snowflake.ml.modeling.semi_supervised.LabelSpreading¶

class snowflake.ml.modeling.semi_supervised.LabelSpreading(*, kernel='rbf', gamma=20, n_neighbors=7, alpha=0.2, max_iter=30, tol=0.001, 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

LabelSpreading model for semi-supervised learning For more details on this class, see sklearn.semi_supervised.LabelSpreading

kernel: {‘knn’, ‘rbf’} or callable, default=’rbf’

String identifier for kernel function to use or the kernel function itself. Only ‘rbf’ and ‘knn’ strings are valid inputs. The function passed should take two inputs, each of shape (n_samples, n_features), and return a (n_samples, n_samples) shaped weight matrix.

gamma: float, default=20

Parameter for rbf kernel.

n_neighbors: int, default=7

Parameter for knn kernel which is a strictly positive integer.

alpha: float, default=0.2

Clamping factor. A value in (0, 1) that specifies the relative amount that an instance should adopt the information from its neighbors as opposed to its initial label. alpha=0 means keeping the initial label information; alpha=1 means replacing all initial information.

max_iter: int, default=30

Maximum number of iterations allowed.

tol: float, default=1e-3

Convergence tolerance: threshold to consider the system at steady state.

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 a semi-supervised label propagation model to X For more details on this function, see sklearn.semi_supervised.LabelSpreading.fit
predict(dataset)	Perform inductive inference across the model For more details on this function, see sklearn.semi_supervised.LabelSpreading.predict
predict_proba(dataset[, output_cols_prefix])	Predict probability for each possible outcome For more details on this function, see sklearn.semi_supervised.LabelSpreading.predict_proba
score(dataset)	Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.semi_supervised.LabelSpreading.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.semi_supervised.LabelSpreading object.

Attributes

model_signatures

Returns model signature of current class.