FeatureHasher

Implements feature hashing, aka the hashing trick.

This class turns sequences of symbolic feature names (strings) into scipy.sparse matrices, using a hash function to compute the matrix column corresponding to a name. The hash function employed is the signed 32-bit version of Murmurhash3.

Feature names of type byte string are used as-is. Unicode strings are converted to UTF-8 first, but no Unicode normalization is done. Feature values must be (finite) numbers.

This class is a low-memory alternative to DictVectorizer and CountVectorizer, intended for large-scale (online) learning and situations where memory is tight, e.g. when running prediction code on embedded devices.

For an efficiency comparision of the different feature extractors, see FeatureHasher and DictVectorizer Comparison.

Constructors

constructor()

Signature

new FeatureHasher(opts?: object): FeatureHasher;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.alternate_sign?`	`boolean`	When `true`, an alternating sign is added to the features as to approximately conserve the inner product in the hashed space even for small n_features. This approach is similar to sparse random projection. `Default Value` `true`
`opts.dtype?`	`any`	The type of feature values. Passed to scipy.sparse matrix constructors as the dtype argument. Do not set this to bool, np.boolean or any unsigned integer type.
`opts.input_type?`	`string`	Choose a string from {‘dict’, ‘pair’, ‘string’}. Either “dict” (the default) to accept dictionaries over (feature_name, value); “pair” to accept pairs of (feature_name, value); or “string” to accept single strings. feature_name should be a string, while value should be a number. In the case of “string”, a value of 1 is implied. The feature_name is hashed to find the appropriate column for the feature. The value’s sign might be flipped in the output (but see non_negative, below). `Default Value` `'dict'`
`opts.n_features?`	`number`	The number of features (columns) in the output matrices. Small numbers of features are likely to cause hash collisions, but large numbers will cause larger coefficient dimensions in linear learners. `Default Value` `2`

Returns

FeatureHasher

Defined in: generated/feature_extraction/FeatureHasher.ts:31 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/feature_extraction/FeatureHasher.ts:117 (opens in a new tab)

fit()

Only validates estimator’s parameters.

This method allows to: (i) validate the estimator’s parameters and (ii) be consistent with the scikit-learn transformer API.

Signature

fit(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`	Not used, present here for API consistency by convention.
`opts.y?`	`any`	Not used, present here for API consistency by convention.

Returns

Promise<any>

Defined in: generated/feature_extraction/FeatureHasher.ts:136 (opens in a new tab)

fit_transform()

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.

Signature

fit_transform(opts: object): Promise<any[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Input samples.
`opts.fit_params?`	`any`	Additional fit parameters.
`opts.y?`	`ArrayLike`	Target values (`undefined` for unsupervised transformations).

Returns

Promise<any[]>

Defined in: generated/feature_extraction/FeatureHasher.ts:176 (opens in a new tab)

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.routing?`	`any`	A `MetadataRequest` encapsulating routing information.

Returns

Promise<any>

Defined in: generated/feature_extraction/FeatureHasher.ts:225 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/feature_extraction/FeatureHasher.ts:75 (opens in a new tab)

set_output()

Set output container.

See Introducing the set_output API for an example on how to use the API.

Signature

set_output(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.transform?`	`"default"` \| `"pandas"`	Configure output of `transform` and `fit\_transform`.

Returns

Promise<any>

Defined in: generated/feature_extraction/FeatureHasher.ts:262 (opens in a new tab)

set_transform_request()

Request metadata passed to the transform method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

set_transform_request(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.raw_X?`	`string` \| `boolean`	Metadata routing for `raw\_X` parameter in `transform`.

Returns

Promise<any>

Defined in: generated/feature_extraction/FeatureHasher.ts:299 (opens in a new tab)

transform()

Transform a sequence of instances to a scipy.sparse matrix.

Signature

transform(opts: object): Promise<any[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.raw_X?`	`any`	Samples. Each sample must be iterable an (e.g., a list or tuple) containing/generating feature names (and optionally values, see the input_type constructor argument) which will be hashed. raw_X need not support the len function, so it can be the result of a generator; n_samples is determined on the fly.