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Feature range parameter support #1044

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Feature range parameter support #1044

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c17baae
added new argument support feature_range
namanmistry May 19, 2023
b0ea077
added new feature feature_range
namanmistry May 19, 2023
7d1a00c
Added new parameter feature_range
namanmistry May 19, 2023
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28 changes: 24 additions & 4 deletions mlxtend/feature_selection/exhaustive_feature_selector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -169,8 +169,8 @@ class ExhaustiveFeatureSelector(BaseEstimator, MetaEstimatorMixin):
def __init__(
self,
estimator,
min_features=1,
max_features=1,
min_features=None,
max_features=None,
print_progress=True,
scoring="accuracy",
cv=5,
Expand All @@ -179,11 +179,13 @@ def __init__(
clone_estimator=True,
fixed_features=None,
feature_groups=None,
feature_range = None
):
self.estimator = estimator
self.min_features = min_features
self.max_features = max_features
self.pre_dispatch = pre_dispatch
self.feature_range = feature_range
# Want to raise meaningful error message if a
# cross-validation generator is inputted
if isinstance(cv, types.GeneratorType):
Expand Down Expand Up @@ -401,8 +403,26 @@ def fit(self, X, y, groups=None, **fit_params):

# candidates in the following lines are the non-fixed-features candidates
# (the fixed features will be added later to each combination)
min_num_candidates = self.min_features - len(self.fixed_features_group_set)
max_num_candidates = self.max_features - len(self.fixed_features_group_set)

if self.min_features == None and self.max_features == None:
if self.feature_range == None:
min_num_candidates = 1
max_num_candidates = 1
elif self.feature_range == "all":
min_num_candidates = 1
max_num_candidates = X.shape[1]
else:
try:
min_num_candidates = self.feature_range[0]
max_num_candidates = self.feature_range[1]
except ValueError:
raise ValueError(
"""feature_range should be of tuple type. First argument should be
minimum number of feature and second argument shoulf be maximum number of feature"""
)
else:
min_num_candidates = self.min_features - len(self.fixed_features_group_set)
max_num_candidates = self.max_features - len(self.fixed_features_group_set)
candidates = chain.from_iterable(
combinations(non_fixed_groups, r=i)
for i in range(min_num_candidates, max_num_candidates + 1)
Expand Down