OrthogonalMatchingPursuit (original) (raw)
class sklearn.linear_model.OrthogonalMatchingPursuit(*, n_nonzero_coefs=None, tol=None, fit_intercept=True, precompute='auto')[source]#
Orthogonal Matching Pursuit model (OMP).
Read more in the User Guide.
Parameters:
n_nonzero_coefsint, default=None
Desired number of non-zero entries in the solution. Ignored if tol is set. When None and tol is also None, this value is either set to 10% ofn_features or 1, whichever is greater.
tolfloat, default=None
Maximum squared norm of the residual. If not None, overrides n_nonzero_coefs.
fit_interceptbool, 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).
precompute‘auto’ or bool, default=’auto’
Whether to use a precomputed Gram and Xy matrix to speed up calculations. Improves performance when n_targets orn_samples is very large. Note that if you already have such matrices, you can pass them directly to the fit method.
Attributes:
**coef_**ndarray of shape (n_features,) or (n_targets, n_features)
Parameter vector (w in the formula).
**intercept_**float or ndarray of shape (n_targets,)
Independent term in decision function.
**n_iter_**int or array-like
Number of active features across every target.
**n_nonzero_coefs_**int or None
The number of non-zero coefficients in the solution or None when tol is set. If n_nonzero_coefs is None and tol is None this value is either set to 10% of n_features or 1, whichever is greater.
**n_features_in_**int
Number of features seen during fit.
Added in version 0.24.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when Xhas feature names that are all strings.
Added in version 1.0.
See also
Solves n_targets Orthogonal Matching Pursuit problems.
Solves n_targets Orthogonal Matching Pursuit problems using only the Gram matrix X.T * X and the product X.T * y.
Compute Least Angle Regression or Lasso path using LARS algorithm.
Least Angle Regression model a.k.a. LAR.
Lasso model fit with Least Angle Regression a.k.a. Lars.
sklearn.decomposition.sparse_encode
Generic sparse coding. Each column of the result is the solution to a Lasso problem.
Cross-validated Orthogonal Matching Pursuit model (OMP).
Notes
Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415. (https://www.di.ens.fr/~mallat/papiers/MallatPursuit93.pdf)
This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad, M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit Technical Report - CS Technion, April 2008.https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
Examples
from sklearn.linear_model import OrthogonalMatchingPursuit from sklearn.datasets import make_regression X, y = make_regression(noise=4, random_state=0) reg = OrthogonalMatchingPursuit().fit(X, y) reg.score(X, y) 0.9991... reg.predict(X[:1,]) array([-78.3854...])
Fit the model using X, y as training data.
Parameters:
Xarray-like of shape (n_samples, n_features)
Training data.
yarray-like of shape (n_samples,) or (n_samples, n_targets)
Target values. Will be cast to X’s dtype if necessary.
Returns:
selfobject
Returns an instance of self.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating routing information.
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.
Predict using the linear model.
Parameters:
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples.
Returns:
Carray, shape (n_samples,)
Returns predicted values.
score(X, y, sample_weight=None)[source]#
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:
Xarray-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_fittedis the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
Returns:
scorefloat
\(R^2\) of self.predict(X) w.r.t. y.
Notes
The \(R^2\) score used when calling score on a regressor usesmultioutput='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 forMultiOutputRegressor).
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.
set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → OrthogonalMatchingPursuit[source]#
Request metadata passed to the score method.
Note that this method is only relevant ifenable_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 toscoreif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it toscore.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.
Added 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 aPipeline. Otherwise it has no effect.
Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in score.
Returns:
selfobject
The updated object.