Sklearn plot confusión matriz con tags

Como se indicó en esta pregunta , debe “abrir” la API de artista de nivel inferior , almacenando los objetos de figura y eje pasados ​​por las funciones de matplotlib que llama (las variables fig , ax y cax continuación). Luego, puede reemplazar las marcas predeterminadas de los ejes x e y usando 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() 

Tenga en cuenta que pasé la lista de labels a la función confusion_matrix para asegurarme de que esté correctamente ordenada, coincidiendo con los tics.

Esto da como resultado la siguiente figura:

Quizás te interese https://github.com/pandas-ml/pandas-ml/

que implementa una implementación de Python Pandas de Confusion Matrix.

Algunas caracteristicas:

• matriz de confusión de la ttwig
• plot normalizada matriz de confusión
• estadísticas de clase
• estadísticas generales

Aquí hay un ejemplo:

 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]:  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 

Creo que vale la pena mencionar el uso de seaborn.heatmap aquí.

 import seaborn as sns import matplotlib.pyplot as plt 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']); 

 from sklearn import model_selection test_size = 0.33 seed = 7 X_train, X_test, y_train, y_test = model_selection.train_test_split(feature_vectors, y, test_size=test_size, random_state=seed) from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix model = LogisticRegression() model.fit(X_train, y_train) result = model.score(X_test, y_test) print("Accuracy: %.3f%%" % (result*100.0)) y_pred = model.predict(X_test) print("F1 Score: ", f1_score(y_test, y_pred, average="macro")) print("Precision Score: ", precision_score(y_test, y_pred, average="macro")) print("Recall Score: ", recall_score(y_test, y_pred, average="macro")) import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.metrics import confusion_matrix def cm_analysis(y_true, y_pred, labels, ymap=None, figsize=(10,10)): """ Generate matrix plot of confusion matrix with pretty annotations. The plot image is saved to disk. args: y_true: true label of the data, with shape (nsamples,) y_pred: prediction of the data, with shape (nsamples,) filename: filename of figure file to save labels: string array, name the order of class labels in the confusion matrix. use `clf.classes_` if using scikit-learn models. with shape (nclass,). ymap: dict: any -> string, length == nclass. if not None, map the labels & ys to more understandable strings. Caution: original y_true, y_pred and labels must align. figsize: the size of the figure plotted. """ if ymap is not None: y_pred = [ymap[yi] for yi in y_pred] y_true = [ymap[yi] for yi in y_true] labels = [ymap[yi] for yi in labels] cm = confusion_matrix(y_true, y_pred, labels=labels) cm_sum = np.sum(cm, axis=1, keepdims=True) cm_perc = cm / cm_sum.astype(float) * 100 annot = np.empty_like(cm).astype(str) nrows, ncols = cm.shape for i in range(nrows): for j in range(ncols): c = cm[i, j] p = cm_perc[i, j] if i == j: s = cm_sum[i] annot[i, j] = '%.1f%%\n%d/%d' % (p, c, s) elif c == 0: annot[i, j] = '' else: annot[i, j] = '%.1f%%\n%d' % (p, c) cm = pd.DataFrame(cm, index=labels, columns=labels) cm.index.name = 'Actual' cm.columns.name = 'Predicted' fig, ax = plt.subplots(figsize=figsize) sns.heatmap(cm, annot=annot, fmt='', ax=ax) #plt.savefig(filename) plt.show() cm_analysis(y_test, y_pred, model.classes_, ymap=None, figsize=(10,10)) 

utilizando https://gist.github.com/hitvoice/36cf44689065ca9b927431546381a3f7

Tenga en cuenta que si usa rocket_r invertirá los colores y de alguna manera se verá más natural y mejor, como a continuación:

Encontré una función que puede trazar la matriz de confusión que se generó a partir de 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() 

Se verá así