snowflake.ml.modeling.linear_model.MultiTaskElasticNetCV¶
- class snowflake.ml.modeling.linear_model.MultiTaskElasticNetCV(*, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, max_iter=1000, tol=0.0001, cv=None, copy_X=True, verbose=0, n_jobs=None, 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:
BaseTransformerMulti-task L1/L2 ElasticNet with built-in cross-validation For more details on this class, see sklearn.linear_model.MultiTaskElasticNetCV
- l1_ratio: float or list of float, default=0.5
The ElasticNet mixing parameter, with 0 < l1_ratio <= 1. For l1_ratio = 1 the penalty is an L1/L2 penalty. For l1_ratio = 0 it is an L2 penalty. For
0 < l1_ratio < 1, the penalty is a combination of L1/L2 and L2. This parameter can be a list, in which case the different values are tested by cross-validation and the one giving the best prediction score is used. Note that a good choice of list of values for l1_ratio is often to put more values close to 1 (i.e. Lasso) and less close to 0 (i.e. Ridge), as in[.1, .5, .7, .9, .95, .99, 1].- eps: float, default=1e-3
Length of the path.
eps=1e-3means thatalpha_min / alpha_max = 1e-3.- n_alphas: int, default=100
Number of alphas along the regularization path.
- alphas: array-like, default=None
List of alphas where to compute the models. If not provided, set automatically.
- 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).
- 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 thantol.- cv: int, cross-validation generator or iterable, default=None
Determines the cross-validation splitting strategy. Possible inputs for cv are:
None, to use the default 5-fold cross-validation,
int, to specify the number of folds.
CV splitter,
An iterable yielding (train, test) splits as arrays of indices.
For int/None inputs,
KFoldis used.Refer User Guide for the various cross-validation strategies that can be used here.
- copy_X: bool, default=True
If
True, X will be copied; else, it may be overwritten.- verbose: bool or int, default=0
Amount of verbosity.
- n_jobs: int, default=None
Number of CPUs to use during the cross validation. Note that this is used only if multiple values for l1_ratio are given.
Nonemeans 1 unless in ajoblib.parallel_backendcontext.-1means using all processors. See Glossary for more details.- 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 MultiTaskElasticNet model with coordinate descent For more details on this function, see sklearn.linear_model.MultiTaskElasticNetCV.fit
predict(dataset)Predict using the linear model For more details on this function, see sklearn.linear_model.MultiTaskElasticNetCV.predict
score(dataset)Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.MultiTaskElasticNetCV.score
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.linear_model.MultiTaskElasticNetCV object.
Attributes
model_signaturesReturns model signature of current class.