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问题:
I want to plot a confusion matrix to visualize the classifer's performance, but it shows only the numbers of the labels, not the labels themselves:
from sklearn.metrics import confusion_matrix import pylab as pl y_test=['business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business'] pred=array(['health', 'business', 'business', 'business', 'business', 'business', 'health', 'health', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'health', 'health', 'business', 'health'], dtype='|S8') cm = confusion_matrix(y_test, pred) pl.matshow(cm) pl.title('Confusion matrix of the classifier') pl.colorbar() pl.show()
How can I add the labels (health, business..etc) to the confusion matrix?
回答1:
As hinted in this question, you have to "open" the lower-level artist API, by storing the figure and axis objects passed by the matplotlib functions you call (the fig, ax and cax variables below). You can then replace the default x- and y-axis ticks using set_xticklabels/set_yticklabels:
from sklearn.metrics import confusion_matrix labels = ['business', 'health'] cm = confusion_matrix(y_test, pred, labels) print(cm) fig = plt.figure() ax = fig.add_subplot(111) cax = ax.matshow(cm) plt.title('Confusion matrix of the classifier') fig.colorbar(cax) ax.set_xticklabels([''] + labels) ax.set_yticklabels([''] + labels) plt.xlabel('Predicted') plt.ylabel('True') plt.show()
Note that I passed the labels list to the confusion_matrix function to make sure it's properly sorted, matching the ticks.
This results in the following figure:
回答2:
You might be interested by https://github.com/pandas-ml/pandas-ml/
which implements a Python Pandas implementation of Confusion Matrix.
Some features:
- plot confusion matrix
- plot normalized confusion matrix
- class statistics
- overall statistics
Here is an example:
In [1]: from pandas_ml import ConfusionMatrix In [2]: import matplotlib.pyplot as plt In [3]: y_test = ['business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business'] In [4]: y_pred = ['health', 'business', 'business', 'business', 'business', 'business', 'health', 'health', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'health', 'health', 'business', 'health'] In [5]: cm = ConfusionMatrix(y_test, y_pred) In [6]: cm Out[6]: Predicted business health __all__ Actual business 14 6 20 health 0 0 0 __all__ 14 6 20 In [7]: cm.plot() Out[7]: <matplotlib.axes._subplots.AxesSubplot at 0x1093cf9b0> In [8]: plt.show()
In [9]: cm.print_stats() Confusion Matrix: Predicted business health __all__ Actual business 14 6 20 health 0 0 0 __all__ 14 6 20 Overall Statistics: Accuracy: 0.7 95% CI: (0.45721081772371086, 0.88106840959427235) No Information Rate: ToDo P-Value [Acc > NIR]: 0.608009812201 Kappa: 0.0 Mcnemar's Test P-Value: ToDo Class Statistics: Classes business health Population 20 20 P: Condition positive 20 0 N: Condition negative 0 20 Test outcome positive 14 6 Test outcome negative 6 14 TP: True Positive 14 0 TN: True Negative 0 14 FP: False Positive 0 6 FN: False Negative 6 0 TPR: (Sensitivity, hit rate, recall) 0.7 NaN TNR=SPC: (Specificity) NaN 0.7 PPV: Pos Pred Value (Precision) 1 0 NPV: Neg Pred Value 0 1 FPR: False-out NaN 0.3 FDR: False Discovery Rate 0 1 FNR: Miss Rate 0.3 NaN ACC: Accuracy 0.7 0.7 F1 score 0.8235294 0 MCC: Matthews correlation coefficient NaN NaN Informedness NaN NaN Markedness 0 0 Prevalence 1 0 LR+: Positive likelihood ratio NaN NaN LR-: Negative likelihood ratio NaN NaN DOR: Diagnostic odds ratio NaN NaN FOR: False omission rate 1 0
回答3:
I think it's worth mentioning the use of seaborn.heatmap here.
import seaborn as sns ax= plt.subplot() sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells # labels, title and ticks ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); ax.set_title('Confusion Matrix'); ax.xaxis.set_ticklabels(['business', 'health']); ax.yaxis.set_ticklabels(['health', 'business']);
回答4:
I found a function that can plot the confusion matrix which generated from sklearn.
import numpy as np def plot_confusion_matrix(cm, target_names, title='Confusion matrix', cmap=None, normalize=True): """ given a sklearn confusion matrix (cm), make a nice plot Arguments --------- cm: confusion matrix from sklearn.metrics.confusion_matrix target_names: given classification classes such as [0, 1, 2] the class names, for example: ['high', 'medium', 'low'] title: the text to display at the top of the matrix cmap: the gradient of the values displayed from matplotlib.pyplot.cm see http://matplotlib.org/examples/color/colormaps_reference.html plt.get_cmap('jet') or plt.cm.Blues normalize: If False, plot the raw numbers If True, plot the proportions Usage ----- plot_confusion_matrix(cm = cm, # confusion matrix created by # sklearn.metrics.confusion_matrix normalize = True, # show proportions target_names = y_labels_vals, # list of names of the classes title = best_estimator_name) # title of graph Citiation --------- http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html """ import matplotlib.pyplot as plt import numpy as np import itertools accuracy = np.trace(cm) / float(np.sum(cm)) misclass = 1 - accuracy if cmap is None: cmap = plt.get_cmap('Blues') plt.figure(figsize=(8, 6)) plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() if target_names is not None: tick_marks = np.arange(len(target_names)) plt.xticks(tick_marks, target_names, rotation=45) plt.yticks(tick_marks, target_names) if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] thresh = cm.max() / 1.5 if normalize else cm.max() / 2 for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): if normalize: plt.text(j, i, "{:0.4f}".format(cm[i, j]), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") else: plt.text(j, i, "{:,}".format(cm[i, j]), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass)) plt.show()
It will look like this
