Pushing the docs to dev/ for branch: master, commit f4cd388569dca52e0af7b4a0d83cd87072be6c76

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n\n# Author: Issam H. Laradji\n# License: BSD 3 clause\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.colors import ListedColormap\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.datasets import make_moons, make_circles, make_classification\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.pipeline import make_pipeline\n\nh = .02  # step size in the mesh\n\nalphas = np.logspace(-5, 3, 5)\nnames = ['alpha ' + str(i) for i in alphas]\n\nclassifiers = []\nfor i in alphas:\n    classifiers.append(make_pipeline(\n                       StandardScaler(),\n                       MLPClassifier(solver='lbfgs', alpha=i,\n                                     random_state=1, max_iter=2000,\n                                     early_stopping=True,\n                                     hidden_layer_sizes=[100, 100])\n                       ))\n\nX, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n                           random_state=0, n_clusters_per_class=1)\nrng = np.random.RandomState(2)\nX += 2 * rng.uniform(size=X.shape)\nlinearly_separable = (X, y)\n\ndatasets = [make_moons(noise=0.3, random_state=0),\n            make_circles(noise=0.2, factor=0.5, random_state=1),\n            linearly_separable]\n\nfigure = plt.figure(figsize=(17, 9))\ni = 1\n# iterate over datasets\nfor X, y in datasets:\n    # preprocess dataset, split into training and test part\n    X = StandardScaler().fit_transform(X)\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)\n\n    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n                         np.arange(y_min, y_max, h))\n\n    # just plot the dataset first\n    cm = plt.cm.RdBu\n    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n    # Plot the training points\n    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n    # and testing points\n    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)\n    ax.set_xlim(xx.min(), xx.max())\n    ax.set_ylim(yy.min(), yy.max())\n    ax.set_xticks(())\n    ax.set_yticks(())\n    i += 1\n\n    # iterate over classifiers\n    for name, clf in zip(names, classifiers):\n        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n        clf.fit(X_train, y_train)\n        score = clf.score(X_test, y_test)\n\n        # Plot the decision boundary. For that, we will assign a color to each\n        # point in the mesh [x_min, x_max]x[y_min, y_max].\n        if hasattr(clf, \"decision_function\"):\n            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n        else:\n            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n\n        # Put the result into a color plot\n        Z = Z.reshape(xx.shape)\n        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n\n        # Plot also the training points\n        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n                   edgecolors='black', s=25)\n        # and testing points\n        ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n                   alpha=0.6, edgecolors='black', s=25)\n\n        ax.set_xlim(xx.min(), xx.max())\n        ax.set_ylim(yy.min(), yy.max())\n        ax.set_xticks(())\n        ax.set_yticks(())\n        ax.set_title(name)\n        ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),\n                size=15, horizontalalignment='right')\n        i += 1\n\nfigure.subplots_adjust(left=.02, right=.98)\nplt.show()"
+        "print(__doc__)\n\n\n# Author: Issam H. Laradji\n# License: BSD 3 clause\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.colors import ListedColormap\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.datasets import make_moons, make_circles, make_classification\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.pipeline import make_pipeline\n\nh = .02  # step size in the mesh\n\nalphas = np.logspace(-1, 1, 5)\n\nclassifiers = []\nnames = []\nfor alpha in alphas:\n    classifiers.append(make_pipeline(\n        StandardScaler(),\n        MLPClassifier(\n            solver='lbfgs', alpha=alpha, random_state=1, max_iter=2000,\n            early_stopping=True, hidden_layer_sizes=[100, 100],\n        )\n    ))\n    names.append(f\"alpha {alpha:.2f}\")\n\nX, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n                           random_state=0, n_clusters_per_class=1)\nrng = np.random.RandomState(2)\nX += 2 * rng.uniform(size=X.shape)\nlinearly_separable = (X, y)\n\ndatasets = [make_moons(noise=0.3, random_state=0),\n            make_circles(noise=0.2, factor=0.5, random_state=1),\n            linearly_separable]\n\nfigure = plt.figure(figsize=(17, 9))\ni = 1\n# iterate over datasets\nfor X, y in datasets:\n    # split into training and test part\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)\n\n    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n                         np.arange(y_min, y_max, h))\n\n    # just plot the dataset first\n    cm = plt.cm.RdBu\n    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n    # Plot the training points\n    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n    # and testing points\n    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)\n    ax.set_xlim(xx.min(), xx.max())\n    ax.set_ylim(yy.min(), yy.max())\n    ax.set_xticks(())\n    ax.set_yticks(())\n    i += 1\n\n    # iterate over classifiers\n    for name, clf in zip(names, classifiers):\n        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n        clf.fit(X_train, y_train)\n        score = clf.score(X_test, y_test)\n\n        # Plot the decision boundary. For that, we will assign a color to each\n        # point in the mesh [x_min, x_max] x [y_min, y_max].\n        if hasattr(clf, \"decision_function\"):\n            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n        else:\n            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n\n        # Put the result into a color plot\n        Z = Z.reshape(xx.shape)\n        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n\n        # Plot also the training points\n        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n                   edgecolors='black', s=25)\n        # and testing points\n        ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n                   alpha=0.6, edgecolors='black', s=25)\n\n        ax.set_xlim(xx.min(), xx.max())\n        ax.set_ylim(yy.min(), yy.max())\n        ax.set_xticks(())\n        ax.set_yticks(())\n        ax.set_title(name)\n        ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),\n                size=15, horizontalalignment='right')\n        i += 1\n\nfigure.subplots_adjust(left=.02, right=.98)\nplt.show()"
       ]
     }
   ],

dev/_downloads/7ce809adad0d67b96c1df3b7fcd74567/plot_mlp_alpha.ipynb

@@ -32,18 +32,19 @@
 
 h = .02  # step size in the mesh
 
-alphas = np.logspace(-5, 3, 5)
-names = ['alpha ' + str(i) for i in alphas]
+alphas = np.logspace(-1, 1, 5)
 
 classifiers = []
-for i in alphas:
+names = []
+for alpha in alphas:
     classifiers.append(make_pipeline(
-                       StandardScaler(),
-                       MLPClassifier(solver='lbfgs', alpha=i,
-                                     random_state=1, max_iter=2000,
-                                     early_stopping=True,
-                                     hidden_layer_sizes=[100, 100])
-                       ))
+        StandardScaler(),
+        MLPClassifier(
+            solver='lbfgs', alpha=alpha, random_state=1, max_iter=2000,
+            early_stopping=True, hidden_layer_sizes=[100, 100],
+        )
+    ))
+    names.append(f"alpha {alpha:.2f}")
 
 X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                            random_state=0, n_clusters_per_class=1)
@@ -59,8 +60,7 @@
 i = 1
 # iterate over datasets
 for X, y in datasets:
-    # preprocess dataset, split into training and test part
-    X = StandardScaler().fit_transform(X)
+    # split into training and test part
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)
 
     x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
@@ -89,7 +89,7 @@
         score = clf.score(X_test, y_test)
 
         # Plot the decision boundary. For that, we will assign a color to each
-        # point in the mesh [x_min, x_max]x[y_min, y_max].
+        # point in the mesh [x_min, x_max] x [y_min, y_max].
         if hasattr(clf, "decision_function"):
             Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
         else: