Ex 4: Varying regularization in Multi-layer Perceptron

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​http://scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_alpha.html#sphx-glr-auto-examples-neural-networks-plot-mlp-alpha-py 
此範例是比較不同的正歸化參數'alpha'，對於使用scikit-learn的資料產生器 ，所產生的circles、 moon 和random n-class classification，三種資料集的成效。  PS:正規化為一種處理無限大、發散以及一些不合理表示式的方法，透過引入一項輔助性的概念——正規子(regulator)，去限制函數使得函數不會發散  此處的Alpha參數即為正規子，目的是去限制權重(Weight,W)的大小，以防萬一overfitting與underfitting的問題，增加alpha值可能可以處理overfitting，反之減小alpha可能可以解決underfitting的問題，至於權重大小，如何影響輸出請看圖1: 
 圖1:比較同樣輸入，對於不同大小權重值，對於輸出的影響左圖為權重為5時，當輸入變動0.1時，輸出增加0.5，即輸出改變10%，右圖為權重為1時，當輸入變動0.1時，輸出增加0.1，即輸出改變2%，通常模型對於input較不敏感，模型表現較好 結果將顯示出:使用不同alpha值去限制權重產生出的決策邊界
(一)引入函式庫
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print(__doc__)
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​
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​
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# Author: Issam H. Laradji
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# License: BSD 3 clause
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​
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import numpy as np
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from matplotlib import pyplot as plt
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from matplotlib.colors import ListedColormap
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.datasets import make_moons, make_circles, make_classification
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from sklearn.neural_network import MLPClassifier
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(二)設定模型參數與產生資料
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h = .02  # step size in the mesh
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​
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alphas = np.logspace(-5, 3, 5)#
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names = []
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for i in alphas:
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    names.append('alpha ' + str(i))
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​
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classifiers = []
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for i in alphas:
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    classifiers.append(MLPClassifier(alpha=i, random_state=1))
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​
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X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
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                           random_state=0, n_clusters_per_class=1)
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rng = np.random.RandomState(2)
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X += 2 * rng.uniform(size=X.shape)
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linearly_separable = (X, y)
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​
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datasets = [make_moons(noise=0.3, random_state=0),
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            make_circles(noise=0.2, factor=0.5, random_state=1),
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            linearly_separable]
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​
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figure = plt.figure(figsize=(17, 9))
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i = 1
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# iterate over datasets
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for X, y in datasets:
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    # preprocess dataset, split into training and test part
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    X = StandardScaler().fit_transform(X)
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    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)
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​
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    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
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    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
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    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
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                         np.arange(y_min, y_max, h))
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(三)繪製圖形
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    # just plot the dataset first
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    cm = plt.cm.RdBu
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    cm_bright = ListedColormap(['#FF0000', '#0000FF'])
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    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
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    # Plot the training points
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    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
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    # and testing points
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    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
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    ax.set_xlim(xx.min(), xx.max())
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    ax.set_ylim(yy.min(), yy.max())
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    ax.set_xticks(())
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    ax.set_yticks(())
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    i += 1
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​
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    # iterate over classifiers
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    for name, clf in zip(names, classifiers):
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        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
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        clf.fit(X_train, y_train)
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        score = clf.score(X_test, y_test)
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​
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        # Plot the decision boundary. For that, we will assign a color to each
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        # point in the mesh [x_min, x_max]x[y_min, y_max].
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        if hasattr(clf, "decision_function"):
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            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
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        else:
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            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
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​
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        # Put the result into a color plot
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        Z = Z.reshape(xx.shape)
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        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)
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​
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        # Plot also the training points
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        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
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        # and testing points
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        ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,
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                   alpha=0.6)
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​
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        ax.set_xlim(xx.min(), xx.max())
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        ax.set_ylim(yy.min(), yy.max())
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        ax.set_xticks(())
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        ax.set_yticks(())
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        ax.set_title(name)
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        ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
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                size=15, horizontalalignment='right')
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        i += 1
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​
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figure.subplots_adjust(left=.02, right=.98)
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plt.show()
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 圖2:不同alpha結果圖，每張子圖右下角是分辨率，alpha值很大，模型的結果明顯underfitting
Ex 3: Compare Stochastic learning strategies for MLPClassifier
決策樹 Decision_trees
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Contents
(一)引入函式庫
(二)設定模型參數與產生資料
(三)繪製圖形