def test_evaluate(self):
ratio_split = RatioSplit(self.data,
exclude_unknowns=True,
verbose=True)
ratio_split.evaluate(MF(), [MAE(), Recall()], user_based=False)
ratio_split = RatioSplit(self.data,
exclude_unknowns=False,
verbose=True)
ratio_split.evaluate(MF(), [MAE(), Recall()], user_based=False)
users = []
items = []
for u, i, r in self.data:
users.append(u)
items.append(i)
for u in users:
for i in items:
self.data.append((u, i, 5))
ratio_split = RatioSplit(self.data,
exclude_unknowns=True,
verbose=True)
ratio_split.evaluate(MF(), [MAE(), Recall()], user_based=True)
ratio_split = RatioSplit(self.data,
exclude_unknowns=False,
verbose=True)
ratio_split.evaluate(MF(), [MAE(), Recall()], user_based=True)
def test_with_ratio_split():
data_file = './tests/data.txt'
data = Reader.read_uir_triplets(data_file)
exp = Experiment(eval_method=RatioSplit(data, verbose=True),
models=[PMF(1, 0)],
metrics=[MAE(), RMSE(),
Recall(1), FMeasure(1)],
verbose=True)
exp.run()
assert (1, 4) == exp.avg_results.shape
assert 1 == len(exp.user_results)
assert 4 == len(exp.user_results['PMF'])
assert 2 == len(exp.user_results['PMF']['MAE'])
assert 2 == len(exp.user_results['PMF']['RMSE'])
assert 2 == len(exp.user_results['PMF']['Recall@1'])
assert 2 == len(exp.user_results['PMF']['F1@1'])
try:
Experiment(None, None, None)
except ValueError:
assert True
try:
Experiment(None, [PMF(1, 0)], None)
except ValueError:
assert True
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_evaluate():
data_file = './tests/data.txt'
data = reader.read_uir(data_file)
ratio_split = RatioSplit(data, exclude_unknowns=True, verbose=True)
ratio_split.evaluate(MF(), [MAE(), Recall()], user_based=False)
ratio_split = RatioSplit(data, exclude_unknowns=False, verbose=True)
ratio_split.evaluate(MF(), [MAE(), Recall()], user_based=False)
users = []
items = []
for u, i, r in data:
users.append(u)
items.append(i)
for u in users:
for i in items:
data.append((u, i, 5))
ratio_split = RatioSplit(data, exclude_unknowns=True, verbose=True)
ratio_split.evaluate(MF(), [MAE(), Recall()], user_based=True)
ratio_split = RatioSplit(data, exclude_unknowns=False, verbose=True)
ratio_split.evaluate(MF(), [MAE(), Recall()], user_based=True)
def test_with_ratio_split(self):
exp = Experiment(eval_method=RatioSplit(self.data, verbose=True),
models=[PMF(1, 0)],
metrics=[MAE(), RMSE(),
Recall(1),
FMeasure(1)],
verbose=True)
exp.run()
try:
Experiment(None, None, None)
except ValueError:
assert True
try:
Experiment(None, [PMF(1, 0)], None)
except ValueError:
assert True
def test_recall(self):
rec = Recall()
self.assertEqual(rec.type, "ranking")
self.assertEqual(rec.name, "Recall@-1")
self.assertEqual(1, rec.compute(np.asarray([1]), np.asarray([0])))
ground_truth = np.asarray([1, 0, 1]) # [1, 0, 1]
rec_list = np.asarray([0, 2, 1]) # [1, 1, 1]
self.assertEqual(1, rec.compute(ground_truth, rec_list))
ground_truth = np.asarray([0, 0, 1]) # [0, 0, 1]
rec_list = np.asarray([1, 2, 0]) # [1, 1, 1]
self.assertEqual(1, rec.compute(ground_truth, rec_list))
rec_2 = Recall(k=2)
self.assertEqual(rec_2.k, 2)
ground_truth = np.asarray([0, 0, 1]) # [0, 0, 1]
rec_list = np.asarray([1, 2, 0]) # [1, 1, 1]
self.assertEqual(1, rec_2.compute(ground_truth, rec_list))
def test_recall():
rec = Recall()
assert rec.type == 'ranking'
assert rec.name == 'Recall@-1'
assert 1 == rec.compute(np.asarray([1]), np.asarray([0]))
ground_truth = np.asarray([1, 0, 1]) # [1, 0, 1]
rec_list = np.asarray([0, 2, 1]) # [1, 1, 1]
assert 1 == rec.compute(ground_truth, rec_list)
ground_truth = np.asarray([0, 0, 1]) # [0, 0, 1]
rec_list = np.asarray([1, 2, 0]) # [1, 1, 1]
assert 1 == rec.compute(ground_truth, rec_list)
rec_2 = Recall(k=2)
assert rec_2.k == 2
ground_truth = np.asarray([0, 0, 1]) # [0, 0, 1]
rec_list = np.asarray([1, 2, 0]) # [1, 1, 1]
assert 1 == rec_2.compute(ground_truth, rec_list)
lambda_d=0.1,
min_user_freq=2,
max_iter=1000,
trainable=True,
verbose=True,
init_params=params,
)
n_items = eval_method.train_set.num_items
k_1 = int(n_items / 100)
k_5 = int(n_items * 5 / 100)
k_10 = int(n_items * 10 / 100)
Experiment(
eval_method,
models=[model],
metrics=[
AUC(),
Recall(k=k_1),
Recall(k=k_5),
Recall(k=k_10),
NDCG(k=k_1),
NDCG(k=k_5),
NDCG(k=k_10),
],
show_validation=True,
save_dir="dist/toy/result",
verbose=True,
).run()
rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123)
# initialize models, here we are comparing: Biased MF, PMF, and BPR
models = [
MF(k=10,
max_iter=25,
learning_rate=0.01,
lambda_reg=0.02,
use_bias=True,
seed=123),
PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123),
BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123),
]
# define metrics to evaluate the models
metrics = [
MAE(),
RMSE(),
Precision(k=10),
Recall(k=10),
NDCG(k=10),
AUC(),
MAP()
]
# put it together in an experiment, voilà!
cornac.Experiment(eval_method=rs,
models=models,
metrics=metrics,
user_based=True).run()
from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP
from cornac.eval_methods import PropensityStratifiedEvaluation
from cornac.experiment import Experiment
# Load the MovieLens 1M dataset
ml_dataset = cornac.datasets.movielens.load_feedback(variant="1M")
# Instantiate an instance of PropensityStratifiedEvaluation method
stra_eval_method = PropensityStratifiedEvaluation(
data=ml_dataset,
n_strata=2, # number of strata
rating_threshold=4.0,
verbose=True)
# define the examined models
models = [
WMF(k=10, seed=123),
BPR(k=10, seed=123),
]
# define the metrics
metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(), AUC(), MAP()]
# run an experiment
exp_stra = Experiment(eval_method=stra_eval_method,
models=models,
metrics=metrics)
exp_stra.run()
