FeatureHasher

class ibex.sklearn.feature_extraction.FeatureHasher(n_features=1048576, input_type='dict', dtype=<class 'numpy.float64'>, alternate_sign=True, non_negative=False)

Bases: sklearn.feature_extraction.hashing.FeatureHasher, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

Read more in the User Guide.

n_features : integer, optional
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_type : string, optional, default “dict”
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).
dtype : numpy type, optional, 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_sign : boolean, optional, 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.
non_negative : boolean, optional, default False

When True, an absolute value is applied to the features matrix prior to returning it. When used in conjunction with alternate_sign=True, this significantly reduces the inner product preservation property.

Deprecated since version 0.19: This option will be removed in 0.21.

>>> 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.]])

DictVectorizer : vectorizes string-valued features using a hash table. sklearn.preprocessing.OneHotEncoder : handles nominal/categorical features

encoded as columns of integers.
fit(X=None, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

No-op.

This method doesn’t do anything. It exists purely for compatibility with the scikit-learn transformer API.

X : array-like

self : FeatureHasher

fit_transform(X, y=None, **fit_params)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
transform(raw_X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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

raw_X : iterable over iterable over raw features, length = n_samples
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.
X : scipy.sparse matrix, shape = (n_samples, self.n_features)
Feature matrix, for use with estimators or further transformers.