def test_standardization_matches_causallib(linear_data_pandas):
w, t, y = linear_data_pandas
causallib_standardization = Standardization(LinearRegression())
causallib_standardization.fit(w, t, y)
individual_potential_outcomes = causallib_standardization.estimate_individual_outcome(
w, t)
causallib_ite_estimates = individual_potential_outcomes[
1] - individual_potential_outcomes[0]
mean_potential_outcomes = causallib_standardization.estimate_population_outcome(
w, t)
causallib_ate_estimate = mean_potential_outcomes[
1] - mean_potential_outcomes[0]
standardization = StandardizationEstimator()
standardization.fit(w, t, y)
assert causallib_ate_estimate == standardization.estimate_ate()
pd.testing.assert_series_equal(causallib_ite_estimates,
standardization.estimate_ite())
class TestStandardizationClassification(TestStandardizationCommon):
@classmethod
def setUpClass(cls):
# Three-class outcome, since decision_function might return a vector when n_classes=2, and we wish to check the
# matrix form of the output behaves as expected:
X, y = make_classification(n_features=3,
n_informative=2,
n_redundant=0,
n_repeated=0,
n_classes=3,
n_clusters_per_class=1,
flip_y=0.0,
class_sep=10.0)
X, a = X[:, :-1], X[:, -1]
a = (a > np.median(a)).astype(int)
cls.data_3cls = {
"X": pd.DataFrame(X),
"a": pd.Series(a),
"y": pd.Series(y)
}
# X, y = make_classification(n_features=2, n_informative=1, n_redundant=0, n_repeated=0, n_classes=2,
# n_clusters_per_class=1, flip_y=0.0, class_sep=10.0)
# X, a = X[:, :-1], X[:, -1]
# a = (a > np.median(a)).astype(int)
# cls.data_2cls = {"X": pd.DataFrame(X), "a": pd.Series(a), "y": pd.Series(y)}
def verify_individual_multiclass_output(self):
self.estimator.fit(self.data_3cls["X"], self.data_3cls["a"],
self.data_3cls["y"])
ind_outcome = self.estimator.estimate_individual_outcome(
self.data_3cls["X"], self.data_3cls["a"])
with self.subTest("Output size, # samples:"):
self.assertEqual(self.data_3cls["X"].shape[0],
ind_outcome.shape[0])
with self.subTest("Output size, # predictions:"):
with self.subTest(
"Output's multiindex level names are describing treatment and outcome"
):
self.assertEqual(["a", "y"], ind_outcome.columns.names)
with self.subTest(
"Output's number of predictions is the same as number of outcome and treatment values"
):
self.assertEqual(
self.data_3cls["a"].nunique() *
self.data_3cls["y"].nunique(), ind_outcome.shape[1])
self.assertEqual(
self.data_3cls["a"].nunique(),
ind_outcome.columns.get_level_values("a").unique().size)
self.assertEqual(
self.data_3cls["y"].nunique(),
ind_outcome.columns.get_level_values("y").unique().size)
return ind_outcome
def test_predict_proba(self):
self.estimator = Standardization(LogisticRegression(C=1e6,
solver='lbfgs'),
predict_proba=True)
ind_outcome = self.verify_individual_multiclass_output()
with self.subTest("Test results are probabilities - sum to 1:"):
for treatment_value, y_pred in ind_outcome.groupby(level="a",
axis="columns"):
pd.testing.assert_series_equal(
pd.Series(1.0, index=y_pred.index),
y_pred.sum(axis="columns"))
def test_decision_function(self):
self.estimator = Standardization(SVC(decision_function_shape='ovr'),
predict_proba=True)
self.verify_individual_multiclass_output()
def test_predict(self):
self.estimator = Standardization(LogisticRegression(C=1e6,
solver='lbfgs'),
predict_proba=False)
self.estimator.fit(self.data_3cls["X"], self.data_3cls["a"],
self.data_3cls["y"])
ind_outcome = self.estimator.estimate_individual_outcome(
self.data_3cls["X"], self.data_3cls["a"])
with self.subTest("Output size, # predictions:"):
self.assertEqual(self.data_3cls["a"].nunique(),
ind_outcome.shape[1])
self.assertNotEqual(self.data_3cls["y"].nunique(),
ind_outcome.shape[1])