3.2.4.1.8. sklearn.linear_model
.OrthogonalMatchingPursuitCV¶

class
sklearn.linear_model.
OrthogonalMatchingPursuitCV
(*, copy=True, fit_intercept=True, normalize=True, max_iter=None, cv=None, n_jobs=None, verbose=False)[source]¶ Crossvalidated Orthogonal Matching Pursuit model (OMP).
See glossary entry for crossvalidation estimator.
Read more in the User Guide.
 Parameters
 copybool, optional
Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortranordered, otherwise a copy is made anyway.
 fit_interceptboolean, optional
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).
 normalizeboolean, optional, default True
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 l2norm. If you wish to standardize, please useStandardScaler
before callingfit
on an estimator withnormalize=False
. max_iterinteger, optional
Maximum numbers of iterations to perform, therefore maximum features to include. 10% of
n_features
but at least 5 if available. cvint, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
None, to use the default 5fold crossvalidation,
integer, to specify the number of folds.
An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs,
KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
Changed in version 0.22:
cv
default value if None changed from 3fold to 5fold. n_jobsint or None, optional (default=None)
Number of CPUs to use during the cross validation.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details. verboseboolean or integer, optional
Sets the verbosity amount
 Attributes
 intercept_float or array, shape (n_targets,)
Independent term in decision function.
 coef_array, shape (n_features,) or (n_targets, n_features)
Parameter vector (w in the problem formulation).
 n_nonzero_coefs_int
Estimated number of nonzero coefficients giving the best mean squared error over the crossvalidation folds.
 n_iter_int or arraylike
Number of active features across every target for the model refit with the best hyperparameters got by crossvalidating across all folds.
See also
orthogonal_mp
orthogonal_mp_gram
lars_path
Lars
LassoLars
OrthogonalMatchingPursuit
LarsCV
LassoLarsCV
decomposition.sparse_encode
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_params
([deep])Get parameters for this estimator.
predict
(X)Predict using the linear model.
score
(X, y[, sample_weight])Return the coefficient of determination R^2 of the prediction.
set_params
(**params)Set the parameters of this estimator.

fit
(X, y)[source]¶ Fit the model using X, y as training data.
 Parameters
 Xarraylike, shape [n_samples, n_features]
Training data.
 yarraylike, shape [n_samples]
Target values. Will be cast to X’s dtype if necessary
 Returns
 selfobject
returns an instance of self.

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
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(X)[source]¶ Predict using the linear model.
 Parameters
 Xarraylike 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 R^2 of the prediction.
The coefficient R^2 is defined as (1  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
 Xarraylike 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, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
R^2 of self.predict(X) wrt. y.
Notes
The R2 score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).

set_params
(**params)[source]¶ 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. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.