snowflake.ml.modeling.manifold.MDS¶

class snowflake.ml.modeling.manifold.MDS(*, n_components=2, metric=True, n_init=4, max_iter=300, verbose=0, eps=0.001, n_jobs=None, random_state=None, dissimilarity='euclidean', normalized_stress='warn', 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

Multidimensional scaling For more details on this class, see sklearn.manifold.MDS

n_components: int, default=2

Number of dimensions in which to immerse the dissimilarities.

metric: bool, default=True

If True, perform metric MDS; otherwise, perform nonmetric MDS. When False (i.e. non-metric MDS), dissimilarities with 0 are considered as missing values.

n_init: int, default=4

Number of times the SMACOF algorithm will be run with different initializations. The final results will be the best output of the runs, determined by the run with the smallest final stress.

max_iter: int, default=300

Maximum number of iterations of the SMACOF algorithm for a single run.

verbose: int, default=0

Level of verbosity.

eps: float, default=1e-3

Relative tolerance with respect to stress at which to declare convergence. The value of eps should be tuned separately depending on whether or not normalized_stress is being used.

n_jobs: int, default=None

The number of jobs to use for the computation. If multiple initializations are used (n_init), each run of the algorithm is computed in parallel.

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

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

Determines the random number generator used to initialize the centers. Pass an int for reproducible results across multiple function calls. See Glossary.

dissimilarity: {‘euclidean’, ‘precomputed’}, default=’euclidean’

Dissimilarity measure to use:

• ‘euclidean’:

  Pairwise Euclidean distances between points in the dataset.

• ‘precomputed’:

  Pre-computed dissimilarities are passed directly to fit and fit_transform.

normalized_stress: bool or “auto” default=False

Whether use and return normed stress value (Stress-1) instead of raw stress calculated by default. Only supported in non-metric MDS.

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 position of the points in the embedding space For more details on this function, see sklearn.manifold.MDS.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.manifold.MDS object.

Attributes

model_signatures

Returns model signature of current class.