Training a perceptron via scikit-learn
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1. Load Data
import numpy as npfrom sklearn import datasetsiris = datasets.load_iris()X = iris.data[:, [2, 3]]y = iris.target2. Split data into train and test
from distutils.version import LooseVersion as Versionfrom sklearn import __version__ as sklearn_versionif Version(sklearn_version) < '0.18': from sklearn.grid_search import train_test_splitelse: from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
3. Preprocessing
from sklearn.preprocessing import StandardScalersc = StandardScaler()sc.fit(X_train)X_train_std = sc.transform(X_train)X_test_std = sc.transform(X_test)
4. Most algorithms in scikit-learn already support multiclass classifcation by default via the One-vs.-Rest(OvR) method :
from sklearn.linear_model import Perceptronppn = Perceptron(n_iter=40, eta0=0.1, random_state=0)ppn.fit(X_train_std, y_train)
5. Having trained a model in scikit-learn, we can make predictions via thepredict method :
y_pred = ppn.predict(X_test_std)
from sklearn.metrics import accuracy_scoreprint('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
7. Define function to show results:
def versiontuple(v): return tuple(map(int, (v.split("."))))def 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', 'gray', '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()) for idx, cl in enumerate(np.unique(y)): plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1], alpha=0.8, c=cmap(idx), marker=markers[idx], label=cl) # highlight test samples if test_idx: # plot all samples if not versiontuple(np.__version__) >= versiontuple('1.9.0'): X_test, y_test = X[list(test_idx), :], y[list(test_idx)] warnings.warn('Please update to NumPy 1.9.0 or newer') else: X_test, y_test = X[test_idx, :], y[test_idx] plt.scatter(X_test[:, 0], X_test[:, 1], c='', alpha=1.0, linewidths=1, marker='o', s=55, label='test set')8. Show Results:
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()
Accuracy: 0.91
Reference: 《Python Machine Learning》
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