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SequentialFeatureSelection Early Stopping Criterion #886

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SequentialFeatureSelection Early Stopping Criterion #886

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8bedc45
adding early stop for sequential feature selection
aldder Feb 2, 2022
fff2163
test and refactoring
aldder Feb 2, 2022
f99fec4
improvements: https://github.com/rasbt/mlxtend/pull/886#issuecomment-…
aldder Feb 2, 2022
4db4c97
refactoring
aldder Feb 2, 2022
d5595a9
fix https://github.com/rasbt/mlxtend/pull/886#discussion_r797950840 a…
aldder Feb 3, 2022
c6e9ca8
fix https://github.com/rasbt/mlxtend/pull/886#discussion_r797954274
aldder Feb 7, 2022
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improvements: #886 (comment)
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@aldder
aldder committed Feb 2, 2022
commit f99fec4c2c16d5d35db8df8d869b812a4eae1d91
21 changes: 9 additions & 12 deletions mlxtend/feature_selection/sequential_feature_selector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,14 +114,11 @@ class SequentialFeatureSelector(_BaseXComposition, MetaEstimatorMixin):
n_jobs : int (default: 1)
The number of CPUs to use for evaluating different feature subsets
in parallel. -1 means 'all CPUs'.
early_stop : bool (default: False)
Determines whether to prematurely stop execution if the score does not
improve after a number of iterations set by the `early_stop_rounds`
parameter.
early_stop_rounds : int (default 3)
Used when `early_stop` is True, it determines the number of iterations
after which, if no performance boost has been seen, execution is
stopped.
early_stop_rounds : int (default 0)
Enable early stopping criterion when > 0, this value determines the
number of iterations after which, if no performance boost has been
seen, execution is stopped.
Used only when `k_features == 'best'` or `k_features == 'parsimonious'`
pre_dispatch : int, or string (default: '2*n_jobs')
Controls the number of jobs that get dispatched
during parallel execution if `n_jobs > 1` or `n_jobs=-1`.
Expand Down Expand Up @@ -186,8 +183,7 @@ def __init__(self, estimator, k_features=1,
forward=True, floating=False,
verbose=0, scoring=None,
cv=5, n_jobs=1,
early_stop=False,
early_stop_rounds=3,
early_stop_rounds=0,
pre_dispatch='2*n_jobs',
clone_estimator=True,
fixed_features=None):
Expand Down Expand Up @@ -216,7 +212,6 @@ def __init__(self, estimator, k_features=1,
'an integer value greater than or equal to 0.'
'Got %d' % early_stop_rounds)
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I think ...Got %d' % early_stop_rounds might not work if early_stop_rounds is not an integer. Maybe it's better to replace it with

...Got %s' % early_stop_rounds

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Right!


self.early_stop = early_stop
self.early_stop_rounds = early_stop_rounds

if fixed_features is not None:
Expand Down Expand Up @@ -571,7 +566,9 @@ def fit(self, X, y, custom_feature_names=None, groups=None, **fit_params):
raise KeyboardInterrupt

# early stop
if self.early_stop and k != k_to_select:
if self.early_stop_rounds \
and k != k_to_select \
and self.k_features in {'best', 'parsimonious'}:
if k_score <= best_score:
early_stop_count -= 1
if early_stop_count == 0:
Expand Down
4 changes: 2 additions & 2 deletions mlxtend/feature_selection/tests/test_sequential_feature_selector.py
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Expand Up @@ -997,7 +997,7 @@ def test_run_forward_earlystop():
knn = KNeighborsClassifier()
esr = 2
sfs = SFS(estimator=knn,
k_features=X_iris_with_noise.shape[1],
k_features='best',
forward=True,
floating=False,
early_stop=True,
Expand All @@ -1021,7 +1021,7 @@ def test_run_backward_earlystop():
knn = KNeighborsClassifier()
esr = 2
sfs = SFS(estimator=knn,
k_features=1,
k_features='best',
forward=False,
floating=False,
early_stop=True,
Expand Down