196 lines
7.1 KiB
Python
196 lines
7.1 KiB
Python
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# Author: Lars Buitinck
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# License: BSD 3 clause
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from numbers import Integral
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from itertools import chain
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import numpy as np
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import scipy.sparse as sp
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from ..base import BaseEstimator, TransformerMixin
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from ._hashing_fast import transform as _hashing_transform
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from ..utils._param_validation import Interval, StrOptions
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def _iteritems(d):
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"""Like d.iteritems, but accepts any collections.Mapping."""
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return d.iteritems() if hasattr(d, "iteritems") else d.items()
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class FeatureHasher(TransformerMixin, BaseEstimator):
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"""Implements feature hashing, aka the hashing trick.
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This class turns sequences of symbolic feature names (strings) into
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scipy.sparse matrices, using a hash function to compute the matrix column
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corresponding to a name. The hash function employed is the signed 32-bit
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version of Murmurhash3.
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Feature names of type byte string are used as-is. Unicode strings are
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converted to UTF-8 first, but no Unicode normalization is done.
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Feature values must be (finite) numbers.
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This class is a low-memory alternative to DictVectorizer and
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CountVectorizer, intended for large-scale (online) learning and situations
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where memory is tight, e.g. when running prediction code on embedded
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devices.
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Read more in the :ref:`User Guide <feature_hashing>`.
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.. versionadded:: 0.13
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Parameters
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----------
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n_features : int, default=2**20
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The number of features (columns) in the output matrices. Small numbers
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of features are likely to cause hash collisions, but large numbers
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will cause larger coefficient dimensions in linear learners.
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input_type : str, default='dict'
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Choose a string from {'dict', 'pair', 'string'}.
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Either "dict" (the default) to accept dictionaries over
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(feature_name, value); "pair" to accept pairs of (feature_name, value);
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or "string" to accept single strings.
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feature_name should be a string, while value should be a number.
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In the case of "string", a value of 1 is implied.
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The feature_name is hashed to find the appropriate column for the
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feature. The value's sign might be flipped in the output (but see
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non_negative, below).
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dtype : numpy dtype, default=np.float64
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The type of feature values. Passed to scipy.sparse matrix constructors
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as the dtype argument. Do not set this to bool, np.boolean or any
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unsigned integer type.
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alternate_sign : bool, default=True
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When True, an alternating sign is added to the features as to
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approximately conserve the inner product in the hashed space even for
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small n_features. This approach is similar to sparse random projection.
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.. versionchanged:: 0.19
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``alternate_sign`` replaces the now deprecated ``non_negative``
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parameter.
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See Also
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--------
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DictVectorizer : Vectorizes string-valued features using a hash table.
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sklearn.preprocessing.OneHotEncoder : Handles nominal/categorical features.
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Notes
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-----
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This estimator is :term:`stateless` and does not need to be fitted.
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However, we recommend to call :meth:`fit_transform` instead of
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:meth:`transform`, as parameter validation is only performed in
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:meth:`fit`.
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Examples
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--------
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>>> from sklearn.feature_extraction import FeatureHasher
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>>> h = FeatureHasher(n_features=10)
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>>> D = [{'dog': 1, 'cat':2, 'elephant':4},{'dog': 2, 'run': 5}]
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>>> f = h.transform(D)
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>>> f.toarray()
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array([[ 0., 0., -4., -1., 0., 0., 0., 0., 0., 2.],
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[ 0., 0., 0., -2., -5., 0., 0., 0., 0., 0.]])
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With `input_type="string"`, the input must be an iterable over iterables of
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strings:
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>>> h = FeatureHasher(n_features=8, input_type="string")
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>>> raw_X = [["dog", "cat", "snake"], ["snake", "dog"], ["cat", "bird"]]
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>>> f = h.transform(raw_X)
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>>> f.toarray()
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array([[ 0., 0., 0., -1., 0., -1., 0., 1.],
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[ 0., 0., 0., -1., 0., -1., 0., 0.],
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[ 0., -1., 0., 0., 0., 0., 0., 1.]])
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"""
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_parameter_constraints: dict = {
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"n_features": [Interval(Integral, 1, np.iinfo(np.int32).max, closed="both")],
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"input_type": [StrOptions({"dict", "pair", "string"})],
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"dtype": "no_validation", # delegate to numpy
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"alternate_sign": ["boolean"],
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}
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def __init__(
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self,
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n_features=(2**20),
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*,
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input_type="dict",
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dtype=np.float64,
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alternate_sign=True,
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):
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self.dtype = dtype
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self.input_type = input_type
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self.n_features = n_features
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self.alternate_sign = alternate_sign
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def fit(self, X=None, y=None):
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"""Only validates estimator's parameters.
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This method allows to: (i) validate the estimator's parameters and
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(ii) be consistent with the scikit-learn transformer API.
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Parameters
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----------
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X : Ignored
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Not used, present here for API consistency by convention.
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y : Ignored
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Not used, present here for API consistency by convention.
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Returns
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-------
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self : object
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FeatureHasher class instance.
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"""
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# repeat input validation for grid search (which calls set_params)
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self._validate_params()
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return self
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def transform(self, raw_X):
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"""Transform a sequence of instances to a scipy.sparse matrix.
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Parameters
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----------
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raw_X : iterable over iterable over raw features, length = n_samples
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Samples. Each sample must be iterable an (e.g., a list or tuple)
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containing/generating feature names (and optionally values, see
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the input_type constructor argument) which will be hashed.
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raw_X need not support the len function, so it can be the result
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of a generator; n_samples is determined on the fly.
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Returns
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-------
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X : sparse matrix of shape (n_samples, n_features)
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Feature matrix, for use with estimators or further transformers.
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"""
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raw_X = iter(raw_X)
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if self.input_type == "dict":
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raw_X = (_iteritems(d) for d in raw_X)
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elif self.input_type == "string":
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first_raw_X = next(raw_X)
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if isinstance(first_raw_X, str):
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raise ValueError(
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"Samples can not be a single string. The input must be an iterable"
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" over iterables of strings."
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)
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raw_X_ = chain([first_raw_X], raw_X)
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raw_X = (((f, 1) for f in x) for x in raw_X_)
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indices, indptr, values = _hashing_transform(
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raw_X, self.n_features, self.dtype, self.alternate_sign, seed=0
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)
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n_samples = indptr.shape[0] - 1
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if n_samples == 0:
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raise ValueError("Cannot vectorize empty sequence.")
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X = sp.csr_matrix(
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(values, indices, indptr),
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dtype=self.dtype,
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shape=(n_samples, self.n_features),
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)
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X.sum_duplicates() # also sorts the indices
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return X
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def _more_tags(self):
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return {"X_types": [self.input_type]}
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