diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 1065312..82881b5 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -12,7 +12,7 @@ variables: TOXENV: py38 RUN_BUILD: 1 RUN_TEST: 1 - RUN_DOCTEST: 1 + RUN_DOCTEST: 0 RUN_LINT: 0 RUN_TYPEHINT: 0 RUN_DOCS: 1 diff --git a/.gitlab/issue_templates/bug_template.md b/.gitlab/issue_templates/bug_template.md new file mode 100644 index 0000000..1addecf --- /dev/null +++ b/.gitlab/issue_templates/bug_template.md @@ -0,0 +1,25 @@ +### 1-2 sentence description + +[I tried to do X and expected to get output Y but instead Z happened.] + +### What did you do? + +Include a code sample, a copy-pastable example if possible. + +```python +# Your code here that produces the bug +# This example should be self-contained, and so not rely on external data. +# It should run in a fresh ipython session, and so include all relevant imports. +``` + +### What happened when you did it? + +``` + +``` + +### What did you expect or want to happen instead? + +A clear and concise description of what you expected or wanted to happen. + +### Anything else? \ No newline at end of file diff --git a/.gitlab/issue_templates/feature_request.md b/.gitlab/issue_templates/feature_request.md new file mode 100644 index 0000000..974090f --- /dev/null +++ b/.gitlab/issue_templates/feature_request.md @@ -0,0 +1,22 @@ +### What problem do you want to solve? + +[I want to be able to do X (or do X more easily).] + +### What would your ideal solution look like? + +A clear and concise description of what you want to happen. Optionally, include some pseudo-code for what the syntax would look like. + +```python +model = SomeNewModel(mean, cov) +model.some_new_method() +``` + +``` + +``` + +### (Optional) What's the best current work-around? + +What alternatives have you considered? If you had to solve the problem with the current version of the software, what would you do? + +### Anything else? \ No newline at end of file diff --git a/README.md b/README.md index 7e023b9..0d7ad0b 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,6 @@ -# Conditional Inference +# Multiple Inference +[![Documentation Status](https://readthedocs.org/projects/dsbowen-conditional-inference/badge/?version=latest)](https://dsbowen-conditional-inference.readthedocs.io/en/latest/?badge=latest) [![pipeline status](https://gitlab.com/dsbowen/conditional-inference/badges/master/pipeline.svg)](https://gitlab.com/dsbowen/conditional-inference/-/commits/master) [![coverage report](https://gitlab.com/dsbowen/conditional-inference/badges/master/coverage.svg)](https://gitlab.com/dsbowen/conditional-inference/-/commits/master) [![PyPI version](https://badge.fury.io/py/conditional-inference.svg)](https://badge.fury.io/py/conditional-inference) @@ -7,4 +8,4 @@ [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gl/dsbowen%2Fconditional-inference/HEAD?urlpath=lab/tree/examples) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) -A statistics package for comparing multiple policies or treatments. [Read the docs](https://dsbowen.gitlab.io/conditional-inference). +A statistics package for comparing multiple parameters. Read the docs [here](https://dsbowen-conditional-inference.readthedocs.io/en/latest/?badge=latest). \ No newline at end of file diff --git a/docs/changelog.rst b/docs/changelog.rst index 9ff95d6..3a4da32 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -1,6 +1,18 @@ Changelog ========= +1.0.0 +----- + +- Added nonparametric empirical Bayes +- Collapsed all normal prior Bayesian estimators (classic, maximum likelihood, and James-Stein) into a single ``Normal`` class +- Created a separate ``Improper`` class for Bayesian models with an improper prior +- Moved projection confidence intervals from ``RQU`` to a separate ``condfidence_set.ConfidenceSet`` class +- Created the ``confidence_set`` module +- Added non-parametric, mixture, and joint Distributions +- Added significance conditional analysis +- Renamed ``RQU`` to the more expressive ``RankCondition`` + 0.0.3 ----- diff --git a/docs/conditional_inference/base.rst b/docs/conditional_inference/base.rst index d0249d7..d4c3a5e 100644 --- a/docs/conditional_inference/base.rst +++ b/docs/conditional_inference/base.rst @@ -17,15 +17,5 @@ conditional\_inference.base .. autosummary:: - ConventionalEstimatesData - ModelBase ResultsBase - - - - - - - - - + ModelBase \ No newline at end of file diff --git a/docs/conditional_inference/bayes/base.rst b/docs/conditional_inference/bayes/base.rst index 7fe7cb8..d36dd7b 100644 --- a/docs/conditional_inference/bayes/base.rst +++ b/docs/conditional_inference/bayes/base.rst @@ -17,7 +17,7 @@ conditional\_inference.bayes.base .. autosummary:: - BayesModelBase + BayesBase BayesResults diff --git a/docs/conditional_inference/bayes/classic.rst b/docs/conditional_inference/bayes/classic.rst deleted file mode 100644 index 9c317d5..0000000 --- a/docs/conditional_inference/bayes/classic.rst +++ /dev/null @@ -1,30 +0,0 @@ -conditional\_inference.bayes.classic -==================================== - -.. automodule:: conditional_inference.bayes.classic - :members: - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - ClassicBayesBase - LinearClassicBayes - - - - - - - - - diff --git a/docs/conditional_inference/bayes/empirical.rst b/docs/conditional_inference/bayes/empirical.rst deleted file mode 100644 index 9fb8701..0000000 --- a/docs/conditional_inference/bayes/empirical.rst +++ /dev/null @@ -1,31 +0,0 @@ -conditional\_inference.bayes.empirical -====================================== - -.. automodule:: conditional_inference.bayes.empirical - :members: - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - EmpiricalBayesBase - LinearEmpiricalBayes - JamesStein - - - - - - - - - diff --git a/docs/conditional_inference/bayes/hierarchical.rst b/docs/conditional_inference/bayes/hierarchical.rst deleted file mode 100644 index a0016d6..0000000 --- a/docs/conditional_inference/bayes/hierarchical.rst +++ /dev/null @@ -1,31 +0,0 @@ -conditional\_inference.bayes.hierarchical -========================================= - -.. automodule:: conditional_inference.bayes.hierarchical - :members: - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - HierarchicalBayesBase - LinearHierarchicalBayes - HierarchicalBayesResults - - - - - - - - - diff --git a/docs/conditional_inference/bayes/improper.rst b/docs/conditional_inference/bayes/improper.rst new file mode 100644 index 0000000..1d06d61 --- /dev/null +++ b/docs/conditional_inference/bayes/improper.rst @@ -0,0 +1,30 @@ +conditional\_inference.bayes.improper +===================================== + +.. automodule:: conditional_inference.bayes.improper + :members: + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + Improper + + + + + + + + + + diff --git a/docs/conditional_inference/bayes/nonparametric.rst b/docs/conditional_inference/bayes/nonparametric.rst new file mode 100644 index 0000000..c2c3188 --- /dev/null +++ b/docs/conditional_inference/bayes/nonparametric.rst @@ -0,0 +1,30 @@ +conditional\_inference.bayes.nonparametric +========================================== + +.. automodule:: conditional_inference.bayes.nonparametric + :members: + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + Nonparametric + + + + + + + + + + diff --git a/docs/conditional_inference/bayes/normal.rst b/docs/conditional_inference/bayes/normal.rst new file mode 100644 index 0000000..10dcc38 --- /dev/null +++ b/docs/conditional_inference/bayes/normal.rst @@ -0,0 +1,30 @@ +conditional\_inference.bayes.normal +=================================== + +.. automodule:: conditional_inference.bayes.normal + :members: + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + Normal + + + + + + + + + + diff --git a/docs/conditional_inference/confidence_set.rst b/docs/conditional_inference/confidence_set.rst new file mode 100644 index 0000000..8baef1c --- /dev/null +++ b/docs/conditional_inference/confidence_set.rst @@ -0,0 +1,37 @@ +conditional\_inference.confidence_set +===================================== + +.. automodule:: conditional_inference.confidence_set + :members: + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + ConfidenceSetResults + ConfidenceSet + AverageComparison + BaselineComparison + PairwiseComparisonResults + PairwiseComparison + MarginalRankingResults + MarginalRanking + SimultaneousRankingResults + SimultaneousRanking + + + + + + + + diff --git a/docs/conditional_inference/index.rst b/docs/conditional_inference/index.rst index a9312af..b141bab 100644 --- a/docs/conditional_inference/index.rst +++ b/docs/conditional_inference/index.rst @@ -11,15 +11,22 @@ API reference .. toctree:: :maxdepth: 2 - :caption: Quantile unbiased inference + :caption: Confidence sets and ranking - Quantile-unbiased estimator + Simultaneous confidence sets + +.. toctree:: + :maxdepth: 2 + :caption: Conditional inference + + Ranking conditions + Significance conditions .. toctree:: :maxdepth: 2 :caption: Bayesian inference Base classes - Classic - Empirical - Hierarchical \ No newline at end of file + Improper prior + Normal prior + Nonparametric prior \ No newline at end of file diff --git a/docs/conditional_inference/rank_condition.rst b/docs/conditional_inference/rank_condition.rst new file mode 100644 index 0000000..2893f69 --- /dev/null +++ b/docs/conditional_inference/rank_condition.rst @@ -0,0 +1,29 @@ +conditional\_inference.rank_condition +===================================== + +.. automodule:: conditional_inference.rank_condition + :members: + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + RankCondition + RankConditionResults + + + + + + + + diff --git a/docs/conditional_inference/rqu.rst b/docs/conditional_inference/rqu.rst deleted file mode 100644 index f19c919..0000000 --- a/docs/conditional_inference/rqu.rst +++ /dev/null @@ -1,31 +0,0 @@ -conditional\_inference.rqu -========================== - -.. automodule:: conditional_inference.rqu - :members: - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - RQU - RQUResults - ProjectionResults - RQUData - - - - - - - - diff --git a/docs/conditional_inference/significance_condition.rst b/docs/conditional_inference/significance_condition.rst new file mode 100644 index 0000000..637d5ef --- /dev/null +++ b/docs/conditional_inference/significance_condition.rst @@ -0,0 +1,29 @@ +conditional\_inference.significance_condition +============================================= + +.. automodule:: conditional_inference.significance_condition + :members: + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + SignificanceCondition + SignificanceConditionResults + + + + + + + + diff --git a/docs/conditional_inference/stats.rst b/docs/conditional_inference/stats.rst index 58ff159..f22a94f 100644 --- a/docs/conditional_inference/stats.rst +++ b/docs/conditional_inference/stats.rst @@ -17,8 +17,12 @@ conditional\_inference.stats .. autosummary:: - truncnorm + joint_distribution + mixture + nonparametric quantile_unbiased + truncnorm + diff --git a/docs/conditional_inference/utils.rst b/docs/conditional_inference/utils.rst index 6ff5177..ee72e29 100644 --- a/docs/conditional_inference/utils.rst +++ b/docs/conditional_inference/utils.rst @@ -14,6 +14,7 @@ conditional\_inference.utils .. autosummary:: expected_wasserstein_distance + holm_bonferroni_correction weighted_cdf weighted_quantile diff --git a/docs/conf.py b/docs/conf.py index acb37a3..33b7cbe 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -19,7 +19,7 @@ sys.path.insert(0, os.path.abspath('../src')) # necessary to run `make doctest` # -- Project information ----------------------------------------------------- -project = 'Conditional Inference' +project = 'Multiple Inference' copyright = "2021, Dillon Bowen" author = 'Dillon Bowen' @@ -52,6 +52,12 @@ extensions = [ 'sphinx.ext.viewcode', ] +# doctest setup +doctest_global_setup = """ +import numpy as np +np.random.seed(0) +""" + # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] diff --git a/docs/index.rst b/docs/index.rst index a74476d..621ebc5 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -3,11 +3,14 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -Conditional Inference documentation -=========================================== +Multiple Inference documentation +================================ -A statistics package for comparing multiple policies or treatments. +A statistics package for comparing multiple parameters (e.g., multiple treatments, policies, or subgroups). +.. image:: https://readthedocs.org/projects/dsbowen-conditional-inference/badge/?version=latest + :target: https://dsbowen-conditional-inference.readthedocs.io/en/latest/?badge=latest + :alt: Documentation Status .. image:: https://gitlab.com/dsbowen/conditional-inference/badges/master/pipeline.svg :target: https://gitlab.com/dsbowen/conditional-inference/-/commits/master .. image:: https://gitlab.com/dsbowen/conditional-inference/badges/master/coverage.svg @@ -22,20 +25,26 @@ A statistics package for comparing multiple policies or treatments. :target: https://github.com/psf/black | -Quickstart +Motivation ========== -Click the badges below to launch a Jupyter Binder with a ready-to-use virtual environment and boilerplate code. +Multiple inference techniques outperform standard methods like OLS and IV estimation for comparing multiple parameters. For example, `this post `_ shows how to apply Bayesian estimators to a randomized control trial testing many interventions to increase vaccination rates. -Use the following binder for quantile-unbiased analysis. +Start here +========== + +Click the badges below to launch a Jupyter Binder with a ready-to-use virtual environment and template code. + +This binder is an 80-20 solution for multiple inference. .. image:: https://mybinder.org/badge_logo.svg - :target: https://mybinder.org/v2/gl/dsbowen%2Fconditional-inference/HEAD?urlpath=lab/tree/examples/rqu.ipynb + :target: https://mybinder.org/v2/gl/dsbowen%2Fconditional-inference/HEAD?urlpath=lab/tree/examples/multiple_inference.ipynb -| Use the following binder for Bayesian analysis. +| This binder is for inference after ranking. .. image:: https://mybinder.org/badge_logo.svg - :target: https://mybinder.org/v2/gl/dsbowen%2Fconditional-inference/HEAD?urlpath=lab/tree/examples/bayes.ipynb + :target: https://mybinder.org/v2/gl/dsbowen%2Fconditional-inference/HEAD?urlpath=lab/tree/examples/rank_conditions.ipynb + | Installation @@ -45,6 +54,11 @@ Installation $ pip install conditional-inference +Issues +====== + +Please submit issues `here `_. + Contents ======== @@ -66,21 +80,16 @@ Citations .. code-block:: - @software(bowen2021conditional-inference, - title={ Conditional Inference }, + @software(multiple-inference, + title={ Multiple Inference }, author={ Bowen, Dillon }, - year={ 2021 }, - url={ https://dsbowen.gitlab.io/conditional-inference } + year={ 2022 }, + url={ https://dsbowen-conditional-inference.readthedocs.io/en/latest/?badge=latest } ) - @techreport{andrews2019inference, - title={ Inference on winners }, - author={ Andrews, Isaiah and Kitagawa, Toru and McCloskey, Adam }, - year={ 2019 }, - institution={ National Bureau of Economic Research } - } - Acknowledgements ================ -I would like to thank Isaiah Andrews, Toru Kitagawa, Adam McCloskey, and Jeff Rowley for invaluable feedback on my early drafts. \ No newline at end of file +I would like to thank Isaiah Andrews, Toru Kitagawa, Adam McCloskey, and Jeff Rowley for invaluable feedback on my early drafts. + +My issue templates are based on the `statsmodels `_ issue templates. \ No newline at end of file diff --git a/examples/bayes.ipynb b/examples/bayes.ipynb deleted file mode 100644 index b737bf7..0000000 --- a/examples/bayes.ipynb +++ /dev/null @@ -1,323 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Template code for Bayesian analysis\r\n", - "\r\n", - "This template provides regression tables, point plots, and reconstruction plots for empirical (James-Stein) and hierarchical Bayesian estimates. For more detail, check out the file in this folder named `bayes_primer.ipynb`.\r\n", - "\r\n", - "Instructions:\r\n", - "\r\n", - "1. Upload a file named `data.csv` to this folder with your conventional estimates. Open `data.csv` to see an example. In this file, we named our dependent variable \"dep_variable\", and have estimated the effects of policies named \"policy0\",..., \"policy3\". The first column of `data.csv` contains the conventional estimates `X` of the true unknown mean. The remaining columns contain consistent estimates of the corresponding covariance matrix $\\Sigma$. In the example `data.csv` provided, $X=(0, 1, 2, 3)$ and $\\Sigma = I$.\r\n", - "2. Modify the code if necessary.\r\n", - "3. Run the notebook." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 1, - "source": [ - "import matplotlib.pyplot as plt\r\n", - "import numpy as np\r\n", - "import seaborn as sns\r\n", - "from scipy.stats import loguniform\r\n", - "\r\n", - "from conditional_inference.bayes.classic import LinearClassicBayes\r\n", - "from conditional_inference.bayes.empirical import JamesStein, LinearEmpiricalBayes\r\n", - "from conditional_inference.bayes.hierarchical import LinearHierarchicalBayes\r\n", - "\r\n", - "data_file = \"data.csv\"\r\n", - "alpha = .05\r\n", - "\r\n", - "np.random.seed(123)\r\n", - "sns.set()" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 2, - "source": [ - "conventional_result = LinearClassicBayes.from_csv(data_file, prior_cov=np.inf)\\\r\n", - " .fit(cols=\"sorted\")\r\n", - "conventional_result.summary(title=\"Conventional estimates\", alpha=alpha)" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Conventional estimates
coef pvalue [0.025 0.975]
policy3 3.000 0.001 1.040 4.960
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Dep. Variable dep_variable
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Conventional estimates \n", - "==================================\n", - " coef pvalue [0.025 0.975]\n", - "----------------------------------\n", - "policy3 3.000 0.001 1.040 4.960\n", - "policy2 2.000 0.023 0.040 3.960\n", - "policy1 1.000 0.159 -0.960 2.960\n", - "policy0 0.000 0.500 -1.960 1.960\n", - "==========================\n", - "Dep. Variable dep_variable\n", - "--------------------------\n", - "\"\"\"" - ] - }, - "metadata": {}, - "execution_count": 2 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 3, - "source": [ - "conventional_result.point_plot(title=\"Conventional estimates\", alpha=alpha)\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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Empirical Bayes estimates
coef pvalue [0.025 0.975]
policy3 2.940 0.002 0.994 4.886
policy2 1.980 0.022 0.049 3.911
policy1 1.020 0.150 -0.911 2.951
policy0 0.060 0.476 -1.886 2.006
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Dep. Variable dep_variable
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Empirical Bayes estimates \n", - "==================================\n", - " coef pvalue [0.025 0.975]\n", - "----------------------------------\n", - "policy3 2.940 0.002 0.994 4.886\n", - "policy2 1.980 0.022 0.049 3.911\n", - "policy1 1.020 0.150 -0.911 2.951\n", - "policy0 0.060 0.476 -1.886 2.006\n", - "==========================\n", - "Dep. Variable dep_variable\n", - "--------------------------\n", - "\"\"\"" - ] - }, - "metadata": {}, - "execution_count": 4 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 5, - "source": [ - "js_result.point_plot(alpha=alpha)\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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" - ] - }, - "metadata": {} - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 6, - "source": [ - "# construct the hyperprior distribution\r\n", - "# i.e. the distribution of the prior standard deviation parameter\r\n", - "_, prior_std_anchor = LinearEmpiricalBayes.from_csv(data_file).estimate_prior_params()\r\n", - "prior_std_distribution = loguniform(prior_std_anchor, 2 * prior_std_anchor)\r\n", - "\r\n", - "hb_result = LinearHierarchicalBayes.from_csv(\r\n", - " data_file, prior_cov_params_distribution=prior_std_distribution\r\n", - ").fit(cols=\"sorted\")\r\n", - "hb_result.summary(alpha=alpha)" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Hierarchical Bayes estimates
coef pvalue [0.025 0.975]
policy3 2.022 0.005 0.689 3.521
policy2 1.662 0.005 0.353 3.002
policy1 1.359 0.023 0.031 2.696
policy0 1.008 0.085 -0.365 2.416
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Dep. Variable dep_variable
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Hierarchical Bayes estimates \n", - "==================================\n", - " coef pvalue [0.025 0.975]\n", - "----------------------------------\n", - "policy3 2.022 0.005 0.689 3.521\n", - "policy2 1.662 0.005 0.353 3.002\n", - "policy1 1.359 0.023 0.031 2.696\n", - "policy0 1.008 0.085 -0.365 2.416\n", - "==========================\n", - "Dep. Variable dep_variable\n", - "--------------------------\n", - "\"\"\"" - ] - }, - "metadata": {}, - "execution_count": 6 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 7, - "source": [ - "hb_result.point_plot(alpha=alpha)\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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85OHhoVdeeUXDhw/XxYsXtWbNGvtt8r+/RboyDh48aA8phmHol19+0YIFC+Tl5WW/9saZfdS7d29NnjxZ33//vf7xj384zBs7dqzi4+P19NNP64EHHlBRUZHeeust7du3z36BcIcOHfTWW2/pT3/6k9q2basTJ05o6dKlioyMLPM4JiQkaODAgRo5cqQefvhheXl5adWqVdq4caNee+01SZevAXrxxRc1Y8YMRUdHKy8vT/Pnz9cdd9xhHyqqX7++fvrpJ+3cuVNt2rRxaj+WpWHDhrrlllu0cuVKNW7cWPXr19fXX39tP1tZvP+Kg+WXX34pPz8/hYaGasyYMZowYYLc3d3VvXt35eXlyWq16sSJE2rVqpXq1q2rVq1aaf78+fL09FRISIgOHTqk9PR0h48eAKoSAQi4jlgsFi1evFjz5s3T66+/rt9++02BgYEaNmxYhReEjhw5Ujk5OVq2bJkWLFigm266SX/5y1/k5uam119/3X4ha2luuukmvfPOO3rllVc0btw41alTR1FRUZozZ478/PyuKAC5u7tryZIlmjVrlubNm6f8/HyFhITorbfesr8pz549W/Pnz9cTTzwhPz8/hYeHa/ny5Ro8eLB2796tkJCQSm/vpZdesv9usVjUoEEDhYeH65VXXrGfAXJmH3l5ealjx47KysoqESI6d+6sN998U/Pnz1dSUpI8PT3VqlUrLV261H7n1ejRo1WnTh2tXr1aCxYskK+vr3r06KGnn366zBpCQ0O1cuVKzZkzR88++6wMw1CLFi20YMEC9ezZU9LlM0uXLl3Se++9p3feeUd169ZVp06d9Mwzz8jT01OSNHz4cL388st67LHHtHTp0krvw4pYrVZNnTrV/jcSHByshQsX6uWXX9bu3bs1ePBgNW/eXP369bMPZX788cd66KGH5OPjozfeeEOrVq3SDTfcoIiICM2aNUtNmjSRdPn4zZ07V2+99ZZ+/fVXNWzYUA8++KBGjx5dZf0Hfs/N4BvrAKCECxcuqGvXrkpISNCjjz7q6u4AqGKcAQKA3yn+FOWtW7fKzc2ND+EDaikCEAD8jsVi0fLly+Xj46M5c+aY+jvEgNqMITAAAGA63AYPAABMhwAEAABMhwAEAABMh4ugy2EYhmy20i+RsljcypxXm1Bn7UKdtQt11i7UWXXtV+brewhA5bDZDGVnnysx3cPDIn9/H+XlnVdhYflfMVCTUWftQp21C3XWLtRZdQICfOTuXnEAYggMAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDl+FAQA1VJHN0P7D2fot74Ia+HipRZMGslgq/goAAC4IQCEhIZo2bZpiY2OVmpqq9PR0ffHFF1fdbn5+vmbNmqXPP/9cZ86cUVhYmJ555hmFh4dffacB4Dqz6+eTemdDpn47fcE+zd/XS3+Paa52IY1c2DOgZnDpENjw4cP1wQcfVElbL7zwgr755hu9+uqrWrdunVq0aKFhw4bpxIkTVdI+AFwv9mScVOoH3zuEH0nKOVOgBek/ak/GSRf1DKg5XDoE5uPjIx8fn6tup6ioSHXq1NHEiRMVGRkpSRo7dqzeeecd7d27V3369LnqbQBXouBikau7YFdkM3ShoFAFF4tq9bdN1/Y6bTZDKzdklrvMOxsPqOXtAbViOKy2H89iZqzT3cV/n04FoJCQEE2YMEFr167V/v37dccdd+ipp55Sz5497ct8+eWXslqtOnDggHx8fNS3b1+NGTNGdevWLdHeH4fATp06pZkzZ2rz5s0qLCxU+/btNX78ePn6+io6OlpTpkxR//797evPnj1bW7du1erVqzVt2jT79LNnz2rx4sXy8fG56iEwD4+SJ8nc3S0O/9ZW1Hn1hk+/+uFdwFk5ZwqUOPcrV3cDKNeyF2Jcun2nzwDNmjVLycnJmj59utasWaMnn3xSK1euVEREhDZs2KCkpCSNGjVKM2bMUFZWliZOnKijR4/KarWW225hYaGGDx8uDw8PWa1WNWjQQNOnT9eIESO0fv16devWTR9++KE9ANlsNq1bt07x8fEO7SxatEhz5syRm5ubpk6dqptuusnZEu0sFjf5+5d9hqp+fe8rbrsmoU4AQFUr7/31WnA6AMXGxmrQoEGSpOTkZO3cuVMrVqxQRESEFi9erHvvvVcJCQmSpKCgIBmGocTERB08eFDBwcFltrtt2zZlZGRo/fr1CgoKkiRNmTJFaWlpOn36tOLi4pSQkKATJ04oMDBQ27ZtU3Z2tvr16+fQTp8+fRQdHa1PP/1UL7zwggICAtS9e3dny5R0+VRzXt75EtPd3S2qX99beXn5KiqqvacqqfPqLXn2yv72qoPF3U31fb2VdyZftiLD1d2pNrW9zoz/5GjWe99VuFzywHCF3OZf/R2qZrX9eBYzY505OeeqZRv163tX6oy+0wEoKirK4XHbtm21ZcsWSVJmZqb69u3rML/4mpzMzMxyA1BmZqb8/Pzs4UeSAgMDlZKSIkmKjo5Ww4YNtXbtWsXHxys9PV09e/aUn5+fQzu33367JKlly5bav3+/li5desUBSFK5Y7FFRbZaPVZbjDqvnKvHuH/Pw92iul4eyj9vUaFRe49nba8z9DZ/+ft6KedMQZnLBPh6KfQ2/1pxDVBtP57FTFmni99XnL7owcPDMTMVFRXJYrncjGGUTK02m63U9Spq94/c3d3Vv39/ffTRRzp//rw2btyoAQMGSJLOnTun9evXKzc312GdFi1acBcYgFrFYnHT32Oal7vMwzHNa0X4AaqT0wHohx9+cHj87bffqlWrVpIuXyS9d+9eh/m7d++WJDVr1qzcdoODg3X69GkdOXLEPi07O1tRUVH67rvvJElxcXHKzMzU8uXL5evrq86dO0u6HLLGjh2r9evXO7T5/fffl3vWCQBqonYhjTTqwTZq6Od4c0mAr5cSB4TxOUBAJTg9BPb222+radOmCgsL0z//+U9lZGRo6tSpkqQRI0Zo9OjRslqt6tOnjw4fPqzJkyere/fuFQagTp06KSwsTCkpKRo/fry8vb01c+ZMBQQE2ANWUFCQIiIiZLVaNXjwYLm7u0uSfH199de//lXz5s1T48aNddttt+m9997Tvn379N577zlbIgBc9zqENlLPqDu0Y98xPgkauAJOB6CBAwcqLS1NmZmZCg0N1ZtvvqnQ0FBJUu/evfXqq69q4cKFslqtCggIUL9+/ZSUlFRhuxaLRVarVdOmTdOwYcPk5uamjh076o033pCnp6d9udjYWO3du9c+/FVs/Pjx8vPz06RJk3Tq1Cm1atVKaWlpCgsLc7ZEAKgR3C1uuvOOAJdfSwHURG5GaRfulOH3X2PhKqmpqdq6davefffdat9WUZFN2dklr1L38LDI399HOTnnavULD3XWLtRZu1Bn7UKdVScgwKd67gJzlT179ujQoUNatmyZXnrpJVd3BwAA1GA1JgBt2rRJK1asUFxcHF9tAQAAropTASgjI6O6+lGh5ORkJScnu2z7AACg9qjdX/IEAABQCgIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQ9XdwAArkc2m6HMo7nKPVegBj5eatGkgSwWN1d3C0AVueYBKCQkRNOmTVNsbKxSU1OVnp6uL7744qrbvXDhghYsWKBPPvlEOTk5CgoKUmJionr27FkFvQZgJnsyTuqdjQeUc6bAPs3f10t/j2mudiGNXNgzAFXFpUNgw4cP1wcffFAlbU2ZMkUfffSRXnzxRX344YeKiYnRk08+qR07dlRJ+wDMYU/GSS1I/9Eh/EhSzpkCLUj/UXsyTrqoZwCqkkuHwHx8fOTj43PV7eTn5+vDDz/Uyy+/rK5du0qSEhIStGPHDq1evVpRUVFXvQ1cvwouFl3V+kU2QxcKClVwsUiFhbYq6tX1hzorZrMZWrkhs9xl3tl4QC1vD3D5cBjHs3Ypshmu7oLpOBWAQkJCNGHCBK1du1b79+/XHXfcoaeeesphmOnLL7+U1WrVgQMH5OPjo759+2rMmDGqW7duifb+OAR26tQpzZw5U5s3b1ZhYaHat2+v8ePHy9fXV9HR0ZoyZYr69+9vX3/27NnaunWrVq5cqUWLFiksLMyhfYvFory8PGdKLMHDo+RJMnd3i8O/tVVNqXP49KsfQgUqK+dMgRLnfuXqbqAW+mj2X67719urdT29rzh9BmjWrFlKTk7W9OnTtWbNGj355JNauXKlIiIitGHDBiUlJWnUqFGaMWOGsrKyNHHiRB09elRWq7XcdgsLCzV8+HB5eHjIarWqQYMGmj59ukaMGKH169erW7du+vDDD+0ByGazad26dYqPj1fdunXVuXNnh/a+//57bd++XS+88IKzJdpZLG7y9y/7DFX9+t5X3HZNYpY6AcDVzPJ6ez3U6XQAio2N1aBBgyRJycnJ2rlzp1asWKGIiAgtXrxY9957rxISEiRJQUFBMgxDiYmJOnjwoIKDg8tsd9u2bcrIyND69esVFBQk6fJ1PWlpaTp9+rTi4uKUkJCgEydOKDAwUNu2bVN2drb69etXoq2srCwlJiaqTZs2+utf/+psiXY2m6G8vPMlpru7W1S/vrfy8vJVVFR7T8nWlDqXPNv9qta3uLupvq+38s7ky1ZUe09DU2fFMv6To1nvfVfhcskDwxVym/8V9rBqcDxrF4v75SHV6/319mpdi/eV+vW9K3WGyekA9Mfradq2bastW7ZIkjIzM9W3b1+H+ZGRkfZ55QWgzMxM+fn52cOPJAUGBiolJUWSFB0drYYNG2rt2rWKj49Xenq6evbsKT8/P4d29u7dq4SEBDVu3FiLFi2Sp6ensyU6KG/MuajIVqvHpItd73W6X+W1GB7uFtX18lD+eYsKjeu3zqtFnRULvc1f/r5eJS6A/r0AXy+F3ubv8muAOJ61i8f//4Z9vb/eVpXroU6nB+E8PBwzU1FRkSyWy80YRsl0brPZSl2vonb/yN3dXf3799dHH32k8+fPa+PGjRowYIDDMv/61780dOhQNW/eXMuXL5e/v2v/hwagZrFY3PT3mOblLvNwTHOXhx8AV8/pAPTDDz84PP7222/VqlUrSZcvkt67d6/D/N27d0uSmjVrVm67wcHBOn36tI4cOWKflp2draioKH333XeSpLi4OGVmZmr58uXy9fV1uO7niy++0JgxY9StWze9+eab8vX1dbY0AFC7kEZKHBAmf18vh+kBvl5KHBDG5wABtYTTQ2Bvv/22mjZtqrCwMP3zn/9URkaGpk6dKkkaMWKERo8eLavVqj59+ujw4cOaPHmyunfvXmEA6tSpk8LCwpSSkqLx48fL29tbM2fOVEBAgD1gBQUFKSIiQlarVYMHD5a7u7sk6fTp00pJSVGrVq30/PPP6/Tp0/Z2PT091aBBA2fLBGBi7UIaqW3zG/kkaKAWczoADRw4UGlpacrMzFRoaKjefPNNhYaGSpJ69+6tV199VQsXLpTValVAQID69eunpKSkCtu1WCyyWq2aNm2ahg0bJjc3N3Xs2FFvvPGGw3U8sbGx2rt3r8Pw11dffaW8vDzt27dP0dHRDu1GRkZq+fLlzpYJwOQsFjeF3s4wOlBbuRmlXbhTht9/jYWrpKamauvWrXr33XerfVtFRTZlZ58rMd3DwyJ/fx/l5Jxz+UVc1Yk6axfqrF2os3ahzqoTEOBTPXeBucqePXt06NAhLVu2TC+99JKruwMAAGqwGhOANm3apBUrViguLk59+vRxdXcAAEAN5lQAysjIqK5+VCg5OVnJycku2z4AAKg9XP9lHAAAANcYAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJiOh6s7AACSZLMZyjyaq9xzBWrg46UWTRrIYnFzdbcA1FLX/AxQSEiI1qxZI0lKTU1Vjx49qnwbH3/8cbW0C6B67Mk4qWcWbtXMd7/V4nU/aea73+qZhVu1J+Okq7sGoJZy6RDY8OHD9cEHH1Rpmxs3btT48eOrtE0A1WdPxkktSP9ROWcKHKbnnCnQgvQfCUEAqoVLh8B8fHzk4+NTJW2dPXtWU6ZM0ccff6xmzZrpzJkzVdIu/p+Ci0Wu7kK1KLIZulBQqIKLRSostLm6O9XmeqzTZjO0ckNmucu8s/GAWt4eUOnhsOuxzupAnVXDq457lbeJmsGpABQSEqIJEyZo7dq12r9/v+644w499dRT6tmzp32ZL7/8UlarVQcOHJCPj4/69u2rMWPGqG7duiXaS01NVXp6ur744gtJ0qlTpzRz5kxt3rxZhYWFat++vcaPHy9fX19FR0drypQp6t+/v3392bNna+vWrVq9erWOHTum//3vf3r//fe1ceNGpaenX+EuceThUfIkmbu7xeHf2uqPdQ6f/oUruwOTyjlToMS5X7m6G6illr0Q4+ouSDLv+4orOX0GaNasWUpOTtb06dO1Zs0aPfnkk1q5cqUiIiK0YcMGJSUladSoUZoxY4aysrI0ceJEHT16VFartdx2CwsLNXz4cHl4eMhqtapBgwaaPn26RowYofXr16tbt2768MMP7QHIZrNp3bp1io+PlySFhobq7bfflnR5GKwqWCxu8vcv+wxV/freVbKd651Z6gRgPuW9xruCWV5vr4c6nQ5AsbGxGjRokCQpOTlZO3fu1IoVKxQREaHFixfr3nvvVUJCgiQpKChIhmEoMTFRBw8eVHBwcJntbtu2TRkZGVq/fr2CgoIkSVOmTFFaWppOnz6tuLg4JSQk6MSJEwoMDNS2bduUnZ2tfv36XUndlWKzGcrLO19iuru7RfXreysvL19FRbX31PMf61zybHdXd6laWNzdVN/XW3ln8mUrMlzdnWpzPdaZ8Z8czXrvuwqXSx4YrpDb/CvV5vVYZ3WgzqqRk3Ouytu8EmZ9X6kO9et7V+oMk9MBKCoqyuFx27ZttWXLFklSZmam+vbt6zA/MjLSPq+8AJSZmSk/Pz97+JGkwMBApaSkSJKio6PVsGFDrV27VvHx8UpPT1fPnj3l5+fnbAlOKW/MuajIVqvH3osV1+leS29J9nC3qK6Xh/LPW1Ro1N7jeT3WGXqbv/x9vUpcAP17Ab5eCr3Nv9LXAF2PdVYH6qwa19truNneV1zJ6UE4Dw/HzFRUVCSL5XIzhlEyndtstlLXq6jdP3J3d1f//v310Ucf6fz589q4caMGDBjgTNcBXGcsFjf9PaZ5ucs8HNOczwMCUOWcDkA//PCDw+Nvv/1WrVq1knT5Ium9e/c6zN+9e7ckqVmzZuW2GxwcrNOnT+vIkSP2adnZ2YqKitJ3330nSYqLi1NmZqaWL18uX19fde7c2dnuA7jOtAtppMQBYfL39XKYHuDrpcQBYWoX0shFPQNQmzk9BPb222+radOmCgsL0z//+U9lZGRo6tSpkqQRI0Zo9OjRslqt6tOnjw4fPqzJkyere/fuFQagTp06KSwsTCkpKRo/fry8vb01c+ZMBQQE2ANWUFCQIiIiZLVaNXjwYLm7c/siUBu0C2mkts1v5JOgAVwzTgeggQMHKi0tTZmZmQoNDdWbb76p0NBQSVLv3r316quvauHChbJarQoICFC/fv2UlJRUYbsWi0VWq1XTpk3TsGHD5Obmpo4dO+qNN96Qp6enfbnY2Fjt3buX4S+glrFY3BR6e+UudAaAq+VmlHbhThlCQkI0bdo0xcbGVmefypWamqqtW7fq3XffrfZtFRXZlJ1d8g4BDw+L/P19lJNzzuUXcVUn6qxdqLN2oc7ahTqrTkCAT/XcBeYqe/bs0aFDh7Rs2TK99NJLru4OAACowWpMANq0aZNWrFihuLg49enTx9XdAQAANZhTASgjI6O6+lGh5ORkJScnu2z7AACg9nD9l3EAAABcYwQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQ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" - ] - }, - "metadata": {} - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [], - "outputs": [], - "metadata": {} - } - ], - "metadata": { - "orig_nbformat": 4, - "language_info": { - "name": "python", - "version": "3.9.0", - "mimetype": "text/x-python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "pygments_lexer": "ipython3", - "nbconvert_exporter": "python", - "file_extension": ".py" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3.9.0 64-bit ('conditional-inference': conda)" - }, - "interpreter": { - "hash": "120d65e34230161c0f4356d19a77763cc2f6669dcb2a194d42d3b2faf517ecd2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/examples/bayes_primer.ipynb b/examples/bayes_primer.ipynb index ef43f02..8f9000d 100644 --- a/examples/bayes_primer.ipynb +++ b/examples/bayes_primer.ipynb @@ -6,17 +6,17 @@ "source": [ "# A primer on Bayesian analysis\n", "\n", - "I designed this notebook to give you a primer on Bayesian analysis: how it works, why you should use it, and how it can change your results. To run Bayesian analysis on your own data, check out the file named `bayes.ipynb` in this folder.\n", + "I designed this notebook to give you a primer on Bayesian analysis: how it works, why you should use it, and how it can change your results. To run Bayesian analysis on your data, check out the `multiple_inference.ipynb` file in this folder.\n", "\n", "First, when should you use Bayesian analysis? You should use Bayesian analysis when comparing 4 or more \"things.\" For example, you should use Bayesian analysis when you run a study comparing the effects of 4 or more treatments or when studying differences between 4 or more groups of people. (The reason we start at 4 instead of 3 or 5 has to do with the [mathematical underpinnings](https://en.wikipedia.org/wiki/James%E2%80%93Stein_estimator) of Bayesian estimators.)\n", "\n", - "Throughout this notebook, I'll illustrate the importance of Bayesian estimators with an example from [A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor's appointment](https://www.pnas.org/content/118/20/e2101165118) published in PNAS. The authors partnered with Penn Medicine to send patients one of 19 text messages encouraging them to get a flu vaccine. Using OLS, the authors reported that their average text message increased vaccination rates by 2.1 people per hundred relative to the control group. The top-performing message was more than twice as effective, increasing vaccination rates by a stunning [4.6 people per hundred](https://twitter.com/katy_milkman/status/1362579547401687040).\n", + "Throughout this notebook, I'll illustrate the importance of Bayesian estimators with an example from [A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor's appointment](https://www.pnas.org/content/118/20/e2101165118) published in PNAS. The authors partnered with Penn Medicine to send patients one of 19 text messages encouraging them to get a flu vaccine. Using OLS, the authors reported their average text message increased vaccination rates by 2.1 people per hundred compared to the control group. The top-performing message was twice as effective, increasing vaccination rates by a stunning [4.6 people per hundred](https://twitter.com/katy_milkman/status/1362579547401687040).\n", "\n", "Many popular media outlets, including the [Economist](https://www.economist.com/by-invitation/2020/11/30/katy-milkman-on-how-to-nudge-people-to-accept-a-covid-19-vaccine), the [Washington Post](https://www.washingtonpost.com/outlook/2021/05/24/nudges-vaccination-psychology-messaging/), [CNBC](https://www.cnbc.com/2021/06/26/return-to-office-and-vaccines-how-companies-can-drum-up-enthusiasm.html), [NPR](https://www.npr.org/2021/05/26/1000616898/the-science-behind-vaccine-incentives), and [CNN](https://kyma.com/cnn-health/2021/06/29/this-simple-text-message-can-encourage-people-to-get-vaccinated-researchers-say/), point to this research as a remarkable example of how behavioral economics can encourage people to get vaccinated and potentially save lives during the COVID-19 pandemic. As [Fortune](https://fortune.com/2021/02/20/covid-vaccine-rollout-getting-people-vaccinated-vaccination-rates-behavioral-nudge-wharto/) reported,\n", "\n", "> What they found was eye-opening. Precisely *how* a message was worded had a huge impact on whether the patient ended up getting the shot.\n", "\n", - "Researchers continue to speculate about why the top-performing message was so much more successful than the others.\n", + "Researchers continue to speculate about why the top-performing message was more successful than the others.\n", "\n", "Let's start by looking at the results reported in PNAS." ] @@ -29,7 +29,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -50,25 +50,21 @@ "import seaborn as sns\n", "import statsmodels.api as sm\n", "from IPython import display\n", - "from scipy.stats import norm, loguniform\n", "from sklearn.model_selection import RepeatedStratifiedKFold\n", "\n", - "from conditional_inference.bayes.classic import LinearClassicBayes\n", - "from conditional_inference.bayes.empirical import LinearEmpiricalBayes, JamesStein\n", - "from conditional_inference.bayes.hierarchical import LinearHierarchicalBayes\n", - "from conditional_inference.utils import weighted_quantile\n", + "from conditional_inference.bayes import Improper, Nonparametric, Normal\n", "\n", "np.random.seed(123)\n", "sns.set()\n", "\n", - "display.Image(url=\"https://www.pnas.org/content/pnas/118/20/e2101165118/F1.large.jpg\")" + "display.Image(url=\"https://www.pnas.org/cms/10.1073/pnas.2101165118/asset/7d1e1f26-cdcd-4d3a-b2a1-167d9d49c6d9/assets/images/large/pnas.2101165118fig01.jpg\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The original data aren't yet available, but we can approximately reproduce the data given what we know about the study. We know that 47,306 participants were evenly assigned to one of 19 treatments or a control condition. The outcome was binary (did the patient get a vaccine or not), and we know the vaccination rate in each treatment from the PNAS publication." + "It's standard for researchers not to post patient health data for privacy reasons. However, we can approximately reproduce the data given what we know about the study. The researchers evenly assigned 47,306 participants to one of 19 treatments or a control condition. The outcome was binary (did the patient get a vaccine or not), and we know the vaccination rate in each treatment from the PNAS publication." ] }, { @@ -78,7 +74,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -133,12 +129,12 @@ "results = sm.OLS(df.vaccinated, X).fit().get_robustcov_results()\n", "treatment_coefficients = [col for col in X.columns if col != \"control\"]\n", "# note that Bayesian analysis with an infinite prior is equivalent to OLS\n", - "ols_results = LinearClassicBayes.from_results(results, prior_cov=np.inf, cols=treatment_coefficients).fit()\n", + "ols_results = Improper.from_results(results, columns=treatment_coefficients).fit(title=\"OLS estimates\")\n", "\n", "# plot the OLS estimates\n", - "ols_results_plot = ols_results.point_plot(title=\"OLS estimates\")\n", + "ols_results_plot = ols_results.point_plot()\n", "ols_results_plot.set_xlabel(XLABEL)\n", - "ols_results_plot.axvline(0)\n", + "ols_results_plot.axvline(0, linestyle=\"--\")\n", "ols_results_plot.set_xlim(XLIM)\n", "plt.show()" ] @@ -151,17 +147,19 @@ "\n", "In Bayesian analysis, we start with a prior belief. For example, we might expect that each of the treatments we're about to test will increase vaccination rates by 4 percentage points relative to the control condition. Then we collect data and update our belief. [Bayes' Theorem](https://en.wikipedia.org/wiki/Bayes%27_theorem) is a mathematical formula that tells us how much we should update our prior belief based on the data. The updated belief is called a *posterior*.\n", "\n", - "A natural question to ask is, \"Where does our prior belief come from?\" This package implements 3 different versions of Bayesian analysis - classical, empirical, and hierarchical - and each version gives a different answer to this question.\n", + "Where do we get our prior belief?\n", "\n", "Classical Bayes takes the prior as a given. For example, you might have a prior belief based on data from previous studies or a survey of subject matter experts.\n", "\n", - "I dislike this approach because I prefer to let my data speak for themselves. That's where empirical Bayes comes to the rescue. Empirical Bayes estimates a prior based on the data. This might sound like a contradiction. By definition, the prior is what you expect *before* seeing any data, so doesn't estimating a prior based on the data undermine what we're trying to do here?\n", + "However, we can often obtain better estimates by using empirical Bayes to estimate the prior from the data. Estimating the prior using data might sound like a contradiction. By definition, the prior is what you expect *before* seeing any data, so doesn't estimating the prior using data undermine what we're trying to do here?\n", "\n", - "To give some intuition for empirical Bayes, imagine we want to predict MLB players' on-base percentage (OBP) next season. For returning players, we might predict that next season's OBP will be the same as this season's OBP. But what do we predict for a rookie who has no batting history? One approach would be to predict that the rookie's OBP next season will be similar to the average rookie's OBP this season. In Bayesian terms, we've constructed a prior belief about the *next* season's rookies' OBP from data about *this* season's rookies' OBP.\n", + "To understand how empirical Bayes estimates the prior, imagine predicting MLB players' on-base percentage (OBP) next season. We might predict that a player's OBP next season will be the same as his OBP in the previous season. But how can we predict the OBP for a rookie with no batting history? One solution is to predict that the rookie's OBP will be similar to last season's rookies' OBP. In Bayesian terms, we've constructed a prior belief about *next* season's rookies' OBP using data from the *previous* season's rookies' rookies' OBP.\n", "\n", - "We can apply the same logic to the flu study. Imagine we randomly select one text message and put the data for that treatment in a locked box. What should our prior belief about the effect of this text message be? Empirical Bayes says, roughly, that our prior belief about the effect of the message we locked in the box should be the average effect of the other 18. We can also use the variability in the effects of the other 18 messages to tell us how confident we should be in our prior, giving us a *prior distribution*.\n", + "We can apply the same logic to the flu study. Imagine we randomly select one text message and put the data for that treatment in a locked box. What should our prior belief about the effect of this text message be? Empirical Bayes says that our prior belief about the effect of the message we locked in the box should be the average effect of the other 18. We can also use the variability in the effects of the other 18 messages to tell us how confident we should be in our prior, giving us a *prior distribution*.\n", "\n", - "Let's take a look at the prior we get from empirical Bayes." + "Empirical Bayes estimators can be parametric or non-parametric. Parametric empirical Bayes assumes the shape of the prior distribution. Nonparametric empirical Bayes does not assume the shape of the prior distribution.\n", + "\n", + "Let's look at the prior from a parametric empirical Bayes estimator assuming a normal prior." ] }, { @@ -173,12 +171,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "95% confidence interval: [0.00112666 0.0416439 ]\n" + "Prior 95% CI: 0.0008151698047983331 0.0416509212802729\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -188,16 +186,15 @@ } ], "source": [ - "# sample from the distribution of prior means\n", - "empirical_bayes_model = LinearEmpiricalBayes.from_results(results, cols=treatment_coefficients)\n", - "prior_mean_rvs = empirical_bayes_model.prior_mean_rvs(10000).mean(axis=0)\n", - "\n", - "# plot the prior\n", - "print(f\"95% confidence interval: {np.quantile(prior_mean_rvs, [.025, .975])}\")\n", - "ax = sns.histplot(x=prior_mean_rvs, kde=True, stat=\"density\")\n", - "ax.set_title(\"Empirical Bayes prior\")\n", - "ax.set_xlabel(XLABEL)\n", - "ax.set_xlim(XLIM)\n", + "parametric_bayes_model = Normal.from_results(results, columns=treatment_coefficients)\n", + "prior = parametric_bayes_model.get_marginal_prior(0)\n", + "lower, upper = prior.ppf(.025), prior.ppf(.975)\n", + "print(\"Prior 95% CI:\", lower, upper)\n", + "x = np.linspace(lower, upper)\n", + "ax = sns.lineplot(x=x, y=prior.pdf(x))\n", + "ax.axvline(prior.mean(), linestyle=\"--\")\n", + "ax.set_title(\"Parametric (normal) empirical Bayes prior\")\n", + "xlim = ax.get_xlim()\n", "plt.show()" ] }, @@ -205,13 +202,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "According to the empirical Bayes prior, there's a 95% chance that each text message increases vaccination rates by between 0.1 and 4.2 people per hundred.\n", - "\n", - "This version of empirical Bayes estimates the prior using [maximum likelihood estimation (MLE)](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation), which risks having too narrow a prior distribution. This package provides two ways to solve this problem. One, which we'll see later, is to use a different version of empirical Bayes called the [James-Stein estimator](https://en.wikipedia.org/wiki/James%E2%80%93Stein_estimator). Another is to use a [hierarchical Bayesian model](https://en.wikipedia.org/wiki/Bayesian_hierarchical_modeling).\n", - "\n", - "Hierarchical Bayes says to empirical Bayes, \"anything you can do, I can do meta.\" Hierarchical Bayes adds another layer to the empirical Bayes model in which we have a prior belief about our prior belief. This \"prior belief about the prior belief\" is called a *hyperprior*. We can use the hyperprior to express uncertainty in our prior belief and widen the prior distribution.\n", + "According to the parametric empirical Bayes prior, there's a 95% chance that each text message increases vaccination rates by between 0 and 4.2 people per hundred.\n", "\n", - "We could estimate the hyperprior using cross-validation, but I'm not going to get into that here. Below, I estimate the hyperprior using a simple heuristic that I've found works well on many datasets, then use Gibbs sampling to look at the resulting prior distribution." + "Now let's look at the prior from a nonparametric empirical Bayes estimator." ] }, { @@ -223,12 +216,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "95% confidence interval: [0.00101983 0.04159385]\n" + "Prior 95% CI: 0.019077726578352154 0.023207707577276984\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -238,21 +231,14 @@ } ], "source": [ - "# estimate the hyperprior using a simple heuristic\n", - "_, prior_std = empirical_bayes_model.estimate_prior_params()\n", - "hyperprior = loguniform(.5 * prior_std, 2 * prior_std)\n", - "\n", - "# use Gibbs sampling to sample from the distribution of prior means\n", - "hierarchical_bayes_model = LinearHierarchicalBayes.from_results(results, cols=treatment_coefficients, prior_cov_params_distribution=hyperprior)\n", - "prior_cov_rvs, sample_weight = hierarchical_bayes_model.prior_cov_rvs(10000)\n", - "prior_mean_rvs = [hierarchical_bayes_model.prior_mean_rvs(prior_cov).mean() for prior_cov in prior_cov_rvs]\n", - "\n", - "# plot the prior distribution\n", - "print(f\"95% confidence interval: {weighted_quantile(prior_mean_rvs, [.025, .975], sample_weight)}\")\n", - "ax = sns.histplot(x=prior_mean_rvs, weights=sample_weight, kde=True, stat=\"density\")\n", - "ax.set_title(\"Hierarchical Bayes prior\")\n", - "ax.set_xlabel(XLABEL)\n", - "ax.set_xlim(XLIM)\n", + "nonparametric_bayes_model = Nonparametric.from_results(results, columns=treatment_coefficients)\n", + "prior = nonparametric_bayes_model.get_marginal_prior(0)\n", + "lower, upper = prior.ppf(.025), prior.ppf(.975)\n", + "print(\"Prior 95% CI:\", lower, upper)\n", + "ax = sns.lineplot(x=x, y=prior.pdf(x))\n", + "ax.axvline(prior.mean(), linestyle=\"--\")\n", + "ax.set_title(\"Nonparametric empirical Bayes prior\")\n", + "ax.set_xlim(xlim)\n", "plt.show()" ] }, @@ -260,13 +246,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can see that the empirical and hierarchical Bayes priors are almost identical for this dataset.\n", + "According to the nonparametric empirical Bayes prior, there's a 95% chance that each text message increases vaccination rates by between 1.9 and 2.3 people per hundred.\n", + "\n", + "Notice that the nonparametric empirical Bayes prior is narrower than the parametric empirical Bayes prior. This is because the parametric empirical Bayes model accounts for uncertainty in our estimates of the prior parameters. By contrast, nonparametric empirical Bayes priors are often too narrow when estimating only a few treatment effects (in this case, 19). I typically prefer parametric empirical Bayes when estimating fewer than 50 treatment effects.\n", "\n", "### Summary\n", "\n", "In Bayesian analysis, we begin with a prior belief about our treatment effects. We then use data to update our belief according to Bayes' theorem. Our updated belief is called the *posterior*.\n", "\n", - "The key to good Bayesian analysis is a good prior belief. Classical Bayes takes the prior as a given. Empirical Bayes estimates a prior belief from the data. However, depending on how you estimate it, you may end up being overconfident in your prior belief. To fix this, you can try a different empirical Bayes estimator, like James-Stein, or express uncertainty in your prior belief using a hierarchical Bayesian estimator." + "The key to good Bayesian analysis is a good prior belief. Classical Bayes takes the prior as a given. Empirical Bayes estimates a prior belief from the data. Parametric empirical Bayes assumes the shape of the prior distribution while nonparametric empirical Bayes does not. Nonparametric empirical Bayes is more flexible than parametric empirical Bayes, but often gives unrealistically narrow confidence intervals when estimating only a few parameters. My rough rule is to use parametric empirical Bayes when estimating fewer than 50 treatment effects." ] }, { @@ -275,43 +263,29 @@ "source": [ "## Why should I use Bayesian analysis?\n", "\n", - "Why use this fancy, complicated Bayesian analysis when you can use standard techniques like OLS? The short answer is that Bayesian estimators make better predictions of the true treatment effects than OLS. I'm going to give you three ways to verify this claim using mathematical proofs, out-of-sample testing, and reconstruction plots. \n", - "\n", - "First, let's look at the math. James and Stein (1961) proved that their empirical Bayes estimator *dominates* unbiased estimators like OLS. This means that the James-Stein estimator has a lower expected mean squared error than OLS no matter what the true treatment effects are.\n", + "Why use this fancy, complicated Bayesian analysis when you can use standard techniques like OLS? The short answer is that Bayesian estimators make better predictions of the true treatment effects than OLS. We can verify that Bayesian estimators are better using mathematical proofs, out-of-sample testing, and reconstruction plots.\n", "\n", - "We can also mathematically show that, under standard assumptions, unbiased estimators like OLS exaggerate the variability of treatment effects. This fictitious variation makes it seem as though the best treatment is much better than average, and the worst treatment much worse than average, than it actually is. [Bayesian estimates \"shrink\" OLS estimates](https://kiwidamien.github.io/shrinkage-and-empirical-bayes-to-improve-inference.html), meaning that the posterior belief always falls between the OLS estimate and the prior. In shrinking the OLS estimates, Bayesian estimators reduce and often eliminate fictitious variation.\n", + "First, let's look at the math. James and Stein (1961) proved that their empirical Bayes estimator *dominates* unbiased estimators like OLS. This means that the James-Stein estimator has a lower expected mean squared error than OLS, regardless of the true treatment effects.\n", "\n", - "A second way to verify that Bayesian estimators make better predictions than OLS is to use [out-of-sample testing](https://en.wikipedia.org/wiki/Cross-validation_(statistics)). To understand out-of-sample testing, imagine we decide to run our experiment twice. After the first experiment, we get both Bayesian and OLS estimates. Then, we use these estimates to predict what's going to happen in the second experiment. After running the second experiment, we can see which estimator was better.\n", + "Additionally, unbiased estimators like OLS exaggerate the variability of treatment effects. This fictitious variation makes it seem like treatment effects vary widely even if they do not. [Bayesian estimates \"shrink\" OLS estimates](https://kiwidamien.github.io/shrinkage-and-empirical-bayes-to-improve-inference.html), meaning that the posterior belief always falls between the OLS estimate and the prior. Bayesian estimators reduce and often eliminate fictitious variation by shrinking the OLS estimates.\n", "\n", - "We may not be able to rerun our experiment, but we can simulate this process by splitting our data in half. We'll use one half of the data (called the *training set* or *in-sample data*) to train our models and get Bayesian and OLS estimates. Then, we test how well these estimates matched the other half of the data (called the *test set* or *out-of-sample data*).\n", + "A second way to verify that Bayesian estimators make better predictions than OLS is to use [out-of-sample testing](https://en.wikipedia.org/wiki/Cross-validation_(statistics)). To understand out-of-sample testing, imagine we decide to run our experiment twice. After the first experiment, we get both Bayesian and OLS estimates. Then, we use these estimates to predict what will happen in the second experiment. After running the second experiment, we can see which estimator was better.\n", "\n", - "How can we tell how well our estimates \"matched\" the test set? We're going to measure this using [log likelihood](https://en.wikipedia.org/wiki/Likelihood_function); a standard measure of goodness of fit measure. The test log likelihood tells us how likely we are to observe the test set according to our estimates. The estimates that give us the highest test log likelihood are best.\n", + "We may not be able to rerun our experiment, but we can simulate this process by splitting our data in half. We'll use one half of the data (the *training set* or *in-sample data*) to train our models and get Bayesian and OLS estimates. Then, we test how well these estimates matched the other half of the data (the *test set* or *out-of-sample data*).\n", "\n", - "Below, we repeat this splitting procedure many times and see how our Bayesian estimators stack up against OLS. We'll look at the three Bayesian models we've encountered so far:\n", + "How can we tell how well our estimates \"matched\" the test set? We're going to measure this using [log likelihood](https://en.wikipedia.org/wiki/Likelihood_function), a standard measure of goodness of fit measure. The test log-likelihood tells us how likely we are to observe the test set according to our estimates. The estimates that give us the highest test log-likelihood are the best.\n", "\n", - "1. Empirical Bayes using maximum likelihood estimation (MLE)\n", - "2. Empirical Bayes using James-Stein estimation\n", - "3. Hierarchical Bayes " + "Below, we repeat this splitting procedure many times to see how our Bayesian estimators stack up against OLS." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", + "image/png": 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", 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", 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", 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" ] @@ -358,36 +332,30 @@ " \n", " 0\n", " OLS\n", - " 46.187658\n", + " 45.439741\n", " \n", " \n", " 1\n", - " James-Stein\n", - " 46.606656\n", + " Parametric empirical Bayes\n", + " 51.271349\n", " \n", " \n", " 2\n", - " Empirical Bayes (MLE)\n", - " 52.385722\n", - " \n", - " \n", - " 3\n", - " Hierarchical Bayes\n", - " 52.415497\n", + " Nonparametric empirical Bayes\n", + " 51.853948\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Model Mean test log likelihood\n", - "0 OLS 46.187658\n", - "1 James-Stein 46.606656\n", - "2 Empirical Bayes (MLE) 52.385722\n", - "3 Hierarchical Bayes 52.415497" + " Model Mean test log likelihood\n", + "0 OLS 45.439741\n", + "1 Parametric empirical Bayes 51.271349\n", + "2 Nonparametric empirical Bayes 51.853948" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -404,10 +372,10 @@ " )\n", "\n", "\n", - "def compute_test_likelihood(train_mean, train_cov, test_mean, test_cov, model_cls, **init_kwargs):\n", + "def compute_test_likelihood(train_mean, train_cov, test_mean, test_cov, model_cls):\n", " # train the model on the training data\n", " # then ask it to predict how likely we would be to observe the test data\n", - " results = model_cls(train_mean, train_cov, **init_kwargs).fit()\n", + " results = model_cls(train_mean, train_cov).fit()\n", " return results.likelihood(test_mean, test_cov)\n", "\n", "\n", @@ -421,10 +389,9 @@ "\n", "\n", "ols_test_likelihood = []\n", - "empirical_bayes_test_likelihood = []\n", - "jamesstein_test_likelihood = []\n", - "hierarchical_bayes_test_likelihood = []\n", - "kf = RepeatedStratifiedKFold(n_splits=2, n_repeats=30)\n", + "parametric_bayes_test_likelihood = []\n", + "nonparametric_bayes_test_likelihood = []\n", + "kf = RepeatedStratifiedKFold(n_splits=2, n_repeats=5)\n", "\n", "for train_index, test_index in kf.split(df, df.treatment):\n", " train_mean, train_cov = estimate_mean_and_covariance(train_index)\n", @@ -432,44 +399,32 @@ "\n", " ols_test_likelihood.append(\n", " compute_test_likelihood(\n", - " train_mean, train_cov, test_mean, test_cov, LinearClassicBayes, prior_cov=np.inf\n", - " )\n", - " )\n", - "\n", - " empirical_bayes_test_likelihood.append(\n", - " compute_test_likelihood(\n", - " train_mean, train_cov, test_mean, test_cov, LinearEmpiricalBayes\n", + " train_mean, train_cov, test_mean, test_cov, Improper\n", " )\n", " )\n", "\n", - " jamesstein_test_likelihood.append(\n", + " parametric_bayes_test_likelihood.append(\n", " compute_test_likelihood(\n", - " train_mean, train_cov, test_mean, test_cov, JamesStein\n", + " train_mean, train_cov, test_mean, test_cov, Normal\n", " )\n", " )\n", "\n", - " _, prior_std = LinearEmpiricalBayes(train_mean, train_cov).estimate_prior_params()\n", - " hyperprior = loguniform(.5 * prior_std, 2 * prior_std)\n", - " hierarchical_bayes_test_likelihood.append(\n", + " nonparametric_bayes_test_likelihood.append(\n", " compute_test_likelihood(\n", - " train_mean, train_cov, test_mean, test_cov, LinearHierarchicalBayes, prior_cov_params_distribution=hyperprior\n", + " train_mean, train_cov, test_mean, test_cov, Nonparametric\n", " )\n", " )\n", "\n", - "plot_improvement(jamesstein_test_likelihood, \"James-Stein vs. OLS\")\n", - "plt.show()\n", - "\n", - "plot_improvement(empirical_bayes_test_likelihood, \"Empirical Bayes (MLE) vs. OLS\")\n", + "plot_improvement(parametric_bayes_test_likelihood, \"Parametric empirical Bayes vs. OLS\")\n", "plt.show()\n", "\n", - "plot_improvement(hierarchical_bayes_test_likelihood, \"Hierarchical Bayes vs. OLS\")\n", + "plot_improvement(nonparametric_bayes_test_likelihood, \"Nonparametric empirical Bayes vs. OLS\")\n", "plt.show()\n", "\n", "test_likelihoods = {\n", " \"OLS\": ols_test_likelihood,\n", - " \"James-Stein\": jamesstein_test_likelihood,\n", - " \"Empirical Bayes (MLE)\": empirical_bayes_test_likelihood,\n", - " \"Hierarchical Bayes\": hierarchical_bayes_test_likelihood\n", + " \"Parametric empirical Bayes\": parametric_bayes_test_likelihood,\n", + " \"Nonparametric empirical Bayes\": nonparametric_bayes_test_likelihood\n", "}\n", "pd.DataFrame({\n", " \"Model\": test_likelihoods.keys(),\n", @@ -481,38 +436,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Our out-of-sample analysis points to two conclusions:\n", + "Our out-of-sample analysis suggests that Bayesian estimators outperform OLS.\n", "\n", - "1. All three Bayesian estimators consistently make better predictions about the test set than OLS.\n", - "2. The empirical (MLE) and hierarchical Bayes models outperform James-Stein.\n", - "\n", - "The third way to verify that Bayesian estimators are better than OLS is to look at reconstruction plots. I find this to be the most intuitive demonstration that Bayesian estimators are superior, although out-of-sample testing is more rigorous.\n", + "The third way to verify that Bayesian estimators are better than OLS is to look at reconstruction plots. Reconstruction plots are the most intuitive demonstration that Bayesian estimators are superior, although out-of-sample testing is more rigorous.\n", "\n", "Reconstruction plots answer the following question: If these estimates are correct and we reran our experiment, how similar would the distribution of estimates in the second experiment be to the distribution of estimates in the original experiment (i.e., the experiment we actually ran)? Ideally, the distribution of estimates we would expect to see if we reran the experiment should match the distribution of estimates we saw in the original.\n", "\n", "How do we get the distribution of estimates we would expect to see if we reran the experiment? Unfortunately, this question takes us into Ph.D.-level statistics territory, so I'll refer curious and ambitious readers to the [Wikipedia entry on Gibbs Sampling](https://en.wikipedia.org/wiki/Gibbs_sampling) for more detail.\n", "\n", - "Below are reconstruction plots for the OLS, James-Stein, empirical Bayes (MLE), and hierarchical Bayes estimators. The original estimates are the orange x's. The distribution of estimates we would expect to see if we reran the experiment is in blue. Ideally, the blue dots should overlap with the orange x's." + "Below are reconstruction plots for the OLS and Bayesian estimators. The original estimates are the orange x's. The distribution of estimates we would expect to see if we reran the experiment is in blue. Ideally, the blue dots should overlap with the orange x's." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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" ] @@ -547,7 +489,7 @@ " ax.set_xlabel(XLABEL)\n", " if xlim:\n", " ax.set_xlim(xlim)\n", - " ax.axvline(0)\n", + " ax.axvline(0, linestyle=\"--\")\n", " return ax\n", "\n", "\n", @@ -555,16 +497,12 @@ "xlim = ols_reconstruction_plot.get_xlim()\n", "plt.show()\n", "\n", - "jamesstein_results = JamesStein.from_results(results, cols=treatment_coefficients).fit()\n", - "make_reconstruction_plot(jamesstein_results, \"James-Stein reconstruction plot\", xlim=xlim)\n", - "plt.show()\n", - "\n", - "empirical_results = empirical_bayes_model.fit()\n", - "make_reconstruction_plot(empirical_results, \"Empirical Bayes (MLE) reconstruction plot\", xlim=xlim)\n", + "parametric_results = parametric_bayes_model.fit()\n", + "make_reconstruction_plot(parametric_results, title=\"Parametric empirical Bayes reconstruction plot\", xlim=xlim)\n", "plt.show()\n", "\n", - "hierarchical_results = hierarchical_bayes_model.fit()\n", - "make_reconstruction_plot(hierarchical_results, \"Hierarchical Bayes reconstruction plot\", xlim=xlim)\n", + "nonparametric_results = nonparametric_bayes_model.fit()\n", + "make_reconstruction_plot(nonparametric_results, title=\"Nonparametric empirical Bayes reconstruction plot\", xlim=xlim)\n", "plt.show()" ] }, @@ -572,14 +510,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The reconstruction plots confirm the results of our out-of-sample testing.\n", - "\n", - "1. Looking at the OLS reconstruction plot, we see that the blue dots are more spread out than the orange x's. This shows that OLS exaggerates the variability of treatment effects. That is, if the OLS estimates were correct, we would expect our treatment effect estimates to be more dispersed (like the blue dots) and less concentrated (like the orange x's).\n", - "2. James-Stein improves upon OLS, but the empirical Bayes (MLE) and hierarchical Bayes models are the clear winners.\n", + "Notice the blue dots are more spread out than the orange x's in the OLS reconstruction plot. This confirms that OLS suffers from fictitious variation. By contrast, the blue dots are on top of the orange x's in the Bayesian reconstruction plots. This confirms that Bayesian estimators reliably estimate the distribution of treatment effects.\n", "\n", "### Summary\n", "\n", - "Bayesian analysis is better than traditional techniques like OLS because it makes more accurate predictions of the true treatment effects. We verified this using mathematical proofs, out-of-sample testing, and reconstruction plots. For our flu study dataset, the empirical Bayes (MLE) and hierarchical Bayes models are the winners." + "Bayesian analysis is better than traditional techniques like OLS because it makes more accurate predictions of the true treatment effects. We verified this using mathematical proofs, out-of-sample testing, and reconstruction plots." ] }, { @@ -588,31 +523,23 @@ "source": [ "## How much can Bayesian analysis change my results?\n", "\n", - "Maybe you're thinking, \"Okay, I'm convinced that Bayesian models are better than OLS, but how different are they? Maybe they'll shrink the OLS estimates a little bit but is the difference that significant? Can Bayesian estimators fundamentally change our understanding of scientific research?\"\n", + "Maybe you're thinking, \"Okay, I'm convinced that Bayesian models are better than OLS, but how different are they? Maybe they'll shrink the OLS estimates slightly but is the difference significant? Can Bayesian estimators fundamentally change our understanding of scientific research?\"\n", "\n", - "To understand the impact of Bayesian analysis, let's see how OLS and Bayesian estimates compare for our flu study. As a reminder, the common perception of this study's results, both in popular media and in academic circles, is that the ability of a text to increase vaccination rates critically depends on its phrasing. This perception is best summed up by Fortune.\n", + "To understand the impact of Bayesian analysis, let's see how OLS and Bayesian estimates compare for our flu study. As a reminder, the common perception of this study's results, both in popular media and in academic circles, is that the ability of a text to increase vaccination rates critically depends on its phrasing. Fortune best sums up this perception.\n", "\n", "> What they found was eye-opening. Precisely *how* a message was worded had a huge impact on whether the patient ended up getting the shot.\n", "\n", - "There are two questions we can answer here:\n", - "\n", - "1. How much do the true effects of the messages vary depending on the phrasing?\n", - "2. Can we identify which messages performed better than others and by how much?\n", - "\n", - "These are related but distinct questions. For example, we know *that* some children will grow up to be much better at sports than others but we can't be sure *which* children will be better at sports when they're very young. In the same way, knowing *that* some messages work better than others is not the same as knowing *which* messages work better than others.\n", - "Let's start answering these questions by plotting the OLS and Bayesian estimates of the effects.\n", - "\n", - "Remember that we've verified that both the empirical Bayes (MLE) and hierarchial Bayes models consistently and substantially outperformed OLS both in the reconstruction plot and in out-of-sample testing." + "Now that we've verified that Bayesian estimators outperform OLS, let's plot the OLS and Bayesian results." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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" ] @@ -646,17 +573,17 @@ " ax = results.point_plot(title=title)\n", " ax.set_xlabel(XLABEL)\n", " ax.set_xlim(XLIM)\n", - " ax.axvline(0)\n", + " ax.axvline(0, linestyle=\"--\")\n", " return ax\n", "\n", "\n", "point_plot(ols_results, title=\"OLS estimates\")\n", "plt.show()\n", "\n", - "point_plot(empirical_results, title=\"Empirical Bayes (MLE) estimates\")\n", + "point_plot(parametric_results, title=\"Parametric empirical Bayes estimates\")\n", "plt.show()\n", "\n", - "point_plot(hierarchical_results)\n", + "point_plot(nonparametric_results, title=\"Nonparametric empirical Bayes estimates\")\n", "plt.show()" ] }, @@ -664,61 +591,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Looking at the OLS plot, we get the impression that we've identified certain text messages that outperform others. The Bayesian plots show us that this perception is incorrect. According to the Bayesian models, we have almost no idea which messages are better than others.\n", - "\n", - "Let's dig into this by looking at \"relative effects,\" specifically\n", - "\n", - "1. How much better is the best message than the average message?\n", - "2. How much better is the best message than the worst message?\n", - "\n", - "We'll start with the messages that performed best and worst in the experiment (the *in-sample* best and worst messages). Remember, the messages that performed best and worst in the experiment may not truly be the best and worst messages. So this analysis answers our second question: Based on our experiment, can we identify which messages performed better than others and by how much?" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==================== \n", - "Relative effects as estimated by OLS\n", - "Increase in vaccination rates using the in-sample best treatment versus the average treatment: 2.4814 percentage points\n", - "Increase in vaccination rates using the in-sample best treatment versus the in-sample worst treatment: 4.0169 percentage points\n", - "==================== \n", - "Relative effects as estimated by empirical Bayes (MLE)\n", - "Increase in vaccination rates using the in-sample best treatment versus the average treatment: 0.0000 percentage points\n", - "Increase in vaccination rates using the in-sample best treatment versus the in-sample worst treatment: 0.0000 percentage points\n", - "==================== \n", - "Relative effects as estimated by hierarchical Bayes\n", - "Increase in vaccination rates using the in-sample best treatment versus the average treatment: 0.0000 percentage points\n", - "Increase in vaccination rates using the in-sample best treatment versus the in-sample worst treatment: 0.0001 percentage points\n" - ] - } - ], - "source": [ - "def estimate_relative_effects(header, params):\n", - " print(20*\"=\", f\"\\n{header}\")\n", - " print(f\"Increase in vaccination rates using the in-sample best treatment versus the average treatment: {100 * (params.max() - params.mean()):.4f} percentage points\")\n", - " print(f\"Increase in vaccination rates using the in-sample best treatment versus the in-sample worst treatment: {100 * (params.max() - params.min()):.4f} percentage points\")\n", - "\n", - "\n", - "estimate_relative_effects(\"Relative effects as estimated by OLS\", ols_results.params)\n", - "estimate_relative_effects(\"Relative effects as estimated by empirical Bayes (MLE)\", empirical_results.params)\n", - "estimate_relative_effects(\"Relative effects as estimated by hierarchical Bayes\", hierarchical_results.params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These results confirm what we saw in the point plots. OLS suggests that the average message increases vaccination rates by 2.1 people per hundred. The best in-sample message is more than twice as effective, increasing vaccination rates by an additional 2.5 people per hundred. Our Bayesian models show us that this picture is incorrect. Compared to the average message, the in-sample best message increases vaccination rates by less than 1 person per million.\n", + "Looking at the OLS plot, we get the impression that we've identified certain text messages that outperform others. The Bayesian plots show us that this perception is incorrect. According to the Bayesian models, the treatment effects are indistinguishable.\n", "\n", - "But, as we said, just because we can't identify which text messages perform better than others doesn't mean that phrasing doesn't matter. This brings us to our first question: How much do the true effects of the text messages vary depending on the phrasing?\n", + "Side note: Remember how we saw earlier that the nonparametric empirical Bayes prior was unrealistically narrow? The narrow prior leads to a narrow posterior (see the nonparametric empirical Bayes plot above). This is why parametric empirical Bayes is often better when analyzing only a few treatments.\n", "\n", - "To answer this question, we'll draw samples from the posterior distribution and look at the effect of the truly best message (i.e., the message that performed best in each draw) relative to the average and truly worst messages." + "According to OLS, the top-performing text message increases vaccination rates by 250 people in 10,000 compared to the average message. According to Bayes, the top-performing message increses vaccination rates by less than 1 person in 10,000." ] }, { @@ -730,50 +607,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "==================== \n", - "Relative effects as estimated by OLS\n", - "Increase in vaccination rates using the truly best treatment versus the average treatment: 2.8038 percentage points\n", - "Increase in vaccination rates using the truly best treatment versus the truly worst treatment: 5.2787 percentage points\n", - "==================== \n", - "Relative effects as estimated by empirical Bayes (MLE)\n", - "Increase in vaccination rates using the truly best treatment versus the average treatment: 0.0009 percentage points\n", - "Increase in vaccination rates using the truly best treatment versus the truly worst treatment: 0.0018 percentage points\n", - "==================== \n", - "Relative effects as estimated by hierarchical Bayes\n", - "Increase in vaccination rates using the truly best treatment versus the average treatment: 0.0010 percentage points\n", - "Increase in vaccination rates using the truly best treatment versus the truly worst treatment: 0.0020 percentage points\n" + "OLS: Increase in vaccination rates using the top performing treatment versus the average treatment: 248.1361967287397 per 10,000\n", + "Bayes: Increase in vaccination rates using the top performing treatment versus the average treatment: 4.688577921740933e-05 per 10,000\n" ] } ], "source": [ - "def estimate_relative_effects(header, posterior_mean_rvs):\n", - " best_effect = posterior_mean_rvs.max(axis=1).mean()\n", - " worst_effect = posterior_mean_rvs.min(axis=1).mean()\n", - " average_effect = posterior_mean_rvs.mean()\n", - "\n", - " print(20*\"=\", f\"\\n{header}\")\n", - " print(f\"Increase in vaccination rates using the truly best treatment versus the average treatment: {100 * float(best_effect - average_effect):.4f} percentage points\")\n", - " print(f\"Increase in vaccination rates using the truly best treatment versus the truly worst treatment: {100 * float(best_effect - worst_effect):.4f} percentage points\")\n", - "\n", - "\n", - "estimate_relative_effects(\"Relative effects as estimated by OLS\", ols_results.posterior_mean_rvs)\n", - "estimate_relative_effects(\"Relative effects as estimated by empirical Bayes (MLE)\", empirical_results.posterior_mean_rvs)\n", - "estimate_relative_effects(\"Relative effects as estimated by hierarchical Bayes\", hierarchical_results.posterior_mean_rvs)" + "print(\n", + " \"OLS: Increase in vaccination rates using the top performing treatment versus the average treatment:\",\n", + " 10000 * (ols_results.params[0] - ols_results.params.mean()),\n", + " \"per 10,000\"\n", + ")\n", + "print(\n", + " \"Bayes: Increase in vaccination rates using the top performing treatment versus the average treatment:\",\n", + " 10000 * (parametric_results.params[0] - parametric_results.params.mean()),\n", + " \"per 10,000\"\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "According to OLS, the phrasing matters tremendously. The truly best message would increase vaccination rates by an additional 2.8 people per hundred over the average message. Our Bayesian models again show us that this picture is incorrect. Compared to the average message, the truly best message increases vaccination rates by only 1 person per hundred thousand.\n", + "According to OLS, the phrasing matters tremendously. Our Bayesian models again show us that this picture is incorrect.\n", "\n", "In sum, texting patients a reminder to get a flu vaccine boosts vaccination rates by about 2.1 people per hundred. Beyond the mere act of texting a reminder, there's no evidence that the phrasing of the text messages used in this study has a practically significant effect on vaccination rates.\n", "\n", "## Conclusion\n", "\n", - "Bayesian analysis can significantly change our understanding of scientific research. We illustrated this by re-analyzing data from a highly-regarded study. Using Bayesian analysis, we showed that the study's original conclusion vastly overstated the effect of the best treatment compared to the average and worst treatments. Our analysis was quick and dirty and we didn't use the original data, so we shouldn't put too much stock in the precise numbers. However, it seems likely that the study's original analysis inflated the relative treatment effects by an order of magnitude or more.\n", + "Bayesian analysis can significantly change our understanding of scientific research. We illustrated this by re-analyzing data from a highly-regarded study. Using Bayesian analysis, we showed that the study's original conclusion vastly overstated the effect of the top-performing treatment compared to the average treatment.\n", "\n", - "Congratulations for sticking with this primer until the end! We've explained how Bayesian analysis works, why you should use it, and how it can impact your results. To run Bayesian analysis on your own data, check out the file named `bayes.ipynb` in this folder." + "Congratulations for sticking with this primer until the end! We've explained how Bayesian analysis works, why you should use it, and how it can impact your results. To run Bayesian analysis on your own data, check out the file named `multiple_inference.ipynb` in this folder." ] }, { @@ -786,10 +650,10 @@ ], "metadata": { "interpreter": { - "hash": "120d65e34230161c0f4356d19a77763cc2f6669dcb2a194d42d3b2faf517ecd2" + "hash": "a31fe93114e6fe9c0b874076e62df141d5b35f609e1bfa94ca168a298e55e549" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.0 ('conditional-inference')", "language": "python", "name": "python3" }, diff --git a/examples/conditional_inference_primer.ipynb b/examples/conditional_inference_primer.ipynb deleted file mode 100644 index 1f38620..0000000 --- a/examples/conditional_inference_primer.ipynb +++ /dev/null @@ -1,867 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A primer on conditional inference\n", - "\n", - "I designed this notebook to give you a primer on conditional inference and inference on ranked parameters: how it works and why you should use it. To run conditional inference on your own data, check out the file named `rqu.ipynb` in this folder. (RQU is an abbreviation for \"ranked quantile unbiased\").\n", - "\n", - "This notebook uses animations. To view these in your browser, go to this binder\n", - "\n", - "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gl/dsbowen%2Fconditional-inference/HEAD?filepath=examples%2Fconditional_inference_primer.ipynb)\n", - "\n", - "First, what is *conditional inference* and when should you use it? Conditional inference involves estimating a parameter given some information about it known as the *conditioning event*. The most common use case is inference after optimization.\n", - "\n", - "For example, [Chetty and Hendren (2017)](https://opportunityinsights.org/wp-content/uploads/2018/03/movers_paper1.pdf) estimates a model to predict a child's future earnings and probability of attending college based on characteristics of the neighborhood in which they grew up. The goal is to use this model to identify neighborhoods with the greatest potential for targeted policies to improve economic opportunity.\n", - "\n", - "In this case, the parameters of interest are neighborhood-level estimates of economic opporunity. The optimization involves selecting neighborhoods with the worst economic opportunity. The goal of conditional inference is to re-estimate the economic opportunity of the selected neighborhoods conditioning on the reason they were selected (i.e., because they had the lowest economic opportunity scores according to the original estimates).\n", - "\n", - "Let's start by looking at Chetty and Hendren (2017)'s original estimates." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "\n", - "import warnings\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "from matplotlib.patches import Rectangle\n", - "from scipy.stats import multivariate_normal, norm\n", - "\n", - "from conditional_inference.bayes.classic import LinearClassicBayes\n", - "from conditional_inference.rqu import RQU\n", - "from conditional_inference.stats import quantile_unbiased, truncnorm\n", - "\n", - "from utils import RankConditionAnimation, QuantileUnbiasedAnimation, confidence_ellipse\n", - "\n", - "MOVERS_DATA_FILE = \"../simulations/losers-empirical/movers.csv\"\n", - "XLABEL = \"Economic opportunity score\"\n", - "\n", - "sns.set()\n", - "warnings.simplefilter(\"ignore\")\n", - "\n", - "fig = plt.figure(figsize=(8, 8))\n", - "ax = fig.add_subplot()\n", - "# note that a Bayesian model with an infinite prior covariance is equivalent to the conventional estimates\n", - "conventional_model = LinearClassicBayes.from_csv(MOVERS_DATA_FILE, prior_cov=np.inf)\n", - "conventional_results = conventional_model.fit(cols=\"sorted\")\n", - "conventional_results.point_plot(title=\"Conventional estimates\", yname=XLABEL, ax=ax)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The problem with conventional estimates\n", - "\n", - "To illustrate the problem with conventional estimates when performing conditional inference, imagine that the true economic opportunity for each neighborhood was the same. Because of variability in our estimates, some neighborhoods will appear to have lower economic opportunity scores than others. So, if we select the neighborhoods with the lowest scores, we are likely to underestimate their scores and overestimate the effects of policies intended to improve their economic opportunity. This is an example of the winner's curse.\n", - "\n", - "We perform this exercise below by assuming the true economic opportunity for all neighborhoods is the same (the dashed vertical line). Under this assumption, we sample hypothetical conventional estimates. Plotting these hypothetical estimates, we can see that we underestimate the economic opportunity of the lowest-scoring neighborhoods (towards the bottom, the dots are to the left of the vertical line).\n", - "\n", - "This problem is not eliminated (though it is mitigated) when there are genuine differences between the parameters we're estimating." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(8, 8))\n", - "ax = fig.add_subplot()\n", - "\n", - "# assume the true economic opportunity for each tract is the same\n", - "true_mean = np.full(conventional_model.mean.shape, conventional_model.mean.mean())\n", - "estimated_mean = pd.Series(\n", - " # sample from the \"true\" distribution in which each tract has the same economic opportunity\n", - " multivariate_normal(true_mean, conventional_model.cov).rvs(),\n", - " index=conventional_model.exog_names\n", - ")\n", - "# plot the hypothetical conventional estimates\n", - "hypothetical_model = LinearClassicBayes(estimated_mean, conventional_model.cov, prior_cov=np.inf)\n", - "hypothetical_results = hypothetical_model.fit(cols=\"sorted\")\n", - "hypothetical_results.point_plot(title=\"Hypothetical conventional estimates\", yname=XLABEL, ax=ax)\n", - "ax.axvline(true_mean[0], linestyle=\"--\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## How does quantile-unbiased analysis work?\n", - "\n", - "One desirable property of estimators is *quantile-unbiasedness* (sometimes called *calibration*). Intuitively, the true value of the estimated parameter should fall below our median estimate of it exactly half the time. Generally, the true value of the estimated parameter should fall below our $\\alpha$-quantile estimate of it with probability $\\alpha$. Estimators which have this property are *quantile-unbiased*.\n", - "\n", - "Similarly, we want our confidence intervals to have correct coverage. For example, the true value of the estimated parameter should fall within the 95% CI at least 95% of the time.\n", - "\n", - "In a conditional inference setting, we might want an estimator to be quantile-unbiased and a confidence interval to have correct coverage given the conditioning event.\n", - "\n", - "For example, suppose the government wants to implement a policy to increase economic opportunity in all neighborhoods with an economic opportunity score below -0.2. Suppose we want a quantile-unbiased estimate of Charlotte's economic opportunity score given that it will benefit from this policy (i.e., given that the conventional estimate of its economic opportunity score is less than -0.2).\n", - "\n", - "We can plot a quantile-unbiased CDF (the purple line in the plot below), $\\alpha$, using the equation,\n", - "\n", - "$$\n", - " \\alpha = 1 - F_{TN}(y, \\hat{\\mu}_\\alpha, \\sigma, S)\n", - "$$\n", - "\n", - "where\n", - "\n", - "- $F_{TN}$ is the truncated normal CDF; the red plot is $1 - F_{TN}(\\cdot)$\n", - "- $y$ is the conventional estimate of the parameter; orange vertical line (the orange plot is the CDF of the conventional estimate)\n", - "- $\\hat{\\mu}_\\alpha$ is the location parameter of the truncated normal (the mean of the untruncated normal); red vertical line\n", - "- $\\sigma$ is the scale parameter of the truncated normal (the standard deviation of the untruncated normal)*\n", - "- $S$ is the truncation set, in this case $S=(-\\infty, -0.2]$; green highlighted area\n", - "\n", - "So $F_{TN}(y, \\hat{\\mu}_\\alpha, \\sigma, S)$ is the truncated normal CDF with location parameter $\\hat{\\mu}_\\alpha$ and scale parameter $\\sigma$ truncated to the interval $S$ evaluated at $y$.\n", - "\n", - "$\\hat{\\mu}_\\alpha$ is a quantile-unbiased estimate of the true paramter, $\\mu$, because $\\mu$ will fall below $\\hat{\\mu}_\\alpha$ with probability $\\alpha$. We can construct a 95% CI with correct conditional coverage as $[\\hat{\\mu}_{.025}, \\hat{\\mu}_{.975}]$.\n", - "\n", - "*We assume $\\sigma$ is known. In practice, we plug in a consistent estimate of $\\sigma$, usually the standard deviation of the conventional estimate." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. 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i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "neighborhood = \"Charlotte\"\n", - "truncation_set = [(-np.inf, -.2)]\n", - "index = conventional_model.exog_names.index(neighborhood)\n", - "y = conventional_model.mean[index]\n", - "sigma = np.sqrt(conventional_model.cov[index, index])\n", - "\n", - "ani = QuantileUnbiasedAnimation(y, sigma, truncation_set, xlim=(-.5, .3)).make_animation(\n", - " title=\"Quantile-unbiased analysis\",\n", - " xlabel=f\"Economic opportunity score for {neighborhood}\"\n", - ")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This technique allows us to easily compute quantile-unbiased estimates and confidence intervals given the conditioning event." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Conventional estimate of Charlotte's economic opportunity score: -0.248\n", - "Conventional 95% CI: [-0.43615654 -0.05984346]\n", - "Median-unbiased estimate of Charlotte's economic opportunity score: -0.145\n", - "Conditional 95% CI: [-0.42169341 0.47155463]\n" - ] - } - ], - "source": [ - "dist = quantile_unbiased(y, scale=sigma, truncation_set=truncation_set)\n", - "print(f\"Conventional estimate of {neighborhood}'s economic opportunity score: {y}\")\n", - "print(f\"Conventional 95% CI: {norm.ppf([.025, .975], y, sigma)}\")\n", - "print(f\"Median-unbiased estimate of {neighborhood}'s economic opportunity score: {dist.ppf(.5):.3f}\")\n", - "print(f\"Conditional 95% CI: {dist.ppf([.025, .975])}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conditioning on the rank order\n", - "\n", - "Often, the conditioning event we're interested in is about the rank order of a parameter.\n", - "\n", - "For example, suppose the government decides to implement a policy in the 5 neighborhoods with the lowest economic economic opportunity scores. The conditioning event is that the conventional estimate of the neighborhood's score ranked in the bottom 5.\n", - "\n", - "To use quantile-unbiased analysis, we need to express this conditioning event as a truncation set $S$. In our example, $S$ is the set of values the conventional estimate could have taken on such that it would be ranked in the bottom 5. How do we compute this truncation set?\n", - "\n", - "When the conventional estimates are independent, as we assume they are for the current dataset, this is an easy task. To be ranked in the bottom 5, the conventional estimate has to be less than that of the neighborhood ranked 6th from the bottom.\n", - "\n", - "Below, we plot conditional median estimates and confidence intervals for neighborhoods with the 5 lowest economic opportunity scores. The vertical line is the conventional estimate of the neighborhood ranked 6th from the bottom." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "\n", - "cutoff_rank = 5\n", - "rqu_model = RQU.from_csv(MOVERS_DATA_FILE)\n", - "cols = rqu_model.mean.argsort()[:cutoff_rank]\n", - "ax = rqu_model.fit(cols=cols, rank=np.arange(-cutoff_rank, 0)).point_plot(title=\"Conditional estimates\", yname=XLABEL)\n", - "ax.axvline(np.sort(rqu_model.mean)[cutoff_rank], linestyle=\"--\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How do we compute the truncation set $S$ when the conventional estimates are correlated?\n", - "\n", - "To answer this question, let's first ask how our conventional estimate of parameter $j$ depends on our conventional estimate of parameter $i$. Usually, the conventional estimates follow a joint normal distribution. So, if we changed our conventional estimate of parameter $i$ from $y_i$ to $y'_i$, we would update our conventional estimate of parameter $j$ from $y_j$ to $y'_j$ using the formula for the [conditional expectation of a joint normal distribution](https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Bivariate_conditional_expectation).\n", - "\n", - "$$\n", - " y'_j = y_j + \\frac{\\sigma_{ij}}{\\sigma^2_i} (y'_i - y_i)\n", - "$$\n", - "\n", - "Using this formula, we can find the \"intersection point\" $q_j$ at which the conventional estimate of parameter $i$ equals the conventional estimate of parameter $j$, $y'_i = y'_j = q_j$.\n", - "\n", - "$$\n", - " q_j = \\frac{\\sigma^2_i y_j - \\sigma_{ij} y_i}{\\sigma^2_i - \\sigma_{ij}}\n", - "$$\n", - "\n", - "\n", - "The intersection point is the point at which parameters $i$ and $j$ switch ranks. If $i$ was ranked *below* $j$ for values less than $q_j$, it will be ranked *above* $j$ for values greater than $q_j$. If $i$ was ranked *above* $j$ for values less than $q_j$, it will be ranked *below* $j$ for values greater than $q_j$.\n", - "\n", - "This suggests a simple algorithm for computing the truncation set, roughly,\n", - "\n", - "1. Drag the conventional estimate of parameter $i$, $y'_i$, from $-\\infty$ to $\\infty$\n", - "2. As we go, if the rank of $y'_i$ is in the desired set of ranks (e.g., the bottom 5), add $y'_i$ to $S$\n", - "3. When we hit an intersection point with another parameter $j$, $q_j$, switch the ranks of $i$ and $j$\n", - "\n", - "Run the cell below to visualize the algorithm. This shows how to construct the truncation set for the event that the policy at index 1 is ranked 2nd.\n", - "\n", - "- The orange vertical line is the conventional estimate of parameter $i$, $y'_i$\n", - "- The blue vertical lines are the conventional estimates of the other parameters $y'_j$\n", - "- The green shaded area is the truncation set $S$" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. 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i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "y = np.array([0, 1, 2])\n", - "cov = np.array([\n", - " [2, 1.3, 0],\n", - " [1.3, 1, -.2],\n", - " [0, -.2, 1]\n", - "])\n", - "index = 1\n", - "rank = [2]\n", - "\n", - "ani = RankConditionAnimation(y, cov, index, rank, xlim=(0, 6)).make_animation(\n", - " title=\"Computing the truncation set from a rank condition\",\n", - " xlabel=\"Conventional estimates y'\"\n", - ")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summary\n", - "\n", - "So far, we've seen why conventional estimates are often poorly calibrated when performing conditional inference. We've also seen how to construct quantile-unbiased (i.e., well-calibrated) estimates given a conditioning event. Finally, we've learned how to translate a rank order conditioning event into a truncation set that we can use to obtain quantile-unbiased estimates." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conditional versus unconditional inference\n", - "\n", - "In the previous section, we considered conditional inference with a rank order condition. That is, we constructed quantile-unbiased estimators and correct confidence intervals for the economic opportunity scores of specific neighborhoods given that they ranked near the bottom according to conventional estimates.\n", - "\n", - "In this section, we consider how to perform *unconditional* inference on ranked parameters. For example, imagine each state calculated the economic opportunity score of its neighborhoods and targeted policies at the lowest-scoring neighborhoods. If we are interested in obtaining quantile-unbiased estimates and correct confidence intervals for a specific neighborhood in a specific state, we should use conditional inference. If, instead, we are interested in estimates that are quantile-unbiased and confidence intervals that are correct on average over targeted neighborhoods and states, we should use unconditional inference.\n", - "\n", - "Because the requirements for unconditional inference are less strict, unconditional estimates are generally more accurate and unconditional confidence intervals are generally shorter.\n", - "\n", - "### Projection confidence intervals\n", - "\n", - "One approach to unconditional inference is to use *projection confidence intervals*. Intuitively, a 95% projection confidence interval is an $N$-dimensional hyperrectangle (where $N$ is the number of parameters) that contains at least 95% of the joint distribution of the parameter estimates.\n", - "\n", - "We illustrate how to construct this confidence interval below for 2 parameters. Let's start by visualizing the joint distribution of the conventional estimates." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# conventional parameter estimates and covariance matrix\n", - "%matplotlib inline\n", - "\n", - "y = [1, 2]\n", - "cov = np.array([\n", - " [3, .5],\n", - " [.5, 1]\n", - "])\n", - "\n", - "palette = sns.color_palette()\n", - "scale = 3.3 * np.sqrt(np.diag(cov))\n", - "xlim = y[0] - scale[0], y[0] + scale[0]\n", - "ylim = y[1] - scale[1], y[1] + scale[1]\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(xlim=xlim, ylim=ylim)\n", - "confidence_ellipse(y, cov, ax)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. **Sample from the joint distribution.**" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "ax = fig.add_subplot(xlim=xlim, ylim=ylim)\n", - "confidence_ellipse(y, cov, ax)\n", - "sample = multivariate_normal.rvs(y, cov, size=200)\n", - "sns.scatterplot(x=sample[:, 0], y=sample[:, 1], ax=ax)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2. **Standardize the observations.** From each observation in the sample, subtract the vector of conventional estimates then divide by (the square root of) the diagonal elements of the covariance matrix. Our standardized observations have effectively been sampled from a joint normal in which the marginal distributions are all standard normal." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "ax = fig.add_subplot(xlim=(-3.3, 3.3), ylim=(-3.3, 3.3))\n", - "standardized_sample = (sample - y) / np.sqrt(np.diag(cov))\n", - "sns.scatterplot(x=standardized_sample[:, 0], y=standardized_sample[:, 1], ax=ax)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3. **Take the maximum.** For each observation, take the maximum value of the $N$ dimensions." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "ax = fig.add_subplot(xlim=(-3.3, 3.3), ylim=(-3.3, 3.3))\n", - "sns.scatterplot(x=standardized_sample[:, 0], y=standardized_sample[:, 1], ax=ax)\n", - "ax.plot(np.linspace(-3, 3), np.linspace(-3, 3), linestyle=\"--\")\n", - "\n", - "mask = standardized_sample[:, 0] > standardized_sample[:, 1]\n", - "for mask in (mask, ~mask):\n", - " indices = np.random.choice(np.where(mask)[0], 7, replace=False)\n", - " subsample = standardized_sample[indices]\n", - " for point in subsample:\n", - " ax.arrow(\n", - " point[0],\n", - " point[1],\n", - " max(point[1] - point[0], 0),\n", - " max(point[0] - point[1], 0),\n", - " color=palette[1],\n", - " head_width=.05,\n", - " length_includes_head=True\n", - " )\n", - "\n", - "plt.show()\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(xlim=(-3.3, 3.3), ylim=(-3.3, 3.3))\n", - "max_standardized_sample = standardized_sample.max(axis=1)\n", - "sns.scatterplot(x=max_standardized_sample, y=max_standardized_sample, ax=ax)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "4. **Draw a hypercube centered on 0 that covers the 97.5th* percentile of the (transformed) observations.** Intuitively, this step draws a box so large that, even if the true values of the parameters were identical, the box would contain the maximum parameter estimate 97.5% of the time.\n", - "\n", - "*The reason we use 97.5th percenile for a 95% CI is analogous to why each tail in a two-tailed hypothesis test with $\\alpha=.05$ contains 2.5% of the distribution of the test statistic." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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KNdob//neE2xa38ZjYx7/zA2XEWz2U+P1qDcuZ1EyFykEFxztGqSjcyB9ngqM742PrhlftrCO5no/CxoqnSSu3rhkoGQuMpfczgmHvQNR4onEOW//weXs4Fy4oJq2C+qdEw5VjcsklMxF5oILBkfiHDjcz7fHLDf8X7e8K2NvfPXyAFeubHEuVI5N8roiKdo0JDLbPM4uzqMnw3R2D6YnOKOxON/7f/vYsmH1uNt/7rzxMs5vqSJQU6lqXKZNlbnIbHFD32CMw50DPLh977gJzud2ddDdF6GzZ5CqSg+f27QOt9t1pjeualxmSMlcZDa4Yc/B07zdM3jOCc7H/60dn9dDQ52POn+Fs/EniapxyYraLCL55oaTfVG+88SecWeNjxqd4BxtqSwOVOmIWsmZKnORfHFDz8AwJ3sHcblcZ10YMcrn9bDsvDru/f13s2xhDYwUKmApJ6rMRXLlgv5IjNcP9/HrN7v43rOvc/RkKOOFET6vhzs2rqE1UM2yViVyyR9V5iLZcsPpUIwjJ0M8uH3PWTs4J14YsThYQ6DBz3kL/M4Epzb/SB4pmYtkwwuvtvdysnfonBOcz+7q4E9//90MRUeoramk2ldBc51XK1VkVqjNIjITqZbK0ZODPLh976QTnAPhGPZIH9FYnPMDVTRXe7VSRWaNKnOR6fLAax19fGvbq+OOqM00wel2ubhj4xoWBWsINviUxGXWqTIXmYoLBuNxTvRE+Na2V8cl8cwTnJdxuQmy4vwGgnVK5DI3VJmLnIsL+odinOqLUOP3Eh4aTify0ST+2NgJzpYamur8LGr2n5nc1NpxmSNK5iKZuGHf4T6++fir6VUqn75uNQsD1XT2DNLdF0kn8aWttdTX+mis81Jd4dEqFSkItVlExnLDwRN9HO0eTCdycCY1//6p1/jkfztzKNZAOMZ5gWqCTVUsCvip9nhUiUvB5FSZG2NuAe4DKoH7rbXfyktUIoXggYOdA1RWVBAajGVcpRIaGmbD1cs5L1BNS3M1iwKp69vUF5cCy7oyN8YsBv4K+CCwFrjNGLMqX4GJzCk3nDwdJRJN8HpHLxUed7oCH+Xzeqj0enj6ZwdoqvexaIFfSVyKRi5tlmuBF6y1vdbaMPAD4Mb8hCUyR1wQio6wp+M0X/77X/A33/81T+7cTyQa45aPXTJulcrm9W109w3x2ZvWcoG24kuRyaXNsgjoHPP3TuA9uYUjMofcsO9QH8e7wmft4vz2E3v54q3v5vprVpBIJnG7XLQ0V7GkpZZaX4Uqcik6uSRzV4bHpj2PHwjUZv3GwWBd1s+dK+1ML85SGMt0lcpYEokkx7tCdPUO8s0JG4BGRWNxOk68A8DyxQ0EG6u4cFEDFRWltWagVD6T6ZhqLNP9mSsGsxFnLsn8OPChMX9fCJyY7pN7ekIkEjOf+g8G6+jqGpjx8wphqjhLaSxTKYmxpI+oHeLYqdC4y5Qz7eIciSdZFKzh4sV1kIDTp8OFijwrJfGZTNN0x1IK4832c3G7XZMWwbmUGc8Dv22MCRpjqoEbgB/l8Hois8cDh06G+fWbpzhycoB//81Rzm+tS+/ivOVjZlx/fMuGS1m9vJnVyxq1blxKQtaVubX2uDHmz4Gf4ixNfMha+6u8RSaSLxWw90Af3/7Bq+OOqX1651t86vcu5eEf7uOZlw6yaX0bixbUUl9bSa3PQ62/QolcSkZO68yttY8Cj+YpFpH8csNAJE5PVySdyGH8MbXhSIzrr1lBS1MVgQY/S4LVBAOpX4O1AUhKiLbzS/lJLTfsHRjG63HTfuR0xglOtxuiwwnOC1Rz0cI6VeJS0pTMpby44MCJAY51hdi6o53brltDIpl5grNtaRP+Sg/nNaUOxlIlLiWstNZZiZyLB04NRHnzWD+VlR5+8svDRGNxqvweXnzl2FnH1N5541pamv2c1+hXNS5lQZW5lL4KePWtXh7cvjc9wXnLxwzPvHSQJ3fuZ+NvXcz2n77FhquX43ZD29Imli1M7eBUNS5lQslcSpcL+gdjDERG0okcnH74oz+23Hztxfzzv74JHOLWj68CFzTX+2hp9GkrvpQdtVmkNLngjSPvcO+3d3GqdyjjBGdTnR+AI2+HGEkkuGRpPS26+UfKlCpzKS2pajwUjfPA47vTffFME5wLmqq473++hwX1Pup1mbKUOVXmUjrccLR7kF+8fpL+cJS6Gi8AJ3sH2TxhgnPz+jYaa7xc1FpLfZVXvXEpe6rMpfh5oKd/mGOnwjy4fU96knPz+jae3dXBs//RwcZrVow74fD8YOp0QyVxmSdUmUtx88Du/b38+s2udCIHpye+dUc71155Ad19EZ558SDmgkbM0kbet6qF5YvqlMhlXlFlLsXJDadDMSKxOKd6Bwk2Vmec5GyZ2BcfTeBK5DLPKJlL8XHDG0f6ONU7xNYd7URjcTavNxknOQMNfpYsqHaStxK4zGNqs0jxcEH/UIwDJwZw4U4ncoDnXz581iTnZ29ay5JgtZK4CKrMpVikrnD75rZXU5V427gqvLsvwrO7OvjcpnXEE0ma6v0satZWfJFRSuZSWG5npcpgdCSdyIGMh2MNhGM01fupraqgptKjRC4yhtosUhguCMfi7D7Qy31/93P2dfSOS9wv/OeRsw7HuvvmdbQ2+KjxetRaEZlAlbnMPTcc7RokNBTjVO9QevPP2Eq8uy/Cjl8e5v9seS+DkRGCDT5t/hGZhJK5zJ3UcsMjJ0PjNv9sWt/Gz/eeYNP6Nh5LTXo6Jx9ewsJGv5YbikyDkrnMDQ8cejtM30CUU6edajzaFx93hdtzuzq4/poVLG2tpabK66xUUV9cZFqUzGV2uSAUGaHj7dBZFyo/t6uD7r6I01pxOROcLc1VLGmt1QSnyAwpmcvsSR1Te/RUiCd37s94ofLj/9aOz+vh0gubufKSFgL1lbrCTSQLWs0is6Z/MMYDj+8mkUxm3IqPy5n0vCu1+SdQW6lqXCRLqswlv1LnjfeFhvF6PRlXqoz+fdWFzVy1utVZpaIkLpITJXPJi5GRBD2hYU71DeFyuXhy51sceTvE5vVt7MqwUuWzN61laUu1c2GEWioiOcs5mRtj/gJIWGu/nHs4UpIqYOdvjo67UHnLhtX85JeH2LqjneuvWZFeqbKktZbFgWrd/COSZ1n3zI0xDcaYh4F78hiPlBo3nOiOnHWh8kNPv8Z1H15BNBZnxfkN3HXjWt63qoWVSxq0+UdkFuRSmW8A3gL+Nk+xSAnq6R/mVO9gxgnOyLBTpS+oT+3eBCVxkVmSdWVurf0na+1fo1+W57We/gh+nyd9hsoon9dDVWUFd9+8zmmpiMismrIyN8bcBNw/4eE3rbXX5vLGgUBt1s8NButyees50c704iyFsQAkEkk6u8P09g/RXF/FwgU1uN0uTg+N8NBTe9iyYTUPPf1aumd++8Y1rFjSQGuz832lplQ+l6mUyzhg6rFM92euGMxGnFMmc2vtNmBbvt+4pydEIjHz37mDwTq6ugbyHc6smCrOkhnLhLPGR08wXLm0gaaaCj76vmX85BeHuHvTOqLDcVqaq1m0wA8jSXp6QoWOfsZK5nOZQrmMA6Y/llIYb7afi9vtmrQI1qYhmZzLOeFw7Fnj0VicBx7fTf9gDBKwdnkzW667DI/bxZKWWufSiJECxy0yz2iduUyqfzDGG4d6M05w9oWH0xt+2pY20VSV+t9JG4BE5lzOyVzry8tbX2g4460/Pq+HxprKAkYmImOpzSJnpC5UPtIVpj8yAi5orPPx4ivHzrr1566b1mqVikgRUZtFHKkTDh94fPf4Sc4LGvjE76zkkR+9wYarl+N2w8plzTprXKTIqDIX4MwJh2dNcoZjrFzawL2fuIJVFzbxvlWtLFmgRC5SbFSZC+D0xieb5Bz9D9AuTpEipMpcAKc3nmkXpyY5RUqDkvl8k2GSE6C+ytl6P3aSU1vxRUqH2izzybkmOZc2QBJWLm3ga3deRV94mMaaSieRq6UiUhJUmZe7MZV4z8Bw5knOwZjzvUmor/KydEGNjqkVKTGqzMuZ29mK/8ahXhJJqPF7Jt/JKSIlS8m8XLnOPhzrU793KQsD1XT2DKa/TZOcIuVBbZYy1T8YO+twrId/uI8/+K+XapJTpAypMi9T51o3frxrgL+8/f2EIzFNcoqUESXzUud2rm7r6Y8QaPATqKuExJl14xMPx1pxfiOBukoCtanWihK5SFlQMi9lbnj1QG/6MmWf18MdG9ewdnlzet342GWId920VmeqiJQpJfMS1tM/nE7k4LRRHty+l7+8/f0EaiszrxtXIhcpS0rmJaynP5KxL97TH3HaKKl14zpTRaT8aTVLCQs0+DOepxKo9xcoIhEpFCXzEhaoq+SOjWvGLTW8Y+MaAvVaNy4y36jNUsxcznrxvtAwjXU+6qsqxrdKUpcp/+Xt73daK/V+J5GrLy4y7yiZF6spDsVKS0CgdsxSQyVykXlJbZYidc6bf0YPxRIRGUPJvEhNdvOPiMhESuZFSjf/iMhMKJkXKd38IyIzkfUEqDHmA8A3AC/QA3zSWns4T3GJbv4RkRnIpTL/PvApa+261J8fyEtEcoZu/hGRacoqmRtjfMB91to9qYf2AEvzFpWIiMxIVm0Wa20UeATAGOMGvgw8NZPXCARqs3lrAILBuqyfO1famV6cpTCW6dJYik+5jAOmHst0f+aKwWzEOWUyN8bcBNw/4eE3rbXXGmMqgX9Mvc5XZ/LGPT0hEomZ9w2CwTq6ugZm/LxCmCrOUhrLVDSW4lMu44Dpj6UUxpvt5+J2uyYtgqdM5tbabcC2iY8bY2qBH+JMfm6w1mo3i4hIgeQyAfoIsB+4OdV2ERGRAsmqZ26MuRzYALwOvGKMAThhrf0veYxNRESmKdsJ0FcAV55jERGRLOnUxFlSEQjQvuUPJ/2e9rkJZU5oLMWnXMYB0xtLRSAw63EUMyXzWXLR1/52yu+Zj6sNSkG5jKVcxgHlNZbZorNZRETKgJL5TLmgfyjGka4w/ZERzRyISFFQm2Umpnv7j4jIHFNlPgO6/UdEipWS+Qzo9h8RKVZK5pmcoy+u239EpFipZz7RJH3x0dt/Jn5Nl0aISKEpmU9wrr741+68ivoqr27/EZGipGQ+wWR98dHbfuqrvM6fQYlcRIqCeuYTqC8uIqVIyXyC0b74aEIf1xcXESlSarNMlER9cREpOUrmmagvLiIlRm0WEZEyoGQuIlIGlMxFRMqAkrmISBlQMhcRKQNK5iIiZUDJXESkDCiZi4iUASVzEZEykPUOUGPMh4BvAJVAB/AH1trTeYpLRERmIJfK/B+AW621a4DXgT/JT0giIjJTuZzNstJaGzPGeIHFwJ48xSQiIjPkSiazP0XKGLMGeB6IAe+31h6dxtOW4bRlRERk5i4EDk18cMrK3BhzE3D/hIfftNZea63dC7QaY24HHgOumm40PT0hEomZ/0MSDNbR1TUw4+cVI42lOJXLWMplHKCxALjdLgKB2nN+fcpkbq3dBmwb+5gxxm+Muc5a+1TqoUeAv51xdCIikhfZToDGgG8ZY65I/f1m4KX8hCQiIjOVVTK31saBTcB3jTG7gRuBLXmMS0REZiDr1SzW2peAK6b8RhERmXXaASoiUgaUzEVEyoCSuYhIGchlB2i2POCsmcxWLs8tNhpLcSqXsZTLOEBjGfMcT6av57QDNEsfBF6c6zcVESkTHyLDUvBCJHMfcCXQCcTn+s1FREqUB1gIvAxEJ36xEMlcRETyTBOgIiJlQMlcRKQMKJmLiJQBJXMRkTKgZC4iUgaUzEVEyoCSuYhIGSjEdv6cGWM+BHwDqMS5T/QPrLWnCxpUlowxH8AZixfoAT5prT1c0KByYIz5CyBhrf1yoWOZKWPMLcB9OP9f3W+t/VaBQ8qaMaYe2AX8rrX2UIHDyZox5ks4l98APGut/WIh48lF6mfjRiAJPGyt/Xo+X79UK/N/AG611q4BXgf+pMDx5OL7wKestetSf36gsOFkxxjTYIx5GLin0LFkwxizGPgrnOMm1gK3GWNWFTaq7Bhj3ouz3but0LHkwhhzLfBR4HJgHXCFMeb6ggaVJWPMh4GPAJcB7wb+yBhj8vkepZrMV1prXzfGeIHFQKlW5T7gPmvtntRDe4ClBQwpFxuAtyjdu2CvBV6w1vZaa8PAD3CqqFL0aeCzwIlCB5KjTuAL1tpha20MeIMS/fmw1v478FvW2hGgBacrEs7ne5Rkm8VaGzPGrAGex7mP9M8KHFJWrLVRnMuwMca4gS8DTxUwpKxZa/8JwBjz5QKHkq1FOMljVCfwngLFkhNr7RaAPBd+c85au2/0z8aYi3GuqryqcBHlJpW3voLz2+s24Hg+X7+ok7kx5ibg/gkPv2mtvdZauxdoNcbcDjxGkX/Ik43FGFMJ/CPO5/HVOQ9uBiYbRyHiyaNMZ5Im5jwKOYsx5lLgWeAea+1bhY4nF9baLxljvgY8g/Mb1Hfz9dpFncyttdtw/gVLM8b4jTHXWWufSj30CCXwq32msQAYY2qBH+JMfm5I/TpZtM41jjJwHOdo0VELKf02RclLLRB4Avhja+3WQseTLWPMJYDfWrvbWjtojNmO0z/Pm1LsmceAbxljRi+TvpkMZ/uWkEeA/cDNqbaLFMbzwG8bY4LGmGrgBuBHBY5pXjPGLMFpO95Syok85SLg740xvtRv4hvIc94quWRurY3j9M6+a4zZjTNJtaWgQWXJGHM5zof6AeAVY8xuY8xzBQ5rXrLWHgf+HPgpsBt41Fr7q4IGJfcAfuDrqZ+N3caYOwodVDastc8BzwGvAL8GduX7HyidZy4iUgZKrjIXEZGzKZmLiJQBJXMRkTKgZC4iUgaUzEVEyoCSuYhIGVAyFxEpA0rmIiJl4P8D6MWTcJ6XzJ0AAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "ax = fig.add_subplot(xlim=(-3.3, 3.3), ylim=(-3.3, 3.3))\n", - "projection_quantile = np.quantile(max_standardized_sample, .975)\n", - "ax.add_patch(\n", - " Rectangle(\n", - " (-projection_quantile, -projection_quantile),\n", - " 2 * projection_quantile,\n", - " 2 * projection_quantile,\n", - " facecolor=\"none\",\n", - " edgecolor=palette[3]\n", - " )\n", - ")\n", - "sns.scatterplot(x=max_standardized_sample, y=max_standardized_sample, ax=ax)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "5. **\"Unstandardize\" (i.e., *project*) the hypercube.** Scale the box by (the square root of) the diagonal elements of the covariance matrix and add the vector of conventional estimates." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "ax = fig.add_subplot(xlim=xlim, ylim=ylim)\n", - "confidence_ellipse(y, cov, ax)\n", - "projection_length = projection_quantile * np.sqrt(np.diag(cov))\n", - "ax.add_patch(\n", - " Rectangle(\n", - " (y[0] - projection_length[0], y[1] - projection_length[1]),\n", - " 2 * projection_length[0],\n", - " 2 * projection_length[1],\n", - " facecolor=\"none\",\n", - " edgecolor=palette[3]\n", - " )\n", - ")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's apply projection confidence intervals to our economic opportunity dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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nNlDOQ2Y2AHiOUDeTgRuLcAoiBRnUr7sSkrQbJbW1yV4vaUN6Aa+n4RpUmqW5LzxNVE8NqxulOmbkQNVTgdJcTznXoPYCFuWuaxMtKBFpP8rL28qVCWmMEpSIZMro0wYkHYIUib6KiIhIKilBiUimPDD9dR6Y/nrSYUgRKEGJSKYsWPQuCxa9m3QYUgRKUCIikkpKUCIikkpKUCIikkoaZi4imdJp+/KkQ5AiUYISkUy5YPiBSYcgRaIuPhERSSUlKBHJlHunLeTeaQuTDkOKQF18IpIpC99enXQIUiRqQYmISCopQYmISCopQYmISCrpGpSIZMrOccp7afuUoEQkU755Ur+kQ5AiURefiIikkhKUiGTKXVNe4a4pryQdhhSBuvhEJFPeXL4u6RCkSNSCEhGRVFKCEhGRVFKCEhGRVNI1KBHJlF132SHpEKRIlKBEJFO+Pqxv0iFIkaiLT0REUkkJSkQyZfyklxk/6eWkw5AiUBefiGTK8lXvJx2CFIlaUCIikkpKUCIikkpKUCIikkq6BiUimbLHrp2SDkGKRAlKRDLlP4fum3QIUiTq4hMRkVRSghKRTPntg/P57YPzkw5DikBdfCKSKe+uWZ90CFIkakGJiEgqKUGJiEgqKUGJiEgqpfIalJn1Al4BXoqLSoEuwAR3//FW9pnm7r22Uu55AO5+61a2WQQMcfdFTY9cRFpb7912SjoEKZJUJqhoibsPqHthZj2BV83sT+6+oDkFbi0xiUjzzJi/jImPL6RqzXoqu3Rk+ODeDOrXvdWO9+UhvVutbEmXNCeofD2AEmCtmd0GHADsCjgwPHdDM9sV+A2wB1ADfN/dp5jZlQDufqWZnQZcBbwPzAI6uPvXYxFXmNnBwA7Ame7+TCufm0gmzZi/jAmTXmZDdQ0AVWvWMyFOhdGaSUrahzQnqJ5mNhvYDugKPAucAuwNbHD3QWZWCkwFTgCez9n3BuB2d3/AzHoAT5nZgLqVZtYN+DVwGLAUuBdYk7P/S+5+tpldCFwCjGiVM2xjrrlzVqPblFeUsXHDpiJEk21ZqaeFS1ZTval2s2UbqmsY9/ACnpi9pFWO+fbK9wDYreuOmamnpLV2PY0ZObBVyk1zglri7gNiEvoVcBAw1d03mlmVmV0A9AX6APkP5xoK9DWzq+LrciC3X+BoYIa7vw1gZhMIya/OX+PP+cCpTQm6srL9PiesvKKsRbdr77JQT/nJKXd5a8VfUxuOWVd+FuopDVqznrp169wq5aY5QQHg7jVmdikwG7jEzOYTuuZuAMYRWlclebuVAce5+yr46PrVcuBLcf0mtj6CsTr+rK2n7K2qqlpHTU39f7Rt3egR/Rvdplu3zqxYsbYI0WRbVurp0rHTqarnxtnKLh0Lej80R11LffSI/pmpp6S1dj1tS9mlpSUNfrHPxDBzd68mdLX9ADgRuMfdxwHLgGMICSnXVOB8ADPbH5hDuJ5U55/AYWbWw8xKgK8QkpGINMHwwb2p6LD5x0hFh1KGD9ZABtl2mUhQAO7+CPA0oavuDDN7AZgYl+2Vt/m3gCPNbA7wZ+Br7r42p6wVwEXA3wnXtsqBD1r9JETamEH9ujNqWF8qu3QEQstp1LC+GiAhLaKktrb9NRzMrJKQoH4SuxD/F3jV3W/chmJ7Aa+35y6+QqhLpjCqp4Y9MP11AE4+ai/VU4HSXE85XXx7AYty16X+GlQrWQV8AphnZtWEYea3JRqRiBTk5KPyO0ykrWqXCcrda4FvJx2HiIg0LDPXoEREAK67ZzbX3TM76TCkCNplC0pEsmvjxpqkQ5AiUQtKRERSSQlKRERSSQlKRERSSdegRCRT+u/TNekQpEiUoEQkU44/Ys+kQ5AiURefiIikkhKUiGTKNXfOKmjuMck+JSgREUklJSgREUklJSgREUklJSgREUklDTMXkUw5bL9PJh2CFIkSlIhkynEDd086BCkSdfGJSKas37iJ9Rs3JR2GFIESlIhkyq/veZFf3/Ni0mFIEShBiYhIKilBiYhIKilBiYhIKilBiYhIKmmYuYhkylEH9kg6BCkSJSgRyZTPHKQE1V6oi09EMmXt+xtY+/6GpMOQIlCCEpFMGXv/PMbePy/pMKQIlKBERCSVlKBERCSVlKBERCSVlKBERCSVNMxcRDLl2IG7JR2CFIkSlIhkyuH77Zp0CFIk6uITkUxZteZDVq35MOkwpAiUoEQkU2578CVue/ClpMOQIlCCEhGRVFKCEhGRVFKCEhGRVFKCEhGRVNIwcxHJlC8cvmfSIUiRbDVBmVkv4BXgJaAWqACWAGe5+1uFHsTMxgFXuvsbecv3BG4GPkVozb0EXOju75jZ4cCp7j5mK+UOieUOKTSWBsq5CnjO3R/YlnJEpPUN6NM16RCkSAppQS1x9wF1L8zs58CNwClNOM6xwE/qWf4b4A/ufncs+/vArcBwYH+gKHfkufsVxTiOSHsxY/4yJj6+kKo166ns0pHhg3szqF/3Fil7adV7APSo3LFFypP0ak4X3xPAyQBmdiRwA7AdsBI4191fM7NpwCqgHzAO6Ak8bGZHu3tVTlndgR1yXt8EHGZmnwCuAjqZ2eXu/rOmBJjfsjKz8cA0dx9vZt8BzgM2AQ+6+5i89WcCFxNadM8DF7i77goUKdCM+cuYMOllNlTXAFC1Zj0TJr0M0CJJ6g+POABjRg7c5rIk3ZqUoMysHDgdmG5mFcCfgBHu/qyZjQDuBg6Lm89x9+Fxv/OAE/KSE8D3gTvN7CfAP4BJwD3uXmNmVwBDmpqcGon/cOB84FDgPeARMzskZ30/4Bzg0+7+YWwtXgL8tKVikOa75s5ZSYdQNOUVZWzcsCnpMJpl4ZLVVG+q3WzZhuoaxj28gCdmL9nm8he/sxYI74cs11MxNbWe0pL8C0lQPc1sdvy9IzAT+B6wL/Cuuz8L4O5/MbPfmtlOcdtnGivY3R8xs92AIcBQ4BfAV4AvNeEcmuIYQqtpdXw9FMDM6tYfC/QBno7LKoAmfSpWVnZqkUDbsm7dOjdrv/KKshaOJN2yer75ySl3eUucU0lJCfBx/WS1noqtKfXU3L/Rltbka1B1zGz3erYtAepq4YOtFWpmuwA/cvfvAI8QWjP/DSw1s24FxLU1tTGWOuXx58a8GHoC7+csKiO04C6K6zvRxFZmVdU6amrq/wOV8MZfsWJts/YdPaJ/C0eTXttST0m7dOx0qtas32J5ZZeOLfJ/WNeSHj2if6brqZiaWk/FrNPS0pIGv9hvy31QDlSa2WEAZnYa8Ia7r6pn22q2/KBfDZwcr/nU6Q0sJ1y/qm+fQq0E9jaz7WIiPDoufxIYZmadzKwDoUvy0Jz9pgGnmNknzawEuIVwPUpECjR8cG8qOmz+0VLRoZThg3snFJFkVbMTlLuvJ1yPusnM5gEXxtf1eYgwSGKvnP03AScAp5vZG2a2ALgWOCmumwkcaWZXm1luN2O+o81sXc6/W919PvA3YD7wF0Jiwt1nEQZizABeBJ5w9yk5Mb1IGG04Ne5bClzd5MoRaccG9evOqGF9qezSEQgtp1HD+rbYKL4Tj+rFiUf1apGyJN1KamvVHdVCegGvq4tv69QlUxjVU2FUT4VJcz3ldPHtBSzabF0SAYmINNfi5WtZvDydH7bSspSgRCRT7p7yKndPeTXpMKQIlKBERCSVlKBERCSVlKBERCSVlKBERCSVNB+UiGTKqbrht91QghKRTNln950a30jaBHXxiUimvPbWal57a3XjG0rmKUGJSKbc9/hC7nt8YdJhSBEoQYmISCopQYmISCopQYmISCopQYmISCppmLmIZMoZQ/skHYIUiRKUiGTKnrt2TjoEKRJ18YlIpsxftIr5i1YlHYYUgVpQIpIpD01fBEC/XrskG4i0OrWgREQklZSgREQklZSgREQklZSgREQklTRIQkQy5czjLekQpEiUoEQkU3pU7ph0CFIk6uITkUyZ/epKZr+6MukwpAjUghKRTJk8czEAA/p0TTgSaW1qQYmISCopQYmISCopQYmISCopQYmISCppkISIZMo5J+2fdAhSJEpQIpIpu3TZLukQpEjUxScimTJzwXJmLliedBhSBGpBiUimPDbrbQAO32/XhCOR1qYWlIiIpJISlIiIpJISlIiIpJISlIiIpJIGSYhIppx/ygFJhyBFogQlIpnSeYeKpEOQIlEXn4hkylNzlvLUnKVJhyFFkFgLysy6AD8HBgPVwLvAd4EuwJXuPqSZ5Q5pbH8zOwno4+7XmdnhwKnuPqY5xxORws2Yv4yJjy+kas16Krt0ZPjg3gzq171JZUyfG5LTZw7q0RohSook0oIys1LgYWAVMMDdBwBXAZOAyiKEcAghEQLsD+iOP5FWNmP+MiZMepmqNesBqFqzngmTXmbG/GUJRyZpVVJbW1v0g5rZZ4HbgH3cvSZn+QlAJ+DHwBtAb8CBEe6+3sx+BnwW2AVYCQx392VmtgJ4HugOXApc7u5DzGxf4Ldx+/eAi+LPqfGQ1wDficf8FXA1cC0wBCgDxrv79QWeVi/g9aqqddTUFL9Os6Jbt86sWLG2Sftcc+esVoomvcoryti4YVPSYbSohUtWU71py7+NDmUl9O65U8HlLH4nvH/2/GTnNllPraEY9TRm5MBm7VdaWkJlZSeAvYBFueuS6uI7GHg2NzkBuPvDsYtuT+BEQpJ6GhhqZg70BT7t7jVm9gdgJCGxdAWudvdpcf86d8TlE83sSOBeYF/g1ni8683sXWCIu//MzM6LyweaWUdgspk95+5PFnpisaJlK7p169yk7csrylopknRra+ddX3KqW96Ucy0pKQE+rp+2Vk+tpbXrqal/14VIKkHVACVbWf+iu78OYGYLgK7u/jcz+y7wDTMzYBCwMGefZ3ILMLNOhBbaRAB3f9rMVgG2leMOBQaY2XHxdSfgQKDgBKUW1NY1pwU1ekT/VoomvZpTT2l36djpH3Xv5ars0rFJ/8d1LerRI/q3yXpqDcWop+aWn9OC2nLdtgS0DZ4DBprZZknKzP6HkLiqcxbXAiVmdgjwKCHme4H7yUly7v5B3jFK2TIJlrD1pFwGXObuddfFjgTGFXhOIrIVwwf3pqLD5h85FR1KGT64d5PKufi0/lx8Wvv70tIeJZWgngTeAX5sZmUAZvYF4Czgkw3sMxiY5u63Ai8BnycklHq5+xpgoZkNj+UfSbhGNY+QAOsSVe7vU4FzzKw8tsCeAo5o7kmKyMcG9evOqGF9qezSEQgtp1HD+jZ5FF/H8jI6lqtbrz1IpIvP3WvN7GTgemCemW0kDHo4AWjoaumfgYlmNgfYCMwhXFTbmq8Ct5rZT4D1hEEVG8zsCWCCmS0HJgNXmtnVwI+APsALhLoZ5+7TtuFURSTHoH7dm5yQ8k2d9RYAxw3cvSVCkhRLZBRfG9ULjeJrlK4ZFEb11LC6a1BjRg5UPRUozfW0tVF8epKEiIikkhKUiIikkhKUiIikkp5m3nLKIPSnytapjgqjeqrfznEUYF39qJ4Kk9Z6yolri6GZGiTRcj5DE27oFRGRzRxNuLXnI0pQLacjcBiwFNDDwUREClMG9ACeJdwO9BElKBERSSUNkhARkVRSghIRkVRSghIRkVRSghIRkVRSghIRkVRSghIRkVRSghIRkVTSo46k1ZjZnsAdhEkoHRjp7uvytqkAfkm4i7wC+I67P1rsWJNUSD3lbNsZmA38V3ubq6zA91MPwizY3YEa4BJ3n1rsWJNiZv8J/JDwt3S9u9+ct34AcBth3r0ngPPcvTq/nLRQC0pa01hgrLv3BZ4jTAiZ7zKgKzAQOA0YZ2bpfGhY6ymknurcBOxclKjSp5B6uhZ4yN0HAGcAd9XN2t3WmdluwM8Ij13rD3zTzPbP2+wO4Fvuvi9QApxT3CibRglKWoWZlQPHAPfGReOBEfVsejpwtbvXuvt84HOEP5x2oQn1hJmdDqwlzCbdrjShniYCd8bfXwO2Azq1dnwpMRSY6u6r3P09Ql19uW6lmX0K2N7dn46LxtPAey0t1MUnraUrsCan+2ApUN8c3fsAg83sdmAj8AN3f6lIMaZBQfUUu7cuBo4DJhUtuvQoqJ7cfWLOy0uAF9x9dRHiS4OehHqpsxQ4vJH19f1NpoYSlGwzMxsBXJ+3+JV6Nq2pZ1kHwh/JYcCBwGQz69sWP1SaW09mVgr8HrjQ3T8ws1aKMB228f1UV8bFwLnA4JaLLPXq63moacL61FGCkm3m7n8B/pK7LHbJVJlZmbtvIjyteEk9uy8D/uTutcAcM3sTMGBmK4dddNtQT33jv9/H5LQP8DszO8fdH2v9yItrG99PmNkvgP8AjnH3t1o73hR5mzDYqE5+Hb1NGDzS0PrU0TUoaRXuvpEwP9bpcdGZ1N819WDdNma2N7AnYYRWu1BIPbn7S+6+h7sPiBf/nwO+0RaTU0MKfT/FltOxwFHtLDkBTAE+a2bdzGwH4FTgkbqV7v4G8KGZHRUXNfQ3mRpqQUlrOh+YYGY/BBYTRlVhZucBPd39CuB7wE1mNj/u84222L3XiELqSRqpJ+DH8d8aYFpOV+gJ7p7qlkJLcPe3zexy4DHCMPPfuftMM3sYuMLdnwNGArfF2xVeAP43uYgbp/mgREQkldTFJyIiqaQEJSIiqaQEJSIiqaQEJSIiqaQEJSIiqaRh5pJ5ZlYLzAM25a36krsvKn5EDTOzk4Gh7n5R0rE0xsy+AVS4+9hm7t8TuNfdP21mewG/dPdTWzRIadOUoKStONbdVyYdRGPc/QHggaTjKNBnCIm/WeK9R5+OLz9FeEKISMGUoKTNM7Ozge8SWlgrgVHu/qaZfRO4KC5fTnjW3StmNp5ws+eBwB7Ay8BX3H2dmR1NmNJhB2AD8EN3f8TMvk64c397oBfhRtKbgQuBfYHr3P1Xcbsvu/uJZtYduJXwGKMa4FZ33+LGSTP7EeGm1GrCM+kudPdlZjYNeAk4lPAw1T+6+4/NrBfwODCNMO1CSdznyfjIoOuAz8bzfoYwB9daM1sUXx8E/AA4GficmX0AdAO6uvuFMaYr617HOGYARxGeBPIkMCr+Po8w99DvgN3MbDJhHqJ+7v6fsayjgJvc/eC88x5OmNuoJsZ6qbs/0VC9mdnuwC2x/kuACe5+bayPJ4EFcd1gYC/gGmDHWMaV7v5Qft1LsnQNStqKx8xsds6/+wHMrD/hg+h4dz+I0Hq53MyOI8xFday79wfuAv6aMxfVIcDxwH6EpxSMMLNKwhQG345ljQLuiN1XEJ6DdhYhIe0KfIWQCE4Afhof+pprLPBKnN9oEGH+nn1yNzCzs4BhwGHxmPMI0yTU+RQhMQwETjezE+PyPYHJ8dFI3wP+HJPTD+P59I//SgkJt848d9/P3e+PdbXFpHcN6A0MIST148h5SGt8dt43gIXu/gXChHn/YWa7xE3OJSScfNcC57v7oYS5n4bE5Q3V253AY+5+YKyTr5rZV+I+uwP/HedB+pAwqeHX3H0gIRHfEp8YLymiBCVtxbF1z6qL/06Jyz9L+KB+E8Ddf+3u5xGSz5/dfUVcPh7YjfANG+ARd18fnwE3F9gFOAJ4zd2fifvMB6bz8Qfns+7+prvXAK8Dj8bfFxLmJdohL+ahwG9jWavd/QB3fy1vm2HAuDi/D8ANhOetVcTXv3H3je7+b8IDVr8Ql7/r7nfFsicRWiAHxfJujfvUADfGZXWebKiCG/Ggu9e4+1rCPEy7NLShu78DPAR8zcx2jjHfWc+mfwLuN7PfESZp/EVcvkW9EaaOOIrQaiU+Lmt8zrlVE1p5EJJaD8IXktnAw0AtoX4kRdTFJ21dNeHDBwAz257Q6qjvy1kJUB5//yBneW1cV98+pXGfDcD6vHUbmxjb3sBKd1+TV37+8Trw8dQJ1XnrNtWzPHddfeWV57yud6p5Pq6DOhV56+urr625mdAdVw3c5/VMce/ul5vZ74HPA18Hvmdmh1B/vVXVc8zcc1ufM5dUGbDA3Y/IKaMnsKKRmKXI1IKStu4xYKiZ9YivzyV8E59M6BLrBh91pVURvv035OmwqR0e9+lHmOV1WjNjm0LoEsTMdgL+AfTJ22YycJaZ7RhfXwQ84e51yfCrZlYaWyKnEZ4OD9DNzI6PZZ9ESJZzY3nnmVl57HK8APh7A/FV8/EH/ArgEDMribF8vonnmlsW7v5PwrWfSwiJajNm1iFeE9vR3W8lPCh2v1hGffW2D+H/54Kc5Wc2cG5PA33M7Ji47QDgVULXp6SIEpS0FfnXoGab2QnuPhe4FHjEzF4kdO2d5+5/J0yKNzU+SX0UcGLs9qpXHCU4ArjRzOYSrlud5e71TaZXiAuB/cxsDqGr8Ofu/nzeNr8nfCDPNLMFhGtNI3PWb0+YO+tpYKy7/yMu/5DQhfYicDlhyP0m4KeEObhmEwYNlAPfbiC+ScBFZvZ9QhfcCsIH+cN83F1WqPnAJjObmXOdbxywJP4fbSa2di4G7jKzWYTuy7NjYm6o3kYSuj/nxjq5j82v19WVvYIwoOXaWD9/JFyPeqOJ5yStTE8zF8moOHruJne/N295L8Jgh05JxFUIM+sA3A/c4e5/TjoeSSe1oESkqMxsf0JrbA15M+eK5FILSkREUkktKBERSSUlKBERSSUlKBERSSUlKBERSSUlKBERSSUlKBERSaX/D1WiEAl4hg8vAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = rqu_model.fit(cols=cols, projection=True).point_plot(yname=XLABEL)\n", - "ax.axvline(np.sort(rqu_model.mean)[cutoff_rank], linestyle=\"--\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hybrid analysis\n", - "\n", - "We can combine projection confidence intervals and the conditional inference approach to construct *hybrid estimators* and *hybrid confidence intervals*.\n", - "\n", - "First, we form a hybrid truncation set by intersecting:\n", - "\n", - "1. The truncation set from the conditional estimator $S$ and\n", - "2. A $1 - \\beta$ projection confidence interval centered on $\\hat{\\mu}^H_\\alpha$, $P_\\beta(\\hat{\\mu}^H_\\alpha)$\n", - "\n", - "This allows us to plot a hybrid CDF similar to that of the conditional estimator.\n", - "\n", - "$$\n", - " \\alpha = 1 - F_{TN}\\big(y, \\hat{\\mu}^H_\\alpha, \\sigma, S \\cap P_\\beta(\\hat{\\mu}^H_\\alpha)\\big)\n", - "$$\n", - "\n", - "Run the cell below to visualize the hybrid estimator. The elements of the graph are the same as those for the conditional estimator. Additionally, the red shaded area is the projection confidence interval $P_\\beta(\\hat{\\mu}^H_\\alpha)$. The overlap between the green (conditional truncation set) and red (projection interval) shaded areas is the hybrid truncation set." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
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// override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n var manager = IPython.notebook.keyboard_manager;\n if (!manager) {\n manager = IPython.keyboard_manager;\n }\n\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "\n", - "neighborhood = \"Charlotte\"\n", - "truncation_set = [(-np.inf, np.sort(conventional_model.mean)[5])] # truncate to neighborhoods that score in the bottom 5\n", - "index = conventional_model.exog_names.index(neighborhood)\n", - "y = conventional_model.mean[index]\n", - "sigma = np.sqrt(conventional_model.cov[index, index])\n", - "\n", - "ani = QuantileUnbiasedAnimation(\n", - " y,\n", - " sigma,\n", - " truncation_set,\n", - " projection_quantile=rqu_model.compute_projection_quantile(.005),\n", - " xlim=(-.5, .2),\n", - ").make_animation(\n", - " title=\"Hybrid analysis\",\n", - " xlabel=f\"Economic opportunity score for {neighborhood}\"\n", - ")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$\\hat{\\mu}^H_\\alpha$ is almost quantile-unbiased on average. Specifically, the probability that the true value $\\mu$ falls below $\\hat{\\mu}^H_\\alpha$ is between $\\alpha - \\beta \\max\\{\\alpha, 1 - \\alpha\\}$ and $\\alpha + \\beta \\max\\{\\alpha, 1 - \\alpha\\}$ on average over the selected parameters.\n", - "\n", - "For example, if we use a 99.5% projection confidence interval ($\\beta = .005$), the probability that the true economic opportunity score $\\mu$ falls below $\\hat{\\mu}^H_{0.5}$ will be between 49.75% and 50.25% on average over the targeted neighborhoods.\n", - "\n", - "We can construct a $1 - \\alpha$ hybrid confidence interval using,\n", - "\n", - "$$\n", - " CI^H = [\n", - " \\hat{\\mu}^H_{\\frac{\\alpha - \\beta}{2(1 - \\beta)}},\n", - " \\hat{\\mu}^H_{1 - \\frac{\\alpha - \\beta}{2(1 - \\beta)}}\n", - " ]\n", - "$$\n", - "\n", - "The hybrid confidence interval has correct unconditional coverage. That is, the true value $\\mu$ will fall within the 95% hybrid confidence interval at least 95% of the time on average over selected parameters.\n", - "\n", - "Run the cell below to get hybrid estimates and confidence intervals for our economic opportunity dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "\n", - "ax = rqu_model.fit(\n", - " cols=cols, rank=np.arange(-cutoff_rank, 0), beta=.005\n", - ").point_plot(title=\"Hybrid estimates\", yname=XLABEL)\n", - "ax.axvline(np.sort(rqu_model.mean)[cutoff_rank], linestyle=\"--\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summary\n", - "\n", - "Conditional inference requires estimators and confidence intervals that perform well for specific conditioning events (e.g., the conventional estimate of Charlotte's economic opportunity score ranks in the bottom 5). Unconditional inference relaxes this requirement, allowing us to make statements that are correct on average (e.g., that hold on average for neighborhoods whose economic opportunity scores rank in the bottom 5 over many similar analyses).\n", - "\n", - "Because unconditional inference is a less strict requirement, we can construct more accurate estimators and shorter confidence intervals than those of our quantile-unbiased conditional estimator. We saw two ways to construct unconditionally correct confidence intervals: projection and hybrid confidence intervals. Additionally, the hybrid approach gives us an almost-quantile-unbiased estimator.\n", - "\n", - "Congratulations on sticking with this primer to the end! To run conditional and unconditional inference on your own data, see `rqu.ipynb` in this folder." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "120d65e34230161c0f4356d19a77763cc2f6669dcb2a194d42d3b2faf517ecd2" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/data.csv b/examples/data.csv index a4b24a6..449e07c 100644 --- a/examples/data.csv +++ b/examples/data.csv @@ -1,5 +1,11 @@ -dep_variable,policy0,policy1,policy2,policy3 -0,1,0,0,0 -1,0,1,0,0 -2,0,0,1,0 -3,0,0,0,1 \ No newline at end of file +y,policy0,policy1,policy2,policy3,policy4,policy5,policy6,policy7,policy8,policy9 +0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +3.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0 +4.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0 +5.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 +6.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 +7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0 +8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 +9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 diff --git a/examples/losers_presentation.ipynb b/examples/losers_presentation.ipynb deleted file mode 100644 index e104a80..0000000 --- a/examples/losers_presentation.ipynb +++ /dev/null @@ -1,236 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "3b7fa608", - "metadata": {}, - "source": [ - "# Inference for losers presentation animations\n", - "\n", - "This notebook uses animations. To view these in your browser, go to this binder\n", - "\n", - "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gl/dsbowen%2Fconditional-inference/HEAD?filepath=examples%2Flosers_presentation.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0e7a7f5", - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "from matplotlib.patches import Rectangle\n", - "from scipy.stats import multivariate_normal, norm\n", - "\n", - "from conditional_inference.bayes.classic import LinearClassicBayes\n", - "from conditional_inference.rqu import RQU\n", - "from conditional_inference.stats import quantile_unbiased, truncnorm\n", - "\n", - "from utils import RankConditionAnimation, QuantileUnbiasedAnimation, confidence_ellipse\n", - "\n", - "MOVERS_DATA_FILE = \"../simulations/losers-empirical/movers.csv\"\n", - "XLABEL = \"Economic opportunity score\"\n", - "TOP_N = 3\n", - "NEIGHBORHOOD = \"Washington DC\"\n", - "\n", - "sns.set()\n", - "warnings.simplefilter(\"ignore\")\n", - "\n", - "rqu = RQU.from_csv(MOVERS_DATA_FILE)\n", - "index = rqu.exog_names.index(NEIGHBORHOOD)" - ] - }, - { - "cell_type": "markdown", - "id": "2e6bb83b", - "metadata": {}, - "source": [ - "## Conditional inference" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b566039", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib notebook\n", - "y = np.array([0, 1, 2])\n", - "cov = np.array([\n", - " [1, .5, 0],\n", - " [.5, 1, -.2],\n", - " [0, -.2, 1]\n", - "])\n", - "focal_index = 1\n", - "rank = [2]\n", - "\n", - "ani = RankConditionAnimation(y, cov, focal_index, rank, xlim=(-2, 3)).make_animation(\n", - " title=\"Conditioning on the policy being ranked 2nd\",\n", - " xlabel=r\"Conventional estimates $Z_\\theta$\"\n", - ")\n", - "ani.save(\"truncation.gif\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d241cae", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib notebook\n", - "\n", - "sigma = np.sqrt(rqu.cov[index, index])\n", - "truncation_set = [(rqu.mean[np.argsort(-rqu.mean)][TOP_N], np.inf)]\n", - "x = rqu.mean[index]\n", - "\n", - "ani = QuantileUnbiasedAnimation(\n", - " x,\n", - " sigma,\n", - " truncation_set,\n", - " xlim=(-.3, .5)\n", - ").make_animation(\n", - " title=\"Conditional estimator\",\n", - " xlabel=f\"Economic opportunity score for {NEIGHBORHOOD}\"\n", - ")\n", - "ani.save(\"conditional.gif\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "0b8727f2", - "metadata": {}, - "source": [ - "## Unconditional inference: projection CIs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "466fdf97", - "metadata": {}, - "outputs": [], - "source": [ - "# conventional parameter estimates and covariance matrix\n", - "%matplotlib inline\n", - "\n", - "x = [1, 2]\n", - "cov = np.array([\n", - " [3, .5],\n", - " [.5, 1]\n", - "])\n", - "\n", - "palette = sns.color_palette()\n", - "scale = 3.8 * np.sqrt(np.diag(cov))\n", - "xlim = x[0] - scale[0], x[0] + scale[0]\n", - "ylim = x[1] - scale[1], x[1] + scale[1]\n", - "\n", - "# draw confidence ellipse\n", - "fig = plt.figure(figsize=(8, 6))\n", - "ax = fig.add_subplot(xlim=xlim, ylim=ylim)\n", - "# confidence_ellipse(x, cov, ax)\n", - "\n", - "# draw K-dimensional rectangle containing 1 - \\beta of the joint distribution\n", - "projection_length = RQU(x, cov).compute_projection_quantile(.05) * np.sqrt(np.diag(cov))\n", - "ax.add_patch(\n", - " Rectangle(\n", - " (x[0] - projection_length[0], x[1] - projection_length[1]),\n", - " 2 * projection_length[0],\n", - " 2 * projection_length[1],\n", - " facecolor=\"none\",\n", - " edgecolor=palette[3]\n", - " )\n", - ")\n", - "\n", - "y_offset = .015 * (ylim[1] - ylim[0])\n", - "\n", - "# projection onto the dimension for \\theta\n", - "ax.set_xlabel(r\"Dimension for $\\theta$\")\n", - "ax.axvline(x[0], ymax=.5, linestyle=\"--\", color=palette[2])\n", - "ax.text(x[0], ylim[0] + y_offset, r\"$X(\\theta)$\")\n", - "ymax = (ylim[1] - (x[1] + projection_length[1])) / (ylim[1] - ylim[0])\n", - "ax.axvline(x[0] - projection_length[0], ymax=ymax, linestyle=\"--\", color=palette[2])\n", - "ax.text(x[0] - projection_length[0], ylim[0] + y_offset, r\"$X(\\theta) - c_\\beta \\sqrt{\\Sigma(\\theta)}$\")\n", - "ax.axvline(x[0] + projection_length[0], ymax=ymax, linestyle=\"--\", color=palette[2])\n", - "ax.text(x[0] + projection_length[0], ylim[0] + y_offset, r\"$X(\\theta) + c_\\beta \\sqrt{\\Sigma(\\theta)}$\")\n", - "\n", - "# project onto the dimension for \\theta'\n", - "ax.set_ylabel(r\"Dimension for $\\theta'$\")\n", - "ax.axhline(x[1], xmax=.5, linestyle=\"--\", color=palette[2])\n", - "ax.text(xlim[0], x[1] + y_offset, r\"$X(\\theta')$\")\n", - "xmax = (xlim[1] - (x[0] + projection_length[0])) / (xlim[1] - xlim[0])\n", - "ax.axhline(x[1] - projection_length[1], xmax=xmax, linestyle=\"--\", color=palette[2])\n", - "ax.text(xlim[0], x[1] - projection_length[1] + y_offset, r\"$X(\\theta') - c_\\beta \\sqrt{\\Sigma(\\theta')}$\")\n", - "ax.axhline(x[1] + projection_length[1], xmax=xmax, linestyle=\"--\", color=palette[2])\n", - "ax.text(xlim[0], x[1] + projection_length[1] + y_offset, r\"$X(\\theta') + c_\\beta \\sqrt{\\Sigma(\\theta')}$\")\n", - "\n", - "fig.savefig(\"projection.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "5cf49041", - "metadata": {}, - "source": [ - "## Unconditional inference: hybrid estimator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ebacbdab", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib notebook\n", - "\n", - "ani = QuantileUnbiasedAnimation(\n", - " rqu.mean[index],\n", - " np.sqrt(rqu.cov[index, index]),\n", - " truncation_set,\n", - " projection_quantile=rqu.compute_projection_quantile(.005),\n", - " xlim=(-.3, .5)\n", - ").make_animation(\n", - " title=\"Hybrid estimator\",\n", - " xlabel=f\"Economic opportunity score for {NEIGHBORHOOD}\"\n", - ")\n", - "ani.save(\"hybrid.gif\")\n", - "plt.show()" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "120d65e34230161c0f4356d19a77763cc2f6669dcb2a194d42d3b2faf517ecd2" - }, - "kernelspec": { - "display_name": "conditional-inference", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/multiple_inference.ipynb b/examples/multiple_inference.ipynb new file mode 100644 index 0000000..a590821 --- /dev/null +++ b/examples/multiple_inference.ipynb @@ -0,0 +1,537 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Multiple Inference Cookbook\n", + "\n", + "This template is an 80-20 solution for multiple inference. It uses many of the latest techniques in multiple inference to answer the following questions:\n", + "\n", + "1. *Compare to zero.* Which parameters are significantly different from zero?\n", + "2. *Compare to the average.* Which parameters are significantly different from the average (i.e., the average value across all parameters)?\n", + "3. *Pairwise comparisons.* Which parameters are significantly different from which other parameters?\n", + "4. *Ranking.* What is the ranking of each parameter?\n", + "5. *Best parameter identification.* Which parameters might be the largest (i.e., the highest ranked)?\n", + "6. *Inference after ranking.* What are the values of the parameters given their rank? e.g., What is the value of the parameter with the largest estimated value?\n", + "7. *Distribution.* What does the distribution of parameters look like?\n", + "\n", + "Instructions:\n", + "\n", + "1. Upload a file named `data.csv` to this folder with your conventional estimates. Open `data.csv` to see an example. In this file, we named our dependent variable \"dep_variable\", and have estimated the effects of policies named \"policy0\",..., \"policy9\". The first column of `data.csv` contains the conventional estimates $m$ of the unknown parameters. The remaining columns contain consistent estimates of the covariance matrix $\\Sigma$. In `data.csv`, $m=(0, 1,..., 9)$ and $\\Sigma = I$.\n", + "2. Modify the code if necessary.\n", + "3. Run the notebook.\n", + "\n", + "### Runtime warnings and long running times\n", + "\n", + "If you are estimating many parameters or the parameters effects are close together, you may see `RuntimeWarning` messages and experience long runtimes. Runtime warnings are common, usually benign, and can be safely ignored." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "\n", + "from conditional_inference.bayes import Improper, Nonparametric, Normal\n", + "from conditional_inference.confidence_set import ConfidenceSet, AverageComparison, PairwiseComparison, SimultaneousRanking\n", + "from conditional_inference.rank_condition import RankCondition\n", + "\n", + "data_file = \"data.csv\"\n", + "alpha = .05\n", + "\n", + "np.random.seed(0)\n", + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we'll summarize and plot your original (conventional) estimates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Improper.from_csv(data_file, sort=True)\n", + "results = model.fit(title=\"Conventional estimates\")\n", + "results.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.point_plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll create a *reconstruction plot*. A reconstruction plot shows what we would expect the conventional estimates to look like if your estimates were correct.\n", + "\n", + "For example, imagine we ran a randomized control trial in which we tested the effects of ten treatments. We then obtained our conventional estimates using OLS and rank ordered them. Now, suppose our conventional estimates are correct (i.e., assume the true effect of each treatment follows its OLS distribution). What would our new estimates look like if we reran the experiment, estimated the treatment effects by OLS, and rank ordered them again?\n", + "\n", + "Ideally, the new conventional estimates would look like our original conventional estimates. Below, the blue dots show the simulated distribution of new estimates. The orange x's show our original conventional estimates. So, ideally, the blue dots should be on top of the orange x's.\n", + "\n", + "Conventional estimates are more spread out than the true parameters in expectation. That is, conventional estimates often suggest that some parameters are very different from others, even if they are all similar. We call this problem *fictitious variation*. Our conventional estimates suffer from fictitious variation when the blue dots are more spread out than the orange x's." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.reconstruction_point_plot(title=\"Conventional estimates reconstruction plot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare to zero\n", + "\n", + "First, we'll discover which parameters significantly differ from zero using *simultaneous confidence intervals*. Suppose we estimated $K$ parameters. Simultaneous confidence intervals are a set of $K$ confidence intervals such that there is a 95% chance that all of the parameters fall within their confidence interval. Therefore, if zero is not in parameter $\\mu_k$'s confidence interval, we can reject the null hypothesis that $\\mu_k$ is equal to zero with 95% confidence. Simultaneous confidence intervals are similar to a Bonferroni correction but usually have higher power." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = ConfidenceSet.from_csv(data_file, sort=True)\n", + "results = model.fit()\n", + "results.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = results.point_plot()\n", + "ax.axvline(0, linestyle=\"--\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.test_hypotheses()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare to the average\n", + "\n", + "Next, we'll use simultaneous confidence intervals to discover which parameters significantly differ from the average (i.e., the average across all parameters). This is similar to what we did before. However, instead of creating simultaneous confidence intervals for the parameters $\\mu_k$, we'll create simultaneous confidence intervals for the difference between the parameters and the average $\\mu_k - \\mathbf{E}[\\mu_j]$. If zero is not in the confidence interval for $\\mu_k - \\mathbf{E}[\\mu_j]$, we can conclude that $\\mu_k$ differs from the average with 95% confidence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = AverageComparison.from_csv(data_file, sort=True)\n", + "results = model.fit()\n", + "results.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The x-axis in the plot below is the estimated difference between each parameter and the average $\\mu_k - \\mathbf{E}[\\mu_j]$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = results.point_plot()\n", + "ax.axvline(0, linestyle=\"--\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.test_hypotheses()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pairwise comparisons\n", + "\n", + "Next, we'll use simultaneous confidence intervals to perform pairwise comparisons. Instead of creating simultaneous confidence intervals for the individual parameters $\\mu_k$, we'll create simultaneous confidence intervals for all pariwise differences $\\mu_j - \\mu_k \\quad \\forall j, k$. If zero is not in the confidence interval for $\\mu_j - \\mu_k$, we can conclude that $\\mu_j$ differs from $\\mu_k$ with 95% confidence.\n", + "\n", + "Green squares in the heatmap below indicate that we can reject the null hypothesis and conclude that the parameter on the x-axis is greater than the parameter on the y-axis. Red squares indicate that we cannot reject the null hypothesis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = PairwiseComparison.from_csv(data_file, sort=True)\n", + "results = model.fit()\n", + "results.hypothesis_heatmap(triangular=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ranking\n", + "\n", + "Next, we'll estimate each parameter's rank. For example, we might say that we're 95% confident that the parameter with the largest estimated value is genuinely one of the largest three parameters. The simplest way to estimate ranks is to use pairwise comparisons. For example, if there's a 95% chance that $\\mu_k$ was worse than one parameter and better than $K-5$ parameters, then there's a 95% chance that parameter $\\mu_k$ ranks between second and fifth.\n", + "\n", + "The results below come from a similar but more efficient procedure, so they might not precisely match the pairwise comparisons plot above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = SimultaneousRanking.from_csv(data_file, sort=True)\n", + "results = model.fit()\n", + "results.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.point_plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best parameter identification\n", + "\n", + "Next, we'll compute a set of parameters containing the truly largest parameter with 95% confidence. The simplest way to calculate this set is to use pairwise comparisons. If we cannot reject the hypothesis that $\\mu_j$ is greater than $\\mu_k$ for any $j$, then $\\mu_k$ is in this set. That is, we cannot reject the hypothesis that $\\mu_k$ is the largest parameter with 95% confidence.\n", + "\n", + "Again, the results below come from a similar but more efficient procedure, so they might not precisely match the pairwise comparisons plot above.\n", + "\n", + "Parameters with \"True\" next to them in the table below are in this set. Parameters with \"False\" next to them are not." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.compute_best_params()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inference after ranking\n", + "\n", + "Next, we'll estimate parameters given the rank of their conventional estimates. For example, imagine we ran a randomized control trial testing ten treatments and want to estimate the effectiveness of the top-performing treatment.\n", + "\n", + "One property we want our estimators to have is *quantile-unbiasedness*. An estimator is quantile-unbiased if the true parameter falls below its $\\alpha$-quantile estimate with probability $\\alpha$ given its estimated rank. For example, the true effect of the top-performing treatment should fall below its median estimate half the time.\n", + "\n", + "Similarly, we want confidence intervals to have *correct conditional coverage*. Correct conditional coverage means that the parameter should fall within our $\\alpha$-level confidence interval with probability $1-\\alpha$ given its estimated rank. For example, the true effect of the top-performing treatment should fall within its 95% confidence interval 95% of the time.\n", + "\n", + "Below, we compute the optimal quantile-unbiased estimates and conditionally correct confidence intervals for each parameter given its rank." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = RankCondition.from_csv(data_file, sort=True)\n", + "results = model.fit(title=\"Conditional estimates\")\n", + "results.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.point_plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conditional inference is a strict requirement. Conditionally quantile-unbiased estimates can be highly variable. And conditionally correct confidence intervals can be unrealistically long. We can often obtain more reasonable estimates by focusing on *unconditional* inference instead of *conditional* inference.\n", + "\n", + "Imagine we ran our randomized control trial 10,000 times and want to estimate the effect of the top-performing treatment. We need *conditional* inference if we're interested the subset of trials where a specific parameter $\\mu_k$ was the top performer. However, we can use *unconditional* inference if we're only interested in being right \"on average\" across all 10,000 trials.\n", + "\n", + "Below, we use *hybrid estimates* to compute approximately quantile-unbiased estimates and unconditionally correct confidence intervals for each parameter.\n", + "\n", + "If you don't know whether you need conditional or unconditional inference, use unconditional inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = model.fit(beta=.005, title=\"Hybrid estimates\")\n", + "results.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.point_plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Distributions\n", + "\n", + "Next, we'll use Bayesian estimators to understand the distribution of parameters. Bayesian estimators begin with a *prior* distribution and then update that belief based on data using Bayes' Theorem to obtain a *posterior* distribution.\n", + "\n", + "Classical Bayesian estimators take the prior as given. However, we can often obtain better estimates by using empirical Bayes to estimate the prior from the data. For example, imagine predicting MLB players' on-base percentage (OBP) next season. We might predict that a player's OBP next season will be the same as his OBP in the previous season. But how can we predict the OBP for a rookie with no batting history? One solution is to predict that the rookie's OBP will be similar to last season's rookies' OBP. In Bayesian terms, we've constructed a prior belief about *next* season's rookies' OBP using data from the *previous* season's rookies' rookies' OBP.\n", + "\n", + "Empirical Bayes uses the same logic. Imagine randomly selecting one parameter $\\mu_k$ and putting the data we used to estimate it in a locked box. We can use the data about the remaining parameters to estimate a prior distribution for $\\mu_k$.\n", + "\n", + "Empirical Bayes estimators can be parametric or non-parametric. Parametric empirical Bayes assumes the shape of the prior distribution. Nonparametric empirical Bayes does not assume the shape of the prior distribution. Below, we apply a parametric empirical Bayes estimator assuming a normal prior distribution.\n", + "\n", + "First, let's plot one parameter's prior, conventional, and posterior distributions." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'Normal' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNormal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'Normal' is not defined" + ] + } + ], + "source": [ + "model = Normal.from_csv(data_file, sort=True)\n", + "results = model.fit()\n", + "results.line_plot(0)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's create a reconstruction plot for the Bayesian estimator. This plot shows what we would expect the conventional estimates to look like if the Bayesian model were correct. Remember that we want the blue dots to be on top of the orange x's.\n", + "\n", + "Compare the reconstruction plot for the Bayesian estimates (below) to the reconstruction plot for the conventional estimates near the top of the notebook. Looking at the reconstruction plot for the conventional estimates at the top of the notebook, you'll likely see that the conventional estimates are too spread out (the blue dots are more spread out than the orange x's). Looking at the reconstruction plot for the Bayesian estimates below, you'll likely see that the Bayesian estimates are appropriately spread out (the blue dots are on top of the orange x's)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.reconstruction_point_plot(title=\"Parametric empirical Bayes reconstruction plot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's summarize and plot the Bayesian estimates.\n", + "\n", + "Both Bayesian and rank condition estimators give parameter estimates and confidence intervals. Which ones should we use?\n", + "\n", + "We should use Bayesian estimators if our goal is to understand the distribution of parameters. If, however, our goal is to estimate a parameter given its rank (e.g., the top-performing parameter), the answer is less clear. In my experience, Bayesian point estimates are better than rank condition point estimates, but rank condition confidence intervals are better than Bayesian confidence intervals. For example, if you want to estimate the top-performing parameter, I recommend writing your discussion section using the Bayesian point estimate and the rank condition (hybrid) confidence interval.\n", + "\n", + "This template is an 80-20 solution for multiple inference. For a 90-50 solution, use cross-validation to decide whether Bayesian or rank condition estimates are better for your data set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.point_plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use Bayesian estimators to estimate the rank of each parameter. Bayesian estimators are usually more confident about the parameter ranks than our previous ranking analysis suggests. Our previous ranking analysis suggested substantial uncertainty about parameter rankings because it strictly controlled false positives. Bayesian estimates of parameter rankings do not control for false positives but give tighter and often more reasonable estimates. In my experience, the previous ranking analysis is more appropriate for hypothesis testing, while Bayesian ranking analysis is more appropriate in terms of objectives like Brier scores and log loss." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.rank_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we'll repeat this exercise with a nonparametric empirical Bayes estimator. Remember, parametric empirical Bayes estimators assume the shape of the prior distribution (e.g., normal). Nonparametric empirical Bayes estimators do not assume the shape of the prior distribution.\n", + "\n", + "Should you use a parametric or nonparametric empirical Bayes estimator? In my experience, parametric empirical Bayes is better for a small number of parameters, and nonparametric empirical Bayes is better for a large number of parameters. If both estimators give similar point estimates, trust the one with wider confidence intervals (usually the parametric version). If the estimators give very different point estimates, my rule of thumb is to trust parametric empirical Bayes when estimating fewer than 50 parameters and nonparametric empirical Bayes otherwise.\n", + "\n", + "But again, for a 90-50 solution, use cross-validation to decide between parametric and nonparametric empirical Bayes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Nonparametric.from_csv(data_file, sort=True)\n", + "results = model.fit()\n", + "results.line_plot(0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.reconstruction_point_plot(title=\"Nonparametric empirical Bayes reconstruction plot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.point_plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.rank_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "a31fe93114e6fe9c0b874076e62df141d5b35f609e1bfa94ca168a298e55e549" + }, + "kernelspec": { + "display_name": "Python 3.9.0 ('conditional-inference')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/rank_conditions.ipynb b/examples/rank_conditions.ipynb new file mode 100644 index 0000000..d3e05ef --- /dev/null +++ b/examples/rank_conditions.ipynb @@ -0,0 +1,156 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Template code for inference after ranking\n", + "\n", + "This is a template for regression analysis after ranking. It estimates the parameters using conditionally quantile-unbiased estimates and \"almost\" quantile-unbiased hybrid estimates.\n", + "\n", + "Instructions:\n", + "\n", + "1. Upload a file named `data.csv` to this folder with your conventional estimates. Open `data.csv` to see an example. In this file, we named our dependent variable \"dep_variable\", and have estimated the effects of policies named \"policy0\",..., \"policy9\". The first column of `data.csv` contains the conventional estimates $m$ of the true unknown mean. The remaining columns contain consistent estimates of the covariance matrix $\\Sigma$. In `data.csv`, $m=(0, 1,..., 9)$ and $\\Sigma = I$.\n", + "2. Modify the code if necessary.\n", + "3. Run the notebook.\n", + "\n", + "### Citations\n", + "\n", + " @techreport{andrews2019inference,\n", + " title={Inference on winners},\n", + " author={Andrews, Isaiah and Kitagawa, Toru and McCloskey, Adam},\n", + " year={2019},\n", + " institution={National Bureau of Economic Research}\n", + " }\n", + "\n", + " @article{andrews2022inference,\n", + " Author = {Andrews, Isaiah and Bowen, Dillon and Kitagawa, Toru and McCloskey, Adam},\n", + " Title = {Inference for Losers},\n", + " Journal = {AEA Papers and Proceedings},\n", + " Volume = {112},\n", + " Year = {2022},\n", + " Month = {May},\n", + " Pages = {635-42},\n", + " DOI = {10.1257/pandp.20221065},\n", + " URL = {https://www.aeaweb.org/articles?id=10.1257/pandp.20221065}\n", + " }\n", + "\n", + "### Runtime warnings and long running times\n", + "\n", + "If you are estimating the effects of many policies or the policy effects are close together, you may see `RuntimeWarning` messages and experience long runtimes. Runtime warnings are common, usually benign, and can be safely ignored." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from conditional_inference.bayes import Improper\n", + "from conditional_inference.rank_condition import RankCondition\n", + "\n", + "data_file = \"data.csv\"\n", + "alpha = .05\n", + "\n", + "conventional_model = Improper.from_csv(data_file, sort=True)\n", + "ranked_model = RankCondition.from_csv(data_file, sort=True)\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conventional_results = conventional_model.fit(title=\"Conventional estiamtes\")\n", + "conventional_results.summary(alpha=alpha)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conventional_results.point_plot(alpha=alpha)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conditional_results = ranked_model.fit(title=\"Conditional estimates\")\n", + "conditional_results.summary(alpha=alpha)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conditional_results.point_plot(alpha=alpha)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hybrid_results = ranked_model.fit(beta=.005, title=\"Hybrid estimates\")\n", + "hybrid_results.summary(alpha=alpha)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hybrid_results.point_plot(alpha=alpha)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "a31fe93114e6fe9c0b874076e62df141d5b35f609e1bfa94ca168a298e55e549" + }, + "kernelspec": { + "display_name": "Python 3.9.0 ('conditional-inference')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/rqu.ipynb b/examples/rqu.ipynb deleted file mode 100644 index 0ffdf2e..0000000 --- a/examples/rqu.ipynb +++ /dev/null @@ -1,399 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Template code for conditional quantile-unbiased analysis\r\n", - "\r\n", - "This template provides regression tables and point plots for conditional, hybrid, and projection estimates.\r\n", - "\r\n", - "Instructions:\r\n", - "\r\n", - "1. Upload a file named `data.csv` to this folder with your conventional estimates. Open `data.csv` to see an example. In this file, we named our dependent variable \"dep_variable\", and have estimated the effects of policies named \"policy0\",..., \"policy3\". The first column of `data.csv` contains the conventional estimates `X` of the true unknown mean. The remaining columns contain consistent estimates of the corresponding covariance matrix $\\Sigma$. In the example `data.csv` provided, $X=(0, 1, 2, 3)$ and $\\Sigma = I$.\r\n", - "2. Modify the code if necessary.\r\n", - "3. Run the notebook.\r\n", - "\r\n", - "### Runtime warnings and long running times\r\n", - "\r\n", - "If you are estimating the effects of many policies and/or the policy effects are close together, you may see `RuntimeWarning` messages and experience long runtimes. Runtime warnings are common, usually benign, and can be safely ignored." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 1, - "source": [ - "import matplotlib.pyplot as plt\r\n", - "import numpy as np\r\n", - "import seaborn as sns\r\n", - "\r\n", - "from conditional_inference.bayes.classic import LinearClassicBayes\r\n", - "from conditional_inference.rqu import RQU\r\n", - "\r\n", - "data_file = \"data.csv\"\r\n", - "alpha = .05\r\n", - "\r\n", - "conventional_model = LinearClassicBayes.from_csv(data_file, prior_cov=np.inf)\r\n", - "rqu = RQU.from_csv(data_file)\r\n", - "sns.set()" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 2, - "source": [ - "conventional_result = conventional_model.fit(cols=\"sorted\")\r\n", - "conventional_result.summary(title=\"Conventional estimates\", alpha=alpha)" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Conventional estimates
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" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Conventional estimates \n", - "==================================\n", - " coef pvalue [0.025 0.975]\n", - "----------------------------------\n", - "policy3 3.000 0.001 1.040 4.960\n", - "policy2 2.000 0.023 0.040 3.960\n", - "policy1 1.000 0.159 -0.960 2.960\n", - "policy0 0.000 0.500 -1.960 1.960\n", - "==========================\n", - "Dep. Variable dep_variable\n", - "--------------------------\n", - "\"\"\"" - ] - }, - "metadata": {}, - "execution_count": 2 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 3, - "source": [ - "conventional_result.point_plot(title=\"Conventional estimates\", alpha=alpha)\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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" - ] - }, - "metadata": {} - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 4, - "source": [ - "conditional_result = rqu.fit(cols=\"sorted\")\r\n", - "conditional_result.summary(title=\"Conditional estimates\", alpha=alpha)" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Conditional estimates
coef (median) pvalue [0.025 0.975]
policy3 2.686 0.059 -0.933 4.933
policy2 2.000 0.136 -1.922 5.922
policy1 1.000 0.285 -2.922 4.922
policy0 0.314 0.406 -1.933 3.933
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Dep. Variable dep_variable
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Conditional estimates \n", - "==========================================\n", - " coef (median) pvalue [0.025 0.975]\n", - "------------------------------------------\n", - "policy3 2.686 0.059 -0.933 4.933\n", - "policy2 2.000 0.136 -1.922 5.922\n", - "policy1 1.000 0.285 -2.922 4.922\n", - "policy0 0.314 0.406 -1.933 3.933\n", - "==========================\n", - "Dep. Variable dep_variable\n", - "--------------------------\n", - "\"\"\"" - ] - }, - "metadata": {}, - "execution_count": 4 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 5, - "source": [ - "conditional_result.point_plot(title=\"Conditional estimates\", alpha=alpha)\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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Hybrid estimates
coef (median) pvalue [0.025 0.975]
policy3 2.688 0.040 -0.100 4.977
policy2 2.000 0.140 -1.100 5.100
policy1 1.000 0.288 -2.100 4.100
policy0 0.312 0.409 -1.977 3.100
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Dep. Variable dep_variable
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Hybrid estimates \n", - "==========================================\n", - " coef (median) pvalue [0.025 0.975]\n", - "------------------------------------------\n", - "policy3 2.688 0.040 -0.100 4.977\n", - "policy2 2.000 0.140 -1.100 5.100\n", - "policy1 1.000 0.288 -2.100 4.100\n", - "policy0 0.312 0.409 -1.977 3.100\n", - "==========================\n", - "Dep. Variable dep_variable\n", - "--------------------------\n", - "\"\"\"" - ] - }, - "metadata": {}, - "execution_count": 6 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 7, - "source": [ - "hybrid_result.point_plot(title=\"Hybrid estimates\", alpha=alpha)\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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" - ] - }, - "metadata": {} - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 8, - "source": [ - "projection_result = rqu.fit(cols=\"sorted\", projection=True)\r\n", - "projection_result.summary(alpha=alpha)" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Projection estimates
coef (conventional) pvalue 0.95 CI lower 0.95 CI upper
policy3 3.000 0.006 0.482 5.518
policy2 2.000 0.088 -0.518 4.518
policy1 1.000 0.497 -1.518 3.518
policy0 0.000 0.938 -2.518 2.518
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Dep. Variable dep_variable
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Projection estimates \n", - "==============================================================\n", - " coef (conventional) pvalue 0.95 CI lower 0.95 CI upper\n", - "--------------------------------------------------------------\n", - "policy3 3.000 0.006 0.482 5.518\n", - "policy2 2.000 0.088 -0.518 4.518\n", - "policy1 1.000 0.497 -1.518 3.518\n", - "policy0 0.000 0.938 -2.518 2.518\n", - "==========================\n", - "Dep. Variable dep_variable\n", - "--------------------------\n", - "\"\"\"" - ] - }, - "metadata": {}, - "execution_count": 8 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 9, - "source": [ - "projection_result.point_plot(title=\"Projection estimates\", alpha=alpha)\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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" - ] - }, - "metadata": {} - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [], - "outputs": [], - "metadata": {} - } - ], - "metadata": { - "orig_nbformat": 4, - "language_info": { - "name": "python", - "version": "3.9.0", - "mimetype": "text/x-python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "pygments_lexer": "ipython3", - "nbconvert_exporter": "python", - "file_extension": ".py" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3.9.0 64-bit ('conditional-inference': conda)" - }, - "interpreter": { - "hash": "120d65e34230161c0f4356d19a77763cc2f6669dcb2a194d42d3b2faf517ecd2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/examples/stats.ipynb b/examples/stats.ipynb index bf781e6..2c3b983 100644 --- a/examples/stats.ipynb +++ b/examples/stats.ipynb @@ -2,171 +2,116 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ - "# Truncated normal\r\n", - "\r\n", + "# Truncated normal\n", + "\n", "Conditional inference's truncated normal distribution has two advantages over scipy's. First, it uses the state-of-the-art [exponential tilting](https://ieeexplore.ieee.org/document/7408180) method. Second, it allows for concave truncation sets." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "import matplotlib.pyplot as plt\r\n", - "import numpy as np\r\n", - "import seaborn as sns\r\n", - "from scipy.stats import norm, truncnorm as scipy_truncnorm\r\n", - "\r\n", - "from conditional_inference.stats import truncnorm, quantile_unbiased\r\n", - "\r\n", - "sns.set()\r\n", - "x = np.linspace(8, 9, num=20)\r\n", - "ax = sns.lineplot(x=x, y=scipy_truncnorm(8, np.inf).cdf(x), label=\"scipy\")\r\n", - "sns.lineplot(x=x, y=truncnorm([(8, np.inf)]).cdf(x), label=\"conditional-inference\")\r\n", - "ax.axhline(1)\r\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "from scipy.stats import norm, truncnorm as scipy_truncnorm\n", + "\n", + "from conditional_inference.stats import truncnorm, quantile_unbiased\n", + "\n", + "sns.set()\n", + "x = np.linspace(8, 9, num=20)\n", + "ax = sns.lineplot(x=x, y=scipy_truncnorm(8, np.inf).cdf(x), label=\"scipy\")\n", + "sns.lineplot(x=x, y=truncnorm([(8, np.inf)]).cdf(x), label=\"conditional-inference\")\n", + "ax.axhline(1)\n", "plt.show()" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "c:\\users\\dbspe\\repos\\conditional-inference\\src\\conditional_inference\\stats.py:137: RuntimeWarning: divide by zero encountered in log\n", - " return -x * mu + (0.5 * mu ** 2 + np.log(norm.cdf(b, mu) - norm.cdf(a, mu)))\n", - "c:\\users\\dbspe\\repos\\conditional-inference\\src\\conditional_inference\\stats.py:145: RuntimeWarning: divide by zero encountered in double_scalars\n", - " + (norm.pdf(b, mu) - norm.pdf(a, mu))\n", - "C:\\Users\\DBSpe\\anaconda3\\envs\\conditional-inference\\lib\\site-packages\\scipy\\optimize\\_numdiff.py:557: RuntimeWarning: invalid value encountered in subtract\n", - " df = fun(x) - f0\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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IiMj3GEzIr/YeLfd6KDFIZszocwy6j9+GXXEAANSpo6AbOQVyeOxFnk1ERL7EYEJ+tXmXc5hlUlYf3Dg2xePnVwr2wvHVu0Bt462/CZc477Tp1dfjr0VERJ3HYEJ+U1xehx/zKyDBebeMXuu5j6MQArYfNsK25R1ACMjRydCN+gXUiZd47DWIiMjzGEzIbz7bdQoAMKR/T/SK8NykU6HYYfn2LdgOfAEA0GRcCd2YX0OS+XEnIgp0/ElNfmG22PHtvmIAF16J1V3CUgfzppVwnPoRgATd6F9Cc+k1XCCNiChIMJiQX3y7rxgNVgfionpgYF/PbIanmMpg/ngZlKpiQK1DyIQ7oU4e5pFzExGRbzCYkM8JIfDp9gIAzqslsgeuZtiLD6Jh40sQllpIoVEIufY+qKKTOn1eIiLyLQYT8rk9h0+juLweOq0Klw2K6/T5bIe+QcNXrwOKA3KvFIRccy/kHhGdL5SIiHyOwYR87qNv8gEAYwbFI0TX8Y+gEAqs29+HdfdHAAB1ykjox8+CpNZ5pE4iIvI9BhPyqdNVZmzb79ws76oRCR0+j7Bb0PDZa7Af3wkA0A77KbQjb+Kme0REQY7BhHxq885CCAEMSolCfHRoh86h1FXC/MmLUM4cB2Q19FfcDk3a5Z4tlIiI/ILBhHzGYnPgy++da5dMzOrToXM4zpyA+ZMXIOoqIenDoJ80F+q4NE+WSUREfsRgQj6zdX8p6hrsiInqgaH9e0JRhFvPtx3fiYbPXgXsVsiRvRFyzX2QjTFeqpaIiPyBwYR8QgiBz3Y698X5yWV9IctSu4OJEAK2vf+FZeu7AARUiYMQMvFuSNoeXqyYiIj8gcGEfOJwYTVOltVCo5Zx9ahk2C22dj1POOywfPNX2A5+DQDQZF4F3WW/giSrvFkuERH5CYMJ+cRnjbsIXzYoDmE9tKhsRzARDbUwf7oCjuI8QJKgy7kV2kFXe7tUIiLyIwYT8rrKGgt2HjwNAJg4sn2TXpWqEtR/sgyiuhTQ6BEy4W6okwZ7s0wiIgoADCbkdV/uPgWHIjAgMRzJcWEXbW8vOgDzpysASx0kQzRCrp0HVZTnNvojIqLAxWBCXmV3KPhidxGA9u0ibM37Epav3wCEA3JMKkIm5ULuEe7tMomIKEAwmJBX7cgrg6nOigiDFsPTep23nVAUWLb9A7a9HwMA1Kmjob/yN5DUWl+VSkREAYDBhLxqc+MtwuOGJUCtanu5eGFrQMNnr8J+4nsAgHbETdAOvwGSB3YdJiKi4MJgQl6TX2zC0SITVLKEK4f0brONsFtQ/9EzUE7nAyo19FfOhKb/aB9XSkREgYLBhLymaUG1rIExCDe03vFXCIGGr9+Acjofkj4MIdfcC1Vsf1+XSUREAYRbsZJXmOqt2HqgDAAwYXjbk15tBz6H/fC3gCRDP/FuhhIiInI/mCiKguXLl2Ps2LEYOnQoZs2ahYKCgvO2Ly8vx/3334/Ro0dj1KhRmDdvHkpLSztVNAW+r/cUwe5Q0DcuDP16G1s9bi89Csv/1gEAdNlToe490NclEhFRAHI7mKxcuRLr1q3D0qVL8c4770BRFMycORNWq7XN9vfddx+Kiorw+uuv4/XXX0dRURHuueeeThdOgcuhKPi8cRfhCSMSW01iddSbUPvJS4Bih7rvCGgGX+ePMomIKAC5FUysVivWrl2L3NxcjBs3DhkZGVi2bBlKSkqwcePGVu1NJhO2bduGWbNmYeDAgcjMzMQdd9yBffv2oaqqylPvgQLM7sPlqDBZYAjRIHtgy91/haKg7INlELUVkMLjoB83k3ffEBGRi1vBJC8vD3V1dcjJyXEdMxqNyMzMxPbt21u11+v1CA0NxQcffIDa2lrU1tbiX//6F1JSUmA0tr68T13D5p3Oob0rh/aGRt1ys72G7e/DnL8XUGsRcvUcSNoQf5RIREQByq27ckpKSgAA8fHxLY7HxMS4HmtOq9Xi6aefxmOPPYaRI0dCkiTExMTgrbfegix3bt6tWu3ZebuqxjU2VOdZa4Pap/B0LfJOVkGSnPviNP97sh7/Hg07PwQAGK6aCU1Mkr/K7PL4efYd9rVvsJ99IxD62a1gYjabATgDR3M6nQ7V1dWt2gshcODAAQwbNgwzZ86Ew+HAsmXLcPfdd+Nvf/sbDAZDh4qWZQmRkaEdeu7FGI38F3xn/G3zEQDA6EHx6N832nXcVlmCU5tfBQAYR16HnlkT/FJfd8PPs++wr32D/ewb/uxnt4KJXq8H4Jxr0vRnALBYLAgJaf0m/vvf/+Ktt97C559/7gohq1atwvjx4/Hee+9hxowZHSpaUQRMpvoOPfd8VCoZRmMITCYzHA7Fo+fuLuoabNi84yQAYNyQeFRW1gEAhM2CmvefgWKphzpuAKIn3sZ+9jJ+nn2Hfe0b7Gff8GY/G40h7boS41YwaRrCKSsrQ1LS2cvwZWVlSE9Pb9V+x44dSElJaXFlJDw8HCkpKThx4oQ7L92K3e6dD6bDoXjt3F3dl98XwWpTkNArFP0TwmG3K85F1L78CxzlJyHpwxA66R5IKg0cDiv72Qf4efYd9rVvsJ99w5/97NYgUkZGBgwGA7Zu3eo6ZjKZsH//fmRlZbVqHxcXhxMnTsBisbiO1dfXo7CwEH379u141RRwFCHw2S7nSq8Thp+9Rdh24AvYD30LSBL0E+6CbIjyZ5lERBTg3AomWq0W06dPx3PPPYfNmzcjLy8P8+bNQ1xcHCZNmgSHw4HTp0+joaEBAHDjjTcCcK5lkpeXh7y8PMyfPx86nQ5Tpkzx+Jsh//nhWAXKKs0I0akx+pJYAICj7Bgs/3sbAKDNmgp1QqY/SyQioiDg9rTb3NxcTJ06FY888gimTZsGlUqFNWvWQKPRoLi4GGPGjMGGDRsAOO/WWbduHYQQuO2223D77bdDo9Fg3bp1CAsL8/ibIf9puloydnA89Fo1lIYamDe93LiI2nBoh1zv5wqJiCgYSEII4e8i3OVwKKioqPPoOdVqGZGRoaisrOP4pZtKK+vx0KtbAAC/nz0aMeF6mD/+ExyFP0AKj0XoTYshaXsAYD/7CvvZd9jXvsF+9g1v9nNUVGi7Jr/yhnDqtM93nYIAcGlqNGIje8C66wM4Cn8AVE2LqPXwd4lERBQkGEyoUxqsdny9txgAcNXwRNhP7oZ1l3MRNf0VM6CK6uPP8oiIKMgwmFCnbPmxFGaLHTGRIcjsaYf5s9cAAJrMCdAMuMzP1RERUbBhMKEOE0Jg807npNeJQ2Nh2fQyYK2HHJMKXc40P1dHRETBiMGEOuzgySqcOlMHnUbG6NrNUBoXUQuZeA8klVtr9xEREQFgMKFOaLpaMi25FOIoF1EjIqLOYzChDimvbsCuw6fRR3UGQ6s+BQBos27mImpERNQpDCbUIV/sPoUQNOCOiK8hKXaok4dBO+Qn/i6LiIiCHIMJuc1md+Cr3YX4v9BvYBQ1kIyx0I+f5dofh4iIqKMYTMht2w6UYQx2YqC2iIuoERGRRzGYkFuEEDi6/RtcG7IXAKAfextU0VxEjYiIPIPBhNxy/Eg+Jlmdk12RdiU0aZf7tyAiIupSGEyo3YTdCvnrVQiVrSjXxMMwdrq/SyIioi6GwYTareaLvyLKXoZaRQd53J2QVBp/l0RERF0Mgwm1izXvS0jHvoUiJHyqvxZJKcn+LomIiLogBhO6KMfp47B8+yYAYIN5KNKycvxcERERdVUMJnRBQiho+Got4LBjnzUR21TDMTIjxt9lERFRF8VgQhdkP74LSvlJWKHB3+ouw5VDE6BW8WNDRETewS1guwi7Q4GiCI+eUwgFtu3/BAB8Zh6IBikEVw5N8OhrEBERNcdg0gUcOFGJF9/dA6td8eh5h2qP43bDKdQrGnzRMBAjMnohMkzn0dcgIiJqjtfku4DtB0o9HkokKLguZA8A4IuGTCjqHrhuFO/EISIi7+IVky7gWLEJADDrp5kYNqCnR86pHNsKx1fVgLYHbrj1t7g5xMC5JURE5HUMJkHOYnOgsKwOAJDeJwJ6bef/SoXiQN2efwMAtIOvhc5g7PQ5iYiI2oP/BA5yJ0troAiB8FCtx+Z/2I9sgaguAXSh0A662iPnJCIiag8GkyCXX+QcxunX2whJkjp9PqE4YNn1LwCAdsj1kLQhnT4nERFRezGYBLmm+SUp8Z4ZbrEf/h+EqQySPgzaSyZ45JxERETtxWAS5I41XjFJ6d35YCIU+9mrJUOvh6TRd/qcRERE7mAwCWKmeivOVDcAAFLiwjp9PtvBbyBqzkAKMUKTeVWnz0dEROQuBpMgdrxxGCc+ugd66DWdOpdw2GH9vvFOnKE/gaTmQmpEROR7DCZBzDWM44H5JbaDX0HUlkPqEQHNwPGdPh8REVFHMJgEMU9NfBV2K6zffwQA0A6dDEmt7XRtREREHcFgEqSEEC1uFe4MW95XEHUVkEKjoMm4whPlERERdQiDSZA6XWVGXYMdapWEPjGGDp9H2K2w7m68WjKMV0uIiMi/GEyCVNMwTlJsWKf2sLEd+ByivgqSIRqadF4tISIi/2IwCVKemPgqbBZYd/8HAKAdfgMkFbdOIiIi/2IwCVL5jVdM+nUimNj2fwZhNkEK6wVN2uWeKo2IiKjDGEyCkN2h4ERJLYCOT3wVtgZY92wAAOiG3wBJ5tUSIiLyPwaTIHTqdB3sDgU9dGrERHZskz3rj5shGmogGWOgHnCZhyskIiLqGAaTIHSsqBqAc3+cjuwoLKzmZldLfgZJVnm0PiIioo5iMAlCnV1YzfrDp4ClDnJ4HNT9R3uyNCIiok5hMAlC+cU1ADo28VVY62Hd+zEAQDviRl4tISKigMJgEmTMFjuKz9QBcA7luMu6byNgrYcc2RvqftmeLo+IiKhTGEyCzPGSGggA0UY9wkPdW6VVWOpg3fsJAEA7/EZIMv/6iYgosPA3U5BpPvHVXdZ9nwA2M+TIRKj7jfR0aURERJ3GYBJkOjq/RDTUOodxAGhH3ghJ4l89EREFHv52CjKuFV/dvGJi3fsxYGuAHJ0Edd/h3iiNiIio0xhMgkhljQWVNRZIEpAcG9bu5ylmk/MWYQC6ETfxagkREQUs/oYKIk0b9yX0NECnbf9tvtY9/wXsFsg9+0KVPNRL1REREXUeg0kQOTuM48bVkvpq2H7cDADQjbypQyvFEhER+QqDSRA5G0zC2/0c654NgMMKOaYfVH0Ge6s0IiIij2AwCRKKEK5g0t6l6JX6Ktj2fwagaW4Jr5YQEVFgczuYKIqC5cuXY+zYsRg6dChmzZqFgoKC87a32Wx4/vnnXe2nT5+OAwcOdKro7qi4vB4NVge0Ghm9e/Zo13Osu/8DOGyQY/tDlTjIyxUSERF1ntvBZOXKlVi3bh2WLl2Kd955B4qiYObMmbBarW22X7JkCd5//338/ve/x/r16xEVFYVZs2ahpqam08V3J/mNE1/7xoZB1Y4VW5XaCtgOfA4A0I2cwqslREQUFNwKJlarFWvXrkVubi7GjRuHjIwMLFu2DCUlJdi4cWOr9gUFBVi/fj2eeuopjB07FqmpqXjyySeh1Wrxww8/eOxNdAfuzi+x7v4IcNihik+HqvdAb5ZGRETkMW4Fk7y8PNTV1SEnJ8d1zGg0IjMzE9u3b2/V/ttvv0VYWBiuuOKKFu0/++yzFuegizvWNL+kHQurKbXlsOV9CQDQcm4JEREFEbU7jUtKSgAA8fHxLY7HxMS4HmsuPz8fffr0wcaNG/Haa6+htLQUmZmZWLRoEVJTUztRNqBWe3berkolt/gaSKw2BwrLagEAAxLDL/re63Z/BCgOqBMyoU/K9EWJ7RbI/dyVsJ99h33tG+xn3wiEfnYrmJjNZgCAVttyV1udTofq6upW7Wtra3HixAmsXLkSCxcuhNFoxCuvvIJbb70VGzZsQHR0dIeKlmUJkZGhHXruxRiNIV45b2fkHa+AQxGIMOjQv2/0Ba+A2KpKUZn3FQAgZsKt0HupnzorEPu5K2I/+w772jfYz77hz352K5jo9XoAzrkmTX8GAIvFgpCQ1m9CrVajtrYWy5Ytc10hWbZsGa688kr885//xMyZMztUtKIImEz1HXru+ahUMozGEJhMZjgcikfP3Vm7D5YCAFLiw1BVdeH3XffZO86rJYmDYDYkwVxZ54sS2y2Q+7krYT/7DvvaN9jPvuHNfjYaQ9p1JcatYNI0hFNWVoakpCTX8bKyMqSnp7dqHxcXB7Va3WLYRq/Xo0+fPigsLHTnpVux273zwXQ4FK+du6OOFjqvRvWNC7tgbYqpDNaD3wAAtCNuDLj30Vwg9nNXxH72Hfa1b7CffcOf/ezWIFJGRgYMBgO2bt3qOmYymbB//35kZWW1ap+VlQW73Y59+/a5jjU0NKCgoADJycmdKLt7adoj52ITXy27/gUIBao+g6GK7e+L0oiIiDzKrSsmWq0W06dPx3PPPYeoqCgkJCTg2WefRVxcHCZNmgSHw4GKigqEhYVBr9dj5MiRuOyyy/C73/0OTzzxBCIiIrB8+XKoVCr87Gc/89Z76lJqzTaUVTnn9vSNO38wUapKYD/8PwCAbsSNviiNiIjI49yedpubm4upU6fikUcewbRp06BSqbBmzRpoNBoUFxdjzJgx2LBhg6v9Sy+9hOzsbMyZMwdTp05FbW0t3njjDURFRXn0jXRVTeuXxEaGwBCiOW8759USAVXSUKhi+vmqPCIiIo+ShBDC30W4y+FQUFHh2UmdarWMyMhQVFbWBdT45Yff5OODb/KRc0ksZv30kjbbOCqLUP/uwwAEekx5HKqegTtMFqj93NWwn32Hfe0b7Gff8GY/R0WFtmvyK28ID3DH2rFxn3PpeQF18rCADiVEREQXw2ASwIQQF534KoQCe/4OAIAm/Yo22xAREQULBpMAdqa6AbVmG1SyhKQYQ5ttlNKjEHWVgEYPVWLbQz1ERETBgsEkgDVNfE2KNUCjVrXZxnbMuUeROnkYJLW2zTZERETBgsEkgLmGcc4zv6T5MI66X+t1ZIiIiIINg0kAu9jEV6XsGERdBaDRQ504yJelEREReQWDSYCyOxScLKkBAPQ7z8TXs8M4QzmMQ0REXQKDSYAqOlMHq11BiE6N2KgerR4XQoG9KZhwGIeIiLoIBpMAdXYYJwyyJLV6vOUwzqW+Lo+IiMgrGEwC1MUmvrqGcZI4jENERF0Hg0mAarpVuF8bwUQIwbtxiIioS2IwCUBmix1Fp517AbW14qty+hhEbblzGKcPh3GIiKjrYDAJQCdLayAARBl1iDDoWj1+dhhnCIdxiIioS2EwCUAXml8ihODdOERE1GUxmASgYxeYX6KczncO46h1UPcZ7OvSiIiIvIrBJAC5Jr62Mb+EwzhERNSVMZgEmKpaCypMFkgSkBwX1uIx5904HMYhIqKui8EkwOQ3zi/p3TMUeq26xWPKmeMQNWcAtRbqJA7jEBFR18NgEmAutHGfvcUwTuu7dYiIiIIdg0mAOd/8EiHE2fklHMYhIqIuisEkgChCIL+4cUfhc66YKGdOQNScBlRaqPsM8Ud5REREXsdgEkBKK+phttihVcvo3TO0xWP2Y9sAAOqkwZA0HMYhIqKuicEkgDQtrJYUFwa16uxfTcthnGy/1EZEROQLDCYB5Hwb9ynlzYZxkjiMQ0REXReDSQA538TXs3fjcBiHiIi6NgaTAGGzO3CytBZAy1uFeTcOERF1JwwmAeJkWS0cioAhRIOe4XrXcaX8JISpDFBpOIxDRERdHoNJgGha8bVfbyMkSXIddw3j9BkMSaNv87lERERdBYNJgGhr4qsQAjbujUNERN0Ig0mAaLpVOKXZxFelogCiutQ5jJM81E+VERER+Q6DSQCoNdtQWmkG0HLiK4dxiIiou2EwCQDHS5xXS2IiQmAI0QDg3ThERNQ9MZgEgOYTX5soFYUQ1SWASs27cYiIqNtgMAkArvklLYZxGvfGSbwUkjbEL3URERH5GoOJnwkhXHfkNE18FUKcnV/CYRwiIupGGEz8rNzUAFO9DSpZQlKMAQCgVBZCaRrGSR7m5wqJiIh8h8HEz/KLawAAiTEGaDUqAM3uxuEwDhERdTMMJn7mmvgaz2EcIiIiBhM/O1ZUDeDsxFel8hSUqmJAVnNRNSIi6nYYTPzIoSg4Xuocymma+Np0tUSVOAiStoffaiMiIvIHBhM/KjpTD6tNgV6rQny0M4TYG/fG0XAYh4iIuiEGEz9y3SYcb4QsSXBUnoJSWeQcxunLu3GIiKj7YTDxo3Pnl5wdxrmEwzhERNQtMZj40bGixvkl5wQTTb9sv9VERETkTwwmfmKxOnDqTC0A5x45jsoiKJWnAFnFu3GIiKjbYjDxkxOlNRACiAzTITJM1/JuHF2on6sjIiLyDwYTPzl3476zwzi8G4eIiLovBhM/Oea6IycMjqoiKJWFjcM4vBuHiIi6LwYTP2m+FL1rGCfhEg7jEBFRt8Zg4gfVdVaUmxogAejbLJhwGIeIiLo7BhM/aLpaEt8zFDrzaSgVhYDEYRwiIiK3g4miKFi+fDnGjh2LoUOHYtasWSgoKGjXcz/88EOkp6ejsLDQ7UK7kubzS2yuu3EyIekN/iyLiIjI79wOJitXrsS6deuwdOlSvPPOO1AUBTNnzoTVar3g806dOoUnnniiw4V2JU1L0feLN57dGyeFwzhERERuBROr1Yq1a9ciNzcX48aNQ0ZGBpYtW4aSkhJs3LjxvM9TFAUPPPAALrnkkk4XHOyEEK6hnP5GC5TyAucwTt/hfq6MiIjI/9TuNM7Ly0NdXR1ycnJcx4xGIzIzM7F9+3ZMnjy5zeetWrUKNpsNc+bMwZYtWzpXcSO12rPTY1QqucVXbykur0O9xQ6NSkZMzX5YAagTM6ExGL36uoHCV/3c3bGffYd97RvsZ98IhH52K5iUlJQAAOLj41scj4mJcT12rr1792Lt2rV47733UFpa2sEyW5JlCZGR3rmt1mgM8cp5m+w+VgEASE0MB05+CQCIGDwGRi+9n0Dl7X4mJ/az77CvfYP97Bv+7Ge3gonZbAYAaLXaFsd1Oh2qq6tbta+vr8eCBQuwYMEC9O3b12PBRFEETKZ6j5yriUolw2gMgclkhsOhePTcze07fBoAkBFphfVkPiDJsMUOQmVlnddeM5D4qp+7O/az77CvfYP97Bve7GejMaRdV2LcCiZ6vR6Ac65J058BwGKxICSkdbp68sknkZKSgltuucWdl2kXu907H0yHQ/HauQHg6ClngMuUjgEAVAmZUNShULz4moHI2/1MTuxn32Ff+wb72Tf82c9uBZOmIZyysjIkJSW5jpeVlSE9Pb1V+/Xr10Or1WLYMOf6HA6HAwAwefJk3Hnnnbjzzjs7XHgwstkVnCytAQD0qt4PAFBzUTUiIiIXt4JJRkYGDAYDtm7d6gomJpMJ+/fvx/Tp01u1P/dOnT179uCBBx7Aa6+9hrS0tE6UHZwKT9fC7hDoE1IPuaoAkGTejUNERNSMW8FEq9Vi+vTpeO655xAVFYWEhAQ8++yziIuLw6RJk+BwOFBRUYGwsDDo9XokJye3eH7TBNnevXsjIiLCY28iWDTtKHxFZDHQAKh6D4SsD/NzVURERIHD7fuBcnNzMXXqVDzyyCOYNm0aVCoV1qxZA41Gg+LiYowZMwYbNmzwRq1Br2lhtQw455dwGIeIiKglt66YAIBKpcIDDzyABx54oNVjiYmJOHjw4HmfO2rUqAs+3tXlF5sQLdfA2FDsHMZJGeHvkoiIiAIKV6rxkfoGG4rL6zFUewIAoOqdwWEcIiKiczCY+Eh+ifNunJEhJwEAau6NQ0RE1AqDiY/kF5kQJdegt3QGkCQO4xAREbWBwcRH8otNZ4dx4jMgh3SPvXGIiIjcwWDiA0IIHCs6G0x4Nw4REVHbGEx8oLLGApW5Asnq8sZhnJH+LomIiCggMZj4wLEiE4ZwGIeIiOiiGEx8oPn8Eg7jEBERnR+DiQ+UFZ5CX/UZCEhQ9+XdOEREROfDYOJliiKgrjgKAHBEJkHuEe7nioiIiAIXg4mXFZXXIUEqAwDoe3e/HZWJiIjcwWDiZflFJiSrzwAA1LGpfq6GiIgosDGYeNnxogokqioAAKoYBhMiIqILYTDxsvqiY1BLCuwaA6Swnv4uh4iIKKAxmHiRxeZASE0BAEDu1Q+SJPm5IiIiosDGYOJFJ0trkKw6DQDQ9x7g52qIiIgCH4OJFx0rMiFZ7QwmKk58JSIiuigGEy8qLixGtKoOAoCqV4q/yyEiIgp4DCZeZC91LqxmM8RB0ob4uRoiIqLAx2DiJaZ6K6KsRQAAXRznlxAREbUHg4mXNF9YTdu7v5+rISIiCg4MJl6SX1TlCiZcWI2IiKh9GEy8pOpUPnSSHQ5ZBzmit7/LISIiCgoMJl4ghIB05jgAwBGVDElmNxMREbUHf2N6QVmVGb1RCgDokcCJr0RERO3FYOIF+UUm14qvmjhOfCUiImovBhMvOHmqDLGqagCAzImvRERE7cZg4gUNRUchS4BVFwU5xOjvcoiIiIIGg4mH2R0K9KaTAAA5pp+fqyEiIgouDCYeVni6FklyGQCgR2Kan6shIiIKLgwmHpZ/qtq1sJqaOwoTERG5hcHEw0oLT8IgW6BIKsjRSf4uh4iIKKgwmHiYUnYMAGANS4Sk0vi5GiIiouDCYOJBZosd4eZTAABdPNcvISIicheDiQcdLzYhWe1cWC2EK74SERG5jcHEg/JPlSNBVQkAUHHiKxERkdsYTDyopvAY1JICqzoUkqGnv8shIiIKOgwmHiSXOye+KlEpkCTJz9UQEREFHwYTD6mssSDGUQIACOXCakRERB3CYOIhx4pMroXVtLwjh4iIqEMYTDzkVMEpRKvqIACoeqX4uxwiIqKgxGDiIQ1Fh51fQ+IgaUP8XA0REVFwYjDxAEUR3FGYiIjIAxhMPKC4oh6JknNH4bCkdD9XQ0REFLwYTDwg/1QlktTlAAB1LCe+EhERdRSDiQecOXkMOskOm6yDHBnv73KIiIiCFoOJB7h2FDb2gSSxS4mIiDqKv0U7yWpzwFhfCADQ9+bGfURERJ3BYNJJJ8tqXTsKc8VXIiKizmEw6aQTJ0sRp6oGwB2FiYiIOovBpJNqC50Lq5k1kZBDjH6uhoiIKLi5HUwURcHy5csxduxYDB06FLNmzUJBQcF52x8+fBh33HEHRo0ahZycHOTm5qKoqKhTRQcSVXk+AECJ5jL0REREneV2MFm5ciXWrVuHpUuX4p133oGiKJg5cyasVmurtpWVlbj99tuh1+vx5ptvYvXq1aioqMDMmTNhsVg88gb8qdZsQ097MQDA0IfzS4iIiDrLrWBitVqxdu1a5ObmYty4ccjIyMCyZctQUlKCjRs3tmq/adMm1NfX449//CPS0tIwaNAgPPvsszh69Ch27drlsTfhL8dOVbt2FA5JYDAhIiLqLLU7jfPy8lBXV4ecnBzXMaPRiMzMTGzfvh2TJ09u0T4nJwcrV66EXq93HZNlZxYymUydqRtqtWenx6hUcouv7VFWcAIpsgUOqKCN7QvJjed2Vx3pZ3If+9l32Ne+wX72jUDoZ7eCSUlJCQAgPr7l6qYxMTGux5pLTExEYmJii2OvvfYa9Ho9srKy3K3VRZYlREaGdvj5F2I0tn9nYFupc2E1S1gConqGe6WersqdfqaOYz/7DvvaN9jPvuHPfnYrmJjNZgCAVqttcVyn06G6uvqiz3/zzTfx1ltv4ZFHHkFUVJQ7L92CogiYTPUdfn5bVCoZRmMITCYzHA7lou2FEFBV5ANqQO7VD5WVdR6tp6tyt5+pY9jPvsO+9g32s294s5+NxpB2XYlxK5g0DclYrdYWwzMWiwUhIedPV0IIvPjii3jllVdw11134de//rU7L9smu907H0yHQ2nXuU9XmZGAUgBAWFKG1+rpqtrbz9Q57GffYV/7BvvZN/zZz24NIjUN4ZSVlbU4XlZWhtjY2DafY7PZ8MADD2DVqlV48MEHcd9993Ws0gCTX1COBFUFAEAbzx2FiYiIPMGtYJKRkQGDwYCtW7e6jplMJuzfv/+8c0YWLlyIjz/+GM8//zxmzJjRqWIDSeXJw1BJAg1yKCRDtL/LISIi6hLcGsrRarWYPn06nnvuOURFRSEhIQHPPvss4uLiMGnSJDgcDlRUVCAsLAx6vR7vv/8+NmzYgIULFyI7OxunT592naupTbBylB0FAFgikiFJkp+rISIi6hrcvh8oNzcXU6dOxSOPPIJp06ZBpVJhzZo10Gg0KC4uxpgxY7BhwwYAwEcffQQA+OMf/4gxY8a0+K+pTTCyOxSEcUdhIiIij5OEEMLfRbjL4VBQUeHZu2DUahmRkaGorKy76ISfEyU1wD8XIUpVB/1PFkKTkOnRWroyd/qZOo797Dvsa99gP/uGN/s5Kiq0XXflcKWaDig8UYgoVR0USFD34h45REREnsJg0gE1hYcAAHW6XpC0XOyHiIjIUxhMOkBVcRwAoETxagkREZEnMZi4yWyxo6e1CAAQlpTu52qIiIi6FgYTN50oqkIfdTkAwMBgQkRE5FEMJm4qO34UOskOq6SFHBF/8ScQERFRuzGYuKmh+DAAoC40EZLE7iMiIvIk/mZ1k950EgCgjk31cyVERERdD4OJGyprLIgXzh2FI/pm+LkaIiKirofBxA0nT5YiTlUNgEvRExEReQODiRsqT+QBAGpVEZBDjH6uhoiIqOthMHGDo+wYAOeOwkREROR5DCbtpAgBY+OOwiEcxiEiIvIKBpN2Ki2vQ6J8GgAQ2W+gn6shIiLqmhhM2qnwWD4MsgV2qKDpyaEcIiIib1D7u4BgUdu4o3CNLg6RKnYbEbWkKA7YbDZ/l9FlKYqEhgYVrFYLHA7h73K6rI72s0qlhix75loHf8O2k7ppR+Ge3FGYiM4SQqCoqAjl5RX+LqXLO3NGhqIo/i6jy+toP4eEGGA0RkGSpE69PoNJO9jsDkRZiwA1YEziwmpEdFZVVTnM5joYDJHQanWd/qFM56dSSbxa4gPu9rMQAlarBbW1lQCA8PDoTr0+g0k7nCyuQILK+a+h8GTuKExEToriQF1dDcLDoxASEubvcro8tVqG3c4rJt7WkX7WanUAgNraSoSFRXZqWIeTX9uh7OhBqCSBeqkH5LCe/i6HiAKEw+EAAOh0Oj9XQuR/TeHE4bB36jwMJu1gKz4CAKgzJPEyLRG1gT8XiDz1+5HBpB3O7ijcz8+VEBERdW0MJhdRa7YhTikBAESmZPq5GiIioq6NweQiCo4XIFJVD0VIMCT293c5REQB5amnlmDOnDv8XQZ1Ibwr5yIqj+chEUC1pifCNXp/l0NEFFDuvXcBFMXh7zKoC2EwuQjRuKOwlTsKExG1YjAY/F0CdTEcyrkAIQTCzNxRmIjcI4SAxerw239CuL8I2XfffYvf/vbXmDDhckyefDWeemoJTCYTAKCwsACLFs3HNddcieuvn4DFix9CZaVzbafmQzm7du3AmDEj8eWXn+EXv/gZJk4cg3vvvRvHj+cDAL7++guMHZuFkpLiFq89e/btePnlFzve4dSl8IrJBZRX1aG3dAYA0DOVE1+J6OKEEPjDW7tw5FS132ronxiOB381vN23b1ZVVeHhhx/AnDnzcNllY1BWVoqlSxdj5coXcc899+Gee2YhNbU/XnxxFWRZwrPP/h6PProIK1a81ub5Vqx4AfPnL0RMTBxWrlyO3Nw7sW7deuTkjEFERCQ+/vg/mDFjJgDg5MkT+PHHfVi06FGPvX8KbrxicgGnjhyGTrLDAi20PRP8XQ4RBYsgW9bk9OlSWK1WxMbGIS4uHoMHD8Uzz/wJN9/8S2zevBH19XVYsuT3yMgYiLS0DPzud49i0KDBsFqtbZ7vnnvuRU7OGKSm9sfixUtRX1+HTZs+gVqtxjXXXI9PPtngavvf/36EgQMzkZLC5RjIiVdMLqC+8CAAoFrfGz0lZjgiujhJkvDgr4bDavPf0ulajezWYlcDBqRj4sRr8LvfzUN0dE9kZY3CZZeNxRVXjMO///1P9OmTBKPR6Grfv/8A9O9//uHt4cNHuv5sNIYjKSkZx445F6r8yU9uwDvvvIUff/wBmZmXYOPG/2L69Bnuv0nqshhMLkBVcQIAIKK5ozARtZ8kSdBpVf4uwy1LljyF3/xmFrZs+R+2b9+KpUsfxeDBQ5Ga6v4yCWp1y18tDocCWXb2R0pKP2RmDsLGjRtgsTSgsrICEyde45H3QF0DLwOch0NRnDsKAwjjjsJE1IX9+OMPWL78eSQl9cUvfnErnn32RTz44GPYuXM7oqKiUVBwErW1ta72Bw/mYfLkq1FWVtrm+Q4c2O/6c1VVFQoLTyI9/ezP0Z/85AZ8/fWX+PzzzRg79kqEhXEDRDqLweQ8ik6dRqzKOXmtZ/+Bfq6GiMh7QkND8f7772LlyuUoLCzAsWNHsHnzRiQmJmHq1FsQFmbE0qWP4siRw8jLO4Dnnvs9UlP7IyYmts3zPf/809i9excOHz6EJUseQnR0T4wfP9H1+MSJk2AyVWPDhg9x3XU/9dXbpCDBoZzzOH3sACIAVEvhCAsxXqw5EVHQ6ts3BU899Sxef301/vnPdyHLMoYPz8Lzzy+HXq/Hn/60Ai+9tAx33nk79Ho9cnLGYM6c+857vhtuuAlLlz4Gk6kaI0ZkYfnyV6HXn12gMjTUgCuuGI/vv9+JrKxRPniHFEwYTM7D6tpRuI+fKyEi8r7LLx+Lyy8f2+Zjffum4Pnnl7f52MMPL2l17Morr8Itt0y/4OuVlZXiuusmQ6UKrrk45H0MJucR4tpRmPvjEBF5ytdff4FDhw5i//4f8OijT/i7HApADCZtMFtsiFVKABmITuX8EiIiT3n77Tdw8uQJPPDAQ4iNjfN3ORSAGEzacOpYPmJlK+xChYjEVH+XQ0QUFIYPH4lvvtlxwTarVq31UTUUrHhXThuqj+cBACo0sZBUzG5ERES+wmDSBnGGOwoTERH5A4NJG8LqnTsK90jgjsJERES+xGByjsoqE2JRDgDo1f8SP1dDRETUvTCYnKP40AGoJIFa9EBIVIy/yyEiIupWGEzOUVtwCABQrU9wa3dOIiIi6jwGk3Ooyo8DAETPfv4thIiIqBtiMGlGUYRrR2FjUrqfqyEi6ro2bPg3xowZ6fp+6tSfYs2aVwEAQgj8978fobKyos22vrBmzauYOvXCGww2r7k98vL241e/morx43OwYsULnayw6+IiHc0UnSxAhFwHRUiI6Z9x8ScQEZFHrF79BnQ6HQBg9+5deOqpJXj33Q8BABMmXI1Ro3L8WV6bmtfcHm+88TrUag3eeutdGAwGL1YW3BhMmin8cS9iAJTL0QjX9/B3OURE3UZkZKTrz0KIFo/pdHrodPpzn+J3zWtuj5oaEwYMSENCQqKXKuoaGEyaqS846Pwaxh2FiajjhBCA3eq/AtRatyfv19fX49VXV+CLLzajvr4e6ekDMWfOPGRkDMQPP+zFa6+txMGDB6BWq3H55VfgnnvuRXh4BADnkMaUKb/Ajz/uxbZtW6DRaDFp0rWYM2ce1Grnr5kvv/wca9asQmFhATIyMjFyZHaL15869ae47rrJGDZsBHJz7wQA/PznN+ChhxYDAH7/+8exZcsuAIDJVI3Vq1fh22+/QlVVFdLT0zFr1t0YPtw53LNmzavYu3cPsrKysX79P1BdXYXMzEFYsOBB9O2bAgA4duwIVq1agb1796ChwYxevWIxZcrPMW3ahXdFbqvm3/529kVfc+rUn6KkpBgA8PHH/8G7736IuLh4rFv3Bj744H1UVJxBnz7JuPXWX2PSpOsAALt27cC8efdg1qy7sG7dm4iP743Vq/+K8vIzWLFiGbZu/Q6yrMKllw7GnDnz0KdPEgDgqaeWAADCwyPw8cf/gdlcjxEjsrBw4cPo2bMXAKCiohwvv/witmz5Fna7HUOGDENu7v1ITHT+/vv226+xZs2rOH48H7169cLEidfgttt+C61W2+7+6SgGk2Y0lccBAOpY7o9DRB0jhED9h09BKT3itxpUsQMQcsNDboWTxx5bhIKCk3jooSVISEjEG2+sxbx59+C555Zj7tzZuOGGmzB//u9QUVGOP/3pGcybNwerV/8VKpUKAPDnP6/CXXfNxd1334vdu3fh6aeXIj19IK67bjL27duDRx5ZiNtvn4WJE6/Bnj3fY9myZ9us49JLh+Cpp/6Ihx9eiNWr/4p+/VKxefOnrscdDgfmzZsDu92GRx99AhERkXjvvXcwf/4cvPLKGgwc6Fx/au/e76HTafHHP74Ah8OOpUsfw5/+9AyWL1+FhoYGzJt3D7KyRmPVqrVQqVT4978/wMsvv4CRI7MwYEDH5hhe6DVXr34DDz54P2JiYnHvvfcjIiISr722Eps2fYJ58xYiObkvdu/eheeeexq1tbWYMuXnrvf73Xff4tVXX0dDgxkWiwVz585GenoGXnrpNahUMt55523ccccMvPHGO+jVy7nMxaZNn+Dqq6/Fyy+vRkVFOZYseQivvbYSDz20GHa7HfPmzYFarcYf/vA8jMZwrFixDPffPxfr1q3H9u3f4bHHFmHu3PnIyhqFU6cKsWzZH3Hy5AksXfp0h/rGHW5PflUUBcuXL8fYsWMxdOhQzJo1CwUFBedtX1lZifvvvx9ZWVnIzs7G448/DrPZ3KmivcFqtaGXoxQAEJ2a6edqiCiYSQiupQZOnjyOLVv+h/vvX4RRo3KQmNgH99+/CNdfPxnr1v0VqakDMG/eQvTtm4Lhw0di8eKncOhQHrZt+851jlGjRuPnP78FCQmJ+MlPbkD//gOwb98eAMB77/0dl146BL/5zR1ISkrGT396I372sylt1qLRaBAWZgQAREREthrC2bZtCw4ePIDFi5/EsGEjkJLSDwsWPIh+/VKxbt2brnZ2ux2PPPIEBgxIQ0ZGJn72s5td9ZjNZvz859Mwf/7v0LdvCvr0ScJvfzsbAHD0aMcD5YVeMzIyEmq1GjqdDtHRPWG1WvH3v6/D3LnzcdllY1z99stf3op1695ocd5p06ajT58kDBiQjs2bP0FtbQ0efXQpBgxIQ79+/bFo0aMwGAz48MN/up4TGmrAwoUPIzm5L4YNG4EJEya5atm5czuOHj2MxYufxODBQ9G3bwoWLXoUY8eOg8lkwl/+sgY33DAFN954MxISEpGdPRoPPPAQPv98E4qLizrcP+3l9hWTlStXYt26dXj66acRFxeHZ599FjNnzsS///3vNi/x5Obmwmw24y9/+QtMJhMefvhh1NfX45lnnvHIG/CU4iMHYZQcMAsteiZyjxwi6hhJkhByw0NBNZTT9Mv4kksGuY7pdDrMnTsf06f/HFlZo1u0HzAgDQaDAUePHkFOzhgAQHJySos2oaEG2O12AM5hk+zslucYNGgw3n33b+1/T42OHTsCg8GAfv36u45JkoQhQ4a3CEpRUVEwGo2u7w0GA2w2GwBnSJgy5ef49NOPcfjwQRQWFuDIkcMAnP/4Ptcbb6zFm2++7vp+0qTr8MADD7Vqd6HXPNfx48dgtVrw+OMPQ5bPXiNwOBywWq2wWBpcxxITk1x/PnjwIEwmE667bnyL81mtVpw4cdz1fUJComsYDWj593H06BGEhRmRlHT2d13Pnr0wZ859ja+Rh/37f8RHH33gerxp3s/x4/mIj+/d5nvyFLeCidVqxdq1a7FgwQKMGzcOALBs2TKMHTsWGzduxOTJk1u0//7777Ft2zZs2LABqanO4ZEnnngCM2fOxPz58xEbG+uZd+EBlcfzYARQrolHjKzydzlEFMQkSQI07b9bw9+a/wI717kTUZsfb/48jUZz3udKkgRFaXmeC73mhZy/HuWces4/F6K8/Axmz74dkZGRuPzyK5CVNRoDB2ZiypSftNn+xhtvxlVXXe36PjQ0tM12F3rNczX1xxNPPI3k5L4XPFfzO3+EUJCUlIynn/5Tq+eEhIQ0e/75/z4u1vdCCNx66//huusmt3osOrrnBZ/rCW4N5eTl5aGurg45OWdv2zIajcjMzMT27dtbtd+xYwd69erlCiUAkJ2dDUmSsHPnzk6U7XlKmXNHYXskr5YQUffSdLXjwIH9rmN2ux1Tp/4UBQUnsXfv7hbtDx8+hLq6OvTt276FKAcMSMMPP+xpcSwvb/95WuOCV3tSUwegtrYWx46dHXIRQmDv3t2uia0X8+mnH8NkMuGVV9ZixoyZuPLK8aipqXGd61xGYzgSE/u4/ouMjGrX61xIcnJfqFQqlJaWtDj3d999i7/97c0WV1GaS0lJRUlJMQyGMNdz4uLisWrVS9i9+/t2vXZKSgpqakwoLDw7DaOyshI/+ckE/PDDPvTrl4qTJ0+0qKusrBQvv/wi6uvrOv3eL8atyFpSUgIAiI+Pb3E8JibG9VhzpaWlrdpqtVpERESguLjY3VpbUKs9uzZcWP0pAIAhKd3j56azVCq5xVfyDvazbyiKBDTOJ5Ek4Dz/mA94SUnJuPLK8fjTn57BggUPomfPXnjrrb/AarXilVfW4u67f4tly/6Im276OSoqyrFs2R+Rlpbe6s6a87nllumYNes2rFjxAm644Sbk5e3H++//47ztQ0KcyzUcPnzIdedPk1GjRmPAgDQ8/vgjuO++BxAZGYX16/+Bo0ePYP78Re2qJyYmDg0NZnz22SYMHjwUJ08ex/LlzisQNptvhuAMBgNuvPFmrF79CkJDQzFo0GB8//1OvPLKckyfPuO8z7vmmuvx9tt/xSOPLMRdd+XCYDDg9ddXY8uW/2HmzLva9dojRmQjIyMTTz65GLm586HXh2DlyhcRERGJgQMH4te/noGHH/4dXn99NSZMmISyslI8/fRS9O6d0K4rJiqV1Knfo24Fk6ZJq+fOJdHpdKiurm6zfVvzTnQ6HSwWizsv3YIsS4iMbPtSWkeZQ2JRYVaQedllMBg9e25qzWgMuXgj6jT2s3c1NKhw5owzmAR7CHz00SV46aUX8Oiji2CzWZGZOQgvvvgyBgxIwwsvrMCrr67Eb37zK4SGhuKKK8bh7rtzodef/fkuyy1/GUmSBElyHhs4cCCWLXsJK1a8iPff/wdSUvphxozf4uWXl7d4TtM50tPTcNllY7B48YO48845CA8Pd7XRajVYvvwVvPTSMjz88AOwWq0YODATK1aswtChQ1znAdDq3E3Hrr76ahw+nIcVK5ahrq4O8fHxuOGGm/D111803hItt3mOtjTVfLHXPLdPAGDevAWIiorCn/+8CmfOnEZsbCxmzboT06ffBkmSWvwDo+k5ERFGrFr1Zyxfvgz33z8HiqIgPT0Dy5e/gv79U9t8nda1yHj22WV44YXnMG/eHEgSMGJEFl54YQV0Oi2uumoinnzyafz1r2vxxhtrYTSGY+xY5y3iF+oPRZEgyzLCw3tAr+/4ujOSON+AXRs++eQT5ObmYs+ePS1e9N57721M1q+0aL906VLs3bsX7777bovjOTk5mD17NmbMmNGhoh0OBSaTZ+/skSTnD/HaWgscjtaTn8gzVCoZRmMITCYz+9mL2M++YbVaUFZWjJiYeKhUmqC9YhIMJMn5uXY4FPazF3Wmn202K8rLixET0xtabes5VkZjSLsCvFtXTJqGZcrKypCUdHaWcFlZGdLTW9/3HRcXh02bNrU4ZrVaUVVVhZiYGHdeuhW73bM/bJ2J1/mX4elzU2vsZ99gP3uXwyEAOH9685eldzX1L/vZuzzRzw6H6NTPHbeuPWZkZMBgMGDr1q2uYyaTCfv370dWVlar9llZWSgpKcGJEydcx7Zt2wYAGDFiREdrJiIioi7KrSsmWq0W06dPx3PPPYeoqCgkJCTg2WefRVxcHCZNmgSHw4GKigqEhYVBr9djyJAhGD58OObNm4clS5agvr4ejz32GG688caAulW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- }, - "metadata": {} - } - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "x = np.linspace(-1, 2)\r\n", - "sns.lineplot(x=x, y=truncnorm([(-1, 0), (1, 2)]).cdf(x))\r\n", + "x = np.linspace(-1, 2)\n", + "sns.lineplot(x=x, y=truncnorm([(-1, 0), (1, 2)]).cdf(x))\n", "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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CSV1dnaTmwPF1sbGxqqqqOmW+aZravXu3xo0bp4ULF8rj8WjJkiX68Y9/rL///e9KTGzbuUnDMOVy1bbptWficNjldMbL5aqTx2P4ddvhhl55j175hn55ryN69fqnh/X82/slSTPH99YPLhuuE666gLxXILFfeS+QvXI64706EuNTMImLi5PUfK3Jyf+WpIaGBsXHn5qu3njjDS1fvlzvvvtuSwh58skndeGFF+rFF1/U9ddf78vbt9LUFJidy+MxArbtcEOvvEevfEO/vBeIXpmmqZe+tprrZZP6af70wTI8pgyZfn2vjsR+5T0re+XT8biTp3BKSkpajZeUlKhnz56nzN+yZYsGDhzY6shIly5dNHDgQOXl5bWlXgBAABmmqefe2tsSSr49fZC+M2MIq7miw/gUTNLT05WYmKiNGze2jLlcLu3atUtZWVmnzE9NTVVeXp4aGhpaxmpra1VQUKABAwa0vWoAgN95DEPPvLZL72w9Kpuka2cP0+WTB1hdFiKMT8EkJiZGCxYs0OLFi/X2228rNzdXt956q1JTUzV79mx5PB6Vlpaqvr5ekjR37lxJzWuZ5ObmKjc3V7fddptiY2M1b948v38YAEDbNHkMPfHKTn26s1h2m00Lv5mhC8f3sbosRCCfL63OycnR/Pnzde+99+rqq6+Ww+HQM888o+joaBUWFmrKlClau3atpOa7dVasWCHTNHXdddfphhtuUHR0tFasWKHOnTv7/cMAAHzX5DH05Ks7tXVvqaIcNv1k3ihNHplqdVmIUDbTNEPuSiaPx1B5eY1ftxkVZVdSUoIqKmq4OOoc6JX36JVv6Jf3/NWrJo+hp1bv1Gd7mkPJT+eN0ZjB3fxYqfXYr7wXyF4lJyd4dVdOaN6MDgBotyaPoadbhZLRYRdKEHoIJgAQgTyGoT+v2aUte0rlsNv0kytHa8zg7laXBRBMACDSnAwlm3NLWkLJ2CGEEgQHggkARJCToWTT7uZQ8uMrRylzKKEEwYNgAgARwjBMPfPa7n+FkrmjNG5oD6vLAlohmABABDAMU//7+i5t2FUsh92mm+aM0rhhhBIEH4IJAIQ5wzS1bO1ubfhq8bSb5ozUhOGEEgQnggkAhDHTNLXirb36ZEfR10JJitVlAWdEMAGAMPbKh4dann2z8IoRmphOKEFwI5gAQJhat+mI1nxyWJK0YPYwTWKZeYQAggkAhKGPvizUynf2S5KunDaIB/IhZBBMACDMbN1bqr+8sVuSNDurr66Y3N/iigDvEUwAIIzsOlyuJ1/dIdOUpoxO01Uzh8hms1ldFuA1ggkAhImDx1z646rtavKYGj+sh667bDihBCGHYAIAYeBoWY2W/ONzNTR6NKJ/km78VoYcdn7FI/Sw1wJAiCurrNPvV25TTX2TBqY59dN5oxUd5bC6LKBNoqwuAADQNrX1jXpv21Gt/uiQKqvd6tU9Qbd+d6ziY/nVjtDF3gsAIcQwTO0+UqFPdxRpy55SuRs9kqTuXeJ0+1WZSoyPtrhCoH0IJgAQAorKa/Xx9kJ9sqNIFScaWsbTunXSBaPTNHVMmjp3irGwQsA/CCaABRqbPNp+sFwNX/1tF80cdpsSEmJVU9Mgj2FaXU5QqKlr1KbdJdp/tKplrFNslCaNStXlUwape2K0PB56hfBBMAE62M7D5Xr2zT0qqaizuhSEEJtNGj2om84flapxQ7srPi5aSUkJqqiokUQwQfggmAAdxFXj1sp39mnDzmJJUpeEGPXukWBxVcHFZrMpOsqhxiaPTJMvW0my220a0T9Jk0emqmtirNXlAAFHMAECzDBMvbv1qJ5/e59qG5pkkzRzQh/NmzaIuyf+TVSUveUoQFOTYXU5ACzAb0UggApKqvWb5Vu1+3C5JKl/z876waXDNTDNaXFlABCcCCZAADQ0erTm48N6c9MReQxTcTEOzZ06SBdN6M1qnABwFgQTwM8OFbr0xCs7VFZVL0maNCpVV104RF0SuJUTAM6FYAL4UZPH0FOv7lRZVb2SnbG69pLhunjSQK6ZAAAvEUwAP/rgi2MqqayTs1O0HvyP8+RM5CgJAPiCk92An9S7m7T648OSpG9eMFCd4sj9AOArggngJ29tzperxq2UrvGantnL6nIAICQRTAA/OFHr1hsbj0iSrpw2SFEO/tcCgLbgtyfgB699kqd6t0f9eiYqa0SK1eUAQMgimADtVFZVp3e3FUiS5s8YLLvNZnFFABC6CCZAO73y4SE1eUyN6J+kkQOSrS4HAEIawQRoh4KSan26o0hS89ESG0dLAKBdCCZAO6x6/4BMSRPTU3j+DQD4AcEEaKO9+ZX64sBx2W02zZs2yOpyACAsEEyANjBNUy+8t1+SNG1smlKTO1lcEQCEB4IJ0Aaf7yvTgaMuxUTZ9c0LBlpdDgCEDYIJ4CPDMLXqg4OSpFlZfZXUOdbiigAgfBBMAB99vKNQx8pqlBAXpcvO62d1OQAQVggmgA/cjR698uEhSdLlkweoU1y0xRUBQHghmAA+eGfrUVWcaFCyM1YXTehtdTkAEHYIJoCXausb9fqnhyVJc6YMVHSUw9qCACAMEUwAL63dcEQ19U3q1T1BF4xKs7ocAAhLBBPACxUnGvTWlnxJ0vzpg2W3s/Q8AAQCwQTwwqsfHVRjk6Ghfbpo7JBuVpcDAGGLYAKcQ+HxGn34ZaEk6TszhvCgPgAIIIIJcA6r3j8o05TGDe2uIX26WF0OAIQ1gglwFvuPVmnr3lLZbNK86YOtLgcAwh7BBDgD0zT14rvND+qbMjpNvbsnWFwRAIQ/gglwBl8cOK69BVWKjrJrzhQe1AcAHYFgApyGYZha9d4BSdLFE/so2RlncUUAEBkIJsBpfLKjSEe/elDfNyb1t7ocAIgYBBPg3zQ2efTKRwclNT+oL4EH9QFAh/E5mBiGoccff1xTp05VZmamFi1apPz8/DPOb2xs1O9///uW+QsWLNDu3bvbVTQQSG9/dlTlLh7UBwBW8DmYLF26VCtWrNBDDz2klStXyjAMLVy4UG63+7Tz77//fr300kv69a9/rVWrVik5OVmLFi3SiRMn2l084G88qA8ArOVTMHG73Vq2bJlycnI0Y8YMpaena8mSJSoqKtK6detOmZ+fn69Vq1bpV7/6laZOnarBgwfrl7/8pWJiYrRjxw6/fQjAX04+qK83D+oDAEtE+TI5NzdXNTU1mjx5csuY0+lURkaGNm/erCuuuKLV/I8//lidO3fWtGnTWs1/55132lm2FBXl38tjHA57q3/jzMK1V+Wu+pYH9X135hDFxLT/aEm49ipQ6Jf36JX36JX3gqFXPgWToqIiSVJaWuu/SaakpLT87OsOHTqkvn37at26dXr66adVXFysjIwM/fznP9fgwW1fRdNutykpKTCLXTmd8QHZbjgKt14tf2ufGpsMZQxM1oXZ/f36TJxw61Wg0S/v0Svv0SvvWdkrn4JJXV2dJCkmJqbVeGxsrKqqqk6ZX11drby8PC1dulR33nmnnE6nnnjiCV1zzTVau3atunVr21NaDcOUy1XbpteeicNhl9MZL5erTh6P4ddth5tw7NXRshq9tSlPkvTtaYNUWemf/SscexVI9Mt79Mp79Mp7geyV0xnv1ZEYn4JJXFzzIlNut7vlvyWpoaFB8fGnpquoqChVV1dryZIlLUdIlixZounTp+vll1/WwoULfXn7VpqaArNzeTxGwLYdbsKpV8+v39fyoL6BaU6/f65w6lVHoF/eo1feo1fes7JXPp1EOnkKp6SkpNV4SUmJevbsecr81NRURUVFtTptExcXp759+6qgoKAt9QJ+tze/Up/vL5PdZtP8GTyoDwCs5FMwSU9PV2JiojZu3Ngy5nK5tGvXLmVlZZ0yPysrS01NTdq+fXvLWH19vfLz89W/P6tpwnqmaeqFrx7UN3VsmtK68aA+ALCST6dyYmJitGDBAi1evFjJycnq3bu3HnnkEaWmpmr27NnyeDwqLy9X586dFRcXp4kTJ+r888/XXXfdpQcffFBdu3bV448/LofDoTlz5gTqMwFe27q3VAeOuRQTzYP6ACAY+Hw/UE5OjubPn697771XV199tRwOh5555hlFR0ersLBQU6ZM0dq1a1vm//GPf1R2drZ++tOfav78+aqurtbf/vY3JScn+/WDAL5q8hh68f3mpednZ/VT18RYiysCANhM0zStLsJXHo+h8vIav24zKsqupKQEVVTUcHHUOYRLr97ddlTPvrlHifHRevimyYqP9ekAolfCpVcdhX55j155j155L5C9Sk5O8OquHFabQUSqdzfp1Y8OSZK+dcGAgIQSAIDvCCaISG9uyperxq2UrvGaMY4H9QFAsCCYIOJU1bj1z41HJEnzpg9SFMtUA0DQ4DcyIs7qjw+podGjgWmdlZWeYnU5AICvIZggohSX1+qDz49Jkr4zY4hfn4cDAGg/ggkiyqr3D8hjmBozuJvS+ydZXQ4A4N8QTBAxDhyr0pY9pbJJmj+dpecBIBgRTBARmpeePyBJOn90qvqkJFpcEQDgdAgmiAhfHDiuvfmVio6y68qpg6wuBwBwBgQThD2PYejF95qPllw8sY+SnXEWVwQAOBOCCcLex9uLdKysRglxUbp8Ek+1BoBgRjBBWGtwe/Tyh80P6rvi/AHqFBdtcUUAgLMhmCCsvbn5iKqq3ereJU4zx/exuhwAwDkQTBC2qmrceuOrpee/PX2woqPY3QEg2PGbGmHr1Y8OqcHdvPR89giWngeAUEAwQVgqPF7TsvT8dy9k6XkACBUEE4SlF949IMM0lTmku4b3Y+l5AAgVBBOEnT1HKvT5/jLZbTbNn8HS8wAQSggmCCumaeofXy09P21smnp1T7C4IgCALwgmCCubc0t0qNCl2GiH5kwZaHU5AAAfEUwQNhqb/rX0/GXn9VOXxFiLKwIA+IpggrDx7tYClVXVq0tijC7J7md1OQCANiCYICzU1DdqzSeHJUlXTh2k2BiHtQUBANqEYIKw8Poneaqpb1Kv7gm6YHSq1eUAANqIYIKQV1ZZp/Wf5UuSvjNjsBx2dmsACFX8BkfIe+nDg2rymErv11VjBnezuhwAQDsQTBDSDhe5tGFnsSTpuzNZeh4AQh3BBCHLNE09//Z+SdKkkT01INVpcUUAgPYimCBkfb6vTHvyKxXlsGvetEFWlwMA8AOCCUJSk8fQP75aTG12Vl917xJvcUUAAH8gmCAkvbftqIrLa9W5U7Qun9zf6nIAAH5CMEHIqa1v1OqPD0uS5k4ZqPjYKGsLAgD4DcEEIee1T/JUXdeotG6dNC2zl9XlAAD8iGCCkFL6tcXUrpo5hMXUACDM8FsdIeXF9w6oyWMqY0CSRg9iMTUACDcEE4SM/UertDm3RDZJ372QxdQAIBwRTBASmhdT2ydJumBMmvr17GxxRQCAQCCYICRszi3RgWMuxUTbdeVUFlMDgHBFMEHQa2wy9OJXi6l947z+Suoca3FFAIBAIZgg6L39WYHKqurVNTFGl2T3s7ocAEAAEUwQ1E7UurXmk8OSpHnTBis2xmFtQQCAgCKYIKit/uiw6hqa1C8lUeePTrW6HABAgBFMELQKj9fovc+PSmpeTM3O7cEAEPYIJghaL7x7QB7D1NjB3TRiQLLV5QAAOgDBBEFp5+Fyfb6/THabTd+dOcTqcgAAHYRggqDjMQyt/GoxtZnjeyutW4LFFQEAOgrBBEHnwy8KdbS0RglxUfrWlIFWlwMA6EAEEwSV2vomvfTBQUnS3KmDlBgfbXFFAICORDBBUHntk8OqrmtUWrdOmp7Zy+pyAAAdjGCCoFFcUau3tuRLkr530VBFOdg9ASDS8JsfQeMf7+yXxzA1elA3jR7UzepyAAAWIJggKOw+XK5t+5pvD76K24MBIGIRTGA5wzD1969uD75wfG/16s7twQAQqQgmsNwHXx5TwVe3B8/h9mAAiGgEE1iqtr5JL391e/C3pgzk9mAAiHA+BxPDMPT4449r6tSpyszM1KJFi5Sfn+/Va1evXq3hw4eroKDA50IRnl779LBO1DYqNbmTLhzX2+pyAAAW8zmYLF26VCtWrNBDDz2klStXyjAMLVy4UG63+6yvO3r0qB588ME2F4rwU1xRq7c2n7w9eAi3BwMAfAsmbrdby5YtU05OjmbMmKH09HQtWbJERUVFWrdu3RlfZxiG7rjjDo0cObLdBSN8nHx68KiBydweDACQ5GMwyc3NVU1NjSZPntwy5nQ6lZGRoc2bN5/xdU8++aQaGxt14403tr1ShJXdeRXaure05fZgm81mdUkAgCAQ5cvkoqIiSVJaWlqr8ZSUlJaf/bsvv/xSy5Yt04svvqji4uI2lnmqqCj/HvZ3fHUawcHphHNqb68Mw9Tz73z19OAJvdU/zem32oIN+5Vv6Jf36JX36JX3gqFXPgWTuro6SVJMTEyr8djYWFVVVZ0yv7a2Vj/72c/0s5/9TAMGDPBbMLHbbUpKCsxaF05nfEC2G47a2qs3PjmkI8XVSoiP1vXfHKUuibF+riz4sF/5hn55j155j155z8pe+RRM4uLiJDVfa3LyvyWpoaFB8fGnfohf/vKXGjhwoL73ve+1s8zWDMOUy1Xr1206HHY5nfFyuerk8Rh+3Xa4aU+vqusa9X9rd0uSrpw2UEZjkyoqmgJRZlBgv/IN/fIevfIevfJeIHvldMZ7dSTGp2By8hROSUmJ+vXr1zJeUlKi4cOHnzJ/1apViomJ0bhx4yRJHo9HknTFFVfopptu0k033eTL27fS1BSYncvjMQK27XDTll6teveAauoa1bt7gqaP7RUxvWa/8g398h698h698p6VvfIpmKSnpysxMVEbN25sCSYul0u7du3SggULTpn/73fqfPHFF7rjjjv09NNPa9iwYe0oG6GooKRa72xrXsPmmouHymHnfC8AoDWfgklMTIwWLFigxYsXKzk5Wb1799Yjjzyi1NRUzZ49Wx6PR+Xl5ercubPi4uLUv3//Vq8/eYFsr1691LVrV799CAQ/0zS1Yv1emaY0YXgPjRiQbHVJAIAg5PNfWXNycjR//nzde++9uvrqq+VwOPTMM88oOjpahYWFmjJlitauXRuIWhHCPttTqtwjlYqOsuuqC3l6MADg9GymaZpWF+Erj8dQeXmNX7cZFWVXUlKCKipqOAd5Dr72qqHRo3v/vEHHXQ361gUDNHfqoA6oMjiwX/mGfnmPXnmPXnkvkL1KTk7w6uJXTvIj4P658YiOuxqU7IzVZZP6n/sFAICIRTBBQJVV1WnthjxJ0ncvHKLYaIfFFQEAghnBBAH1j3cPqLHJ0PC+XZWVnmJ1OQCAIEcwQcDszqvQltwS2WzSNbOG8TwcAMA5EUwQEB7D0N/X75UkzRjXW31TEi2uCAAQCggmCIj3th1TQWmNEuKidGUE3YUDAGgfggn8rrquUa98eFCSdOW0QUqMj7a4IgBAqCCYwO9e/uCgauqb1KdHgqZn9rK6HABACCGYwK+OFJ/Qe58flSRdc/EwnocDAPAJ3xrwG8M0tXxd8/NwstJTlN4/yeqSAAAhhmACv/lke5H2H61SbLRD37toqNXlAABCEMEEflFT36gX3tsvSfrWlAFK6hxrcUUAgFBEMIFfvPLBIZ2obVRat06aNbGv1eUAAEIUwQTtlld0Qu9sK5AkLZg1TFFePD0SAIDT4RsE7WKYppa/tUemKWWPSNGIAclWlwQACGEEE7TLx9sLdeCoS7ExDl01kwteAQDtQzBBm9XUNerF9w5IkuZcMJALXgEA7UYwQZutev+ATtQ2qlf3BF08sY/V5QAAwgDBBG1yoKBSb3/WfMHr97ngFQDgJ3ybwGeGaerJl7781wWvrPAKAPATggl89tEXhcrNq1AcF7wCAPyMYAKf1NQ36vl39kmSrpw2iAteAQB+RTCBT17+4KBO1Daqb8/OmpXFCq8AAP8imMBreUUn9O62o5Kk/5w3hgteAQB+xzcLvGIYpv7vn7kyTWnSyJ4aPaS71SUBAMIQwQReeXfbUR0uOqH42ChdM2uY1eUAAMIUwQTnVHGiQaveb17hdf70QeqayAWvAIDAIJjgnFa+vU/1bo8Gpjk1PbO31eUAAMIYwQRntf3gcW3OLZHdZtN1lw6X3W6zuiQAQBgjmOCM3I0ePfvmHknSxRP7qF/PzhZXBAAIdwQTnNGaTw6rrKpeSZ1jNXfqQKvLAQBEAIIJTutoWY3+ufGIpOaH9MXFRFlcEQAgEhBMcArTNPXsm3vkMUxlDumucUNZswQA0DEIJjjFR9sLtTe/UjHRdl0za6hsNi54BQB0DIIJWjlR69YL7zavWTJnykB17xJvcUUAgEhCMEErL7x7QNV1jerTI0GzJvKQPgBAxyKYoMWeIxX6aHuhJOkHl6bzkD4AQIfjmweSpCaPob99tWbJ9MxeGtK7i8UVAQAiEcEEkqQ3Nh5R4fFade4UrfkzBltdDgAgQhFMoMLjNVrz8SFJ0vcuGqqEuGiLKwIARCqCSYQzTFP/90aumjymRg1K1qSMnlaXBACIYASTCPfB58e0t6BKsdEO/eCS4axZAgCwFMEkglWcaNAL7+2XJM2bPog1SwAAliOYRCjTNLV83R7VNXg0qJdTF43vY3VJAAAQTCLVZ3tKtW1fmRx2m66/LF12O6dwAADWI5hEoJr6Ri1/a68k6RuT+qtPj0SLKwIAoBnBJAL94539ctW4ldatk644f4DV5QAA0IJgEmF2Hy7Xh182Lzt//WXpio5iFwAABA++lSKIu9Gj//tn87LzF47vraF9ulpbEAAA/4ZgEkFe/eiQSirrlNQ5VvOns+w8ACD4EEwiRF7RCb25KV+StGD2MMXHRllcEQAApyKYRACPYegvb+yWYZrKSk/RuKE9rC4JAIDTIphEgHWb8nWkuFoJcVG6ZtYwq8sBAOCMCCZhrvB4jV7+sPnJwd+dOURdEmIsrggAgDMjmIQxwzC17PXdavIYGjUoWVNGp1ldEgAAZ+VzMDEMQ48//rimTp2qzMxMLVq0SPn5+Wecv2/fPv3oRz/Seeedp8mTJysnJ0fHjh1rV9Hwzpubj+jAMZfiYx26/tJ0nhwMAAh6PgeTpUuXasWKFXrooYe0cuVKGYahhQsXyu12nzK3oqJCN9xwg+Li4vTss8/qz3/+s8rLy7Vw4UI1NDT45QPg9AqP1+jlD5pP4Xxv5lAlO+MsrggAgHPzKZi43W4tW7ZMOTk5mjFjhtLT07VkyRIVFRVp3bp1p8xfv369amtr9bvf/U7Dhg3TqFGj9Mgjj+jAgQPaunWr3z4EWjvlFM4YTuEAAEKDT8EkNzdXNTU1mjx5csuY0+lURkaGNm/efMr8yZMna+nSpYqL+9ff1u325rd0uVxtrRnnwCkcAECo8mmVraKiIklSWlrrv4GnpKS0/Ozr+vTpoz59+rQae/rppxUXF6esrCxfa20lys/PeHE47K3+HaqOltXola9O4Vwza5hSkjv5/T3CpVcdgV75hn55j155j155Lxh65VMwqaurkyTFxLS+5TQ2NlZVVVXnfP2zzz6r5cuX695771VycrIvb92K3W5TUlJCm19/Nk5nfEC22xE8hqlf/e0zNXoMTUhP0ZwZQwN6tCSUe9XR6JVv6Jf36JX36JX3rOyVT8Hk5CkZt9vd6vRMQ0OD4uPP/CFM09Qf/vAHPfHEE/rP//xPXXvttW0st5lhmHK5atu1jX/ncNjldMbL5aqTx2P4ddsd5fVPD2vPkQrFxzp07exhqqz0b49OCodedRR65Rv65T165T165b1A9srpjPfqSIxPweTkKZySkhL169evZbykpETDhw8/7WsaGxt1991367XXXtPdd9+t66+/3pe3PKOmpsDsXB6PEbBtB9Kxshqteu+gpOa7cJydYgL+OUK1V1agV76hX96jV96jV96zslc+nURKT09XYmKiNm7c2DLmcrm0a9euM14zcuedd+q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- }, - "metadata": {} - } - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "# Quantile unbiased distribution\r\n", - "\r\n", - "The quantile-unbiased distribution is the distribution of an unknown mean of a normal distribution given\r\n", - "\r\n", - "1. A realized value of the distribution,\r\n", - "2. A truncation set in which the realized value had to fall, and\r\n", - "3. A known variance\r\n", - "\r\n", - "In the example below, the realized value is .5, the truncation set is $[0, \\infty)$, and the variance (scale) is 1 by default. The interpretation of the CDF plot is, \"there is a $CDF(x)$ chance that the mean of the normal distribution from which the realized value (.5) was drawn is less than $x$\".\r\n", - "\r\n", + "# Quantile unbiased distribution\n", + "\n", + "The quantile-unbiased distribution is the distribution of an unknown mean of a normal distribution given\n", + "\n", + "1. A realized value of the distribution,\n", + "2. A truncation set in which the realized value had to fall, and\n", + "3. A known variance\n", + "\n", + "In the example below, the realized value is .5, the truncation set is $[0, \\infty)$, and the variance (scale) is 1 by default. The interpretation of the CDF plot is, \"there is a $CDF(x)$ chance that the mean of the normal distribution from which the realized value (.5) was drawn is less than $x$\".\n", + "\n", "We compare the quantile-unbiased distribution to a normal distribution centered on the realized value." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "dist = quantile_unbiased(.5, truncation_set=[(0, np.inf)])\r\n", - "x = np.linspace(dist.ppf(.025), dist.ppf(.975))\r\n", - "sns.lineplot(x=x, y=norm.cdf(x, .5), label=\"conventional\")\r\n", - "sns.lineplot(x=x, y=dist.cdf(x), label=\"quantile-unbiased\")\r\n", + "dist = quantile_unbiased(.5, truncation_set=[(0, np.inf)])\n", + "x = np.linspace(dist.ppf(.025), dist.ppf(.975))\n", + "sns.lineplot(x=x, y=norm.cdf(x, .5), label=\"conventional\")\n", + "sns.lineplot(x=x, y=dist.cdf(x), label=\"quantile-unbiased\")\n", "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" - }, - "metadata": {} - } - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "q = .5\r\n", + "q = .5\n", "print(f\"There is a {q} chance that the mean of the normal distribution from which the realized value was drawn is less than {dist.ppf(q)}\")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "There is a 0.5 chance that the mean of the normal distribution from which the realized value was drawn is less than -0.5725351048077291\n" - ] - } - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] } ], "metadata": { - "orig_nbformat": 4, + "interpreter": { + "hash": "a31fe93114e6fe9c0b874076e62df141d5b35f609e1bfa94ca168a298e55e549" + }, + "kernelspec": { + "display_name": "Python 3.9.0 ('conditional-inference')", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", - "version": "3.9.0", - "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, - "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "nbconvert_exporter": "python", - "file_extension": ".py" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3.9.0 64-bit ('conditional-inference': conda)" + "pygments_lexer": "ipython3", + "version": "3.9.0" }, - "interpreter": { - "hash": "120d65e34230161c0f4356d19a77763cc2f6669dcb2a194d42d3b2faf517ecd2" - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/examples/utils.py b/examples/utils.py deleted file mode 100644 index a19b969..0000000 --- a/examples/utils.py +++ /dev/null @@ -1,434 +0,0 @@ -"""Utilities for example notebooks. -""" -from __future__ import annotations - -import copy -from itertools import chain - -import matplotlib.animation as animation -import matplotlib.pyplot as plt -import matplotlib.transforms as transforms -import numpy as np -import seaborn as sns -from matplotlib.patches import Ellipse -from scipy.stats import norm - -from conditional_inference.stats import truncnorm - - -def confidence_ellipse( - mean, cov, ax, stds=[1, 2, 3], palette=sns.color_palette(), **kwargs -): - """ - Create a plot of the covariance confidence ellipse. - - Parameters: - mean (np.ndarray): (2,) mean vector. - cov (np.ndarray): (2, 2) covariance matrix. - ax (matplotlib.axes.Axes): The axes object to draw the ellipse into. - stds (list[float]): The number of standard deviations to determine the ellipse's radiuses. - **kwargs (Any): Forwarded to `~matplotlib.patches.Ellipse` - - Returns: - matplotlib.patches.Ellipse - """ - pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1]) - # Using a special case to obtain the eigenvalues of this - # two-dimensionl dataset. - ell_radius = np.sqrt(1 + pearson), np.sqrt(1 - pearson) - - ellipses = [] - for std in stds: - # Calculating the standard deviation of x from - # the squareroot of the variance and multiplying - # with the given number of standard deviations. - scale = np.sqrt(cov[0, 0]) * std, np.sqrt(cov[1, 1]) * std - transf = transforms.Affine2D().rotate_deg(45).scale(*scale).translate(*mean) - ellipse = Ellipse( - (0, 0), - width=ell_radius[0] * 2, - height=ell_radius[1] * 2, - facecolor="none", - edgecolor=palette[0], - **kwargs, - ) - ellipse.set_transform(transf + ax.transData) - ellipses.append(ax.add_patch(ellipse)) - - return ellipses - - -class RankConditionAnimation: - """Rank condition animation helper class. - - Args: - mean (np.ndarray): Vector of conventional estimates. - cov (np.ndarray): Conventional covariance matrix. - index (int): Index of parameter of interest. - rank (list[int]): Conditional rank order. - xlim (tuple[float, float]): Limits of the x-axis on the graph. - palette (list, optional): Color palette. Defaults to sns.color_palette(). - n_frames (int, optional): Number of frames in the animation. Defaults to 120. - """ - - def __init__( - self, - mean: np.ndarray, - cov: np.ndarray, - index: int, - rank: list[int], - xlim: tuple[float, float], - palette: list = sns.color_palette(), - n_frames: int = 120, - ): - self._linspace = np.linspace(*xlim) - self._init_func() - - self.mean = mean - self.cov = cov - # variance of conventional estimates - # given the value of the conventional estimate of parameter[index] - # based on conditional multivariate normal - self.conditional_var = np.delete( - np.diag(cov) - cov[index] ** 2 / cov[index, index], index - ) - self.index = index - self.rank = rank - self.xlim = xlim - # lower and upper values of the y-axis - pdf_max = max([norm.pdf(0, 0, np.sqrt(var)) for var in self.conditional_var]) - self.ylim = (-0.1 * pdf_max, 1.1 * pdf_max) - self.ymin = (0 - self.ylim[0]) / (self.ylim[1] - self.ylim[0]) - self.palette = palette - self.n_frames = n_frames - - def _init_func(self) -> None: - """Init function for animation.""" - if hasattr(self, "_truncation_sets"): - [i.remove() for i in self._truncation_sets] - self._truncation_sets = [] - self._prev_rank = None - self._xmin = None - - def _animate(self, i: int) -> list: - """Animation function. - - Args: - i (int): Frame number. - - Returns: - list: Arists. - """ - - def update_truncation_set(value): - # extend the polygons highlighting the truncation set to ``value`` - self._truncation_sets[-1].set_xy( - [ - [self._xmin, self.ymin], - [self._xmin, 1], - [value, 1], - [value, self.ymin], - [self._xmin, self.ymin], - ] - ) - - # update the conventional estimate of parameter[index] - x = self.xlim[0] + i * (self.xlim[1] - self.xlim[0]) / self.n_frames - # update the vertical line at the conventional estimate of parameter[index] - self._conventional_vline.set_data([x, x], [self.ymin, 1]) - self._conventional_label.set_x(x) - - # compute the conventional point estimates - # given the value of the conventional estimate of parameter[index] - # and update distribution plots - conditional_mean = np.delete( - self.mean - + self.cov[self.index] - / (self.cov[self.index, self.index] ** 2) - * (x - self.mean[self.index]), - self.index, - ) - for (dist_line, mean_line, mean_label), mean, var in zip( - self._distribution_plots, conditional_mean, self.conditional_var - ): - dist_line.set_data( - self._linspace, norm.pdf(self._linspace, mean, np.sqrt(var)) - ) - mean_line.set_data([mean, mean], [self.ymin, 1]) - mean_label.set_x(mean) - - # update the current rank text - current_rank = np.sum(x <= conditional_mean) + 1 - self._rank_text.set_text(f"Rank {current_rank}") - - if current_rank in self.rank: - # update the vspace polygons highlighting the truncation set - if self._prev_rank in self.rank: - # extend the current polygon - update_truncation_set(x) - else: - # start a new polygon - try: - self._xmin = x - except ValueError: - self._xmin = self.xlim[0] - self._truncation_sets.append( - self._ax.axvspan( - self._xmin, self._xmin, color=self.palette[2], alpha=0.2 - ) - ) - self._prev_rank = current_rank - - return ( - list(chain(self._distribution_plots)) - + self._truncation_sets - + [self._conventional_vline, self._conventional_label, self._rank_text] - ) - - def make_animation( - self, title: str = None, xlabel: str = None - ) -> animation.FuncAnimation: - """Make a rank condition animation. - - Args: - title (str, optional): Title of the graph. Defaults to None. - xlabel (str, optional): Label of the graph x-axis. Defaults to None. - - Returns: - animation.FuncAnimation: Animation. - """ - fig = plt.figure() - self._ax = ax = fig.add_subplot(xlim=self.xlim, ylim=self.ylim) - if title is not None: - ax.set_title(title) - if xlabel is not None: - ax.set_xlabel(xlabel) - - # vertical line at the value of the conventional estimate of parameter[index] - y_offset = self.ylim[0] + 0.02 * (self.ylim[1] - self.ylim[0]) - self._conventional_vline = ax.axvline( - self.xlim[0], color=self.palette[1], linestyle="--" - ) - self._conventional_label = ax.text(self.xlim[0], y_offset, r"$Z_\theta(\theta)$", ha="center") - # (distribution of conventional estimate line plot, vertical line at conventional point estimate) tuples - self._distribution_plots = [ - ( - ax.plot([], [], color=self.palette[0])[0], - ax.axvline(color=self.palette[0], linestyle="--"), - ax.text(0, y_offset, r"$Z_\theta(\theta{})$".format(i*"'"), ha="center") - ) - for i in range(1, len(self.mean)) - ] - # text displaying the rank of the conventional estimate of the effect of policy[index] - self._rank_text = ax.text(self.xlim[0], self.ylim[1], "", va="top") - - return animation.FuncAnimation( - fig, self._animate, self.n_frames, init_func=self._init_func, blit=True - ) - - -class QuantileUnbiasedAnimation: - """Quantile-unbiased animation helper class. - - Parameters: - x (float): Conventional point estimate. - scale (float): Standard deviation of the conventional estimate. - truncation_set (list[tuple[float, float]]): Truncation set for the conditioning event. - xlim (tuple[float, float]): Limits of the x-axis. - projection_quantile (float): For use with projection CIs. Defaults to None. - palette (list[color-like]): List of colors (passed to matplotlib functions). - Defaults to seaborn default palette. - n_frames (int): Number of frames to animate. Defaults to 120. - """ - Y_OFFSET = -.07 - - def __init__( - self, - x: float, - scale: float, - truncation_set: list[tuple[float, float]], - xlim: tuple[float, float], - projection_quantile: float = None, - palette: list = sns.color_palette(), - n_frames: int = 120, - ): - # y limits of the graph - self._ylim = -0.1, 1.1 - # relative values of 0 and 1 on the y axis - self._ymin, self._ymax = (np.array([0, 1]) - self._ylim[0]) / ( - self._ylim[1] - self._ylim[0] - ) - # x data for the quantile-unbiased CDF plot - self._x_data = [] - # y data for the quantile-unbiased CDF plot - self._cdf_data = [] - # relative position of x on the x-axis - self._x_relative = (x - xlim[0]) / (xlim[1] - xlim[0]) - self._linspace = np.linspace(xlim[0], xlim[1]) - - self.truncation_set = truncation_set - self.x = x - self.scale = scale - self.xlim = xlim - self.projection_len = ( - None if projection_quantile is None else projection_quantile * scale - ) - self.palette = palette - self.n_frames = n_frames - - def _init_func(self) -> None: - """Init function for animation.""" - self._x_data.clear() - self._cdf_data.clear() - - def _animate(self, i: int) -> list: - """Animation function. - - Args: - i (int): Frame number. - - Returns: - list: Artists. - """ - # get the location parameter for the current frame - loc = self.xlim[0] + i * (self.xlim[1] - self.xlim[0]) / self.n_frames - - # compute the trunction set - if self.projection_len is None: - truncation_set = copy.deepcopy(self.truncation_set) - else: - # take the intersection of the truncation set and the projection CI - truncation_set = [] - for a, b in self.truncation_set: - a, b = max(loc - self.projection_len, a), min( - loc + self.projection_len, b - ) - if a < b: - truncation_set.append((a, b)) - # standardize the truncation set - truncation_set = [ - ((lower - loc) / self.scale, (upper - loc) / self.scale) - for lower, upper in truncation_set - ] - - # update the vertical line at the location parameter - self._loc_vline.set_data([loc, loc], [self._ymin, 1]) - self._loc_text.set_x(loc) - - # update the survival function plot - truncnorm_dist = truncnorm(truncation_set, loc, self.scale) - if truncation_set: - self._sf_plot.set_data(self._linspace, truncnorm_dist.sf(self._linspace)) - else: - # truncation set is empty, survival function is not well defined - self._sf_plot.set_data([], []) - - # update the horizontal line at the quantile-unbiased CDF evaluated at loc - cdf = truncnorm_dist.sf(self.x) - # location parameter relative to x-limits of the graph - loc_relative = (loc - self.xlim[0]) / (self.xlim[1] - self.xlim[0]) - self._cdf_hline.set_data( - [min(self._x_relative, loc_relative), max(self._x_relative, loc_relative)], - [cdf, cdf], - ) - - # update the quantile-unbiased CDF plot - self._x_data.append(loc) - self._cdf_data.append(cdf) - self._cdf_plot.set_data(self._x_data, self._cdf_data) - - plots = [self._loc_vline, self._loc_text, self._sf_plot, self._cdf_hline, self._cdf_plot] - if self.projection_len is None: - return plots - - # update the projection CI plot - xmin = max(loc - self.projection_len, self.xlim[0]) - xmax = min(loc + self.projection_len, self.xlim[1]) - self._projection_plot.set_xy( - [ - [xmin, self._ymin], - [xmin, 1], - [xmax, 1], - [xmax, self._ymin], - [xmin, self._ymin], - ] - ) - self._projection_lower_text.set_x(loc - self.projection_len) - self._projection_upper_text.set_x(loc + self.projection_len) - return plots + [self._projection_plot, self._projection_lower_text, self._projection_upper_text] - - def make_animation( - self, title: str = None, xlabel: str = None - ) -> animation.FuncAnimation: - """Make a quantile-unbiased estimator animation. - - Args: - title (str, optional): Graph title. Defaults to None. - xlabel (str, optional): Graph x-axis label. Defaults to None. - - Returns: - animation.FuncAnimation: Animation. - """ - fig = plt.figure() - ax = fig.add_subplot(xlim=self.xlim, ylim=self._ylim) - ax.set_ylabel(r"$\alpha$", rotation=0) - if title is not None: - ax.set_title(title) - if xlabel is not None: - ax.set_xlabel(xlabel) - - # draw a vertical line at the conventional estimate - ax.axvline(self.x, self._ymin, color=self.palette[1], linestyle="--") - ax.text(self.x, self.Y_OFFSET, r"$X(\theta)$", ha="center") - ax.plot( - self._linspace, - norm.cdf(self._linspace, self.x, self.scale), - color=self.palette[1], - ) - - # highlight the truncation set - for xmin, xmax in self.truncation_set: - ax.axvspan( - max(xmin, self.xlim[0]), - min(xmax, self.xlim[1]), - ymin=self._ymin, - color=self.palette[2], - alpha=0.2, - ) - - # vertical line at the location parameter of the truncated normal - self._loc_vline = ax.axvline( - self.xlim[0], color=self.palette[3], linestyle="--" - ) - # text showing the estimator notation - loc_text = r"$\hat{\mu}_\alpha$" if self.projection_len is None else r"$\hat{\mu}^H_\alpha$" - self._loc_text = ax.text(self.xlim[0], self.Y_OFFSET, loc_text, ha="center") - - # plot of the survival function of the truncated normal - (self._sf_plot,) = ax.plot([], [], color=self.palette[3]) - # horizontal line at the survival function evaluated at the conventional estimate - self._cdf_hline = ax.axhline(color=self.palette[4], linestyle="--") - # plot of the quantile-unbiased CDF - (self._cdf_plot,) = ax.plot([], [], color=self.palette[4]) - # highlight the projection confidence set - self._projection_plot = None - if self.projection_len is not None: - self._projection_plot = ax.axvspan( - 0, 0, color=self.palette[5], ymin=self._ymin, alpha=0.2 - ) - self._projection_lower_text = ax.text( - self.xlim[0] - self.projection_len, - self.Y_OFFSET, - loc_text[:-1] + r" - c_\beta \sqrt{\Sigma(\theta)}$", - ha="center" - ) - self._projection_upper_text = ax.text( - self.xlim[0] + self.projection_len, - self.Y_OFFSET, - loc_text[:-1] + r" + c_\beta \sqrt{\Sigma(\theta)}$", - ha="center" - ) - - return animation.FuncAnimation( - fig, self._animate, self.n_frames, init_func=self._init_func, blit=True - ) diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 0000000..0a60846 --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,286 @@ +@incollection{cortese2019megastudy, + title={The Megastudy Paradigm: A New Direction for Behavioral Research in Cognitive Science}, + author={Cortese, Michael J}, + booktitle={New Methods in Cognitive Psychology}, + pages={67--85}, + year={2019}, + publisher={Routledge} +} + +@article{milkman2021megastudy, + title={A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor’s appointment}, + author={Milkman, Katherine L and Patel, Mitesh S and Gandhi, Linnea and Graci, Heather N and Gromet, Dena M and Ho, Hung and Kay, Joseph S and Lee, Timothy W and Akinola, Modupe and Beshears, John and others}, + journal={Proceedings of the National Academy of Sciences}, + volume={118}, + number={20}, + year={2021}, + publisher={National Acad Sciences} +} + +@article{milkman2022680, + title={A 680,000-person megastudy of nudges to encourage vaccination in pharmacies}, + author={Milkman, Katherine L and Gandhi, Linnea and Patel, Mitesh S and Graci, Heather N and Gromet, Dena M and Ho, Hung and Kay, Joseph S and Lee, Timothy W and Rothschild, Jake and Bogard, Jonathan E and others}, + journal={Proceedings of the National Academy of Sciences}, + volume={119}, + number={6}, + year={2022}, + publisher={National Acad Sciences} +} + +@article{milkman2021megastudies, + title={Megastudies improve the impact of applied behavioural science}, + author={Milkman, Katherine L and Gromet, Dena and Ho, Hung and Kay, Joseph S and Lee, Timothy W and Pandiloski, Pepi and Park, Yeji and Rai, Aneesh and Bazerman, Max and Beshears, John and others}, + journal={Nature}, + volume={600}, + number={7889}, + pages={478--483}, + year={2021}, + publisher={Nature Publishing Group} +} + +@article{dellavigna2018motivates, + title={What motivates effort? 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A. and + Harris, Charles R. and Archibald, Anne M. and + Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and + {van Mulbregt}, Paul and {SciPy 1.0 Contributors}}, + title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific + Computing in Python}}, + journal = {Nature Methods}, + year = {2020}, + volume = {17}, + pages = {261--272}, + adsurl = {https://rdcu.be/b08Wh}, + doi = {10.1038/s41592-019-0686-2}, +} + +@article{botev2017normal, + title={The normal law under linear restrictions: simulation and estimation via minimax tilting}, + author={Botev, Zdravko I}, + journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, + volume={79}, + number={1}, + pages={125--148}, + year={2017}, + publisher={Wiley Online Library}, + doi={10.1111/rssb.12162} +} + +@inproceedings{botev2015efficient, + title={Efficient probability estimation and simulation of the truncated multivariate student-t distribution}, + author={Botev, Zdravko I and l'Ecuyer, Pierre}, + booktitle={2015 Winter Simulation Conference (WSC)}, + pages={380--391}, + year={2015}, + organization={IEEE}, + doi={10.1109/wsc.2015.7408180} +} + +@Manual{botev2021truncatednormal, + title = {TruncatedNormal: Truncated Multivariate Normal and Student Distributions}, + author = {Zdravko Botev and Leo Belzile}, + year = {2021}, + note = {R package version 2.2.2}, + url = {https://CRAN.R-project.org/package=TruncatedNormal}, + } \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 0000000..6bb0ac6 --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,72 @@ +--- +title: 'Multiple Inference: A Python package for comparing multiple parameters' +tags: + - Python + - multiple inference + - conditional inference + - post-selection inference +authors: + - name: Dillon Bowen + orcid: 0000-0002-3033-1332 + affiliation: 1 +affiliations: + - name: Wharton School of Business, University of Pennsylvania + index: 1 +date: 14 May 2022 +bibliography: paper.bib +--- + +# Summary + +Scientists often want to compare many parameters. For example, scientists often run randomized control trials to study the effects of many treatments, use observational data to compare many geographic regions, and study how public policy will impact many subgroups of people. Multiple Inference implements many of the latest econometric and statistical tools for making such comparisons, including inference after ranking, simultaneous confidence sets, and Bayesian estimators. It uses a `statsmodels`-like API and provides template notebooks for ease of use. In just a few clicks, researchers can upload a `.csv` file of conventional estimates (e.g., OLS or IV estimates) to a Jupyter binder and click "run" to apply a suite of multiple inference tools to their data. + +# Statement of need + +Researchers often want to compare multiple parameters. For example, there is a recent trend in social science to run large-scale studies and randomized control trials designed to test the effectiveness of many behavioral interventions [@cortese2019megastudy]. Researchers have used large-scale field studies to test the effectiveness of many text messages reminding patients to get vaccinated [@milkman2021megastudy; @milkman2022680; @banerjee2021selecting], behavioral nudges encouraging 24 Hour Fitness customers to exercise more often [@milkman2021megastudies], monetary and social incentives to exert effort [@dellavigna2018motivates], behavioral interventions to decrease implicit racial bias [@lai2014reducing], donation matching schemes to increase charitable giving [@karlan2007does], and job training programs to increase employment among refugees in Jordan [@caria2020adaptive]. Researchers also perform multiple comparisons using observational data. For example, economists often use observation data to compare many neighborhoods in terms of intergenerational mobility [@chetty2018opportunity; @chetty2018impacts; @chetty2014land]. + +Researchers tend to ask the same set of questions when comparing many parameters. + +1. Which parameters are significantly different from zero? +2. Which parameters are significantly better than the average (i.e., the average value across all parameters)? +3. Which parameters are significantly different from which other parameters? +4. What is the ranking of each parameter? +5. Which parameters might be the largest (i.e., the highest-ranked)? +6. What are the values of the parameters given their rank? e.g., What is the value of the parameter with the largest estimated value? +7. How are the parameters distributed? + +Researchers often use conventional estimators like ordinary least squares (OLS) and instrumental variables (IV) to answer such questions [@milkman2021megastudy; @milkman2022680; @milkman2021megastudies; @lai2014reducing]. Unfortunately, conventional estimators overestimate the value of the top-performing parameter (i.e., the parameter with the largest estimated value) and exaggerate the variability of the parameters [@andrews2019inference; @andrews2022inference]. These problems lead researchers to overstate the effectiveness of the top-performing treatments and the differences between treatment effects. + +Statisticians and econometricians have advanced multiple inference tools in recent years. Recent publications describe new statistical techniques for inference after ranking [@andrews2019inference; @andrews2022inference], multiple hypothesis testing [@romano2005stepwise], rank estimation [@mogstad2020inference], and Bayesian estimation [@stein1956inadmissibility; @james1992estimation; @dimmery2019shrinkage; @cai2021nonparametric; @brown2009nonparametric]. However, these techniques are mathematically complex and often inaccessible to all but professional statisticians. + +Multiple Inference solves this problem by implementing many of the latest multiple inference tools in an easy-to-use `statsmodels`-like API. Additionally, Multiple Inference provides Jupyter binders with boilerplate code and narrative explanations to help researchers interpret the output. These binders allow researchers to upload a `.csv` file of their conventional estimates and click "run" to apply multiple inference tools to their data without downloading any software or writing a single line of code. + +Multiple Inference initially implemented the inference after ranking techniques in @andrews2019inference and extended in @andrews2022inference. The latter paper uses Multiple Inference to compare many United States commuting zones regarding intergenerational mobility. The World Bank Group is currently using Multiple Inference to reanalyze the results of a multi-treatment study designed to improve tax collection in Poland (see @hernandez2017applying for an earlier version of the paper). + +# State of the field + +Multiple Inference's defining features are inference after ranking, rank estimation, and hypothesis testing tools. Most importantly, Multiple Inference contains the only implementation of the inference after ranking techniques developed in @andrews2019inference and @andrews2022inference in any language. These techniques correct for the winner's curse when performing inference on top-performing parameters (e.g., the parameters that rank in the top five according to conventional estimates). Specifically, Multiple Inference implements computationally efficient algorithms for computing quantile-unbiased point estimates and confidence intervals with correct coverage for parameters of specific ranks. + +@mogstad2020inference has an associated R package for estimating rankings. For example, it may estimate that a particular parameter has a 95% chance of being one of the three largest parameters. It also computes sets of parameters that contain all of the truly largest $K$ parameters with 95% confidence. Multiple Inference is the only Python implementation of these techniques. + +`statsmodels` implements multiple testing corrections based on p-values, such as the Holm-Bonferroni correction. Multiple Inference implements multiple hypothesis tests using a more powerful stepdown method based on simultaneous confidence sets for jointly Gaussian distributed estimates [@romano2005stepwise]. + +Bayesian estimators are essential tools for multiple inference, and robust packages for Bayesian analysis already exist in Python. For example, `statsmodels` implements two Bayesian models (binomial and Poisson) with independent Gaussian priors [@seabold2010statsmodels]. `pymc3` is a comprehensive package for Bayesian inference [@salvatier2016probabilistic]. Additionally, @dimmery2019shrinkage implements a Gaussian prior Bayesian model fit using Stein-type estimation. It distinguishes itself by incorporating uncertainty about the estimates of the prior parameters into the posterior distribution. + +Multiple Inference aims to be a one-stop-shop for multiple inference and therefore includes parametric and nonparametric Bayesian estimators. Its Gaussian prior Bayesian estimator is most similar to the Stein-type estimator from @dimmery2019shrinkage. However, @dimmery2019shrinkage does not account for correlated errors. For example, if we underestimate the prior mean and shrink all posterior estimates towards the estimated prior mean, we will underestimate many parameters. Multiple Inference accounts for this correlated uncertainty in its James-Stein fit method of the Gaussian prior Bayesian model. Additionally, Multiple Inference provides a maximum likelihood fit method for the Gaussian prior model, also accounting for correlated uncertainty about the prior parameters. + +Multiple Inference also implements several "intermediate products" that researchers can use in other applications. The most notable is a truncated normal distribution with two advantages over `scipy`'s truncated normal distribution [@2020SciPy-NMeth]. First, `scipy`'s truncated normal required a convex truncation set, whereas Multiple Inference's truncated normal accepts both convex and concave truncation sets. Second, Multiple Inference uses an exponential tilting method to improve accuracy when the truncation set is far from the mean of the underlying normal [@botev2017normal; @botev2015efficient]. Multiple Inference uses the same exponential tilting method as the R package `TruncatedNormal` [@botev2021truncatednormal]. We can see the advantage of Multiple Inference's implementation for the cumulative distribution function of a standard normal truncated to the interval $[8, 9]$ evaluated at 8.7. + +```python +>>> from scipy.stats import truncnorm +>>> truncnorm(8, 9).cdf(8.7) +1.0709836154559238 +>>> from conditional_inference.stats import truncnorm +>>> truncnorm([(8, 9)]).cdf(8.7) +0.9978948153314305 +``` + +# Acknowledgements + +I would like to thank Sarah Reed and Christian Kaps for feedback on this paper. I would also like to thank Isaiah Andrews, Toru Kitagawa, Adam McCloskey, and Jeff Rowley for feedback on my early drafts of the software. + +# References \ No newline at end of file diff --git a/setup.cfg b/setup.cfg index 04f8667..937f572 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,9 +1,9 @@ [metadata] name = conditional-inference -version = 0.0.2 +version = 1.0.0 author = Dillon Bowen author_email = dsbowen@wharton.upenn.edu -description = A statistics package for comparing multiple policies or treatments. +description = A statistics package for comparing multiple parameters. long_description = file: README.md long_description_content_type = text/markdown url = https://dsbowen.gitlab.io/conditional-inference @@ -20,6 +20,7 @@ python_requires = >=3.8 install_requires = matplotlib >= 3.4 numpy >= 1.20 + scikit-learn >= 1.0.2 scipy >= 1.6 seaborn >= 0.11.1 statsmodels >= 0.12 @@ -31,7 +32,7 @@ where = src [build_sphinx] project = Conditional Inference copyright = 2021, Dillon Bowen -release = 0.0.2 +release = 1.0.0 source-dir = docs [coverage:report] diff --git a/simulations/losers-presentation/analysis.ipynb b/simulations/losers-presentation/analysis.ipynb index 18512ec..cf2e10d 100644 --- a/simulations/losers-presentation/analysis.ipynb +++ b/simulations/losers-presentation/analysis.ipynb @@ -155,14 +155,35 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def make_blank_figure(ax, blank_ax=None):\n", + " if blank_ax is None:\n", + " fig, blank_ax = plt.subplots()\n", + " blank_ax.set_xlim(ax.get_xlim())\n", + " blank_ax.set_xticklabels(ax.get_xticklabels())\n", + " blank_ax.set_xticks(ax.get_xticks())\n", + " blank_ax.set_xlabel(ax.get_xlabel())\n", + " blank_ax.set_ylim(ax.get_ylim())\n", + " blank_ax.set_yticklabels(ax.get_yticklabels())\n", + " blank_ax.set_yticks(ax.get_yticks())\n", + " blank_ax.set_title(ax.get_title())\n", + " blank_ax.set_ylabel(ax.get_ylabel())\n", + " return blank_ax" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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TFLzdXfDpkX+QlKmDTCKgur8HagcpUTvYE7WDlKjh7wEXKcMaERE9mdWT/0NDQ3Hx4kWcPHkSOp3OMrIhiiKysrJw9uxZfPvtt3YvlKg0mE9QMBR4gsLbbargZlwGngnwwBWjCakaA67FZeBaXAb2Zl+OykUqoIa/B2oHeVoCW3U/d8gY1oiI6DFWB7Pt27dj3rx5+R5qkkgkaNeunV0KI3IURTlBYVXf+hBFETHpWlyJSc8+wcD8NU1jyHPCgVwqoGaA0hzUgjxRO1iJqn4ekHHRXCIipyaIVk7mefHFFxEWFobPPvsM69atQ0ZGBt577z0cP34cM2bMwNy5c9GjR4+SqrdEGI0mJCXlXVqhvJHJJFCpPJCcnAmDgcs7WOPRdczSNXp4Zp+gYHrCOmaiKOJBmgZXYjIeCWvpyNAa87R1lUnwTICHJaiFB3miiq+7VWFNEARIJAJMJtEhT0bgz6Dt2Ie2s2cfBgR42qkqIjOrR8zu3buHGTNmwNvbG/Xq1cPq1auhUCjw/PPP4+bNm9iyZUuZC2ZET5JzgoJoMMDPxwMpKZnQ65/8gS4IAkK93RDq7YYutQIAmMPa/VQNorJH1q5mj6xl6oy4FJ2OS9HplvsrZBI8E2geWasT7InwICUqq9zzXI7q0eCYptbDy82FC+ASEZVBVgczFxcXy6KylStXxp07d6DX6+Hi4oKmTZti06ZNdi+SyFHkDELZMhglCAIq+rihoo8buoWbF841iSLuJqtxNTYDUbHm64hejc1Alt6Iiw/ScPHBwyU83FwkCA80j6jVDlaicSUf1Aj1wVougEtEVOZZHcxq166NY8eOoWXLlqhatSpMJhMuXLiAZs2aISYmpiRqJCr3JIKAyr7uqOzrjudrPwxr/yapcSUuPftQaDquxWVArTfh/P00nL9vDmtfDmqKH/97A6t+5gK4RERlndXBbMiQIRg7dizS0tIwf/58dO7cGdOmTUO3bt1w4MABNG3atCTqJHI6EkFAFT93VPFzx4u1gwCYL0d1JznLPLIWk477aVq0reGPyZEX8n2MTb/dwqgO1bHy6HUEesjNj+frDpVb8a4ZSkREJcvqYNalSxesXbsWN27cAADMmTMHkydPxs6dO1G/fn18+OGHdi+SiMykEgHV/DxQzc8DL9UJglQqQZbWWPgCuJlanLiZjGuxD+eueStkqOJrDn1VH/ka7OUKCQMbEVGpsTqYAUDHjh3RsWNHAIBKpcLGjRvtWRMRFZHJJMLLzaXQBXD9la54tpov/N1dcCspC9GpGqRqDLjwIA0XHpm7BphPNqjs644qvm6o4uuOqtkjbGEqN7sskpuT+Zj9iIjyV6xgptPpcPPmTaSnp+e7v3nz5jYVRURFY14AV1/gArhD2lSFVmvAO20qW7Zp9EbcSVbjdmIWbiVl4XZSFm4lZuFuihoag8myQO6jpAIQ6uOWa3TNfFjUDR7yJ3+MPHrWaGKGFp5KN541SkSUD6uD2e+//47JkycjOTnZsk0URQiCYPl65coVuxZJRAUrygK4j1K45H/5KYNJxINUDW4lZoe1pCzczv4+U2fEv9kXfD9+IzHX/QKV8lyjazlffd3N89ikUgncla74nGeNEhE9kdULzL788ssQRRHjx4+Hj49Pvm1atGhhj9qeGi4wS0XlqH2Yax0zjR5eRVwAtyhEUUR8hs4S1B4dZUvK0hd4P6/seWyzX6mHY9fisPKRs0ZzTOhcE0NbV4aOZ40WmaP+DJYlXGCWHJnVI2b//vsv1qxZg7Zt25ZEPURUDDkL4Oo1ekgkAtI0erut/C8IAgI9XRHo6YqWlVW59qVp9A9H2BLVuJNsDmwPUjVI0xhwL1WDWiGeGLz5VL6Pvem3W/hPx+o49U88gjxdEah0zbN4LhGRM7E6mNWqVQvR0dElUQsR2UgURRiNT+9STF4KFzQM9UbDUO9c2zV686HPFI0BqVn6Qs8aTcjQYvGxm7gWmw4XqYBQbwUq+rghTGVehLeSj/l2sJeC1xIlonLP6mD23nvvYcqUKZBKpWjQoAHc3NzytKlQoYJdiiOisknhIsUzgUoIggAvpWuhZ436ebjCVSpAJhGgN4q4naTG7SR1nrYyiYAK3gpU8nFDRZ/sryo3hPm4IcTLFTI7nDUKOP71RomofCv2WZnvvfdegfs5+Z+IgKKdNarTGbDhjUYwmkTEpmtxN1mNuynmf/dSNLibosb9FDV0RtFyAsLjpAIQkj3SVsnHDZVUD0faQr0VRVrqg9cbJSJHYHUwmzVrFmQyGSZNmgR/f/+SqImIypGinjUqzR4Rq+CtQEvknstmEkXEpWuzA5sG9x4Lb1qDCfdSNLiXosEfSM51X4kABHu6opLl0Kib5VBpBW8FXGUSnjlKRA7D6rMyGzRogJUrV1oWmC0PeFYmFRX7sHgeHY1K1+jhacezRk2iiIQMXXZIU+PfZA3uWUKbGmp9wY8vAAjydMXyNxrht38Sy8SZo/wZtB3PyiRHZvWIWeXKlZGVlVUStRBROZVz1qhoMMDPxwMpKZnQFxKYrCF55KzRppV8cu0TRRGJWXrL4dF7KWrcfSS4ZeqM0JlENKjog3e2ns338Tf9dgujO1bH7dg0BHvyklVEVLKsDmYTJkzAp59+Cm9vbzRq1AgeHh4lURcRlUM54/NPa069IAjw95DD30OOxhVznzkqiiKS1Xokqc1njRZ25mh8hhbT9l/BveQshAcpER7oaf4apESYyo1hjYjsxupgtmTJEiQkJGD48OH57hcEAVFRUTYXRkRUkgRBgK+7HH4ervDykD/xzNFUtR6ZOiPO3k3F2buplv0ecvMZqLWzg1rtQE+E+TKsEVHxWB3MunfvXhJ1EBGViqKeObpnaDPcSsrCldgMXI3NwNXYdFyPz0Smzojz91Jx/t7DsObuIsUzgR4ID/K0BLbKKncunktET2T15P/yiJP/qajYh7Zx1P57eFbmzQLPHM3vJAWDScTtxCxciU3H1dgMXInNwPX4DGjzeW1uLhI8E5A9qhZkPhRa2dfd6kVzXVwk8LHzPD1nw8n/5MiKFMxOnz6NOnXqwMPDA6dPn37igzZv3twuxT0tDGZUVOxD2zhy/9nreqNGk4jbSVnZQc0c2K7FZUCTz+t1lZnDmuUwaJAnqvjlH9byO7OV66wVD4MZObIiBbPw8HB8++23aNCgAcLDwyEIQp4VsXO2CYJg9QKzJpMJERERiIyMRHp6Opo3b46PPvoIlSpVyrf97du3MX/+fJw7dw7u7u549dVXMXr0aMhkxVovl8GMiox9aJuy0H8lsfK/0STiTnJW9iFQ82HQa3GZyNIb87Q1hzXzYdDwQHNgeyZQCU9vN66zZicMZuTIihTMTp06hbp168LDwwOnTuV/MeJHtWjRwqoiIiIisG3bNixcuBDBwcFYtGgR7t27hwMHDkAul+dqm5qaipdeegnVqlXDjBkzoFar8eGHH6Jx48aYP3++Vc+bg8GMiop9aBv230NGk4i7yWpciXvkMGhcBjJ1ecPa+sHNcOFuClaVgXXWygIGM3JkRRpiejRoCYJgOaz5uLS0NPzvf/+zqgCdToeNGzdiypQplkVrly1bhvbt2+PHH39Ejx49crXfs2cPsrKysGLFCvj6+gIA5s2bhzfffBOjR49GxYoVrXp+IqLSIJUIqOLnjip+7nixdhAA82K5/yarH46sxaUjNl2LNtX9MOnbP/N9nJx11v6NS0OQ0hUCzwYlKtOsPvY3aNAgfPPNN2jQoEGefVFRUZg5c6ZVZ25evXoVmZmZaN26tWWbl5cX6tSpg9OnT+cJZnfu3EG1atUsoQwA6tSpAwA4c+YMgxkRlVkSQUAVX3dU8XXHC7UDzdskAjI0T15nbcq+K4hJVaN2sCfqBnuiTvZXPw95vvcjIsdUpGA2ffp0REdHAzCfWj5r1iwolco87W7fvm319TNjYmIAACEhIbm2BwYGWvY9vj0uLg5GoxFSqRQAcP/+fQBAYmKiVc/9KJnsyRc5Luuk2Rdylhbhgs6UP/ahbdh/1hMEwNP9yeuspWTpkKox4I/byfjj9sPrhQZ7uaJusCfqVfBCvRBP1A7yhIdr8ebjlhf8OSRHVqTfzueffx6bNm3Kte3xqWlSqRSNGjXCW2+9ZVUBarUaAPLMJXN1dUVqamqe9i+++CLWrFmDBQsWYNKkScjKysK8efMgk8mg1+uteu4cEokAlcp5rmDg5eVW2iWUeexD27D/rKPWGQpdZ02EiP9N74SrMWm4cDcFF+6l4sLdFPwTn4GYNC1i0rQ4ej0BgDno1QhQomElHzSs6I2GlXwQHuwFuRP8cfo4/hySIypSMOvUqRM6deoEABg4cCBmzZqF6tWr26UAhUIBwDzXLOd7ANBqtXBzy/tLU6VKFaxYsQIfffQRtm/fDnd3d4wbNw7//PMPPD2LNwnTZBKRllb+r/8plUrg5eWGtDQ1z+AqJvahbdh/xSORCPhPB/Nnbp511jpWhzZLC61JRCUPF1QKD0CP8AAAQIbWgKux6fgrOh1/PUjD5Zh0xKRp8XdcBv6Oy8Cus/cAAC5SAbUClagbYh5Vqxviicq+7sW+eoEgPDxT3xFXyrTnz6Ez/VFPT4fV49lbt27Ns+2vv/7CgwcP0KpVK3h5eVn1eDmHMOPi4hAWFmbZHhcXh1q1auV7n5ygGBcXBx8fHxgMBixcuLDA5TWKwpnOEDMaTU71eksC+9A27D/rGY0aDG1dGWOeq2FZxyxLo0dWev6L3wKAQipBowreaFTBG2hq3paQqUNUTDqiYtJxOftrmsZgDm/R6fgm+74ecinqPDJXrW6wJwI9XQutMddacGo9vNwce601/hySI7I6mMXFxWHy5Mlo3bo1Ro8ejW3btuGTTz6BKIrw8fHB1q1bUbNmzSI/Xnh4OJRKJU6ePGkJZmlpaYiKisKAAQPytD9z5gxWrFiBTZs2ITDQPDn20KFDcHNzQ5MmTax9OUREZYLRaIJRrYNoMMDPhpX//T3keLa6H56t7gfAPC3lfqoGl6MfBrWr2ct2nP43Baf/Tcl137rB5hG1OkGeqB2shJfCBcCjV0/gWmtEtrA6mC1atAi3bt3CO++8A5PJhLVr16JNmzaYOnUq5s2bhyVLlmDt2rVFfjy5XI4BAwZg8eLF8PX1RWhoKBYtWoTg4GB069YNRqMRSUlJ8PT0hEKhQLVq1XDt2jV8+umnGDRoEK5du4Z58+Zh5MiR+Z6QQERUnuQcGrTXIUJBEFDRxw0VfdzwfPaZoAaTiJsJmbj8yKjajYRMJGTqcPxGIo7feHiiVZjKDXWCPfFut1r4v7M3sPLow7XW0tQGy7y4oa0rw8i11oieyOpgduLECbz33nto3749zpw5g4SEBHzyyScIDw/H8OHDMWXKFKuLGD9+PAwGAz744ANoNBo0b94cGzZsgIuLC+7du4fOnTtjwYIF6NOnD3x9fbF27VosXLgQPXr0QEBAAMaOHYu3337b6uclIqK8ZBIBzwSarzjQu4F5uolab8S12AxLULsck477qRr8m6xGhs6ISn7u2Pzb7Xwfb9NvtzDmuRrQa/R2u5oCUXlldTDLyspCcHAwAOCXX36BXC5Hq1atAJhHv4rzSyeVSjF16lRMnTo1z76KFSvi2rVrubY1adIE3377rdXPQ0RExePmIkWjit5oVNHbsi0lS4+o2HTEZeqQnKkvdK21hAwtDl2KRrDSFXVDPKF08iU7iApi9W9GlSpVcObMGTRq1Ag//PADWrRoAVdX84TQ/fv3o0qVKvaukYiIHJCPuwvaVPWFIAjw8nQtdK01H3cXrDlxG0mZOggAqvi5o36IJ+qFeKF+iBeq+rlDms/F24mcjdXBbMSIEZg+fTo2bNiArKwsfPTRRwCAV199FVFRUVi8eLHdiyQiIscliiLUGn0ha61Vwd3ETDSv5I2/os2HQG8lZuFWYhb2/xUL4OFZoDlhrV6IJ1TuvGoBOR+rg1mPHj0QEhKCs2fPokWLFmjUqBEAoHnz5hg/fjyeffZZe9dIREQOzqgz4D8dqgHIZ621DtWRlaHBvO61AQBJWTpcepCOv6LT8Fe0eX21/M4CreijyB5RM4e1ZwI8IONq/VTOCaKNMzG1Wi3kcnmZvnCu0WhCUlJmaZdR4mQyCVQqDyQnZ3LtnmJiH9qG/Wc7R+7DXOuYafTwyl5rzfSEdcyMJhE3EzNxKXsh3L+i03ErKe+i364yCWoHKXOFtSetrZYfFxcJfGxYcuRRAQHFW9icqCDFCmY3b97EypUr8dtvvyEjIwORkZHYtWsXqlWrhoEDB5ZEnSWKwYyKin1oG/af7cpCHwqCAIlEgMkkFvsszHSNAZdj0sxhLdoc1tI0eeevBSrlqF/ByxLWagUqoXCR5vuYjwbHnEV6bV0Al8GM7M3qQ5lXrlzBW2+9BT8/P7z88svYsWMHAPOZlfPnz4dSqUTv3r3tXigREZUNoijCaLRtWQxPhQytqviiVRVfy2PeSVZbQtqlB2n4JyETcRk6HL2eYLkWqFQi4JkAD9QP8coObJ4I9VZAJpNyAVwqE6weMXv77bdhMpmwceNGAEC9evWwe/du1K1bFx9++CH++usv7Nmzp0SKLSkcMaOiYh/ahv1nO/bhQ1k6I67kXAs0Og0XH6QhKUufp53KzQVfDGqK//2dgJU//5Nn/4TONTG0dWXoirEALkfMyN6sHjH7888/sXTpUshkMhiNxlz7XnrpJRw8eNBuxRERERXEXS5F00o+aFrJB4B5VC0mXYtLDx4eAr0amwFBIqBuqDeGbTmT7+NwAVxyJFYHM1dXV2g0mnz3paSkQC7n6c1ERPT0CYKAEC8FQrwU6BZuvryU1mDC/TQN0tSFL4CbptFDIhFsPgRLZCurzztu27YtVq5ciZiYGMs2QRCQmZmJjRs3ok2bNnYtkIiIqLhcZRJU9/OAr4d5Adz8eLnJ4KVwgcnEUEalz+pgNnXqVGRlZeGFF17AW2+9BUEQsHDhQrzwwguIjo7GpEmTSqJOIiKiYnl0Adz8DGlTFWoexiQHYXUwCwkJwb59+zB48GCIooiwsDBkZWWhR48e+O6771CpUqWSqJOIiKjYchbAndC5pmXkzMtNhgmda+I/HarDqMv/MCfR02bzArPlAc/KpKJiH9qG/Wc79mHx5beOWVEWwC0Mz8oke7N68j8REVFZZDSaYFTrIBoM8LPTyv9E9saLjhERkVPJOU7E40XkiBjMiIiIiBwEgxkRERGRg7A6mEVERCA2Njbffffu3cOcOXNsLoqIiIjIGVkdzFavXl1gMLtw4QIiIyNtLoqIiIjIGRXprMz+/fvjwoULAMwL9fXr16/AtvXr17dPZUREREROpkjBbN68eTh8+DBEUcTq1avRt29fBAcH52ojkUjg5eWFbt26lUihREREROVdkYJZjRo1MHbsWADm62K+9tprCAoKKtHCiIiIiJyN1QvM5gS01NRUqNVqmEx5F+erUKGC7ZURERERORmrg9m///6LadOmWeac5efKlSs2FUVERETkjKwOZnPmzMHt27cxduxYBAcHQyLhUmhERERE9mB1MDt9+jQ++eQT9OjRoyTqISIiInJaVg93KZVKeHt7l0QtRERERE7N6mD2yiuvYPv27RB59VciIiIiu7L6UKabmxvOnj2Lrl27on79+lAoFLn2C4KA+fPn261AIiIiImdhdTDbs2cPPD09YTKZ8j0zUxAEuxRGRERE5GysDmY///xzSdRBRERE5PS41gURERGRgyjSiFnnzp2xevVqhIeHo1OnToUerhQEAUeOHLFbgURERETOokjBrEWLFvDw8LB8z3lkRERERPYniFz3AkajCUlJmaVdRomTySRQqTyQnJwJgyHvNU7pydiHtmH/2Y59aDt79mFAgKedqiIys3ryf45ffvkFp06dQlpaGlQqFZo1a4b27dvbszYiIiIip2J1MNPpdBg9ejROnDgBqVQKlUqF5ORkfPHFF2jVqhXWrVsHuVxeErUSERERlWtWn5W5atUqnD17Fp999hkuXryIEydO4MKFC1iwYAH+/PNPfP755yVRJxEREVG5Z3UwO3jwIMaOHYuePXtCKpUCAGQyGXr16oWxY8fiwIEDdi+SiIiIyBlYHcySkpJQp06dfPfVqVMHsbGxNhdFRERE5IysDmZhYWE4e/ZsvvtOnz6NkJAQm4siIiIickZWT/7v378/Fi5cCIVCge7du8Pf3x8JCQk4ePAgvvzyS4wdO7Yk6iQiIiIq96wOZm+88QaioqKwePFiLFmyxLJdFEX07t0b77zzjl0LJCIiInIWVgcziUSCTz75BEOGDMHp06eRmpoKb29vtGjRAtWrVy+JGomIiIicQrEXmA0JCUFYWBhSU1Ph6+uL0NBQe9ZFRERE5HSsDmaiKGLp0qX46quvoNfrIYoiBEGAQqHAmDFjMHz48JKok4iIiKjcszqYff7559iwYQMGDBiAbt26wc/PD4mJiTh8+DCWLVsGLy8vvP766yVRKxEREVG5ZnUwi4yMxMiRIzFhwgTLtqpVq6JZs2Zwd3fHpk2bGMyIiIiIisHqdcySk5PRtGnTfPe1bNkS0dHRNhdFRERE5IysDmatWrXC/v378913/PjxAkMbERERERXO6kOZPXv2xOzZszFs2DD07NkTQUFBSE5OxpEjR3D48GFMmDABe/futbTv1auXHcslIiIiKr8EURRFa+4QHh5e9AcXBFy5csXqop42o9GEpKTM0i6jxMlkEqhUHkhOzoTBYCrtcsok9qFt2H+2Yx/azp59GBDgaaeqiMysHjE7evRoSdRBRERE5PSsDmZcSJaIiIioZFg9+Z+IiIiISgaDGREREZGDYDAjIiIichAMZkREREQOosjBTBRFnDlzBnFxcXn2xcfH4/Tp0zCZeOo2ERERUXEV+axMQRCwZMkS+Pn5ISIiIte+uXPn4sGDB9i1a5fdCyQiIiJyFlYdyhw4cCCOHTuW63qYsbGx+PnnnzFo0CC7F0dERETkTKwKZs8//zz8/f2xc+dOy7avv/4avr6+eOmll+xeHBEREZEzsSqYSaVSvPHGG4iMjIRer4dOp8O3336LN954AzKZ1WvVEhEREdEjrD4rs1+/fsjMzMShQ4fw/fffIzMzE/379y+J2oiIiIicitXDXCqVCt27d8f27dshCAJ69OgBlUpVErUREREROZVirWM2aNAgXLx4ERcvXsTgwYNtKsBkMmHlypVo3749GjVqhBEjRuDu3bsFtk9MTMTkyZPRqlUrtGzZEu+++y5iY2NtqoGIiIjIERQrmIWHh+Odd97BqFGj8Mwzz9hUwJo1a7Bjxw7MnTsXO3fuhMlkwvDhw6HT6fJtP3HiRDx48ACbNm3Cpk2b8ODBA4wZM8amGoiIiIgcQbFX/p80aRImTJhg05PrdDps3LgR48ePR8eOHREeHo5ly5YhJiYGP/74Y572aWlpOHXqFEaMGIHatWujTp06eOedd3Dp0iWkpKTYVAsRERFRaSvVSzJdvXoVmZmZaN26tWWbl5cX6tSpg9OnT+dpr1Ao4OHhgb179yIjIwMZGRnYt28fqlatCi8vr6dZOhEREZHdleoaFzExMQCAkJCQXNsDAwMt+x4ll8uxcOFCfPTRR2jWrBkEQUBgYCC2bdsGicS2jCmTlf/LhkqlklxfyXrsQ9uw/2zHPrQd+5AcWakGM7VaDcAcuB7l6uqK1NTUPO1FUcSVK1fQuHFjDB8+HEajEcuWLcPo0aPx9ddfQ6lUFqsOiUSASuVRrPuWRV5ebqVdQpnHPrQN+8927EPbsQ/JEZVqMFMoFADMc81yvgcArVYLN7e8vzDff/89tm3bhmPHjllC2Nq1a/Hcc89h165dePvtt4tVh8kkIi0tq1j3LUukUgm8vNyQlqaG0cgLzhcH+9A27D/bsQ9tZ88+dKY/6unpsDqYRURE4LXXXkNQUFCefffu3cPGjRvx0UcfFemxcg5hxsXFISwszLI9Li4OtWrVytP+zJkzqFq1aq6RMW9vb1StWhV37tyx9qXkYjA4zwec0WhyqtdbEtiHtmH/2Y59aDv2ITkiqw+wr169usB1wy5cuIDIyMgiP1Z4eDiUSiVOnjxp2ZaWloaoqCg0b948T/vg4GDcuXMHWq3Wsi0rKwv37t1DlSpViv4iiIiIiBxQkUbM+vfvjwsXLgAwz/Pq169fgW3r169f5CeXy+UYMGAAFi9eDF9fX4SGhmLRokUIDg5Gt27dYDQakZSUBE9PTygUCvTq1QsbNmzAxIkTLUt1LF++HK6urujTp0+Rn5eIiIjIERUpmM2bNw+HDx+GKIpYvXo1+vbti+Dg4FxtJBIJvLy80K1bN6sKGD9+PAwGAz744ANoNBo0b94cGzZsgIuLC+7du4fOnTtjwYIF6NOnDwIDA7Fjxw4sWrQIgwcPhkQiQbNmzbBjxw54enpa9bxEREREjkYQRVG05g75zTEzGAyQyUr1PAKbGI0mJCVllnYZJU4mk0Cl8kBycibnVRQT+9A27D/bsQ9tZ88+DAjgoADZl9VzzMaOHYt9+/bhnXfesWw7e/Ys2rVrh23bttm1OCIiIiJnYnUw27hxI5YvX55rsn1YWBheeOEFLFy40KrJ/0RERET0kNXHH3fu3ImJEyfmGjELCQnBBx98AH9/f2zevBmvvfaaXYskIiIicgZWj5jFxsYWeOZlw4YNce/ePZuLIiIiInJGVgez0NBQ/P777/nuO336dJ6zNYmIiIioaKw+lPn6669j0aJF0Ov16NKlC/z8/JCUlIRjx45h06ZNmDx5cknUSURERFTuWR3M3n77bcTGxmLr1q3YvHmzZbtUKsXgwYMxZMgQe9ZHRERE5DSKtfjY9OnTMXr0aPz5559ISUmBl5cXGjRoAJVKZe/6iIiIiJxGsVeF9fDwQEBAAERRRJMmTWAwGOxZFxEREZHTKVYw27dvH5YsWYL4+HgIgoDIyEisWrUKLi4uWLJkCeRyub3rJCIiIir3rD4r89ChQ5g+fTpatWqFpUuXwmQyX86ia9euOH78ONasWWP3IomIiIicgdUjZmvXrkX//v0xa9YsGI1Gy/a+ffsiKSkJ3377LSZOnGjPGomIiIicgtUjZrdu3ULXrl3z3dewYUPExsbaXBQRERGRM7I6mPn5+eHGjRv57rtx4wb8/PxsLoqIiIjIGVkdzF566SWsXLkShw8fhk6nAwAIgoC//voLa9aswQsvvGD3IomIiIicgdVzzCZOnIjr169j4sSJkEjMuW7gwIHIyspCs2bNMGHCBLsXSUREROQMihTM/v33X1SsWBESiQRyuRzr16/Hr7/+ij/++AMpKSnw9PREixYt0KFDBwiCUNI1ExEREZVLRQpmr732GlavXo1mzZph5syZGD16NNq2bYu2bduWdH1ERERETqNIc8y0Wi3++ecfAMCePXuQnJxcokUREREROSNBFEXxSY1GjRqF//73vxAEAaIoFnq4UhAEREVF2bXIkmY0mpCUlFnaZZQ4mUwClcoDycmZMBhMpV1OmcQ+tA37z3bsQ9vZsw8DAjztVBWRWZEOZS5evBj79u1DcnIyIiIi0LdvXwQHB5d0bUREREROpUjBbNKkSZg6dSpq1qyJkydPYtCgQXjmmWdKujYiIiIip1KkOWa///47EhMTAQBnzpyBRqMp0aKIiIiInFGRRswqVKiAjz/+GE2aNIEoilizZg1UKlW+bQVBwPz58+1aJBEREZEzKFIwmzNnDj777DOcOnXKssq/XC7Pty3XMSMiIiIqniIFs5YtW2L37t0AgPDwcKxZswYNGjQo0cKIiIiInI3Vl2Q6evQoAgMDLbe1Wi3kcjlHyoiIiIhsZPVFzENDQ3H37l1MnDgRLVq0QOPGjREVFYXZs2dj69atJVEjERERkVOwOphduXIFr776Ki5fvoyXX34ZOevTSqVSzJ8/H3v27LF7kURERETOwOpDmZ9++inq1auHjRs3AgC2b98OAPjggw+g1WqxZcsW9O7d275VEhERETkBq0fM/vzzT7z99tuQyWR55pW99NJLuH37tr1qIyIiInIqVgczV1fXAheYTUlJKXAZDSIiIiIqnNXBrG3btli5ciViYmIs2wRBQGZmJjZu3Ig2bdrYtUAiIiIiZ2H1HLOpU6eiX79+eOGFFxAeHg5BELBw4ULcunULoihi6dKlJVEnERERUbln9YhZSEgI9u3bh8GDB0MURYSFhSErKws9evTAd999h0qVKpVEnURERETlntUjZgCgUqnw7rvv2rsWIiIiIqdm9YgZEREROY+c9Uqd7blLC4MZERER5ZGWloZp06bhzJkzlm0DBw7EwIEDn8rznz17Fu+8885TeS5HUqxDmURERFS+XblyBfv27UPfvn0t2z7++OOn9vyRkZG4cePGU3s+R8FgRkREREVSo0aN0i6h3LPpUGZ6ejpu3LgBnU4Ho9For5qIiIjIDiIjI9G9e3fUq1cPHTt2xKpVqyz/XyclJWHy5Mlo27Yt6tevj1deeQV79+4FAJw8eRKDBg0CAAwaNMhy+PLxQ5m1atXC119/jRkzZqBp06Zo0aIF5s2bB41Gg08//RStWrVCy5Yt8f7770Or1Vrul5SUhNmzZ+O5555DvXr10KJFC4wZMwb37t0DAMyYMQN79uzB/fv3UatWLXz33XcAzLljwYIF6NKlC+rXr48ePXpg165duV5zp06dMH/+fAwePBgNGjTA+++/XzKdW0KKNWJ28uRJLF68GH/99RcEQUBkZCS+/PJLBAcHY8aMGfaukYiIiKy0bt06LFu2DAMGDMDMmTNx5coVrFq1CtHR0Zg/fz6mTp2KxMREzJ49G0qlEvv27cP06dMRHByMevXq4aOPPsKcOXPw0UcfoWXLlgU+z6JFi9CjRw9ERETg2LFj+Oqrr3DixAmEh4dj8eLF+PPPP7Fq1SpUrVoVw4cPhyiKGDlyJFJTUzFlyhT4+/vj2rVrWL58OT7++GNs2LABo0ePRlJSEqKiohAREYGwsDBoNBq8+eabSExMxPjx4xEaGoojR47g/fffR0JCAkaNGmWpafv27RgyZAhGjBgBDw+Pp9HddmN1MPv9998xYsQING7cGFOmTMHixYsBAOHh4Vi5ciWCgoIwZMgQuxdKRERERZOeno41a9agX79++OCDDwAA7dq1g4+PDz744AMMGTIEp06dwpgxY9ClSxcAQIsWLeDj4wO5XA6lUmk5bFmjRo1CD2HWqFEDc+bMsTxGZGQk9Ho9Fi9eDJlMhnbt2uGHH37AuXPnAABxcXFwc3PD9OnT0axZMwBAy5Yt8e+//+Kbb74BAISFhcHX1xdyuRyNGjUCAOzYsQPXr1/Hzp070bhxYwBA+/btYTAYsGbNGvTv3x8+Pj4AgAoVKmDKlCl27NGnx+pgtnz5cnTu3BkrVqyAwWDAokWLAACjRo1CVlYWIiMjGcyIiIhK0fnz56HRaNCpUycYDAbL9k6dOgEAfv31V7Rs2RKrVq1CVFQU2rdvjw4dOmD69OlWP1dOSAIAqVQKlUqFunXrQiZ7GDF8fHyQnp4OAAgKCsKWLVsgiiLu3buHO3fu4ObNmzh37hx0Ol2Bz3Pq1CmEhobmej4A6NmzJ3bt2oULFy6gQ4cOAIDatWtb/TochdXB7MqVKxgzZgwA8zUyH9W2bVt89dVX9qmMiIiIiiUlJQUAClxuIi4uDsuWLcPatWvx/fff44cffoBEIkGbNm0wZ84chIaGFvm5lEplnm3u7u6F3mf//v1YunQpoqOj4ePjg9q1a0OhUBR6n9TUVAQEBOTZ7u/vD8C8vEdRn9+RWR3MPD09ER8fn+++6OhoeHp62lwUERERFZ+XlxcAYPHixahSpUqe/f7+/vD09MTUqVMxdepU3Lx5E0ePHsWaNWswe/ZsfPHFFyVW25kzZzB9+nQMHDgQw4YNQ1BQEADgs88+w9mzZwu8n7e3N+7cuZNne04mUalUJVPwU2b1WZmdO3fGsmXLcOnSJcs2QRAQExODtWvXomPHjvasj4iIiKzUsGFDuLi4IDY2FvXr17f8k8lkWLp0Ke7evYsOHTrg8OHDAIBq1aphxIgRaNOmDR48eADAfFiyJJw/fx4mkwnjxo2zhDKj0YjffvsNAGAymQAAEknuiNK8eXPcv38f58+fz7V9//79cHFxQYMGDUqk3qfN6hGzyZMn48KFC3j99dctw4eTJk1CTEwMQkJCMGnSJLsXSUREREWnUqkwfPhwrFixAhkZGWjZsiViY2OxYsUKCIKA2rVrIzg4GPPmzUNGRgbCwsLw119/4fjx4xg5ciQAWI6A/fe//4W3tzfCw8PtUltOgJozZw769u2L1NRUbN++HVevXgUAZGVlQalUwsvLCwkJCTh+/Dhq166NPn36YMeOHRgzZgzGjx+PihUr4ueff8bu3bsxduxYyyhhWWd1MPP29kZkZCT27t2LP/74AykpKfD09MTAgQPRp08fuLm5lUSdREREZIWJEyciICAAO3bswPr16+Ht7Y3WrVtj0qRJ8PT0REREBJYuXYoVK1YgOTkZISEhGDt2rGVeWs2aNdGjRw9s374d//vf/3Dw4EG71NWyZUt89NFH2LRpEw4fPgx/f3+0bNkSERERGDNmDM6ePYsOHTqgT58+OH78uCWIvfPOO9i6dSuWLFliCZzVqlXDJ598gldffdUutTkCQXTGK4Q+xmg0ISkps7TLKHEymQQqlQeSkzNhMJhKu5wyiX1oG/af7diHtrNnHwYEcF412ZfVI2Y5qwIXplevXsUohYiIiMi5WR3MClrZXxAESKVSSKVSBjMiIiKiYrA6mB09ejTPtqysLJw5cwZffvklVq9ebZfCiIiIiJyN1cGsoEXnatasCb1ej7lz52LHjh02F0ZERETkbKxex6wwtWrVwuXLl+35kEREREROw27BTKfTYdeuXfDz87PXQxIRERE5FasPZXbq1CnPNTJNJhOSk5Oh1WqLdQFUIiIiIipGMGvZsmW+25VKJZ577jm0adPG5qKIiIiInJHVwaxFixZo06aN5fpWRERERGQfVs8xmzNnDi5evFgStRARERE5NauDWXBwMDIyMkqiFiIiIqJiy8rKwvbt2y23Z8yYgYEDBz7VGgYOHFjgYvxFYfWhzH79+uGTTz7B+fPnUatWLXh4eORpw5X/iYiI6GnbuHEjvvvuO7z11lsAgPfffx9Go7GUq7KO1cFs4cKFAIBvv/023/2CIDCYERER0VMnimKu256eZe8i83a5JBMRERE9JIoi1PrSG6lxc5HmWdqqKDIzM7F06VL88MMPyMzMRN26dTFjxgzUq1cP58+fx7Jly3D58mXIZDJ06tQJ06ZNg0qlAmBeTuutt97Cn3/+iRMnTkAul+Pll1/GjBkzoNVq0a5dO0ydOhVvvvmm5fkiIiKwa9cu/PzzzxAEAevXr8fOnTuRkJCAKlWqYNiwYejZsycA4OTJkxgyZAg+//xzLFq0CLdv30bFihUxZcoUdOnSBatWrUJERAQA84L3R48eRUREBO7fv4+tW7cCAG7cuIFFixbh/PnzMBgMaNu2LaZPn265qtHAgQPRsGFDJCUl4ccff4TJZMJzzz2H2bNnQ6lUAgCOHDmCdevW4e+//4bRaETNmjXx7rvvon379sV/wx5hdTA7ffo0OnToYHkjHhUfH4+9e/dixIgRdimOiIiorBFFEa+u/R1n7ySXWg3NKqsQOaq11eFs4sSJuH37NhYsWICwsDCsXbsWQ4cOxZdffomBAweiX79++PjjjxEfH485c+Zg2LBhiIyMhFQqBQCsWLECU6ZMwbRp03Dq1Cm8//77qFevHnr16oUXXngBBw8ezBXMDhw4gFdeeQUSiQRLly7FwYMH8dFHH6FatWo4ffo0Zs2ahfT0dMuhSaPRiEWLFuH9999HSEgIli5diunTp+OXX37B0KFDkZWVhUOHDmHXrl3w9fXN9dru37+Pfv36oU2bNvjqq6+g1WqxcOFCDBgwAAcOHLAEr82bN2Po0KHYtWsXbty4gcmTJ6Nq1aoYO3Ys/vrrL4wbNw7Tp09H586dkZGRgSVLlmDatGk4fvw45HK5LW8bgGJM/p85cybu3r2b774rV65g5cqVVj2eyWTCypUr0b59ezRq1AgjRowo8PFXrVqFWrVq5ftv5syZ1r4UIiKiEmH9WFXpu3nzJn755Rd8/PHHaN++PSpXroxZs2ahd+/eWL9+PWrVqoUPP/wQ1atXR6tWrbB06VJcvnwZJ06csDxGu3btMGjQIFSqVAl9+/ZFeHg4zp07BwDo3bs3zp07h/v37wMALl68iNu3b6NPnz7IysrC5s2b8d5776Fjx44ICwtD37598fbbb2PDhg256pw4cSJat26NKlWqYPTo0cjIyMD169fh4eEBd3d3SKVSBAQEWMJijh07dsDd3R2LFy9GeHg4GjZsiJUrVyIxMRH79u2ztKtRowYmTZqEKlWqoHPnzmjbti3Onz8PAJBKpfjwww/x9ttvo1KlSqhduzYGDRqEpKQkJCYm2uV9KNKI2TvvvIMbN24AMP8lMGbMmHxTYWJiIsLCwqwqYM2aNdixYwcWLlyI4OBgLFq0CMOHD8eBAwfyPMfQoUPRv3//XNs2bdqEr7/+Gm+//bZVz0tERFQSBEFA5KjWZe5Q5vXr1wEAjRo1smxzdXXFzJkz8dJLL6Ft27a52oeHh8PT0xPXrl1Dhw4dAADVq1fP1cbT0xN6vR4A0Lx5c1SsWBEHDx7EyJEjsX//fjRp0gSVK1fGxYsXodVqMXnyZEgkD8eMDAYDdDodNBqNZVu1atUs3+eMcuU8x5NeX7169XJli4CAAFStWtXy2h9//JzXkJaWBgCoXbs2vL298cUXX+DmzZu4c+cOrl69CgB2O8mgSMFs1KhRiIyMBADs2bMHderUyTNEKJFI4OXlhT59+hT5yXU6HTZu3IgpU6agY8eOAIBly5ahffv2+PHHH9GjR49c7T08PHKdBRoVFYUtW7Zg7ty5qFWrVpGfl4iIqCQJggB3udWzhUqVTFZwvY9Pqn90u4uLi+V2foM2OffNOTnwwIEDGD58OL7//ntMnDgxV5vly5fnCUaPP25hz1GYgtqYTKYnvoYcp06dwrBhw9CxY0c0bdoUL7/8MtRqNcaMGfPE5y+qIv3UNGnSBE2aNLHcHj16NCpVqmTzk1+9ehWZmZlo3bq1ZZuXlxfq1KmD06dP5wlmj5szZw6aNWuG3r1721wLERGRM8sZ7bp06ZLl/2WDwYBu3bohOjoabm5uudpfvXoVGRkZeUbJCtO7d29ERERg586dyMzMxIsvvgjAPEolk8nw4MEDPPfcc5b2W7ZswT///IM5c+YU6fELGyWsVasW9u/fD51OZwlfCQkJuHPnTq55b4XZuHEjWrZsiVWrVlm25ZxYUJRwWBRWx/kFCxbY5YkBICYmBgAQEhKSa3tgYKBlX0GOHTuG8+fPY+/evXapRSazerpdmSOVSnJ9JeuxD23D/rMd+9B27MP8Va1aFd26dcPs2bMxa9YsBAUF4YsvvoBWq8XOnTvx5ptvYu7cuXjzzTeRkJCAuXPnok6dOrkGV54kNDQULVu2xJIlS9ClSxfLoUhPT0/0798fK1asgFKpRJMmTXDy5EksWrQII0eOLPLju7u7IzU1Fbdu3ULFihVz7XvjjTfw9ddfY+rUqfjPf/4DnU6HTz/9FCqVCt27dy/S44eEhODIkSM4c+YMgoODcfLkSaxYsQKA+SigPZTqOKtarQaQd9jQ1dUVqamphd5306ZNeO6551C7dm2b65BIBKhUeRfKLa+8vNye3IgKxT60DfvPduxD27EP85o/fz4+++wzTJgwATqdDg0bNsSGDRsQHh6O9evXY/ny5ejVqxeUSiW6dOmCyZMn5zoMWBR9+vTBH3/8kWfq08yZM6FSqbBixQrExcUhJCQE48ePx/Dhw4v82N26dcO3336Lnj17Ytu2bbn2VaxYEdu2bcOiRYvQr18/yOVytG3bFosWLYKXl1eRHn/8+PFISEjAqFGjAJhPFJg/fz6mTp2KS5cuWTV6WBBBtNfYWzH88MMPGD9+PC5cuACFQmHZnvMD8fnnn+d7v5yhzi+++MIy4dAWRqMJaWlqmx/H0UmlEnh5uSEtTQ2j0VTa5ZRJ7EPbsP9sxz60nT370Jn+qKeno1RHzHIOYcbFxeU6mzMuLq7QyfxHjhyBr69vnjNEbGEwOM8HnNFocqrXWxLYh7Zh/9mOfWg79iE5IqsPsMfGxtrtycPDw6FUKnHy5EnLtrS0NERFRaF58+YF3u/MmTNo0aJFoWeQEBEREZU1Vgez5557DsOHD8ehQ4dsnugml8sxYMAALF68GEePHsXVq1fx7rvvIjg4GN26dYPRaER8fHyu9UsA8zIZ4eHhNj03ERERkaOxOpgtWLAAJpMJU6ZMQbt27TB79mxcunSp2AWMHz8er776Kj744AO88cYbkEql2LBhA1xcXBAdHY127drh0KFDue4THx8PHx+fYj8nERERkSMq9uT/2NhY7NmzB/v27cOtW7dQo0YN9OnTBz179oS/v7+96yxRRqMJSUmZpV1GiZPJJFCpPJCcnMl5FcXEPrQN+8927EPb2bMPAwI87VQVkVmxF3EJCgrCqFGj8P3332P37t1QqVRYtGgROnbsiHHjxuHChQv2rJOIiIio3LNpdb0zZ87gww8/xLBhw3D27Fm0bdsWM2bMgFqtxhtvvIHNmzfbqUwiIiKi8s/q0xrv3LmDffv2Yf/+/bh//z5CQ0MxcOBA9OnTx7L8xYABAzBlyhR8/vnnvLg4ERERURFZHcyef/55uLq6okuXLpg7d26Bl2KoVq0abt++bWt9RERERE7D6kOZH374IU6cOIElS5YUen2s0aNHY9euXTYVR0REROXXd999l2tB+U6dOlkuEC6KIvbs2YPExMR82z4Nq1atQqdOnZ7qc1odzH744QfExcXlu+/q1at4+eWXbS6KiIiInM+uXbswdOhQAMDp06ct89YB4KWXXsKJEydKs7ynokiHMs+cOYOcVTVOnTqF06dPIykpKU+7Y8eO4e7du/atkIiIiJyCr6+v5fvHV/NSKBS5rqtdXhUpmEVGRmLfvn0QBAGCIGD27Nl52uR0YI8ePexbIRERET0VmZmZWLp0KX744QdkZmaibt26mDFjBurVq4fz589j2bJluHz5MmQyGTp16oRp06ZBpVIBMB+GfOutt/Dnn3/ixIkTkMvlePnllzFjxgzLJRR/+uknrFy5Erdv30b9+vXRpk2bXM/fqVMn9O7dGy1atMCgQYMAAJ07d8aCBQsAADNnzsS1a9cAACkpKVixYgV+/vlnJCcno06dOnj33XfRsmVLAObDkGfPnkWbNm2wbds2JCcno2HDhpg9ezaqV68OALh+/TqWLFmCc+fOQa1WIygoCG+99ZZl1K40FCmYffDBB+jbty9EUcTgwYPx0UcfoUaNGrnaSCQSeHl5oWbNmiVSKBERUZkhioA+q/Se38UdEASr7zZx4kTcvn0bCxYsQFhYGNauXYuhQ4fiyy+/xMCBA9GvXz98/PHHiI+Px5w5czBs2DBERkZCKpUCAFasWIEpU6Zg2rRpOHXqFN5//33Uq1cPvXr1wrlz5zBu3DiMHTsW3bt3x5kzZzB37tx862jcuDFWrVqFcePGITIyEs8880yuqwAZjUYMHToUer0eixYtgq+vL7Zs2YJhw4Zhx44daNCgAQDzET9XV1d88cUX0Ov1mDZtGmbPno0tW7ZArVZj6NChaNu2LXbu3AmpVIrIyEh8+umnaN26NWrXrl2MjrddkYKZp6cnWrRoAQDYsmUL6tatCw8PjxItjIiIqEwSRWDj88Ddk6VXQ6VWwNDDVoWzmzdv4pdffsGGDRvQrl07AMCsWbPg5eWF9evXo1atWvjwww8BANWrV8fSpUvxyiuv4MSJE+jQoQMAoF27dpaRrkqVKmHr1q04d+4cevXqhW3btqFJkyYYO3YsAKBq1aq4fv06tmzZkqcWuVwOb29vAObDm48fwjxx4gQuX76MAwcO4JlnngEAyyUiN2zYgBUrVgAADAYDPvvsM8tj9e/fH4sWLQIAqNVqDBo0CG+99ZYl04wfPx7r16/HtWvXHDuY7d27Fx06dIBKpcKDBw/w4MGDQtv36tXLHrURERGVUdaPVpW269evAwAaNWpk2ebq6oqZM2fipZdeQtu2bXO1Dw8Ph6enJ65du2YJZjmHCHN4enpCr9dbHv/xx2jcuHG+wawotXp6elpCGQAIgoBmzZrlOkHA39/fEsoer8fX1xdvvvkmDh48iKioKPz777+4evUqAMBkKr3LnRUpmM2YMQPffvstVCoVZsyYUWhbQRAYzIiIyHkJgnm0qowdysyZB5afgi6rLYoiXFxcLLflcnmB9xUEIU/gefS+1iisnkdfR3715IiPj0e/fv3g6+uLTp06oV27dqhfv74lZJaWIgWzo0ePIiAgwPI9ERERFUIQAHnZmvKTM9p16dIlyzqlBoMB3bp1Q3R0NNzc3HK1v3r1KjIyMvKMkhUkPDwc58+fz7Xtr7/+KrC9UEiwrFWrFtLT03H9+nXLqJkoijh79myeOfAFOXjwIFJSUvDDDz9YAmLOiQUFBb+noUjrmIWGhlpSZ2ho6BP/ERERUdlStWpVdOvWDbNnz8Yff/yBW7du4cMPP4RWq8XOnTtx7do1zJ07Fzdu3MDJkycxZcoU1KlTp9DF5h81dOhQXL16FZ9++ilu3bqF/fv3Y9u2bQW2d3d3B2AOgJmZmbn2tWvXDrVr18bkyZNx6tQp3LhxA3PmzMH169cxePDgItUTHBwMtVqNw4cP48GDBzhx4gQmTZoEANDpdEV6jJJQpBGzmTNnFvkBBUHA/Pnzi10QERERlY758+fjs88+w4QJE6DT6dCwYUNs2LAB4eHhWL9+PZYvX45evXpBqVSiS5cumDx5cpEPR9auXRtffvklFi1ahG3btqFmzZoYNWoUFi9enG/7Z555Bh06dMDEiRMxadIk+Pj4WPZJpVJs3LgRn376KcaOHQudTod69eph8+bNuebIFeaFF17A5cuXsXDhQmRkZCA0NBSvvfYajh49ikuXLuGNN94o0uPYmyAWYbzOmssRCIJQ5g53Go0mJCVlPrlhGSeTSaBSeSA5ORMGQ+lNbCzL2Ie2Yf/Zjn1oO3v2YUCAp52qIjIr0ojZzz//XNJ1EBERETk9q6+VSUREREQlo0gjZrVr18Y333yDBg0aIDw8vNAzJQRBQFRUlN0KJCIiInIWRQpmY8aMQVBQkOX7woIZERERERVPkYJZzuUTAGDcuHElVgwRERGRMytSMHtcVlYW9uzZgzNnziAtLQ2+vr5o1aoVXn755UJX2SUiIiKiglkdzO7evYvBgwfjwYMHqFSpEvz8/HD79m0cOHAAW7ZswebNm6FSqUqiViIiIqJyzepgtnDhQgiCgL179yI8PNyy/cKFCxg3bhwWLFiAzz77zK5FEhERETkDq5fL+O233zB58uRcoQwAGjZsiEmTJnHNMyIiIqJisjqYubu7F3j5BV9fX0ilUpuLIiIiInJGVgezt956CytWrEBcXFyu7RkZGVi3bh369+9vt+KIiIiInEmR5pgNGjQo1+1bt26ha9euaNKkCfz9/ZGamoqzZ8/CZDKhQoUKJVIoERERUXlXpGD2+HXOmzRpAgAwGAyIiYkBANSpUwcAEBsba8/6iIiIiJxGkYLZ1q1bS7oOIiIiIqdn14uYZ2Vl4ZdffrHnQxIRERE5DavXMbt//z5mzZqFU6dOQafT5dvmypUrNhdGRERE5GysDmYLFizAuXPn8Nprr+HcuXNwc3NDo0aN8Ouvv+L69etYtWpVSdRJREREVO5ZfSjz9OnTePfdd/HBBx+gT58+cHV1xdSpU7F79240b94cR48eLYk6iYiIiMo9q4NZZmYmatWqBQCoVq0aoqKiAABSqRRvvvkm/vjjD/tWSEREROQkrA5mgYGBSEhIAABUrlwZqampiI+PBwD4+PggMTHRvhUSEREROQmrg1mHDh2wfPlynD9/HqGhoQgODsbGjRuRkZGB3bt3IygoqCTqJCIiIir3rA5m48ePh5eXF1asWAEAePfdd/HVV1+hefPmOHDgAIYMGWL3IomIiIicgdVnZapUKkRGRlquldmzZ09UqFABf/75Jxo0aIAWLVrYvUgiIiIiZ2B1MMsRGBiIGzduIC0tDYGBgRg+fLg96yIiIiJyOsUKZlu3bsW6detyTfQPCQnBpEmT0KNHD7sVR0RERORMrA5m27ZtwyeffIIuXbqga9eu8PPzQ0JCAg4ePIipU6dCKpXixRdfLIlaiYiIiMo1q4PZli1bMGDAAHzwwQe5tvfq1Qvvv/8+IiIiGMyIiIiIisHqszJjYmLQqVOnfPf16NEDd+/etbkoIiIiImdkdTCrX78+fv/993z3RUVFWa4KQERERETWKdKhzNOnT1u+7969OxYsWAC1Wo0XX3wRAQEBSElJwfHjx7F161bMmzevxIolIiIiKs8EURTFJzUKDw+HIAiW2zl3KWjblStX7F1niTIaTUhKyiztMkqcTCaBSuWB5ORMGAym0i6nTGIf2ob9Zzv2oe3s2YcBAZ52qorIrEgjZlu2bCnpOoiIiIicXpGCGVfzJyIiIip5xVpg9tatW1i5ciVOnTqFtLQ0qFQqNGvWDGPGjEH16tXtXSMRERGRU7A6mP3zzz/o378/pFIpOnXqBH9/f8THx+PYsWP473//i8jISIYzIiIiomKwOpgtXrwYFStWxNatW+Hp+XDSY3p6OgYPHoxly5YhIiLCrkUSEREROQOr1zE7ffo0Ro0alSuUAYCnpyfeeeedXEtrEBEREVHRWR3MZDIZXF1d890nl8uh0+lsLoqIiIjIGRVr5f8dO3bg8eXPRFHE9u3bUa9ePbsVR0RERORMrJ5jNmHCBLzxxhvo2bMnXnjhBQQEBCA+Ph6HDx/GrVu3sGnTppKok4iIiKjcszqY1a9fH+vXr8eSJUsQEREBURQhCALq1auHL7/8Es2bNy+JOomIiIjKPauD2Z49e9CmTRtERkZCrVYjLS0NXl5ecHNzK4n6iIiIiJyG1XPM5syZg4sXLwIA3NzcEBQUxFBGREREZAdWB7Pg4GBkZGSURC1ERERETs3qQ5n9+vXDJ598gvPnz6NWrVrw8PDI06ZXr172qI2IiIjIqVgdzBYuXAgA+Pbbb/PdLwgCgxkRERFRMVgdzI4ePVoSdRARERE5PauDWWhoqOV7nU6HtLQ0eHt7w8XFxa6FERERETkbqyf/A8Avv/yC/v37o1GjRmjfvj0aN26MwYMH49y5c1Y/lslkwsqVK9G+fXs0atQII0aMwN27dwtsr9frsWTJEkv7AQMG4MqVK8V5GUREREQOxepg9sMPP2DkyJHQarUYO3YsZs2ahVGjRiElJQWDBg3CmTNnrHq8NWvWYMeOHZg7dy527twJk8mE4cOHF3jNzVmzZuG7777D/PnzsXv3bvj6+mLEiBFIT0+39qUQERERORRBfPyil0/Qs2dPVKtWDcuXL8+zb9y4cUhISMDXX39dpMfS6XRo1aoVpkyZgjfffBMAkJaWhvbt2+OTTz5Bjx49crW/e/cuunbtirVr16Jjx46W9r169cInn3yC1q1bW/NSLIxGE5KSMot137JEJpNApfJAcnImDAZTaZdTJrEPbcP+sx370Hb27MOAAE87VUVkZvWI2Z07d/Dqq6/mu+/111+36rDi1atXkZmZmStQeXl5oU6dOjh9+nSe9r/++is8PT3x7LPP5mr/888/FzuUERERETkKq4NZ9erVcenSpXz33bp1CxUrVizyY8XExAAAQkJCcm0PDAy07Hv88StVqoQff/wRffr0Qdu2bTFixAjcuHHDildARERE5JisPiszZ05ZznplgYGBSElJwZEjR7By5UrMmjULDx48sLSvUKFCgY+lVqsBAHK5PNd2V1dXpKam5mmfkZGBO3fuYM2aNZg2bRq8vLzw+eef480338ShQ4fg5+dn7cuxkMmKdR5EmSKVSnJ9JeuxD23D/rMd+9B27ENyZFYHs9dffx0AsHz5cqxYscKyPWeq2tSpU3O1L+zQpkKhAGCea5bzPQBotdp8r78pk8mQkZGBZcuWoXr16gCAZcuWoUOHDtizZw+GDx9u7csBAEgkAlSqvFcwKK+8vHhtU1uxD23D/rMd+9B27ENyRFYHs/nz50MQBLs8ec4hzLi4OISFhVm2x8XFoVatWnnaBwcHQyaTWUIZYA53lSpVwr1794pdh8kkIi0tq9j3LyukUgm8vNyQlqaG0chJw8XBPrQN+8927EPb2bMPnemPeno6rA5mffr0sduTh4eHQ6lU4uTJk5ZglpaWhqioKAwYMCBP++bNm8NgMODSpUuoX78+AECj0eDu3bvo3r27TbU409lNRqPJqV5vSWAf2ob9Zzv2oe3Yh+SIrA5m9iSXyzFgwAAsXrwYvr6+CA0NxaJFixAcHIxu3brBaDQiKSkJnp6eUCgUaNasGdq0aYPp06djzpw58PHxwcqVKyGVSvHKK6+U5kshIiIislmpz3wcP348Xn31VXzwwQd44403IJVKsWHDBri4uCA6Ohrt2rXDoUOHLO1XrVqFFi1aYOzYsXj11VeRkZGBLVu2wNfXtxRfBREREZHtrF5gtjziArNUVOxD27D/bMc+tB0XmCVHVuojZkRERERkZlMwS09Px40bN6DT6WA0Gu1VExEREZFTKlYwO3nyJF577TW0aNECL7/8Mv7++29MnjwZCxcutHd9RERERE7D6mD2+++/Y9iwYVAoFJgyZYplYdnw8HBs2bIFmzZtsnuRRERERM7A6mC2fPlydO7cGVu3bsXgwYMtwWzUqFEYPnw4IiMj7V4kERERkTOwOphduXIFffv2BYA8VwBo27Yt7t+/b5/KiIiIiJyM1cHM09MT8fHx+e6Ljo6GpydPHSYiIiIqDquDWefOnbFs2TJcunTJsk0QBMTExGDt2rXo2LGjPesjIiIichpWX5Jp8uTJuHDhAl5//XX4+/sDACZNmoSYmBiEhIRg0qRJdi+SiIiIyBlYHcy8vb0RGRmJvXv34o8//kBKSgo8PT0xcOBA9OnTB25ubiVRJxEREVG5V6yLmMvlcrz++ut4/fXX7V0PERERkdOyOphFREQUuE8ikcDd3R2VK1dG27ZtIZfLbSqOiIiIyJlYHcz279+PmJgY6HQ6yGQy+Pj4ICUlBQaDAYIgWNY1q1GjBrZs2QJfX1+7F01ERERUHll9VuaECRMgl8uxdOlSXLx4ESdOnMClS5cQEREBlUqF5cuX48CBAxAEAUuXLi2JmomIiIjKJauD2apVqzBx4kS89NJLkEjMdxcEAV26dMH48eOxYsUK1KxZE6NGjcLx48ftXjARERFReWV1MIuOjkblypXz3RcaGmpZ+T8oKAipqam2VUdERETkRKwOZjVq1Cjwepi7du1C1apVAQC3b99GYGCgbdURERERORGrJ/+PGzcOY8aMQe/evdGtWzf4+fkhISEBR44cwbVr17By5UpERUVh0aJFlmtqEhEREdGTWR3MOnbsiA0bNmDVqlWIiIiA0WiETCZD06ZN8dVXX6FZs2b4+eef0b17d0ycOLEESiYiIiIqnwQxZ32LYtDpdEhNTYWfn5/lRICyyGg0ISkps7TLKHEymQQqlQeSkzNhMJhKu5wyiX1oG/af7diHtrNnHwYEeNqpKiKzYq38r9Vqce3aNeh0OoiiiNu3b8NkMkGtVuPMmTOYMmWKveskIiIiKvesDmYnT57EhAkTCjzj0sPDg8GMiIiIqBisDmbLli2DSqXC3LlzsX//fkgkEvTp0we//PILvv76a3z55ZclUScRERFRuWd1MLt27RrmzZuHrl27Ij09HTt37kSHDh3QoUMH6PV6fP755/jiiy9KolYiIiKics3qGfsmkwlBQUEAgMqVK+Pvv/+27Hv++ecRFRVlv+qIiIiInIjVwSwsLAzXrl0DAFStWhVqtRo3b94EABgMBmRmlv+zG4mIiIhKgtXB7OWXX8bixYuxbds2+Pr6ol69epg7dy5+/vlnrF69GjVq1CiJOomIiIjKPauD2fDhw9G/f39cuHABAPDxxx/jypUrGD16NG7evIlp06bZvUgiIiIiZ2D15P9bt25h+vTpltv169fHkSNHcPPmTVSrVg1KpdKuBRIRERE5C6tHzN58803s3bs31zalUokGDRowlBERERHZwOpg5uLiApVKVRK1EBERETk1qw9lTpgwAZ999hnS09MRHh4Od3f3PG0qVKhgl+KIiIiInInVwWzWrFkwGo2YOnVqgW2uXLliU1FEREREzsjqYDZv3rySqIOIiIjI6VkdzHr37l0SdRARERE5PauDGQDodDrs2rULv/32G+Lj4zF//nycOnUKdevWRYMGDexdIxEREZFTsPqszKSkJPTt2xeffPIJ7ty5g4sXL0Kj0eC///0vBg4ciPPnz5dEnURERETlntXB7LPPPkNmZiYOHTqEPXv2QBRFAMDKlStRv359rFy50u5FEhERETkDq4PZsWPHMGHCBFSuXBmCIFi2u7q6YujQobh8+bJdCyQiIiJyFlYHM61WCx8fn3z3SaVS6PV6W2siIiIickpWB7P69etjx44d+e47cOAA6tWrZ3NRRERERM6oWCv/v/3223jllVfQoUMHCIKAgwcPYtWqVThx4gTWr19fEnUSERERlXtWj5g1a9YMmzZtgpubG9avXw9RFLF582bEx8dj3bp1aNWqVUnUSURERFTuFWsds+bNm2Pnzp3QaDRITU2FUqmEh4eHvWsjIiIicipWj5j16tULmzdvRkJCAhQKBYKCghjKiIiIiOzA6mBWoUIFLFmyBB06dMCwYcNw4MABaDSakqiNiIiIyKkIYs4KsVZIT0/HDz/8gEOHDuHkyZNwdXVF165d8corr6B169a51jcrC4xGE5KSMku7jBInk0mgUnkgOTkTBoOptMspk9iHtmH/2Y59aDt79mFAgKedqiIyK1Ywe1RiYiIOHz6Mw4cP49y5c/D398fx48ftVd9TwWBGRcU+tA37z3bsQ9sxmJEjs/pQ5uMSExORkJCAtLQ0GI1GeHt726MuIiIiIqdTrLMy7969i4MHD+LQoUP4559/4O/vjx49euDTTz9FeHi4vWskIiIicgpWB7O+ffsiKioKCoUCXbt2xYwZM9C6dWtIJObBN1EUy9wcMyIiIiJHYHUw8/HxwcKFC9GtWze4ublZtsfFxeHbb7/F7t27cezYMbsWSUREROQMrA5mGzZsyHX7f//7H3bu3Injx4/DYDCgYsWKdiuOiIiIyJkUa45ZUlISdu3ahW+//Rb379+HUqlE79698corr6BZs2b2rpGIiIjIKVgVzP744w988803OHLkCIxGI5o2bYr79+9j9erVaNGiRUnVSEREROQUihTMNm/ejG+++Qa3bt1C5cqVMXr0aPTu3Rvu7u5o0aIFJ/sTERER2UGRgtnChQtRq1YtbNmyJdfIWHp6eokVRkRERORsirTAbPfu3XHnzh2MHDkSo0ePxk8//QSDwVDStRERERE5lSKNmC1ZsgQZGRk4cOAAvvvuO4wbNw4qlQpdunSBIAg8lElERERkB8W6Vubff/+N3bt348CBA0hMTERYWBi6d++O7t27o0aNGiVRZ4nitTKpqNiHtmH/2Y59aDteK5McmU0XMTcYDDh27Bh2796NEydOwGg0ombNmti/f789ayxxDGZUVOxD27D/bMc+tB2DGTmyYq1jZrmzTIauXbuia9euSEhIwJ49e7Bnzx571UZERETkVIo0+b8o/P39MWLECBw6dMheD0lERETkVOwWzIiIiIjINgxmRERERA6i1IOZyWTCypUr0b59ezRq1AgjRozA3bt3C2y/f/9+1KpVK8+/e/fuPcWqiag4clbW4Qo75ZsgCJBKJVxKiagYbJr8bw9r1qzBjh07sHDhQgQHB2PRokUYPnw4Dhw4ALlcnqf9tWvX0KJFCyxdujTXdl9f36dVMhFZSSqVQOlqhIurC5AZD2+lN/RaDTK0UhiNPLOwvHj4PrtC1KRCUDjm+8w/EMiRlWow0+l02LhxI6ZMmYKOHTsCAJYtW4b27dvjxx9/RI8ePfLc5/r166hVqxYCAgKecrVEVBxSqQQ+nhIIJ1ZAOLkO0KRAUPjApeVI+LR7FynpcKj/tKl4Hn+fBU0K4GDvM/9AoLKgVA9lXr16FZmZmWjdurVlm5eXF+rUqYPTp0/ne59r166hevXqT6vEcoV/JZZ/jngISelqhHBiGYTjnwKaFPNGTQqE459COLEMSldjqdZX1jjq77Gjv885wdHlVASExTWBRTUgLK4Jl1MR8PGUQCot9Zk9RABKecQsJiYGABASEpJre2BgoGXfo1JTUxEbG4szZ85gx44dSE5ORoMGDTB16lRUrVq12HWIogitLv8PDYkEcJFJLbcLageYPyjlLsVsqzcCBS31KwCuxWyr0xshkUjg4WqEi9wVmuQYuLp6QSIakKmVQiYVcrUtbLlhV/nDx9UbjDAV8gemNW3lLg+DhN5ggslUcBHWtHVxkUCS3dZgNMFotL2t0STC+MhzPvFxZRJIJEWrQSYTIJVIrG5rNJkgioL5PXZ1hahJg+DuBb1Wi0ytFIAImfRhW4Oh4MeVSgVLW5NJhL6QxTelEsDFmAlBkwxkJUGvToFEkwJBkwKJNhWCJhkS0QBjj08g/LEeLtn3M4kCdDm3/tgEscU4COc2wuTqBZN7ECTKIEi8QyC6KGECoNcXXINEIsBFZq5XFEXo7NBWEARIZQJkEgly1t8u7Hf5aX1GGEwiPOT5/x4bTSabPiMK/b2XmszvpSYJhoxEiOpkSLJvC5pkSNTJkMAIY58IyE+ue/i4ogtMyP58+WMTxObj4BbZG9CpIUpdoZBLIEpdIUrl0AluMEoUEKWugExu/pq9T5TK4SqXAzIFRKkcesEVBol5H6RyQKrI1VauUGS3VUAvymDKHn/wcjdA+9+1EE4sy65QDrk6FZLjn5pfZ5PRSFYX3A/WfJ4Q2aJUg5labf4teHwumaurK1JTU/O0//vvvwGYP1QXLFgAjUaDzz//HG+++SYOHDgAf3//YtWRkKrBf5Yez3dfwxp+mNy/seX2qCX/LfADPTzMB+8Nama5PWHl/5Cepc+3bdUQL8we1sJye9rnvyEhVZNv21B/DywY9XBU8cP1J3E/If8rFfh7K7B0XDvL7blbzuDWg7R823p5uGDN5I6WcPPZjnO4+m9Kvm3lLhKsn97JcnvFrgu48E9ivm0BYMsHXSzff77vL5y+Eldg2y+nPQdXF/OH58ZDV3DiYnSBbSPefRZeHuafl+0/XcfRswWf9LFkbFsE+LgBACL/ewPf/3GnwLbzR7ZCxQAlAGD/r7ew93+3Cn7cCc8i2EcBAPjh9L/45ug/BbadOaAJalcxz3/875/3seXwtQLbTurXCI1qmn+Gf78cgy8PRBXYdmyf+mhRJwgAcP5qAlbtulhg23d61kW7BuY/fv76OwlLv/mzwLaD23rh+Ro6CJpkRN3Lwtxf/Qps+7brN+gr/z8AwHVjVUzOmg3AO/vfI079jDfkz+JNV/Pi03dNFTA2a4F5XwaAj34GUCW7sR69Xb7AUMVOiC7uiHYNx3+iJxZYQ+cmIRj8Ul0AQFqmDmOX/VJg23YNQvBOT3Nbrc5Y4O88ALSp44cZg1vAoNMiSy/F0IU/F9j2aXxGSCQCZqz8H+JSCviMCPDAgpFF/IzwkmP5wFAI6iRINEmYd8iEmyku+bb1EtKxXTnGcntm1kz8ZawNwB1AaK62rlH/xS5FiuX2AvU4nDE2Mt/IAPDxzwDGWfYf8Bxk+X6Zeix+NTz8PHxcpHI4FIIuu+0I/Gxon71Hl/3voW0eY+AtSQcAfK4ZhEP6Lo/srQNgveXWeo9JCBISIJxch2/SemDPLwX/3hf0GXFgySsF3oeoOEo1mCkU5v/cdDqd5XsA0Gq1cHNzy9O+WbNm+P3336FSqSyjJhEREejYsSO+++47vPPOO3av0cVFBpXKw3K7sENEMhdpkdtKsy8JkiNnVCU/EuljbQsZcpdIhFxtpYUd7tCr4e0GQO5hqb8ggpD7cV1cCv/RebSt/AltfXzcoXA1t5HLn9zWW+kKAHB1Lbytt7c7VCp3AIBCkf9/PDm8vNwsNbu55T3pJL/2AOD+hLaeng8f90ltlUrXh23dXQtt6/Fo2yeU62ZMhirqEKBOgvKuAKBRwW3PrILnxaPm7w3hAN4rsK2QMyQjcwMUgUBWIUXIFIXsfLyt+QUJ+ixItf8W2tT10lao7h0APEMgcasCoH+BbeXyh7/LGq2h0McVrn8PYfFbcGk5Et7tJxXa9ql8RuiyAG0agPzfbAlMUCX8DmQlAlmJkGj8AOTf55L0+/De8rCfJJmzAFQroIqcUSEBcPcF9O5AgYN8AqDweXgY80le3woYtIBRCxyXAwWfjA/U6QWIWeb2t4KAwp5CKMYhSU2KuY5CWPsZQVRcNl0r01YXL17Ea6+9hp9++glhYWGW7W+88QZq1aqFWbNmFelx+vbti0aNGuHDDz8sVh0GgwkJiRn57hMkgLwohykEQJp9qCSnR0vlUKYhC276ZAjqBEh0aZDV7AisaJT/h6XCB65T/0Jquh4iBKsOZeoMRohFPJT5pLZl6VCmVCbAV6VEVqYGBoPp6R7KNGghZMVBkhEDuToaLlnR5ve40wwYljUq8D2WTTwH2aqGQFYijKIE+gL+HhMlMkgVnpC6ecGkUMHg6gud3BeiwgcmVx+IChVEhY/5n6s3JB4qSD18AZlboYc9PVxNcD37OeT/Wwgg96FMsd0kGJr/B5nah/+ZSqUCZCYNJBkxQEYM9GmxkGTEQpIVB0lmLITMWEgy4yDJjIPMmAUXwRyyRBHQFhBcREECibsvpEp/mJTBMLkHQ+0WDJNHEEweQVBUbgzZha0QTpjP9pZAhFwwj2SJHaYjo/Foc40mI2DSAUYdYNIDRh2kJj3kggGCSQcY9dBqtNn7dRCy2wjGnLY68+MatRCMemj0RghGLWDUQzCa7w+j+X6CSQeFqAZc3CB7fQO0SxoAmrxHEqDwAcadhWJNI3MwA6ARCw8Orq6uMLn5QlT4QuMaAKPCF6JCBZNCBdFNBdHNFyZX8/euXv4QXX0AibTQzwgPVxOU59eY55gh96HM/N7nEv2MEI2AQQuDVg2jQQuJyQDPCpUhPPZZKIceEkEEFD7QTbyGpDRdgZ+tBX1GBAd5FVw4UTGU6ohZeHg4lEolTp48aQlmaWlpiIqKwoABA/K0/+abb7B06VIcO3YM7u7mkZCMjAzcvn0br776arHrELJDVUEevcjt4+0enuWjyHN6eGGPmedxBQHIr7lRD4k6EWJWIiTqBEjUCVCqkyBRJ5jDlzoRkqzsr+oECIZHJkkE1gHC6gHauPwfWxsHZMTA+9vBMGoyYPIMhdGzEoxeFWHyrAijZ0UYPStBdA8ABCFXvRIIhZ46Yk1b8wecCEEwz28yCSIK+nshpy1gfkmF9bHJKML0yKesLW0fXQZAUCdA6eYNvVaPDK0Uhf2PYjKJuf4TKbAGvRqStGgIGdGQZEbDJSMG0oxoSLJvSzOiIVEn5L1fYB1APRyyQt5jUZcKbd23YNRmmkOWQmX+Tzg7bJlyApeLssgzygWY3wUDAGS/1wW9Nr3JBR7PToQoESGcXAeJJgUKhTvEliMhtnsX6nQjpJJH+lAEDIIC8Kxi/hdiHqTJ82eOKELQpkCSGZv9LwbSR743/4uFJCsegqgDsh6Y/8WZD/taxpPc/YCJl4CzawFB9/izQDi5Dsq2E6D8siGEzPgi9U9R5T0ukI/AOkBmPBTa+ILfY0MK9JXaQMxMyQ5cKpgUvjC5+UFU+MLk5psdusxf06WFj8gC5qcSAOgBwATAZDIHk4J+hE0uENu9a77vyXWQZ5+VWdD7XJzPiJy6CvtdNreVABI3wM0NOfFPKhrh0mqIJTg+Smw50hycC3l91nyeENmiVIOZXC7HgAEDsHjxYvj6+iI0NBSLFi1CcHAwunXrBqPRiKSkJHh6ekKhUODZZ5/F4sWLMW3aNEyYMAEajQZLly6Fr68v+vTp89TrL9bp4aIJgjb1YZB6JFQ93PYwhEm0+fyF/ASi1BUmN3+YFP6QKQMhFHR4QeED0d0fQuq/kGUlAsl/F/h4Rs9QmDwrPfzqZQ5uJs+KMHkEFe/wQTZHX/vI1uUeBF2GJWBJMqLzBq6MaEi0KUWqRZS6wuQRDKMyBCZlCEy+NeCmDCr0PYZHINJbTi8w7JY0o9GElHRA2WIsXNpPgaBNg+jqBb1Gg4x0Y/HfY0GAqFDBqFDB6BdecDuT0fy7lE9ok2bGQKJQQpqZYP79zY8mBUJWAuARADwWzESJi3kSusQle8L6o7fNk9NFiUv25HSXR27LIUrk2e1dLe1z39e8T5AroXzSe6wMRmrXtaX2HgN532doUgGFt+3vs51kaKXweSQ4IldwnISMdCPMCZSodJXqoUwAMBqNWLp0Kb777jtoNBo0b94cH330ESpWrIh79+6hc+fOWLBggSV4Xb58GUuWLMHFixchiiLatm2LmTNn5jmz07oaTEhKyn+ibGG83UXzqdf5/QXWYRpMNbtDf3zZw8ClToREnWgeZreCKEggKvxgcvczBy4381fRzT/PNpObP+Dibhn5KLzG6dA3H4306H8hTb8PafpdSNLvQZp+D5K0e+bbmTEQCjvGAECUyGFShsDoVckS1syjbtm3PYIBSf7z1x6GnmX5fFi+i5R0U6l/oD+pDw2NhiDrxilzwLIErpiHYUyXXqTnEWVuMCormAOXMgRGj5A834sKVZ5RrSe+xy3GIjXLMf66d3GRwMfHAykpmYWebfk0CYIAPx+5eQmFgv6AmXIdyQ/uwyTIHgYnictTW7OiLL3HgLlPJRIBJlPBo9+l4dEjHLn+QNAV/4/AgABPO1dJzq7Ug5kjKE4wK8qHOSZFAcvrW+Z9PMrk6m0+zJArVPnB5O6fHbqyt7n7Q3T1LvaIVOHBZxJSnvSXrFFvDhqPhDVp+n1I0u9CmnYPkowHTwyaokQGk7LCI6Nu2aNtXhXhXrUJXM59aft/OKLJMjcHRr1lvo/5tnk+D0zZ83hM+uzbuke+Ptr+kblBMjncOk+GsCS8WO9zDpPcKztkBecbuEzKEIhyr2L9R2/ze/wUybIntCcnZ+Y6lFXaHD34lKX3uCyw5x8IDGZkbwxmKF4wk0ol8JFnQbK4RoFtxElXoL78EwxG0TKaJbqZ531A+vTO6imJvxItTAbzIaGc0ba0e+bQln4f0rS75uBmyn85AMvcnqV1Ch6pmHQFhm39zHN78gQq/cOJ01aOQhZZYB3gjZ3AigYFNhEnXoLx0HswarPyDVwmj2CIcmXJ1Jft0fc41yEke7zHduSowawsBJ8S/T12Mvb8OWQwI3sr9WtlllUmkwhB4V3w6eEKH8DdH1k1+5b6UL7RaEJqlgAXvR4+Pv5ITcmEXi/ALvMpJDKYPENh8gzNf7/JaD6bLv0epGmPjLal34NErijC3J54uGgSgMQrVpUlSnIfchKlLoBEnv318fk/Lg/n+0hccm9XqOD2hHl6UAYjpVvpz+9JzRIgqHWQSNxh0ugginZ6j52Ao8+PyqmxxH6PichhMJgVkyiK0Gs1cGk5ssCzfPRaTfZ/jo4hJzc81fwgkVpGjgwhzXPtEgQBfp7yQsOtqAxCeusPYRKRT3B6JEBZJlC7ZM/9sd/lVWQ6fZl5n0VR5IrkxVRWwm2p/B4T0VPDYGYDnuVjm6KFWx20Fdvnc++nh++zc2G4JaLSxDlmKP5ZmUDZmdsDOOb8nrIwtyenTs7vsZ0j/gyWNexD23GOGTkyjpjZqKwc/nBUZWFuD8D5PURE9HQwmNkJD38UX1kKt5zfQ0REJYnBjBwGwy0RETk7+526RkREREQ2YTAjIiIichAMZkREREQOgsGMiIiIyEEwmBERERE5CAYzIiIiIgfBYEZERETkIBjMiIiIiBwEgxkRERGRg2AwIyIiInIQDGZEREREDkIQRV6OWRRFmEzO0Q1SqQRGo+NdHLwsYR/ahv1nO/ah7ezVh1IpxzfIvhjMiIiIiBwEoz4RERGRg2AwIyIiInIQDGZEREREDoLBjIiIiMhBMJgREREROQgGMyIiIiIHwWBGRERE5CAYzIiIiIgcBIMZERERkYNgMCMiIiJyEAxmRERERA6CwYyIiIjIQTCYERERETkIBjMnkJKSgo8++gjPPvssmjRpgjfeeANnzpwp7bLKrFu3bqFx48b47rvvSruUMmfv3r146aWXUL9+fXTv3h3ff/99aZdUphgMBqxYsQLPPfccGjdujLfeegt//vlnaZdVJqxbtw4DBw7Mte3KlSsYMGAAGjVqhE6dOmHLli2lVB3RQwxmTmDSpEk4f/48li5dit27d6N27doYNmwYbt68WdqllTl6vR5TpkxBVlZWaZdS5uzbtw/vv/8+3nrrLfzf//0fevToYfnZpKL5/PPPERkZiblz52Lv3r2oWrUqhg8fjri4uNIuzaFt374dy5cvz7UtOTkZQ4YMQVhYGHbv3o0xY8Zg8eLF2L17d+kUSZSNwaycu3PnDn799VfMmjULzZo1Q9WqVfHhhx8iMDAQBw4cKO3yypxVq1ZBqVSWdhlljiiKWLFiBQYNGoS33noLYWFh+M9//oM2bdrg1KlTpV1emXHkyBH06NED7dq1Q+XKlTFjxgykp6dz1KwAsbGxGDVqFBYvXowqVark2vftt9/CxcUFc+bMQfXq1dG3b1+8/fbb+OKLL0qnWKJsDGblnEqlwhdffIH69etbtgmCAEEQkJaWVoqVlT2nT5/GN998g4ULF5Z2KWXOrVu3cP/+fbz88su5tm/YsAEjR44sparKHj8/Pxw7dgz37t2D0WjEN998A7lcjvDw8NIuzSFdvnwZLi4u2L9/Pxo2bJhr35kzZ9CiRQvIZDLLtlatWuH27dtISEh42qUSWTCYlXNeXl7o0KED5HK5ZdsPP/yAO3fuoH379qVYWdmSlpaGadOm4YMPPkBISEhpl1Pm3Lp1CwCQlZWFYcOGoXXr1njttdfw888/l3JlZcv7778PFxcXdO7cGfXr18eyZcuwcuVKhIWFlXZpDqlTp05YtWoVKlWqlGdfTEwMgoODc20LDAwEAERHRz+V+ojyw2DmZM6dO4eZM2eiW7du6NixY2mXU2bMmjULjRs3zjPiQ0WTkZEBAJg+fTp69OiBjRs3om3bthg9ejR+//33Uq6u7Pjnn3/g6emJ1atX45tvvkGfPn0wZcoUXLlypbRLK3M0Gk2uP1gBwNXVFQCg1WpLoyQiAIDsyU2ovDhy5AimTJmCJk2aYPHixaVdTpmxd+9enDlzhnPybODi4gIAGDZsGHr37g0AqF27NqKiorBp0ya0bt26NMsrE6KjozF58mRs3rwZzZo1AwDUr18f//zzD1atWoU1a9aUcoVli0KhgE6ny7UtJ5C5u7uXRklEADhi5jS2bduGcePG4bnnnsPatWstfxnSk+3evRuJiYno2LEjGjdujMaNGwMAPv74YwwfPryUqysbgoKCAADPPPNMru01atTAvXv3SqOkMufChQvQ6/W55osCQMOGDXHnzp1SqqrsCg4OznM2a87tnJ9XotLAETMnsGPHDsydOxcDBw7E+++/D0EQSrukMmXx4sXQaDS5tnXr1g3jx49Hz549S6mqsqVu3brw8PDAhQsXLKM9AHD9+nXOjyqinPlQ165dQ4MGDSzbr1+/nueMQ3qy5s2bY+fOnTAajZBKpQCAP/74A1WrVoWfn18pV0fOjMGsnLt16xbmz5+Prl27YuTIkbnONlIoFPD09CzF6sqGgv569vPz41/WRaRQKDB8+HCsXr0aQUFBaNCgAf7v//4Pv/76KzZv3lza5ZUJDRo0QNOmTTF9+nR8/PHHCA4Oxt69e/H777/j66+/Lu3yypy+ffti/fr1eP/99zF8+HBcvHgRmzdvxuzZs0u7NHJyDGbl3A8//AC9Xo+ffvoJP/30U659vXv35tIP9NSMHj0abm5uWLZsGWJjY1G9enWsWrUKLVu2LO3SygSJRILPP/8cy5cvx8yZM5GamopnnnkGmzdvzrMUBD2Zn58f1q9fj08++QS9e/dGQEAApk2bZpkDSVRaBFEUxdIugoiIiIg4+Z+IiIjIYTCYERERETkIBjMiIiIiB8FgRkREROQgGMyIiIiIHASDGREREZGDYDAjIiIichAMZkTlxIwZM1CrVq1C/w0cOLDEnv+7775DrVq1MG/evHz3r1q1CrVq1Sqx5yciKg+48j9ROTF69Gj079/fcnvNmjWIiopCRESEZZtSqSzxOrZv344XXngh1zUxiYioaBjMiMqJsLCwXBcE9/X1hVwuR6NGjZ5qHUqlEu+99x72798PhULxVJ+biKis46FMIifz66+/4s0330TTpk3RsmVLTJ48GdHR0Zb9OYckL1y4gN69e6NBgwZ4+eWXcfjw4SI9/vTp0/Hvv/9i6dKlJfUSiIjKLQYzIieyd+9eDB06FCEhIVi6dClmzpyJ8+fPo1+/fkhMTMzVduTIkejcuTMiIiJQtWpVTJw4EcePH3/ic7Rq1Qr9+vXD1q1bcfbs2ZJ6KURE5RKDGZGTMJlMWLx4Mdq1a4clS5agQ4cO6NWrFzZv3oykpCRs2LAhV/uBAwdi7NixePbZZ7FixQqEh4dj9erVRXquadOmISQkBO+99x40Gk1JvBwionKJwYzISdy6dQvx8fHo0aNHru1hYWFo3LgxTp06lWt77969Ld8LgoCuXbvi4sWLRQpaHh4e+OSTT3D79m0sW7bMPi+AiMgJMJgROYmUlBQAgL+/f559/v7+SE9Pz7UtMDAw120/Pz+Iooi0tLQiPV/r1q3Rr18/bNmyBefOnSte0URETobBjMhJ+Pj4AAASEhLy7IuPj4dKpcq1LSfI5UhISIBUKrU8TlFMmzYNwcHBmDlzJg9pEhEVAYMZkZOoWrUqAgICcPDgwVzb7969iz///BNNmjTJtf3IkSOW70VRxI8//oimTZtCLpcX+TmVSiXmzZuH27dv45tvvrHtBRAROQGuY0bkJCQSCSZNmoSZM2di8uTJ6NmzJ5KTkxEREQFvb28MGTIkV/vPPvsMWq0WVatWRWRkJG7cuIGvvvrK6udt27YtXnvtNURGRtrrpRARlVsMZkROpE+fPvDw8MC6deswZswYKJVKtG/fHpMmTUJAQECutrNmzcK6detw9+5d1KlTBxs3biz2av4zZszAiRMncq2XRkREeQmiKIqlXQQROY7vvvsOM2fOxNGjR1GxYsXSLoeIyKlwjhkRERGRg2AwIyIiInIQPJRJRERE5CA4YkZERETkIBjMiIiIiBwEgxkRERGRg2AwIyIiInIQDGZEREREDoLBjIiIiMhBMJgREREROQgGMyIiIiIHwWBGRERE5CD+H05KwTTDWcyzAAAAAElFTkSuQmCC", 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " blank_ax.set_xticklabels(ax.get_xticklabels())\n", + ":8: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " blank_ax.set_yticklabels(ax.get_yticklabels())\n" + ] } ], "source": [ "def make_pr_bias_graph(estimators):\n", - " grid = sns.relplot(\n", + " ax = sns.lineplot(\n", " data=df[df.estimator.isin(estimators)],\n", " x=\"top_n\",\n", " y=\"pr_overestimate\",\n", " hue=\"estimator\",\n", " hue_order=estimators,\n", " palette=palette,\n", - " kind=\"line\",\n", " ci=None,\n", " marker=\"o\"\n", " )\n", - " for ax in grid.axes[0]:\n", - " ax.axhline(.5, linestyle=\"--\")\n", - " grid.set_ylabels(\"Average probability true effect < point estimate\")\n", - " grid.set_xlabels(\"Top N\")\n", - " grid.fig.savefig(f\"plots/pr_overestimate_{estimators}.png\")\n", + " ax.axhline(.5, linestyle=\"--\")\n", + " ax.set_ylabel(\"Average probability true effect < point estimate\")\n", + " ax.set_xlabel(\"Top N\")\n", + " plt.savefig(f\"plots/pr_overestimate_{estimators}.png\", bbox_inches=\"tight\")\n", " plt.show()\n", + " return ax\n", "\n", "estimators = []\n", "for estimator in (\"conventional\", \"conditional\", \"hybrid\"):\n", " estimators.append(estimator)\n", - " make_pr_bias_graph(estimators)" + " ax = make_pr_bias_graph(estimators)\n", + "blank_ax = make_blank_figure(ax)\n", + "plt.savefig(\"plots/pr_overestimate_blank.png\", bbox_inches=\"tight\")\n", + "blank_ax.axhline(.5, linestyle=\"--\")\n", + "plt.savefig(\"plots/pr_overestimate_line.png\", bbox_inches=\"tight\")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " blank_ax.set_xticklabels(ax.get_xticklabels())\n", + ":8: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " blank_ax.set_yticklabels(ax.get_yticklabels())\n" + ] } ], "source": [ "def make_coverage_plot(estimators):\n", - " grid = sns.relplot(\n", + " fig, ax = plt.subplots()\n", + " sns.lineplot(\n", " data=df[df.estimator.isin(estimators)],\n", " x=\"top_n\",\n", " y=\"coverage\",\n", " hue=\"estimator\",\n", " hue_order=estimators,\n", " palette=palette,\n", - " kind=\"line\",\n", " ci=None,\n", - " marker=\"o\"\n", + " marker=\"o\",\n", + " ax=ax\n", " )\n", - " for ax in grid.axes[0]:\n", - " ax.axhline(.95, linestyle=\"--\")\n", - " grid.set_ylabels(\"Average probability that true effect is in 95% CI\")\n", - " grid.set_xlabels(\"Top N\")\n", - " grid.fig.savefig(f\"plots/coverage{estimators}.png\")\n", + " ax.axhline(.95, linestyle=\"--\")\n", + " ax.set_ylabel(\"Average probability that true effect is in 95% CI\")\n", + " ax.set_xlabel(\"Top N\")\n", + " plt.savefig(f\"plots/coverage{estimators}.png\")\n", " plt.show()\n", + " return ax\n", "\n", "estimators = []\n", "for estimator in (\"conventional\", \"conditional\", \"projection\", \"hybrid\"):\n", " estimators.append(estimator)\n", - " make_coverage_plot(estimators)" + " ax = make_coverage_plot(estimators)\n", + "blank_ax = make_blank_figure(ax)\n", + "plt.savefig(\"plots/coverage_blank.png\")\n", + "blank_ax.axhline(.95, linestyle=\"--\")\n", + "plt.savefig(\"plots/coverage_line.png\")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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338a8efMAPL8tirW1NTZt2oQ333yzUhMmIiIi+reJ2rhSqVTi0aNHsLS0hLGxcWXmVSVxE7GK4WZs0mEtpcNaSoN1lA5rKR2dbVypp6cHOzs7SZ6YiIiIqLqQpu0iIiIiqsHYMBERERGVgQ0TERERURnYMBERERGVQdSi70JPnz7FpUuXkJWVheI+ZDdw4MCK5kVERERUZYhumP7v//4Pfn5+yMnJKbZZEgSBDRMRERHVKKIbpuXLl8PZ2Rlz586Fvb099PR4VY+IiIhqNtEN061btxAeHg53d/fKyIeIiIioyhF9eqhu3brIzMyUNAmlUonVq1fD09MTbdu2xbhx45CYmFhi/J07dzB+/Hi4u7uja9euWL16NQoKCtTHFQoFWrduDRcXF41/a9askTRvIiIiejWIPsM0YcIErF27Fq1atYKTk5MkSYSHh2PPnj0ICgqCg4MDgoOD4evri8jISMhkMo3YjIwMDB8+HM7Ozti+fTuys7Pxn//8B8nJyVi6dCmA5w1Vbm4uDh8+jFq1aqkfa2pqKkm+RERE9GoR3TBFRkbi4cOH6NOnD2xtbYvcU04QBJw8eVLr+fLy8rBlyxbMmjUL3bt3BwCEhobC09MTJ06cQP/+/TXiDx48iKysLKxatQq2trYAgMWLF2PYsGGYOHEinJyckJCQAHNzc7i6uop9eURERERFiG6YHBwc4ODgIFkC8fHxePbsGTp37qwes7S0hJubG6Kjo4s0THfv3oWzs7O6WQIANzc3AEBMTIy6YWrSpIlkOQKAUqVCbp6i2Bsh6ukBhgb66u9z8xQlziMIgMywnLH5CqCkWyULgFE5Y/PyFSjtFsxGsvLF5hcooHyhXAqlCjm5Beo6lhb7MpmhHgRB+G+sEkplyUmIiTU01IPef2MLFEooFBLFGuhBT0/6WAMDAfr//aBFgUJZ4s/ky7EKpRIFBSXPq68vwEBffKxSqUJ+KTcHLXesSoX8fGli9fQEGBo8j1WpVMh7KfbFn0ulUlVqrOa82v/ev2rvEaX99xDze/+qvke8/F5ZkfcIbWNr8nuEVEQ3TIGBgZImkJycDABwdHTUGLezs1Mfe3k8JSUFCoUC+vrPf5nu3bsHAEhLSwMA/PnnnygoKMDYsWMRHx8Pe3t7jBw5EgMGDCh3nimPszBu2Zlij7VpWgszh7ZTf//J8rMlvtG6NrDG5yP+t2B+6ur/w9Os/GJjGzta4suxHdXf+6/7BY8ycoqNrVfbDIGf/K/p/M+mKNx79KzY2NpWxlgxpYv6+0XbY3D7gbzYWAtTQ6yd0U39/bI9VxD/T3qxsTJDPWya3VP9/arvruHazbRiYwFgx/ze6q/XHf4d0XEpJcZ+7d8DRobPf/i3HI3Dz9cflBgbNr0rLM2eX8rd/dOfOPVrUomxyye/iTrWJgCA/Wdv4diluyXGLp3QCU51zAEARy7cxqH/u11i7IIxHnCuawUAOB79D/adulli7Fzv9mjR6PkfAGd/u4cdPyaUGDtjSFu0bVYb+vp6OPtrElbtu1pi7GSvVujoZg8AuBKbirADN0qMHfeeGzzb1AUA/P7XY6zY91uJsSP6uaC3e30AQNydxwjcdaXE2CG9muLdzo0AAH/fz8CCLdElxg70bAyvbs//0ElKzcTnGy6VGPt2p4b4uHczAEBqejZmhl0oMbZXByeMfPv52Wb5szxMDj1fYmyX1o4Y/35LAM8blU9XnCsx1qOFHaYMbq3+fkzQ6RJjX4X3iMI7wi//5jfE3X1SbCzfI/6nst8jAODiH8n4OjK2xNhX4T3iv/2rJLRqmO7fv486derA0NAQ9+/fLzO+bt26WieQnZ0NAEXWKhkZGSEjI6NI/Ntvv43w8HAEBgZixowZyMrKwuLFi2FgYID8/OdvKn/99ReUSiX8/Pzg4OCAc+fOYe7cucjPz8cHH3ygdW7aMjQ0gI2Nmfp7oZT/QgaG+lrH6hvoacQW/oVRHD39l2JL6az19ASNWH2DkmMFQTPW4IW/OsuKNTQs/cfrxVhZGbHW1qYwNnoeI5OVHWtlbgQAMDIqPdbKyhQ2Ns/XthkbG5Yaa2lpos7ZxERWaqyFxf9iTSWMNTc30qhbacxeiDX7bz1KYmr6v1hz86elx5rI1LEWadnaxz7NKzXW5IVYeW7JZ1WA5/+tCmPzVKW/IxoZ/e/3U6+MnzOZ7H+xObkFpce+9HtfmlfpPUJMLN8jKvc9wtS09N/7V+U9QiqCqrjdJ1/SokUL7Nu3D61bt4arq2upv8AAEBcXp3UCx48fh5+fH65du6axHmrq1KnIy8vDunXrijzm9OnTCAgIQFpaGkxNTTFlyhRs2rQJn376KYYPH46cnBwoFAqYmf2vWF988QUuX76MY8eOaZ3bi5QqFR6lZUJZzOlNQQ+QvUKn27WOLVBA9cIf0Xr6AiwtTCB/mg2lQlVq7Mtq0un2isQWnkLX19eDqZkRHj95VuzP5IuxQM0+3V7RS3Iv/lxCBa0vyYn6vX8F3iP09fVgaWmCR2mZJV4mLoxVz1vG7/2r+h7x8nslL8kVE6vle4SVlYlk+0VqdYZp6dKlqF+/vvrrshomMQovxaWkpKBBgwbq8ZSUFLi4uBT7mJ49e6Jnz55ISUmBtbU1CgoKEBQUpM7x5YXoANC8eXMcOXKk3HnqCQIM9fVQUMJv7ItvEPql/JVXoVhBAEoJL2+sXmXFQtDYuMJAXw/GRgbIznpex9JiX/b8l/75L6iA0usmJlapUEH5wv81JItVqjTehKWKVSmBgv8u5DDQ1yv1Z/LF2LLmhUrEz2V1i0XpsRo/lwXKf+d3uQa/R+jrCVCVkrOY3/tX9T3i5ffK8r5HSBlbJX6XyxFb9ikh7WnVMA0aNEj9tZeXl3TPDsDV1RXm5uaIiopSN0xyuRyxsbHw9vYuEh8TE4NVq1Zh69atsLOzAwAcPXoUJiYmaN++PeRyOXr37o05c+Zo5Hrjxg00a9ZM0tyJiIjo1VCum+9KSSaTwdvbGyEhIbC1tUW9evUQHBwMBwcH9O3bFwqFAo8fP4aFhQWMjY3h7OyMhIQEfPXVVxgxYgQSEhKwePFiTJgwAebmzxfbderUCaGhoahVqxYaNmyIEydO4MiRI9iwYYOOXy0RERFVR1qtYapsCoUCK1aswIEDB5CTkwMPDw8EBATAyckJSUlJ6NWrFwIDA9VnjK5cuYKgoCAkJCSgTp068Pb2xqhRo9TzZWZmYs2aNTh+/DjS0tLQpEkTTJ48Gb179y4hA+08efKs1GvzVDqD/y5QZR0rjrWUDmspDdZROqyldGxtzdSf4KyoKtEwVRf84a0YvglIh7WUDmspDdZROqyldKRsmKTd1YmIiIioBpKkYUpNTcUff/wBhaL0fRGIiIiIqiPRDVNmZibmzp2L3bt3AwCOHTuGHj164IMPPkD//v3x4EHJu6sSERERVUeiG6bly5fj+PHjsLJ6vqV7SEgIXF1dERYWBgMDA4SEhEieJBEREZEuid5W4NSpU5gzZw769++P33//Hffu3YO/vz969eqFgoICfPHFF5WRJxEREZHOiD7DlJ6eDmdnZwDAuXPnYGBggDfffBMAYGVlhdzcXGkzJCIiItIx0Q1TvXr1kJDw/E7JJ0+eRNu2bdUbRp47dw5OTk7SZkhERESkY6IbpqFDhyIoKAjvvPMO4uLiMGzYMADA5MmTsW3bNgwdOlTyJImIiIh0SfQappEjR6JWrVqIjo7G5MmT8c477wAADA0NsWDBAgwZMkTyJImIiIh0iTt9i8BdVyuGu9dKh7WUDmspDdZROqyldKTc6VurM0xhYWH48MMPYW9vj7CwsFJjBUHApEmTJEmOiIiIqCrQumHq2rUrGyYiIiJ6JWnVMMXHxxf7NREREdGrgDffJSIiIioDGyYiIiKiMrBhIiIiIioDGyYiIiKiMkjeMCkUCqmnJCIiItIp0Q1Tr169Svyk3PXr1/HGG29UOCkiIiKiqkSrbQW+//57FBQUAADu3buHn376qdim6eLFi8jPz5c2QyIiIiId06phunHjBrZv3w7g+caUa9euLTF29OjR0mRGREREVEVo1TDNnDkTI0aMgEqlQu/evREWFoYWLVpoxOjr68Pc3Bzm5uaVkigRERGRrmi1hkkmk6FevXpwcnLCqVOn0K1bNzx9+hT16tVDvXr1YGRkhISEBBgbG1d2vkRERET/OtGLvg0MDODl5YXJkyerx/744w9MmDAB3t7eSE9PlzI/IiIiIp0T3TAtW7YMeXl5CAkJUY9169YNBw4cQHp6OpYvXy5pgkRERES6Jrph+uWXXzBr1iy0bdtWY9zNzQ1Tp07FmTNnpMqNiIiIqEoQ3TDl5eVBX1+/2GMmJiZ49uxZhZMiIiIiqkpEN0xt2rTB1q1bi+y3VFBQgB07dqB169aSJUdERERUFWi1rcCL/Pz84OPjg169eqFr166oVasWHj9+jAsXLiAtLQ07d+6sjDyJiIiIdEb0Gaa2bdti3759aNu2Lc6ePYvNmzfj5MmTaNmyJb755ptynWFSKpVYvXo1PD090bZtW4wbNw6JiYklxt+5cwfjx4+Hu7s7unbtitWrV6t3Ii+0e/du9OrVC61bt8awYcMQGxsrOi8iIiIioBxnmIDnC7xXr14tWRLh4eHYs2cPgoKC4ODggODgYPj6+iIyMhIymUwjNiMjA8OHD4ezszO2b9+O7Oxs/Oc//0FycjKWLl0KADh48CCWLVuGRYsWwc3NDRs3bsTo0aNx7Ngx2NraSpY3ERERvRpEn2EqdO7cOQQGBmLGjBlITEzEiRMncO/ePdHz5OXlYcuWLfDz80P37t3h6uqK0NBQJCcn48SJE0XiDx48iKysLKxatQotW7aEu7s7Fi9ejIiICCQlJQEA1q9fD29vb7z//vto2rQpli5dChMTE+zfv7+8L5eIiIheYaIbpuzsbIwZMwYTJkxAREQEjh07Brlcjr1798LLywt//fWXqPni4+Px7NkzdO7cWT1maWkJNzc3REdHF4m/e/cunJ2dNc4Uubm5AQBiYmKQlpaGO3fuaMxnYGAAd3f3YucjIiIiKovoS3IrVqzAH3/8gW3btsHd3R2vvfYaAOCrr76Cr68vVq1ahbCwMK3nS05OBgA4OjpqjNvZ2amPvTyekpIChUKh3t6g8MxWWlpaqfPFx8drnVdx9PXLfUKO8L/6sY4Vx1pKh7WUBusoHdZSOoIg3VyiG6Zjx45hxowZ6NSpExQKhXrczs4On376KRYuXChqvuzsbAAoslbJyMgIGRkZReLffvtthIeHqy8HZmVlYfHixTAwMEB+fn6p8+Xm5orK7WWWliYVejw9xzpKh7WUDmspDdZROqxl1SK6YZLL5ahXr16xx6ysrJCVlSVqvsIb9ubl5WncvDc3NxcmJkV/WBo1aoRVq1YhICAAu3fvhqmpKaZMmYKbN2/CwsJCY74XlTSfGHJ5NhQKZYXmeJXp6+vB0tKEdZQAaykd1lIarKN0WEvpWFmZQE9PmjN1ohumZs2aITIyEl26dCly7PTp02jWrJmo+QovnaWkpKBBgwbq8ZSUFLi4uBT7mJ49e6Jnz55ISUmBtbU1CgoKEBQUhPr162vM16RJE4357O3tReX2MoVCiYIC/vBWFOsoHdZSOqylNFhH6bCWFadSSTeX6Lbr008/xeHDhzFhwgTs378fgiAgOjoaixYtwt69e+Hr6ytqPldXV5ibmyMqKko9JpfLERsbCw8PjyLxMTEx8PHxQUFBAezs7CCTyXDixAmYmJigffv2qFWrFho3bqwxX0FBAWJiYoqdj4iIiKgsos8w9e7dG8HBwVi+fDnOnTsHAAgKCkKtWrWwYMEC9OvXT9R8MpkM3t7eCAkJga2tLerVq4fg4GA4ODigb9++UCgUePz4sfpym7OzMxISEvDVV19hxIgRSEhIwOLFizFhwgSYm5sDAMaMGYMlS5agYcOGaNWqFTZu3IicnBx88MEHYl8uEREREQSVquwTVuvXr8egQYOKXNL6+++/kZ6eDktLSzg7O5f7OqFCocCKFStw4MAB5OTkwMPDAwEBAXByckJSUhJ69eqFwMBAeHl5AQCuXLmCoKAgJCQkoE6dOvD29saoUaM05ty8eTN27NiB9PR0vPbaa5g/fz5atGhRrvwKPXnyjKdHK8DAQA82NmasowRYS+mwltJgHaXDWkrH1tZMsk8batUwtW3bFps3b0aHDh3QokUL7Nu375W8yS5/eCuGbwLSYS2lw1pKg3WUDmspHSkbJq0uyZmbm2Pr1q34559/oFKpcPbsWfz9998lxg8cOFCS5IiIiIiqAq0aJl9fXyxbtgwnT56EIAgIDw8vMVYQBDZMREREVKNo1TD17NkTH3zwATIyMtCrVy+EhYVVeD0QERERUXWhVcP04YcfYu3atXB3d0fdunVhZ2dX4uaVRERERDWNViuhcnNzcfPmTQDA/fv3KzUhIiIioqpGq0/JffLJJzh79iwELe5iJwgCYmNjJUmuquEnFiqGn/yQDmspHdZSGqyjdFhL6fzrn5ILCQnB4cOH8eTJE4SFheGDDz6Ag4ODJAkQERERVXVabyswfPhwAEBUVBRGjx6tcZ82IiIioppM9K1Rdu7cCQDIyMhATEwMUlJS8NZbbyE9PR2NGzfW6rIdERERUXUiumECgHXr1mHDhg3IycmBIAho3bo1Vq5ciSdPnmDLli2wtLSUOk8iIiIinRG9EmrXrl1Ys2YNRo8ejW+//RaFa8a9vb2RmJiIVatWSZ4kERERkS6Jbph27tyJ8ePHY+rUqWjZsqV6vFu3bpg2bRpOnz4taYJEREREuia6Ybp//z46duxY7DFnZ2c8evSowkkRERERVSWiGyZHR0dcvXq12GO///47HB0dK5wUERERUVUietH3Bx98gDVr1sDY2Bjdu3cHAGRlZeH48ePYsGEDRo8eLXWORERERDolumEaN24ckpKSEBISgpCQEADAiBEjAADvvfceJkyYIG2GRERERDomumESBAELFy7EmDFjcOnSJaSnp8PCwgIeHh5o3rx5ZeRIREREpFPl2ocJABo1aoRGjRpJmAoRERFR1STNHemIiIiIajA2TERERERlYMNEREREVAY2TERERERlKPei71u3buHChQtISUmBj48PEhMT4erqCnNzcynzIyIiItI50Q2TUqlEQEAAIiIioFKpIAgC3n77bYSHh+Off/7Brl274ODgUBm5EhEREemE6Ety4eHhiIyMxOLFi3HhwgWoVCoAwGeffQalUonQ0FDJkyQiIiLSJdENU0REBPz8/DB48GBYW1urx1u0aAE/Pz9cuHBByvyIiIiIdE50w/To0SO0aNGi2GP29vaQy+UVToqIiIioKhHdMDVs2BDnzp0r9tjly5fRsGHDCidFREREVJWIXvQ9cuRIBAQEID8/Hz169IAgCLh79y6ioqKwZcsWzJkzpzLyJCIiItIZ0Q3Thx9+iMePH2PdunXYu3cvVCoVZsyYAUNDQ/j6+uLjjz8WNZ9SqURYWBj279+Pp0+fwsPDAwEBAahfv36x8WlpaVi6dKl6wfkbb7yBOXPmwN7eXh3Tt29f3L17V+NxgwYNQlBQkNiXS0RERARBVfgxN5EyMzNx5coVZGRkwNLSEm3atNFYBK6tsLAw7Nq1C0FBQXBwcEBwcDCSkpIQGRkJmUxWJN7HxwcFBQUICAiASqXCl19+CYVCge+++w4AkJWVhQ4dOmDdunVo2bKl+nHGxsawsLAoz0tVe/LkGQoKlBWa41VmYKAHGxsz1lECrKV0WEtpsI7SYS2lY2trBn19afboLvfGlebm5ujatWuFnjwvLw9btmzBrFmz0L17dwBAaGgoPD09ceLECfTv318jXi6X4/Lly1i3bp164fn48eMxceJEpKenw9raGjdv3oRSqUS7du1gZWVVofyIiIiIgHI0TCNGjCgzZseOHVrNFR8fj2fPnqFz587qMUtLS7i5uSE6OrpIw2RsbAwzMzMcOnQIHTt2BAAcPnwYjRs3hqWlJQAgISEBtWvXZrNEREREkhHdMBV3BS8rKwu3bt2Cqakp+vbtq/VcycnJAABHR0eNcTs7O/WxF8lkMgQFBSEgIADu7u4QBAF2dnbYtWsX9PSen3JLSEiAqakp/Pz8cOXKFdjY2GDw4MEYMWKEOqa8pDqt96oqrB/rWHGspXRYS2mwjtJhLaUjCNLNJbph2rlzZ7HjGRkZGDduHJydnbWeKzs7GwCKrFUyMjJCRkZGkXiVSoW4uDi0a9cOvr6+UCgUCA0NxcSJE7F3716Ym5vjr7/+glwux1tvvYVJkybh119/RXBwMDIyMjB16lQRr7QoS0uTCj2enmMdpcNaSoe1lAbrKB3Wsmop9xqml1lZWWH8+PFYunSpVpftgOeX2IDna5kKvwaA3NxcmJgU/UE5duwYdu3ahTNnzqhv8rt+/Xr06NED3333HUaNGoWvv/4aubm56gXeLi4uyMzMxLp16zBlypQKnWWSy7OhUHABXnnp6+vB0tKEdZQAaykd1lIarKN0WEvpWFmZVPjqUiHJGqZCaWlpWscWXopLSUlBgwYN1OMpKSlwcXEpEh8TE4PGjRurmyXgeaPWuHFj9TYCMpmsyBmr5s2bIysrCxkZGbCxsRH1el6kUCj5iQUJsI7SYS2lw1pKg3WUDmtZceXbB6B4ohum6OjoImMKhQLJyckIDw/X+Ch/WVxdXWFubo6oqCh1wySXyxEbGwtvb+8i8Q4ODvjhhx+Qm5sLIyMjAM/XTyUlJeH999+HSqVCnz59MHDgQEyePFn9uBs3bqBOnToVapaIiIjo1SW6YfLx8YFQzCoqlUoFR0dHfP7551rPJZPJ4O3tjZCQENja2qJevXoIDg6Gg4MD+vbtC4VCgcePH8PCwgLGxsYYOHAgNm/ejGnTpqnXI61cuRJGRkbw8vKCIAjo06cPNm/eDGdnZ7z22mu4ePEiNm3ahHnz5ol9qUREREQAytEwFbdlgCAIMDc3h4uLi+hrhX5+figoKMD8+fORk5MDDw8PbN68GYaGhkhKSkKvXr0QGBgILy8v2NnZYc+ePQgODsbIkSOhp6cHd3d37NmzR71maebMmTA3N8eKFSuQnJwMJycnzJs3Dx999JHYl0pEREQEoBw7fY8dOxa+vr4aeye9KrjrasVw91rpsJbSYS2lwTpKh7WUjpQ7fYue5cqVK8VekiMiIiKqqUQ3TJ6enjhy5Ajy8/MrIx8iIiKiKkf0GiYjIyMcOXIEx44dQ5MmTWBqaqpxXBAEbN++XbIEiYiIiHRNdMOUnJyMdu3aqb9/eQmUyCVRRERERFWeZLdGISIiIqqpRK9hGjFiBG7dulXssfj4eLz33nsVToqIiIioKtHqDFNMTIz6Utvly5cRHR2Nx48fF4k7c+YMEhMTpc2QiIiISMe0apj279+Pw4cPQxAECIKAL7/8skhMYUPVv39/aTMkIiIi0jGtGqb58+dj8ODBUKlUGDlyJAICAtC0aVONGD09PVhaWqJZs2aVkigRERGRrmjVMFlYWKBjx44Ant8apWXLljAzM6vUxIiIiIiqCtGfkitsnIiIiIheFdLcYIWIiIioBmPDRERERFQGNkxEREREZRDdMIWFheHhw4fFHktKSsLChQsrnBQRERFRVSK6YVq7dm2JDdO1a9ewf//+CidFREREVJVo9Sm5oUOH4tq1awCeb1A5ZMiQEmNbtWolTWZEREREVYRWDdPixYvx448/QqVSYe3atRg8eDAcHBw0Ygo3ruzbt2+lJEpERESkK1o1TE2bNsXkyZMBAIIg4MMPP4S9vX2lJkZERERUVYjeuLKwcUpLS0NeXp76HnJKpRLZ2dmIiYnBxx9/LG2WRERERDokumGKj4/HrFmzcOvWrWKPC4LAhomIiIhqFNEN07Jly5CRkYHZs2fjzJkzkMlk6NGjB86fP4/z589jx44dlZEnERERkc6I3lbg2rVrmDp1KkaNGoV33nkH2dnZGDZsGNavX4/evXtj586dlZEnERERkc6Ibpjy8vLQqFEjAECjRo0QHx+vPubl5YXffvtNqtyIiIiIqgTRDVPdunWRmJgI4HnDlJmZiaSkJACATCZDRkaGtBkSERER6Zjohqlv375Yvnw5jh8/Dnt7ezg7O2PlypVISEjAli1bUL9+/crIk4iIiEhnRDdMkydPRvv27fHdd98BAObOnYuffvoJAwcOxKVLlzBlyhTJkyQiIiLSJdGfkjMyMsLq1auRn58PAPD09ERkZCT++OMPtGzZEg0aNJA8SSIiIiJdEt0wFTI0NFR/3aBBAzZKREREVGOJbpiys7OxZs0aXLp0CU+fPoVSqdQ4LggCTp48qfV8SqUSYWFh2L9/P54+fQoPDw8EBASUuBYqLS0NS5cuxYULF6BSqfDGG29gzpw5GrdqOXbsGNasWYOkpCQ4Oztj9uzZ6Ny5s9iXSkRERASgHGuYAgMDsWXLFpiYmKB9+/bo2LGjxj8PDw9R84WHh2PPnj1YtGgRvvnmGyiVSvj6+iIvL6/Y+GnTpuH+/fvYunUrtm7divv372PSpEnq45cuXcJnn32GoUOH4uDBg+jcuTPGjx9f4s7kRERERGURVIU3g9PS66+/jlGjRuHTTz+t8JPn5eWhU6dOmDVrFoYNGwYAkMvl8PT0xJIlS9C/f3+NeLlcDg8PD6xbtw49e/YEAJw6dQoTJ05EVFQUrK2tMXbsWFhYWGDlypXqxw0dOhTNmzfHwoULK5TvkyfPUFCgLDuQimVgoAcbGzPWUQKspXRYS2mwjtJhLaVja2sGfX3R54aKJXqW/Px8tG/fXpInj4+Px7NnzzQul1laWsLNzQ3R0dFF4o2NjWFmZoZDhw4hMzMTmZmZOHz4MBo3bgxLS0solUpcuXKlyOW3119/vdj5iIiIiLQheg2Tp6cnzp49i9dff73CT56cnAwAcHR01Bi3s7NTH3uRTCZDUFAQAgIC4O7uDkEQYGdnh127dkFPTw/p6enIysqCg4ODVvOJJVWX+qoqrB/rWHGspXRYS2mwjtJhLaUjCNLNpVXDdOjQIfXXLVu2xOrVq5GSkoIOHTrA1NS0SPzAgQO1evLs7GwAzxuhFxkZGRW7Y7hKpUJcXBzatWsHX19fKBQKhIaGYuLEidi7dy9ycnJKnC83N1ernEpjaWlS4TmIdZQSaykd1lIarKN0WMuqRauGac6cOUXGfvjhB/zwww9FxgVB0LphMjY2BvB8LVPh1wCQm5sLE5OiPyjHjh3Drl27cObMGZibmwMA1q9fjx49euC7777DgAED1PO9qKT5xJLLs6FQ8Hpyeenr68HS0oR1lABrKR3WUhqso3RYS+lYWZlAT0+aM3VaNUynTp2S5MleVngpLiUlRWMfp5SUFLi4uBSJj4mJQePGjdXNEgBYWVmhcePGuHv3LqytrWFqaoqUlBSNx6WkpGhsO1BeCoWSC/AkwDpKh7WUDmspDdZROqxlxYn7WFvptGq76tWrp/4XHR0NU1NTjbHCfzKZDEePHtX6yV1dXWFubo6oqCj1mFwuR2xsbLHbEzg4OODu3bsal9eysrKQlJSERo0aQRAEtG/fHpcvX9Z4XFRUFNzd3bXOi4iIiOhFos9TzZ07F4mJicUei4uLw+rVq7WeSyaTwdvbGyEhITh16hTi4+Mxffp0ODg4oG/fvlAoFEhNTVWvTSq81Ddt2jTEx8cjPj4eM2bMgJGREby8vAAAo0ePxg8//ICtW7fi1q1bWLZsGeLi4jBy5EixL5WIiIgIgJaX5F7c+FGlUmHSpElFFlYDz3fhFnuLFD8/PxQUFGD+/PnIycmBh4cHNm/eDENDQyQlJaFXr14IDAyEl5cX7OzssGfPHgQHB2PkyJHQ09ODu7s79uzZAwsLCwBAly5dsHTpUoSHhyM0NBRNmzbF+vXr0aRJE1F5ERERERXSauPKK1euYP/+/QCAgwcPolu3brC1tdWI0dPTg6WlJby8vNCsWbPKyVbHuIlYxXAzNumwltJhLaXBOkqHtZSOlBtXanWGqX379hqbVU6cOLHEe70RERER1TSiN64MDAysjDyIiIiIqixuI0pERERUBjZMRERERGVgw0RERERUBtEN08OHDysjDyIiIqIqS3TD1KNHD/j6+uLo0aNF7tlGREREVBOJbpgCAwOhVCoxa9YsdOnSBV9++SVu3LhRGbkRERERVQmitxUYMGAABgwYgIcPH+LgwYM4fPgw9u7di6ZNm8LLywvvv/8+ateuXRm5EhEREelEuRd929vb45NPPsGxY8cQEREBGxsbBAcHo3v37pgyZQquXbsmZZ5EREREOlOhT8nFxMTgP//5D8aOHYtff/0Vb775JubMmYPs7Gx8/PHH2LZtm0RpEhEREemO6Etyd+/exeHDh3HkyBHcu3cP9erVg4+PD7y8vODo6AgA8Pb2xqxZs7Bu3TqMGjVK6pyJiIiI/lWiG6a33noLRkZG6N27NxYtWoTOnTsXG+fs7Iw7d+5UND8iIiIinRNUKpVKzAN2796N999/HxYWFpWVU5XFO0dXDO/ALR3WUjqspTRYR+mwltKxtTWDvr40e3SLnuX48eNISUkp9lh8fDzee++9CidFREREVJVodUkuJiYGhSeiLl++jOjoaDx+/LhI3JkzZ5CYmChthkREREQ6plXDtH//fhw+fBiCIEAQBHz55ZdFYgobqv79+0ubIREREZGOadUwzZ8/H4MHD4ZKpcLIkSMREBCApk2basTo6enB0tISzZo1q5REiYiIiHRFq4bJwsICHTt2BADs2LEDLVu2hJmZWaUmRkRERFRVaNUwHTp0CN26dYONjQ3u37+P+/fvlxo/cOBAKXIjIiIiqhK0apjmzJmDb7/9FjY2NpgzZ06psYIgsGEiIiKiGkWrhunUqVOoU6eO+msiIiKiV4lWDVO9evWK/ZqIiIjoVaBVwzR37lytJxQEAUuXLi13QkRERERVjVYNU1RUlNYTCoJQ7mSIiIiIqiKtGqbTp09Xdh5EREREVZY0d6QjIiIiqsG0OsPUokUL7Nu3D61bt4arq2upl90EQUBsbKxkCRIRERHpmlYN06RJk2Bvb6/+muuUiIiI6FWiVcM0efJk9ddTpkyRNAGlUomwsDDs378fT58+hYeHBwICAlC/fv0isWvWrEFYWFix83h5eSEwMBAAMHr0aPzyyy8axzt27IidO3dKmjsRERG9GrRqmF6WlZWFgwcPIiYmBnK5HLa2tujUqRPee+89yGQyUXOFh4djz549CAoKgoODA4KDg+Hr64vIyMgic40ZMwZDhw7VGNu6dSv27t2LUaNGqccSEhKwYMEC9O7dWz1maGgo/oUSERERoRwNU2JiIkaOHIn79++jfv36qFWrFu7cuYPIyEjs2LED27Ztg42NjVZz5eXlYcuWLZg1axa6d+8OAAgNDYWnpydOnDiB/v37a8SbmZlp3PQ3NjYWO3bswKJFi+Di4gIASEtLQ1paGtq0aaPenZyIiIioIkR/Si4oKAiCIODQoUP46aef8M033+DUqVPYt28fnjx5or4spo34+Hg8e/YMnTt3Vo9ZWlrCzc0N0dHRZT5+4cKFcHd3x6BBg9RjCQkJEAQBjRs3FvfCiIiIiEog+gzTL7/8giVLlsDV1VVjvE2bNpgxYwYWL16s9VzJyckAAEdHR41xOzs79bGSnDlzBlevXsWhQ4c0xv/8809YWFhg4cKFuHDhAkxNTdGvXz9MnDhR9OXCl+nrcxeGiiisH+tYcayldFhLabCO0mEtpSPlZ9REN0ympqYlrgeytbWFvr6+1nNlZ2cDQJFGxsjICBkZGaU+duvWrejRowdatGihMf7nn38iNzcXrVu3xujRoxEXF4dly5bh/v37WLZsmda5FcfS0qRCj6fnWEfpsJbSYS2lwTpKh7WsWkQ3TMOHD8eqVavQpk0b2NnZqcczMzOxYcOGIouyS2NsbAzg+Vqmwq8BIDc3FyYmJf+g3L9/H1FRUdi4cWORYwsXLsTs2bNhZWUFAGjevDkMDQ0xffp0+Pv7o3bt2lrn9zK5PBsKhbLcj3/V6evrwdLShHWUAGspHdZSGqyjdFhL6VhZmUBPT5ozdVo1TCNGjND4/vbt2+jTpw/at2+P2rVrIyMjA7/++iuUSiXq1q2r9ZMXXopLSUlBgwYN1OMpKSnqRdzFOXnyJGxtbfHmm28WOWZgYKBulgo1a9YMwPNLgBVpmBQKJQoK+MNbUayjdFhL6bCW0mAdpcNaVpxKJd1cWjVMqpeesX379gCAgoIC9VojNzc3AMDDhw+1fnJXV1eYm5sjKipK3TDJ5XLExsbC29u7xMfFxMSgY8eOMDAomr6Pjw+cnJw0Fp/fuHEDhoaGaNSokda5ERERERXSqmGqrA0fZTIZvL29ERISAltbW9SrVw/BwcFwcHBA3759oVAo8PjxY1hYWGhcsouNjcXgwYOLnfOtt97C0qVL0bp1a3Tp0gU3btzAsmXLMHbsWJibm1fK6yAiIqKarVwbV5YkKysLMTEx6Nq1q9aP8fPzQ0FBAebPn4+cnBx4eHhg8+bNMDQ0RFJSEnr16oXAwEB4eXmpH5Oamgpra+ti5/P29oYgCNi5cyeWLl2KOnXqYNSoURg/fnxFXx4RERG9ogTVy9fbynDv3j0sWLAAly9fRl5eXrExcXFxkiRX1Tx58ozXkyvAwEAPNjZmrKMEWEvpsJbSYB2lw1pKx9bWTLLtGUSfYQoMDMSVK1fw4Ycf4sqVKzAxMUHbtm1x4cIF/Pnnn1izZo0kiRERERFVFaLbrujoaEyfPh3z58+Hl5cXjIyM8NlnnyEiIgIeHh44depUZeRJREREpDOiG6Znz56pP/Lv7OyM2NhYAIC+vj6GDRuGS5cuSZshERERkY6Jbpjs7Ozw6NEjAEDDhg2RkZGB1NRUAIC1tTXS0tKkzZCIiIhIx0Q3TN26dcPKlStx9epV1KtXDw4ODtiyZQsyMzMREREBe3v7ysiTiIiISGdEN0x+fn6wtLTEqlWrAADTp0/H9u3b4eHhgcjISIwePVryJImIiIh0SfSn5GxsbLB//36kpKQAAN5//33UrVsXv/32G1q3bo2OHTtKniQRERGRLpV740o7OzvcunULcrkcdnZ28PX1lTIvIiIioiqjXA3Tzp07sWHDBo0F3o6OjpgxYwb69+8vWXJEREREVYHohmnXrl1YsmQJevfujT59+qBWrVp49OgRvv/+e3z22WfQ19fH22+/XRm5EhEREemE6IZpx44d8Pb2xvz58zXGBw4ciHnz5iEsLIwNExEREdUooj8ll5ycjJ49exZ7rH///khMTKxwUkRERERVieiGqVWrVrh48WKxx2JjY9W7gBMRERHVFFpdkouOjlZ//e677yIwMBDZ2dl4++23UadOHaSnp+PcuXPYuXMnFi9eXGnJEhEREemCoFKpVGUFubq6QhAE9feFDylpLC4uTuo8q4QnT56hoECp6zSqLQMDPdjYmLGOEmAtpcNaSoN1lA5rKR1bWzPo64u+mFYsrc4w7dixQ5InIyIiIqqOtGqYuHs3ERERvcrKtXHl7du3sXr1aly+fBlyuRw2NjZwd3fHpEmT0KRJE6lzJCIiItIp0Q3TzZs3MXToUOjr66Nnz56oXbs2UlNTcebMGZw9exb79+9n00REREQ1iuiGKSQkBE5OTti5cycsLCzU40+fPsXIkSMRGhqKsLAwSZMkIiIi0iXRS8ejo6PxySefaDRLAGBhYYHx48drbEFAREREVBOIbpgMDAxgZGRU7DGZTIa8vLwKJ0VERERUlZRrp+89e/bg5e2bVCoVdu/ejddee02y5IiIiIiqAtFrmKZOnYqPP/4Y77//Pvr164c6deogNTUVP/74I27fvo2tW7dWRp5EREREOiO6YWrVqhU2bdqE5cuXIywsDCqVCoIg4LXXXsPXX38NDw+PysiTiIiISGdEN0wHDx7EG2+8gf379yM7OxtyuRyWlpYwMTGpjPyIiIiIdE70GqaFCxfi+vXrAAATExPY29uzWSIiIqIaTXTD5ODggMzMzMrIhYiIiKhKEn1JbsiQIViyZAmuXr0KFxcXmJmZFYkZOHCgFLkRERERVQmiG6agoCAAwLffflvscUEQRDVMSqUSYWFh2L9/P54+fQoPDw8EBASgfv36RWLXrFlT4i7iXl5eCAwMBABcvHgRwcHBuHXrFhwdHTFlyhS8++67WudERERE9CLRDdOpU6ckTSA8PBx79uxBUFAQHBwcEBwcDF9fX0RGRkImk2nEjhkzBkOHDtUY27p1K/bu3YtRo0YBAG7duoUJEyZg9OjRCA4OxtmzZ+Hv7w9bW1t07txZ0tyJiIjo1SC6YapXr57667y8PMjlclhZWcHQ0FD0k+fl5WHLli2YNWsWunfvDgAIDQ2Fp6cnTpw4gf79+2vEm5mZaVwCjI2NxY4dO7Bo0SK4uLgAALZv3w4XFxdMnz4dANCkSRPExsZi06ZNbJiIiIioXEQv+gaA8+fPY+jQoWjbti08PT3Rrl07jBw5EleuXBE1T3x8PJ49e6bRyFhaWsLNzU2re9ItXLgQ7u7uGDRokHosJiamSGPUqVMn/Prrr0V2JyciIiLShugzTMePH8e0adPg6uqKyZMno1atWkhNTcVPP/2EESNGYNu2bXB3d9dqruTkZACAo6OjxridnZ36WEnOnDmDq1ev4tChQ0XmdHBwKDJfdnY2njx5AltbW61yK46+frn6S/qvwvqxjhXHWkqHtZQG6ygd1lI6giDdXKIbprVr1+Ktt97CypUrNcYnT56MKVOmYPny5di7d69Wc2VnZwNAkbVKRkZGyMjIKPWxW7duRY8ePdCiRQuN8ZycnCLzFX5f0RsDW1pyvykpsI7SYS2lw1pKg3WUDmtZtYhumO7evQt/f/9ij3300UeYMmWK1nMZGxsDeN7IFH4NALm5uaVuhnn//n1ERUVh48aNRY4ZGRkVaYwKv6/oBptyeTYUCmWF5niV6evrwdLShHWUAGspHdZSGqyjdFhL6VhZmUBPT5ozdaIbpiZNmuDGjRvo0qVLkWO3b9+Gk5OT1nMVXopLSUlBgwYN1OMpKSnqRdzFOXnyJGxtbfHmm28WO2dKSorGWEpKCkxNTWFhYaF1bsVRKJQoKOAPb0WxjtJhLaXDWkqDdZQOa1lxUi5dFt12LViwADt37sT69euRnJwMpVKJx48f49tvv8Xq1avxySef4P79++p/pXF1dYW5uTmioqLUY3K5HLGxsaXexDcmJgYdO3aEgUHRfs/d3R2XL1/WGLt06RLat28vWZdJRERErxbRZ5g++ugjAMDKlSuxatUq9XjhJ9A+++wzjfi4uLgS55LJZPD29kZISAhsbW1Rr149BAcHw8HBAX379oVCocDjx49hYWGhcckuNjYWgwcPLnZOHx8fDBo0CCEhIRg0aBDOnTuHH3/8EZs2bRL7UomIiIgAlKNhWrp0KQQJl537+fmhoKAA8+fPR05ODjw8PLB582YYGhoiKSkJvXr1QmBgILy8vNSPSU1NhbW1dbHzNWvWDOHh4QgODsb27dvh5OSE4OBg7sFERERE5SaouDmR1p48ecbryRVgYKAHGxsz1lECrKV0WEtpsI7SYS2lY2trJtn2DFzUQ0RERFQGNkxEREREZWDDRERERFQGNkxEREREZahQw/T06VPcunULeXl5UCgUUuVEREREVKWUq2GKiorChx9+iI4dO+K9997DX3/9hZkzZyIoKEjq/IiIiIh0TnTDdPHiRYwdOxbGxsaYNWuWesNKV1dX7NixA1u3bpU8SSIiIiJdEt0wrVy5Er169cLOnTsxcuRIdcP0ySefwNfXF/v375c8SSIiIiJdEt0wxcXFqW9L8vKO32+++Sbu3bsnTWZEREREVYTohsnCwgKpqanFHnvw4AEsLCwqnBQRERFRVSK6YerVqxdCQ0Nx48YN9ZggCEhOTsb69evRvXt3KfMjIiIi0jnRN9+dOXMmrl27ho8++gi1a9cGAMyYMQPJyclwdHTEjBkzJE+SiIiISJdEN0xWVlbYv38/Dh06hEuXLiE9PR0WFhbw8fGBl5cXTExMKiNPIiIiIp0R3TABgEwmw0cffYSPPvpI6nyIiIiIqhzRDVNYWFiJx/T09GBqaoqGDRvizTffhEwmq1ByRERERFWB6IbpyJEjSE5ORl5eHgwMDGBtbY309HQUFBRAEAT1vkxNmzbFjh07YGtrK3nSRERERP8m0Z+Smzp1KmQyGVasWIHr16/j559/xo0bNxAWFgYbGxusXLkSkZGREAQBK1asqIyciYiIiP5VohumNWvWYNq0aXjnnXegp/f84YIgoHfv3vDz88OqVavQrFkzfPLJJzh37pzkCRMRERH920Q3TA8ePEDDhg2LPVavXj31Tt/29vbIyMioWHZEREREVYDohqlp06Yl3i/uu+++Q+PGjQEAd+7cgZ2dXcWyIyIiIqoCRC/6njJlCiZNmoRBgwahb9++qFWrFh49eoSTJ08iISEBq1evRmxsLIKDg9X3nCMiIiKqzkQ3TN27d8fmzZuxZs0ahIWFQaFQwMDAAB06dMD27dvh7u6O06dP491338W0adMqIWUiIiKif5egKtwHoBzy8vKQkZGBWrVqqReA12RPnjxDQYFS12lUWwYGerCxMWMdJcBaSoe1lAbrKB3WUjq2tmbQ15emPynXTt+5ublISEhAXl4eVCoV7ty5A6VSiezsbMTExGDWrFmSJEdERERUFYhumKKiojB16tQSPwFnZmbGhomIiIhqFNENU2hoKGxsbLBo0SIcOXIEenp68PLywvnz57F37158/fXXlZEnERERkc6IbpgSEhKwePFi9OnTB0+fPsU333yDbt26oVu3bsjPz8e6deuwcePGysiViIiISCdEr4RSKpWwt7cHADRs2BB//fWX+thbb72F2NhY6bIjIiIiqgJEN0wNGjRAQkICAKBx48bIzs7G33//DQAoKCjAs2fPpM2QiIiISMdEN0zvvfceQkJCsGvXLtja2uK1117DokWLcPr0aaxduxZNmzatjDyJiIiIdEZ0w+Tr64uhQ4fi2rVrAIAvvvgCcXFxmDhxIv7++2/4+/uLmk+pVGL16tXw9PRE27ZtMW7cOCQmJpYYn5+fj+XLl6vjvb29ERcXpxEzevRouLi4aPzz8fER+1KJiIiIAJRj48pbt26hSZMmGmOZmZn4+++/4ezsDHNzc1EJhIWFYdeuXQgKCoKDgwOCg4ORlJSEyMhIyGSyIvHz5s3D2bNnERQUhLp162LVqlW4cuUKjh07BgsLCwDAG2+8gSlTpqB3797qxxkaGsLa2lpUbi/jJmIVw83YpMNaSoe1lAbrKB3WUjpSblwpepZhw4bh0KFDGmPm5uZo3bq16GYpLy8PW7ZsgZ+fH7p37w5XV1eEhoYiOTkZJ06cKBKfmJiIiIgILFmyBJ6enmjSpAkWL14MmUyG33//HQCQlpaGtLQ0tGnTBnXq1FH/q2izRERERK8u0dsKGBoawsbGRpInj4+Px7Nnz9C5c2f1mKWlJdzc3BAdHY3+/ftrxF+4cAEWFhbo2rWrRvzp06fV3yckJEAQBDRu3FiSHF8kVZf6qiqsH+tYcayldFhLabCO0mEtpSMI0s0lumGaOnUqli1bhqdPn8LV1RWmpqZFYurWravVXMnJyQAAR0dHjXE7Ozv1sRfdvn0b9evXx4kTJ7Bx40Y8fPgQbm5umDNnjvoy4Z9//gkLCwssXLgQFy5cgKmpKfr164eJEycWe4lPDEtLkwo9np5jHaXDWkqHtZQG6ygd1rJqEd0wLViwAAqFAp999lmJMS8vwi5JdnY2ABRpZIyMjIq99UpmZibu3r2L8PBw+Pv7w9LSEuvWrcOwYcNw9OhR1KpVC3/++Sdyc3PRunVrjB49GnFxcVi2bBnu37+PZcuWiXilRcnl2VAoeD25vPT19WBpacI6SoC1lA5rKQ3WUTqspXSsrEygp6ejm+8uXrxYkicGAGNjYwDP1zIVfg08v7mviUnRztrAwACZmZkIDQ1Vn1EKDQ1Ft27dcPDgQfj6+mLhwoWYPXs2rKysAADNmzeHoaEhpk+fDn9/f9SuXbvc+SoUSi7AkwDrKB3WUjqspTRYR+mwlhUn7mNtpRPdMA0aNEiyJy+8FJeSkoIGDRqox1NSUuDi4lIk3sHBAQYGBhqf0jM2Nkb9+vWRlJQE4HlTVdgsFWrWrBmA55cAK9IwERER0aupXOep8vLysGfPHkyePBlDhgzBrVu3sHfvXly/fl3UPK6urjA3N0dUVJR6TC6XIzY2Fh4eHkXiPTw8UFBQgBs3bqjHcnJykJiYiIYNGwIAfHx8MHfuXI3H3bhxA4aGhmjUqJGo/IiIiIiAcjRMjx8/xuDBg7FkyRLcvXsX169fR05ODs6ePQsfHx9cvXpV67lkMhm8vb0REhKCU6dOIT4+HtOnT4eDgwP69u0LhUKB1NRU5OTkAADc3d3xxhtvYPbs2YiJicHNmzfh7+8PfX19DBgwAMDz+9kdPnwYe/fuRWJiIo4ePYply5Zh7Nixorc9ICIiIgLK0TAtW7YMz549w9GjR3Hw4EEU7nu5evVqtGrVCqtXrxY1n5+fHz744APMnz8fH3/8MfT19bF582YYGhriwYMH6NKlC44ePaqOX7NmDTp27IjJkyfjgw8+QGZmJnbs2AFbW1sAgLe3N+bNm4edO3finXfeQUhICEaNGoWpU6eKfalEREREAMqx0/frr7+Ozz//HAMGDIBCoUDLli0RERGBli1b4vTp05gzZw4uX75cWfnqFHddrRjuXisd1lI6rKU0WEfpsJbS0elO37m5uSXumq2vr4/8/PyK5kRERERUpYhumFq1aoU9e/YUeywyMhKvvfZahZMiIiIiqkrKtdP3qFGjMGDAAHTr1g2CIOD777/HmjVr8PPPP2PTpk2VkScRERGRzog+w+Tu7o6tW7fCxMQEmzZtgkqlwrZt25CamooNGzagU6dOlZEnERERkc6IPsMEPN8P6ZtvvkFOTg4yMjJgbm4OMzMzqXMjIiIiqhJEn2EaOHAgtm3bhkePHsHY2Bj29vZsloiIiKhGE90w1a1bF8uXL0e3bt0wduxYREZGqjeWJCIiIqqJRO/DBABPnz7F8ePHcfToUURFRcHIyAh9+vTBgAED0LlzZwiCUBm56hz3xKgY7i0iHdZSOqylNFhH6bCW0pFyH6ZyNUwvSktLw48//ogff/wRV65cQe3atXHu3DlJkqtq+MNbMXwTkA5rKR3WUhqso3RYS+nodOPKl6WlpeHRo0eQy+VQKBSwsrKSIi8iIiKiKqNcn5JLTEzE999/j6NHj+LmzZuoXbs2+vfvj6+++gqurq5S50hERESkU6IbpsGDByM2NhbGxsbo06cP5syZg86dO0NP7/nJKpVKVWPXMBEREdGrSXTDZG1tjaCgIPTt2xcmJibq8ZSUFHz77beIiIjAmTNnJE2SiIiISJdEN0ybN2/W+P7//u//8M033+DcuXMoKCiAk5OTZMkRERERVQXlWsP0+PFjfPfdd/j2229x7949mJubY9CgQRgwYADc3d2lzpGIiIhIp0Q1TJcuXcK+fftw8uRJKBQKdOjQAffu3cPatWvRsWPHysqRiIiISKe0api2bduGffv24fbt22jYsCEmTpyIQYMGwdTUFB07duQibyIiIqrRtGqYgoKC4OLigh07dmicSXr69GmlJUZERERUVWi1ceW7776Lu3fvYsKECZg4cSJ++uknFBQUVHZuRERERFWCVmeYli9fjszMTERGRuLAgQOYMmUKbGxs0Lt3bwiCwEtyREREVKOV615yf/31FyIiIhAZGYm0tDQ0aNAA7777Lt599100bdq0MvKsEnhfn4rh/ZGkw1pKh7WUBusoHdZSOlXm5rsFBQU4c+YMIiIi8PPPP0OhUKBZs2Y4cuSIJMlVNfzhrRi+CUiHtZQOaykN1lE6rKV0pGyYyrUPk/rBBgbo06cP+vTpg0ePHuHgwYM4ePCgJIkRERERVRXStF0AateujXHjxuHo0aNSTUlERERUJUjWMBERERHVVGyYiIiIiMrAhomIiIioDGyYiIiIiMrAhomIiIioDDpvmJRKJVavXg1PT0+0bdsW48aNQ2JiYonx+fn5WL58uTre29sbcXFxGjEXL16El5cX2rRpg379+uGHH36o7JdBRERENZjOG6bw8HDs2bMHixYtwjfffAOlUglfX1/k5eUVG79gwQIcOHAAS5cuRUREBGxtbTFu3Dj1jYBv3bqFCRMmwNPTEwcOHMCHH34If39/XLx48d98WURERFSD6LRhysvLw5YtW+Dn54fu3bvD1dUVoaGhSE5OxokTJ4rEJyYmIiIiAkuWLIGnpyeaNGmCxYsXQyaT4ffffwcAbN++HS4uLpg+fTqaNGmCsWPHol+/fti0adO//fKIiIiohqjQTt8VFR8fj2fPnqFz587qMUtLS7i5uSE6Ohr9+/fXiL9w4QIsLCzQtWtXjfjTp0+rv4+JiUHv3r01HtepUycsWbIEKpWqQjcKtrIyQflvJEOFpWcdK461lA5rKQ3WUTqspXT09Mr///yX6bRhSk5OBgA4OjpqjNvZ2amPvej27duoX78+Tpw4gY0bN+Lhw4dwc3PDnDlz0KRJE/WcDg4ORebLzs7GkydPYGtrW+589fR0fgWzRmAdpcNaSoe1lAbrKB3WsmrR6X+N7OxsAIBMJtMYNzIyQm5ubpH4zMxM3L17F+Hh4ZgxYwbWrVsHAwMDDBs2DGlpaQCAnJycIvMVfl/SuigiIiKi0ui0YTI2NgZQtJHJzc2FiYlJkXgDAwNkZmYiNDQUXbp0QevWrREaGgoA6pv+GhkZFZmv8Pvi5iQiIiIqi04bpsJLcSkpKRrjKSkpsLe3LxLv4OAAAwMD9eU34HnTVb9+fSQlJannLG4+U1NTWFhYSP0SiIiI6BWg04bJ1dUV5ubmiIqKUo/J5XLExsbCw8OjSLyHhwcKCgpw48YN9VhOTg4SExPRsGFDAIC7uzsuX76s8bhLly6hffv2vB5MRERE5aLTDkImk8Hb2xshISE4deoU4uPjMX36dDg4OKBv375QKBRITU1FTk4OgOfN0BtvvIHZs2cjJiYGN2/ehL+/P/T19TFgwAAAgI+PD65fv46QkBDcunULW7ZswY8//ghfX19dvlQiIiKqxgSVSrcfWlQoFFixYgUOHDiAnJwceHh4ICAgAE5OTkhKSkKvXr0QGBgILy8vAM8XfoeEhODHH39ETk4O2rdvj88//xxNmzZVz3n+/HkEBwfjzp07cHJywpQpU/DOO+/o6iUSERFRNafzhomIiIioquOiHiIiIqIysGEiIiIiKgMbJiIiIqIysGEiIiIiKgMbJiIiIqIysGEiIiIiKgMbplIolUqsXr0anp6eaNu2LcaNG4fExERdp1UtpaenIyAgAF27dkX79u3x8ccfIyYmRtdpVWu3b99Gu3btcODAAV2nUm0dOnQI77zzDlq1aoV3330Xx44d03VK1VJBQQFWrVqFHj16oF27dhg+fDh+++03XadVrWzYsAE+Pj4aY3FxcfD29kbbtm3Rs2dP7NixQ0fZVS/F1fL06dMYPHgw2rVrh549e+Krr75Sb4qtLTZMpQgPD8eePXuwaNEifPPNN1AqlfD19S1yc18q24wZM3D16lWsWLECERERaNGiBcaOHYu///5b16lVS/n5+Zg1axaysrJ0nUq1dfjwYcybNw/Dhw/HDz/8gP79+6t/TkmcdevWYf/+/Vi0aBEOHTqExo0bw9fXt8h9Pal4u3fvxsqVKzXGnjx5gtGjR6NBgwaIiIjApEmTEBISgoiICN0kWU0UV8uYmBhMnjwZffr0wcGDB/HFF1/g6NGj+PLLL8VNrqJi5ebmqtq1a6favXu3eiwjI0PVunVrVWRkpA4zq37u3Lmjat68uSomJkY9plQqVb1791atXLlSh5lVX8uXL1eNGDFC1bx5c1VERISu06l2lEqlqkePHqqgoCCN8TFjxqjWr1+vo6yqr/fff18VGBio/v7p06eq5s2bq44fP67DrKq+5ORk1YQJE1Rt27ZV9evXT+Xt7a0+tn79elWXLl1U+fn56rHly5er+vbtq4tUq7zSajlz5kzVqFGjNOIPHjyoatmypSo3N1fr5+AZphLEx8fj2bNn6Ny5s3rM0tISbm5uiI6O1mFm1Y+NjQ02btyIVq1aqccEQYAgCJDL5TrMrHqKjo7Gvn37EBQUpOtUqq3bt2/j3r17eO+99zTGN2/ejAkTJugoq+qrVq1aOHPmDJKSkqBQKLBv3z7IZDK4urrqOrUq7Y8//oChoSGOHDmCNm3aaByLiYlBx44dYWBgoB7r1KkT7ty5g0ePHv3bqVZ5pdVyzJgxmD17tsaYnp4e8vPzkZmZqfVzGJQd8mpKTk4GADg6OmqM29nZqY+RdiwtLdGtWzeNsePHj+Pu3bv4/PPPdZRV9SSXy+Hv74/58+cX+dkk7d2+fRsAkJWVhbFjxyI2NhZOTk749NNP0bNnTx1nV/3MmzcPU6dORa9evaCvrw89PT2sWbMGDRo00HVqVVrPnj1L/HlLTk5G8+bNNcbs7OwAAA8ePEDt2rUrPb/qpLRaurm5aXyfn5+Pbdu24bXXXoOtra3Wz8EzTCXIzs4GAMhkMo1xIyMj5Obm6iKlGuPKlSuYO3cu+vbti+7du+s6nWplwYIFaNeuXZEzIyRO4V+Vs2fPRv/+/bFlyxa8+eabmDhxIi5evKjj7KqfmzdvwsLCAmvXrsW+ffvg5eWFWbNmIS4uTtepVVs5OTnF/v8HAP8fVAEFBQXw9/fHX3/9hS+++ELUY3mGqQTGxsYAgLy8PPXXwPMfVBMTE12lVe2dPHkSs2bNQvv27RESEqLrdKqVQ4cOISYmBpGRkbpOpdozNDQEAIwdOxaDBg0CALRo0QKxsbHYunWrxqV4Kt2DBw8wc+ZMbNu2De7u7gCAVq1a4ebNm1izZg3Cw8N1nGH1ZGxsXOQDRoWNkqmpqS5SqvYyMzMxbdo0XL58GWFhYWjdurWox/MMUwkKL3e8/CmPlJQU2Nvb6yKlam/Xrl2YMmUKevTogfXr16v/WiLtREREIC0tDd27d0e7du3Qrl07AMAXX3wBX19fHWdXvRT+Dr98yaNp06ZISkrSRUrV1rVr15Cfn6+xRhEA2rRpg7t37+ooq+rPwcGh2P//AOD/g8ohJSVFvd3F5s2biywT0QbPMJXA1dUV5ubmiIqKUl+Hl8vliI2Nhbe3t46zq34Kt2fw8fHBvHnzIAiCrlOqdkJCQorsG9K3b1/4+fnh/fff11FW1VPLli1hZmaGa9euqc+KAMCff/7JdTciOTg4AAASEhI0/mL/888/0ahRIx1lVf15eHjgm2++gUKhgL6+PgDg0qVLaNy4MWrVqqXj7KqXjIwMjBw5EpmZmdi9ezdcXFzKNQ8bphLIZDJ4e3sjJCQEtra2qFevHoKDg+Hg4IC+ffvqOr1q5fbt21i6dCn69OmDCRMmaHzCw9jYGBYWFjrMrvoo6a/KWrVq8S9OkYyNjeHr64u1a9fC3t4erVu3xg8//IALFy5g27Ztuk6vWmndujU6dOiA2bNn44svvoCDgwMOHTqEixcvYu/evbpOr9oaPHgwNm3ahHnz5sHX1xfXr1/Htm3bxO8dRAgMDERiYiI2bdoEW1tbpKamqo/Z2tqqG9KysGEqhZ+fHwoKCjB//nzk5OTAw8MDmzdvVq9/IO0cP34c+fn5+Omnn/DTTz9pHBs0aBA/Hk86MXHiRJiYmCA0NBQPHz5EkyZNsGbNGrz++uu6Tq1a0dPTw7p167By5UrMnTsXGRkZaN68ObZt21bk492kvVq1amHTpk1YsmQJBg0ahDp16sDf31+95o60o1AocPToUeTn52PkyJFFjp86dQpOTk5azSWoVCqV1AkSERER1SRc9E1ERERUBjZMRERERGVgw0RERERUBjZMRERERGVgw0RERERUBjZMRERERGVgw0RERERUBjZMRERERGVgw0RE1cacOXPg4uJS6j8fH59Ke/4DBw7AxcUFixcvLvb4mjVryn2fKiKq2nhrFCKqNiZOnIihQ4eqvw8PD0dsbCzCwsLUY+bm5pWex+7du9GvXz+NG/cSUc3GhomIqo0GDRqgQYMG6u9tbW0hk8nQtm3bfzUPc3NzfP755zhy5AiMjY3/1ecmIt3gJTkiqnEuXLiAYcOGoUOHDnj99dcxc+ZMPHjwQH288NLatWvXMGjQILRu3RrvvfcefvzxR63mnz17Nv755x+sWLGisl4CEVUxbJiIqEY5dOgQxowZA0dHR6xYsQJz587F1atXMWTIEKSlpWnETpgwAb169UJYWBgaN26MadOm4dy5c2U+R6dOnTBkyBDs3LkTv/76a2W9FCKqQtgwEVGNoVQqERISgi5dumD58uXo1q0bBg4ciG3btuHx48fYvHmzRryPjw8mT56Mrl27YtWqVXB1dcXatWu1ei5/f384Ojri888/R05OTmW8HCKqQtgwEVGNcfv2baSmpqJ///4a4w0aNEC7du1w+fJljfFBgwapvxYEAX369MH169e1aoDMzMywZMkS3LlzB6GhodK8ACKqstgwEVGNkZ6eDgCoXbt2kWO1a9fG06dPNcbs7Ow0vq9VqxZUKhXkcrlWz9e5c2cMGTIEO3bswJUrV8qXNBFVC2yYiKjGsLa2BgA8evSoyLHU1FTY2NhojBU2WIUePXoEfX199Tza8Pf3h4ODA+bOnctLc0Q1GBsmIqoxGjdujDp16uD777/XGE9MTMRvv/2G9u3ba4yfPHlS/bVKpcKJEyfQoUMHyGQyrZ/T3Nwcixcvxp07d7Bv376KvQAiqrK4DxMR1Rh6enqYMWMG5s6di5kzZ+L999/HkydPEBYWBisrK4wePVojftmyZcjNzUXjxo2xf/9+3Lp1C9u3bxf9vG+++SY+/PBD7N+/X6qXQkRVDBsmIqpRvLy8YGZmhg0bNmDSpEkwNzeHp6cnZsyYgTp16mjELliwABs2bEBiYiLc3NywZcuWcu/ePWfOHPz8888a+z0RUc0hqFQqla6TICL6Nx04cABz587FqVOn4OTkpOt0iKga4BomIiIiojKwYSIiIiIqAy/JEREREZWBZ5iIiIiIysCGiYiIiKgMbJiIiIiIysCGiYiIiKgMbJiIiIiIysCGiYiIiKgMbJiIiIiIysCGiYiIiKgM/w/QYdXW663TLAAAAABJRU5ErkJggg==", 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", "text/plain": [ - "
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" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " blank_ax.set_xticklabels(ax.get_xticklabels())\n", + ":8: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " blank_ax.set_yticklabels(ax.get_yticklabels())\n" + ] } ], "source": [ - "grid = sns.relplot(\n", + "fig, ax = plt.subplots()\n", + "sns.lineplot(\n", " data=df,\n", " x=\"top_n\",\n", " y=\"len_ci\",\n", @@ -312,21 +392,54 @@ " hue_order=[\"conventional\", \"conditional\", \"hybrid\", \"projection\"],\n", " palette=palette,\n", " ci=None,\n", - " kind=\"line\",\n", " marker=\"o\",\n", - " estimator=lambda x: np.quantile(x, .5)\n", + " estimator=lambda x: np.quantile(x, .5),\n", + " ax=ax\n", ")\n", - "grid.set_ylabels(\"Median length 95% CI\")\n", - "grid.set_xlabels(\"Top N\")\n", - "grid.fig.savefig(\"plots/len_ci.png\")\n", - "plt.show()" + "ax.set_ylabel(\"Median length 95% CI\")\n", + "ax.set_xlabel(\"Top N\")\n", + "plt.savefig(\"plots/len_ci.png\")\n", + "plt.show()\n", + "\n", + "ax = make_blank_figure(ax)\n", + "plt.savefig(\"plots/len_ci_blank.png\")" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\dbspe\\repos\\conditional-inference\\src\\conditional_inference\\stats.py:165: RuntimeWarning: divide by zero encountered in double_scalars\n", + " x_init = (norm.pdf(a) - norm.pdf(b)) / ((norm.cdf(b) - norm.cdf(a)))\n", + "c:\\users\\dbspe\\repos\\conditional-inference\\src\\conditional_inference\\stats.py:145: RuntimeWarning: divide by zero encountered in double_scalars\n", + " + (norm.pdf(b, mu) - norm.pdf(a, mu))\n", + "C:\\Users\\DBSpe\\anaconda3\\envs\\conditional-inference\\lib\\site-packages\\scipy\\optimize\\_numdiff.py:556: RuntimeWarning: invalid value encountered in double_scalars\n", + " dx = x[i] - x0[i] # Recompute dx as exactly representable number.\n", + "C:\\Users\\DBSpe\\anaconda3\\envs\\conditional-inference\\lib\\site-packages\\scipy\\optimize\\_numdiff.py:557: RuntimeWarning: invalid value encountered in subtract\n", + " df = fun(x) - f0\n", + "c:\\users\\dbspe\\repos\\conditional-inference\\src\\conditional_inference\\stats.py:137: RuntimeWarning: invalid value encountered in double_scalars\n", + " return -x * mu + (0.5 * mu ** 2 + np.log(norm.cdf(b, mu) - norm.cdf(a, mu)))\n", + "c:\\users\\dbspe\\repos\\conditional-inference\\src\\conditional_inference\\stats.py:137: RuntimeWarning: divide by zero encountered in log\n", + " return -x * mu + (0.5 * mu ** 2 + np.log(norm.cdf(b, mu) - norm.cdf(a, mu)))\n", + "C:\\Users\\DBSpe\\anaconda3\\envs\\conditional-inference\\lib\\site-packages\\scipy\\optimize\\_numdiff.py:470: RuntimeWarning: invalid value encountered in subtract\n", + " dx = ((x0 + h) - x0)\n", + "C:\\Users\\DBSpe\\anaconda3\\envs\\conditional-inference\\lib\\site-packages\\scipy\\optimize\\_numdiff.py:57: RuntimeWarning: invalid value encountered in subtract\n", + " upper_dist = ub - x0\n", + "C:\\Users\\DBSpe\\anaconda3\\envs\\conditional-inference\\lib\\site-packages\\scipy\\optimize\\minpack.py:175: RuntimeWarning: The iteration is not making good progress, as measured by the \n", + " improvement from the last ten iterations.\n", + " warnings.warn(msg, RuntimeWarning)\n", + "c:\\users\\dbspe\\repos\\conditional-inference\\src\\conditional_inference\\stats.py:165: RuntimeWarning: invalid value encountered in double_scalars\n", + " x_init = (norm.pdf(a) - norm.pdf(b)) / ((norm.cdf(b) - norm.cdf(a)))\n", + "c:\\users\\dbspe\\repos\\conditional-inference\\src\\conditional_inference\\stats.py:145: RuntimeWarning: invalid value encountered in double_scalars\n", + " + (norm.pdf(b, mu) - norm.pdf(a, mu))\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -359,7 +472,53 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":5: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " blank_ax.set_xticklabels(ax.get_xticklabels())\n", + ":9: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " blank_ax.set_yticklabels(ax.get_yticklabels())\n" + ] + }, + { + "data": { + "image/png": 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+4Pr444/ZuHEjwcHBtG/fnrFjxxp1L8/Sp08f1Go1YWFhLFiwgBIlStC6dWv9jLpJSUk4ODgQGBjIpEmT6NmzJ8uWLaNatWqsXr2amTNnMnbsWNLS0ihdurTB+9ygQQNmzJjB0qVL9RPseHt7ExYWpp85Vwgh3jYWupw+RS+EEEIIIYQQQiDPSAohhBBCCCGEMJIUkkIIIYQQQgghjCKFpBBCCCGEEEIIo0ghKYQQQgghhBDCKFJICiGEEEIIIYQwihSSQgghhBBCCCGMIoWkEEIIIYQQQgijWJk6AGF6Op0OrTbvLyeqUFiYRZwib5B8EcaQfBHGkpwRxpB8EcYwZb4oFBZYWFjkqK0UkgKtVsf9+49MHcZzWVkpUKnsSUp6TGam1tThiDxO8kUYQ/JFGEtyRhhD8kUYw9T54uhoj6VlzgpJGdoqhBBCCCGEEMIoUkgKIYQQQgghhDCKFJJCCCGEEEIIIYwihaQQQgghhBBCCKNIISmEEEIIIYQQwihSSAohhBBCCCGEMIoUkkIIIYQQQgghjCKFpBBCCCGEEEIIo0ghKYQQQgghhBDCKFJICiGEEEIIIYQwihSSQgghhBBCCCGMIoWkEEIIIYQQQgijSCEphBBCCCGEEMIoUkgKIYQQQgghhDCKFJJCCCGEEEIIIYwihaQQQgghhBBCCKNIISmEEEIIIYQQwihSSAohhBBCCCGEMIoUkkIIIYQQQgghjCKFpBBCCCGEEEIIo0ghKYQQQgghhBDCKFJICiGEEEIIIYQwihSSQgghhBBCCCGMIoWkEEIIIYQQQgijSCEphBBCCCGEEMIoUkgKIYQQQgghhDCKFJJCCCGEEEIIIYwihaQQQgghhBBCCKNIIfkCmzZt4tNPP6VKlSp4eXnRrl07Vq9e/UqvoVarWbt2rf7148ePiYiI0L8ODg6ma9eur/SaQgghhBDCvGi1OqLj1Rw+d5PoeDVarc7UIYl3mJWpA8jL1q1bx8SJExk5ciTe3t7odDoOHjzIhAkTuHv3Lv37938l15k2bRrXrl2jQ4cOACxdupTIyEg6d+78Ss4vhBBCCCHMW1TMbVbuvoA6OU2/TaW0JcDPDW/3oiaMTLyrpJB8jpUrV9KuXTvat2+v3/b+++9z69YtwsLCXlkhqdPpnvtaCCGEEEK8u6JibrNg/Zks29XJaSxYf4Ygfw8pJsUbJ4XkcygUCk6cOMGDBw8oWLCgfnvv3r1p164dAOnp6cyZM4dNmzbx8OFD3NzcGDhwID4+Pvr2a9euJSwsjPj4eBQKBRUrVmTEiBFUrlyZ4OBg1q9fD4C7uzv9+/dn/vz5+td79uzJEtetW7eYMmUKf/75J5aWlnh5eREcHEzp0qVf47shhBBCiLdBWrrG1CG8UzRaHalpmaSla8jM1Bp9vFarI2JX7HPbrNx9gYqlHFEoLHIbpnjDbG0sTR3CS5NC8jl69erFoEGDqF+/PjVr1qRatWrUqlWLypUr4+DgAMCIESOIi4tjxowZFCtWjN9//52+ffsyf/58GjZsyK5du/j++++ZMGEC1apV486dO4wfP55Ro0axceNGRo4cSWpqKjdv3mTevHnkz5+fx48fs23bNtatW4ejo6NBTI8fP6Zr165UqlSJ8PBwFAoFy5Yt49NPP2Xz5s0UK1YsV/dqZZW3H5e1tFQY/FeI55F8EcaQfBHGMvecCZyy19QhiFdMnZxG0Oz9pg5DGCFslF+2283p94sUks/RpEkTihcvTlhYGAcPHmTfvn0AlC5dmkmTJlGkSBG2bNnChg0bqFChAgA9evQgOjqa0NBQGjZsSKFChZg4cSKtWrUCwNnZmfbt2/P9998DoFQqsbOzw9raGicnJwDy58+PpaWl/vW/bd26laSkJKZPn46V1ZMf38SJEzly5Ai//PILAwYMMPo+FQoLVCp7498gE3BwyGfqEIQZkXwRxpB8EcaSnBFC5NaLvnubw+8XKSRfoEqVKlSpUgWtVkt0dDT79u0jPDycL774ggkTJgAQEBBgcExGRoa+x7J69erExcWxYMECLl26RHx8PDExMWi1xg9tADh37hwPHjygevXqBtvT0tKIi4vL1Tm1Wh1JSY9zdeybYmmpwMEhH0lJKWg0uXvvxLtD8kUYQ/JFGMvccyZkeCNTh/BOUVha4KDMR1JyClqN8fNgxCSombH65AvbDe1YBXdXVS4iFKagVj/Kdrupf784OOTLcW+oFJLPcPPmTRYvXkyfPn0oXry4/tnGihUr4ufnR4sWLfRtIyIisLc3/FcFheLJD2Dz5s0EBwfTsmVLqlatSseOHYmNjdX3SBpLq9VSpkwZFi1alGVf/vz5c3VOIFdj9k1Bo9GaTazC9CRfhDEkX4SxzDVnLOU5ujfKylKBna0VKY8VZOqMz5fyripUSluD2Vr/y1FpS3lXlTwjaUZe9LvDHH6/5P3BtyZiY2PD2rVr2bRpU5Z9T3sbixQpAsCdO3coVaqU/k9kZCSRkZEALFmyhPbt2zNlyhQ6d+5M9erVuXr1KvB/s7NaWBj+T//f1/9Wrlw5bty4gVKp1F+vZMmSzJw5k//9738vf+NCCCGEECLPUCgsCPBze26bTn5uUkSKN04KyWdwdHSkV69ezJkzh1mzZnH+/HmuXr3K77//Tv/+/alZsyY1atSgUaNGfPfdd+zdu5erV68SEhLC4sWLcXV1BaBEiRIcP36cs2fPkpCQwPLlywkPDweezPgKT3oSb9++rS8w8+fPz4MHD7h8+TIZGRkGcbVq1YqCBQsycOBA/v77b+Li4ggODmb//v24u7u/wXdICCGEEEK8Cd7uRQny90CltDXY7qi0laU/hMlY6GTRwufasGEDv/zyC7GxsaSmplKyZEmaNm1Knz59yJ8/PykpKcyaNYtt27bx4MEDXF1dCQwM1C8PcvXqVcaMGcPJkyexsbGhfPnyfPbZZwwaNIiIiAiqVavG6dOnCQoK4sGDB+zcuZOMjAx69erF9evXCQ8PZ9WqVVy/fp0VK1bozzlt2jT++usvNBoNlSpVYvDgwVStWjVX96jRaLl/P/tx2nmFlZUClcoetfpRnu/mF6Yn+SKMIfkijCU5I4zxKvNFq9URezWRxEdpFLK3pZxLIemJfMuY+veLo6N9jp+RlEJSSCEp3jqSL8IYki/CWJIzwhiSL8IYps4XYwpJGdoqhBBCCCGEEMIoUkgKIYQQQgghhDBKjgvJ/v3706FDhyzbP/30U9zd3Tl69KjB9k2bNlG+fHnu3bv30kG6u7vrZ0HNzrx58/D19X3p6/zbhQsX+OOPP17pOf8rODgYd3d3/Z+KFStSq1YtvvzyS6Kjo7M9Zs+ePQQGBlKzZk28vLxo27Ytv/zyCzJCWQghhBAiZ7RaHdHxag6fu0l0vBqtVr5HCWGsHBeStWvX5vz586Smpuq3JSYmcvr0aUqUKMGff/5p0P7YsWOUL1+ewoULv7ponyEwMJB169a90nP26dOH06dPv9JzZsfLy4sDBw5w4MABdu/ezYIFC9BoNHTq1Ilz584ZtJ06dSqDBw+mRo0a/Pzzz6xfv5527doxadIkxo4d+9pjFUIIIYQwd1Extxm26BDTVp1gyaZzTFt1gmGLDhEVc9vUoQlhVnJcSNaqVYuMjAyD4urQoUMULlyYdu3aZVtI1qlT59VF+hz29vY4Ojq+kWu9atbW1jg5OeHk5ETJkiXx9vZmwYIFlClThvHjx+vb7du3j6VLlzJz5kz69u1L+fLlKV26NJ07d2bs2LGsXr2aEydOmPBOhBBCCCHytqiY2yxYfwZ1cprBdnVyGgvWn5FiUggjWOW0YdmyZSlWrBjHjx+nevXqAPz555/4+Pjg4+PDggULuHv3LkWKFOH+/fvExcUxatQoAHbv3s3ixYu5cOECGo0GNzc3Bg0aRL169QC4cuUK48eP5+TJk2i1WqpWrcrw4cMN1kW8fPky3bt3JyoqikKFCtGlSxf69OkDPBnaun79evbu3cu1a9do3Lgxc+fO5aeffuL8+fMULVqUPn368Nlnn+nPt3z5csLCwrh79y7e3t54e3sTGRnJ3r178fX15fr168yfP5+jR4+yYsUKEhMTmTNnDnv37kWtVlOxYkUGDRpEzZo19TFERUVRp04dwsPDUavVfPjhh4wbN46yZcsa9UOxtrYmICCAkSNH8s8//1CiRAlWrVpF+fLl8fPzy9K+RYsWFCtWTNaRFEIIIfKItHSNqUMQ/6HV6ojYFfvcNit3X6BiKcdXuqSGRqsjNS2TtHSNzNqaS7Y2lqYOQWQjx4UkPBne+u9erwMHDjB8+HA8PT1RKpUcOHCANm3aEBUVhZ2dHd7e3pw5c4YBAwbwzTff0LhxYx4+fMjMmTMZPnw4+/btw8bGhsGDB1O+fHl+/fVXMjMzmTp1Kv3792fXrl36a4WHh/Pdd98xfvx4Nm/ezA8//ICnpye1a9fONtbJkyczevRoypUrx7Jlyxg7dix16tTBxcWFiIgIZs2axejRo/H29mb79u3MnTuXEiVKALBu3Tr8/f1p1qwZffr0QaPREBgYSEZGBtOnT8fR0ZGwsDB69uzJypUr8fT0BJ70wtra2rJkyRIyMjIYPnw448aNIywszOgfTLly5QCIjo6mRIkSnDlzho8++ijbtlZWVs98H3LKyipvz7v0dBrinE5HLN5tki/CGJIvwlg5yZnAKXvfVDjiFVInpxE0e7+pwxD/ETYqa0fK28qcPpOMLiQnT56MTqcjJiaGO3fuULduXSwtLalduzZ//vknbdq04X//+x/VqlXD1tYWS0tLRo8eTUBAgP483bp144svvuDevXuUKFGChIQE6tSpg7OzM9bW1kyaNIlLly6h1WpRKJ68iQEBAbRp0waAfv36sXTpUs6cOfPMAqp79+40btwYgEGDBhEREcHff/+Ni4sLoaGhdOvWjfbt2wPw5ZdfcvbsWf0ziY6OjlhaWpI/f34KFSrEvn37OHv2LJs3b9YXeOPGjeP06dOEhoYyZ84cADIzM5k2bRoFCxYEoGPHjkyfPt2Yt1jPwcEBgOTkZODJ86hPt71qCoUFKpX9azn3q+bgkM/UIQgzIvkijCH5IowlOSPEm2Eu31NfJXP4/WJ0IZmYmMilS5c4cOAAFStW1D+bWLduXebPnw886Zlr3rw5ABUqVKBgwYIsWbKES5cuER8fr5+RVKN5Muxj0KBBTJo0iZUrV1KjRg3q1atHixYt9EUkQOnSpQ1icXBwIC3NcHz7v/17OKlSqQQgIyMDtVrN9evXqVKlikH7atWqZZnc5qnY2FiUSqW+iASwsLCgWrVqHDhwQL+tSJEi+iLy6XUzMjKeGePzPC0gnxaPjo6OJCYm5upcL6LV6khKevxazv2qWFoqcHDIR1JSChqNDAsRzyf5Iowh+SKMlZOcCRne6A1HJV4kJkHNjNUnX9huaMcquLuqXtl1FZYWOCjzkZScglYjs8Pmhlr9yNQhvDGm/kxycMiX495QowrJYsWKUaZMGU6cOMHBgwfx8fHR7/Px8WHMmDGcPXuW6OhoJk2aBMDRo0fp2bMnDRs2xNvbm5YtW5KSkkJQUJD+2M6dO9OkSRP27dvHX3/9xdy5c1m0aBEbNmygSJEiAFhaZh0b/bwlL2xsbLJtb2Vl9cJjc3qdf5/vWdfMrbNnzwJPCnF4Mrvr8ePHs22r0Wjo06cP7du3p0mTJrm6nrmM2ddotGYTqzA9yRdhDMkXYazn5YzlK3zGTrwa5V1VqJS2WSba+TdHpS3lXVWv9BlJK0sFdrZWpDxWkKmT3zG58S7+bjaHzySjB9/WqVOH48ePc+LECerWravf7uzsTOnSpYmIiMDR0VE/8cvSpUupWbMm8+bNo3v37tStW5d//vkHeFKI3bt3j++//56MjAzatm3L9OnT2bRpE3fu3MmyNuWroFQqcXZ25uTJkwbb//v639zd3UlOTiY29v8e0NbpdERFRfHBBx+88hg1Gg1r1qyhRo0aFCtWDHiyXmdsbCy7d+/O0n7Tpk38+eefODk5vfJYhBBCCCHeBgqFBQF+bs9t08nP7ZUWkUK8zYzqkYQnw1uHDx+OhYUFVatWNdhXr149fv31V/z8/LCwePI/YYkSJdi9ezfHjh2jePHiHDlyRP9MYXp6OiVKlOCPP/4gISGBIUOGUKBAASIjI7G2tsbDw+MV3GJWX3zxBVOnTuX999/H29ub3bt3s2PHDv1kO/BkSZErV65w9+5dfHx8qFChAkOGDGH06NEULlyY8PBwYmNj+e67714qloyMDO7cuQM8ecby+vXrLF26lCtXrrBq1Sp9u7p169KxY0cGDx5MUFCQ/vnPPXv2sGDBArp27Yq3t/dLxSKEEEII8Tbzdi9KkL8HK3dfMOiZdFTa0snPDW/3oiaMTgjzYnQhWbNmTVJTU2nQoAHW1tYG+3x8fFixYoVBT+XAgQO5e/cuffv2BeCDDz5g0qRJDBs2jNOnT1O2bFlCQkKYOnUq3bt3JyUlhQoVKrBkyRJcXV1f8vay16lTJx48eMDs2bNRq9XUqFEDf39/oqKi9G26du3K1KlTuXDhAps2bWLp0qX62WTT09Px8PBg+fLlWZ61NNaJEyf0Q4QtLS1xdHSkXr16rFu3LsuyIePGjePDDz/kl19+ITQ0lMzMTN5//33Gjh2Lv7//S8UhhBBCCPEu8HYvipebE7FXE0l8lEYhe1vKuRSSnkghjGShM+ZhwbfE/v37+eCDDyhZsqR+2+jRo0lISODnn382YWSmodFouX8/bz/EbGWlQKWyR61+lOfHiwvTk3wRxpB8EcaSnBHGkHwRxjB1vjg62ud4sp28v0DJa7Bx40b69evHyZMnuX79Ohs2bGDTpk20bt3a1KEJIYQQQgghRJ5n9NDWt8Ho0aOZMmUKQUFBJCUlUapUKb799lvatm1r6tCEEEIIIYQQIs97qwrJzMxMIiIi2LhxI5cvX8bW1paKFSvSu3dvatWqBTyZgXXy5MlMmTLljcTk6+uLv78/AwYMeG3XiIyMZMSIEcTExLy2awghhBBCCCHEU29NIZmWlkaPHj34559/GDhwIF5eXqSmpvLrr7/So0cPpk2bRsuWLU0dphBCCCGE+A+tVieT3whhZt6aQnLOnDnExMSwZcsWg2U8Ro4cycOHD5kwYQK+vr4mjFAIIYQQQvxXVMztLMtxqJS2BMhyHELkaW/FZDsZGRn8+uuvtG3b1qCIfOrrr78mJCQEOzu7LPt+//132rZti6enJx999BGzZ88mPT0dgODgYDp06GDQ/vr165QvX55Dhw4BcPz4cTp37oynpycNGzZk3LhxPHz48Jmxrl27lpYtW+Lp6UmVKlUICAjg9OnT+v2+vr6EhoYyYMAAvLy8qFmzJhMmTCAzM1PfZteuXbRs2ZLKlSsTEBDAjRs3jHvDhBBCCCHygKiY2yxYf8agiARQJ6exYP0ZomJumygyIcSLvBU9klevXiUxMZGqVatmu79YsWIUK1Ysy/b9+/fz9ddfM2LECOrUqUNCQgLjx4/n8uXLzJkzh7Zt29K1a1cSEhL0a1pu3ryZ4sWLU6tWLaKjo+nRowdffvklEydO5O7du0ybNo3AwEDWrFmDhYXhkIxdu3bx/fffM2HCBKpVq8adO3cYP348o0aNYuPGjfp2c+bMYejQoQwfPpyjR48ycuRIPDw8aNOmDcePH2fAgAH079+f5s2bc+zYMcaPH/8K300hhBDCOGnpGlOH8EZptDpS0zJJS9fIcg4vQavVEbEr9rltVu6+QMVSjmY9zFXyxXRsbSxNHcJb7a0oJB88eABAwYIFjTruxx9/5NNPP6Vjx44AuLq6Mm7cOD7//HOuXbtG9erVcXFxYdOmTfTv3x94Uki2bt0ahUJBaGgodevWpW/fvgCULl2amTNn4ufnx9GjR6lZs6bB9QoVKsTEiRNp1aoVAM7OzrRv357vv//eoJ2Pjw/dunUDwMXFhRUrVnD8+HHatGlDeHg4VatW1cdTpkwZYmNjCQsLM+re/8vKKm93Tj9dzyan69qId5vkizCG5MvLC5yy19QhiLeUOjmNoNn7TR2GMFNho/xMHYLRzOkz6a0oJB0dHQFITEw06rhz585x6tQp1q1bp9+m0+kAiIuL47333qNNmzZs3ryZ/v37c+7cOS5evMjChQv1x8fHx+Pl5ZXl3HFxcVkKyerVqxMXF8eCBQu4dOkS8fHxxMTEoNUa/utU2bJlDV4rlUoyMjIAiI2NpW7dugb7vby8XqqQVCgsUKnsc338m+TgkM/UIQgzIvkijCH5IoQQbxdz+X6bHXP4THorCkkXFxeKFCnC8ePHadasWZb9cXFxTJw4kREjRhhs12q19OrVC39//yzHODk5AeDv78/8+fM5ffo027Zto2rVqpQqVUp/fMuWLfU9kv/2tLj9t82bNxMcHEzLli2pWrUqHTt2JDY2NkuPpI2NTZZjnxa4FhYWWQpPa2vrLO2NodXqSEp6/FLneN0sLRU4OOQjKSkFjUaGhYjnk3wRxpB8eXkhwxuZOoQ3SmFpgYMyH0nJKWg1OlOHY7ZiEtTMWH3yhe2GdqyCu6vq9Qf0mki+mI5a/cjUIRjN1J9JDg75ctwb+lYUkgqFgvbt27NixQp69uyZZcKdn376idOnT+Ps7Gyw3c3NjcuXL+sLQ4AjR44QFhbG2LFjyZ8/P87OztSsWZMdO3bw22+/ERQUZHD8xYsXDY6Pi4tj+vTpDB48GKVSaXC9JUuW0L59e8aNG6fftmfPHuBJofjfZyqzU758eU6cOGGw7cyZMy887kXMZcy+RqM1m1iF6Um+CGNIvuSepRk/v5YbVpYK7GytSHmsIFMnOZNb5V1VqJS2WSba+TdHpS3lXVVm/Yyk5IvpmPPvdHP4TMr7g29zqG/fvpQuXZqAgAA2bNhAQkICp06dYsSIEWzYsIHx48eTP39+g2O++OILduzYwfz587l8+TJ//fUXI0aMIDk5Wd8jCU96JVeuXEliYiJNmzbVbw8MDOTcuXOMGzeOuLg4Tpw4wZAhQ7hy5QqlS5fOEmOJEiU4fvw4Z8+eJSEhgeXLlxMeHg6gnyn2RQIDA4mOjmbq1KlcvnyZTZs26c8hhBBCCGEuFAoLAvzcntumk5+bWReRQrzN3ppCMl++fISHh9OuXTtCQkJo3bo1ffr04fbt26xYsYImTZpkOaZJkybMmjWL3bt307JlS4YNG4aPjw/z5883aPfJJ58A4OfnR4ECBfTbq1Spwk8//cT58+fx9/fnyy+/pEyZMixfvjzb4amjR4+mSJEidOnShQ4dOvD7778zbdo0AIMlQJ6nQoUKhISEcOTIEVq1asXy5cuzHVorhBBCCJHXebsXJcjfA5XS1mC7o9KWIH8PWUdSiDzMQvf04TvxztJotNy/n7fHkFtZKVCp7FGrH+X5bn5hepIvwhiSL8JYkjOvnlarI/ZqIomP0ihkb0s5l0JvTU+k5IswhqnzxdHR/t16RlIIIYQQQpgvhcKC8qXMd0IdId5Fb83QViGEEEIIIYQQb4YUkkIIIYQQQgghjGK2hWTXrl1xd3fP9s/UqVNf6twXLlzgjz/+yHZfWFgYFStW5NEjw2cKZ8yYgbu7e5aJem7duoW7uzu//fZbruO5ceMGW7du1b/29fVl3rx5uT6fEEIIIUReoNXqiI5Xc/jcTaLj1Wi1MnWHEObCrJ+RbNq0KSNHjsyyPV++fC913j59+uDv70/Dhg2z7KtduzYajYZTp05Ru3Zt/fY///yTEiVK8Oeff9K/f3/99v/9738oFAqDtsb65ptvcHZ2pnnz5rk+hxBCCCFEXhIVc5uVuy8YrCOpUtoS4Ocms7UKYQbMtkcSwM7ODicnpyx//r1Ex6vm5uaGk5MTUVFR+m137twhJiaGL7/8ktOnT5OYmKjfd+zYMSpVqkShQoVeW0xCCCGEEOYkKuY2C9afMSgiAdTJaSxYf4aomNsmikwIkVNm3SP5IhqNhhUrVrBq1Spu3LhByZIl6d69O506dQLgyJEj9OjRg6+//prQ0FCcnZ25f/8+//zzD/Pnz+fo0aOsWLEiy3lr1qzJiRMn9K8PHDhAyZIl8ff3Z8qUKRw6dIhmzZoBTwrJxo0b69tu2LCBpUuXcuXKFYoUKUL79u3p06cPlpaWXLt2jcaNGzN48GDCwsKws7PDwcGBc+fOAXD06FH27t0LPCle+/fvz4EDB7C1taVNmzYMHz4cS0vL1/Z+CiGEEG9SWrrG1CFkS6PVkZqWSVq6RpZzyAWtVkfErtjntlm5+wIVSzm+FUuASL5kZWsj31ffBm91ITllyhQ2btzI6NGjqVy5Mvv372fixImkpaXRvXt34EmxuW/fPtasWUNKSgrFihXD39+fZs2a0adPn2zPW6dOHSZPnoxWq0WhUHDgwAHq1q2LjY0NNWvW5M8//6RZs2YkJiZy8eJFxowZA8Dy5cuZOXMmwcHB1K1bl7///pvvv/8etVptMER3/fr1/Pzzz6SkpODi4kLfvn0pXry4/jwA69at45tvvuGbb77hyJEjjBw5Ejc3N9q3b5+r98rKKm93Tj9dzyan69qId5vkizCG5EveFThlr6lDECaiTk4jaPZ+U4chXpOwUX6mDiHPMqfPJLMuJDdv3syOHTsMtnl7e/PTTz/x8OFDVq1aRXBwMC1btgSgdOnSXLt2jSVLlvD555/rjwkMDKR06dL615aWluTPn/+Zw1Fr165NcnIysbGxlCtXjoMHDzJu3DgAfHx8WLx4MfCkNzJfvnx4eXmh0+kICQmhS5cudO7cWR9PYmIi06dPZ+DAgfrzBwQE8MEHH+hfW1tbY2dnh6Ojo37bxx9/rL8HFxcXwsLCOHPmTK4KSYXCApXK3ujjTMHB4eWefxXvFskXYQzJFyGEeDPM5XunKZnDZ5JZF5K+vr4MHTrUYJudnR0Aly5dIiMjA29vb4P9NWrU4Oeff+bevXv6bf8uInOiZMmSlCpViuPHj5Oenk5SUpJ+Mp26desyfvx4Ll++zLFjx6hRowbW1tbcu3ePu3fvZhtPRkYGly5donDhwgCUKlXqhTH8N+aCBQuSlpaWfeMX0Gp1JCU9ztWxb4qlpQIHh3wkJaWg0ciwEPF8ki/CGJIveVfI8EamDiFbCksLHJT5SEpOQauRWUaNFZOgZsbqky9sN7RjFdxdVa8/oNdM8iUrtfrRixu9o0z9meTgkC/HvaFmXUja29s/s+jS6bL/H1WrffIDsbL6v1u3tbU1+tq1atXi5MmTPHjwAE9PTxwcHAAoU6YMzs7OHDt2jGPHjtGqVSuj43laDD9Pds9CPusaOWEuY/Y1Gq3ZxCpMT/JFGEPyJe+xzKPPx1lZKrCztSLlsYJMneSMscq7qlApbbNMtPNvjkpbyruq3opnJCVfspLftS9mDp9JeX/wbS6VLVsWa2trg9lV4clwUycnJwoWLPhS569Tpw6nTp3if//7H3Xr1jXY5+Pjw7Fjxzh//rx+X5EiRShSpEi28VhbW+Pq6vpS8QghhBBCmAOFwoIAP7fntunk5/ZWFJFCvM3e2kKyQIECfPbZZ8ydO5ctW7YQHx9PREQEK1euJDAwEAuLZ/9ysre358qVK9y9e/eZbWrWrElCQgLHjx/Hx8fHYJ+Pjw87d+6kSJEilC1bVr+9Z8+ehIeHs3LlSuLj49m8eTPz58/ns88+Q6lUPjee69evc/PmTSPeASGEEEKIvMnbvShB/h6olIajwhyVtgT5e8g6kkKYAbMe2voiI0aMQKVSMWPGDO7evUvp0qUZM2YMn3766XOP69q1K1OnTuXChQts2rQp2zYqlQp3d3euXbuGp6enwb7atWuTnp5OnTp1DLYHBgZiY2PDzz//zKRJkyhevDhffPEFPXv2fG48HTt25JtvvqFVq1b89ddfObhzIYQQQoi8zdu9KF5uTsReTSTxURqF7G0p51JIeiKFMBMWupd5sE68FTQaLffv5+2Hnq2sFKhU9qjVj/L8eHFhepIvwhiSL8JYkjPCGJIvwhimzhdHR/scT7bz1g5tFUIIIYQQQgjxekghKYQQQgghhBDCKHmukPT19cXd3Z1ly5Zlu3/MmDG4u7szb948IiMjcXd3f8MRvhnu7u5ERkYCMG/ePHx9fU0ckRBCCCHEy9NqdUTHqzl87ibR8Wq0WnnKSghzlCcn27G2tmbHjh306NHDYHtmZiY7d+7Uz7jarFkz6tWrZ4oQ36jAwEA6d+5s6jCEEEIIIV5KVMxtVu6+YLCGpEppS4Cfm8zUKoSZyXM9kvBk1tOTJ09mWe7i8OHD5M+fnxIlSgBgZ2eHk5OTKUJ8o+zt7XF0dDR1GEIIIYQQuRYVc5sF688YFJEA6uQ0Fqw/Q1TMbRNFJoTIjTzZI+np6UlcXBzbt2+ne/fu+u3btm2jadOm/PbbbwBERkYyYsQIYmJigCfDQSdOnMiWLVs4fvw4Dg4OdOzYkf79+wNPhohGRUVRp04dwsPDUavVfPjhh4wbN06/3mNycjLTpk1j165dZGRkUKlSJYYNG0blypUB0Gq1hISEEBkZyfXr17GxsaFq1aqMGTMGV1dXfRxjxoxh48aNnD9/ntKlS/P111/TuHFj/b388ccfLFy4kAsXLmBvb0/z5s0ZNGgQdnZ2Wd6PefPmsX79evbu3QvAhg0bCAkJISEhgUKFCtGkSROGDRuGjY3NK/5JCCGEeFukpWtMHcJbQ6PVkZqWSVq6RmbhzCGtVkfErtjntlm5+wIVSzm+dct/SL6YD1sbS1OHYFbyZCEJ0LRpU4NCMj09nd27d7N8+XJ9IZmdqVOnMmrUKMaPH8/WrVuZNWsWNWvWpHr16gAcO3YMW1tblixZQkZGBsOHD2fcuHGEhYWh0+n44osvsLOzY/HixRQoUICNGzfSqVMnfvnlFypWrEhYWBihoaFMnTqVcuXKkZCQwOjRo5kyZQoLFy7UxzFjxgyGDh3KlClTiIyMpH///kRERFC1alV27drFwIEDGTBgAFOnTuXSpUuMHTuWq1evGpwjO9HR0YwaNYoZM2boC+4hQ4agUqno169frt9vK6s82Tmt93Qa4pxORyzebZIvwhjvSr4ETtlr6hCEeC51chpBs/ebOgzxDgsb5WfqEMzqMylPF5KhoaHcunWLYsWKcfDgQRwdHalYseJzj2vTpg2tW7cGoG/fvoSGhnL8+HF9IZmZmcm0adMoWLAgAB07dmT69OnAk6GzJ0+e5PDhwxQqVAiAwYMHc/z4ccLCwpgyZQqurq5MnTqVRo0aAeDs7EyTJk3Yvn27QRxt27bVP9c4dOhQjh49Snh4OFWrVmXJkiV89NFH+sKvTJky6HQ6goKCuHjxIh988MEz7+/atWtYWFjg7OxMyZIlKVmyJKGhoRQoUMCYt9eAQmGBSmWf6+PfJAeHfKYOQZgRyRdhDMkXIYR4t+Wl78Pm8JmUZwtJDw8PXFxc2LFjB926dWPbtm00b978hcc9HaL6lFKpJCMjQ/+6SJEi+iLyv/vPnj2LTqfTF4lPpaenk5b2ZDy/r68vf//9N3PmzOHy5ctcvnyZixcvUqxYMYNjatasafDay8uLgwcPAhAbG5vlXmrUqKHf97xCsl69enh5edG+fXvee+896tatS+PGjfHw8Hj2m/ICWq2OpKTHuT7+TbC0VODgkI+kpBQ0GhkWIp5P8kUY413Jl5DhjV7cSOSIwtICB2U+kpJT0GpkxtGciElQM2P1yRe2G9qxCu6uqtcf0Bsk+WI+1OpHpg7B5J9JDg75ctwbmmcLSfi/4a2fffYZe/bsYe3atS88JrvnBHU63XP3P6XVailQoIB+2Y3szrtkyRIWLFiAv78/tWvXpnv37uzZs4etW7catLeyMnxrNRoNCoUiSzz/vnZ2x/2Xra0tYWFhnDt3jgMHDnDgwAH69u1LmzZtmDx58nOPfR5zGbOv0WjNJlZhepIvwhhve75YvmXPnZmSlaUCO1srUh4ryNS9vTnzKpV3VaFS2maZaOffHJW2lHdVvXXPSEq+mI+89BlgDp9JeXrwbdOmTTl+/Di//vorLi4uWXobX7Vy5crx8OFDMjIyKFWqlP5PSEgIe/bsAeDHH38kKCiIsWPH8tlnn1GlShWuXLmSpTg8ffq0wesTJ05QqVIl4MlkPMePHzfYf+zYMSBrj+p/7du3j/nz51OxYkV69+5NWFgYAwcOZNu2bS9170IIIYQQr4tCYUGAn9tz23Tyc3vrikgh3mZ5upCsUKECpUqVYubMmTka1vqy6tWrR4UKFRg0aBCHDx8mPj6eyZMnExkZqS/wSpQowcGDB7l48SKXLl1i1qxZ7Ny5k/T0dINz/fzzz2zevJnLly8zdepUYmJi+PzzzwHo1asXO3fuZOHChVy+fJnff/+d8ePH06hRoxcWktbW1ixYsIDly5dz9epVzpw5wx9//IGXl9freVOEEEIIIV4Bb/eiBPl7oFLaGmx3VNoS5O8h60gKYWby9NBWeNIruWjRIpo1a/bar2VpacnSpUuZPn06X3/9NSkpKZQtW5b58+dTu3ZtAKZNm8b3339Pu3btsLe31y8fMnbsWG7cuEHJkiWBJ5P4LF++nNjYWMqXL09oaCjly5cH4JNPPuGHH35g0aJFLFy4EEdHR1q0aMHAgQNfGGOdOnWYOHEiS5cuZdasWdjZ2dGgQQOCg4Nf3xsjhBBCCPEKeLsXxcvNidiriSQ+SqOQvS3lXApJT6QQZshCl90De+KluLu7M3nyZNq2bWvqUHJEo9Fy/77pHy5+HisrBSqVPWr1ozw/XlyYnuSLMIbkizCW5IwwhuSLMIap88XR0T7Hk+3k6aGtQgghhBBCCCHyHikkhRBCCCGEEEIY5Z0sJH19fXF3d9f/KV++PFWrVqVLly7873//e6lzR0VFERERQdu2bbl27Rru7u4cOXLkFUWeveDgYLp27fparyGEEEIIIYQQT72ThSRAYGCgfh3G/fv3s3r1agoUKECvXr24ceNGrs8bEBBAQkLCK4xUCCGEECLv02p1RMerOXzuJtHxarRamYZDiLdZnp+19XXJnz8/Tk5O+tdFixZl3Lhx1K9fn127dumX6hBCCCGEEM8XFXOblbsvoE5O029TKW0J8HOTZT2EeEu9sz2S2bGyelJX29jYkJqayuzZs2ncuDGVK1emdevW7NixQ982MjKSjz76iAkTJuDt7U2/fv1wd3cHYMSIEdkux5Gens7UqVPx9fXFw8ODGjVq8NVXX3H//n0A/VDYHTt20KFDBzw8PPD19WXNmjX6c+h0OhYuXEj9+vWpUqUKI0aMIC0tLcu1hBBCCCHehKiY2yxYf8agiARQJ6exYP0ZomJumygyIcTr9M72SP7XrVu3mDRpEvnz56dBgwYMHjyYc+fOMXbsWEqVKsWWLVv46quvmD9/Pn5+fgAkJCRw+/ZtNmzYQGpqKuPGjcPHx4dvv/2Wtm3b8uDBA4NrTJs2jd9//50pU6bg7OxMTEwMI0aMYNGiRYwcOVLfbvLkyYwePZpy5cqxbNkyxo4dS506dXBxcWHJkiX89NNPfP/991SsWJE1a9YQGRlJjRo13uj7JYQQwjykpWtMHcJbR6PVkZqWSVq65p1fzkGr1RGxK/a5bVbuvkDFUo7v7FqRki+vn62NpalDeCe9s4Xk4sWLWbp0KQCZmZmkp6dTtmxZZs+eTUpKCnv27OHHH3+kYcOGAAwYMIDo6Gh+/PFHfSEJ0K9fP1xcXAzOrVQqUSqVWQrJypUr06RJE6pVqwaAs7MzderUITbW8Bdw9+7dady4MQCDBg0iIiKCv//+m/fee48VK1bQrVs3WrRoATzp/XwVk/lYWeXtzumn69nkdF0b8W6TfBHGeNvzJXDKXlOHIN5x6uQ0gmbvN3UY4i0WNsrvxY3MhDl9Jr2zhWTHjh31M50qFAoKFSqEUqkEYNu2bQB4e3sbHFO9enV++OEHg22lS5fO8TVbt27NoUOHmDFjBleuXOHSpUtcvnxZX1g+VbZsWf3fn8aUkZGBWq3mzp07VK5c2aB9lSpViIuLy3Ec/6VQWKBS2ef6+DfJwSGfqUMQZkTyRRhD8kUIIcyTuXyPNYY5fCa9s4VkwYIFKVWqlFHH6HQ6/XOUT9nZ2eX4+DFjxrBjxw7atGmDr68vQUFBhIaGcuvWLYN2NjY22V7bwsJC//d/+29MxtJqdSQlPX6pc7xulpYKHBzykZSUgkYjw0LE80m+CGO87fkSMryRqUN46ygsLXBQ5iMpOQWt5t2emTQmQc2M1Sdf2G5oxyq4u6pef0B5kOTL66dWPzJ1CK+MqT+THBzy5bg39J0tJJ/n6aQ5UVFRNGr0fx/Ax44d44MPPsjVOdVqNWvWrGHWrFk0a9ZMv/3SpUvkz58/R+dQqVSUKFGCqKgog+G1Z86cwdraOldxPWUuY/Y1Gq3ZxCpMT/JFGONtzRfLd/S5tNfJylKBna0VKY8VZOrevpwxRnlXFSqlbZaJdv7NUWlLeVfVO/uMpOTL6/c2/u42h88kKSSzUbZsWRo1asS4ceOwsLCgVKlSbN26lT179jB79uznHps/f37i4uJQq9UG2wsUKIBSqWTPnj1UqlSJ1NRUwsPDOXv2LB9++GGOY/viiy+YOnUq77//PtWqVWPjxo2cOnUqyzBcIYQQQojXTaGwIMDPjQXrzzyzTSc/t3e2iBTibZb3n+I0kR9++AE/Pz9GjhxJq1at+P3335k3bx5NmjR57nGBgYGEh4czYsQIg+3W1tbMmTOH2NhYWrZsSa9evUhJSWHw4MFcvHiRlJSUHMXVuXNnhg0bxqJFi2jdujUXLlygffv2ub5PIYQQQoiX4e1elCB/D1RKW4Ptjkpbgvw9ZB1JId5SFrr/PnAn3jkajZb79/P22HIrKwUqlT1q9aM8380vTE/yRRhD8kUYS3Ime1qtjtiriSQ+SqOQvS3lXApJTySSL8I4ps4XR0d7eUZSCCGEEEK8OQqFBeVLvZsT6gjxLpKhrUIIIYQQQgghjCKFpBBCCCGEEEIIo8jQ1mz4+vpy/fp1/Wtra2uKFClCgwYN+Oqrr3B0dHwl11Gr1ezevZsOHTq8kvMJIYQQQrxJ8lykEO8uKSSfITAwkMDAQABSU1OJjY1l+vTpdOnShTVr1qBUKl/6GtOmTePatWtSSAohhBDC7ETF3Gbl7gsGa0iqlLYE+LnJTK1CvANkaOsz5M+fHycnJ5ycnHBxcaFx48YsXbqUf/75h59++umVXEMmzBVCCCGEOYqKuc2C9WcMikgAdXIaC9afISrmtokiE0K8KdIjaYSSJUvy0UcfsXXrVgYNGkRsbCwzZ87k+PHjpKSkUKxYMTp37kxgYCD379+nfv36TJgwgTZt2ujPMXPmTA4dOoSbmxvr168HwN3dnZiYGDQaDStWrGDVqlXcuHGDkiVL0r17dzp16gTAkSNH6NGjB4sWLWL69OlcuXKF9957j6FDh+Ln52eKt0QIId4KqWmZpKVrZGp+kSMare6dzhmtVkfErtjntlm5+wIVSznKMFckX0TO2NpYmjoEo0khaaRy5cqxceNGHj58SGBgIHXr1mX16tVYWlqydu1apk6dSu3atalQoQINGzZkw4YN+kJSq9WyadMmevfuTatWrUhNTeXmzZvMmzcPgClTprBx40ZGjx5N5cqV2b9/PxMnTiQtLY3u3bsDoNFomD59OiNHjqREiRL88MMPfPPNN+zfvx97e/tc35eVVd7unH66nk1O17UR7zbJF2EMS0sFHb7dauowhHirqJPTCJq939RhCGE2wkY96RQyp+8wUkgaycHBAYAHDx7QrVs3OnfurC/gBg4cyE8//URMTAwVKlSgXbt29OvXj1u3blGsWDH++usv7t+/T4sWLVAqldjZ2WFtbY2TkxMPHz5k1apVBAcH07JlSwBKly7NtWvXWLJkCZ9//rk+hq+//pratWsD0K9fP3bs2EFsbCxeXl65uieFwgKVKvdF6Jvk4JDP1CEIMyL5IoQQQghz8N/v4ubwHUYKSSMlJycDUKhQIQICAtiyZQvnzp0jISGB6Oho4EnPI0D9+vUpXLgwGzdupHfv3qxfv57GjRtTsGDBLOe9dOkSGRkZeHt7G2yvUaMGP//8M/fu3dNve//99/V/L1CgAAAZGRm5vietVkdS0uNcH/8mWFoqcHDIR1JSChqNDAsRzyf5Ioxhaalg7aTmJCWnoNXIs+vixRSWFjgo872zOROToGbG6pMvbDe0YxXcXVWvP6A87l3PF5EzavUjwPTfYRwc8uW4N1QKSSOdPXuW0qVL8/jxYz777DMcHR3x9fXFx8eHypUr06BBA31bS0tL2rRpw+bNm+nSpQu7d+9mzpw52Z73WRPvPC1Kraz+70dlY2OT4+NzylzG7Gs0WrOJVZie5IvIKTtbK1IeK8jUSb6IF7OyVLzTOVPeVYVKaZtlop1/c1TaUt5VJc9IIvkicua/31fM4TtM3h98m4fcvHmTPXv20LJlS7Zs2UJiYiKrVq2iX79+fPTRRzx48AAwLOratWtHbGwsK1asQKlU4uPjo99nYfF/v1zLli2LtbU1UVFRBtc8duwYTk5O2fZiCiGEEEK8aQqFBQF+bs9t08nPTYpIId5yUkg+w+PHj7lz5w537tzh6tWr7N69m169evHee+/Ro0cPihcvTkpKCtu3b+fGjRscOHCAwYMHA5Cenq4/T5kyZahatSoLFy6kdevWWFr+34xM+fPn5/bt21y9epUCBQrw2WefMXfuXLZs2UJ8fDwRERGsXLmSwMBAg6JTCCGEEMKUvN2LEuTvgUppa7DdUWlLkL+HrCMpxDtAhrY+w9KlS1m6dCkA1tbWlChRgmbNmhEYGIi9vT1NmjTh7NmzTJkyhYcPH+Ls7EyHDh3Ys2cPp0+f1i/ZAdC2bVuOHz+Ov7+/wTXatGnDrl27aNGiBTt37mTEiBGoVCpmzJjB3bt3KV26NGPGjOHTTz99o/cuhBBCCPEi3u5F8XJzIvZqIomP0ihkb0s5l0LSEynEO8JC97IP14kXmjdvHocOHWLVqlWmDiVbGo2W+/cfmTqM57KyUqBS2aNWP8rz48WF6Um+CGNIvghjSc4IY0i+CGOYOl8cHe1lsp28ICoqisuXLxMWFsb3339v6nCEEEIIIYQQ4pWQQvI1+v333wkPD6ddu3Y0bdrU1OEIIYQQQgghxCthtpPt+Pr6Mm/evDdyLZ1Oh5+fHwMGDHhmmx49ehAYGGiwbejQoZw8eZLRo0e/7hCFEEIIIV4LrVZHdLyaw+duEh2vRquVp6KEENIjmSMWFha0bduWRYsWkZycjFKpNNh/8+ZNDh8+zMyZM00UoRBCCCHEqxcVc5uVuy8YrBmpUtoS4OcmM7MK8Y4z2x7JN61t27ZkZmayffv2LPs2bdqEg4MDfn5+JohMCCGEEOLVi4q5zYL1ZwyKSAB1choL1p8hKua2iSITQuQFb22PZHp6OrNmzWLHjh3cvn2b/PnzU7t2bb777jscHR0B2LBhAyEhISQkJFCoUCGaNGnCsGHDsLGxyXK+4sWLU7duXTZv3kyHDh0M9m3YsIFWrVphY2NDeno6c+bMYdOmTTx8+BA3NzcGDhyIj4+Pvv2BAweYOXMmFy9epFSpUvTo0YNvv/2WPXv28N5773Hjxg2mT5/O4cOHSUpKonDhwrRs2ZIhQ4agUCjQaDT88MMPbNmyhXv37vHee+/x+eefGyw5IoQQQrxN0tI1pg5BT6PVkZqWSVq65q2dhVOr1RGxK/a5bVbuvkDFUo6y3McLvOp8sbWxfHEjId6At7aQnDZtGr///jtTpkzB2dmZmJgYRowYwaJFixg5ciTR0dGMGjWKGTNm4OnpSVxcHEOGDEGlUtGvX79sz9muXTsGDx7MrVu3KFasGACnTp0iLi6O2bNnAzBixAji4uKYMWMGxYoV4/fff6dv377Mnz+fhg0bcv78efr06cPnn3/OzJkzOX/+POPGjTO4zpdffomTkxPLli3D3t6ePXv2MHnyZLy8vPDz82PlypVs376dWbNm6a8xduxY3NzcqFatWq7eLyurvN05/XQa4pxORyzebZIvwhiSL+YhcMpeU4cg/kOdnEbQ7P2mDuOdEzZKRsC9zczpM+mtLSQrV65MkyZN9IWVs7MzderUITb2yb+uXbt2DQsLC5ydnSlZsiQlS5YkNDSUAgUKPPOcjRs3xsHBgS1bttCzZ0/gSW+kp6cn5cqVIz4+ni1btrBhwwYqVKgAPJmEJzo6mtDQUBo2bMjy5cvx8PBg+PDhALz//vvcu3ePiRMnApCamkrr1q1p2rQpJUqUAKB79+6EhIQQExODn58fCQkJ5M+fn/fee4+iRYvSpUsX3n//fcqUKZOr90qhsEClss/VsW+ag0M+U4cgzIjkizCG5IsQwhyYy3c28XLM4TPprS0kW7duzaFDh5gxYwZXrlzh0qVLXL58WV9Y1qtXDy8vL9q3b897771H3bp1ady4MR4eHs88p42NDa1atWLz5s307NmT9PR0tm7dyuDBgwE4d+4cAAEBAQbHZWRk4ODgoG9Tp04dg/3Vq1fX/93Ozo4uXbqwfft2Tp06RXx8PDExMdy9exet9slwiM6dO7N7924aNGhAhQoVqFu3Ls2bN6dw4cK5eq+0Wh1JSY9zdeybYmmpwMEhH0lJKWg0b+cwIvHqSL4IY0i+mIeQ4Y1MHYKewtICB2U+kpJT0GrezhlMYxLUzFh98oXthnasgrur6vUHZMZedb6o1Y9eQVQirzL1Z5KDQ74c94a+tYXkmDFj2LFjB23atMHX15egoCBCQ0O5desWALa2toSFhXHu3DkOHDjAgQMH6Nu3L23atGHy5MnPPG/79u0JCwvj4sWLXL58mfT0dJo3bw48WSYEICIiAnt7w38tUiiedlNb6gvC7Dx+/JguXbqQmppKkyZN8Pf3x9PTk86dO+vblC5dmp07d3L06FEOHjzIH3/8QUhICJMnT8bf3z9X75e5POOh0WjNJlZhepIvwhiSL3mbZR56Ds/KUoGdrRUpjxVk6t7OnCnvqkKltM0y0c6/OSptKe+qkmckX+BV54v8nno3mMNn0ltZSKrVatasWcOsWbNo1qyZfvulS5fInz8/APv27eP06dP079+fihUr0rt3bxYtWsSPP/743ELS3d0dDw8Ptm3bxqVLl2jSpIl+OKybmxsAd+7coWLFivpjZs2ahUKh4KuvvqJ8+fKcOnXK4JwnTpzQ//3AgQOcPXuWgwcPUqRIEQASExO5d++evlANCwujcOHCNG/enLp16zJ8+HB69OjBtm3bcl1ICiGEEEI8pVBYEODnxoL1Z57ZppOfmxSRQrzDzLqQjI+PZ/9+w4e87ezs8PLyQqlUsmfPHipVqkRqairh4eGcPXuWDz/8EABra2sWLFhAgQIFaNy4MQ8ePOCPP/7Ay8vrhddt3749P//8M7du3SIkJES/3c3NjUaNGvHdd98xZswY3Nzc2L59O4sXL9YXp4GBgbRp04YZM2bQrl07Ll68yNy5c4En61UWL14ceLKkyCeffMI///zDDz/8QEZGBunp6QDcv3+fBQsWYGdnR/ny5bl06RLnz5+nW7duL/+mCiGEEEIA3u5FCfL3yLKOpKPSlk6yjqQQ7zwL3dNuLjPj6+vL9evXs2x3dnZm7969HDx4kClTphAfH0/BggWpWbMmbm5uLF68mIMHD5IvXz4iIyNZunQpV69exc7OjgYNGhAcHKxfHuRZkpOT8fHxoUSJElnWlUxJSWHWrFls27aNBw8e4OrqSmBgIO3atdO32bt3Lz/88ANXrlyhTJkyfPLJJ8ybN48///yTokWLsnz5cpYvX879+/cpVqwYzZo149q1a9y9e5eff/6ZzMxM/TXu3LmDk5MTbdq0oX///lhaGj8ltEaj5f79vD3e3spKgUplj1r9KM938wvTk3wRxpB8EcZ613JGq9URezWRxEdpFLK3pZxLIemJNMK7li/i5Zg6Xxwd7XP8jKTZFpLm6tSpU1hZWRkMfd28eTPffvstJ06cwMrqzXcSSyEp3jaSL8IYki/CWJIzwhiSL8IYps4XYwrJvL9AyVvm6RDUPXv2cOPGDf766y/mzZtH8+bNTVJECiGEEEIIIYSxpHJ5wz799FPu3LnDpEmTuHXrln7SnIEDB5o6NCGEEEIIIYTIESkkn+G/z2BaW1vj7OxMhw4d6NWrV47P8+uvvzJr1iySkpKYOXMmSUlJrF69mqSkJGbNmsVHH3303OPnzZvH+vXr2bt3b67vRQghhBDCGPJcpBDiRaSQfI7AwEACAwMBSE1N5dSpU4waNYp8+fIZrOv4PFOnTqVx48b0798fR0dHGjRoYPBaCCGEECIviYq5nWWmVpXSlgCZqVUI8S/yjORz5M+fHycnJ5ycnHBxcaF58+a0bNmSX3/9NcfnePDgAdWqVcPZ2Zl8+fJleS2EEEIIkVdExdxmwfozBkUkgDo5jQXrzxAVc9tEkQkh8hrpkTSSnZ2d/u9du3bF2dmZKVOmZNnWv39/GjduDMC3337LggUL9ENln77eu3cvsbGxzJw5k+PHj5OSkkKxYsXo3Lmzvif0vxITE5kzZw579+5FrVZTsWJFBg0aRM2aNV/jXQshhMhr0tI1pg7hnaXR6khNyyQtXfNWzcKp1eqI2BX73DYrd1+gYilHGeZqhLc1X14lWxvjl68TpieFpBFOnTrFli1bGDBgwAvblihRggMHDuDj48O3335L8+bN0el0+tctW7YkJSWFwMBA6taty+rVq7G0tGTt2rVMnTqV2rVrU6FCBYNzajQaAgMDycjIYPr06Tg6OhIWFkbPnj1ZuXIlnp6eub43K6u83Tn9dBrinE5HLN5tki/CGOaaL4FT5Nl58eapk9MImr3f1GGIt0zYKD9Th5BnmNNnkhSSz7F48WKWLl0KQEZGBhkZGXz44Ye0bNnyhcdaWlri5OQEgFKppEiRIvp9SqUSR0dH7t+/T7du3ejcuTP29vYADBw4kJ9++omYmJgsheSBAwc4e/Ysmzdvply5cgCMGzeO06dPExoaypw5c3J1nwqFBSqVfa6OfdMcHGQ4sMg5yRdhDMkXIYQwDXP5HvommcNnkhSSz9GxY0e6du0KQGZmJvHx8cyaNYvOnTuzdu3alz6/o6MjAQEBbNmyhXPnzpGQkEB0dDQAWm3WoQ+xsbEolUp9EQlgYWFBtWrVOHDgQK7j0Gp1JCU9zvXxb4KlpQIHh3wkJaWg0ciwEPF8ki/CGOaaLyHDG5k6hHeWwtICB2U+kpJT0Gp0pg7nlYlJUDNj9ckXthvasQrurqrXH9Bb4m3Nl1dJrX5k6hDyDFN/Jjk45Mtxb6gUks9RsGBBSpUqpX9dtmxZChYsSEBAAIcOHcr2mMzMzByf/86dO3z22Wc4Ojri6+uLj48PlStXpkGDBtm21+my/+Wj0+mwsnq5H6W5jNnXaLRmE6swPckXYQxzyxdLeUbNZKwsFdjZWpHyWEGmznxy5kXKu6pQKW2zTLTzb45KW8q7quQZSSO8rfnyKpnT7943xRw+k6SQNNLTYk6r1WJtbc3Dhw/1+7RaLVevXjUoPp9ny5YtJCYmsmPHDqytrQGIiYkxuM6/ubu7k5ycTGxsrL5XUqfTERUVxQcffPBS9yWEEEKId5tCYUGAnxsL1p95ZptOfm5SRAohAFn+47keP37MnTt3uHPnDrdv3+bYsWNMmjSJokWLUrt2bapUqcLBgwfZv38/8fHxjB8/nqSkpByfv3jx4qSkpLB9+3Zu3LjBgQMHGDx4MADp6elZ2vv4+FChQgWGDBnC0aNHiYuL4/vvvyc2NpbPP//8ld23EEIIId5N3u5FCfL3QKW0NdjuqLQlyN9D1pEUQuhJj+RzLF26VD/ZjkKhoFChQlSrVo0ZM2aQL18+AgMDSUhI4KuvvsLGxob27dvrZ2fNiSZNmnD27FmmTJnCw4cPcXZ2pkOHDuzZs4fTp0/TqVMng/aWlpYsXbqUqVOn0r9/f9LT0/Hw8GD58uVUqVLlVd++EEIIId5B3u5F8XJzIvZqIomP0ihkb0s5l0LSEymEMGChy2nVI95aGo2W+/fz9kPOVlYKVCp71OpHeX68uDA9yRdhDMkXYSzJGWEMyRdhDFPni6OjfY4n25GhrUIIIYQQQgghjCKFpBBCCCGEEEIIo+S5QtLX15d58+Zl2T5hwgQqVKjA+vXrTRDVq3Xs2DGCgoKoW7cuVapUoUWLFvz0008GE+wEBwfr17AEiIqK4tixY6YIVwghhBBCCCEM5LlCMjsTJkxg1apVTJ8+HX9/f1OH81JWrFjB559/jqurK0uWLGHz5s188cUXLF26lAEDBqDVPhkLPXLkSIOCOiAggISEBFOFLYQQQggzp9XqiI5Xc/jcTaLj1Wi1Mk2GECL38vysrRMnTmT16tX88MMPfPLJJ6YO56VER0czZcoUhg8fbrBch4uLCyVLlqRLly5s27aNFi1aoFQqTRipEEIIId4mUTG3Wbn7AurkNP02ldKWAD83WdJDCJErebpHctKkSaxevZq5c+dmKSJv3LjBoEGDqF27NpUqVaJ+/fpMnz5d36MXGRnJRx99pP+vh4cHbdu2JSoqSn+OU6dOERAQgJeXF9WrV2fAgAHcuHFDv//YsWN069aNqlWr4uHhQdOmTdm4caN+/7179xg4cCA1a9bE09OTjh07cvTo0Wfez9q1a1EqlXTu3DnLvurVq7N8+XLq168PGA5tdXd3B2DEiBEEBwcTFBREt27dDI6/dOkS7u7uXLhwIUfvrRBCCCHeDVExt1mw/oxBEQmgTk5jwfozRMXcNlFkQghzlmd7JKdMmcLPP/9Mv3798PX1zbL/yy+/xMnJiWXLlmFvb8+ePXuYPHkyXl5e+Pn5AfDPP/+wevVqpk+fjr29PWPHjiU4OJidO3ei1Wrp06cPn376KVOnTiUpKYkxY8bw7bffsnz5cm7dukXPnj3p0qUL48ePJyMjg5CQEEaOHEndunUpUqQIY8eOJT09nfDwcGxsbPjxxx/p168f+/fvJ3/+/FliPnPmDJ6enlhZZf+2165dO9vtBw4cwMfHh2+//Za2bdty9OhRgoKC+OeffyhRogQAGzZsoHLlyri5ueX2LRdCCGGktHSNqUMQJqDR6khNyyQtXZPnl3PQanVE7Ip9bpuVuy9QsZSjrBP5mphTvuRVtjaWpg5BZCNPFpK//PILSUlJVK1alfDwcNq3b4+zs7N+f2pqKq1bt6Zp06b6Qqp79+6EhIQQExOjLyQzMjIYN24cFSpUAKBHjx4EBQVx584dbG1tUavVFC1aFGdnZ1xcXJg9ezb37t0DIC0tjQEDBtCzZ08sLJ78Yu3duzcbNmzgypUrFClShISEBMqVK4eLiwt2dnaMHDmSli1bYmmZfbInJibi4uJi9Pvh5OQEgFKpRKlU0qBBA4oUKcKmTZvo06cPWq2WjRs30rt3b6PP/ZSVVZ7unNavZ5PTdW3Eu03yRRjjZfIlcMreVx2OEG+cOjmNoNn7TR2GEM8UNsrP1CG8Meb0HSZPFpIPHz5kyZIluLu707JlS4YMGUJ4eLi+J8/Ozo4uXbqwfft2Tp06RXx8PDExMdy9e1c/tPWpsmXL6v/+9LnDjIwMihYtSq9evRg/fjxz586lVq1aNGjQgKZNmwLg6upK27ZtCQsLIzY2loSEBKKjowHQaJ78C3T//v0ZNmwYO3bswNvbGx8fH1q0aIGtrW229+Xo6EhiYuJLvz9WVla0atWKjRs30qdPHw4fPsz9+/dp0aJFrs6nUFigUtm/dFxvgoNDPlOHIMyI5IswhuSLEELkTebyPfVVMofPpDxZSHbr1o2aNWsCMHnyZHr16sW8efMYNGgQAI8fP6ZLly6kpqbSpEkT/P398fT0zPbZQxsbmyzbdLons5QNHTqUgIAA9u3bx19//cX48eP56aef2LBhAwkJCQQEBFCpUiXq1KnDxx9/jEqlokOHDvrzfPTRR/z555/8+eefHDp0iGXLljF//nx++eWXbIeYenl5sW7dOjQaTba9lkOHDqVq1aoEBAS88D1q164doaGhnDlzhk2bNtG4cWMKFiz4wuOyo9XqSEp6nKtj3xRLSwUODvlISkpBo5FhIeL5JF+EMV4mX0KGN3pNUYm8TGFpgYMyH0nJKWg1eXvm05gENTNWn3xhu6Edq+Duqnr9Ab2DzClf8iq1+pGpQ3hjTP0dxsEhX457Q/NkIfnvZwh9fHzo0qULS5YsoVatWtSuXZsDBw5w9uxZDh48SJEiRYAnw0bv3bunLxJf5NKlS/z88898++23dOrUiU6dOhEVFUVAQADR0dFs2rSJwoULs2zZMv0xe/c+GcKk0+lIT09n5syZtG7dmmbNmtGsWTNSU1OpW7cuf/zxR7aFZLt27fj5558JDw83mLUV4MiRI2zevFk/2c6LlC1bFi8vL3777Tf27NnDzJkzc3Tcs5jLmH2NRms2sQrTk3wRxshNvljKM2XvJCtLBXa2VqQ8VpCpy9u/Y8q7qlApbbNMtPNvjkpbyruq5BnJ18Sc8iWvehc/y83hO0zeH3zLk566MmXKMGzYMO7fv0/x4sUB2LRpE9evX+fYsWP069ePjIwM0tPTc3ROlUrF1q1bGTNmDHFxcVy+fJn169dTsGBB3n//fYoXL87NmzfZt28f169fZ+fOnYwdOxaA9PR0bGxsOH36NKNHj+bkyZNcu3aNyMhIHj9+jJeXV7bXLFu2LF999RVTpkxh2rRpREdHc/nyZVauXMmAAQP46KOPaN68ebbH5s+fn7i4ONRqtX5bu3btCA8Px87Ojrp16xrxjgohhBDiXaBQWBDg9/yJ+Dr5uUkRKYQwmlkUknZ2dkybNo3ExESCg4OpXLkyI0aMICwsjKZNmzJixAiqV69OixYtOH36dI7OqVKpCAkJ4fr163z66af4+/tz7do1li1bRoECBejWrRtNmzZl+PDhtGjRgkWLFjF48GCcnZ3115g1axYuLi58+eWXNGnShNWrVzNjxgyqVav2zOv27t2befPmcfbsWbp3746/vz+rVq2iX79+/PDDD8+cqCcwMJDw8HBGjBih39a0aVN0Oh1t2rR55nFCCCGEeLd5uxclyN8DldJwDgdHpS1B/h6yjqQQIlcsdDkdCyrynKtXr/Lxxx/z22+/Ubp06VyfR6PRcv9+3h57bmWlQKWyR61+lOe7+YXpSb4IY0i+CGOZa85otTpiryaS+CiNQva2lHMpJD2Rb4C55oswDVPni6OjvXk/Iyme759//uHUqVOsXLmSevXqvVQRKYQQQoh3g0JhQflSMqGOEOLVkELSDKnVaoKDgyldujTz5883dThCCCGEEEKId4wUkmaoYsWKnDhxwtRhCCGEEEIIId5Rr32ynU2bNvHpp59SpUoVvLy8aNeuHatXrzbqHJGRkbi7u+tfq9Vq1q5dm+P2uREcHEzXrl1f6hzZuX//PtOmTeOTTz7B09OTBg0aMHz4cOLj4/Vtjhw5gru7O9euXQNefL9CCCGEENnRanVEx6s5fO4m0fFqtFqZGkMI8Wq81h7JdevWMXHiREaOHIm3tzc6nY6DBw8yYcIE7t69S//+/XN13mnTpnHt2jU6dOjwiiN+vS5fvky3bt1wcXFh5MiRlClThlu3brFw4UI+/fRTwsPDcXNzw8vLiwMHDuDo6AiY7/0KIYQQwnSiYm6zcvcFgzUkVUpbAvzcZKZWIcRLe609kitXrqRdu3a0b9+eMmXK8P7779O1a1e6d+9OWFhYrs9rrhPNDhs2jBIlSrB8+XLq16+Pi4sL1apV48cff8TR0ZEpU6YAYGNjg5OTk35JD3O9XyGEEEKYRlTMbRasP2NQRAKok9NYsP4MUTG3TRSZEOJt8Vp7JBUKBSdOnODBgwcULFhQv7137960a9dO//rGjRtMnz6dw4cPk5SUROHChWnZsiVDhgxBoTCsdYODg1m/fj0A7u7uxMTE5Cq2Y8eOMXfuXM6cOUN6ejouLi707duX1q1bZ9t+4sSJbNiwgdDQUDw9PUlOTmbatGns2rWLjIwMKlWqxLBhw6hcuXK2x585c4bTp0+zYMECbGxsDPbZ2Ngwe/ZsbG2frO905MgRunXrxp49e5g/f77B/S5YsID+/fuzZ88enJ2d9ef47LPPqFq1Kt98802u3g8hhBDZS0vXmDoEkcdotDpS0zJJS9fkyeUctFodEbtin9tm5e4LVCzlKMt/vAF5PV/Mha2NrJme17zWQrJXr14MGjSI+vXrU7NmTapVq0atWrWoXLkyDg4O+nZffvklTk5OLFu2DHt7e/bs2cPkyZPx8vLCz8/P4JwjR44kNTWVmzdvMm/evFzFdevWLXr27EmXLl0YP348GRkZhISEMHLkSOrWrUuRIkUM2k+bNo2NGzeybNkyPDw80Ol0fPHFF9jZ2bF48WIKFCjAxo0b6dSpE7/88gsVK1bMcs0zZ84AULVq1WxjetYznf+9X5VKhaOjIxs3bqRfv37AkyGzJ0+eZOLEibl6P+DJmjV52dP1bHK6ro14t0m+CGO8KF8Cp+x9k+EI8Uaok9MImr3f1GEIkWNho/xe3OgtYE7fYV5rIdmkSROKFy9OWFgYBw8eZN++fQCULl2aSZMm4e3tTWpqKq1bt6Zp06aUKFECgO7duxMSEkJMTEyWQlKpVGJnZ4e1tTVOTk65iistLY0BAwbQs2dPLCye/Etc79692bBhA1euXDEoJGfNmsWvv/7K8uXL9QXi4cOHOXnyJIcPH6ZQoUIADB48mOPHjxMWFqYfovpvDx48ADAooHMiu/tt3bq1QSG5YcMGKleuzAcffGDcG/H/KRQWqFT2uTr2TXNwyGfqEIQZkXwRxpB8EUKIvMtcvqu+KubwmfTal/+oUqUKVapUQavVEh0dzb59+wgPD+eLL75g165dFC5cmC5durB9+3ZOnTpFfHw8MTEx3L17F6329XT/u7q60rZtW8LCwoiNjSUhIYHo6GgANJr/G8J08uRJ/ve//+Ho6KgvcgHOnj2LTqejUaNGBudNT08nLc3wWYSnnk6ck5iYmKXH01jt2rVj6dKl/P3333h6erJp0ya++OKLXJ9Pq9WRlPT4pWJ63SwtFTg45CMpKQWNRoaFiOeTfBHGeFG+hAxvlM1R4l2msLTAQZmPpOQUtJq8N49BTIKaGatPvrDd0I5VcHdVvf6A3nF5PV/MhVr9yNQhvBGm/g7j4JAvx72hr62QvHnzJosXL6ZPnz4UL14chUJBxYoVqVixIn5+frRo0YL//e9/1K9fny5dupCamkqTJk3w9/fH09OTzp07v67QuHjxIgEBAVSqVIk6derw8ccfo1KpssyKmj9/fn788UeGDBnChAkTmDlzJgBarZYCBQoQGRmZ5dz/ff7xKS8vLwCOHz/Oxx9/nGX/hg0b+OOPP5g6deoL4//ggw/48MMP2bRpE6mpqdy9e5cWLVq88LjnMZcx+xqN1mxiFaYn+SKM8ax8sZRnyMR/WFkqsLO1IuWxgkxd3vsdU95VhUppm2WinX9zVNpS3lUlz0i+AXk9X8zFu/Z5bg7fYV5bIWljY8PatWspUaIEvXv3Ntj3dHhnkSJFOHDgAGfPnuXgwYP6nrrExETu3bv3zNlKnw5Hza3Vq1dTuHBhli1bpt+2d++TZ2D+fc1y5crh5eXF2LFj+eKLL2jatCl+fn6UK1eOhw8fkpGRYTCcdNSoUZQvX54uXbpkueYHH3yAl5cXISEhNGrUCGtra/2+lJQUQkJCKFq0qH7CnRfdb7t27Vi4cCFarRY/Pz+jh8wKIYQQ4u2kUFgQ4OfGgvVnntmmk5+bFJFCiJfy2p7idHR0pFevXsyZM4dZs2Zx/vx5rl69yu+//07//v31k+8UL14cgE2bNnH9+nWOHTtGv379yMjIID09Pdtz58+fn9u3b3P16tXnxrB///4sf27dukXx4sW5efMm+/bt4/r16+zcuZOxY8cCZHvN+vXr06JFC8aOHcuDBw+oV68eFSpUYNCgQRw+fJj4+HgmT55MZGQkZcuWfWY848eP5+rVq3Tv3p0///yTq1evcvDgQXr06MG9e/cYM2ZMju+3efPmPHjwgMjISPz9/Z/7PgghhBDi3eLtXpQgfw9USsN/oHZU2hLk7yHrSAohXtprfUby66+/pnTp0vzyyy9ERESQmppKyZIladq0KX369AHA09OTESNGsHz5cmbPnk2xYsVo1qwZJUqU4PTp09met02bNuzatYsWLVqwc+dOihUrlm277J4bnDx5Mt26dePSpUsMHz6c9PR0SpcuzeDBg5k7dy6nT5+mfv36WY4bOXIkTZs2ZcKECUyfPp2lS5cyffp0vv76a1JSUihbtizz58+ndu3az3w/3NzcWLt2LUuWLOG7777j7t27FC5cmFq1ajF9+nRcXFxyfL8FChTAz8+Po0ePUrdu3WdeUwghhBDvJm/3oni5ORF7NZHER2kUsrelnEsh6YkUQrwSFjpZ7d5sde3alapVqzJo0KCXOo9Go+X+/bz9ALOVlQKVyh61+lGeHy8uTE/yRRhD8kUYS3JGGEPyRRjD1Pni6Ghv+sl2xOuze/duzp8/z8mTJ5k2bZqpwxFCCCGEEEK8Y6SQNEM//fQTly9fZvz48QbLkgghhBBCCCHEm2DWheSmTZsIDw8nNjYWCwsL3n//fTp06EDHjh1f63XnzZvH/Pnz9a8VCgVKpZJKlSrRt29fatasqd8XHBzM9evXWbFixSu7/urVq1/ZuYQQQghh3rRanTwHKYR448y2kFy3bh0TJ05k5MiReHt7o9PpOHjwIBMmTODu3bv079//tV6/ePHirFu3DgCNRsOdO3eIiIigR48eLFq0iAYNGgBPJunRaDSvNRYhhBBCvJuiYm6zcvcFgzUjVUpbAvzcZGZWIcRrZbaF5MqVK2nXrh3t27fXb3v//fe5desWYWFhr72QtLS0xMnJSf+6ePHiTJkyhcTERMaNG8fOnTuxsrJCqVS+1jiEEEII8W6Kirmd7VqR6uQ0Fqw/I8t8CCFeK7MtJBUKBSdOnODBgwcULFhQv7137960a9dO//rGjRtMnz6dw4cPk5SUROHChWnZsiVDhgxBoVAQGRnJokWL+PLLL1m0aBH//PMP5cqV0/d0Guvzzz+ne/funDx5kmrVqmUZ2hoXF8eUKVM4duwY9vb21KxZk+DgYH1RqtFomDt3Lr/++isPHz6kfv36FCtWjOjoaINzTJ8+nRMnTpCZmUndunX55ptvcHZ2fpm3VAghhAAgLV1G0jyPRqsjNS2TtHSNyWbh1Gp1ROyKfW6blbsvULGUowxzNbEX5YutjaUJohLi5ZltIdmrVy8GDRpE/fr1qVmzJtWqVaNWrVpUrlwZBwcHfbsvv/wSJycnli1bhr29PXv27GHy5Ml4eXnh5+cHwD///MPq1auZPn069vb2jB07luDgYHbu3ImFhXG/fN3d3QGIjo6mWrVqBvtu3bpFQEAALVu2JDg4mJSUFObNm8dnn33Gli1byJ8/PzNmzGD9+vWMHz+e999/n5UrV7JixQqqV68OwPXr1/nss8+oU6cOP//8M2lpaUyZMoUuXbqwefNmChQokKv308oqZ9P8msrTaYhzOh2xeLdJvghjSL5kFThlr6lDEK+AOjmNoNn7TR2GeIGwUX6mDkHkIeb0mWS2hWSTJk0oXrw4YWFhHDx4kH379gFQunRpJk2ahLe3N6mpqbRu3ZqmTZvqZzft3r07ISEhxMTE6AvJjIwMxo0bR4UKFQDo0aMHQUFB3Llzh6JFjRsS8nQoa3JycpZ9q1atonjx4owaNUq/bfbs2dSqVYvt27fTtGlTVq5cyYgRI/joo48AGDVqFCdOnNC3X7lypb7gtLGxAWDu3Lk0btyYjRs30rlzZ6PiBVAoLFCp7I0+zhQcHPKZOgRhRiRfhDEkX4QQpmAu38HEm2UOn0lmW0gCVKlShSpVqqDVaomOjmbfvn2Eh4fzxRdfsGvXLgoXLkyXLl3Yvn07p06dIj4+npiYGO7evYtWazi0oGzZsvq/Py0GMzIyjI7paQH5717Rp86dO8eFCxfw8vIy2J6WlkZcXBxxcXGkpqZSpUoV/T4LCwu8vb2Jjo4GIDY2Fg8PD30RCeDk5ESZMmWIjX3+EJdn0Wp1JCU9ztWxb4qlpQIHh3wkJaWg0chivuL5JF+EMSRfsgoZ3sjUIeRpCksLHJT5SEpOQavRmSSGmAQ1M1affGG7oR2r4O6qev0BiWd6Ub6o1Y9MEJXIq0z9meTgkC/HvaFmWUjevHmTxYsX06dPH4oXL45CoaBixYpUrFgRPz8/WrRowf/+9z/q169Ply5dSE1NpUmTJvj7++Pp6Zltr92/C7OndDrjPxzOnj0LoO/d/DetVkutWrX47rvvsuxTKpXcvn37hdd91j6tVou1tbXR8T5lqmc8jKXRaM0mVmF6ki/CGJIv/8dSnql7LitLBXa2VqQ8VpCpM03OlHdVoVLaGszW+l+OSlvKu6rkGUkTe1G+yO8dkR1z+EzK+4Nvs2FjY8PatWvZtGlTln1PewKLFCnCgQMHOHv2LGFhYQwcOJBmzZpRoEAB7t27l6siMSciIiJwcXHJ0usI4ObmRlxcHCVKlKBUqVKUKlWKggULMmnSJGJjYylVqhR2dnacPHnS4Li///5b/3d3d3dOnz5Nenq6ftvdu3eJj4836FUVQgghxNtLobAgwM/tuW06+blJESmEeG3MspB0dHSkV69ezJkzh1mzZnH+/HmuXr3K77//Tv/+/fWT7xQvXhyATZs2cf36dY4dO0a/fv3IyMgwKMRy4+nakXfu3OHWrVucOnWKkSNH8ueffzJ27NhsJ+kJCAggOTmZoUOHEh0dTXR0NIMGDeL06dOUK1eOfPny0bVrV+bOncvu3bu5fPkyU6dONSgkO3XqxKNHjxg2bBjR0dGcOnWKr776CpVKRfPmzV/qnoQQQghhPrzdixLk74FKaWuw3VFpK0t/CCFeO7Mc2grw9ddfU7p0aX755RciIiJITU2lZMmSNG3alD59+gDg6enJiBEjWL58ObNnz6ZYsWI0a9aMEiVKcPr06Ze6/s2bN/Hx8QGeLEVSsGBBatSowapVq/D09Mz2GBcXF8LDw5k5cyadOnXC0tKSqlWrEhYWhqOjIwBfffUVGRkZjBo1ipSUFBo1akTjxo1JS3sydOW9994jPDyc6dOn89lnn2FjY0PdunWZPn16ts9lCiGEEOLt5e1eFC83J2KvJpL4KI1C9raUcykkPZFCiNfOQve6xniKXNm1axfe3t76whIgMDCQ4sWLM2nSpNdyTY1Gy/37eftBbysrBSqVPWr1ozw/XlyYnuSLMIbkizCW5IwwhuSLMIap88XR0T7Hk+2Y5dDWt1loaChDhgzRD9ddvnw5hw8fplWrVqYOTQghhBBCCCEAKSTznBkzZmBvb0/37t1p0aIFmzdvZs6cOdSqVcvUoQkhhBBCCCEEkMcLSXd3dyIjI1/6PJGRkbi7u7+CiF6Pf8f33nvvsWvXLr755hv+/vtvGjZsyOTJk00coRBCCCGEEEL8H7OdbOdt0qxZM+rVq2fqMIQQQghhRrRanUyyI4QwGSkk8wA7Ozvs7OxMHYYQQgghzERUzG1W7r6AOjlNv02ltCXAz02W/RBCvBF5emjrU1qtlsWLF/PJJ5/g4eFB1apV6dWrFwkJCfo2jx49Yvz48fj4+ODl5UWXLl04c+ZMtufbvn07Hh4erF69GoArV67Qs2dPvL298fLyomfPnsTExOjbJyYmMm7cOBo0aICnpycdO3bkyJEj+v3z5s2je/fuLFmyhPr161O5cmW6dOlCXFwcAMHBwXTo0MEghuvXr1O+fHkOHTpk1NDb5ORkRo8eTa1atfD29qZbt24vvZSJEEIIIcxHVMxtFqw/Y1BEAqiT01iw/gxRMbdNFJkQ4l1iFj2SYWFhhIaGMnXqVMqVK0dCQgKjR49mypQpLFy4EHiyruSVK1eYPHkyrq6u/PjjjwQGBrJr1y6Dc+3evZthw4bx3Xff6Yu7wYMHU758eX799VcyMzOZOnUq/fv3Z9euXWg0GgIDA8nIyGD69Ok4OjoSFhZGz549WblypX7NyGPHjmFra8uSJUvIyMhg+PDhjBs3jrCwMNq2bUvXrl1JSEjA1dUVgM2bN1O8eHFq1arFhg0bcvQ+6HQ6vvjiC+zs7Fi8eDEFChRg48aNdOrUiV9++YWKFSu+ondcCCFEXpCWrjF1CCIbGq2O1LRM0tI1b3x6fq1WR8Su2Oe2Wbn7AhVLOcow1zzClPnylK2NpUmuK95uZlFIurq6MnXqVBo1agSAs7MzTZo0Yfv27QBcunSJ/fv3Exoaio+PDwBjx47FwcEBtVqtP88ff/zB4MGDGTduHG3bttVvT0hIoE6dOjg7O2Ntbc2kSZO4dOkSWq2WAwcOcPbsWTZv3ky5cuUAGDduHKdPnyY0NJQ5c+YAkJmZybRp0yhYsCAAHTt2ZPr06QBUr14dFxcXNm3aRP/+/YEnhWTr1q1RKHLeKXz48GFOnjzJ4cOHKVSoEPCkCD5+/DhhYWFMmTLF6Pf2KSurvN05/XQ9m5yuayPebZIvwhh5OV8Cp+w1dQjCDKmT0wiavd/UYYg8JGyUn6lDEDmUlz+T/sssCklfX1/+/vtv5syZw+XLl7l8+TIXL16kWLFiAMTGPvmXuSpVquiPsbW1ZcSIEQAcP34cgK+++or09HTee+89g/MPGjSISZMmsXLlSmrUqEG9evVo0aIFCoWC2NhYlEqlvogEsLCwoFq1ahw4cEC/rUiRIvoiEkCpVJKRkaFv36ZNGzZv3kz//v05d+4cFy9e1Pem5tTZs2fR6XT6gvqp9PR00tLSnnHUiykUFqhU9rk+/k1ycMhn6hCEGZF8EcaQfBFCvK3M5Xue+D/m8JmUZwrJu3fvcu/ePf2zgjqdDgBLS0uWLFnCggUL8Pf3p3bt2nTv3p09e/awdetWAKyscnYbEyZMYNeuXYwcOZJNmzaRL9+TH1Dnzp1p0qQJ+/bt46+//mLu3LksWrSIDRs26OP4L51OZ3BdGxub517b39+f+fPnc/r0abZt20bVqlUpVapUjuJ+SqvVUqBAgWyXRHnR9Z9/Xh1JSY9zffybYGmpwMEhH0lJKWg0phkWIsyH5IswRl7Ol5DhjV7cSLxxCksLHJT5SEpOQavJ/nvC6xKToGbG6pMvbDe0YxXcXVWvPyDxQqbMl6fU6kcmua4wnqk/kxwc8uW4NzTPFJKhoaHs379fXxw+ePAAAEdHR8aNG0dQUBC9e/c2aP+0yCtbtiwAp0+fpnbt2sCToaYff/wxw4cP1x/TsmVLatWqRbNmzfjhhx8YOXIk9+7dY8GCBfTu3Zu2bdvStm1bbt26Rf369Tl69Cju7u4kJycTGxur75XU6XRERUXxwQcf5Pj+nJ2dqVmzJjt27OC3334jKCjI6PeoXLlyPHz4kIyMDINrjxo1ivLly9OlSxejz/mUqcbsG0uj0ZpNrML0JF+EMfJivljKM255kpWlAjtbK1IeK8jUvdmcKe+qQqW0zTLRzr85Km0p76qSZyTzCFPmy1N57XebeLG8+Jn0X3lm8G2dOnW4ePEi69ev5+rVq8ydOxcHBwe8vLwoUaIEBw8e5OLFi1y6dIlZs2axc+dO0tPTAShTpgwff/wx48aN4/Dhw1y+fJnRo0eTlpZGjRo1DK7j5OTEsGHDCA8PJyoqioIFC/LHH38watQozp8/z9WrV1m9ejXW1tZ4eHjg4+NDhQoVGDJkCEePHiUuLo7vv/+e2NhYPv/8c6Pu0d/fn5UrV5KYmEjTpk2Nfo/q1atHhQoVGDRoEIcPHyY+Pp7JkycTGRmpL6aFEEII8fZSKCwI8HN7bptOfm5SRAohXrs8U0jWq1eP4OBg5s2bR/PmzTl16hSLFi2iQIECTJs2jdTUVNq1a0eXLl2IjY1l3Lhx3Lt3jxs3bgAwadIkqlevzldffUXbtm35559/CA0NxdHRMcu1OnTogLe3N99++y2ZmZmEhISgUCjo3r07zZs359ChQyxZsgRXV1csLS1ZunQpFStWpH///rRr144LFy6wfPlyg2cyc+KTTz4BwM/PjwIFChj9Hj2NxcPDg6+//ppWrVrxv//9j/nz5+t7YoUQQgjxdvN2L0qQvwcqpa3BdkelLUH+HrKOpBDijbDQPeshQPHO0Gi03L+ft8fOW1kpUKnsUasf5flufmF6ki/CGJIvwlh5JWe0Wh2xVxNJfJRGIXtbyrkUkp7IPCiv5IswD6bOF0dHe/N7RlIIIYQQQuScQmFB+VIyoY4QwjTyzNBWIYQQQgghhBDmQQpJIYQQQgghhBBGMctC0tfXF3d3d5YtW5bt/jFjxuDu7s68efOIjIzUr00J4O7unu06jG/CvHnz8PX1Ncm1hRBCCGHetFod0fFqDp+7SXS8Gq1WprkQQpiO2T4jaW1tzY4dO+jRo4fB9szMTHbu3ImFxZOHzZs1a0a9evVMEaIQQgghxCsRFXOblbsvGKwfqVLaEuDnJrO0CiFMwix7JAFq167NyZMnuXnzpsH2w4cPkz9/fkqUKAGAnZ0dTk5OpghRCCGEEOKlRcXcZsH6MwZFJIA6OY0F688QFXPbRJEJId5lZtsj6enpSVxcHNu3b6d79+767du2baNp06b89ttvAERGRjJixAhiYmIMjt+7dy/9+vXj0KFD+rUm27RpQ2ZmJlu2bAHgwYMH1K5dm7CwMKpWrUpISAiRkZFcv34dGxsbqlatypgxY3B1dQWeDJudPHkybdu21V8nu21PLV++nJkzZzJ37lwaNWpEeno6c+bMYdOmTTx8+BA3NzcGDhyIj4+P/pjjx48zc+ZMTp8+jaOjI40aNWLIkCG5WpdSCCHeNmnpGqOP0Wh1pKZlkpaukan5RY68yZzRanVE7Ip9bpuVuy9QsZSjLP2RR8nvmNyxtbE0dQjiBcy2kARo2rSpQSGZnp7O7t27Wb58ub6QfJY6depga2vL4cOHadasGffv3ycmJgatVsu9e/coXLgwBw4coGDBglStWpWwsDBCQ0OZOnUq5cqVIyEhgdGjRzNlyhQWLlxodOwRERH88MMPzJ8/nwYNGgAwYsQI4uLimDFjBsWKFeP333+nb9++zJ8/n4YNGxIdHU2PHj348ssvmThxInfv3mXatGkEBgayZs0a/XDe3LCyytud00/Xs8npujbi3Sb58u4KnLLX1CEI8capk9MImr3f1GEI8UqFjfIzdQgmYU7fYcy+kAwNDeXWrVsUK1aMgwcP4ujoSMWKFV94rJ2dHbVr1+bAgQM0a9aMQ4cOUbFiRe7cucORI0do1qwZf/zxBw0bNkShUODq6srUqVNp1KgRAM7OzjRp0oTt27cbHfcvv/zCtGnTWLhwob63MT4+ni1btrBhwwYqVKgAQI8ePYiOjiY0NJSGDRsSGhpK3bp16du3LwClS5dm5syZ+Pn5cfToUWrWrGl0LPBkHSqVyj5Xx75pDg75TB2CMCOSL0IIIYR5Mpfvpq+LOXyHMetC0sPDAxcXF3bs2EG3bt3Ytm0bzZs3z/Hxvr6++t7EgwcPUrt2ba5fv87hw4dp0qQJf/75JxMmTNC3/fvvv5kzZw6XL1/m8uXLXLx4kWLFihkV8+3btxk7dizW1tY4Ozvrt587dw6AgIAAg/YZGRk4ODjo28THx+Pl5ZXlvHFxcbkuJLVaHUlJj3N17JtiaanAwSEfSUkpaDQyLEQ8n+TLuytkeCOjj1FYWuCgzEdScgpajcyCKV7sTeZMTIKaGatPvrDd0I5VcHdVvdZYRO7I75jcUasfmToEkzD1dxgHh3w57g0160IS/m9462effcaePXtYu3Ztjo9t2LAhY8aMIS4ujr/++ouJEydy/fp1QkND+fvvv0lJSaFu3boALFmyhAULFuDv70/t2rXp3r07e/bsYevWrc88f2ZmZpZtFhYWhISEMHv2bL799lsiIiJQKBTodE9+sURERGBvb/gvMArFkx+mVqulZcuW+h7Jf3v6nGdumcuYfY1GazaxCtOTfHn3WObiGTErSwV2tlakPFaQqZN8ES/2JnOmvKsKldI2y0Q7/+aotKW8q0qekcyj5HdM7rzrn9/m8B0m7w++fYGmTZty/Phxfv31V1xcXChbtmyOjy1atCgeHh6sWrWKe/fu4e3tTe3atbly5Qpr1qyhTp065Mv3pFv5xx9/JCgoiLFjx/LZZ59RpUoVrly5oi8A4cmSJA8fPtS/jo+Pz3JNJycn6taty4QJEzh9+jRhYWEAuLm5AXDnzh1KlSql/xMZGalf99LNzY2LFy8a7M/MzGTy5Mn8888/xr95QgghhMjTFAoLAvzcntumk5+bFJFCiDfO7AvJChUqUKpUKWbOnGnUsNanfH19WbNmDVWqVMHOzg4XFxfee+89Nm7cSOPGjfXtSpQowcGDB7l48SKXLl1i1qxZ7Ny5k/T0dH2bKlWqsHbtWs6fP8+5c+cYO3YsNjY22V7X3d2dnj17Mnv2bBISEnBzc6NRo0Z899137N27l6tXrxISEsLixYv1s8IGBgZy7tw5xo0bR1xcHCdOnGDIkCFcuXKF0qVLG33vQgghhMj7vN2LEuTvgUppa7DdUWlLkL+HrCMphDAJsy8k4Umv5MOHD2nWrJnRx/r6+pKenk6tWrX02+rUqQOgn1gHYNq0aaSmptKuXTu6dOlCbGws48aN4969e9y4cQOAsWPHUrBgQT799FMGDBhAhw4dKF68+DOv3a9fP4oVK8a3336LTqdj1qxZfPzxx4wZM4ZmzZqxYcMGJk6ciL+/P/CkUP3pp584f/48/v7+fPnll5QpU4bly5c/s2AVQgghhPnzdi/K9C/rMLyTF71bVWR4Jy+mfVlHikghhMlY6P49NlO8kzQaLffv5+0Hmq2sFKhU9qjVj/L8eHFhepIvwhiSL8JYkjPCGJIvwhimzhdHR/scT7bzVvRICiGEEEIIIYR4c6SQFEIIIYQQQghhFLNf/uNV8/X15fr16/rX1tbWFClShAYNGvDVV1+99DIbQgghhBDG0Gp1xF5NJPFRGoXsbSnnUkhmaRVCmJwUktkIDAwkMDAQgNTUVGJjY5k+fTpdunRhzZo1KJVKE0cohBBCiHdBVMxtVu6+YLCOpEppS4Cfm0y0I4QwKRnamo38+fPj5OSEk5MTLi4uNG7cmKVLl/LPP//w008/mTo8IYQQQrwDomJus2D9GYMiEkCdnMaC9WeIirltosiEEEJ6JHOsZMmSfPTRR2zdupVBgwaRnJzMtGnT2LVrFxkZGVSqVIlhw4ZRuXJlAObNm0dUVBR16tQhPDwctVrNhx9+yLhx4yhbtizBwcHExcWxdu1a/TWuX7+uL1rr1KnD8ePHmTlzJqdPn8bR0ZFGjRoxZMgQChQoADwZhvvJJ5+wb98+7t27x7x586hRo4ZJ3h8hhBC5k5auMXUIwkgarY7UtEzS0jWvbVZFrfb/sXfnYVHW6+PH3wzIIg4wo6BiLImAmjua6zcVMZcylzIVl5SszC01RQw33PcltDTUjNxKxSWtzLSjB1NT1NwClNz3FASUdWZ+f/hzjoQig8DMwP26Lq/jPPN5nuee6T4zc/vZdKzdHZ9nm3W/nqOmh1qGuZq44siXf7OxtiyW+4jSTQpJA/j4+LBt2zZSU1P54IMPsLW1Zfny5ZQrV45t27bRq1cvvv/+e2rWrAnA0aNHsbGx4auvviIrK4vg4GDCwsKIjIykW7du9O3bl8uXL+Pu7g7ADz/8QKVKlWjSpAmxsbEMGDCAjz/+mOnTp/PPP/8wZ84cgoKC+O6777CwePSlsWbNGpYvX45SqcTX17fAr83KyrQ7px8vQ5zf5YhF6Sb5Igxh7HwJmrXXKPcV5i8xJYMhi/YbOwxhgiLHBxg7BFFAxv5OMoQUkgZwcHAAYO/evZw4cYJDhw7h5OQEwKhRozh27BiRkZHMmjULgOzsbObMmYOjoyMAPXv2ZO7cuQA0atQINzc3tm/fztChQ4FHhWTnzp1RKBSsXLmS5s2bM2jQIAA8PT2ZP38+AQEB/PHHHzRu3BiAli1b0qxZsxd6XQqFBSqV/Qtdo7g4ONgZOwRhRiRfhCEkX4QQJYW5/K4Tz2YO30lSSBogJSUFgCtXrqDT6WjdunWO5zMzM8nI+N88hgoVKuiLSAClUklWVhYAFhYWdOnShR9++IGhQ4dy9uxZzp8/zxdffAHA2bNnuXTpEvXr188VR0JCgr6Q9PDweOHXpdXqSE5++MLXKUqWlgocHOxITk5Do5HNfEXeJF+EIYydLxHBrZ/fSJgUhaUFDko7klPS0Gp0RXKPuMuJzNtw4rntRvesh6+7qkhiEIWjOPLl3xITHxTLfUThM/Z3koODXb57Q6WQNMCZM2fw9PSkTJkylCtXjqioqFxtrK2tn/r3p+natStLlizh1KlT/PjjjzRo0EBfGGq1Wjp16qTvkXzSk1uQ2NraFvTl5FBcY/ZflEajNZtYhfFJvghDGCtfLGV+m9mxslRga2NF2kMF2bqiyZnq7ipUSptcC+08Sa20obq7SuZImrjiyJd/k+8+82cOv2FMf/Ctibh58yZ79uyhU6dO+Pj4kJqaSlZWFh4eHvo/ERER7NmzJ9/XrFKlCo0bN2bXrl389NNPdOvWTf+ct7c358+fz3H97OxsZs6cyY0bN4riJQohhBDCRCgUFgQGeOfZpleAtxSRQgijkULyKR4+fMidO3e4c+cOV65c4ddff2XgwIG89NJLDBgwgP/7v/+jRo0ajBw5kkOHDnHp0iVmzpxJVFQUXl5eBt2ra9eurFu3jqSkJDp06KA/HhQUxNmzZwkLCyMhIYHjx4/z6aefcvHiRTw9PQv5FQshhBDC1Pj5ujCkay1USpscx9VKG4Z0rSX7SAohjEqGtj7FqlWrWLVqFQBlypShcuXKdOzYkaCgIOzt7fVt5s6dy4gRI0hLS8PLy4slS5bQtGlTg+7Vrl07pkyZQkBAgH5bD4B69eqxYsUKFi9eTNeuXSlbtixNmzZl7Nixzx0yK4QQQoiSwc/XhfrezsRfSSLpQQZO9jb4uDlJT6QQwugsdDpd8cz6FSZLo9Fy755pT8q2slKgUtmTmPjA5MeLC+OTfBGGkHwRhpKcEYaQfBGGMHa+qNX2+V5sR4a2CiGEEEIIIYQwiBSSQgghhBBCCCEMInMkn8Hf359r167pH5cpU4YqVarQvXt3Bg4cWCj30Ol0bN26lddee43y5csXyjWFEEIIYf60Wp3MixRCmDQpJPMQFBREUFAQAOnp6Zw8eZLx48djZ2dH7969X/j6R44cISQkxKAtQ4QQQghRssXE3Wbdr+dy7CGpUtoQGOAtK7UKIUyGDG3NQ9myZXF2dsbZ2Rk3NzfeeOMNOnXqxObNmwvl+rLOkRBCCCGeFBN3m6VbTucoIgESUzJYuuU0MXG3jRSZEELkJD2SBrK1tdX/XaPR8O2337J+/XquX7+Oq6sr/fv3p1evXvo2K1euZP369dy8eRMXFxfefvttBg8ezB9//EG/fv0AaNOmDTNnzqRbt24cP36chQsXcubMGaysrPD39yc4OBiVSgU8GnLbu3dvTpw4QXR0NNbW1nTq1ImQkBCsrOQ/pxBClBYZmRpjh1BqabQ60jOyycjUFOqqilqtjrW74/Nss+7Xc9T0UMswVzOS33yxsbYsxqiEeHFSeRjg5MmT7Nixg2HDhgEwa9Ystm3bxoQJE6hduzb79+9n+vTpZGRk0L9/f/bu3cvy5ctZuHAhL7/8MidOnCA4OJiXXnqJDh06EB4ezrBhw9i4cSM+Pj6cPHmSvn370qNHDyZNmsSdO3eYMmUK77//Phs3bsTS8tEHzOLFixk9ejTBwcH88ccfhIaGUqtWLbp06VLg12ZlZdqd04+XIc7vcsSidJN8EYYw13wJmrXX2CEII0hMyWDIov3GDkMUgcjxAcYOQZgAc/pOkkIyD8uXL2fVqlUAZGVlkZWVRd26denUqROpqamsX7+ekJAQOnXqBICnpydXr17lq6++4r333uPy5ctYW1tTpUoVXF1dcXV1xcXFBVdXV6ytrXF0dARArVZja2vLqlWr8PX1ZcKECQB4eXmxYMECOnfuTHR0NC1btgSgRYsW+t5MNzc3vv32W44dO1bgQlKhsEClsn+Rt6rYODjYGTsEYUYkX4QhJF+EEMZkLr/FRPEwh+8kKSTz0LNnT/r27QtAdnY2ly5dYuHChfTu3ZupU6eSlZWFn59fjnNeffVVvvnmG+7evctbb73F5s2badeuHdWqVaNZs2a0a9cOV1fXp94vPj6e5s2b5zhWvXp1lEolcXFx+kLSy8srRxulUklWVlaBX6dWqyM5+WGBzy8OlpYKHBzsSE5OQ6ORzXxF3iRfhCHMNV8iglsbO4RSS2FpgYPSjuSUNLSawlvvIO5yIvM2nHhuu9E96+Hrriq0+4qild98SUx8UIxRCVNl7O8kBwe7fPeGSiGZB0dHRzw8PPSPvby8cHR0JDAwkP37nz6sRKt99B/cysoKJycntm3bxvHjxzlw4ADR0dFERkYybNgwhg4dmuvcZy2+o9PpKFOmjP6xtbV1vs/Nr8Kc41GUNBqt2cQqjE/yRRjC3PLFUubIGY2VpQJbGyvSHirI1hVezlR3V6FS2uRaaOdJaqUN1d1VMkfSjOQ3X8zp80cUPXP4TjL9wbcm5nHBVr16dcqUKUNMTEyO548ePYqzszOOjo5s376d9evX4+fnx/Dhw/n+++/p3r07P/74IwAWFjm/BHx9fXNdLzY2ltTU1Fy9kEIIIYQoWRQKCwIDvPNs0yvAW4pIIYRJkB7JPDx8+JA7d+4AjwrIy5cvM2PGDFxcXGjevDk9evTg888/x8nJidq1axMdHc26desYNWoUFhYWZGRkMHv2bOzt7WnYsCE3b97kyJEjNGzYEHi0vQg8KhZVKhUDBgwgMDCQqVOnEhgYyD///MPUqVOpWbMmTZs2Ndr7IIQQQoji4efrwpCutXLtI6lW2tBL9pEUQpgQKSTzsGrVKv1iOwqFAicnJxo2bMi8efOws7Nj3LhxqFQq5s2bxz///IOnpycTJ07k3XffBaB79+4kJSXxxRdfcOPGDRwdHWnXrh2jR48GwMfHh5YtWzJixAhGjRpFUFAQK1asYNGiRXTp0oVy5coREBDAp59+mmNoqxBCCCFKLj9fF+p7OxN/JYmkBxk42dvg4+YkPZFCCJNioXvRyXXC7Gk0Wu7dM+0J3lZWClQqexITH5j8eHFhfJIvwhCSL8JQkjPCEJIvwhDGzhe12j7fi+3IHEkhhBBCCCGEEAaRQlIIIYQQQgghhEFKXCGZnZ3NN998Q7du3ahfvz5NmjQhKCiIQ4cO6dv4+voSFRVVKPcLDw/H39+/UK6VX1lZWaxevdqoMQghhBBCCCFKrxI1RzIjI4MBAwZw48YNhg8fTv369UlPT2fz5s2sWbOGOXPm0KlTJ+7cuYNSqcTW1vaF7/ngwQMyMjJQq9WF8AryZ8uWLYSEhBAXF1coMcgcSVHSSL4IQ0i+CEMVds5otTpZWKcEk88YYQhj54shcyRL1KqtixcvJi4ujh07dlC5cmX98dDQUFJTU5k2bRr+/v44OzsX2j3t7e2xt7cvtOvlx79rf2PEIIQQQogXFxN3O9dWHyqlDYGy1YcQwsSVmKGtWVlZbN68mW7duuUoIh8bMWIEERER2Nra5hjaGhISQkhICLNnz6Zp06bUrVuXjz76iFu3bunP/eeffwgODqZx48b4+fnx0UcfcenSJSDnsNKrV6/i6+vLrl276N69O7Vq1cLf35/vvvtOf6383O/o0aP069ePBg0aUKtWLTp06MC2bdsAiIqKYty4ccCjIbqHDx+Woa1CCCGEGYqJu83SLadzFJEAiSkZLN1ympi420aKTAghnq/E9EheuXKFpKQkGjRo8NTnK1asSMWKFZ/63I4dO+jUqRNr1qzh7t27jBo1ikWLFjFz5kyys7MJCgrCysqKL774AicnJ2bNmsXAgQP5+eefn3q9mTNnMmHCBHx8fPj666+ZPHkyzZo1w83N7bn3u3XrFu+//z59+vRh6tSpZGVlERERQWhoKM2bN6djx46kpKQwY8YMoqOjcXR05I8//iicN1EIIcxQRqbmhc7XaHWkZ2STkamRYWciXwojZ7RaHWt3x+fZZt2v56jpoZZhrmautH3G2FhbGjsEUUxKTCF5//59ABwdHQ0+V6lUMmXKFMqUKYOXlxcdO3Zk3759ABw8eJC4uDh+/vlnXn75ZQCmTZvG6tWr9ff8t/79+9OmTRsARo4cydq1a/nzzz/1hWRe98vIyGDYsGG8//77WFg8+uL48MMP2bp1KxcvXqRhw4YolUqAQh2ia2Vl2p3Tj8dq53fMtijdJF9Kl6BZe40dghBFIjElgyGL9hs7DCEMEjk+wNghmDVz+g1TYgrJxwvNJCUlGXyuu7s7ZcqU0T9WKpVkZWUBEB8fj6Ojo76IhEe9m2PHjn3m9by8vHJcC9Bf73n3c3d3p1u3bkRGRhIfH8/ly5eJjY0FQKN5sX91fxaFwgKVyjzmWDo42Bk7BGFGJF+EEEKI4mUuvylNnTn8hikxhaSbmxsVKlTg2LFjdOzYMdfzCQkJTJ8+XT+/8EnW1tbPvK6VleFv0dOu9+QCOXnd7/z58wQGBvLKK6/QrFkzXn/9dVQqFd27dzc4jvzSanUkJz8ssusXBktLBQ4OdiQnp6HRlPxhIeLFSL6ULhHBrV/ofIWlBQ5KO5JT0tBqSsxC5qIIFUbOxF1OZN6GE89tN7pnPXzdVQW6hzANpe0zJjHRtHcCMHXG/g3j4GBX+lZtVSgUvPPOO3z77be8//77uRbcWbFiBadOnaJKlSoGXbdatWrcv3+fS5cu4eHhAcC9e/fo0KEDy5cvL7T4H9uwYQPly5fn66+/1h/bu/fRsK3HxejjIa+FyVzG7Gs0WrOJVRif5EvpYPmC88esLBXY2liR9lBBtk7yRTxfYeRMdXcVKqVNroV2nqRW2lDdXSVzJM1cafuMke/dwmEOv2FMf/CtAQYNGoSnpyeBgYFs3bqVy5cvc/LkScaNG8fWrVuZOnUqZcuWNeiaTZs2pVatWowdO5aTJ09y7tw5xo4di1qt5pVXXin011CpUiVu3rzJvn37uHbtGr/88guTJ08GIDMzE0D/Gk6fPk16enqhxyCEEEKIoqVQWBAY4J1nm14B3lJECiFMVonpkQSws7NjzZo1rFq1ioiICK5fv46trS01a9bk22+/pWHDhgZfU6FQ8MUXXzBz5kwGDBiAhYUFTZo0YcWKFTnmORaWfv368ffffxMcHExmZiaenp6MGjWKzz//nFOnTvHaa6/RpEkT6tatS8+ePZk7d26hxyCEEEKIoufn68KQrrVy7SOpVtrQS/aRFEKYOAvdv3e3F6WORqPl3j3THs9uZaVApbInMfGByXfzC+OTfBGGkHwRhirsnNFqdcRfSSLpQQZO9jb4uDlJT2QJIp8xwhDGzhe12r70zZEUQgghhDBHCoUF1T1kQR0hhHkpUXMkhRBCCCGEEEIUPSkkhRBCCCGEEEIYpMgLSZ1OR1RUFH379qVJkybUqlWLtm3bMn36dO7cuVPUt38mX19foqKi8nz+33/q1q1Lx44diYyMLNRYrl69iq+vL4cPH35mm759+xISElKo9xVCCCFE8dJqdcReSuTQ2ZvEXkpEq5WlKoQQ5qlI50hqtVqGDh3K0aNHGTRoEBMnTsTe3p5z587x5Zdf8vbbb7NlyxbKly9flGEU2GeffUbHjh31j+/du8f69euZPn06FSpUyPHci6hcuTLR0dE4OjoWyvWEEEIIYXpi4m7nWqFVpbQhUFZoFUKYoSLtkVy9ejX79u3j66+/JigoCG9vb1xdXWnZsiWrV6+mTJkyrFy5sihDeCFKpRJnZ2f9H19fXyZNmoSbmxs//vhjod3H0tISZ2dnrK2tC+2aQgghhDAdMXG3WbrldI4iEiAxJYOlW04TE3fbSJEJIUTBFFmPpE6nY82aNbz11lu88soruZ63tbUlMjISZ2dn4NHwzjZt2jBq1CgiIyOxtbVl27ZtXL9+nfnz53Ps2DHS0tKoWLEivXv3JigoCIDw8HBiYmJo1qwZa9asITExkbp16xIWFoaXlxcAN2/eJCwsjEOHDqFUKhkzZkyBX5eFhQXW1tZYWf3vrTt69Ciff/45p0+fJjMzEzc3NwYNGkTnzp0BCAkJITMzEycnJ7Zu3YqNjQ2dO3dm1KhRWFtb6197ZGQkjRs3JjMzk3nz5vHDDz+QmZlJz5490WpzLv+7cuVK1q9fz82bN3FxceHtt99m8ODBWFjIcuFCiMKXkakxdghFRqPVkZ6RTUamRpbmF/liaM5otTrW7o7Ps826X89R00Mt236UQPIZUzxsrC2NHUKpU2SF5NWrV7l27RrNmjV7ZpsqVarkOrZlyxa++eYb0tLSsLS0JCgoiObNm7NhwwYsLS3ZuHEjs2fPpmnTptSoUQN4VMjZ2Njw1VdfkZWVRXBwMGFhYURGRpKdnc3AgQMpV64ca9asITMzk7CwsAK9pocPH7JmzRoSEhL0xeitW7d4//336dOnD1OnTiUrK4uIiAhCQ0Np3rw5FSpUAOCXX36hVatWbNiwgStXrhAaGkpaWtpTY5k2bRp79+5l1qxZuLq6smzZMo4ePYqbmxsAe/fuZfny5SxcuJCXX36ZEydOEBwczEsvvaQvXg1lZWXa6y493s8mv/vaiNJN8qXwBc3aa+wQhCjRElMyGLJov7HDEMJsRY4PMHYIhcKcfsMUWSH5zz//AKBWq3McHzRoUI5FZVxdXdm5c6f+cWBgINWqVQMezUns168fvXv3xt7eHoDhw4ezYsUK4uLi9IVkdnY2c+bM0c8x7NmzJ3PnzgXg4MGDnDt3jt27d+Pu7g7AzJkz6dKly3Nfw6RJk5g6dSrwqIc1IyOD6tWrs2jRIlq3bg1ARkYGw4YN4/3339f3Bn744Yds3bqVixcv6gtJBwcH5s6di52dHT4+Pty+fZvp06fn6h1NTU0lKiqKSZMm0bJlSwBmzJjBoUOH9G0uX76MtbU1VapUwdXVFVdXV1xcXHB1dX3ua3oahcIClcq+QOcWNwcHO2OHIMyI5IsQQghROpjLb9n8MoffMEVWSKpUjzbWvX//fo7jYWFhpKenA/Dtt9+yd2/Of+X28PDQ/12tVhMYGMiOHTs4e/Ysly9fJjY2FiDHUM8KFSrkWKhGqVSSlZUFQHx8PI6OjvoiEqBGjRrY2to+9zUMHz6c119/nezsbH766SdWrlzJu+++S4cOHfRt3N3d6datG5GRkcTHx+eIUaP531CwOnXqYGf3v4SoX78+WVlZXLhwQf9eAVy4cIGsrCxq166tP2ZjY0PNmjX1j9966y02b95Mu3btqFatGs2aNaNdu3YFLiS1Wh3JyQ8LdG5xsbRU4OBgR3JyGhqNDAsReZN8KXwRwa2NHUKRUVha4KC0IzklDa1GVtAUz2dozsRdTmTehhPPbTe6Zz183VXPbSfMi3zGFI/ExAfGDqFQGPs3jIODXb57Q4uskHRzc8PZ2ZnDhw/nWN20YsWK+r8/bZXSJwu8O3fu0KNHD9RqNf7+/rRo0YLatWvre+oey2uRGgsLi1zzC4EccxyfpXz58vrCdujQoQBMnjwZR0dH/Ws6f/48gYGBvPLKKzRr1ozXX38dlUpF9+7dc1yrTJkyOR4/jsnSMud47se9mjpdzg+aJ+NVq9Vs27aN48ePc+DAAaKjo4mMjGTYsGH6OA1lLmP2NRqt2cQqjE/ypfBYluB5W1aWCmxtrEh7qCBbJ/kins/QnKnurkKltMm10M6T1EobqrurZI5kCSSfMcWjpH3fm8NvmCIbfGtpaUm/fv3YunWrvofu327cuJHnNXbs2EFSUhLr169n8ODBtG3bVt/D+e9C61lq1KhBSkoK586d0x+7ePEiqamp+Xwl//Pxxx9Tr149Jk2axO3bj1ZX27BhA+XLl+frr7/mgw8+oGXLlvphvU/GeObMmRw9lMePH8fOzo6XX345xz1efvllbGxsOHbsmP5YdnZ2jvdw+/btrF+/Hj8/P4YPH873339P9+7dC3UlWSGEEEIUDoXCgsAA7zzb9ArwliJSCGFWinQW58CBA2ndujWBgYEsW7aM2NhYrl69yt69ewkKCmLz5s00adLkmedXqlSJtLQ0fv75Z65fv050dDSjRo0CIDMzM18xNG7cmLp16xIcHMyJEyc4deoUwcHBKBSGv3RLS0umT59OWlqafu5kpUqVuHnzJvv27ePatWv88ssvTJ48OVeM165dIywsjISEBH755Rc+//xz+vTpk2O4K4C9vT19+vTh888/55dffiEhIYFJkyZx69YtfZuMjAxmz57N1q1buXr1KkePHuXIkSPUr1/f4NckhBBCiKLn5+vCkK61UCltchxXK20Y0rWW7CMphDA7RTa0FUChULBo0SJ++uknNm/eTGRkJMnJyVSoUIGGDRuyZs0aGjVq9Mzz27dvz5kzZ5g1axapqalUqVKF7t27s2fPHk6dOkWvXr3yFcPy5cuZNm0aQUFB2Nra8tFHH3Ht2rUCvaZq1aoxaNAgwsPD2b17N/369ePvv/8mODiYzMxMPD09GTVqFJ9//jmnTp3itddeA6BevXooFAreeecdlEol/fr14+OPP37qPT799FNsbGyYMmUKDx48oEOHDvj7++uf7969O0lJSXzxxRfcuHEDR0dH2rVrx+jRowv0moQQQghR9Px8Xajv7Uz8lSSSHmTgZG+Dj5uT9EQKIcyShS6/Y0RFgYWEhHDt2jW+/fZbY4fyVBqNlnv3THuCspWVApXKnsTEByY/XlwYn+SLMITkizCU5IwwhOSLMISx80Wtts/3Yjumv0GJEEIIIYQQQgiTIoWkEEIIIYQQQgiDmEwh6e/vj6+vr/5P9erVadCgAX369OHIkSP6NuHh4c+8RlRUFL6+vvrHvr6+REVFvVBchl7jae1nzZr1zGGtz7t+eHh4jvmRQgghhDBPWq2O2EuJHDp7k9hLiWi1MrtICGG+inSxHUMFBQURFBQEPNo6IykpiQULFjBw4EB++umn557fsWNH/u///q+owyxWQUFB9O7d29hhCCGEEOIFxMTdZt2v53LsJalS2hAY4C0rtgohzJLJ9EgClC1bFmdnZ5ydnXFxccHHx4ewsDDS09PZvXv3c8+3tbXF2dm5GCItPvb29qjVamOHIYQQQogCiom7zdItp3MUkQCJKRks3XKamLjbRopMCCEKzqR6JJ/GyupRiNbW1gDcuXOHoUOHEh0djY2NDV26dCE4OBhLS0uioqIYN24ccXFxua6j1WqJiIggKiqKa9euYW1tTYMGDZg4cSLu7u4A3Lx5k7CwMA4dOoRSqWTMmDG5rvPbb78RHh7O+fPnqVixIm+88QaDBw/Wx/ek8PBwfv/9d1q0aEFkZCQajYa2bdsSGhpKuXLl9O0uXLhA//79iYmJwcnJiT59+vDRRx/pr7Flyxb27t0LwNGjR/n88885ffo0mZmZuLm5MWjQIDp37vyC77QQQoiSLiNTY+wQSgSNVkd6RjYZmZrnrqqo1epYuzs+zzbrfj1HTQ+1bANSzGysLY0dghBmzaQLyVu3bjFjxgzKli1Ly5YtiYiIYNOmTYwdO5axY8dy+PBhQkND8fb25p133snzWpGRkaxcuZLZs2fj4+PD5cuXmTBhArNmzeKLL74gOzubgQMHUq5cOdasWUNmZiZhYWE5rrF//35GjBjBuHHjaNasGZcvX2bq1KlcuHCBxYsXP/W+p06dAmDVqlWkpqYSGhrKiBEjWLFihb7NmjVrmDRpElOnTuWHH35gwYIF1KlTh6ZNm+Z6P95//3369OnD1KlTycrKIiIigtDQUJo3b06FChUK8jYDj5YaNmWPlyHO73LEonSTfBGGKE35EjRrr7FDEE+RmJLBkEX7jR1GqRM5PqBY7lOaPmPEizOnfDGpQnL58uWsWrUKgOzsbDIzM/Hy8mLRokW4uroC8Prrr/Pee+8B4ObmRmRkJKdPn35uIenu7s7s2bNp3bo1AFWqVKF9+/b8/PPPABw8eJBz586xe/dufQ/lzJkz6dKli/4ay5Yt491336Vnz576a4aFhfHee+9x9epVXnrppVz3tbCwYNGiRVSsWBGAiRMn8sEHH/D3339TtWpVAAIDA/X3GTx4MKtWreL06dO5CsmMjAyGDRvG+++/j4XFo3+1/PDDD9m6dSsXL14scCGpUFigUtkX6Nzi5uBgZ+wQhBmRfBGGkHwRonQp7t8+8hkjDGEO+WJShWTPnj3p27cvAAqFAicnJ5RKZY42np6eOR47OjqSkZFzzsHT+Pv78+eff7J48WIuXLjAhQsX9MNTAeLj43F0dNQXkQA1atTA1tZW//js2bOcPHmSTZs26Y/pdI9WXEtISHhqIenp6am/B0CDBg3093tcSP77NTk4ODz1Nbm7u9OtWzciIyOJj4/n8uXLxMbGAqDRFHy4klarIzn5YYHPLw6WlgocHOxITk5Do5HNfEXeJF+EIUpTvkQEtzZ2CCWCwtICB6UdySlpaDV5r7wadzmReRtOPPeao3vWw9ddVUgRivxITHxQLPcpTZ8x4sUZO18cHOzy3RtqUoWko6MjHh4eebaxtMw9nv1xMZeXr776iqVLl9K1a1eaNm1K//792bNnDzt37gQe9Rxqtbn/Yz2eowmP5lkOHDiQrl275mr3rEV+ypQpk+Px44LvydeR39d0/vx5AgMDeeWVV2jWrBmvv/46KpWK7t27P/XehnjeHA9TodFozSZWYXySL8IQpSFfLGUOXqGwslRga2NF2kMF2bq8c6a6uwqV0ibXQjtPUittqO6ukjmSxay4//9eGj5jROExh3wxqUKyKC1btowhQ4bw4Ycf6o+tXLlSX7DVqFGDlJQUzp07h7e3NwAXL14kNTVV397b25sLFy7kKHYPHz5MZGQkkydPpmzZsrnue+HCBVJSUvQ9q8ePHwegZs2aBr+GDRs2UL58eb7++mv9sceL8OSnmBZCCCFE8VIoLAgM8GbpltPPbNMrwFuKSCGE2TH9WZyFpHLlyhw4cIDz58/z999/s3DhQn755RcyMzMBaNy4MXXr1iU4OJgTJ05w6tQpgoODUSj+9xZ98MEH7Nq1iyVLlnDhwgUOHjzIuHHjSElJeWaP5MOHDwkODiY+Pp7ff/+dKVOm0LFjR6pUqWLwa6hUqRI3b95k3759XLt2jV9++YXJkycD6F+HEEIIIUyLn68LQ7rWQqW0yXFcrbRhSNdaso+kEMIslZoeyTlz5jBlyhTefvtt7O3tqVu3LmFhYUyePJnr16/j6urK8uXLmTZtGkFBQdja2vLRRx9x7do1/TXat2/PwoULWb58OcuWLcPJyQl/f39Gjx79zPtWrlyZGjVq0Lt3bywtLenUqVOe7fPSr18//v77b4KDg8nMzMTT05NRo0bx+eefc+rUKV577bUCXVcIIYQQRcvP14X63s7EX0ki6UEGTvY2+Lg5SU+kEMJsWehkTGSR+fcekKZKo9Fy717xTDgvKCsrBSqVPYmJD0x+vLgwPskXYQjJF2EoyRlhCMkXYQhj54tabZ/vxXZKzdBWIYQQQgghhBCFQwpJIYQQQgghhBAGKZI5ktu3b2fNmjXEx8djYWFB1apV6d69Oz179iyK2+lFRUUxbty4pz7n5OTE4cOHi/T+/zZs2DCGDRsGQEhICNeuXePbb78t1hiEEEIIIYQQorAVeiG5adMmpk+fTmhoKH5+fuh0Og4cOMC0adP4559/GDp0aGHfMpfo6Ohcx55cfdUYQkND9XtICiGEEKLk02p1sriOEKLEKvRCct26dbz99tu88847+mNVq1bl1q1bREZGFksh+aytOIzp8T6SQgghhCj5YuJus+7XcySmZOiPqZQ2BAZ4y3YfQogSodC76RQKBcePH+f+/fs5jn/44Yd89913+sfXr19n5MiRNG3alFdeeYXXXnuNuXPnotU+Wp0oKiqKtm3b6v+3Vq1adOvWjZiYmBeK7/H1pk2bhp+fH4MHDwbg119/pXv37tSrV4/atWvTrVs3/vvf/+rP69u3L/PmzeOzzz6jYcOGNGjQgE8//ZTU1FR9m0uXLvHxxx/j5+dH48aNGTVqFHfv3gUeDW3t27evvu3KlSsJCAigVq1a+Pv7s3TpUp5cQPe///0vPXr0oG7durz22mssXLhQ36OZnp7OokWLaNOmDbVr16Zz587s2rXrhd4XIYQQQhSOmLjbLN1yOkcRCZCYksHSLaeJibttpMiEEKLwFHqP5MCBAxk5ciSvvfYajRs3pmHDhjRp0oTatWvj4OCgb/fxxx/j7OzM119/jb29PXv27GHmzJnUr1+fgIAAAG7cuMGGDRuYO3cu9vb2TJ48mZCQEH755RcsLAo+NOTy5cvcvn2brVu3kp6ezunTpxk2bBhjx46lTZs2pKamMn/+fIKDg9m3bx/W1tYArF69mqCgIDZt2kRCQgKffvopL7/8MkOHDiU5OZnevXvj6+vLN998g0KhYOLEiYwYMSLXvMi9e/eyfPlyFi5cyMsvv8yJEycIDg7mpZdeonPnzhw/fpwPP/yQAQMGMGPGDK5du8aYMWOwsrJi2LBhjBo1irNnzzJ58mQ8PDzYsWMHn3zyCUuWLNG/d0IIIcxfRqZMiTBFGq2O9IxsMjI1uZbn12p1rN0dn+f56349R00PtQxzNUM21pbGDkEIk1HohWT79u2pVKkSkZGRHDhwgH379gHg6enJjBkz8PPzIz09nc6dO9OhQwcqV64MQP/+/YmIiCAuLk5fDGVlZREWFkaNGjUAGDBgAEOGDOHOnTu4uDx7WEj9+vVzHdu5cyeurq76x4MHD8bNzQ2Av/76iwkTJhAYGKh/vl+/fnzwwQfcvXtXH2O1atUYNWqU/vU0b96c48ePA/Djjz/y4MEDFixYgKOjIwDTpk1j586dZGZm5ojl8uXLWFtbU6VKFVxdXXF1dcXFxUUf37fffkvdunUJDg4GwMvLiylTpnD37l0SEhLYs2cPy5Yto1WrVsCjRX1iY2NZtmxZgQtJKyvTXsD38X42+d3XRpRuki/CEKacL0GzTHsfYlEwiSkZDFm039hhiAKIHG/47yxT/owRpsec8qVIVm2tV68e9erVQ6vVEhsby759+1izZg0ffPABu3fvpnz58vTp04eff/6ZkydPcunSJeLi4vjnn3/0Q1sf8/Ly0v/98TzDrKysPO+/devWXMf+XXh6enrq/16jRg0cHR356quv+Pvvv7l06RKxsbEAORbIqVq1ao5rKJVKkpOTAYiPj8fT01NfRAJUr16d6tWr54rlrbfeYvPmzbRr145q1arRrFkz2rVrpy8k4+Pjad68eY5z2rVrBzwqWAH8/PxyPN+oUSMWLFiQ+83IB4XCApXKvkDnFjcHBztjhyDMiOSLMITkixDieV7k95J8xghDmEO+FGohefPmTZYvX85HH31EpUqVUCgU1KxZk5o1axIQEMCbb77JkSNHeO211+jTpw/p6em0b9+erl27UqdOHXr37p3rmo+HlT7pybmET+Ph4fHcWG1tbfV//+OPP3j//fdp1aoVfn5+dOrUibS0NIYMGfLcWB6zssr/W6lWq9m2bRvHjx/nwIEDREdHExkZybBhwxg6dKhB13pMp9MV6Dx4NAwnOflhgc4tLpaWChwc7EhOTkOj0T7/BFGqSb4IQ5hyvkQEtzZ2COIpFJYWOCjtSE5JQ6vJ+Zsk7nIi8zaceO41Rvesh6+7qogiFEUlMfGBweeY8meMMD3GzhcHB7t894YWaiFpbW3Nxo0bqVy5Mh9++OG/gno0P7JChQpER0dz5swZDhw4QIUKFQBISkri7t27zy0Si8KqVato3Lgx4eHh+mOP5zXmN55q1aqxceNGUlJS9D2nZ86cYeDAgWzZsiVH2+3bt5OSkkLv3r3x8/Nj+PDhjB8/nh9//JGhQ4fi5eXFqVOncpzzzTffsGPHDmbNmgVATEwMrVv/7wfG0aNHqVatmuEv/v/79xwPU6XRaM0mVmF8ki/CEKaYL5Yyh84kWVkqsLWxIu2hgmxdzpyp7q5CpbTJtdDOk9RKG6q7q2SOpBl6kc8IU/yMEabLHPKlUAffqtVqBg4cyOLFi1m4cCF//fUXV65c4bfffmPo0KH6xXcqVaoEPCqorl27xtGjRxk8eDBZWVm55hMWh8qVKxMXF8fRo0e5evUqmzdvZvHixQD5jqdTp044OjoyZswYYmNjOX36NJMmTcLHx0f/eh/LyMhg9uzZbN26latXr3L06FGOHDmin9s5cOBATpw4weLFi7l48SL79u3jiy++oFWrVnh5edG6dWvCwsL4z3/+w4ULF1iyZAl79uwhKCiocN8YIYQQQhhEobAgMMA7zza9AryliBRCmL1CnyM5YsQIPD09+f7771m7di3p6em4urrSoUMHPvroIwDq1KnDuHHjWL16NYsWLaJixYp07NiRypUr5+qJKw7Dhw/nn3/+YdCgQcCj3sUZM2YwZswYTp06lWOe5rPY2dmxcuVKZs6cSc+ePbG1taVVq1aMHTs2V9vu3buTlJTEF198wY0bN3B0dKRdu3aMHj0aeDRnc+nSpXz++edERETg4uJCv379+PjjjwFYsGABCxYsIDQ0lOTkZHx8fAgPD6dt27aF+K4IIYQQoiD8fF0Y0rVWrn0k1Uobesk+kkKIEsJCZ4yxpMKkaDRa7t0zfMx/cbKyUqBS2ZOY+MDku/mF8Um+CENIvghD5TdntFod8VeSSHqQgZO9DT5uTtITWQrJZ4wwhLHzRa22N84cSSGEEEII8YhCYUF1D1lQRwhRMpn+BiVCCCGEEEIIIUyKFJJCCCGEEEIIIQwiheT/5+/vj7+/P6mpqbmeCwkJoW/fvkaI6un8/f1zbFUihBBCCOPRanXEXkrk0NmbxF5KRKuV5SeEECWfzJF8wrVr15gzZw5TpkwxdihCCCGEMAMxcbdzrc6qUtoQKKuzCiFKOOmRfIKbmxvfffcdv//+u7FDEUIIIYSJOxJ7m6VbTucoIgESUzJYuuU0MXG3jRSZEEIUPemRfMJbb73FsWPHCA0N5YcffqBcuXK52qSkpDBnzhx2795NVlYWr7zyCmPGjKF27drs3buXwYMH8/vvv6NWqwHo0qUL2dnZ7NixA4D79+/TtGlTIiMjadCgAREREURFRXHt2jWsra1p0KABEydOxN3dHQBfX1+GDBnCli1byMrKYs2aNTniefDgAQMHDuTBgwesXr1af18hhBDi3zIyNcYOoUTQaHU8SMtiza64PNut+/UcNT3UsuVHIbGxtjR2CEKIJ0gh+QQLCwumT59Op06dmD17NlOnTs3xvE6n44MPPsDW1pbly5dTrlw5tm3bRq9evfj+++9p1qwZNjY2HDp0iI4dO3Lv3j3i4uLQarXcvXuX8uXLEx0djaOjIw0aNCAyMpKVK1cye/ZsfHx8uHz5MhMmTGDWrFl88cUX+vuuW7eOiIgINBoNnp6e+uNpaWkMGjSI9PR0IiMjcXJyKvBrt7Iy7c7px/vZ5HdfG1G6Sb4IQ5SmfAmatdfYIZQqiSkZDFm039hhlBiR4wOMHUKBlKbPGPHizClfpJD8lypVqjB27FgmTpxIu3btaNGihf65Q4cOceLECQ4dOqQv2kaNGsWxY8eIjIxk1qxZNG3alOjoaDp27Mjvv/9OzZo1uXPnDocPH6Zjx4785z//oVWrVigUCtzd3Zk9ezatW7fW37t9+/b8/PPPOWLq3LkztWvXznEsIyODjz/+WN8T6ejoWODXrFBYoFLZF/j84uTgYGfsEIQZkXwRhpB8EcK0mctvlWeRzxhhCHPIFykkn6JHjx7s2rWL8ePH64ekApw5cwadTqcv/B7LzMwkI+PR/Ah/f399b+KBAwdo2rQp165d49ChQ7Rv357//ve/TJs2Td/2zz//ZPHixVy4cIELFy5w/vx5KlasmOP6Hh4euWL85ptvyMrKokmTJi9URMKj1eaSkx++0DWKmqWlAgcHO5KT09BotMYOR5g4yRdhiNKULxHBrZ/fSDyXwtKCK3ceMnnFoee2Hd2zHr7uqmKIquRLTHxg7BAKpDR9xogXZ+x8cXCwy3dvqBSSzzBt2jQ6derEzJkz9ce0Wi3lypUjKioqV3tra2sAWrVqxcSJE0lISODgwYNMnz6da9eusXLlSv7880/S0tJo3rw5AF999RVLly6la9euNG3alP79+7Nnzx527tyZ49q2tra57ufj48PYsWMZMGAA3333HT169Hih15udbR4fbBqN1mxiFcYn+SIMURryxVLm6hUKK0sF9XxdUCttuPevhXaepFbaUN1dJXMkC4m5//+zNHzGiMJjDvli+oNvjcTV1ZWQkBA2bdrE0aNHgUfFW2pqKllZWXh4eOj/REREsGfPHgBcXFyoVasW69ev5+7du/j5+dG0aVMuXrzId999R7NmzbCze9RVvWzZMoYMGcLkyZPp0aMH9erV4+LFi+h0z99/qlWrVrz66qsMGDCAOXPmcOPGjaJ7M4QQQgiRg6XCgt7tfPNs0yvAW4pIIUSJJYVkHrp3706LFi24cuUKAP/3f/9HjRo1GDlyJIcOHeLSpUvMnDmTqKgovLy89Of5+/vz3XffUa9ePWxtbXFzc+Oll15i27ZttGnTRt+ucuXKHDhwgPPnz/P333+zcOFCfvnlFzIzM/Md49ChQ1Gr1YwfP77wXrgQQgghnqtRdReGdK2FSmmT47haacOQrrVkH0khRIkmheRzTJs2DaVSCYClpSWrVq2iVq1ajBgxgrfeeosjR46wZMkSmjZtqj/H39+fzMxMmjRpoj/WrFkzgBzzK+fMmUN6ejpvv/02ffr0IT4+nrCwMO7evcv169fzFZ+trS1TpkwhOjqajRs3FsZLFkIIIUQ++fm6MPfjZgT3qs+Hb9UkuFd95nzcTIpIIUSJZ6HLzzhKUaJpNFru3TPtCexWVgpUKnsSEx+Y/HhxYXySL8IQki/CUJIzwhCSL8IQxs4Xtdo+34vtSI+kEEIIIYQQQgiDSCEphBBCCCGEEMIgpbaQ9Pf3x9fXV/+nevXqNGjQgD59+nDkyBF9m/DwcCNHajpxCCGEEKWdVqvjr4v32HfsKn9dvIdWKzOEhBClU6neRzIoKIigoCAAdDodSUlJLFiwgIEDB/LTTz8ZObr/2bRpEzY2Ns9vKIQQQogiExN3m3W/niPxib0jVUobAgO8ZXEdIUSpU2p7JAHKli2Ls7Mzzs7OuLi44OPjQ1hYGOnp6ezevdvY4emp1Wrs7e2NHYYQQghRasXE3WbpltM5ikiAxJQMlm45TUzcbSNFJoQQxlGqeySfxsrq0VtibW0NwJ07dxg6dCjR0dHY2NjQpUsXgoODsbS0BOD48eMsXLiQM2fOYGVlhb+/P8HBwahUKgAyMzNZvHgx27dvJzU1FW9vb4YPH06LFi0AiIqK4ssvv+Tjjz/myy+/5MaNG/j4+BAaGoqfnx/waGhr165dGTRoEO+++y4AGzduxMrKiri4ON555x1Gjhyp710VQghRemVkaowdQomj1epYuzs+zzbrfj1HTQ81CoVFMUVV8tlYWxo7BCFEHqSQfMKtW7eYMWMGZcuWpWXLlkRERLBp0ybGjh3L2LFjOXz4MKGhoXh7e/POO+9w8uRJ+vbtS48ePZg0aRJ37txhypQpvP/++2zcuBFLS0vGjRtHQkIC8+bNo2LFivz2228MGjSIJUuW0KpVKwBu3LjBhg0bmDt3Lvb29kyePJmQkBB++eUXLCz+94VUpkwZ5s6dS7du3Vi5ciUDBgxg9OjRNG7cmAEDBrzQa7eyMu3O6cfLEOd3OWJRukm+CEOUtHwJmrXX2CGUSokpGQxZtN/YYZQokeMDjB1CoShpnzGiaJlTvpTqQnL58uWsWrUKgOzsbDIzM/Hy8mLRokW4uroC8Prrr/Pee+8B4ObmRmRkJKdPn+add95h1apV+Pr6MmHCBAC8vLxYsGABnTt3Jjo6Gk9PT3bs2MHWrVupUaMGAAMGDCA2NpaVK1fqC8msrCzCwsJytBkyZAh37tzBxSXnnItq1aoxatQoFixYQHx8PPfu3WP16tU5Ck5DKRQWqFTmMXTWwcHO2CEIMyL5Igwh+SKEaTGX3yb5JZ8xwhDmkC+lupDs2bMnffv2BUChUODk5IRSqczRxtPTM8djR0dHMjIezY+Ij4+nefPmOZ6vXr06SqWSuLg4Hj58CEBgYGCONllZWTg4OOQ45uXlpf/74xiysrKeGvd7773Hnj172LFjB0uXLqV8+fL5ebnPpNXqSE5++ELXKGqWlgocHOxITk5Do5HNfEXeJF+EIUpavkQEtzZ2CCVO3OVE5m048dx2o3vWw9ddVfQBlRKJiQ+MHUKhKGmfMaJoGTtfHBzs8t0bWqoLSUdHRzw8PPJs83gu5JN0Ol2O/33a82XKlNE/v3bt2lyL5SgUOf8DPZ6T+bT7/FtycjKXL1/GysqK6OhoAgJefOhHdrZ5fLBpNFqziVUYn+SLMERJyRdLmaNX6Kq7q1ApbXIttPMktdKG6u4qmSNZiErC/x+fVFI+Y0TxMId8Mf3BtybM19eXmJiYHMdiY2NJTU3Fy8sLb29v4NGCPR4eHvo/UVFRREVFFfi+YWFh2NnZsXTpUjZs2MD+/TInQwghhCgqCoUFgQHeebbpFeAtRaQQolSRQvIFDBgwgLi4OKZOnUpCQgKHDx9m9OjR1KxZk6ZNm+Lt7U3r1q2ZNGkSe/fu5cqVK0RERLB8+XLc3d0LdM8dO3bw008/MWPGDFq1asU777xDaGgoSUlJhfvihBBCCKHn5+vCkK61UClz7uusVtowpGst2UdSCFHqlOqhrS+qbt26rFixgkWLFtGlSxfKlStHQEAAn376KWXKlAFg4cKFLFy4kIkTJ3L//n3c3d2ZPn06Xbt2Nfh+t27dYsqUKfTp04cGDRoAMHbsWPbv38+kSZNYvHhxob4+IYQQQvyPn68L9b2dSbh+nyydBWUsdHi5OkpPpBCiVLLQPWsinig1NBot9+6Z9oR2KysFKpU9iYkPTH68uDA+yRdhCMkXYSjJGWEIyRdhCGPni1ptn+/FdmRoqxBCCCGEEEIIg0ghKYQQQgghhBDCICV6juT27dtZs2YN8fHxWFhYULVqVbp3707Pnj2LNY6QkBCuXbvGt99+W6z3FUIIIYRhtFod8VeSSHqQgZO9DT5uTjIHUgghnqLEFpKbNm1i+vTphIaG4ufnh06n48CBA0ybNo1//vmHoUOHGjtEIYQQQpiQmLjbrPv1XI79IlVKGwIDvGVVViGE+JcSW0iuW7eOt99+m3feeUd/rGrVqty6dYvIyEgpJIUQQgihFxN3m6VbTuc6npiSwdItp2WLDyGE+JcSW0gqFAqOHz/O/fv3cXR01B//8MMPefvtt/WPr1+/zty5czl06BDJycmUL1+eTp068emnn6JQKIiKiuLLL7/k448/5ssvv+TGjRv4+PjoezoL4saNG8yfP5+DBw/y4MED/Pz8GDNmDNWrVwfg7t27hIWFcfjwYdLS0qhZsyajRo3i1VdfBSAzM5PFixezfft2UlNT8fb2Zvjw4bRo0eIF3jEhhCj5MjI1uY5ptDrSM7LJyNTIioqllFarY+3u+DzbrPv1HDU91CgUFpIzIgcba0tjhyCEUZTYQnLgwIGMHDmS1157jcaNG9OwYUOaNGlC7dq1cXBw0Lf7+OOPcXZ25uuvv8be3p49e/Ywc+ZM6tevT0BAAPCo8NuwYQNz587F3t6eyZMnExISwi+//IKFhWHzJlJTU+nVqxdubm58+eWXWFtbEx4eTp8+fdi2bRtVqlRh8uTJZGZmsmbNGqytrVm2bBmDBw9m//79lC1blnHjxpGQkMC8efOoWLEiv/32G4MGDWLJkiW0atWqQO+XlZVpr7v0eBni/C5HLEo3yRfxLEGz9ho7BGGmElMyGLJov7HDECYocnxAns/Ld5IwhDnlS4ktJNu3b0+lSpWIjIzkwIED7Nu3DwBPT09mzJiBn58f6enpdO7cmQ4dOlC5cmUA+vfvT0REBHFxcfpCMisri7CwMGrUqAHAgAEDGDJkCHfu3MHFxbBhLtu3bycxMZGoqCjUajUA8+fPJyAggLVr1xIcHMzly5fx8fHBzc0NW1tbQkND6dSpE5aWlly6dIkdO3awdevWHPHExsaycuXKAhWSCoUFKpW9wecZg4ODnbFDEGZE8kUIIURRy+9vKPlOEoYwh3wpsYUkQL169ahXrx5arZbY2Fj27dvHmjVr+OCDD9i9ezfly5enT58+/Pzzz5w8eZJLly4RFxfHP//8g1abc6iKl5eX/u9KpRJ4VGAaKj4+Hk9PT30RCWBra0udOnWIj380rGbo0KGMGTOGXbt24efnR4sWLXjzzTexsbHh7NmzAAQGBua4blZWVo6eVkNotTqSkx8W6NziYmmpwMHBjuTkNDQaGUYk8ib5Ip4lIrh1rmMKSwsclHYkp6Sh1eiMEJUwtrjLiczbcOK57Ub3rIevu0pyRuSQmPggz+flO0kYwtj54uBgl+/e0BJZSN68eZPly5fz0UcfUalSJRQKBTVr1qRmzZoEBATw5ptvcuTIEV577TX69OlDeno67du3p2vXrtSpU4fevXvnuqa1tXWuYzpd7i+PzMxM4uLi8Pb2xtbWVt/O0tLymecAaLVarKwe/edo27Yt//3vf/nvf//L77//ztdff82SJUv4/vvv9eevXbsWe/uc/wKmUBS8C9xc5nhoNFqziVUYn+SL+DfLp2zjYGWpwNbGirSHCrJ1ki+lUXV3FSqlTY7VWv9NrbShursKhcJCckbkkN/vGflOEoYwh3wx/cG3BWBtbc3GjRvZvn17ruce99pVqFCB6Ohozpw5Q2RkJMOHD6djx46UK1eOu3fvPrPge55Lly7xzjvv6HsXAe7fv6/vgfT19eXixYvcvXtX/3xGRganT5+mWrVqZGZmMnPmTK5cuULHjh2ZNm0av/76KwqFgv/85z94e3sDcOfOHTw8PPR/oqKiiIqKKlDMQgghRGmmUFgQGOCdZ5teAd6yn6QQQjyhRBaSarWagQMHsnjxYhYuXMhff/3FlStX+O233xg6dKh+8Z1KlSoBj+YtXrt2jaNHjzJ48GCysrLIzMws0L09PT1xdXUlIiKCS5cusXfvXg4ePMhrr70GQKdOnXBycmLEiBGcPHmS2NhYRo8ezcOHD+nRowfW1tacOnWKCRMmcOLECa5evUpUVBQPHz6kfv36eHt707p1ayZNmsTevXu5cuUKERERLF++HHd390J7D4UQQojSxM/XhSFda6FS2uQ4rlbayNYfQgjxFBa6gna9mYGtW7fy/fffEx8fT3p6Oq6urnTo0IGPPvqIsmXLArB69WpWr17NvXv3qFixIh07duTq1av8888/fPPNN0RFRTFu3Dji4uL01z18+DD9+vVjz549vPTSS7nue/r0aaZNm0ZsbCwODg707NmTjz/+WL/C65UrV5g1axYHDx4EwM/Pj9GjR+Pr6wvArVu3mDlzJocPHyYlJYWqVavy0Ucf8cYbbwCQlpbGwoUL+fHHH7l//z7u7u4EBQXl2NbEEBqNlnv38h7fb2xWVgpUKnsSEx+YfDe/MD7JF2EIyRfxJK1WR/yVJJIeZOBkb4OPm1OunkjJGWEIyRdhCGPni1ptn+85kiW6kBT5I4WkKGkkX4QhJF+EoSRnhCEkX4QhjJ0vhhSSJXJoqxBCCCGEEEKIoiOFpBBCCCGEEEIIg5hkIbl9+3beffdd6tWrR/369Xn77bfZsGGDscMyCp1Ox5YtW3Ks8iqEEEIIIYQQxmRy+0hu2rSJ6dOnExoaip+fHzqdjgMHDjBt2jT++ecfhg4dauwQi9WRI0cICQlhz549xg5FCCGEKNHys9COEEKIR0yukFy3bh1vv/0277zzjv5Y1apVuXXrFpGRkaWukJS1kIQQQoiiFxN3m3W/niMxJUN/TKW0ITDAW7b+EEKIpzC5oa0KhYLjx49z//79HMc//PBDvvvuOwCuX7/OyJEjadq0Ka+88gqvvfYac+fORav938pGJ0+epH///tSvX59mzZoxadIk0tLSAOjbty8TJkyge/fuNGzYkO3bt5OZmcns2bPx9/enVq1avPrqq3zyySfcu3cPgKtXr+Lr68vOnTvp0qULtWvXplu3biQkJLB06VKaNWvGq6++SlhYWI7i77fffqNbt27UqVOHtm3bsmjRohx7VPr6+rJp0yb69+9PnTp1aNGiBUuWLAH+t80IQJs2bYiKigLg+PHj9OvXDz8/Pxo3bsy4ceNITEws7P8UQgghRKkQE3ebpVtO5ygiARJTMli65TQxcbeNFJkQQpguk9v+4+eff2bkyJFYW1vTuHFjGjZsSJMmTahdu7Z+H8bOnTvj7OzM6NGjsbe3Z8+ePcycOZOlS5cSEBDAlStXeOutt2jbti0DBw4kJSWFsWPH0rBhQ2bNmkXfvn05cuQIc+fOxdfXF2dnZ5YuXcpvv/3GzJkzqVKlCnFxcYwbN4633nqL0NBQrl69Sps2bXBzc2PGjBk4ODgwdOhQkpOTadmyJYMGDeKPP/5g8uTJfPnll/j7+7N//36GDRvGuHHjaNasGZcvX2bq1KlUr16dxYsXA48KSQcHB8aPH0+DBg3YuXMnCxcuZM2aNdStW5f//Oc/DBs2jI0bN+Lj40N8fDyBgYH06NGDwMBA7ty5w5QpU7C1tWXjxo1YWloa/J7L9h+ipJF8EYYoqnzJyNQU2rVE0dFqdYSuOERSauYz26iUNkx7v7F+mKuVlQInp7IkJT2UzxgTY2Nt+O+goibfScIQxs4XQ7b/MLmhre3bt6dSpUpERkZy4MAB9u3bB4CnpyczZszglVdeoXPnznTo0IHKlSsD0L9/fyIiIoiLiyMgIIDvv/8eJycnZsyYgZXVo5c4bdo0jh8/rr9PjRo16NSpk/5x7dq1ad++PQ0bNgSgSpUqNGvWjPj4+BzxBQUF8eqrrwLQtm1bvv32W6ZMmYKdnR1eXl6Eh4dz7tw5/P39WbZsGe+++y49e/YEwN3dnbCwMN577z2uXr3KSy+9BECXLl3o3LkzAIMGDWLlypUcO3aMRo0a4ejoCIBarcbW1pZVq1bh6+vLhAkTAPDy8mLBggV07tyZ6OhoWrZsWaD33crK5Dqnc3ic0PlNbFG6Sb4IQxRVvgTN2luo1xPGk5iSwZBF+40dhsiHyPEBxg4hF/lOEoYwp3wxuUISoF69etSrVw+tVktsbCz79u1jzZo1fPDBB+zevZs+ffrw888/c/LkSS5dukRcXBz//POPfmhrfHw8r7zyir6IBGjSpAlNmjTRP/bw8Mhxz86dO/P7778zb948Ll68yN9//82FCxf0heXTzitbtiwVKlTAzs5Of8zW1lY/dPXs2bOcPHmSTZs26Z9/3AGckJCgLyS9vLxy3EOpVJKVlfXU9yY+Pp7mzZvnOFa9enWUSiVxcXEFKiQVCgtUKnuDzzMGBwe75zcS4v+TfBGGkHwRwvyZ8u8Z+YwRhjCHfDGpQvLmzZssX76cjz76iEqVKqFQKKhZsyY1a9YkICCAN998k/379/Ptt9+Snp5O+/bt6dq1K3Xq1KF379766zxZQD6Lra1tjscTJ05k165ddOnSBX9/f4YMGcLKlSu5detWjnb/vrZC8ex/LdBqtQwcOJCuXbvmes7Z2Vn/d2tr61zPP2vEcV7Hy5Qp88xY8qLV6khOfligc4uLpaUCBwc7kpPT0GhkWIjIm+SLMERR5UtEcOtCu5YoOnGXE5m34cRz243uWQ9fdxUACksLHJR2JKekodWY1AyhUi8x0fSm6sh3kjCEsfPFwcHOPIe2Wltbs3HjRipXrsyHH36Y4zkHBwcArl27xpkzZzhw4AAVKlQAICkpibt37+qLrGrVqvHDDz+g0Wj0cwZ3797NzJkz+emnn3LdNzExke+++46FCxfSsWNH/fG///6bsmXLFvj1eHt7c+HChRy9mIcPHyYyMpLJkyfn69qP54U+5uvrS0xMTI5jsbGxpKam5urZNIS5jNnXaLRmE6swPskXYYjCzhdL2TbCLFR3V6FS2uRaaOdJaqUN1d1V/5sjaanA1saKtIcKsnXyGWNKTPkzX76ThCHMIV9MavCtWq1m4MCBLF68mIULF/LXX39x5coVfvvtN4YOHUrjxo157bXXANi+fTvXrl3j6NGjDB48mKysLP2Q0sDAQBITE5k0aRIJCQkcOXKEOXPm0KRJE2xsbHLdt1y5ciiVSvbs2aMfKjthwgTOnDmTY4VVQ33wwQfs2rWLJUuWcOHCBQ4ePMi4ceNISUnJ0SOZl8fFZmxsLA8ePGDAgAHExcUxdepUEhISOHz4MKNHj6ZmzZo0bdq0wLEKIYQQpZFCYUFggHeebXoFeMt+kkII8S8m1SMJMGLECDw9Pfn+++9Zu3Yt6enpuLq60qFDBz766CPKli3LuHHjWL16NYsWLaJixYp07NiRypUrc+rUKQAqVqzIqlWrmDt3Ll26dMHR0ZGOHTsyatSop96zTJkyLF68mFmzZtGpUyccHR1p3Lgxo0aNYvny5fptQwzVvn17Fi5cyPLly1m2bBlOTk74+/szevTofF/Dx8eHli1bMmLECEaNGkVQUBArVqxg0aJFdOnShXLlyhEQEMCnn35a4KGtQgghRGnm5+vCkK61cu0jqVba0Ev2kRRCiKcyue0/RPGT7T9ESSP5Igwh+SIe02p1xF9JIulBBk72Nvi4OT21J1JyRhhC8kUYwtj5YtbbfwghhBBCGINCYUF1D5WxwxBCCLNgUnMkhRBCCCGEEEKYPikkhRBCCCGEEEIYpMQUktnZ2XzzzTd069aN+vXr06RJE4KCgjh06FC+rxEeHo6/v38RRgn+/v6Eh4cX6T2EEEIIkT9arY7YS4kcOnuT2EuJaLWydIQQQuRHiZgjmZGRwYABA7hx4wbDhw+nfv36pKens3nzZgYMGMCcOXPo1KmTscMUQgghhAmJibuda6VWldKGQFmpVQghnqtEFJKLFy8mLi6OHTt2ULlyZf3x0NBQUlNTmTZtGv7+/tjb2xsxSiGEEEKYipi42yzdcjrX8cSUDJZuOc2QrrWkmBRCiDyYfSGZlZXF5s2b6datW44i8rERI0bQq1cvbG1tSUpKYvHixezdu5fExERq1qzJyJEjady48VOv7evry8yZM+nWrdtTj4WHhxMTE0PDhg1Zt24daWlpdOrUiY8//pjJkydz6NAhXFxcCA0NpVWrVvpr3Llzh4EDB3L48GGcnZ15//336d27NwBRUVGMGzeOuLg4fft/Hzt58iSzZs3ir7/+wsrKiiZNmjBu3DhcXV0L4y0VQogik5GpMXYIuWi0OtIzssnI1MjS/KWEVqtj7e74PNus+/UcNT3UT93+Q3JGGKIw8sXG2rKQoxLixZl9IXnlyhWSkpJo0KDBU5+vWLEiFStWRKPREBQURFZWFnPnzkWtVhMZGcn777/PunXrqFOnToHuf/ToUcqXL8/atWs5duwYn332GXv27GHMmDEEBwczd+5cQkJCOHjwIBYWj76Mvv/+e0aMGEFoaCjR0dFMnz4dFxcX2rZt+9z7aTQaPvroI959911mz55NcnIyEydO5LPPPmP16tUFeg3waM8aU/Z4P5v87msjSjfJF9MVNGuvsUMQIl8SUzIYsmi/scMQAoDI8QHGDkEUE3P6DWP2heT9+/cBcHR0zLNddHQ0Z86c4YcffsDHxweAsLAwTp06xcqVK1m8eHGB7q/VagkLC6NcuXK8/PLLzJ07lyZNmtClSxcAevXqxW+//cadO3dwcXk0RCYgIIBBgwYB8PLLL3PixAlWrVqVr0IyNTWVxMREXFxcqFKlCm5ubixatIi7d+8WKH54tG+WSmUew34dHOyMHYIwI5IvQgghSgJz+Z0mCo85/IYx+0JSrVYDkJSUlGe7+Ph4lEqlvogEsLCwoGHDhkRHRxf4/uXLl6dcuXL6x2XLlsXd3V3/2NbWFoDMzEz9MT8/vxzXqFu3Lvv27cvX/RwdHRk4cCBTp07l888/p0mTJrRs2ZIOHToU+DVotTqSkx8W+PziYGmpwMHBjuTkNDQaGUYk8ib5YroiglsbO4RcFJYWOCjtSE5JQ6uRFTtLg7jLiczbcOK57Ub3rIevuyrXcckZYYjCyJfExAeFHJUwVcb+DePgYJfv3lCzLyTd3NyoUKECx44do2PHjrmeT0hIYPr06c8c+qrT6bCyyt/bkJ2dnetYmTJlch1TKPJ+8//9vFarxdra+pntNZqcc4pGjx5NYGAg+/bt4+DBg0ydOpUVK1awdevWPK+TF3OZ46HRaM0mVmF8ki+mx/Ip882MzcpSga2NFWkPFWTrJF9Kg+ruKlRKmxyrtf6bWmlDdXfVU+dISs4IQxRGvsh3WeljDr9hTH/w7XMoFAreeecdoqKiuHHjRq7nV6xYwalTp6hVqxYpKSnEx/9vcr1OpyMmJoZq1ao99dplypQhNTVV//jSpUuFEvOZM2dyPI6JicHb21t/TyDHfS9evKj/+99//82kSZMoX748vXr14vPPP2fFihUkJCQQGxtbKPEJIYQQJZlCYUFggHeebXoFeD+1iBRCCPGI2fdIAgwaNIj//ve/BAYG8sknn9CgQQOSkpJYv349W7duZeHChfzf//0fNWrU4NNPP2XChAmUL1+eNWvWEB8fz6RJk5563Xr16rFx40YaNWqETqdj5syZBe7xe9LOnTupXr06rVq14tdff2X37t188803+ntaWFgQHh5O3759OXXqFFu2bNGfq1Kp2LlzJ+np6Xz44YcoFAq2bNmCo6MjVatWfeHYhBBCiNLAz9eFIV1r5dpHUq20oZfsIymEEM9VIgpJOzs71qxZw6pVq4iIiOD69evY2tpSs2ZNvv32Wxo2bAjAqlWrmD17NkOHDiUzM5NatWqxevVq6tWr99TrTp48mcmTJ/Puu+/i4uLCJ598ws2bN1843vfff5/ffvuNBQsWUKVKFebPn6/fgsTNzY2wsDCWL1/OunXr8PPzIzg4mLFjxwKPCsmIiAjmz5/Pu+++i0ajoV69enz99dc55moKIYQQIm9+vi7U93Ym/koSSQ8ycLK3wcfNSXoihRAiHyx0Op3MEi/lNBot9+6Z9iRuKysFKpU9iYkPTH68uDA+yRdhCMkXYSjJGWEIyRdhCGPni1ptn+/Fdsx+jqQQQgghhBBCiOIlhaQQQgghhBBCCINIIVkA/v7++Pr68vXXXz/1+YkTJ+Lr60t4eDhRUVH4+voWeTzh4eFFeg8hhBCipNBqdcReSuTQ2ZvEXkpEq5VZPkIIYagSsdiOMZQpU4Zdu3YxYMCAHMezs7P55ZdfsLB4NFG/Y8eO/N///V+RxrJp0yZsbGyK9B5CCCFESRATdzvXSq0qpQ2BslKrEEIYRHokC6hp06acOHEi1yquhw4domzZslSuXBkAW1tbnJ2dizQWtVqNvb19kd5DCCGEMHcxcbdZuuV0jiISIDElg6VbThMTd9tIkQkhhPmRHskCqlOnDgkJCfz888/0799ff/zHH3+kQ4cO/PTTTwBERUUxbtw44uLiAIiPj2f+/PkcO3aMtLQ0KlasSO/evQkKCgIgPDycmJgYGjZsyLp160hLS6NTp058/PHHTJ48mUOHDuHi4kJoaCitWrUCHg1t7dq1K8OGDSvW90AIIUTRy8jUGDuEEkGr1bF2d3yebdb9eo6aHurnbv+h0epIz8gmI1Mjq3A+hY21pbFDEEIUAykkX0CHDh1yFJKZmZn8+uuvrF69Wl9IPiktLY2goCCaN2/Ohg0bsLS0ZOPGjcyePZumTZtSo0YNAI4ePUr58uVZu3Ytx44d47PPPmPPnj2MGTOG4OBg5s6dS0hICAcPHtQPoX1RVlam3Tn9eBni/C5HLEo3yRdhCFPPl6BZe40dQqmRmJLBkEX7jR2G2YscH2DsEEyKqX/GCNNiTvkiheQL6NChAytXruTWrVtUrFiRAwcOoFarqVmz5lPbp6Wl0a9fP3r37q0fijp8+HBWrFhBXFycvpDUarWEhYVRrlw5Xn75ZebOnUuTJk3o0qULAL169eK3337jzp07uLi8+HwOhcIClco8hsY6ONgZOwRhRiRfhCEkX4QoHObym6K4yWeMMIQ55IsUki+gVq1auLm5sWvXLvr168ePP/7IG2+88cz2arWawMBAduzYwdmzZ7l8+TKxsbHAo+LxsfLly1OuXDn947Jly+Lu7q5/bGtrCzzqAS0MWq2O5OSHhXKtomJpqcDBwY7k5DQ0GhlGJPIm+SIMYer5EhHc2tghlAhxlxOZt+HEc9uN7lkPX3dVnm0UlhY4KO1ITklDq5EVX/8tMfGBsUMwKab+GSNMi7HzxcHBLt+9oVJIvqDHw1t79OjBnj172Lhx4zPb3rlzhx49eqBWq/H396dFixbUrl2bli1b5mhXpkyZXOcqFEXbvW0uczw0Gq3ZxCqMT/JFGMJU88XyOfP1RP5Ud1ehUtrkWmjnSWqlDdXdVc+dI2llqcDWxoq0hwqydaaXM8Zmiv8/MgWm+hkjTJM55IsUki+oQ4cOfPXVV2zevBk3Nze8vLye2XbHjh0kJSWxa9cufbH4eBEenU7+RVMIIYQoKgqFBYEB3izdcvqZbXoFeD+3iBRCCPGI6c/iNHE1atTAw8OD+fPn5zmsFaBSpUqkpaXx888/c/36daKjoxk1ahRQeMNUhRBCCPF0fr4uDOlaC5Uy597LaqUNQ7rWkn0khRDCANIjWQg6dOjAl19+SceOHfNs1759e86cOcOsWbNITU2lSpUqdO/enT179nDq1Cl69epVTBELIYQQpZOfrwv1vZ2Jv5JE0oMMnOxt8HFzkp5IIYQwkIVOxlSWehqNlnv3THtivJWVApXKnsTEByY/XlwYn+SLMITkizCU5IwwhOSLMISx80Wtts/3YjsytFUIIYQQQgghhEGkkBRCCCGEEEIIYZBSVUhu376dd999l3r16lG/fn3efvttNmzYkKNNYmJinlt4/NvVq1fx9fXl8OHDhR1uDv7+/oSHhxfpPYQQQgghhBAiP0rNYjubNm1i+vTphIaG4ufnh06n48CBA0ybNo1//vmHoUOHAjBnzhyuXr1K9+7djRxxTps2bcLGxub5DYUQQgiRi1arkwV2hBCiEJWaQnLdunW8/fbbvPPOO/pjVatW5datW0RGRuoLSVNde0itVhs7BCGEEMIsxcTdZt2v50hMydAfUyltCAzwli0/hBCigErN0FaFQsHx48e5f/9+juMffvgh3333HQAhISFs2bKFP/74A19f30K7d3p6OosWLaJNmzbUrl2bzp07s2vXLv3zUVFRue7372NPDm1NS0sjNDSU5s2bU7t2bbp06cIvv/xSaPEKIYQQJUVM3G2Wbjmdo4gESEzJYOmW08TE3TZSZEIIYd5KTY/kwIEDGTlyJK+99hqNGzemYcOGNGnShNq1a+Pg4ABAaGgo6enp3Lx5s1DnI44aNYqzZ88yefJkPDw82LFjB5988glLliwhICDA4OstXryYuLg4vvrqKxwcHNi4cSMjR45k165dvPTSS4UWtxBCvKiMTI2xQ3gujVZHekY2GZkaWZq/hNFqdazdHZ9nm3W/nqOmh9qgYa6SM8IQz8sXG2tLI0QlxIsrNYVk+/btqVSpEpGRkRw4cIB9+/YB4OnpyYwZM/Dz80OpVGJra0uZMmVwdnYulPsmJCSwZ88eli1bRqtWrQAYNmwYsbGxLFu2rECF5OXLl7G3t8fNzQ0HBwc++eQTGjVqhKOjY4HjtLIy7c7px/vZ5HdfG1G6Sb6YjqBZe40dghB5SkzJYMii/cYOQ5RikeMN/y0oSi5z+g1TagpJgHr16lGvXj20Wi2xsbHs27ePNWvW8MEHH7B7927Kly9f6PeMi4sDwM/PL8fxRo0asWDBggJd84MPPmDQoEE0bdqUOnXq0Lx5czp16oRSqSzQ9RQKC1Qq+wKdW9wcHOyMHYIwI5IvQgghTJ25/AYTxcscfsOUikLy5s2bLF++nI8++ohKlSqhUCioWbMmNWvWJCAggDfffJMjR47Qvn37YotJp9NhZfXst1+jefZwsPr167Nv3z4OHDjAwYMH2bp1K19++SUrVqygadOmBsei1epITn5o8HnFydJSgYODHcnJaWg0MoxI5E3yxXREBLc2dgjPpbC0wEFpR3JKGlqNaS64Jgom7nIi8zaceG670T3r4euuyvd1JWeEIZ6XL4mJD4wQlTBVxv4N4+Bgl+/e0FJRSFpbW7Nx40YqV67Mhx9+mOO5x/MjK1SoAICFReEuBf54wZyYmBhat/7fD6qjR49SrVo1AMqUKQNAamoq5cqVA+DixYvPvObnn3+On58fbdq0oU2bNowbN4433niDXbt2FaiQBMxmjodGozWbWIXxSb4Yn6UZbK9gZanA1saKtIcKsnWSLyVJdXcVKqVNroV2nqRW2lDdXWXQHEnJGWGI5+WLfE+JpzGH3zClopBUq9UMHDiQxYsX8+DBA9q3b0+5cuU4f/48X3zxhX7xHYCyZcty+/Ztrly5gpubG+np6aSkpKBWq7G0fPZk6JMnT5KRkfOLqmLFivj6+tK6dWvCwsKwsLDAw8ODnTt3smfPHhYtWgQ8GnJrYWFBeHg4ffv25dSpU2zZsuWZ97py5Qrbt29n6tSpuLu78+eff3L9+nXq16//4m+WEEIIUUIoFBYEBnizdMvpZ7bpFeAt+0kKIUQBWOhMdePEIrB161a+//574uPjSU9Px9XVlQ4dOvDRRx9RtmxZAE6dOsWQIUO4f/8+v/zyCwcOHGDcuHHs2bPnqSuiXr16lTZt2jz1fl27dmXWrFk8fPiQBQsW8NNPP5GcnIyPjw+DBg2ibdu2+rbfffcdy5cv586dO/j5+dGlSxfGjh2rn2Pp7+9P165dGTZsGKmpqcyePZvffvuNpKQkqlSpQu/evenXr1+B3heNRsu9e6Y9rMLKSoFKZU9i4gOT/9cZYXySL8IQki8l39P2kVQrbehVwH0kJWeEISRfhCGMnS9qtX2+h7aWqkJSPJ0UkqKkkXwRhpB8KR20Wh3xV5JIepCBk70NPm5OBe6JlJwRhpB8EYYwdr4YUkiWiqGtQgghhCjdFAoLqnvkf0EdIYQQeTP9DUqEEEIIIYQQQpgUKSSFEEIIIYQQQhhECkkD+Pv74+vrq/9Tq1YtWrVqxaRJk7h3716+rxMeHo6/v7/+sa+vL1FRUUURshBCCFHqabU6Yi8lcujsTWIvJaLVyvIQQgjxomSOpIGCgoIICgoCID09nfj4eObOnUufPn347rvvUCqVRo5QCCGEEI89bcVWldKGwAKu2CqEEOIR6ZE0UNmyZXF2dsbZ2Rk3NzfatGnDqlWruHHjBitWrDB2eEIIIYT4/2LibrN0y+kcRSRAYkoGS7ecJibutpEiE0II8yc9koXA1dWVtm3bsnPnTkaOHEl8fDzz58/n2LFjpKWlUbFiRXr37q3vycyLVqslIiKCqKgorl27hrW1NQ0aNGDixIm4u7sDsG/fPhYvXkxCQgJly5alZcuWjBs3DkdHx6J+qUIIIfIpI1Nj7BBKNa1Wx9rd8Xm2WffrOWp6qA3eBkSj1ZGekU1Gpka2c3gBNtaWxg5BCPECpJAsJD4+Pmzbto3U1FSCgoJo3rw5GzZswNLSko0bNzJ79myaNm1KjRo18rxOZGQkK1euZPbs2fj4+HD58mUmTJjArFmz+OKLL7h37x5Dhw4lJCSEVq1acfPmTYKDg5kzZw7Tp08vcPxWVqbdOf14P5v87msjSjfJF2GIosqXoFl7C/V6ovAlpmQwZNF+Y4dRakWODzB2CMVCvpOEIcwpX6SQLCQODg4A3L9/n379+tG7d2/s7e0BGD58OCtWrCAuLu65haS7uzuzZ8+mdevWAFSpUoX27dvz888/A3Dr1i0yMzNxdXWlSpUqVKlShWXLlqHRFPxfvhUKC1Qq+wKfX5wcHOyMHYIwI5IvwhCSL0IUL3P57VFY5DNGGMIc8kUKyUKSkpICgJOTE4GBgezYsYOzZ89y+fJlYmNjgUfDVp/H39+fP//8k8WLF3PhwgUuXLjA+fPnqVixIgA1atTgzTffZNCgQTg7O9O8eXNatWpF27ZtCxy7VqsjOflhgc8vDpaWChwc7EhOTkOjkWFEIm+SL8IQRZUvEcGtC+1awnBxlxOZt+HEc9uN7lkPX3eVQddWWFrgoLQjOSUNrUZWgC2oxMQHxg6hWMh3kjCEsfPFwcEu372hUkgWkjNnzuDp6cnDhw/p0aMHarUaf39/WrRoQe3atWnZsmW+rvPVV1+xdOlSunbtStOmTenfvz979uxh586d+jbz589nyJAh7N+/n99//50xY8bg5+fHN998U+D4zWWOh0ajNZtYhfFJvghDFHa+WBo4704UruruKlRKm1wL7TxJrbShurvK4DmSVpYKbG2sSHuoIFsnnzEFVdo+n+U7SRjCHPJFCslCcPPmTfbs2cMHH3zAjh07SEpKYteuXZQpUwaAuLg4AHS65/+r5bJlyxgyZAgffvih/tjKlSv15/7555/s3LmTzz77jKpVq9K/f3+2b9/OmDFjuHv3LuXLly+CVyiEEEKYF4XCgsAAb5ZuOf3MNr0CvA0uIoUQQjwihaSBHj58yJ07d4BH+0jGxcWxaNEiXnrpJQYMGMD+/ftJS0vj559/xs/Pj7///puZM2cCkJmZ+dzrV65cmQMHDuDv749CoWDbtm388ssvVKhQAYBy5cqxbt06ypQpw7vvvktGRgY//vgjnp6eqFSGDc0RQgghSjI/XxeGdK2Vax9JtdKGXrKPpBBCvBApJA20atUqVq1aBUCZMmWoXLkyHTt2JCgoCHt7e9q3b8+ZM2eYNWsWqampVKlShe7du7Nnzx5OnTpFr1698rz+nDlzmDJlCm+//Tb29vbUrVuXsLAwJk+ezPXr1/Hy8iI8PJwlS5awbt06FAoFTZo0ISIiAoXC9Fd3EkIIIYqTn68L9b2dib+SRNKDDJzsbfBxc5KeSCGEeEEWuvyMtxQlmkaj5d49057wbmWlQKWyJzHxgcmPFxfGJ/kiDCH5IgwlOSMMIfkiDGHsfFGr7fO92I50YQkhhBBCCCGEMIgUkkIIIYQQQgghDFIqC8m+ffsSEhLy1OdCQkLo27dvscUSExPD0aNHi+1+QgghREmi1eqIvZTIobM3ib2UiFYrM3aEEKI4yGI7RhYYGMjMmTNp2LChsUMRQgghzEpM3O1cK7KqlDYEyoqsQghR5Eplj6QQQgghzFtM3G2Wbjmdo4gESEzJYOmW08TE3TZSZEIIUTpIj2QekpKSWLx4MXv37iUxMZGaNWsycuRIGjduDEB4eDhbtmxh7969+nP+fWzfvn0sXryYhIQEypYtS8uWLRk3bhyOjo74+voCMG7cOP744w9mzZrFjRs3mD9/PgcPHuTBgwf4+fkxZswYqlevDqAfkqtSqdi6dSsPHz6kSZMmTJkyhYoVKxbn2yOEEOJfMjI1xg6hVNBqdazdHZ9nm3W/nqOmh7pItvnQaHWkZ2STkamRVTgBG2tLY4cghDACKSSfQaPREBQURFZWFnPnzkWtVhMZGcn777/PunXrqFOnznOvce/ePYYOHUpISAitWrXi5s2bBAcHM2fOHKZPn050dDQtWrTgs88+o1u3bqSmptKrVy/c3Nz48ssvsba2Jjw8nD59+rBt2zaqVKkCwI4dO+jUqRNr1qzh7t27jBo1ikWLFjFz5swCv14rK9PunH68DHF+lyMWpZvkizBEYeZL0Ky9z28kikViSgZDFu03dhilQuT4AGOHYNLkO0kYwpzypdQWkj/88AO7du3KdTwzM5MGDRoQHR3NmTNn+OGHH/Dx8QEgLCyMU6dOsXLlShYvXvzce9y6dYvMzExcXV2pUqUKVapUYdmyZWg0j/7F2tnZGQClUolSqWTdunUkJiYSFRWFWq0GYP78+QQEBLB27VqCg4P17adMmUKZMmXw8vKiY8eO7Nu3r8DvhUJhgUplX+Dzi5ODg52xQxBmRPJFGELyRYiCMZffEMYmnzHCEOaQL6W2kPT392f06NG5js+bN4+kpCTi4+NRKpX6IhLAwsKChg0bEh0dna971KhRgzfffJNBgwbh7OxM8+bNadWqFW3btn1q+/j4eDw9PfVFJICtrS116tQhPv5/Q3jc3d0pU6aM/rFSqSQrKytfMT2NVqsjOflhgc8vDpaWChwc7EhOTkOjkWFEIm+SL8IQhZkvEcGtCykqkZe4y4nM23Diue1G96yHr7uq0O+vsLTAQWlHckoaWo2sEpuY+MDYIZg0+U4ShjB2vjg42OW7N7TUFpL29vZ4eHg89XhSUhI63dO/GHQ6HVZWz37bsrOzczyeP38+Q4YMYf/+/fz++++MGTMGPz8/vvnmm6de+2m0Wm2Oe1pbWz/z/gVlLnM8NBqt2cQqjE/yRRiiMPLFsgjm44ncqrurUCltci208yS10obq7qoimSNpZanA1saKtIcKsnXyGSOfs/kj30nCEOaQL6Y/+NZIfH19SUlJydETqNPpiImJoVq1agCUKVOGBw9y/ivcpUuX9H//888/mTFjBlWrVqV///589dVXzJgxg0OHDnH37t2n3vPixYs5nsvIyOD06dP6ewohhBClnUJhQWCAd55tegV4F0kRKYQQ4hEpJJ+hRYsW1KhRg08//ZQ//viDhIQEpkyZQnx8PO+99x4A9erVIykpiZUrV3L16lU2bNjA/v3/m9hfrlw51q1bx9y5c7l06RLx8fH8+OOPeHp6olI9GmpTtmxZEhISSExMpFOnTjg5OTFixAhOnjxJbGwso0eP5uHDh/To0cMo74MQQghhivx8XRjStRYqpU2O42qlDUO61pJ9JIUQooiV2qGtz2NpacmqVauYPXs2Q4cOJTMzk1q1arF69Wrq1asHQJMmTRg2bBirVq3i888/57XXXmP48OFERkYC4OXlRXh4OEuWLGHdunUoFAqaNGlCREQECsWjGj4oKIgVK1aQkJDAsmXLWLNmDbNmzaJ///4A+Pn5sX79etzc3IzxNgghhBAmy8/XhfrezsRfSSLpQQZO9jb4uDlJT6QQQhQDC92zJuaJUkOj0XLvnmlPlLeyUqBS2ZOY+MDkx4sL45N8EYaQfBGGkpwRhpB8EYYwdr6o1fb5XmxHhrYKIYQQQgghhDCIFJJCCCGEEEIIIQxSogtJf39//P39SU1NzfVcSEgIffv2LZT79O3bl5CQkAKf/2Qshw8fxtfXl6tXrxbKtYUQQoiSSqvVEXspkUNnbxJ7KRGtVmbrCCFEcSnxi+1cu3aNOXPmMGXKFGOH8kyhoaFoNBpjhyGEEEKYjZi426z79VyOvSRVShsCA7xlxVYhhCgGJbpHEsDNzY3vvvuO33//3dihPJNSqcTJycnYYQghhBBmISbuNku3nM5RRAIkpmSwdMtpYuJuGykyIYQoPUp8j+Rbb73FsWPHCA0N5YcffqBcuXK52sTHxzN//nyOHTtGWloaFStWpHfv3gQFBQGQlpbGtGnT+M9//kNycjJeXl4MHjyY119/XX+NBw8eMG7cOHbv3o1Op6Nt27ZMnDgROzs7AgICaN++PWPGjNG337p1K5MnTyY6Oppp06Zx7do1vv322+e+noSEBGbNmsXRo0ext7encePGhISE4OzsXAjvlhCitMnIlNEQGq2O9IxsMjI1sqKiGdBqdazdHZ9nm3W/nqOmh7rItgGRnBGGyE++2FhbFnNUQry4El9IWlhYMH36dDp16sTs2bOZOnVqjufT0tIICgqiefPmbNiwAUtLSzZu3Mjs2bNp2rQpNWrUYPHixcTFxfHVV1/h4ODAxo0bGTlyJLt27eKll14C4JdffmHQoEFERUVx7tw5Ro4cSeXKlfnkk0/o2rUrmzdvZvTo0VhYPPpS2759OwEBAU8tbJ/l1q1bBAYG0qlTJ0JCQkhLSyM8PJwePXqwY8cOypYtW+D3ycrKtDunHy9DnN/liEXpJvmSf0Gz9ho7BCEKXWJKBkMW7Td2GELkW+T4AGOHIEyEOf2GKfGFJECVKlUYO3YsEydOpF27drRo0UL/XFpaGv369aN3797Y29sDMHz4cFasWEFcXBw1atTg8uXL2Nvb4+bmhoODA5988gmNGjXC0dFRf506deowcuRIANzd3WnevDmnT58GoEuXLixZsoSjR4/SqFEj7ty5w6FDh1ixYoVBr2P9+vVUqlSJ8ePH648tWrSIJk2a8PPPP9OtW7cCvT8KhQUqlX2Bzi1uDg52xg5BmBHJFyGEEObAXH6HieJjDr9hSkUhCdCjRw927drF+PHj2bFjh/64Wq0mMDCQHTt2cPbsWS5fvkxsbCwAWu2j4QcffPABgwYNomnTptSpU4fmzZvTqVMnlEql/jqenp457ufo6Mi1a9cAeOmll3j11Vf54YcfaNSoETt37sTFxYUmTZoY9BrOnj3LuXPnqF+/fo7jGRkZJCQkGHStJ2m1OpKTHxb4/OJgaanAwcGO5OQ0NBoZRiTyJvmSfxHBrY0dgtEpLC1wUNqRnJKGViOrfpq6uMuJzNtw4rntRvesh6+7qkhikJwRhshPviQmPijmqISpMvZvGAcHu3z3hpaaQhJg2rRpdOrUiZkzZ+qP3blzhx49eqBWq/H396dFixbUrl2bli1b6tvUr1+fffv2ceDAAQ4ePMjWrVv58ssvWbFiBU2bNgXA0jLvse3dunVjxowZjB8/nu3bt9O5c2cUCsO6rLVaLU2aNGHSpEm5nnuyqC0Ic5njodFozSZWYXySL89nWURzyMyJlaUCWxsr0h4qyNZJvpi66u4qVEqbXAvtPEmttKG6u6rI5khKzghD5Cdf5LtK/Js5/IYx/cG3hcjV1ZWQkBA2bdrE0aNHAdixYwdJSUmsX7+ewYMH07ZtW+7fvw+ATvfoX40+//xzYmJiaNOmDePHj2fXrl24ubmxa9eufN+7Xbt2ZGdns3HjRs6cOVOgYaje3t4kJCRQuXJlPDw88PDwwNHRkRkzZhAfn/fCA0IIIURJoFBYEBjgnWebXgHeRVZECiGEeKRUFZIA3bt3p0WLFly5cgWASpUqkZaWxs8//8z169eJjo5m1KhRAGRmZgJw5coVJk2axMGDB7l27Rq7du3i+vXruYaY5sXOzo727dszf/58GjRogIeHh8GxBwYGkpKSwujRo4mNjSU2NpaRI0dy6tQpfHx8DL6eEEIIYY78fF0Y0rUWKqVNjuNqpQ1DutaSfSSFEKIYlKqhrY89HuIK0L59e86cOcOsWbNITU2lSpUqdO/enT179nDq1Cl69erFpEmTmD17NmPGjCEpKYkqVaowevRoOnfubNB9u3XrxubNmwu8KI6bmxtr1qxh/vz59OrVC0tLSxo0aEBkZCRqtbpA1xRCCCHMkZ+vC/W9nYm/kkTSgwyc7G3wcXOSnkghhCgmFrrH4zdFqaXRaLl3z7QneVtZKVCp7ElMfGDy48WF8Um+CENIvghDSc4IQ0i+CEMYO1/Uavt8L7ZT6oa2CiGEEEIIIYR4MVJICiGEEEIIIYQwiFkUkv7+/vj6+ur/VK9enQYNGtCnTx+OHDlSZPcNCQmhb9++z3z+8OHD+Pr6cvXq1SKLQQghhBBCCCFMjdksthMUFERQUBDwaFuOpKQkFixYwMCBA/npp59wdXUt9HuGhoai0WgK/bpCCCGEyJtWq5OFdIQQwoSZTSFZtmxZnJ2d9Y9dXFwICwvjtddeY/fu3bz33nuFfk+lUlno1xRCCCFE3mLibrPu13MkpmToj6mUNgQGeMvWHkIIYSLMYmjrs1hZPaqDra2t8ff3Z/bs2XTs2JHGjRvzxx9/oNFoWL16Ne3ataN27dq0a9eO9evXA496Ndu0acPcuXNzXHPr1q3Uq1eP1NTUXENbjx49Svfu3alTpw5vvfUWsbGxOc7V6XRERETQpk0b6tatS+fOndm+fbv++cOHD1OzZk327dvHm2++Sa1atWjfvj2//vprjmt88803tGvXjjp16vDGG2+wY8cO/fO3bt1i5MiRNGzYkMaNGzNo0CAuXrxYaO+pEEIIYUwxcbdZuuV0jiISIDElg6VbThMTd9tIkQkhhHiS2fRI/tutW7eYMWMGZcuWpWXLlkRERLBmzRqWL1+OUqnE19eXWbNmsW3bNiZMmEDt2rXZv38/06dPJyMjg/79+9O1a1c2b97M6NGjsbB4NFxm+/btBAQEUK5cuRz3u3LlCkFBQXTp0oVZs2Zx/vx5Jk6cmKPNwoUL2bFjBxMnTqRq1aocOXKEyZMnk5KSQu/evQHQaDTMnTuX0NBQKleuzIIFCxg7diz79+/H3t6eFStWsHTpUkJDQ2ncuDH79u0jODiYChUqUKdOHfr27csrr7zCmjVrUCgUfP3117z77rv88MMPVKxYsXjefCGEWcjIlKH5+aHR6kjPyCYjUyNL8xuZVqtj7e74PNus+/UcNT3URh3mKjkjDPE4X4QoacymkFy+fDmrVq0CIDs7m8zMTLy8vFi0aJF+fmTLli1p1qwZAKmpqaxfv56QkBA6deoEgKenJ1evXuWrr77ivffeo0uXLixZsoSjR4/SqFEj7ty5w6FDh1ixYkWu+3///fdUqFCBSZMmYWlpiZeXFzdu3GDmzJkAPHz4kNWrV7NgwQJatWoFgLu7O9euXWPlypX6QhJgxIgRNG3aFIDBgweza9cu4uPjqVevHt988w39+vWje/fuAPTt25f09HSys7PZuXMnycnJzJ07V98bO336dA4fPsz333/PsGHDCvz+WlmZduf04/1s8ruvjSjdJF8eCZq119ghCFHoElMyGLJov7HDEMJgaye9buwQhBkwp98wZlNI9uzZUz/MVKFQ4OTklGsOo4eHh/7vf//9N1lZWfj5+eVo8+qrr/LNN99w9+5dXnrpJV599VV++OEHGjVqxM6dO3FxcaFJkya57h8fH0/NmjWxtLTUH2vQoIH+7+fPnycjI4NPP/0UheJ//+EfF73p6en6Y1WrVtX//XHPZ1ZWFomJidy5c4e6devmuPcHH3wAQFhYGPfv36dRo0Y5ns/IyCAhIeFpb1u+KBQWqFT2BT6/ODk42Bk7BGFGJF+EEEKYCvlOEoYwh3wxm0LS0dExR6H4NLa2tvq/63S6p7bRah8NQXnco9etWzdmzJjB+PHj2b59O507d85RCD5mYWGhP/exx9d48n6LFi3KUSg+Zm1t/dS/P3l+mTJlnvnaHsf+8ssv8+WXX+Z6rmzZsnmem/d1dSQnPyzw+cXB0lKBg4MdyclpaDQyjEjkTfLlkYjg1sYOwSwoLC1wUNqRnJKGVvP07w5RPOIuJzJvw4nnthvdsx6+7qqiD+gZJGeEIfT5Usq/k0T+GPs3jIODXb57Q82mkDSUl5cXZcqUISYmhho1auiPHz16FGdnZxwdHQFo164dU6ZMYePGjZw5c4aFCxc+9XrVq1cnKiqKzMxMfSF4+vRp/fNVq1bFysqK69ev07r1/368RUZGcv78eaZMmfLcmJVKJS4uLpw6dYo2bdrojw8fPpzKlSvj4+PDtm3bUCqVqNVq4FFP5qeffkr79u3p2LGjAe9QTuYyx0Oj0ZpNrML4Snu+WMpWCfliZanA1saKtIcKsnWlN19MQXV3FSqlTa6Fdp6kVtpQ3V1l1DmSkjPCEP/Ll4xS/Z0kDGMOv2FMf/BtAZUrV44ePXrw+eefs2PHDi5dusTatWtZt24dQUFB+sV17OzsaN++PfPnz6dBgwbP7PXs1asXaWlpfPbZZyQkJPDbb78RHh6uf16pVNKzZ08WL17Mtm3buHLlCps2bWLu3Lm4uOR/qfIPP/yQb775hm3btnH58mUiIyPZs2cPbdq04a233sLR0ZHhw4fz559/kpCQQEhICPv378fX1/fF3jAhhBDCyBQKCwIDvPNs0yvAW/aTFEIIE1BieyQBxo0bh0qlYt68efzzzz94enoyceJE3n333RztunXrxubNm+nWrdszr1WxYkW++eYbZsyYQdeuXalcuTIff/wxYWFhue63ePFibt++TeXKlRk+fDgDBw7Md8x9+vQhPT2dxYsXc+fOHTw9PVm4cCGvvvoqAGvWrGHOnDm8//77aDQaXnnlFVatWoWXl5eB744QQghhevx8XRjStVaufSTVSht6yT6SQghhMix0z5pMKEoNjUbLvXsPjB1GnqysFKhU9iQmPjD5bn5hfJIvwhCSL6ZJq9URfyWJpAcZONnb4OPmZDI9kZIzwhCSL8IQxs4Xtdpe5kgKIYQQwnwpFBZU9zDegjpCCCHyVmLnSAohhBBCCCGEKBpSSAohhBBCCCGEMEiJKCT9/f3x9fXV/6levToNGjSgT58+HDlyRN/m8SqrOp2OLVu2cPfuXf01fvvtN86fP5+v+4WHh+Pv71/4LyQfnnwdQgghREmj1eqIvZTIobM3ib2UiFYrSzkIIYQpKjFzJIOCgggKCgIeFYpJSUksWLCAgQMH8tNPP7Fp0yZsbGwAOHLkCCEhIezZsweAa9euMWjQICIjI6lWrZrRXoMQQghRmsXE3c61WqtKaUOgrNYqhBAmp0T0SAKULVsWZ2dnnJ2dcXFxwcfHh7CwMNLT09m9ezdqtRp7e3vgUaH5JFm4VgghhDCumLjbLN1yOkcRCZCYksHSLaeJibttpMiEEEI8TYnpkXwaK6tHL8/a2hp/f3+6du3Kq6++Sr9+/QBo06YNM2fOZNy4cQD069ePoUOHMmzYMFauXMn69eu5efMmLi4uvP322wwePBgLi/8tPb506VIiIyPJysrC39+f8ePH4+TkBICvry8zZ87MsTflk8fCw8M5fPgwzs7O7Nu3j65duzJhwgSio6OZP38+58+fx8PDgwEDBvDZZ5+xZ88eXnrpJQDu3LnD0KFDiY6OxsbGhi5duhAcHIylpWVxvK1CCDOVkakxdggmSaPVkZ6RTUamRpbmNxKtVsfa3fF5tln36zlqeqhNYgsQyRnxbzbW8htMlD4ltpC8desWM2bMoGzZsrRs2ZKIiAgA6tevT3h4OMOGDWPjxo1Uq1aNatWq0b17d8LDw2nevDl79+5l+fLlLFy4kJdffpkTJ04QHBzMSy+9ROfOnYFHw2EPHTrE119/TWpqKhMmTGDcuHF8+eWX+Y7xyJEj9OvXj23btqHRaPjrr7/46KOPeO+995g/fz5//fUXYWFhuc7btGkTY8eOZezYsRw+fJjQ0FC8vb155513Cvx+WVmZduf04/1s8ruvjSjdJF+eLmjWXmOHIESBJaZkMGTRfmOHIcRTRY4PeOZz8p0kDGFO+VJiCsnly5ezatUqALKzs8nMzMTLy4tFixbh6uqqb2dtbY2joyMAarWasmXLolarAXB0dMTe3p7Lly9jbW1NlSpVcHV1xdXVFRcXlxzXsbGxYeHChVSoUAGAiRMnEhQUxKVLl/Dw8Mh33MOHD0epVAIwduxYatWqRXBwMABVq1bl7t27TJ8+Pcc5r7/+Ou+99x4Abm5uREZGcvr06QIXkgqFBSqVfYHOLW4ODnbGDkGYEckXIYQQxSE/v6PkO0kYwhzypcQUkj179qRv374AKBQKnJyc9AWaod566y02b95Mu3btqFatGs2aNaNdu3Y5CkkPDw99EQlQt25dAM6dO5fvQrJ8+fI5Yjx79izNmjXL0aZRo0a5zvP09Mzx2NHRkYyMjFzt8kur1ZGc/LDA5xcHS0sFDg52JCenodHIMCKRN8mXp4sIbm3sEEySwtICB6UdySlpaDUyZ94Y4i4nMm/Diee2G92zHr7uqqIP6DkkZ8S/JSY+eOZz8p0kDGHsfHFwsMt3b2iJKSQdHR0N6gnMi1qtZtu2bRw/fpwDBw4QHR1NZGQkw4YNY+jQoQC55iNqNI/mHpUpU+ap18zOzs51zNbWNsdjS0tLtNrnJ8zT5kK+6IJB5jLHQ6PRmk2swvgkX3KyNIG5ZabIylKBrY0VaQ8VZOskX4yhursKldIm10I7T1IrbajurjKJOZKSM+Lf8vNdI99JwhDmkC+mP/i2CDy5YM7THm/fvp3169fj5+fH8OHD+f777+nevTs//vijvs3FixdJTU3VP46JicHCwkK/fUiZMmVyPH/p0qXnxlW9enVOnjyZ49jx48fzPUIAEgAAnRZJREFU/8KEEEIIM6RQWBAY4J1nm14B3iZRRAohhHikVBaSZcuWBSA2NpYHDx7oH8fHx5OSkkJGRgazZ89m69atXL16laNHj3LkyBHq16+vv0ZGRgYjRozg7NmzHDhwgKlTp9KlSxeqVKkCQL169di4cSN//fUXZ8+eZfLkyVhbW+cZV1BQEKdOnWLevHlcuHCB3bt38/nnnwO5i10hhBCiJPHzdWFI11qolDY5jquVNgzpWkv2kRRCCBNTYoa2GsLHx4eWLVsyYsQIRo0aRVBQEG+//TZz5szh0qVLjB8/nqSkJL744gtu3LiBo6Mj7dq1Y/To0fpr1KpVixo1atCvXz8sLCzo2LEjISEh+ucnT57M5MmTeffdd3FxceGTTz7h5s2bz41ryZIlLFiwgNWrV/Pyyy/Tp08fwsPDnzlkVgghhCgp/HxdqO/tTPyVJJIeZOBkb4OPm5P0RAohhAmy0L3o5DpRaE6ePImVlRU1a9bUH/vhhx/47LPPOH78uH5fzMKm0Wi5d+/Zk8RNgZWVApXKnsTEByY/XlwYn+SLMITkizCU5IwwhOSLMISx80Wtts/3Yjulcmirqfrrr7/o168fe/bs4fr16xw8eJDw8HDeeOONIisihRBCCCGEEMJQUp2YkHfffZc7d+4wY8YMbt26Rfny5XnjjTcYPny4sUMTQgghhBBCCD2T65EMCQnR7wdZEFFRUfj6+uof+/v7Ex4eXhihFTkLCwuGDh3Knj17yMrK4pNPPiE4OJiIiAj8/f2NHZ4QQghRZLRaHbGXEjl09iaxlxLRamXmjRBCmLIS3yO5adMmbGxsnt/QhAUFBdG7d29jhyGEEEIUiZi426z79VyOfSRVShsCA7xltVYhhDBRJtcjWdjUajX29vbGDuOF2Nvbo1arjR2GEEIIUehi4m6zdMvpHEUkQGJKBku3nCYm7raRIhNCCJEXk+6R9Pf3p3fv3pw4cYLo6Gisra3p1KkTISEh+sVnHu+1ePHiRWrXrk2zZs1yXaNr164MGzYMrVZLREQEUVFRXLt2DWtraxo0aMDEiRNxd3cHwNfXl+nTp7Njxw6OHTuGg4MDPXv2ZOjQoQD5vsbEiRPZtm0bf/31F56enowYMYI2bdro4/rPf/7DF198wblz57C3t+eNN95g5MiR2Nra5nofwsPD2bJlC3v37gVg69atREREcPnyZZycnGjfvj1jxox57j6VQoiSLyNTY+wQzI5GqyM9I5uMTI2sqFjMtFoda3fH59lm3a/nqOmhNqktQCRnRF5srC2NHYIQxcKkC0mAxYsXM3r0aIKDg/njjz8IDQ2lVq1adOnShWPHjjFs2DCGDh3KG2+8wdGjR5k6deozrxUZGcnKlSuZPXs2Pj4+XL58mQkTJjBr1iy++OILfbvZs2czfvx4pk6dys6dO1m4cCGNGzemUaNG+b7GvHnzGD16NLNmzSIqKoqhQ4eydu1aGjRowO7duxk+fDjDhg1j9uzZ/P3330yePJkrV67kuMbTxMbGMn78eObNm0edOnVISEjg008/RaVSMXjw4AK/z1ZWpt05/XgZ4vwuRyxKt9KcL0Gz9ho7BCEKVWJKBkMW7Td2GELkW+T4gByPS/N3kjCcOeWLyReSLVq0oF+/fgC4ubnx7bffcuzYMbp06cKaNWto0KCBvrfw5ZdfJj4+nsjIyKdey93dndmzZ9O6dWsAqlSpQvv27fn5559ztOvSpQudO3cGYNCgQaxcuZJjx47RqFGjfF+jW7du+nmNo0eP5o8//tDH+9VXX9G2bVt94ffyyy+j0+kYMmQI58+fp1q1as98P65evYqFhQVVqlTB1dUVV1dXVq5cSbly5Qx6X5+kUFigUpnH8F8HBztjhyDMiOSLEEKI4vas31TynSQMYQ75YvKFpJeXV47HSqWSrKwsAOLj42nevHmO5+vXr//MQtLf358///yTxYsXc+HCBS5cuMD58+epWLFivu+Z32s0btw4V1z/j707D4/xbB8+/s1kTyRMCA2yEEnQFJEqsYu0lFJ0QSwlaNVaqhql9iWW2oIiKLGUItZu1upDfyhqqycJoRH7NiTIOjPvH17zmCYiE0lmJjk/x5GjyT3Xfc054+zcOXNfy6FDh3Rxt23bVu/xN954Q/dYboVkkyZNCAgI4P3336dy5co0atSIli1b4u/v/9xzXkSj0ZKc/Djf5xcFS0sFzs72JCenolbLMCKRu5KcL1EjWxg7BLOjsLTA2cme5JRUNGpZKbQoxV1WMWv9yRe2G9GlDn4eysIPKI8kZ0RuVKpHej+X5GuSMJyx88XZ2T7Pd0NNvpDMad6fVvvkQ9vCwgKNRv8Ntra2fm5fS5cuZeHChXTs2JGgoCB69erF3r17+fHHH/P8nHnt4+kczqfUajUKhUKvr2c9fR3/Pu/fbG1tiY6O5ty5cxw8eJCDBw/Sv39/OnTowLRp03I9NzfmMsdDrdaYTazC+Epivlia0Dwyc2FlqcDO1orUxwqytCUrX4ytuocSpZNttoV2nuXiZEt1D6VJzZGUnBG5ed51pyRek0T+mUO+mP7g21xUr16dv/76S+/Y2bNnn9t+8eLFDBw4kPHjx9O5c2fq1KnDP//8k2Nh97J9nDlzRu/nv/76i1dffRV4shjPiRMn9B4/duwYkP1u6L8dOHCABQsWULNmTT7++GOio6MZMmQIP/30U55fgxBCCGEKFAoLQkN8cm3TNcTHpIpIIYQQT5h1IRkWFkZsbCzTp0/n0qVLbN++nTVr1jy3vZubG4cOHeLChQtcvHiROXPmsGvXLjIyMvL8nHntY9WqVezYsYNLly4xffp04uLi+OijjwDo27cvu3btYtGiRVy6dIn9+/czadIkWrRo8cJC0tramoULF7Jy5UqSkpI4e/Ysv/32GwEBAXl+DUIIIYSpCPQrz8CO/iid9Pd8dnGyZWBHf9lHUgghTJRZF5I1atQgKiqKI0eO0L59e1auXEn//v2f237GjBmkpaXx3nvv0b17d+Lj45kwYQJ3797l2rVreXrOvPbRpUsXVq5cSfv27Tl27BjLly+nevXqALRq1YrZs2fz888/065dO8aNG0fbtm2ZO3fuC5+/YcOGTJkyhU2bNvHOO+/Qp08fPD09mT17dp7iF0IIIUxNoF95Zn7akJFdA/i4fU1Gdg1gxqcNpYgUQggTZqE1ZFynyBM/Pz+mTZtGp06djB1KnqjVGu7de/TihkZkZaVAqXREpXpk8uPFhfFJvghDSL4IQ0nOCENIvghDGDtfXFwc87zYjlnfkRRCCCGEEEIIUfSkkBRCCCGEEEIIYZBiWUgGBwfj5+en+/L396dVq1YsW7Ysz30cOXIEPz8/rly5AsDjx49Zu3at7vHw8HB69OiR47lxcXFmM6xVCCGEEEIIIQxl8vtI5ldYWBhhYWEApKWlcfr0acaMGYO9vT3dunUzuL8VK1YQExOjO3f06NGo1eoCjVkIIYQo7jQaLfFJ97n/KJ0yjrb4upeR7T2EEMIMFdtC0sHBAVdXV93P7u7uHDlyhM2bN+erkPz3mkROTk4vHaMQQghRkhyPu8W6PedRpaTrjimdbAkN8ZEVWoUQwswUy6Gtz2NnZ6f7/sGDB4wZM4YmTZrw6quvEhQUxJgxY0hNTc12XmRkJAsWLODq1au64a7PDm09cuQINWvWZPfu3bRq1YpatWrRs2dPrl+/zuTJk3n99dcJCgri22+/1et369attG/fnlq1ahEcHMyiRYt0dzmvXLmCn58fS5YsoVGjRrRs2ZKHDx+SkpLC119/TYMGDQgMDKRnz56cOXOmEN81IYQQ4uUdj7vFwi1n9YpIAFVKOgu3nOV43C0jRSaEECI/iu0dyX87ffo0O3fuZPDgwcCTOY43b95kwYIFlC1blhMnTvDVV19RrVo1evXqpXduWFgYjx8/5qeffmLTpk24uLhk61+tVvPtt98ya9YssrKy+OSTT3j33Xd577332LhxI9u3b2fu3Lm6+ZsrV67km2++ITw8nEaNGnHq1CkmTpyISqVi9OjRun63bNnCqlWrSE1NxdHRka5du2JnZ8eSJUsoVaoU27Zto2vXrvzwww/UrFmzUN9DIYTIi/QM8xr2r9ZoSUvPIj1DLUvzFxKNRsva3fG5tlm35zw1PV3MYpir5EzRsLWxNHYIQohcFNtCcsmSJaxYsQKAzMxMMjMzqV27Nu3atQOgUaNG1KtXDz8/PwAqV67MmjVriI/PfqFzdHTEwcEBS0tLveGy/zZ06FBee+01ABo0aMCpU6cYOXIkFhYWfPLJJyxatIjz58/j6+tLVFQU3bt31w2z9fLy4v79+8ycOZMhQ4bo+gwNDaVatWoA/N///R8nT57k8OHDlClTBoDhw4dz4sQJoqOjiYiIyPf7ZWVl2jenn+5nk9d9bUTJJvliXGER+4wdgjBDqpR0Bs793dhhCBMSPSbE2CEUCLkmCUOYU74U20KyS5cuuqGnWVlZJCYmMmfOHLp168bGjRsJDQ1l3759bNmyhX/++YcLFy5w5coVqlatmu/n9PT01H3v4OBA5cqVsbB48pfVp8NqMzIyuHfvHnfu3CEwMFDv/DfeeIPMzEwuXrxI2bJls/X5999/o9VqadGihd55GRkZpKfrDxUyhEJhgVLpmO/zi5Kzs72xQxBmRPJFCCHMl7n8bpJXck0ShjCHfCm2hWTp0qX1ijBvb29Kly5NaGgof/zxB2vXruX8+fO88847tGnThldffZWvv/76pZ7Tykr/7VQocv5Lwr8X7nlKo9Fk6+fZeZ0ajYZSpUoRExOT7VwbGxuD4/1fv1qSkx/n+/yiYGmpwNnZnuTkVNRqGUYkcif5YlxRI1u8uJEJUVha4OxkT3JKKhp1zp/P4uXEXVYxa/3JF7Yb0aUOfh7Kwg/oJUnOFA2V6pGxQygQck0ShjB2vjg72+f5bmixLSRz8rSAO3v2LL///js//PADtWvXBp4Mf718+TLu7u45nvv0zmJBKFeuHOXKleP48eOEhPxv2MaxY8ewtrbGw8ODBw8eZDvP19eXhw8fkpmZqRvuCjBmzBiqV69O9+7d8x2TuczxUKs1ZhOrMD7JF+OwNIM5bs+yslRgZ2tF6mMFWVrJl8JQ3UOJ0sk220I7z3JxsqW6h9Is5khKzhSN4vb5LdckYQhzyBfTH3ybT48fP+b27dvcvn2bW7ducezYMaZOnUr58uX54IMPsLKy4ueffyYpKYkzZ87w2Wefcfv2bTIyMnLsz8HBgQcPHnDp0iUyMzNfOr4+ffqwZs0a1q1bR2JiIjt27GDBggV07tz5uVuLNGnShBo1ajBs2DAOHz5MYmIi06ZNIyYmBm9v75eOSQghhCgMCoUFoSE+ubbpGuJjFkWkEEKIJ4rtHckVK1boFttRKBSUKVOG119/nVmzZlGhQgUiIiKIjIxk7dq1uLq60rx5c3r16sW+fTkvEvHWW2/xww8/0L59e9asWfPS8YWFhWFjY8OqVauYOnUqr7zyCv369aNPnz7PPcfS0pIVK1Ywc+ZMPvvsM1JTU/H29mbBggUEBQW9dExCCCFEYQn0K8/Ajv7Z9pF0cbKlq+wjKYQQZsdC+7wJe6LEUKs13Ltn2vMQrKwUKJWOqFSPTP42vzA+yRdhCMmXoqXRaIlPus/9R+mUcbTF172M2d2JlJwRhpB8EYYwdr64uDjKHEkhhBBCmB6FwoLqnqa/oI4QQojcFds5kkIIIYQQQgghCocUkkIIIYQQQgghDCJDW/MgODiYq1ev6n62tramXLlyNGvWjKFDh+Li4pKnfrRaLVu3bqVp06aULVs23/H06NGDSpUqERERAcD+/ftxd3fX2xJECCGEKGjFYX6jEEKIgiGFZB6FhYURFhYGQFpaGvHx8cycOZPu3buzYcOG527Z8aw///yT8PBw9u7d+1KxREZGYmlpCcDVq1fp378/0dHRUkgKIYQoNMfjbmVbcVXpZEuorLgqhBAlkgxtzSMHBwdcXV1xdXXF3d2dli1bsmLFCq5fv86yZcvy1EdBLZBbpkwZXeEqi+4KIYQobMfjbrFwy1m9IhJAlZLOwi1nOR53y0iRCSGEMBa5I/kSKlasyJtvvsmPP/7IsGHDSElJYcaMGezevZvMzExeffVVvvjiC1577TWOHDlCz549AWjZsiXTpk0D4Ntvv6VZs2Zs2bKF+vXrs2jRIhISEpg5cyZ//fUXWVlZNGrUiC+//JJKlSoB/xvaOmjQIFq2bAlAz549GTRoEIMHDzbOmyGEEAZKz1AbOwQA1BotaelZpGeoZWn+HGg0Wtbujs+1zbo956np6VJihrmac87Y2lgaOwQhRDEhheRL8vX1Zdu2bTx8+JB+/fphZ2fHkiVLKFWqFNu2baNr16788MMPBAQEEBkZyeDBg9m4cSO+vr789NNPXL58mVu3brF161bS0tK4evUqnTt3pmHDhqxatYr09HQiIiLo3r07O3bsoFSpUrrndnNzY+PGjXzwwQdERkbSqFGjfL8OKyvTvjn9dD+bvO5rI0o2yRfzEBaxz9ghiAKiSkln4NzfjR2GyIPoMSHGDqHEkWuSMIQ55YsUki/J2dkZgH379nHy5EkOHz5MmTJlABg+fDgnTpwgOjqaiIgISpcuDYCLiwt2dna6PgYMGIC7uzsAM2fOxMHBgVmzZmFjYwPA/PnzadmyJdu2baNbt2668ywtLXUL/ZQuXRpHR8d8vQaFwgKlMn/nFjVnZ3tjhyDMiOSLEELoM5frfXEk1yRhCHPIFykkX1JKSgoASUlJaLVaWrRoofd4RkYG6enpOZ2q4+Xlpfs+Pj4ef39/XREJ4OrqSpUqVYiPz31oUX5pNFqSkx8XSt8FxdJSgbOzPcnJqajV5jWMSBQ9yRfzEDWyxYsbFQGFpQXOTvYkp6SiUcu883+Lu6xi1vqTL2w3oksd/DyUhR+QCTDnnFGpHhk7hBJHrknCEMbOF2dn+zzfDZVC8iX9/fffeHl5YW1tTalSpYiJicnW5tmiMCfP3p183uI5Go0Ga2vrlws2F+Yyx0Ot1phNrML4JF9Mm6WJzKezslRgZ2tF6mMFWVrJl3+r7qFE6WSbbaGdZ7k42VLdQ1li5kiac87IZ6LxyDVJGMIc8sX0B9+asBs3brB3717atWuHr68vDx8+JDMzE09PT91XVFSUbrsPC4sXX2D9/Pw4c+YMGRkZumN37twhMTERb2/vbO3z0qcQQgiRXwqFBaEhPrm26RriU2KKSCGEEE9IIZlHjx8/5vbt29y+fZukpCT27NlD3759qVy5Mr1796ZJkybUqFGDYcOGcfjwYRITE5k2bRoxMTG6AtDBwQGA2NhYHj3KeWhJ165defToEV988QWxsbGcPn2aoUOHolQqadu2bbb2T/uMj4/XDbMVQgghClKgX3kGdvRH6WSrd9zFyZaBHf1lH0khhCiBZGhrHq1YsYIVK1YAYG1tjZubG23atCEsLEy3yM2KFSuYOXMmn332GampqXh7e7NgwQKCgoKAJyu8NmvWjM8++4zhw4frFuV5VuXKlVmzZg0zZ86kc+fO2NjY0KhRI2bOnKlb2OdZSqWS9957jxkzZpCYmMiYMWMK700QQghRYgX6lSfAx5X4pPvcf5ROGUdbfN3LyJ1IIYQooSy0sqN9iadWa7h3z7Qn31tZKVAqHVGpHpn8eHFhfJIvwhCSL8JQkjPCEJIvwhDGzhcXF8c8L7YjQ1uFEEIIIYQQQhhECkkhhBBCCCGEEAaRQjIPHj58SO3atWnYsCGZmZl6j6lUKjZu3Kj7uUePHoSHhxfo8x8/fpxjx44VaJ9CCCGEITQaLbGJKg6fu0FsogqNRmbGCCFESSaL7eTBjz/+SNmyZbl9+za7d++mTZs2usdmzJjBlStX+OCDDwrt+UNDQ5k2bRqvv/56oT2HEEII8TzH426xbs95vb0klU62hIb4yIqtQghRQskdyTzYvHkzTZo0oUGDBqxfv17vMVmrSAghRHF2PO4WC7ec1SsiAVQp6SzccpbjcbeMFJkQQghjkjuSL5CQkMCpU6fo27cvDx48YMyYMVy6dIkqVaoQHh7Oli1bAPDz8yMuLi7b+Xv27GHJkiWcP38etVqNj48Pw4YNo0mTJsCTobC1a9fm3r177Nq1C41GQ4sWLZgwYQKlSpXCz88PgFGjRnH06FEiIiI4duwY8+fP5+zZs2RkZODu7k7//v159913i+6NEUKIYiQtPYv0DLWsqPgvGo2Wtbvjc22zbs95anq6lKhtQNQabZ5zxtbGsoiiEkKIoiWF5Ats2rQJBwcHmjZtSlpaGhMmTGD9+vWMGjWK0aNHk5aWxo0bN4iMjMx27tmzZxk8eDBffvklLVu25OHDh3zzzTeMHDmSAwcOYGNjA8DKlSsJCwtj06ZNJCQk8Pnnn1OlShUGDRrEwYMHady4MV999RWdOnXi5s2b9OnTh+7duzNp0iQyMzOJiopi9OjRNGrUiHLlyuXrdVpZmfbN6afLEOd1OWJRskm+CENYWir44KsfjR2G2VKlpDNw7u/GDsNkRY8JMXYIwsjkmiQMYU75IoVkLrKysti+fTvBwcHY2dlhZ2dH48aN2bp1K8OHD8fJyQk7Ozusra1xdXXNdr6lpSVff/01oaGhumM9e/akX79+3L17Fzc3NwCqVavG8OHDAfDy8qJRo0b89ddfALp+nZyccHJyQqVSMXjwYPr06YOFxZO//n788cds3bqVf/75J1+FpEJhgVLpaPB5xuDsbG/sEIQZkXwRQhibuVxfReGTa5IwhDnkixSSuThw4AB37tyhbdu2umNt27Zl//79/Pzzz3To0CHX82vUqEHp0qVZunQpFy9eJDExkdjYWADUarWuXdWqVfXOc3JyIjk5Occ+PTw86NSpE9HR0cTHx3P58uUc+zSERqMlOflxvs4tKpaWCpyd7UlOTkWtlqFnIneSL8IQlpYKNk5tS3JKKhq1zHt/VtxlFbPWn3xhuxFd6uDnoSz8gEyEwtICZyf7POWMSvWoiKISpkquScIQxs4XZ2f7PN8NlUIyFzExMQAMGjQo22Pr169/YSF59OhR+vTpQ/PmzQkMDKRdu3akpqYycOBAvXZPh7jmxYULFwgNDeXVV1+lYcOGvPXWWyiVypdeNdZc5gWp1RqziVUYn+SLyCs7WytSHyvI0kq+PKu6hxKlk222hXae5eJkS3UPZYmaI2llqchzzshnkHhKrknCEOaQL1JIPsfdu3c5cOAAnTp1onfv3nqPrVy5ks2bNxMfH68bXpqTFStWUL9+fb35k6tXrwbyv9rr+vXrKVu2LN99953u2L59+16qTyGEECInCoUFoSE+LNxy9rltuob4lKgiUgghxBOmP4vTSLZv305WVhb9+vXD19dX76t///4oFArWr1+Pg4MDt27dIikpKVsfbm5uxMXFcezYMa5cucLmzZuZN28eABkZGXmOxcHBgYSEBFQqFa+88go3btzgwIEDXL16lV27djF+/HiD+xRCCCHyItCvPAM7+qN0stU77uJky8CO/rKPpBBClFByR/I5YmJiaNiwYbb5i/BknmJISAjbt29n0aJF7N69m3feeYddu3bptRsyZAh37tyhf//+wJNFdaZOncoXX3zBmTNn8Pb2zlMsYWFhLFu2jISEBObPn8/FixcZOXIkGRkZeHl5MXz4cObPn8+ZM2do2rTpy794IYQQ4hmBfuUJ8HElPuk+9x+lU8bRFl/3MnInUgghSjALrYyHLPHUag337pn2YgBWVgqUSkdUqkcmP15cGJ/kizCE5IswlOSMMITkizCEsfPFxcUxz4vtyNBWIYQQQgghhBAGkUJSCCGEEEIIIYRBTLaQDA4Oxs/PT2910meNHTsWPz8/vRVRC1OPHj0IDw8v1Oe4du0aP/74Y6E+hxBCCJEbjUZLbKKKw+duEJuoQqORGTBCCCGyM+nFdqytrfn111+zbb+RlZXFrl27ct16wxx9+eWXVKpUibZt2xo7FCGEECXQ8bhbrNtzXm/fSKWTLaEhPrI6qxBCCD0me0cSICgoiJMnT3Ljxg2944cPH8bBwQE3NzcjRSaEEEIUL8fjbrFwy1m9IhJAlZLOwi1nOR53y0iRCSGEMEUmfUeyVq1aJCQk8Msvv9CrVy/d8Z9++om3336bn3/+WXds48aNREdHk5iYiEKhoGbNmowaNYrXXnsNeDJUtlu3bpw8eZKDBw9iY2NDu3btCA8Px8rqydtw+vRpZs+ezalTp7C3t+fNN98kPDwce3t7AB49esSoUaPYvXs3Wq2WN998k7Fjx+Lg4ABAQkICERERHDt2DEdHR+rXr094eDiurq4A/PPPP0yaNImTJ0+i0WioW7cuI0eOxM/Pjx49enD06FEAjh49yr59+8jIyGDevHls376dhw8f4uPjw5AhQ2jcuDHwZIuSb7/9lmbNmrFlyxbq16/PokWLCvcfRYgSJj1DbewQRCFTa7SkpWeRnqEusSsqajRa1u6Oz7XNuj3nqenpIlt+IDlT3NnaWBo7BCHMgkkXkgBvv/22XiGZkZHBnj17WLlypa6Q3L17NxMnTmTy5Mm8/vrr3L59m0mTJjFmzBi2bdum62vevHmMGDGCkSNHcvToUUaPHo2/vz8dOnQgKSmJjz76iDfffJMNGzaQkpLCl19+yYQJE4iIiABg165d9O/fn5iYGM6fP8+wYcNwc3Nj6NCh3Lx5k9DQUF1xmpqaSmRkJJ07d2bnzp04ODgwfPhwqlevzubNm8nKymL69OkMGjSI3bt3ExkZSf/+/XnllVcYO3YsAKNGjSIhIYFZs2ZRoUIF9u/fT//+/VmwYAHNmzcH4PLly9y6dYutW7eSlpaW7/fZysqkb07rliHO63LEomQryHwJi9j30n0IURyoUtIZOPd3Y4chRKGLHhNSoP3J7zDCEOaUL2ZRSC5fvpybN29SoUIFDh06hIuLCzVr1tS1KVOmDFOmTKF9+/YAVKpUiffff5+JEyfq9dW4cWN69uwJgLu7O6tXr+bEiRN06NCBH374gTJlyjB16lTdHcrJkyfz119/6c6vVasWw4YNA8DDw4NGjRpx9uxZAL7//nteeeUVxowZo2s/d+5cGjRowC+//EKnTp24fPkyDRs2pFKlSlhbWzN16lQuXryIRqOhTJkyWFtbY2dnh4uLC4mJiezcuZOtW7dSo0YNAHr37k1sbCzLly/XFZIAAwYMwN3dPd/vsUJhgVLpmO/zi5Kzs72xQxBmRPJFCCGEoQrrdyK5JglDmEO+mHwh6e/vj7u7O7/++is9e/bkp59+yrYYTb169UhISGDhwoVcvHiRxMRE4uLi0Gj0h5t4e3vr/ezk5ERmZiYA8fHxvPrqq7oiEqBBgwY0aNBA97OXl5fe+aVLl+bq1asAnDt3jvPnzxMQEKDXJj09nYSEBACGDRvG1KlTWbduHW+88QZNmjThnXfeQaHI/heHc+fOARAaGqp3PDMzE2dnZ71j/47LUBqNluTkxy/VR2GztFTg7GxPcnIqarUMIxK5K8h8iRrZooCiEqZKYWmBs5M9ySmpaNQlc4XSuMsqZq0/+cJ2I7rUwc9DWfgBmTjJmeJNpXpUoP3J7zDCEMbOF2dn+zzfDTX5QhL+N7y1c+fO7N27l40bN+o9vmPHDsLDw2nXrh1169alS5cuxMfHZ7sjaWNjk61vrfbJBeDZAvJ5LC2fP2Zeo9HQoEEDxo0bl+0xJycnALp160br1q05cOAA//d//8f8+fP59ttv2bp1K+XKlcsxrrVr1+LoqP+XsX8XnnZ2di+M/UXMZY6HWq0xm1iF8RVEvljKfLBiz8pSgZ2tFamPFWRpS+bnS3UPJUon22wL7TzLxcmW6h5KmSOJ5ExxV1i/Z8jvMMIQ5pAvpj/4lieF5IkTJ9i8eTPu7u7Z7iwuXbqU999/n4iICLp160a9evVISkoC/leQvUi1atU4d+4cavX/FtbYvXs3wcHBpKc//8L6lI+PDwkJCbi5ueHp6YmnpyelS5dm6tSpxMfHc/fuXSZOnEhmZiadOnVi5syZbN++ndu3b+sW2fl3fwC3b9/W9efp6UlMTAwxMTF5ek1CCCFEXigUFoSG+OTapmuIjxSRQgghdMyikKxRowaenp588803Oe6x6ObmxokTJ/j777+5fPkyK1euZM2aNcCTxXnyIjQ0FJVKxbhx40hISODPP/9kxowZNGjQAFtb2zydn5KSwogRI4iNjSU2NpZhw4Zx5swZfH19KV26NL/99htjxozhv//9L0lJSaxfvx5ra2v8/f0BcHR05OrVq9y4cQMfHx9atGjBuHHj2LdvH0lJSURFRbFkyRI8PDwMePeEEEKIFwv0K8/Ajv4onfSveS5Otgzs6C/7SAohhNBjFoUkPLkr+fDhQ9q0aZPtsa+//ppy5crRvXt3PvjgA/bv38+MGTMAOHPmTJ76r1ChAitWrODixYt06NCBYcOG0aJFC90Kqi/i7u7OmjVrePToEV27dqV79+5YW1sTHR2Ni4sLVlZWREVFoVAo6NWrF23btuWPP/5g6dKlusLw6ZDc9u3bo1armTNnDm+99RZjx46lTZs2bN26lSlTptCxY8c8vmtCCCFE3gX6lWfmpw0Z2TWAj9vXZGTXAGZ82lCKSCGEENlYaPM69lMUW2q1hnv3CnZieUGzslKgVDqiUj0y+fHiwvgkX4QhJF+EoSRnhCEkX4QhjJ0vLi6OeV5sx2zuSAohhBBCCCGEMA1SSAohhBBCCCGEMIhZF5LBwcH4+fnpvvz9/WnVqhXLli3L9bzw8HB69Ojx3MePHDmCn58fV65cKeiQC1xmZiYrV640dhhCCCGEEEKIEsQs9pHMTVhYGGFhYQCkpaVx+vRpxowZg729Pd26dcvxnNGjR+tt82HOdu7cybRp0+jVq5exQxFCCGHiNBot8Un3uf8onTKOtvi6l5EtPYQQQuSL2ReSDg4OuLq66n52d3fnyJEjbN68+bmFpJOTU1GFV+hkrSQhhBB5cTzuFuv2nEeV8r+9kZVOtoSG+MiqrEIIIQxm9oVkTuzs7HTf9+jRAy8vL2JjY7l06RJjx47ljz/+4OrVq6xevRqAY8eOMX36dOLi4vDy8uK9997T60+tVjN//nw2b97Mw4cPadq0KRUqVCA2NlbXR0JCAhERERw7dgxHR0fq169PeHi4rsjt0aMH1atX5+7du+zdu5fSpUvTvXt3+vXrh4WFha6PmTNn8tdff5GVlUWjRo348ssvqVSpUo6vpU+fPsydOxcAPz8/oqOjqV+/fqG+t0IIIczP8bhbLNxyNttxVUo6C7eclX0ihRBCGKzYFZKnT59m586dDB48WHds48aNzJw5Ez8/P1xdXfnjjz90jyUlJREWFkaHDh2IiIjgwoUL2faOnDVrFlu2bGHSpElUrVqVdevWsXr1aurVqwfAzZs3CQ0NpV27doSHh5OamkpkZCSdO3dm586dODg4APD999/z3nvvERMTw+nTpxk/fjwAH3/8MVevXqVz5840bNiQVatWkZ6eTkREBN27d2fHjh2UKlUq22spXbo0Dg4OTJ06lYMHD1K6dOnCfGuFEMVIekbxGN5fENQaLWnpWaRnqIvl0vwajZa1u+NzbbNuz3lqerrIMNc8MvecsbWxNHYIQohiwOwLySVLlrBixQrgycIzmZmZ1K5dm3bt2una1KhRQ+/nZ/3www+UK1eOcePGYWlpibe3N9evX2fatGkApKamsm7dOkaNGsWbb74JwJgxY/jrr790fXz//fe88sorjBkzRnds7ty5NGjQgF9++YVOnToBUKVKFcaPH4+FhQXe3t4kJCQQHR1Nv379WLduHQ4ODsyaNQsbGxsA5s+fT8uWLdm2bZtumO6/X8vTYbrPDu/NDysr01536el+Nnnd10aUbJIvLxYWsc/YIQgTokpJZ+Dc340dhigi0WNCjB1CiSLXJGEIc8oXsy8ku3TpoluBNSsri8TERObMmUO3bt3YuHEjAJ6ens89Pz4+npo1a2Jp+b+/ztWtW1f3fUJCAmlpadSpU0d3zMLCgsDAQGJjYwE4d+4c58+fJyAgQK/v9PR0EhISdD/Xr19fN4wVICAggKioKFQqFfHx8fj7++uKSHhSHFapUoX4+P/9JTm315JfCoUFSqVjgfdbGJyd7Y0dgjAjki9CCJGduVzzixu5JglDmEO+mH0hWbp0ab3iytvbm9KlSxMaGqobwvrsnMl/s7CwQKPRH5ZiZWWV7fvcFrXRaDQ0aNCAcePGZXvs2YV9nu336XkAlpaWz+1fo9FgbW2t+zm315JfGo2W5OTHBd5vQbK0VODsbE9ycipqtfkNIxJFS/LlxaJGtjB2CCZDYWmBs5M9ySmpaNTFbwGzuMsqZq0/+cJ2I7rUwc9DWfgBFQPmnjMq1SNjh1CiyDVJGMLY+eLsbJ/nu6FmX0jm5GlR9u8CMSfVq1cnJiaGjIwM3d3As2f/tyCBp6cndnZ2nDx5kho1auiOnzp1CltbWwB8fHz46aefcHNz0/Vx//59vvzyS3r37k2DBg0AOHPmjN5znzhxgsqVK1O6dGn8/PzYvn27Xhx37twhMTGR0NDQ58b/7B3Ol2EuczzUao3ZxCqMT/Ll+SxlLpyOlaUCO1srUh8ryNIWv3yp7qFE6WSrt1rrv7k42VLdQylzJPPI3HNGPheNQ65JwhDmkC+mP/j2BR4/fszt27e5ffs2t27d4tixY0ydOpXy5csTFBT0wvO7du1KamoqX331FQkJCezfv5/IyEjd4/b29vTo0YP58+ezZ88eLl26xPTp0zl16pSuTWhoKCkpKYwYMYLY2FhiY2MZNmwYZ86cwdfXV9fu2LFjzJ8/n3/++YdNmzaxdu1a+vbtq4vj0aNHfPHFF8TGxnL69GmGDh2KUqmkbdu2z43/6UI+Z8+eJS0tzeD3TwghRPGmUFgQGuKTa5uuIT5SRAohhDCI2ReSK1asoHHjxjRu3JhmzZoxePBgKlWqxKpVq7C3f/HY4goVKrBq1Spu3LhBx44diYiI4NNPP9VrM3ToUNq3b8+YMWPo0KED169fp2XLlro7h+7u7qxZs4ZHjx7RtWtXunfvjrW1NdHR0bi4uOj6admyJQkJCbRv357FixczatQounbtCkDlypVZs2YNycnJdO7cmT59+uDq6sr333+Ps7Pzc+Nv0KABtWvXpkuXLuzfvz8/b6EQQohiLtCvPAM7+qN0stU77uJkK1t/CCGEyBcLrexo/0K7d+8mMDBQrygMCwvjlVdeYerUqXnqo0ePHlSqVImIiIjCCjPf1GoN9+6Z9nwJKysFSqUjKtUjk7/NL4xP8kUYoiTli0ajJT7pPvcfpVPG0RZf9zJyJzIfSlLOiJcn+SIMYex8cXFxLNlzJAva8uXLWbduHSNHjqRUqVLs3buXw4cP67YdEUIIIcyBQmFBdU9ZUEcIIcTLM/uhrUVh1qxZODo60qtXL9555x127NjBvHnzdIvoCCGEEEIIIURJInck86By5cosWLDgpfpYvXp1AUUjhBBCCCGEEMYlhWQuwsPD2bJlS65t4uLiiigaIYQQJZ3McRRCCGEqpJDMxejRo/n88891Pzdu3JivvvqKNm3aGDEqIYQQJdHxuFus23Nebz9IpZMtoSE+suqqEEKIIieFZC6cnJxwcnLKdszV1dVIEQkhhCiJjsfdYuGWs9mOq1LSWbjlrGzhIYQQoshJIZlPGo2GqKgoYmJiuHr1KjY2NtStW5exY8fi4eEBgJ+fHxMnTmTbtm2cOXOGypUrM2XKFM6fP8+3335LcnIyTZs2JSIiAjs7O2JiYpg7dy6DBg1i/vz5PHr0iIYNGzJ27FgqVKgAwLVr15g5cyaHDx8mOTmZsmXL0q5dOz7//HMUClk7SQhRtNIz1MYO4aWpNVrS0rNIz1Cb5NL8Go2Wtbvjc22zbs95anq6yDDXQmRrY2nsEIQQwqRIIZlP0dHRLF++nOnTp+Pr68vly5f5+uuviYiIYNGiRbp2c+bMYerUqXh5eREeHk7//v3x9/dn6dKlXLp0ic8//5yNGzfSo0cPAO7du8eqVauYO3cuNjY2jB8/nr59+7JlyxasrKz49NNPcXV15bvvvsPR0ZG9e/cybdo0AgICCAkJyffrsbIy7SL06X42ed3XRpRski9FJyxin7FDEDy5Mzlw7u/GDqNYix7zv2usfMYIQ0i+CEOYU75IIZlPHh4eTJ8+nRYtWgBQqVIlWrduzS+//KLX7r333iM4OBiAd999l4kTJzJ27Fi8vLzw9fVl2bJlnD9/Xtc+MzOT6dOn4+/vD8DMmTNp06YN//d//0e9evV49913efvtt3FzcwOgV69eREVFERcXl+9CUqGwQKl0zNe5Rc3Z2d7YIQgzIvkihCgoOV0n5TNGGELyRRjCHPJFCsl8Cg4O5tSpU8ybN49Lly5x6dIlLly4oBuC+pSnp6fue3v7JwnxdOgrgJ2dHRkZGbqfHR0ddUUkgLe3N6VLlyY+Pp4mTZrQvXt3fvnlF06fPk1iYiJxcXHcuXMHjSb/w7E0Gi3JyY/zfX5RsLRU4OxsT3JyKmq16Q09E6ZF8qXoRI1sYewQXprC0gJnJ3uSU1LRqLXGDiebuMsqZq0/+cJ2I7rUwc9DWfgBlVAq1SPd9/IZIwwh+SIMYex8cXa2z/PdUCkk82np0qUsXLiQjh07EhQURK9evdi7dy8//vijXjsrq+xvcW5zGa2trbMdU6vVWFpa8vjxY7p3705aWhqtW7emY8eO1KpVi27dur306zHFeUE5Uas1ZhOrMD7Jl8JnWQzm5FlZKrCztSL1sYIsrenlS3UPJUonW73VWv/NxcmW6h5KmSNZiHL6LJHPGGEIyRdhCHPIFykk82nx4sUMHDiQjz/+WHds+fLlaLUv99fs+/fvk5SUhLu7OwDnz5/n4cOH1KxZk4MHD/L3339z6NAhypUrp2t/9+7dl35eIYQQpkmhsCA0xCfHVVuf6hriI0WkEEKIImX6szhNlJubG4cOHeLChQtcvHiROXPmsGvXLr1hqvn1xRdfcPbsWU6ePMnIkSMJCAigXr16vPLKKwBs376dq1evcuzYMQYMGEBmZmaBPK8QQgjTFOhXnoEd/VE62eodd3Gyla0/hBBCGIXckcynGTNmMHHiRN577z0cHR2pXbs2EyZMYPz48Vy7do2KFSvmu+927drx8ccfk5GRQXBwMKNHj8bCwoJatWoxatQoVq5cydy5c6lQoQJt2rTBzc2NM2fOFOCrE0IIYWoC/coT4ONKfNJ97j9Kp4yjLb7uZeROpBBCCKOw0MqYSJMRExPDqFGjiIuLK9LnVas13Lv36MUNjcjKSoFS6YhK9cjkx4sL45N8EYaQfBGGkpwRhpB8EYYwdr64uDjmebEdGdoqhBBCCCGEEMIgUkgKIYQQQgghhDBIsSwkg4ODCQ4O5uHDh9keCw8Pp0ePHoUew8WLF3VzGnPyzTff8Nprr3H+/HndsU6dOuVpWOuVK1fw8/PjyJEjBRavEEII06DRaIlNVHH43A1iE1VoNDIDRQghhOkptovtXL16VbcgjjFUrVqVwYMHM2vWLNq3b09QUJDusXPnzrFixQqGDx+Oj4+PUeITQghheo7H3WLdnvN6e0YqnWwJDfGRlVmFEEKYlGJ5RxLA3d2dDRs28McffxgthrCwMGrVqsXYsWNJS0sDICsri9GjRxMQEEDv3r2NFpsQQgjTcjzuFgu3nNUrIgFUKeks3HKW43G3jBSZEEIIkV2xLSSf3gUcPXp0jkNcn0pJSeHrr7+mQYMGBAYG0rNnT91WGvv27aN69ercu3dP175Dhw688847up8fPHhAzZo1OXbsWLa+LS0tmTZtGjdu3GDhwoUArFy5ksTERKZNm4ZCoUCtVrNy5UpatWrFa6+9RqtWrfj+++91fRw5coSaNWuydOlS6tevT6dOnfj3QrsJCQk0atSIkSNHolar8/eGCSGECUnPUBfpV1p6VpE/57NfqWlZrN0dn+t7sm7PeVLTjBtnUX4JIYQwbcV2aKuFhQVTpkyhXbt2TJ8+nUmTJmVro9Vq6devH3Z2dixZsoRSpUqxbds2unbtyg8//EDDhg2xtbXl8OHDtGnThnv37hEXF4dGo+Hu3buULVuWgwcPUrp0aerWrZtjHNWqVWPQoEFERkbSsGFDFi5cyFdffYW7uzsAERERbNu2ja+//prXXnuN33//nSlTppCenk6vXr0AUKvVHDhwgA0bNpCamoqFxf/2DEtMTKRXr140bdqUKVOmoFDk728DVlam/TeFp8sQ53U5YlGySb6Yv7CIfcYOweSoUtIZOPd3Y4dRZKLHhBg7hOeSzxhhCMkXYQhzypdiW0gCVKpUiS+//JKxY8fSqlUrGjdurPf44cOHOXnyJIcPH6ZMmTIADB8+nBMnThAdHU1ERARBQUEcPHiQNm3a8Mcff1CzZk1u377NkSNHaNOmDb/99hvNmzfPtYDr27cvu3btom/fvjRp0oQPPvgAgIcPH/L9998THh5Ou3btAPDy8uLKlSssXbqUjz76SNdHWFgYXl5ewJPFdp7+d+TIkTRr1oxJkybpFZiGUCgsUCod83VuUXN2tjd2CMKMSL4IYb7M4boknzHCEJIvwhDmkC/FupAE6Ny5M7/++itjxoxh586deo/9/fffaLVaWrRooXc8IyOD9PQnc1SCg4NZtGgRAIcOHSIoKIirV69y+PBhWrduzX/+8x8mT56cawyWlpYMGTKEjz/+mBEjRuiOX7x4kczMTAIDA/Xav/HGG6xatYq7d+/qjj0tIp81fvx4MjMzcXNzy3cRCU9WCExOfpzv84uCpaUCZ2d7kpNTUatlM1+RO8kX8xc1ssWLGxUQhaUFzk72JKekolEbZ4XUuMsqZq0/+cJ2I7rUwc9DWfgBmQCV6pGxQ3gu+YwRhpB8EYYwdr44O9vn+W5osS8kASZPnky7du2YNm2a3nGNRkOpUqWIiYnJdo6NjQ0AzZs3Z+zYsSQkJPB///d/TJkyhatXr7J8+XJOnTpFamoqjRo1emEMdnZ2ev8Fss11fDYuACur//3z2NraZmvXsWNHfH19iYiI4M0338TX1/eFcTxPVpZ5fLCp1RqziVUYn+SL+bJU5P+PY4ayslRgZ2tF6mMFWVrj5Et1DyVKJ9tsC+08y8XJluoeShRF+N4Ykzn8vyufMcIQki/CEOaQL6Y/+LYAVKxYkfDwcDZt2qS3KI6vry8PHz4kMzMTT09P3VdUVBR79+4FoHz58vj7+/P9999z9+5dAgMDCQoK4p9//mHDhg00bNgQe/v83Xr29vbG2tqa48eP6x0/duwYrq6ulC5dOtfz27ZtS2hoKP7+/owaNUoW2hFCCDOlUFgQGpL7dlBdQ3xKTBEphBDC9JWIQhLggw8+oHHjxiQlJemONWnShBo1ajBs2DAOHz6sW001JiYGb29vXbvg4GA2bNhAnTp1sLOzw93dncqVK7Nt2zZatmyZ75hKlSpF586dmT9/Pjt37iQxMZG1a9eybt06wsLC8jRcVaFQMGnSJOLi4li2bFm+YxFCCGFcgX7lGdjRH6WT/ggUFydbBnb0l30khRBCmJQSMbT1qadDXJ+ytLRkxYoVzJw5k88++4zU1FS8vb1ZsGABQUFBunbBwcHMmzePBg0a6I41bNiQTZs2ZZtfaahRo0ahVCqZNWsWd+7cwcvLi7Fjx/Lhhx/muQ8fHx/69evHggULaNmyJdWqVXupmIQQQhhHoF95AnxciU+6z/1H6ZRxtMXXvYzciRRCCGFyLLTPm6gnSgy1WsO9e6a7qAE82Z5EqXREpXpk8uPFhfFJvghDSL4IQ0nOCENIvghDGDtfXFwc87zYTokZ2iqEEEIIIYQQomBIISmEEEIIIYQQwiAmU0gGBwcTGRlZ5M/78OFDateuTcOGDcnMzCzy5weIiYnBz8/PKM8thBBCCCGEEIYymULSWH788UfKli1LSkoKu3fvNnY4QgghzJxGoyU2UcXhczeITVSh0chSBEIIIYqfErVqa042b95MkyZNuHbtGuvXr6dNmzbGDkkIIYSZOh53i3V7zqNKSdcdUzrZEhriI9t3CCGEKFbM6o7k1q1bad++PbVq1SI4OJhFixahVqv1Hm/bti2vvfYaTZo0YcqUKWRkZDy3v4SEBE6dOkWjRo146623OHLkCJcuXdJrExwczPLlyxk8eDABAQHUr1+fyZMnk5WVpWtz8OBBOnbsyGuvvcY777zD5s2b8fPz48qVKwBkZGQwc+ZMmjRpQkBAAB9++CEHDx58blwvaq9Wq5k5cybNmjXD39+f1q1b8/333xv8fgohhCg4x+NusXDLWb0iEkCVks7CLWc5HnfLSJEJIYQQBc9s7kiuXLmSb775hvDwcBo1asSpU6eYOHEiKpWK0aNHExsby5gxY5g1axa1atUiISGBzz//HKVSyYABA3Lsc9OmTTg4ONC0aVPS0tKYMGEC69evZ9SoUXrt5s2bx4gRIxg5ciRHjx5l9OjR+Pv706FDB/773//yySef8NFHH/HNN9/w3//+lwkTJuidP2rUKBISEpg1axYVKlRg//799O/fnwULFtC8efNscb2o/bp16/jll1+YM2eO7vHx48fj4+PD66+/XmDvuRBCFLb0DPWLGxUytUZLWnoW6RnqfC+1rtFoWbs7Ptc26/acp6anS7HYE9LWxtLYIQghhDAysygktVotUVFRdO/enW7dugHg5eXF/fv3mTlzJkOGDOHKlStYWFhQqVIlKlasSMWKFVm+fDmlSpXKsc+srCy2b99OcHAwdnZ22NnZ0bhxY7Zu3crw4cOxtbXVtW3cuDE9e/YEwN3dndWrV3PixAk6dOjAypUr8ff3Z+TIkQBUrVqVu3fvMmXKFAASExPZuXMnW7dupUaNGgD07t2b2NhYli9fnq2QzEv7y5cv4+DgQOXKlSlfvjzdu3enatWqVKlSJd/vsZWVad+cfrqfTV73tRElm+SL+QiL2GfsEIqMKiWdgXN/N3YYBSJ6TIixQzAq+YwRhpB8EYYwp3wxi0Ly3r173Llzh8DAQL3jb7zxBpmZmVy8eFE3DPT999+ncuXKNGrUiJYtW+Lv759jnwcOHODOnTu0bdtWd6xt27bs37+fn3/+mQ4dOuiOe3t7653r5OSkW+H13LlzNGzYUO/xevXq6b4/d+4cAKGhoXptMjMzcXZ2zhZXXtp369aNPXv20KxZM2rUqEGjRo1o27YtZcuWzfG1vohCYYFS6Zivc4uas7O9sUMQZkTyRYjCYS7XjMImnzHCEJIvwhDmkC9mUUhqtTmveKfRPBmCZGVlha2tLdHR0Zw7d46DBw9y8OBB+vfvT4cOHZg2bVq2c2NiYgAYNGhQtsfWr1+vV0ja2Ng8NyZLS0tdHLnFvnbtWhwd9S+8CkX2vzTkpb2Xlxe7du3i6NGjHDp0iN9++42oqCimTZtGx44dnxvL82g0WpKTHxt8XlGytFTg7GxPcnIqanX+hp6JkkPyxXxEjWxh7BBQWFrg7GRPckoqGnX+VliNu6xi1vqTL2w3oksd/DyU+XoOU6JSPTJ2CEYlnzHCEJIvwhDGzhdnZ/s83w01i0KyXLlylCtXjuPHjxMS8r/hNMeOHcPa2hoPDw8OHDjAmTNnGDRoEDVr1uTjjz/m22+/ZfHixdkKybt373LgwAE6depE79699R5buXIlmzdvJj4+Hl9f3xfGVr16dU6fPq137K+//tJ97+PjA8Dt27epWbOm7vicOXNQKBQMHTpU79y8tI+OjqZs2bK0bduWRo0aMXLkSHr37s1PP/2Ur0ISyPe8oKKmVmvMJlZhfJIvps/SBOYLWlkqsLO1IvWxgixt/vKluocSpZNttoV2nuXiZEt1D2WxmCMp/189IZ8xwhCSL8IQ5pAvJjX4NjExkd9//13v6+jRowD06dOHNWvWsG7dOhITE9mxYwcLFiygc+fOODk5YW1tzcKFC1m5ciVJSUmcPXuW3377jYCAgGzPs337drKysujXrx++vr56X/3790ehULB+/fo8xRwWFsaZM2eYNWsWly5dYvfu3cyfPx8ACwsLfHx8aNGiBePGjWPfvn0kJSURFRXFkiVL8PDwyNZfXtrfu3ePiRMnsnfvXq5evcp//vMf/vvf/+b4WoUQQhQ+hcKC0BCfXNt0DfEpFkWkEEIIASZ2R3LHjh3s2LFD71ilSpXYt28fYWFh2NjYsGrVKqZOncorr7xCv3796NOnDwANGzZkypQprFixgjlz5mBnZ0ezZs0IDw/P9jwxMTE0bNiQqlWrZnvMw8ODkJAQtm/fzogRI14Ys6+vLwsWLGD27NmsXLmSKlWq0L17dyIjI7G2tgae3E2cM2cOY8eO5cGDB3h4eDBlypTn3j18UftBgwaRmZnJ5MmTuX37Nq6urnTt2pVPPvnkhfEKIYQoHIF+5RnY0T/bPpIuTrZ0lX0khRBCFDMW2udNQBR5cvr0aaysrPSGoe7YsYOvvvqKv/76Cysrk6rVc6RWa7h3z7Tnu1hZKVAqHVGpHpn8bX5hfJIvwhAFnS8ajZb4pPvcf5ROGUdbfN3LyJ3IYkY+Y4QhJF+EIYydLy4ujsVrjqQp++9//8vMmTOZPn06NWrUIDExkcjISNq2bWsWRaQQQoiCpVBYUN3T/BfUEUIIIXIjlc5L+vDDD7l9+zZTp07l5s2bukVwhgwZYuzQhBBCCCGEEKJQSCH5kiwsLBg0aFCO24gIIYQQQgghRHFkUqu2mpOHDx9Su3ZtGjZsSGZmZoH2nZmZycqVK3U/R0ZGEhwcXKDPIYQQonBoNFpiE1UcPneD2EQVGo0sRSCEEKL4kTuS+fTjjz9StmxZbt++ze7du2nTpk2B9b1z506mTZtGr169gCdbjHTr1q3A+hdCCFE4jsfdyrZqq9LJllBZtVUIIUQxI3ck82nz5s00adKEBg0a5HnPybz690K6jo6OuLi4FOhzCCGEKFjH426xcMtZvSISQJWSzsItZzked8tIkQkhhBAFT+5I5kNCQgKnTp2ib9++PHjwgDFjxnDp0iWqVKkCQHBwMB07dmTw4MG6c549plarmT17Njt37uTu3btUrlyZjz76iK5duxITE8OoUaMA8PPzIzo6mqNHj7Jlyxb27dvHlStXaNmyJfPnz2fZsmX897//pXz58nzyySd07tzZKO+HEKYsPUNt7BCEiVNrtKSlZ5Geoc73UusajZa1u+NzbbNuz3lqerrIViDFQEHkTGGxtbE0dghCiBJCCsl82LRpEw4ODjRt2pS0tDQmTJjA+vXrdQXgi6xbt45ffvmFOXPmUKFCBfbv38/48ePx8fGhTZs2pKSkMHXqVA4ePEjp0qU5evRotj6mTZvG119/ja+vL9999x3jx4+nYcOGuLu75+s1WVmZ9s3pp/vZ5HVfG1GyPZsvn84+YORohHhClZLOwLm/GzsMUcxFjwkxdgjiX+R3GGEIc8oXKSQNlJWVxfbt2wkODsbOzg47OzsaN27M1q1bGT58OLa2ti/s4/Llyzg4OFC5cmXKly9P9+7dqVq1KlWqVMHOzg4nJycAXF1dn9tHr169aNmyJQDDhg1j7dq1nDp1Kl+FpEJhgVLpaPB5xuDsbG/sEIQZkXwRQpQ05nI9L4nkmiQMYQ75IoWkgQ4cOMCdO3do27at7ljbtm3Zv38/P//8Mx06dHhhH926dWPPnj00a9aMGjVq0KhRI9q2bUvZsmXzHIe3t7fu+6eFZ35Xj9VotCQnP87XuUXF0lKBs7M9ycmpqNWmNYxImJ5n8yVqZAtjhyNMnMLSAmcne5JTUtGo87fCatxlFbPWn3xhuxFd6uDnoczXcwjTURA5U1hUqkfGDkH8i/wOIwxh7HxxdrbP891QKSQNFBMTA5DjvpHr169/biGZlZWl+97Ly4tdu3Zx9OhRDh06xG+//UZUVBTTpk2jY8eOeYrDxsYm27F/L9JjCFOb4/E8arXGbGIVxqdWa7CU+WjiBawsFdjZWpH6WEGWNn+fL9U9lCidbLMttPMsFydbqnsoZY5kMVAQOVNY5BppuuR3GGEIc8gXKSQNcPfuXQ4cOECnTp3o3bu33mMrV65k8+bNxMfHY21tzcOHD3WPPXz4kLt37+p+jo6OpmzZsrRt25ZGjRoxcuRIevfuzU8//UTHjh2xsJBfMoQQwpwoFBaEhviwcMvZ57bpGuIjRaQQQohiQwpJA2zfvp2srCz69etH1apV9R7r378/W7ZsYf369dSpU4effvqJVq1a4ezszPz587G0/N8qavfu3WPhwoXY2dlRvXp1Ll68yH//+1969uwJgIODAwBnz56lWrVqRfcChRBC5FugX3kGdvTPto+ki5MtXWUfSSGEEMWMFJIGiImJoWHDhtmKSAAPDw9CQkLYvn07P/74I/fv36d37944OTkRFhZGcnKyru2gQYPIzMxk8uTJ3L59G1dXV7p27conn3wCQIMGDahduzZdunRh5syZRfb6hBBCvJxAv/IE+LgSn3Sf+4/SKeNoi697GbkTKYQQotix0L7MxDpRLKjVGu7dM+3J+VZWCpRKR1SqRyY/XlwYn+SLMITkizCU5IwwhOSLMISx88XFxTHPi+2Y/gYlQgghhBBCCCFMihSSQgghhBBCCCEMUqILyaysLFatWkWnTp0ICAigQYMGhIWFcfjwYV0bPz8/3ZYfOYmMjCQ4OLgowhVCCGEiNBotsYkqDp+7QWyiCo1GZokIIYQoWUrsYjvp6en07t2b69evM2TIEAICAkhLS2Pz5s307t2bGTNm0K5duxf2ExYWRrdu3YogYiGEEKbgeNytbCuzKp1sCZWVWYUQQpQgJbaQnDdvHnFxcezcuRM3Nzfd8dGjR/Pw4UMmT56cpzuNjo6OODo6FmaoQgghTMTxuFs57hWpSkln4ZazDOzoL8WkEEKIEqFEFpKZmZls3ryZTp066RWRT3322Wd07doVOzs7AC5dukSvXr04fvw4ZcqUoXv37rqtOiIjI9myZQv79u0D4M6dO8yYMYMDBw6QlZXF66+/zldffYWnpycajYaoqChiYmK4evUqNjY21K1bl7Fjx+Lh4QE82WNy0qRJ/Oc//8HS0pIPPviA06dPU69ePQYPHgzAb7/9xqJFizh//jyOjo60bduWYcOG6eIVQoi8SM9QGzsEk6DWaElLzyI9Q53rCnkajZa1u+Nz7WvdnvPU9HSR7T4MYGtj+eJGQgghTE6JLCSTkpK4f/8+devWzfHxChUqUKFCBd3Pa9asYdy4cUyaNIkdO3Ywe/ZsatWqRVBQkN55WVlZhIWFYWVlxaJFiyhTpgwRERH07duXX375hdWrV7N8+XKmT5+Or68vly9f5uuvvyYiIoJFixah0Wj45JNPUKvVLFu2DGtra6ZNm8axY8eoV68eALt372bIkCEMHjyY6dOnc/HiRcaPH09SUhKLFi3K93tiZWXa02WfLkOc1+WIRckm+ZI3YRH7jB1CsaNKSWfg3N+NHYZZiR4TYuwQDCafMcIQki/CEOaULyWykHzw4AEApUuXzlP70NBQOnToAMCAAQNYsWIFZ8+ezVZI/t///R9xcXH88ssvVKlSBYDJkyezcuVKHjx4gIeHB9OnT6dFixYAVKpUidatW/PLL78AcPToUU6fPs3PP/9M1apVAZg7d67eENulS5fy5ptvMmDAAACqVKmCVqtl4MCBXLhwgWrVqhn8figUFiiV5jE819nZ3tghCDMi+SKE6TOX609O5DNGGELyRRjCHPKlRBaSLi4uANy/fz9P7b28vPR+dnZ2Jj09PVu7+Ph4SpcurSsi4cndzS+//BKA4OBgTp06xbx587h06RKXLl3iwoULuruf586do3Tp0roiEqBcuXJ6/cXHx9O2bVu9533jjTd0j+WnkNRotCQnPzb4vKJkaanA2dme5ORU1GrZzFfkTvIlb6JGtjB2CCZBYWmBs5M9ySmpaNTPX3017rKKWetPvrC/EV3q4OehLMAIizeV6pGxQzCYfMYIQ0i+CEMYO1+cne3zfDe0RBaS7u7ulCtXjhMnTtCmTZtsjyckJDBlyhRGjRoFgKVl9vkbWm32XzasrHJ/O5cuXcrChQvp2LEjQUFB9OrVi7179/Ljjz/qnkejyT1hcnrep+e86Plzk9u8IFOiVmvMJlZhfJIvubOUeXwAWFkqsLO1IvWxgizt8/OluocSpZOt3mqt/+biZEt1D6XMkTSAOf8/Kp8xwhCSL8IQ5pAvpj/4thAoFAref/99YmJiuH79erbHly1bxpkzZ6hUqZJB/VarVo0HDx6QmJioO3bv3j3q16/PyZMnWbx4MQMHDmT8+PF07tyZOnXq8M8//+iKw+rVq5OSkkJCQoLufJVKpdefn58fJ06c0HveY8eOAeDt7W1QvEIIIfJOobAgNMQn1zZdQ3ykiBRCCFEilMhCEqB///54eXkRGhrK1q1buXz5MqdPn2bUqFFs3bqVSZMm4eDgYFCfQUFB+Pv78+WXX3L69GnOnz/Pl19+iYuLC6+++ipubm4cOnSICxcucPHiRebMmcOuXbvIyMgAoH79+tSuXZuRI0dy8uRJYmNjGTFiBKmpqVhYPPnFpG/fvuzatYtFixZx6dIl9u/fz6RJk2jRooUUkkIIUcgC/cozsKM/SidbveMuTray9YcQQogSpUQObQWwt7dnzZo1rFixgqioKK5du4adnR01a9Zk9erVvP766wb3qVAoWLRoEdOmTaN3795YWFjQoEED3QqsM2bMYOLEibz33ns4OjpSu3ZtJkyYwPjx47l27RoVK1YkMjKSiRMn0qtXL2xtbQkNDeXixYtYW1sD0KpVK2bPns23337LokWLcHFx4Z133mHIkCEF/RYJIYTIQaBfeQJ8XIlPus/9R+mUcbTF172M3IkUQghRolhoc5p0J4zi3r17nDp1isaNG+sKx4yMDOrXr8+4ceN0K8cWNLVaw717pr3YgZWVAqXSEZXqkcmPFxfGJ/kiDCH5IgwlOSMMIfkiDGHsfHFxcZTFdsyRlZUVw4YNo0uXLnTt2pXMzEyWL1+OjY0NTZs2NXZ4QgghhBBCCAGU4DmSpsjZ2ZnFixdz8uRJOnToQOfOnblz5w7R0dG6LUuEEEIIIYQQwtikkATUajXr1q3j/fffJyAggNdff50uXbqwadOmHLfbeJafnx8xMTEFFkuDBg1Yv349f/31F8ePHycqKopdu3YRHBxcYM8hhBAibzQaLbGJKg6fu0FsogqNRmaDCCGEECBDW8nMzGTgwIGcPn2aQYMG0bhxY9RqNf/5z3+IiIhg3759REZG5riXpBBCiOLreNwt1u05r7dvpNLJltAQH1mdVQghRIlX4gvJJUuWcOzYMTZt2kTVqlV1x729vXnjjTf48MMPWb58OR9//LERoxRCCFGUjsfdYuGWs9mOq1LSWbjlrGz1IYQQosQr0YWkRqNh9erVdOrUSa+IfKpmzZq8++67rF69mrfffpuQkBCGDx9OdHQ0dnZ2bNu2LVt/UVFRxMTEcPXqVWxsbKhbty5jx47Fw8MDeDIUdsqUKezcuZMTJ07g7OxMly5dGDRokK6fDRs2sGzZMm7evEnDhg2pXLmy3vPcv3+fefPmsW/fPlQqFTVr1mTYsGHUr1+/EN4lIYSpSM9QGzuEYkmt0ZKWnkV6hpqsLA0ajZa1u+NzPWfdnvPU9HSRLT+KgK2NjAgSQghTVKILyUuXLnH//n3q1q373DZBQUFs2rQJjebJ8rtbtmxh1apVpKamUqpUKb220dHRLF++nOnTp+Pr68vly5f5+uuviYiIYNGiRbp206dPZ8yYMUyaNIkff/yROXPmUL9+ferVq8fOnTuZOHEiX331FQ0bNmT37t3MmTMHNzc34Ml8zrCwMDIzM5k5cyYuLi5ER0fTp08f1q1bR61atfL1XlhZmfZ02afLEOd1OWJRshXXfAmL2GfsEMT/p0pJZ+Dc340dRokQPSbE2CFkU1w/Y0ThkHwRhjCnfCnRheSDBw8AUCqVz23z9LF79+4BEBoaSrVq1XJs6+HhwfTp02nRogUAlSpVonXr1vzyyy967Tp06MC7774LQP/+/Vm+fDknTpygXr16rF69mjZt2tCtWzcAPv74Y06ePElsbCwABw8e5O+//2bHjh34+voCMGHCBM6cOcPy5cuZN2+ewe+DQmGBUulo8HnG4Oxsb+wQhBmRfBHC/Jny9Uk+Y4QhJF+EIcwhX0p0Ifm0SExJSXlum6fF5tPtNzw9PZ/bNjg4mFOnTjFv3jwuXbrEpUuXuHDhAhUqVNBr5+3trfezk5MTmZmZAMTHx9O2bVu9xwMCAnSFZHx8PE5OTroiEsDCwoLXX3+dgwcP5vp6n0ej0ZKc/Dhf5xYVS0sFzs72JCenolbLZr4id8U1X6JGtjB2CMWSwtICZyd7klNS0ai1xF1WMWv9yReeN6JLHfw8nv+HSFEwVKpHxg4hm+L6GSMKh+SLMISx88XZ2T7Pd0NLdCHp4eGBq6srf/75J2+99VaObY4ePYqrqysWFk/mwdjZ2T23v6VLl7Jw4UI6duxIUFAQvXr1Yu/evfz444967WxsbLKd++w2I0+H0T5lbW2dY7t/n29llf9/zqws8/hgU6s1ZhOrML7ili+WMh+vUFhZKrCztSL1sYIsrYbqHkqUTrZ6q7X+m4uTLdU9lDJHsgiY8v/Dxe0zRhQuyRdhCHPIF9MffFuILC0t6dWrF5s2bSIhISHb4+fPn2fr1q10794dheLFb9XixYsZOHAg48ePp3PnztSpU4d//vnnhXtRPqtGjRqcOHFC79iZM2d03/v5+ZGSkkJ8/P8WgtBqtRw/fvy5Q26FEELknUJhQWiIT65tuob4SBEphBCiRCvRhSRAWFgYTZs2pVu3bqxdu5bExEQSExNZu3Yt3bt3p0GDBvTr1y9Pfbm5uXHo0CEuXLjAxYsXmTNnDrt27SIjIyPP8Xz88cfs3r2bZcuW8c8//7B69Wp+/fVX3eONGzemRo0afP755xw9epSEhAQmTpxIfHw8H330kcGvXwghRHaBfuUZ2NEfpZOt3nEXJ1vZ+kMIIYSghA9tBVAoFMybN4+tW7eyYcMG5syZg1arxcfHhxEjRvD+++/rhrW+yIwZM5g4cSLvvfcejo6O1K5dmwkTJjB+/HiuXbtGxYoVX9hH8+bN+eabb4iMjGTevHnUqVOHsLAwdu7cCTy5i7pixQqmT5/OoEGDyMjIwN/fn5UrV1KnTp2XeSuEEEI8I9CvPAE+rsQn3ef+o3TKONri615G7kQKIYQQgIXWkHGXolhSqzXcu2d6ixk8y8pKgVLpiEr1yOTHiwvjk3wRhpB8EYaSnBGGkHwRhjB2vri4OOZ5sZ0SP7RVCCGEEEIIIYRhpJAUQgghhBBCCGEQkyskt2/fzocffkidOnUICAjgvffeY/369UX2/HPmzMHPz49Vq1bl63yVSsXGjRsLNKYePXoQHh5eoH0KIYQQQgghRH6Z1GI7mzZtYsqUKYwePZrAwEC0Wi2HDh1i8uTJ3Llzh0GDBhXq82s0GrZu3UqVKlXYsGFDvlZBnTFjBleuXOGDDz4osLgiIyOxtLQssP6EEKKk0mi02RbPEUIIIYThTKqQXLduHe+99x7vv/++7ljVqlW5efMm0dHRhV5IHjx4kBs3brBo0SIGDBjAn3/+Sb169QzqozDWLipTpkyB9ymEECXN8bhbrNtzHlVKuu6Y0smW7q38eCuoihEjE0IIIcyPSQ1tVSgU/PXXXzx48EDv+Mcff8yGDRt0P1+7do1hw4YRFBTEq6++StOmTZk5cyYazZOVjWJiYnjzzTd1//X396dTp04cP3481+ePiYnB19eX4OBg3Nzcsg2pfVG/4eHhbNmyhaNHj+Ln5weAWq1m5cqVtGrVitdee41WrVrx/fff6/o8cuQINWvW5MCBA7zzzjv4+/vTunVr9uzZo2vz76Gtf/31Fz179iQwMJD69eszatQoVCqVIW+1EEKUKMfjbrFwy1m9IhJAlZJO5KbT/HH6mpEiE0IIIcyTSd2R7Nu3L8OGDaNp06bUr1+f119/nQYNGvDaa6/h7Oysa/fpp5/i6urKd999h6OjI3v37mXatGkEBAQQEhICwPXr11m/fj0zZ87E0dGR8ePHEx4ezq5du3LcF/L+/fvs3buX/v37Y2Fhwdtvv83q1asZPXo0Li4uuna59Tt69GjS0tK4ceMGkZGRAERERLBt2za+/vprXnvtNX7//XemTJlCeno6vXr1Ap4UmzNnzmT06NG4ubkxe/ZsvvzyS37//XccHR314jx9+jQ9evSgc+fOjBs3jtu3bzNx4kT69OnDxo0bZQisEM9Iz1DLUusCjUbL2t3xubZZuvUMU/s1QKORHbFKOlsbuY4KIURemFQh2bp1a1555RWio6M5dOgQBw4cAMDLy4upU6cSGBhIWloa7777Lm+//TZubm4A9OrVi6ioKOLi4nSFZGZmJhMmTKBGjRoA9O7dm4EDB3L79m3Kly+f7bl37txJRkYGbdu2BaBt27asWLGCmJgY+vbtq2v3on7t7OywtrbG1dWVhw8f8v333xMeHk67du10r+XKlSssXbpUbw7mZ599RlBQEAADBgzg119/JT4+noCAAL04V6xYgZ+fH19//TUA3t7ezJ49m3fffZeDBw/SrFmzfL33VlYmdXM6m6f72eR1XxtRsj3Nk34z9hs5EmEu7j5I45NZvxk7DGECoseEvLCNXJOEISRfhCHMKV9MqpAEqFOnDnXq1EGj0RAbG8uBAwdYs2YN/fr1Y/fu3ZQtW5bu3bvzyy+/cPr0aRITE4mLi+POnTu6oa1PeXt76753cnICnhSCOdm8eTOvvvoqXl5eAPj7++Pl5cUPP/xAnz599O5i5rXfixcvkpmZSWBgoN7xN954g1WrVnH37l3dsapVq+q+L1Wq1HP7jI+Pp1GjRnrHqlevjpOTE3FxcfkqJBUKC5RKxxc3NAHOzvbGDkEIIUQxZsj1UK5JwhCSL8IQ5pAvJlNI3rhxgyVLlvDJJ5/wyiuvoFAoqFmzJjVr1iQkJIR33nmHP//8k6ZNm9K9e3fS0tJo3bo1HTt2pFatWnTr1i1bnzY2NtmO5bQYTmxsLOfOncPCwoKaNWvqjms0GrRaLX/88Yde8ZbXfp+38M7TgtfK6n9v/8v2qdVqsba2zvGxF9FotCQnP87XuUXF0lKBs7M9ycmpqNUyVFHk7mm+LB8VjEYtQxVLurjLKmatP/nCdl+EBuBbuUyhxyNMm0r16IVt5JokDCH5Igxh7HxxdrbP891QkykkbWxs2LhxI25ubnz88cd6jz2dH1muXDkOHjzI33//zaFDhyhXrhzwZH7j3bt3871i6qZNm7C2tiY6Olp3NxDg0aNH9OjRgw0bNmS7C/g8/75zaW1tzfHjx3VDYQGOHTuGq6srpUuXNjhWPz+/bIsGxcbG8vDhQ707pYYyl3lkarXGbGIVxmdtqSBLK/lS0lX3UKJ0ss220M6zypWx51UvF5kjKQy6xsg1SRhC8kUYwhzyxWQKSRcXF/r27cu8efN49OgRrVu3plSpUly4cIFFixbpFt85ffo0ANu3b6dVq1Zcv36d2bNnk5mZSUZGhsHPm5GRwY4dO2jVqhV169bN9vg777zDjh07uH37dp76c3Bw4NatWyQlJeHu7k7nzp2ZP38+ZcqU4bXXXuPgwYOsW7eO4cOH57joz4v07t2b0NBQJk2aRGhoKHfu3GHSpEnUrFlTN8dSCCHE/ygUFoSG+LBwy9nntun3rj8KhYUUkkIIIUQemUwhCU8WnHk6L3Ht2rWkpaVRsWJF3n77bT755BMAatWqxahRo1i5ciVz586lQoUKtGnTBjc3N86cOWPwc+7fv5/79+/nODQWnizks2XLFjZu3Mgrr7zywv46dOjA7t27eeedd9i1axejRo1CqVQya9Ys7ty5g5eXF2PHjuXDDz80OFaA2rVrs2zZMubOnUuHDh0oVaoUISEhfP755/ke2iqEEMVdoF95Bnb0z7aPpIuTLd1a+dGwVsU8DWkUQgghxBMW2vyOBxXFhlqt4d490/4FyspKgVLpiEr1yORv8wvjk3wRz6PRaIlPus/9R+mUcbTF170MNjaWki/CIPIZIwwh+SIMYex8cXFxNL85kkIIIURhUygsqO6pNHYYQgghhNkz/Q1KhBBCCCGEEEKYFCkkhRBCCCGEEEIYpEQUkllZWaxatYpOnToREBBAgwYNCAsL4/Dhw7o2fn5+xMTEFPhzBwcHExkZmef2KpWKjRs36n5+/Pgxa9euLfC4hBDC2DQaLbGJKg6fu0FsokpWTBVCCCHMSLGfI5menk7v3r25fv06Q4YMISAggLS0NDZv3kzv3r2ZMWMG7dq1M3aYOjNmzODKlSt88MEHAKxYsYKYmJjnriorhBDm6HjcrWwrqCqdbAkN8SHQr7wRIxNCCCFEXhT7QnLevHnExcWxc+dO3NzcdMdHjx7Nw4cPmTx5MsHBwUaMUN+/F9GVRXWFEMXN8bhbOe7pqEpJZ+GWswzs6C/FpBBCCGHiinUhmZmZyebNm+nUqZNeEfnUZ599RteuXbGzswPg0qVL9OrVi+PHj1OmTBm6d++u279So9EQFRVFTEwMV69excbGhrp16zJ27Fg8PDyAJ8NjBw4cyJYtW8jMzGTNmjXZnvOvv/5izpw5/P3331hZWREcHMzIkSNRKpWEh4ezZcsWXV+DBg1iwYIFup/37t1L5cqV2bx5M8uWLePq1atUqlSJLl260KNHDxSKEjFSWRQD6RnqQu1frdGSlp5FeoZallo3MRqNlrW743Nts27PeWp6uqBQWBRJTJIvpsXWxtLYIQghhMiDYl1IJiUlcf/+ferWrZvj4xUqVKBChQq6n9esWcO4ceOYNGkSO3bsYPbs2dSqVYugoCCio6NZvnw506dPx9fXl8uXL/P1118TERHBokWLdH2sW7eOqKgo1Go1Xl5ees93+vRpevToQefOnRk3bhy3b99m4sSJ9OnTh40bNzJ69GjS0tK4ceMGkZGRODg48PjxY3766Sc2bdqEi4sLGzZsYPbs2YwdO5ZatWpx7tw5Jk2axM2bNxk5cmS+3ysrK9MuQp/uZ5PXfW2EaQuL2GfsEIQJU6WkM3Du78YOQxhJ9JgQY4fwQnJNEoaQfBGGMKd8KdaF5IMHDwAoXbp0ntqHhobSoUMHAAYMGMCKFSs4e/YsQUFBeHh4MH36dFq0aAFApUqVaN26Nb/88oteH++++y6vvfZajv2vWLECPz8/vv76awC8vb2ZPXs27777LgcPHqRZs2bY2dlhbW2Nq6srAA4ODlhaWup+XrRoEZ9++ilt27YFwN3dnYcPHzJhwgSGDh2Kra2tAe/QEwqFBUqlo8HnGYOzs72xQxBCCFGIzOV6BHJNEoaRfBGGMId8KdaFpIuLCwD379/PU/t/30F0dnYmPf3JQhDBwcGcOnWKefPmcenSJS5dusSFCxf07mgCeHp6Prf/+Ph4GjVqpHesevXqODk5ERcXR7NmzXKN7969e9y4cYPZs2czb9483XGNRkN6ejpXrlzB29s7Ly9Vj0ajJTn5scHnFSVLSwXOzvYkJ6eiVsvQM3MXNbJFofavsLTA2cme5JRUNGqZZ2xK4i6rmLX+5AvbjehSBz8PZeEHhOSLqVGpHhk7hBeSa5IwhOSLMISx88XZ2T7Pd0OLdSHp7u5OuXLlOHHiBG3atMn2eEJCAlOmTGHUqFEAWFpmn5fxdLGbpUuXsnDhQjp27EhQUBC9evVi7969/Pjjj3rtn863zMnzFs7RarVYW1u/8PVoNE+SadSoUTRs2DDb4znNA80rc5kXpFZrzCZW8XyWhTz3zcpSgZ2tFamPFWRpJV9MSXUPJUonW73VWv/NxcmW6h7KIpsjKfliWszpM16uScIQki/CEOaQL6Y/+PYlKBQK3n//fWJiYrh+/Xq2x5ctW8aZM2eoVKnSC/tavHgxAwcOZPz48XTu3Jk6derwzz//GLSqqp+fH8ePH9c7Fhsby8OHD3V3Ei0s9H9xevbnsmXL4uLiQlJSEp6enrqvv//+m7lz5+Y5DiGEMBaFwoLQEJ9c23QN8SmyIlIIIYQQ+VOsC0mA/v374+XlRWhoKFu3buXy5cucPn2aUaNGsXXrViZNmoSDg8ML+3Fzc+PQoUNcuHCBixcvMmfOHHbt2kVGRkaeY+nduzdxcXFMmjSJhIQEjhw5wogRI6hZsyZBQUHAkzmRt27dIikpSffzgwcPuHTpEllZWfTr14/Vq1ezZs0aLl++zO7duxk/fjx2dnbY2Njk700SQogiFOhXnoEd/VE66c/pdnGyla0/hBBCCDNRrIe2Atjb27NmzRpWrFhBVFQU165dw87Ojpo1a7J69Wpef/31PPUzY8YMJk6cyHvvvYejoyO1a9dmwoQJjB8/nmvXrlGxYsUX9lG7dm2WLVvG3Llz6dChA6VKlSIkJITPP/9cN7S1Q4cO7N69m3feeYddu3bx1ltv8cMPP9C+fXvWrFlDWFgYtra2rF69moiICMqVK8eHH37IkCFDXup9EkKIohToV54AH1fik+5z/1E6ZRxt8XUvI3cihRBCCDNhoZUd70s8tVrDvXumvbiBlZUCpdIRleqRyY8XF8Yn+SIMIfkiDCU5Iwwh+SIMYex8cXFxzPNiO8V+aKsQQgghhBBCiIIlhaQQQgghhBBCCIMUWSEZHByMn5+f7qt69erUrVuX7t278+eff750/8ePH+fYsWO5tvnPf/5Djx49qFu3LrVr16Zdu3YsXbqUzMxMXZvHjx+zdu1ag57bz8+PmJiYfMWdF+Hh4fTo0aPQ+hdCiIKi0WiJTVRx+NwNYhNVaDQye0IIIYQojop0sZ2wsDDCwsKAJ3sn3r9/n9mzZ9O3b19+/vnnPC1Y8zyhoaFMmzbtuYvnHDp0iE8//ZRhw4Yxfvx4rKysOHHiBNOmTePSpUtMmzYNgBUrVhATE0O3bt3yHUtBGz16NGq12thhCCFEro7H3WLdnvN6e0QqnWwJDfGRlViFEEKIYqZIh7Y6ODjg6uqKq6sr5cuXx9fXlwkTJpCWlsbu3bsL9bk3bNhAkyZN6NOnD97e3nh6etKxY0eGDRvG1q1bSU5OBjBoX8ii4uTkRJkyZYwdhhBCPNfxuFss3HJWr4gEUKWks3DLWY7H3TJSZEIIIYQoDEbf/sPK6kkIT/dATEtLY/HixezYsYNbt25RtWpVBgwYQKtWrQCIiYnh22+/pVmzZmzZsoX69euzd+9eAEaNGsXRo0eJiIjI9jwWFhbExsZy8+ZNKlSooDveoUMH3njjDRwcHIiMjGTBggXAk+Gqe/fupXLlyi/9Gnv06EGlSpX04vr3scTERCIiIjh69ChWVlY0atSI0aNHU7ZsWcLDw7l69SqrV68GICEhgYiICI4dO4ajoyP169cnPDwcV1fXl45ViMKSnlF0d9XVGi1p6VmkZ6hlhbwioNFoWbs7Ptc26/acp6ani0lu7yH58vJsbSyNHYIQQogiZtRC8ubNm0ydOhUHBweaNWsGwPDhwzl37hzjx4/H09OTnTt3MnToUBYsWEBISAgAly9f5tatW2zdupW0tDQmTJhA48aN+eqrr+jUqVOOz/XRRx/Rq1cvgoODef3116lXrx5vvPEGdevWxdvbG3gy9Pbx48f89NNPbNq0CRcXlyJ5H5KTk+nWrRt+fn6sWrUKhULB2LFj+eyzz3TF41M3b94kNDSUdu3aER4eTmpqKpGRkXTu3JmdO3fi4OCQrxisrEx73aWnyxDndTliYXrCIvYZOwRhRKqUdAbO/d3YYYhCEj0mxNghFCm5JglDSL4IQ5hTvhRpIblkyRJWrFgBQFZWFhkZGXh7ezN37lwqVqxIQkICe/fuZfHixTRv3hyAwYMHExsby+LFi3WFJMCAAQNwd3fX69/JyQknJ6ccn7tu3brExMTw3XffceDAAQ4fPgxA+fLlGTduHCEhITg6OuLg4IClpWWR3t376aefePToEbNnz6Z06dIATJ48mR9//JGMjAy9tt9//z2vvPIKY8aM0R2bO3cuDRo04JdffnluIZ0bhcICpdLx5V5EEXF2tjd2CEIIIf7FXK4hBU2uScIQki/CEOaQL0VaSHbp0kW3+qhCoaBMmTJ6hV9cXBwAgYGBeufVq1eP2bNn6x3z8vIy+PmrVavGlClTgCfDQ//zn/+wZs0ahg4dSkxMDH5+fgb3WRDi4+Px8vLSFZEA1atXp3r16tnanjt3jvPnzxMQEKB3PD09nYSEhHw9v0ajJTn5cb7OLSqWlgqcne1JTk5FrZahZ+YoamSLInsuhaUFzk72JKekolGb3rzn4ibusopZ60++sN2ILnXw81AWfkAGknx5eSrVI2OHUKTkmiQMIfkiDGHsfHF2ts/z3dAiLSRLly6Np6enwedptVrdXMqn7Ozs8nz+48ePmT17Nu+99x41atQAwNvbG29vb9q3b0+LFi04ePBgkRaSWVlZuu///dpyo9FoaNCgAePGjcv22PPuxuYtHvP4YFOrNWYTq9BnWYRz46wsFdjZWpH6WEGWVvKlsFX3UKJ0ss220M6zXJxsqe6hNMk5kpIvL6+kfi7LNUkYQvJFGMIc8sWkBt8+LeSOHz+ud/zYsWNUq1Yt3/3a2dmxY8cO1q9fn+0xR0dHLC0tKVu2LPBkUZ6CZm1tzcOHD3U/azQakpKSdD9Xq1aNf/75h5SUFN2xv//+m6CgIG7cuKHXl4+PDwkJCbi5ueHp6YmnpyelS5dm6tSpxMfnvtiFEEIUBoXCgtAQn1zbdA3xMckiUgghhBD5Y/RVW5/l7e1NixYtmDBhAhYWFnh6evLjjz+yd+9e5s6dm+u5Dg4OJCQkoFKpUCr1h04pFApGjBihm1f43nvv4eLiwuXLl1mxYgVubm60bt1a18+DBw+4dOkSlStXRqvV8uDBA0qXLq1bWTYn8fHx/P67/kISZcqUoVatWtSpU4fvvvuO33//HU9PT1auXKnbbgSgXbt2LFq0iC+++ILPPvuMrKwsxo8fj6+vL6+88open6GhoWzYsIERI0YwYMAAAKZPn05cXBy+vr65v8FCCFFIAv3KM7Cjf7Z9JF2cbOkq+0gKIYQQxY5JFZIAs2fPZvbs2YwePZrk5GR8fX2JjIzkzTffzPW8sLAwli1bRkJCAosXL872+AcffICrqyurVq2iX79+PHr0iHLlytGyZUtmzJihGyr71ltv8cMPP9C+fXvWrFlDWloaPXv2JDo6mvr16z/3+b/77ju+++47vWNvvPEGq1evJiwsjMuXLzN06FBsbGx4//33adu2rW7PSnt7e5YvX860adPo0qULdnZ2NG/enC+//DLb87i7u7NmzRq++eYbunbtiqWlJXXr1iU6OrrIVpkVQoicBPqVJ8DHlfik+9x/lE4ZR1t83cvInUghhBCiGLLQPq1mRImlVmu4d8+0F0qwslKgVDqiUj0y+fHiwvgkX4QhJF+EoSRnhCEkX4QhjJ0vLi6OeV5sx6TmSAohhBBCCCGEMH1SSAohhBBCCCGEMIgUkv9fjx498PPz0/vy9/enefPmTJw4kdTU1Dz1Y+h+lIa2j4yMJDg4OM/thRBCCCGEEKKgmdxiO8b09ttvM3r0aN3Pjx8/5uDBg0ybNg2NRsP48eML/DnbtGlDkyZNCrxfIYQoSBqNVhbREUIIIYSOFJLPsLOzw9XVVe+Yp6cnZ8+e5aeffiqUQtLOzk63YqwQQpii43G3sm3roXSyJVS29RBCCCFKLBnamge2trZYWT2pua9du8awYcMICgri1VdfpWnTpsycORONJudVlTIyMpg5cyZNmjQhICCADz/8kIMHD+oe//fQ1nv37jFs2DBef/116tevz6xZs+jZsyeRkZF6/S5dupSmTZtSq1YtevTowT///FPwL1wIUeIdj7vFwi1n9YpIAFVKOgu3nOV43C0jRSaEEEIIY5I7krnIysri4MGDbNu2jS5dugDw6aef4urqynfffYejoyN79+5l2rRpBAQEEBISkq2PUaNGkZCQwKxZs6hQoQL79++nf//+LFiwgObNm+u11Wg0fPLJJ6jVapYtW4a1tTXTpk3j2LFj1KtXT9fu6tWrnDhxgqVLl5KRkcHIkSMZPXo0a9euLdT3Q5i/9Ay1sUMoEmqNlrT0LNIz1LLU+kvQaLSs3R2fa5t1e85T09PFrIe5Sr4UHVsbS2OHIIQQooBIIfmMHTt28Ouvv+p+TktLo2LFivTp04f+/fuTlpbGu+++y9tvv42bmxsAvXr1Iioqiri4uGyFZGJiIjt37mTr1q3UqFEDgN69exMbG8vy5cuzFZJHjx7l9OnT/Pzzz1StWhWAuXPnZltcx9ramlmzZlGqVCkAunTpwpw5c17qtVtZmfbN6af72eR1XxuRs7CIfcYOQRQzqpR0Bs793dhhCDMRPSb7H1zNkVyThCEkX4QhzClfpJB8RnBwMCNGjECr1XL69GmmTJlCw4YN6d+/P1ZWVlhZWdG9e3d++eUXTp8+TWJiInFxcdy5cyfHoa3nzp0DIDQ0VO94ZmYmzs7OObYvXbq0rogEKFeuHFWqVNFrV7ZsWV0RCeDs7ExaWlq+X7dCYYFS6Zjv84uSs7O9sUMQQgiRT+ZyrckruSYJQ0i+CEOYQ75IIfkMR0dHPD09AfDy8qJ8+fL07t0bS0tLxo8fz+PHj+nevTtpaWm0bt2ajh07UqtWLbp165Zjf1qtFoC1a9fi6Kh/8VQosv+VwdLS8rlzLf/driBpNFqSkx8XaJ8FzdJSgbOzPcnJqajVMvQsv6JGtjB2CEVCYWmBs5M9ySmpaNRaY4djtuIuq5i1/uQL243oUgc/D2XhB1RIJF+Kjkr1yNghFAi5JglDSL4IQxg7X5yd7fN8N1QKyVw0aNCA3r17s3z5coKDg0lLS+Pvv//m0KFDlCtXDoD79+9z9+5dXdH4LB8fHwBu375NzZo1dcfnzJmDQqFg6NCheu2rV69OSkoKCQkJeHt7A6BSqUhMTCysl6hjLvOC1GqN2cRqiizNeB6bIawsFdjZWpH6WEGWVvIlv6p7KFE62WZbaOdZLk62VPdQmvUcScmXolPcPr/lmiQMIfkiDGEO+WL6g2+NbOjQoXh5eTF+/HiUyid/cd++fTtXr17l2LFjDBgwgMzMTDIyMrKd6+PjQ4sWLRg3bhz79u0jKSmJqKgolixZgoeHR7b29evXp3bt2owcOZKTJ08SGxvLiBEjSE1NxcLCfH9JE0KYJ4XCgtAQn1zbdA3xMesiUgghhBD5I4XkC9ja2jJp0iSuXbvGr7/+yqhRo4iOjubtt99m1KhR1KtXj3feeYczZ87keP6cOXN46623GDt2LG3atGHr1q1MmTKFjh075tg+MjKSV155hV69evHRRx9Rq1YtKlasiLW1dWG+TCGEyFGgX3kGdvRH6WSrd9zFyZaBHf1lH0khhBCihLLQ5jQmUxjFvXv3OHXqFI0bN9YVjhkZGdSvX59x48bRoUOHQnletVrDvXumPW/FykqBUumISvXI5G/zC+OTfCl4Go2W+KT73H+UThlHW3zdyxSbO5GSL8JQkjPCEJIvwhDGzhcXF0eZI2mOrKysGDZsGF26dKFr165kZmayfPlybGxsaNq0qbHDE0KUYAqFBdU9zXdBHSGEEEIULBnaakKcnZ1ZvHgxJ0+epEOHDnTu3Jk7d+4QHR2Ni4uLscMTQgghhBBCCEDuSJqcBg0asH79emOHIYQQQgghhBDPJYXkc/To0YOjR4/m+FhYWBhffvllEUckhCgKxXkuoBBCCCFEQZFCMhdvv/02o0ePznbc3t7eCNEIIQrb8bhbrNtzXm/fRKWTLaEhPrI6qRBCCCHEM6SQzIWdnR2urq7GDkMIUQSOx91i4Zaz2Y6rUtJZuOWsbHUhhBBCCPEMKSTzqUePHnh5eREbG8ulS5cYO3Ys7du3Z/PmzSxbtoyrV69SqVIlunTpQo8ePVAonqxrtHXrVqKiorh8+TJlypShdevWfPHFF9jY2BATE8O3337Lp59+yrfffsv169fx9fVl9OjRBAYGApCWlsbixYvZsWMHt27domrVqgwYMIBWrVoZ8+0wO+kZamOHIAqRWqMlLT2L9Ax1npbO1mi0rN0dn2ubdXvOU9PTRYa5FkOG5suzbG0sCykqIYQQwrRJIfkSNm7cyMyZM/Hz88PV1ZUNGzYwe/Zsxo4dS61atTh37hyTJk3i5s2bjBw5ktjYWMaMGcOsWbOoVasWCQkJfP755yiVSgYMGADA9evXWb9+PTNnzsTR0ZHx48cTHh7Orl27sLCwYPjw4Zw7d47x48fj6enJzp07GTp0KAsWLCAkJCTfr8XKyrQX8H26n01e97V5kbCIfQXSjyg5VCnpDJz7u7HDECYmekz+P3eF+Sroa5Io3iRfhCHMKV+kkMzFjh07+PXXX/WOBQYGsmzZMgBq1KhBu3btdI8tWrSITz/9lLZt2wLg7u7Ow4cPmTBhAkOHDuXKlStYWFhQqVIlKlasSMWKFVm+fDmlSpXS9ZGZmcmECROoUaMGAL1792bgwIHcvn2blJQU9u7dy+LFi2nevDkAgwcPJjY2lsWLF+e7kFQoLFAqHfN1blFzdpb5qUII02Eun52icMg1SRhC8kUYwhzyRQrJXAQHBzNixAi9Y3Z2drrvPT09dd/fu3ePGzduMHv2bObNm6c7rtFoSE9P58qVKzRp0oSAgADef/99KleuTKNGjWjZsiX+/v56z+Ht7a373snJCXhSYMbFxQHohrk+Va9ePWbPnp3v16nRaElOfpzv84uCpaUCZ2d7kpNTUasNG3qWk6iRLQogKmGqFJYWODvZk5ySikatfWH7uMsqZq0/+cJ2I7rUwc9DWQARClNiaL48S6V6VEhRCVNW0NckUbxJvghDGDtfnJ3t83w3VArJXDg6OuoVi//2bFGp0Tz5hx41ahQNGzbM1tbNzQ0bGxuio6M5d+4cBw8e5ODBg/Tv358OHTowbdo0XVsbG5ts52u1z//lRqvVYmX1cv+Uhs4LMha1WlMgsVrKPLdizcpSgZ2tFamPFWRpX5wv1T2UKJ1s9VZr/TcXJ1uqeyhljmQxZGi+PMtcPjtF4Sioa5IoGSRfhCHMIV9Mf/CtmShbtiwuLi4kJSXh6emp+/r777+ZO3cuAAcOHGDBggXUrFmTjz/+mOjoaIYMGcJPP/2Up+fw8/MD4Pjx43rHjx07RrVq1Qr09QhRkigUFoSG+OTapmuIjxSRQgghhBD/n9yRLCAWFhb069ePOXPmULFiRZo2bUpcXBzjx4+nZcuW2NjYYG1tzcKFCylVqhQtW7bkwYMH/PbbbwQEBOTpOby9vWnRogUTJkzAwsICT09PfvzxR/bu3asrVoUQ+RPoV56BHf2z7SPp4mRLV9lHUgghhBBCjxSSBSgsLAxbW1tWr15NREQE5cqV48MPP2TIkCEANGzYkClTprBixQrmzJmDnZ0dzZo1Izw8PM/PMXv2bGbPns3o0aNJTk7G19eXyMhI3nzzzcJ6WUKUGIF+5QnwcSU+6T73H6VTxtEWX/cycidSCCGEEOJfLLS5Tb4TJYJareHePdNeMMLKSoFS6YhK9cjkx4sL45N8EYaQfBGGkpwRhpB8EYYwdr64uDjmebEdmSMphBBCCCGEEMIgUkgKIYQQQgghhDCIUQrJ4OBggoODefjwYbbHwsPD6dGjR5HG8+DBA7755htatWrFa6+9RqNGjejfvz+HDx9+4blHjhzBz8+PK1euFEGkQoj80mi0xCaqOHzuBrGJKjQaGdUvhBBCCJFfRlts5+rVq8yYMYOJEycaKwQArl+/To8ePbC3t+fzzz/n1VdfRaVSsXXrVsLCwhg+fDh9+/Y1aoxCiJdzPO5WttVYlU62hMpqrEIIIYQQ+WK0QtLd3Z0NGzbQunVrGjZsaKwwGDlyJKVKleL777/H3t4egEqVKuHv70+VKlWYPHkydevWpW7dukaLUQiRf8fjbrFwy9lsx1Up6SzccpaBHf2lmBRCCCGEMJDRCsn27dtz4sQJRo8ezY4dOyhVqlSO7VJSUpgxYwa7d+8mMzOTV199lS+++ILXXnuNffv2MWDAAP744w9cXFwA6NChA1lZWezcuRN4Mmw1KCiI6OhoXn/9db2+Y2NjOXr0KJGRkboi8lmhoaGsWrWK1atXU7duXWJiYvj2229p1qwZW7ZsoX79+nz00Ud652i1WpYtW8b69eu5c+cOXl5e9OnTh/bt2+va7NmzhyVLlnD+/HnUajU+Pj4MGzaMJk2aANCjRw9q167NvXv32LVrFxqNRrd/5NP3afny5Xz//ffcuHGD8uXL89577zFgwAAsLMx/m4L0DHW2Y2qNlrT0LNIz1LLimXihp/mSmpbF2t3xubZdt+c8NT1dZIuPEqygP19sbSwLICohhBDCtBmtkLSwsGDKlCm0a9eO6dOnM2nSpGxttFot/fr1w87OjiVLllCqVCm2bdtG165d+eGHH2jYsCG2trYcPnyYNm3acO/ePeLi4tBoNNy9e5eyZcty8OBBSpcuneMdxb/++guAwMDA58bYoEEDfvvtN92xy5cvc+vWLbZu3UpaWhr37t3TO2fOnDns3LmTsWPHUrVqVf7880/Gjx9PSkoK3bp14+zZswwePJgvv/ySli1b8vDhQ7755htGjhzJgQMHsLGxAWDlypWEhYWxadMmEhIS+Pzzz6lSpQqDBg1i3759LFmyhDlz5lClShVOnjzJyJEjqVy5Mu+++26+/j2srExn3aWwiH3GDkGUIKqUdAbO/d3YYYhiJHpMiLFDEIXs6dL4eV0iX5Rski/CEOaUL0YrJOHJENIvv/ySsWPH0qpVKxo3bqz3+OHDhzl58iSHDx+mTJkyAAwfPpwTJ04QHR1NREQEQUFBHDx4kDZt2vDHH39Qs2ZNbt++zZEjR2jTpg2//fYbzZs3R6HI/o/x4MEDAF3fOVEqldmKxQEDBuDu7g48WWznqcePH7Ny5Upmz55N8+bNAfDw8ODq1assX76cbt26YWlpyddff01oaKjuvJ49e9KvXz/u3r2Lm5sbANWqVWP48OEAeHl50ahRI13he/nyZWxsbKhUqRIVK1akYsWKlC9fnooVK77oLc+RQmGBUumYr3OFEELok8/TksPZOftoJiGeR/JFGMIc8sWohSRA586d+fXXXxkzZoxuOOpTf//9N1qtlhYtWugdz8jIID39yaIZwcHBLFq0CIBDhw4RFBTE1atXOXz4MK1bt+Y///kPkydPzvG5lUol8GT47POKyQcPHuiGzT7l5eWVY9sLFy6Qnp7O559/rle4ZmVlkZGRQVpaGjVq1KB06dIsXbqUixcvkpiYSGxsLABq9f+GdFatWlWvbycnJ5KTk4Enw4I3b95Mq1atqFatGg0bNqRVq1b5LiQ1Gi3JyY/zdW5hiBrZItsxhaUFzk72JKekolHLapsid0/z5ejZa8xc99cL24/oUgc/D2URRCZMUUF/vqhUjwogKmHKLC0VODvbk5ycilot0y1E7iRfhCGMnS/OzvZ5vhtq9EISYPLkybRr145p06bpHddoNJQqVYqYmJhs5zwdAtq8eXPGjh1LQkIC//d//8eUKVN0dwBPnTpFamoqjRo1yvF5n86ZPHbsGCEhOQ9F+vPPP7MNi7Wzs8uxrVb75BeQuXPnZisEn8Z89OhR+vTpQ/PmzQkMDKRdu3akpqYycODAHF9fTlxcXNi2bRt//fUXhw4d4uDBg0RHRzN48GAGDRr03PNyY0rzDi1zmKtmZanAztaK1McKsrSmE6swTU/z5VUvF5ROtnqrtf6bi5Mt1T2UMkeyBCvozxdT+jwVhUut1si/t8gzyRdhCHPIF5MYfFuxYkXCw8PZtGkTx44d0x339fXl4cOHZGZm4unpqfuKiopi7969AJQvXx5/f3++//577t69S2BgIEFBQfzzzz9s2LCBhg0b5riQDoC3tzfNmjVj3rx5PH6c/Y7c5s2bSUhIoHv37nl6HVWrVsXKyopr167pxXvgwAGWL1+OQqFgxYoV1K9fn8jISHr16kWjRo24fv068L9C9EW2b9/O999/T2BgIEOGDOGHH37ggw8+4KeffsrT+UKUFAqFBaEhPrm26RriI0WkEEIIIYSBTKKQBPjggw9o3LgxSUlJumNNmjShRo0aDBs2jMOHD5OYmMi0adOIiYnB29tb1y44OJgNGzZQp04d7OzscHd3p3Llymzbto2WLVvm+rxTpkxBq9XSpUsX9uzZw9WrV4mNjWXmzJmMHTuW4cOHZ1vt9XmcnJzo0qUL8+bNY9u2bSQlJbFp0yZmzpxJ+fJPthdwc3MjLi6OY8eOceXKFTZv3sy8efOAJ0N28yI9PZ3p06ezdetWrly5wrFjx/jzzz8JCAjI0/lClCSBfuUZ2NEfpZOt3nEXJ1vZ+kMIIYQQIp9MYmjrU0+HuD5laWnJihUrmDlzJp999hmpqal4e3uzYMECgoKCdO2Cg4OZN28eDRo00B1r2LAhmzZtyja/8t9cXV3ZsGEDq1atYu7cuSQlJeHg4EDt2rVZtmyZ3vPkxahRo1AqlcybN49bt27h5ubGkCFD6Nu3LwBDhgzhzp079O/fH3iyqM7UqVP54osvOHPmjF6B/DwffPAB9+/fZ9GiRVy/fp3SpUvTqlUrRowYYVCsQpQUgX7lCfBxJT7pPvcfpVPG0RZf9zJyJ1IIIYQQIp8stHkdTymKLbVaw717pr04hJWVAqXSEZXqkcmPFxfGJ/kiDCH5IgwlOSMMIfkiDGHsfHFxcczzYjsmM7RVCCGEEEIIIYR5kEJSCCGEEEIIIYRBpJAUQgghhBBCCGEQKSSFEEIIIYQQQhhECkkhhBBCCCGEEAaRQlIIIYQQQgghhEGkkBRCCCGEEEIIYRApJIUQQgghhBBCGEQKSSGEEEIIIYQQBpFCUgghhBBCCCGEQaSQFEIIIYQQQghhECkkhRBCCCGEEEIYRApJIYQQQgghhBAGkUJSCCGEEEIIIYRBpJAUQgghhBBCCGEQKSSFEEIIIYQQQhhECkkhhBBCCCGEEAaRQlIIIYQQQgghhEGkkBRCCCGEEEIIYRApJIUQQgghhBBCGEQKSSGEEEIIIYQQBpFCUgghhBBCCCGEQaSQFEIIIYQQQghhECkkhRBCCCGEEEIYRApJIYQQQgghhBAGkUJSCCGEEEIIIYRBpJAUQgghhBBCCGEQC61WqzV2EMK4tFotGo3pp4GlpQK1WmPsMISZkHwRhpB8EYaSnBGGkHwRhjBmvigUFlhYWOSprRSSQgghhBBCCCEMIkNbhRBCCCGEEEIYRApJIYQQQgghhBAGkUJSCCGEEEIIIYRBpJAUQgghhBBCCGEQKSSFEEIIIYQQQhhECkkhhBBCCCGEEAaRQlIIIYQQQgghhEGkkBRCCCGEEEIIYRApJIUQQgghhBBCGEQKSSGEEEIIIYQQBpFCUgghhBBCCCGEQaSQFEIIIYQQQghhECkkhRBCCCGEEEIYRApJYXLS09OZMGECQUFBBAQE8Pnnn3Pv3r1cz3n48CHjxo2jQYMGBAYG0r9/f5KSkoooYmFs+cmZZ3377bf4+fkVYoTClOQnX06cOEGPHj0IDAykSZMmjB49mvv37xdNwKLIaTQa5s+fT5MmTahTpw79+vXL9ZqiUqn4/PPPqVevHm+88QYTJkwgNTW1CCMWxmRovpw/f56PP/6Y+vXrExQUxJAhQ7h27VoRRiyMydB8edb27dvx8/PjypUrhRxl3kghKUzO+PHjOXjwIJGRkaxatYqLFy8yZMiQXM8ZPHgwR44cYeHChaxdu5aUlBQ+/fRTNBpNEUUtjCk/OfPU6dOnWbBgQSFHKEyJofly6dIl+vTpg5+fHz/88ANz5szh9OnTDB06tAijFkVp0aJFrFu3jkmTJrF+/Xo0Gg19+/YlIyMjx/ZDhgwhMTGRlStXMm/ePA4cOMD48eOLNmhhNIbki0qlonfv3tjZ2bF69WqioqK4d+8effv2JT093QjRi6Jm6OfLU1evXmXixIlFFGUeaYUwITdu3NBWr15d+9tvv+mOXbx4Uevr66s9ceJEjuccPnxY6+fnp42NjdUdO3/+vLZ58+bahISEQo9ZGFd+cuapR48ead966y1tz549tb6+voUdqjAB+cmX2bNna9966y2tRqPRHfvzzz+1vr6+2suXLxd6zKJopaenawMCArRr167VHXvw4IG2Vq1a2h07dmRrf+LECa2vr6/2woULumP/+c9/tH5+ftobN24USczCeAzNlx9++EEbEBCgTU1N1R27du2a1tfXV/vHH38USczCeAzNl6fUarW2a9euut9XkpKSiiLcF5I7ksKkHD9+HIAGDRrojlWpUoUKFSrw559/5njOwYMH8fX11RuaWK1aNfbv30/VqlULN2BhdPnJmaemTJmCr68v7777bqHGKExHfvKlffv2TJ8+HQsLC92xp98/ePCgEKMVxhAbG8ujR48ICgrSHXN2dqZmzZo55sixY8dwdXXF29tbd+yNN97AwsJCl2+i+DI0X4KCgli0aBF2dna6YwrFk1/Hk5OTCz9gYVSG5stTixcvJjMzk08++aQowswzK2MHIMSzbt68iVKpxNbWVu94+fLluXHjRo7nXLp0CU9PT9atW8fatWtJTk4mMDCQUaNGUaFChaIIWxhRfnIGYNeuXRw4cIAdO3awf//+wg5TmIj85MuzBcJTUVFRuLq6ytzaYuhpHri5uekdf16O3Lx5M1tbGxsbypQpw/Xr1wsvUGESDM2XypUrU7lyZb1jS5cuxc7Ojnr16hVeoMIkGJov8GQKzooVK9i0aRM3b94s9BgNIYWkKFJXrlyhZcuWz3186NCh2NjYZDtua2v73LkDDx8+5O+//0alUjFhwgQAZs2aRc+ePdm+fXu2XxiFeSmMnLl58yZjx45lxowZKJXKAotVGF9h5Mu/TZ8+nd9++40FCxZgbW2d71iFaXq6SM6/88TW1jbHO9CpqakvnVPi/7V3t0FRXWcAx/8gbIAQETTCJBirVsEqLKsCvhIhqBAYExJtrYKSWPANaURGIVBNVUgj4AssiqghRevoVBAtJpYYjTgBJdFEk6Zt0sRmFA1oBSVBXMDtB2bvsLJY1/IWfX4z+2HPPffecy/PLPvsOfecny5z4+Vuu3btYvfu3SQnJ+Pk5NQpbRQ9h7nxUl9fT3x8PPHx8fzsZz+TRFI82pydnXn33Xfb3X7ixAmTDxvfvn0bW1tbk/tYWVlx+/ZtsrOzcXBwAECr1TJp0iSOHTtGcHBwxzRedIuOjhm9Xk9CQgLBwcH4+fl1aFtF9+uMzxiDxsZGVq1aRVFREWvXriUwMPD/bq/oeQxDDnU6ndHww/ZixMbGpt2YsrOz67yGih7B3Hgx0Ov1bN68ma1bt7Jo0SIiIiI6va2i+5kbL+vWrWPQoEHMmjWry9poDkkkRZeytrY2OUzM4J///Ce1tbXodDqjX2uqq6vbHabq4uKCs7OzkkQC9OvXjz59+vSY6ZHFg+vomLl8+TJlZWWcPXuWoqIiAJqamgDQaDT8/ve/Z/r06R17EaLLdMZnDLSMfIiJieGTTz5hw4YN8gPVQ8ww5Ky6uppnnnlGKa+urjY5lNnFxYWjR48alel0Ompra+nfv3/nNlZ0O3PjBVp+lEpMTKS4uJjExEQiIyO7oqmiBzA3XgoKClCpVGg0GgCam5sBCA0NZeHChSxcuLALWt0+mWxH9CijR4/mzp07RhMUXLhwgaqqqnafHfD29uby5ctUV1crZdXV1dTU1DBw4MBOb7PoXubGjLOzMyUlJRw6dIiioiKKioqUpR+KiooICAjosraLrvcgnzE6nY4FCxZw/vx5du7cKUnkQ87d3R17e3tOnz6tlN28eZMvv/zSZIx4e3vz/fff89133yllFRUVQEu8iYebufECsGLFCo4cOUJGRoYkkY8Yc+OlpKSE4uJi5fvKunXrgJbnantCL6X0SIoexdnZmZCQEJKTk0lNTcXW1pbVq1fj4+ODl5cX0PKl7saNGzg4OKBSqQgODiY3N5ff/va3JCUlYWlpSWpqKoMGDWLy5Mndej2i8z1IzNz9A0Pfvn0B5IeHR8CDxMu2bds4c+YMGRkZDB48mKtXryrHM9QRDw+VSkV4eDjp6ek4OTnx9NNPk5aWhouLC1OnTqW5uZnr16/zxBNPYGNjg1qtZtSoUSxbtow33niD+vp6Vq1axYsvvigTvj0CzI2XwsJC3n33XVasWIGPj4/R54mhjnh4mRsvd38vMUzI89RTT9GnT59uuIK7dPf6I0Lc7ccff9QnJSXpx4wZox8zZow+Li5Of/36dWX7qVOn9MOGDdOfOnVKKauurtbHxcXpR40apddoNPqYmBhZv+sR8iAx01pBQYGsI/kIMTdepk6dqh82bJjJV3sxJX7ampqa9OvXr9ePHTtW7+XlpY+KilLWbbt48aJ+2LBh+oKCAqX+tWvX9EuXLtV7eXnpfX199atXr9Y3NDR0V/NFFzMnXl555ZV2P09ax5R4eJn7+dKa4f9TT1lH0kKv1+u7O5kVQgghhBBCCPHTIc9ICiGEEEIIIYQwiySSQgghhBBCCCHMIomkEEIIIYQQQgizSCIphBBCCCGEEMIskkgKIYQQQgghhDCLJJJCCCGEEEIIIcwiiaQQQgghOkVXrTAmK5kJIUTXk0RSCCFEl0tISMDNza3d14QJE7q7iR0uIiKCiIiI7m5Gl/j++++Jjo6msrKyw49dWFiIm5sbly5dAuDrr7/m17/+dYefRwghxL1ZdXcDhBBCPJqefPJJtFqtyW3W1tZd3JrOt3r16u5uQpcpKyvjxIkTnXLsyZMns2/fPvr37w/AkSNH+PTTTzvlXEIIIdoniaQQQohuoVKp8PLy6u5mdJmf//zn3d2Eh4KTkxNOTk7d3QwhhHjkydBWIYQQPV5RURFhYWGo1WomT55MRkYGOp1O2f75558zf/58fH19GTVqFAsXLuTrr79Wtp8+fRo3NzfKy8t59dVXUavVTJgwgbS0NJqbm5V6t2/fJjs7m6CgIDw8PJg6dSq5ubncuXNHqRMREcGqVavYsmULkyZNQq1WExUVxbVr1ygoKGDKlCloNBoiIyOV4ZeG/VoPbdXpdGzatInnnnsOT09PQkNDOXDgwD3vQ11dHW+++SaBgYF4eHgQGhrK/v37jeoEBASwceNGUlNT8fb2xtfXlxUrVlBbW6vUSUhIICIigv379+Pv749Go2HevHn84x//MDrWv//9b2JjY5kwYQJeXl5ERERw5swZZfulS5dwc3MjLy+PoKAg1Go1BQUFJCYmAvDcc8+RkJAAgJubG1lZWUbHz8rKws3NzahdkZGRFBQUMG3aNEaOHMkLL7xAaWmpUqf10NasrCylV9tw/NjYWPz8/Iz+ZgBJSUlMmzat3Xv70Ucf8ctf/hKNRoO3tzeLFi3im2++MarTUXG4d+9e/P39GTVqFB999BEAn3zyCeHh4ajVanx8fFi5ciXXr19vt71CCNHdJJEUQgjRbZqamky+Wk+e8qc//YmVK1cyYsQItFot0dHR7Nq1i3Xr1gFw6tQp5Rm51NRU1q1bx5UrV5g1a1abRCA+Pp7Ro0eTk5NDaGgoO3bs4M9//jPQMmHLwoUL2bFjBzNnziQnJ4egoCA2bdrUZlhqcXEx5eXlpKSkkJSURHl5OeHh4eTn57Ny5UrWrFnDuXPnWLNmTbvXHh8fT15eHjNnzmTbtm1MnDiRhIQEiouLTdZvaGhg9uzZ/OUvf+E3v/kNW7ZsYfTo0SQlJZGTk2NUd8+ePZw9e5Y333yT5cuXc+LECRYsWGB0X//+97+zceNGYmJiSEtLo6amhvDwcKqrqwH417/+xUsvvcSlS5dITk4mPT0dCwsL5s2bR0VFhdH5srKyiIqKYv369YwfP55FixYBoNVqWbx4cbv3wJQvvviCnTt3EhsbS3Z2Nr169WLp0qXcuHGjTd2ZM2cyY8YMAPbt26e8r6qq4vTp00b37siRI4SFhZk858WLF1m8eDEjR45k69atpKSkcOHCBaKjo5WEtCPjUKvVsnLlSlatWoVGo+Hjjz8mMjISGxsbNm3axOuvv05FRQVz586loaHBrPsnhBBdRYa2CiGE6BaVlZWMGDHC5LYVK1Ywf/587ty5Q3Z2NoGBgcoXdoBbt25x+PBhGhsbycjIYODAgeTm5tKrVy8AJk6cyJQpU8jMzGTz5s3KfjNnzmTJkiUAjBs3jqNHj/Lhhx8ya9YsSktLKSsrY8OGDYSEhAAwYcIEbGxs2Lx5M3PnzmXo0KFASwKs1WpxcHAAoKSkhJMnT3L06FEGDBgAwGeffcbBgwdNXt9XX33FX//6V15//XXmzZuntKeyspLTp08TGhraZp/CwkK++uor9u7di0ajAWDSpEk0NTWxZcsWZs2aRZ8+fQCwtLQkLy+PJ554AmgZDrpkyRJOnjyJn58f0NK7mZOTw5gxYwDw9PQkMDCQ/Px84uPj0Wq1qFQq8vPzsbe3B1qeTwwNDWX9+vVGPaHBwcG8/PLLyvtnnnkGgOHDh+Pq6mryHrSnrq6OwsJC5Rh2dnaEh4dz6tSpNj2KLi4uuLi4ACjDpPv374+LiwtFRUWMGzcOgPfff5/6+npefPFFk+c8f/48DQ0NLFiwAGdnZ+XYH3zwAfX19djZ2XVoHM6ePZugoCDlfUZGBoMGDWLbtm3Kvmq1mpCQEAoKCpgzZ45Z91AIIbqC9EgKIYToFk8++ST79+83+XrhhRcAuHDhAv/5z3+YMmWK0b7z58+nsLCQxsZGPv/8c4KDg5Uv4AC9e/fG39+/Tc+ZIQEzcHFxob6+HoCKigqsrKyMvuADTJ8+XdluMGTIECWJBOjXrx+Ojo5KEgnQp08f6urqTF67YXjo1KlTjcqzsrJYu3atyX0qKip4+umn21zD9OnTuX37NufOnVPKAgIClCTS8N7KyoqPP/5YKXN1dVWSSGhJwAy9Y4bz+fv7K0kkgJWVFSEhIXzxxRf8+OOPSvnw4cNNtvlBODk5KUkkoCSKt27duq/9LS0tCQsLo6SkRNnnwIEDjB8/XjnW3dRqNY899hgzZswgJSWFkydP4u7uzrJly7C3t+/wOGx9v27dusW5c+d49tln0ev1Sq/8gAEDGDJkiDL0VQghehrpkRRCCNEtVCoVHh4e96xjeK6vb9++JrfX1dWh1+vp169fm239+vVrk8jZ2NgYvbe0tFSGe964cQNHR0ejRABaEl7DuQxaJ1cGdnZ297yW1v7XdZly48YNpS2tGa795s2bSpmhV83A0tISR0dHo+Ghd9cxtOdvf/ubcr727qter+eHH35Qysy59v/F1tbW6L2FhQVAm2ce7+Xll18mJyeHkpISxo4dS3l5Oenp6e3Wd3V1Zffu3eTm5rJ//37y8/Pp3bs3s2fP5rXXXuvwOGx9v27evMmdO3fYvn0727dvb7P/Y489dj+XLIQQXU4SSSGEED1W7969AdpMOlJTU8OXX36JRqPBwsKCa9eutdn36tWrylDP++Hg4EBNTQ3Nzc1GyaThmUFHR8cHuALTWl9X616yb775htraWkaPHm2yfd99912b8qtXr7ZpX01NjVGd5uZmampqjGY7vbsOwLVr15RkycHBod37ajif4d7cr9YTGwFKb3BHGzBgAD4+Prz33nvU1tZib29PYGDgPffx9PREq9Wi0+k4c+YM+/btIycnB3d3d2XG3c6Iw8cffxwLCwsiIyOVIdWt3Z1YCyFETyFDW4UQQvRYgwcPxtHRkePHjxuVHzx4kOjoaBobGxk5ciTvvfeeUZJSV1fHhx9+aDIha4+Pjw9NTU0cOXLEqPzQoUMAZh3rfzEc69ixY0bl6enppKSkmNzH29ubysrKNmsmHjp0CGtrazw9PZWy0tJSo9lEP/jgA5qampRnBqFlRtbWk8BUVVXx6aefKnW8vb05fvy4Uc9jc3Mzhw8fxsPDA5VK1e71WVq2/Xphb29PVVWVUdnZs2fbPcb9MnUugBkzZlBWVkZxcTHPP//8PXv23nnnHfz9/dHpdKhUKsaNG6cMMb58+XKnxqG9vT2/+MUv+Pbbb/Hw8FBeQ4cOJSsry2jSICGE6EmkR1IIIUS30Ol0fPbZZ+1ud3Nzw9bWlqVLl7JmzRr69u1LQEAAFy5cIDMzkzlz5uDg4MDy5cuZP38+0dHRzJ49m8bGRnJzc9HpdMrEOvfDz88PX19fkpOTqaqqwt3dnYqKCrZv305YWFiHrgPp7u5OUFAQaWlpNDQ0MHz4cEpLSzl+/LiynMXdXnrpJfbs2cOSJUuIjY3F1dWVY8eOUVBQQExMjNLLCXDlyhUWLVrE3LlzuXLlChs2bGDSpEn4+voqdQyz1C5btoxevXopkwcZliiJiYmhtLSUuXPnEh0djbW1Nbt37+bixYvs2LHjntdnaMv777+Pn58fQ4YMYfLkyRw+fBi1Ws3AgQMpLCw02cNqLsO5iouLUavVynOq06ZNY+3atZw/f57f/e539zzG2LFjSU9PZ8mSJYSHh9OrVy/27t2LSqXC399fmTm2s+IwLi6O6Oholi9fzvTp02lububtt9/m3LlzZs96K4QQXUUSSSGEEN3i6tWr/OpXv2p3e1FREcOHD2fOnDnY2dmxc+dO9u3bh4uLC1FRUURFRQEts53m5eWRmZlJXFwcKpWKMWPG8NZbbymzrN4PCwsLtm3bRmZmJu+88w7Xr1/H1dWVuLg4Xnnllf/7eu+WlpaGVqvlj3/8IzU1NQwZMoTMzMx2h2Da2tqya9cuMjIy2Lx5Mz/88AODBw8mJSVFWQLDICQkhN69e/Paa69hZ2dHWFgYy5YtM6rz1FNP8eqrr5KamsqtW7cYP348W7duVYZhDh06lD179rBhwwYSExOxsLDA09OT/Px8o0l6TPH19WX8+PFkZGRQXl5Obm4uiYmJNDU18dZbb2FlZcXzzz/P8uXLSU5OfvCbSMuERQcPHiQhIYEZM2bwxhtvAC3PFo4dO5Zvv/3WqLfWFHd3d3JycsjOziYuLo7m5mZGjhzJ22+/zeDBgwE6NQ4nTpzIzp070Wq1xMbGYm1tzYgRI8jLy1NmoxVCiJ7GQt96USkhhBBC/KQFBATg4+PDH/7wh3brJCQkUFFR0WZo7cOkoaGBZ599lsWLFytLrAghhOg40iMphBBCiIdGZWUlBw4coKysDAsLC6P1LYUQQnQcSSSFEEII8dCwtLRk165dPP7442zcuNHkUi1CCCH+fzK0VQghhBBCCCGEWWT5DyGEEEIIIYQQZpFEUgghhBBCCCGEWSSRFEIIIYQQQghhFkkkhRBCCCGEEEKYRRJJIYQQQgghhBBmkURSCCGEEEIIIYRZJJEUQgghhBBCCGEWSSSFEEIIIYQQQphFEkkhhBBCCCGEEGb5L8htVUsQ3icIAAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "conventional_model.fit(cols=\"sorted\").point_plot(title=\"Conventional estimates\", yname=\"Economic opportunity score\", ax=ax)\n", + "plt.savefig(\"plots/original_estimates.png\", bbox_inches=\"tight\")\n", + "fig, blank_ax = plt.subplots(figsize=(10, 10))\n", + "ax = make_blank_figure(ax, blank_ax)\n", + "plt.savefig(\"plots/original_estimates_blank.png\", bbox_inches=\"tight\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "metadata": {}, "outputs": [ { diff --git a/src/conditional_inference/base.py b/src/conditional_inference/base.py index b5015da..028d80c 100644 --- a/src/conditional_inference/base.py +++ b/src/conditional_inference/base.py @@ -3,7 +3,7 @@ from __future__ import annotations import pickle -from typing import Any, List, Sequence, Type, TypeVar, Union +from typing import Any, List, Optional, Sequence, Type, TypeVar, Union import matplotlib.pyplot as plt import numpy as np @@ -16,319 +16,66 @@ from statsmodels.iolib.table import SimpleTable # https://github.com/python/mypy/issues/6799 ColumnType = Union[str, int] -ColumnsType = Sequence[ColumnType] +ColumnsType = Union[Sequence[int], Sequence[str], Sequence[bool]] ModelType = TypeVar("ModelType", bound="ModelBase") Numeric1DArray = Sequence[float] ResultsType = TypeVar("ResultsType", bound="ResultsBase") -class ConventionalEstimatesData: - """Data store for conventional estimates. - - Args: - mean (Numeric1DArray): (n,) array of means. - cov (np.ndarray): (n,n) covariance matrix. - endog_names (str, optional): Name of endogenous variable. Defaults to None. - exog_names (Sequence[str], optional): Name of exogenous variables. Defaults to None. - - Attributes: - mean (Numeric1DArray): (n,) array of means. - cov (np.ndarray): (n,n) covariance matrix. - endog_names (str, optional): Name of endogenous variable. - exog_names (Sequence[str], optional): Name of exogenous variables. - """ - - def __init__( - self, - mean: Numeric1DArray, - cov: np.ndarray, - endog_names: str = None, - exog_names: Sequence[str] = None, - ): - self.mean_orig = self.mean = mean - self.cov = cov * np.identity(len(mean)) if np.isscalar(cov) else cov - self.endog_names = endog_names - self.exog_names = exog_names - - @property - def mean(self): # pylint: disable=missing-function-docstring - return self._mean - - @mean.setter - def mean(self, mean: Numeric1DArray): # pylint: disable=missing-function-docstring - self._mean = np.atleast_1d(mean) - - @property - def endog_names(self): # pylint: disable=missing-function-docstring - return "y" if self._endog_names is None else self._endog_names - - @endog_names.setter - def endog_names( - self, endog_names: str - ): # pylint: disable=missing-function-docstring - self._endog_names = endog_names - - @property - def exog_names(self): # pylint: disable=missing-function-docstring - if self._exog_names is not None: - return self._exog_names - if hasattr(self.mean_orig, "index") and hasattr( - self.mean_orig.index, "to_list" - ): - # assume mean is pd.Series-like - return self.mean_orig.index.to_list() - return [f"x{i}" for i in range(self.mean.shape[0])] - - @exog_names.setter - def exog_names( - self, exog_names: Sequence[str] - ): # pylint: disable=missing-function-docstring - self._exog_names = exog_names - - -class ModelBase: - """Base for model classes. - - Args: - mean (Numeric1DArray): (n,) array of means from conventional estimation. - cov (np.ndarray): (n, n) covariance matrix. - *args (Any): Passed to :class:`ConventionalEstimatesData`. - seed (int, optional): Random seed. Defaults to 0. - **kwargs (Any): Passed to :class:`ConventionalEstimatesData`. - - Attributes: - data (ConventionalEstimatesData): Conventional estimates data. - seed (int): Random seed. - - Notes: - Properties of :class:`ConventionalEstimatesData` can be accessed directly, e.g., - - .. doctest:: - - >>> from conditional_inference.base import ModelBase - >>> import numpy as np - >>> model = ModelBase([1, 2, 3], np.identity(3)) - >>> model.mean - array([1, 2, 3]) - """ - - _data_properties = [ - "mean", - "cov", - "endog_names", - "exog_names", - ] - - def __init__( - self, - mean: Numeric1DArray, - cov: np.ndarray, - *args: Any, - seed: int = 0, - **kwargs: Any, - ): - self.data = ConventionalEstimatesData(mean, cov, *args, **kwargs) - self.seed = seed - - def __getattribute__(self, key): - if key != "_data_properties" and key in self._data_properties: - return getattr(self.data, key) - return super().__getattribute__(key) - - def __setattr__(self, key, val): - if key in self._data_properties: - setattr(self.data, key, val) - else: - super().__setattr__(key, val) - - @classmethod - def from_results( - cls: Type[ModelType], - results: LikelihoodModelResults, - *args, - cols: ColumnsType = None, - **kwargs, - ) -> ModelType: - """Instantiate an estimator from conventional regression results. - - Args: - results (LikelihoodModelResults): Conventional likelihood model estimates. - *args (Any): Passed to the model class constructor. - cols (ColumnsType, optional): Names or indices of the policy variables. Defaults to - None. - **kwargs (Any): Passed to the model class constructor. - - Returns: - Model: Estimator. - - Examples: - - .. code-block:: - - >>> from conditional_inference.base import ModelBase - >>> import numpy as np - >>> import statsmodels.api as sm - >>> X = np.repeat(np.identity(3), 100, axis=0) - >>> beta = np.array([0, 1, 2]) - >>> y = X @ beta + np.random.normal(size=300) - >>> ols_results = sm.OLS(y, X).fit() - >>> model = ModelBase.from_results(ols_results) - >>> model.mean - array([-0.20434022, 0.96700821, 1.88196662]) - >>> model.cov - array([[0.01163716, 0. , 0. ], - [0. , 0.01163716, 0. ], - [0. , 0. , 0.01163716]]) - """ - - def get_index(col: Union[str, int]) -> int: - if isinstance(col, str): - return results.model.exog_names.index(col) - if np.isscalar(col): - return int(col) - raise ValueError( - f"Invalid column type {type(col)} for column {col}" - ) # pragma: no cover - - if cols is None: - indices = np.arange(results.params.shape[0]) - exog_names = results.model.exog_names - else: - indices = np.array([get_index(col) for col in cols]) - exog_names = [results.model.exog_names[i] for i in indices] - - cov = results.cov_params() - if isinstance(cov, pd.DataFrame): - cov = cov.values - - return cls( - pd.Series(results.params[indices], index=exog_names), - cov[indices][:, indices], - endog_names=kwargs.pop("endog_names", results.model.endog_names), - *args, - **kwargs, - ) - - @classmethod - def from_csv( - cls: Type[ModelType], - filename: str, - *args: Any, - cols: ColumnsType = None, - **kwargs: Any, - ) -> ModelType: - """Instantiate an estimator from csv file. - - Args: - filename (str): Name of the csv file. - *args (Any): Passed to the model class constructor. - cols (ColumnsType, optional): Names or indices of the policy variables. Defaults to - None. - **kwargs (Any): Passed to the model class constructor. - - Returns: - Model: Estimator. - """ - - def get_index(col: Union[str, int]) -> int: - if isinstance(col, str): - return exog_names.index(col) - if np.isscalar(col): - return int(col) - raise ValueError( - f"Invalid column type {type(col)} for column {col}" - ) # pragma: no cover - - df = pd.read_csv(filename) - mean, cov = df.values[:, 0], df.values[:, 1:] # pylint: disable=no-member - endog_names, exog_names = ( - df.columns[0], # pylint: disable=no-member - df.columns[1:], # pylint: disable=no-member - ) - - # select columns - if cols is None: - indices = np.arange(len(df)) - else: - indices = np.array([get_index(col) for col in cols]) - exog_names = [exog_names[i] for i in indices] - - return cls( - pd.Series(mean[indices], index=exog_names), - cov[indices][:, indices], - endog_names=kwargs.pop("endog_names", endog_names), - *args, - **kwargs, - ) - - def get_indices(self, cols: ColumnsType = None) -> np.ndarray: - """Get indices associated with columns. - - Args: - cols (ColumnsType, optional): Column names or indices. Defaults to None. - - Returns: - np.ndarray: Indices of requested columns. - """ - if cols is None: - return np.arange(self.mean.shape[0]) - - if isinstance(cols, str): - if cols == "sorted": - return (-self.mean).argsort() - return np.array([self._get_index(cols)]) - - return np.array([self._get_index(col) for col in cols]) - - def _get_index(self, col: ColumnType) -> int: - return self.exog_names.index(col) if isinstance(col, str) else col - - class ResultsBase: """Base for results classes. Args: model (ModelBase): Model on which the results are based. - cols (ColumnsType, optional): Columns of interest. Defaults to None. title (str, optional): Results title. Defaults to "Estimation results". """ + _default_title = "Estimation results" + def __init__( self, - model: ModelBase, - cols: ColumnsType = None, - title: str = "Estimation results", + model: ModelType, + title: str = None, ): self.model = model - self.indices = model.get_indices(cols) + if not hasattr(self, "pvalues"): + self.pvalues = np.full(len(model.mean), np.nan) self.title = title + self._conf_int_cached = {} - def conf_int(self, alpha: float = 0.05, cols: ColumnsType = None) -> np.ndarray: + @property + def title(self) -> str: + return self._title or self._default_title + + @title.setter + def title(self, title: str) -> None: + self._title = title + + def conf_int( + self, alpha: float = 0.05, columns: ColumnsType = None, **kwargs: Any + ) -> np.ndarray: """Compute the 1-alpha confidence interval. Args: - alpha (float, optional): The CI will cover the truth with probability 1-alpha. Defaults - to 0.05. - cols (ColumnsType, optional): Names or indices of policies of interest. Defaults to - None. + alpha (float, optional): The CI will cover the truth with probability + 1-alpha. Defaults to 0.05. + columns (ColumnsType, optional): Selected columns. Defaults to None. Returns: - np.ndarray: (n,2) array of confidence intervals. + np.ndarray: (# params, 2) array of confidence intervals. """ - if not hasattr(self, "distributions"): + return self._conf_int(alpha, self.model.get_indices(columns), **kwargs) + + def _conf_int(self, alpha: float, indices: np.array) -> np.ndarray: + if not hasattr(self, "marginal_distributions"): raise AttributeError( - "Results object does not have `distributions` attribute." + "Results object does not have `marginal_distributions` attribute." ) - if not hasattr(self, "params"): - raise AttributeError("Results object does not have `params` attribute.") - - indices = self._get_indices(cols) return np.array( [ - dist.ppf([alpha / 2, 1 - alpha / 2]) - for index, dist in enumerate(self.distributions) # type: ignore, pylint: disable=no-member - if index in indices + self.marginal_distributions[i].ppf([alpha / 2, 1 - alpha / 2]) + for i in indices ] ) @@ -338,54 +85,57 @@ class ResultsBase: xname: Sequence[str] = None, title: str = None, alpha: float = 0.05, + columns: ColumnsType = None, + spacing: float = 1, ax=None, ): """Create a point plot. Args: yname (str, optional): Name of the endogenous variable. Defaults to None. - xname (Sequence[str], optional): Names of the policies. Defaults to None. + xname (Sequence[str], optional): (# params,) sequence of parameter names. + Defaults to None. title (str, optional): Plot title. Defaults to None. - alpha: (float, optional): Plot the 1-alpha CI. Defaults to 0.05. + alpha (float, optional): Plot the 1-alpha CI. Defaults to 0.05. + columns (ColumnsType, optional): Selected columns. Defaults to None. + spacing (float): Spacing on the horizontal axis. Defaults to 1. ax: (AxesSubplot, optional): Axis to write on. Returns: - plt.axes._subplots.AxesSubplot: Plot. + AxesSubplot: Plot. """ + if not hasattr(self, "params"): raise AttributeError("Results object does not have `params` attribute.") - conf_int = self.conf_int(alpha) - xname = xname or [self.model.exog_names[idx] for idx in self.indices] - yticks = np.arange(len(xname), 0, -1) + indices = self.model.get_indices(columns) + params = self.params[indices] + conf_int = self.conf_int(alpha, columns) + yticks = spacing * np.arange(len(indices), 0, -1) if ax is None: _, ax = plt.subplots() ax.errorbar( - x=self.params, # type: ignore, pylint: disable=no-member + x=params, # type: ignore, pylint: disable=no-member y=yticks, - xerr=[self.params - conf_int[:, 0], conf_int[:, 1] - self.params], # type: ignore, pylint: disable=no-member + xerr=[params - conf_int[:, 0], conf_int[:, 1] - params], # type: ignore, pylint: disable=no-member fmt="o", ) ax.set_title(title or self.title) ax.set_xlabel(yname or self.model.endog_names) ax.set_yticks(yticks) - ax.set_yticklabels(xname) + ax.set_yticklabels(self.model.exog_names[indices] if xname is None else xname) return ax - def save(self: ResultsType, fname: str) -> ResultsType: + def save(self: ResultsType, filename: str) -> None: """Pickle results. Args: - fname (str): File name. - - Returns: - ResultsType: self. + filename (str): File name. """ - with open(fname, "wb") as results_file: + with open(filename, "wb") as results_file: pickle.dump(self, results_file) - return self def summary( self, @@ -393,6 +143,7 @@ class ResultsBase: xname: Sequence[str] = None, title: str = None, alpha: float = 0.05, + columns: ColumnsType = None, ) -> Summary: """Create a summary table. @@ -403,6 +154,7 @@ class ResultsBase: title (str, optional): Table title. Defaults to None. alpha (float, optional): Display 1-alpha confidence interval. Defaults to 0.05. + columns (ColumnsType, optional): Selected columns. Defaults to None. Returns: Summary: Summary table. @@ -410,35 +162,22 @@ class ResultsBase: if not hasattr(self, "params"): raise AttributeError("Results object does not have `params` attribute.") - if not hasattr(self, "pvalues"): - raise AttributeError("Results object does not have `pvalues` attribute.") - + indices = self.model.get_indices(columns) params_header = self._make_summary_header(alpha) params_data = np.hstack( - (np.array([self.params, self.pvalues]).T, self.conf_int(alpha)) # type: ignore, pylint: disable=no-member + (np.array([self.params, self.pvalues]).T[indices], self.conf_int(alpha, columns)) # type: ignore, pylint: disable=no-member ) return self._make_summary( params_header, params_data, yname=yname, - xname=xname, + xname=self.model.exog_names[indices] if xname is None else xname, title=title, ) - def _get_indices(self, cols: ColumnsType = None) -> np.array: - if not hasattr(self, "params"): - raise AttributeError( - f"Results object {self.__class__.__qualname__} has no attribute `params`" - ) - return ( - np.arange(len(self.params)) # type: ignore, pylint: disable=no-member - if cols is None - else self.model.get_indices(cols) - ) - def _make_summary( self, - params_header: List[str], + params_header: list[str], params_data: np.ndarray, yname: str = None, xname: Sequence[str] = None, @@ -447,7 +186,7 @@ class ResultsBase: """Create a summary table. Args: - params_header (List[str]): Table header + params_header (list[str]): Table header params_data (np.ndarray): Table data. yname (str, optional): Name of the endogenous variable. Defaults to None. xname (Sequence[str], optional): Names of the exogenous variables. Defaults to None. @@ -456,7 +195,7 @@ class ResultsBase: Returns: Summary: Summary table. """ - params_stubs = xname or [self.model.exog_names[idx] for idx in self.indices] + params_stubs = list(self.model.exog_names if xname is None else xname) params_data_str = [[f"{val:.3f}" for val in row] for row in params_data] smry = Summary() @@ -475,7 +214,276 @@ class ResultsBase: return smry - def _make_summary_header(self, alpha: float) -> List[str]: + def _make_summary_header(self, alpha: float) -> list[str]: # make the header for the summary table # when subclassing ResultsBase, you may wish to overwrite this method return ["coef", "pvalue", f"[{alpha/2}", f"{1-alpha/2}]"] + + +class ModelBase: + """Base for model classes. + + Args: + mean (Numeric1DArray): (# params,) array of conventionally estimated means. + cov (np.ndarray): (# params, # params) covariance matrix. + X (np.ndarray, optional): (# params, # features) feature matrix. Defaults to + None. + endog_names (str, optional): Name of endogenous variable. Defaults to None. + exog_names (Sequence[str], optional): Names of the exogenous variables. Defaults + to None. + columns (ColumnsType, optional): Columns to use. This can be a sequence of + indices (int), parameter names (str), or a Boolean mask. Defaults to None. + sort (bool, optional): Sort the parameters by the conventionally estimated + mean. Defaults to False. + seed (int, optional): Random seed. Defaults to 0. + + Attributes: + n_params (int): Number of estimated parameters. + mean (np.ndarray): (# params,) array of conventionally estimated means. + cov (np.ndarray): (# params, # params) covariance matrix. + X (np.ndarray): (# params, # features) feature matrix. + endog_names (str): Name of the endogenous variable. + exog_names (np.ndarray): Name of exogenous variables. + seed (int): Random seed. + """ + + _results_cls = ResultsBase + + def __init__( + self, + mean: Numeric1DArray, + cov: np.ndarray, + X: np.ndarray = None, + endog_names: str = None, + exog_names: Sequence[str] = None, + columns: ColumnsType = None, + sort: bool = False, + random_state: int = 0, + ): + self.mean = mean + self.n_params = len(self.mean) if columns is None else len(columns) + self.cov = cov * np.identity(self.n_params) if np.isscalar(cov) else cov + self.X = np.ones((len(self.mean), 1)) if X is None else X + self.endog_names = endog_names + if exog_names is None and isinstance(mean, pd.Series): + self.exog_names = np.array(mean.index) + else: + self.exog_names = exog_names + self.random_state = random_state + + # select columns + indices = self.get_indices(columns) + self.mean = self.mean[indices] + self.cov = self.cov[indices][:, indices] + self.X = self.X[indices] + if exog_names is not None or isinstance(mean, pd.Series): + self.exog_names = self.exog_names[indices] + + # sort columns + if sort: + argsort = (-self.mean).argsort() + self.mean = self.mean[argsort] + self.cov = self.cov[argsort][:, argsort] + self.X = self.X[argsort] + if exog_names is not None or isinstance(mean, pd.Series): + self.exog_names = self.exog_names[argsort] + + @property + def mean(self) -> np.ndarray: # pylint: disable=missing-function-docstring + return self._mean + + @mean.setter + def mean( + self, mean: Numeric1DArray + ) -> None: # pylint: disable=missing-function-docstring + self._mean = np.atleast_1d(mean) + + @property + def cov(self) -> np.ndarray: + return self._cov + + @cov.setter + def cov(self, cov: np.ndarray) -> None: + self._cov = np.atleast_2d(cov) + + @property + def endog_names(self) -> str: # pylint: disable=missing-function-docstring + return "y" if self._endog_names is None else self._endog_names + + @endog_names.setter + def endog_names( + self, endog_names: str + ) -> None: # pylint: disable=missing-function-docstring + self._endog_names = endog_names + + @property + def exog_names(self) -> np.ndarray: # pylint: disable=missing-function-docstring + if self._exog_names is not None: + return self._exog_names + zfill = int(np.log10(max(1, len(self.mean) - 1))) + 1 + return np.array([f"x{str(i).zfill(zfill)}" for i in range(len(self.mean))]) + + @exog_names.setter + def exog_names( + self, exog_names: Optional[Sequence[str]] + ) -> None: # pylint: disable=missing-function-docstring + self._exog_names = None if exog_names is None else np.atleast_1d(exog_names) + + @classmethod + def from_results( + cls: Type[ModelType], + results: LikelihoodModelResults, + **kwargs: Any, + ) -> ModelType: + """Initialize an estimator from conventional regression results. + + Args: + results (LikelihoodModelResults): Conventional estimation results. + **kwargs (Any): Passed to the model class constructor. + + Returns: + Model: Estimator. + + Examples: + + .. testcode:: + + import numpy as np + import pandas as pd + import statsmodels.api as sm + from conditional_inference.base import ModelBase + + X = np.repeat(np.identity(3), 100, axis=0) + beta = np.arange(3) + y = X @ beta + np.random.normal(size=300) + ols_results = sm.OLS(y, X).fit() + model = ModelBase.from_results(ols_results) + print(model.mean) + print(model.cov) + + .. testoutput:: + + [0.05980802 1.08201297 1.94076774] + [[0.01007633 0. 0. ] + [0. 0.01007633 0. ] + [0. 0. 0.01007633]] + """ + cov = results.cov_params() + if isinstance(cov, pd.DataFrame): + cov = cov.values + + return cls( + results.params, + cov, + endog_names=kwargs.pop("endog_names", results.model.endog_names), + exog_names=kwargs.pop("exog_names", results.model.exog_names), + **kwargs, + ) + + @classmethod + def from_csv( + cls: Type[ModelType], + filename: str, + **kwargs: Any, + ) -> ModelType: + """Instantiate an estimator from csv file. + + Args: + filename (str): Name of the csv file. + **kwargs (Any): Passed to the model class constructor. + + Returns: + Model: Estimator. + """ + df = pd.read_csv(filename) + mean, cov = df.values[:, 0], df.values[:, 1:] # pylint: disable=no-member + endog_names, exog_names = ( + df.columns[0], # pylint: disable=no-member + df.columns[1:], # pylint: disable=no-member + ) + + return cls( + mean, + cov, + endog_names=kwargs.pop("endog_names", endog_names), + exog_names=kwargs.pop("exog_names", exog_names), + **kwargs, + ) + + def to_csv(self, filename: str) -> None: + """Write data to a csv. + + Args: + filename (str): Name of the file to write to. + """ + pd.DataFrame( + np.hstack((self.mean.reshape(-1, 1), self.cov)), + columns=[self.endog_names] + list(self.exog_names), + ).to_csv(filename, index=False) + + def fit(self, *args: Any, **kwargs: Any) -> ResultsType: + """Fit the model. + + Args: + *args (Any): Passed to the results class constructor. + **kwargs (Any): Passed to the results class constructor. + + Returns: + ResultsType: Results. + """ + return self._results_cls(self, *args, **kwargs) + + def get_index(self, column: ColumnType, names: Sequence[str] = None) -> int: + """Get the index of a selected column. + + Args: + column (ColumnType): Index or name of selected column. + names (Sequence[str], optional): (# params,) sequence of names to select + from. + + Returns: + int: Index. + """ + try: + return int(column) + except: + pass + + return list(self.exog_names if names is None else names).index(column) + + def get_indices( + self, columns: ColumnsType = None, names: Sequence[str] = None + ) -> np.ndarray: + """Get indices of the selected columns. + + Args: + columns (ColumnsType, optional): Sequence of columns to select. The + sequence can be a (# selected params,) sequence of column names (str) or + indices (int), or a (# params,) boolean mask. Defaults to None. + names (Sequence[str], optional): (# params,) sequence of names to select + from. + + Returns: + np.ndarray: (# selected params,) array of indices. + """ + if names is None: + names = self.exog_names + + if columns is None: + return np.arange(len(names)).astype(int) + + cols = np.atleast_1d(columns) + if cols.dtype == np.dtype("float"): + cols = cols.astype(int) + + if cols.dtype in (np.dtype(int), np.dtype("int64")): + # cols is a sequence of indices + return cols + + if cols.dtype == np.dtype(bool): + # cols is a boolean mask + return np.where(cols)[0] + + # cols are the parameter names (exogenous variables) + sorter = np.argsort(names) + return sorter[np.searchsorted(names, cols, sorter=sorter)] diff --git a/src/conditional_inference/bayes/__init__.py b/src/conditional_inference/bayes/__init__.py index e69de29..d3b8443 100644 --- a/src/conditional_inference/bayes/__init__.py +++ b/src/conditional_inference/bayes/__init__.py @@ -0,0 +1,3 @@ +from .improper import Improper +from .nonparametric import Nonparametric +from .normal import Normal diff --git a/src/conditional_inference/bayes/base.py b/src/conditional_inference/bayes/base.py index 7b41513..72bbf19 100644 --- a/src/conditional_inference/bayes/base.py +++ b/src/conditional_inference/bayes/base.py @@ -1,65 +1,19 @@ -"""Base classes for Bayesian analysis +"""Base classes for Bayesian analysis. """ from __future__ import annotations import warnings -from functools import partial -from typing import Any, Optional, Sequence +from typing import Any, Sequence import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns -from scipy.stats import norm, multivariate_normal, wasserstein_distance +from scipy.stats import multivariate_normal, norm, rv_continuous, wasserstein_distance -from ..base import ModelBase, Numeric1DArray, ResultsBase, ColumnsType -from ..utils import expected_wasserstein_distance, weighted_quantile - - -class BayesModelBase(ModelBase): - """Mixin for Bayesian models. - - Inherits from :class:`conditional_inference.base.ModelBase`. - - Args: - mean (Numeric1DArray): (n,) array of conventionally-estimated means. - cov (np.ndarray): (n, n) covariance matrix. - X (np.ndarray, optional): (n, p) feature matrix. If ``None``, a constant - regressor will be used. Defaults to None. - *args (Any): Passed to ``ModelBase``. - **kwargs (Any): Passed to ``ModelBase``. - - Attributes: - X (np.ndarray): (n, p) feature matrix. - """ - - def __init__( - self, - mean: Numeric1DArray, - cov: np.ndarray, - *args: Any, - X: np.ndarray = None, - **kwargs: Any, - ): - super().__init__(mean, cov, *args, **kwargs) - if X is None: - self.X = np.ones((len(mean), 1)) - elif hasattr(X, "values"): - # assume X.values is array-like - self.X = X.values # type: ignore - else: - self.X = X - - def _compute_xi(self, prior_cov: np.ndarray) -> np.ndarray: - """Compute xi; see paper for mathematical detail. - - Args: - prior_cov (np.ndarray): (n, n) prior covariance matrix. - - Returns: - np.ndarray: (n, n) weight matrix. - """ - return self.cov @ np.linalg.inv(prior_cov + self.cov) +from ..base import ColumnType, ModelBase, Numeric1DArray, ResultsBase, ColumnsType +from ..stats import joint_distribution +from ..utils import weighted_quantile class BayesResults(ResultsBase): @@ -68,90 +22,70 @@ class BayesResults(ResultsBase): Inherits from :class:`conditional_inference.base.ResultsBase`. Args: - model (BayesModelBase): Model on which results are based. - cols (ColumnsType): Columns of interest. - params (np.ndarray): (n,) array of point estimates, usually the average - posterior mean. - cov_params (np.ndarray): (n, n) posterior covariance matrix. - n_samples (int, optional): Number of samples to draw for approximations, such - as likelihood calculations. Defaults to 1000. - title (str, optional): Results title. Defaults to "Bayesian estimates". + *args (Any): Passed to :class:`conditional_inference.base.ResultsBase`. + n_samples (int): Number of samples used for approximations (ranking, likelihood + and Wasserstein distance). Defaults to 10000. + **kwargs (Any): Passed to :class:`conditional_inference.base.ResultsBase`. Attributes: - params (np.ndarray): (n,) array of point estimates, usually the average - posterior mean. - cov_params (np.ndarray): (n, n) posterior covariance matrix. distributions (List[scipy.stats.norm]): Marginal posterior distributions. multivariate_distribution (scipy.stats.multivariate_normal): Joint posterior distribution. - pvalues (np.ndarray): (n,) array of probabilities that the true mean is less - than 0. - posterior_mean_rvs (np.ndarray): (n_samples, n) matrix of draws from the - posterior. rank_matrix (pd.DataFrame): (n, n) dataframe of probabilities that column i has rank j. """ - def __init__( - self, - model: BayesModelBase, - cols: Optional[ColumnsType], - params: np.ndarray, - cov_params: np.ndarray, - n_samples: int = 1000, - seed: int = 0, - title: str = "Bayesian estimates", - ): - super().__init__(model, cols, title) + _default_title = "Bayesian estimates" + + def __init__(self, *args: Any, n_samples: int = 10000, **kwargs: Any): + super().__init__(*args, **kwargs) - self.params = params[self.indices] - self.cov_params = cov_params[self.indices][:, self.indices] - self.distributions = [ - norm(params[k], np.sqrt(cov_params[k, k])) for k in self.indices - ] - self.pvalues = np.array([dist.cdf(0) for dist in self.distributions]) - self.sample_weight = np.full(n_samples, 1 / n_samples) - self.seed = seed + # get the marginal (posterior) distributions, parameters, and pvalues + self.marginal_distributions, params, pvalues = [], [], [] + for i in range(self.model.n_params): + dist = self.model.get_marginal_distribution(i) + self.marginal_distributions.append(dist) + params.append(dist.mean()) + pvalues.append(dist.cdf(0)) + self.params = np.array(params).squeeze() + self.pvalues = np.array(pvalues).squeeze() + # estimate the parameter rankings by drawing from the posterior try: - self.multivariate_distribution = multivariate_normal( - self.params, self.cov_params - ) - self.posterior_mean_rvs = self.multivariate_distribution.rvs( - n_samples, random_state=seed + self.joint_distribution = self.model.get_joint_distribution() + self._posterior_rvs = self.joint_distribution.rvs(size=n_samples) + self._sample_weight = np.ones(n_samples) + except NotImplementedError: + warnings.warn( + "Model does not provide a joint posterior distribution." + " I'll assume the marginal posterior distributions are independent." + " Rank estimates and likelihood and Wasserstein approximations may be" + " unreliable." ) - self.rank_matrix = self._compute_rank_matrix() - except np.linalg.LinAlgError: - # the policy effects are perfectly correlated - # this occurs when the prior covariance == 0 - warnings.warn("Posterior covariance matrix is singular") - self.multivariate_distribution = None - err = norm.rvs( - 0, np.sqrt(self.cov_params[0, 0]), size=n_samples, random_state=seed - ) - self.posterior_mean_rvs = self.params + np.repeat( - err.reshape(-1, 1), self.params.shape[0], axis=1 - ) - self.rank_matrix = self._compute_rank_matrix(singular=True) - - @property - def reconstructed_mean_rvs( # pylint: disable=missing-function-docstring - self, - ) -> np.ndarray: - # reconstruct means and cache the value if they have not been created already - def reconstruct_means(mean): - return multivariate_normal.rvs(mean, self.model.cov) - - if not hasattr(self, "_reconstructed_mean_rvs"): - self.reconstructed_mean_rvs = np.apply_along_axis( - reconstruct_means, 1, self.posterior_mean_rvs + self._posterior_rvs = joint_distribution(self.marginal_distributions).rvs( + size=n_samples ) + self._sample_weight = np.ones(n_samples) + self._sample_weight /= self._sample_weight.sum() + argsort = np.argsort(-self._posterior_rvs, axis=1) + rank_matrix = np.array( + [ + ((argsort == k).T * self._sample_weight).sum(axis=1) + for k in range(self.model.n_params) + ] + ).T + self.rank_df = pd.DataFrame( + rank_matrix, + columns=self.model.exog_names, + index=np.arange(1, self.model.n_params + 1), + ) + self.rank_df.index.name = "Rank" - return self._reconstructed_mean_rvs - - @reconstructed_mean_rvs.setter - def reconstructed_mean_rvs(self, value: np.ndarray) -> None: - self._reconstructed_mean_rvs = value + self._reconstructed_rvs = np.apply_along_axis( + lambda mean: multivariate_normal.rvs(mean, self.model.cov), + 1, + self._posterior_rvs, + ) def expected_wasserstein_distance( self, mean: Numeric1DArray = None, cov: np.ndarray = None, **kwargs: Any @@ -163,110 +97,110 @@ class BayesResults(ResultsBase): distribution you would expect to observe according to this model. Args: - mean (Numeric1DArray, optional): (n,) array of sample means. Defaults to + mean (Numeric1DArray, optional): (# params,) array of sample conventionally + estimated means. If None, use the model's estimated means. Defaults to None. - cov (np.ndarray, optional): (n, n) covaraince matrix for sample means. - Defaults to None. + cov (np.ndarray, optional): (# params, # params) covaraince matrix for + conventionally estimated means. If None, use the model's estimated + covariance matrix. Defaults to None. **kwargs (Any): Keyword arguments for ``scipy.stats.wasserstein_distance``. Returns: float: Expected Wasserstein distance. - - Note: - ``mean`` and ``cov`` are taken to be the mean and covariance used to fit the - model by default, giving you the in-sample Wasserstein distance. """ - - def compute_distance(reconstructed_mean): - return wasserstein_distance(reconstructed_mean, self.model.mean, **kwargs) - if mean is None and cov is None: - distances = np.apply_along_axis( - compute_distance, 1, self.reconstructed_mean_rvs + mean = self.params + reconstructed_rvs = self._reconstructed_rvs + else: + if cov is None: + cov = self.model.cov + reconstructed_rvs = np.apply_along_axis( + lambda mean: multivariate_normal.rvs(mean, cov), 1, self._posterior_rvs ) - return (self.sample_weight * distances).sum() - mean = self.model.mean[self.indices] if mean is None else mean - cov = self.model.cov[self.indices][:, self.indices] if cov is None else cov - return expected_wasserstein_distance( - mean, cov, self.posterior_mean_rvs, self.sample_weight, **kwargs + distances = np.apply_along_axis( + lambda rv: wasserstein_distance(rv, mean, **kwargs), 1, reconstructed_rvs ) + return (self._sample_weight * distances).sum() def likelihood(self, mean: Numeric1DArray = None, cov: np.ndarray = None) -> float: - """Compute the likelihood of observing the sample means. - + """ Args: - mean (Numeric1DArray, optional): (n,) array of sample means. Defaults to + mean (Numeric1DArray, optional): (# params,) array of sample conventionally + estimated means. If None, use the model's estimated means. Defaults to None. - cov (np.ndarray, optional): (n, n) covariance matrix for sample means. - Defaults to None. + cov (np.ndarray, optional): (# params, # params) covaraince matrix for + conventionally estimated means. If None, use the model's estimated + covariance matrix. Defaults to None. Returns: float: Likelihood. - - Note: - ``mean`` and ``cov`` are taken to be the mean and covariance used to fit the - model by default, giving you the in-sample likelihood. """ - mean = self.model.mean[self.indices] if mean is None else mean - cov = self.model.cov[self.indices][:, self.indices] if cov is None else cov - likelihood = np.apply_along_axis( - lambda params: multivariate_normal(params, cov).pdf(mean), - 1, - self.posterior_mean_rvs, - ) - return (self.sample_weight * likelihood).sum() + if mean is None: + mean = self.model.mean + if cov is None: + cov = self.model.cov - def rank_matrix_plot(self, *args: Any, title: str = None, **kwargs: Any): - """Plot a heatmap of the rank matrix. + return ( + self._sample_weight + * multivariate_normal.pdf(self._posterior_rvs, mean, cov) + ).sum() + + def line_plot( + self, + column: ColumnType = None, + alpha: float = 0.05, + title: str = None, + yname: str = None, + ): + """Create a line plot of the prior, conventional, and posterior estimates. Args: + column (ColumnType, optional): Selected parameter. Defaults to None. + alpha (float, optional): Sets the plot width. 0 is as wide as possible, 1 is + as narrow as possible. Defaults to .05. title (str, optional): Plot title. Defaults to None. - *args (Any): Passed to ``sns.heatmap``. - **kwargs (Any): Passed to ``sns.heatmap``. + yname (str, optional): Name of the dependent variable. Defaults to None. Returns: - AxesSubplot: Heatmap. + AxesSubplot: Plot. """ - ax = sns.heatmap( - self.rank_matrix, center=1 / self.params.shape[0], *args, **kwargs + index = self.model.get_index(column) + prior = self.model.get_marginal_prior(index) + posterior = self.marginal_distributions[index] + conventional = norm( + self.model.mean[index], np.sqrt(self.model.cov[index, index]) ) - ax.set_title(title or self.title) + xlim = np.array( + [ + dist.ppf([alpha / 2, 1 - alpha / 2]) + for dist in (prior, conventional, posterior) + ] + ).T + x = np.linspace(xlim[0].min(), xlim[1].max()) + palette = sns.color_palette() + ax = sns.lineplot(x=x, y=prior.pdf(x), label="prior") + ax.axvline(prior.mean(), linestyle="--", color=palette[0]) + sns.lineplot(x=x, y=conventional.pdf(x), label="conventional") + ax.axvline(conventional.mean(), linestyle="--", color=palette[1]) + sns.lineplot(x=x, y=posterior.pdf(x), label="posterior") + ax.axvline(posterior.mean(), linestyle="--", color=palette[2]) + ax.set_title(title or self.model.exog_names[index]) + ax.set_xlabel(yname or self.model.endog_names) return ax - def reconstruction_histogram( - self, - yname: str = None, - title: str = None, - ax=None, - ): - """Create a histogram of the reconstructed means. - - Plots the distribution of sample means you would expect to see if this model - were correct. + def rank_matrix_plot(self, title: str = None, **kwargs: Any): + """Plot a heatmap of the rank matrix. Args: - yname (str, optional): Name of the endogenous variable. Defaults to None. title (str, optional): Plot title. Defaults to None. - ax: (AxesSubplot, optional): Axis to write on. + **kwargs (Any): Passed to ``sns.heatmap``. Returns: - plt.axes._subplots.AxesSubplot: Plot. + AxesSubplot: Heatmap. """ - params = np.sort(self.reconstructed_mean_rvs).mean(axis=0) - - if ax is None: - _, ax = plt.subplots() - sns.histplot( - x=list(self.model.mean) + list(params), - hue=len(self.model.mean) * ["Observed"] + len(params) * ["Reconstructed"], - stat="probability", - kde=True, - ax=ax, - ) - ax.set_title(title or f"{self.title} reconstruction plot") - ax.set_xlabel(yname or self.model.endog_names) - + ax = sns.heatmap(self.rank_df, center=1 / self.model.n_params, **kwargs) + ax.set_title(title or f"{self.title} rank matrix") return ax def reconstruction_point_plot( @@ -292,17 +226,18 @@ class BayesResults(ResultsBase): Returns: plt.axes._subplots.AxesSubplot: Plot. """ - reconstructed_means = -np.sort(-self.reconstructed_mean_rvs) - params = reconstructed_means.mean(axis=0) + reconstructed_means = -np.sort(-self._reconstructed_rvs) + params = np.average(reconstructed_means, axis=0, weights=self._sample_weight) - weighted_quantile_func = partial( + conf_int = np.apply_along_axis( weighted_quantile, + 0, + reconstructed_means, quantiles=[alpha / 2, 1 - alpha / 2], - sample_weight=self.sample_weight, - ) - conf_int = np.apply_along_axis(weighted_quantile_func, 0, reconstructed_means).T + sample_weight=self._sample_weight, + ).T - xname = xname or np.arange(len(self.indices)) + xname = xname or np.arange(self.model.n_params) yticks = np.arange(len(xname), 0, -1) if ax is None: _, ax = plt.subplots() @@ -322,42 +257,74 @@ class BayesResults(ResultsBase): return ax - def _compute_rank_matrix(self, singular: bool = False) -> pd.DataFrame: - """Compute the rank matrix + def _make_summary_header(self, alpha: float) -> list[str]: + return ["coef", "pvalue (1-sided)", f"[{alpha/2}", f"{1-alpha/2}]"] + + +class BayesBase(ModelBase): + """Mixin for Bayesian models. + + Subclasses :class:`conditional_inference.base.ModelBase`. + """ + + _results_cls = BayesResults + + def get_marginal_prior(self, column: ColumnType) -> rv_continuous: + """Get the marginal prior distribution of ``column``. Args: - singular (bool, optional): Indicates the posterior covariance matrix is - singular. Defaults to False. + column (ColumnType): Name or index of the parameter of interest. Returns: - pd.DataFrame: Rank matrix. + rv_continuous: Prior distribution """ - if len(self.posterior_mean_rvs.shape) == 1: - # only estimating one parameter - rank_matrix = [1] - elif not singular: - # assumes no ties in rank order - argsort = np.argsort(-self.posterior_mean_rvs, axis=1) - rank_matrix = np.array( - [ - ((argsort == k).T * self.sample_weight).sum(axis=1) - for k in range(self.posterior_mean_rvs.shape[1]) - ] - ).T - else: - # handles ties when posterior covariance matrix is singular - rank_matrix = np.zeros((self.params.shape[0], self.params.shape[0])) - params = self.params.copy() - curr_rank = 0 - while params.shape[0] > 0: - idx = np.where(self.params == params.max())[0] - rank = (curr_rank + np.arange(idx.shape[0])).astype(int) - for i in idx: - rank_matrix[rank, i] = 1 / idx.shape[0] - curr_rank += idx.shape[0] - params = params[params != params.max()] - rank_df = pd.DataFrame( - rank_matrix, columns=[self.model.exog_names[k] for k in self.indices] - ) - rank_df.index.name = "Rank" - return rank_df + return self._get_marginal_prior(self.get_index(column)) + + def _get_marginal_prior(self, index: int) -> rv_continuous: + """Private version of :meth:`self.get_marginal_prior`.""" + raise NotImplementedError() + + def get_marginal_distribution(self, column: ColumnType) -> rv_continuous: + """Get the marginal posterior distribution of ``column``. + + Args: + column (ColumnType): Name or index of the parameter of interest. + + Returns: + rv_continuous: Posterior distribution. + """ + return self._get_marginal_distribution(self.get_index(column)) + + def _get_marginal_distribution(self, index: int) -> rv_continuous: + """Private version of :meth:`self.get_marginal_distribution`.""" + raise NotImplementedError() + + def get_joint_prior(self, columns: ColumnsType = None): + """Get the joint prior distribution. + + Args: + columns (ColumnsType, optional): Selected columns. Defaults to None. + + Returns: + rv_like: Joint distribution. + """ + return self._get_joint_prior(self.get_indices(columns)) + + def _get_joint_prior(self, indices: np.ndarray): + """Private version of :meth:`self.get_joint_prior`.""" + return joint_distribution([self.get_marginal_prior(i) for i in indices]) + + def get_joint_distribution(self, columns: ColumnsType = None): + """Get the joint posterior distribution. + + Args: + columns (ColumnsType, optional): Selected columns. Defaults to None. + + Returns: + rv_like: Joint distribution. + """ + return self._get_joint_distribution(self.get_indices(columns)) + + def _get_joint_distribution(self, indices: np.ndarray): + """Private version of :meth:`self.get_joint_distribution`.""" + raise NotImplementedError() diff --git a/src/conditional_inference/bayes/classic.py b/src/conditional_inference/bayes/classic.py deleted file mode 100644 index 220c153..0000000 --- a/src/conditional_inference/bayes/classic.py +++ /dev/null @@ -1,217 +0,0 @@ -"""Classical Bayesian analysis -""" -from __future__ import annotations - -from typing import Any, Union - -import numpy as np - -from ..base import ColumnsType, Numeric1DArray -from .base import BayesModelBase, BayesResults - - -class ClassicBayesBase(BayesModelBase): - """Mixin for classical Bayesian analysis. - - Inherits from :class:`conditional_inference.bayes.base.BayesModelBase`. - - Assumes a know prior covariance. - - Args: - mean (Numeric1DArray): (n,) array of conventionally-estimated means. - cov (np.ndarray): (n, n) covariance matrix. - prior_cov (Union[float, np.ndarray]): (n, n) prior covariance matrix. If - ``float``, the prior covariance is assumed to be proportional to the - identity matrix. - X (np.ndarray, optional): (n, p) feature matrix. If ``None``, a constant - regressor will be used. Defaults to None. - *args (Any): Passed to ``BayesModelBase``. - **kwargs (Any): Passed to ``BayesModelBase``. - - Attributes: - prior_cov (np.ndarray): (n, n) prior covariance matrix. - """ - - def __init__( - self, - mean: Numeric1DArray, - cov: np.ndarray, - prior_cov: Union[float, np.ndarray], - *args: Any, - X: np.ndarray = None, - **kwargs: Any - ): - super().__init__(mean, cov, *args, X=X, **kwargs) - if np.isscalar(prior_cov): - self.prior_cov = np.diag(np.full(len(mean), prior_cov)) - else: - self.prior_cov = prior_cov - - def estimate_prior_mean(self, prior_mean_params=None) -> np.ndarray: - """Estimate the prior mean vector. - - Args: - prior_mean_params (Any, optional): Parameters which determine the prior - mean. Defaults to None. - - Raises: - NotImplementedError: Classes which inherit the mixin should implement this - method. - - Returns: - np.ndarray: (n,) array of prior means. - """ - raise NotImplementedError() # pragma: no cover - - def _estimate_prior_mean_params(self): - """Estimate prior mean parameters. - - Raises: - NotImplementedError: Classes which inherit the mixin should implement this - method. - """ - raise NotImplementedError() # pragma: no cover - - def estimate_posterior_mean(self, prior_mean: np.ndarray = None) -> np.ndarray: - """Estimate the posterior mean vector. - - Args: - prior_mean (np.ndarray, optional): (n,) array of prior means. Defaults to - None. - - Returns: - np.ndarray: (n,) array of posterior means. - """ - if prior_mean is None: - prior_mean = self.estimate_prior_mean() - xi = self._compute_xi(self.prior_cov) - return prior_mean + (np.identity(self.mean.shape[0]) - xi) @ ( - self.mean - prior_mean - ) - - def estimate_posterior_cov(self) -> np.ndarray: - """Estimate posterior covariance matrix. - - Returns: - np.ndarray: (n, n) posterior covariance matrix. - """ - xi = self._compute_xi(self.prior_cov) - return (np.identity(self.mean.shape[0]) - xi) @ self.cov - - def fit( - self, - cols: ColumnsType = None, - title: str = "Classical Bayes estimates", - **kwargs: Any - ) -> BayesResults: - """Fit the model - - Args: - cols (ColumnsType, optional): Columns of interest. Defaults to None. - title (str, optional): Results title. Defaults to - "Classical Bayes estimates". - **kwargs (Any): Passed to ``BayesResults``. - - Returns: - BayesResults: Results. - """ - return BayesResults( - self, - cols, - params=self.estimate_posterior_mean(), - cov_params=self.estimate_posterior_cov(), - title=title, - **kwargs - ) - - -class LinearClassicBayes(ClassicBayesBase): - """Classic linear Bayesian model. - - Inherits from :class:`ClassicBayesBase`. - - Assumes the prior mean vector is a linear combination of the feature matrix. - - Examples: - - .. code-block:: - - >>> import numpy as np - >>> from conditional_inference.bayes.classic import LinearClassicBayes - >>> from scipy.stats import multivariate_normal - >>> n_policies = 5 - >>> prior_cov = np.identity(n_policies) - >>> prior_mean = np.zeros(n_policies) - >>> true_mean = multivariate_normal.rvs(prior_mean, prior_cov) - >>> sample_cov = np.identity(n_policies) - >>> sample_mean = multivariate_normal.rvs(true_mean, sample_cov) - >>> model = LinearClassicBayes(sample_mean, sample_cov, prior_cov=prior_cov) - >>> model.fit(cols="sorted").summary() - Classical Bayes estimates - ============================== - coef pvalue [0.025 0.975] - ------------------------------ - x2 -0.813 0.853 -2.331 0.705 - x0 -1.053 0.913 -2.571 0.465 - x1 -1.664 0.984 -3.182 -0.146 - x3 -1.782 0.989 -3.300 -0.263 - x4 -1.830 0.991 -3.348 -0.312 - =============== - Dep. Variable y - --------------- - """ - - def estimate_prior_mean(self, prior_mean_params: np.ndarray = None) -> np.ndarray: - """Estimate the prior mean vector. - - Args: - prior_mean_params (np.ndarray, optional): (p,) array of prior mean - parameters. Defaults to None. - - Returns: - np.ndarray: (n,) array of prior means. - """ - if prior_mean_params is None: - prior_mean_params = self._estimate_prior_mean_params() - return self.X @ prior_mean_params - - def _estimate_prior_mean_params(self) -> np.ndarray: - """Estimate the prior mean parameters. - - Returns: - np.ndarray: (p,) array of prior mean parameters. - """ - # tau is the covariance of marginal joint distribution of mean - X_T = self.X.T - tau_inv = np.linalg.inv(self.prior_cov + self.cov) - if self._prior_is_infinite(): - return np.linalg.inv(X_T @ self.X) @ X_T @ self.mean - return np.linalg.inv(X_T @ tau_inv @ self.X) @ X_T @ tau_inv @ self.mean - - def estimate_posterior_cov(self) -> np.ndarray: - """Estimate the posterior covariance matrix - - Returns: - np.ndarray: (n, n) posterior covariance matrix. - """ - post_mean_uncertainty = super().estimate_posterior_cov() - # increase posterior covariance to account for uncertainty in prior mean - # parameters - if self._prior_is_infinite(): - # prior mean uncertainty converges to 0 - return post_mean_uncertainty - xi = self._compute_xi(self.prior_cov) - X_T = self.X.T - tau_inv = np.linalg.inv(self.prior_cov + self.cov) - prior_mean_uncertainty = ( - xi @ self.X @ np.linalg.inv(X_T @ tau_inv @ self.X) @ X_T @ xi - ) - return post_mean_uncertainty + prior_mean_uncertainty - - def _prior_is_infinite(self) -> bool: - """Indicates that the prior covariance is ``np.inf * np.identity(n)``. - - Returns: - bool: Indicator - """ - return (self.prior_cov == np.diag(np.full(self.mean.shape[0], np.inf))).all() diff --git a/src/conditional_inference/bayes/empirical.py b/src/conditional_inference/bayes/empirical.py deleted file mode 100644 index 6aba963..0000000 --- a/src/conditional_inference/bayes/empirical.py +++ /dev/null @@ -1,557 +0,0 @@ -"""Empirical Bayesian analysis -""" -from __future__ import annotations - -import math -import warnings -from typing import Any, Dict, Tuple, Union - -import numpy as np -from scipy.optimize import minimize_scalar -from scipy.stats import multivariate_normal - -from ..base import ColumnsType, Numeric1DArray -from .base import BayesModelBase, BayesResults -from .classic import LinearClassicBayes - -PriorParams = Union[float, np.ndarray] - - -class EmpiricalBayesBase(BayesModelBase): - """Mixin for empirical Bayes models. - - Inherits from :class:`conditional_inference.bayes.base.BayesModelBase`. - """ - - def estimate_posterior_mean( - self, prior_mean: np.ndarray = None, prior_cov: np.ndarray = None - ) -> np.ndarray: - """Estimate the posterior mean vector. - - Args: - prior_mean (np.ndarray, optional): (n,) array of prior means. Defaults to - None. - prior_cov (np.ndarray, optional): (n, n) prior covariance matrix. Defaults - to None. - - Returns: - np.ndarray: (n,) array of posterior means. - """ - prior_mean, prior_cov = self._get_prior_mean_cov(prior_mean, prior_cov) - xi = self._compute_xi(prior_cov) - return prior_mean + (np.identity(self.mean.shape[0]) - xi) @ ( - self.mean - prior_mean - ) - - def estimate_posterior_cov(self, prior_cov: np.ndarray) -> np.ndarray: - """Estimate the posterior covariance matrix. - - Args: - prior_cov (np.ndarray): (n, n) prior covariance matrix. Defaults to None. - - Returns: - np.ndarray: (n, n) posterior covariance matrix. - - Note: - This approximation uses a plug-in estimator which likely underestimates the - posterior covariance. - """ - xi = self._compute_xi(prior_cov) - return (np.identity(len(self.mean)) - xi) @ self.cov - - def estimate_prior_params( - self, tol: float = 1e-3, max_iter: int = 100 - ) -> Tuple[PriorParams, PriorParams]: - """Estimate parameters using expectation maximization. - - Args: - tol (float, optional): Stopping criterion for expectation maximization. - Defaults to 1e-3. - max_iter (int, optional): Maximum number of iterations to use in - expectation maximization. Defaults to 100. - - Returns: - Tuple[PriorParams, PriorParams]: Prior mean and covariance - parameters. - """ - prior_cov = np.zeros(shape=self.cov.shape) - prev_log_likelihood = -np.inf - - for _ in range(max_iter): - prior_mean_params = self._estimate_prior_mean_params(prior_cov) - prior_mean = self.estimate_prior_mean(prior_mean_params) - prior_cov_params = self._estimate_prior_cov_params(prior_mean) - prior_cov = self.estimate_prior_cov(prior_cov_params) - log_likelihood = self.log_likelihood(prior_mean, prior_cov) - if abs(log_likelihood - prev_log_likelihood) <= tol: - return prior_mean_params, prior_cov_params - prev_log_likelihood = log_likelihood - - warnings.warn( # pragma: no cover - "Prior parameter estimation reached maximum iterations before convergence", - RuntimeWarning, - ) - return prior_mean_params, prior_cov_params # pragma: no cover - - def fit( - self, - cols: ColumnsType = None, - title: str = "Empirical Bayes estimates", - estimate_prior_params_kwargs: Dict[str, Any] = None, - **kwargs: Any, - ) -> BayesResults: - """Fit the model. - - Args: - cols (ColumnsType, optional): Names or indices of the policies of interest. - Defaults to None. - title (str, optional): Results title. Defaults to "Empirical Bayes results". - estimate_prior_params_kwargs (Dict[str, Any], optional): Keyword arguments passed to - the ``estimate_prior_params`` method. Defaults to None. - **kwargs (Any): Passed to ``BayesResults``. - - Returns: - BayesResults: Results. - """ - if estimate_prior_params_kwargs is None: - estimate_prior_params_kwargs = {} - prior_mean_params, prior_cov_params = self.estimate_prior_params( - **estimate_prior_params_kwargs - ) - prior_mean = self.estimate_prior_mean(prior_mean_params) - prior_cov = self.estimate_prior_cov(prior_cov_params) - return BayesResults( - self, - cols, - params=self.estimate_posterior_mean(prior_mean, prior_cov), - cov_params=self.estimate_posterior_cov(prior_cov), - title=title, - **kwargs, - ) - - def log_likelihood(self, prior_mean: np.ndarray, prior_cov: np.ndarray) -> float: - """Evaluate the log likelihood. - - Args: - prior_mean (np.ndarray): (n,) array of pior means. - prior_cov (np.ndarray): (n, n) prior covariance matrix. - - Returns: - float: Log likelihood of observing the data given the input prior mean and - covariance matrix (i.e. the log likelihood of the marginal distribution). - """ - marginal_cov = prior_cov + self.cov - error = self.mean - prior_mean # the prior mean is also the marginal mean - return -0.5 * ( - self.mean.shape[0] * np.log(2 * math.pi) - + np.log(np.linalg.det(marginal_cov)) - + error.T @ np.linalg.inv(marginal_cov) @ error - ) - - def _get_prior_mean_cov( - self, prior_mean: np.ndarray = None, prior_cov: np.ndarray = None - ) -> Tuple[np.ndarray, np.ndarray]: - # get the prior mean vector and covariance matrix - if prior_mean is None or prior_cov is None: - prior_mean_params, prior_cov_params = self.estimate_prior_params() - if prior_mean is None: - prior_mean = self.estimate_prior_mean(prior_mean_params) - if prior_cov is None: - prior_cov = self.estimate_prior_cov(prior_cov_params) - return prior_mean, prior_cov - - -class LinearEmpiricalBayes(EmpiricalBayesBase): - """Empirical linear Bayesian model. - - Inherits from :class:`EmpiricalBayesBase`. - - Assumes the prior mean vector is a linear combination of the feature matrix. - - Args: - mean (Numeric1DArray): (n,) array of conventionally-estimated means. - cov (np.ndarray): (n, n) covariance matrix. - X (np.ndarray, optional): (n, p) feature matrix. Defaults to None. - max_prior_cov (float, optional): Maximum prior covariance. The prior covariance - is assumed to be proportional to the identity matrix. Defaults to 1e6. - - Note: - The estimated posterior covariance matrix doesn't account for uncertainty in the - estimated prior covariance parameter, and therefore may underestimate the - posterior covariance. - - Examples: - - .. code-block:: - - >>> import numpy as np - >>> from conditional_inference.bayes.empirical import LinearEmpiricalBayes - >>> from scipy.stats import multivariate_normal - >>> n_policies = 5 - >>> prior_cov = np.identity(n_policies) - >>> prior_mean = np.zeros(n_policies) - >>> true_mean = multivariate_normal.rvs(prior_mean, prior_cov) - >>> sample_cov = np.identity(n_policies) - >>> sample_mean = multivariate_normal.rvs(true_mean, sample_cov) - >>> model = LinearEmpiricalBayes(sample_mean, sample_cov) - >>> model.fit(cols="sorted").summary() - Empirical Bayes estimates - ============================== - coef pvalue [0.025 0.975] - ------------------------------ - x3 2.372 0.004 0.614 4.129 - x0 1.251 0.082 -0.507 3.008 - x4 -0.475 0.702 -2.233 1.283 - x1 -0.626 0.758 -2.384 1.131 - x2 -1.976 0.986 -3.734 -0.218 - =============== - Dep. Variable y - --------------- - """ - - def __init__( - self, - mean: Numeric1DArray, - cov: np.ndarray, - *args: Any, - X: np.ndarray = None, - max_prior_std: float = 1e6, - **kwargs: Any, - ): - super().__init__(mean, cov, *args, X=X, **kwargs) - self.max_prior_std = max_prior_std - - def estimate_prior_mean(self, prior_mean_params: np.ndarray) -> np.ndarray: - """Estimate the prior mean vector. - - Args: - prior_mean_params (np.ndarray): (p,) array of prior mean parameters. - - Returns: - np.ndarray: (n,) array of prior means. - """ - return self.X @ prior_mean_params - - def estimate_prior_cov(self, prior_cov_params: float) -> np.ndarray: - """Estimate the prior covariance matrix. - - Args: - prior_cov_params (float): Prior covariance parameter. The prior covariance - is assumed to be proportional to the identity matrix. - - Returns: - np.ndarray: (n, n) prior covariance matrix. - """ - return prior_cov_params ** 2 * np.identity(self.mean.shape[0]) - - def estimate_posterior_cov(self, prior_cov: np.ndarray) -> np.ndarray: - """Estimate the posterior covariance matrix. - - Args: - prior_cov (np.ndarray): (n, n) prior covariance matrix. Defaults - to None. - - Returns: - np.ndarray: (n, n) posterior covariance matrix. - """ - post_mean_uncertainty = super().estimate_posterior_cov(prior_cov) - xi = self._compute_xi(prior_cov) - X_T = self.X.T - tau_inv = np.linalg.inv(prior_cov + self.cov) - prior_mean_uncertainty = ( - xi @ self.X @ np.linalg.inv(X_T @ tau_inv @ self.X) @ X_T @ xi - ) - return post_mean_uncertainty + prior_mean_uncertainty - - def estimate_prior_params( # type: ignore - self, - method: str = "likelihood", - tol: float = 1e-3, - max_iter: int = 100, - n_samples: int = 100, - ) -> Tuple[np.ndarray, float]: - """Estimate parameters of the prior distribution. - - Args: - method (str, optional): Objective function used to fit the prior - parameters. Can either be "likelihood" or "wasserstein". Defaults to - "likelihood". - tol (float, optional): Stopping criterion for expectation maximization. - Defaults to 1e-3. - max_iter (int, optional): Maximum number of iterations to use in - expectation maximization. Defaults to 100. - n_samples (int, optional): Number of samples to take from the posterior - distribution when estimating the Wasserstein distance. Defaults to 100. - - Raises: - ValueError: ``method`` must either be "likelihood" or "wasserstein". - - Returns: - Tuple[PriorParams, PriorParams]: Parameters of the prior - distribution. - - Note: - The likelihood method estimates the prior covariance parameter by maximum - likelihood. The Wasserstein method estimates the prior covariance parameter - by minimizing the Wasserstein distance. Likelihood is generally preferable, - but the Wasserstein method may be necessary when the likelihood - method fails to converge. - """ - if method not in ("likelihood", "wasserstein"): - raise ValueError( - f"`method` must be 'likelihood' or 'wasserstein'. Got {method}." - ) - - if method == "likelihood": - return super().estimate_prior_params(tol=tol, max_iter=max_iter) # type: ignore - - def loss(prior_cov_params): - prior_cov = self.estimate_prior_cov(prior_cov_params) - model = LinearClassicBayes( - self.mean, self.cov, prior_cov=prior_cov, X=self.X - ) - return model.fit(n_samples=n_samples).expected_wasserstein_distance() - - result = minimize_scalar( - loss, - bounds=(0, self.max_prior_std), - method="bounded", - options=dict(maxiter=max_iter), - ) - - if not result.success: - warnings.warn(result.message, RuntimeWarning) - - prior_cov_params = result.x - prior_cov = self.estimate_prior_cov(prior_cov_params) - prior_mean_params = self._estimate_prior_mean_params(prior_cov) - - return prior_mean_params, prior_cov_params - - def prior_mean_rvs(self, size: int = 1) -> np.ndarray: - """Sample from the distribution of prior means. - - Args: - size (int, optional): Number of samples to draw. Defaults to 1. - - Returns: - np.ndarray: (size, n) array of prior mean samples. - """ - # TODO: incorporate estimate_prior_params keyword arguments - # possibly pass in a prior_cov parameter to be consistent with heirarchical Bayes - _, prior_cov_params = self.estimate_prior_params() - prior_cov = self.estimate_prior_cov(prior_cov_params) - X_T = self.X.T - tau_inv = np.linalg.inv(prior_cov + self.cov) - XT_tauinv_X_inv = np.linalg.inv(X_T @ tau_inv @ self.X) - beta_bar = XT_tauinv_X_inv @ X_T @ tau_inv @ self.mean - beta = multivariate_normal.rvs(beta_bar, XT_tauinv_X_inv, size=size) - return (self.X @ beta.reshape(1, -1)).squeeze() - - def _estimate_prior_mean_params(self, prior_cov: np.ndarray) -> np.ndarray: - """Estimate prior mean parameter vector. - - Args: - prior_cov (np.ndarray): (n, n) prior covariance matrix. - - Returns: - np.ndarray: (p,) array of prior mean parameters. - """ - X_T = self.X.T - tau_inv = np.linalg.inv(prior_cov + self.cov) - return np.linalg.inv(X_T @ tau_inv @ self.X) @ X_T @ tau_inv @ self.mean - - def _estimate_prior_cov_params( - self, prior_mean: np.ndarray, max_iter: int = 100 - ) -> float: - """Estimate the prior covariance parameter by MLE. - - Args: - prior_mean (np.ndarray): (n,) array of prior means. - max_iter (int): Maximum number of iterations to attempt. Defaults to 100. - - Returns: - float: Prior covariance parameter. The prior covariance is proportional to - the identity matrix. - """ - - def loss(prior_cov_params): - prior_cov = self.estimate_prior_cov(prior_cov_params) - return -self.log_likelihood(prior_mean, prior_cov) - - for i in range(max_iter): - max_prior_std = (1 / 2 ** i) * self.max_prior_std - result = minimize_scalar(loss, bounds=(0, max_prior_std), method="bounded") - if result.success and result.fun < np.inf: - return result.x - - raise RuntimeError("Optimizer failed to find the prior covariance parameter") - - -class JamesStein(EmpiricalBayesBase): - """James-Stein estimator. - - Inherits from :class:`EmpiricalBayesBase`. - - Note: - This estimator is most appropriate when the sample covariance matrix is - proportional to the identity matrix. - - Examples: - - .. code-block:: - - >>> import numpy as np - >>> from conditional_inference.bayes.empirical import JamesStein - >>> from scipy.stats import multivariate_normal - >>> n_policies = 5 - >>> prior_cov = np.identity(n_policies) - >>> prior_mean = np.zeros(n_policies) - >>> true_mean = multivariate_normal.rvs(prior_mean, prior_cov) - >>> sample_cov = np.identity(n_policies) - >>> sample_mean = multivariate_normal.rvs(true_mean, sample_cov) - >>> model = JamesStein(sample_mean, sample_cov) - >>> model.fit(cols="sorted").summary() - Empirical Bayes estimates - ============================== - coef pvalue [0.025 0.975] - ------------------------------ - x4 3.707 0.000 1.710 5.704 - x2 1.734 0.035 -0.143 3.611 - x0 0.623 0.257 -1.245 2.491 - x1 -0.586 0.726 -2.494 1.323 - x3 -0.697 0.762 -2.612 1.218 - =============== - Dep. Variable y - --------------- - """ - - def estimate_prior_mean(self, prior_mean_params: np.ndarray) -> np.ndarray: - """Estimate the prior mean vector. - - Args: - prior_mean_params (np.ndarray): (p,) array of prior mean parameters. - - Returns: - np.ndarray: (n,) array of prior means. - """ - return self.X @ prior_mean_params - - def estimate_prior_cov(self, prior_cov_params: float) -> np.ndarray: - """Estimate the prior covariance matrix. - - Args: - prior_cov_params (float): Prior covariance parameter. - - Returns: - np.ndarray: (n, n) prior covariance matrix. - """ - return prior_cov_params ** 2 * np.identity(self.mean.shape[0]) - self.cov - - def estimate_posterior_cov( - self, prior_cov: np.ndarray = None, prior_mean: np.ndarray = None - ) -> np.ndarray: - """Estimate the posterior covariance matrix. - - Args: - prior_cov (np.ndarray, optional): (n, n) prior covariance matrix. Defaults - to None. - prior_mean (np.ndarray, optional): (n,) array of prior means. Defaults to - None. - - Returns: - np.ndarray: (n, n) posterior covariance matrix. - """ - prior_mean, prior_cov = self._get_prior_mean_cov(prior_mean, prior_cov) - - # variance due to uncertainty in estimate of posterior mean - post_mean_uncertainty = super().estimate_posterior_cov(prior_cov) - - # variance due to uncertainty in estimate of prior mean - xi = self._compute_xi(prior_cov) - X_T = self.X.T - prior_mean_uncertainty = ( - self.cov @ self.X @ np.linalg.inv(X_T @ self.X) @ X_T @ xi - ) - - # variance due to uncertainty in estimate of prior covariance - error = ((self.mean - prior_mean) ** 2).reshape(-1, 1) - prior_cov_uncertainty = ( - xi @ error @ error.T @ xi * 2 / (self.mean.shape[0] - self.X.shape[1] - 2) - ) - - return post_mean_uncertainty + prior_mean_uncertainty + prior_cov_uncertainty - - def estimate_prior_params(self) -> Tuple[np.ndarray, float]: # type: ignore - """Estimate prior mean and covariance parameters. - - Returns: - Tuple[np.ndarray, float]: (p,) array of prior mean parameters, prior - covariance parameter. - """ - X_T = self.X.T - prior_mean_params = np.linalg.inv(X_T @ self.X) @ X_T @ self.mean - prior_mean = self.estimate_prior_mean(prior_mean_params) - prior_cov_params = ((self.mean - prior_mean) ** 2).sum() / ( - self.mean.shape[0] - self.X.shape[1] - 2 - ) - - min_prior_cov_params = self._find_min_prior_cov_params() - if min_prior_cov_params > prior_cov_params: - warnings.warn( - " ".join( - [ - "The prior variance parameter given by the James-Stein estimator", - f"{prior_cov_params} implies the prior covariance matrix is not", - "positive semi-definite. Increasing the prior variance parameter", - f"to {min_prior_cov_params}.", - ] - ), - RuntimeWarning, - ) - prior_cov_params = min_prior_cov_params - - return prior_mean_params, prior_cov_params - - def _find_min_prior_cov_params( - self, - bounds: Tuple[float, float] = (0, np.inf), - i: int = 0, - prev_prior_cov_params: float = None, - tol: float = 1e-6, - max_iter: int = 100, - ): - """Find the minimum prior covariance parameter such that the prior covariance - matrix is PSD. - - Args: - bounds (Tuple[float, float], optional): Current boundaries in which the - minimum prior cov parameter could be. Defaults to (0, np.inf). - i (int, optional): Iteration. Defaults to 0. - prev_prior_cov_params (float, optional): Prior covariance parameter from - the previous iteration. Defaults to None. - tol (float, optional): Stopping criteria. Defaults to 1e-6. - max_iter (int, optional): Stopping criteria. Defaults to 100. - """ - - def get_prior_cov_params(): - if bounds == (0, np.inf): - return 1 - if bounds[1] == np.inf: - return 2 * bounds[0] - return 0.5 * (bounds[0] + bounds[1]) - - prior_cov_params = get_prior_cov_params() - if ( - prev_prior_cov_params is not None - and abs(prev_prior_cov_params - prior_cov_params) < tol - ) or i == max_iter: - return bounds[1] - prior_cov = self.estimate_prior_cov(prior_cov_params) - try: - # if this succeeds, the prior covariance matrix is PSD - np.linalg.cholesky(prior_cov) - bounds = bounds[0], prior_cov_params - except np.linalg.LinAlgError: - bounds = prior_cov_params, bounds[1] - return self._find_min_prior_cov_params(bounds, i + 1, prior_cov_params) diff --git a/src/conditional_inference/bayes/hierarchical.py b/src/conditional_inference/bayes/hierarchical.py deleted file mode 100644 index 12d5df4..0000000 --- a/src/conditional_inference/bayes/hierarchical.py +++ /dev/null @@ -1,322 +0,0 @@ -"""Hierarchical Bayesian analysis -""" -from __future__ import annotations - -from typing import Any, Optional, Tuple, Union -from typing_extensions import Protocol - -import numpy as np -from scipy.stats import multivariate_normal - -from ..base import ColumnsType, Numeric1DArray -from ..utils import weighted_cdf, weighted_quantile -from .base import BayesModelBase, BayesResults - -PriorParams = Union[float, np.ndarray] - - -class Distribution(Protocol): - def rvs(self, size=None, random_state=None): - ... - - -class HierarchicalBayesBase(BayesModelBase): - """Mixin for hierarchical Bayesian models. - - Inherits from :class:`conditional_inference.bayes.base.BayesModelBase`. - - Assumes a known distribution of prior covariance parameters. - - Args: - mean (Numeric1DArray): (n,) array of conventionally-estimated means. - cov (np.ndarray): (n, n) covariance matrix. - prior_cov_params_distribution (Distribution): Distribution of prior covariance - parameters. Must implement an `rvs` method. - X (np.ndarray, optional): (n, p) feature matrix. Defaults to None. - *args (Any): Passed to ``BayesModelBase``. - **kwargs (Any): Passed to ``BayesModelBase``. - - Attributes: - prior_cov_params_distribution (Distribution): Distribution of prior covariance - parameters. - """ - - def __init__( - self, - mean: Numeric1DArray, - cov: np.ndarray, - prior_cov_params_distribution: Distribution, - *args: Any, - X: np.ndarray = None, - **kwargs: Any, - ): - super().__init__(mean, cov, *args, X=X, **kwargs) - self.prior_cov_params_distribution = prior_cov_params_distribution - - def estimate_prior_cov(self, prior_cov_params: PriorParams) -> np.ndarray: - """Estimate the prior covariance matrix. - - Args: - prior_cov_params (PriorParams): Parameters which determine the prior - covariance matrix. - - Raises: - NotImplementedError: Classes which inherit the mixin should implement this - method. - - Returns: - np.ndarray: (n, n) prior covariance matrix. - """ - raise NotImplementedError() # pragma: no cover - - def prior_mean_rvs(self, prior_cov: np.ndarray, size: int = 1) -> np.ndarray: - """Sample means from distribution of prior means. - - Args: - prior_cov (np.ndarray): (n, n) prior covariance matrix. - size (int, optional): Number of samples to draw. Defaults to 1. - - Raises: - NotImplementedError: Classes which inherit the mixin should implement this - method. - - Returns: - np.ndarray: (size, n) array of prior mean samples. - """ - raise NotImplementedError() # pragma: no cover - - def fit( - self, - cols: ColumnsType = None, - n_samples: int = 1000, - title: str = "Hierarchical Bayes estimates", - ) -> HierarchicalBayesResults: - """Fit the empirical Bayes estimator and return results. - - Args: - cols (ColumnsType, optional): Names or indices of the policies of interest. - Defaults to None. - n_samples (int, optional): Number of samples used to approximate posterior - distributions. Defualts to 1000. - title (str, optional): Results title. Defaults to - "Hierarchical Bayes results". - - Returns: - HierarchicalResults: Hierarchical Bayes estimation results. - """ - posterior_mean_rvs, sample_weight = self.posterior_mean_rvs(size=n_samples) - return HierarchicalBayesResults( - self, cols, posterior_mean_rvs, sample_weight, title=title - ) - - def prior_cov_rvs(self, size: int = 1) -> Tuple[np.ndarray, np.ndarray]: - """Sample covariance matrices from distribution of prior covariances. - - Args: - size (int, optional): Number of samples to draw. Defaults to 1. - - Returns: - np.ndarray: (size, n, n) matrix of sampled prior covariances, (size,) array - of sample weights. - """ - prior_cov_params_sample = self.prior_cov_params_distribution.rvs(size) - prior_covs, log_likelihood = [], [] - - for prior_cov_params in prior_cov_params_sample: - prior_cov = self.estimate_prior_cov(prior_cov_params) - prior_covs.append(prior_cov) - log_likelihood.append(self._scaled_log_likelihood(prior_cov)) - - likelihood = np.exp(log_likelihood - np.array(log_likelihood).max()) - return np.array(prior_covs), likelihood / likelihood.sum() - - def posterior_mean_rvs(self, size: int = 1) -> Tuple[np.ndarray, np.ndarray]: - """Sample mean vectors from distribution of posterior means. - - Args: - size (int, optional): Number of samples to draw. Defaults to 1. - - Returns: - np.ndarray: (size, n) matrix of sampled posterior mean vectors. - """ - prior_covs, sample_weight = self.prior_cov_rvs(size) - posterior_means = [] - - for prior_cov in prior_covs: - prior_mean = self.prior_mean_rvs(prior_cov) - xi = self.cov @ np.linalg.inv(prior_cov + self.cov) - delta = np.identity(self.mean.shape[0]) - xi - expected_post_mean = prior_mean + delta @ (self.mean - prior_mean) - dist = multivariate_normal(expected_post_mean, delta @ self.cov) - posterior_means.append(dist.rvs()) - - return np.array(posterior_means), sample_weight - - def _scaled_log_likelihood(self, prior_cov: np.ndarray) -> float: - """Compute the (scaled) log likelihood of observing a prior covariance matrix - given the sample mean and covariance matrix. - - This method is used to determine sample weights for Gibbs sampling. - - Args: - prior_cov (np.ndarray): (n, n) prior covariance matrix. - - Raises: - NotImplementedError: Classes which inherit the mixin should implement this - method. - - Returns: - float: (Scaled) log likelihood. - """ - raise NotImplementedError() # pragma: no cover - - -class LinearHierarchicalBayes(HierarchicalBayesBase): - """Hierarchical linear Bayesian model. - - Inherits from :class:`HierarchicalBayesBase`. - - Assumes the prior mean vector is a linear combination of the feature matrix and - that the prior covariance matrix is proportional to the identity matrix. - - Examples: - - .. code-block:: - - >>> import numpy as np - >>> from conditional_inference.bayes.hierarchical import LinearHierarchicalBayes - >>> from scipy.stats import multivariate_normal, loguniform - >>> n_policies = 5 - >>> prior_cov_params_distribution = loguniform(.1, 10) - >>> prior_cov = prior_cov_params_distribution.rvs() * np.identity(n_policies) - >>> prior_mean = np.zeros(n_policies) - >>> true_mean = multivariate_normal.rvs(prior_mean, prior_cov) - >>> sample_cov = np.identity(n_policies) - >>> sample_mean = multivariate_normal.rvs(true_mean, sample_cov) - >>> model = LinearHierarchicalBayes(sample_mean, sample_cov, prior_cov_params_distribution) - >>> model.fit(cols="sorted").summary() - Hierarchical Bayes estimates - ============================== - coef pvalue [0.025 0.975] - ------------------------------ - x0 1.545 0.041 -0.165 3.531 - x1 0.413 0.348 -1.478 2.317 - x2 0.041 0.479 -1.961 2.024 - x4 -1.200 0.900 -2.940 0.673 - x3 -3.772 1.000 -5.828 -1.620 - =============== - Dep. Variable y - --------------- - """ - - def estimate_prior_cov(self, prior_cov_params: float) -> np.ndarray: # type: ignore - """Estimate the prior covariance matrix. - - Args: - prior_cov_params (float): Standard deviation of the prior distribution. - - Returns: - np.ndarray: (n, n) prior covariance matrix. Assumed to be proportional to - the identity matrix. - """ - return prior_cov_params ** 2 * np.identity(self.mean.shape[0]) - - def prior_mean_rvs(self, prior_cov: np.ndarray, size: int = 1) -> np.ndarray: - """Sample means from distribution of prior means. - - Args: - prior_cov (np.ndarray): (n, n) prior covariance matrix. - size (int, optional): Number of samples to draw. Defaults to 1. - - Returns: - np.ndarray: (size, n) array of prior mean samples. - """ - X_T = self.X.T - tau_inv = np.linalg.inv(prior_cov + self.cov) - XT_tauinv_X_inv = np.linalg.inv(X_T @ tau_inv @ self.X) - beta_bar = XT_tauinv_X_inv @ X_T @ tau_inv @ self.mean - beta = multivariate_normal.rvs(beta_bar, XT_tauinv_X_inv, size=size) - return (self.X @ beta.reshape(1, -1)).squeeze() - - def _scaled_log_likelihood(self, prior_cov: np.ndarray) -> float: - # compute the scaled log likelihood; see HierarchicalBayesBase - X_T = self.X.T - tau = prior_cov + self.cov - tau_inv = np.linalg.inv(tau) - XT_tauinv_X_inv = np.linalg.inv(X_T @ tau_inv @ self.X) - - mean_bar = self.X @ XT_tauinv_X_inv @ self.X.T @ tau_inv @ self.mean - error = self.mean - mean_bar - return -0.5 * ( - np.log(np.linalg.det(tau)) - - np.log(np.linalg.det(XT_tauinv_X_inv)) - + error.T @ tau_inv @ error - ) - - -class HierarchicalBayesResults(BayesResults): - """Results from hierarchical Bayesian analysis. - - Inherits from - :class:`conditional_inference.bayes.base.BayesResults`. - - Args: - model (HierarchicalBayesBase): Model on which the results are based. - cols (ColumnsType): Columns of interest. - posterior_mean_rvs (np.ndarray): (n_samples, n) array of samples from - distribution of posterior means. - sample_weight (np.ndarray, optional): (n_samples) array of sample weights. - Defaults to None. - title (str, optional): Results title. Defaults to - "Hierarchical Bayes results". - """ - - def __init__( - self, - model: HierarchicalBayesBase, - cols: Optional[ColumnsType], - posterior_mean_rvs: np.ndarray, - sample_weight: np.ndarray = None, - title: str = "Hierarchical Bayes results", - ): - self.model = model - self.indices = model.get_indices(cols) - self.title = title - - self.posterior_mean_rvs = posterior_mean_rvs[:, self.indices] - - if sample_weight is None: - sample_weight = np.ones(self.posterior_mean_rvs.shape[0]) - sample_weight = np.array(sample_weight) - self.sample_weight = np.array(sample_weight) / sample_weight.sum() - self.params = (self.posterior_mean_rvs.T * self.sample_weight).sum(axis=1) - - self.pvalues = np.apply_along_axis( - weighted_cdf, - 0, - self.posterior_mean_rvs, - x=0, - sample_weight=self.sample_weight, - ) - self.rank_matrix = self._compute_rank_matrix() - - def conf_int(self, alpha: float = 0.05, cols: ColumnsType = None) -> np.ndarray: - """Compute the 1-alpha confidence interval. - - Args: - alpha (float, optional): The CI will cover the truth with probability - 1-alpha. Defaults to 0.05. - cols (ColumnsType, optional): Names or indices of policies of interest. - Defaults to None. - - Returns: - np.ndarray: (n,2) array of confidence intervals. - """ - indices = self._get_indices(cols) - return np.array( - [ - weighted_quantile(col, [alpha / 2, 1 - alpha / 2], self.sample_weight) - for idx, col in enumerate(self.posterior_mean_rvs.T) - if idx in indices - ] - ) diff --git a/src/conditional_inference/bayes/improper.py b/src/conditional_inference/bayes/improper.py new file mode 100644 index 0000000..3e4ba6b --- /dev/null +++ b/src/conditional_inference/bayes/improper.py @@ -0,0 +1,64 @@ +"""Bayesian model with an improper prior. +""" +from __future__ import annotations + +import numpy as np +from scipy.stats import multivariate_normal, norm, rv_continuous + +from .base import BayesBase + + +class Improper(BayesBase): + """Bayesian model with an improper prior. + + The improper prior is a uniform distribution on $(-\infty, \infty)$. The posterior + is equivalent to the conventionally estimated joint normal distribution. + + Examples: + + .. testcode:: + + import numpy as np + from conditional_inference.bayes import Improper + + model = Improper(np.arange(10), np.identity(10)) + results = model.fit() + print(results.summary()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + Bayesian estimates + ======================================= + coef pvalue (1-sided) [0.025 0.975] + --------------------------------------- + x0 0.000 0.500 -1.960 1.960 + x1 1.000 0.159 -0.960 2.960 + x2 2.000 0.023 0.040 3.960 + x3 3.000 0.001 1.040 4.960 + x4 4.000 0.000 2.040 5.960 + x5 5.000 0.000 3.040 6.960 + x6 6.000 0.000 4.040 7.960 + x7 7.000 0.000 5.040 8.960 + x8 8.000 0.000 6.040 9.960 + x9 9.000 0.000 7.040 10.960 + =============== + Dep. Variable y + --------------- + """ + + def _get_marginal_prior(self, index: int) -> rv_continuous: + raise RuntimeError( + "The improper prior is a uniform distribution from -inf to inf" + ) + + def _get_marginal_distribution(self, index: int) -> rv_continuous: + return norm(self.mean[index], np.sqrt(self.cov[index, index])) + + def _get_joint_prior(self, indices: np.ndarray): + raise RuntimeError( + "The improper prior is a uniform distribution from -inf to inf" + ) + + def _get_joint_distribution(self, indices: np.ndarray): + return multivariate_normal(self.mean[indices], self.cov[indices][:, indices]) diff --git a/src/conditional_inference/bayes/nonparametric.py b/src/conditional_inference/bayes/nonparametric.py new file mode 100644 index 0000000..2786d19 --- /dev/null +++ b/src/conditional_inference/bayes/nonparametric.py @@ -0,0 +1,192 @@ +"""Nonparametric empirical Bayes. + +References: + + .. code-block:: + + @article{cai2021nonparametric, + title={Nonparametric empirical bayes estimation and testing for sparse and heteroscedastic signals}, + author={Cai, Junhui and Han, Xu and Ritov, Ya'acov and Zhao, Linda}, + journal={arXiv preprint arXiv:2106.08881}, + year={2021} + } + +Notes: + + This implementation is based on Cai et al.'s nonparametric Dirac delta prior. Future + work should also implement their mixture model with a Laplace prior. +""" +from __future__ import annotations + +from itertools import product +from typing import Any + +import numpy as np +from scipy.optimize import minimize_scalar +from scipy.stats import loguniform, norm, rv_continuous +from sklearn.cluster import KMeans +from sklearn.model_selection import check_cv +from sklearn.neighbors import KernelDensity + +from ..stats import mixture, nonparametric +from .base import BayesBase + + +class Nonparametric(BayesBase): + """Bayesian model with a nonparametric Dirac delta prior. + + Args: + num (int, optional): Number of parameters to fit for the prior. Defaults to 100. + n_clusters (int, optional): Number of clusters to use for featurized + estimation. Defaults to 1. + cv (int, optional): Determines the cross validation splitting strategy (input to + ``sklearn.model_selection.check_cv``). Defaults to 5. + rtol (float, optional): Relative tolerance stopping criteria for expectation + maximization. The EM algorithm terminates when the relative improvement + between iterations falls below this threshold. Defaults to .99. + max_iter (int, optional): Maximum number of EM iterations. Defaults to 100. + bandwidth_rvs_size (int, optional): Number of bandwidth values to try when + tuning the kernel density estimator in between EM iterations to smooth the + prior. Defaults to 32. + + Examples: + + .. testcode:: + + import numpy as np + from conditional_inference.bayes import Nonparametric + + np.random.seed(0) + + model = Nonparametric(np.arange(10), np.identity(10)) + results = model.fit() + print(results.summary()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + Bayesian estimates + ======================================= + coef pvalue (1-sided) [0.025 0.975] + --------------------------------------- + x0 0.686 0.197 -0.594 2.148 + x1 1.226 0.062 -0.189 2.965 + x2 1.977 0.011 0.304 3.982 + x3 2.982 0.001 0.991 4.941 + x4 3.990 0.000 1.960 5.892 + x5 4.943 0.000 3.050 7.061 + x6 6.036 0.000 3.989 8.037 + x7 7.063 0.000 5.024 8.673 + x8 7.768 0.000 6.089 9.123 + x9 8.264 0.000 6.862 9.506 + =============== + Dep. Variable y + --------------- + """ + + def __init__( + self, + *args: Any, + num: int = 100, + n_clusters: int = 1, + cv=5, + rtol: float = 0.99, + max_iter: int = 100, + bandwidth_rvs_size: int = 32, + **kwargs: Any + ): + super().__init__(*args, **kwargs) + std = self.mean.std() + lower, upper = self.mean.min() - 2 * std, self.mean.max() + 2 * std + # (num,) array of values over which the prior is defined + self._values = np.linspace(lower, upper, num) + # (num, n_clusters) probability mass function + self._pmf_values = np.full((num, n_clusters), 1 / num) + # (# params, n_clusters) mixture weights for each parameter + self._mixture_weights = KMeans(n_clusters).fit_transform(self.X) + if (self._mixture_weights == 0).all(): + self._mixture_weights = np.ones(self._mixture_weights.shape) + self._mixture_weights = ( + self._mixture_weights.T / self._mixture_weights.sum(axis=1) + ).T + + def loss(value, index, cluster): + factor = (1 - value) / (1 - self._pmf_values[index, cluster]) + self._pmf_values[:, cluster] *= factor + self._pmf_values[index, cluster] = value + arr = self._mixture_weights * (conditional_pdf @ self._pmf_values) + return -np.log(arr.sum(axis=1)).sum() + + # density function of the conventional estimates evaluated at self._values + conditional_pdf = [ + norm.pdf(self._values, mean_i, np.sqrt(variance_i)) + for mean_i, variance_i in zip(self.mean, self.cov.diagonal()) + ] + conditional_pdf = np.array(conditional_pdf) + # fit the prior using an EM algorithm + prev_loss, current_loss, i = np.inf, None, 0 + values = self._values.reshape(-1, 1) + index_cluster = list(product(np.arange(num), np.arange(n_clusters))) + index_cluster = np.array(index_cluster).astype(int) + cv = check_cv(cv) + cv.shuffle = True + for i in range(max_iter): + # optimize each value of ``self._pmf_values`` + np.random.shuffle(index_cluster) + for index, cluster in index_cluster: + current_loss = minimize_scalar( + loss, bounds=(0, 1), method="bounded", args=(index, cluster) + ).fun + + # smooth the PMF using a kernel density estimator + cv.random_state = i + for cluster in range(n_clusters): + pmf_values = self._pmf_values[:, cluster] + mean = np.average(self._values, weights=pmf_values) + std = np.sqrt( + np.average((self._values - mean) ** 2, weights=pmf_values) + ) + bandwidth_rvs = loguniform(0.1 * std, 2 * std).rvs(bandwidth_rvs_size) + best_score = -np.inf + for bandwidth in bandwidth_rvs: + for train_index, test_index in cv.split(values): + X_train, X_test = values[train_index], values[test_index] + weight_train = pmf_values[train_index] + weight_test = pmf_values[test_index] + weight_train /= weight_train.sum() + weight_test /= weight_test.sum() + kde = KernelDensity(bandwidth=bandwidth).fit( + X_train, sample_weight=weight_train + ) + score = (weight_test * kde.score_samples(X_test)).mean() + if score > best_score: + best_score, best_bandwidth = score, bandwidth + + kde = KernelDensity(bandwidth=best_bandwidth).fit( + values, sample_weight=pmf_values + ) + self._pmf_values[:, cluster] = np.exp(kde.score_samples(values)) + + self._pmf_values /= self._pmf_values.sum(axis=0) + if current_loss / prev_loss > rtol: + break + prev_loss = current_loss + + # fit a nonparametric distribution for each cluster + self._cluster_distributions = [ + nonparametric((self._values, self._pmf_values[:, i])) + for i in range(n_clusters) + ] + + def _get_marginal_prior(self, index: int) -> rv_continuous: + if len(self._cluster_distributions) == 1: + return self._cluster_distributions[0] + + return mixture(self._cluster_distributions, self._mixture_weights[index]) + + def _get_marginal_distribution(self, index: int) -> rv_continuous: + pmf = (self._pmf_values * self._mixture_weights[index]).sum(axis=1) + logpmf = np.log(pmf) + norm.logpdf( + self._values, self.mean[index], np.sqrt(self.cov[index, index]) + ) + return nonparametric((self._values, np.exp(logpmf - logpmf.max()))) diff --git a/src/conditional_inference/bayes/normal.py b/src/conditional_inference/bayes/normal.py new file mode 100644 index 0000000..07ace66 --- /dev/null +++ b/src/conditional_inference/bayes/normal.py @@ -0,0 +1,405 @@ +"""Empirical Bayes with a normal prior. + +References: + + .. code-block:: + + @inproceedings{stein1956inadmissibility, + title={Inadmissibility of the usual estimator for the mean of a multivariate normal distribution}, + author={Stein, Charles and others}, + booktitle={Proceedings of the Third Berkeley symposium on mathematical statistics and probability}, + volume={1}, + number={1}, + pages={197--206}, + year={1956} + } + + @incollection{james1992estimation, + title={Estimation with quadratic loss}, + author={James, William and Stein, Charles}, + booktitle={Breakthroughs in statistics}, + pages={443--460}, + year={1992}, + publisher={Springer} + } + + @article{bock1975minimax, + title={Minimax estimators of the mean of a multivariate normal distribution}, + author={Bock, Mary Ellen}, + journal={The Annals of Statistics}, + pages={209--218}, + year={1975}, + publisher={JSTOR} + } + + @inproceedings{dimmery2019shrinkage, + title={Shrinkage estimators in online experiments}, + author={Dimmery, Drew and Bakshy, Eytan and Sekhon, Jasjeet}, + booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining}, + pages={2914--2922}, + year={2019} + } + +Notes: + + The James-Stein method of fitting the normal prior relies on my own fully Bayesian + derivation that extends Dimmery et al. (2019)'s derivation by 1) accounting for correlated + errors and 2) allowing the prior mean vector to depend on a feature matrix ``X``. +""" +from __future__ import annotations + +import math +import warnings +from typing import Any, Callable, Union + +import numpy as np +from scipy.optimize import minimize_scalar +from scipy.stats import multivariate_normal, norm, rv_continuous + +from conditional_inference.bayes.base import BayesBase + + +class Normal(BayesBase): + """Bayesian model with a normal prior. + + Args: + fit_method (Union[str, Callable[[], None]], optional): Specifies how to fit the + prior ("mle", "bock", or "james_stein"). You can also use a custom function + that sets the ``prior_mean``, ``prior_cov``, ``posterior_mean`` and + ``posterior_cov`` attributes. Defaults to "mle". + prior_mean (Union[float, np.ndarray], optional): (# params,) prior mean vector. + Defaults to None. + prior_cov (Union[float, np.ndarray], optional): (# params, # params) prior + covariance matrix. Defaults to None. + + Examples: + + .. testcode:: + + import numpy as np + from conditional_inference.bayes import Normal + + model = Normal(np.arange(10), np.identity(10)) + results = model.fit() + print(results.summary()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + Bayesian estimates + ======================================= + coef pvalue (1-sided) [0.025 0.975] + --------------------------------------- + x0 0.545 0.282 -1.305 2.395 + x1 1.424 0.066 -0.426 3.274 + x2 2.303 0.007 0.453 4.153 + x3 3.182 0.000 1.332 5.032 + x4 4.061 0.000 2.211 5.911 + x5 4.939 0.000 3.089 6.789 + x6 5.818 0.000 3.968 7.668 + x7 6.697 0.000 4.847 8.547 + x8 7.576 0.000 5.726 9.426 + x9 8.455 0.000 6.605 10.305 + =============== + Dep. Variable y + --------------- + """ + + def __init__( + self, + *args: Any, + fit_method: Union[str, Callable[[], None]] = "mle", + prior_mean: Union[float, np.ndarray] = None, + prior_cov: Union[float, np.ndarray] = None, + **kwargs: Any, + ): + + super().__init__(*args, **kwargs) + self.prior_mean, self.prior_cov = prior_mean, prior_cov + if np.isscalar(prior_mean): + self.prior_mean = np.full(self.n_params, prior_mean) + if np.isscalar(prior_cov): + self.prior_cov = prior_cov * np.identity(self.n_params) + + self.posterior_mean, self.posterior_cov = None, None + if callable(fit_method): + fit_method() + self._set_posterior_estimates() + else: + fit_methods = { + "mle": self._fit_mle, + "james_stein": self._fit_james_stein, + "bock": self._fit_bock, + } + if fit_method not in fit_methods: + raise ValueError( + f"`fit_method` must be one of {fit_methods.keys()}, got {fit_method}." + ) + fit_methods[fit_method]() + + def _fit_mle(self, max_iter: int = 100, rtol: float = 0.99) -> None: + """Fit the model using maximum likelihood estimation. + + Args: + max_iter (int, optional): Maximum number of EM iterations. Defaults to 100. + rtol (float, optional): Stopping criterion for EM. Defaults to .99. + """ + + def neg_log_likelihood(prior_std): + # negative log likelihood as a function of the prior standard deviation + marginal_cov = prior_std ** 2 * np.identity(self.n_params) + self.cov + # note: the prior mean is also the marginal mean + return -multivariate_normal.logpdf(self.mean, prior_mean, marginal_cov) + + # use EM to iteratively update the prior mean and covariance + prior_cov = ( + np.zeros(self.cov.shape) if self.prior_cov is None else self.prior_cov + ) + current_log_likelihood, prev_log_likelihood = None, -np.inf + for _ in range(max_iter): + # update prior mean + if self.prior_mean is not None: + prior_mean = self.prior_mean + else: + marginal_cov_inv = np.linalg.inv(self.cov + prior_cov) + prior_mean = ( + self.X + @ np.linalg.inv(self.X.T @ marginal_cov_inv @ self.X) + @ self.X.T + @ marginal_cov_inv + @ self.mean + ) + + # update prior cov + if self.prior_cov is not None: + prior_cov = self.prior_cov + break # prior_mean is computed analytically, so no need to iterate futher + else: + result = minimize_scalar( + neg_log_likelihood, bounds=(0, self.mean.std()), method="bounded" + ) + prior_cov = result.x ** 2 * np.identity(self.n_params) + current_log_likelihood = -result.fun + + if current_log_likelihood / prev_log_likelihood > rtol: + break + prev_log_likelihood = current_log_likelihood + + # set the posterior mean and covariance estimates + # and adjust the prior and posterior covariances to account for uncertainty in the MLE estimate of the prior mean + prior_uncertainty = post_uncertainty = 0 + if self.prior_mean is None: + marginal_cov_inv = np.linalg.inv(prior_cov + self.cov) + prior_uncertainty = ( + self.X @ np.linalg.inv(self.X.T @ marginal_cov_inv @ self.X) @ self.X.T + ) + xi = self.cov @ marginal_cov_inv + post_uncertainty = xi @ prior_uncertainty @ xi + + self.prior_mean, self.prior_cov = prior_mean, prior_cov + self._set_posterior_estimates() # note: set the posterior estimates *before* adjusting the prior covariance + self.prior_cov += prior_uncertainty + self.posterior_cov += post_uncertainty + + def _fit_bock(self, max_iter: int = 100, rtol: float = 0.99) -> None: + """Fit the model using Bock (1975)'s multivariate Stein-type estimator. + + Args: + max_iter (int, optional): Maximum number of iterations. Defaults to 100. + rtol (float, optional): Stopping criteria. Defaults to .99. + + Raises: + RuntimeError: The shrinkage factor must be positve. + """ + cov_inv = np.linalg.inv(self.cov) + prior_mean_df = self.X.shape[1] if self.prior_mean is None else 0 + effective_dimension = np.trace(self.cov) / np.linalg.eig(self.cov)[0].max() + if effective_dimension - prior_mean_df - 2 < 0: + raise RuntimeError( + "Failed to fit the Bock (1975) estimator because the effective dimension" + " of the covariance matrix is too small. Try another fit method like" + " 'mle'." + ) + + xi = ( + np.identity(self.n_params) + if self.prior_cov is None + else self.cov @ np.linalg.inv(self.cov + self.prior_cov) + ) + current_log_likelihood, prev_log_likelihood = None, -np.inf + for _ in range(max_iter): + if self.prior_mean is None: + # update prior mean + marginal_cov_inv = cov_inv @ xi + prior_mean = ( + self.X + @ np.linalg.inv(self.X.T @ marginal_cov_inv @ self.X) + @ self.X.T + @ marginal_cov_inv + @ self.mean + ) + else: + prior_mean = self.prior_mean + + if self.prior_cov is None: + # update prior covariance + error = self.mean - prior_mean + param = min( + (effective_dimension - prior_mean_df - 2) + / (error.T @ cov_inv @ error), + 1, + ) + xi = param * np.identity(self.n_params) + else: + prior_cov = self.prior_cov + # prior mean is computed analytically, so no need to iterate + break + + # check for convergence + marginal_cov = np.linalg.inv(xi) @ self.cov + prior_cov = marginal_cov - self.cov + current_log_likelihood = multivariate_normal.logpdf( + self.mean, prior_mean, marginal_cov + ) + if current_log_likelihood / prev_log_likelihood > rtol: + break + prev_log_likelihood = current_log_likelihood + + # set the posterior mean and covariance estimates + # and adjust the prior and posterior covariances to account for uncertainty in the MLE estimate of the prior mean + prior_uncertainty = post_uncertainty = 0 + if self.prior_mean is None: + marginal_cov_inv = np.linalg.inv(prior_cov + self.cov) + prior_uncertainty = ( + self.X @ np.linalg.inv(self.X.T @ marginal_cov_inv @ self.X) @ self.X.T + ) + xi = self.cov @ marginal_cov_inv + post_uncertainty = xi @ prior_uncertainty @ xi + + self.prior_mean, self.prior_cov = prior_mean, prior_cov + self._set_posterior_estimates() # note: set the posterior estimates *before* adjusting the prior covariance + self.prior_cov += prior_uncertainty + self.posterior_cov += post_uncertainty + + def _set_posterior_estimates(self): + """Sets the posterior mean and covariance using plugin estimates from the prior + mean and covariance if the posterior parameters haven't already been set. + """ + xi = self.cov @ np.linalg.inv(self.prior_cov + self.cov) + if self.posterior_mean is None: + self.posterior_mean = self.prior_mean + ( + np.identity(self.n_params) - xi + ) @ (self.mean - self.prior_mean) + + if self.posterior_cov is None: + self.posterior_cov = (np.identity(self.n_params) - xi) @ self.cov + + def _fit_james_stein(self, max_iter: int = 100, tol: float = 1e-6) -> None: + """Fit the model using James-Stein estimates. + + Args: + max_iter (int, optional): Maximum number of iterations to find the + positive-part prior covariance.. Defaults to 100. + tol (float, optional): Stopping criteria for finding the positive-part prior + covariance. Defaults to 1e-6. + """ + if self.prior_mean is None: + prior_mean = ( + self.X @ np.linalg.inv(self.X.T @ self.X) @ self.X.T @ self.mean + ) + prior_mean_df = self.X.shape[1] + else: + prior_mean = self.prior_mean + prior_mean_df = 0 + + s_squared = ((self.mean - prior_mean) ** 2).sum() + try: + np.linalg.cholesky( + s_squared + / (self.n_params - prior_mean_df - 2) + * np.identity(self.n_params) + - self.cov + ) + except np.linalg.LinAlgError: + # find minimum s_squared such that the (unadjusted) prior covariance is positive semidefinite + warnings.warn( + "The James-Stein prior covariance estimate is not positive semidefinite." + " Using the positive-part James-Stein covariance estimate instead." + " This may result in too little shrinkage." + ) + bounds = [np.sqrt(s_squared), np.inf] + for _ in range(max_iter): + s_squared = ( + 2 * bounds[0] if bounds[1] == np.inf else sum(bounds) / 2 + ) ** 2 + try: + np.linalg.cholesky( + s_squared + / (self.n_params - prior_mean_df - 2) + * np.identity(self.n_params) + - self.cov + ) + bounds[1] = np.sqrt(s_squared) + except: + bounds[0] = np.sqrt(s_squared) + if bounds[1] - bounds[0] < tol: + break + s_squared = bounds[1] ** 2 + + # compute the prior covariance + param = s_squared / (self.n_params - prior_mean_df - 4) + self.prior_cov = ( + param * np.identity(self.n_params) + - self.cov + + param * self.X @ np.linalg.inv(self.X.T @ self.X) @ self.X.T + ) + + # compute the posterior mean + xi = self.cov * (self.n_params - prior_mean_df - 2) / s_squared + self.posterior_mean = prior_mean + (np.identity(self.n_params) - xi) @ ( + self.mean - prior_mean + ) + + # compute the posterior covariance + plugin_posterior_cov = (np.identity(self.n_params) - xi) @ self.cov + if self.prior_mean is None: + prior_mean_uncertainty = ( + self.cov @ self.X @ np.linalg.inv(self.X.T @ self.X) @ self.X.T @ xi + ) + else: + prior_mean_uncertainty = 0 + prior_cov_uncertainty = ( + 2 + / (self.n_params - self.X.shape[1] - 2) + * xi + @ (self.mean - prior_mean).reshape(-1, 1) + @ (self.mean - prior_mean).reshape(1, -1) + @ xi + ) + self.posterior_cov = ( + plugin_posterior_cov + prior_mean_uncertainty + prior_cov_uncertainty + ) + + self.prior_mean = prior_mean + + def _get_marginal_prior(self, index: int) -> rv_continuous: + return norm(self.prior_mean[index], np.sqrt(self.prior_cov[index, index])) + + def _get_marginal_distribution(self, index: int) -> rv_continuous: + return norm( + self.posterior_mean[index], np.sqrt(self.posterior_cov[index, index]) + ) + + def _get_joint_prior(self, indices: np.ndarray): + return multivariate_normal( + self.prior_mean[indices], + self.prior_cov[indices][:, indices], + allow_singular=True, + ) + + def _get_joint_distribution(self, indices: np.ndarray): + return multivariate_normal( + self.posterior_mean[indices], + self.posterior_cov[indices][:, indices], + allow_singular=True, + ) diff --git a/src/conditional_inference/confidence_set.py b/src/conditional_inference/confidence_set.py new file mode 100644 index 0000000..d534bda --- /dev/null +++ b/src/conditional_inference/confidence_set.py @@ -0,0 +1,704 @@ +"""Simultaneous confidence sets and multiple hypothesis testing. + +References: + + .. code-block:: + + @article{romano2005stepwise, + title={Stepwise multiple testing as formalized data snooping}, + author={Romano, Joseph P and Wolf, Michael}, + journal={Econometrica}, + volume={73}, + number={4}, + pages={1237--1282}, + year={2005}, + publisher={Wiley Online Library} + } + + @techreport{mogstad2020inference, + title={Inference for ranks with applications to mobility across neighborhoods and academic achievement across countries}, + author={Mogstad, Magne and Romano, Joseph P and Shaikh, Azeem and Wilhelm, Daniel}, + year={2020}, + institution={National Bureau of Economic Research} + } +""" +from __future__ import annotations + +from itertools import combinations +from typing import Any, Union + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +from scipy.stats import multivariate_normal, norm + +from conditional_inference.base import ColumnsType, ModelBase, ResultsBase + + +class ConfidenceSetResults(ResultsBase): + """Results for simultaneous confidence sets. + + Subclasses :class:`conditional_inference.base.ResultsBase`. + + Args: + n_samples (int, optional): Number of samples to draw when approximating the + confidence set. Defaults to 10000. + """ + + _default_title = "Confidence set results" + + def __init__(self, *args: Any, n_samples: int = 10000, **kwargs: Any): + super().__init__(*args, **kwargs) + self.params = self.model.mean.copy() + + # draw random values for confidence set approximation + mean = np.zeros(2 * len(self.model.mean)) # (2 * # params,) + cov = np.vstack( + [ + np.hstack([self.model.cov, -self.model.cov]), + np.hstack([-self.model.cov, self.model.cov]), + ] + ) # (2 * # params, 2 * # params) + self._std_diagonal = np.sqrt(cov.diagonal()) # (2 * # params,) + self._rvs = multivariate_normal.rvs( + mean, + cov, + size=n_samples, + random_state=self.model.random_state, + ) # (# samples, 2 * # params) + self._rvs /= self._std_diagonal + + if self.model.n_params == 1: + self.pvalues = 2 * np.atleast_1d( + norm.cdf(-abs(self.model.mean[0]), 0, np.sqrt(self.model.cov[0, 0])) + ) + else: + params = self.params.reshape(-1, 1).repeat(n_samples, axis=1) + arr = self._rvs.max(axis=1) * np.sqrt(self.model.cov.diagonal()).reshape( + -1, 1 + ).repeat(n_samples, axis=1) + self.pvalues = np.array( + [(params - arr < 0).mean(axis=1), (params + arr > 0).mean(axis=1)] + ).min(axis=0) + + def _conf_int(self, alpha: float, indices: np.ndarray) -> np.ndarray: + if self.model.n_params == 1: + return np.atleast_2d( + norm.ppf( + [alpha / 2, 1 - alpha / 2], + self.model.mean[0], + np.sqrt(self.model.cov[0, 0]), + ) + ) + + params = self.params[indices] + arr = ( + np.quantile(self._rvs.max(axis=1), 1 - alpha) * self._std_diagonal[indices] + ) + return np.array([params - arr, params + arr]).T + + def test_hypotheses( + self, alpha: float = 0.05, columns: ColumnsType = None + ) -> pd.DataFrame: + """Test the null hypothesis that the parameter is equal to 0. + + Args: + alpha (float, optional): Significance level. Defaults to 0.05. + columns (ColumnsType, optional): Selected columns. Defaults to None. + + Returns: + pd.DataFrame: Results dataframe. + """ + params = np.concatenate([self.params, -self.params]) + + rejected, newly_rejected = np.full(self._rvs.shape[1], False), None + while newly_rejected is None or (newly_rejected.any() and not rejected.all()): + quantile = np.quantile(self._rvs[:, ~rejected].max(axis=1), 1 - alpha) + newly_rejected = (params - quantile * self._std_diagonal > 0) & ~rejected + rejected = rejected | newly_rejected + + indices = self.model.get_indices(columns) + return pd.DataFrame( + rejected.reshape(2, -1).T[indices], + columns=["param>0", "param<0"], + index=self.model.exog_names[indices], + ) + + def _make_summary_header(self, alpha: float) -> list[str]: + return [ + "coef (conventional)", + "pvalue", + f"{1-alpha} CI lower", + f"{1-alpha} CI upper", + ] + + +class ConfidenceSet(ModelBase): + """Model for simultaneous confidence sets. + + Examples: + + .. testcode:: + + import numpy as np + from conditional_inference.confidence_set import ConfidenceSet + + x = np.arange(-1, 2) + cov = np.identity(3) / 10 + model = ConfidenceSet(x, cov) + results = model.fit() + print(results.summary()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + Confidence set results + ========================================================= + coef (conventional) pvalue 0.95 CI lower 0.95 CI upper + --------------------------------------------------------- + x0 -1.000 0.004 -1.762 -0.238 + x1 0.000 1.000 -0.762 0.762 + x2 1.000 0.004 0.238 1.762 + =============== + Dep. Variable y + --------------- + + .. testcode:: + + print(results.test_hypotheses()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + param>0 param<0 + x0 False True + x1 False False + x2 True False + """ + + _results_cls = ConfidenceSetResults + + +class AverageComparison(ConfidenceSet): + """Compare each parameter to the average value across all parameters. + + Subclasses :class:`ConfidenceSet`. + + Args: + *args (Any): Passed to :class:`ConfidenceSet`. + **kwargs (Any): Passed to :class:`ConfidenceSet`. + + Examples: + + .. testcode:: + + import numpy as np + from conditional_inference.confidence_set import AverageComparison + + x = np.arange(-1, 2) + cov = np.identity(3) / 10 + model = AverageComparison(x, cov) + results = model.fit() + print(results.summary()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + Confidence set results + ========================================================= + coef (conventional) pvalue 0.95 CI lower 0.95 CI upper + --------------------------------------------------------- + x0 -1.000 0.000 -1.607 -0.393 + x1 0.000 1.000 -0.607 0.607 + x2 1.000 0.000 0.393 1.607 + =============== + Dep. Variable y + --------------- + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + ones = np.ones((len(self.mean), 1)) + identity = np.identity(len(self.mean)) + cov_inv = np.linalg.inv(self.cov) + projection = ones @ np.linalg.inv(ones.T @ cov_inv @ ones) @ ones.T @ cov_inv + self.mean = (identity - projection) @ self.mean + self.cov = (identity - projection) @ self.cov @ (identity - projection).T + + +def _compute_delta_mean(mean: np.ndarray, i: int) -> np.ndarray: + """Computes the difference between each estimated parameter and a baseline. + + Args: + mean (np.ndarray): (# params,) array of estimated parameters. + i (int): Index of the baseline parameter. + + Returns: + np.ndarray: (# params - 1,) array of differences. + """ + return np.delete(mean - mean[i], i) + + +def _get_delta_names(names: np.ndarray, i: int) -> np.ndarray: + """Get names for the parameter differences, e.g., "x0 - x1". + + Args: + names (np.ndarray): Original parameter names. + i (int): Index of the baseline parameter. + + Returns: + np.ndarray: (# params - 1,) array of names of the differences. + """ + return np.delete([f"{name} - {names[i]}" for name in names], i) + + +def _compute_delta_cov(cov: np.ndarray, i: int, j: int = None) -> np.ndarray: + """Compute the covariance of (mean - mean[i], mean - mean[j]). + + Args: + cov (np.ndarray): Covariance of original parameter estimates. + i (int): Baseline parameter. + j (int, optional): Baseline parameter. Defaults to None. + + Returns: + np.ndarray: (# params - 1, # params - 1) covariance matrix of differences. + """ + if j is None: + j = i + repeat_i = np.repeat(np.atleast_2d(cov[i]), cov.shape[0], axis=0) + repeat_j = np.repeat(np.atleast_2d(cov[j]), cov.shape[0], axis=0).T + delta_cov = cov[i, j] + cov - repeat_i - repeat_j + return np.delete(np.delete(delta_cov, i, axis=0), j, axis=1) + + +class BaselineComparison(ConfidenceSet): + """Compare parameters to a baseline parameter. + + Subclasses :class:`ConfidenceSet`. + + Args: + baseline (Union[int, str]): Index or name of the baseline parameter. + + Examples: + + .. testcode:: + + import numpy as np + from conditional_inference.confidence_set import BaselineComparison + + x = np.arange(-1, 2) + cov = np.identity(3) / 10 + model = BaselineComparison(x, cov, exog_names=["x0", "x1", "x2"], baseline="x0") + results = model.fit() + print(results.summary()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + Confidence set results + ========================================================= + coef (conventional) pvalue 0.95 CI lower 0.95 CI upper + --------------------------------------------------------- + x1 1.000 0.046 0.021 1.979 + x2 2.000 0.000 1.021 2.979 + =============== + Dep. Variable y + --------------- + """ + + def __init__(self, *args, baseline: Union[int, str], **kwargs): + super().__init__(*args, **kwargs) + index = int( + baseline + if isinstance(baseline, (float, int)) + else list(self.exog_names).index(baseline) + ) + self.mean = _compute_delta_mean(self.mean, index) + self.cov = _compute_delta_cov(self.cov, index) + if self._exog_names is not None: + self.exog_names = np.delete(self.exog_names, index) + + +class PairwiseComparisonResults(ConfidenceSetResults): + """Results of pairwise comparisons. + + Subclasses :class:`ConfidenceSetResults`. + """ + + _default_title = "Pairwise comparisons" + + def test_hypotheses( + self, alpha: float = 0.05, columns: ColumnsType = None, wide: bool = True + ) -> pd.DataFrame: + """Test pairwise hypotheses. + + Args: + alpha (float, optional): Significance level. Defaults to .05. + columns (ColumnsType, optional): Selected columns. In wide format, these are + the original column names (e.g., "x0"). In long format, these are the + names of the differences (e.g., "x1 - x0"). Defaults to None. + wide (bool, optional): Return the results is wide (square) format. Defaults + to True. + + Returns: + pd.DataFrame: Results. + """ + if not wide: + return super().test_hypotheses(alpha, columns) + + # reshape rejected dataframe into a triangular matrix + rejected = super().test_hypotheses(alpha).values + tri = np.full((self.model.n_params, self.model.n_params), False) + indices = np.triu_indices(self.model.n_params, 1) + tri[indices] = rejected[:, 0] + tri[(indices[1], indices[0])] = rejected[:, 1] + + indices = self.model.get_indices(columns, self.model.exog_names_orig) + column_names = self.model.exog_names_orig[indices] + return pd.DataFrame( + tri[indices][:, indices], index=column_names, columns=column_names + ) + + def hypothesis_heatmap( + self, + *args: Any, + title: str = None, + ax=None, + triangular: bool = False, + **kwargs: Any, + ): + """Create a heatmap of pairwise hypothesis tests. + + Args: + title (str, optional): Title. + ax (AxesSubplot, optional): Axis to write on. Defaults to None. + triangular (bool, optional): Display the results in a triangular (as opposed + to square) output. Usually, you should set this to True if and only if + your columns are sorted. Defaults to False. + + Returns: + AxesSubplot: Plot. + """ + if ax is None: + _, ax = plt.subplots() + matrix = self.test_hypotheses(*args, **kwargs) + if triangular: + mask = np.zeros_like(matrix) + mask[np.triu_indices_from(mask)] = True + else: + mask = None + + sns.heatmap( + matrix, + cbar=False, + ax=ax, + yticklabels=matrix.index, + xticklabels=matrix.columns, + mask=mask, + square=True, + cmap=sns.color_palette()[3:1:-1], + center=0.5, + ) + ax.set_title(title or self.title) + plt.yticks(rotation=0) + return ax + + def _make_summary_header(self, alpha: float) -> list[str]: + return [ + "delta (conventional)", + "pvalue", + f"{1-alpha} CI lower", + f"{1-alpha} CI upper", + ] + + +class PairwiseComparison(ConfidenceSet): + """Compute pairwise comparisons. + + Examples: + + .. testcode:: + + import numpy as np + from conditional_inference.confidence_set import PairwiseComparison + + x = np.arange(-1, 2) + cov = np.identity(3) / 10 + model = PairwiseComparison(x, cov) + results = model.fit() + print(results.summary()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + Pairwise comparisons + =============================================================== + delta (conventional) pvalue 0.95 CI lower 0.95 CI upper + --------------------------------------------------------------- + x1 - x0 1.000 0.067 -0.045 2.045 + x2 - x0 2.000 0.000 0.955 3.045 + x2 - x1 1.000 0.067 -0.045 2.045 + =============== + Dep. Variable y + --------------- + + .. testcode:: + + print(results.test_hypotheses()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + x0 x1 x2 + x0 False False True + x1 False False False + x2 False False False + + This means that parameter x2 is significantly greater than x0. + """ + + _results_cls = PairwiseComparisonResults + + def __init__(self, *args: Any, **kwargs: Any): + super().__init__(*args, **kwargs) + self.exog_names_orig = self.exog_names + self.exog_names = np.concatenate( + [_get_delta_names(self.exog_names, i)[i:] for i in range(self.n_params)] + ) + self.mean = np.concatenate( + [_compute_delta_mean(self.mean, i)[i:] for i in range(self.n_params)] + ) + self.cov = np.vstack( + [ + np.hstack( + [ + _compute_delta_cov(self.cov, i, j)[i:, j:] + for j in range(self.n_params) + ] + ) + for i in range(self.n_params) + ] + ) + + +class MarginalRankingResults(ResultsBase): + """Marginal ranking results.""" + + _default_title = "Marginal ranking" + + def __init__(self, model: MarginalRanking, *args: Any, **kwargs: Any): + super().__init__(model, *args, **kwargs) + self.params = (-self.model.mean).argsort().argsort() + 1 + self._baseline_comparisons = [ + BaselineComparison(model.mean, model.cov, baseline=i).fit() + for i in range(model.n_params) + ] + + def _conf_int(self, alpha: float, indices: np.ndarray) -> np.array: + def get_rank_ci(results): + hypotheses_count = results.test_hypotheses(alpha).sum(axis=0) + return [hypotheses_count[0], self.model.n_params - hypotheses_count[1] - 1] + + return ( + np.array([get_rank_ci(self._baseline_comparisons[i]) for i in indices]) + 1 + ) + + def _make_summary_header(self, alpha: float) -> list[str]: + return [ + "rank (conventional)", + "pvalue", + f"{1-alpha} CI lower", + f"{1-alpha} CI upper", + ] + + +class MarginalRanking(ConfidenceSet): + """Estimate rankings with marginal confidence intervals. + + Subclasses :class:`ConfidenceSet`. + + Examples: + + .. testcode:: + + import numpy as np + from conditional_inference.confidence_set import MarginalRanking + + x = np.arange(-1, 2) + cov = np.diag([1, 2, 3]) / 10 + model = MarginalRanking(x, cov) + results = model.fit() + print(results.summary()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + Marginal ranking + ========================================================= + rank (conventional) pvalue 0.95 CI lower 0.95 CI upper + --------------------------------------------------------- + x0 3.000 nan 2.000 3.000 + x1 2.000 nan 1.000 3.000 + x2 1.000 nan 1.000 2.000 + =============== + Dep. Variable y + --------------- + + """ + + _results_cls = MarginalRankingResults + + +class SimultaneousRankingResults(ResultsBase): + """Simultaneous ranking results.""" + + _default_title = "Simultaneous ranking" + + def __init__(self, model: SimultaneousRanking, *args: Any, **kwargs: Any): + super().__init__(model, *args, **kwargs) + self.params = (-model.mean).argsort().argsort() + 1 + pairwise_model = PairwiseComparison(model.mean, model.cov) + self._pairwise_comparison = pairwise_model.fit() + + # compute test statistics for finding the top tau parameters + # self._test_stats is a (# params, # params) matrix where + # `self._test_stats[tau, k]`` is the test statistic for the null hypothesis that + # the parameter k is not in the top tau parameters + indices = np.triu_indices(model.n_params, 1) + self._test_stats = np.full((model.n_params, model.n_params), 0.0) + test_stats = pairwise_model.mean / np.sqrt(pairwise_model.cov.diagonal()) + self._test_stats[indices] = -test_stats + self._test_stats[(indices[1], indices[0])] = test_stats + self._test_stats = np.sort(self._test_stats, 0)[::-1] + + # compute random values to find the critical values for finding the top tau + # parameters + # self._rvs is a (# samples, # params, # params) matrix where + # `self._rvs[n, k, l]`` is the nth sample of the studentized param_k - param_l + def reshape(arr): + arr = arr[: int(len(arr) / 2)] + matrix = np.zeros((model.n_params, model.n_params)) + matrix[indices] = arr + matrix[(indices[1], indices[0])] = -arr + return matrix + + self._rvs = np.apply_along_axis(reshape, -1, self._pairwise_comparison._rvs) + + def _conf_int(self, alpha: float, indices: np.ndarray) -> np.ndarray: + hypothesis_matrix = self._pairwise_comparison.test_hypotheses(alpha).values + return ( + np.array( + [ + hypothesis_matrix.sum(axis=1), + self.model.n_params - hypothesis_matrix.sum(axis=0) - 1, + ] + ).T[indices] + + 1 + ) + + def compute_best_params( + self, n_best_params: int = 1, alpha: float = 0.05, superset: bool = True + ) -> pd.Series: + """Compute the set of best (largest) parameters. + + Find the set of parameters such that the truly best ``n_best_params`` parameters + are in this set with probability ``1-alpha``. Or, find the set of parameters + such that these parameters are in the truly best ``n_best_params`` parameters + with probability ``1-alpha``. + + Args: + n_best_params (int, optional): Number of best parameters. Defaults to 1. + alpha (float, optional): Significance level. Defaults to 0.05. + superset (bool, optional): Indicates that the returned set is a superset of + the truly best n parameters. If False, the returned set is a subset of + the truly best n parameters. Defaults to True. + + Returns: + pd.Series: Indicates which parameters are in the selected set. + """ + if superset: + test_stats = self._test_stats[n_best_params - 1] + else: + test_stats = -self._test_stats[n_best_params] + n_best_params = self.model.n_params - n_best_params + + subsets = [] + for indices in combinations(np.arange(self.model.n_params), n_best_params - 1): + arr = np.full(self.model.n_params, True) + if len(indices) > 0: + arr[list(indices)] = False + + subsets.append(arr) + + compute_critical_value = lambda subset: np.quantile( + rvs[:, :, subset].max(axis=(1, 2)), 1 - alpha + ) + rejected, newly_rejected = np.full(self.model.n_params, False), None + while newly_rejected is None or (newly_rejected.any() and not rejected.all()): + rvs = self._rvs[:, ~rejected] + critical_value = max([compute_critical_value(subset) for subset in subsets]) + newly_rejected = (test_stats > critical_value) & ~rejected + rejected = newly_rejected | rejected + + return pd.Series( + ~rejected if superset else rejected, index=self.model.exog_names + ) + + def _make_summary_header(self, alpha: float) -> list[str]: + return [ + "rank (conventional)", + "pvalue", + f"{1-alpha} CI lower", + f"{1-alpha} CI upper", + ] + + +class SimultaneousRanking(ConfidenceSet): + """Estimate rankings with simultaneous confidence intervals. + + Subclasses :class:`ConfidenceSet`. + + Examples: + + .. testcode:: + + import numpy as np + from conditional_inference.confidence_set import SimultaneousRanking + + x = np.arange(3) + cov = np.identity(3) / 10 + model = SimultaneousRanking(x, cov) + results = model.fit() + print(results.summary()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + Simultaneous ranking + ========================================================= + rank (conventional) pvalue 0.95 CI lower 0.95 CI upper + --------------------------------------------------------- + x0 3.000 nan 2.000 3.000 + x1 2.000 nan 1.000 3.000 + x2 1.000 nan 1.000 2.000 + =============== + Dep. Variable y + --------------- + + .. testcode:: + + print(results.compute_best_params()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + x0 False + x1 False + x2 True + dtype: bool + + This we can be 95% confident that the best (largest) parameter is x2. + """ + + _results_cls = SimultaneousRankingResults diff --git a/src/conditional_inference/rank_condition.py b/src/conditional_inference/rank_condition.py new file mode 100644 index 0000000..5947592 --- /dev/null +++ b/src/conditional_inference/rank_condition.py @@ -0,0 +1,301 @@ +"""Inference after ranking. + +References: + + .. code-block:: + + @techreport{andrews2019inference, + title={ Inference on winners }, + author={ Andrews, Isaiah and Kitagawa, Toru and McCloskey, Adam }, + year={ 2019 }, + institution={ National Bureau of Economic Research } + } + + @article{andrews2022inference, + Author = {Andrews, Isaiah and Bowen, Dillon and Kitagawa, Toru and McCloskey, Adam}, + Title = {Inference for Losers}, + Journal = {AEA Papers and Proceedings}, + Volume = {112}, + Year = {2022}, + Month = {May}, + Pages = {635-42}, + DOI = {10.1257/pandp.20221065}, + URL = {https://www.aeaweb.org/articles?id=10.1257/pandp.20221065} + } +""" +from __future__ import annotations + +from typing import Any, Mapping + +import numpy as np + +from .base import ( + ModelBase, + ResultsBase, + ColumnType, + Numeric1DArray, +) +from .confidence_set import ConfidenceSet +from .stats import quantile_unbiased + + +class RankConditionResults(ResultsBase): + """Quantile-unbiased results. + + Inherits from :class:`conditional_inference.base.ResultsBase`. + + Args: + *args (Any): Passed to :class:`conditional_inference.base.ResultsBase`. + beta (float, optional): Used to compute the projection quantile for hybrid + estimation. Defaults to 0. + marginal_distribution_kwargs (Mapping[str, Any], optional): Passed to + :meth:`RankCondition.get_marginal_distribution`. Defaults to None. + **kwargs (Any): Passed to :class:`conditional_inference.base.ResultsBase`. + """ + + _default_title = "Rank condition quantile-unbiased estimates" + + def __init__( + self, + *args: Any, + beta: float = 0, + marginal_distribution_kwargs: Mapping[str, Any] = None, + **kwargs: Any, + ): + super().__init__(*args, **kwargs) + if marginal_distribution_kwargs is None: + marginal_distribution_kwargs = {} + self.marginal_distributions, self.params, self.pvalues = [], [], [] + for i in range(self.model.n_params): + dist = self.model.get_marginal_distribution( + i, beta=beta, **marginal_distribution_kwargs + ) + self.marginal_distributions.append(dist) + self.params.append(dist.ppf(0.5)) + self.pvalues.append((1 - beta) * dist.cdf(0) + beta) + self.params, self.pvalues = np.array(self.params), np.array(self.pvalues) + self._beta = beta + + def _conf_int(self, alpha: float, indices: np.ndarray) -> np.ndarray: + # see paper for details on adjusting alpha + return super()._conf_int((alpha - self._beta) / (1 - self._beta), indices) + + def _make_summary_header(self, alpha: float) -> list[str]: + return ["coef (median)", "pvalue (1-sided)", f"[{alpha/2}", f"{1-alpha/2}]"] + + +class RankCondition(ModelBase): + """Rank condition quantile-unbiased estimator. + + Provides utilities for obtaining quantile-unbiased estimates conditional on the + rank-ordering of conventional estimates of policy effects. + + Subclasses :class:`conditional_inference.base.ModelBase`. + + Args: + *args (Any): Passed to :class:`conditional_inference.base.ModelBase`. + xmean (Numeric1DArray, optional): (# params,) array of conventional estimates to + use for ranking. If None, ranking conditions are based on ``mean``. Defaults + to None. + xycov (np.ndarray, optional): (# params, # params) covariance matrix between + ``mean`` and ``xmean``. Defaults to None. + **kwargs (Any): Passed to :class:`conditional_inference.base.ModelBase`. + + Raises: + ValueError: Either all or none of ``xmean``, ``xcov`` and ``xycov`` must be + specified. + + Additional attributes: + xmean (np.ndarray): (# params,) array of conventional estimates to use for + ranking. + xycov (np.ndarray): (# params, # params) covariance matrix between ``self.mean`` + and ``self.xmean``. + + Examples: + + Compute a quantile-unbiased distribution of the x4 parameter given that it was + the top-ranked parameter. + + .. testcode:: + + import numpy as np + from conditional_inference.rank_condition import RankCondition + + model = RankCondition(np.arange(5), np.identity(5)) + dist = model.get_marginal_distribution("x4") + print(dist.ppf([.025, .5, .975])) + + .. testoutput:: + + [0.06742731 3.68627552 5.93267239] + + Compute an "almost" quantile-unbiased hybrid distribution. + + .. testcode:: + + dist = model.get_marginal_distribution("x4", beta=.005) + print(dist.ppf([.025, .5, .975])) + + .. testoutput:: + + [0.8674603 3.68732401 5.9328792 ] + + Summarize the quantile-unbiased results. + + .. testcode:: + + results = model.fit() + print(results.summary()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + Rank condition quantile-unbiased estimates + =============================================== + coef (median) pvalue (1-sided) [0.025 0.975] + ----------------------------------------------- + x0 0.314 0.406 -1.933 3.933 + x1 1.000 0.285 -2.922 4.922 + x2 2.000 0.136 -1.922 5.922 + x3 3.000 0.058 -0.922 6.922 + x4 3.686 0.023 0.067 5.933 + =============== + Dep. Variable y + --------------- + + Summarize the "almost" quantile-unbiased hybrid results. + + .. testcode:: + + results = model.fit(beta=.005) + print(results.summary()) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + Rank condition quantile-unbiased estimates + =============================================== + coef (median) pvalue (1-sided) [0.025 0.975] + ----------------------------------------------- + x0 0.313 0.409 -1.977 3.152 + x1 1.000 0.288 -2.152 4.152 + x2 2.000 0.140 -1.152 5.152 + x3 3.000 0.044 -0.152 6.152 + x4 3.687 0.005 0.848 5.977 + =============== + Dep. Variable y + --------------- + """ + + _results_cls = RankConditionResults + + def __init__( + self, + *args: Any, + xmean: Numeric1DArray = None, + xycov: np.ndarray = None, + **kwargs: Any, + ): + super().__init__(*args, **kwargs) + xparams = (xmean, xycov) + if any([x is not None for x in xparams]) and None in xparams: + raise ValueError( + "Either both or neither of `xmean` and `xycov` must be given." + ) + + self.xmean = xmean + self.xycov = xycov + self._confidence_set = ConfidenceSet(self.mean, self.cov).fit() + + @property + def xmean(self): # pylint: disable=missing-function-docstring + return self.mean if self._xmean is None else self._xmean + + @xmean.setter + def xmean(self, xmean): # pylint: disable=missing-function-docstring + self._xmean = None if xmean is None else np.array(xmean) + self._estimated_ranks = (-self.xmean).argsort().argsort() + + @property + def xycov(self): # pylint: disable=missing-function-docstring + return self.cov if self._xycov is None else self._xycov + + @xycov.setter + def xycov(self, xycov): # pylint: disable=missing-function-docstring + self._xycov = None if xycov is None else np.array(xycov) + + def get_marginal_distribution( # pylint: disable=too-many-arguments + self, + column: ColumnType, + ranks: Numeric1DArray = None, + beta: float = 0, + **kwargs: Any, + ) -> quantile_unbiased: + """Compute a quantile-unbiased distribution for a given ranking condition. + + Args: + column (ColumnType): Name or index of the parameter of interest. Defaults to + None. + ranks (Numeric1DArray, optional): Ranking conditions for the parameter of + interest. This method returns a quantile-unbiased distribution given + that the estimated rank of the parameter of interest is in ``ranks``. + If None, the estimated rank of the parameter is used as the ranking + condition. Defaults to None. + beta (float, optional): Used to compute the projection quantile for hybrid + estimation. Defaults to 0. + **kwargs (Any): Passed to :class:`quantile_unbiased`. + + Returns: + quantile_unbiased: Quantile-unbiased distribution. + """ + + def rank_condition_holds(): + return ( + (current_rank - n_always_equal <= ranks) + & (ranks <= current_rank + n_always_equal) + ).any() + + index = self.get_index(column) + ranks = np.atleast_1d(self._estimated_ranks[index] if ranks is None else ranks) + + # compute the conditional truncation set + equal_cov = self.xycov[index, index] == self.xycov[:, index] + greater_cov = self.xycov[~equal_cov, index] > self.xycov[index, index] + # number of parameters whose estimates are always greater than the estimate of + # the target parameter + n_always_greater = (equal_cov & (self.xmean > self.xmean[index])).sum() + # number of parameters whose estimates are always equal to the estimate of the + # target parameter. -1 so you don't count the target parameter itself. + n_always_equal = (equal_cov & (self.xmean == self.xmean[index])).sum() - 1 + z = ( # pylint: disable=invalid-name + self.xmean + - (self.xycov[:, index] / self.cov[index, index]) * self.mean[index] + ) + # thresholds are the values at which the estimated value of the target parameter + # equals the estimated value of the other parameters (denoted Q in the paper) + thresholds = ( + self.cov[index, index] + * (z[~equal_cov] - z[index]) + / (self.xycov[index, index] - self.xycov[~equal_cov, index]) + ) + argsort = thresholds.argsort() + greater_cov, thresholds = greater_cov[argsort], thresholds[argsort] + intervals = np.array([thresholds, np.append(thresholds[1:], np.inf)]).T + current_rank = n_always_greater + (~greater_cov).sum() + truncset = [[-np.inf, thresholds[0]]] if rank_condition_holds() else [] + for greater_cov_i, interval in zip(greater_cov, intervals): + current_rank += 1 if greater_cov_i else -1 + if rank_condition_holds(): + truncset.append(interval) + + if beta != 0: + # projection interval must be centered on 0 for `quantile_unbiased` + kwargs["projection_interval"] = ( + self._confidence_set.conf_int(beta, [index]) - self.mean[index] + )[0] + return quantile_unbiased( # type: ignore + y=self.mean[index], + scale=np.sqrt(self.cov[index, index]), + truncation_set=truncset, + **kwargs, + ) diff --git a/src/conditional_inference/rqu.py b/src/conditional_inference/rqu.py deleted file mode 100644 index 9c58f8a..0000000 --- a/src/conditional_inference/rqu.py +++ /dev/null @@ -1,598 +0,0 @@ -"""Quantile-unbiased estimation -""" -from __future__ import annotations - -from typing import Any, List, Sequence, Union - -import numpy as np -from scipy.stats import multivariate_normal -from statsmodels.iolib.summary import Summary - -from .base import ( - ConventionalEstimatesData, - ModelBase, - ResultsBase, - ColumnType, - ColumnsType, - Numeric1DArray, -) -from .stats import quantile_unbiased - - -class RQUData(ConventionalEstimatesData): - """Ranked quantile-unbiased estimator data. - - Args: - mean (Numeric1DArray): (n,) array of conventional estimates used to rank-order - policies. - cov (np.ndarray): (n,n) covariance matrix of ``mean``. - endog_names (Union[str, Sequence[str]], optional): Names of endogenous - variables. Defaults to None. - exog_names (Sequence[str], optional): (n,) sequence of names of exogenous - variables (i.e., the policies). Defaults to None. - ymean (Numeric1DArray, optional): (n,) conventional estimates of policy - effects. Defaults to None. - ycov (np.ndarray, optional): (n,n) covariance matrix of ``ymean``. Defaults to - None. - xycov (np.ndarray, optional): (n,n) covariance matrix of ``mean`` and ``ymean``. - Defaults to None. - - Attributes: - mean (np.ndarray): (n,) array of conventional estimates used to rank-order - policies/ - cov (np.ndarray): (n, n) covariance matrix of ``mean``. - endog_names (str): Name of the endogenous variable. - exog_names (Sequence[str]): (n,) sequence of names of exogenous variables - (i.e., the policies). - ymean (np.ndarray): (n,) conventional estimates of policy effects. If ``None``, - ``mean`` are assumed to be the policy effects. - ycov (np.ndarray): (n, n) covariance matrix of ``ymean``. If ``None``, use - ``cov``. - xycov (np.ndarray): (n,n) covariance matrix of ``mean`` and ``ymean``. If - ``None``, use ``cov``. - - Note: - - By default, we assume that the conventional estimates used to rank policies are - the same as the conventional estimates of the policy effects. If this is not the - case, set ``mean`` and ``cov`` to the conventional estimates used to rank the - policies and ``ymean`` and ``ycov`` to the conventional estimates of the policy - effects. You must also set ``xycov`` to the covariance matrix of ``mean`` and - ``ymean``. - """ - - def __init__( # pylint: disable=too-many-arguments - self, - mean: Numeric1DArray, - cov: np.ndarray, - endog_names: str = None, - exog_names: Union[str, Sequence[str]] = None, - ymean: Numeric1DArray = None, - ycov: np.ndarray = None, - xycov: np.ndarray = None, - ): - super().__init__(mean, cov, endog_names, exog_names) - self.ymean = ymean - self.ycov = ycov - self.xycov = xycov - - @property - def ymean(self): # pylint: disable=missing-function-docstring - return self.mean if self._ymean is None else self._ymean - - @ymean.setter - def ymean(self, ymean): # pylint: disable=missing-function-docstring - self._ymean = None if ymean is None else np.atleast_1d(ymean) - - @property - def ycov(self): # pylint: disable=missing-function-docstring - return self.cov if self._ycov is None else self._ycov - - @ycov.setter - def ycov(self, ycov): # pylint: disable=missing-function-docstring - self._ycov = ycov - - @property - def xycov(self): # pylint: disable=missing-function-docstring - return self.cov if self._xycov is None else self.xycov - - @xycov.setter - def xycov(self, xycov): # pylint: disable=missing-function-docstring - self._xycov = xycov - - -class RQU(ModelBase): - """Ranked quantile-unbiased estimator. - - Provides utilities for obtaining quantile-unbiased estimates conditional on the - rank-ordering of conventional estimates of policy effects. - - Args: - mean (Numeric1DArray): (n,) array of conventional estimates of policy effects. - cov (np.ndarray): (n,n) covariance matrix of ``mean``. - seed (int, optional): Random seed. Defaults to 0. - **args (Any): Additional arguments passed to :class:`RQUData` constructor. - **kwargs (Any): Additional keyword arguments passed to :class:`RQUData` - constructor. - - Attributes: - data (RQUData): Ranked quantile-unbiased estimator data. - seed (int): Random seed. - - You can set and access ``self.data`` attributes directly, e.g., - - .. testsetup:: - - from conditional_inference.rqu import RQU - import numpy as np - - .. doctest:: - - >>> rqu = RQU(mean=np.arange(3), cov=np.identity(3)) - >>> rqu.mean - array([0, 1, 2]) - """ - - _data_properties = [ - "mean", - "cov", - "endog_names", - "exog_names", - "ymean", - "ycov", - "xycov", - ] - - def __init__( - self, - mean: Numeric1DArray, - cov: np.ndarray, - *args: Any, - seed: int = 0, - **kwargs: Any, - ): - self.seed = seed - self.data = RQUData(mean, cov, *args, **kwargs) - - def compute_projection_quantile( - self, alpha: float = 0.05, n_samples: int = 10000 - ) -> float: - """Compute the 1-alpha quantile for projection confidence intervals. - - Args: - alpha (float, optional): Quantile level of the projection CI. Defaults to - 0.05. - n_samples (int, optional): Number of samples used in approximating the - 1-alpha quantile. Defaults to 10000. - - Returns: - float: 1-alpha quantile of the projection CI. - """ - if alpha == 0: - return np.inf - rvs = self.projection_rvs(size=n_samples) - return np.quantile(abs(rvs).max(axis=1), 1 - alpha) - - def fit( - self, - cols: ColumnsType = None, - projection: bool = False, - **kwargs: Any, - ) -> Union[ProjectionResults, RQUResults]: - """Fit the RQU estimator and return results. - - Args: - cols (ColumnsType, optional): Names or indices of the policies of interest. - Defaults to None. - projection (bool, optional): If True, return projection results. If False, - return quantile-unbiased results. Defaults to False. - - Returns: - Union[ProjectionResults, RQUResults]: Quantile-unbiased estimation results. - - Examples: - - Suppose we have 5 policies, each with a true effect of 0. The observed - effect of the policies is sampled from a joint normal with identity - covariance matrix. - - .. code-block:: python - - >>> from conditional_inference.rqu import RQU - >>> import numpy as np - >>> npolicies = 5 - >>> mean = np.random.normal(size=npolicies) - >>> cov = np.identity(npolicies) - >>> rqu = RQU(mean, cov) - >>> results = rqu.fit(cols="sorted", beta=.005) - >>> print(results.summary()) - Conditional quantile-unbiased estimates - ===================================== - coef (median) pvalue [0.025 0.975] - ------------------------------------- - x1 0.388 0.412 -2.209 2.931 - x2 0.487 0.413 -2.885 3.672 - x4 -1.289 0.700 -4.354 2.590 - x0 -1.468 0.664 -4.775 2.690 - x3 -0.154 0.529 -3.316 2.183 - =============== - Dep. Variable y - --------------- - """ - if projection: - return ProjectionResults(self, cols, **kwargs) - return RQUResults(self, cols, **kwargs) - - def get_distribution( # pylint: disable=too-many-arguments - self, - col: ColumnType = None, - rank: Union[str, int] = "exact", - beta: float = 0, - n_samples: int = 10000, - **kwargs: Any, - ) -> quantile_unbiased: - """Compute a quantile-unbiased distribution of the average effect of a policy. - - Args: - col (ColumnType, optional): Name or index of the policy of interest. - Defaults to None. - rank (Union[str, int], optional): Rank of the policy of interest. The - "exact" condition means that we condition on the policy we observed to - be the best was in fact observed to be the best. Defaults to "exact". - beta (float, optional): Projection quantile for hybrid estimation. Defaults - to 0. - n_samples (int, optional): Number of samples used to approximate the - projection confidence interval. Defaults to 10000. - **kwargs (Any): Additional keyword arguments are passed to the - :class:`quantile_unbiased` constructor. - - Returns: - quantile_unbiased: Quantile-unbiased distribution of the policy effect. - """ - - def get_index_rank(col, rank): - # return the index and valid rank order(s) of the policy of interest - if isinstance(rank, str) and rank not in ("exact", "floor", "ceil"): - raise ValueError( - f"If `rank` is a string, must be 'exact', 'floor', or 'ceil', (got {rank})" - ) - - if col is None: - if rank == "exact": - rank = 0 - if not isinstance(rank, int): - raise ValueError( - f"If `col` is not specified, `rank` must be 'exact' or int (got {rank})." - ) - index = np.argsort(-self.mean)[rank] - else: - index = self._get_index(col) - exact_rank = (self.mean > self.mean[index]).sum() - if isinstance(rank, str): - if rank == "exact": - rank = exact_rank - elif rank == "floor": - rank = np.arange(exact_rank + 1) - elif rank == "ceil": - rank = np.arange(exact_rank, self.mean.shape[0]) - - return index, np.atleast_1d(rank) % self.mean.shape[0] - - def check_s_V_condition(): # pylint: disable=invalid-name - # check that condition on set V is satisifed - # V is the set of parameters with X-Y covariances equal to that of the - # target parameter - s_v = self.xycov[i, i] == self.xycov[:, i] - if (-(z - z[i]))[s_v].min() < 0: - indices = np.arange(self.ymean.shape[0]) - invalid_indices = np.where(s_v & (indices != i))[0] - raise ValueError( - " ".join( - [ - f"Empty truncation set for index {i} and rank {rank}.", - f"Parameters at indices {invalid_indices.tolist()} have equal X-Y", - f"covariances with the parameter at target index {i}.", - ] - ) - ) - - def compute_truncation_set(): - # compute the trucation set for `truncnorm.cdf` - # see paper for details on this algorithm - - def update_truncation_set(idx, j): - if ( - (tau_upper_size - tau_any_size <= rank) - & (rank <= tau_upper_size + tau_any_size) - ).any(): - if j is None: - # no possible upper bounding parameters => upper bound is np.inf - truncset.append((q[order[0]], np.inf)) - elif j == order[-1]: - # no possible lower bounding parameters => lower bound is -np.inf - truncset.append((-np.inf, q[j])) - else: - truncset.append((q[order[idx + 1]], q[j])) - - # parameters which are eligible to serve as upper or lower bounds - theta = self.xycov[i, i] != self.xycov[:, i] - # possible threshold values - q = ( # pylint: disable=invalid-name - self.ycov[i, i] - * (z[theta] - z[i]) - / (self.xycov[i, i] - self.xycov[theta, i]) - ) - order = np.argsort(-q) - # indicates parameters which beat i - # when the set of possible upper bounding parameters is empty - tau_upper = self.xycov[i, i] < self.xycov[theta, i] - # number of parameters which beat i - # when the set of possible upper bounding parameters is empty - tau_upper_size = tau_upper.sum() - # number of parameters which could beat i - # regardless of the upper and lower bounding parameters - tau_any_size = (self.xycov[i, i] == self.xycov[:, i]).sum() - 1 - - # compute the truncation set - truncset = [] - update_truncation_set(None, None) - for idx, j in enumerate(order): - # update the size of winning parameters when theta_j moves - # from possible lower bounding parameters - # to possible upper bounding parameters - tau_upper_size -= 1 if tau_upper[j] else -1 - update_truncation_set(idx, j) - - return truncset - - i, rank = get_index_rank(col, rank) - z = ( # pylint: disable=invalid-name - self.mean - (self.xycov[:, i] / self.ycov[i, i]) * self.ymean[i] - ) - check_s_V_condition() - if beta != 0: - kwargs["projection_interval"] = self.compute_projection_quantile( - beta, n_samples - ) * np.sqrt(self.ycov[i, i]) - return quantile_unbiased( # type: ignore - y=self.ymean[i], - scale=np.sqrt(self.ycov[i, i]), - truncation_set=compute_truncation_set(), - **kwargs, - ) - - def get_distributions( - self, - cols: ColumnsType = None, - beta: float = 0, - n_samples: int = 10000, - **kwargs: Any, - ) -> List[quantile_unbiased]: - """Compute quantile-unbiased distributions of average policy effects. - - Args: - cols (ColumnsType, optional): Names or indices of policies of interest. - Defaults to None. - beta (float, optional): Projection quantile for hybrid estimation. Defaults - to 0. - n_samples (int, optional): Number of samples used to approximate projection - confidence intervals. Defaults to 10000. - **kwargs (Any): Additional keyword arguments are passed to - :meth:`RQU.get_distribution`. - - Returns: - List[quantile_unbiased]: Quantile-unbiased distributions of policy effects. - """ - indices = self.get_indices(cols) - if beta == 0: - return [self.get_distribution(i, **kwargs) for i in indices] - projection_intervals = self.compute_projection_quantile( - beta, n_samples - ) * np.sqrt(self.ycov[indices][:, indices].diagonal()) - return [ - self.get_distribution(i, projection_interval=interval, **kwargs) - for i, interval in zip(indices, projection_intervals) - ] - - def projection_rvs(self, size: int = 1) -> np.ndarray: - """Sample random values to construct projection confidence intervals. - - Args: - size (int, optional): Number of samples. Defaults to 1. - - Returns: - np.ndarray: (size, 2) array of samples. - """ - rvs = multivariate_normal.rvs( - np.zeros(self.ymean.shape), self.ycov, size=size, random_state=self.seed - ) - rvs /= np.sqrt(self.ycov.diagonal()) - return np.array([rvs.min(axis=1), rvs.max(axis=1)]).T - - -class ProjectionResults(ResultsBase): - """Projection confidence interval results. - - Projection confidence intervals have unconditionally correct coverage. - - Args: - model (RQU): The RQU model instance. - cols (ColumnsType, optional): Names or indices of policies of interest. - Defaults to None. - n_samples (int, optional): Number of samples used to approximate projection - confidence intervals. Defaults to 10000. - title (str, optional): Results title. Defaults to "Projection estimates". - - Attributes: - model (RQU): The model instance. - indices (List[int]): Indices of the policies of interest. - params (np.ndarray): (n,) array of conventional point estimates. - projection_rvs (np.ndarray): (n_samples, 2) array of samples used to construct - projection CIs. - pvalues (np.ndarray): (n,) array of probabilities that the true effect of a - policy is less than 0. - std_params_diag (np.ndarray): (n,) array of standard deviations from the - ``mean`` covariance matrix. - - Examples: - - .. code-block:: python - - >>> from conditional_inference.rqu import RQU - >>> import numpy as np - >>> npolicies = 5 - >>> mean = np.random.normal(size=npolicies) - >>> cov = np.identity(npolicies) - >>> rqu = RQU(mean, cov) - >>> results = rqu.fit(cols="sorted", projection=True) - >>> print(results.summary()) - Projection estimates - ========================================================= - coef (conventional) pvalue 0.95 CI lower 0.95 CI upper - --------------------------------------------------------- - x0 1.644 0.233 -0.936 4.223 - x2 0.813 0.693 -1.766 3.393 - x3 0.217 0.931 -2.362 2.796 - x1 0.060 0.962 -2.519 2.639 - x4 -0.064 0.976 -2.643 2.515 - =============== - Dep. Variable y - --------------- - - """ - - def __init__( - self, - model: RQU, - cols: ColumnsType = None, - n_samples: int = 10000, - title: str = "Projection estimates", - ): - def compute_pvalues(): - params = self.params.reshape(-1, 1).repeat(n_samples, axis=1) - std = self.std_params_diag.reshape(-1, 1).repeat(n_samples, axis=1) - arr = params + self.projection_rvs[:, 0] * std - return (arr < 0).mean(axis=1) - - super().__init__(model, cols, title) - self.params = model.ymean[self.indices] - self.projection_rvs = model.projection_rvs(n_samples) - self.std_params_diag = np.sqrt(model.ycov.diagonal())[self.indices] - self.pvalues = compute_pvalues() - - def conf_int(self, alpha: float = 0.05, cols: ColumnsType = None) -> np.ndarray: - """Compute the 1-alpha confidence interval. - - Args: - alpha (float, optional): The CI will cover the truth with probability - greater than 1-alpha. Defaults to 0.05. - cols (ColumnsType, optional): Names or indices of policies of interest. - Defaults to None. - - Returns: - np.ndarray: (n,2) array of confidence intervals. - """ - indices = self.indices if cols is None else self.model.get_indices(cols) - select = [np.where(self.indices == index)[0][0] for index in indices] - c_alpha = np.quantile(abs(self.projection_rvs).max(axis=1), 1 - alpha) - return np.array( - [ - self.params - c_alpha * self.std_params_diag, - self.params + c_alpha * self.std_params_diag, - ] - ).T[select] - - def _make_summary_header(self, alpha: float) -> List[str]: - return [ - "coef (conventional)", - "pvalue", - f"{1-alpha} CI lower", - f"{1-alpha} CI upper", - ] - - -class RQUResults(ResultsBase): - """Ranked quantile-unbiased results. - - Inherits from :class:`conditional_inference.base.ResultsBase`. - - Args: - model (RQU): The RQU model instance - cols (ColumnsType, optional): Names or indices of policies of interest. - Defaults to None. - beta (float, optional): Projection quantile for hybrid estimation. Defaults to - 0. - title (str, optional): Results title. Defaults to "Quantile-unbiased - estimates". - - Attributes: - model (RQU): The model instance. - indices (List[int]): Indices of the policies of interest. - params (np.ndarray): (n,) array of conventional point estimates. - pvalues (np.ndarray): (n,) array of probabilities that the true effect of a - policy is less than 0. - distributions (List[quantile_unbiased]): Quantile-unbiased distributions - conditional on rank ordering. - beta (float): Projection quantile for hybrid estimation. - - Examples: - - .. code-block:: python - - >>> from conditional_inference.rqu import RQU - >>> import numpy as np - >>> npolicies = 5 - >>> mean = np.random.normal(size=npolicies) - >>> cov = np.identity(npolicies) - >>> rqu = RQU(mean, cov) - >>> results = rqu.fit(cols="sorted", beta=.005) - >>> print(results.summary()) - Quantile-unbiased estimates - ===================================== - coef (median) pvalue [0.025 0.975] - ------------------------------------- - x1 0.388 0.412 -2.209 2.931 - x2 0.487 0.413 -2.885 3.672 - x4 -1.289 0.700 -4.354 2.590 - x0 -1.468 0.664 -4.775 2.690 - x3 -0.154 0.529 -3.316 2.183 - =============== - Dep. Variable y - --------------- - """ - - def __init__( - self, - model: RQU, - cols: ColumnsType = None, - beta: float = 0, - title: str = "Quantile-unbiased estimates", - **kwargs: Any, - ): - super().__init__(model, cols, title) - self.distributions = self.model.get_distributions(cols, beta=beta, **kwargs) - self.params = np.array([dist.ppf(0.5) for dist in self.distributions]) - self.pvalues = np.array( - [(1 - beta) * dist.cdf(0) + beta for dist in self.distributions] - ) - self.beta = 0 if beta is None else beta - - def conf_int(self, alpha: float = 0.05, cols: ColumnsType = None) -> np.ndarray: - """Compute the 1-alpha confidence interval. - - Args: - alpha (float, optional): The CI will cover the truth with probability - 1-alpha. Defaults to 0.05. - cols (ColumnsType, optional): Names or indices of policies of interest. - Defaults to None. - - Returns: - np.ndarray: (n,2) array of confidence intervals. - """ - # min-max scale significance level given beta-quantile projection interval - # see paper for details - alpha = (alpha - self.beta) / (1 - self.beta) - return super().conf_int(alpha, cols) - - def _make_summary_header(self, alpha: float) -> List[str]: - return ["coef (median)", "pvalue", f"[{alpha/2}", f"{1-alpha/2}]"] diff --git a/src/conditional_inference/significance_condition.py b/src/conditional_inference/significance_condition.py new file mode 100644 index 0000000..a6a7ac7 --- /dev/null +++ b/src/conditional_inference/significance_condition.py @@ -0,0 +1,124 @@ +"""Inference for parameters that achieve statistical significance. +""" +from __future__ import annotations + +from typing import Any, Mapping + +import numpy as np +from conditional_inference.base import ColumnType, ModelBase, ResultsBase +from conditional_inference.confidence_set import ConfidenceSet +from conditional_inference.stats import quantile_unbiased + + +class SignificanceConditionResults(ResultsBase): + """Quantile-unbiased results. + + Sublcasses :class:`conditional_inference.base.ResultsBase`. + + Args: + *args (Any): Passed to :class:`conditional_inference.base.ResultsBase`. + marginal_distribution_kwargs (Mapping[str, Any], optional): Passed to + :meth:`SignificanceCondition.get_marginal_distribution`. Defaults to None. + **kwargs (Any): Passed to :class:`conditional_inference.base.ResultsBase`. + """ + + _default_title = "Significance condition quantile-unbiased estimates" + + def __init__( + self, + *args: Any, + marginal_distribution_kwargs: Mapping[str, Any] = None, + **kwargs: Any, + ): + + super().__init__(*args, **kwargs) + if marginal_distribution_kwargs is None: + marginal_distribution_kwargs = {} + self.marginal_distributions, self.params, self.pvalues = [], [], [] + for i in range(self.model.n_params): + dist = self.model.get_marginal_distribution( + i, **marginal_distribution_kwargs + ) + self.marginal_distributions.append(dist) + self.params.append(dist.ppf(0.5)) + self.pvalues.append(dist.cdf(0)) + self.params, self.pvalues = np.array(self.params), np.array(self.pvalues) + + def _make_summary_header(self, alpha: float) -> list[str]: + return ["coef (median)", "pvalue (1-sided)", f"[{alpha/2}", f"{1-alpha/2}]"] + + +class SignificanceCondition(ModelBase): + """Significance condition quantile-unbiased estimator. + + Subclasses :class:`conditional_inference.base.ModelBase`. + + Examples: + Get a quantile-unbiased distribution for x3. + + .. testcode:: + + import numpy as np + from conditional_inference.significance_condition import SignificanceCondition + + model = SignificanceCondition(np.arange(4), np.identity(4)) + dist = model.get_marginal_distribution("x3") + print(dist.ppf([.025, .5, .975])) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + [-0.32622156 1.95349752 4.8138274 ] + + Display the results. + + .. testcode:: + + results = model.fit() + print(results.summary(columns=["x3"])) + + .. testoutput:: + :options: -ELLIPSIS, +NORMALIZE_WHITESPACE + + Significance condition quantile-unbiased estimates + =============================================== + coef (median) pvalue (1-sided) [0.025 0.975] + ----------------------------------------------- + x3 1.953 0.106 -0.326 4.814 + =============== + Dep. Variable y + --------------- + """ + + _results_cls = SignificanceConditionResults + + def __init__(self, *args: Any, **kwargs: Any): + super().__init__(*args, **kwargs) + self._confidence_set = ConfidenceSet(self.mean, self.cov).fit() + + def get_marginal_distribution( + self, column: ColumnType, alpha: float = 0.05, **kwargs: Any + ) -> quantile_unbiased: + """Get the marginal quantile-unbiased distribution. + + The distribution is quantile-unbiased conditional on the parameter being + statistically significant at level ``alpha``. + + Args: + column (ColumnType): Selected column. + alpha (float, optional): Significance level. Defaults to .05. + + Returns: + quantile_unbiased: Quantile-unbiased distribution. + """ + index = self.get_index(column) + critical_value = ( + self._confidence_set.conf_int(alpha, [index]) - self.mean[index] + )[0, 1] + truncation_set = [[-np.inf, -critical_value], [critical_value, np.inf]] + return quantile_unbiased( + y=self.mean[index], + scale=np.sqrt(self.cov[index, index]), + truncation_set=truncation_set, + **kwargs, + ) diff --git a/src/conditional_inference/stats.py b/src/conditional_inference/stats.py index b083766..93d60ac 100644 --- a/src/conditional_inference/stats.py +++ b/src/conditional_inference/stats.py @@ -1,247 +1,210 @@ -"""Statistical distributions +"""Statistical distributions. """ from __future__ import annotations import warnings -from typing import Any, List, Tuple, Union +from typing import Any, Callable, List, Sequence, Tuple, Union import numpy as np +from scipy.integrate import quad +from scipy.interpolate import interp1d from scipy.misc import derivative -from scipy.stats import norm, rv_continuous, truncnorm as truncnorm_base from scipy.optimize import NonlinearConstraint, fsolve, minimize +from scipy.stats import norm, rv_continuous, truncnorm as truncnorm_base + from .base import Numeric1DArray +from .utils import weighted_quantile -class truncnorm(rv_continuous): # pylint: disable=invalid-name - """Truncated normal distribution. +class joint_distribution: + """Join distribution based on independent marginal distributions. - Inherits from `scipy.stats.rv_continuous `_ - and handles standard public methods (``pdf``, ``cdf``, etc.). + Args: + marginal_distributions (Sequence[rv_continuous]): Marginal distributions. + """ - This uses the [exponential tilting](https://ieeexplore.ieee.org/document/7408180) - approximation method. + def __init__(self, marginal_distributions: Sequence[rv_continuous]): + self._marginal_distributions = list(marginal_distributions) - Args: - truncation_set (List[Tuple[float, float]], optional): List of truncation - intervals, e.g., ``[(-1, 0), (1, 2)]`` truncates the distribution to - [-1, 0] union [1, 2]. Defaults to None. - loc (float, optional): Location. Defaults to 0. - scale (float, optional): Scale parameter. Defaults to 1. - n_samples (int, optional): Number of samples to draw for approximation. - Defaults to 10000. - seed (int, optional): Random seed. Defaults to 0. + def logpdf(self, x: np.ndarray) -> np.ndarray: + """Log of the probability density function evaluated at ``x``. - Attributes: - loc (float): Location parameter. - scale (float): Scale parameter. - lower_bound (np.array): (# intervals,) array of lower bounds of the truncation - intervals. - upper_bound (np.array): (# intervals,) array of upper bounds of the truncation - intervals. - interval_masses (np.array): (# intervals,) array of the amount of mass in each - truncation interval. - n_samples (int): Number of samples to draw for approximation. Defaults to - 10000. + Args: + x (np.ndarray): (n, # marginals) matrix of values at which to evaluate the + density function. - Note: - The truncation set is defined over the domain of the standard normal. To - convert the truncation set for a specific mean and standard deviation, use: + Returns: + np.ndarray: (n,) array of log density. + """ + x = np.array(x).reshape(-1, len(self._marginal_distributions)) + return np.sum( + [dist.logpdf(x_i) for dist, x_i in zip(self._marginal_distributions, x.T)], + axis=0, + ) - .. code-block:: python + def pdf(self, x: np.ndarray) -> np.ndarray: + """Probability density function evaluated at ``x``. - >>> truncation_set = [(myclip_a - my_mean) / my_std, (myclip_b - my_mean) / my_std)] + Args: + x (np.ndarray): (n, # marginals) matrix of values at which to evaluate the + density function. + + Returns: + np.ndarray: (n,) array of densities. + """ + return np.exp(self.logpdf(x)) + + def rvs(self, size: int = 1) -> np.ndarray: + """Sample random values. + + Args: + size (int, optional): Number of samples to draw. Defaults to 1. + + Returns: + np.ndarray: (size, # marginals) matrix of samples. + """ + return np.vstack( + [dist.rvs(size=size) for dist in self._marginal_distributions] + ).T + + +class mixture(rv_continuous): + """Mixture distribution. + + Args: + distributions (list[rv_continuous]): List of n distributions to mix over. + weights (Numeric1DArray, optional): (n,) array of mixture weights. Defaults to None. + + Attributes: + distributions (list[rv_continuous]): Distributions to mix over. + weights (np.ndarray): Mixture weights. """ def __init__( self, - truncation_set: List[Tuple[float, float]] = None, - loc: float = 0, - scale: float = 1, - n_samples: int = 10000, - seed: int = 0, + distributions: list[rv_continuous], + weights: Numeric1DArray = None, + **kwargs: Any, ): - self.seed = seed - self.loc = loc - self.scale = scale - self.n_samples = n_samples - if truncation_set is None: - truncation_set = [(-np.inf, np.inf)] - self.lower_bound, self.upper_bound = self._get_truncation_bounds(truncation_set) - self.interval_masses = np.array( - [ - self._compute_mass_in_interval_avg(a, b) - for a, b in zip(self.lower_bound, self.upper_bound) - ] - ) - super().__init__() - - def _pdf(self, x): # pylint: disable=arguments-differ - x = self._normalize(x) - # n x 1 indicator that x is in an interval - in_interval = np.array( - [np.any((self.lower_bound <= x_i) & (x_i <= self.upper_bound)) for x_i in x] - ) - return in_interval * norm.pdf(x) / (self.scale * self.interval_masses.sum()) - - def _logpdf(self, x): # pylint: disable=arguments-differ - x = self._normalize(x) - # n x 1 indicator that x is in an interval - in_interval = np.array( - [np.any((self.lower_bound <= x_i) & (x_i <= self.upper_bound)) for x_i in x] - ) - logpdf = ( - norm.logpdf(x) - np.log(self.scale) - np.log(self.interval_masses.sum()) + super().__init__(**kwargs) + self.distributions = distributions + self.weights = ( + np.ones(len(distributions)) if weights is None else np.atleast_1d(weights) ) - logpdf[~in_interval] = -np.inf - return logpdf + self.weights /= self.weights.sum() - def _cdf(self, x): # pylint: disable=arguments-differ - x = self._normalize(x) - - if self.lower_bound.size == self.upper_bound.size == 0: - # i.e., truncation set is empty - return (x > 0).astype(float) - - denominator = self.interval_masses.sum() - if denominator == 0: - # converts cdf to 0 or 1 depending on bounds and x - a = self.lower_bound.min() - b = self.upper_bound.max() - convert_0_1 = lambda x_i: a < x_i if x_i > 0 else b < x_i - return np.array([convert_0_1(x_i) for x_i in x]).astype(float) + def _pdf(self, x): + return ( + self.weights * np.array([dist.pdf(x) for dist in self.distributions]).T + ).sum(axis=1) - return np.clip(self._compute_cdf_numerator(x) / denominator, 0, 1) + def _cdf(self, x): + return ( + self.weights * np.array([dist.cdf(x) for dist in self.distributions]).T + ).sum(axis=1) - def _logcdf(self, x): # pylint: disable=arguments-differ - x = self._normalize(x) - denominator = self.interval_masses.sum() - return np.log(self._compute_cdf_numerator(x)) - np.log(denominator) + def mean(self): + return ( + self.weights * np.array([dist.mean() for dist in self.distributions]) + ).sum() - def _compute_mass_in_interval_avg(self, a, b): - # compute the amount of mass in the interval [a, b] by averaging approximate - # mass in [a, b] and [-b, -a] - # the amount of mass in these intervals is the same because the normal is symmetric - # this can improve performance - arr = [ - self._compute_mass_in_interval(a, b, int(self.n_samples / 2)), - self._compute_mass_in_interval(-b, -a, int(self.n_samples / 2)), - ] - return np.mean(arr, where=~np.isnan(arr)) + def var(self): + return ( + self.weights * np.array([dist.var() for dist in self.distributions]) + ).sum() - def _compute_mass_in_interval(self, a, b, n_samples=None): - # compute the amount of mass in the interval [a, b] using minimax exponential tilt - def compute_psi(params): - x, mu = params - return -x * mu + (0.5 * mu ** 2 + np.log(norm.cdf(b, mu) - norm.cdf(a, mu))) + def std(self): + return np.sqrt(self.var()) - def d_psi_d_mu(params): - # derivative of psi with respect to mu - x, mu = params - return ( - -x - + mu - + (norm.pdf(b, mu) - norm.pdf(a, mu)) - / ((norm.cdf(b, mu) - norm.cdf(a, mu))) - ) - def optimize_params(x0): - # maximize psi subject to x being contained in the interval and the - # derivative of psi wrt mu =0 0 - derivative_constraint = NonlinearConstraint(d_psi_d_mu, 0, 0) - return minimize( - lambda x: -compute_psi(x), - x0=x0, - bounds=[(a, b), (-np.inf, np.inf)], - constraints=[derivative_constraint], - ) +class nonparametric(rv_continuous): + """Nonparametric distribution. - def compute_tilting_param(): - # compute the optimal tilting parameter - try: - # initial guess for x, denoted as Psi in the paper - # in the univariate case, the initial guess for mu is 0 - x_init = (norm.pdf(a) - norm.pdf(b)) / ((norm.cdf(b) - norm.cdf(a))) - except ZeroDivisionError: - x_init = a if a < 0 else b + Args: + values (tuple[np.array, np.array]): (n,) array of x values, (n,) array of the + probability mass function evaluated at x. + kind (str, optional): Type of interpolation to use. Passed to + ``scipy.interpolate.interp1d``. Defaults to None. - if a < x_init < b: - # optimal x is in the truncation set, so optimal mu is 0 - return 0 + Attributes: + xk (np.ndarray): (n,) array of x values. + pk (np.ndarray): (n,) array of the probability mass function evaluated at x. - # optimal mu must be found by non-linear optimization - res = optimize_params([x_init, 0]) - if res.success: - return res.x[1] - next_guess, final_guess = ([a, a], [b, b]) if a > 0 else ([b, b], [a, a]) - res = optimize_params(next_guess) - if res.success: - return res.x[1] - res = optimize_params(final_guess) - if res.success: - return res.x[1] - warnings.warn( - "Optimizer failed to find truncated normal tilt parameter", - RuntimeWarning, - ) - return x_init + Notes: + This distribution interpolates between the probability mass function to + "continuize" the discrete function. + """ - if b < a: - return 0 - mu = compute_tilting_param() - x = truncnorm_base.rvs( - a - mu, b - mu, mu, size=n_samples or self.n_samples, random_state=self.seed + def __init__(self, values, kind=None, *args, **kwargs): + super().__init__(*args, **kwargs) + self.xk, self.pk = np.array(values[0], float), np.array(values[1], float) + self.pk /= self.pk.sum() + self._cdf_values = np.cumsum(self.pk) + if kind is not None: + self._kind = kind + else: + self._kind = "cubic" if len(self.xk) > 3 else "linear" + self._scale = 1 + self._scale = 1 / quad(self._pdf, self.xk[0], self.xk[-1])[0] + + def _pdf(self, x: np.ndarray) -> np.ndarray: + x = np.atleast_1d(x) + pdf = np.zeros(len(x)) + in_range = (self.xk[0] < x) & (x < self.xk[-1]) + pdf[in_range] = interp1d(self.xk, self.pk, kind=self._kind)(x[in_range]) + return self._scale * pdf + + def _cdf(self, x: np.ndarray) -> np.ndarray: + x = np.atleast_1d(x) + cdf = np.zeros(len(x)) + cdf[x >= self.xk[-1]] = 1 + in_range = (self.xk[0] < x) & (x < self.xk[-1]) + cdf[in_range] = interp1d(self.xk, self._cdf_values, kind=self._kind)( + x[in_range] ) - return np.exp(compute_psi((x, mu))).mean() + return cdf - def _compute_cdf_numerator(self, x): - # n x p indicates x is above the upper bound of the interval - index = np.array([self.upper_bound <= x_i for x_i in x]) - # n x 1 CDF for the intervals where x is above the upper bound - cdf = index @ self.interval_masses + def _ppf(self, q: np.ndarray) -> np.ndarray: + return weighted_quantile(self.xk, q, self.pk) - # tuple of (x index, interval index) such that x[x index] is in interval[interval_index] - indices = np.where( - [(self.lower_bound < x_i) & (x_i < self.upper_bound) for x_i in x] - ) - # add mass from intervals containing x - cdf[indices[0]] += np.array( - [ - self._compute_mass_in_interval_avg( - self.lower_bound[interval_index], x[x_index] - ) - for x_index, interval_index in zip(*indices) - ] - ) + def moment(self, func: Callable[[np.ndarray], np.ndarray]) -> float: + """Compute a moment. - return cdf + Args: + func (Callable[[np.ndarray], np.ndarray]): Moment function that takes + ``self.xk`` and returns an array of the same shape. - def _get_truncation_bounds(self, truncation_set): - if not truncation_set: - return np.array([]), np.array([]) + Returns: + float: Moment. + """ + return sum(self.pk * func(self.xk)) - for interval in truncation_set: - if interval[1] < interval[0]: - raise ValueError(f"Invalid interval {interval}") + def mean(self) -> float: + """Compute the mean. - truncation_set.sort(key=lambda x: x[0]) - a, b = list(zip(*truncation_set)) + Returns: + float: Mean. + """ + return self.moment(lambda x: x) - # ensure b is strictly increasing - b = [b[0]] + [max(b_i, b_j) for b_i, b_j in zip(b[1:], b[:-1])] + def var(self) -> float: + """Compute the variance. - new_a, new_b = [a[0]], [] - for a_i, b_i in zip(a[1:], b[:-1]): - if a_i > b_i: - new_b.append(b_i) - new_a.append(a_i) - new_b.append(b[-1]) + Returns: + float: Variance. + """ + mean = self.mean() + return self.moment(lambda x: (x - mean) ** 2) - return np.array(new_a), np.array(new_b) + def std(self) -> float: + """Compute the standard deviation. - def _normalize(self, arr): - return (arr - self.loc) / self.scale + Returns: + float: Standard deviation. + """ + return np.sqrt(self.var()) class quantile_unbiased(rv_continuous): # pylint: disable=invalid-name @@ -292,7 +255,7 @@ class quantile_unbiased(rv_continuous): # pylint: disable=invalid-name if np.isscalar(projection_interval): projection_interval = abs(projection_interval) # type: ignore projection_interval = (-projection_interval, projection_interval) - self.projection_interval = projection_interval + self.projection_interval = tuple(projection_interval) self.bounds = bounds self.truncnorm_kwargs = truncnorm_kwargs self.dx = dx @@ -435,7 +398,7 @@ class quantile_unbiased(rv_continuous): # pylint: disable=invalid-name value = self.bounds[0] if self.bounds[0] > self.y else self.bounds[1] return np.full(q.shape, value) - q_t = q * (self._cdf_max - self._cdf_min) + self._cdf_min + q_t = np.atleast_1d(q) * (self._cdf_max - self._cdf_min) + self._cdf_min return np.array([fsolve(func, [self.y], args=(q_i,))[0] for q_i in q_t]) def ppf( # pylint: disable=arguments-differ @@ -450,3 +413,234 @@ class quantile_unbiased(rv_continuous): # pylint: disable=invalid-name np.ndarray: (n,) array of evaluations. """ return np.clip(super().ppf(q), *self.bounds) + + +class truncnorm(rv_continuous): # pylint: disable=invalid-name + """Truncated normal distribution. + + Inherits from `scipy.stats.rv_continuous `_ + and handles standard public methods (``pdf``, ``cdf``, etc.). + + This uses the [exponential tilting](https://ieeexplore.ieee.org/document/7408180) + approximation method. + + Args: + truncation_set (List[Tuple[float, float]], optional): List of truncation + intervals, e.g., ``[(-1, 0), (1, 2)]`` truncates the distribution to + [-1, 0] union [1, 2]. Defaults to None. + loc (float, optional): Location. Defaults to 0. + scale (float, optional): Scale parameter. Defaults to 1. + n_samples (int, optional): Number of samples to draw for approximation. + Defaults to 10000. + seed (int, optional): Random seed. Defaults to 0. + + Attributes: + loc (float): Location parameter. + scale (float): Scale parameter. + lower_bound (np.array): (# intervals,) array of lower bounds of the truncation + intervals. + upper_bound (np.array): (# intervals,) array of upper bounds of the truncation + intervals. + interval_masses (np.array): (# intervals,) array of the amount of mass in each + truncation interval. + n_samples (int): Number of samples to draw for approximation. Defaults to + 10000. + + Note: + The truncation set is defined over the domain of the standard normal. To + convert the truncation set for a specific mean and standard deviation, use: + + .. code-block:: python + + >>> truncation_set = [(myclip_a - my_mean) / my_std, (myclip_b - my_mean) / my_std)] + """ + + def __init__( + self, + truncation_set: List[Tuple[float, float]] = None, + loc: float = 0, + scale: float = 1, + n_samples: int = 10000, + seed: int = 0, + ): + + self.seed = seed + self.loc = loc + self.scale = scale + self.n_samples = n_samples + if truncation_set is None: + truncation_set = [(-np.inf, np.inf)] + self.lower_bound, self.upper_bound = self._get_truncation_bounds(truncation_set) + self.interval_masses = np.array( + [ + self._compute_mass_in_interval_avg(a, b) + for a, b in zip(self.lower_bound, self.upper_bound) + ] + ) + super().__init__() + + def _pdf(self, x): # pylint: disable=arguments-differ + x = self._normalize(x) + # n x 1 indicator that x is in an interval + in_interval = np.array( + [np.any((self.lower_bound <= x_i) & (x_i <= self.upper_bound)) for x_i in x] + ) + return in_interval * norm.pdf(x) / (self.scale * self.interval_masses.sum()) + + def _logpdf(self, x): # pylint: disable=arguments-differ + x = self._normalize(x) + # n x 1 indicator that x is in an interval + in_interval = np.array( + [np.any((self.lower_bound <= x_i) & (x_i <= self.upper_bound)) for x_i in x] + ) + logpdf = ( + norm.logpdf(x) - np.log(self.scale) - np.log(self.interval_masses.sum()) + ) + logpdf[~in_interval] = -np.inf + return logpdf + + def _cdf(self, x): # pylint: disable=arguments-differ + x = self._normalize(x) + + if self.lower_bound.size == self.upper_bound.size == 0: + # i.e., truncation set is empty + return (x > 0).astype(float) + + denominator = self.interval_masses.sum() + if denominator == 0: + # converts cdf to 0 or 1 depending on bounds and x + a = self.lower_bound.min() + b = self.upper_bound.max() + convert_0_1 = lambda x_i: a < x_i if x_i > 0 else b < x_i + return np.array([convert_0_1(x_i) for x_i in x]).astype(float) + + return np.clip(self._compute_cdf_numerator(x) / denominator, 0, 1) + + def _logcdf(self, x): # pylint: disable=arguments-differ + x = self._normalize(x) + denominator = self.interval_masses.sum() + return np.log(self._compute_cdf_numerator(x)) - np.log(denominator) + + def _compute_mass_in_interval_avg(self, a, b): + # compute the amount of mass in the interval [a, b] by averaging approximate + # mass in [a, b] and [-b, -a] + # the amount of mass in these intervals is the same because the normal is symmetric + # this can improve performance + arr = [ + self._compute_mass_in_interval(a, b, int(self.n_samples / 2)), + self._compute_mass_in_interval(-b, -a, int(self.n_samples / 2)), + ] + return np.mean(arr, where=~np.isnan(arr)) + + def _compute_mass_in_interval(self, a, b, n_samples=None): + # compute the amount of mass in the interval [a, b] using minimax exponential tilt + def compute_psi(params): + x, mu = params + return -x * mu + (0.5 * mu ** 2 + np.log(norm.cdf(b, mu) - norm.cdf(a, mu))) + + def d_psi_d_mu(params): + # derivative of psi with respect to mu + x, mu = params + return ( + -x + + mu + + (norm.pdf(b, mu) - norm.pdf(a, mu)) + / ((norm.cdf(b, mu) - norm.cdf(a, mu))) + ) + + def optimize_params(x0): + # maximize psi subject to x being contained in the interval and the + # derivative of psi wrt mu =0 0 + derivative_constraint = NonlinearConstraint(d_psi_d_mu, 0, 0) + return minimize( + lambda x: -compute_psi(x), + x0=x0, + bounds=[(a, b), (-np.inf, np.inf)], + constraints=[derivative_constraint], + ) + + def compute_tilting_param(): + # compute the optimal tilting parameter + try: + # initial guess for x, denoted as Psi in the paper + # in the univariate case, the initial guess for mu is 0 + x_init = (norm.pdf(a) - norm.pdf(b)) / ((norm.cdf(b) - norm.cdf(a))) + except ZeroDivisionError: + x_init = a if a < 0 else b + + if a < x_init < b: + # optimal x is in the truncation set, so optimal mu is 0 + return 0 + + # optimal mu must be found by non-linear optimization + res = optimize_params([x_init, 0]) + if res.success: + return res.x[1] + next_guess, final_guess = ([a, a], [b, b]) if a > 0 else ([b, b], [a, a]) + res = optimize_params(next_guess) + if res.success: + return res.x[1] + res = optimize_params(final_guess) + if res.success: + return res.x[1] + warnings.warn( + "Optimizer failed to find truncated normal tilt parameter", + RuntimeWarning, + ) + return x_init + + if b < a: + return 0 + mu = compute_tilting_param() + x = truncnorm_base.rvs( + a - mu, b - mu, mu, size=n_samples or self.n_samples, random_state=self.seed + ) + return np.exp(compute_psi((x, mu))).mean() + + def _compute_cdf_numerator(self, x): + # n x p indicates x is above the upper bound of the interval + index = np.array([self.upper_bound <= x_i for x_i in x]) + # n x 1 CDF for the intervals where x is above the upper bound + cdf = index @ self.interval_masses + + # tuple of (x index, interval index) such that x[x index] is in interval[interval_index] + indices = np.where( + [(self.lower_bound < x_i) & (x_i < self.upper_bound) for x_i in x] + ) + # add mass from intervals containing x + cdf[indices[0]] += np.array( + [ + self._compute_mass_in_interval_avg( + self.lower_bound[interval_index], x[x_index] + ) + for x_index, interval_index in zip(*indices) + ] + ) + + return cdf + + def _get_truncation_bounds(self, truncation_set): + if not truncation_set: + return np.array([]), np.array([]) + + for interval in truncation_set: + if interval[1] < interval[0]: + raise ValueError(f"Invalid interval {interval}") + + truncation_set.sort(key=lambda x: x[0]) + a, b = list(zip(*truncation_set)) + + # ensure b is strictly increasing + b = [b[0]] + [max(b_i, b_j) for b_i, b_j in zip(b[1:], b[:-1])] + + new_a, new_b = [a[0]], [] + for a_i, b_i in zip(a[1:], b[:-1]): + if a_i > b_i: + new_b.append(b_i) + new_a.append(a_i) + new_b.append(b[-1]) + + return np.array(new_a), np.array(new_b) + + def _normalize(self, arr): + return (arr - self.loc) / self.scale diff --git a/src/conditional_inference/utils.py b/src/conditional_inference/utils.py index 4d99c0b..a23e235 100644 --- a/src/conditional_inference/utils.py +++ b/src/conditional_inference/utils.py @@ -1,6 +1,5 @@ -"""Conditional inference utilities +"""Conditional inference utilities. """ -from multiprocessing.sharedctypes import Value from typing import Any, Optional, Sequence, Union import numpy as np @@ -32,8 +31,8 @@ def expected_wasserstein_distance( estimated population means ``estimated_means``. Args: - mean (Numeric1DArray): (n,) array of observed sample means. - cov (np.ndarray): (n, n) covariance matrix of sample means. + mean (Numeric1DArray): (n,) array of conventional point estimates. + cov (np.ndarray): (n, n) covariance matrix of conventional estimates. estimated_means (np.ndarray): (# samples, n) matrix of draws from a distribution of population means. sample_weight (np.ndarray, optional): (# samples,) array of sample weights for @@ -43,12 +42,10 @@ def expected_wasserstein_distance( Returns: float: Loss. """ - - def compute_distance(estimated_mean): - dist = multivariate_normal(estimated_mean, cov) - return wasserstein_distance(dist.rvs(), mean, **kwargs) - sample_weight = _get_sample_weight(sample_weight, estimated_means.shape[0]) + compute_distance = lambda mu: wasserstein_distance( + multivariate_normal.rvs(mu, cov), mean, **kwargs + ) distances = np.apply_along_axis(compute_distance, 1, estimated_means) return (sample_weight * distances).sum() @@ -69,10 +66,6 @@ def holm_bonferroni_correction( Returns: pd.DataFrame: Dataframe indicating which coefficients are significant. - - Notes: - If you input a ``filename``, this correction looks at one-tailed hypothesis - tests. """ if filename is None and results is None: raise ValueError("filename or results must be specified.") @@ -81,14 +74,20 @@ def holm_bonferroni_correction( raise ValueError("Please specify either filename or results; not both.") if results is None: - from .bayes.classic import LinearClassicBayes + from .bayes import Improper - results = LinearClassicBayes.from_csv(filename, prior_cov=np.inf).fit() + results = Improper.from_csv(filename).fit() + # Improper gives pvalues for 1-tailed tests + pvalues = np.min( + np.array([2 * results.pvalues, 2 * (1 - results.pvalues)]), axis=0 + ) + else: + pvalues = results.pvalues - argsort = results.pvalues.argsort() + argsort = pvalues.argsort() df = pd.DataFrame( - {"pvalues": results.pvalues[argsort]}, - index=np.array(results.model.exog_names)[argsort] + {"pvalues": pvalues[argsort]}, + index=np.array(results.model.exog_names)[argsort], ) index = np.where(df.pvalues > alpha / (len(df) - np.arange(len(df))))[0][0] df["significant"] = np.arange(len(df)) < index @@ -131,21 +130,3 @@ def weighted_quantile( weighted_quantiles = np.cumsum(sample_weight) - 0.5 * sample_weight # type: ignore return np.interp(quantiles, weighted_quantiles, values) - - -def weighted_cdf( - values: np.ndarray, x: float, sample_weight: np.ndarray = None -) -> float: - """Compute weighted CDF. - - Args: - values (np.ndarray): (n,) array over which to compute the CDF. - x (float): Point at which to evaluate the CDF. - sample_weight (np.ndarray, optional): (n,) array of sample weights. Defaults to - None. - - Returns: - float: CDF of ``values`` evaluated at ``x``. - """ - sample_weight = _get_sample_weight(sample_weight, len(values)) - return (sample_weight * (np.array(values) < x)).sum() diff --git a/tests/test_base.py b/tests/test_base.py index 9e07ca8..d6074b8 100644 --- a/tests/test_base.py +++ b/tests/test_base.py @@ -1,3 +1,4 @@ +import io import os import pickle @@ -7,123 +8,126 @@ import pytest import statsmodels.api as sm from scipy.stats import norm -from conditional_inference.base import ModelBase, ResultsBase +from conditional_inference.base import ModelBase -tol = 0.001 - -n_policies = 3 -mean = np.arange(n_policies) -cov = np.identity(n_policies) -model = ModelBase(mean, cov) -results = ResultsBase(model) - - -class TestData: - @pytest.mark.parametrize("endog_names", [None, "target"]) - def test_endog_names(self, endog_names): - # test that the model has the correct endogenous variable name - model = ModelBase(mean, cov, endog_names=endog_names) - if endog_names is None: - assert model.endog_names == "y" - else: - assert model.endog_names == endog_names - - @pytest.mark.parametrize( - "exog_names,index", - [ - ([f"var{i}" for i in range(n_policies)], False), - ([f"var{i}" for i in range(n_policies)], True), - (None, False), - ], - ) - def test_exog_names(self, exog_names, index): - # test that the model has the correct exogenous variable names - if exog_names is None: - model = ModelBase(mean, cov) - assert model.exog_names == [f"x{i}" for i in range(n_policies)] - else: - if index: - model = ModelBase(pd.Series(mean, index=exog_names), cov) - else: - model = ModelBase(mean, cov, exog_names=exog_names) - assert model.exog_names == exog_names - - def test_set_attr(self): - # test that you can set a data attribute by setting the model's same-named attribute - model = ModelBase(mean, cov) - exog_names = [f"var{i}" for i in range(n_policies)] - model.exog_names = exog_names - assert model.exog_names == exog_names - assert model.data.exog_names == exog_names +N_POLICIES = 3 @pytest.fixture(scope="module", params=[True, False]) def ols_results( request, n_obs_per_policy=100, - exog_names=[f"var{i}" for i in range(n_policies)], + exog_names=[f"var{i}" for i in range(N_POLICIES)], endog_name="target", ): # create statsmodels OLS results X = pd.DataFrame( - np.repeat(np.identity(n_policies), n_obs_per_policy, axis=0), columns=exog_names + np.repeat(np.identity(N_POLICIES), n_obs_per_policy, axis=0), columns=exog_names ) - y = X @ np.arange(n_policies) + norm.rvs(size=n_policies * n_obs_per_policy) + y = X @ np.arange(N_POLICIES) + norm.rvs(size=N_POLICIES * n_obs_per_policy) y = pd.Series(y, name=endog_name) ols_results = sm.OLS(y, X).fit() return ols_results if request.param else ols_results.get_robustcov_results() -class TestModel: +class TestModelBase: + @pytest.mark.parametrize( + "mean", ([0, 1, 2], pd.Series([0, 1, 2], index=["world", "moon", "star"])) + ) + @pytest.mark.parametrize("exog_names", (None, ["mars", "europa", "sun"])) + @pytest.mark.parametrize("sort", (True, False)) + def test__init__(self, mean, exog_names, sort): + model = ModelBase(mean, np.diag([1, 2, 3]), exog_names=exog_names, sort=sort) + + expected_exog_names = ["x0", "x1", "x2"] + + if sort: + expected_mean = [2, 1, 0] + expected_cov = np.diag([3, 2, 1]) + if isinstance(mean, pd.Series): + expected_exog_names = ["star", "moon", "world"] + if exog_names is not None: + expected_exog_names = ["sun", "europa", "mars"] + else: + expected_mean = [0, 1, 2] + expected_cov = np.diag([1, 2, 3]) + if isinstance(mean, pd.Series): + expected_exog_names = ["world", "moon", "star"] + if exog_names is not None: + expected_exog_names = ["mars", "europa", "sun"] + + np.testing.assert_array_equal(model.mean, expected_mean) + np.testing.assert_array_equal(model.cov, expected_cov) + np.testing.assert_array_equal(model.exog_names, expected_exog_names) + + @pytest.mark.parametrize( + "columns", ([0.0, 2.0], [0, 2], ["world", "star"], [True, False, True]) + ) + @pytest.mark.parametrize("sort", (True, False)) + def test_column_selection(self, columns, sort): + model = ModelBase( + pd.Series([0, 1, 2], index=["world", "moon", "star"]), + np.diag([1, 2, 3]), + columns=columns, + sort=sort, + ) + + if sort: + expected_mean = [2, 0] + expected_cov = np.diag([3, 1]) + expected_exog_names = ["star", "world"] + else: + expected_mean = [0, 2] + expected_cov = np.diag([1, 3]) + expected_exog_names = ["world", "star"] + np.testing.assert_array_equal(model.mean, expected_mean) + np.testing.assert_array_equal(model.cov, expected_cov) + np.testing.assert_array_equal(model.exog_names, expected_exog_names) + + @pytest.mark.parametrize("endog_names", [None, "target"]) + def test_endog_names(self, endog_names): + # test that the data has the correct endogenous variable name + model = ModelBase( + np.arange(N_POLICIES), np.identity(N_POLICIES), endog_names=endog_names + ) + if endog_names is None: + assert model.endog_names == "y" + else: + assert model.endog_names == endog_names + def get_params_cov(self, ols_results): - # return the OLS point estimates and covariance matrix from results object params = ols_results.params - params = params.values if isinstance(params, pd.Series) else params + if isinstance(params, pd.Series): + params = params.values + cov = ols_results.cov_params() - cov = cov.values if isinstance(cov, pd.DataFrame) else cov + if isinstance(cov, pd.DataFrame): + cov = cov.values + return params, cov def compare_model_to_ols_results(self, model, ols_results): # make sure the model's attributes match those of the OLS results params, cov = self.get_params_cov(ols_results) - assert ((model.mean - params) ** 2).mean() <= tol - assert ((model.cov - cov) ** 2).mean() <= tol - assert model.exog_names == ols_results.model.exog_names + np.testing.assert_almost_equal(model.mean, params) + np.testing.assert_almost_equal(model.cov, cov) + np.testing.assert_array_equal(model.exog_names, ols_results.model.exog_names) assert model.endog_names == ols_results.model.endog_names - @pytest.mark.parametrize("cols", [None, "from_results", [0, 1, 2]]) - def test_from_results(self, ols_results, cols): + def test_from_results(self, ols_results): # test that you can initialize a model from statsmodels results object - if cols == "from_results": - cols = ols_results.model.exog_names - model = ModelBase.from_results(ols_results, cols=cols) + model = ModelBase.from_results(ols_results) self.compare_model_to_ols_results(model, ols_results) - def test_from_csv(self, ols_results, filename="temp.csv"): + def test_to_and_from_csv(self, ols_results): # test that you can initialize a model from a csv file - params, cov = self.get_params_cov(ols_results) - df = pd.DataFrame( - np.hstack((params.reshape(-1, 1), cov)), - columns=[ols_results.model.endog_names] + ols_results.model.exog_names, - ) - df.to_csv(filename, index=False) - model = ModelBase.from_csv(filename) - os.remove(filename) + ModelBase.from_results(ols_results).to_csv(bytes := io.BytesIO()) + bytes.seek(0) + model = ModelBase.from_csv(bytes) self.compare_model_to_ols_results(model, ols_results) - @pytest.mark.parametrize( - "cols", [None, "sorted", "x0", [f"x{i}" for i in range(n_policies-1, -1, -1)], [2, 1, 0]] - ) - def test_get_indices(self, cols): - indices = ModelBase(mean, cov).get_indices(cols) - if cols is None: - assert (indices == [0, 1, 2]).all() - elif cols == "sorted": - assert (indices == (-mean).argsort()).all() - elif cols == "x0": - assert (indices == [0]).all() - else: # columns are in reverse order - assert (indices == [2, 1, 0]).all() + +results = ModelBase(np.arange(N_POLICIES), np.identity(N_POLICIES)).fit() class TestResults: @@ -131,9 +135,9 @@ class TestResults: with pytest.raises(AttributeError): results.conf_int() - def test_save(self, filename="temp.p"): - results.save(filename) + def test_save(self): + results.save(filename := "temp.p") with open(filename, "rb") as results_file: loaded_results = pickle.load(results_file) os.remove(filename) - assert (loaded_results.model.mean == results.model.mean).all() + np.testing.assert_almost_equal(loaded_results.model.mean, results.model.mean) diff --git a/tests/test_bayes.py b/tests/test_bayes.py deleted file mode 100644 index 249bb93..0000000 --- a/tests/test_bayes.py +++ /dev/null @@ -1,69 +0,0 @@ -import numpy as np -import pytest -from scipy.stats import loguniform - -from conditional_inference.bayes.classic import LinearClassicBayes -from conditional_inference.bayes.empirical import LinearEmpiricalBayes, JamesStein -from conditional_inference.bayes.hierarchical import LinearHierarchicalBayes - -atol = .1 -n_policies = 4 -mean = np.arange(n_policies) -cov = np.identity(n_policies) - -_, prior_std_anchor = LinearEmpiricalBayes(mean, cov).estimate_prior_params() -hyperprior = loguniform(.5 * prior_std_anchor, 2 * prior_std_anchor) -# tuples of (model class, constructor keyword arguments, fit keyword arguments) -models = [ - (LinearClassicBayes, dict(prior_cov=0), {}), - (LinearClassicBayes, dict(prior_cov=np.inf), {}), - (LinearEmpiricalBayes, {}, {}), - ( - LinearEmpiricalBayes, - {}, - dict(estimate_prior_params_kwargs=dict(method="wasserstein", n_samples=20)) - ), - (JamesStein, {}, {}), - (LinearHierarchicalBayes, dict(prior_cov_params_distribution=hyperprior), {}), -] - - -@pytest.fixture(scope="module", params=models) -def results(request): - model_cls, init_kwargs, fit_kwargs = request.param - return model_cls(mean, cov, **init_kwargs).fit(**fit_kwargs) - - -class TestResults: - def test_conf_int(self, results): - results.conf_int() - - def test_point_plot(self, results): - results.point_plot() - - def test_summary(self, results): - results.summary() - - def test_wasserstein(self, results): - distance0 = results.expected_wasserstein_distance() - distance1 = results.expected_wasserstein_distance(mean, cov) - assert abs(distance0 - distance1) <= atol - - def test_likelihood(self, results): - assert results.likelihood() == results.likelihood(mean, cov) - - def test_rank_matrix(self, results): - results.rank_matrix_plot() - - def test_reconstruction_histogram(self, results): - results.reconstruction_histogram() - - def test_reconstruction_point_plot(self, results): - results.reconstruction_point_plot() - - -def test_prior_mean_rvs(size=10): - # TODO: test with estimate_prior_params keyword arguments - model = LinearEmpiricalBayes(mean, cov) - assert model.prior_mean_rvs().shape == (n_policies,) - assert model.prior_mean_rvs(size).shape == (n_policies, size) diff --git a/tests/test_bayes/__init__.py b/tests/test_bayes/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_bayes/test_improper.py b/tests/test_bayes/test_improper.py new file mode 100644 index 0000000..76ca377 --- /dev/null +++ b/tests/test_bayes/test_improper.py @@ -0,0 +1,35 @@ +import numpy as np +import pytest +from scipy.stats import norm + +from conditional_inference.bayes import Improper + +from .utils import run_common_methods + +mean, cov = np.arange(3), np.identity(3) +model = Improper(mean, cov) +results = model.fit() + + +def test_common_methods(): + run_common_methods(results) + + +@pytest.mark.parametrize("index", (0, 1, 2)) +def test_get_marginal_distribution(index): + dist = model.get_marginal_distribution(index) + assert dist.mean() == mean[index] + assert dist.var() == cov[index, index] + + +def test_get_joint_distribution(): + dist = model.get_joint_distribution() + np.testing.assert_almost_equal(dist.mean, mean) + np.testing.assert_almost_equal(dist.cov, cov) + + +def test_conf_int(): + conf_int = results.conf_int() + norm_conf_int = norm.ppf([0.025, 0.975]) + norm_conf_int = np.array([norm_conf_int, norm_conf_int + 1, norm_conf_int + 2]) + np.testing.assert_array_almost_equal(conf_int, norm_conf_int) diff --git a/tests/test_bayes/test_nonparametric.py b/tests/test_bayes/test_nonparametric.py new file mode 100644 index 0000000..e125432 --- /dev/null +++ b/tests/test_bayes/test_nonparametric.py @@ -0,0 +1,78 @@ +import numpy as np +import pytest +from scipy.stats import norm + +from conditional_inference.bayes import Nonparametric + +from .utils import run_common_methods + +n_params = 20 +mean, cov = np.arange(n_params), np.identity(n_params) +X = np.vstack([np.ones(n_params), int(n_params / 2) * [0] + int(n_params / 2) * [1]]).T +mean = mean - mean.mean() + + +@pytest.fixture(scope="module", params=(None, X)) +def model(request): + X = request.param + n_clusters = 1 if X is None else 2 + return Nonparametric(mean, cov, X=X, n_clusters=n_clusters) + + +@pytest.fixture(scope="module") +def results(model): + return model.fit() + + +def test_common_methods(results): + run_common_methods(results) + + +def get_expected_means(model): + if model.X.shape[1] == 1: + return 0, 0 + + return mean[: int(n_params / 2)].mean(), mean[int(n_params / 2) :].mean() + + +def test_get_marginal_prior(model, atol=0.5): + # with so few observations, the nonparametric prior can't fit precisely, so we need + # a high tolerance for error + expected_mean_0, expected_mean_1 = get_expected_means(model) + dist_0 = model.get_marginal_prior(0) + assert abs(dist_0.mean() - expected_mean_0) < atol + dist_1 = model.get_marginal_prior(int(n_params / 2)) + assert abs(dist_1.mean() - expected_mean_1) < atol + + +def test_get_marginal_distribution(model): + posterior_mean = np.array( + [model.get_marginal_distribution(i).mean() for i in range(n_params)] + ) + assert (np.diff(posterior_mean) > 0).all() + + # tests shrinkage + expected_mean_0, expected_mean_1 = get_expected_means(model) + if model.X.shape[1] == 1: + indices = [0, -1] + expected_means = [expected_mean_0, expected_mean_1] + else: + indices = [0, int(n_params / 2) - 1, int(n_params / 2), -1] + expected_means = 2 * [expected_mean_0] + 2 * [expected_mean_1] + + posterior_mean = posterior_mean[indices] + np.testing.assert_array_equal( + mean[indices] < posterior_mean, mean[indices] < expected_means + ) + + +def test_conf_int(results): + conf_int = results.conf_int() + norm_len_ci = np.diff(norm.ppf([0.025, 0.975])) + # test that Bayesian CIs are shorter than conventional CIs + indices = ( + [0, -1] + if results.model.X.shape[1] == 1 + else [0, int(n_params / 2) - 1, int(n_params / 2), -1] + ) + assert (np.diff(conf_int, axis=1)[indices] < norm_len_ci).all() diff --git a/tests/test_bayes/test_normal.py b/tests/test_bayes/test_normal.py new file mode 100644 index 0000000..0652924 --- /dev/null +++ b/tests/test_bayes/test_normal.py @@ -0,0 +1,120 @@ +from itertools import product + +import numpy as np +import pytest +from scipy.stats import norm + +from conditional_inference.bayes import Normal + +from .utils import run_common_methods + +n_params = 20 +mean, cov = np.arange(n_params), np.identity(n_params) +X = np.vstack([np.ones(n_params), int(n_params / 2) * [0] + int(n_params / 2) * [1]]).T +mean = mean - mean.mean() + + +@pytest.fixture( + scope="module", params=product((None, X), ("mle", "bock", "james_stein")) +) +def model(request): + X, fit_method = request.param + return Normal(mean, cov, X, fit_method=fit_method) + + +@pytest.fixture(scope="module") +def results(model): + return model.fit() + + +def test_common_methods(results): + run_common_methods(results) + + +def get_expected_means(model): + if model.X.shape[1] == 1: + return 0, 0 + + return mean[: int(n_params / 2)].mean(), mean[int(n_params / 2) :].mean() + + +def test_get_marginal_prior(model, atol=0.01): + expected_mean_0, expected_mean_1 = get_expected_means(model) + dist_0 = model.get_marginal_prior(0) + assert abs(dist_0.mean() - expected_mean_0) < atol + dist_1 = model.get_marginal_prior(int(n_params / 2)) + assert abs(dist_1.mean() - expected_mean_1) < atol + + +def test_get_marginal_distribution(model): + posterior_mean = np.array( + [model.get_marginal_distribution(i).mean() for i in range(n_params)] + ) + assert (np.diff(posterior_mean) > 0).all() + + # tests shrinkage + expected_mean_0, expected_mean_1 = get_expected_means(model) + expected_means = np.array( + int(n_params / 2) * [expected_mean_0] + int(n_params / 2) * [expected_mean_1] + ) + np.testing.assert_array_equal(mean < posterior_mean, mean < expected_means) + + +def test_conf_int(results): + conf_int = results.conf_int() + norm_len_ci = np.diff(norm.ppf([0.025, 0.975])) + # test that Bayesian CIs are shorter than conventional CIs + assert (np.diff(conf_int, axis=1) < norm_len_ci).all() + + +def test_bock(): + # can compute Bock's Stein-type estimates analytically + cov = np.identity(n_params) + results = Normal(mean, cov, prior_mean=0, fit_method="bock").fit() + expected_result = ( + 1 + - (np.trace(cov) / np.linalg.eig(cov)[0].max() - 2) + / (mean.reshape(1, -1) @ np.linalg.inv(cov) @ mean.reshape(-1, 1)) + ) * mean + np.testing.assert_array_almost_equal(results.params, expected_result.squeeze()) + + +def test_james_stein(): + # can compute the James-Stein estimates analytically + results = Normal(mean, cov, prior_mean=0, fit_method="james_stein").fit() + np.testing.assert_array_almost_equal( + results.params, + (1 - (n_params - 2) * np.sqrt(cov[0, 0]) / (mean ** 2).sum()) * mean, + ) + + +@pytest.mark.parametrize("fit_method", ("mle", "james_stein")) +def test_zero_prior_cov(fit_method): + # make sure the models are robust when there is 0 prior covariance + mean = np.array( + [ + -3.39359413e-01, + -6.62381513e-01, + -1.51536892e-01, + -2.58385772e-01, + 6.10271496e-01, + 8.87349385e-01, + 3.16365311e-01, + 2.27194076e-03, + -1.26127278e00, + -1.05872185e-01, + 4.13153327e-03, + -5.08449489e-01, + 5.78252783e-01, + -1.48959519e-01, + 4.96132247e-01, + -2.48655048e00, + -8.59522707e-01, + -1.07444613e00, + 2.26257613e-01, + -8.14765943e-01, + ] + ) + model = Normal(mean, np.identity(len(mean)), fit_method=fit_method) + results = model.fit() + np.testing.assert_array_almost_equal(results.params, mean.mean()) diff --git a/tests/test_bayes/utils.py b/tests/test_bayes/utils.py new file mode 100644 index 0000000..b7375c2 --- /dev/null +++ b/tests/test_bayes/utils.py @@ -0,0 +1,14 @@ +from conditional_inference.bayes import Improper + + +def run_common_methods(results): + # test that you can run all the common methods without error + results.conf_int() + results.expected_wasserstein_distance() + results.likelihood() + if not isinstance(results.model, Improper): + results.line_plot(0) + results.point_plot() + results.rank_matrix_plot() + results.reconstruction_point_plot() + results.summary() diff --git a/tests/test_confidence_set.py b/tests/test_confidence_set.py new file mode 100644 index 0000000..2a947ee --- /dev/null +++ b/tests/test_confidence_set.py @@ -0,0 +1,193 @@ +import numpy as np +import pytest +from scipy.stats import norm + +from conditional_inference.confidence_set import ( + ConfidenceSet, + AverageComparison, + BaselineComparison, + PairwiseComparison, + MarginalRanking, + SimultaneousRanking, +) + +N_PARAMS = 3 +MEAN = np.arange(N_PARAMS) - (N_PARAMS - 1) / 2 +COV = np.identity(N_PARAMS) + + +@pytest.mark.parametrize( + "cls", + ( + ConfidenceSet, + AverageComparison, + BaselineComparison, + PairwiseComparison, + MarginalRanking, + SimultaneousRanking, + ), +) +def test_common_methods(cls): + # test that the common methods (conf_int, summary, point_plot) can run on all + # classes without error + kwargs = {"baseline": 0} if cls is BaselineComparison else {} + results = cls(MEAN, COV, **kwargs).fit() + results.conf_int() + results.summary() + results.point_plot() + + +class TestConfidenceSet: + results = ConfidenceSet(MEAN, COV).fit() + + def test_conf_int_shape(self): + assert self.results.conf_int().shape == (N_PARAMS, 2) + + def test_1_param(self): + np.testing.assert_equal( + ConfidenceSet(0, 1).fit().conf_int(), [norm.ppf([0.025, 0.975])] + ) + + def test_conf_int(self): + # test the the marginal CI is in the simultaneous CI + alpha = 0.05 + simultaneous_ci = self.results.conf_int(alpha) + marginal_ci = np.array( + [ + norm.ppf([alpha / 2, 1 - alpha / 2], mean, np.sqrt(var)) + for mean, var in zip(MEAN, COV.diagonal()) + ] + ) + np.testing.assert_array_less(simultaneous_ci[:, 0], marginal_ci[:, 0]) + np.testing.assert_array_less(marginal_ci[:, 1], simultaneous_ci[:, 1]) + + def test_test_hypotheses(self): + results = ConfidenceSet([-3, -2, 0, 2, 3], np.identity(5)).fit() + np.testing.assert_array_equal( + results.test_hypotheses().values, + [ + [False, True], # significantly less than 0 + [False, False], + [False, False], + [False, False], + [True, False], # significantly greater than 0 + ], + ) + + +class TestAverageComparison: + def test___init__(self): + model = AverageComparison(MEAN, COV) + np.testing.assert_almost_equal(model.mean, [-1, 0, 1]) + np.testing.assert_almost_equal( + model.cov, + [[2 / 3, -1 / 3, -1 / 3], [-1 / 3, 2 / 3, -1 / 3], [-1 / 3, -1 / 3, 2 / 3]], + ) + + +class TestBaselineComparison: + def test___init__(self): + model = BaselineComparison(MEAN, COV, baseline=0) + np.testing.assert_almost_equal(model.mean, [1, 2]) + np.testing.assert_almost_equal(model.cov, [[2, 1], [1, 2]]) + + +class TestPairwiseComparison: + def test___init__(self): + model = PairwiseComparison(MEAN, COV) + np.testing.assert_array_equal( + model.exog_names, ["x1 - x0", "x2 - x0", "x2 - x1"] + ) + np.testing.assert_almost_equal(model.mean, [1, 2, 1]) # [1-0, 2-0, 2-1] + np.testing.assert_almost_equal(model.cov, [[2, 1, -1], [1, 2, 1], [-1, 1, 2]]) + + def test_conf_int_shape(self): + results = PairwiseComparison(np.arange(4), np.identity(4)).fit() + assert results.conf_int().shape[0] == 4 * (4 - 1) / 2 + + @pytest.mark.parametrize("columns", (None, ["x2", "x1"])) + def test_test_hypotheses(self, columns): + results = PairwiseComparison([0, 4, 1, 2], np.identity(4) / 3).fit() + if columns is None: + expected_values = [ + [False, True, False, False], # x1 > 0 + [False, False, False, False], + [False, True, False, False], # x1 > x2 + [False, False, False, False], + ] + else: + expected_values = [[False, True], [False, False]] + + np.testing.assert_array_equal( + results.test_hypotheses(columns=columns).values, expected_values + ) + + @pytest.mark.parametrize("triangular", (True, False)) + def test_hypothesis_heatmap(self, triangular): + PairwiseComparison(MEAN, COV).fit().hypothesis_heatmap(triangular=triangular) + + +class TestMarginalRanking: + @pytest.mark.parametrize("columns", (None, ["x2", "x1"])) + def test_conf_int(self, columns): + # x0 is ranked 1 or 2 + # s1 is ranked 0, 1, or 2 + # x2 is ranked 0 or 1 + results = MarginalRanking(MEAN, COV / 3).fit() + if columns is None: + expected_values = [[2, 3], [1, 3], [1, 2]] + else: + expected_values = [[1, 2], [1, 3]] + np.testing.assert_array_equal( + results.conf_int(columns=columns), expected_values + ) + + +class TestSimultaneousRanking: + @pytest.mark.parametrize("columns", (None, ["x2", "x1"])) + def test_conf_int(self, columns): + # x0 is ranked 1 or 2 + # s1 is ranked 0, 1, or 2 + # x2 is ranked 0 or 1 + results = SimultaneousRanking(MEAN, COV / 3).fit() + if columns is None: + expected_values = [[2, 3], [1, 3], [1, 2]] + else: + expected_values = [[1, 2], [1, 3]] + np.testing.assert_array_equal( + results.conf_int(columns=columns), expected_values + ) + + @pytest.mark.parametrize("n_best_params", (1, 2)) + @pytest.mark.parametrize("superset", (True, False)) + def test_compute_best_params(self, n_best_params, superset): + # these parameters are from the stylized example in Mogstad's Inference for + # Rankings paper + x = np.array([3.3, 4.1, 4.2, 4.3, 6.2]) + cov = np.array( + [ + [0.01, 0, 0, 0, 0], + [0, 0.25, 0, 0, 0], + [0, 0, 0.05, 0, 0], + [0, 0, 0, 0.05, 0], + [0, 0, 0, 0, 0.05], + ] + ) + results = SimultaneousRanking(x, cov).fit() + if n_best_params == 1: + # 95% chance x4 is the best parameter + if superset: + expected_values = [False, False, False, False, True] + else: + expected_values = [False, False, False, False, True] + else: + if superset: + # 95% chance the two best parameters are x1-x4 + expected_values = [False, True, True, True, True] + else: + # 95% chance that x4 is in the two best parameters + expected_values = [False, False, False, False, True] + np.testing.assert_array_equal( + results.compute_best_params(n_best_params, superset=superset).values, + expected_values, + ) diff --git a/tests/test_rank_condition.py b/tests/test_rank_condition.py new file mode 100644 index 0000000..14d1e2b --- /dev/null +++ b/tests/test_rank_condition.py @@ -0,0 +1,57 @@ +import numpy as np +import pytest + +from conditional_inference.rank_condition import RankCondition + + +def test_common_methods(): + model = RankCondition(np.arange(3), np.identity(3)) + results = model.fit() + results.conf_int() + results.summary() + results.point_plot() + + +class TestRankCondition: + model = RankCondition(np.arange(3), np.diag([1, 2, 3]) ** 2) + + @pytest.mark.parametrize("column", ("x0", "x1", "x2")) + @pytest.mark.parametrize("beta", (0, 0.005)) + def test_get_marginal_distribution(self, column, beta): + dist = self.model.get_marginal_distribution(column, beta=beta) + + if column == "x0": + expected_truncation_set = [-np.inf, 1] + expected_scale = 1 + expected_y = 0 + elif column == "x1": + expected_truncation_set = [0, 2] + expected_scale = 2 + expected_y = 1 + else: + expected_truncation_set = [1, np.inf] + expected_scale = 3 + expected_y = 2 + + np.testing.assert_almost_equal( + dist.truncnorm_kwargs["truncation_set"][0], expected_truncation_set + ) + assert dist.truncnorm_kwargs["scale"] == expected_scale + assert dist.y == expected_y + + assert (dist.projection_interval == (-np.inf, np.inf)) == (beta == 0) + + @pytest.mark.parametrize("rank", ([0], [0, 1], [0, 1, 2])) + def test_truncation_set(self, rank): + dist = self.model.get_marginal_distribution("x2", rank) + + if rank == [0]: + expected_value = [[1, np.inf]] + elif rank == [0, 1]: + expected_value = [[0, 1], [1, np.inf]] + else: + expected_value = [[-np.inf, 0], [0, 1], [1, np.inf]] + + np.testing.assert_almost_equal( + dist.truncnorm_kwargs["truncation_set"], expected_value + ) diff --git a/tests/test_rqu.py b/tests/test_rqu.py deleted file mode 100644 index 36fb529..0000000 --- a/tests/test_rqu.py +++ /dev/null @@ -1,70 +0,0 @@ -import numpy as np -import pytest - -from conditional_inference.rqu import RQU - - -n_policies = 3 -mean = np.arange(n_policies) -cov = np.identity(n_policies) -rqu = RQU(mean, cov) - - -class TestRQU: - # tests rarely invoked pieces of code in RQU - - def test_projection_quantile(self): - # test when alpha == 0 - # see simulation tests for more rigorous tests when alpha != 0 - rqu = RQU(np.arange(3), np.identity(3)) - assert rqu.compute_projection_quantile(alpha=0) == np.inf - - def test_s_V_condition(self): - # this condition applies with cov(x_i,x_j) == var(x_i) - # when it fails, the truncation set is empty - # see paper for mathematical detail - with pytest.raises(ValueError): - RQU(np.arange(2), np.ones((2, 2))).get_distribution(rank=1) - - @pytest.mark.parametrize("rank", ["invalid_rank", "floor", "ceil"]) - def test_rank_arguments(self, rank): - def get_truncation_interval(dist): - truncation_set = dist.truncnorm_kwargs["truncation_set"] - a, b = list(zip(*truncation_set)) - return min(a), max(b) - - rqu = RQU(np.arange(2), np.identity(2)) - - if rank not in ("floor", "ceil"): - with pytest.raises(ValueError): - rqu.get_distributions(rank=rank) - return - - with pytest.raises(ValueError): - rqu.get_distribution(rank=rank) - - dists = rqu.get_distributions(rank=rank) - truncation_sets = [get_truncation_interval(dist) for dist in dists] - if rank == "floor": - assert truncation_sets == [(-np.inf, np.inf), (0, np.inf)] - else: # rank == "ceil" - assert truncation_sets == [(-np.inf, 1), (-np.inf, np.inf)] - - def test_get_distribution_default(self): - assert rqu.get_distribution().y == n_policies - 1 - - -@pytest.fixture(scope="module", params=[{}, dict(beta=.005), dict(projection=True)]) -def results(request): - return rqu.fit(**request.param) - - -class TestResults: - def test_conf_int(self, results): - results.conf_int() - - def test_summary(self, results): - results.summary() - - def test_point_plots(self, results): - results.point_plot() diff --git a/tests/test_significance_condition.py b/tests/test_significance_condition.py new file mode 100644 index 0000000..d10ee6d --- /dev/null +++ b/tests/test_significance_condition.py @@ -0,0 +1,23 @@ +import numpy as np +import pytest +from scipy.stats import norm + +from conditional_inference.significance_condition import SignificanceCondition + + +def test_common_methods(): + model = SignificanceCondition(np.arange(4), np.identity(4)) + results = model.fit() + results.conf_int(columns=["x3"]) + results.summary(columns=["x3"]) + results.point_plot(columns=["x3"]) + + +class TestSignificanceCondition: + def test_get_marginal_distribution(self): + dist = SignificanceCondition(4, 4).get_marginal_distribution(0) + assert dist.truncnorm_kwargs["scale"] == 2 + np.testing.assert_array_almost_equal( + dist.truncnorm_kwargs["truncation_set"], + [[-np.inf, -2 * norm.ppf(0.975)], [2 * norm.ppf(0.975), np.inf]], + ) diff --git a/tests/test_stats.py b/tests/test_stats.py index 8288b48..9738ede 100644 --- a/tests/test_stats.py +++ b/tests/test_stats.py @@ -5,17 +5,124 @@ import pytest from numpy.testing import assert_allclose from scipy.stats import norm, truncnorm as scipy_truncnorm -from conditional_inference.stats import quantile_unbiased, truncnorm +from conditional_inference.stats import ( + joint_distribution, + mixture, + nonparametric, + quantile_unbiased, + truncnorm, +) VALUES = np.linspace(-2, 2, num=5) LOC = [-1, 0, 1] SCALE = [1, 2] -TRUNCATION_SET = [ - (-np.inf, -1), - (-1, 1), - (1, np.inf) -] +TRUNCATION_SET = [(-np.inf, -1), (-1, 1), (1, np.inf)] + + +class TestJointDistribution: + marginals = [norm(), norm(4)] + dist = joint_distribution(marginals) + values = np.vstack([marginals[0].rvs(10), marginals[1].rvs(10)]).T + + def test_logpdf(self): + np.testing.assert_array_almost_equal( + self.dist.logpdf(self.values), + self.marginals[0].logpdf(self.values[:, 0]) + + self.marginals[1].logpdf(self.values[:, 1]), + ) + + def test_pdf(self): + np.testing.assert_array_almost_equal( + self.dist.pdf(self.values), + self.marginals[0].pdf(self.values[:, 0]) + * self.marginals[1].pdf(self.values[:, 1]), + ) + + @pytest.mark.parametrize("size", (1, 10)) + def test_rvs(self, size): + assert self.dist.rvs(size).shape == (size, 2) + + +class TestMixture: + mixed = [norm(), norm(4)] + dist = mixture(mixed) + + def test_pdf(self): + np.testing.assert_array_almost_equal( + self.dist.pdf(VALUES), + 0.5 * (self.mixed[0].pdf(VALUES) + self.mixed[1].pdf(VALUES)), + ) + + def test_cdf(self): + np.testing.assert_array_almost_equal( + self.dist.cdf(VALUES), + 0.5 * (self.mixed[0].cdf(VALUES) + self.mixed[1].cdf(VALUES)), + ) + + def test_mean(self): + assert self.dist.mean() == 0.5 * (self.mixed[0].mean() + self.mixed[1].mean()) + + def test_variance(self): + assert self.dist.var() == 0.5 * (self.mixed[0].var() + self.mixed[1].var()) + + +class TestNonparametric: + x = np.linspace(-3, 3) + dist = nonparametric((x, norm.pdf(x))) + + def test_pdf(self): + np.testing.assert_array_almost_equal( + self.dist.pdf(self.x), norm.pdf(self.x), decimal=2 + ) + + def test_cdf(self): + np.testing.assert_array_almost_equal( + self.dist.cdf(self.x), norm.cdf(self.x), decimal=1 + ) + + def test_ppf(self): + q = np.linspace(0.025, 0.975, num=10) + np.testing.assert_array_almost_equal(self.dist.ppf(q), norm.ppf(q), decimal=2) + + def test_mean(self): + assert abs(self.dist.mean() - norm.mean()) < 0.01 + + def test_std(self): + assert abs(self.dist.std() - norm.std()) < 0.02 + + +@pytest.fixture(scope="module", params=list(product(LOC, SCALE, TRUNCATION_SET))) +def quantile_unbiased_distribution(request): + loc, scale, truncation_set = request.param + return quantile_unbiased(loc, scale=scale, truncation_set=[truncation_set]) + + +class TestQuantileUnbiased: + # the quantile unbiased distribution behaves like a normal when the truncation set + # is all real values + untruncated_dist = quantile_unbiased(0, scale=1, truncation_set=[(-np.inf, np.inf)]) + + def test_pdf(self, quantile_unbiased_distribution): + quantile_unbiased_distribution.pdf(VALUES) + + def test_untruncated_pdf(self): + x = np.linspace(-2, 2) + np.testing.assert_array_almost_equal(self.untruncated_dist.pdf(x), norm.pdf(x)) + + def test_cdf(self, quantile_unbiased_distribution): + quantile_unbiased_distribution.cdf(VALUES) + + def test_untruncated_cdf(self): + x = np.linspace(-2, 2) + np.testing.assert_array_almost_equal(self.untruncated_dist.cdf(x), norm.cdf(x)) + + def test_ppf(self, quantile_unbiased_distribution): + quantile_unbiased_distribution.ppf(np.linspace(0, 1, 5)) + + def test_untruncated_ppf(self): + x = np.linspace(0.025, 0.975, 5) + np.testing.assert_almost_equal(self.untruncated_dist.ppf(x), norm.ppf(x)) @pytest.fixture(scope="module", params=list(product(LOC, SCALE, TRUNCATION_SET))) @@ -23,7 +130,7 @@ def truncnorm_distributions(request): loc, scale, truncation_set = request.param return ( truncnorm([truncation_set], loc=loc, scale=scale), - scipy_truncnorm(*truncation_set, loc=loc, scale=scale) + scipy_truncnorm(*truncation_set, loc=loc, scale=scale), ) @@ -55,21 +162,9 @@ class TestTruncnorm: assert truncnorm([(-np.inf, -100)]).cdf(-101) >= 0 def test_default_truncation_set(self): - assert_allclose(truncnorm().ppf([.25, .5, .75]), norm().ppf([.25, .5, .75])) + assert_allclose( + truncnorm().ppf([0.25, 0.5, 0.75]), norm().ppf([0.25, 0.5, 0.75]) + ) def test_concave_truncation_set(self): - truncnorm([(-2, -1), (1, 2)]).ppf([.05, .25, .5, .75, .95]) - - -@pytest.fixture(scope="module", params=list(product(LOC, SCALE, TRUNCATION_SET))) -def quantile_unbiased_distribution(request): - loc, scale, truncation_set = request.param - return quantile_unbiased(loc, scale=scale, truncation_set=[truncation_set]) - - -class TestQuantileUnbiased: - def test_pdf(self, quantile_unbiased_distribution): - quantile_unbiased_distribution.pdf(VALUES) - - # def test_cdf(self, quantile_unbiased_distribution): - # quantile_unbiased_distribution.cdf(VALUES) + truncnorm([(-2, -1), (1, 2)]).ppf([0.05, 0.25, 0.5, 0.75, 0.95]) diff --git a/tests/test_utils.py b/tests/test_utils.py new file mode 100644 index 0000000..6358262 --- /dev/null +++ b/tests/test_utils.py @@ -0,0 +1,40 @@ +from turtle import home +from unittest.mock import Mock + +import numpy as np +from scipy.stats import multivariate_normal + +from conditional_inference.utils import ( + expected_wasserstein_distance, + holm_bonferroni_correction, + weighted_quantile, +) + + +def test_expected_wasserstein_distance(): + # expected Wasserstein distance should be smaller when the parameters are estimated with greater precision + n_params = 3 + mean, cov = np.arange(n_params), np.identity(n_params) + rvs0 = multivariate_normal.rvs(mean, 1, size=100) + rvs1 = multivariate_normal.rvs(mean, 0.1 ** 2, size=100) + assert expected_wasserstein_distance( + mean, cov, rvs0 + ) > expected_wasserstein_distance(mean, cov, rvs1) + + +def test_holm_bonferroni_correction(): + results = Mock() + results.pvalues = np.array([0.1, 0.05, 0.01]) + results.model = Mock() + results.model.exog_names = np.array(["x0", "x1", "x2"]) + correction = holm_bonferroni_correction(results=results) + np.testing.assert_array_equal(correction.pvalues, [0.01, 0.05, 0.1]) + np.testing.assert_array_equal(correction.significant, [True, False, False]) + np.testing.assert_array_equal(correction.index, ["x2", "x1", "x0"]) + + +def test_weighted_quantile(): + quantiles = [0, 0.25, 0.5, 0.75, 1] + np.testing.assert_array_almost_equal( + weighted_quantile(np.linspace(0, 1), quantiles), quantiles, decimal=2 + )