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CLN blacken first 15 notebooks #4148

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Oct 5, 2020
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CLN blacken first 15 notebooks #4148

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0afdf54
blacken first 15 files
MarcoGorelli Sep 29, 2020
a377d78
ignore black for data block
MarcoGorelli Sep 29, 2020
0b5adb0
turn off black for block in GLM-hierarchical-binominal-model
MarcoGorelli Sep 29, 2020
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2 changes: 1 addition & 1 deletion docs/source/notebooks/AR.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,7 @@
"%config InlineBackend.figure_format = 'retina'\n",
"RANDOM_SEED = 8927\n",
"np.random.seed(RANDOM_SEED)\n",
"az.style.use('arviz-darkgrid')"
"az.style.use(\"arviz-darkgrid\")"
]
},
{
Expand Down
66 changes: 37 additions & 29 deletions docs/source/notebooks/BEST.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@
"import pandas as pd\n",
"import pymc3 as pm\n",
"\n",
"print(f'Running on PyMC3 v{pm.__version__}')"
"print(f\"Running on PyMC3 v{pm.__version__}\")"
]
},
{
Expand All @@ -37,7 +37,7 @@
"outputs": [],
"source": [
"%config InlineBackend.figure_format = 'retina'\n",
"az.style.use('arviz-darkgrid')"
"az.style.use(\"arviz-darkgrid\")"
]
},
{
Expand Down Expand Up @@ -94,18 +94,22 @@
}
],
"source": [
"# fmt: off\n",
"drug = (101,100,102,104,102,97,105,105,98,101,100,123,105,103,100,95,102,106,\n",
" 109,102,82,102,100,102,102,101,102,102,103,103,97,97,103,101,97,104,\n",
" 96,103,124,101,101,100,101,101,104,100,101)\n",
"placebo = (99,101,100,101,102,100,97,101,104,101,102,102,100,105,88,101,100,\n",
" 104,100,100,100,101,102,103,97,101,101,100,101,99,101,100,100,\n",
" 101,100,99,101,100,102,99,100,99)\n",
"# fmt: on\n",
"\n",
"y1 = np.array(drug)\n",
"y2 = np.array(placebo)\n",
"y = pd.DataFrame(dict(value=np.r_[y1, y2], group=np.r_[['drug']*len(drug), ['placebo']*len(placebo)]))\n",
"y = pd.DataFrame(\n",
" dict(value=np.r_[y1, y2], group=np.r_[[\"drug\"] * len(drug), [\"placebo\"] * len(placebo)])\n",
")\n",
"\n",
"y.hist('value', by='group', figsize=(12, 4));"
"y.hist(\"value\", by=\"group\", figsize=(12, 4));"
]
},
{
Expand Down Expand Up @@ -146,8 +150,8 @@
"μ_s = y.value.std() * 2\n",
"\n",
"with pm.Model() as model:\n",
" group1_mean = pm.Normal('group1_mean', mu=μ_m, sd=μ_s)\n",
" group2_mean = pm.Normal('group2_mean', mu=μ_m, sd=μ_s)"
" group1_mean = pm.Normal(\"group1_mean\", mu=μ_m, sd=μ_s)\n",
" group2_mean = pm.Normal(\"group2_mean\", mu=μ_m, sd=μ_s)"
]
},
{
Expand Down Expand Up @@ -176,8 +180,8 @@
"σ_high = 10\n",
"\n",
"with model:\n",
" group1_std = pm.Uniform('group1_std', lower=σ_low, upper=σ_high)\n",
" group2_std = pm.Uniform('group2_std', lower=σ_low, upper=σ_high)"
" group1_std = pm.Uniform(\"group1_std\", lower=σ_low, upper=σ_high)\n",
" group2_std = pm.Uniform(\"group2_std\", lower=σ_low, upper=σ_high)"
]
},
{
Expand Down Expand Up @@ -210,9 +214,9 @@
],
"source": [
"with model:\n",
" ν = pm.Exponential('ν_minus_one', 1/29.) + 1\n",
" ν = pm.Exponential(\"ν_minus_one\", 1 / 29.0) + 1\n",
"\n",
"pm.kdeplot(np.random.exponential(30, size=10000), fill_kwargs={'alpha': 0.5});"
"pm.kdeplot(np.random.exponential(30, size=10000), fill_kwargs={\"alpha\": 0.5});"
]
},
{
Expand All @@ -234,11 +238,11 @@
"outputs": [],
"source": [
"with model:\n",
" λ1 = group1_std**-2\n",
" λ2 = group2_std**-2\n",
" λ1 = group1_std ** -2\n",
" λ2 = group2_std ** -2\n",
"\n",
" group1 = pm.StudentT('drug', nu=ν, mu=group1_mean, lam=λ1, observed=y1)\n",
" group2 = pm.StudentT('placebo', nu=ν, mu=group2_mean, lam=λ2, observed=y2)"
" group1 = pm.StudentT(\"drug\", nu=ν, mu=group1_mean, lam=λ1, observed=y1)\n",
" group2 = pm.StudentT(\"placebo\", nu=ν, mu=group2_mean, lam=λ2, observed=y2)"
]
},
{
Expand All @@ -262,10 +266,11 @@
"outputs": [],
"source": [
"with model:\n",
" diff_of_means = pm.Deterministic('difference of means', group1_mean - group2_mean)\n",
" diff_of_stds = pm.Deterministic('difference of stds', group1_std - group2_std)\n",
" effect_size = pm.Deterministic('effect size', \n",
" diff_of_means / np.sqrt((group1_std**2 + group2_std**2) / 2))"
" diff_of_means = pm.Deterministic(\"difference of means\", group1_mean - group2_mean)\n",
" diff_of_stds = pm.Deterministic(\"difference of stds\", group1_std - group2_std)\n",
" effect_size = pm.Deterministic(\n",
" \"effect size\", diff_of_means / np.sqrt((group1_std ** 2 + group2_std ** 2) / 2)\n",
" )"
]
},
{
Expand Down Expand Up @@ -369,8 +374,11 @@
}
],
"source": [
"pm.plot_posterior(trace, var_names=['group1_mean','group2_mean', 'group1_std', 'group2_std', 'ν_minus_one'],\n",
" color='#87ceeb');"
"pm.plot_posterior(\n",
" trace,\n",
" var_names=[\"group1_mean\", \"group2_mean\", \"group1_std\", \"group2_std\", \"ν_minus_one\"],\n",
" color=\"#87ceeb\",\n",
");"
]
},
{
Expand Down Expand Up @@ -412,9 +420,12 @@
}
],
"source": [
"pm.plot_posterior(trace, var_names=['difference of means','difference of stds', 'effect size'],\n",
" ref_val=0,\n",
" color='#87ceeb');"
"pm.plot_posterior(\n",
" trace,\n",
" var_names=[\"difference of means\", \"difference of stds\", \"effect size\"],\n",
" ref_val=0,\n",
" color=\"#87ceeb\",\n",
");"
]
},
{
Expand Down Expand Up @@ -454,8 +465,7 @@
}
],
"source": [
"pm.forestplot(trace, var_names=['group1_mean',\n",
" 'group2_mean']);"
"pm.forestplot(trace, var_names=[\"group1_mean\", \"group2_mean\"]);"
]
},
{
Expand Down Expand Up @@ -488,9 +498,7 @@
}
],
"source": [
"pm.forestplot(trace, var_names=['group1_std',\n",
" 'group2_std',\n",
" 'ν_minus_one']);"
"pm.forestplot(trace, var_names=[\"group1_std\", \"group2_std\", \"ν_minus_one\"]);"
]
},
{
Expand Down Expand Up @@ -607,7 +615,7 @@
}
],
"source": [
"pm.summary(trace, varnames=['difference of means', 'difference of stds', 'effect size'])"
"pm.summary(trace, varnames=[\"difference of means\", \"difference of stds\", \"effect size\"])"
]
},
{
Expand Down
4 changes: 1 addition & 3 deletions docs/source/notebooks/Bayes_factor.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -340,9 +340,7 @@
}
],
"source": [
"BF_smc = np.exp(\n",
" traces[1].report.log_marginal_likelihood - traces[0].report.log_marginal_likelihood\n",
")\n",
"BF_smc = np.exp(traces[1].report.log_marginal_likelihood - traces[0].report.log_marginal_likelihood)\n",
"np.round(BF_smc)"
]
},
Expand Down
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