snowflake.ml.modeling.cluster.Birch¶

class snowflake.ml.modeling.cluster.Birch(*, threshold=0.5, branching_factor=50, n_clusters=3, compute_labels=True, copy=True, 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

Implements the BIRCH clustering algorithm For more details on this class, see sklearn.cluster.Birch

threshold: float, default=0.5

The radius of the subcluster obtained by merging a new sample and the closest subcluster should be lesser than the threshold. Otherwise a new subcluster is started. Setting this value to be very low promotes splitting and vice-versa.

branching_factor: int, default=50

Maximum number of CF subclusters in each node. If a new samples enters such that the number of subclusters exceed the branching_factor then that node is split into two nodes with the subclusters redistributed in each. The parent subcluster of that node is removed and two new subclusters are added as parents of the 2 split nodes.

n_clusters: int, instance of sklearn.cluster model or None, default=3

Number of clusters after the final clustering step, which treats the subclusters from the leaves as new samples.

• None: the final clustering step is not performed and the subclusters are returned as they are.

• sklearn.cluster Estimator: If a model is provided, the model is fit treating the subclusters as new samples and the initial data is mapped to the label of the closest subcluster.

• int: the model fit is AgglomerativeClustering with n_clusters set to be equal to the int.

compute_labels: bool, default=True

Whether or not to compute labels for each fit.

copy: bool, default=True

Whether or not to make a copy of the given data. If set to False, the initial data will be overwritten.

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)	Build a CF Tree for the input data For more details on this function, see sklearn.cluster.Birch.fit
predict(dataset)	Predict data using the centroids_ of subclusters For more details on this function, see sklearn.cluster.Birch.predict
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.cluster.Birch object.
transform(dataset)	Transform X into subcluster centroids dimension For more details on this function, see sklearn.cluster.Birch.transform

Attributes

model_signatures

Returns model signature of current class.