8000 Test CI by jrbourbeau · Pull Request #873 · dask/dask-ml · GitHub
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Test CI #873

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jrbourbeau
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As title

@VibhuJawa
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This failure is because of this change in sckit-learn 1.01 scikit-learn/scikit-learn#21389.

Previously sklearn kept feature-names even if we called fit again on a numpy array. PR scikit-learn/scikit-learn#21389. fixed that.

See below:

In sckit-learn=1.0.0:

>>> import numpy as np
>>> import pandas as pd
>>> from sklearn.preprocessing import StandardScaler
>>> 
>>> est = StandardScaler()
>>> est.fit(X=pd.DataFrame({'a':[0,1],'b':[2,3]}))
StandardScaler()
>>> est.fit(X=np.asarray([[0,1],[2,3]]))
StandardScaler()
>>> 
>>> print(est.feature_names_in_)
['a' 'b']

In sckit-learn=1.0.1 after PR 21389 :

>>> import numpy as np
>>> import pandas as pd
>>> from sklearn.preprocessing import StandardScaler
>>> 
>>> est = StandardScaler()
>>> est.fit(X=pd.DataFrame({'a':[0,1],'b':[2,3]}))
StandardScaler()
>>> est.fit(X=np.asarray([[0,1],[2,3]]))
StandardScaler()
>>> 
>>> print(est.feature_names_in_)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'StandardScaler' object has no attribute 'feature_names_in_'

This was referenced Oct 31, 2021
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@jrbourbeau
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Thanks for investigating @VibhuJawa. Closing this PR in favor of #872

@jrbourbeau jrbourbeau closed this Nov 1, 2021
@jrbourbeau jrbourbeau deleted the test-co branch November 1, 2021 19:15
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@jrbourbeau @VibhuJawa
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