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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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 usesklearn.preprocessing.StandardScaler
before callingfit
on an estimator withnormalize=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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
-
predict
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Predict using the linear model
- X : {array-like, sparse matrix}, shape = (n_samples, n_features)
- Samples.
- C : array, shape = (n_samples,)
- Returns predicted values.
- A parameter
-
score
(X, y, sample_weight=None)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
- A parameter