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    Class PCA (0.25.0) 

      PCA 
 ( 
 n_components 
 : 
 int 
 = 
 3 
 )

    Principal component analysis (PCA).

    Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space. The input data is centered but not scaled for each feature before applying the SVD.

    It uses the LAPACK implementation of the full SVD or a randomized truncated SVD by the method of Halko et al. 2009, depending on the shape of the input data and the number of components to extract.

    It can also use the scipy.sparse.linalg ARPACK implementation of the truncated SVD.

    Parameter

    Name
    Description
    n_components
    Optional[int], default 3

    Number of components to keep. if n_components is not set all components are kept.

    Properties

    components_

    Principal axes in feature space, representing the directions of maximum variance in the data.

    Returns
    Type
    Description
    bigframes.dataframe.DataFrame
    DataFrame of principal components, containing following columns: principal_component_id: An integer that identifies the principal component. feature: The column name that contains the feature. numerical_value: If feature is numeric, the value of feature for the principal component that principal_component_id identifies. If feature isn't 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 component.

    explained_variance_

    The amount of variance explained by each of the selected components.

    Returns
    Type
    Description
    bigframes.dataframe.DataFrame
    DataFrame containing following columns: principal_component_id: An integer that identifies the principal component. explained_variance: The factor by which the eigenvector is scaled. Eigenvalue and explained variance are the same concepts in PCA.

    explained_variance_ratio_

    Percentage of variance explained by each of the selected components.

    Returns
    Type
    Description
    bigframes.dataframe.DataFrame
    DataFrame containing following columns: principal_component_id: An integer that identifies the principal component. explained_variance_ratio: the total variance is the sum of variances, also known as eigenvalues, of all of the individual principal components. The explained variance ratio by a principal component is the ratio between the variance, also known as eigenvalue, of that principal component and the total variance.

    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

    Fit the model according to the given training data.

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

    Series or DataFrame of shape (n_samples, n_features). Training vector, where n_samples is the number of samples and n_features is the number of features.

    y
    default None

    Ignored.

    Returns
    Type
    Description
    PCA
    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 for each sample in X.

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

    Series or a DataFrame to predict.

    Returns
    Type
    Description
    bigframes.dataframe.DataFrame
    predicted DataFrames.

    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
    Name
    Description
    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 
 = 
 None 
 , 
 y 
 = 
 None 
 ) 
 - 
> bigframes 
 . 
 dataframe 
 . 
 DataFrame

    Calculate evaluation metrics of the model.

    Parameters
    Name
    Description
    X
    default None

    Ignored.

    y
    default None

    Ignored.

    Returns
    Type
    Description
    bigframes.dataframe.DataFrame
    DataFrame that represents model metrics.

    to_gbq

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

    Save the model to BigQuery.

    Parameters
    Name
    Description
    model_name
    str

    the name of the model.

    replace
    bool, default False

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

    Returns
    Type
    Description
    PCA
    saved model.
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