class dask_ml.feature_extraction.text.FeatureHasher(n_features=1048576, *, input_type='dict', dtype=<class 'numpy.float64'>, alternate_sign=True)

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.

New in version 0.13.

n_featuresint, default=2**20

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.

input_typestr, default=’dict’

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).

dtypenumpy dtype, default=np.float64

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.

alternate_signbool, default=True

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.

Changed in version 0.19: alternate_sign replaces the now deprecated non_negative parameter.

See also


Vectorizes string-valued features using a hash table.


Handles nominal/categorical features.


This estimator is stateless and does not need to be fitted. However, we recommend to call fit_transform() instead of transform(), as parameter validation is only performed in fit().


>>> from sklearn.feature_extraction import FeatureHasher
>>> h = FeatureHasher(n_features=10)
>>> D = [{'dog': 1, 'cat':2, 'elephant':4},{'dog': 2, 'run': 5}]
>>> f = h.transform(D)
>>> f.toarray()
array([[ 0.,  0., -4., -1.,  0.,  0.,  0.,  0.,  0.,  2.],
       [ 0.,  0.,  0., -2., -5.,  0.,  0.,  0.,  0.,  0.]])

With input_type=”string”, the input must be an iterable over iterables of strings:

>>> h = FeatureHasher(n_features=8, input_type="string")
>>> raw_X = [["dog", "cat", "snake"], ["snake", "dog"], ["cat", "bird"]]
>>> f = h.transform(raw_X)
>>> f.toarray()
array([[ 0.,  0.,  0., -1.,  0., -1.,  0.,  1.],
       [ 0.,  0.,  0., -1.,  0., -1.,  0.,  0.],
       [ 0., -1.,  0.,  0.,  0.,  0.,  0.,  1.]])


fit([X, y])

Only validates estimator's parameters.

fit_transform(X[, y])

Fit to data, then transform it.


Get metadata routing of this object.


Get parameters for this estimator.

set_output(*[, transform])

Set output container.


Set the parameters of this estimator.

set_transform_request(*[, raw_X])

Request metadata passed to the transform method.


__init__(n_features=1048576, *, input_type='dict', dtype=<class 'numpy.float64'>, alternate_sign=True)