bigframes.ml.decomposition.MatrixFactorization#

classbigframes.ml.decomposition.MatrixFactorization(*,feedback_type:Literal['explicit','implicit']='explicit',num_factors:int,user_col:str,item_col:str,rating_col:str='rating',l2_reg:float=1.0)[source]#

Matrix Factorization (MF).

Examples:

>>>importbigframes.pandasasbpd>>>frombigframes.ml.decompositionimportMatrixFactorization>>>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:

  • 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 orauto,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.

Attributes

rating_col

The rating column name.

Methods

__init__(*[, feedback_type, rating_col, l2_reg])

fit(X[, y])

Fit the model according to the given training data.

fit_predict(X[, y])

Fit the model with X and generate a predicted rating for every user-item row combination for a matrix factorization model.

get_params([deep])

Get parameters for this estimator.

predict(X)

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

register([vertex_ai_model_id])

Register the model to Vertex AI.

score([X, y])

Calculate evaluation metrics of the model.

to_gbq(model_name[, replace])

Save the model to BigQuery.

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