FeatureGenerator

class atom.feature_engineering.FeatureGenerator(strategy="dfs", n_features=None, operators=None, n_jobs=1, verbose=0, logger=None, random_state=None, **kwargs)[source]

Generate new features.

Create new combinations of existing features to capture the non-linear relations between the original features.

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

Warning

Using the div, log or sqrt operators can return new features with inf or NaN values. Check the warnings that may pop up or use atom's nans attribute.
When using dfs with n_jobs>1, make sure to protect your code with if __name__ == "__main__". Featuretools uses dask, which uses python multiprocessing for parallelization. The spawn method on multiprocessing starts a new python process, which requires it to import the __main__ module before it can do its task.
gfg can be slow for very large populations.

Tip

dfs can create many new features and not all of them will be useful. Use the FeatureSelector class to reduce the number of features.

Parameters

strategy: str, default="dfs"

Strategy to crate new features. Choose from:

"dfs": Deep Feature Synthesis.
"gfg": Genetic Feature Generation.

n_features: int or None, default=None

Maximum number of newly generated features to add to the dataset. If None, select all created features.

operators: str, sequence or None, default=None

Mathematical operators to apply on the features. None to use all. Choose from: add, sub, mul, div, abs, sqrt, log, inv, sin, cos, tan.

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.

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.

logger: str, Logger or None, default=None

If None: Doesn't save a logging file.
If str: Name of the log file. Use "auto" for automatic naming.
Else: Python logging.Logger instance.

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

Additional keyword arguments for the SymbolicTransformer instance. Only for the gfg strategy.

Attributes

gfg: SymbolicTransformer

Object used to calculate the genetic features. Only for the gfg strategy.

genetic_features: pd.DataFrame

Information on the newly created non-linear features. Only for the gfg strategy. Columns include:

name: Name of the feature (generated automatically).
description: Operators used to create this feature.
fitness: Fitness score.

feature_names_in_: np.array

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_generation(strategy="dfs", n_features=5, verbose=2)

Fitting FeatureGenerator...
Generating new features...
 --> 5 new features were added.

>>> # Note the texture error / worst symmetry column
>>> print(atom.dataset)

     mean radius  mean texture  ...  texture error / worst symmetry  target
0          15.75         19.22  ...                        3.118963       0
1          12.10         17.72  ...                        5.418170       1
2          20.16         19.66  ...                        2.246481       0
3          12.88         18.22  ...                        4.527498       1
4          13.03         18.42  ...                       11.786613       1
..           ...           ...  ...                             ...     ...
564        21.75         20.99  ...                        4.772326       0
565        13.64         16.34  ...                        3.936061       1
566        10.08         15.11  ...                        4.323219       1
567        12.91         16.33  ...                        3.004630       1
568        11.60         18.36  ...                        2.385047       1

[569 rows x 36 columns]

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

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

>>> fg = FeatureGenerator(strategy="dfs", n_features=5, verbose=2)
>>> X = fg.fit_transform(X, y)

Fitting FeatureGenerator...
Generating new features...
 --> 5 new features were added.

>>> # Note the radius error * worst smoothness column
>>> print(X)

     mean radius  ...  radius error * worst smoothness
0          17.99  ...                         0.177609
1          20.57  ...                         0.067285
2          19.69  ...                         0.107665
3          11.42  ...                         0.103977
4          20.29  ...                         0.104039
..           ...  ...                              ...
564        21.56  ...                         0.165816
565        20.13  ...                         0.089257
566        16.60  ...                         0.051984
567        20.60  ...                         0.119790
568         7.76  ...                         0.034698

[569 rows x 35 columns]

Methods

fit	Fit to data.
fit_transform	Fit to data, then transform it.
get_params	Get parameters for this estimator.
inverse_transform	Does nothing.
log	Print message and save to log file.
save	Save the instance to a pickle file.
set_params	Set the parameters of this estimator.
transform	Generate new features.

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

Fit to data.

Parameters

X: dataframe-like

Feature set with shape=(n_samples, n_features).

y: int, str, sequence, dataframe-like or None, default=None

Target column corresponding to X.

If None: y is ignored.
If int: Position of the target column in X.
If str: Name of the target column in X.
If sequence: Target column with shape=(n_samples,) or sequence of column names or positions for multioutput tasks.
If dataframe-like: Target columns with shape=(n_samples, n_targets) for multioutput tasks.

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: int, str, sequence, dataframe-like or None, default=None

Target column corresponding to X.

If None: y is ignored.
If int: Position of the target column in X.
If str: Name of the target column in X.
If sequence: Target column with shape=(n_samples,) or sequence of column names or positions for multioutput tasks.
If dataframe-like: Target columns with shape=(n_samples, n_targets) for multioutput tasks.

**fit_params

Additional keyword arguments for the fit method.

Returns

dataframe

Transformed feature set. Only returned if provided.

series

Transformed target column. Only returned if provided.

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)[source]

Does nothing.

Parameters

X: dataframe-like or None, default=None

Feature set with shape=(n_samples, n_features). If None, X is ignored.

y: int, str, sequence, dataframe-like or None, default=None

Target column corresponding to X.

If None: y is ignored.
If int: Position of the target column in X.
If str: Name of the target column in X.
If sequence: Target column with shape=(n_samples,) or sequence of column names or positions for multioutput tasks.
If dataframe-like: Target columns with shape=(n_samples, n_targets) for multioutput tasks.

Returns

dataframe

Transformed feature set. Only returned if provided.

series

Transformed target column. Only returned if provided.

method log(msg, level=0, severity="info")[source]

Print message and save to log file.

Parameters

msg: int, float or str

Message to save to the logger and print to stdout.

level: int, default=0

Minimum verbosity level to print the message.

severity: str, default="info"

Severity level of the message. Choose from: debug, info, warning, error, critical.

method save(filename="auto", save_data=True)[source]

Save the instance to a pickle file.

Parameters

filename: str, default="auto"

Name of the file. Use "auto" for automatic naming.

save_data: bool, default=True

Whether to save the dataset with the instance. This parameter is ignored if the method is not called from atom. If False, add the data to the load method.

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]

Generate new features.

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