experiment

def experiment(validation_datas,validation_labels,original_datas,ground_true,ground_true_times,validation_original_outputs, validation_output_times, nb_repeat, reg):
    error_scores = list()
    #get only the close data
    ground_true = ground_true[:,:,0].reshape(-1)
    ground_true_times = ground_true_times.reshape(-1)
    ground_true_times = pd.to_datetime(ground_true_times, unit='s')
    validation_output_times = pd.to_datetime(validation_output_times.reshape(-1), unit='s')
    for i in range(nb_repeat):
        model = fit_lstm(reg)
        predicted = model.predict(validation_datas)
        predicted_inverted = []
        scaler.fit(original_datas[:,0].reshape(-1,1))
        predicted_inverted.append(scaler.inverse_transform(predicted))
        # since we are appending in the first dimension
        predicted_inverted = np.array(predicted_inverted)[0,:,:].reshape(-1)
        error_scores.append(mean_squared_error(validation_original_outputs[:,:,0].reshape(-1),predicted_inverted))
    return error_scores

regs = [regularizers.l1(0),regularizers.l1(0.1), regularizers.l1(0.01), regularizers.l1(0.001), regularizers.l1(0.0001),regularizers.l2(0.1), regularizers.l2(0.01), regularizers.l2(0.001), regularizers.l2(0.0001)]
nb_repeat = 30
results = pd.DataFrame()
for reg in regs:
    
    name = ('l1 %.4f,l2 %.4f' % (reg.l1, reg.l2))
    print "Training "+ str(name)
    results[name] = experiment(validation_datas,validation_labels,original_datas,ground_true,ground_true_times,validation_original_outputs, validation_output_times, nb_repeat,reg)

results.describe().to_csv('result/lstm_bias_reg.csv')
results.describe()