How to create image of confusion matrix in Python

Written by- Sharif 5278 times views

A confusion matrix is a table that summarizes the performance of an estimator, in terms of its accuracy and its false alarm rate.

There are two types of confusion matrices:

The first type is called a “true positives” (TP) to “false positives” (FP) matrix. This matrix shows the number of positives that were correctly identified as such, and the number of negatives that were incorrectly identified as positive. The second type is called a “true negatives” (TN) to “false negatives” (FN) matrix. This matrix shows the number of negatives that were correctly identified as such, and the number of positives that were incorrectly identified as negative.


How to create image of confusion matrix in Python

Solution 1:

OPTION 1:

After you get array of the confusion matrix from sklearn.metrics, you can use matplotlib.pyplot.matshow() or seaborn.heatmap to generate the plot of the confusion matrix from that array.

e.g.

import pandas as pd
import seaborn as sn
import matplotlib.pyplot as plt

cfm = [[35, 0, 6],
       [0, 0, 3],
       [5, 50, 1]]
classes = ["0", "1", "2"]

df_cfm = pd.DataFrame(cfm, index = classes, columns = classes)
plt.figure(figsize = (10,7))
cfm_plot = sn.heatmap(df_cfm, annot=True)
cfm_plot.figure.savefig("cfm.png")

enter image description here


OPTION 2:

You can use plot_confusion_matrix() from sklearn to create image of confusion matrix directly from an estimater (i.e. classifier).

e.g.

cfm_plot = plot_confusion_matrix(<estimator>, <X>, <Y>)
cfm_plot.savefig("cfm.png")

Both options use savefig() to save the result as the png file.

REF: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html


Solution 2:

To see classification report visually, maybe a better method rather than saving odd plots, is saving it as a table, or some table-like object.

See sklearn's classification_report, it produces a nice table as an output, it has an argument output_dict which is False by default, pass this as true like

import json
from sklearn.metrics import classification_report

def save_json(obj, path):
    with open(path, 'w') as jf:
        json.dump(obj, jf)

report = classification_report(y_true, y_pred, output_dict=True)
save_json(report, 'path/to/save_dir/myreport.json'

you can also try to get dataframe of that resulting dict with

import pandas as pd

report_df = pd.DataFrame(report)
report_df.to_csv('saving/path/df.csv')

Thank you for reading the article.




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