# Alternative syntax, which assumes cv receives 'groups' by default, and that a
# method-based API is provided on meta-estimators:
# lr = LogisticRegressionCV(
# cv=GroupKFold(),
# scoring='accuracy',
# ).add_prop_route(scoring='sample_weight')
cross_validate(
lr,
X,
y,
cv=GroupKFold(),
props={
'sample_weight': my_weights,
'groups': my_groups
},
scoring='accuracy',
prop_routing={
'estimator': '*', # pass all props
'cv': ['groups'],
'scoring': ['sample_weight'],
})
# Error handling: if props={'sample_eight': my_weights, ...} was passed
# instead, LogisticRegressionCV would have to identify that a key was passed
# that could not be routed nor used, in order to raise an error.
# %%
# Case B: weighted scoring and unweighted fitting
def fit(self, X, y):
return super().fit(X, y, sample_weight=MY_WEIGHTS.loc[X.index])
acc_scorer = get_scorer('accuracy')
def wrapped_weighted_acc(est, X, y, sample_weight=None):
return acc_scorer(est, X, y, sample_weight=MY_WEIGHTS.loc[X.index])
lr = WeightedLogisticRegressionCV(
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 B: weighted scoring and unweighted fitting
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,
# Here we presume that GroupKFold requests `groups` by default.
# We need to explicitly request weights in make_scorer and for
# LogisticRegressionCV. Both of these consumers understand the meaning
# of the key "sample_weight".
weighted_acc = make_scorer(accuracy, request_props=['sample_weight'])
lr = LogisticRegressionCV(
cv=group_cv,
scoring=weighted_acc,
).request_sample_weight(fit=['sample_weight'])
cross_validate(lr,
X,
y,
cv=group_cv,
props={
'sample_weight': my_weights,
'groups': my_groups
},
scoring=weighted_acc)
# Error handling: if props={'sample_eight': my_weights, ...} was passed,
# cross_validate would raise an error, since 'sample_eight' was not requested
# by any of its children.
# %%
# Case B: weighted scoring and unweighted fitting
# Since LogisticRegressionCV requires that weights explicitly be requested,
# removing that request means the fitting is unweighted.
acc_scorer = get_scorer('accuracy')
def wrapped_weighted_acc(est, X, y, sample_weight=None):
return acc_scorer(est, unwrap_X(X), y, sample_weight=X[:, WEIGHT_IDX])
lr = WrappedLogisticRegressionCV(
cv=wrapped_group_cv,
scoring=wrapped_weighted_acc,
).set_props_request(['sample_weight'])
cross_validate(lr,
np.hstack([X, my_weights, my_groups]),
y,
cv=wrapped_group_cv,
scoring=wrapped_weighted_acc)
# %%
# Case B: weighted scoring and unweighted fitting
class UnweightedWrappedLogisticRegressionCV(LogisticRegressionCV):
def fit(self, X, y):
return super().fit(unwrap_X(X), y)
lr = UnweightedWrappedLogisticRegressionCV(
cv=wrapped_group_cv,
scoring=wrapped_weighted_acc,
from defs import (accuracy_score, GroupKFold, make_scorer, SelectKBest,
LogisticRegressionCV, cross_validate, make_pipeline, X, y,
my_groups, my_weights, my_other_weights)
# %%
# Case A: weighted scoring and fitting
lr = LogisticRegressionCV(
cv=GroupKFold(),
scoring='accuracy',
)
cross_validate(lr,
X,
y,
cv=GroupKFold(),
props={
'sample_weight': my_weights,
'groups': my_groups
},
scoring='accuracy')
# Error handling: if props={'sample_eight': my_weights, ...} was passed
# instead, the estimator would fit and score without weight, silently failing.
# %%
# Case B: weighted scoring and unweighted fitting
class MyLogisticRegressionCV(LogisticRegressionCV):
def fit(self, X, y, props=None):
props = props.copy()
my_weights, my_other_weights)
# %%
# Case A: weighted scoring and fitting
lr = LogisticRegressionCV(
cv=GroupKFold(),
scoring='accuracy',
)
props = {'cv__groups': my_groups,
'estimator__cv__groups': my_groups,
'estimator__sample_weight': my_weights,
'scoring__sample_weight': my_weights,
'estimator__scoring__sample_weight': my_weights}
cross_validate(lr, X, y, cv=GroupKFold(),
props=props,
scoring='accuracy')
# error handling: if props={'estimator__sample_eight': my_weights, ...} was
# passed instead, the estimator would raise an error.
# %%
# Case B: weighted scoring and unweighted fitting
lr = LogisticRegressionCV(
cv=GroupKFold(),
scoring='accuracy',
)
props = {'cv__groups': my_groups,
'estimator__cv__groups': my_groups,
'scoring__sample_weight': my_weights,
# %%
# Case A: weighted scoring and fitting
lr = LogisticRegressionCV(
cv=group_cv,
scoring='accuracy',
)
props = {
'cv__groups': my_groups,
'estimator__cv__groups': my_groups,
'estimator__sample_weight': my_weights,
'scoring__sample_weight': my_weights,
'estimator__scoring__sample_weight': my_weights
}
cross_validate(lr, X, y, cv=group_cv, props=props, scoring='accuracy')
# error handling: if props={'estimator__sample_eight': my_weights, ...} was
# passed instead, the estimator would raise an error.
# %%
# Case B: weighted scoring and unweighted fitting
lr = LogisticRegressionCV(
cv=group_cv,
scoring='accuracy',
)
props = {
'cv__groups': my_groups,
'estimator__cv__groups': my_groups,
'scoring__sample_weight': my_weights,
