Encoder

---

class atom.data_cleaning.Encoder(strategy="Target", max_onehot=10, ordinal=None, infrequent_to_value=None, value="infrequent", n_jobs=1, verbose=0, **kwargs)[source]

Perform encoding of categorical features.

The encoding type depends on the number of classes in the column:

• If n_classes=2 or ordinal feature, use Ordinal-encoding.
• If 2 < n_classes <= max_onehot, use OneHot-encoding.
• If n_classes > max_onehot, use strategy-encoding.

Missing values are propagated to the output column. Unknown classes encountered during transforming are imputed according to the selected strategy. Infrequent classes can be replaced with a value in order to prevent too high cardinality.

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

Warning

Three category-encoders estimators are unavailable:

• OneHotEncoder: Use the max_onehot parameter.
• HashingEncoder: Incompatibility of APIs.
• LeaveOneOutEncoder: Incompatibility of APIs.

Parameters

strategy: str or transformer, default="Target" Type of encoding to use for high cardinality features. Choose from any of the estimators in the category-encoders package or provide a custom one. max_onehot: int or None, default=10 Maximum number of unique values in a feature to perform one-hot encoding. If None, strategy-encoding is always used for columns with more than two classes. ordinal: dict or None, default=None Order of ordinal features, where the dict key is the feature's name and the value is the class order, e.g., {"salary": ["low", "medium", "high"]}. infrequent_to_value: int, float or None, default=None Replaces infrequent class occurrences in categorical columns with the string in parameter value. This transformation is done before the encoding of the column. If None: Skip this step. If int: Minimum number of occurrences in a class. If float: Minimum fraction of occurrences in a class. value: str, default="infrequent" Value with which to replace rare classes. This parameter is ignored if infrequent_to_value=None. 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 - value. 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. **kwargs Additional keyword arguments for the strategy estimator.

Attributes

mapping_: dict of dicts Encoded values and their respective mapping. The column name is the key to its mapping dictionary. Only for ordinal encoding. feature_names_in_: np.ndarray Names of features seen during fit. n_features_in_: int Number of features seen during fit.

See Also

Cleaner

Applies standard data cleaning steps on a dataset.

Imputer

Handle missing values in the data.

Pruner

Prune outliers from the data.

Example

atomstand-alone

>>> from atom import ATOMClassifier
>>> from sklearn.datasets import load_breast_cancer
>>> from numpy.random import randint

>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)
>>> X["cat_feature_1"] = [f"x{i}" for i in randint(0, 2, len(X))]
>>> X["cat_feature_2"] = [f"x{i}" for i in randint(0, 3, len(X))]
>>> X["cat_feature_3"] = [f"x{i}" for i in randint(0, 20, len(X))]

>>> atom = ATOMClassifier(X, y, random_state=1)
>>> print(atom.X)

     mean radius  mean texture  mean perimeter  mean area  mean smoothness  mean compactness  mean concavity  mean concave points  mean symmetry  ...  worst smoothness  worst compactness  worst concavity  worst concave points  worst symmetry  worst fractal dimension  cat_feature_1  cat_feature_2  cat_feature_3
0          13.48         20.82           88.40      559.2          0.10160           0.12550         0.10630              0.05439         0.1720  ...            0.1610            0.42250           0.5030               0.22580          0.2807                  0.10710             x1             x2            x10
1          18.31         20.58          120.80     1052.0          0.10680           0.12480         0.15690              0.09451         0.1860  ...            0.1492            0.25360           0.3759               0.15100          0.3074                  0.07863             x0             x1             x6
2          17.93         24.48          115.20      998.9          0.08855           0.07027         0.05699              0.04744         0.1538  ...            0.1315            0.18060           0.2080               0.11360          0.2504                  0.07948             x0             x2             x9
3          15.13         29.81           96.71      719.5          0.08320           0.04605         0.04686              0.02739         0.1852  ...            0.1148            0.09866           0.1547               0.06575          0.3233                  0.06165             x0             x2            x11
4           8.95         15.76           58.74      245.2          0.09462           0.12430         0.09263              0.02308         0.1305  ...            0.1179            0.18790           0.1544               0.03846          0.1652                  0.07722             x0             x0             x3
..           ...           ...             ...        ...              ...               ...             ...                  ...            ...  ...               ...                ...              ...                   ...             ...                      ...            ...            ...            ...
564        14.34         13.47           92.51      641.2          0.09906           0.07624         0.05724              0.04603         0.2075  ...            0.1297            0.15250           0.1632               0.10870          0.3062                  0.06072             x1             x2            x11
565        13.17         21.81           85.42      531.5          0.09714           0.10470         0.08259              0.05252         0.1746  ...            0.1503            0.39040           0.3728               0.16070          0.3693                  0.09618             x0             x1             x4
566        17.30         17.08          113.00      928.2          0.10080           0.10410         0.12660              0.08353         0.1813  ...            0.1416            0.24050           0.3378               0.18570          0.3138                  0.08113             x0             x1            x10
567        17.68         20.74          117.40      963.7          0.11150           0.16650         0.18550              0.10540         0.1971  ...            0.1418            0.34980           0.3583               0.15150          0.2463                  0.07738             x0             x1            x16
568        14.80         17.66           95.88      674.8          0.09179           0.08890         0.04069              0.02260         0.1893  ...            0.1226            0.18810           0.2060               0.08308          0.3600                  0.07285             x1             x1             x2

[569 rows x 33 columns]

>>> atom.encode(strategy="target", max_onehot=10, verbose=2)

Fitting Encoder...
Encoding categorical columns...
 --> Ordinal-encoding feature cat_feature_1. Contains 2 classes.
 --> OneHot-encoding feature cat_feature_2. Contains 3 classes.
 --> Target-encoding feature cat_feature_3. Contains 20 classes.

>>> # Note the one-hot encoded column with name [feature]_[class]
>>> print(atom.X)

     mean radius  mean texture  mean perimeter  mean area  mean smoothness  mean compactness  mean concavity  mean concave points  mean symmetry  ...  worst concavity  worst concave points  worst symmetry  worst fractal dimension  cat_feature_1  cat_feature_2_x2  cat_feature_2_x1  cat_feature_2_x0  cat_feature_3
0          13.48         20.82           88.40      559.2          0.10160           0.12550         0.10630              0.05439         0.1720  ...           0.5030               0.22580          0.2807                  0.10710            1.0               1.0               0.0               0.0       0.604275
1          18.31         20.58          120.80     1052.0          0.10680           0.12480         0.15690              0.09451         0.1860  ...           0.3759               0.15100          0.3074                  0.07863            0.0               0.0               1.0               0.0       0.495404
2          17.93         24.48          115.20      998.9          0.08855           0.07027         0.05699              0.04744         0.1538  ...           0.2080               0.11360          0.2504                  0.07948            0.0               1.0               0.0               0.0       0.604073
3          15.13         29.81           96.71      719.5          0.08320           0.04605         0.04686              0.02739         0.1852  ...           0.1547               0.06575          0.3233                  0.06165            0.0               1.0               0.0               0.0       0.657228
4           8.95         15.76           58.74      245.2          0.09462           0.12430         0.09263              0.02308         0.1305  ...           0.1544               0.03846          0.1652                  0.07722            0.0               0.0               0.0               1.0       0.660063
..           ...           ...             ...        ...              ...               ...             ...                  ...            ...  ...              ...                   ...             ...                      ...            ...               ...               ...               ...            ...
564        14.34         13.47           92.51      641.2          0.09906           0.07624         0.05724              0.04603         0.2075  ...           0.1632               0.10870          0.3062                  0.06072            1.0               1.0               0.0               0.0       0.657228
565        13.17         21.81           85.42      531.5          0.09714           0.10470         0.08259              0.05252         0.1746  ...           0.3728               0.16070          0.3693                  0.09618            0.0               0.0               1.0               0.0       0.616927
566        17.30         17.08          113.00      928.2          0.10080           0.10410         0.12660              0.08353         0.1813  ...           0.3378               0.18570          0.3138                  0.08113            0.0               0.0               1.0               0.0       0.604275
567        17.68         20.74          117.40      963.7          0.11150           0.16650         0.18550              0.10540         0.1971  ...           0.3583               0.15150          0.2463                  0.07738            0.0               0.0               1.0               0.0       0.675771
568        14.80         17.66           95.88      674.8          0.09179           0.08890         0.04069              0.02260         0.1893  ...           0.2060               0.08308          0.3600                  0.07285            1.0               0.0               1.0               0.0       0.591592

[569 rows x 35 columns]

>>> from atom.data_cleaning import Encoder
>>> from sklearn.datasets import load_breast_cancer
>>> from numpy.random import randint

>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)
>>> X["cat_feature_1"] = [f"x{i}" for i in randint(0, 2, len(X))]
>>> X["cat_feature_2"] = [f"x{i}" for i in randint(0, 3, len(X))]
>>> X["cat_feature_3"] = [f"x{i}" for i in randint(0, 20, len(X))]
>>> print(X)

     mean radius  mean texture  mean perimeter  mean area  mean smoothness  mean compactness  mean concavity  mean concave points  mean symmetry  ...  worst smoothness  worst compactness  worst concavity  worst concave points  worst symmetry  worst fractal dimension  cat_feature_1  cat_feature_2  cat_feature_3
0          17.99         10.38          122.80     1001.0          0.11840           0.27760         0.30010              0.14710         0.2419  ...           0.16220            0.66560           0.7119                0.2654          0.4601                  0.11890             x1             x2             x5
1          20.57         17.77          132.90     1326.0          0.08474           0.07864         0.08690              0.07017         0.1812  ...           0.12380            0.18660           0.2416                0.1860          0.2750                  0.08902             x1             x2            x13
2          19.69         21.25          130.00     1203.0          0.10960           0.15990         0.19740              0.12790         0.2069  ...           0.14440            0.42450           0.4504                0.2430          0.3613                  0.08758             x0             x0            x15
3          11.42         20.38           77.58      386.1          0.14250           0.28390         0.24140              0.10520         0.2597  ...           0.20980            0.86630           0.6869                0.2575          0.6638                  0.17300             x0             x2            x10
4          20.29         14.34          135.10     1297.0          0.10030           0.13280         0.19800              0.10430         0.1809  ...           0.13740            0.20500           0.4000                0.1625          0.2364                  0.07678             x1             x1            x17
..           ...           ...             ...        ...              ...               ...             ...                  ...            ...  ...               ...                ...              ...                   ...             ...                      ...            ...            ...            ...
564        21.56         22.39          142.00     1479.0          0.11100           0.11590         0.24390              0.13890         0.1726  ...           0.14100            0.21130           0.4107                0.2216          0.2060                  0.07115             x1             x1            x12
565        20.13         28.25          131.20     1261.0          0.09780           0.10340         0.14400              0.09791         0.1752  ...           0.11660            0.19220           0.3215                0.1628          0.2572                  0.06637             x0             x2            x14
566        16.60         28.08          108.30      858.1          0.08455           0.10230         0.09251              0.05302         0.1590  ...           0.11390            0.30940           0.3403                0.1418          0.2218                  0.07820             x0             x1             x3
567        20.60         29.33          140.10     1265.0          0.11780           0.27700         0.35140              0.15200         0.2397  ...           0.16500            0.86810           0.9387                0.2650          0.4087                  0.12400             x1             x0             x2
568         7.76         24.54           47.92      181.0          0.05263           0.04362         0.00000              0.00000         0.1587  ...           0.08996            0.06444           0.0000                0.0000          0.2871                  0.07039             x1             x1            x11

[569 rows x 33 columns]

>>> encoder = Encoder(strategy="target", max_onehot=10, verbose=2)
>>> X = encoder.fit_transform(X, y)

Fitting Encoder...
Encoding categorical columns...
 --> Ordinal-encoding feature cat_feature_1. Contains 2 classes.
 --> OneHot-encoding feature cat_feature_2. Contains 3 classes.
 --> Target-encoding feature cat_feature_3. Contains 20 classes.

>>> # Note the one-hot encoded column with name [feature]_[class]
>>> print(X)

     mean radius  mean texture  mean perimeter  mean area  mean smoothness  mean compactness  mean concavity  mean concave points  mean symmetry  ...  worst concavity  worst concave points  worst symmetry  worst fractal dimension  cat_feature_1  cat_feature_2_x2  cat_feature_2_x0  cat_feature_2_x1  cat_feature_3
0          17.99         10.38          122.80     1001.0          0.11840           0.27760         0.30010              0.14710         0.2419  ...           0.7119                0.2654          0.4601                  0.11890            1.0               1.0               0.0               0.0       0.645086
1          20.57         17.77          132.90     1326.0          0.08474           0.07864         0.08690              0.07017         0.1812  ...           0.2416                0.1860          0.2750                  0.08902            1.0               1.0               0.0               0.0       0.604148
2          19.69         21.25          130.00     1203.0          0.10960           0.15990         0.19740              0.12790         0.2069  ...           0.4504                0.2430          0.3613                  0.08758            0.0               0.0               1.0               0.0       0.675079
3          11.42         20.38           77.58      386.1          0.14250           0.28390         0.24140              0.10520         0.2597  ...           0.6869                0.2575          0.6638                  0.17300            0.0               1.0               0.0               0.0       0.706297
4          20.29         14.34          135.10     1297.0          0.10030           0.13280         0.19800              0.10430         0.1809  ...           0.4000                0.1625          0.2364                  0.07678            1.0               0.0               0.0               1.0       0.716566
..           ...           ...             ...        ...              ...               ...             ...                  ...            ...  ...              ...                   ...             ...                      ...            ...               ...               ...               ...            ...
564        21.56         22.39          142.00     1479.0          0.11100           0.11590         0.24390              0.13890         0.1726  ...           0.4107                0.2216          0.2060                  0.07115            1.0               0.0               0.0               1.0       0.598024
565        20.13         28.25          131.20     1261.0          0.09780           0.10340         0.14400              0.09791         0.1752  ...           0.3215                0.1628          0.2572                  0.06637            0.0               1.0               0.0               0.0       0.683185
566        16.60         28.08          108.30      858.1          0.08455           0.10230         0.09251              0.05302         0.1590  ...           0.3403                0.1418          0.2218                  0.07820            0.0               0.0               0.0               1.0       0.472908
567        20.60         29.33          140.10     1265.0          0.11780           0.27700         0.35140              0.15200         0.2397  ...           0.9387                0.2650          0.4087                  0.12400            1.0               0.0               1.0               0.0       0.585452
568         7.76         24.54           47.92      181.0          0.05263           0.04362         0.00000              0.00000         0.1587  ...           0.0000                0.0000          0.2871                  0.07039            1.0               0.0               0.0               1.0       0.516759

[569 rows x 35 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	Do nothing.
set_output	Set output container.
set_params	Set the parameters of this estimator.
transform	Encode the data.

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

Fit to data.

Note that leaving y=None can lead to errors if the strategy encoder requires target values. For multioutput tasks, only the first target column is used to fit the encoder.

Parameters	X: dataframe-like Feature set with shape=(n_samples, n_features). y: sequence or dataframe-like 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_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]

Encode 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 Encoded dataframe.