Class KMeans (0.16.0)

KMeans(n_clusters: int = 8)

K-Means clustering.

Parameter

NameDescription
n_clusters int, default 8

The number of clusters to form as well as the number of centroids to generate. Default to 8.

Properties

cluster_centers_

Information of cluster centers.

Returns
TypeDescription
bigframes.dataframe.DataFrameDataFrame of cluster centers, containing following columns: centroid_id: An integer that identifies the centroid. feature: The column name that contains the feature. numerical_value: If feature is numeric, the value of feature for the centroid that centroid_id identifies. If feature is not numeric, the value is NULL. categorical_value: An list of mappings containing information about categorical features. Each mapping contains the following fields: categorical_value.category: The name of each category. categorical_value.value: The value of categorical_value.category for the centroid that centroid_id identifies. The output contains one row per feature per centroid.

Methods

__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values

fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> bigframes.ml.base._T

Compute k-means clustering.

Parameters
NameDescription
X bigframes.dataframe.DataFrame or bigframes.series.Series

DataFrame of shape (n_samples, n_features). Training data.

y default None

Not used, present here for API consistency by convention.

Returns
TypeDescription
KMeansFitted Estimator.

get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

Parameter
NameDescription
deep bool, default True

Default True. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
TypeDescription
DictionaryA dictionary of parameter names mapped to their values.

predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict the closest cluster each sample in X belongs to.

Parameter
NameDescription
X bigframes.dataframe.DataFrame or bigframes.series.Series

DataFrame of shape (n_samples, n_features). New data to predict.

Returns
TypeDescription
bigframes.dataframe.DataFrameDataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted labels.

register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

After register, go to Google Cloud Console (https://console.cloud.google.com/vertex-ai/models) to manage the model registries. Refer to https://cloud.google.com/vertex-ai/docs/model-registry/introduction for more options.

Parameter
NameDescription
vertex_ai_model_id Optional[str], default None

optional string id as model id in Vertex. If not set, will by default to 'bigframes_{bq_model_id}'. Vertex Ai model id will be truncated to 63 characters due to its limitation.

score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y=None
) -> bigframes.dataframe.DataFrame

Calculate evaluation metrics of the model.

Parameters
NameDescription
X bigframes.dataframe.DataFrame or bigframes.series.Series

DataFrame of shape (n_samples, n_features). New Data.

y default None

Not used, present here for API consistency by convention.

Returns
TypeDescription
bigframes.dataframe.DataFrameDataFrame of the metrics.

to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.cluster.KMeans

Save the model to BigQuery.

Parameters
NameDescription
model_name str

the name of the model.

replace bool, default False

whether to replace if the model already exists. Default to False.

Returns
TypeDescription
KMeanssaved model.