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, 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

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.

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 GraphicalLasso model to X For more details on this function, see sklearn.covariance.GraphicalLasso.fit
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_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.covariance.GraphicalLasso object.

Attributes

model_signatures

Returns model signature of current class.