'scoring': {
'sample_weight': 'scoring_weight'
},
})
# %%
# Case C: unweighted feature selection
lr = LogisticRegressionCV(cv=GroupKFold(),
scoring='accuracy',
prop_routing={
'cv': ['groups'],
'scoring': ['sample_weight'],
})
pipe = make_pipeline(
SelectKBest(),
lr,
prop_routing={'logisticregressioncv': ['sample_weight', 'groups']})
cross_validate(lr,
X,
y,
cv=GroupKFold(),
props={
'sample_weight': my_weights,
'groups': my_groups
},
scoring='accuracy',
prop_routing={
'estimator': '*',
'cv': ['groups'],
'scoring': ['sample_weight'],
})
lr = LogisticRegressionCV(
cv=wrapped_group_cv,
scoring=wrapped_weighted_acc,
).set_props_request(['sample_weight'])
cross_validate(lr, X, y, cv=wrapped_group_cv, scoring=wrapped_weighted_acc)
# %%
# Case C: unweighted feature selection
lr = WeightedLogisticRegressionCV(
cv=wrapped_group_cv,
scoring=wrapped_weighted_acc,
).set_props_request(['sample_weight'])
sel = SelectKBest()
pipe = make_pipeline(sel, lr)
cross_validate(pipe, X, y, cv=wrapped_group_cv, scoring=wrapped_weighted_acc)
# %%
# Case D: different scoring and fitting weights
def other_weighted_acc(est, X, y, sample_weight=None):
return acc_scorer(est, X, y, sample_weight=MY_OTHER_WEIGHTS.loc[X.index])
lr = WeightedLogisticRegressionCV(
cv=wrapped_group_cv,
scoring=other_weighted_acc,
).set_props_request(['sample_weight'])
sel = SelectKBest()
props = {'cv__groups': my_groups,
'estimator__cv__groups': my_groups,
'scoring__sample_weight': my_weights,
'estimator__scoring__sample_weight': my_weights}
cross_validate(lr, X, y, cv=GroupKFold(),
props=props,
scoring='accuracy')
# %%
# Case C: unweighted feature selection
lr = LogisticRegressionCV(
cv=GroupKFold(),
scoring='accuracy',
)
pipe = make_pipeline(SelectKBest(), lr)
props = {'cv__groups': my_groups,
'estimator__logisticregressioncv__cv__groups': my_groups,
'estimator__logisticregressioncv__sample_weight': my_weights,
'scoring__sample_weight': my_weights,
'estimator__scoring__sample_weight': my_weights}
cross_validate(pipe, X, y, cv=GroupKFold(),
props=props,
scoring='accuracy')
# %%
# Case D: different scoring and fitting weights
lr = LogisticRegressionCV(
cv=GroupKFold(),
scoring='accuracy',
