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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
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 |
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 |
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. |