[pre-commit.ci] pre-commit autoupdate (#1942)

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -13,7 +13,7 @@ repos:
           - --exclude=scripts/
           - --start-year=2022
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.12
+    rev: v0.13.0
     hooks:
       - id: ruff-check
         types_or: [python, pyi, jupyter]
@@ -23,7 +23,7 @@ repos:
         types_or: [python, pyi, jupyter]
         exclude: ^docs/source/notebooks/clv/dev/
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.17.1
+    rev: v1.18.1
     hooks:
       - id: mypy
         args: [--ignore-missing-imports]

docs/source/notebooks/mmm/mmm_lift_test.ipynb

@@ -617,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "302995b6",
    "metadata": {
     "tags": [
@@ -678,7 +678,7 @@
     "        original_scale=True\n",
     "    )\n",
     "\n",
-    "    fig, ax = plt.subplots(\n",
+    "    _, ax = plt.subplots(\n",
     "        nrows=2,\n",
     "        figsize=(15, 8),\n",
     "        ncols=1,\n",

docs/source/notebooks/mmm/mmm_tvp_example.ipynb

@@ -273,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -293,7 +293,7 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
+    "_, ax = plt.subplots()\n",
     "DATA.set_index(\"Weeks\").plot(ax=ax)\n",
     "ax.set_title(\"Time series of the data\")\n",
     "ax.set_ylabel(\"Spending\")\n",
@@ -320,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -374,7 +374,7 @@
     "    target = pm.draw(true_model.y, random_seed=SEED)\n",
     "\n",
     "    # Plot the simulated target series\n",
-    "    fig, ax = plt.subplots()\n",
+    "    _, ax = plt.subplots()\n",
     "    ax.set_title(\"Target\")\n",
     "    ax.plot(DATA.Weeks, target, label=\"Sales\")\n",
     "    ax.plot(DATA.Weeks, synthetic_intercept, label=\"Synthetic intercept\")\n",
@@ -531,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -610,7 +610,7 @@
     "    mmm.y = target_series.values\n",
     "\n",
     "    # Plot the posterior predictive\n",
-    "    fig, ax = plt.subplots()\n",
+    "    _, ax = plt.subplots()\n",
     "    mmm.plot_posterior_predictive(original_scale=True, add_mean=False, ax=ax)\n",
     "    split_index = DATA.shape[0] - SPLIT_N\n",
     "    ax.axvline(\n",
@@ -727,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -761,7 +761,7 @@
     "    )\n",
     "\n",
     "    # Plot posterior intercept versus actual\n",
-    "    fig, ax = plt.subplots()\n",
+    "    _, ax = plt.subplots()\n",
     "    ax.set_title(\"Posterior intercept vs actual\")\n",
     "    ax.plot(\n",
     "        mmm.posterior_predictive.date, intercept_posterior_mean, label=\"Posterior mean\"\n",

pymc_marketing/customer_choice/nested_logit.py

@@ -536,7 +536,7 @@ def make_exp_nest(
         lambda_lkup = self.lambda_lkup
         N = U.shape[0]
         if "_" in nest:
-            parent, child = nest.split("_")
+            parent, _ = nest.split("_")
         else:
             parent = None
         y_nest = U[:, nest_indices[level][nest]]
@@ -707,7 +707,7 @@ def make_model(self, X, W, y) -> pm.Model:
 
                 for idx, n in enumerate(middle_nests):
                     is_last = idx == len(middle_nests) - 1
-                    parent, child = n.split("_")
+                    parent, _ = n.split("_")
                     P_nest = nest_prob_m[n]
                     P_y_given_nest = cond_prob_m[n]["P_y_given"]
                     prod = pm.Deterministic(

pymc_marketing/mmm/events.py

@@ -118,7 +118,7 @@ def create_basis_matrix(df_events: pd.DataFrame, model_dates: np.ndarray):
 BasisMeta = create_registration_meta(BASIS_TRANSFORMATIONS)
 
 
-class Basis(Transformation, metaclass=BasisMeta):  # type: ignore[misc]
+class Basis(Transformation, metaclass=BasisMeta):  # type: ignore[metaclass]
     """Basis transformation associated with an event model."""
 
     prefix: str = "basis"

scripts/data_generators/multidimensional_data_generation.py

@@ -357,7 +357,7 @@ def main(
     rng = np.random.default_rng(seed)
 
     # Generate dates
-    dates, n_dates = get_dates(start_date, end_date)
+    dates, _ = get_dates(start_date, end_date)
 
     # Generate initial dataframe
     df = generate_dataframe(dates, geo_index, rng)

tests/clv/models/test_basic.py

@@ -143,7 +143,7 @@ def test_fit_advi_with_wrong_chains_advi_kwargs(self, mocker):
 
         with pytest.warns(
             UserWarning,
-            match="The 'chains' parameter must be 1 with 'advi'. Sampling only 1 chain despite the provided parameter.",
+            match=r"The 'chains' parameter must be 1 with 'advi'. Sampling only 1 chain despite the provided parameter.",  # noqa: E501
         ):
             model.fit(
                 method="advi",
@@ -220,7 +220,7 @@ def mock_property(self):
         monkeypatch.setattr(CLVModelTest, "id", property(mock_property))
         with pytest.raises(
             DifferentModelError,
-            match="The file 'test_model'",
+            match=r"The file 'test_model'",
         ):
             CLVModelTest.load("test_model")
         os.remove("test_model")
@@ -244,7 +244,7 @@ def test_model_config_warns(self) -> None:
         model_config = {
             "x": {"dist": "StudentT", "kwargs": {"mu": 0, "sigma": 5, "nu": 15}},
         }
-        with pytest.warns(DeprecationWarning, match="x is automatically"):
+        with pytest.warns(DeprecationWarning, match=r"x is automatically"):
             model = CLVModelTest(model_config=model_config)
 
         assert model.model_config == {
@@ -274,7 +274,7 @@ def test_deprecation_warning_on_old_config(self):
             "x_prior": {"dist": "Normal", "kwargs": {"mu": 0, "sigma": 1}}
         }
         with pytest.warns(
-            DeprecationWarning, match="The key 'x_prior' in model_config is deprecated"
+            DeprecationWarning, match=r"The key 'x_prior' in model_config is deprecated"
         ):
             model = CLVModelTest(model_config=old_model_config)
 

tests/clv/models/test_beta_geo.py

@@ -183,7 +183,7 @@ def test_customer_id_duplicate(self):
         )
 
         with pytest.raises(
-            ValueError, match="Column customer_id has duplicate entries"
+            ValueError, match=r"Column customer_id has duplicate entries"
         ):
             BetaGeoModel(
                 data=data,
@@ -340,7 +340,7 @@ def test_model_convergence(self, method, rtol, model_config):
 
     def test_fit_result_without_fit(self, mocker, model_config):
         model = BetaGeoModel(data=self.data, model_config=model_config)
-        with pytest.raises(RuntimeError, match="The model hasn't been fit yet"):
+        with pytest.raises(RuntimeError, match=r"The model hasn't been fit yet"):
             model.fit_result
 
         mocker.patch("pymc.sample", mock_sample)

tests/clv/models/test_beta_geo_beta_binom.py

@@ -208,7 +208,7 @@ def test_missing_cols(self):
 
     def test_customer_id_duplicate(self):
         with pytest.raises(
-            ValueError, match="Column customer_id has duplicate entries"
+            ValueError, match=r"Column customer_id has duplicate entries"
         ):
             data = pd.DataFrame(
                 {
@@ -224,7 +224,7 @@ def test_customer_id_duplicate(self):
             )
 
     def test_T_homogeneity(self):
-        with pytest.raises(ValueError, match="Column T has non-homogeneous entries"):
+        with pytest.raises(ValueError, match=r"Column T has non-homogeneous entries"):
             data = pd.DataFrame(
                 {
                     "customer_id": np.asarray([1, 2]),
@@ -312,7 +312,7 @@ def test_model_convergence(self, method, rtol, model_config):
 
     def test_fit_result_without_fit(self, mocker, model_config):
         model = BetaGeoBetaBinomModel(data=self.pred_data, model_config=model_config)
-        with pytest.raises(RuntimeError, match="The model hasn't been fit yet"):
+        with pytest.raises(RuntimeError, match=r"The model hasn't been fit yet"):
             model.fit_result
 
         mocker.patch("pymc.sample", mock_sample)

tests/clv/models/test_modified_beta_geo.py

@@ -152,7 +152,7 @@ def test_missing_cols(self, missing_column):
 
     def test_customer_id_duplicate(self):
         with pytest.raises(
-            ValueError, match="Column customer_id has duplicate entries"
+            ValueError, match=r"Column customer_id has duplicate entries"
         ):
             data = pd.DataFrame(
                 {
@@ -239,7 +239,7 @@ def test_model_convergence(self, method, rtol, model_config):
 
     def test_fit_result_without_fit(self, mocker, model_config):
         model = ModifiedBetaGeoModel(data=self.data, model_config=model_config)
-        with pytest.raises(RuntimeError, match="The model hasn't been fit yet"):
+        with pytest.raises(RuntimeError, match=r"The model hasn't been fit yet"):
             model.fit_result
 
         mocker.patch("pymc.sample", mock_sample)
@@ -283,7 +283,7 @@ def tests_expected_probability_no_purchases_raises_exception(self):
 
         with pytest.raises(
             NotImplementedError,
-            match="The MBG/NBD model does not support this method.",
+            match=r"The MBG/NBD model does not support this method.",
         ):
             mbg_model.expected_probability_no_purchase(t=test_t, data=data)