snowflake.ml.modeling.linear_model.ElasticNet¶
- class snowflake.ml.modeling.linear_model.ElasticNet(*, alpha=1.0, l1_ratio=0.5, fit_intercept=True, precompute=False, max_iter=1000, copy_X=True, 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:
BaseTransformerLinear regression with combined L1 and L2 priors as regularizer For more details on this class, see sklearn.linear_model.ElasticNet
- alpha: float, default=1.0
Constant that multiplies the penalty terms. Defaults to 1.0. See the notes for the exact mathematical meaning of this parameter.
alpha = 0is equivalent to an ordinary least square, solved by theLinearRegressionobject. For numerical reasons, usingalpha = 0with theLassoobject is not advised. Given this, you should use theLinearRegressionobject.- l1_ratio: float, default=0.5
The ElasticNet mixing parameter, with
0 <= l1_ratio <= 1. Forl1_ratio = 0the penalty is an L2 penalty.For l1_ratio = 1it is an L1 penalty. For0 < l1_ratio < 1, the penalty is a combination of L1 and L2.- fit_intercept: bool, default=True
Whether the intercept should be estimated or not. If
False, the data is assumed to be already 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
Falseto preserve sparsity.- max_iter: int, default=1000
The maximum number of iterations.
- copy_X: bool, default=True
If
True, X will be copied; else, it may be overwritten.- 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 thantol, 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.ElasticNet.fit
predict(dataset)Predict using the linear model For more details on this function, see sklearn.linear_model.ElasticNet.predict
score(dataset)Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.ElasticNet.score
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.linear_model.ElasticNet object.
Attributes
model_signaturesReturns model signature of current class.