o ci@sVddlmZmZddlZddlZddlmZddl m Z e ddGdddeZ dS) )ListOptionalN) Preprocessor) PublicAPIalpha) stabilityc@sleZdZdZdddddddZdZ dd d d eed eeefd dZ de j fddZ ddZ d S) Normalizera 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"``. cCst|jddSNaxis)npabssumcolsrU/home/ubuntu/.local/lib/python3.10/site-packages/ray/data/preprocessors/normalizer.py\zNormalizer.cCstt|djddS)Nr r )r sqrtpowerrrrrrr]scCstjt|ddSr )r maxrrrrrr^r)l1l2rFrN)output_columnscolumnsrcCsB||_||_||jvrtd|d|jt|||_dS)NzNorm z( is not supported.Supported values are: )rnorm _norm_fns ValueErrorkeysr#_derive_and_validate_output_columnsr)selfrrrrrr__init__cs  zNormalizer.__init__dfcCs<|jdd|jf}|j|j|}|j|dd||j<|S)Nrr )locrrrdivr)r#r%r column_normsrrr_transform_pandaswszNormalizer._transform_pandascCs&|jjd|jd|jd|jdS)Nz (columns=z, norm=z, output_columns=)) __class____name__rrr)r#rrr__repr__~s zNormalizer.__repr__)r)r, __module__ __qualname____doc__r _is_fittablerstrrr$pd DataFramer)r-rrrrr s"P   r) typingrrnumpyr pandasr3ray.data.preprocessorrray.util.annotationsrrrrrrs