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Computing ranking metrics on first positive label #230

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Computing ranking metrics on first positive label #230

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4 changes: 2 additions & 2 deletions python/ml4ir/applications/ranking/model/metrics/helpers/aux_metrics_helper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -267,7 +267,7 @@ def compute_aux_metrics_on_query_group(query_group: pd.DataFrame,
compute_aux_metrics(
aux_label_values=query_group[aux_label],
ranks=query_group[old_rank_col],
click_rank=query_group[query_group[label_col] == 1][old_rank_col].values[0]
click_rank=query_group[query_group[label_col] == 1][old_rank_col].min()
if (query_group[label_col] == 1).sum() != 0
else float("inf"),
prefix="old_",
Expand All @@ -277,7 +277,7 @@ def compute_aux_metrics_on_query_group(query_group: pd.DataFrame,
compute_aux_metrics(
aux_label_values=query_group[aux_label],
ranks=query_group[new_rank_col],
click_rank=query_group[query_group[label_col] == 1][new_rank_col].values[0]
click_rank=query_group[query_group[label_col] == 1][new_rank_col].min()
if (query_group[label_col] == 1).sum() != 0
else float("inf"),
prefix="new_",
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3 changes: 3 additions & 0 deletions python/ml4ir/applications/ranking/model/metrics/helpers/metrics_helper.py
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Expand Up @@ -66,6 +66,9 @@ def get_grouped_stats(
df_clicked = df[df[label_col] == 1.0]
df = df[df[query_key_col].isin(df_clicked[query_key_col])]

# Pick most relevant record for each query to compute ranking metrics
df_clicked = df_clicked.groupby(query_key_col).apply(min)

# Compute metrics on aux labels
df_aux_metrics = pd.DataFrame()
if aux_label:
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1 change: 1 addition & 0 deletions python/ml4ir/base/model/scoring/prediction_helper.py
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Expand Up @@ -86,6 +86,7 @@ def _predict_score(features, label):
if is_compiled:
scores = infer(features)[output_name]
else:
import pdb; pdb.set_trace()
scores = infer(**features)[output_name]

# Set scores of padded records to 0
Expand Down