snowflake.ml.modeling.linear_model.Lasso¶

class snowflake.ml.modeling.linear_model.Lasso(*, alpha=1.0, fit_intercept=True, precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic', 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

Linear Model trained with L1 prior as regularizer (aka the Lasso) For more details on this class, see sklearn.linear_model.Lasso

alpha: float, default=1.0

Constant that multiplies the L1 term, controlling regularization strength. alpha must be a non-negative float i.e. in [0, inf).

When alpha = 0, the objective is equivalent to ordinary least squares, solved by the LinearRegression object. For numerical reasons, using alpha = 0 with the Lasso object is not advised. Instead, you should use 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).

precompute: bool or array-like of shape (n_features, n_features), default=False

Whether to use a precomputed Gram matrix to speed up calculations. The Gram matrix can also be passed as argument. For sparse input this option is always False to preserve sparsity.

copy_X: bool, default=True

If True, X will be copied; else, it may be overwritten.

max_iter: int, default=1000

The maximum number of iterations.

tol: float, default=1e-4

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 than tol, see Notes below.

warm_start: bool, default=False

When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary.

positive: bool, default=False

When set to True, forces the coefficients to be positive.

random_state: int, RandomState instance, default=None

The seed of the pseudo random number generator that selects a random feature to update. Used when selection == ‘random’. Pass an int for reproducible output across multiple function calls. See Glossary.

selection: {‘cyclic’, ‘random’}, default=’cyclic’

If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e-4.

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 model with coordinate descent For more details on this function, see sklearn.linear_model.Lasso.fit
predict(dataset)	Predict using the linear model For more details on this function, see sklearn.linear_model.Lasso.predict
score(dataset)	Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.Lasso.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.linear_model.Lasso object.

Attributes

model_signatures

Returns model signature of current class.