Merge pull request #57 from PenGuln/1.3.0

Fix import error when importing metrics

Author: GangLii

libauc/metrics/__init__.py

@@ -1 +1,2 @@
+from .metrics_k import *
 from .metrics import *

libauc/metrics/metrics.py

@@ -81,94 +81,6 @@ def pauc_roc_score(y_true, y_pred, max_fpr=1.0, min_tpr=0.0, reduction='mean', *
     selected_pred = np.concatenate((y_pred[pos_idx][selected_pos], y_pred[neg_idx][selected_neg]))
     return roc_auc_score(selected_target, selected_pred, **kwargs)
 
-
-# Reference: https://www.kaggle.com/code/nandeshwar/mean-average-precision-map-k-metric-explained-code
-def precision_and_recall_at_k(y_true, y_pred, k, pos_label=1, **kwargs):
-    # referece: https://github.com/NicolasHug/Surprise/blob/master/examples/precision_recall_at_k.py
-    def calc_metrics(y_true, y_pred):
-        y_true = y_true == pos_label 
-        desc_sort_order = np.argsort(y_pred)[::-1]
-        y_true_sorted = y_true[desc_sort_order]
-        true_positives = y_true_sorted[:k].sum()
-        total_positives = sum(y_true)
-
-        precision_k = true_positives / min(k, total_positives)
-        recall_k = true_positives / total_positives
-        return precision_k, recall_k
-
-    y_true = check_array_shape(y_true, (-1, 1))
-    y_pred = check_array_shape(y_pred, (-1, 1))
-
-    if y_true.shape[-1] != 1 and len(y_true.shape) > 1:
-        metrics_list = [calc_metrics(y_true[:, i], y_pred[:, i]) for i in range(y_true.shape[-1])]
-        precision_k_list, recall_k_list = zip(*metrics_list)
-        return precision_k_list, recall_k_list
-    else:
-        y_true = y_true.flatten()
-        y_pred = y_pred.flatten()
-        precision_k, recall_k = calc_metrics(y_true, y_pred)
-        return precision_k, recall_k
-
-def precision_at_k(y_true, y_pred, k, pos_label=1, **kwargs):
-    r"""Evaluation function of Precision@K"""
-    precision_k, _ = precision_and_recall_at_k(y_true, y_pred, k, pos_label=1, **kwargs)
-    return precision_k
-
-def recall_at_k(y_true, y_pred, k, pos_label=1, **kwargs):
-    r"""Evaluation function of Recall@K"""
-    _, recall_k = precision_and_recall_at_k(y_true, y_pred, k, pos_label=1, **kwargs)
-    return recall_k
-
-def ap_at_k(y_true, y_pred, k=10):
-    r"""Evaluation function of AveragePrecision@K"""
-    # adapted from https://github.com/benhamner/Metrics/blob/master/Python/ml_metrics/average_precision.py
-    y_true = check_array_shape(y_true, (-1,))
-    y_pred = check_array_shape(y_pred, (-1,))
-    if len(y_pred)>k:
-        y_pred = y_pred[:k]
-    score = 0.0
-    num_hits = 0.0
-    for i,p in enumerate(y_pred):
-        if p in y_true and p not in y_pred[:i]:
-            num_hits += 1.0
-            score += num_hits / (i+1.0)
-    return score / min(len(y_true), k)
-
-def map_at_k(y_true, y_pred, k=10):
-    r"""Evaluation function of meanAveragePrecision@K"""
-    # adapted from https://github.com/benhamner/Metrics/blob/master/Python/ml_metrics/average_precision.py
-    assert len(y_true.shape) == 2 and len(y_true.shape) == 2 
-    assert k > 0, 'Value of k is not valid!'
-    if isinstance(y_true, np.ndarray):
-        y_true = y_true.tolist()
-    if isinstance(y_pred, np.ndarray):
-        y_pred = y_pred.tolist()
-    return np.mean([ap_at_k(a,p,k) for a,p in zip(y_true, y_pred)])
-
-
-def ndcg_at_k(y_true, y_pred, k=5):
-    r"""
-        Evaluation function of NDCG@K
-    """
-    assert isinstance(y_pred, np.ndarray)
-    assert isinstance(y_true, np.ndarray)
-    assert len(y_pred.shape) == 2 and len(y_pred.shape) == 2
-
-    num_of_users, num_pos_items = y_true.shape
-    sorted_ratings = -np.sort(-y_true)            # descending order !!
-    discounters = np.tile([np.log2(i+1) for i in range(1, 1+num_pos_items)], (num_of_users, 1))
-    normalizer_mat = (np.exp2(sorted_ratings) - 1) / discounters
-    
-    sort_idx = (-y_pred).argsort(axis=1)    # index of sorted predictions (max->min)
-    gt_rank = np.array([np.argwhere(sort_idx == i)[:, 1]+1 for i in range(num_pos_items)]).T  # rank of the ground-truth (start from 1)
-    hit = (gt_rank <= k)
-    
-    # calculate the normalizer first
-    normalizer = np.sum(normalizer_mat[:, :k], axis=1)
-    # calculate DCG
-    DCG = np.sum(((np.exp2(y_true) - 1) / np.log2(gt_rank+1)) * hit.astype(float), axis=1)
-    return np.mean(DCG / normalizer)
-
 # TODO: automatic detect classificaiton task or ranking task?
 def evaluator(y_true, y_pred, metrics=['auroc', 'auprc', 'pauroc'], return_str=False, format='%.4f(%s)', **kwargs):
     results = {}

libauc/metrics/metrics_k.py

@@ -0,0 +1,106 @@
+import numpy as np
+
+def check_array_type(array):
+    # convert to array type 
+    if not isinstance(array, (np.ndarray, np.generic)):
+        array = np.array(array)
+    return array
+
+def check_array_shape(array, shape):
+    # check array shape
+    array = check_array_type(array)  
+    if array.size == 0:
+        raise ValueError("Array is empty.")
+    if array.shape != shape and len(array.shape) != 1:
+        try:
+            array = array.reshape(shape)
+        except ValueError as e:
+            raise ValueError(f"Could not reshape array of shape {array.shape} to {shape}.") from e
+    return array
+
+# Reference: https://www.kaggle.com/code/nandeshwar/mean-average-precision-map-k-metric-explained-code
+def precision_and_recall_at_k(y_true, y_pred, k, pos_label=1, **kwargs):
+    # referece: https://github.com/NicolasHug/Surprise/blob/master/examples/precision_recall_at_k.py
+    def calc_metrics(y_true, y_pred):
+        y_true = y_true == pos_label 
+        desc_sort_order = np.argsort(y_pred)[::-1]
+        y_true_sorted = y_true[desc_sort_order]
+        true_positives = y_true_sorted[:k].sum()
+        total_positives = sum(y_true)
+
+        precision_k = true_positives / min(k, total_positives)
+        recall_k = true_positives / total_positives
+        return precision_k, recall_k
+
+    y_true = check_array_shape(y_true, (-1, 1))
+    y_pred = check_array_shape(y_pred, (-1, 1))
+
+    if y_true.shape[-1] != 1 and len(y_true.shape) > 1:
+        metrics_list = [calc_metrics(y_true[:, i], y_pred[:, i]) for i in range(y_true.shape[-1])]
+        precision_k_list, recall_k_list = zip(*metrics_list)
+        return precision_k_list, recall_k_list
+    else:
+        y_true = y_true.flatten()
+        y_pred = y_pred.flatten()
+        precision_k, recall_k = calc_metrics(y_true, y_pred)
+        return precision_k, recall_k
+
+def precision_at_k(y_true, y_pred, k, pos_label=1, **kwargs):
+    r"""Evaluation function of Precision@K"""
+    precision_k, _ = precision_and_recall_at_k(y_true, y_pred, k, pos_label=1, **kwargs)
+    return precision_k
+
+def recall_at_k(y_true, y_pred, k, pos_label=1, **kwargs):
+    r"""Evaluation function of Recall@K"""
+    _, recall_k = precision_and_recall_at_k(y_true, y_pred, k, pos_label=1, **kwargs)
+    return recall_k
+
+def ap_at_k(y_true, y_pred, k=10):
+    r"""Evaluation function of AveragePrecision@K"""
+    # adapted from https://github.com/benhamner/Metrics/blob/master/Python/ml_metrics/average_precision.py
+    y_true = check_array_shape(y_true, (-1,))
+    y_pred = check_array_shape(y_pred, (-1,))
+    if len(y_pred)>k:
+        y_pred = y_pred[:k]
+    score = 0.0
+    num_hits = 0.0
+    for i,p in enumerate(y_pred):
+        if p in y_true and p not in y_pred[:i]:
+            num_hits += 1.0
+            score += num_hits / (i+1.0)
+    return score / min(len(y_true), k)
+
+def map_at_k(y_true, y_pred, k=10):
+    r"""Evaluation function of meanAveragePrecision@K"""
+    # adapted from https://github.com/benhamner/Metrics/blob/master/Python/ml_metrics/average_precision.py
+    assert len(y_true.shape) == 2 and len(y_true.shape) == 2 
+    assert k > 0, 'Value of k is not valid!'
+    if isinstance(y_true, np.ndarray):
+        y_true = y_true.tolist()
+    if isinstance(y_pred, np.ndarray):
+        y_pred = y_pred.tolist()
+    return np.mean([ap_at_k(a,p,k) for a,p in zip(y_true, y_pred)])
+
+
+def ndcg_at_k(y_true, y_pred, k=5):
+    r"""
+        Evaluation function of NDCG@K
+    """
+    assert isinstance(y_pred, np.ndarray)
+    assert isinstance(y_true, np.ndarray)
+    assert len(y_pred.shape) == 2 and len(y_pred.shape) == 2
+
+    num_of_users, num_pos_items = y_true.shape
+    sorted_ratings = -np.sort(-y_true)            # descending order !!
+    discounters = np.tile([np.log2(i+1) for i in range(1, 1+num_pos_items)], (num_of_users, 1))
+    normalizer_mat = (np.exp2(sorted_ratings) - 1) / discounters
+    
+    sort_idx = (-y_pred).argsort(axis=1)    # index of sorted predictions (max->min)
+    gt_rank = np.array([np.argwhere(sort_idx == i)[:, 1]+1 for i in range(num_pos_items)]).T  # rank of the ground-truth (start from 1)
+    hit = (gt_rank <= k)
+    
+    # calculate the normalizer first
+    normalizer = np.sum(normalizer_mat[:, :k], axis=1)
+    # calculate DCG
+    DCG = np.sum(((np.exp2(y_true) - 1) / np.log2(gt_rank+1)) * hit.astype(float), axis=1)
+    return np.mean(DCG / normalizer)