Normalizer
class atom.data_cleaning.Normalizer(strategy="yeojohnson", device="cpu", engine=None, verbose=0, random_state=None, **kwargs)[source]
Transform the data to follow a Normal/Gaussian distribution.
This transformation is useful for modeling issues related to heteroscedasticity (non-constant variance), or other situations where normality is desired. Missing values are disregarded in fit and maintained in transform. Categorical columns are ignored.
This class can be accessed from atom through the normalize method. Read more in the user guide.
Warning
The quantile strategy performs a non-linear transformation. This may distort linear correlations between variables measured at the same scale but renders variables measured at different scales more directly comparable.
Note
The yeojohnson and boxcox strategies scale the data after
transforming. Use the kwargs to change this behavior.
| Parameters |
strategy: str, default="yeojohnson"
The transforming strategy. Choose from:
device: str, default="cpu"
Device on which to run the estimators. Use any string that
follows the SYCL_DEVICE_FILTER filter selector, e.g.
engine: str or None, default=Nonedevice="gpu" to use the GPU. Read more in the
user guide.
Execution engine to use for estimators.
If None, the default value is used. Choose from:
verbose: int, default=0
Verbosity level of the class. Choose from:
random_state: int or None, default=None
Seed used by the quantile strategy. If None, the random
number generator is the
**kwargsRandomState used by np.random.
Additional keyword arguments for the strategy estimator.
|
| Attributes | {#normalizer-[strategy]}
[strategy]: sklearn transformer
Object with which the data is transformed, e.g.,
feature_names_in_: np.ndarraynormalizer.yeojohnson for the default strategy.
Names of features seen during
n_features_in_: intfit.
Number of features seen during fit.
|
See Also
Applies standard data cleaning steps on a dataset.
Prune outliers from the data.
Scale the data.
Example
>>> 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.plot_distribution(columns=0)
>>> atom.normalize(verbose=2)
Fitting Normalizer...
Normalizing features...
>>> 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 -0.017068 0.464087 0.031104 -0.020222 0.390628 0.620790 0.562136 0.426774 -0.280554 ... 0.251532 0.081524 1.224389 1.206519 1.189835 1.522769 -0.043007 1.378960 0
1 1.182066 0.411242 1.183030 1.200556 0.741209 0.608244 1.100342 1.256472 0.256014 ... 1.119375 1.218096 0.759546 0.244492 0.726989 0.650523 0.424017 -0.164104 0
2 1.105309 1.197684 1.018344 1.106437 -0.552214 -0.652544 -0.230044 0.226950 -1.050816 ... 0.973194 1.037232 0.002307 -0.374986 -0.128679 0.107299 -0.647198 -0.100126 0
3 0.455144 2.077941 0.379512 0.486019 -0.966587 -1.447057 -0.438308 -0.480189 0.226570 ... 0.337722 0.483003 -0.785100 -1.301043 -0.483292 -0.722786 0.676588 -1.783846 0
4 -1.898537 -0.815757 -1.745528 -1.873415 -0.102067 0.599235 0.374346 -0.662103 -2.173761 ... -1.869111 -2.095123 -0.633206 -0.305478 -0.485431 -1.278472 -2.898859 -0.273347 1
.. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
564 0.238929 -1.546154 0.209113 0.257899 0.214334 -0.482480 -0.225132 0.183841 0.996371 ... 0.346743 0.373205 -0.079012 -0.660736 -0.423384 0.029761 0.404215 -1.894769 1
565 -0.115233 0.675396 -0.105672 -0.125511 0.078814 0.213069 0.222118 0.375009 -0.177404 ... 0.194134 0.082260 0.804177 1.061384 0.714032 0.778530 1.315113 0.913117 0
566 0.972621 -0.443853 0.950416 0.971288 0.335466 0.200161 0.804757 1.074782 0.080964 ... 0.880583 0.920102 0.443592 0.144776 0.561298 1.086695 0.527842 0.020173 0
567 1.053489 0.446545 1.084407 1.040647 1.046541 1.237987 1.321388 1.410770 0.650180 ... 0.925288 1.016604 0.452080 0.855688 0.652219 0.657243 -0.735710 -0.260751 0
568 0.366875 -0.289945 0.346701 0.359700 -0.309357 -0.150999 -0.574459 -0.683107 0.375972 ... 0.207028 0.284140 -0.407994 -0.303600 -0.141124 -0.402554 1.196110 -0.638106 1
[569 rows x 31 columns]
>>> atom.plot_distribution(columns=0)
>>> from atom.data_cleaning import Normalizer
>>> from sklearn.datasets import load_breast_cancer
>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)
>>> normalizer = Normalizer(verbose=2)
>>> X = normalizer.fit_transform(X)
Fitting Normalizer...
Normalizing features...
>>> 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 1.134881 -2.678666 1.259822 1.126421 1.504114 2.165938 1.862988 1.848558 1.953067 ... -1.488367 1.810506 1.652210 1.282792 1.942737 1.730182 1.935654 2.197206 1.723624
1 1.619346 -0.264377 1.528723 1.633946 -0.820227 -0.384102 0.291976 0.820609 0.102291 ... -0.288382 1.430616 1.610022 -0.325080 -0.296580 0.070746 1.101594 -0.121997 0.537179
2 1.464796 0.547806 1.454664 1.461645 0.963977 1.163977 1.403673 1.683104 0.985668 ... 0.071406 1.321941 1.425307 0.580301 1.209701 1.005512 1.722744 1.218181 0.453955
3 -0.759262 0.357721 -0.514886 -0.836238 2.781494 2.197843 1.642391 1.423004 2.360528 ... 0.228089 -0.039480 -0.436860 2.857821 2.282276 1.675087 1.862378 3.250202 2.517606
4 1.571260 -1.233520 1.583340 1.595120 0.343932 0.762392 1.407479 1.410929 0.090964 ... -1.637882 1.316582 1.309486 0.284367 -0.131829 0.817474 0.807077 -0.943554 -0.279402
.. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
564 1.781795 0.785604 1.746492 1.823030 1.052829 0.460810 1.653784 1.783067 -0.232645 ... 0.212151 1.547961 1.657442 0.438013 -0.077871 0.859079 1.503734 -1.721528 -0.751459
565 1.543335 1.845150 1.485601 1.545430 0.168014 0.207602 0.984746 1.320730 -0.129120 ... 1.832201 1.365939 1.443167 -0.667317 -0.245277 0.480804 0.810995 -0.480093 -1.210527
566 0.828589 1.817618 0.811329 0.835270 -0.835509 0.183969 0.375105 0.396882 -0.808189 ... 1.320625 0.786129 0.796192 -0.799337 0.626487 0.566826 0.526136 -1.301164 -0.170872
567 1.624440 2.016299 1.702747 1.551036 1.468642 2.162820 1.994466 1.884414 1.899087 ... 1.968949 1.810506 1.513198 1.387135 2.284642 2.136932 1.931990 1.744693 1.850944
568 -2.699432 1.203224 -2.827766 -2.703256 -3.834325 -1.481409 -1.658319 -1.845392 -0.821560 ... 0.810681 -2.231436 -2.149403 -2.064647 -1.731936 -1.819966 -2.131070 0.103122 -0.820663
[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_params | Get parameters for this estimator. |
| inverse_transform | Apply the inverse transformation to the data. |
| set_output | Set output container. |
| set_params | Set the parameters of this estimator. |
| transform | Apply the transformations to the data. |
method fit(X, y=None)[source]
Fit to data.
method fit_transform(X=None, y=None, **fit_params)[source]
Fit to data, then transform it.
method get_feature_names_out(input_features=None)[source]
Get output feature names for transformation.
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.
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.
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]
Apply the transformations to the data.