python - scikit - Sklearn plot confusión matriz con etiquetas

scikit learn cost matrix

Question

Quiero trazar una matriz de confusión para visualizar el rendimiento del clasificador, pero muestra solo los números de las etiquetas, no las etiquetas 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 etiquetas (salud, negocio, etc.) a la matriz de confusión?

Answer 1

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 a las que llama (las variables fig , ax y cax continuación). Luego, puede reemplazar las marcas predeterminadas de los ejes xy 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:

Answer 2

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'']);

Answer 3

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

Answer 4

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 trama
• parcela 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]: <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