add model comparision

Author: ksopyla

img/precision_recall_curve_model_comparision.png

@@ -51,5 +51,3 @@
 plt.title('2-class Precision-Recall curve')
 plt.legend()
 plt.show()          
-
-# %%

img/precision_recall_curve_model_comparision_easy.png

@@ -1,5 +1,5 @@
 """Example of computing precision recall curve for random and ideal
-classificator.
+classifier.
 """
 # %%
 

metrics/precision_recall_curve.py

@@ -0,0 +1,67 @@
+"""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()
+
+
+# %%