Python SGDClassifier.train方法代码示例

# 需要导入模块: from sklearn.linear_model import SGDClassifier [as 别名]
# 或者: from sklearn.linear_model.SGDClassifier import train [as 别名]
class SVMTest(TestCase):
    def set_up(self):
        corpus = CommentsCorpus(document_class=BagOfWords)
        train = corpus[0:1000]
        test = corpus[1000:1200]
        svm = SVM(corpus.label, corpus.featureDict)
        svm.setUp(train, test)
        X = svm.x_matrix
        y = svm.y_matrix
        # self.y = np.ravel(self.svm.y_matrix)
        test_X = svm.x_test
        test_y = svm.y_test
        return X, y, test_X, test_y, len(corpus.featureDict)

    def test_soft_margin_kernl_svm(self):
        pass
        # self.set_up()
        # self.svm.train(train, test)

    def test_sgd(self):
        """Test self write svm sgd"""
        train_x, train_y, test_x, test_y, n_features = self.set_up()
        self.svm = SVM_SGD(n_features)
        self.svm.train(train_x, train_y)
        self.svm.classify(test_x, test_y)

    def test_with_skleart_sgd(self):
        """Test svm sgd with sklearn"""
        train_x, train_y, test_x, test_y, n_features = self.set_up()
        train_y = np.ravel(train_y)
        self.svm = SGDClassifier(loss="hinge", penalty="l2")
        self.svm.fit(train_x, train_y)
        self.accuracy(test_x, test_y, "sgd")

    # def test_with_sklearn_svc(self):
    #     # pass
    #     """Test svc in sklearn"""
    #     train_x, train_y, test_x, test_y, n_features = self.set_up()
    #     train_y = np.ravel(train_y)
    #     self.svm = svm.SVC()
    #     self.svm.fit(train_x, train_y)
    #     self.accuracy(test_x, test_y, "svc")

    def test_with_sklearn_linearsvc(self):
        # pass
        """Test linear svc in sklearn """
        train_x, train_y, test_x, test_y, n_features = self.set_up()
        train_y = np.ravel(train_y)
        self.svm = svm.LinearSVC()
        self.svm.fit(train_x, train_y)
        self.accuracy(test_x, test_y, "linear svc")

    def accuracy(self, test_x, test_y, classfier_type):
        """Test the accuracy of the classifier"""
        result = []
        for i in test_x:
            result.append(self.svm.predict([i]))
        accuracy = float(np.sum(result == test_y)) / len(test_y)
        print("The accuracy of the " + classfier_type + " classifier is: %.3f%%" % (accuracy * 100))

# 需要导入模块: from sklearn.linear_model import SGDClassifier [as 别名]
# 或者: from sklearn.linear_model.SGDClassifier import train [as 别名]
#GaussianNB_classifier=SklearnClassifier(GaussianNB())
#GaussianNB_classifier.train(training_set)
#print("Gaussian Naive Bayes Algorithm Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(GaussianNB_classifier,testing_set))*100)

BernoulliNB_classifier=SklearnClassifier(BernoulliNB())
BernoulliNB_classifier.train(training_set)
print("Bernuolli Naive Bayes Algorithm Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(BernoulliNB_classifier,testing_set))*100)


LogisticRegression_classifier=SklearnClassifier(LogisticRegression())
LogisticRegression_classifier.train(training_set)
print("Logistic Regression Algorithm Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(LogisticRegression_classifier,testing_set))*100)

SGDClassifier=SklearnClassifier(SGDClassifier())
SGDClassifier.train(training_set)
print("SGD Classifier Algorithm Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(SGDClassifier,testing_set))*100)

SVC_Classifier=SklearnClassifier(SVC())
SVC_Classifier.train(training_set)
print("SVC classifier Algorithm Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(SVC_Classifier,testing_set))*100)

LinearSVC=SklearnClassifier(LinearSVC())
LinearSVC.train(training_set)
print("Linear SVC Algorithm Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(LinearSVC,testing_set))*100)

#NuSVC_Classifier=SklearnClassifier(NuSVC())
#NuSVC_Classifier.train(training_set)
#print("NuSVC Algorithm Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(NuSVC_Classifier,testing_set))*100)

voted_classifier=VoteClassifier(classifier,MNB_classifier,BernoulliNB_classifier,LogisticRegression_classifier,SGDClassifier,LinearSVC)