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# `sklearn.linear_model`.OrthogonalMatchingPursuitCV

class sklearn.linear_model.OrthogonalMatchingPursuitCV(*, copy=True, fit_intercept=True, normalize='deprecated', max_iter=None, cv=None, n_jobs=None, verbose=False)

  Cross-validated Orthogonal Matching Pursuit model (OMP).

  See glossary entry for cross-validation estimator.

  Read more in the User Guide.

  Parameters:
    copy bool, default=True

      Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway.

    fit_intercept bool, default=True

      Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).

    normalize bool, default=False

      This parameter is ignored when `fit_intercept` is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use `StandardScaler` before calling `fit` on an estimator with `normalize=False`.

      Changed in version 1.2: default changed from True to False in 1.2.

      Deprecated since version 1.2: `normalize` was deprecated in version 1.2 and will be removed in 1.4.

    max_iter int, default=None

      Maximum numbers of iterations to perform, therefore maximum features to include. 10% of `n_features` but at least 5 if available.

    cv int, cross-validation generator or iterable, default=None

      Determines the cross-validation splitting strategy. Possible inputs for cv are:

      - None, to use the default 5-fold cross-validation,

      - integer, to specify the number of folds.

      - CV splitter,

      - An iterable yielding (train, test) splits as arrays of indices.

      For integer/None inputs, `KFold` is used.

      Refer User Guide for the various cross-validation strategies that can be used here.

      Changed in version 0.22: `cv` default value if None changed from 3-fold to 5-fold.

    n_jobs int, default=None

      Number of CPUs to use during the cross validation. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See Glossary for more details.

    verbose bool or int, default=False

      Sets the verbosity amount.

  Attributes:
    intercept_float or ndarray of shape (n_targets,)

      Independent term in decision function.

    coef_ndarray of shape (n_features,) or (n_targets, n_features)

      Parameter vector (w in the problem formulation).

    n_nonzero_coefs_int

      Estimated number of non-zero coefficients giving the best mean squared error over the cross-validation folds.

    n_iter_int or array-like

      Number of active features across every target for the model refit with the best hyperparameters got by cross-validating across all folds.

    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

  `orthogonal_mp`

    Solves n_targets Orthogonal Matching Pursuit problems.

  `orthogonal_mp_gram`

    Solves n_targets Orthogonal Matching Pursuit problems using only the Gram matrix X.T * X and the product X.T * y.

  `lars_path`

    Compute Least Angle Regression or Lasso path using LARS algorithm.

  `Lars`

    Least Angle Regression model a.k.a. LAR.

  `LassoLars`

    Lasso model fit with Least Angle Regression a.k.a. Lars.

  `OrthogonalMatchingPursuit`

    Orthogonal Matching Pursuit model (OMP).

  `LarsCV`

    Cross-validated Least Angle Regression model.

  `LassoLarsCV`

    Cross-validated Lasso model fit with Least Angle Regression.

  `sklearn.decomposition.sparse_encode`

    Generic sparse coding. Each column of the result is the solution to a Lasso problem.

  Notes

  In `fit`, once the optimal number of non-zero coefficients is found through cross-validation, the model is fit again using the entire training set.

  Examples

      >>> from sklearn.linear_model import OrthogonalMatchingPursuitCV
      >>> from sklearn.datasets import make_regression
      >>> X, y = make_regression(n_features=100, n_informative=10,
      ...                        noise=4, random_state=0)
      >>> reg = OrthogonalMatchingPursuitCV(cv=5).fit(X, y)
      >>> reg.score(X, y)
      0.9991...
      >>> reg.n_nonzero_coefs_
      10
      >>> reg.predict(X[:1,])
      array([-78.3854...])

  Methods

  `fit`(X, y) | Fit the model using X, y as training data.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `predict`(X) | Predict using the linear model.
  `score`(X, y[, sample_weight]) | Return the coefficient of determination of the prediction.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  fit(X, y)

    Fit the model using X, y as training data.

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

        Training data.

      y array-like of shape (n_samples,)

        Target values. Will be cast to X’s dtype if necessary.

    Returns:
      self object

        Returns an instance of self.

  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.

  predict(X)

    Predict using the linear model.

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

        Samples.

    Returns:
      C array, shape (n_samples,)

        Returns predicted values.

  score(X, y, sample_weight=None)

    Return the coefficient of determination of the prediction.

    The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares `((y_true - y_pred)** 2).sum()` and \(v\) is the total sum of squares `((y_true - y_true.mean()) ** 2).sum()`. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of `y`, disregarding the input features, would get a \(R^2\) score of 0.0.

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

        Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape `(n_samples, n_samples_fitted)`, where `n_samples_fitted` is the number of samples used in the fitting for the estimator.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True values for `X`.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

        \(R^2\) of `self.predict(X)` w.r.t. `y`.

    Notes

    The \(R^2\) score used when calling `score` on a regressor uses `multioutput='uniform_average'` from version 0.23 to keep consistent with default value of `r2_score`. This influences the `score` method of all the multioutput regressors (except for `MultiOutputRegressor`).

  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.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → OrthogonalMatchingPursuitCV

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

## Examples using `sklearn.linear_model.OrthogonalMatchingPursuitCV`

Orthogonal Matching Pursuit

Orthogonal Matching Pursuit