import os

import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix

from NewsSentiment.fxlogger import get_logger

logger = get_logger()


def create_save_plotted_confusion_matrix(conf_matrix, expected_labels, basepath):
    ax, title = plot_confusion_matrix(conf_matrix, expected_labels, normalize=False)
    filepath = os.path.join(basepath, 'stats.png')
    plt.savefig(filepath, bbox_inches='tight')
    logger.debug("created confusion matrices in path: {}".format(filepath))


def plot_confusion_matrix(cm, 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`.
    based on https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py
    """
    if not title:
        if normalize:
            title = 'Normalized confusion matrix'
        else:
            title = 'Confusion matrix, without normalization'

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        logger.debug("Normalized confusion matrix")
    else:
        logger.debug('Confusion matrix, without normalization')

    logger.debug(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, title


if __name__ == '__main__':
    y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
    y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
    confmat = confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"])

    create_save_plotted_confusion_matrix(confmat, ["ant", "bird", "cat"], '.')