Scaler

---

class atom.data_cleaning.Scaler(strategy="standard", include_binary=False, device="cpu", engine=None, verbose=0, **kwargs)[source]

Scale the data.

Apply one of sklearn's scaling strategies. Categorical columns are ignored.

This class can be accessed from atom through the scale method. Read more in the user guide.

Parameters

strategy: str, default="standard" Strategy with which to scale the data. Choose from: "standard": Remove mean and scale to unit variance. "minmax": Scale features to a given range. "maxabs": Scale features by their maximum absolute value. "robust": Scale using statistics that are robust to outliers. include_binary: bool, default=False Whether to scale binary columns (only 0s and 1s). device: str, default="cpu" Device on which to run the estimators. Use any string that follows the SYCL_DEVICE_FILTER filter selector, e.g. device="gpu" to use the GPU. Read more in the user guide. engine: str or None, default=None Execution engine to use for estimators. If None, the default value is used. Choose from: "sklearn" (default) "cuml" verbose: int, default=0 Verbosity level of the class. Choose from: 0 to not print anything. 1 to print basic information. **kwargs Additional keyword arguments for the strategy estimator.

Attributes

{#scaler-[strategy]} [strategy]: sklearn transformer Object with which the data is scaled, e.g., scaler.standard for the default strategy. feature_names_in_: np.ndarray Names of features seen during fit. n_features_in_: int Number of features seen during fit.

See Also

Balancer

Balance the number of samples per class in the target column.

Normalizer

Transform the data to follow a Normal/Gaussian distribution.

Scaler

Scale the data.

Example

atomstand-alone

>>> from atom import ATOMClassifier
>>> from sklearn.datasets import load_breast_cancer

>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)

>>> atom = ATOMClassifier(X, y, random_state=1)
>>> print(atom.dataset)

     mean radius  mean texture  mean perimeter  mean area  mean smoothness  mean compactness  mean concavity  mean concave points  mean symmetry  ...  worst perimeter  worst area  worst smoothness  worst compactness  worst concavity  worst concave points  worst symmetry  worst fractal dimension  target
0          13.48         20.82           88.40      559.2          0.10160           0.12550         0.10630              0.05439         0.1720  ...           107.30       740.4            0.1610            0.42250           0.5030               0.22580          0.2807                  0.10710       0
1          18.31         20.58          120.80     1052.0          0.10680           0.12480         0.15690              0.09451         0.1860  ...           142.20      1493.0            0.1492            0.25360           0.3759               0.15100          0.3074                  0.07863       0
2          17.93         24.48          115.20      998.9          0.08855           0.07027         0.05699              0.04744         0.1538  ...           135.10      1320.0            0.1315            0.18060           0.2080               0.11360          0.2504                  0.07948       0
3          15.13         29.81           96.71      719.5          0.08320           0.04605         0.04686              0.02739         0.1852  ...           110.10       931.4            0.1148            0.09866           0.1547               0.06575          0.3233                  0.06165       0
4           8.95         15.76           58.74      245.2          0.09462           0.12430         0.09263              0.02308         0.1305  ...            63.34       270.0            0.1179            0.18790           0.1544               0.03846          0.1652                  0.07722       1
..           ...           ...             ...        ...              ...               ...             ...                  ...            ...  ...              ...         ...               ...                ...              ...                   ...             ...                      ...     ...
564        14.34         13.47           92.51      641.2          0.09906           0.07624         0.05724              0.04603         0.2075  ...           110.40       873.2            0.1297            0.15250           0.1632               0.10870          0.3062                  0.06072       1
565        13.17         21.81           85.42      531.5          0.09714           0.10470         0.08259              0.05252         0.1746  ...           105.50       740.7            0.1503            0.39040           0.3728               0.16070          0.3693                  0.09618       0
566        17.30         17.08          113.00      928.2          0.10080           0.10410         0.12660              0.08353         0.1813  ...           130.90      1222.0            0.1416            0.24050           0.3378               0.18570          0.3138                  0.08113       0
567        17.68         20.74          117.40      963.7          0.11150           0.16650         0.18550              0.10540         0.1971  ...           132.90      1302.0            0.1418            0.34980           0.3583               0.15150          0.2463                  0.07738       0
568        14.80         17.66           95.88      674.8          0.09179           0.08890         0.04069              0.02260         0.1893  ...           105.90       829.5            0.1226            0.18810           0.2060               0.08308          0.3600                  0.07285       1

[569 rows x 31 columns]

>>> atom.scale(verbose=2)

Fitting Scaler...
Scaling features...

>>> # Note the reduced number of rows
>>> print(atom.dataset)

     mean radius  mean texture  mean perimeter  mean area  mean smoothness  mean compactness  mean concavity  mean concave points  mean symmetry  ...  worst perimeter  worst area  worst smoothness  worst compactness  worst concavity  worst concave points  worst symmetry  worst fractal dimension  target
0      -0.181875      0.356669       -0.147122  -0.270991         0.340268          0.381628        0.214571             0.125567      -0.345050  ...         0.000933   -0.246244          1.240292           1.077359         1.116229              1.667157       -0.162964                 1.326816       0
1       1.162216      0.300578        1.159704   1.097856         0.707625          0.368288        0.852572             1.148598       0.172744  ...         1.025723    1.042996          0.719898          -0.011475         0.500961              0.537309        0.280594                -0.308640       0
2       1.056470      1.212060        0.933833   0.950360        -0.581659         -0.670877       -0.407166            -0.051653      -1.018183  ...         0.817241    0.746639         -0.060694          -0.482078        -0.311813             -0.027615       -0.666328                -0.259812       0
3       0.277287      2.457753        0.188054   0.174273        -0.959614         -1.132432       -0.534892            -0.562913       0.143156  ...         0.083151    0.080948         -0.797185          -1.010314        -0.569828             -0.750385        0.544735                -1.284055       0
4      -1.442482     -0.825921       -1.343434  -1.143186        -0.152840          0.358760        0.042209            -0.672815      -1.879941  ...        -1.289891   -1.052061         -0.660471          -0.435018        -0.571280             -1.162598       -2.081728                -0.389638       1
..           ...           ...             ...        ...              ...               ...             ...                  ...            ...  ...              ...         ...               ...                ...              ...                   ...             ...                      ...     ...
564     0.057446     -1.361124        0.018651  -0.043220         0.160827         -0.557108       -0.404013            -0.087607       0.967929  ...         0.091960   -0.018751         -0.140077          -0.663228        -0.528681             -0.101629        0.260659                -1.337478       1
565    -0.268141      0.588045       -0.267318  -0.347933         0.025188         -0.014753       -0.084382             0.077883      -0.248889  ...        -0.051921   -0.245730          0.768409           0.870422         0.485954              0.683827        1.308918                 0.699518       0
566     0.881154     -0.517419        0.845098   0.753978         0.283751         -0.026187        0.470528             0.868616      -0.001087  ...         0.693914    0.578760          0.384728          -0.095926         0.316526              1.061450        0.386915                -0.165028       0
567     0.986900      0.337972        1.022568   0.852586         1.039660          1.162956        1.213182             1.426285       0.583281  ...         0.752641    0.715804          0.393548           0.608690         0.415763              0.544861       -0.734440                -0.380446       0
568     0.185455     -0.381865        0.154577   0.050111        -0.352767         -0.315850       -0.612688            -0.685055       0.294796  ...        -0.040176   -0.093611         -0.453195          -0.433728        -0.321494             -0.488617        1.154420                -0.640672       1

[569 rows x 31 columns]

>>> from atom.data_cleaning import Scaler
>>> from sklearn.datasets import load_breast_cancer

>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)

>>> scaler = Scaler(verbose=2)
>>> X = scaler.fit_transform(X)

Fitting Scaler...
Scaling features...

>>> # Note the reduced number of rows
>>> print(X)

     mean radius  mean texture  mean perimeter  mean area  mean smoothness  mean compactness  mean concavity  mean concave points  mean symmetry  ...  worst texture  worst perimeter  worst area  worst smoothness  worst compactness  worst concavity  worst concave points  worst symmetry  worst fractal dimension
0       1.097064     -2.073335        1.269934   0.984375         1.568466          3.283515        2.652874             2.532475       2.217515  ...      -1.359293         2.303601    2.001237          1.307686           2.616665         2.109526              2.296076        2.750622                 1.937015
1       1.829821     -0.353632        1.685955   1.908708        -0.826962         -0.487072       -0.023846             0.548144       0.001392  ...      -0.369203         1.535126    1.890489         -0.375612          -0.430444        -0.146749              1.087084       -0.243890                 0.281190
2       1.579888      0.456187        1.566503   1.558884         0.942210          1.052926        1.363478             2.037231       0.939685  ...      -0.023974         1.347475    1.456285          0.527407           1.082932         0.854974              1.955000        1.152255                 0.201391
3      -0.768909      0.253732       -0.592687  -0.764464         3.283553          3.402909        1.915897             1.451707       2.867383  ...       0.133984        -0.249939   -0.550021          3.394275           3.893397         1.989588              2.175786        6.046041                 4.935010
4       1.750297     -1.151816        1.776573   1.826229         0.280372          0.539340        1.371011             1.428493      -0.009560  ...      -1.466770         1.338539    1.220724          0.220556          -0.313395         0.613179              0.729259       -0.868353                -0.397100
..           ...           ...             ...        ...              ...               ...             ...                  ...            ...  ...            ...              ...         ...               ...                ...              ...                   ...             ...                      ...
564     2.110995      0.721473        2.060786   2.343856         1.041842          0.219060        1.947285             2.320965      -0.312589  ...       0.117700         1.752563    2.015301          0.378365          -0.273318         0.664512              1.629151       -1.360158                -0.709091
565     1.704854      2.085134        1.615931   1.723842         0.102458         -0.017833        0.693043             1.263669      -0.217664  ...       2.047399         1.421940    1.494959         -0.691230          -0.394820         0.236573              0.733827       -0.531855                -0.973978
566     0.702284      2.045574        0.672676   0.577953        -0.840484         -0.038680        0.046588             0.105777      -0.809117  ...       1.374854         0.579001    0.427906         -0.809587           0.350735         0.326767              0.414069       -1.104549                -0.318409
567     1.838341      2.336457        1.982524   1.735218         1.525767          3.272144        3.296944             2.658866       2.137194  ...       2.237926         2.303601    1.653171          1.430427           3.904848         3.197605              2.289985        1.919083                 2.219635
568    -1.808401      1.221792       -1.814389  -1.347789        -3.112085         -1.150752       -1.114873            -1.261820      -0.820070  ...       0.764190        -1.432735   -1.075813         -1.859019          -1.207552        -1.305831             -1.745063       -0.048138                -0.751207

[569 rows x 30 columns]

Methods

fit	Fit to data.
fit_transform	Fit to data, then transform it.
get_feature_names_out	Get output feature names for transformation.
get_metadata_routing	Get metadata routing of this object.
get_params	Get parameters for this estimator.
inverse_transform	Apply the inverse transformation to the data.
set_fit_request	Request metadata passed to the fit method.
set_output	Set output container.
set_params	Set the parameters of this estimator.
transform	Perform standardization by centering and scaling.

method fit(X, y=None, sample_weight=None)[source]

Fit to data.

Parameters	X: dataframe-like Feature set with shape=(n_samples, n_features). y: sequence, dataframe-like or None, default=None Do nothing. Implemented for continuity of the API. sample_weight: sequence or None, default=None Sample weights with shape=(n_samples,).
Returns	Self Estimator instance.

method fit_transform(X=None, y=None, **fit_params)[source]

Fit to data, then transform it.

Parameters	X: dataframe-like or None, default=None Feature set with shape=(n_samples, n_features). If None, X is ignored. y: sequence, dataframe-like or None, default=None Target column(s) corresponding to X. If None, y is ignored. **fit_params Additional keyword arguments for the fit method.
Returns	dataframe Transformed feature set. Only returned if provided. series or dataframe Transformed target column. Only returned if provided.

method get_feature_names_out(input_features=None)[source]

Get output feature names for transformation.

Parameters

input_features : array-like of str or None, default=None Input features. If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"]. If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns

feature_names_out : ndarray of str objects Same as input features.

method get_metadata_routing()[source]

Get metadata routing of this object.

Returns

routing : MetadataRequest A :class:~sklearn.utils.metadata_routing.MetadataRequest encapsulating routing information.

method get_params(deep=True)[source]

Get parameters for this estimator.

Parameters	deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns	params : dict Parameter names mapped to their values.

method inverse_transform(X, y=None)[source]

Apply the inverse transformation to the data.

Parameters	X: dataframe-like Feature set with shape=(n_samples, n_features). y: sequence, dataframe-like or None, default=None Do nothing. Implemented for continuity of the API.
Returns	dataframe Scaled dataframe.

method set_fit_request(sample_weight="$UNCHANGED$")[source]

Request metadata passed to the fit method.

Parameters	sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight parameter in fit.
Returns	self : object The updated object.

method set_output(transform=None)[source]

Set output container.

See sklearn's user guide on how to use the set_output API. See here a description of the choices.

Parameters	transform: str or None, default=None Configure the output of the transform, fit_transform, and inverse_transform method. If None, the configuration is not changed. Choose from: "numpy" "pandas" (default) "pandas-pyarrow" "polars" "polars-lazy" "pyarrow" "modin" "dask" "pyspark" "pyspark-pandas"
Returns	Self Estimator instance.

method set_params(**params)[source]

Set the parameters of this estimator.

Parameters	**params : dict Estimator parameters.
Returns	self : estimator instance Estimator instance.

method transform(X, y=None)[source]

Perform standardization by centering and scaling.

Parameters	X: dataframe-like Feature set with shape=(n_samples, n_features). y: sequence, dataframe-like or None, default=None Do nothing. Implemented for continuity of the API.
Returns	dataframe Scaled dataframe.