train fuction

train.py

from sklearn.multiclass import OneVsRestClassifier
from sklearn import model_selection
from sklearn.metrics import accuracy_score, classification_report, f1_score, roc_auc_score
from sklearn.metrics import multilabel_confusion_matrix

### OneVsRestClassifier
def train_model(classifier,X, y, max_feature = 1000, embedding= 'bow' ):

    #Train-test split
    print("... Performing train test split")
    X_train, X_test, y_train, y_test = model_selection.train_test_split(X,y,
                                                                    test_size=0.25,random_state=42)
    
    ## Features extraction with word embedding
    print("... Extracting features")
    Xv_train, Xv_test, vectorizer = get_embeddings(X_train, X_test,
                                                          max_feature = max_feature , embedding_type= embedding)
    
    # train the model 
    print('... Training {} model'.format(classifier.__class__.__name__))
    clf = OneVsRestClassifier(classifier)
    clf.fit(Xv_train, y_train)

    # compute the test accuracy
    print("... Computing accuracy")
    prediction = clf.predict(Xv_test)

    ## Accuracy score
    score = (accuracy_score(y_test, prediction))
    type2_score = j_score(y_test, prediction)
    f1_s = f1_score(y_test, prediction,average='macro')
    roc_auc = roc_auc_score(y_test, prediction)
    confusion_matrix = multilabel_confusion_matrix(y_test, prediction)
    score_sumry = [score, type2_score, f1_s, roc_auc]

    print('\n')
    print("Model evaluation")
    print("------")
    print(print_score(prediction,y_test, classifier))
    print('Accuracy is {}'.format(score))
    print("ROC_AUC - {}".format(roc_auc))
    print("------")
    print("Multilabel confusion matrix \n {}".format(confusion_matrix))
    
    return clf, vectorizer, score_sumry