snowflake.ml.modeling.kernel_approximation.PolynomialCountSketch¶

class snowflake.ml.modeling.kernel_approximation.PolynomialCountSketch(*, gamma=1.0, degree=2, coef0=0, n_components=100, 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

Polynomial kernel approximation via Tensor Sketch For more details on this class, see sklearn.kernel_approximation.PolynomialCountSketch

gamma: float, default=1.0

Parameter of the polynomial kernel whose feature map will be approximated.

degree: int, default=2

Degree of the polynomial kernel whose feature map will be approximated.

coef0: int, default=0

Constant term of the polynomial kernel whose feature map will be approximated.

n_components: int, default=100

Dimensionality of the output feature space. Usually, n_components should be greater than the number of features in input samples in order to achieve good performance. The optimal score / run time balance is typically achieved around n_components = 10 * n_features, but this depends on the specific dataset being used.

random_state: int, RandomState instance, default=None

Determines random number generation for indexHash and bitHash initialization. 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

fit(dataset)	Fit the model with X For more details on this function, see sklearn.kernel_approximation.PolynomialCountSketch.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.kernel_approximation.PolynomialCountSketch object.
transform(dataset)	Generate the feature map approximation for X For more details on this function, see sklearn.kernel_approximation.PolynomialCountSketch.transform

Attributes

model_signatures

Returns model signature of current class.