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Three changes (one in _glm.py and two in _glm_cv.py) to enable more detailed analysis of CV performance #935

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Three changes (one in _glm.py and two in _glm_cv.py) to enable more detailed analysis of CV performance #935

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3 changes: 3 additions & 0 deletions src/glum/_glm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -285,6 +285,9 @@ def _convert_from_pandas(

return X

# expose the method as public so can create predictions outside of class
convert_from_pandas = _convert_from_pandas

def _set_up_for_fit(self, y: np.ndarray) -> None:
#######################################################################
# 1. input validation #
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10 changes: 7 additions & 3 deletions src/glum/_glm_cv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -543,7 +543,7 @@ def fit(
_stype = ["csc"]
else:
_stype = ["csc", "csr"]

def _fit_path(
self,
train_idx,
Expand Down Expand Up @@ -571,6 +571,8 @@ def _fit_path(
y[test_idx],
sample_weight[test_idx],
)
# test weights need to sum to 1 too, else deviance is not properly scaled
w_test /= w_test.sum()

if offset is not None:
offset_train = offset[train_idx]
Expand Down Expand Up @@ -667,8 +669,8 @@ def _get_deviance(coef):
)
deviance_path_ = [_get_deviance(_coef) for _coef in coef_path_]

return intercept_path_, coef_path_, deviance_path_

return intercept_path_, coef_path_, deviance_path_, train_idx
jobs = (
joblib.delayed(_fit_path)(
self,
Expand Down Expand Up @@ -706,6 +708,8 @@ def _get_deviance(coef):
(cv.get_n_splits(), len(l1_ratio), len(alphas[0])),
)

self.train_indices_ = [elmt[3] for elmt in paths_data]

avg_deviance = self.deviance_path_.mean(axis=0) # type: ignore

best_l1, best_alpha = np.unravel_index(
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