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# | |
import sklearn.metrics as skm | |
import numpy as np | |
# binary problem | |
# true labels | |
y_true = np.array([0, 0, 1, 0, 0, 1, 0, 0, 1, 1], dtype=float) | |
# classificator predict scores for each object | |
y_scores = np.array([0.01, 0.12, 0.89, .99, .05, .76, .14, .87, .44, .32]) | |
# casts scores to labels, | |
y_pred = y_scores > 0.5 | |
# compute confusion_matrix | |
cm = skm.confusion_matrix(y_true, y_pred) | |
print(cm) | |
# compute classification | |
print(skm.classification_report(y_true, y_pred)) | |
report = skm.classification_report(y_true, y_pred, output_dict=True) | |
print(report) | |
#%% multiclass problem | |
y_true = [2, 0, 2, 2, 0, 1, 1, 1, 1, 1] | |
y_pred = [0, 0, 2, 2, 0, 2, 1, 0, 2, 2] | |
cm = skm.confusion_matrix(y_true, y_pred) | |
print(cm) | |
labels = ["A", "B", "C"] | |
print(skm.classification_report(y_true, y_pred, target_names=labels)) | |
report = skm.classification_report(y_true, y_pred, output_dict=True) | |
print(report) | |
# multilabel problems | |
# 2 label binary problem | |
y_true = np.array([ | |
[0, 0], [1, 1], [0, 0], [1, 1] | |
]) | |
y_pred = np.array([ | |
[0, 0], [1, 1], [0, 1], [0, 1] | |
]) | |
cm = skm.multilabel_confusion_matrix(y_true, y_pred) | |
print(cm) | |
print(skm.classification_report(y_true, y_pred, | |
target_names=['label0==1', 'label1==1'])) | |
# 3 label binary problem | |
y_true = np.array([ | |
[0, 0, 0], [1, 1, 1], [0, 0, 0], [1, 1, 1] | |
]) | |
y_pred = np.array([ | |
[0, 0, 0], [1, 1, 1], [0, 1, 0], [0, 1, 1] | |
]) | |
cm = skm.multilabel_confusion_matrix(y_true, y_pred) | |
print(cm) | |
print(skm.classification_report(y_true, y_pred, | |
target_names=['label1==1', 'label2==1', 'label3==1'])) | |
# 2 label multiclass problem | |
# !!!!! multi label multi output not supported | |
# y_true = np.array([ | |
# [0,0], [1,1], [2,2], [1,1] | |
# ]) | |
# y_pred = np.array([ | |
# [0,0], [1,1], [2,2], [0,1] | |
# ]) | |
# cm = skm.multilabel_confusion_matrix(y_true, y_pred) | |
# print(cm) | |
# print( skm.classification_report(y_true,y_pred, target_names=['label1==1', 'label2==1'] )) | |
# %% |