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'
