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Made categorical dictionary more cohesive to overall structure #1454

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Merged
mfeurer merged 11 commits into from
May 9, 2022
Merged

Made categorical dictionary more cohesive to overall structure #1454

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8d47cc9
change treatment of string features. In file `smbo.py` string columns…
Louquinze Mar 24, 2022
9263e85
change treatment of string features. In file `smbo.py` string columns…
Louquinze Mar 24, 2022
9836f99
Merge remote-tracking branch 'origin/development' into development
Louquinze May 2, 2022
b9da740
made categorical dictionary more cohesive. Use the previously create …
Louquinze May 2, 2022
230bed7
change test files since they now get the feat type dictionary and no …
Louquinze May 2, 2022
ed79585
change test files since they now get the feat type dictionary and no …
Louquinze May 3, 2022
b2dea63
moved feat_type to categorical conv. to the helper functions
Louquinze May 4, 2022
893d8ff
fixed minor issues
Louquinze May 5, 2022
15cec29
fixed minor issues
Louquinze May 6, 2022
b299111
fixed minor issues
Louquinze May 9, 2022
fc9628d
fixed bug in metalearning tests
Louquinze May 9, 2022
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27 changes: 9 additions & 18 deletions autosklearn/metalearning/metafeatures/metafeatures.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1061,7 +1061,7 @@ def _calculate(self, X, y, logger, categorical):


def calculate_all_metafeatures_encoded_labels(
X, y, categorical, dataset_name, logger, calculate=None, dont_calculate=None
X, y, feat_type, dataset_name, logger, calculate=None, dont_calculate=None
):
"""
Calculate only metafeatures for which a 1HotEncoded feature matrix is necessery.
Expand All @@ -1073,7 +1073,7 @@ def calculate_all_metafeatures_encoded_labels(
return calculate_all_metafeatures(
X,
y,
categorical,
feat_type,
dataset_name,
calculate=calculate,
dont_calculate=dont_calculate,
Expand All @@ -1082,7 +1082,7 @@ def calculate_all_metafeatures_encoded_labels(


def calculate_all_metafeatures_with_labels(
X, y, categorical, dataset_name, logger, calculate=None, dont_calculate=None
X, y, feat_type, dataset_name, logger, calculate=None, dont_calculate=None
):
if dont_calculate is None:
dont_calculate = set()
Expand All @@ -1092,7 +1092,7 @@ def calculate_all_metafeatures_with_labels(
return calculate_all_metafeatures(
X,
y,
categorical,
feat_type,
dataset_name,
calculate=calculate,
dont_calculate=dont_calculate,
Expand All @@ -1103,7 +1103,7 @@ def calculate_all_metafeatures_with_labels(
def calculate_all_metafeatures(
X,
y,
categorical,
feat_type,
dataset_name,
logger,
calculate=None,
Expand Down Expand Up @@ -1138,19 +1138,7 @@ def calculate_all_metafeatures(
# sparse matrices because of wrong sparse format)
sparse = scipy.sparse.issparse(X)

feat_type = {
key: "categorical" if value else "numerical"
for key, value in categorical.items()
}

# TODO make this more cohesive to the overall structure (quick bug fix)
if isinstance(X, pd.DataFrame):
for key in X.select_dtypes(include="string").columns:
feat_type[key] = "string"

DPP = FeatTypeSplit(
# The difference between feat_type and categorical, is that
# categorical has True/False instead of categorical/numerical
feat_type=feat_type,
force_sparse_output=True,
)
Expand Down Expand Up @@ -1189,7 +1177,10 @@ def calculate_all_metafeatures(
else:
X_ = X
y_ = y
categorical_ = categorical
categorical_ = {
col: True if feat_type.lower() == "categorical" else False
for col, feat_type in feat_type.items()
}

dependency = metafeatures.get_dependency(name)
if dependency is not None:
Expand Down
13 changes: 2 additions & 11 deletions autosklearn/smbo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -101,11 +101,6 @@ def _calculate_metafeatures(
# == Calculate metafeatures
with stopwatch.time("Calculate meta-features") as task_timer:

categorical = {
col: True if feat_type.lower() in {"categorical", "string"} else False
for col, feat_type in data_feat_type.items()
}

EXCLUDE_META_FEATURES = (
EXCLUDE_META_FEATURES_CLASSIFICATION
if data_info_task in CLASSIFICATION_TASKS
Expand All @@ -123,7 +118,7 @@ def _calculate_metafeatures(
result = calculate_all_metafeatures_with_labels(
x_train,
y_train,
categorical=categorical,
feat_type=data_feat_type,
dataset_name=basename,
dont_calculate=EXCLUDE_META_FEATURES,
logger=logger_,
Expand Down Expand Up @@ -159,15 +154,11 @@ def _calculate_metafeatures_encoded(
)

with stopwatch.time("Calculate meta-features encoded") as task_timer:
categorical = {
col: True if feat_type.lower() in {"categorical", "string"} else False
for col, feat_type in data_feat_type.items()
}

result = calculate_all_metafeatures_encoded_labels(
x_train,
y_train,
categorical=categorical,
feat_type=data_feat_type,
dataset_name=basename,
dont_calculate=EXCLUDE_META_FEATURES,
logger=logger_,
Expand Down
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