Remove ruff E501 ignore (#619)

remove ruff E501 ignore

docs/source/notebooks/mmm/mmm_budget_allocation_example.ipynb

@@ -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": {
@@ -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)"
    ]
   },
@@ -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",
     ")"
    ]
   },
@@ -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",
+    ")"
    ]
   },
   {
@@ -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}"
    ]
   },
@@ -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"
@@ -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"
    ]
@@ -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",
     "}"
    ]
   },
@@ -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",
+    ")"
    ]
   },
   {
@@ -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",
+    "    )"
    ]
   },
   {
@@ -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",
+    ")"
    ]
   },
   {
@@ -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"
@@ -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",
     ")"
    ]
   },
@@ -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"
@@ -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",
+    ")"
    ]
   },
   {
@@ -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": {

pymc_marketing/clv/distributions.py

@@ -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::
@@ -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
 

pymc_marketing/clv/models/beta_geo.py

@@ -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.
 

pymc_marketing/clv/models/gamma_gamma.py

@@ -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
@@ -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.
 
@@ -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.