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    Class KMeans (1.34.0) 

      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.

    Examples:

     >>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> from bigframes.ml.cluster import KMeans

>>> X = bpd.DataFrame({"feat0": [1, 1, 1, 10, 10, 10], "feat1": [2, 4, 0, 2, 4, 0]})
>>> kmeans = KMeans(n_clusters=2).fit(X)
>>> kmeans.predict(bpd.DataFrame({"feat0": [0, 12], "feat1": [0, 3]}))["CENTROID_ID"] # doctest:+SKIP
0    1
1    2
Name: CENTROID_ID, dtype: Int64

>>> kmeans.cluster_centers_ # doctest:+SKIP
centroid_id feature  numerical_value categorical_value
0            1   feat0              5.5                []
1            1   feat1              1.0                []
2            2   feat0              5.5                []
3            2   feat1              4.0                []

[4 rows x 4 columns]

    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 
 , 
 pandas 
 . 
 core 
 . 
 frame 
 . 
 DataFrame 
 , 
 pandas 
 . 
 core 
 . 
 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 
 , 
 pandas 
 . 
 core 
 . 
 frame 
 . 
 DataFrame 
 , 
 pandas 
 . 
 core 
 . 
 series 
 . 
 Series 
 , 
 ], 
 y 
 : 
 typing 
 . 
 Optional 
 [ 
 typing 
 . 
 Union 
 [ 
 bigframes 
 . 
 dataframe 
 . 
 DataFrame 
 , 
 bigframes 
 . 
 series 
 . 
 Series 
 , 
 pandas 
 . 
 core 
 . 
 frame 
 . 
 DataFrame 
 , 
 pandas 
 . 
 core 
 . 
 series 
 . 
 Series 
 , 
 ] 
 ] 
 = 
 None 
 , 
 ) 
 - 
> bigframes 
 . 
 ml 
 . 
 base 
 . 
 _T

    Compute k-means clustering.

    Parameters
    Name
    Description
    X
    bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.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 
 , 
 pandas 
 . 
 core 
 . 
 frame 
 . 
 DataFrame 
 , 
 pandas 
 . 
 core 
 . 
 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 or pandas.core.frame.DataFrame or pandas.core.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 
 , 
 pandas 
 . 
 core 
 . 
 frame 
 . 
 DataFrame 
 , 
 pandas 
 . 
 core 
 . 
 series 
 . 
 Series 
 , 
 ], 
 y 
 = 
 None 
 , 
 ) 
 - 
> bigframes 
 . 
 dataframe 
 . 
 DataFrame

    Calculate evaluation metrics of the model.

    Parameters
    Name
    Description
    X
    bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.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.
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