# Modules to import
import torch
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()


# define a function to plot confusion matrix
def plot_confusion(input_sample, num_classes, des_output, actual_output):
    confusion = torch.zeros(num_classes, num_classes)
    for i in range(input_sample):
        actual_class = actual_output[i]
        predicted_class = des_output[i]

        confusion[actual_class][predicted_class] += 1

    return confusion


# Confusion matrix visualization
# Referred to: https://github.com/DTrimarchi10/confusion_matrix
def make_confusion_matrix(cf,
                          group_names=None,
                          categories='auto',
                          count=True,
                          percent=True,
                          cbar=True,
                          xyticks=True,
                          xyplotlabels=True,
                          figsize=None,
                          cmap='Blues',
                          title=None):
    '''
    This function will make a pretty plot of an sklearn Confusion Matrix cm using a Seaborn heatmap visualization.
    Arguments
    ---------
    cf:            confusion matrix to be passed in
    group_names:   List of strings that represent the labels row by row to be shown in each square.
    categories:    List of strings containing the categories to be displayed on the x,y axis. Default is 'auto'
    count:         If True, show the raw number in the confusion matrix. Default is True.
    normalize:     If True, show the proportions for each category. Default is True.
    cbar:          If True, show the color bar. The cbar values are based off the values in the confusion matrix.
                   Default is True.
    xyticks:       If True, show x and y ticks. Default is True.
    xyplotlabels:  If True, show 'True Label' and 'Predicted Label' on the figure. Default is True.
    sum_stats:     If True, display summary statistics below the figure. Default is True.
    figsize:       Tuple representing the figure size. Default will be the matplotlib rcParams value.
    cmap:          Colormap of the values displayed from matplotlib.pyplot.cm. Default is 'Blues'
                   See http://matplotlib.org/examples/color/colormaps_reference.html

    title:         Title for the heatmap. Default is None.
    '''

    # CODE TO GENERATE TEXT INSIDE EACH SQUARE
    blanks = ['' for i in range(cf.size)]

    if group_names and len(group_names) == cf.size:
        group_labels = ["{}\n".format(value) for value in group_names]
    else:
        group_labels = blanks

    if count:
        group_counts = ["{0:0.0f}\n".format(value) for value in cf.flatten()]
    else:
        group_counts = blanks

    if percent:
        group_percentages = ["{0:.2%}".format(value) for value in cf.flatten() / np.sum(cf)]
    else:
        group_percentages = blanks

    box_labels = [f"{v1}{v2}{v3}".strip() for v1, v2, v3 in zip(group_labels, group_counts, group_percentages)]
    box_labels = np.asarray(box_labels).reshape(cf.shape[0], cf.shape[1])

    # Accuracy is sum of diagonal divided by total observations
    accuracy = np.trace(cf) / float(np.sum(cf))

    # if it is a binary confusion matrix, show some more stats
    if len(cf) == 2:
        # Metrics for Binary Confusion Matrices
        precision = cf[1, 1] / sum(cf[:, 1])
        recall = cf[1, 1] / sum(cf[1, :])
        f1_score = 2 * precision * recall / (precision + recall)
        stats_text = "\n\nAccuracy={:0.3f}\nPrecision={:0.3f}\nRecall={:0.3f}\nF1 Score={:0.3f}".format(
            accuracy, precision, recall, f1_score)
    else:
        stats_text = "\n\nAccuracy={:0.3f}".format(accuracy)

    # SET FIGURE PARAMETERS ACCORDING TO OTHER ARGUMENTS
    if figsize == None:
        # Get default figure size if not set
        figsize = plt.rcParams.get('figure.figsize')

    if xyticks == False:
        # Do not show categories if xyticks is False
        categories = False

    # MAKE THE HEATMAP VISUALIZATION
    plt.figure(figsize=figsize)
    sns.heatmap(cf, annot=box_labels, fmt="", cmap=cmap, cbar=cbar, xticklabels=categories, yticklabels=categories)

    if xyplotlabels:
        plt.ylabel('True Label')
        plt.xlabel('Predicted Label' + stats_text)
    else:
        plt.xlabel(stats_text)

    if title:
        plt.title(title)
