Model Comparism Curve

def roc_auc_multiple(df, model_1, model_2, model_3, model_4, model_5, model_6, model_7, model_8):
    
    import sklearn.metrics as metrics
    
    num_label = {'ok': 1, 'nok' : 0}
    Y_test = df_test['class'].copy().map(num_label).astype('int')
    
    # df.reset()
    predictions_1 = model_1.predict(df, steps=len(df), verbose=0)
    pred_labels_1 = np.where(predictions_1>0.5, 1, 0)
 
    predictions_2 = model_2.predict(df, steps=len(df), verbose=0)
    pred_labels_2 = np.where(predictions_2>0.5, 1, 0)
 
    predictions_3 = model_3.predict(df, steps=len(df), verbose=0)
    pred_labels_3 = np.where(predictions_3>0.5, 1, 0)
 
    predictions_4 = model_4.predict(df, steps=len(df), verbose=0)
    pred_labels_4 = np.where(predictions_4>0.5, 1, 0)
 
    predictions_5 = model_5.predict(df, steps=len(df), verbose=0)
    pred_labels_5 = np.where(predictions_5>0.5, 1, 0)
    
    predictions_6 = model_6.predict(df, steps=len(df), verbose=0)
    pred_labels_6 = np.where(predictions_5>0.5, 1, 0)
    
    predictions_7 = model_7.predict(df, steps=len(df), verbose=0)
    pred_labels_7 = np.where(predictions_5>0.5, 1, 0)
    
    predictions_8 = model_8.predict(df, steps=len(df), verbose=0)
    pred_labels_8 = np.where(predictions_5>0.5, 1, 0)
    
    roc_auc_1 = metrics.roc_auc_score(Y_test, predictions_1)
    roc_auc_2 = metrics.roc_auc_score(Y_test, predictions_2)
    roc_auc_3 = metrics.roc_auc_score(Y_test, predictions_3)
    roc_auc_4 = metrics.roc_auc_score(Y_test, predictions_4)
    roc_auc_5 = metrics.roc_auc_score(Y_test, predictions_5)
    roc_auc_6 = metrics.roc_auc_score(Y_test, predictions_6)
    roc_auc_7 = metrics.roc_auc_score(Y_test, predictions_7)
    roc_auc_8 = metrics.roc_auc_score(Y_test, predictions_8)
    
    
    print('ROC_AUC_1: ', roc_auc_1)
    print('ROC_AUC_2: ', roc_auc_2)
    print('ROC_AUC_3: ', roc_auc_3)
    print('ROC_AUC_4: ', roc_auc_4)
    print('ROC_AUC_5: ', roc_auc_5)
    print('ROC_AUC_6: ', roc_auc_6)
    print('ROC_AUC_7: ', roc_auc_7)
    print('ROC_AUC_8: ', roc_auc_8)
 
    plt.rcParams['figure.figsize'] = (20 , 20)
    
    fpr_1, tpr_1, thresholds_1 = metrics.roc_curve(Y_test, predictions_1)
    plt.plot(fpr_1, tpr_1, label = 'Model: 01 = %0.3f' % roc_auc_1)
    
    fpr_2, tpr_2, thresholds_2 = metrics.roc_curve(Y_test, predictions_2)
    plt.plot(fpr_2, tpr_2, label = 'Model: 02 = %0.3f' % roc_auc_2)
 
    fpr_3, tpr_3, thresholds_3 = metrics.roc_curve(Y_test, predictions_3)
    plt.plot(fpr_3, tpr_3, label = 'Model: 03 = %0.3f' % roc_auc_3)
 
    fpr_4, tpr_4, thresholds_4 = metrics.roc_curve(Y_test, predictions_4)
    plt.plot(fpr_4, tpr_4, label = 'Model: 04 = %0.3f' % roc_auc_4)
 
    fpr_5, tpr_5, thresholds_5 = metrics.roc_curve(Y_test, predictions_5)
    plt.plot(fpr_5, tpr_5, label = 'Model: 05 = %0.3f' % roc_auc_5)
    
    fpr_6, tpr_6, thresholds_6 = metrics.roc_curve(Y_test, predictions_6)
    plt.plot(fpr_5, tpr_5, label = 'Model: 06 = %0.3f' % roc_auc_6)
     
    fpr_7, tpr_7, thresholds_7 = metrics.roc_curve(Y_test, predictions_7)
    plt.plot(fpr_7, tpr_7, label = 'Model 07 = %0.3f' % roc_auc_7)
     
    fpr_8, tpr_8, thresholds_8 = metrics.roc_curve(Y_test, predictions_8)
    plt.plot(fpr_8, tpr_8, label = 'Model_08 = %0.3f' % roc_auc_8)
 
    plt.xlabel("False Positive Rate", fontsize= 12)
    plt.ylabel("True Positive Rate", fontsize= 12)
    plt.legend(loc="lower right")
 
    fig1 = plt.gcf()
    plt.show()
    plt.draw()
    fig1.savefig('roc_auc_multiple.png', dpi=50) 

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def plot_performance_validation_loss(hist, hist_one, hist_two, hist_three, hist_four, hist_five, hist_six, hist_seven):
    plt.rcParams['figure.figsize'] = (20, 20)
    
    hist_00 = hist.history
    epochs_00 = hist.epoch
    
    hist_01 = hist_one.history
    epochs_01 = hist_one.epoch
    
    hist_02 = hist_two.history
    epochs_02 = hist_two.epoch
    
    hist_03 = hist_three.history
    epochs_03 = hist_three.epoch
    
    hist_04 = hist_four.history
    epochs_04 = hist_four.epoch
    
    hist_05 = hist_five.history
    epochs_05 = hist_five.epoch
    
    hist_06 = hist_six.history
    epochs_06 = hist_six.epoch
    
    hist_07 = hist_seven.history
    epochs_07 = hist_seven.epoch
    
    
    # plt.subplot(1, 2, 1) # row 1, col 2 index 1
    # plt.plot(epochs_00, hist_00['loss'], label='Learning Rate: 0.1')
    # plt.plot(epochs_01, hist_01['loss'], label='Learning Rate: 0.01')
    # plt.plot(epochs_02, hist_02['loss'], label='Learning Rate: 0.001')
    # plt.plot(epochs_03, hist_03['loss'], label='Learning Rate: 0.0001')
    # plt.plot(epochs_04, hist_04['loss'], label='Learning Rate: 0.00001')
    # plt.plot(epochs_05, hist_05['loss'], label='Learning Rate: 0.00001')
    
    # plt.xlabel('Epochs')
    # plt.ylabel('Loss')
    # plt.ylim([-0.001, 2.0])
    # plt.title('Training loss with Adam')
    # plt.legend(loc = 'upper right')
    
    # plt.subplot(1, 2, 2) # row 1, col 2 index 1
    plt.plot(epochs_00, hist_00['val_loss'], label='Model: 01')
    plt.plot(epochs_01, hist_01['val_loss'], label='Model: 02')
    plt.plot(epochs_02, hist_02['val_loss'], label='Model: 03')
    plt.plot(epochs_03, hist_03['val_loss'], label='Model: 04')
    plt.plot(epochs_04, hist_04['val_loss'], label='Model: 05')
    plt.plot(epochs_05, hist_05['val_loss'], label='Model: 06')
    plt.plot(epochs_06, hist_06['val_loss'], label='Model: 07')
    plt.plot(epochs_07, hist_07['val_loss'], label='Model: 08')
    
    
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    # plt.ylim([-0.001, 0.8])
    plt.title('Validation loss with SGD')
    plt.legend(loc = 'upper right')
 
    # plt.tight_layout(2)
    fig1 = plt.gcf()
    plt.show()
    plt.draw()
    fig1.savefig('loss_comparism_validation.png', dpi=50)


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def plot_performance_train_validation_loss(hist, hist_one, hist_two, hist_three, hist_four, hist_five, hist_six, hist_seven):
    plt.rcParams['figure.figsize'] = (20, 20)
    
    hist_00 = hist.history
    epochs_00 = hist.epoch
    
    hist_01 = hist_one.history
    epochs_01 = hist_one.epoch
    
    hist_02 = hist_two.history
    epochs_02 = hist_two.epoch
    
    hist_03 = hist_three.history
    epochs_03 = hist_three.epoch
    
    hist_04 = hist_four.history
    epochs_04 = hist_four.epoch
    
    hist_05 = hist_five.history
    epochs_05 = hist_five.epoch
    
    hist_06 = hist_six.history
    epochs_06 = hist_six.epoch
    
    hist_07 = hist_seven.history
    epochs_07 = hist_seven.epoch
    
    
    plt.subplot(1, 2, 1) # row 1, col 2 index 1
    plt.plot(epochs_00, hist_00['loss'], label='Model: 01')
    plt.plot(epochs_01, hist_01['loss'], label='Model: 02')
    plt.plot(epochs_02, hist_02['loss'], label='Model: 03')
    plt.plot(epochs_03, hist_03['loss'], label='Model: 04')
    plt.plot(epochs_04, hist_04['loss'], label='Model: 05')
    plt.plot(epochs_05, hist_05['loss'], label='Model: 06')
    plt.plot(epochs_06, hist_06['loss'], label='Model: 07')
    plt.plot(epochs_07, hist_07['loss'], label='Model: 08')
    
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    # plt.ylim([-0.001, 2.0])
    plt.title('Training loss')
    plt.legend(loc = 'upper right')
    
    plt.subplot(1, 2, 2) # row 1, col 2 index 1
    plt.plot(epochs_00, hist_00['val_loss'], label='Model: 01')
    plt.plot(epochs_01, hist_01['val_loss'], label='Model: 02')
    plt.plot(epochs_02, hist_02['val_loss'], label='Model: 03')
    plt.plot(epochs_03, hist_03['val_loss'], label='Model: 04')
    plt.plot(epochs_04, hist_04['val_loss'], label='Model: 05')
    plt.plot(epochs_05, hist_05['val_loss'], label='Model: 06')
    plt.plot(epochs_06, hist_06['val_loss'], label='Model: 07')
    plt.plot(epochs_07, hist_07['val_loss'], label='Model: 08')
    
    
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    # plt.ylim([-0.001, 0.8])
    plt.title('Validation loss')
    plt.legend(loc = 'upper right')
 
    # plt.tight_layout(2)
    fig1 = plt.gcf()
    plt.show()
    plt.draw()
    fig1.savefig('train_validation_loss_com.png', dpi=50)