class dislib.recommendation.als.base.ALS(random_state=None, n_f=100, lambda_=0.065, tol=0.0001, max_iter=100, arity=5, check_convergence=True, verbose=False)[source]

Bases: sklearn.base.BaseEstimator

Alternating Least Squares recommendation.

Implements distributed alternating least squares recommendation based on Zhou et al. [1].

  • max_iter (int, optional (default=100)) – Maximum number of iterations to perform.
  • tol (float, optional (default=1e-4)) – Tolerance for the stopping criterion.
  • n_f (int, optional (default=100)) – Number of latent factors (i.e. dimensions) for the matrices U and I.
  • lambda_ (float, optional (default=0.065)) – Regularization parameters value.
  • check_convergence (boolean, optional (default=True)) – Whether to test for convergence at the end of each iteration.
  • random_state (int, orNone, optional (default=None)) – The seed of the pseudo random number generator used to initialize the items matrix I.
  • arity (int, optional (default=5)) – The arity of the tasks during the merge of each matrix chunk.
  • verbose (boolean, optional (default=False)) – Whether to print progress information.
  • users (np.array) – User matrix.
  • items (np.array) – Items matrix.
  • converged (boolean) – Whether the model has converged.


[1]Zhou Y., Wilkinson D., Schreiber R., Pan R. (2008) Large-Scale Parallel Collaborative Filtering for the Netflix Prize. In: Fleischer R., Xu J. (eds) Algorithmic Aspects in Information and Management. AAIM 2008. Lecture Notes in Computer Science, vol 5034. Springer, Berlin, Heidelberg


>>> import dislib as ds
>>> from dislib.recommendation import ALS
>>> import numpy as np
>>> from scipy.sparse import csr_matrix
>>> if __name__ == '__main__':
>>>     data = np.array([[0, 0, 5], [3, 0, 5], [3, 1, 2]])
>>>     ratings = csr_matrix(data).transpose().tocsr()
>>>     train = ds.array(ratings, block_size=(1, 3))
>>>     als = ALS()
>>>     als.fit(train)
>>>     print('Ratings for user 0: %s' % als.predict_user(user_id=0))
fit(x, test=None)[source]

Fits a model using training data. Training data is also used to check for convergence unless test data is provided.

  • x (ds-array, shape=(n_ratings, n_users)) – ds-array where each row is the collection of ratings given by a user
  • test (csr_matrix) – Sparse matrix used to check convergence with users as rows and items as columns. If not passed, uses training data to check convergence.

Returns the expected ratings for user_id. Each index represents the rating for i-th item. If the user was not present in the training set, a np.NaN vector is returned.

Parameters:user_id (int)
Return type:np.array containing all estimated items ratings for user_id.