MNT Fix several FutureWarnings (#810)

Author: ArturoAmorQ

notebooks/datasets_bike_rides.ipynb

@@ -271,7 +271,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_ride.resample(\"60S\").mean().plot()\n",
+    "data_ride.resample(\"60s\").mean().plot()\n",
     "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
     "_ = plt.title(\"Sensor values for different cyclist measurements\")"
    ]

notebooks/ensemble_adaboost.ipynb

@@ -271,7 +271,7 @@
     "\n",
     "estimator = DecisionTreeClassifier(max_depth=3, random_state=0)\n",
     "adaboost = AdaBoostClassifier(\n",
-    "    estimator=estimator, n_estimators=3, algorithm=\"SAMME\", random_state=0\n",
+    "    estimator=estimator, n_estimators=3, random_state=0\n",
     ")\n",
     "adaboost.fit(data, target)"
    ]

notebooks/linear_models_regularization.ipynb

@@ -618,7 +618,7 @@
     "ridge = make_pipeline(\n",
     "    MinMaxScaler(),\n",
     "    PolynomialFeatures(degree=2, include_bias=False),\n",
-    "    RidgeCV(alphas=alphas, store_cv_values=True),\n",
+    "    RidgeCV(alphas=alphas, store_cv_results=True),\n",
     ")"
    ]
   },
@@ -677,7 +677,7 @@
     "It indicates that our model is not overfitting.\n",
     "\n",
     "When fitting the ridge regressor, we also requested to store the error found\n",
-    "during cross-validation (by setting the parameter `store_cv_values=True`). We\n",
+    "during cross-validation (by setting the parameter `store_cv_results=True`). We\n",
     "can plot the mean squared error for the different `alphas` regularization\n",
     "strengths that we tried. The error bars represent one standard deviation of the\n",
     "average mean square error across folds for a given value of `alpha`."
@@ -690,7 +690,7 @@
    "outputs": [],
    "source": [
     "mse_alphas = [\n",
-    "    est[-1].cv_values_.mean(axis=0) for est in cv_results[\"estimator\"]\n",
+    "    est[-1].cv_results_.mean(axis=0) for est in cv_results[\"estimator\"]\n",
     "]\n",
     "cv_alphas = pd.DataFrame(mse_alphas, columns=alphas)\n",
     "cv_alphas = cv_alphas.aggregate([\"mean\", \"std\"]).T\n",

notebooks/parameter_tuning_nested.ipynb

@@ -70,6 +70,7 @@
     "        (\"cat_preprocessor\", categorical_preprocessor, categorical_columns),\n",
     "    ],\n",
     "    remainder=\"passthrough\",\n",
+    "    force_int_remainder_cols=False,  # Silence a warning in scikit-learn v1.6.\n",
     ")"
    ]
   },

notebooks/parameter_tuning_randomized_search.ipynb

@@ -121,6 +121,7 @@
     "preprocessor = ColumnTransformer(\n",
     "    [(\"cat_preprocessor\", categorical_preprocessor, categorical_columns)],\n",
     "    remainder=\"passthrough\",\n",
+    "    force_int_remainder_cols=False,  # Silence a warning in scikit-learn v1.6.\n",
     ")"
    ]
   },

python_scripts/datasets_bike_rides.py

@@ -155,7 +155,7 @@
 # smoother visualization.
 
 # %%
-data_ride.resample("60S").mean().plot()
+data_ride.resample("60s").mean().plot()
 plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
 _ = plt.title("Sensor values for different cyclist measurements")
 

python_scripts/ensemble_adaboost.py

@@ -190,7 +190,7 @@
 
 estimator = DecisionTreeClassifier(max_depth=3, random_state=0)
 adaboost = AdaBoostClassifier(
-    estimator=estimator, n_estimators=3, algorithm="SAMME", random_state=0
+    estimator=estimator, n_estimators=3, random_state=0
 )
 adaboost.fit(data, target)
 

python_scripts/linear_models_regularization.py

@@ -421,7 +421,7 @@
 ridge = make_pipeline(
     MinMaxScaler(),
     PolynomialFeatures(degree=2, include_bias=False),
-    RidgeCV(alphas=alphas, store_cv_values=True),
+    RidgeCV(alphas=alphas, store_cv_results=True),
 )
 
 # %%
@@ -458,14 +458,14 @@
 # It indicates that our model is not overfitting.
 #
 # When fitting the ridge regressor, we also requested to store the error found
-# during cross-validation (by setting the parameter `store_cv_values=True`). We
+# during cross-validation (by setting the parameter `store_cv_results=True`). We
 # can plot the mean squared error for the different `alphas` regularization
 # strengths that we tried. The error bars represent one standard deviation of the
 # average mean square error across folds for a given value of `alpha`.
 
 # %%
 mse_alphas = [
-    est[-1].cv_values_.mean(axis=0) for est in cv_results["estimator"]
+    est[-1].cv_results_.mean(axis=0) for est in cv_results["estimator"]
 ]
 cv_alphas = pd.DataFrame(mse_alphas, columns=alphas)
 cv_alphas = cv_alphas.aggregate(["mean", "std"]).T

python_scripts/parameter_tuning_grid_search.py

@@ -89,6 +89,9 @@
 preprocessor = ColumnTransformer(
     [("cat_preprocessor", categorical_preprocessor, categorical_columns)],
     remainder="passthrough",
+    # Silence a deprecation warning in scikit-learn v1.6 related to how the
+    # ColumnTransformer stores an attribute that we do not use in this notebook
+    force_int_remainder_cols=False,
 )
 
 # %% [markdown]

python_scripts/parameter_tuning_nested.py

@@ -56,6 +56,7 @@
         ("cat_preprocessor", categorical_preprocessor, categorical_columns),
     ],
     remainder="passthrough",
+    force_int_remainder_cols=False,  # Silence a warning in scikit-learn v1.6.
 )
 
 # %%

python_scripts/parameter_tuning_randomized_search.py

@@ -73,6 +73,7 @@
 preprocessor = ColumnTransformer(
     [("cat_preprocessor", categorical_preprocessor, categorical_columns)],
     remainder="passthrough",
+    force_int_remainder_cols=False,  # Silence a warning in scikit-learn v1.6.
 )
 
 # %%