def test_gridsearchcv_refit(u1_ml100k):
"""Test refit function of GridSearchCV."""
data_file = os.path.join(os.path.dirname(__file__), './u1_ml100k_test')
data = Dataset.load_from_file(data_file, Reader('ml-100k'))
param_grid = {
'n_epochs': [5],
'lr_all': [0.002, 0.005],
'reg_all': [0.4, 0.6],
'n_factors': [2]
}
# assert gs.fit() and gs.test will use best estimator for mae (first
# appearing in measures)
gs = GridSearchCV(SVD,
param_grid,
measures=['mae', 'rmse'],
cv=2,
refit=True)
gs.fit(data)
gs_preds = gs.test(data.construct_testset(data.raw_ratings))
mae_preds = gs.best_estimator['mae'].test(
data.construct_testset(data.raw_ratings))
assert gs_preds == mae_preds
# assert gs.fit() and gs.test will use best estimator for rmse
gs = GridSearchCV(SVD,
param_grid,
measures=['mae', 'rmse'],
cv=2,
refit='rmse')
gs.fit(data)
gs_preds = gs.test(data.construct_testset(data.raw_ratings))
rmse_preds = gs.best_estimator['rmse'].test(
data.construct_testset(data.raw_ratings))
assert gs_preds == rmse_preds
# test that predict() can be called
gs.predict(2, 4)
# assert test() and predict() cannot be used when refit is false
gs = GridSearchCV(SVD,
param_grid,
measures=['mae', 'rmse'],
cv=2,
refit=False)
gs.fit(data)
with pytest.raises(ValueError):
gs_preds = gs.test(data.construct_testset(data.raw_ratings))
with pytest.raises(ValueError):
gs.predict('1', '2')
# test that error is raised if used with load_from_folds
gs = GridSearchCV(SVD,
param_grid,
measures=['mae', 'rmse'],
cv=2,
refit=True)
with pytest.raises(ValueError):
gs.fit(u1_ml100k)
def test_gridsearchcv_refit(u1_ml100k):
"""Test refit function of GridSearchCV."""
data_file = os.path.join(os.path.dirname(__file__), './u1_ml100k_test')
data = Dataset.load_from_file(data_file, Reader('ml-100k'),
rating_scale=(1, 5))
param_grid = {'n_epochs': [5], 'lr_all': [0.002, 0.005],
'reg_all': [0.4, 0.6], 'n_factors': [2]}
# assert gs.fit() and gs.test will use best estimator for mae (first
# appearing in measures)
gs = GridSearchCV(SVD, param_grid, measures=['mae', 'rmse'], cv=2,
refit=True)
gs.fit(data)
gs_preds = gs.test(data.construct_testset(data.raw_ratings))
mae_preds = gs.best_estimator['mae'].test(
data.construct_testset(data.raw_ratings))
assert gs_preds == mae_preds
# assert gs.fit() and gs.test will use best estimator for rmse
gs = GridSearchCV(SVD, param_grid, measures=['mae', 'rmse'], cv=2,
refit='rmse')
gs.fit(data)
gs_preds = gs.test(data.construct_testset(data.raw_ratings))
rmse_preds = gs.best_estimator['rmse'].test(
data.construct_testset(data.raw_ratings))
assert gs_preds == rmse_preds
# test that predict() can be called
gs.predict(2, 4)
# assert test() and predict() cannot be used when refit is false
gs = GridSearchCV(SVD, param_grid, measures=['mae', 'rmse'], cv=2,
refit=False)
gs.fit(data)
with pytest.raises(ValueError):
gs_preds = gs.test(data.construct_testset(data.raw_ratings))
with pytest.raises(ValueError):
gs.predict('1', '2')
# test that error is raised if used with load_from_folds
gs = GridSearchCV(SVD, param_grid, measures=['mae', 'rmse'], cv=2,
refit=True)
with pytest.raises(ValueError):
gs.fit(u1_ml100k)
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import make_scorer
year_data = np.array(release_year)
labels_year = []
for i in year_data:
labels_year.append(i[0])
#split into training and testing
xtrain_y, xtest_y, ytrain_y, ytest_y = train_test_split(v,
labels_year,
test_size=0.25)
clf = LinearRegression().fit(xtrain_y, ytrain_y)
y_pred = clf.predict(xtest_y)
regression = mean_squared_error(ytest_y, y_pred)
print('regression mse: ', regression) #regression mse
# In[51]:
#calculate naive mase
naive_y = np.mean(ytrain_y)
naive_y_test = []
for i in range(len(ytest_y)):
naive_y_test.append(int(naive_y))
naive = mean_squared_error(naive_y_test, ytest_y)
print('naive mse: ', naive) #naive mse
