def test_predictive_evaluation_10(model, test_interactions_ds):
"""Invalid n_test_predictions value (< 0)."""
try:
predictive_evaluation(model,
test_interactions_ds,
count_none_predictions=False,
n_test_predictions=-1,
skip_errors=True)
assert False
except Exception as e:
assert str(e) == 'The number of test users (-1) should be > 0.'
def test_predictive_evaluation_11(model, test_interactions_ds):
"""Invalid metrics value (not a list)."""
try:
predictive_evaluation(model,
test_interactions_ds,
count_none_predictions=False,
n_test_predictions=None,
skip_errors=True,
metrics={})
assert False
except Exception as e:
assert str(e) == 'Expected "metrics" argument to be a list and found <class \'dict\'>. ' \
'Should contain instances of PredictiveMetricABC.'
def test_predictive_evaluation_3(model, test_interactions_ds):
"""Evaluation without skip errors."""
try:
predictive_evaluation(model,
test_interactions_ds,
count_none_predictions=True,
n_test_predictions=None,
skip_errors=False) == {
'MSE': 0.75,
'RMSE': 0.866
}
assert False
except Exception as e:
assert str(e) == 'Item 6 was not found.'
def test_predictive_evaluation_12(model, test_interactions_ds):
"""Invalid metrics value (list with non-PredictiveMetricABC instances)."""
fun = lambda x: 1
try:
predictive_evaluation(model,
test_interactions_ds,
count_none_predictions=False,
n_test_predictions=None,
skip_errors=True,
metrics=[fun])
assert False
except Exception as e:
assert str(
e
) == f'Expected metric {fun} to be an instance of type PredictiveMetricABC.'
def test_predictive_evaluation_8(model):
"""Evaluation on the training set."""
assert predictive_evaluation(model,
count_none_predictions=False,
n_test_predictions=None,
skip_errors=True) == {
'MSE': 5.2485,
'RMSE': 2.291
}
def test_predictive_evaluation_1(model, test_interactions_ds):
"""Evaluation counting None predictions."""
assert predictive_evaluation(model,
test_interactions_ds,
count_none_predictions=True,
n_test_predictions=None,
skip_errors=True) == {
'MSE': 0.75,
'RMSE': 0.866
}
def test_predictive_evaluation_0(model, test_interactions_ds):
"""Evaluation without counting None predictions."""
assert predictive_evaluation(model,
test_interactions_ds,
count_none_predictions=False,
n_test_predictions=None,
skip_errors=True) == {
'MSE': 0.6667,
'RMSE': 0.8165
}
def test_predictive_evaluation_7(model, test_interactions_ds):
"""Evaluation on the first 2 test predictions."""
assert predictive_evaluation(model,
test_interactions_ds,
count_none_predictions=False,
n_test_predictions=2,
skip_errors=True) == {
'MSE': 0.5,
'RMSE': 0.7071
}
def test_predictive_evaluation_6(model, test_interactions_ds):
"""Evaluation on the first test prediction."""
assert predictive_evaluation(model,
test_interactions_ds,
count_none_predictions=False,
n_test_predictions=1,
skip_errors=True) == {
'MSE': 1.0,
'RMSE': 1.0
}
def test_predictive_evaluation_5(model, test_interactions_ds):
"""Evaluation using the MSE metric only."""
assert predictive_evaluation(model,
test_interactions_ds,
count_none_predictions=False,
n_test_predictions=None,
skip_errors=True,
metrics=[MSE()]) == {
'MSE': 0.6667
}
from DRecPy.Recommender.Baseline import ItemKNN
from DRecPy.Dataset import get_train_dataset
from DRecPy.Dataset import get_test_dataset
from DRecPy.Evaluation.Processes import predictive_evaluation
import time
ds_train = get_train_dataset('ml-100k')
ds_test = get_test_dataset('ml-100k')
start_train = time.time()
item_cf = ItemKNN(k=15,
m=1,
shrinkage=100,
sim_metric='adjusted_cosine',
verbose=True)
item_cf.fit(ds_train)
print("Training took", time.time() - start_train)
start_evaluation = time.time()
print(predictive_evaluation(item_cf, ds_test))
print("Evaluation took", time.time() - start_evaluation)
