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:
BaseTransformerLasso 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 = 0is equivalent to an ordinary least square, solved byLinearRegression. For numerical reasons, usingalpha = 0with 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_interceptis 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 useStandardScalerbefore callingfiton an estimator withnormalize=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
tolparameter 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
Truethe full path is stored in thecoef_path_attribute. If you compute the solution for a large problem or many targets, settingfit_pathtoFalsewill 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_signaturesReturns model signature of current class.