I am currently reading the "Hands-On Machine Learning with Scikit-Learn & TensorFlow". I get an error when I am trying to recreate the Transformation Pipelines code. How can I fix this?
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
num_pipeline = Pipeline([('imputer', Imputer(strategy = "median")),
('attribs_adder', CombinedAttributesAdder()),
('std_scaler', StandardScaler()),
])
housing_num_tr = num_pipeline.fit_transform(housing_num)
from sklearn.pipeline import FeatureUnion
num_attribs = list(housing_num)
cat_attribs = ["ocean_proximity"]
num_pipeline = Pipeline([
('selector', DataFrameSelector(num_attribs)),
('imputer', Imputer(strategy = "median")),
('attribs_adder', CombinedAttributesAdder()),
('std_scaler', StandardScaler()),
])
cat_pipeline = Pipeline([('selector', DataFrameSelector(cat_attribs)),
('label_binarizer', LabelBinarizer()),
])
full_pipeline = FeatureUnion(transformer_list = [("num_pipeline", num_pipeline),
("cat_pipeline", cat_pipeline),
])
# And we can now run the whole pipeline simply:
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-350-3a4a39e5bc1c> in <module>()
43
44 num_pipeline = Pipeline([
---> 45 ('selector', DataFrameSelector(num_attribs)),
46 ('imputer', Imputer(strategy = "median")),
47 ('attribs_adder', CombinedAttributesAdder()),
NameError: name 'DataFrameSelector' is not defined
DataFrameSelector
is not being found, and will need to be imported. It is not part of sklearn
, but something of the same name is available in sklearn-features:
from sklearn_features.transformers import DataFrameSelector
(DOCS)