如何绘制分类报告？

Expanding on Bin‘s answer:

import matplotlib.pyplot as pltimport numpy as npdef 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):