import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import LogNorm
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.utils.multiclass import unique_labels

def plot_confusion_matrix(y_true, y_pred, classes,
                          normalize=False,
                          title=None,
                          cmap=plt.cm.Blues,
                          fig_size=10):
    """
    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]*100
    else:
        cm = cm
    

    fig, ax = plt.subplots(figsize=(fig_size,fig_size))
    im = ax.imshow(cm,norm=LogNorm(), cmap=cmap,
                interpolation='nearest')
    ax.figure.colorbar(im, ax=ax)
    # We want to show all ticks...
    ylim=ax.get_ylim()
    ax.set(
        ylim=ylim,
        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 = '.1f' 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")
    fig.tight_layout()
    return ax