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Fix FutureWarning: pass include_groups=False to groupby().apply() #182

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Fix FutureWarning: pass include_groups=False to groupby().apply() #182

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6 changes: 4 additions & 2 deletions numerblox/evaluation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -148,7 +148,8 @@ def per_era_corrs(
return dataf.groupby(dataf[self.era_col]).apply(
lambda d: self._normalize_uniform(d[pred_col].fillna(0.5)).corr(
d[target_col]
)
),
include_groups=False,
)

def mean_std_sharpe(
Expand Down Expand Up @@ -207,7 +208,8 @@ def max_feature_exposure(
) -> np.float64:
"""Maximum exposure over all features."""
max_per_era = dataf.groupby(self.era_col).apply(
lambda d: d[dataf.feature_cols].corrwith(d[pred_col]).abs().max()
lambda d: d[dataf.feature_cols].corrwith(d[pred_col]).abs().max(),
include_groups=False,
)
max_feature_exposure = max_per_era.mean(skipna=True)
return max_feature_exposure
Expand Down
3 changes: 2 additions & 1 deletion numerblox/postprocessing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -184,7 +184,8 @@ def __init__(
def transform(self, dataf: NumerFrame) -> NumerFrame:
feature_names = self.feature_names if self.feature_names else dataf.feature_cols
neutralized_preds = dataf.groupby(dataf.meta.era_col).apply(
lambda x: self.normalize_and_neutralize(x, [self.pred_name], feature_names)
lambda x: self.normalize_and_neutralize(x, [self.pred_name], feature_names),
include_groups=False,
)
dataf.loc[:, self.new_col_name] = MinMaxScaler().fit_transform(
neutralized_preds
Expand Down