FeatureExtractor

class atom.feature_engineering.FeatureExtractor(features=['day', 'month', 'year'], fmt=None, encoding_type="ordinal", drop_columns=True, verbose=0, logger=None)[source]

Extract features from datetime columns.

Create new features extracting datetime elements (day, month, year, etc...) from the provided columns. Columns of dtype datetime64 are used as is. Categorical columns that can be successfully converted to a datetime format (less than 30% NaT values after conversion) are also used.

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

Warning

Decision trees based algorithms build their split rules according to one feature at a time. This means that they will fail to correctly process cyclic features since the sin/cos features should be considered one single coordinate system.

Parameters

features: str or sequence, default=["day", "month", "year"]

Features to create from the datetime columns. Note that created features with zero variance (e.g. the feature hour in a column that only contains dates) are ignored. Allowed values are datetime attributes from pandas.Series.dt.

fmt: str, sequence or None, default=None

Format (strptime) of the categorical columns that need to be converted to datetime. If sequence, the n-th format corresponds to the n-th categorical column that can be successfully converted. If None, the format is inferred automatically from the first non NaN value. Values that can not be converted are returned as NaT.

encoding_type: str, default="ordinal"

Type of encoding to use. Choose from:

"ordinal": Encode features in increasing order.
"cyclic": Encode features using sine and cosine to capture their cyclic nature. This approach creates two columns for every feature. Non-cyclic features still use ordinal encoding.

drop_columns: bool, default=True

Whether to drop the original columns after transformation.

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.

Attributes

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)
>>> X["date"] = pd.date_range(start="1/1/2018", periods=len(X))

>>> atom = ATOMClassifier(X, y)
>>> atom.feature_extraction(features=["day"], fmt="%d/%m/%Y", verbose=2)

Extracting datetime features...
 --> Extracting features from column date.
   --> Creating feature date_day.

>>> # Note the date_day column
>>> print(atom.dataset)

     mean radius  mean texture  ...  date_day  target
0         11.300         18.19  ...        31       1
1         16.460         20.11  ...        27       0
2         11.370         18.89  ...        17       1
3          8.598         20.98  ...         3       1
4         12.800         17.46  ...         2       1
..           ...           ...  ...       ...     ...
564       17.060         21.00  ...         2       0
565       11.940         20.76  ...        14       1
566       19.590         25.00  ...        28       0
567       12.360         18.54  ...        18       1
568       18.450         21.91  ...        15       0

[569 rows x 32 columns]

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

>>> X, _ = load_breast_cancer(return_X_y=True, as_frame=True)
>>> X["date"] = pd.date_range(start="1/1/2018", periods=len(X))

>>> fe = FeatureExtractor(features=["day"], fmt="%Y-%m-%d", verbose=2)
>>> X = fe.transform(X)

Extracting datetime features...
 --> Extracting features from column date.
   --> Creating feature date_day.

>>> # Note the date_day column
>>> print(X)

     mean radius  mean texture  ...  worst fractal dimension  date_day
0          17.99         10.38  ...                  0.11890         1
1          20.57         17.77  ...                  0.08902         2
2          19.69         21.25  ...                  0.08758         3
3          11.42         20.38  ...                  0.17300         4
4          20.29         14.34  ...                  0.07678         5
..           ...           ...  ...                      ...       ...
564        21.56         22.39  ...                  0.07115        19
565        20.13         28.25  ...                  0.06637        20
566        16.60         28.08  ...                  0.07820        21
567        20.60         29.33  ...                  0.12400        22
568         7.76         24.54  ...                  0.07039        23

[569 rows x 31 columns]

Methods

fit	Does nothing.
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	Extract the new features.

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

Does nothing.

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: int, str, dict, sequence 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.
Else: Array with shape=(n_samples,) to use as target.

**fit_params

Additional keyword arguments for the fit method.

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, dict, sequence 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.
Else: Array with shape=(n_samples,) to use as target.

**fit_params

Additional keyword arguments for the fit method.

Returns

pd.DataFrame

Transformed feature set. Only returned if provided.

pd.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, dict, sequence 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. Else: Array with shape=(n_samples,) to use as target.
Returns	pd.DataFrame Transformed feature set. Only returned if provided. pd.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, remember to add the data to ATOMLoader when loading the file.

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]

Extract the new features.

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