snowflake.ml.modeling.cluster.AffinityPropagation¶

class snowflake.ml.modeling.cluster.AffinityPropagation(*, damping=0.5, max_iter=200, convergence_iter=15, copy=True, preference=None, affinity='euclidean', verbose=False, random_state=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

Perform Affinity Propagation Clustering of data For more details on this class, see sklearn.cluster.AffinityPropagation

damping: float, default=0.5

Damping factor in the range [0.5, 1.0) is the extent to which the current value is maintained relative to incoming values (weighted 1 - damping). This in order to avoid numerical oscillations when updating these values (messages).

max_iter: int, default=200

Maximum number of iterations.

convergence_iter: int, default=15

Number of iterations with no change in the number of estimated clusters that stops the convergence.

copy: bool, default=True

Make a copy of input data.

preference: array-like of shape (n_samples,) or float, default=None

Preferences for each point - points with larger values of preferences are more likely to be chosen as exemplars. The number of exemplars, ie of clusters, is influenced by the input preferences value. If the preferences are not passed as arguments, they will be set to the median of the input similarities.

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

Which affinity to use. At the moment ‘precomputed’ and euclidean are supported. ‘euclidean’ uses the negative squared euclidean distance between points.

verbose: bool, default=False

Whether to be verbose.

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

Pseudo-random number generator to control the starting state. Use an int for reproducible results across function calls. See the Glossary.

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 clustering from features, or affinity matrix For more details on this function, see sklearn.cluster.AffinityPropagation.fit
predict(dataset)	Predict the closest cluster each sample in X belongs to For more details on this function, see sklearn.cluster.AffinityPropagation.predict
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.cluster.AffinityPropagation object.

Attributes

model_signatures

Returns model signature of current class.