import logging
from typing import List, Optional
import numpy as np
import pandas as pd
from ray.data.preprocessor import Preprocessor
from ray.util.annotations import PublicAPI
logger = logging.getLogger(__name__)
[docs]
@PublicAPI(stability="alpha")
class Concatenator(Preprocessor):
"""Combine numeric columns into a column of type
:class:`~ray.air.util.tensor_extensions.pandas.TensorDtype`. Only columns
specified in ``columns`` will be concatenated.
This preprocessor concatenates numeric columns and stores the result in a new
column. The new column contains
:class:`~ray.air.util.tensor_extensions.pandas.TensorArrayElement` objects of
shape :math:`(m,)`, where :math:`m` is the number of columns concatenated.
The :math:`m` concatenated columns are dropped after concatenation.
The preprocessor preserves the order of the columns provided in the ``colummns``
argument and will use that order when calling ``transform()`` and ``transform_batch()``.
Examples:
>>> import numpy as np
>>> import pandas as pd
>>> import ray
>>> from ray.data.preprocessors import Concatenator
:py:class:`Concatenator` combines numeric columns into a column of
:py:class:`~ray.air.util.tensor_extensions.pandas.TensorDtype`.
>>> df = pd.DataFrame({"X0": [0, 3, 1], "X1": [0.5, 0.2, 0.9]})
>>> ds = ray.data.from_pandas(df) # doctest: +SKIP
>>> concatenator = Concatenator(columns=["X0", "X1"])
>>> concatenator.transform(ds).to_pandas() # doctest: +SKIP
concat_out
0 [0.0, 0.5]
1 [3.0, 0.2]
2 [1.0, 0.9]
By default, the created column is called `"concat_out"`, but you can specify
a different name.
>>> concatenator = Concatenator(columns=["X0", "X1"], output_column_name="tensor")
>>> concatenator.transform(ds).to_pandas() # doctest: +SKIP
tensor
0 [0.0, 0.5]
1 [3.0, 0.2]
2 [1.0, 0.9]
>>> concatenator = Concatenator(columns=["X0", "X1"], dtype=np.float32)
>>> concatenator.transform(ds) # doctest: +SKIP
Dataset(num_rows=3, schema={Y: object, concat_out: TensorDtype(shape=(2,), dtype=float32)})
When ``flatten=True``, nested vectors in the columns will be flattened during concatenation:
>>> df = pd.DataFrame({"X0": [[1, 2], [3, 4]], "X1": [0.5, 0.2]})
>>> ds = ray.data.from_pandas(df) # doctest: +SKIP
>>> concatenator = Concatenator(columns=["X0", "X1"], flatten=True)
>>> concatenator.transform(ds).to_pandas() # doctest: +SKIP
concat_out
0 [1.0, 2.0, 0.5]
1 [3.0, 4.0, 0.2]
Args:
columns: A list of columns to concatenate. The provided order of the columns
will be retained during concatenation.
output_column_name: The desired name for the new column.
Defaults to ``"concat_out"``.
dtype: The ``dtype`` to convert the output tensors to. If unspecified,
the ``dtype`` is determined by standard coercion rules.
raise_if_missing: If ``True``, an error is raised if any
of the columns in ``columns`` don't exist.
Defaults to ``False``.
flatten: If ``True``, nested vectors in the columns will be flattened during
concatenation. Defaults to ``False``.
Raises:
ValueError: if `raise_if_missing` is `True` and a column in `columns` or
doesn't exist in the dataset.
""" # noqa: E501
_is_fittable = False
def __init__(
self,
columns: List[str],
output_column_name: str = "concat_out",
dtype: Optional[np.dtype] = None,
raise_if_missing: bool = False,
flatten: bool = False,
):
self.columns = columns
self.output_column_name = output_column_name
self.dtype = dtype
self.raise_if_missing = raise_if_missing
self.flatten = flatten
def _validate(self, df: pd.DataFrame) -> None:
missing_columns = set(self.columns) - set(df)
if missing_columns:
message = (
f"Missing columns specified in '{self.columns}': {missing_columns}"
)
if self.raise_if_missing:
raise ValueError(message)
else:
logger.warning(message)
def _transform_pandas(self, df: pd.DataFrame):
self._validate(df)
if self.flatten:
concatenated = df[self.columns].to_numpy()
concatenated = [
np.concatenate(
[
np.atleast_1d(elem)
if self.dtype is None
else np.atleast_1d(elem).astype(self.dtype)
for elem in row
]
)
for row in concatenated
]
else:
concatenated = df[self.columns].to_numpy(dtype=self.dtype)
df = df.drop(columns=self.columns)
# Use a Pandas Series for column assignment to get more consistent
# behavior across Pandas versions.
df.loc[:, self.output_column_name] = pd.Series(list(concatenated))
return df
def __repr__(self):
default_values = {
"output_column_name": "concat_out",
"columns": None,
"dtype": None,
"raise_if_missing": False,
"flatten": False,
}
non_default_arguments = []
for parameter, default_value in default_values.items():
value = getattr(self, parameter)
if value != default_value:
non_default_arguments.append(f"{parameter}={value}")
return f"{self.__class__.__name__}({', '.join(non_default_arguments)})"