<reponame>davidggphy/modelling_week_2019
import matplotlib.pyplot as plt
import numpy as np

from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels


def plot_confusion_matrix(y_true, y_pred, labels=None,
                          normalize=False,
                          title=None,
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    Parameters
    ----------
    y_true : array, shape = [n_samples]
      Ground truth (correct) target values.
    y_pred : array, shape = [n_samples]
      Estimated targets as returned by a classifier.
    labels : array, shape = [n_classes], optional
      List of labels to index the matrix. This may be used to reorder
      or select a subset of labels.
      If none is given, those that appear at least once
      in ``y_true`` or ``y_pred`` are used in sorted order.
    sample_weight : array-like of shape = [n_samples], optional
      Sample weights.
    Returns
    -------
    C : array, shape = [n_classes, n_classes]
      Confusion matrix
    References
    ----------
    https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
    """
    if not title:
        if normalize:
            title = 'Normalized confusion matrix'
        else:
            title = 'Confusion matrix (Counts)'

    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)
    # Only use the labels that appear in the data
    if labels is None:
        labels = np.array(unique_labels(y_true, y_pred), dtype='str')
    else:
        labels = np.array(labels)
        labels = labels[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 (Counts)')

    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=labels, yticklabels=labels,
           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




import matplotlib.pyplot as plt
import numpy as np

from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels


def plot_confusion_matrix(y_true, y_pred, labels=None,
                          normalize=False,
                          title=None,
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    Parameters
    ----------
    y_true : array, shape = [n_samples]
      Ground truth (correct) target values.
    y_pred : array, shape = [n_samples]
      Estimated targets as returned by a classifier.
    labels : array, shape = [n_classes], optional
      List of labels to index the matrix. This may be used to reorder
      or select a subset of labels.
      If none is given, those that appear at least once
      in ``y_true`` or ``y_pred`` are used in sorted order.
    sample_weight : array-like of shape = [n_samples], optional
      Sample weights.
    Returns
    -------
    C : array, shape = [n_classes, n_classes]
      Confusion matrix
    References
    ----------
    https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
    """
    if not title:
        if normalize:
            title = 'Normalized confusion matrix'
        else:
            title = 'Confusion matrix (Counts)'

    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)
    # Only use the labels that appear in the data
    if labels is None:
        labels = np.array(unique_labels(y_true, y_pred), dtype='str')
    else:
        labels = np.array(labels)
        labels = labels[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 (Counts)')

    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=labels, yticklabels=labels,
           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`.     Parameters     ----------     y_true : array, shape = [n_samples]       Ground truth (correct) target values.     y_pred : array, shape = [n_samples]       Estimated targets as returned by a classifier.     labels : array, shape = [n_classes], optional       List of labels to index the matrix. This may be used to reorder       or select a subset of labels.       If none is given, those that appear at least once       in ``y_true`` or ``y_pred`` are used in sorted order.     sample_weight : array-like of shape = [n_samples], optional       Sample weights.     Returns     -------     C : array, shape = [n_classes, n_classes]       Confusion matrix     References     ----------     https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html # 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.