scripts/example_basic_classifier.py
def plot_confusion_matrix(y_true, y_pred,
                          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 = 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")
    fig.tight_layout()
    return ax

    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
This function prints and plots the confusion matrix . Normalization can be applied by setting ` normalize = True ` .
def plot_confusion_matrix(y_true, y_pred,
                          normalize=False,
                          title=None,
                          cmap=plt.cm.Blues):
    if not title:
        if normalize:
            title = 'Normalized confusion matrix'
        else:
            title = 'Confusion matrix, without normalization'
    cm = confusion_matrix(y_true, y_pred)
    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)
    ax.set(xticks=np.arange(cm.shape[1]),
           yticks=np.arange(cm.shape[0]),
           xticklabels=classes, yticklabels=classes,
           title=title,
           ylabel='True label',
           xlabel='Predicted label')
    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor")
    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")
    fig.tight_layout()
    return ax
def plot_confusion_matrix ( y_true , y_pred , normalize = False , title = None , cmap = plt . cm . Blues ) : if not title : if normalize : title = 'Normalized confusion matrix' else : title = 'Confusion matrix, without normalization' cm = confusion_matrix ( y_true , y_pred ) 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 ) ax . set ( xticks = np . arange ( cm . shape [ 1 ] ) , yticks = np . arange ( cm . shape [ 0 ] ) , xticklabels = classes , yticklabels = classes , title = title , ylabel = 'True label' , xlabel = 'Predicted label' ) plt . setp ( ax . get_xticklabels ( ) , rotation = 45 , ha = "right" , rotation_mode = "anchor" ) 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" ) fig . tight_layout ( ) return ax
This function prints and plots the confusion matrix.
This function prints and plots the confusion matrix .
"""
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """ # Compute confusion matrix # Only use the labels that appear in the data # We want to show all ticks... # ... and label them with the respective list entries # Rotate the tick labels and set their alignment. # Loop over data dimensions and create text annotations.
y_true
y_pred
normalize
title
cmap