FeatureExtractor

---

class atom.feature_engineering.FeatureExtractor(features=('year', 'month', 'day'), fmt=None, encoding_type="ordinal", drop_columns=True, from_index=False, verbose=0)[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=("year", "month", "day") 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, dict or None, default=None Format (strptime) of the categorical columns that need to be converted to datetime. If dict, use the column's name as key and the format as value. If None, the format is inferred automatically from the first non NaN value. Values that cannot 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. from_index: bool, default=False Whether to use the index as the datetime column to convert. drop_columns: bool, default=True Whether to drop the original columns after transformation. This parameter is ignored if from_index=True. 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.

Attributes

feature_names_in_: np.ndarray Names of features seen during fit. n_features_in_: int Number of features seen during fit.

See Also

FeatureGenerator

Generate new features.

FeatureGrouper

Extract statistics from similar features.

FeatureSelector

Reduce the number of features in the data.

Example

atomstand-alone

>>> import pandas as pd
>>> from atom import ATOMClassifier
>>> from sklearn.datasets import load_breast_cancer

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

>>> # Add a datetime column
>>> 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)

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

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

     mean radius  mean texture  mean perimeter  mean area  mean smoothness  mean compactness  mean concavity  mean concave points  mean symmetry  ...  worst area  worst smoothness  worst compactness  worst concavity  worst concave points  worst symmetry  worst fractal dimension  date_day  target
0          13.49         22.30           86.91      561.0          0.08752           0.07698         0.04751              0.03384         0.1809  ...       698.8            0.1162            0.17110          0.22820               0.12820          0.2871                  0.06917        19       1
1          19.27         26.47          127.90     1162.0          0.09401           0.17190         0.16570              0.07593         0.1853  ...      1813.0            0.1509            0.65900          0.60910               0.17850          0.3672                  0.11230         3       0
2          13.00         25.13           82.61      520.2          0.08369           0.05073         0.01206              0.01762         0.1667  ...       628.5            0.1218            0.10930          0.04462               0.05921          0.2306                  0.06291         4       1
3          23.29         26.67          158.90     1685.0          0.11410           0.20840         0.35230              0.16200         0.2200  ...      1986.0            0.1536            0.41670          0.78920               0.27330          0.3198                  0.08762        22       0
4          12.77         22.47           81.72      506.3          0.09055           0.05761         0.04711              0.02704         0.1585  ...       653.6            0.1419            0.15230          0.21770               0.09331          0.2829                  0.08067        16       0
..           ...           ...             ...        ...              ...               ...             ...                  ...            ...  ...         ...               ...                ...              ...                   ...             ...                      ...       ...     ...
564        12.77         29.43           81.35      507.9          0.08276           0.04234         0.01997              0.01499         0.1539  ...       594.7            0.1234            0.10640          0.08653               0.06498          0.2407                  0.06484         7       1
565        11.15         13.08           70.87      381.9          0.09754           0.05113         0.01982              0.01786         0.1830  ...       440.8            0.1341            0.08971          0.07116               0.05506          0.2859                  0.06772         3       1
566        19.17         24.80          132.40     1123.0          0.09740           0.24580         0.20650              0.11180         0.2397  ...      1332.0            0.1037            0.39030          0.36390               0.17670          0.3176                  0.10230        13       0
567        11.43         15.39           73.06      399.8          0.09639           0.06889         0.03503              0.02875         0.1734  ...       462.0            0.1190            0.16480          0.13990               0.08476          0.2676                  0.06765        18       1
568        13.48         20.82           88.40      559.2          0.10160           0.12550         0.10630              0.05439         0.1720  ...       740.4            0.1610            0.42250          0.50300               0.22580          0.2807                  0.10710         9       0

[569 rows x 32 columns]

>>> import pandas as pd
>>> from atom.feature_engineering import FeatureExtractor
>>> from sklearn.datasets import load_breast_cancer

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

>>> # Add a datetime column
>>> 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  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  date_day
0          17.99         10.38          122.80     1001.0          0.11840           0.27760         0.30010              0.14710         0.2419  ...           184.60      2019.0           0.16220            0.66560           0.7119                0.2654          0.4601                  0.11890         1
1          20.57         17.77          132.90     1326.0          0.08474           0.07864         0.08690              0.07017         0.1812  ...           158.80      1956.0           0.12380            0.18660           0.2416                0.1860          0.2750                  0.08902         2
2          19.69         21.25          130.00     1203.0          0.10960           0.15990         0.19740              0.12790         0.2069  ...           152.50      1709.0           0.14440            0.42450           0.4504                0.2430          0.3613                  0.08758         3
3          11.42         20.38           77.58      386.1          0.14250           0.28390         0.24140              0.10520         0.2597  ...            98.87       567.7           0.20980            0.86630           0.6869                0.2575          0.6638                  0.17300         4
4          20.29         14.34          135.10     1297.0          0.10030           0.13280         0.19800              0.10430         0.1809  ...           152.20      1575.0           0.13740            0.20500           0.4000                0.1625          0.2364                  0.07678         5
..           ...           ...             ...        ...              ...               ...             ...                  ...            ...  ...              ...         ...               ...                ...              ...                   ...             ...                      ...       ...
564        21.56         22.39          142.00     1479.0          0.11100           0.11590         0.24390              0.13890         0.1726  ...           166.10      2027.0           0.14100            0.21130           0.4107                0.2216          0.2060                  0.07115        19
565        20.13         28.25          131.20     1261.0          0.09780           0.10340         0.14400              0.09791         0.1752  ...           155.00      1731.0           0.11660            0.19220           0.3215                0.1628          0.2572                  0.06637        20
566        16.60         28.08          108.30      858.1          0.08455           0.10230         0.09251              0.05302         0.1590  ...           126.70      1124.0           0.11390            0.30940           0.3403                0.1418          0.2218                  0.07820        21
567        20.60         29.33          140.10     1265.0          0.11780           0.27700         0.35140              0.15200         0.2397  ...           184.60      1821.0           0.16500            0.86810           0.9387                0.2650          0.4087                  0.12400        22
568         7.76         24.54           47.92      181.0          0.05263           0.04362         0.00000              0.00000         0.1587  ...            59.16       268.6           0.08996            0.06444           0.0000                0.0000          0.2871                  0.07039        23

[569 rows x 31 columns]

Methods

fit	Do nothing.
fit_transform	Fit to data, then transform it.
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	Extract the new features.

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

Do 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: 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	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_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]

Extract the new features.

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.