Class FeatureHasherScikitsLearnNode

Implements feature hashing, aka the hashing trick.

This node has been automatically generated by wrapping the ``sklearn.feature_extraction.hashing.FeatureHasher`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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 :ref:`User Guide <feature_hashing>`.

**Parameters**

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.
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.
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).
non_negative : boolean, optional, default False
    Whether output matrices should contain non-negative values only;
    effectively calls abs on the matrix prior to returning it.
    When True, output values can be interpreted as frequencies.
    When False, output values will have expected value zero.

**Examples**

>>> 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.]])

See also

DictVectorizer : vectorizes string-valued features using a hash table.
sklearn.preprocessing.OneHotEncoder : handles nominal/categorical features
  encoded as columns of integers.

Overrides: object.__init__

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

This node has been automatically generated by wrapping the sklearn.feature_extraction.hashing.FeatureHasher class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

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.

y : (ignored)

Returns

X : scipy.sparse matrix, shape = (n_samples, self.n_features)

Feature matrix, for use with estimators or further transformers.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

No-op.

This node has been automatically generated by wrapping the sklearn.feature_extraction.hashing.FeatureHasher class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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

Returns

self : FeatureHasher

Overrides: Node.stop_training