def test_make_plr_turrell2018_return_types():
np.random.seed(3141)
res = make_plr_turrell2018(n_obs=100, return_type='DoubleMLData')
assert isinstance(res, DoubleMLData)
res = make_plr_turrell2018(n_obs=100, return_type='DataFrame')
assert isinstance(res, pd.DataFrame)
x, y, d = make_plr_turrell2018(n_obs=100, return_type='array')
assert isinstance(x, np.ndarray)
assert isinstance(y, np.ndarray)
assert isinstance(d, np.ndarray)
with pytest.raises(ValueError, match=msg_inv_return_type):
_ = make_plr_turrell2018(n_obs=100, return_type='matrix')
def generate_data2(request):
n_p = request.param
np.random.seed(1111)
# setting parameters
n = n_p[0]
p = n_p[1]
theta = 0.5
# generating data
data = make_plr_turrell2018(n, p, theta)
return data
def generate_data2(request):
N_p = request.param
np.random.seed(1111)
# setting parameters
N = N_p[0]
p = N_p[1]
theta = 0.5
# generating data
datasets = []
for i in range(n_datasets):
data = make_plr_turrell2018(N, p, theta)
datasets.append(data)
return datasets