One Hot
- class category_encoders.one_hot.OneHotEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_missing='value', handle_unknown='value', use_cat_names=False)[source]
Onehot (or dummy) coding for categorical features, produces one feature per category, each binary.
- Parameters:
- verbose: int
integer indicating verbosity of the output. 0 for none.
- cols: list
a list of columns to encode, if None, all string columns will be encoded.
- drop_invariant: bool
boolean for whether or not to drop columns with 0 variance.
- return_df: bool
boolean for whether to return a pandas DataFrame from transform (otherwise it will be a numpy array).
- use_cat_names: bool
if True, category values will be included in the encoded column names. Since this can result in duplicate column names, duplicates are suffixed with ‘#’ symbol until a unique name is generated. If False, category indices will be used instead of the category values.
- handle_unknown: str
options are ‘error’, ‘return_nan’, ‘value’, and ‘indicator’. The default is ‘value’.
‘error’ will raise a ValueError at transform time if there are new categories. ‘return_nan’ will encode a new value as np.nan in every dummy column. ‘value’ will encode a new value as 0 in every dummy column. ‘indicator’ will add an additional dummy column (in both training and test data).
- handle_missing: str
options are ‘error’, ‘return_nan’, ‘value’, and ‘indicator’. The default is ‘value’.
‘error’ will raise a ValueError if missings are encountered. ‘return_nan’ will encode a missing value as np.nan in every dummy column. ‘value’ will encode a missing value as 0 in every dummy column. ‘indicator’ will treat missingness as its own category, adding an additional dummy column (whether there are missing values in the training set or not).
References
[1]Contrast Coding Systems for Categorical Variables, from
https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/
[2]Gregory Carey (2003). Coding Categorical Variables, from
- Attributes:
- category_mapping
- feature_names_out_
Methods
fit(X[, y])Fits the encoder according to X and y.
fit_transform(X[, y])Fit to data, then transform it.
get_dummies(X_in)Convert numerical variable into dummy variables
Returns the names of all input columns present when fitting.
Returns the names of all transformed / added columns.
get_params([deep])Get parameters for this estimator.
inverse_transform(X_in)Perform the inverse transformation to encoded data.
reverse_dummies(X, mapping)Convert dummy variable into numerical variables
set_output(*[, transform])Set output container.
set_params(**params)Set the parameters of this estimator.
transform(X[, override_return_df])Perform the transformation to new categorical data.
generate_mapping
get_feature_names
- Parameters:
- verbose: int
integer indicating verbosity of output. 0 for none.
- cols: list
a list of columns to encode, if None, all string and categorical columns will be encoded.
- drop_invariant: bool
boolean for whether or not to drop columns with 0 variance.
- return_df: bool
boolean for whether to return a pandas DataFrame from transform and inverse transform (otherwise it will be a numpy array).
- handle_missing: str
how to handle missing values at fit time. Options are ‘error’, ‘return_nan’, and ‘value’. Default ‘value’, which treat NaNs as a countable category at fit time.
- handle_unknown: str, int or dict of {columnoption, …}.
how to handle unknown labels at transform time. Options are ‘error’ ‘return_nan’, ‘value’ and int. Defaults to None which uses NaN behaviour specified at fit time. Passing an int will fill with this int value.
- kwargs: dict.
additional encoder specific parameters like regularisation.
- Attributes:
- category_mapping
- feature_names_out_
Methods
fit(X[, y])Fits the encoder according to X and y.
fit_transform(X[, y])Fit to data, then transform it.
get_dummies(X_in)Convert numerical variable into dummy variables
Returns the names of all input columns present when fitting.
Returns the names of all transformed / added columns.
get_params([deep])Get parameters for this estimator.
inverse_transform(X_in)Perform the inverse transformation to encoded data.
reverse_dummies(X, mapping)Convert dummy variable into numerical variables
set_output(*[, transform])Set output container.
set_params(**params)Set the parameters of this estimator.
transform(X[, override_return_df])Perform the transformation to new categorical data.
generate_mapping
get_feature_names
- fit(X, y=None, **kwargs)
Fits the encoder according to X and y.
- Parameters:
- Xarray-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of features.
- yarray-like, shape = [n_samples]
Target values.
- Returns:
- selfencoder
Returns self.
- fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns:
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- get_dummies(X_in)[source]
Convert numerical variable into dummy variables
- Parameters:
- X_in: DataFrame
- Returns:
- dummiesDataFrame
- get_feature_names_in() List[str]
Returns the names of all input columns present when fitting. These columns are necessary for the transform step.
- get_feature_names_out() List[str]
Returns the names of all transformed / added columns.
- Returns:
- feature_names: list
A list with all feature names transformed or added. Note: potentially dropped features (because the feature is constant/invariant) are not included!
- get_params(deep=True)
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- inverse_transform(X_in)[source]
Perform the inverse transformation to encoded data.
- Parameters:
- X_inarray-like, shape = [n_samples, n_features]
- Returns:
- p: array, the same size of X_in
- reverse_dummies(X, mapping)[source]
Convert dummy variable into numerical variables
- Parameters:
- XDataFrame
- mapping: list-like
Contains mappings of column to be transformed to it’s new columns and value represented
- Returns:
- numerical: DataFrame
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”}, default=None
Configure output of transform and fit_transform.
“default”: Default output format of a transformer
“pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns:
- selfestimator instance
Estimator instance.
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). The latter have parameters of the form<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- transform(X, override_return_df=False)
Perform the transformation to new categorical data.
- Parameters:
- Xarray-like, shape = [n_samples, n_features]
- override_return_dfbool
override self.return_df to force to return a data frame
- Returns:
- parray or DataFrame, shape = [n_samples, n_features_out]
Transformed values with encoding applied.