classifiers.py

# This file is used to fit different type od classifiers on the featureset generated through tf and tfidf.
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model.stochastic_gradient import SGDClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier

from UnigramTfFeatureGeneration import create_feature_set_and_labels
from UnigramTfifdFeaturesetGeneration import get_features


def begin_test(train_x, test_x, train_y, test_y):
    x = train_x + test_x
    y = train_y + test_y

    clf1 = LinearRegression()
    clf2 = LogisticRegression()
    clf3 = SGDClassifier()
    clf4 = SVC()
    clf5 = KNeighborsClassifier()
    clf6 = MLPClassifier()
    clf7 = DecisionTreeClassifier()
    clf8 = MultinomialNB()
    # clf1.fit(train_x, train_y)
    # y_pred = clf1.predict(test_x)
    # print("LinearRegressionAccuracy   ", accuracy_score(test_y, y_pred.round()))
    eclf = VotingClassifier(
        estimators=[('logr', clf2), ('sgd', clf3), ('svm', clf4), ('kn', clf5), ('nn', clf6), ('dt', clf7)],
        voting='hard')

    for label, clf in zip(
            ['LogisticRegressionClassifier', 'SGDClassifierClassifier', 'SVCClassifier',
             'NearestNeighbourClassifier', 'NeuralNetworkClassifier', 'DecisionTreeClassifier',
             'MultinomialNB', 'EnsembleClassifier'],
            [clf2, clf3, clf4, clf5, clf6, clf7, clf8, eclf]):
        scores = cross_val_score(clf, x, y, cv=10, scoring='accuracy')
        f_measure = cross_val_score(clf, x, y, cv=10, scoring='f1')
        # print(scores)
        print(label, "Accuracy:  ", scores.mean(), "+/- ", scores.std())
        print(label, "F-measure:  ", f_measure.mean())


def test_by_tf():
    train_x, train_y, test_x, test_y = create_feature_set_and_labels \
        ('pos_hindi.txt', 'neg_hindi.txt')
    begin_test(train_x, test_x, train_y, test_y)


def test_by_tfidf():
    train_x, train_y, test_x, test_y = get_features()
    begin_test(train_x, test_x, train_y, test_y)


if __name__ == '__main__':
    print("="*10)
    print("Unigram+Tf Accuracies")
    test_by_tf()
    print("=" * 10)
    print("Unigram+Tfidf Accuracies")
    test_by_tfidf()