snowflake.ml.modeling.linear_model.TheilSenRegressor¶

class snowflake.ml.modeling.linear_model.TheilSenRegressor(*, fit_intercept=True, copy_X=True, max_subpopulation=10000.0, n_subsamples=None, max_iter=300, tol=0.001, random_state=None, n_jobs=None, verbose=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

Theil-Sen Estimator: robust multivariate regression model For more details on this class, see sklearn.linear_model.TheilSenRegressor

fit_intercept: bool, default=True

Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations.

copy_X: bool, default=True

If True, X will be copied; else, it may be overwritten.

max_subpopulation: int, default=1e4

Instead of computing with a set of cardinality ‘n choose k’, where n is the number of samples and k is the number of subsamples (at least number of features), consider only a stochastic subpopulation of a given maximal size if ‘n choose k’ is larger than max_subpopulation. For other than small problem sizes this parameter will determine memory usage and runtime if n_subsamples is not changed. Note that the data type should be int but floats such as 1e4 can be accepted too.

n_subsamples: int, default=None

Number of samples to calculate the parameters. This is at least the number of features (plus 1 if fit_intercept=True) and the number of samples as a maximum. A lower number leads to a higher breakdown point and a low efficiency while a high number leads to a low breakdown point and a high efficiency. If None, take the minimum number of subsamples leading to maximal robustness. If n_subsamples is set to n_samples, Theil-Sen is identical to least squares.

max_iter: int, default=300

Maximum number of iterations for the calculation of spatial median.

tol: float, default=1e-3

Tolerance when calculating spatial median.

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

A random number generator instance to define the state of the random permutations generator. Pass an int for reproducible output across multiple function calls. See Glossary.

n_jobs: int, default=None

Number of CPUs to use during the cross validation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

verbose: bool, default=False

Verbose mode when fitting the model.

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 linear model For more details on this function, see sklearn.linear_model.TheilSenRegressor.fit
predict(dataset)	Predict using the linear model For more details on this function, see sklearn.linear_model.TheilSenRegressor.predict
score(dataset)	Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.TheilSenRegressor.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.linear_model.TheilSenRegressor object.

Attributes

model_signatures

Returns model signature of current class.