from typing import Any, Dict, List, Optional
import numpy as np
import pandas as pd
from ray.data.preprocessor import SerializablePreprocessorBase
from ray.data.preprocessors.utils import _Computed, _PublicField, migrate_private_fields
from ray.data.preprocessors.version_support import SerializablePreprocessor
from ray.util.annotations import PublicAPI
[docs]
@PublicAPI(stability="alpha")
@SerializablePreprocessor(version=1, identifier="io.ray.preprocessors.normalizer")
class Normalizer(SerializablePreprocessorBase):
r"""Scales each sample to have unit norm.
This preprocessor works by dividing each sample (i.e., row) by the sample's norm.
The general formula is given by
.. math::
s' = \frac{s}{\lVert s \rVert_p}
where :math:`s` is the sample, :math:`s'` is the transformed sample,
:math:\lVert s \rVert`, and :math:`p` is the norm type.
The following norms are supported:
* `"l1"` (:math:`L^1`): Sum of the absolute values.
* `"l2"` (:math:`L^2`): Square root of the sum of the squared values.
* `"max"` (:math:`L^\infty`): Maximum value.
Examples:
>>> import pandas as pd
>>> import ray
>>> from ray.data.preprocessors import Normalizer
>>>
>>> df = pd.DataFrame({"X1": [1, 1], "X2": [1, 0], "X3": [0, 1]})
>>> ds = ray.data.from_pandas(df) # doctest: +SKIP
>>> ds.to_pandas() # doctest: +SKIP
X1 X2 X3
0 1 1 0
1 1 0 1
The :math:`L^2`-norm of the first sample is :math:`\sqrt{2}`, and the
:math:`L^2`-norm of the second sample is :math:`1`.
>>> preprocessor = Normalizer(columns=["X1", "X2"])
>>> preprocessor.fit_transform(ds).to_pandas() # doctest: +SKIP
X1 X2 X3
0 0.707107 0.707107 0
1 1.000000 0.000000 1
The :math:`L^1`-norm of the first sample is :math:`2`, and the
:math:`L^1`-norm of the second sample is :math:`1`.
>>> preprocessor = Normalizer(columns=["X1", "X2"], norm="l1")
>>> preprocessor.fit_transform(ds).to_pandas() # doctest: +SKIP
X1 X2 X3
0 0.5 0.5 0
1 1.0 0.0 1
The :math:`L^\infty`-norm of the both samples is :math:`1`.
>>> preprocessor = Normalizer(columns=["X1", "X2"], norm="max")
>>> preprocessor.fit_transform(ds).to_pandas() # doctest: +SKIP
X1 X2 X3
0 1.0 1.0 0
1 1.0 0.0 1
:class:`Normalizer` can also be used in append mode by providing the
name of the output_columns that should hold the normalized values.
>>> preprocessor = Normalizer(columns=["X1", "X2"], output_columns=["X1_normalized", "X2_normalized"])
>>> preprocessor.fit_transform(ds).to_pandas() # doctest: +SKIP
X1 X2 X3 X1_normalized X2_normalized
0 1 1 0 0.707107 0.707107
1 1 0 1 1.000000 0.000000
Args:
columns: The columns to scale. For each row, these colmumns are scaled to
unit-norm.
norm: The norm to use. The supported values are ``"l1"``, ``"l2"``, or
``"max"``. Defaults to ``"l2"``.
output_columns: The names of the transformed columns. If None, the transformed
columns will be the same as the input columns. If not None, the length of
``output_columns`` must match the length of ``columns``, othwerwise an error
will be raised.
Raises:
ValueError: if ``norm`` is not ``"l1"``, ``"l2"``, or ``"max"``.
"""
_norm_fns = {
"l1": lambda cols: np.abs(cols).sum(axis=1),
"l2": lambda cols: np.sqrt(np.power(cols, 2).sum(axis=1)),
"max": lambda cols: np.max(abs(cols), axis=1),
}
_is_fittable = False
def __init__(
self,
columns: List[str],
norm: str = "l2",
*,
output_columns: Optional[List[str]] = None,
):
super().__init__()
self._columns = columns
self._norm = norm
if norm not in self._norm_fns:
raise ValueError(
f"Norm {norm} is not supported."
f"Supported values are: {self._norm_fns.keys()}"
)
self._output_columns = (
SerializablePreprocessorBase._derive_and_validate_output_columns(
columns, output_columns
)
)
@property
def columns(self) -> List[str]:
return self._columns
@property
def norm(self) -> str:
return self._norm
@property
def output_columns(self) -> List[str]:
return self._output_columns
def _transform_pandas(self, df: pd.DataFrame):
columns = df.loc[:, self._columns]
column_norms = self._norm_fns[self._norm](columns)
df[self._output_columns] = columns.div(column_norms, axis=0)
return df
def __repr__(self):
return (
f"{self.__class__.__name__}(columns={self._columns!r}, "
f"norm={self._norm!r}, "
f"output_columns={self._output_columns!r})"
)
def _get_serializable_fields(self) -> Dict[str, Any]:
return {
"columns": self._columns,
"norm": self._norm,
"output_columns": self._output_columns,
}
def _set_serializable_fields(self, fields: Dict[str, Any], version: int):
# required fields
self._columns = fields["columns"]
self._norm = fields["norm"]
self._output_columns = fields["output_columns"]
def __setstate__(self, state: Dict[str, Any]) -> None:
super().__setstate__(state)
migrate_private_fields(
self,
fields={
"_columns": _PublicField(public_field="columns"),
"_norm": _PublicField(public_field="norm", default="l2"),
"_output_columns": _PublicField(
public_field="output_columns",
default=_Computed(lambda obj: obj._columns),
),
},
)