Running hyperopt for a stacking classifier

hyperopt_stacking.py

from hyperopt import fmin, tpe, hp, STATUS_OK, Trials

def run_voting_clf(model_weights):
    y_pred_prob = 0
    for model_name, model in model_dict.items():
        y_pred_prob += (model.predict_proba(test_features)[:,1] * model_weights[model_name])

    y_pred_prob += (simple_nn.predict(test_features.todense()).ravel() * model_weights['simple_nn'])
    y_pred_prob /= sum(model_weights.values())
    f1 = print_model_metrics(y_test, y_pred_prob, return_metrics = True, verbose = 0)[0]
    
    return {'loss' : -f1, 'status' : STATUS_OK}  #return negative of F1 since hyperopt is running fmin


trials = Trials()
model_weights = fmin(run_voting_clf,
    space= {
        'LR' : hp.uniform('LR', 0, 1),
        'SVM' : hp.uniform('SVM', 0, 1),
        'NB' : hp.uniform('NB', 0, 1),
        'KNN' : hp.uniform('KNN', 0, 1),
        'RF' : hp.uniform('RF', 0, 1),
        'XGB' : hp.uniform('XGB', 0, 1),
        'simple_nn' : hp.uniform('simple_nn', 0, 1),
    },
    algo=tpe.suggest,
    max_evals=500,
    trials = trials)