Python Machine Learning

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Chapter 3:A Tour of Machine Learning Classifiers Using Scikit-learn

3.1：Training a perceptron via scikit-learn

from sklearn import datasetsimport numpy as npiris = datasets.load_iris()X = iris.data[:, [2, 3]]y = iris.targetnp.unique(y)from sklearn.cross_validation import train_test_split#從150個樣本中隨機抽取30%的樣本作為test_dataX_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=0)#資料歸一化#StandardScaler estimated the parameters μ(sample mean) and (standard deviation)#(x - mean)/(standard deviation)from sklearn.preprocessing import StandardScalersc = StandardScaler()sc.fit(X_train)X_train_std = sc.transform(X_train)X_test_std = sc.transform(X_test)#Perceptron分類#eta0 is equivalent to the learning ratefrom sklearn.linear_model import Perceptronppn = Perceptron(n_iter=40, eta0=0.1, random_state=0)ppn.fit(X_train_std, y_train)y_pred = ppn.predict(X_test_std)#y_test != y_pred‘‘‘array([False, False, False, False, False, False, False, False, False,       False,  True, False, False, False, False, False, False, False,       False, False, False, False, False, False, False, False, False,       False,  True, False, False, False, False, False, False,  True,       False,  True, False, False, False, False, False, False, False])‘‘‘print(‘Misclassified samples: %d‘ % (y_test != y_pred).sum())#Misclassified samples: 4#Thus, the misclassification error on the test dataset is 0.089 or 8.9 percent (4/45)#the metrics module:performance metricsfrom sklearn.metrics import accuracy_scoreprint(‘Accuracy: %.2f‘ % accuracy_score(y_test, y_pred))#Accuracy:0.91#plot_decision_regions:visualize how well it separates the different flower samplesfrom matplotlib.colors import ListedColormapimport matplotlib.pyplot as pltdef plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):      #setup marker generator and color map      markers = (‘s‘, ‘x‘, ‘o‘, ‘^‘, ‘v‘)      colors = (‘red‘, ‘blue‘, ‘lightgreen‘, ‘black‘, ‘cyan‘)      cmap = ListedColormap(colors[:len(np.unique(y))])      # plot the decision surface      x1_min, x1_max = X[:, 0].min() -1, X[:, 0].max() + 1      x2_min, x2_max = X[:, 1].min() -1, X[:, 1].max() + 1      xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))      Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)      Z = Z.reshape(xx1.shape)      plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)      plt.xlim(xx1.min(), xx1.max())      plt.ylim(xx2.min(), xx2.max())      # plot all samples      for idx, c1 in enumerate(np.unique(y)):            print idx,c1            plt.scatter(x=X[y == c1, 0], y=X[y == c1, 1], alpha=0.8, c=cmap(idx),marker=markers[idx],label=c1)      #highlight test samples      if test_idx :            X_test, y_test = X[test_idx, :], y[test_idx]            #把 corlor 設定為空白，通過edgecolors來控制顏色            plt.scatter(X_test[:, 0],X_test[:, 1], color=‘‘,edgecolors=‘black‘, alpha=1.0, linewidths=2, marker=‘o‘,s=150, label=‘test set‘)            X_combined_std = np.vstack((X_train_std, X_test_std))y_combined = np.hstack((y_train, y_test))plot_decision_regions(X=X_combined_std, y=y_combined, classifier=ppn, test_idx=range(105,150))plt.xlabel(‘petal length [standardized]‘)plt.ylabel(‘petal width [standardized]‘)plt.legend(loc=‘upper left‘)plt.show()

Python Machine Learning