Pushing the docs to dev/ for branch: main, commit 7bfa9ccd7c4a6d73bb2b0a99baf6f9515e681951

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\n\nfrom time import time\nimport scipy.stats as stats\nfrom sklearn.utils.fixes import loguniform\n\nfrom sklearn.model_selection import GridSearchCV, RandomizedSearchCV\nfrom sklearn.datasets import load_digits\nfrom sklearn.linear_model import SGDClassifier\n\n# get some data\nX, y = load_digits(return_X_y=True)\n\n# build a classifier\nclf = SGDClassifier(loss=\"hinge\", penalty=\"elasticnet\", fit_intercept=True)\n\n\n# Utility function to report best scores\ndef report(results, n_top=3):\n    for i in range(1, n_top + 1):\n        candidates = np.flatnonzero(results[\"rank_test_score\"] == i)\n        for candidate in candidates:\n            print(\"Model with rank: {0}\".format(i))\n            print(\n                \"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n                    results[\"mean_test_score\"][candidate],\n                    results[\"std_test_score\"][candidate],\n                )\n            )\n            print(\"Parameters: {0}\".format(results[\"params\"][candidate]))\n            print(\"\")\n\n\n# specify parameters and distributions to sample from\nparam_dist = {\n    \"average\": [True, False],\n    \"l1_ratio\": stats.uniform(0, 1),\n    \"alpha\": loguniform(1e-4, 1e0),\n}\n\n# run randomized search\nn_iter_search = 20\nrandom_search = RandomizedSearchCV(\n    clf, param_distributions=param_dist, n_iter=n_iter_search\n)\n\nstart = time()\nrandom_search.fit(X, y)\nprint(\n    \"RandomizedSearchCV took %.2f seconds for %d candidates parameter settings.\"\n    % ((time() - start), n_iter_search)\n)\nreport(random_search.cv_results_)\n\n# use a full grid over all parameters\nparam_grid = {\n    \"average\": [True, False],\n    \"l1_ratio\": np.linspace(0, 1, num=10),\n    \"alpha\": np.power(10, np.arange(-4, 1, dtype=float)),\n}\n\n# run grid search\ngrid_search = GridSearchCV(clf, param_grid=param_grid)\nstart = time()\ngrid_search.fit(X, y)\n\nprint(\n    \"GridSearchCV took %.2f seconds for %d candidate parameter settings.\"\n    % (time() - start, len(grid_search.cv_results_[\"params\"]))\n)\nreport(grid_search.cv_results_)"
+        "import numpy as np\n\nfrom time import time\nimport scipy.stats as stats\nfrom sklearn.utils.fixes import loguniform\n\nfrom sklearn.model_selection import GridSearchCV, RandomizedSearchCV\nfrom sklearn.datasets import load_digits\nfrom sklearn.linear_model import SGDClassifier\n\n# get some data\nX, y = load_digits(return_X_y=True, n_class=3)\n\n# build a classifier\nclf = SGDClassifier(loss=\"hinge\", penalty=\"elasticnet\", fit_intercept=True)\n\n\n# Utility function to report best scores\ndef report(results, n_top=3):\n    for i in range(1, n_top + 1):\n        candidates = np.flatnonzero(results[\"rank_test_score\"] == i)\n        for candidate in candidates:\n            print(\"Model with rank: {0}\".format(i))\n            print(\n                \"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n                    results[\"mean_test_score\"][candidate],\n                    results[\"std_test_score\"][candidate],\n                )\n            )\n            print(\"Parameters: {0}\".format(results[\"params\"][candidate]))\n            print(\"\")\n\n\n# specify parameters and distributions to sample from\nparam_dist = {\n    \"average\": [True, False],\n    \"l1_ratio\": stats.uniform(0, 1),\n    \"alpha\": loguniform(1e-2, 1e0),\n}\n\n# run randomized search\nn_iter_search = 15\nrandom_search = RandomizedSearchCV(\n    clf, param_distributions=param_dist, n_iter=n_iter_search\n)\n\nstart = time()\nrandom_search.fit(X, y)\nprint(\n    \"RandomizedSearchCV took %.2f seconds for %d candidates parameter settings.\"\n    % ((time() - start), n_iter_search)\n)\nreport(random_search.cv_results_)\n\n# use a full grid over all parameters\nparam_grid = {\n    \"average\": [True, False],\n    \"l1_ratio\": np.linspace(0, 1, num=10),\n    \"alpha\": np.power(10, np.arange(-2, 1, dtype=float)),\n}\n\n# run grid search\ngrid_search = GridSearchCV(clf, param_grid=param_grid)\nstart = time()\ngrid_search.fit(X, y)\n\nprint(\n    \"GridSearchCV took %.2f seconds for %d candidate parameter settings.\"\n    % (time() - start, len(grid_search.cv_results_[\"params\"]))\n)\nreport(grid_search.cv_results_)"
       ]
     }
   ],