snowflake.ml.modeling.decomposition.KernelPCA¶

class snowflake.ml.modeling.decomposition.KernelPCA(*, n_components=None, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None, alpha=1.0, fit_inverse_transform=False, eigen_solver='auto', tol=0, max_iter=None, iterated_power='auto', remove_zero_eig=False, random_state=None, copy_X=True, 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

Kernel Principal component analysis (KPCA) [1]_ For more details on this class, see sklearn.decomposition.KernelPCA

n_components: int, default=None

Number of components. If None, all non-zero components are kept.

kernel: {‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘cosine’, ‘precomputed’} or callable, default=’linear’

Kernel used for PCA.

gamma: float, default=None

Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels. If gamma is None, then it is set to 1/n_features.

degree: int, default=3

Degree for poly kernels. Ignored by other kernels.

coef0: float, default=1

Independent term in poly and sigmoid kernels. Ignored by other kernels.

kernel_params: dict, default=None

Parameters (keyword arguments) and values for kernel passed as callable object. Ignored by other kernels.

alpha: float, default=1.0

Hyperparameter of the ridge regression that learns the inverse transform (when fit_inverse_transform=True).

fit_inverse_transform: bool, default=False

Learn the inverse transform for non-precomputed kernels (i.e. learn to find the pre-image of a point). This method is based on [2]_.

eigen_solver: {‘auto’, ‘dense’, ‘arpack’, ‘randomized’}, default=’auto’

Select eigensolver to use. If n_components is much less than the number of training samples, randomized (or arpack to a smaller extent) may be more efficient than the dense eigensolver. Randomized SVD is performed according to the method of Halko et al [3]_.

auto :

the solver is selected by a default policy based on n_samples (the number of training samples) and n_components: if the number of components to extract is less than 10 (strict) and the number of samples is more than 200 (strict), the ‘arpack’ method is enabled. Otherwise the exact full eigenvalue decomposition is computed and optionally truncated afterwards (‘dense’ method).

dense :

run exact full eigenvalue decomposition calling the standard LAPACK solver via scipy.linalg.eigh, and select the components by postprocessing

arpack :

run SVD truncated to n_components calling ARPACK solver using scipy.sparse.linalg.eigsh. It requires strictly 0 < n_components < n_samples

randomized :

run randomized SVD by the method of Halko et al. [3]_. The current implementation selects eigenvalues based on their module; therefore using this method can lead to unexpected results if the kernel is not positive semi-definite. See also [4]_.

tol: float, default=0

Convergence tolerance for arpack. If 0, optimal value will be chosen by arpack.

max_iter: int, default=None

Maximum number of iterations for arpack. If None, optimal value will be chosen by arpack.

iterated_power: int >= 0, or ‘auto’, default=’auto’

Number of iterations for the power method computed by svd_solver == ‘randomized’. When ‘auto’, it is set to 7 when n_components < 0.1 * min(X.shape), other it is set to 4.

remove_zero_eig: bool, default=False

If True, then all components with zero eigenvalues are removed, so that the number of components in the output may be < n_components (and sometimes even zero due to numerical instability). When n_components is None, this parameter is ignored and components with zero eigenvalues are removed regardless.

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

Used when eigen_solver == ‘arpack’ or ‘randomized’. Pass an int for reproducible results across multiple function calls. See Glossary.

copy_X: bool, default=True

If True, input X is copied and stored by the model in the X_fit_ attribute. If no further changes will be done to X, setting copy_X=False saves memory by storing a reference.

n_jobs: int, default=None

The number of parallel jobs to run. 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 the model from data in X For more details on this function, see sklearn.decomposition.KernelPCA.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.decomposition.KernelPCA object.
transform(dataset)	Transform X For more details on this function, see sklearn.decomposition.KernelPCA.transform

Attributes

model_signatures

Returns model signature of current class.