Pushing the docs to dev/ for branch: main, commit c29fe81218833f8f14b28cf6d5374201ac8f3c13

Author: sklearn-ci

dev/_downloads/02f111fb3dd79805b161e14c564184fc/plot_sgd_comparison.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Comparing various online solvers\n\nAn example showing how different online solvers perform\non the hand-written digits dataset.\n"
+        "\n# Comparing various online solvers\nAn example showing how different online solvers perform\non the hand-written digits dataset.\n"
       ]
     },
     {
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Rob Zinkov <rob at zinkov dot com>\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import SGDClassifier, Perceptron\nfrom sklearn.linear_model import PassiveAggressiveClassifier\nfrom sklearn.linear_model import LogisticRegression\n\nheldout = [0.95, 0.90, 0.75, 0.50, 0.01]\nrounds = 20\nX, y = datasets.load_digits(return_X_y=True)\n\nclassifiers = [\n    (\"SGD\", SGDClassifier(max_iter=100)),\n    (\"ASGD\", SGDClassifier(average=True)),\n    (\"Perceptron\", Perceptron()),\n    (\n        \"Passive-Aggressive I\",\n        PassiveAggressiveClassifier(loss=\"hinge\", C=1.0, tol=1e-4),\n    ),\n    (\n        \"Passive-Aggressive II\",\n        PassiveAggressiveClassifier(loss=\"squared_hinge\", C=1.0, tol=1e-4),\n    ),\n    (\"SAG\", LogisticRegression(solver=\"sag\", tol=1e-1, C=1.0e4 / X.shape[0])),\n]\n\nxx = 1.0 - np.array(heldout)\n\nfor name, clf in classifiers:\n    print(\"training %s\" % name)\n    rng = np.random.RandomState(42)\n    yy = []\n    for i in heldout:\n        yy_ = []\n        for r in range(rounds):\n            X_train, X_test, y_train, y_test = train_test_split(\n                X, y, test_size=i, random_state=rng\n            )\n            clf.fit(X_train, y_train)\n            y_pred = clf.predict(X_test)\n            yy_.append(1 - np.mean(y_pred == y_test))\n        yy.append(np.mean(yy_))\n    plt.plot(xx, yy, label=name)\n\nplt.legend(loc=\"upper right\")\nplt.xlabel(\"Proportion train\")\nplt.ylabel(\"Test Error Rate\")\nplt.show()"
+        "# Author: Rob Zinkov <rob at zinkov dot com>\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import SGDClassifier, Perceptron\nfrom sklearn.linear_model import PassiveAggressiveClassifier\nfrom sklearn.linear_model import LogisticRegression\n\nheldout = [0.95, 0.90, 0.75, 0.50, 0.01]\n# Number of rounds to fit and evaluate an estimator.\nrounds = 10\nX, y = datasets.load_digits(return_X_y=True)\n\nclassifiers = [\n    (\"SGD\", SGDClassifier(max_iter=110)),\n    (\"ASGD\", SGDClassifier(max_iter=110, average=True)),\n    (\"Perceptron\", Perceptron(max_iter=110)),\n    (\n        \"Passive-Aggressive I\",\n        PassiveAggressiveClassifier(max_iter=110, loss=\"hinge\", C=1.0, tol=1e-4),\n    ),\n    (\n        \"Passive-Aggressive II\",\n        PassiveAggressiveClassifier(\n            max_iter=110, loss=\"squared_hinge\", C=1.0, tol=1e-4\n        ),\n    ),\n    (\n        \"SAG\",\n        LogisticRegression(max_iter=110, solver=\"sag\", tol=1e-1, C=1.0e4 / X.shape[0]),\n    ),\n]\n\nxx = 1.0 - np.array(heldout)\n\nfor name, clf in classifiers:\n    print(\"training %s\" % name)\n    rng = np.random.RandomState(42)\n    yy = []\n    for i in heldout:\n        yy_ = []\n        for r in range(rounds):\n            X_train, X_test, y_train, y_test = train_test_split(\n                X, y, test_size=i, random_state=rng\n            )\n            clf.fit(X_train, y_train)\n            y_pred = clf.predict(X_test)\n            yy_.append(1 - np.mean(y_pred == y_test))\n        yy.append(np.mean(yy_))\n    plt.plot(xx, yy, label=name)\n\nplt.legend(loc=\"upper right\")\nplt.xlabel(\"Proportion train\")\nplt.ylabel(\"Test Error Rate\")\nplt.show()"
       ]
     }
   ],

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -2,10 +2,8 @@
 ==================================
 Comparing various online solvers
 ==================================
-
 An example showing how different online solvers perform
 on the hand-written digits dataset.
-
 """
 
 # Author: Rob Zinkov <rob at zinkov dot com>
@@ -21,22 +19,28 @@
 from sklearn.linear_model import LogisticRegression
 
 heldout = [0.95, 0.90, 0.75, 0.50, 0.01]
-rounds = 20
+# Number of rounds to fit and evaluate an estimator.
+rounds = 10
 X, y = datasets.load_digits(return_X_y=True)
 
 classifiers = [
-    ("SGD", SGDClassifier(max_iter=100)),
-    ("ASGD", SGDClassifier(average=True)),
-    ("Perceptron", Perceptron()),
+    ("SGD", SGDClassifier(max_iter=110)),
+    ("ASGD", SGDClassifier(max_iter=110, average=True)),
+    ("Perceptron", Perceptron(max_iter=110)),
     (
         "Passive-Aggressive I",
-        PassiveAggressiveClassifier(loss="hinge", C=1.0, tol=1e-4),
+        PassiveAggressiveClassifier(max_iter=110, loss="hinge", C=1.0, tol=1e-4),
     ),
     (
         "Passive-Aggressive II",
-        PassiveAggressiveClassifier(loss="squared_hinge", C=1.0, tol=1e-4),
+        PassiveAggressiveClassifier(
+            max_iter=110, loss="squared_hinge", C=1.0, tol=1e-4
+        ),
+    ),
+    (
+        "SAG",
+        LogisticRegression(max_iter=110, solver="sag", tol=1e-1, C=1.0e4 / X.shape[0]),
     ),
-    ("SAG", LogisticRegression(solver="sag", tol=1e-1, C=1.0e4 / X.shape[0])),
 ]
 
 xx = 1.0 - np.array(heldout)