Class: 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 comparison of the different feature extractors, see FeatureHasher and DictVectorizer Comparison.
Read more in the User Guide.
Constructors
new FeatureHasher()
new FeatureHasher(
opts?):FeatureHasher
Parameters
| Parameter | 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. |
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). |
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. |
Returns FeatureHasher
Defined in generated/feature_extraction/FeatureHasher.ts:31
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/feature_extraction/FeatureHasher.ts:29 |
_isInitialized | boolean | false | generated/feature_extraction/FeatureHasher.ts:28 |
_py | PythonBridge | undefined | generated/feature_extraction/FeatureHasher.ts:27 |
id | string | undefined | generated/feature_extraction/FeatureHasher.ts:24 |
opts | any | undefined | generated/feature_extraction/FeatureHasher.ts:25 |
Accessors
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/feature_extraction/FeatureHasher.ts:62
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/feature_extraction/FeatureHasher.ts:114
fit()
fit(
opts):Promise<any>
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.
Parameters
| Parameter | 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:133
fit_transform()
fit_transform(
opts):Promise<any[]>
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
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.fit_params? | any | Additional fit parameters. |
opts.X? | ArrayLike[] | Input samples. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns Promise<any[]>
Defined in generated/feature_extraction/FeatureHasher.ts:172
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/feature_extraction/FeatureHasher.ts:216
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/feature_extraction/FeatureHasher.ts:75
set_output()
set_output(
opts):Promise<any>
Set output container.
See Introducing the set_output API for an example on how to use the API.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | "polars" | Configure output of transform and fit_transform. |
Returns Promise<any>
Defined in generated/feature_extraction/FeatureHasher.ts:252
set_transform_request()
set_transform_request(
opts):Promise<any>
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:
Parameters
| Parameter | 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:288
transform()
transform(
opts):Promise<any[]>
Transform a sequence of instances to a scipy.sparse matrix.
Parameters
| Parameter | 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. |
Returns Promise<any[]>
Defined in generated/feature_extraction/FeatureHasher.ts:322