sklearn function transformer in pipeline

Question

Writing my first pipeline for sk-learn I stumbled upon some issues when only a subset of columns is put into a pipeline:

mydf = pd.DataFrame({'classLabel':[0,0,0,1,1,0,0,0],
                   'categorical':[7,8,9,5,7,5,6,4],
                   'numeric1':[7,8,9,5,7,5,6,4],
                   'numeric2':[7,8,9,5,7,5,6,"N.A"]})
columnsNumber = ['numeric1']
XoneColumn = X[columnsNumber]

I use the functionTransformer like:

def extractSpecificColumn(X, columns):
    return X[columns]

pipeline = Pipeline([
    ('features', FeatureUnion([
        ('continuous', Pipeline([
            ('numeric', FunctionTransformer(columnsNumber)),
            ('scale', StandardScaler())
        ]))
    ], n_jobs=1)),
    ('estimator', RandomForestClassifier(n_estimators=50, criterion='entropy', n_jobs=-1))
])

cv.cross_val_score(pipeline, XoneColumn, y, cv=folds, scoring=kappaScore)

This results in: TypeError: 'list' object is not callable when the function transformer is enabled.

edit:

If I instantiate a ColumnExtractor like below no error is returned. But isn't the functionTransformer meant just for simple cases like this one and should just work?

class ColumnExtractor(TransformerMixin):
    def __init__(self, columns):
        self.columns = columns

    def transform(self, X, *_):
        return X[self.columns]

    def fit(self, *_):
        return self

Answer 1

FunctionTransformer is used to "lift" a function to a transformation which I think can help with some data cleaning steps. Imagine you have a mostly numeric array and you want to transform it with a Transformer that that will error out if it gets a nan (like Normalize). You might end up with something like

df.fillna(0, inplace=True)
...
cross_val_score(pipeline, ...)

but maybe you that fillna is only required in one transformation so instead of having the fillna like above, you have

normalize = make_pipeline(
    FunctionTransformer(np.nan_to_num, validate=False),
    Normalize()
)

which ends up normalizing it as you want. Then you can use that snippet in more places without littering your code with .fillna(0)

In your example, you're passing in ['numeric1'] which is a list and not an extractor like the similarly typed df[['numeric1']]. What you may want instead is more like

FunctionTransformer(operator.itemgetter(columns))

but that still wont work because the object that is ultimately passed into the FunctionTransformer will be an np.array and not a DataFrame.

In order to do operations on particular columns of a DataFrame, you may want to use a library like sklearn-pandas which allows you to define particular transformers by column.