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Remove ruff E501 ignore #619

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Apr 12, 2024
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Remove ruff E501 ignore #619

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87ab632
remove ruff E501 ignore
juanitorduz Apr 8, 2024
b82c3b6
max limitt 140
juanitorduz Apr 8, 2024
e2b8229
max limitt 130
juanitorduz Apr 8, 2024
52bf89e
max limitt 120
juanitorduz Apr 8, 2024
9622993
make message consistent
juanitorduz Apr 8, 2024
a7af969
make message consistent
juanitorduz Apr 8, 2024
832d06a
make message consistent
juanitorduz Apr 8, 2024
06a922d
fix test
juanitorduz Apr 11, 2024
c715113
rebase fix
juanitorduz Apr 11, 2024
ef791a7
rebase fix 2
juanitorduz Apr 11, 2024
cdff191
Merge branch 'main' into fix_E501
juanitorduz Apr 12, 2024
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169 changes: 79 additions & 90 deletions docs/source/notebooks/mmm/mmm_budget_allocation_example.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -34,21 +34,6 @@
"Before delving into the specifics of budget allocation, the initial step is to install the PyMC-Marketing library and ascertain its version. This step will confirm support for the budget allocation function. The following pip command can be run on your Jupyter Notebook:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-LnDMZnXIh-x",
"outputId": "8d14a8c8-d5c7-4894-8333-eb437b69cd17"
},
"outputs": [],
"source": [
"!pip install pymc-marketing"
]
},
{
"cell_type": "markdown",
"metadata": {
Expand Down Expand Up @@ -119,7 +104,7 @@
},
"outputs": [],
"source": [
"name = '/pymc-marketing/data/budget_optimizer_model.nc'\n",
"name = \"/pymc-marketing/data/budget_optimizer_model.nc\"\n",
"mmm = DelayedSaturatedMMM.load(name)"
]
},
Expand Down Expand Up @@ -308,7 +293,7 @@
],
"source": [
"sigmoid_response_curve_fig = mmm.plot_direct_contribution_curves(\n",
"show_fit = True, method='michaelis-menten'\n",
" show_fit=True, method=\"michaelis-menten\"\n",
")"
]
},
Expand Down Expand Up @@ -337,9 +322,13 @@
},
"outputs": [],
"source": [
"sigmoid_params = mmm.compute_channel_curve_optimization_parameters_original_scale(method='sigmoid')\n",
"sigmoid_params = mmm.compute_channel_curve_optimization_parameters_original_scale(\n",
" method=\"sigmoid\"\n",
")\n",
"\n",
"menten_params = mmm.compute_channel_curve_optimization_parameters_original_scale(method='michaelis-menten')"
"menten_params = mmm.compute_channel_curve_optimization_parameters_original_scale(\n",
" method=\"michaelis-menten\"\n",
")"
]
},
{
Expand Down Expand Up @@ -431,15 +420,15 @@
},
"outputs": [],
"source": [
"total_budget = 5 #Imagine is 5K or 5M\n",
"#Define your channels\n",
"channels = ['x1','x2']\n",
"#The initial split per channel\n",
"total_budget = 5 # Imagine is 5K or 5M\n",
"# Define your channels\n",
"channels = [\"x1\", \"x2\"]\n",
"# The initial split per channel\n",
"budget_per_channel = total_budget / len(channels)\n",
"#Initial budget per channel as dictionary.\n",
"# Initial budget per channel as dictionary.\n",
"initial_budget_dict = {channel: budget_per_channel for channel in channels}\n",
"#bounds for each channel\n",
"min_budget, max_budget = 1,5\n",
"# bounds for each channel\n",
"min_budget, max_budget = 1, 5\n",
"budget_bounds = {channel: [min_budget, max_budget] for channel in channels}"
]
},
Expand Down Expand Up @@ -733,10 +722,10 @@
],
"source": [
"result_sigmoid = mmm.optimize_channel_budget_for_maximum_contribution(\n",
" method = 'sigmoid', #define saturation function\n",
" total_budget = total_budget,\n",
" parameters = sigmoid_params,\n",
" budget_bounds = budget_bounds\n",
" method=\"sigmoid\", # define saturation function\n",
" total_budget=total_budget,\n",
" parameters=sigmoid_params,\n",
" budget_bounds=budget_bounds,\n",
")\n",
"\n",
"result_sigmoid"
Expand Down Expand Up @@ -1032,10 +1021,10 @@
],
"source": [
"result_menten = mmm.optimize_channel_budget_for_maximum_contribution(\n",
" method = 'michaelis-menten',\n",
" total_budget = total_budget,\n",
" parameters = menten_params,\n",
" budget_bounds = budget_bounds\n",
" method=\"michaelis-menten\",\n",
" total_budget=total_budget,\n",
" parameters=menten_params,\n",
" budget_bounds=budget_bounds,\n",
")\n",
"result_menten"
]
Expand All @@ -1059,18 +1048,16 @@
},
"outputs": [],
"source": [
"#Use the function `calculate_expected_contribution` to estimate\n",
"#the contribution of your initial budget based on the curve parameters.\n",
"# Use the function `calculate_expected_contribution` to estimate\n",
"# the contribution of your initial budget based on the curve parameters.\n",
"initial_contribution = calculate_expected_contribution(\n",
" method='sigmoid',\n",
" parameters = sigmoid_params,\n",
" budget = initial_budget_dict\n",
" method=\"sigmoid\", parameters=sigmoid_params, budget=initial_budget_dict\n",
")\n",
"\n",
"# Initial budget & contribution dictionary\n",
"initial_scenario = {\n",
" 'initial_contribution': initial_contribution,\n",
" 'initial_budget': initial_budget_dict\n",
" \"initial_contribution\": initial_contribution,\n",
" \"initial_budget\": initial_budget_dict,\n",
"}"
]
},
Expand Down Expand Up @@ -1114,9 +1101,11 @@
}
],
"source": [
"#Use the function `compare_budget_scenearios` to validate\n",
"#The estimated contribution from one scenario agains the other\n",
"figure_ = mmm.plot_budget_scenearios(base_data=initial_scenario, method='sigmoid', scenarios_data=[result_sigmoid])"
"# Use the function `compare_budget_scenearios` to validate\n",
"# The estimated contribution from one scenario agains the other\n",
"figure_ = mmm.plot_budget_scenearios(\n",
" base_data=initial_scenario, method=\"sigmoid\", scenarios_data=[result_sigmoid]\n",
")"
]
},
{
Expand Down Expand Up @@ -1158,30 +1147,37 @@
},
"outputs": [],
"source": [
"#Initialize two variables to save the results and base conditions for each scenario.\n",
"# Initialize two variables to save the results and base conditions for each scenario.\n",
"scenarios_result = []\n",
"scenarios_base = []\n",
"\n",
"for scenario in np.array([0.6, 0.8, 1.2, 1.8]):\n",
" scenarios_result.append(\n",
" mmm.optimize_channel_budget_for_maximum_contribution(\n",
" method = 'sigmoid', #define saturation function\n",
" total_budget = total_budget * scenario,\n",
" parameters = sigmoid_params,\n",
" budget_bounds = {channel: [1, total_budget * scenario] for channel in channels}\n",
" ).to_dict()\n",
" )\n",
"\n",
" scenarios_base.append(\n",
" {'initial_contribution': calculate_expected_contribution(\n",
" method='sigmoid', #define saturation function\n",
" parameters = sigmoid_params,\n",
" budget = {channel: total_budget * scenario / len(channels) for channel in channels}\n",
" ),\n",
"\n",
" 'initial_budget': {channel: total_budget * scenario / len(channels) for channel in channels}\n",
" }\n",
" )"
" scenarios_result.append(\n",
" mmm.optimize_channel_budget_for_maximum_contribution(\n",
" method=\"sigmoid\", # define saturation function\n",
" total_budget=total_budget * scenario,\n",
" parameters=sigmoid_params,\n",
" budget_bounds={\n",
" channel: [1, total_budget * scenario] for channel in channels\n",
" },\n",
" ).to_dict()\n",
" )\n",
"\n",
" scenarios_base.append(\n",
" {\n",
" \"initial_contribution\": calculate_expected_contribution(\n",
" method=\"sigmoid\", # define saturation function\n",
" parameters=sigmoid_params,\n",
" budget={\n",
" channel: total_budget * scenario / len(channels)\n",
" for channel in channels\n",
" },\n",
" ),\n",
" \"initial_budget\": {\n",
" channel: total_budget * scenario / len(channels) for channel in channels\n",
" },\n",
" }\n",
" )"
]
},
{
Expand Down Expand Up @@ -1222,9 +1218,11 @@
}
],
"source": [
"#Use the function `compare_budget_scenearios` to validate\n",
"#The estimated contribution from one scenario agains the other\n",
"_figure = mmm.plot_budget_scenearios(base_data=initial_scenario, method='sigmoid', scenarios_data=scenarios_result)"
"# Use the function `compare_budget_scenearios` to validate\n",
"# The estimated contribution from one scenario agains the other\n",
"_figure = mmm.plot_budget_scenearios(\n",
" base_data=initial_scenario, method=\"sigmoid\", scenarios_data=scenarios_result\n",
")"
]
},
{
Expand Down Expand Up @@ -1526,12 +1524,10 @@
],
"source": [
"platform_base_optimization = mmm.optimize_channel_budget_for_maximum_contribution(\n",
" method = 'sigmoid',\n",
" total_budget = total_budget,\n",
" parameters = sigmoid_params,\n",
" budget_bounds = {'x1':[0,1.5],\n",
" 'x2':[0,1.5]\n",
" }\n",
" method=\"sigmoid\",\n",
" total_budget=total_budget,\n",
" parameters=sigmoid_params,\n",
" budget_bounds={\"x1\": [0, 1.5], \"x2\": [0, 1.5]},\n",
")\n",
"\n",
"platform_base_optimization"
Expand Down Expand Up @@ -1580,7 +1576,7 @@
],
"source": [
"sigmoid_response_curve_fig = mmm.plot_direct_contribution_curves(\n",
"show_fit = True, method='sigmoid', xlim_max=3\n",
" show_fit=True, method=\"sigmoid\", xlim_max=3\n",
")"
]
},
Expand Down Expand Up @@ -1874,12 +1870,10 @@
],
"source": [
"platform_base_optimization = mmm.optimize_channel_budget_for_maximum_contribution(\n",
" method = 'sigmoid',\n",
" total_budget = total_budget,\n",
" parameters = sigmoid_params,\n",
" budget_bounds = {'x1':[0,1.2],\n",
" 'x2':[0,1.5]\n",
" }\n",
" method=\"sigmoid\",\n",
" total_budget=total_budget,\n",
" parameters=sigmoid_params,\n",
" budget_bounds={\"x1\": [0, 1.2], \"x2\": [0, 1.5]},\n",
")\n",
"\n",
"platform_base_optimization"
Expand Down Expand Up @@ -1923,7 +1917,11 @@
}
],
"source": [
"_figure = mmm.plot_budget_scenearios(base_data=initial_scenario, method='sigmoid', scenarios_data=[platform_base_optimization])"
"_figure = mmm.plot_budget_scenearios(\n",
" base_data=initial_scenario,\n",
" method=\"sigmoid\",\n",
" scenarios_data=[platform_base_optimization],\n",
")"
]
},
{
Expand Down Expand Up @@ -1952,15 +1950,6 @@
"\n",
"Consequently, your engagements, feedback, and thoughts are not merely welcomed but actively solicited to make this tool as practical and universally applicable as possible."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Hy7ueyLb4F4Q"
},
"outputs": [],
"source": []
}
],
"metadata": {
Expand Down
6 changes: 4 additions & 2 deletions pymc_marketing/clv/distributions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -350,7 +350,9 @@ class ParetoNBD(PositiveContinuous):
Population-level distribution class for a continuous, non-contractual, Pareto/NBD process,
based on Schmittlein, et al. in [2]_.

The likelihood function is derived from equations (22) and (23) of [3]_, with terms rearranged for numerical stability.
The likelihood function is derived from equations (22) and (23) of [3]_, with terms
rearranged for numerical stability.

The modified expression is provided below:

.. math::
Expand Down Expand Up @@ -389,7 +391,7 @@ class ParetoNBD(PositiveContinuous):
.. [3] Fader, Peter & G. S. Hardie, Bruce (2005).
"A Note on Deriving the Pareto/NBD Model and Related Expressions."
http://brucehardie.com/notes/009/pareto_nbd_derivations_2005-11-05.pdf
"""
""" # noqa: E501

rv_op = pareto_nbd

Expand Down
6 changes: 4 additions & 2 deletions pymc_marketing/clv/models/beta_geo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,10 +37,12 @@ class BetaGeoModel(CLVModel):
DataFrame containing the following columns:
* `frequency`: number of repeat purchases (with possible values 0, 1, 2, ...)
* `recency`: time between the first and the last purchase (with possible values 0, 1, 2, ...)
* `T`: time between the first purchase and the end of the observation period (with possible values 0, 1, 2, ...)
* `T`: time between the first purchase and the end of the observation
period (with possible values 0, 1, 2, ...)
* `customer_id`: unique customer identifier
model_config: dict, optional
Dictionary of model prior parameters. If not provided, the model will use default priors specified in the `default_model_config` class attribute.
Dictionary of model prior parameters. If not provided, the model will use default priors specified in
the `default_model_config` class attribute.
sampler_config: dict, optional
Dictionary of sampler parameters. Defaults to None.

Expand Down
10 changes: 6 additions & 4 deletions pymc_marketing/clv/models/gamma_gamma.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,8 +52,8 @@ def expected_customer_spend(

Eq 5 from [1], p.3

Adapted from: https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/lifetimes/fitters/gamma_gamma_fitter.py#L117 # noqa: E501
"""
Adapted from: https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/lifetimes/fitters/gamma_gamma_fitter.py#L117
""" # noqa: E501

mean_transaction_value, frequency = to_xarray(
customer_id, mean_transaction_value, frequency
Expand Down Expand Up @@ -162,7 +162,8 @@ class GammaGammaModel(BaseGammaGammaModel):
- mean_transaction_value: Mean transaction value of each customer.
- frequency: Number of transactions observed for each customer.
model_config: dict, optional
Dictionary of model prior parameters. If not provided, the model will use default priors specified in the `default_model_config` class attribute.
Dictionary of model prior parameters. If not provided, the model will use default priors specified in the
`default_model_config` class attribute.
sampler_config: dict, optional
Dictionary of sampler parameters. Defaults to None.

Expand Down Expand Up @@ -295,7 +296,8 @@ class GammaGammaModelIndividual(BaseGammaGammaModel):
coming from the same customer.
- individual_transaction_value: Value of individual transactions.
model_config: dict, optional
Dictionary of model prior parameters. If not provided, the model will use default priors specified in the `default_model_config` class attribute.
Dictionary of model prior parameters. If not provided, the model will use default priors specified in the
`default_model_config` class attribute.
sampler_config: dict, optional
Dictionary of sampler parameters. Defaults to None.

Expand Down
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