ALS¶
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class
pyspark.mllib.recommendation.ALS¶ Alternating Least Squares matrix factorization
Methods
train(ratings, rank[, iterations, lambda_, …])Train a matrix factorization model given an RDD of ratings by users for a subset of products.
trainImplicit(ratings, rank[, iterations, …])Train a matrix factorization model given an RDD of ‘implicit preferences’ of users for a subset of products.
Methods Documentation
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classmethod
train(ratings: Union[pyspark.rdd.RDD[pyspark.mllib.recommendation.Rating], pyspark.rdd.RDD[Tuple[int, int, float]]], rank: int, iterations: int = 5, lambda_: float = 0.01, blocks: int = - 1, nonnegative: bool = False, seed: Optional[int] = None) → pyspark.mllib.recommendation.MatrixFactorizationModel¶ Train a matrix factorization model given an RDD of ratings by users for a subset of products. The ratings matrix is approximated as the product of two lower-rank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a configurable level of parallelism.
- Parameters
- ratings
pyspark.RDD RDD of Rating or (userID, productID, rating) tuple.
- rankint
Number of features to use (also referred to as the number of latent factors).
- iterationsint, optional
Number of iterations of ALS. (default: 5)
- lambda_float, optional
Regularization parameter. (default: 0.01)
- blocksint, optional
Number of blocks used to parallelize the computation. A value of -1 will use an auto-configured number of blocks. (default: -1)
- nonnegativebool, optional
A value of True will solve least-squares with nonnegativity constraints. (default: False)
- seedbool, optional
Random seed for initial matrix factorization model. A value of None will use system time as the seed. (default: None)
- ratings
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classmethod
trainImplicit(ratings: Union[pyspark.rdd.RDD[pyspark.mllib.recommendation.Rating], pyspark.rdd.RDD[Tuple[int, int, float]]], rank: int, iterations: int = 5, lambda_: float = 0.01, blocks: int = - 1, alpha: float = 0.01, nonnegative: bool = False, seed: Optional[int] = None) → pyspark.mllib.recommendation.MatrixFactorizationModel¶ Train a matrix factorization model given an RDD of ‘implicit preferences’ of users for a subset of products. The ratings matrix is approximated as the product of two lower-rank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a configurable level of parallelism.
- Parameters
- ratings
pyspark.RDD RDD of Rating or (userID, productID, rating) tuple.
- rankint
Number of features to use (also referred to as the number of latent factors).
- iterationsint, optional
Number of iterations of ALS. (default: 5)
- lambda_float, optional
Regularization parameter. (default: 0.01)
- blocksint, optional
Number of blocks used to parallelize the computation. A value of -1 will use an auto-configured number of blocks. (default: -1)
- alphafloat, optional
A constant used in computing confidence. (default: 0.01)
- nonnegativebool, optional
A value of True will solve least-squares with nonnegativity constraints. (default: False)
- seedint, optional
Random seed for initial matrix factorization model. A value of None will use system time as the seed. (default: None)
- ratings
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classmethod