Hashing

class category_encoders.hashing.HashingEncoder(max_process=0, max_sample=0, verbose=0, n_components=8, cols=None, drop_invariant=False, return_df=True, hash_method='md5')[source]

A multivariate hashing implementation with configurable dimensionality/precision.

The advantage of this encoder is that it does not maintain a dictionary of observed categories. Consequently, the encoder does not grow in size and accepts new values during data scoring by design.

It’s important to read about how max_process & max_sample work before setting them manually, inappropriate setting slows down encoding.

Default value of ‘max_process’ is 1 on Windows because multiprocessing might cause issues, see in : https://github.com/scikit-learn-contrib/categorical-encoding/issues/215 https://docs.python.org/2/library/multiprocessing.html?highlight=process#windows

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).
hash_method: str: which hashing method to use. Any method from hashlib works.
max_process: int: how many processes to use in transform(). Limited in range(1, 64). By default, it uses half of the logical CPUs. For example, 4C4T makes max_process=2, 4C8T makes max_process=4. Set it larger if you have a strong CPU. It is not recommended to set it larger than is the count of the logical CPUs as it will actually slow down the encoding.
max_sample: int: how many samples to encode by each process at a time. This setting is useful on low memory machines. By default, max_sample=(all samples num)/(max_process). For example, 4C8T CPU with 100,000 samples makes max_sample=25,000, 6C12T CPU with 100,000 samples makes max_sample=16,666. It is not recommended to set it larger than the default value.
n_components: int: how many bits to use to represent the feature. By default, we use 8 bits. For high-cardinality features, consider using up-to 32 bits.

References

[1]

Feature Hashing for Large Scale Multitask Learning, from

https://alex.smola.org/papers/2009/Weinbergeretal09.pdf .. [R8dde675226a2-2] Don’t be tricked by the Hashing Trick, from https://booking.ai/dont-be-tricked-by-the-hashing-trick-192a6aae3087

Attributes:

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_feature_names_in`()	Returns the names of all input columns present when fitting.
`get_feature_names_out`()	Returns the names of all transformed / added columns.
`get_params`([deep])	Get parameters for this estimator.
`hashing_trick`(X_in[, hashing_method, N, ...])	A basic hashing implementation with configurable dimensionality/precision
`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.

get_feature_names
require_data

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:

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_feature_names_in`()	Returns the names of all input columns present when fitting.
`get_feature_names_out`()	Returns the names of all transformed / added columns.
`get_params`([deep])	Get parameters for this estimator.
`hashing_trick`(X_in[, hashing_method, N, ...])	A basic hashing implementation with configurable dimensionality/precision
`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.

get_feature_names
require_data

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_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.

static hashing_trick(X_in, hashing_method='md5', N=2, cols=None, make_copy=False)[source]

A basic hashing implementation with configurable dimensionality/precision

Performs the hashing trick on a pandas dataframe, X, using the hashing method from hashlib identified by hashing_method. The number of output dimensions (N), and columns to hash (cols) are also configurable.

Parameters:

X_in: pandas dataframe: description text
hashing_method: string, optional: description text
N: int, optional: description text
cols: list, optional: description text
make_copy: bool, optional: description text

Returns:

outdataframe: A hashing encoded dataframe.

References

Cite the relevant literature, e.g. [R6b702480991a-1]. You may also cite these references in the notes section above. .. [R6b702480991a-1] Kilian Weinberger; Anirban Dasgupta; John Langford; Alex Smola; Josh Attenberg (2009). Feature Hashing for Large Scale Multitask Learning. Proc. ICML.

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.