# print(feature)
# print('feature counts: {0}'.format(len(features)))
X_train = vect_val.transform(X_train)
X_val = vect_val.transform(X_val)
print('******** GridSearch ********')
param_grid = {
'n_estimators': [40, 60, 80, 100, 120],
'learning_rate': [0.1, 0.15, 0.2],
'max_depth': [6, 7, 8, 9, 10]
}
scorer = make_scorer(f2)
gs = GridSearch(model=GradientBoostingClassifier())
gs.fit(X_train, y_train, param_grid, X_val, y_val, scoring=scorer)
print('params: ', gs.get_best_params())
print('Test Score for Optimized Parameters:', gs.score(X_val, y_val))
# print('******** GradientBoostingClassifier ********')
# gb = GradientBoostingClassifier(learning_rate=0.1, n_estimators=80, max_depth=7)
# gb, preds_train, preds = train_and_predict(gb, X_train, y_train, X_val)
# print_scores(y_val, preds, y_train, preds_train)
#
# print('******** AdamBoostingClassifier ********')
# ada = AdaBoostClassifier()
# ada, preds_train, preds = train_and_predict(ada, X_train, y_train, X_val)
# print_scores(y_val, preds, y_train, preds_train)
#
# print('******** XgBoostClassifier ********')
# xgb = XGBClassifier()
# xgb, preds_train, preds = train_and_predict(xgb, X_train, y_train, X_val)
print("Length of Train/Test:",len(X_train),len(X_dev))
params = {
'lr' : np.random.uniform(0,1,[5]).tolist() + [1.0],
'ada' : np.random.uniform(0,1,[5]).tolist() + [1.0],
'et' : np.random.uniform(0,1,[5]).tolist() + [1.0],
'xgb' : np.random.uniform(0,1,5).tolist() + [1.0],
'gb' : np.random.uniform(0,1,[5]).tolist() + [1.0],
'rf' : np.random.uniform(0,1,[5]).tolist() + [1.0],
'br' : np.random.uniform(0,1,5).tolist() + [1.0],
'lasso' : np.random.uniform(0,1,[5]).tolist() + [1.0],
'lrf' : [math.floor,math.ceil,avg],
'adaf' : [math.floor,math.ceil,avg],
'etf' : [math.floor,math.ceil,avg],
'xgbf' : [math.floor,math.ceil,avg],
'gbf' : [math.floor,math.ceil,avg],
'rff' :[math.floor,math.ceil,avg],
'brf' : [math.floor,math.ceil,avg],
'lassof' : [math.floor,math.ceil,avg],
}
model = GridSearch(N2C2Classifier(),param_grid=params)
model.fit(X_train,y_train,X_dev,y_dev,verbose=True)
print(model.get_best_params())
print(model.get_best_score())