CLASSIFIERS

from sklearn.naive_bayes import MultinomialNB
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import BernoulliNB
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import f1_score, confusion_matrix

clfs = {
    'mnb': MultinomialNB(),
    'gnb': GaussianNB(),
    'svm1': SVC(kernel='linear'),
    'svm2': SVC(kernel='rbf'),
    'svm3': SVC(kernel='sigmoid'),
    'mlp1': MLPClassifier(),
    'mlp2': MLPClassifier(hidden_layer_sizes=[100, 100]),
    'ada': AdaBoostClassifier(),
    'dtc': DecisionTreeClassifier(),
    'rfc': RandomForestClassifier(),
    'gbc': GradientBoostingClassifier(),
    'lr': LogisticRegression()
}

f1_scores = dict()

for clf_name in clfs:
    
    clf = clfs[clf_name]
    clf.fit(train_x, test_x)
    y_pred = clf.predict(train_y)
    f1_scores[clf_name] = f1_score(y_pred, test_y)
    print(clf_name, f1_scores[clf_name])