#THIS IS THE CONFUSION MATRIX FUNCTION

def plot_confusion_matrix(y_true, y_pred, classes,
                          normalize=False,
                          title=None,
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if not title:
        if normalize:
            title = 'Normalized confusion matrix'
        else:
            title = 'Confusion matrix, without normalization'

    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)
    # Only use the labels that appear in the data
    #classes = classes[unique_labels(y_true, y_pred)]
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    fig, ax = plt.subplots()
    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
    ax.figure.colorbar(im, ax=ax)
    # We want to show all ticks...
    ax.set(xticks=np.arange(cm.shape[1]),
           yticks=np.arange(cm.shape[0]),
           # ... and label them with the respective list entries
           xticklabels=classes, yticklabels=classes,
           title=title,
           ylabel='True label',
           xlabel='Predicted label')

    # Rotate the tick labels and set their alignment.
    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor")

    # Loop over data dimensions and create text annotations.
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i in range(cm.shape[0]):
        for j in range(cm.shape[1]):
            ax.text(j, i, format(cm[i, j], fmt),
                    ha="center", va="center",
                    color="white" if cm[i, j] > thresh else "black")
    return ax


from sklearn.metrics import f1_score
from sklearn.metrics import balanced_accuracy_score
from sklearn.metrics import confusion_matrix

#1.             predict labels
Y_true = Y_test
Y_pred_test = knn_clf.predict(X_test)
Y_pred_test_pitch = knn_clf_pitch.predict(X_test)
Y_pred_test_time = knn_clf_time.predict(X_test)
Y_pred_test_reverb = knn_clf_reverb.predict(X_test)
Y_pred_test_all = knn_clf_all.predict(X_test)


#2.             compute balanced accuracy score

score_train = balanced_accuracy_score(Y_true, Y_pred_test)
score_pitch = balanced_accuracy_score(Y_true, Y_pred_test_pitch)
score_time = balanced_accuracy_score(Y_true, Y_pred_test_time)
score_reverb = balanced_accuracy_score(Y_true, Y_pred_test_reverb)
score_all = balanced_accuracy_score(Y_true, Y_pred_test_all)

#confusion matrices
from sklearn.utils.multiclass import unique_labels

#CONFUSION MATRIX FOR 1) the original training data
Y_pred = Y_pred_test

#              compute cm
cm = confusion_matrix(Y_true, Y_pred)

#              plot cm
np.set_printoptions(precision=2)
# Plot normalized confusion matrix
plot_confusion_matrix(Y_test, Y_pred, classes=instruments, normalize=True,
                      title='Original Training Data')
plt.show()