Module decomposition (2.2.0)

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

Classes

MatrixFactorization

MatrixFactorization(
 *,
 feedback_type: typing.Literal["explicit", "implicit"] = "explicit",
 num_factors: int,
 user_col: str,
 item_col: str,
 rating_col: str = "rating",
 l2_reg: float = 1.0
)

Matrix Factorization (MF).

Examples:

>>> import bigframes.pandas as bpd
>>> from bigframes.ml.decomposition import MatrixFactorization
>>> bpd.options.display.progress_bar = None
>>> X = bpd.DataFrame({
... "row": [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6],
... "column": [0,1] * 7,
... "value": [1, 1, 2, 1, 3, 1.2, 4, 1, 5, 0.8, 6, 1, 2, 3],
... })
>>> model = MatrixFactorization(feedback_type='explicit', num_factors=6, user_col='row', item_col='column', rating_col='value', l2_reg=2.06)
>>> W = model.fit(X)

Parameters
Name	Description
`feedback_type`	`'explicit' 'implicit'` Specifies the feedback type for the model. The feedback type determines the algorithm that is used during training.
`num_factors`	`int or auto, default auto` Specifies the number of latent factors to use.
`user_col`	`str` The user column name.
`item_col`	`str` The item column name.
`l2_reg`	`float, default 1.0` A floating point value for L2 regularization. The default value is 1.0.

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.

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Classes

MatrixFactorization

PCA

Module decomposition (2.2.0)