Class MatrixFactorization (2.0.0)
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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.

Properties

rating_col

str: The rating column name. Defaults to 'rating'.

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,
        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

Fit the model according to the given training data.

Parameters
Name	Description
`X`	`bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.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
`bigframes.ml.decomposition.MatrixFactorization`	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

Generate a predicted rating for every user-item row combination for a matrix factorization model.

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

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=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.MatrixFactorization

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
`MatrixFactorization`	Saved model.