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# `sklearn.kernel_approximation`.AdditiveChi2Sampler

class sklearn.kernel_approximation.AdditiveChi2Sampler(*, sample_steps=2, sample_interval=None)

  Approximate feature map for additive chi2 kernel.

  Uses sampling the fourier transform of the kernel characteristic at regular intervals.

  Since the kernel that is to be approximated is additive, the components of the input vectors can be treated separately. Each entry in the original space is transformed into 2*sample_steps-1 features, where sample_steps is a parameter of the method. Typical values of sample_steps include 1, 2 and 3.

  Optimal choices for the sampling interval for certain data ranges can be computed (see the reference). The default values should be reasonable.

  Read more in the User Guide.

  Parameters:
    sample_steps int, default=2

      Gives the number of (complex) sampling points.

    sample_interval float, default=None

      Sampling interval. Must be specified when sample_steps not in {1,2,3}.

  Attributes:
    sample_interval_float

      Stored sampling interval. Specified as a parameter if `sample_steps` not in {1,2,3}.

      Deprecated since version 1.3: `sample_interval_` serves internal purposes only and will be removed in 1.5.

    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

  `SkewedChi2Sampler`

    A Fourier-approximation to a non-additive variant of the chi squared kernel.

  `sklearn.metrics.pairwise.chi2_kernel`

    The exact chi squared kernel.

  `sklearn.metrics.pairwise.additive_chi2_kernel`

    The exact additive chi squared kernel.

  Notes

  This estimator approximates a slightly different version of the additive chi squared kernel then `metric.additive_chi2` computes.

  This estimator is stateless and does not need to be fitted. However, we recommend to call `fit_transform` instead of `transform`, as parameter validation is only performed in `fit`.

  References

  See “Efficient additive kernels via explicit feature maps” A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence, 2011

  Examples

      >>> from sklearn.datasets import load_digits
      >>> from sklearn.linear_model import SGDClassifier
      >>> from sklearn.kernel_approximation import AdditiveChi2Sampler
      >>> X, y = load_digits(return_X_y=True)
      >>> chi2sampler = AdditiveChi2Sampler(sample_steps=2)
      >>> X_transformed = chi2sampler.fit_transform(X, y)
      >>> clf = SGDClassifier(max_iter=5, random_state=0, tol=1e-3)
      >>> clf.fit(X_transformed, y)
      SGDClassifier(max_iter=5, random_state=0)
      >>> clf.score(X_transformed, y)
      0.9499...

  Methods

  `fit`(X[, y]) | Only validates estimator's parameters.
  `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 approximate feature map to X.

  fit(X, y=None)

    Only validates estimator’s parameters.

    This method allows to: (i) validate the estimator’s parameters and (ii) be consistent with the scikit-learn transformer API.

    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 transformer.

  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.

    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 approximate feature map to X.

    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:
      X_new{ndarray, sparse matrix}, shape = (n_samples, n_features * (2*sample_steps - 1))

        Whether the return value is an array or sparse matrix depends on the type of the input X.