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Fix feature alignment when applying XGBoost/HistGradientBoosting weights without specifying ss_main_score #167

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Fix feature alignment when applying XGBoost/HistGradientBoosting weights without specifying ss_main_score #167

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b72b341
Initial plan
Copilot Oct 21, 2025
d5e3637
Store ss_main_score metadata with XGBoost/HistGradientBoosting models
Copilot Oct 21, 2025
6268549
Update ss_use_dynamic_main_score flag when restoring ss_main_score
Copilot Oct 21, 2025
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16 changes: 14 additions & 2 deletions pyprophet/io/_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -528,7 +528,11 @@ def _save_tsv_weights(self, weights):

def _save_bin_weights(self, weights):
"""
Save the model weights to a binary file.
Save the model weights to a binary file with metadata.

For XGBoost/HistGradientBoosting classifiers, saves the model along with
metadata including ss_main_score and feature names to ensure proper
feature alignment when applying weights.

Args:
weights: Model weights or trained object.
Expand All @@ -537,8 +541,16 @@ def _save_bin_weights(self, weights):
f"trained_model_path_{self.level}"
)
if trained_weights_path is not None:
# For XGBoost/HistGradientBoosting, wrap model with metadata
# to ensure feature alignment when applying weights
model_data = {
"model": weights,
"ss_main_score": self.config.runner.ss_main_score,
"classifier": self.classifier,
"level": self.level,
}
with open(trained_weights_path, "wb") as file:
self.persisted_weights = pickle.dump(weights, file)
pickle.dump(model_data, file)
logger.success("%s written." % trained_weights_path)
else:
logger.error(f"Trained model path {trained_weights_path} not found. ")
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14 changes: 11 additions & 3 deletions pyprophet/io/scoring/osw.py
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Original file line number Diff line number Diff line change
Expand Up @@ -723,9 +723,17 @@ def save_weights(self, weights):

weights.to_sql("PYPROPHET_WEIGHTS", con, index=False, if_exists="append")

elif self.classifier == "XGBoost":
elif self.classifier == "XGBoost" or self.classifier == "HistGradientBoosting":
con = sqlite3.connect(self.outfile)

# Wrap model with metadata for feature alignment
model_data = {
"model": weights,
"ss_main_score": self.config.runner.ss_main_score,
"classifier": self.classifier,
"level": self.level,
}

c = con.cursor()
if self.glyco and self.level in ["ms2", "ms1ms2"]:
c.execute(
Expand All @@ -743,7 +751,7 @@ def save_weights(self, weights):

c.execute(
"INSERT INTO GLYCOPEPTIDEPROPHET_XGB VALUES(?, ?)",
[self.level, pickle.dumps(weights)],
[self.level, pickle.dumps(model_data)],
)
else:
c.execute(
Expand All @@ -758,7 +766,7 @@ def save_weights(self, weights):

c.execute(
"INSERT INTO PYPROPHET_XGB VALUES(?, ?)",
[self.level, pickle.dumps(weights)],
[self.level, pickle.dumps(model_data)],
)
con.commit()
c.close()
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