snowflake.ml.modeling.covariance.EllipticEnvelope¶

class snowflake.ml.modeling.covariance.EllipticEnvelope(*, store_precision=True, assume_centered=False, support_fraction=None, contamination=0.1, 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: BaseTransformer

An object for detecting outliers in a Gaussian distributed dataset For more details on this class, see sklearn.covariance.EllipticEnvelope

store_precision: bool, default=True

Specify if the estimated precision is stored.

assume_centered: bool, default=False

If True, the support of robust location and covariance estimates is computed, and a covariance estimate is recomputed from it, without centering the data. Useful to work with data whose mean is significantly equal to zero but is not exactly zero. If False, the robust location and covariance are directly computed with the FastMCD algorithm without additional treatment.

support_fraction: float, default=None

The proportion of points to be included in the support of the raw MCD estimate. If None, the minimum value of support_fraction will be used within the algorithm: [n_sample + n_features + 1] / 2. Range is (0, 1).

contamination: float, default=0.1

The amount of contamination of the data set, i.e. the proportion of outliers in the data set. Range is (0, 0.5].

random_state: int, RandomState instance or None, default=None

Determines the pseudo random number generator for shuffling the data. Pass an int for reproducible results across multiple function calls. See Glossary.

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

decision_function(dataset[, output_cols_prefix])	Compute the decision function of the given observations For more details on this function, see sklearn.covariance.EllipticEnvelope.decision_function
fit(dataset)	Fit the EllipticEnvelope model For more details on this function, see sklearn.covariance.EllipticEnvelope.fit
predict(dataset)	Predict labels (1 inlier, -1 outlier) of X according to fitted model For more details on this function, see sklearn.covariance.EllipticEnvelope.predict
score(dataset)	Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.covariance.EllipticEnvelope.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.covariance.EllipticEnvelope object.

Attributes

model_signatures

Returns model signature of current class.