Add roc_curve example

Author: ksopyla

.gitignore

@@ -102,3 +102,5 @@ venv.bak/
 
 # mypy
 .mypy_cache/
+.idea/*
+.vscode/*

metrics.py

@@ -1,15 +1,13 @@
 import sklearn.metrics as skm
 import numpy as np
 
-
 # #binary problem
 
 y_true = np.array([0, 0, 1, 0, 0, 1, 0, 0, 1, 1], dtype=float)
-#y_pred = [0, 0, 1, 1, 0, 1, 0, 1, 0, 0]
+# y_pred = [0, 0, 1, 1, 0, 1, 0, 1, 0, 0]
 y_pred = np.array([0.01, 0.12, 0.89, .99, .05, .76, .14, .87, .44, .32])
 y_pred = y_pred > 0.5
 
-
 y_pred = np.array([0, 0, 1, 1])
 y_true = np.array([0, 0, 1, 0])
 
@@ -49,7 +47,6 @@
 print(skm.classification_report(y_true, y_pred,
                                 target_names=['label1==1', 'label2==1']))
 
-
 # 3 label binary problem
 y_true = np.array([
     [0, 0, 0], [1, 1, 1], [0, 0, 0], [1, 1, 1]
@@ -64,6 +61,7 @@
                                 target_names=['label1==1', 'label2==1', 'label3==1']))
 
 
+
 # 2 label multiclass problem
 # !!!!! multi label multi output not supported
 # y_true = np.array([

roc_curve.py

@@ -0,0 +1,21 @@
+import numpy as np
+from sklearn import metrics
+import matplotlib.pyplot as plt
+
+
+y = np.array([0, 0, 1, 1,1])
+scores = np.array([0.1, 0.4, 0.35, 0.6, 0.8])
+fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=1)
+
+print(f'\nfpr={fpr}\ntpr={tpr}\ntre={thresholds}')
+
+plt.plot(fpr, tpr, lw=1)
+plt.scatter(fpr,tpr)
+
+plt.xlim([-0.05, 1.05])
+plt.ylim([-0.05, 1.05])
+plt.xlabel('False Positive Rate')
+plt.ylabel('True Positive Rate')
+plt.title('Receiver operating characteristic example')
+#plt.legend(loc="lower right")
+plt.show()