Exponential + ARD model. ''' import sys, os project_location = os.path.dirname(__file__)+"/../../../../" sys.path.append(project_location) from BMF_Priors.code.models.bmf_gaussian_exponential_ard import BMF_Gaussian_Exponential_ARD from BMF_Priors.data.movielens.load_data import load_movielens_100K from BMF_Priors.experiments.convergence.convergence_experiment import measure_convergence_time import matplotlib.pyplot as plt ''' Run the experiment. ''' R, M = load_movielens_100K() model_class = BMF_Gaussian_Exponential_ARD settings = { 'R': R, 'M': M, 'K': 20, 'hyperparameters': { 'alpha':1., 'beta':1., 'alpha0':1., 'beta0':1. }, 'init': 'random', 'iterations': 200, } fout_performances = './results/performances_gaussian_exponential_ard.txt' fout_times = './results/times_gaussian_exponential_ard.txt' repeats = 10 performances, times = measure_convergence_time( repeats, model_class, settings, fout_performances, fout_times)
''' Measure convergence on the MovieLens 100K dataset, with Poisson likelihood, Gamma priors, and Gamma hierarchical priors. ''' import sys, os project_location = os.path.dirname(__file__) + "/../../../../" sys.path.append(project_location) from BMF_Priors.code.models.bmf_poisson_gamma_gamma import BMF_Poisson_Gamma_Gamma from BMF_Priors.data.movielens.load_data import load_movielens_100K from BMF_Priors.experiments.convergence.convergence_experiment import measure_convergence_time import matplotlib.pyplot as plt ''' Run the experiment. ''' R, M = load_movielens_100K() model_class = BMF_Poisson_Gamma_Gamma settings = { 'R': R, 'M': M, 'K': 20, 'hyperparameters': { 'a': 1., 'ap': 1., 'bp': 1. }, 'init': 'random', 'iterations': 200, } fout_performances = './results/performances_poisson_gamma_gamma.txt' fout_times = './results/times_poisson_gamma_gamma.txt'