'''
Run cross-validation experiment on the MovieLens 100K dataset, with the
row-average baseline.
'''
import sys, os
project_location = os.path.dirname(__file__) + "/../../../../"
sys.path.append(project_location)
from BMF_Priors.code.models.baseline_average_row import RowAverage
from BMF_Priors.code.cross_validation.matrix_single_cross_validation import MatrixSingleCrossValidation
from BMF_Priors.data.movielens.load_data import load_processed_movielens_100K
''' Settings BMF model. '''
method = RowAverage
R, M = load_processed_movielens_100K()
hyperparameters = {}
train_config = {'iterations': 0, 'init': ''}
predict_config = {'burn_in': 0, 'thinning': 0}
parameters = {'K': 0, 'hyperparameters': hyperparameters}
''' Settings nested cross-validation. '''
no_folds = 5
folder_results = './results/baseline_average_row/'
output_file = folder_results + 'results.txt'
''' Run the cross-validation framework. '''
crossval = MatrixSingleCrossValidation(
method=method,
R=R,
M=M,
K=no_folds,
parameters=parameters,
train_config=train_config,
Measure convergence on the MovieLens 100K dataset, with the Gaussian + Laplace + IG model.
'''
import sys, os
project_location = os.path.dirname(__file__)+"/../../../../"
sys.path.append(project_location)
from BMF_Priors.code.models.bmf_gaussian_laplace_inversegaussian import BMF_Gaussian_Laplace_IG
from BMF_Priors.data.movielens.load_data import load_processed_movielens_100K
from BMF_Priors.experiments.convergence.convergence_experiment import measure_convergence_time
import matplotlib.pyplot as plt
''' Run the experiment. '''
R, M = load_processed_movielens_100K()
model_class = BMF_Gaussian_Laplace_IG
settings = {
'R': R,
'M': M,
'K': 20,
'hyperparameters': { 'alpha':1., 'beta':1. },
'init': 'random',
'iterations': 200,
}
fout_performances = './results/performances_gaussian_laplace_ig.txt'
fout_times = './results/times_gaussian_laplace_ig.txt'
repeats = 10
performances, times = measure_convergence_time(
repeats, model_class, settings, fout_performances, fout_times)