How to plot scikit learn classification report?

Question

Is it possible to plot with matplotlib scikit-learn classification report?. Let's assume I print the classification report like this:

print '\n*Classification Report:\n', classification_report(y_test, predictions)     confusion_matrix_graph = confusion_matrix(y_test, predictions) 

and I get:

Clasification Report:              precision    recall  f1-score   support            1       0.62      1.00      0.76        66           2       0.93      0.93      0.93        40           3       0.59      0.97      0.73        67           4       0.47      0.92      0.62       272           5       1.00      0.16      0.28       413  avg / total       0.77      0.57      0.49       858 

How can I "plot" the avobe chart?.

Answer 1

Expanding on Bin's answer:

import matplotlib.pyplot as plt import numpy as np  def show_values(pc, fmt="%.2f", **kw):     '''     Heatmap with text in each cell with matplotlib's pyplot     Source: https://stackoverflow.com/a/25074150/395857      By HYRY     '''     from itertools import izip     pc.update_scalarmappable()     ax = pc.get_axes()     #ax = pc.axes# FOR LATEST MATPLOTLIB     #Use zip BELOW IN PYTHON 3     for p, color, value in izip(pc.get_paths(), pc.get_facecolors(), pc.get_array()):         x, y = p.vertices[:-2, :].mean(0)         if np.all(color[:3] > 0.5):             color = (0.0, 0.0, 0.0)         else:             color = (1.0, 1.0, 1.0)         ax.text(x, y, fmt % value, ha="center", va="center", color=color, **kw)   def cm2inch(*tupl):     '''     Specify figure size in centimeter in matplotlib     Source: https://stackoverflow.com/a/22787457/395857     By gns-ank     '''     inch = 2.54     if type(tupl[0]) == tuple:         return tuple(i/inch for i in tupl[0])     else:         return tuple(i/inch for i in tupl)   def heatmap(AUC, title, xlabel, ylabel, xticklabels, yticklabels, figure_width=40, figure_height=20, correct_orientation=False, cmap='RdBu'):     '''     Inspired by:     - https://stackoverflow.com/a/16124677/395857      - https://stackoverflow.com/a/25074150/395857     '''      # Plot it out     fig, ax = plt.subplots()         #c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap='RdBu', vmin=0.0, vmax=1.0)     c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap=cmap)      # put the major ticks at the middle of each cell     ax.set_yticks(np.arange(AUC.shape[0]) + 0.5, minor=False)     ax.set_xticks(np.arange(AUC.shape[1]) + 0.5, minor=False)      # set tick labels     #ax.set_xticklabels(np.arange(1,AUC.shape[1]+1), minor=False)     ax.set_xticklabels(xticklabels, minor=False)     ax.set_yticklabels(yticklabels, minor=False)      # set title and x/y labels     plt.title(title)     plt.xlabel(xlabel)     plt.ylabel(ylabel)            # Remove last blank column     plt.xlim( (0, AUC.shape[1]) )      # Turn off all the ticks     ax = plt.gca()         for t in ax.xaxis.get_major_ticks():         t.tick1On = False         t.tick2On = False     for t in ax.yaxis.get_major_ticks():         t.tick1On = False         t.tick2On = False      # Add color bar     plt.colorbar(c)      # Add text in each cell      show_values(c)      # Proper orientation (origin at the top left instead of bottom left)     if correct_orientation:         ax.invert_yaxis()         ax.xaxis.tick_top()             # resize      fig = plt.gcf()     #fig.set_size_inches(cm2inch(40, 20))     #fig.set_size_inches(cm2inch(40*4, 20*4))     fig.set_size_inches(cm2inch(figure_width, figure_height))    def plot_classification_report(classification_report, title='Classification report ', cmap='RdBu'):     '''     Plot scikit-learn classification report.     Extension based on https://stackoverflow.com/a/31689645/395857      '''     lines = classification_report.split('\n')      classes = []     plotMat = []     support = []     class_names = []     for line in lines[2 : (len(lines) - 2)]:         t = line.strip().split()         if len(t) < 2: continue         classes.append(t[0])         v = [float(x) for x in t[1: len(t) - 1]]         support.append(int(t[-1]))         class_names.append(t[0])         print(v)         plotMat.append(v)      print('plotMat: {0}'.format(plotMat))     print('support: {0}'.format(support))      xlabel = 'Metrics'     ylabel = 'Classes'     xticklabels = ['Precision', 'Recall', 'F1-score']     yticklabels = ['{0} ({1})'.format(class_names[idx], sup) for idx, sup  in enumerate(support)]     figure_width = 25     figure_height = len(class_names) + 7     correct_orientation = False     heatmap(np.array(plotMat), title, xlabel, ylabel, xticklabels, yticklabels, figure_width, figure_height, correct_orientation, cmap=cmap)   def main():     sampleClassificationReport = """             precision    recall  f1-score   support            Acacia       0.62      1.00      0.76        66           Blossom       0.93      0.93      0.93        40           Camellia       0.59      0.97      0.73        67           Daisy       0.47      0.92      0.62       272           Echium       1.00      0.16      0.28       413          avg / total       0.77      0.57      0.49       858"""       plot_classification_report(sampleClassificationReport)     plt.savefig('test_plot_classif_report.png', dpi=200, format='png', bbox_inches='tight')     plt.close()  if __name__ == "__main__":     main()     #cProfile.run('main()') # if you want to do some profiling 

outputs:

Example with more classes (~40):

Answer 2

No string processing + sns.heatmap

The following solution uses the output_dict=True option in classification_report to get a dictionary and then a heat map is drawn using seaborn to the dataframe created from the dictionary.

---

import numpy as np import seaborn as sns from sklearn.metrics import classification_report import pandas as pd 

Generating data. Classes: A,B,C,D,E,F,G,H,I

true = np.random.randint(0, 10, size=100) pred = np.random.randint(0, 10, size=100) labels = np.arange(10) target_names = list("ABCDEFGHI") 

Call classification_report with output_dict=True

clf_report = classification_report(true,                                    pred,                                    labels=labels,                                    target_names=target_names,                                    output_dict=True) 

Create a dataframe from the dictionary and plot a heatmap of it.

# .iloc[:-1, :] to exclude support sns.heatmap(pd.DataFrame(clf_report).iloc[:-1, :].T, annot=True)