Module decomposition (2.0.0.dev0)

Version latest

• 2.0.0-dev0 (latest)
• 1.42.0
• 1.41.0
• 1.40.0
• 1.39.0
• 1.38.0
• 1.37.0
• 1.36.0
• 1.35.0
• 1.34.0
• 1.33.0
• 1.32.0
• 1.31.0
• 1.30.0
• 1.29.0
• 1.28.0
• 1.27.0
• 1.26.0
• 1.25.0
• 1.24.0
• 1.22.0
• 1.21.0
• 1.20.0
• 1.19.0
• 1.18.0
• 1.17.0
• 1.16.0
• 1.15.0
• 1.14.0
• 1.13.0
• 1.12.0
• 1.11.1
• 1.10.0
• 1.9.0
• 1.8.0
• 1.7.0
• 1.6.0
• 1.5.0
• 1.4.0
• 1.3.0
• 1.2.0
• 1.1.0
• 1.0.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.1
• 0.19.2
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.1
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.0
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0

Matrix Decomposition models. This module is styled after Scikit-Learn's decomposition module: https://scikit-learn.org/stable/modules/decomposition.html.

Classes

PCA

PCA(
    n_components: typing.Optional[typing.Union[int, float]] = None,
    *,
    svd_solver: typing.Literal["full", "randomized", "auto"] = "auto"
)

Principal component analysis (PCA).

Examples:

>>> import bigframes.pandas as bpd
>>> from bigframes.ml.decomposition import PCA
>>> bpd.options.display.progress_bar = None
>>> X = bpd.DataFrame({"feat0": [-1, -2, -3, 1, 2, 3], "feat1": [-1, -1, -2, 1, 1, 2]})
>>> pca = PCA(n_components=2).fit(X)
>>> pca.predict(X) # doctest:+SKIP
    principal_component_1  principal_component_2
0              -0.755243               0.157628
1               -1.05405              -0.141179
2              -1.809292               0.016449
3               0.755243              -0.157628
4                1.05405               0.141179
5               1.809292              -0.016449
<BLANKLINE>
[6 rows x 2 columns]
>>> pca.explained_variance_ratio_ # doctest:+SKIP
    principal_component_id  explained_variance_ratio
0                       1                   0.00901
1                       0                   0.99099
<BLANKLINE>
[2 rows x 2 columns]

Parameters
Name	Description
n_components	int, float or None, default None Number of components to keep. If n_components is not set, all components are kept, n_components = min(n_samples, n_features). If 0 < n_components < 1, select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by n_components.
svd_solver	"full", "randomized" or "auto", default "auto" The solver to use to calculate the principal components. Details: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-pca#pca_solver.