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Bumped sktime to >=0.30.0 and scikit-learn to >=1.5.0 #1238

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merged 5 commits into from
Dec 5, 2024
Merged

Bumped sktime to >=0.30.0 and scikit-learn to >=1.5.0 #1238

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847747f
bumped sktime to >=0.30.0 and scikit-learn to >=1.5.0
MinuraPunchihewa Dec 3, 2024
8a58e13
Merge branch 'staging' of https://github.com/mindsdb/lightwood into f…
MinuraPunchihewa Dec 4, 2024
58c4b0d
updated the poetry.lock file
MinuraPunchihewa Dec 4, 2024
db8038e
updated use of OneHotEncoder with bumped version
MinuraPunchihewa Dec 4, 2024
404eb1c
updated a few more calls to OneHotEncoder
MinuraPunchihewa Dec 4, 2024
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2 changes: 1 addition & 1 deletion lightwood/analysis/nc/calibrate.py
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Expand Up @@ -59,7 +59,7 @@ def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, object]:
all_classes = np.array([ns.encoded_val_data.encoders[ns.target].rev_map[idx] for idx in class_keys])

if data_type != dtype.tags:
enc = OneHotEncoder(sparse=False, handle_unknown='ignore')
enc = OneHotEncoder(sparse_output=False, handle_unknown='ignore')
enc.fit(all_classes.reshape(-1, 1))
output['label_encoders'] = enc # needed to repr cat labels inside nonconformist
else:
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2 changes: 1 addition & 1 deletion lightwood/encoder/helpers.py
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Expand Up @@ -45,7 +45,7 @@ class CatNormalizer:
def __init__(self, encoder_class='one_hot'):
self.encoder_class = encoder_class
if encoder_class == 'one_hot':
self.scaler = OneHotEncoder(sparse=False, handle_unknown='ignore')
self.scaler = OneHotEncoder(sparse_output=False, handle_unknown='ignore')
else:
self.scaler = OrdinalEncoder()

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