snowflake.ml.modeling.kernel_approximation.RBFSampler¶

class snowflake.ml.modeling.kernel_approximation.RBFSampler(*, gamma=1.0, 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

Approximate a RBF kernel feature map using random Fourier features For more details on this class, see sklearn.kernel_approximation.RBFSampler

gamma: ‘scale’ or float, default=1.0

Parameter of RBF kernel: exp(-gamma * x^2). If gamma='scale' is passed then it uses 1 / (n_features * X.var()) as value of gamma.

n_components: int, default=100

Number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.

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

Pseudo-random number generator to control the generation of the random weights and random offset when fitting the training data. Pass an int for reproducible output 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.RBFSampler.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.kernel_approximation.RBFSampler object.
transform(dataset)	Apply the approximate feature map to X For more details on this function, see sklearn.kernel_approximation.RBFSampler.transform

Attributes

model_signatures

Returns model signature of current class.