def test_make_pliv_CHS2015_return_types():
np.random.seed(3141)
res = make_pliv_CHS2015(n_obs=100, return_type='DoubleMLData')
assert isinstance(res, DoubleMLData)
res = make_pliv_CHS2015(n_obs=100, return_type='DataFrame')
assert isinstance(res, pd.DataFrame)
x, y, d, z = make_pliv_CHS2015(n_obs=100, return_type='array')
assert isinstance(x, np.ndarray)
assert isinstance(y, np.ndarray)
assert isinstance(d, np.ndarray)
assert isinstance(z, np.ndarray)
with pytest.raises(ValueError, match=msg_inv_return_type):
_ = make_pliv_CHS2015(n_obs=100, return_type='matrix')
def test_pliv_callable_not_implemented():
np.random.seed(3141)
dml_data_pliv_2z = make_pliv_CHS2015(n_obs=100, dim_z=2)
pliv_score = dml_pliv._score_elements
dml_pliv_callable_score = DoubleMLPLIV._partialX(dml_data_pliv_2z,
Lasso(),
Lasso(),
Lasso(),
score=pliv_score)
msg = 'Callable score not implemented for DoubleMLPLIV.partialX with several instruments.'
with pytest.raises(NotImplementedError, match=msg):
dml_pliv_callable_score.fit()
dml_pliv_callable_score = DoubleMLPLIV._partialZ(dml_data_pliv_2z,
Lasso(),
score=pliv_score)
msg = 'Callable score not implemented for DoubleMLPLIV.partialZ.'
with pytest.raises(NotImplementedError, match=msg):
dml_pliv_callable_score.fit()
dml_pliv_callable_score = DoubleMLPLIV._partialXZ(dml_data_pliv_2z,
Lasso(),
Lasso(),
Lasso(),
score=pliv_score)
msg = 'Callable score not implemented for DoubleMLPLIV.partialXZ.'
with pytest.raises(NotImplementedError, match=msg):
dml_pliv_callable_score.fit()
def test_obj_vs_from_arrays():
np.random.seed(3141)
dml_data = make_plr_CCDDHNR2018(n_obs=100)
dml_data_from_array = DoubleMLData.from_arrays(
dml_data.data[dml_data.x_cols], dml_data.data[dml_data.y_col],
dml_data.data[dml_data.d_cols])
assert dml_data_from_array.data.equals(dml_data.data)
dml_data = _make_pliv_data(n_obs=100)
dml_data_from_array = DoubleMLData.from_arrays(
dml_data.data[dml_data.x_cols], dml_data.data[dml_data.y_col],
dml_data.data[dml_data.d_cols], dml_data.data[dml_data.z_cols])
assert dml_data_from_array.data.equals(dml_data.data)
dml_data = make_pliv_CHS2015(n_obs=100, dim_z=5)
dml_data_from_array = DoubleMLData.from_arrays(
dml_data.data[dml_data.x_cols], dml_data.data[dml_data.y_col],
dml_data.data[dml_data.d_cols], dml_data.data[dml_data.z_cols])
assert np.array_equal(dml_data_from_array.data,
dml_data.data) # z_cols name differ
dml_data = make_plr_CCDDHNR2018(n_obs=100)
df = dml_data.data.copy().iloc[:, :10]
df.columns = [f'X{i+1}' for i in np.arange(7)] + ['y', 'd1', 'd2']
dml_data = DoubleMLData(df, 'y', ['d1', 'd2'],
[f'X{i+1}' for i in np.arange(7)])
dml_data_from_array = DoubleMLData.from_arrays(
dml_data.data[dml_data.x_cols], dml_data.data[dml_data.y_col],
dml_data.data[dml_data.d_cols])
assert np.array_equal(dml_data_from_array.data, dml_data.data)
def test_obj_vs_from_arrays():
np.random.seed(3141)
dml_data = make_plr_CCDDHNR2018(n_obs=100)
dml_data_from_array = DoubleMLData.from_arrays(dml_data.data[dml_data.x_cols],
dml_data.data[dml_data.y_col],
dml_data.data[dml_data.d_cols])
assert dml_data_from_array.data.equals(dml_data.data)
dml_data = _make_pliv_data(n_obs=100)
dml_data_from_array = DoubleMLData.from_arrays(dml_data.data[dml_data.x_cols],
dml_data.data[dml_data.y_col],
dml_data.data[dml_data.d_cols],
dml_data.data[dml_data.z_cols])
assert dml_data_from_array.data.equals(dml_data.data)
dml_data = make_pliv_CHS2015(n_obs=100, dim_z=5)
dml_data_from_array = DoubleMLData.from_arrays(dml_data.data[dml_data.x_cols],
dml_data.data[dml_data.y_col],
dml_data.data[dml_data.d_cols],
dml_data.data[dml_data.z_cols])
assert np.array_equal(dml_data_from_array.data, dml_data.data) # z_cols name differ
dml_data = make_plr_CCDDHNR2018(n_obs=100)
df = dml_data.data.copy().iloc[:, :10]
df.columns = [f'X{i+1}' for i in np.arange(7)] + ['y', 'd1', 'd2']
dml_data = DoubleMLData(df, 'y', ['d1', 'd2'], [f'X{i+1}' for i in np.arange(7)])
dml_data_from_array = DoubleMLData.from_arrays(dml_data.data[dml_data.x_cols],
dml_data.data[dml_data.y_col],
dml_data.data[dml_data.d_cols])
assert np.array_equal(dml_data_from_array.data, dml_data.data)
dml_data = make_pliv_multiway_cluster_CKMS2021(N=10, M=10)
dml_data_from_array = DoubleMLClusterData.from_arrays(dml_data.data[dml_data.x_cols],
dml_data.data[dml_data.y_col],
dml_data.data[dml_data.d_cols],
dml_data.data[dml_data.cluster_cols],
dml_data.data[dml_data.z_cols])
df = dml_data.data.copy()
df.rename(columns={'cluster_var_i': 'cluster_var1',
'cluster_var_j': 'cluster_var2',
'Y': 'y', 'D': 'd', 'Z': 'z'},
inplace=True)
assert dml_data_from_array.data.equals(df)
# with a single cluster variable
dml_data_from_array = DoubleMLClusterData.from_arrays(dml_data.data[dml_data.x_cols],
dml_data.data[dml_data.y_col],
dml_data.data[dml_data.d_cols],
dml_data.data[dml_data.cluster_cols[1]],
dml_data.data[dml_data.z_cols])
df = dml_data.data.copy().drop(columns='cluster_var_i')
df.rename(columns={'cluster_var_j': 'cluster_var',
'Y': 'y', 'D': 'd', 'Z': 'z'},
inplace=True)
assert dml_data_from_array.data.equals(df)
def generate_data_pliv_partialX(request):
n_p = request.param
np.random.seed(1111)
# setting parameters
n = n_p
theta = 1.
# generating data
data = make_pliv_CHS2015(n, alpha=theta, dim_z=5)
return data
def generate_data_iv(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_pliv_CHS2015(n_obs=n, dim_x=p, alpha=theta, dim_z=1, return_type=pd.DataFrame)
return data
def generate_data_pliv_partialX(request):
N_p = request.param
np.random.seed(1111)
# setting parameters
N = N_p
theta = 1.
# generating data
datasets = []
for i in range(n_datasets):
data = make_pliv_CHS2015(N, alpha=theta, dim_z=5)
datasets.append(data)
return datasets
def generate_data_iv(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_pliv_CHS2015(n_obs=N,
dim_x=p,
alpha=theta,
dim_z=1,
return_type=pd.DataFrame)
datasets.append(data)
return datasets
from doubleml.datasets import make_plr_CCDDHNR2018, make_irm_data, make_pliv_CHS2015, make_iivm_data,\
make_pliv_multiway_cluster_CKMS2021
from sklearn.linear_model import Lasso, LogisticRegression
from sklearn.base import BaseEstimator
np.random.seed(3141)
dml_data = make_plr_CCDDHNR2018(n_obs=10)
ml_g = Lasso()
ml_m = Lasso()
ml_r = Lasso()
dml_plr = DoubleMLPLR(dml_data, ml_g, ml_m)
dml_data_irm = make_irm_data(n_obs=10)
dml_data_iivm = make_iivm_data(n_obs=10)
dml_data_pliv = make_pliv_CHS2015(n_obs=10, dim_z=1)
dml_cluster_data_pliv = make_pliv_multiway_cluster_CKMS2021(N=10, M=10)
(x, y, d, z) = make_iivm_data(n_obs=30, return_type="array")
y[y > 0] = 1
y[y < 0] = 0
dml_data_irm_binary_outcome = DoubleMLData.from_arrays(x, y, d)
dml_data_iivm_binary_outcome = DoubleMLData.from_arrays(x, y, d, z)
@pytest.mark.ci
def test_doubleml_exception_data():
msg = 'The data must be of DoubleMLData type.'
with pytest.raises(TypeError, match=msg):
_ = DoubleMLPLR(pd.DataFrame(), ml_g, ml_m)
# PLR with IV
