series.py

#!/usr/bin/env python

import pandas as pd

import pandas_ml.skaccessors as skaccessors
import pandas_ml.util as util
from pandas_ml.compat import Appender, cache_readonly
from pandas_ml.core.generic import ModelTransformer, _shared_docs


class ModelSeries(ModelTransformer, pd.Series):
    """
    Wrapper for ``pandas.Series`` to support ``sklearn.preprocessing``
    """

    @property
    def _constructor(self):
        return ModelSeries

    def _call(self, estimator, method_name, *args, **kwargs):
        method = self._check_attr(estimator, method_name)

        # needed for preprocessing
        data = self.values.reshape(-1, 1)
        result = method(data, *args, **kwargs)

        return result

    def _wrap_transform(self, transformed, columns=None):
        """
        Wrapper for transform methods
        """
        if len(transformed.shape) == 2:
            if (util._is_1d_harray(transformed) or util._is_1d_varray(transformed)):
                transformed = transformed.flatten()
            else:
                from pandas_ml.core.frame import ModelFrame
                return ModelFrame(transformed, index=self.index)
        return self._constructor(transformed, index=self.index,
                                 name=self.name)

    @Appender(_shared_docs['estimator_methods'] %
              dict(funcname='transform', returned='returned : transformed result'))
    def transform(self, estimator, *args, **kwargs):
        try:
            transformed = super(ModelSeries, self).transform(estimator, *args, **kwargs)
            return transformed
        except ImportError:
            # raise patsy error
            raise
        except:    # noqa
            return pd.Series.transform(self, estimator, *args, **kwargs)

    @property
    @Appender(_shared_docs['skaccessor'] % dict(module='preprocessing'))
    def preprocessing(self):
        return self._preprocessing

    @property
    @Appender(_shared_docs['skaccessor'] % dict(module='preprocessing'))
    def pp(self):
        return self._preprocessing

    @cache_readonly
    def _preprocessing(self):
        return skaccessors.PreprocessingMethods(self)

    @Appender(pd.Series.to_frame.__doc__)
    def to_frame(self, name=None):
        # ported from pandas 0.16
        from pandas_ml.core.frame import ModelFrame

        if name is None:
            df = ModelFrame(self)
        else:
            df = ModelFrame({name: self})

        return df

    @Appender(pd.core.generic.NDFrame.groupby.__doc__)
    def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True,
                group_keys=True, squeeze=False):
        from pandas_ml.core.groupby import groupby
        if level is None and by is None:
            raise TypeError("You have to supply one of 'by' and 'level'")
        axis = self._get_axis_number(axis)
        return groupby(self, by=by, axis=axis, level=level, as_index=as_index,
                       sort=sort, group_keys=group_keys, squeeze=squeeze)