IUM_06

metrics.py

@@ -1,8 +1,4 @@
-from sklearn.metrics import (
-    accuracy_score,
-    precision_score,
-    recall_score,
-)
+from sklearn.metrics import confusion_matrix
 import pandas as pd
 
 
@@ -10,14 +6,15 @@ def main():
     y_test = pd.read_csv("data/y_test.csv")
     y_pred = pd.read_csv("evaluation/y_pred.csv", header=None)
 
-    accuracy = accuracy_score(y_test, y_pred)
-    precision_micro = precision_score(y_test, y_pred, average="micro")
-    recall_micro = recall_score(y_test, y_pred, average="micro")
+    cm = confusion_matrix(y_test, y_pred)
+    print(
+        "Recall metric in the testing dataset: ",
+        cm[1, 1] / (cm[1, 0] + cm[1, 1]),
+    )
+    accuracy = cm[1, 1] / (cm[1, 0] + cm[1, 1])
 
     with open(r"evaluation/metrics.txt", "a") as f:
         f.write(f"Accuracy: {accuracy}\n")
-        f.write(f"Micro-average Precision: {precision_micro}\n")
-        f.write(f"Micro-average Recall: {recall_micro}\n")
         f.write(f"\n")
 
 

predict.py

@@ -11,6 +11,7 @@ import numpy as np
 def main():
     model = load_model("model/model.keras")
     X_test = pd.read_csv("data/X_test.csv")
+    y_test = pd.read_csv("data/y_test.csv")
 
     y_pred = model.predict(X_test)
     y_pred = y_pred >= 0.5