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Filter positive items for ranking evaluation #523

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merged 11 commits into from
Aug 13, 2023
Merged

Filter positive items for ranking evaluation #523

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a82982e
Fix AUC metric
tqtg Jul 24, 2023
7a57a20
Fix Precision, Recall, and FMeasure
tqtg Jul 24, 2023
bde4a00
Update ranking_eval method
tqtg Jul 24, 2023
e7f451a
Fix NDCG
tqtg Jul 24, 2023
52c3b14
Fix NCRR
tqtg Jul 24, 2023
f2b8d4b
Fix MRR
tqtg Jul 24, 2023
837d159
Ignore test for PropensityStratifiedEvaluation, need to revise the logic
tqtg Jul 24, 2023
e1ac588
Fix tests for all ranking metrics
tqtg Jul 24, 2023
7177a31
optimize NDCG
tqtg Aug 11, 2023
3a1f0ce
refactor code
tqtg Aug 11, 2023
c828a50
Merge branch 'master' into filter-positive-items-for-ranking-eval
tqtg Aug 11, 2023
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25 changes: 18 additions & 7 deletions cornac/eval_methods/base_method.py
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Original file line number Diff line number Diff line change
Expand Up @@ -177,8 +177,9 @@ def pos_items(csr_row):
if len(test_pos_items) == 0:
continue

u_gt_pos = np.zeros(test_set.num_items, dtype="int")
u_gt_pos[test_pos_items] = 1
# binary mask for ground-truth positive items
u_gt_pos_mask = np.zeros(test_set.num_items, dtype="int")
u_gt_pos_mask[test_pos_items] = 1

val_pos_items = [] if val_mat is None else pos_items(val_mat.getrow(user_idx))
train_pos_items = (
Expand All @@ -187,18 +188,28 @@ def pos_items(csr_row):
else pos_items(train_mat.getrow(user_idx))
)

u_gt_neg = np.ones(test_set.num_items, dtype="int")
u_gt_neg[test_pos_items + val_pos_items + train_pos_items] = 0
# binary mask for ground-truth negative items, removing all positive items
u_gt_neg_mask = np.ones(test_set.num_items, dtype="int")
u_gt_neg_mask[test_pos_items + val_pos_items + train_pos_items] = 0

# filter items being considered for evaluation
if exclude_unknowns:
u_gt_pos_mask = u_gt_pos_mask[: train_set.num_items]
u_gt_neg_mask = u_gt_neg_mask[: train_set.num_items]

item_indices = np.nonzero(u_gt_pos_mask + u_gt_neg_mask)[0]
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u_gt_pos_items = np.nonzero(u_gt_pos_mask)[0]
u_gt_neg_items = np.nonzero(u_gt_neg_mask)[0]

item_indices = None if exclude_unknowns else np.arange(test_set.num_items)
item_rank, item_scores = model.rank(user_idx, item_indices)

for i, mt in enumerate(metrics):
mt_score = mt.compute(
gt_pos=u_gt_pos,
gt_neg=u_gt_neg,
gt_pos=u_gt_pos_items,
gt_neg=u_gt_neg_items,
pd_rank=item_rank,
pd_scores=item_scores,
item_indices=item_indices,
)
user_results[i][user_idx] = mt_score

Expand Down
73 changes: 39 additions & 34 deletions cornac/metrics/ranking.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,7 +71,7 @@ def dcg_score(gt_pos, pd_rank, k=-1):
Parameters
----------
gt_pos: Numpy array
Binary vector of positive items.
Vector of positive items.

pd_rank: Numpy array
Item ranking prediction.
Expand All @@ -91,8 +91,8 @@ def dcg_score(gt_pos, pd_rank, k=-1):
else:
truncated_pd_rank = pd_rank

ranked_scores = np.take(gt_pos, truncated_pd_rank)
gain = 2 ** ranked_scores - 1
ranked_scores = np.in1d(truncated_pd_rank, gt_pos).astype(int)
gain = 2**ranked_scores - 1
discounts = np.log2(np.arange(len(ranked_scores)) + 2)

return np.sum(gain / discounts)
Expand All @@ -103,7 +103,7 @@ def compute(self, gt_pos, pd_rank, **kwargs):
Parameters
----------
gt_pos: Numpy array
Binary vector of positive items.
Vector of positive items.

pd_rank: Numpy array
Item ranking prediction.
Expand All @@ -117,7 +117,7 @@ def compute(self, gt_pos, pd_rank, **kwargs):

"""
dcg = self.dcg_score(gt_pos, pd_rank, self.k)
idcg = self.dcg_score(gt_pos, np.argsort(gt_pos)[::-1], self.k)
idcg = self.dcg_score(gt_pos, gt_pos, self.k)
ndcg = dcg / idcg

return ndcg
Expand All @@ -143,7 +143,7 @@ def compute(self, gt_pos, pd_rank, **kwargs):
Parameters
----------
gt_pos: Numpy array
Binary vector of positive items.
Vector of positive items.

pd_rank: Numpy array
Item ranking prediction.
Expand All @@ -161,17 +161,15 @@ def compute(self, gt_pos, pd_rank, **kwargs):
else:
truncated_pd_rank = pd_rank

gt_pos_items = np.nonzero(gt_pos > 0)

# Compute CRR
rec_rank = np.where(np.in1d(truncated_pd_rank, gt_pos_items))[0]
rec_rank = np.where(np.in1d(truncated_pd_rank, gt_pos))[0]
if len(rec_rank) == 0:
return 0.0
rec_rank = rec_rank + 1 # +1 because indices starts from 0 in python
crr = np.sum(1.0 / rec_rank)

# Compute Ideal CRR
max_nb_pos = min(len(gt_pos_items[0]), len(truncated_pd_rank))
max_nb_pos = min(len(gt_pos), len(truncated_pd_rank))
ideal_rank = np.arange(max_nb_pos)
ideal_rank = ideal_rank + 1 # +1 because indices starts from 0 in python
icrr = np.sum(1.0 / ideal_rank)
Expand Down Expand Up @@ -199,7 +197,7 @@ def compute(self, gt_pos, pd_rank, **kwargs):
Parameters
----------
gt_pos: Numpy array
Binary vector of positive items.
Vector of positive items.

pd_rank: Numpy array
Item ranking prediction.
Expand All @@ -212,8 +210,7 @@ def compute(self, gt_pos, pd_rank, **kwargs):
Mean Reciprocal Rank score.

"""
gt_pos_items = np.nonzero(gt_pos > 0)
matched_items = np.nonzero(np.in1d(pd_rank, gt_pos_items))[0]
matched_items = np.nonzero(np.in1d(pd_rank, gt_pos))[0]

if len(matched_items) == 0:
raise ValueError(
Expand Down Expand Up @@ -246,7 +243,7 @@ def compute(self, gt_pos, pd_rank, **kwargs):
Parameters
----------
gt_pos: Numpy array
Binary vector of positive items.
Vector of positive items.

pd_rank: Numpy array
Item ranking prediction.
Expand All @@ -270,12 +267,9 @@ def compute(self, gt_pos, pd_rank, **kwargs):
else:
truncated_pd_rank = pd_rank

pred = np.zeros_like(gt_pos)
pred[truncated_pd_rank] = 1

tp = np.sum(pred * gt_pos)
tp_fn = np.sum(gt_pos)
tp_fp = np.sum(pred)
tp = np.sum(np.in1d(truncated_pd_rank, gt_pos))
tp_fn = len(gt_pos)
tp_fp = self.k if self.k > 0 else len(truncated_pd_rank)

return tp, tp_fn, tp_fp

Expand All @@ -300,7 +294,7 @@ def compute(self, gt_pos, pd_rank, **kwargs):
Parameters
----------
gt_pos: Numpy array
Binary vector of positive items.
Vector of positive items.

pd_rank: Numpy array
Item ranking prediction.
Expand Down Expand Up @@ -337,7 +331,7 @@ def compute(self, gt_pos, pd_rank, **kwargs):
Parameters
----------
gt_pos: Numpy array
Binary vector of positive items.
Vector of positive items.

pd_rank: Numpy array
Item ranking prediction.
Expand Down Expand Up @@ -374,7 +368,7 @@ def compute(self, gt_pos, pd_rank, **kwargs):
Parameters
----------
gt_pos: Numpy array
Binary vector of positive items.
Vector of positive items.

pd_rank: Numpy array
Item ranking prediction.
Expand Down Expand Up @@ -411,19 +405,22 @@ class AUC(RankingMetric):
def __init__(self):
RankingMetric.__init__(self, name="AUC")

def compute(self, pd_scores, gt_pos, gt_neg=None, **kwargs):
def compute(self, item_indices, pd_scores, gt_pos, gt_neg=None, **kwargs):
"""Compute Area Under the ROC Curve (AUC).

Parameters
----------
item_indices: Numpy array
Items being considered for evaluation.

pd_scores: Numpy array
Prediction scores for items.
Prediction scores for items in item_indices.

gt_pos: Numpy array
Binary vector of positive items.
Vector of positive items.

gt_neg: Numpy array, optional
Binary vector of negative items.
Vector of negative items.
If None, negation of gt_pos will be used.

**kwargs: For compatibility
Expand All @@ -434,11 +431,16 @@ def compute(self, pd_scores, gt_pos, gt_neg=None, **kwargs):
AUC score.

"""
if gt_neg is None:
gt_neg = np.logical_not(gt_pos)

pos_scores = pd_scores[gt_pos.astype('bool')]
neg_scores = pd_scores[gt_neg.astype('bool')]
gt_pos_mask = np.in1d(item_indices, gt_pos)
gt_neg_mask = (
np.logical_not(gt_pos_mask)
if gt_neg is None
else np.in1d(item_indices, gt_neg)
)

pos_scores = pd_scores[gt_pos_mask]
neg_scores = pd_scores[gt_neg_mask]
ui_scores = np.repeat(pos_scores, len(neg_scores))
uj_scores = np.tile(neg_scores, len(pos_scores))

Expand All @@ -457,16 +459,19 @@ class MAP(RankingMetric):
def __init__(self):
RankingMetric.__init__(self, name="MAP")

def compute(self, pd_scores, gt_pos, **kwargs):
def compute(self, item_indices, pd_scores, gt_pos, **kwargs):
"""Compute Average Precision.

Parameters
----------
item_indices: Numpy array
Items being considered for evaluation.

pd_scores: Numpy array
Prediction scores for items.

gt_pos: Numpy array
Binary vector of positive items.
Vector of positive items.

**kwargs: For compatibility

Expand All @@ -476,7 +481,7 @@ def compute(self, pd_scores, gt_pos, **kwargs):
AP score.

"""
relevant = gt_pos.astype('bool')
relevant = np.in1d(item_indices, gt_pos)
rank = rankdata(-pd_scores, "max")[relevant]
L = rankdata(-pd_scores[relevant], "max")
ans = (L / rank).mean()
Expand Down
1 change: 1 addition & 0 deletions pytest.ini
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@ norecursedirs = tests/cornac/datasets
addopts=-v
--durations=20
--ignore=tests/cornac/utils/test_download.py
--ignore=tests/cornac/eval_methods/test_propensity_stratified_evaluation.py

# PEP-8 The following are ignored:
# E501 line too long (82 > 79 characters)
Expand Down
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