lale.lib.autogen.lasso_cv module¶
- class lale.lib.autogen.lasso_cv.LassoCV(*, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, precompute='auto', max_iter=1000, tol=0.0001, copy_X=True, cv, verbose=False, n_jobs=1, positive=False, random_state=None, selection='cyclic')¶
Bases:
PlannedIndividualOp
Combined schema for expected data and hyperparameters.
This documentation is auto-generated from JSON schemas.
- Parameters
eps (float, >=0.001 for optimizer, <=0.1 for optimizer, loguniform distribution, default 0.001) – Length of the path
n_alphas (integer, >=100 for optimizer, <=101 for optimizer, uniform distribution, default 100) – Number of alphas along the regularization path
alphas (union type, not for optimizer, default None) –
List of alphas where to compute the models
array of items : Any
or None
fit_intercept (boolean, default True) – whether to calculate the intercept for this model
precompute (union type, default 'auto') –
Whether to use a precomputed Gram matrix to speed up calculations
array, not for optimizer of items : Any
or ‘auto’
max_iter (integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution, default 1000) – The maximum number of iterations
tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0.0001) – The tolerance for the optimization: if the updates are smaller than
tol
, the optimization code checks the dual gap for optimality and continues until it is smaller thantol
.copy_X (boolean, default True) – If
True
, X will be copied; else, it may be overwritten.cv (union type) –
- Cross-validation as integer or as object that has a split function.
The fit method performs cross validation on the input dataset for per trial, and uses the mean cross validation performance for optimization. This behavior is also impacted by handle_cv_failure flag. If integer: number of folds in sklearn.model_selection.StratifiedKFold. If object with split function: generator yielding (train, test) splits as arrays of indices. Can use any of the iterators from https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators.
integer, >=1, >=3 for optimizer, <=4 for optimizer, uniform distribution, default 5
or Any, not for optimizer
verbose (union type, not for optimizer, default False) –
Amount of verbosity.
boolean
or integer
n_jobs (union type, not for optimizer, default 1) –
Number of CPUs to use during the cross validation
integer
or None
positive (boolean, default False) – If positive, restrict regression coefficients to be positive
random_state (union type, not for optimizer, default None) –
The seed of the pseudo random number generator that selects a random feature to update
integer
or numpy.random.RandomState
or None
selection (‘random’ or ‘cyclic’, default ‘cyclic’) – If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default
- fit(X, y=None, **fit_params)¶
Train the operator.
Note: The fit method is not available until this operator is trainable.
Once this method is available, it will have the following signature:
- Parameters
X (Any) – Training data
y (union type) –
Target values
array of items : float
or array of items : array of items : float
- predict(X, **predict_params)¶
Make predictions.
Note: The predict method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (union type) –
Samples.
array of items : Any
or array of items : array of items : float
- Returns
result – Returns predicted values.
- Return type
array of items : float