/usr/lib/python3/dist-packages/sklearn/feature_extraction/hashing.py is in python3-sklearn 0.17.0-4.
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# License: BSD 3 clause
import numbers
import numpy as np
import scipy.sparse as sp
from . import _hashing
from ..base import BaseEstimator, TransformerMixin
def _iteritems(d):
"""Like d.iteritems, but accepts any collections.Mapping."""
return d.iteritems() if hasattr(d, "iteritems") else d.items()
class FeatureHasher(BaseEstimator, TransformerMixin):
"""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 :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.
"""
def __init__(self, n_features=(2 ** 20), input_type="dict",
dtype=np.float64, non_negative=False):
self._validate_params(n_features, input_type)
self.dtype = dtype
self.input_type = input_type
self.n_features = n_features
self.non_negative = non_negative
@staticmethod
def _validate_params(n_features, input_type):
# strangely, np.int16 instances are not instances of Integral,
# while np.int64 instances are...
if not isinstance(n_features, (numbers.Integral, np.integer)):
raise TypeError("n_features must be integral, got %r (%s)."
% (n_features, type(n_features)))
elif n_features < 1 or n_features >= 2 ** 31:
raise ValueError("Invalid number of features (%d)." % n_features)
if input_type not in ("dict", "pair", "string"):
raise ValueError("input_type must be 'dict', 'pair' or 'string',"
" got %r." % input_type)
def fit(self, X=None, y=None):
"""No-op.
This method doesn't do anything. It exists purely for compatibility
with the scikit-learn transformer API.
Returns
-------
self : FeatureHasher
"""
# repeat input validation for grid search (which calls set_params)
self._validate_params(self.n_features, self.input_type)
return self
def transform(self, raw_X, y=None):
"""Transform a sequence of instances to a scipy.sparse matrix.
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.
"""
raw_X = iter(raw_X)
if self.input_type == "dict":
raw_X = (_iteritems(d) for d in raw_X)
elif self.input_type == "string":
raw_X = (((f, 1) for f in x) for x in raw_X)
indices, indptr, values = \
_hashing.transform(raw_X, self.n_features, self.dtype)
n_samples = indptr.shape[0] - 1
if n_samples == 0:
raise ValueError("Cannot vectorize empty sequence.")
X = sp.csr_matrix((values, indices, indptr), dtype=self.dtype,
shape=(n_samples, self.n_features))
X.sum_duplicates() # also sorts the indices
if self.non_negative:
np.abs(X.data, X.data)
return X
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