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Supporting code for running predict() on data other than training dat… #111

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Supporting code for running predict() on data other than training dat…
jmarcusse Mar 28, 2026
3e817b4
Revisions to support providing non-training data to predict function …
jmarcusse Mar 28, 2026
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42 changes: 34 additions & 8 deletions fife/lgb_modelers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -322,14 +322,20 @@ def train_single_model(
return model

def predict(
self, subset: Union[None, pd.core.series.Series] = None, cumulative: bool = True
self,
subset: Union[None, pd.core.series.Series] = None,
custom_data: Union[None, pd.core.frame.DataFrame] = None,
cumulative: bool = True
) -> np.ndarray:
"""Use trained LightGBM models to predict the outcome for each observation and time horizon.

Args:
subset: A Boolean Series that is True for observations for which
predictions will be produced. If None, default to all
observations.
custom_data: A DataFrame in the same format as the input data for
which predictions will be produced. If None, default to the
assigned input data.
cumulative: If True, produce cumulative survival probabilies.
If False, produce marginal survival probabilities (i.e., one
minus the hazard rate).
Expand All @@ -338,10 +344,19 @@ def predict(
A numpy array of predictions by observation and lead
length.
"""
subset = default_subset_to_all(subset, self.data)
predict_data = self.data[self.categorical_features + self.numeric_features][
subset
]
if custom_data is not None:
data = self.transform_features(custom_data)
if not set(self.categorical_features + self.numeric_features).issubset(
set(data.columns)
):
raise KeyError(
f"Columns {[i for i in (self.categorical_features + self.numeric_features) if i not in data.columns]} not found in data or are of an incompatible type"
)
else:
data = self.data.copy(deep=True)
subset = default_subset_to_all(subset, data)

predict_data = data[self.categorical_features + self.numeric_features][subset]
predictions = np.array(
[
lead_specific_model.predict(predict_data)
Expand All @@ -352,9 +367,20 @@ def predict(
predictions = np.cumprod(predictions, axis=1)
return predictions

def transform_features(self) -> pd.DataFrame:
"""Transform features to suit model training."""
data = self.data.copy(deep=True)
def transform_features(self,
custom_data: Union[None, pd.core.frame.DataFrame] = None
) -> pd.DataFrame:
"""Transform features to suit model training.

Args:
custom_data: A DataFrame in the same format as the input data for
which predictions will be produced. If None, default to the
assigned input data.
"""
if custom_data is not None:
data = custom_data.copy(deep=True)
else:
data = self.data.copy(deep=True)
if self.config.get("DATETIME_AS_DATE", True):
date_cols = list(data.select_dtypes("datetime").columns) + [
col
Expand Down