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, 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

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.

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)	Compute the embedding vectors for data X For more details on this function, see sklearn.manifold.Isomap.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns 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.