Sklearn plot confusión matriz con tags

Quiero trazar una matriz de confusión para visualizar el rendimiento del clasificador, pero muestra solo los números de las tags, no las tags en sí:

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() 

¿Cómo puedo agregar las tags (salud, negocio, etc.) a la matriz de confusión?

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:

introduzca la descripción de la imagen aquí

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() 

Parcela de matriz de confusión

 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']); 

introduzca la descripción de la imagen aquí

 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)) 

introduzca la descripción de la imagen aquí

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: introduzca la descripción de la imagen aquí

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 introduzca la descripción de la imagen aquí