&`i~ddlmZmZddlZddlZddlmZddl m Z e dGddeZ dS) )ListOptionalN) Preprocessor) PublicAPIalpha) stabilityceZdZdZddddZdZ ddd d eed eeeffd Z d e j fdZ dZ xZS) 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"``. cRtj|dSNaxis)npabssumcolss u/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/data/preprocessors/normalizer.pyzNormalizer.\s26$<<+++33cxtjtj|ddS)Nr r)rsqrtpowerrrs rrzNormalizer.]s-2728D!#4#4#8#8a#8#@#@AArcHtjt|dSr )rmaxrrs rrzNormalizer.^sBF3t991555r)l1l2rFrN)output_columnscolumnsr ct||_||_||jvr,t d|d|jtj|||_ dS)NzNorm z( is not supported.Supported values are: ) super__init__r!norm _norm_fns ValueErrorkeysr#_derive_and_validate_output_columnsr )selfr!r%r __class__s rr$zNormalizer.__init__cs   t~ % %AAA)-)<)<)>)>AA  +N ^  rdfc|jdd|jf}|j|j|}||d||j<|S)Nrr)locr!r&r%divr )r*r,r! column_normss r_transform_pandaszNormalizer._transform_pandasxsR&DL)0t~di099 ")++l+"C"C4  rcP|jjd|jd|jd|jdS)Nz (columns=z, norm=z, output_columns=))r+__name__r!r%r )r*s r__repr__zNormalizer.__repr__sK~& 7 7 7 7I 7 7"1 7 7 7 r)r)r4 __module__ __qualname____doc__r& _is_fittablerstrrr$pd DataFramer1r5 __classcell__)r+s@rr r sMM`43AA55I L  /3    c !c+       *BL       rr ) typingrrnumpyrpandasr;ray.data.preprocessorrray.util.annotationsrr rrrDs!!!!!!!!......****** Wy y y y y y y y y y r