class_docstr: 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.
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code: 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
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/feature_extraction/hashing.py
imports: import numbers
  import numpy
  import scipy.sparse
  from .None import _hashing
  from ..base import BaseEstimator, TransformerMixin
label_desc: Use image node_id 1 to create a new FeatureHasher object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = FeatureHasher(n_features, input_type, dtype, non_negative)