FeatureSelector

class atom.feature_engineering.FeatureSelector(strategy=None, solver=None, n_features=None, min_repeated=2, max_repeated=1.0, max_correlation=1.0, n_jobs=1, device="cpu", engine=None, verbose=0, random_state=None, **kwargs)[source]

Reduce the number of features in the data.

Apply feature selection or dimensionality reduction, either to improve the estimators' accuracy or to boost their performance on very high-dimensional datasets. Additionally, remove multicollinear and low-variance features.

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

Warning

Ties between features with equal scores are broken in an unspecified way.
For strategy="rfecv", the n_features parameter is the minimum number of features to select, not the actual number of features that the transformer returns. It may very well be that it returns more!

Info

The "sklearnex" and "cuml" engines are only supported for strategy="pca" with dense datasets.
If strategy="pca" and the data is dense and unscaled, it's scaled to mean=0 and std=1 before fitting the PCA transformer.
If strategy="pca" and the provided data is sparse, the used estimator is TruncatedSVD, which works more efficiently with sparse matrices.

Tip

Use the plot_pca and plot_components methods to examine the results after using strategy="pca".
Use the plot_rfecv method to examine the results after using strategy="rfecv".
Use the plot_feature_importance method to examine how much a specific feature contributes to the final predictions. If the model doesn't have a feature_importances_ attribute, use plot_permutation_importance instead.

Parameters

strategy: str or None, default=None

Feature selection strategy to use. Choose from:

None: Do not perform any feature selection strategy.
"univariate": Univariate statistical F-test.
"pca": Principal Component Analysis.
"sfm": Select best features according to a model.
"sfs": Sequential Feature Selection.
"rfe": Recursive Feature Elimination.
"rfecv": RFE with cross-validated selection.
"pso": Particle Swarm Optimization.
"hho": Harris Hawks Optimization.
"gwo": Grey Wolf Optimization.
"dfo": Dragonfly Optimization.
"go": Genetic Optimization.

solver: str, func, predictor or None, default=None

Solver/estimator to use for the feature selection strategy. See the corresponding documentation for an extended description of the choices. If None, the default value is used (only if strategy="pca"). Choose from:

If strategy="univariate":
- "f_classif"
- "f_regression"
- "mutual_info_classif"
- "mutual_info_regression"
- "chi2"
- Any function with signature func(X, y) -> tuple[scores, p-values].
If strategy="pca":
- If data is dense:
  - If engine="sklearn":
    - "auto" (default)
    - "full"
    - "arpack"
    - "randomized"
  - If engine="sklearnex":
    - "full" (default)
  - If engine="cuml":
    - "full" (default)
    - "jacobi"
- If data is sparse:
  - "randomized" (default)
  - "arpack"
for the remaining strategies:
The base estimator. For sfm, rfe and rfecv, it should have either a feature_importances_ or coef_ attribute after fitting. You can use one of the predefined models. Add _class or _reg after the model's name to specify a classification or regression task, e.g., solver="LGB_reg" (not necessary if called from atom). No default option.

n_features: int, float or None, default=None

Number of features to select.

If None: Select all features.
If <1: Fraction of the total features to select.
If >=1: Number of features to select.

If strategy="sfm" and the threshold parameter is not specified, the threshold is automatically set to -inf to select n_features number of features.

If strategy="rfecv", n_features is the minimum number of features to select.

This parameter is ignored if any of the following strategies is selected: pso, hho, gwo, dfo, go.

min_repeated: int, float or None, default=2

Remove categorical features if there isn't any repeated value in at least min_repeated rows. The default is to keep all features with non-maximum variance, i.e., remove the features which number of unique values is equal to the number of rows (usually the case for names, IDs, etc...).

If None: No check for minimum repetition.
If >1: Minimum repetition number.
If <=1: Minimum repetition fraction.

max_repeated: int, float or None, default=1.0

Remove categorical features with the same value in at least max_repeated rows. The default is to keep all features with non-zero variance, i.e., remove the features that have the same value in all samples.

If None: No check for maximum repetition.
If >1: Maximum number of repeated occurences.
If <=1: Maximum fraction of repeated occurences.

max_correlation: float or None, default=1.0

Minimum absolute Pearson correlation to identify correlated features. For each group, it removes all except the feature with the highest correlation to y (if provided, else it removes all but the first). The default value removes equal columns. If None, skip this step.

n_jobs: int, default=1

Number of cores to use for parallel processing.

If >0: Number of cores to use.
If -1: Use all available cores.
If <-1: Use number of cores - 1 + n_jobs.

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.
2 to print detailed information.

random_state: int or None, default=None

Seed used by the random number generator. If None, the random number generator is the RandomState used by np.random.

**kwargs

Any extra keyword argument for the strategy estimator. See the corresponding documentation for the available options.

Attributes

collinear_: pd.DataFrame

Information on the removed collinear features. Columns include:

drop: Name of the dropped feature.
corr_feature: Names of the correlated features.
corr_value: Corresponding correlation coefficients.

[strategy]_: sklearn transformer

Object used to transform the data, e.g., fs.pca for the pca strategy.

feature_names_in_: np.ndarray

Names of features seen during fit.

n_features_in_: int

Number of features seen during fit.

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)
>>> atom.feature_selection(strategy="pca", n_features=12, verbose=2)

Fitting FeatureSelector...
Performing feature selection ...
 --> Applying Principal Component Analysis...
   --> Scaling features...
   --> Keeping 12 components.
   --> Explained variance ratio: 0.971


>>> # Note that the column names changed
>>> print(atom.dataset)

         pca0      pca1      pca2      pca3      pca4      pca5      pca6      pca7      pca8      pca9     pca10     pca11  target
0    4.948620 -3.211108  2.813983 -0.261623  0.662504  0.936950 -1.313074 -1.081034  0.183677 -0.642439 -0.958280 -0.694207       0
1   -2.502149  0.932102 -0.895617 -1.858217  0.880730 -0.382548  1.100790  0.968928 -0.732717  0.286001  0.244840  0.416763       1
2   -2.100882  1.231940  0.369006  1.182547  0.391909  1.736365  0.241540  1.028575  0.405305 -0.192192 -0.242827  0.557610       1
3    0.578704  0.584978 -0.309733  0.976049  0.234536 -0.909241 -0.646571  0.550037 -0.113273  0.082274  0.216308 -0.754310       0
4   -3.276672 -1.035308 -1.652544  0.431873  0.465152  0.398455  0.067745  0.154959  0.279904 -0.290049  0.077331  0.217252       1
..        ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...     ...
564 -2.260973  1.561488 -0.020353  0.305708  0.625720 -1.525210 -0.817414 -0.238004  0.062477 -0.267512 -0.077557 -0.328817       1
565 -1.405854  0.363970  2.123404  0.810231  2.482462  0.637570  0.796672  0.323181 -0.377214 -0.044153  0.197700 -0.263923       1
566 -4.144479 -1.360866  0.549383 -0.769523  0.146979  1.059392  0.735636 -0.428059  0.130815  0.439454 -0.255789  0.127913       1
567 -4.570773 -0.772144 -0.585513  0.652783  0.201551  0.125336  0.565221  0.726564  0.173535  0.099568  0.059806  0.483699       1
568  3.328853  2.664665  6.591582 -0.123192  3.077119 -4.408566 -3.108804 -4.132658 -1.858832  3.522249 -1.427187 -0.948496       0

[569 rows x 13 columns]

>>> from atom.feature_engineering import FeatureSelector
>>> from sklearn.datasets import load_breast_cancer

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

>>> fs = FeatureSelector(strategy="pca", n_features=12, verbose=2)
>>> X = fs.fit_transform(X)

Fitting FeatureSelector...
Performing feature selection ...
 --> Applying Principal Component Analysis...
   --> Scaling features...
   --> Keeping 12 components.
   --> Explained variance ratio: 0.97


>>> # Note that the column names changed
>>> print(X)

          pca0       pca1      pca2      pca3      pca4      pca5      pca6      pca7      pca8      pca9     pca10     pca11
0     9.192837   1.948583 -1.123166  3.633731 -1.195110  1.411424  2.159370 -0.398407 -0.157118 -0.877402  0.262955 -0.859014
1     2.387802  -3.768172 -0.529293  1.118264  0.621775  0.028656  0.013358  0.240988 -0.711905  1.106995  0.813120  0.157923
2     5.733896  -1.075174 -0.551748  0.912083 -0.177086  0.541452 -0.668166  0.097374  0.024066  0.454275 -0.605604  0.124387
3     7.122953  10.275589 -3.232790  0.152547 -2.960878  3.053422  1.429911  1.059565 -1.405440 -1.116975 -1.151514  1.011316
4     3.935302  -1.948072  1.389767  2.940639  0.546747 -1.226495 -0.936213  0.636376 -0.263805  0.377704  0.651360 -0.110515
..         ...        ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...
564   6.439315  -3.576817  2.459487  1.177314 -0.074824 -2.375193 -0.596130 -0.035471  0.987929  0.256989 -0.062651  0.123342
565   3.793382  -3.584048  2.088476 -2.506028 -0.510723 -0.246710 -0.716326 -1.113360 -0.105207 -0.108632  0.244804  0.222753
566   1.256179  -1.902297  0.562731 -2.089227  1.809991 -0.534447 -0.192758  0.341887  0.393917  0.520877 -0.840512  0.096473
567  10.374794   1.672010 -1.877029 -2.356031 -0.033742  0.567936  0.223082 -0.280239 -0.542035 -0.089296 -0.178628 -0.697461
568  -5.475243  -0.670637  1.490443 -2.299157 -0.184703  1.617837  1.698952  1.046354  0.374101 -0.047726 -0.144094 -0.179496

[569 rows x 12 columns]

Methods

fit	Fit the feature selector to the 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	Do nothing.
set_output	Set output container.
set_params	Set the parameters of this estimator.
transform	Transform the data.

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

Fit the feature selector to the data.

The univariate, sfm (when model is not fitted), sfs, rfe and rfecv strategies need a target column. Leaving it None raises an exception.

Parameters	X: dataframe-like Feature set with shape=(n_samples, n_features). y: sequence, dataframe-like or None, default=None Target column(s) corresponding to `X`.
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: sequence or None, default=None Only used to validate feature names with the names seen in `fit`.
Returns	np.ndarray Transformed feature names.

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=None, y=None, **fit_params)[source]

Do nothing.

Returns the input unchanged. Implemented for continuity of the API.

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.
Returns	dataframe Feature set. Only returned if provided. series or dataframe Target column(s). Only returned if provided.

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]

Transform 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 Transformed feature set.