snowflake.ml.modeling.kernel_approximation.Nystroem¶

class snowflake.ml.modeling.kernel_approximation.Nystroem(*, kernel='rbf', gamma=None, coef0=None, degree=None, kernel_params=None, n_components=100, random_state=None, n_jobs=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 kernel map using a subset of the training data For more details on this class, see sklearn.kernel_approximation.Nystroem

kernel: str or callable, default=’rbf’

Kernel map to be approximated. A callable should accept two arguments and the keyword arguments passed to this object as kernel_params, and should return a floating point number.

gamma: float, default=None

Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels.

coef0: float, default=None

Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.

degree: float, default=None

Degree of the polynomial kernel. Ignored by other kernels.

kernel_params: dict, default=None

Additional parameters (keyword arguments) for kernel function passed as callable object.

n_components: int, default=100

Number of features to construct. How many data points will be used to construct the mapping.

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

Pseudo-random number generator to control the uniform sampling without replacement of n_components of the training data to construct the basis kernel. Pass an int for reproducible output across multiple function calls. See Glossary.

n_jobs: int, default=None

The number of jobs to use for the computation. This works by breaking down the kernel matrix into n_jobs even slices and computing them in parallel.

None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

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 estimator to data For more details on this function, see sklearn.kernel_approximation.Nystroem.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.kernel_approximation.Nystroem object.
transform(dataset)	Apply feature map to X For more details on this function, see sklearn.kernel_approximation.Nystroem.transform

Attributes

model_signatures

Returns model signature of current class.