lale.lib.autogen.lasso_lars_ic module¶
- class lale.lib.autogen.lasso_lars_ic.LassoLarsIC(*, criterion='aic', fit_intercept=True, verbose=False, precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, positive=False)¶
Bases:
PlannedIndividualOp
Combined schema for expected data and hyperparameters.
This documentation is auto-generated from JSON schemas.
- Parameters
criterion (‘bic’ or ‘aic’, default ‘aic’) – The type of criterion to use.
fit_intercept (boolean, default True) – whether to calculate the intercept for this model
verbose (union type, not for optimizer, default False) –
Sets the verbosity amount
boolean
or integer
precompute (union type, default 'auto') –
Whether to use a precomputed Gram matrix to speed up calculations
array, not for optimizer of items : Any
or boolean
or ‘auto’
See also constraint-2, constraint-3.
max_iter (integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution, default 500) – Maximum number of iterations to perform
eps (float, >=0.001 for optimizer, <=0.1 for optimizer, loguniform distribution, default 2.220446049250313e-16) – The machine-precision regularization in the computation of the Cholesky diagonal factors
copy_X (boolean, default True) – If True, X will be copied; else, it may be overwritten.
positive (boolean, default False) – Restrict coefficients to be >= 0
Notes
constraint-1 : any type
constraint-2 : union type
X cannot be None if Gram is not NoneUse lars_path_gram to avoid passing X and y.)
any type
or precompute : None
constraint-3 : union type
From /linear_model/_least_angle.py:None:_lars_path_solver, Exception: raise ValueError(‘X and Gram cannot both be unspecified.’)
precompute : negated type of None or False
or any type
- 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 (array of items : array of items : float) – training data.
y (array of items : float) – target values
copy_X (boolean, optional, default True) – If
True
, X will be copied; else, it may be overwritten.
- 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