OneVsRestClassifier.txt

x=df.iloc[:,1]
y= df.iloc[:,0]

from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(analyzer = 'word',lowercase = True)
x_poly = cv.fit_transform(x).toarray()

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x_poly,y,train_size =0.80,random_state=42)

from sklearn.multiclass import OneVsRestClassifier
from sklearn import tree
from sklearn.naive_bayes import MultinomialNB
base_clf = MultinomialNB(alpha=1)

clf6 = tree.DecisionTreeClassifier()
clf6 = OneVsRestClassifier(clf6).fit(x_train, y_train)

clf2 = OneVsRestClassifier(base_clf).fit(x_train, y_train)

clf5 = OneVsRestClassifier(LogisticRegression()).fit(x_train, y_train)

y_pred2 = clf2.predict(x_test)
y_pred5 = clf5.predict(x_test)
y_pred6 = clf6.predict(x_test)

from sklearn.metrics import confusion_matrix 
cm = confusion_matrix(y_test, y_pred2)
print(cm)

cm5 = confusion_matrix(y_test, y_pred5)
print(cm5)

cm6 = confusion_matrix(y_test, y_pred5)
print(cm6)

from sklearn.metrics import accuracy_score, classification_report
print("OneVsRestClassifier with DecisionTree:",accuracy_score(y_test, y_pred2))
print("OneVsRestClassifier with MultinomialNB",accuracy_score(y_test, y_pred5))
print("OneVsRestClassifier with LogisticRegression",accuracy_score(y_test, y_pred6))
# print(classification_report(y_pred2, y_test))
# print(classification_report(y_pred5, y_test))