snowflake.ml.modeling.manifold.Isomap¶

class snowflake.ml.modeling.manifold.Isomap(*, n_neighbors=5, radius=None, n_components=2, eigen_solver='auto', tol=0, max_iter=None, path_method='auto', neighbors_algorithm='auto', n_jobs=None, metric='minkowski', p=2, metric_params=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, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Isomap Embedding For more details on this class, see sklearn.manifold.Isomap

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, sample_weight_col, and passthrough_cols parameters are considered input columns. Input columns can also be set after initialization with the set_input_cols method.

label_cols: Optional[Union[str, List[str]]]

This parameter is optional and will be ignored during fit. It is present here for API consistency by convention.

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 must match the expected number of output columns from the specific predictor or transformer class used. If you omit this parameter, output column names are derived by adding an OUTPUT_ prefix to the label column names for supervised estimators, or OUTPUT_<IDX>for unsupervised estimators. These inferred output column names work for predictors, but output_cols must be set explicitly for transformers. In general, explicitly specifying output column names is clearer, especially if you don’t specify the input column names. To transform in place, pass the same names for input_cols and output_cols. be set explicitly for transformers. Output columns can also be set after initialization with the set_output_cols method.

sample_weight_col: Optional[str]

A string representing the column name containing the sample weights. This argument is only required when working with weighted datasets. Sample weight column can also be set after initialization with the set_sample_weight_col method.

passthrough_cols: Optional[Union[str, List[str]]]

A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference. Passthrough columns can also be set after initialization with the set_passthrough_cols method.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

n_neighbors: int or None, default=5

Number of neighbors to consider for each point. If n_neighbors is an int, then radius must be None.

radius: float or None, default=None

Limiting distance of neighbors to return. If radius is a float, then n_neighbors must be set to None.

n_components: int, default=2

Number of coordinates for the manifold.

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

‘auto’: Attempt to choose the most efficient solver for the given problem.

‘arpack’: Use Arnoldi decomposition to find the eigenvalues and eigenvectors.

‘dense’: Use a direct solver (i.e. LAPACK) for the eigenvalue decomposition.

tol: float, default=0

Convergence tolerance passed to arpack or lobpcg. not used if eigen_solver == ‘dense’.

max_iter: int, default=None

Maximum number of iterations for the arpack solver. not used if eigen_solver == ‘dense’.

path_method: {‘auto’, ‘FW’, ‘D’}, default=’auto’

Method to use in finding shortest path.

‘auto’: attempt to choose the best algorithm automatically.

‘FW’: Floyd-Warshall algorithm.

‘D’: Dijkstra’s algorithm.

neighbors_algorithm: {‘auto’, ‘brute’, ‘kd_tree’, ‘ball_tree’}, default=’auto’

Algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance.

n_jobs: int or None, 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.

metric: str, or callable, default=”minkowski”

The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by sklearn.metrics.pairwise_distances() for its metric parameter. If metric is “precomputed”, X is assumed to be a distance matrix and must be square. X may be a Glossary.

p: int, default=2

Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

metric_params: dict, default=None

Additional keyword arguments for the metric function.

Methods

fit(dataset)	Compute the embedding vectors for data X For more details on this function, see sklearn.manifold.Isomap.fit
get_input_cols()	Input columns getter.
get_label_cols()	Label column getter.
get_output_cols()	Output columns getter.
get_params([deep])	Get parameters for this transformer.
get_passthrough_cols()	Passthrough columns getter.
get_sample_weight_col()	Sample weight column getter.
get_sklearn_args([default_sklearn_obj, ...])	Get sklearn keyword arguments.
set_drop_input_cols([drop_input_cols])
set_input_cols(input_cols)	Input columns setter.
set_label_cols(label_cols)	Label column setter.
set_output_cols(output_cols)	Output columns setter.
set_params(**params)	Set the parameters of this transformer.
set_passthrough_cols(passthrough_cols)	Passthrough columns setter.
set_sample_weight_col(sample_weight_col)	Sample weight column setter.
to_sklearn()	Get sklearn.manifold.Isomap object.
transform(dataset)	Transform X For more details on this function, see sklearn.manifold.Isomap.transform

Attributes

model_signatures

Returns model signature of current class.