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import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def make_confusion_matrix(cf,
group_names=None,
categories='auto',
count=True,
percent=True,
cbar=True,
cbar_range=(None, None),
xyticks=True,
xyplotlabels=True,
sum_stats=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])
# CODE TO GENERATE SUMMARY STATISTICS & TEXT FOR SUMMARY STATS
if sum_stats:
# 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)
else:
stats_text = ""
# 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, vmin=cbar_range[0], vmax=cbar_range[1], 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)
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