Class KMeans (1.19.0) 

KMeans(
    n_clusters: int = 8,
    *,
    init: typing.Literal["kmeans++", "random", "custom"] = "kmeans++",
    init_col: typing.Optional[str] = None,
    distance_type: typing.Literal["euclidean", "cosine"] = "euclidean",
    max_iter: int = 20,
    tol: float = 0.01,
    warm_start: bool = False
)

K-Means clustering.

Parameters
Name Description
n_clusters int, default 8

The number of clusters to form as well as the number of centroids to generate. Default to 8.
init "kmeans++", "random" or "custom", default "kmeans++"

The method of initializing the clusters. Default to "kmeans++" kmeas++: Initializes a number of centroids equal to the n_clusters value by using the k-means++ algorithm. Using this approach usually trains a better model than using random cluster initialization. random: Initializes the centroids by randomly selecting a number of data points equal to the n_clusters value from the input data. custom: Initializes the centroids using a provided column of type bool. Uses the rows with a value of True as the initial centroids. You specify the column to use by using the init_col option.
init_col str or None, default None

The name of the column to use to initialize the centroids. This column must have a type of bool. If this column contains a value of True for a given row, then uses that row as an initial centroid. The number of True rows in this column must be equal to the value you have specified for the n_clusters option. Only works with init method "custom". Default to None.
distance_type "euclidean" or "cosine", default "euclidean"

The type of metric to use to compute the distance between two points. Default to "euclidean".
max_iter int, default 20

The maximum number of training iterations, where one iteration represents a single pass of the entire training data. Default to 20.
tol float, default 0.01

The minimum relative loss improvement that is necessary to continue training. For example, a value of 0.01 specifies that each iteration must reduce the loss by 1% for training to continue. Default to 0.01.
warm_start bool, default False

Determines whether to train a model with new training data, new model options, or both. Unless you explicitly override them, the initial options used to train the model are used for the warm start run. Default to False.

Properties

cluster_centers_

Information of cluster centers.

Returns
Type Description
bigframes.dataframe.DataFrame DataFrame 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.

detect_anomalies

detect_anomalies(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    contamination: float = 0.1
) -> bigframes.dataframe.DataFrame

Detect the anomaly data points of the input.

Parameters
Name Description
X bigframes.dataframe.DataFrame or bigframes.series.Series

Series or a DataFrame to detect anomalies.
contamination float, default 0.1

Identifies the proportion of anomalies in the training dataset that are used to create the model. The value must be in the range [0, 0.5].
Returns
Type Description
bigframes.dataframe.DataFrame detected DataFrame.

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
Name Description
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
Type Description
KMeans Fitted estimator.

get_params

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

Get parameters for this estimator.

Parameter
Name Description
deep bool, default True

Default True. If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns
Type Description
Dictionary A 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
Name Description
X bigframes.dataframe.DataFrame or bigframes.series.Series

DataFrame of shape (n_samples, n_features). New data to predict.
Returns
Type Description
bigframes.dataframe.DataFrame DataFrame 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 the 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
Name Description
vertex_ai_model_id Optional[str], default None

Optional string id as model id in Vertex. If not set, will 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
Name Description
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
Type Description
bigframes.dataframe.DataFrame DataFrame of the metrics.

to_gbq

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

Save the model to BigQuery.

Parameters
Name Description
model_name str

The name of the model.
replace bool, default False

Determine whether to replace if the model already exists. Default to False.
Returns
Type Description
KMeans Saved model.