форкнуто от main/is_dnn
Sergey Kolpinskiy
2 лет назад
Родитель
3f260f388c
Сommit
6b9946cbe5
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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def make_confusion_matrix(cf,
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group_names=None,
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categories='auto',
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count=True,
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percent=True,
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cbar=True,
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xyticks=True,
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xyplotlabels=True,
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sum_stats=True,
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figsize=None,
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cmap='Blues',
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title=None,
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f_name=None,):
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'''
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This function will make a pretty plot of an sklearn Confusion Matrix cm using a Seaborn heatmap visualization.
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Arguments
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---------
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cf: confusion matrix to be passed in
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group_names: List of strings that represent the labels row by row to be shown in each square.
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categories: List of strings containing the categories to be displayed on the x,y axis. Default is 'auto'
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count: If True, show the raw number in the confusion matrix. Default is True.
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percent: If True, show the proportions for each category. Default is True.
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cbar: If True, show the color bar. The cbar values are based off the values in the confusion matrix.
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Default is True.
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xyticks: If True, show x and y ticks. Default is True.
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xyplotlabels: If True, show 'True Label' and 'Predicted Label' on the figure. Default is True.
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sum_stats: If True, display summary statistics below the figure. Default is True.
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figsize: Tuple representing the figure size. Default will be the matplotlib rcParams value.
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cmap: Colormap of the values displayed from matplotlib.pyplot.cm. Default is 'Blues'
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See http://matplotlib.org/examples/color/colormaps_reference.html
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title: Title for the heatmap. Default is None.
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f_name: Filename for saving picture. Default is None, which means no saving.
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'''
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# CODE TO GENERATE TEXT INSIDE EACH SQUARE
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blanks = ['' for i in range(cf.size)]
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if group_names and len(group_names)==cf.size:
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group_labels = ["{}\n".format(value) for value in group_names]
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else:
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group_labels = blanks
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if count:
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group_counts = ["{0:0.0f}\n".format(value) for value in cf.flatten()]
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else:
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group_counts = blanks
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if percent:
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group_percentages = ["{0:.2%}".format(value) for value in cf.flatten()/np.sum(cf)]
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else:
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group_percentages = blanks
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box_labels = [f"{v1}{v2}{v3}".strip() for v1, v2, v3 in zip(group_labels,group_counts,group_percentages)]
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box_labels = np.asarray(box_labels).reshape(cf.shape[0],cf.shape[1])
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# CODE TO GENERATE SUMMARY STATISTICS & TEXT FOR SUMMARY STATS
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if sum_stats:
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#Accuracy is sum of diagonal divided by total observations
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accuracy = np.trace(cf) / float(np.sum(cf))
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#if it is a binary confusion matrix, show some more stats
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if len(cf)==2:
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#Metrics for Binary Confusion Matrices
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precision = cf[1,1] / sum(cf[:,1])
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recall = cf[1,1] / sum(cf[1,:])
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f1_score = 2*precision*recall / (precision + recall)
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stats_text = "\n\nAccuracy={:0.3f}\nPrecision={:0.3f}\nRecall={:0.3f}\nF1 Score={:0.3f}".format(
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accuracy,precision,recall,f1_score)
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else:
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stats_text = "\n\nAccuracy={:0.3f}".format(accuracy)
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else:
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stats_text = ""
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# SET FIGURE PARAMETERS ACCORDING TO OTHER ARGUMENTS
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if figsize==None:
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#Get default figure size if not set
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figsize = plt.rcParams.get('figure.figsize')
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if xyticks==False:
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#Do not show categories if xyticks is False
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categories=False
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# MAKE THE HEATMAP VISUALIZATION
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plt.figure(figsize=figsize)
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sns.heatmap(cf,annot=box_labels,fmt="",cmap=cmap,cbar=cbar,xticklabels=categories,yticklabels=categories,annot_kws={"size": 20})
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if xyplotlabels:
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plt.ylabel('True label')
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plt.xlabel('Predicted label' + stats_text)
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else:
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plt.xlabel(stats_text)
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if title:
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plt.title(title)
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if f_name:
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plt.savefig(fname = f_name, dpi=None, facecolor='w', edgecolor='w', orientation='portrait', pad_inches=0.1)
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