pyrfm.random_feature.RandomProjection¶
-
class
pyrfm.random_feature.RandomProjection(n_components=100, distribution='rademacher', p_sparse='auto', random_state=None)[source]¶ Bases:
sklearn.base.BaseEstimator,sklearn.base.TransformerMixinApproximates feature map of the linear product kernel by Random Projection.
- Parameters
n_components (int (default=100)) – Number of Monte Carlo samples per original features. Equals the dimensionality of the computed (mapped) feature space.
distribution (str or function (default="rademacher")) – A function for sampling random bases. Its arguments must be random_state and size. If None, the Rademacher distribution is used.
p_sparse (float (default="auto")) – Sparsity parameter for “sparse_rademacher” distribution. If p_sparse = 0, “sparse_rademacher” is equivalent to “rademacher”. The relationship between p_sparse and s in [1] is s = 1/(1-p). If auto, p_sparse = 1 - 1/sqrt(n_features), recommended in [1].
random_state (int, RandomState instance or None, optional (default=None)) – If int, random_state is the seed used by the random number generator; If np.RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
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random_weights_¶ The sampled basis.
- Type
array, or sparse matrix, shape (n_features, n_components)
References
[1] Very Sparse Random Projections. Ping Li, T. Hastie, and K. W. Church. In KDD 2006. (https://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf)
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fit(X, y=None)[source]¶ Generate random weights according to n_features.
- Parameters
X ({array-like, sparse matrix}, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.
- Returns
self – Returns the transformer.
- Return type
object
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fit_transform(X, y=None, **fit_params)¶ 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
X (numpy array of shape [n_samples, n_features]) – Training set.
y (numpy array of shape [n_samples]) – Target values.
- Returns
X_new – Transformed array.
- Return type
numpy array of shape [n_samples, n_features_new]
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get_params(deep=True)¶ Get parameters for this estimator.
- Parameters
deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
mapping of string to any
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set_params(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>so that it’s possible to update each component of a nested object.- Returns
- Return type
self
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transform(X)[source]¶ Apply the approximate feature map to X.
- Parameters
X ({array-like, sparse matrix}, shape (n_samples, n_features)) – New data, where n_samples is the number of samples and n_features is the number of features.
- Returns
X_new
- Return type
array-like, shape (n_samples, n_components)