Pushing the docs to dev/ for branch: master, commit 55a7752f2ee8a57394fbba5e36dd567f028f2912

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.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\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(MLPClassifier(alpha=i, random_state=1))\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\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(MLPClassifier(solver='lbfgs', alpha=i, random_state=1,\n                                     hidden_layer_sizes=[100, 100]))\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()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -36,7 +36,8 @@
 
 classifiers = []
 for i in alphas:
-    classifiers.append(MLPClassifier(alpha=i, random_state=1))
+    classifiers.append(MLPClassifier(solver='lbfgs', alpha=i, random_state=1,
+                                     hidden_layer_sizes=[100, 100]))
 
 X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                            random_state=0, n_clusters_per_class=1)

dev/_downloads/plot_mlp_alpha.ipynb

@@ -123,7 +123,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import load_boston\nfrom sklearn.preprocessing import QuantileTransformer, quantile_transform\n\ndataset = load_boston()\ntarget = np.array(dataset.feature_names) == \"DIS\"\nX = dataset.data[:, np.logical_not(target)]\ny = dataset.data[:, target].squeeze()\ny_trans = quantile_transform(dataset.data[:, target],\n                             output_distribution='normal').squeeze()"
+        "from sklearn.datasets import load_boston\nfrom sklearn.preprocessing import QuantileTransformer, quantile_transform\n\ndataset = load_boston()\ntarget = np.array(dataset.feature_names) == \"DIS\"\nX = dataset.data[:, np.logical_not(target)]\ny = dataset.data[:, target].squeeze()\ny_trans = quantile_transform(dataset.data[:, target],\n                             n_quantiles=300,\n                             output_distribution='normal',\n                             copy=True).squeeze()"
       ]
     },
     {
@@ -159,7 +159,7 @@
       },
       "outputs": [],
       "source": [
-        "f, (ax0, ax1) = plt.subplots(1, 2, sharey=True)\n\nregr = RidgeCV()\nregr.fit(X_train, y_train)\ny_pred = regr.predict(X_test)\n\nax0.scatter(y_test, y_pred)\nax0.plot([0, 10], [0, 10], '--k')\nax0.set_ylabel('Target predicted')\nax0.set_xlabel('True Target')\nax0.set_title('Ridge regression \\n without target transformation')\nax0.text(1, 9, r'$R^2$=%.2f, MAE=%.2f' % (\n    r2_score(y_test, y_pred), median_absolute_error(y_test, y_pred)))\nax0.set_xlim([0, 10])\nax0.set_ylim([0, 10])\n\nregr_trans = TransformedTargetRegressor(\n    regressor=RidgeCV(),\n    transformer=QuantileTransformer(output_distribution='normal'))\nregr_trans.fit(X_train, y_train)\ny_pred = regr_trans.predict(X_test)\n\nax1.scatter(y_test, y_pred)\nax1.plot([0, 10], [0, 10], '--k')\nax1.set_ylabel('Target predicted')\nax1.set_xlabel('True Target')\nax1.set_title('Ridge regression \\n with target transformation')\nax1.text(1, 9, r'$R^2$=%.2f, MAE=%.2f' % (\n    r2_score(y_test, y_pred), median_absolute_error(y_test, y_pred)))\nax1.set_xlim([0, 10])\nax1.set_ylim([0, 10])\n\nf.suptitle(\"Boston housing data: distance to employment centers\", y=0.035)\nf.tight_layout(rect=[0.05, 0.05, 0.95, 0.95])\n\nplt.show()"
+        "f, (ax0, ax1) = plt.subplots(1, 2, sharey=True)\n\nregr = RidgeCV()\nregr.fit(X_train, y_train)\ny_pred = regr.predict(X_test)\n\nax0.scatter(y_test, y_pred)\nax0.plot([0, 10], [0, 10], '--k')\nax0.set_ylabel('Target predicted')\nax0.set_xlabel('True Target')\nax0.set_title('Ridge regression \\n without target transformation')\nax0.text(1, 9, r'$R^2$=%.2f, MAE=%.2f' % (\n    r2_score(y_test, y_pred), median_absolute_error(y_test, y_pred)))\nax0.set_xlim([0, 10])\nax0.set_ylim([0, 10])\n\nregr_trans = TransformedTargetRegressor(\n    regressor=RidgeCV(),\n    transformer=QuantileTransformer(n_quantiles=300,\n                                    output_distribution='normal'))\nregr_trans.fit(X_train, y_train)\ny_pred = regr_trans.predict(X_test)\n\nax1.scatter(y_test, y_pred)\nax1.plot([0, 10], [0, 10], '--k')\nax1.set_ylabel('Target predicted')\nax1.set_xlabel('True Target')\nax1.set_title('Ridge regression \\n with target transformation')\nax1.text(1, 9, r'$R^2$=%.2f, MAE=%.2f' % (\n    r2_score(y_test, y_pred), median_absolute_error(y_test, y_pred)))\nax1.set_xlim([0, 10])\nax1.set_ylim([0, 10])\n\nf.suptitle(\"Boston housing data: distance to employment centers\", y=0.035)\nf.tight_layout(rect=[0.05, 0.05, 0.95, 0.95])\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/plot_mlp_alpha.py

@@ -138,7 +138,9 @@
 X = dataset.data[:, np.logical_not(target)]
 y = dataset.data[:, target].squeeze()
 y_trans = quantile_transform(dataset.data[:, target],
-                             output_distribution='normal').squeeze()
+                             n_quantiles=300,
+                             output_distribution='normal',
+                             copy=True).squeeze()
 
 ###############################################################################
 # A :class:`sklearn.preprocessing.QuantileTransformer` is used such that the
@@ -184,7 +186,8 @@
 
 regr_trans = TransformedTargetRegressor(
     regressor=RidgeCV(),
-    transformer=QuantileTransformer(output_distribution='normal'))
+    transformer=QuantileTransformer(n_quantiles=300,
+                                    output_distribution='normal'))
 regr_trans.fit(X_train, y_train)
 y_pred = regr_trans.predict(X_test)