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# `sklearn.kernel_approximation`.SkewedChi2Sampler

class sklearn.kernel_approximation.SkewedChi2Sampler(*, skewedness=1.0, n_components=100, random_state=None)

  Approximate feature map for “skewed chi-squared” kernel.

  Read more in the User Guide.

  Parameters:
    skewedness float, default=1.0

      “skewedness” parameter of the kernel. Needs to be cross-validated.

    n_components int, default=100

      Number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.

    random_state int, RandomState instance or None, default=None

      Pseudo-random number generator to control the generation of the random weights and random offset when fitting the training data. Pass an int for reproducible output across multiple function calls. See Glossary.

  Attributes:
    random_weights_ndarray of shape (n_features, n_components)

      Weight array, sampled from a secant hyperbolic distribution, which will be used to linearly transform the log of the data.

    random_offset_ndarray of shape (n_features, n_components)

      Bias term, which will be added to the data. It is uniformly distributed between 0 and 2*pi.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

  See also

  `AdditiveChi2Sampler`

    Approximate feature map for additive chi2 kernel.

  `Nystroem`

    Approximate a kernel map using a subset of the training data.

  `RBFSampler`

    Approximate a RBF kernel feature map using random Fourier features.

  `SkewedChi2Sampler`

    Approximate feature map for “skewed chi-squared” kernel.

  `sklearn.metrics.pairwise.chi2_kernel`

    The exact chi squared kernel.

  `sklearn.metrics.pairwise.kernel_metrics`

    List of built-in kernels.

  References

  See “Random Fourier Approximations for Skewed Multiplicative Histogram Kernels” by Fuxin Li, Catalin Ionescu and Cristian Sminchisescu.

  Examples

      >>> from sklearn.kernel_approximation import SkewedChi2Sampler
      >>> from sklearn.linear_model import SGDClassifier
      >>> X = [[0, 0], [1, 1], [1, 0], [0, 1]]
      >>> y = [0, 0, 1, 1]
      >>> chi2_feature = SkewedChi2Sampler(skewedness=.01,
      ...                                  n_components=10,
      ...                                  random_state=0)
      >>> X_features = chi2_feature.fit_transform(X, y)
      >>> 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_feature_names_out`([input_features]) | Get output feature names for transformation.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `transform`(X) | Apply the approximate feature map to X.

  fit(X, y=None)

    Fit the model with X.

    Samples random projection according to n_features.

    Parameters:
      X array-like, shape (n_samples, n_features)

        Training data, where `n_samples` is the number of samples and `n_features` is the number of features.

      y array-like, shape (n_samples,) or (n_samples, n_outputs), default=None

        Target values (None for unsupervised transformations).

    Returns:
      self object

        Returns the instance itself.

  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 array-like of shape (n_samples, n_features)

        Input samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs), default=None

        Target values (None for unsupervised transformations).

      **fit_params dict

        Additional fit parameters.

    Returns:
      X_new ndarray array of shape (n_samples, n_features_new)

        Transformed array.

  get_feature_names_out(input_features=None)

    Get output feature names for transformation.

    The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: `["class_name0", "class_name1", "class_name2"]`.

    Parameters:
      input_features array-like of str or None, default=None

        Only used to validate feature names with the names seen in `fit`.

    Returns:
      feature_names_out ndarray of str objects

        Transformed feature names.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

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

    Returns:
      params dict

        Parameter names mapped to their values.

  set_output(*, transform=None)

    Set output container.

    See Introducing the set_output API for an example on how to use the API.

    Parameters:
      transform{“default”, “pandas”}, default=None

        Configure output of `transform` and `fit_transform`.

        - `"default"`: Default output format of a transformer

        - `"pandas"`: DataFrame output

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**params)

    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:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  transform(X)

    Apply the approximate feature map to X.

    Parameters:
      X array-like, shape (n_samples, n_features)

        New data, where `n_samples` is the number of samples and `n_features` is the number of features. All values of X must be strictly greater than “-skewedness”.

    Returns:
      X_new array-like, shape (n_samples, n_components)

        Returns the instance itself.