sklearn.kernel_approximation.PolynomialCountSketch  
class sklearn.kernel_approximation.PolynomialCountSketch(*, gamma=1.0, degree=2, coef0=0, n_components=100, random_state=None) [source]
 
Polynomial kernel approximation via Tensor Sketch. Implements Tensor Sketch, which approximates the feature map of the polynomial kernel: K(X, Y) = (gamma * <X, Y> + coef0)^degree
 by efficiently computing a Count Sketch of the outer product of a vector with itself using Fast Fourier Transforms (FFT). Read more in the User Guide.  New in version 0.24.   Parameters 
 
gammafloat, default=1.0 

Parameter of the polynomial kernel whose feature map will be approximated.  
degreeint, default=2 

Degree of the polynomial kernel whose feature map will be approximated.  
coef0int, default=0 

Constant term of the polynomial kernel whose feature map will be approximated.  
n_componentsint, default=100 

Dimensionality of the output feature space. Usually, n_components should be greater than the number of features in input samples in order to achieve good performance. The optimal score / run time balance is typically achieved around n_components = 10 * n_features, but this depends on the specific dataset being used.  
random_stateint, RandomState instance, default=None 

Determines random number generation for indexHash and bitHash initialization. Pass an int for reproducible results across multiple function calls. See Glossary.    Attributes 
 
indexHash_ndarray of shape (degree, n_features), dtype=int64 

Array of indexes in range [0, n_components) used to represent the 2-wise independent hash functions for Count Sketch computation.  
bitHash_ndarray of shape (degree, n_features), dtype=float32 

Array with random entries in {+1, -1}, used to represent the 2-wise independent hash functions for Count Sketch computation.     Examples >>> from sklearn.kernel_approximation import PolynomialCountSketch
>>> from sklearn.linear_model import SGDClassifier
>>> X = [[0, 0], [1, 1], [1, 0], [0, 1]]
>>> y = [0, 0, 1, 1]
>>> ps = PolynomialCountSketch(degree=3, random_state=1)
>>> X_features = ps.fit_transform(X)
>>> clf = SGDClassifier(max_iter=10, tol=1e-3)
>>> clf.fit(X_features, y)
SGDClassifier(max_iter=10)
>>> clf.score(X_features, y)
1.0
 Methods  
fit(X[, y]) Fit the model with X.  
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(X) Generate the feature map approximation for X.    
fit(X, y=None) [source]
 
Fit the model with X. Initializes the internal variables. The method needs no information about the distribution of data, so we only care about n_features in X.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

Training data, where n_samples in the number of samples and n_features is the number of features.    Returns 
 
selfobject 

Returns the transformer.     
  
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(X) [source]
 
Generate the feature map approximation for X.  Parameters 
 
X{array-like}, shape (n_samples, n_features) 

New data, where n_samples in the number of samples and n_features is the number of features.    Returns 
 
X_newarray-like, shape (n_samples, n_components) 
   
 
 Examples using sklearn.kernel_approximation.PolynomialCountSketch
 
  Release Highlights for scikit-learn 0.24  

  Scalable learning with polynomial kernel aproximation