Source code for forml.pipeline.payload._convert

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Dataframe conversion tools.
import functools
import itertools
import logging
import typing

import numpy
import pandas
from pandas.core import generic as pdtype

from forml import flow
from forml.pipeline import wrap

LOGGER = logging.getLogger(__name__)

def pandas_read(data: typing.Any, columns: typing.Optional[typing.Sequence[str]] = None) -> pdtype.NDFrame:
    """Pandas converter implementation.

        data: Argument to be converted.
        columns: Optional column names.

        Converted pandas object.

    def from_rows() -> pandas.DataFrame:
        """Helper conversion from list of rows."""
        return pandas.DataFrame(data, columns=columns).infer_objects()

    def from_vector() -> pandas.Series:
        """Helper conversion for a single series."""
        return pandas.Series(data).infer_objects()

    if isinstance(data, pdtype.NDFrame):
        return data
    if isinstance(data, numpy.ndarray):
        return from_vector() if data.ndim == 1 else from_rows()
    if isinstance(data, (tuple, list)):
        return from_vector() if data and not isinstance(data[0], (tuple, list)) else from_rows()
    LOGGER.warning('Unknown NDFrame conversion strategy for %s: %.1024s', type(data), data)
    return data

[docs]def pandas_params( method: typing.Callable[[flow.Actor, pdtype.NDFrame], typing.Any] ) -> typing.Callable[[flow.Actor, typing.Any], typing.Any]: """Decorator for converting the decorated *actor* method input parameters to Pandas format. The parameters will be converted to :class:`pandas:pandas.DataFrame` or :class:`pandas:pandas.Series` depending on the dimensionality. Args: method: Actor method to be decorated expecting input to be in Pandas format. Returns: Decorated method converting its input payload to Pandas. Warning: The conversion is attempted using an internal logic - if unsuccessful, the payload is passed through unchanged emitting a warning. Examples:: class Concat(flow.Actor[pdtype.NDFrame, None, pandas.DataFrame]): @payload.pandas_params def apply(self, *features: pdtype.NDFrame) -> pandas.DataFrame: return pandas.concat(features) Todo: Make the internal conversion logic customizable. """ @functools.wraps(method) def wrapper(*args: typing.Any, **kwargs: typing.Any) -> typing.Any: """Decorating wrapper. Args: *args: Input positional arguments to be converted - first one might be actor (self) in which case it is skipped. **kwargs: Input keyword arguments. Returns: Original output. """ if args: idx = 1 if isinstance(args[0], flow.Actor) else 0 args = itertools.chain(args[:idx], (pandas_read(a) for a in args[idx:])) return method(*args, **{k: pandas_read(v) for k, v in kwargs.items()}) return wrapper
[docs]@wrap.Operator.apply @wrap.Operator.train @wrap.Operator.label @wrap.Actor.apply def ToPandas( # pylint: disable=invalid-name data: typing.Any, *, columns: typing.Optional[typing.Sequence[str]] = None, converter: typing.Callable[[typing.Any, typing.Optional[typing.Sequence[str]]], pdtype.NDFrame] = pandas_read, ) -> pdtype.NDFrame: """ToPandas(data: typing.Any, *, columns: typing.Optional[typing.Sequence[str]] = None, converter: typing.Callable[[typing.Any, typing.Optional[typing.Sequence[str]]], pandas.core.generic.NDFrame] = pandas_read) Simple *1:1* operator that attempts to convert the data on each of apply/train/label segments to Pandas :class:`DataFrame <pandas:pandas.DataFrame>`/:class:`Series <pandas:pandas.Series>`. Args: data: Input data. columns: Optional column names. converter: Optional function to be used for attempting the conversion. It will receive two parameters - the data and the column names (if provided). Warning: The default converter is using an internal logic - if unsuccessful, the payload is passed through unchanged emitting a warning. Returns: Pandas :class:`DataFrame <pandas:pandas.DataFrame>`/:class:`Series <pandas:pandas.Series>`. Examples: >>> SOURCE >>= payload.ToPandas( ... columns=[ for f in SOURCE.extract.train.schema] ... ) """ # pylint: disable=line-too-long # noqa: E501 return converter(data, columns)