snowflake.ml.modeling.covariance.GraphicalLasso¶

class snowflake.ml.modeling.covariance.GraphicalLasso(*, alpha=0.01, mode='cd', covariance=None, tol=0.0001, enet_tol=0.0001, max_iter=100, verbose=False, eps=2.220446049250313e-16, assume_centered=False, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Sparse inverse covariance estimation with an l1-penalized estimator For more details on this class, see sklearn.covariance.GraphicalLasso

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, sample_weight_col, and passthrough_cols parameters are considered input columns. Input columns can also be set after initialization with the set_input_cols method.

label_cols: Optional[Union[str, List[str]]]

This parameter is optional and will be ignored during fit. It is present here for API consistency by convention.

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 must match the expected number of output columns from the specific predictor or transformer class used. If you omit this parameter, output column names are derived by adding an OUTPUT_ prefix to the label column names for supervised estimators, or OUTPUT_<IDX>for unsupervised estimators. These inferred output column names work for predictors, but output_cols must be set explicitly for transformers. In general, explicitly specifying output column names is clearer, especially if you don’t specify the input column names. To transform in place, pass the same names for input_cols and output_cols. be set explicitly for transformers. Output columns can also be set after initialization with the set_output_cols method.

sample_weight_col: Optional[str]

A string representing the column name containing the sample weights. This argument is only required when working with weighted datasets. Sample weight column can also be set after initialization with the set_sample_weight_col method.

passthrough_cols: Optional[Union[str, List[str]]]

A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference. Passthrough columns can also be set after initialization with the set_passthrough_cols method.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

alpha: float, default=0.01

The regularization parameter: the higher alpha, the more regularization, the sparser the inverse covariance. Range is (0, inf].

mode: {‘cd’, ‘lars’}, default=’cd’

The Lasso solver to use: coordinate descent or LARS. Use LARS for very sparse underlying graphs, where p > n. Elsewhere prefer cd which is more numerically stable.

covariance: “precomputed”, default=None

If covariance is “precomputed”, the input data in fit is assumed to be the covariance matrix. If None, the empirical covariance is estimated from the data X.

tol: float, default=1e-4

The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped. Range is (0, inf].

enet_tol: float, default=1e-4

The tolerance for the elastic net solver used to calculate the descent direction. This parameter controls the accuracy of the search direction for a given column update, not of the overall parameter estimate. Only used for mode=’cd’. Range is (0, inf].

max_iter: int, default=100

The maximum number of iterations.

verbose: bool, default=False

If verbose is True, the objective function and dual gap are plotted at each iteration.

eps: float, default=eps

The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Default is np.finfo(np.float64).eps.

assume_centered: bool, default=False

If True, data are not centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If False, data are centered before computation.

Methods

fit(dataset)	Fit the GraphicalLasso model to X For more details on this function, see sklearn.covariance.GraphicalLasso.fit
get_input_cols()	Input columns getter.
get_label_cols()	Label column getter.
get_output_cols()	Output columns getter.
get_params([deep])	Get parameters for this transformer.
get_passthrough_cols()	Passthrough columns getter.
get_sample_weight_col()	Sample weight column getter.
get_sklearn_args([default_sklearn_obj, ...])	Get sklearn keyword arguments.
score(dataset)	Compute the log-likelihood of X_test under the estimated Gaussian model For more details on this function, see sklearn.covariance.GraphicalLasso.score
set_drop_input_cols([drop_input_cols])
set_input_cols(input_cols)	Input columns setter.
set_label_cols(label_cols)	Label column setter.
set_output_cols(output_cols)	Output columns setter.
set_params(**params)	Set the parameters of this transformer.
set_passthrough_cols(passthrough_cols)	Passthrough columns setter.
set_sample_weight_col(sample_weight_col)	Sample weight column setter.
to_sklearn()	Get sklearn.covariance.GraphicalLasso object.

Attributes

model_signatures

Returns model signature of current class.