Pushing the docs to dev/ for branch: main, commit 49a937e974190b4ab20c7506052ce8a67c129da1

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from time import time\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import RandomizedSearchCV\nfrom sklearn.datasets import fetch_lfw_people\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import ConfusionMatrixDisplay\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\nfrom sklearn.svm import SVC\nfrom sklearn.utils.fixes import loguniform"
+        "from time import time\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import RandomizedSearchCV\nfrom sklearn.datasets import fetch_lfw_people\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import ConfusionMatrixDisplay\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\nfrom sklearn.svm import SVC\nfrom scipy.stats import loguniform"
       ]
     },
     {

dev/_downloads/23e3d7fa2388aef4e9a60c4a6caf166d/plot_face_recognition.ipynb

@@ -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_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_)"
+        "import numpy as np\n\nfrom time import time\nimport scipy.stats as stats\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\": stats.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_)"
       ]
     }
   ],

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -23,7 +23,7 @@
 from sklearn.preprocessing import StandardScaler
 from sklearn.decomposition import PCA
 from sklearn.svm import SVC
-from sklearn.utils.fixes import loguniform
+from scipy.stats import loguniform
 
 
 # %%

dev/_downloads/733ff7845fe2f197ecd0c72afcf23651/plot_randomized_search.ipynb

@@ -163,7 +163,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.model_selection import RandomizedSearchCV\nfrom sklearn.utils.fixes import loguniform\n\nparam_distributions = {\n    \"alpha\": loguniform(1e0, 1e3),\n    \"kernel__length_scale\": loguniform(1e-2, 1e2),\n    \"kernel__periodicity\": loguniform(1e0, 1e1),\n}\nkernel_ridge_tuned = RandomizedSearchCV(\n    kernel_ridge,\n    param_distributions=param_distributions,\n    n_iter=500,\n    random_state=0,\n)\nstart_time = time.time()\nkernel_ridge_tuned.fit(training_data, training_noisy_target)\nprint(f\"Time for KernelRidge fitting: {time.time() - start_time:.3f} seconds\")"
+        "from sklearn.model_selection import RandomizedSearchCV\nfrom scipy.stats import loguniform\n\nparam_distributions = {\n    \"alpha\": loguniform(1e0, 1e3),\n    \"kernel__length_scale\": loguniform(1e-2, 1e2),\n    \"kernel__periodicity\": loguniform(1e0, 1e1),\n}\nkernel_ridge_tuned = RandomizedSearchCV(\n    kernel_ridge,\n    param_distributions=param_distributions,\n    n_iter=500,\n    random_state=0,\n)\nstart_time = time.time()\nkernel_ridge_tuned.fit(training_data, training_noisy_target)\nprint(f\"Time for KernelRidge fitting: {time.time() - start_time:.3f} seconds\")"
       ]
     },
     {

dev/_downloads/b3a994b2ad66fe78bcedaf151ab78b07/plot_face_recognition.py

@@ -177,7 +177,7 @@
 
 # %%
 from sklearn.model_selection import RandomizedSearchCV
-from sklearn.utils.fixes import loguniform
+from scipy.stats import loguniform
 
 param_distributions = {
     "alpha": loguniform(1e0, 1e3),

dev/_downloads/c3bc3113489c0b7c9a698b430d691ddc/plot_compare_gpr_krr.ipynb

@@ -24,7 +24,6 @@
 
 from time import time
 import scipy.stats as stats
-from sklearn.utils.fixes import loguniform
 
 from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
 from sklearn.datasets import load_digits
@@ -57,7 +56,7 @@ def report(results, n_top=3):
 param_dist = {
     "average": [True, False],
     "l1_ratio": stats.uniform(0, 1),
-    "alpha": loguniform(1e-2, 1e0),
+    "alpha": stats.loguniform(1e-2, 1e0),
 }
 
 # run randomized search