# OrthogonalMatchingPursuitCV¶

class ibex.sklearn.linear_model.OrthogonalMatchingPursuitCV(copy=True, fit_intercept=True, normalize=True, max_iter=None, cv=None, n_jobs=1, verbose=False)

Bases: sklearn.linear_model.omp.OrthogonalMatchingPursuitCV, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Cross-validated Orthogonal Matching Pursuit model (OMP)

Read more in the User Guide.

copy : bool, optional
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 : boolean, optional
whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
normalize : boolean, 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 l2-norm. If you wish to standardize, please use sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False.
max_iter : integer, optional
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 an iterable, optional

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

• None, to use the default 3-fold cross-validation,
• integer, to specify the number of folds.
• An object to be used as a cross-validation generator.
• An iterable yielding train/test splits.

For integer/None inputs, KFold is used.

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

n_jobs : integer, optional
Number of CPUs to use during the cross validation. If -1, use all the CPUs
verbose : boolean or integer, optional
Sets the verbosity amount
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 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.

orthogonal_mp orthogonal_mp_gram lars_path Lars LassoLars OrthogonalMatchingPursuit LarsCV LassoLarsCV decomposition.sparse_encode

fit(X, y)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit the model using X, y as training data.

X : array-like, shape [n_samples, n_features]
Training data.
y : array-like, shape [n_samples]
Target values. Will be cast to X’s dtype if necessary
self : object
returns an instance of self.
predict(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Predict using the linear model

X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.
C : array, shape = (n_samples,)
Returns predicted values.
score(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Returns 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.

X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
True values for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
score : float
R^2 of self.predict(X) wrt. y.