CODEWITHSUNDEEP
pythonnumpyscikit-learnconverterspipeline
Georg Heiler
Georg Heiler

Reputation: 17676

pandas to numpy array for sklearn pipeline

I have a transformer which calculates the percentage of the values per group. Initially, pandas was used because I started with pandas and colnames are nicer to handle. However, now I need to integrate into sklearn-pipeline.

How can I convert my Transformer to support numpy arrays from a sklearn pipeline instead of pandas data frames? The point is that self.colname cant be used for numpy arrays and I think the grouping needs to be performed differently.

How to implement persistence of such a transformer as these weights need to be loadable from disk in order to deploy such a Transformer in a pipeline.

class PercentageTransformer(TransformerMixin):
    def __init__(self, colname,typePercentage='totalTarget', _target='TARGET', _dropOriginal=True):
        self.colname = colname
        self._target = _target
        self._dropOriginal = _dropOriginal
        self.typePercentage = typePercentage

    def fit(self, X, y, *_):
        original = pd.concat([y,X], axis=1)
        grouped = original.groupby([self.colname, self._target]).size()
        if self.typePercentage == 'totalTarget':
            df = grouped / original[self._target].sum()
        else:
            df = (grouped / grouped.groupby(level=0).sum())

        if self.typePercentage == 'totalTarget':
            nameCol = "pre_" + self.colname
        else:
            nameCol = "pre2_" + self.colname
        self.nameCol = nameCol
        grouped = df.reset_index(name=nameCol)
        groupedOnly = grouped[grouped[self._target] == 1]
        groupedOnly = groupedOnly.drop(self._target, 1)

        self.result =  groupedOnly
        return self

    def transform(self, dataF):
        mergedThing = pd.merge(dataF, self.result, on=self.colname, how='left')
        mergedThing.loc[(mergedThing[self.nameCol].isnull()), self.nameCol] = 0
        if self._dropOriginal:
            mergedThing = mergedThing.drop(self.colname, 1)
        return mergedThing

It would be used in a pipeline like this:

pipeline =  Pipeline([
    ('features', FeatureUnion([
        ('continuous', Pipeline([
            ('extract', ColumnExtractor(CONTINUOUS_FIELDS)),
        ])),
        ('factors', Pipeline([
            ('extract', ColumnExtractor(FACTOR_FIELDS)),
            # using labelencoding and all bias
            ('bias',  PercentageAllTransformer(FACTOR_FIELDS, _dropOriginal=True, typePercentage='totalTarget')),
        ]))
    ], n_jobs=-1)),
    ('estimator', estimator)
])

The pipeline will be fitted with X and y where both are data frames. I am unsure of X.as_matrix would help.

Upvotes: 1

Views: 2461

Answers (1)

yikes.gov
yikes.gov

Reputation: 165

  • Converting Things Back and Forth

Pandas has a .to_records() method, and, as you mentioned, a .as_matrix() method. The .to_records() method will actually keep your column names for you. Numpy does support named columns in arrays. See here.

  • Persistence

Pandas has a pandas.to_pickle(obj, filename) method, which takes a pandas object and pickles it. There is a corresponding pandas.read_pickle(filename) method.

Numpy has a save and load function as well.

Upvotes: 3

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