precision_recall_curve_model_comparision.py

"""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()


# %%