sklearn.feature_extraction.FeatureHasher  
class sklearn.feature_extraction.FeatureHasher(n_features=1048576, *, input_type='dict', dtype=<class 'numpy.float64'>, alternate_sign=True) [source]
 
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.  New in version 0.13.   Parameters 
 
n_featuresint, default=2**20 

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{“dict”, “pair”, “string”}, 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).  
dtypenumpy dtype, 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_signbool, 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.  .. versionchanged:: 0.19

alternate_sign replaces the now deprecated non_negative parameter.      See also  
DictVectorizer


Vectorizes string-valued features using a hash table.  
sklearn.preprocessing.OneHotEncoder


Handles nominal/categorical features.    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.]])
 Methods  
fit([X, y]) No-op.  
fit_transform(X[, y]) Fit to data, then transform it.  
get_params([deep]) Get parameters for this estimator.  
set_params(**params) Set the parameters of this estimator.  
transform(raw_X) Transform a sequence of instances to a scipy.sparse matrix.    
fit(X=None, y=None) [source]
 
No-op. This method doesn’t do anything. It exists purely for compatibility with the scikit-learn transformer API.  Parameters 
 
Xndarray 
  Returns 
 
selfFeatureHasher 
   
  
fit_transform(X, y=None, **fit_params) [source]
 
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Input samples.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None 

Target values (None for unsupervised transformations).  
**fit_paramsdict 

Additional fit parameters.    Returns 
 
X_newndarray array of shape (n_samples, n_features_new) 

Transformed array.     
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

Parameter names mapped to their values.     
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
  
transform(raw_X) [source]
 
Transform a sequence of instances to a scipy.sparse matrix.  Parameters 
 
raw_Xiterable 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.    Returns 
 
Xsparse matrix of shape (n_samples, n_features) 

Feature matrix, for use with estimators or further transformers.     
 
 Examples using sklearn.feature_extraction.FeatureHasher
 
  FeatureHasher and DictVectorizer Comparison