Zhur homework 8

lecture_1_intro_knn/homework/metrics.py

@@ -16,10 +16,17 @@ def binary_classification_metrics(y_pred, y_true):
     # https://en.wikipedia.org/wiki/Precision_and_recall
     # https://en.wikipedia.org/wiki/F1_score
 
-    """
-    YOUR CODE IS HERE
-    """
-    pass
+    tp = np.sum((y_pred == 1) & (y_true == 1))
+    tn = np.sum((y_pred == 0) & (y_true == 0))
+    fp = np.sum((y_pred == 1) & (y_true == 0))
+    fn = np.sum((y_pred == 0) & (y_true == 1))
+
+    precision = tp / (tp + fp) if (tp + fp) > 0 else 0
+    recall = tp / (tp + fn) if (tp + fn) > 0 else 0
+    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
+    accuracy = (tp + tn) / (tp + tn + fp + fn)
+
+    return precision, recall, f1, accuracy
 
 
 def multiclass_accuracy(y_pred, y_true):
@@ -31,11 +38,8 @@ def multiclass_accuracy(y_pred, y_true):
     Returns:
     accuracy - ratio of accurate predictions to total samples
     """
+    return np.mean(y_pred == y_true)
 
-    """
-    YOUR CODE IS HERE
-    """
-    pass
 
 
 def r_squared(y_pred, y_true):
@@ -47,11 +51,10 @@ def r_squared(y_pred, y_true):
     Returns:
     r2 - r-squared value
     """
-
-    """
-    YOUR CODE IS HERE
-    """
-    pass
+    ss_res = np.sum((y_true - y_pred) ** 2)
+    ss_tot = np.sum((y_true - np.mean(y_true)) ** 2)
+    r2 = 1 - (ss_res / ss_tot)
+    return r2
 
 
 def mse(y_pred, y_true):
@@ -63,11 +66,7 @@ def mse(y_pred, y_true):
     Returns:
     mse - mean squared error
     """
-
-    """
-    YOUR CODE IS HERE
-    """
-    pass
+    return np.mean((y_pred - y_true) ** 2)
 
 
 def mae(y_pred, y_true):
@@ -79,9 +78,5 @@ def mae(y_pred, y_true):
     Returns:
     mae - mean absolut error
     """
-
-    """
-    YOUR CODE IS HERE
-    """
-    pass
+    return np.mean(np.abs(y_pred - y_true))