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test_aif360.py
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# Copyright 2020-2023 IBM Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import traceback
import unittest
import urllib.request
import zipfile
import aif360
import jsonschema
import numpy as np
import pandas as pd
import sklearn.model_selection
try:
import cvxpy # noqa because the import is only done as a check and flake fails
cvxpy_installed = True
except ImportError:
cvxpy_installed = False
try:
import numba # noqa because the import is only done as a check and flake fails
numba_installed = True
except ImportError:
numba_installed = False
try:
import tensorflow as tf
except ImportError:
tf = None
import lale.helpers
import lale.lib.aif360
import lale.lib.aif360.util
from lale.datasets.data_schemas import NDArrayWithSchema
from lale.lib.aif360 import (
LFR,
AdversarialDebiasing,
BaggingOrbisClassifier,
CalibratedEqOddsPostprocessing,
DisparateImpactRemover,
EqOddsPostprocessing,
GerryFairClassifier,
MetaFairClassifier,
OptimPreproc,
Orbis,
PrejudiceRemover,
Redacting,
RejectOptionClassification,
Reweighing,
count_fairness_groups,
fair_stratified_train_test_split,
)
from lale.lib.aif360.orbis import _orbis_pick_sizes
from lale.lib.lale import ConcatFeatures, Project
from lale.lib.rasl import mockup_data_loader
from lale.lib.sklearn import (
FunctionTransformer,
LinearRegression,
LogisticRegression,
OneHotEncoder,
)
class TestAIF360Datasets(unittest.TestCase):
downloaded_h181 = False
downloaded_h192 = False
@classmethod
def setUpClass(cls) -> None:
cls.downloaded_h181 = cls._try_download_csv("h181.csv")
cls.downloaded_h192 = cls._try_download_csv("h192.csv")
@classmethod
def tearDownClass(cls) -> None:
if cls.downloaded_h181:
cls._cleanup_meps("h181.csv")
if cls.downloaded_h192:
cls._cleanup_meps("h192.csv")
def _attempt_dataset(
self, X, y, fairness_info, n_rows, n_columns, set_y, di_expected
):
self.assertEqual(X.shape, (n_rows, n_columns))
self.assertEqual(y.shape, (n_rows,))
self.assertEqual(set(y), set_y)
di_scorer = lale.lib.aif360.disparate_impact(**fairness_info)
di_measured = di_scorer.score_data(X=X, y_pred=y)
self.assertAlmostEqual(di_measured, di_expected, places=3)
bat3 = ((None, by, bX) for bX, by in mockup_data_loader(X, y, 3, "pandas"))
di_bat3 = di_scorer.score_data_batched(bat3)
self.assertEqual(di_measured, di_bat3)
def test_dataset_adult_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_adult_df(preprocess=False)
self._attempt_dataset(X, y, fairness_info, 48_842, 14, {"<=50K", ">50K"}, 0.227)
def test_dataset_adult_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_adult_df(preprocess=True)
self._attempt_dataset(X, y, fairness_info, 48_842, 100, {0, 1}, 0.227)
def test_dataset_bank_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_bank_df(preprocess=False)
self._attempt_dataset(X, y, fairness_info, 45_211, 16, {1, 2}, 0.840)
def test_dataset_bank_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_bank_df(preprocess=True)
self._attempt_dataset(X, y, fairness_info, 45_211, 51, {0, 1}, 0.840)
def test_dataset_compas_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_compas_df(preprocess=False)
self._attempt_dataset(X, y, fairness_info, 6_172, 51, {0, 1}, 0.747)
def test_dataset_compas_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_compas_df(preprocess=True)
self._attempt_dataset(X, y, fairness_info, 5_278, 10, {0, 1}, 0.687)
def test_dataset_compas_violent_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_compas_violent_df(preprocess=False)
self._attempt_dataset(X, y, fairness_info, 4_020, 51, {0, 1}, 0.852)
def test_dataset_compas_violent_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_compas_violent_df(preprocess=True)
self._attempt_dataset(X, y, fairness_info, 3_377, 10, {0, 1}, 0.822)
def test_dataset_creditg_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_creditg_df(preprocess=False)
self._attempt_dataset(X, y, fairness_info, 1_000, 20, {"bad", "good"}, 0.748)
def test_dataset_creditg_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_creditg_df(preprocess=True)
self._attempt_dataset(X, y, fairness_info, 1_000, 58, {0, 1}, 0.748)
def test_dataset_default_credit_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_default_credit_df()
self._attempt_dataset(X, y, fairness_info, 30_000, 24, {0, 1}, 0.957)
def test_dataset_heart_disease_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_heart_disease_df()
self._attempt_dataset(X, y, fairness_info, 303, 13, {0, 1}, 0.589)
def test_dataset_law_school_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_law_school_df()
self._attempt_dataset(X, y, fairness_info, 20_800, 11, {"FALSE", "TRUE"}, 0.704)
def test_dataset_nlsy_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_nlsy_df()
self._attempt_dataset(X, y, fairness_info, 4908, 15, {"0", "1"}, 0.668)
def test_dataset_nursery_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_nursery_df(preprocess=False)
self._attempt_dataset(
X,
y,
fairness_info,
12_960,
8,
{"not_recom", "recommend", "very_recom", "priority", "spec_prior"},
0.461,
)
def test_dataset_nursery_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_nursery_df(preprocess=True)
self._attempt_dataset(X, y, fairness_info, 12_960, 25, {0, 1}, 0.461)
def test_dataset_ricci_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_ricci_df(preprocess=False)
self._attempt_dataset(
X, y, fairness_info, 118, 5, {"No promotion", "Promotion"}, 0.498
)
def test_dataset_ricci_pd_cat_bool(self):
X, y, fairness_info = lale.lib.aif360.fetch_ricci_df(preprocess=False)
y = y == "Promotion"
self.assertIs(y.dtype, np.dtype("bool"))
fairness_info = {**fairness_info, "favorable_labels": [True]}
self._attempt_dataset(X, y, fairness_info, 118, 5, {False, True}, 0.498)
def test_dataset_ricci_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_ricci_df(preprocess=True)
self._attempt_dataset(X, y, fairness_info, 118, 6, {0, 1}, 0.498)
def test_dataset_speeddating_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_speeddating_df(preprocess=False)
self._attempt_dataset(X, y, fairness_info, 8_378, 122, {"0", "1"}, 0.853)
def test_dataset_speeddating_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_speeddating_df(preprocess=True)
self._attempt_dataset(X, y, fairness_info, 8_378, 70, {0, 1}, 0.853)
def test_dataset_student_math_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_student_math_df()
self._attempt_dataset(X, y, fairness_info, 395, 32, {0, 1}, 0.894)
def test_dataset_student_por_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_student_por_df()
self._attempt_dataset(X, y, fairness_info, 649, 32, {0, 1}, 0.858)
def test_dataset_boston_housing_pd_cat(self):
X, y, fairness_info = lale.lib.aif360._fetch_boston_housing_df(preprocess=False)
# TODO: consider better way of handling "set_y" parameter for regression problems
self._attempt_dataset(X, y, fairness_info, 506, 13, set(y), 0.814)
def test_dataset_boston_housing_pd_num(self):
X, y, fairness_info = lale.lib.aif360._fetch_boston_housing_df(preprocess=True)
# TODO: consider better way of handling "set_y" parameter for regression problems
self._attempt_dataset(X, y, fairness_info, 506, 13, set(y), 0.814)
def test_dataset_titanic_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_titanic_df(preprocess=False)
self._attempt_dataset(X, y, fairness_info, 1_309, 13, {"0", "1"}, 0.263)
def test_dataset_titanic_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_titanic_df(preprocess=True)
self._attempt_dataset(X, y, fairness_info, 1_309, 37, {0, 1}, 0.263)
def test_dataset_tae_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_tae_df(preprocess=False)
self._attempt_dataset(X, y, fairness_info, 151, 5, {1, 2, 3}, 0.449)
def test_dataset_tae_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_tae_df(preprocess=True)
self._attempt_dataset(X, y, fairness_info, 151, 6, {0, 1}, 0.449)
def test_dataset_us_crime_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_us_crime_df()
self._attempt_dataset(X, y, fairness_info, 1_994, 102, {0, 1}, 0.888)
@classmethod
def _try_download_csv(cls, filename):
directory = os.path.join(
os.path.dirname(os.path.abspath(aif360.__file__)), "data", "raw", "meps"
)
csv_exists = os.path.exists(
os.path.join(
directory,
filename,
)
)
if csv_exists:
return False
else:
filename_without_extension = os.path.splitext(filename)[0]
zip_filename = f"{filename_without_extension}ssp.zip"
urllib.request.urlretrieve(
f"https://meps.ahrq.gov/mepsweb/data_files/pufs/{zip_filename}",
os.path.join(directory, zip_filename),
)
with zipfile.ZipFile(os.path.join(directory, zip_filename), "r") as zip_ref:
zip_ref.extractall(directory)
ssp_filename = f"{filename_without_extension}.ssp"
df = pd.read_sas(os.path.join(directory, ssp_filename), format="xport")
df.to_csv(os.path.join(directory, filename), index=False)
return True
@classmethod
def _cleanup_meps(cls, filename):
directory = os.path.join(
os.path.dirname(os.path.abspath(aif360.__file__)), "data", "raw", "meps"
)
filename_without_extension = os.path.splitext(filename)[0]
zip_filename = f"{filename_without_extension}ssp.zip"
ssp_filename = f"{filename_without_extension}.ssp"
os.remove(os.path.join(directory, filename))
os.remove(os.path.join(directory, zip_filename))
os.remove(os.path.join(directory, ssp_filename))
def test_dataset_meps_panel19_fy2015_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_meps_panel19_fy2015_df(
preprocess=False
)
self._attempt_dataset(X, y, fairness_info, 16578, 1825, {0, 1}, 0.496)
def test_dataset_meps_panel19_fy2015_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_meps_panel19_fy2015_df(
preprocess=True
)
self._attempt_dataset(X, y, fairness_info, 15830, 138, {0, 1}, 0.490)
def test_dataset_meps_panel20_fy2015_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_meps_panel20_fy2015_df(
preprocess=False
)
self._attempt_dataset(X, y, fairness_info, 18849, 1825, {0, 1}, 0.493)
def test_dataset_meps_panel20_fy2015_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_meps_panel20_fy2015_df(
preprocess=True
)
self._attempt_dataset(X, y, fairness_info, 17570, 138, {0, 1}, 0.488)
def test_dataset_meps_panel21_fy2016_pd_cat(self):
X, y, fairness_info = lale.lib.aif360.fetch_meps_panel21_fy2016_df(
preprocess=False
)
self._attempt_dataset(X, y, fairness_info, 17052, 1936, {0, 1}, 0.462)
def test_dataset_meps_panel21_fy2016_pd_num(self):
X, y, fairness_info = lale.lib.aif360.fetch_meps_panel21_fy2016_df(
preprocess=True
)
self._attempt_dataset(X, y, fairness_info, 15675, 138, {0, 1}, 0.451)
class TestAIF360Num(unittest.TestCase):
@classmethod
def _creditg_pd_num(cls):
X, y, fairness_info = lale.lib.aif360.fetch_creditg_df(preprocess=True)
cv = lale.lib.aif360.FairStratifiedKFold(**fairness_info, n_splits=3)
splits = []
lr = LogisticRegression()
for train, test in cv.split(X, y):
train_X, train_y = lale.helpers.split_with_schemas(lr, X, y, train)
assert isinstance(train_X, pd.DataFrame), type(train_X)
assert isinstance(train_y, pd.Series), type(train_y)
test_X, test_y = lale.helpers.split_with_schemas(lr, X, y, test, train)
assert isinstance(test_X, pd.DataFrame), type(test_X)
assert isinstance(test_y, pd.Series), type(test_y)
splits.append(
{
"train_X": train_X,
"train_y": train_y,
"test_X": test_X,
"test_y": test_y,
}
)
result = {"splits": splits, "fairness_info": fairness_info}
return result
@classmethod
def _creditg_np_num(cls):
train_X = cls.creditg_pd_num["splits"][0]["train_X"].to_numpy()
train_y = cls.creditg_pd_num["splits"][0]["train_y"].to_numpy()
test_X = cls.creditg_pd_num["splits"][0]["test_X"].to_numpy()
test_y = cls.creditg_pd_num["splits"][0]["test_y"].to_numpy()
assert isinstance(train_X, np.ndarray), type(train_X)
assert not isinstance(train_X, NDArrayWithSchema), type(train_X)
assert isinstance(train_y, np.ndarray), type(train_y)
assert not isinstance(train_y, NDArrayWithSchema), type(train_y)
assert isinstance(test_X, np.ndarray), type(test_X)
assert not isinstance(test_X, NDArrayWithSchema), type(test_X)
assert isinstance(test_y, np.ndarray), type(test_y)
assert not isinstance(test_y, NDArrayWithSchema), type(test_y)
pd_columns = cls.creditg_pd_num["splits"][0]["train_X"].columns
fairness_info = {
"favorable_labels": [1],
"protected_attributes": [
{
"feature": pd_columns.get_loc("sex"),
"reference_group": [1],
},
{
"feature": pd_columns.get_loc("age"),
"reference_group": [1],
},
],
}
result = {
"train_X": train_X,
"train_y": train_y,
"test_X": test_X,
"test_y": test_y,
"fairness_info": fairness_info,
}
return result
@classmethod
def _boston_pd_num(cls):
# TODO: Consider investigating test failure when preprocess is set to True
# (eo_diff is not less than 0 in this case; perhaps regression model learns differently?)
orig_X, orig_y, fairness_info = lale.lib.aif360._fetch_boston_housing_df(
preprocess=False
)
train_X, test_X, train_y, test_y = sklearn.model_selection.train_test_split(
orig_X, orig_y, test_size=0.33, random_state=42
)
assert isinstance(train_X, pd.DataFrame), type(train_X)
assert isinstance(train_y, pd.Series), type(train_y)
assert isinstance(test_X, pd.DataFrame), type(test_X)
assert isinstance(test_y, pd.Series), type(test_y)
result = {
"train_X": train_X,
"train_y": train_y,
"test_X": test_X,
"test_y": test_y,
"fairness_info": fairness_info,
}
return result
@classmethod
def _boston_np_num(cls):
train_X = cls.boston_pd_num["train_X"].to_numpy()
train_y = cls.boston_pd_num["train_y"].to_numpy()
test_X = cls.boston_pd_num["test_X"].to_numpy()
test_y = cls.boston_pd_num["test_y"].to_numpy()
assert isinstance(train_X, np.ndarray), type(train_X)
assert not isinstance(train_X, NDArrayWithSchema), type(train_X)
assert isinstance(train_y, np.ndarray), type(train_y)
assert not isinstance(train_y, NDArrayWithSchema), type(train_y)
assert isinstance(test_X, np.ndarray), type(test_X)
assert not isinstance(test_X, NDArrayWithSchema), type(test_X)
assert isinstance(test_y, np.ndarray), type(test_y)
assert not isinstance(test_y, NDArrayWithSchema), type(test_y)
pd_columns = cls.boston_pd_num["train_X"].columns
# pulling attributes off of stored fairness_info to avoid recomputing medians
fairness_info = {
"favorable_labels": cls.boston_pd_num["fairness_info"]["favorable_labels"],
"protected_attributes": [
{
"feature": pd_columns.get_loc("B"),
"reference_group": cls.boston_pd_num["fairness_info"][
"protected_attributes"
][0]["reference_group"],
},
],
}
result = {
"train_X": train_X,
"train_y": train_y,
"test_X": test_X,
"test_y": test_y,
"fairness_info": fairness_info,
}
return result
@classmethod
def setUpClass(cls):
cls.creditg_pd_num = cls._creditg_pd_num()
cls.creditg_np_num = cls._creditg_np_num()
cls.boston_pd_num = cls._boston_pd_num()
cls.boston_np_num = cls._boston_np_num()
def test_fair_stratified_train_test_split(self):
X = self.creditg_np_num["train_X"]
y = self.creditg_np_num["train_y"]
fairness_info = self.creditg_np_num["fairness_info"]
z = range(X.shape[0])
(
train_X,
test_X,
train_y,
test_y,
train_z,
test_z,
) = fair_stratified_train_test_split(X, y, z, **fairness_info)
self.assertEqual(train_X.shape[0], train_y.shape[0])
self.assertEqual(train_X.shape[0], len(train_z))
self.assertEqual(test_X.shape[0], test_y.shape[0])
self.assertEqual(test_X.shape[0], len(test_z))
self.assertEqual(train_X.shape[0] + test_X.shape[0], X.shape[0])
def _attempt_scorers(self, fairness_info, estimator, test_X, test_y):
fi = fairness_info
disparate_impact_scorer = lale.lib.aif360.disparate_impact(**fi)
impact = disparate_impact_scorer(estimator, test_X, test_y)
self.assertLess(impact, 0.9)
if estimator.is_classifier():
blended_scorer = lale.lib.aif360.accuracy_and_disparate_impact(**fi)
else:
blended_scorer = lale.lib.aif360.r2_and_disparate_impact(**fi)
blended = blended_scorer(estimator, test_X, test_y)
self.assertLess(0.0, blended)
self.assertLess(blended, 1.0)
parity_scorer = lale.lib.aif360.statistical_parity_difference(**fi)
parity = parity_scorer(estimator, test_X, test_y)
self.assertLess(parity, 0.0)
eo_diff_scorer = lale.lib.aif360.equal_opportunity_difference(**fi)
eo_diff = eo_diff_scorer(estimator, test_X, test_y)
self.assertLess(eo_diff, 0.0)
ao_diff_scorer = lale.lib.aif360.average_odds_difference(**fi)
ao_diff = ao_diff_scorer(estimator, test_X, test_y)
self.assertLess(ao_diff, 0.1)
theil_index_scorer = lale.lib.aif360.theil_index(**fi)
theil_index = theil_index_scorer(estimator, test_X, test_y)
self.assertGreater(theil_index, 0.1)
symm_di_scorer = lale.lib.aif360.symmetric_disparate_impact(**fi)
symm_di = symm_di_scorer(estimator, test_X, test_y)
self.assertLess(symm_di, 0.9)
def test_scorers_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
trainable = LogisticRegression(max_iter=1000)
train_X = self.creditg_pd_num["splits"][0]["train_X"]
train_y = self.creditg_pd_num["splits"][0]["train_y"]
trained = trainable.fit(train_X, train_y)
test_X = self.creditg_pd_num["splits"][0]["test_X"]
test_y = self.creditg_pd_num["splits"][0]["test_y"]
self._attempt_scorers(fairness_info, trained, test_X, test_y)
def test_scorers_np_num(self):
fairness_info = self.creditg_np_num["fairness_info"]
trainable = LogisticRegression(max_iter=1000)
train_X = self.creditg_np_num["train_X"]
train_y = self.creditg_np_num["train_y"]
trained = trainable.fit(train_X, train_y)
test_X = self.creditg_np_num["test_X"]
test_y = self.creditg_np_num["test_y"]
self._attempt_scorers(fairness_info, trained, test_X, test_y)
def test_scorers_regression_pd_num(self):
fairness_info = self.boston_pd_num["fairness_info"]
trainable = LinearRegression()
train_X = self.boston_pd_num["train_X"]
train_y = self.boston_pd_num["train_y"]
trained = trainable.fit(train_X, train_y)
test_X = self.boston_pd_num["test_X"]
test_y = self.boston_pd_num["test_y"]
self._attempt_scorers(fairness_info, trained, test_X, test_y)
def test_scorers_regression_np_num(self):
fairness_info = self.boston_np_num["fairness_info"]
trainable = LinearRegression()
train_X = self.boston_np_num["train_X"]
train_y = self.boston_np_num["train_y"]
trained = trainable.fit(train_X, train_y)
test_X = self.boston_np_num["test_X"]
test_y = self.boston_np_num["test_y"]
self._attempt_scorers(fairness_info, trained, test_X, test_y)
def test_scorers_blend_acc(self):
dummy_fairness_info = {
"favorable_labels": ["fav"],
"protected_attributes": [{"feature": "prot", "reference_group": ["ref"]}],
}
scorer = lale.lib.aif360.accuracy_and_disparate_impact(**dummy_fairness_info)
for acc in [0.2, 0.8, 1]:
for di in [0.7, 0.9, 1.0]:
score = scorer._blend_metrics(acc, di)
self.assertLess(0.0, score)
self.assertLessEqual(score, 1.0)
for di in [0.0, float("inf"), float("-inf"), float("nan")]:
score = scorer._blend_metrics(acc, di)
self.assertEqual(score, 0.5 * acc)
def test_scorers_blend_r2(self):
dummy_fairness_info = {
"favorable_labels": ["fav"],
"protected_attributes": [{"feature": "prot", "reference_group": ["ref"]}],
}
scorer = lale.lib.aif360.r2_and_disparate_impact(**dummy_fairness_info)
for r2 in [-2, 0, 0.5, 1]:
for di in [0.7, 0.9, 1.0]:
score = scorer._blend_metrics(r2, di)
self.assertLess(0.0, score)
self.assertLessEqual(score, 1.0)
for di in [0.0, float("inf"), float("-inf"), float("nan")]:
score = scorer._blend_metrics(r2, di)
self.assertEqual(score, 0.5 / (2.0 - r2))
def _attempt_remi_creditg_pd_num(
self, fairness_info, trainable_remi, min_di, max_di
):
splits = self.creditg_pd_num["splits"]
disparate_impact_scorer = lale.lib.aif360.disparate_impact(**fairness_info)
di_list = []
for split in splits:
if tf is not None: # for AdversarialDebiasing
tf.compat.v1.reset_default_graph()
tf.compat.v1.disable_eager_execution()
train_X = split["train_X"]
train_y = split["train_y"]
trained_remi = trainable_remi.fit(train_X, train_y)
test_X = split["test_X"]
test_y = split["test_y"]
di_list.append(disparate_impact_scorer(trained_remi, test_X, test_y))
di = pd.Series(di_list)
_, _, function_name, _ = traceback.extract_stack()[-2]
print(f"disparate impact {di.mean():.3f} +- {di.std():.3f} {function_name}")
if min_di > 0:
self.assertLessEqual(min_di, di.mean())
self.assertLessEqual(di.mean(), max_di)
def test_disparate_impact_remover_np_num(self):
fairness_info = self.creditg_np_num["fairness_info"]
trainable_orig = LogisticRegression(max_iter=1000)
trainable_remi = DisparateImpactRemover(**fairness_info) >> trainable_orig
train_X = self.creditg_np_num["train_X"]
train_y = self.creditg_np_num["train_y"]
trained_orig = trainable_orig.fit(train_X, train_y)
trained_remi = trainable_remi.fit(train_X, train_y)
test_X = self.creditg_np_num["test_X"]
test_y = self.creditg_np_num["test_y"]
disparate_impact_scorer = lale.lib.aif360.disparate_impact(**fairness_info)
impact_orig = disparate_impact_scorer(trained_orig, test_X, test_y)
self.assertTrue(0.6 < impact_orig < 1.0, f"impact_orig {impact_orig}")
impact_remi = disparate_impact_scorer(trained_remi, test_X, test_y)
self.assertTrue(0.75 < impact_remi < 1.0, f"impact_remi {impact_remi}")
def test_adversarial_debiasing_pd_num(self):
if tf is not None:
fairness_info = self.creditg_pd_num["fairness_info"]
tf.compat.v1.reset_default_graph()
trainable_remi = AdversarialDebiasing(**fairness_info)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.0, 1.5)
def test_calibrated_eq_odds_postprocessing_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
estim = LogisticRegression(max_iter=1000)
trainable_remi = CalibratedEqOddsPostprocessing(
**fairness_info, estimator=estim
)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.65, 0.85)
def test_disparate_impact_remover_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
trainable_remi = DisparateImpactRemover(**fairness_info) >> LogisticRegression(
max_iter=1000
)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.75, 0.88)
def test_eq_odds_postprocessing_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
estim = LogisticRegression(max_iter=1000)
trainable_remi = EqOddsPostprocessing(**fairness_info, estimator=estim)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.82, 1.02)
def test_gerry_fair_classifier_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
trainable_remi = GerryFairClassifier(**fairness_info)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.677, 0.678)
def test_lfr_pd_num(self):
if numba_installed:
fairness_info = self.creditg_pd_num["fairness_info"]
trainable_remi = LFR(**fairness_info) >> LogisticRegression(max_iter=1000)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.95, 1.05)
def test_meta_fair_classifier_pd_num(self):
if aif360.__version__ >= "4.0.0":
fairness_info = self.creditg_pd_num["fairness_info"]
trainable_remi = MetaFairClassifier(**fairness_info)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.62, 0.87)
def test_orbis_mixed_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
estim = LogisticRegression(max_iter=1000)
trainable_remi = Orbis(
estimator=estim, **fairness_info, sampling_strategy="over"
)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.70, 0.92)
def test_orbis_over_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
estim = LogisticRegression(max_iter=1000)
trainable_remi = Orbis(
estimator=estim, **fairness_info, sampling_strategy="over"
)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.70, 0.92)
def test_orbis_under_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
estim = LogisticRegression(max_iter=1000)
trainable_remi = Orbis(
estimator=estim, **fairness_info, sampling_strategy="under"
)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.70, 1.05)
def test_prejudice_remover_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
trainable_remi = PrejudiceRemover(**fairness_info)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.70, 0.83)
def test_redacting_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
redacting = Redacting(**fairness_info)
logistic_regression = LogisticRegression(max_iter=1000)
trainable_remi = redacting >> logistic_regression
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.78, 0.94)
def test_reject_option_classification_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
estim = LogisticRegression(max_iter=1000)
trainable_remi = RejectOptionClassification(**fairness_info, estimator=estim)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.88, 0.98)
def test_reweighing_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
estim = LogisticRegression(max_iter=1000)
trainable_remi = Reweighing(estimator=estim, **fairness_info)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.82, 0.95)
def test_sans_mitigation_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
trainable_remi = LogisticRegression(max_iter=1000)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.5, 1.0)
class TestAIF360OrbisPickSizes(unittest.TestCase):
def setUp(self):
from mystic.tools import random_seed
random_seed(42)
self.maxDiff = None
def test_pick_sizes_mixed_single_pa_single_class_normal(self):
nsizes = _orbis_pick_sizes(
osizes={"00": 100, "01": 200, "10": 300, "11": 400},
imbalance_repair_level=1,
bias_repair_level=1,
favorable_labels={1},
sampling_strategy="mixed",
)
self.assertDictEqual(nsizes, {"00": 122, "01": 122, "10": 398, "11": 398})
def test_pick_sizes_mixed_single_pa_single_class_reversed(self):
nsizes = _orbis_pick_sizes(
osizes={"00": 400, "01": 300, "10": 200, "11": 100},
imbalance_repair_level=1,
bias_repair_level=1,
favorable_labels={1},
sampling_strategy="mixed",
)
self.assertDictEqual(nsizes, {"00": 398, "01": 398, "10": 122, "11": 122})
def test_pick_sizes_mixed_multi_pa_multi_class_normal(self):
osizes = {
"000": 570,
"001": 670,
"002": 770,
"010": 870,
"011": 970,
"012": 1070,
"100": 7070,
"101": 7170,
"102": 7270,
"110": 7370,
"111": 7471,
"112": 7571,
}
nsizes = _orbis_pick_sizes(
osizes,
imbalance_repair_level=1,
bias_repair_level=1,
favorable_labels={1},
sampling_strategy="mixed",
)
self.assertDictEqual(
nsizes,
{
"000": 573,
"001": 670,
"002": 725,
"010": 923,
"011": 970,
"012": 1059,
"100": 7246,
"101": 7170,
"102": 7137,
"110": 7406,
"111": 7471,
"112": 7495,
},
)
def test_pick_sizes_mixed_multi_pa_multi_class_reversed(self):
osizes = {
"112": 100,
"111": 200,
"110": 300,
"102": 400,
"101": 500,
"100": 600,
"012": 700,
"011": 800,
"010": 900,
"002": 3000,
"001": 1100,
"000": 1200,
}
nsizes = _orbis_pick_sizes(
osizes,
imbalance_repair_level=1,
bias_repair_level=1,
favorable_labels={2},
sampling_strategy="mixed",
)
self.assertDictEqual(
nsizes,
{
"002": 1406,
"000": 1200,
"001": 1308,
"012": 700,
"010": 900,
"011": 800,
"102": 400,
"100": 600,
"101": 500,
"112": 306,
"110": 111,
"111": 200,
},
)
def test_pick_sizes_over_single_pa_single_class_normal(self):
nsizes = _orbis_pick_sizes(
osizes={"00": 100, "01": 200, "10": 300, "11": 400},
imbalance_repair_level=1,
bias_repair_level=1,
favorable_labels={1},
sampling_strategy="over",
)
self.assertDictEqual(nsizes, {"00": 200, "01": 200, "10": 400, "11": 400})
def test_pick_sizes_over_single_pa_single_class_reversed(self):
nsizes = _orbis_pick_sizes(
osizes={"00": 400, "01": 300, "10": 200, "11": 100},
imbalance_repair_level=1,
bias_repair_level=1,
favorable_labels={1},
sampling_strategy="over",
)
self.assertDictEqual(nsizes, {"00": 400, "01": 400, "10": 200, "11": 200})
def test_pick_sizes_over_multi_pa_multi_class_normal(self):
osizes = {
"000": 570,
"001": 670,
"002": 770,
"010": 870,
"011": 970,
"012": 1070,
"100": 7070,
"101": 7170,
"102": 7270,
"110": 7370,
"111": 7471,
"112": 7571,
}
nsizes = _orbis_pick_sizes(
osizes,
imbalance_repair_level=1,
bias_repair_level=1,
favorable_labels={1},
sampling_strategy="over",
)
self.assertDictEqual(
nsizes,
{
"000": 570,
"001": 670,
"002": 770,
"010": 884,
"011": 970,
"012": 1070,
"100": 7244,
"101": 7223,
"102": 7270,
"110": 7571,
"111": 7541,
"112": 7571,
},
)
def test_pick_sizes_over_multi_pa_multi_class_reversed(self):
osizes = {
"112": 100,
"111": 200,
"110": 300,
"102": 400,
"101": 500,
"100": 600,
"012": 700,
"011": 800,
"010": 900,
"002": 3000,
"001": 1100,
"000": 1200,
}
nsizes = _orbis_pick_sizes(
osizes,
imbalance_repair_level=1,
bias_repair_level=1,
favorable_labels={2},
sampling_strategy="over",
)
self.assertDictEqual(
nsizes,
{
"112": 403,
"111": 200,
"110": 300,
"102": 400,
"101": 500,
"100": 600,
"012": 700,
"011": 800,
"010": 900,
"002": 3000,
"001": 2981,
"000": 2688,
},
)
def test_pick_sizes_under_single_pa_single_class_normal(self):
nsizes = _orbis_pick_sizes(
osizes={"00": 100, "01": 200, "10": 300, "11": 400},
imbalance_repair_level=1,
bias_repair_level=1,
favorable_labels={1},
sampling_strategy="under",
)
self.assertDictEqual(nsizes, {"00": 100, "01": 100, "10": 300, "11": 300})
def test_pick_sizes_under_single_pa_single_class_reversed(self):
nsizes = _orbis_pick_sizes(
osizes={"00": 400, "01": 300, "10": 200, "11": 100},
imbalance_repair_level=1,
bias_repair_level=1,
favorable_labels={1},
sampling_strategy="under",
)
self.assertDictEqual(nsizes, {"00": 300, "01": 300, "10": 100, "11": 100})
def test_pick_sizes_under_multi_pa_multi_class_normal(self):
osizes = {
"000": 570,
"001": 670,
"002": 770,
"010": 870,
"011": 970,
"012": 1070,
"100": 7070,
"101": 7170,
"102": 7270,
"110": 7370,
"111": 7471,
"112": 7571,
}
nsizes = _orbis_pick_sizes(
osizes,
imbalance_repair_level=1,
bias_repair_level=1,
favorable_labels={1},
sampling_strategy="under",
)
self.assertDictEqual(
nsizes,
{
"000": 570,
"001": 627,
"002": 761,
"010": 870,
"011": 968,
"012": 976,
"100": 7070,
"101": 7170,
"102": 7129,
"110": 7370,
"111": 7381,
"112": 7414,
},
)
def test_pick_sizes_under_multi_pa_multi_class_reversed(self):
osizes = {
"112": 100,
"111": 200,
"110": 300,
"102": 400,
"101": 500,
"100": 600,
"012": 700,
"011": 800,
"010": 900,
"002": 3000,
"001": 1100,
"000": 1200,
}
nsizes = _orbis_pick_sizes(
osizes,
imbalance_repair_level=1,
bias_repair_level=1,
favorable_labels={2},
sampling_strategy="under",
)
self.assertDictEqual(
nsizes,
{
"112": 100,
"111": 100,
"110": 100,
"102": 400,
"101": 498,
"100": 304,
"012": 700,