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"""Example of computing precision recall curve | |
""" | |
# %% | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import sklearn.metrics as skm | |
y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1]) | |
# model1 curve will dominate the model0 | |
# output from model0 | |
y_score0 = np.array([0.75, 0.5, 0.3, 0.35, 0.45, 0.7, 0.3, 0.33, 0.5, 0.8]) | |
# output from model1 | |
y_score1 = np.array([0.6, 0.3, 0.3, 0.55, 0.65, 0.4, 0.55, 0.33, 0.75, 0.3]) | |
# model 1 is better | |
# # output from model0 | |
# y_score0 = np.array([0.75, 0.5, 0.3, 0.35, 0.45, 0.7, 0.3, 0.33, 0.5, 0.8]) | |
# # output from model1 | |
# y_score1 = np.array([0.7, 0.3, 0.3, 0.55, 0.75, 0.4, 0.5, 0.33, 0.72, 0.3]) | |
# looking only at curves it is not so obvious, which one is better | |
# output from model0 | |
y_score0 = np.array([0.7, 0.45, 0.3, 0.35, 0.45, 0.7, 0.3, 0.33, 0.55, 0.8]) | |
# output from model1 | |
y_score1 = np.array([0.6, 0.3, 0.3, 0.55, 0.65, 0.4, 0.5, 0.33, 0.75, 0.3]) | |
# % | |
# first model | |
precision0, recall0, tresholds0 = skm.precision_recall_curve(y_true, y_score0) | |
# second model | |
precision1, recall1, tresholds1 = skm.precision_recall_curve(y_true, y_score1) | |
avg_prec0 = skm.average_precision_score(y_true, y_score0) | |
auc0 = skm.auc(recall0,precision0) | |
print(f"Model 0 average_precision={avg_prec0} area under curve={auc0}") | |
avg_prec1 = skm.average_precision_score(y_true, y_score1) | |
auc1 = skm.auc(recall1,precision1) | |
print(f"Model 1 average_precision={avg_prec1} area under curve={auc1}") | |
# % plot curve | |
plt.plot(recall0, precision0, 'ro') | |
plt.plot(recall0, precision0, 'r', label='model 0') | |
plt.plot(recall1, precision1, 'bo') | |
plt.plot(recall1, precision1, 'b', label='model 1') | |
plt.xlabel('Recall') | |
plt.ylabel('Precision') | |
plt.ylim([0.0, 1.05]) | |
plt.xlim([0.0, 1.0]) | |
plt.title('Precision-Recall curve for 2 ml models') | |
plt.legend() | |
plt.show() | |
# %% |