Pushing the docs to dev/ for branch: main, commit 1d697842a36fbd93e89d7756bf899f527baf6323

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "def _count_nonzero_coefficients(estimator):\n    a = estimator.coef_.toarray()\n    return np.count_nonzero(a)\n\n\nconfigurations = [\n    {\n        \"estimator\": SGDClassifier,\n        \"tuned_params\": {\n            \"penalty\": \"elasticnet\",\n            \"alpha\": 0.001,\n            \"loss\": \"modified_huber\",\n            \"fit_intercept\": True,\n            \"tol\": 1e-3,\n        },\n        \"changing_param\": \"l1_ratio\",\n        \"changing_param_values\": [0.25, 0.5, 0.75, 0.9],\n        \"complexity_label\": \"non_zero coefficients\",\n        \"complexity_computer\": _count_nonzero_coefficients,\n        \"prediction_performance_computer\": hamming_loss,\n        \"prediction_performance_label\": \"Hamming Loss (Misclassification Ratio)\",\n        \"postfit_hook\": lambda x: x.sparsify(),\n        \"data\": classification_data,\n        \"n_samples\": 5,\n    },\n    {\n        \"estimator\": NuSVR,\n        \"tuned_params\": {\"C\": 1e3, \"gamma\": 2 ** -15},\n        \"changing_param\": \"nu\",\n        \"changing_param_values\": [0.05, 0.1, 0.2, 0.35, 0.5],\n        \"complexity_label\": \"n_support_vectors\",\n        \"complexity_computer\": lambda x: len(x.support_vectors_),\n        \"data\": regression_data,\n        \"postfit_hook\": lambda x: x,\n        \"prediction_performance_computer\": mean_squared_error,\n        \"prediction_performance_label\": \"MSE\",\n        \"n_samples\": 15,\n    },\n    {\n        \"estimator\": GradientBoostingRegressor,\n        \"tuned_params\": {\n            \"loss\": \"squared_error\",\n            \"learning_rate\": 0.05,\n            \"max_depth\": 2,\n        },\n        \"changing_param\": \"n_estimators\",\n        \"changing_param_values\": [10, 25, 50, 75, 100],\n        \"complexity_label\": \"n_trees\",\n        \"complexity_computer\": lambda x: x.n_estimators,\n        \"data\": regression_data,\n        \"postfit_hook\": lambda x: x,\n        \"prediction_performance_computer\": mean_squared_error,\n        \"prediction_performance_label\": \"MSE\",\n        \"n_samples\": 15,\n    },\n]"
+        "def _count_nonzero_coefficients(estimator):\n    a = estimator.coef_.toarray()\n    return np.count_nonzero(a)\n\n\nconfigurations = [\n    {\n        \"estimator\": SGDClassifier,\n        \"tuned_params\": {\n            \"penalty\": \"elasticnet\",\n            \"alpha\": 0.001,\n            \"loss\": \"modified_huber\",\n            \"fit_intercept\": True,\n            \"tol\": 1e-1,\n            \"n_iter_no_change\": 2,\n        },\n        \"changing_param\": \"l1_ratio\",\n        \"changing_param_values\": [0.25, 0.5, 0.75, 0.9],\n        \"complexity_label\": \"non_zero coefficients\",\n        \"complexity_computer\": _count_nonzero_coefficients,\n        \"prediction_performance_computer\": hamming_loss,\n        \"prediction_performance_label\": \"Hamming Loss (Misclassification Ratio)\",\n        \"postfit_hook\": lambda x: x.sparsify(),\n        \"data\": classification_data,\n        \"n_samples\": 5,\n    },\n    {\n        \"estimator\": NuSVR,\n        \"tuned_params\": {\"C\": 1e3, \"gamma\": 2 ** -15},\n        \"changing_param\": \"nu\",\n        \"changing_param_values\": [0.05, 0.1, 0.2, 0.35, 0.5],\n        \"complexity_label\": \"n_support_vectors\",\n        \"complexity_computer\": lambda x: len(x.support_vectors_),\n        \"data\": regression_data,\n        \"postfit_hook\": lambda x: x,\n        \"prediction_performance_computer\": mean_squared_error,\n        \"prediction_performance_label\": \"MSE\",\n        \"n_samples\": 15,\n    },\n    {\n        \"estimator\": GradientBoostingRegressor,\n        \"tuned_params\": {\n            \"loss\": \"squared_error\",\n            \"learning_rate\": 0.05,\n            \"max_depth\": 2,\n        },\n        \"changing_param\": \"n_estimators\",\n        \"changing_param_values\": [10, 25, 50, 75, 100],\n        \"complexity_label\": \"n_trees\",\n        \"complexity_computer\": lambda x: x.n_estimators,\n        \"data\": regression_data,\n        \"postfit_hook\": lambda x: x,\n        \"prediction_performance_computer\": mean_squared_error,\n        \"prediction_performance_label\": \"MSE\",\n        \"n_samples\": 15,\n    },\n]"
       ]
     },
     {

dev/_downloads/3ed102fa8211c8d36f2331f0c5e1dcef/plot_model_complexity_influence.ipynb

@@ -165,7 +165,8 @@ def _count_nonzero_coefficients(estimator):
             "alpha": 0.001,
             "loss": "modified_huber",
             "fit_intercept": True,
-            "tol": 1e-3,
+            "tol": 1e-1,
+            "n_iter_no_change": 2,
         },
         "changing_param": "l1_ratio",
         "changing_param_values": [0.25, 0.5, 0.75, 0.9],