def test_with_cross_validation(self):
exp = Experiment(eval_method=CrossValidation(self.data),
models=[PMF(1, 0)],
metrics=[MAE(), RMSE(),
Recall(1),
FMeasure(1)],
verbose=True)
exp.run()
def test_with_cross_validation(self):
Experiment(eval_method=CrossValidation(
self.data + [(self.data[0][0], self.data[1][1], 5.0)],
exclude_unknowns=False,
verbose=True),
models=[PMF(1, 0)],
metrics=[Recall(1), FMeasure(1)],
verbose=True).run()
def test_with_cross_validation():
data_file = './tests/data.txt'
data = reader.read_uir(data_file)
exp = Experiment(eval_method=CrossValidation(data),
models=[PMF(1, 0)],
metrics=[MAE(), RMSE(),
Recall(1), FMeasure(1)],
verbose=True)
exp.run()
def test_get_train_test_sets_next_fold():
data = reader.read_uir('./tests/data.txt')
nfolds = 5
cv = CrossValidation(data=data, n_folds=nfolds)
for n in range(cv.n_folds):
cv._get_train_test()
assert cv.current_fold == n
assert cv.train_set.matrix.shape == (8, 8)
cv._next_fold()
def test_partition_data():
data = reader.read_uir('./tests/data.txt')
nfolds = 5
cv = CrossValidation(data=data, n_folds=nfolds)
ref_set = set(range(nfolds))
res_set = set(cv.partition)
fold_sizes = np.unique(cv.partition, return_counts=True)[1]
assert len(data) == len(cv.partition)
assert res_set == ref_set
assert np.all(fold_sizes == 2)
def test_validate_partition():
data = reader.read_uir('./tests/data.txt')
nfolds = 5
cv = CrossValidation(data=data, n_folds=nfolds)
try:
cv._validate_partition([0, 0, 1, 1])
except:
assert True
try:
cv._validate_partition([0, 0, 1, 1, 2, 2, 2, 2, 3, 3])
except:
assert True
def setUp(self):
self.data = Reader().read('./tests/data.txt')
self.n_folds = 5
self.cv = CrossValidation(data=self.data,
n_folds=self.n_folds,
exclude_unknowns=False)
def setUp(self):
self.data = Reader().read('./tests/data.txt')
self.n_folds = 5
self.cv = CrossValidation(data=self.data, n_folds=self.n_folds)
def select_eval(method, dataset):
if method == "ratio_split":
return RatioSplit(data=dataset, test_size=0.2,
rating_threshold=4.0, exclude_unknowns=False)
elif method == "cross_validation":
return CrossValidation(data=dataset, rating_threshold=1.0, exclude_unknowns=False)