snowflake.ml.modeling.linear_model.LassoLars¶

class snowflake.ml.modeling.linear_model.LassoLars(*, alpha=1.0, fit_intercept=True, verbose=False, normalize='deprecated', precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False, jitter=None, random_state=None, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Lasso model fit with Least Angle Regression a For more details on this class, see sklearn.linear_model.LassoLars

alpha: float, default=1.0: Constant that multiplies the penalty term. Defaults to 1.0. alpha = 0 is equivalent to an ordinary least square, solved by LinearRegression. For numerical reasons, using alpha = 0 with the LassoLars object is not advised and you should prefer the LinearRegression object.
fit_intercept: bool, default=True: 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).
verbose: bool or int, default=False: Sets the verbosity amount.
normalize: bool, default=False: 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 StandardScaler before calling fit on an estimator with normalize=False.
precompute: bool, ‘auto’ or array-like, default=’auto’: Whether to use a precomputed Gram matrix to speed up calculations. If set to 'auto' let us decide. The Gram matrix can also be passed as argument.
max_iter: int, default=500: Maximum number of iterations to perform.
eps: float, default=np.finfo(float).eps: The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the tol parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization.
copy_X: bool, default=True: If True, X will be copied; else, it may be overwritten.
fit_path: bool, default=True: If True the full path is stored in the coef_path_ attribute. If you compute the solution for a large problem or many targets, setting fit_path to False will lead to a speedup, especially with a small alpha.
positive: bool, default=False: Restrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set True by default. Under the positive restriction the model coefficients will not converge to the ordinary-least-squares solution for small values of alpha. Only coefficients up to the smallest alpha value (alphas_[alphas_ > 0.].min() when fit_path=True) reached by the stepwise Lars-Lasso algorithm are typically in congruence with the solution of the coordinate descent Lasso estimator.
jitter: float, default=None: Upper bound on a uniform noise parameter to be added to the y values, to satisfy the model’s assumption of one-at-a-time computations. Might help with stability.
random_state: int, RandomState instance or None, default=None: Determines random number generation for jittering. Pass an int for reproducible output across multiple function calls. See Glossary. Ignored if jitter is None.
input_cols: Optional[Union[str, List[str]]]: A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols and sample-weight_col parameters are considered input columns.
label_cols: Optional[Union[str, List[str]]]: A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).
output_cols: Optional[Union[str, List[str]]]: A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.
sample_weight_col: Optional[str]: A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.
drop_input_cols: Optional[bool], default=False: If set, the response of predict(), transform() methods will not contain input columns.

Methods

`fit`(dataset)	Fit the model using X, y as training data For more details on this function, see sklearn.linear_model.LassoLars.fit
`predict`(dataset)	Predict using the linear model For more details on this function, see sklearn.linear_model.LassoLars.predict
`score`(dataset)	Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.LassoLars.score
`set_input_cols`(input_cols)	Input columns setter.
`to_sklearn`()	Get sklearn.linear_model.LassoLars object.

Attributes

model_signatures

Returns model signature of current class.