from sklearn.metrics import f1_score,accuracy_score
import numpy as np
from utilities.tools import load_model
import pandas as pd

def predict_MSRP_test_data(n_models,nb_words,nlp_f,test_data_1,test_data_2,test_labels):

    models=[]
    n_h_features=nlp_f.shape[1]
    print('loading the models...')
    for i in range(n_models):
        models.append(load_model(i+1,nb_words,n_h_features))

    preds=[]
    print('predicting the test data...\n')
    i=0
    for m in models:
      i+=1
      preds_prob=m.predict([test_data_1, test_data_2,nlp_f], batch_size=64, verbose=0)
      preds.append(preds_prob[:,1])

    preds=
np.asarray(preds)