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# Licensed under the MIT License.
import copy
import numpy as np
import pandas as pd
import pytest
from fairlearn.postprocessing._constants import DEMOGRAPHIC_PARITY, EQUALIZED_ODDS
from fairlearn.postprocessing import ThresholdOptimizer
from fairlearn.postprocessing._threshold_optimizer import \
(_vectorized_prediction,
_threshold_optimization_demographic_parity,
_threshold_optimization_equalized_odds,
DIFFERENT_INPUT_LENGTH_ERROR_MESSAGE,
EMPTY_INPUT_ERROR_MESSAGE,
NON_BINARY_LABELS_ERROR_MESSAGE,
INPUT_DATA_FORMAT_ERROR_MESSAGE,
NOT_SUPPORTED_CONSTRAINTS_ERROR_MESSAGE,
PREDICT_BEFORE_FIT_ERROR_MESSAGE,
MULTIPLE_DATA_COLUMNS_ERROR_MESSAGE)
from fairlearn.postprocessing._postprocessing import \
PREDICTOR_OR_ESTIMATOR_REQUIRED_ERROR_MESSAGE, EITHER_PREDICTOR_OR_ESTIMATOR_ERROR_MESSAGE, \
MISSING_FIT_PREDICT_ERROR_MESSAGE, MISSING_PREDICT_ERROR_MESSAGE
from fairlearn.postprocessing._roc_curve_utilities import DEGENERATE_LABELS_ERROR_MESSAGE
from .test_utilities import (sensitive_features_ex1, sensitive_features_ex2, labels_ex,
degenerate_labels_ex, scores_ex, sensitive_feature_names_ex1,
sensitive_feature_names_ex2, _get_predictions_by_sensitive_feature,
_format_as_list_of_lists, ExamplePredictor, ExampleEstimator,
ExampleNotPredictor, ExampleNotEstimator1, ExampleNotEstimator2)
ALLOWED_INPUT_DATA_TYPES = [lambda x: x, np.array, pd.DataFrame, pd.Series]
@pytest.mark.parametrize("X_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("sensitive_features_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("predict_method_name", ['predict', '_pmf_predict'])
@pytest.mark.parametrize("constraints", [DEMOGRAPHIC_PARITY, EQUALIZED_ODDS])
def test_predict_before_fit_error(X_transform, sensitive_features_transform, predict_method_name,
constraints):
X = X_transform(_format_as_list_of_lists(sensitive_features_ex1))
sensitive_features = sensitive_features_transform(sensitive_features_ex1)
adjusted_predictor = ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
constraints=constraints)
with pytest.raises(ValueError, match=PREDICT_BEFORE_FIT_ERROR_MESSAGE):
getattr(adjusted_predictor, predict_method_name)(X, sensitive_features=sensitive_features)
@pytest.mark.parametrize("constraints", [DEMOGRAPHIC_PARITY, EQUALIZED_ODDS])
def test_both_predictor_and_estimator_error(constraints):
with pytest.raises(ValueError, match=EITHER_PREDICTOR_OR_ESTIMATOR_ERROR_MESSAGE):
ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
estimator=ExampleEstimator(),
constraints=constraints)
@pytest.mark.parametrize("constraints", [DEMOGRAPHIC_PARITY, EQUALIZED_ODDS])
def test_no_predictor_or_estimator_error(constraints):
with pytest.raises(ValueError, match=PREDICTOR_OR_ESTIMATOR_REQUIRED_ERROR_MESSAGE):
ThresholdOptimizer(constraints=constraints)
def test_constraints_not_supported():
with pytest.raises(ValueError, match=NOT_SUPPORTED_CONSTRAINTS_ERROR_MESSAGE):
ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
constraints="UnsupportedConstraints")
@pytest.mark.parametrize("not_estimator", [ExampleNotEstimator1(), ExampleNotEstimator2()])
@pytest.mark.parametrize("constraints", [DEMOGRAPHIC_PARITY, EQUALIZED_ODDS])
def test_not_estimator(not_estimator, constraints):
with pytest.raises(ValueError, match=MISSING_FIT_PREDICT_ERROR_MESSAGE):
ThresholdOptimizer(estimator=not_estimator,
constraints=constraints)
@pytest.mark.parametrize("constraints", [DEMOGRAPHIC_PARITY, EQUALIZED_ODDS])
def test_not_predictor(constraints):
with pytest.raises(ValueError, match=MISSING_PREDICT_ERROR_MESSAGE):
ThresholdOptimizer(unconstrained_predictor=ExampleNotPredictor(),
constraints=constraints)
@pytest.mark.parametrize("X", [None, _format_as_list_of_lists(sensitive_features_ex1)])
@pytest.mark.parametrize("y", [None, labels_ex])
@pytest.mark.parametrize("sensitive_features", [None, sensitive_features_ex1])
@pytest.mark.parametrize("constraints", [DEMOGRAPHIC_PARITY, EQUALIZED_ODDS])
def test_inconsistent_input_data_types(X, y, sensitive_features, constraints):
adjusted_predictor = ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
constraints=constraints)
error_message = INPUT_DATA_FORMAT_ERROR_MESSAGE.format(type(X).__name__,
type(y).__name__,
type(sensitive_features).__name__)
if X is None or y is None and sensitive_features is None:
with pytest.raises(TypeError) as exception:
adjusted_predictor.fit(X, y, sensitive_features=sensitive_features)
assert str(exception.value) == error_message
@pytest.mark.parametrize("X_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("y_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("sensitive_features_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("constraints", [DEMOGRAPHIC_PARITY, EQUALIZED_ODDS])
def test_threshold_optimization_non_binary_labels(X_transform, y_transform,
sensitive_features_transform, constraints):
non_binary_labels = copy.deepcopy(labels_ex)
non_binary_labels[0] = 2
X = X_transform(_format_as_list_of_lists(sensitive_features_ex1))
y = y_transform(non_binary_labels)
sensitive_features = sensitive_features_transform(sensitive_features_ex1)
adjusted_predictor = ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
constraints=constraints)
with pytest.raises(ValueError, match=NON_BINARY_LABELS_ERROR_MESSAGE):
adjusted_predictor.fit(X, y, sensitive_features=sensitive_features)
@pytest.mark.parametrize("X_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("y_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("sensitive_features_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("constraints", [DEMOGRAPHIC_PARITY, EQUALIZED_ODDS])
def test_threshold_optimization_degenerate_labels(X_transform, y_transform,
sensitive_features_transform, constraints):
X = X_transform(_format_as_list_of_lists(sensitive_features_ex1))
y = y_transform(degenerate_labels_ex)
sensitive_features = sensitive_features_transform(sensitive_features_ex1)
adjusted_predictor = ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
constraints=constraints)
with pytest.raises(ValueError, match=DEGENERATE_LABELS_ERROR_MESSAGE.format('A')):
adjusted_predictor.fit(X, y, sensitive_features=sensitive_features)
@pytest.mark.parametrize("X_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("y_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("sensitive_features_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("constraints", [DEMOGRAPHIC_PARITY, EQUALIZED_ODDS])
def test_threshold_optimization_different_input_lengths(X_transform, y_transform,
sensitive_features_transform,
constraints):
n = len(sensitive_features_ex1)
for permutation in [(0, 1), (1, 0)]:
with pytest.raises(ValueError, match=DIFFERENT_INPUT_LENGTH_ERROR_MESSAGE
.format("X, sensitive_features, and y")):
X = X_transform(_format_as_list_of_lists(
sensitive_features_ex1)[:n - permutation[0]])
y = y_transform(labels_ex[:n - permutation[1]])
sensitive_features = sensitive_features_transform(sensitive_features_ex1)
adjusted_predictor = ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
constraints=constraints)
adjusted_predictor.fit(X, y, sensitive_features=sensitive_features)
# try providing empty lists in all combinations
for permutation in [(0, n), (n, 0)]:
X = X_transform(_format_as_list_of_lists(
sensitive_features_ex1)[:n - permutation[0]])
y = y_transform(labels_ex[:n - permutation[1]])
sensitive_features = sensitive_features_transform(sensitive_features_ex1)
adjusted_predictor = ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
constraints=constraints)
with pytest.raises(ValueError, match=EMPTY_INPUT_ERROR_MESSAGE):
adjusted_predictor.fit(X, y, sensitive_features=sensitive_features)
@pytest.mark.parametrize("score_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("y_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("sensitive_features_transform", ALLOWED_INPUT_DATA_TYPES)
def test_threshold_optimization_demographic_parity(score_transform, y_transform,
sensitive_features_transform):
y = y_transform(labels_ex)
sensitive_features = sensitive_features_transform(sensitive_features_ex1)
scores = score_transform(scores_ex)
adjusted_predictor = create_adjusted_predictor(_threshold_optimization_demographic_parity,
sensitive_features, y, scores)
# For Demographic Parity we can ignore p_ignore since it's always 0.
# sensitive feature value A
value_for_less_than_2_5 = 0.8008
assert np.isclose(value_for_less_than_2_5,
adjusted_predictor([sensitive_feature_names_ex1[0]], [0]))
assert np.isclose(value_for_less_than_2_5,
adjusted_predictor([sensitive_feature_names_ex1[0]], [2.499]))
assert 0 == adjusted_predictor([sensitive_feature_names_ex1[0]], [2.5])
assert 0 == adjusted_predictor([sensitive_feature_names_ex1[0]], [100])
# sensitive feature value B
value_for_less_than_0_5 = 0.00133333333333
assert np.isclose(value_for_less_than_0_5,
adjusted_predictor([sensitive_feature_names_ex1[1]], [0]))
assert np.isclose(value_for_less_than_0_5,
adjusted_predictor([sensitive_feature_names_ex1[1]], [0.5]))
assert 1 == adjusted_predictor([sensitive_feature_names_ex1[1]], [0.51])
assert 1 == adjusted_predictor([sensitive_feature_names_ex1[1]], [1])
assert 1 == adjusted_predictor([sensitive_feature_names_ex1[1]], [100])
# sensitive feature value C
value_between_0_5_and_1_5 = 0.608
assert 0 == adjusted_predictor([sensitive_feature_names_ex1[2]], [0])
assert 0 == adjusted_predictor([sensitive_feature_names_ex1[2]], [0.5])
assert np.isclose(value_between_0_5_and_1_5,
adjusted_predictor([sensitive_feature_names_ex1[2]], [0.51]))
assert np.isclose(value_between_0_5_and_1_5,
adjusted_predictor([sensitive_feature_names_ex1[2]], [1]))
assert np.isclose(value_between_0_5_and_1_5,
adjusted_predictor([sensitive_feature_names_ex1[2]], [1.5]))
assert 1 == adjusted_predictor([sensitive_feature_names_ex1[2]], [1.51])
assert 1 == adjusted_predictor([sensitive_feature_names_ex1[2]], [100])
# Assert Demographic Parity actually holds
predictions_by_sensitive_feature = _get_predictions_by_sensitive_feature(
adjusted_predictor, sensitive_features_ex1, scores_ex, labels_ex)
def _average_prediction(sensitive_feature_value, predictions_by_sensitive_feature):
relevant_predictions = predictions_by_sensitive_feature[sensitive_feature_value]
predictions = [lp.prediction for lp in relevant_predictions]
return np.sum(predictions) / len(relevant_predictions)
average_probabilities_by_sensitive_feature = []
for sensitive_feature_value in sorted(predictions_by_sensitive_feature):
average_probabilities_by_sensitive_feature \
.append(_average_prediction(sensitive_feature_value,
predictions_by_sensitive_feature))
assert np.isclose(average_probabilities_by_sensitive_feature, [0.572] * 3).all()
@pytest.mark.parametrize("score_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("y_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("sensitive_features_transform", ALLOWED_INPUT_DATA_TYPES)
def test_threshold_optimization_equalized_odds(score_transform, y_transform,
sensitive_features_transform):
y = y_transform(labels_ex)
sensitive_features = sensitive_features_transform(sensitive_features_ex1)
scores = score_transform(scores_ex)
adjusted_predictor = create_adjusted_predictor(_threshold_optimization_equalized_odds,
sensitive_features, y, scores)
# For Equalized Odds we need to factor in that the output is calculated by
# p_ignore * prediction_constant + (1 - p_ignore) * (p0 * pred0(x) + p1 * pred1(x))
# with p_ignore != 0 and prediction_constant != 0 for at least some sensitive feature values.
prediction_constant = 0.334
# sensitive feature value A
# p_ignore is almost 0 which means there's almost no adjustment
p_ignore = 0.001996007984031716
base_value = prediction_constant * p_ignore
value_for_less_than_2_5 = base_value + (1 - p_ignore) * 0.668
assert np.isclose(value_for_less_than_2_5,
adjusted_predictor([sensitive_feature_names_ex1[0]], [0]))
assert np.isclose(value_for_less_than_2_5,
adjusted_predictor([sensitive_feature_names_ex1[0]], [2.499]))
assert base_value == adjusted_predictor([sensitive_feature_names_ex1[0]], [2.5])
assert base_value == adjusted_predictor([sensitive_feature_names_ex1[0]], [100])
# sensitive feature value B
# p_ignore is the largest among the three classes indicating a large adjustment
p_ignore = 0.1991991991991991
base_value = prediction_constant * p_ignore
value_for_less_than_0_5 = base_value + (1 - p_ignore) * 0.001
assert np.isclose(value_for_less_than_0_5,
adjusted_predictor([sensitive_feature_names_ex1[1]], [0]))
assert np.isclose(value_for_less_than_0_5,
adjusted_predictor([sensitive_feature_names_ex1[1]], [0.5]))
assert base_value + 1 - \
p_ignore == adjusted_predictor([sensitive_feature_names_ex1[1]], [0.51])
assert base_value + 1 - \
p_ignore == adjusted_predictor([sensitive_feature_names_ex1[1]], [1])
assert base_value + 1 - \
p_ignore == adjusted_predictor([sensitive_feature_names_ex1[1]], [100])
# sensitive feature value C
# p_ignore is 0 which means there's no adjustment
p_ignore = 0
base_value = prediction_constant * p_ignore
value_between_0_5_and_1_5 = base_value + (1 - p_ignore) * 0.501
assert base_value == adjusted_predictor([sensitive_feature_names_ex1[2]], [0])
assert base_value == adjusted_predictor([sensitive_feature_names_ex1[2]], [0.5])
assert np.isclose(value_between_0_5_and_1_5,
adjusted_predictor([sensitive_feature_names_ex1[2]], [0.51]))
assert np.isclose(value_between_0_5_and_1_5,
adjusted_predictor([sensitive_feature_names_ex1[2]], [1]))
assert np.isclose(value_between_0_5_and_1_5,
adjusted_predictor([sensitive_feature_names_ex1[2]], [1.5]))
assert base_value + 1 - \
p_ignore == adjusted_predictor([sensitive_feature_names_ex1[2]], [1.51])
assert base_value + 1 - \
p_ignore == adjusted_predictor([sensitive_feature_names_ex1[2]], [100])
# Assert Equalized Odds actually holds
predictions_by_sensitive_feature = _get_predictions_by_sensitive_feature(
adjusted_predictor, sensitive_features_ex1, scores_ex, labels_ex)
def _average_prediction_for_label(label, sensitive_feature_value,
predictions_by_sensitive_feature):
relevant_predictions = predictions_by_sensitive_feature[sensitive_feature_value]
predictions_for_label = [lp.prediction for lp in relevant_predictions if lp.label == label]
sum_of_predictions_for_label = np.sum(predictions_for_label)
n_predictions_for_label = len([lp for lp in relevant_predictions if lp.label == label])
return sum_of_predictions_for_label / n_predictions_for_label
predictions_based_on_label = {0: [], 1: []}
for label in [0, 1]:
for sensitive_feature_value in sorted(predictions_by_sensitive_feature):
predictions_based_on_label[label] \
.append(_average_prediction_for_label(label, sensitive_feature_value,
predictions_by_sensitive_feature))
# assert counts of positive predictions for negative labels
assert np.isclose(predictions_based_on_label[0], [0.334] * 3).all()
# assert counts of positive predictions for positive labels
assert np.isclose(predictions_based_on_label[1], [0.66733333] * 3).all()
@pytest.mark.parametrize("sensitive_features,sensitive_feature_names,expected_p0,expected_p1",
[(sensitive_features_ex1, sensitive_feature_names_ex1, 0.428, 0.572),
(sensitive_features_ex2, sensitive_feature_names_ex2, 0.6, 0.4)])
@pytest.mark.parametrize("X_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("y_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("sensitive_features_transform", ALLOWED_INPUT_DATA_TYPES)
def test_threshold_optimization_demographic_parity_e2e(sensitive_features,
sensitive_feature_names,
expected_p0, expected_p1,
X_transform, y_transform,
sensitive_features_transform):
X = X_transform(_format_as_list_of_lists(sensitive_features))
y = y_transform(labels_ex)
sensitive_features_ = sensitive_features_transform(sensitive_features)
adjusted_predictor = ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
constraints=DEMOGRAPHIC_PARITY)
adjusted_predictor.fit(X, y, sensitive_features=sensitive_features_)
predictions = adjusted_predictor._pmf_predict(X, sensitive_features=sensitive_features_)
# assert demographic parity
for sensitive_feature_name in sensitive_feature_names:
average_probs = np.average(
predictions[np.array(sensitive_features) == sensitive_feature_name], axis=0)
assert np.isclose(average_probs[0], expected_p0)
assert np.isclose(average_probs[1], expected_p1)
@pytest.mark.parametrize("sensitive_features,sensitive_feature_names,"
"expected_positive_p0,expected_positive_p1,"
"expected_negative_p0,expected_negative_p1",
[(sensitive_features_ex1, sensitive_feature_names_ex1,
0.33266666, 0.66733333, 0.666, 0.334),
(sensitive_features_ex2, sensitive_feature_names_ex2,
0.112, 0.888, 0.334, 0.666)])
@pytest.mark.parametrize("X_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("y_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("sensitive_features_transform", ALLOWED_INPUT_DATA_TYPES)
def test_threshold_optimization_equalized_odds_e2e(
sensitive_features, sensitive_feature_names, expected_positive_p0, expected_positive_p1,
expected_negative_p0, expected_negative_p1, X_transform, y_transform,
sensitive_features_transform):
X = X_transform(_format_as_list_of_lists(sensitive_features))
y = y_transform(labels_ex)
sensitive_features_ = sensitive_features_transform(sensitive_features)
adjusted_predictor = ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
constraints=EQUALIZED_ODDS)
adjusted_predictor.fit(X, y, sensitive_features=sensitive_features_)
predictions = adjusted_predictor._pmf_predict(X, sensitive_features=sensitive_features_)
# assert equalized odds
for a in sensitive_feature_names:
positive_indices = (np.array(sensitive_features) == a) * \
(np.array(labels_ex) == 1)
negative_indices = (np.array(sensitive_features) == a) * \
(np.array(labels_ex) == 0)
average_probs_positive_indices = np.average(
predictions[positive_indices], axis=0)
average_probs_negative_indices = np.average(
predictions[negative_indices], axis=0)
assert np.isclose(
average_probs_positive_indices[0], expected_positive_p0)
assert np.isclose(
average_probs_positive_indices[1], expected_positive_p1)
assert np.isclose(
average_probs_negative_indices[0], expected_negative_p0)
assert np.isclose(
average_probs_negative_indices[1], expected_negative_p1)
@pytest.mark.parametrize("sensitive_features,sensitive_feature_names",
[(sensitive_features_ex1, sensitive_feature_names_ex1),
(sensitive_features_ex2, sensitive_feature_names_ex2)])
@pytest.mark.parametrize("X_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("y_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("sensitive_features_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("constraints", [DEMOGRAPHIC_PARITY, EQUALIZED_ODDS])
def test_predict_output_0_or_1(sensitive_features, sensitive_feature_names, X_transform,
y_transform, sensitive_features_transform, constraints):
X = X_transform(_format_as_list_of_lists(sensitive_features))
y = y_transform(labels_ex)
sensitive_features_ = sensitive_features_transform(sensitive_features)
adjusted_predictor = ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
constraints=constraints)
adjusted_predictor.fit(X, y, sensitive_features=sensitive_features_)
predictions = adjusted_predictor.predict(X, sensitive_features=sensitive_features_)
for prediction in predictions:
assert prediction in [0, 1]
@pytest.mark.parametrize("sensitive_features,sensitive_feature_names",
[(sensitive_features_ex1, sensitive_feature_names_ex1),
(sensitive_features_ex2, sensitive_feature_names_ex2)])
@pytest.mark.parametrize("X_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("y_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("constraints", [DEMOGRAPHIC_PARITY, EQUALIZED_ODDS])
def test_predict_multiple_sensitive_features_columns_error(
sensitive_features, sensitive_feature_names, X_transform, y_transform, constraints):
X = X_transform(_format_as_list_of_lists(sensitive_features))
y = y_transform(labels_ex)
sensitive_features_ = pd.DataFrame({"A1": sensitive_features, "A2": sensitive_features})
adjusted_predictor = ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
constraints=constraints)
adjusted_predictor.fit(X, y, sensitive_features=sensitive_features_)
with pytest.raises(ValueError,
match=MULTIPLE_DATA_COLUMNS_ERROR_MESSAGE.format("sensitive_features")):
adjusted_predictor.predict(X, sensitive_features=sensitive_features_)
@pytest.mark.parametrize("sensitive_features,sensitive_feature_names",
[(sensitive_features_ex1, sensitive_feature_names_ex1),
(sensitive_features_ex2, sensitive_feature_names_ex2)])
@pytest.mark.parametrize("X_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("y_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("sensitive_features_transform", ALLOWED_INPUT_DATA_TYPES)
@pytest.mark.parametrize("constraints", [DEMOGRAPHIC_PARITY, EQUALIZED_ODDS])
def test_predict_different_argument_lengths(sensitive_features, sensitive_feature_names,
X_transform, y_transform,
sensitive_features_transform, constraints):
X = X_transform(_format_as_list_of_lists(sensitive_features))
y = y_transform(labels_ex)
sensitive_features_ = sensitive_features_transform(sensitive_features)
adjusted_predictor = ThresholdOptimizer(unconstrained_predictor=ExamplePredictor(),
constraints=constraints)
adjusted_predictor.fit(X, y, sensitive_features=sensitive_features_)
with pytest.raises(ValueError, match=DIFFERENT_INPUT_LENGTH_ERROR_MESSAGE
.format("X and sensitive_features")):
adjusted_predictor.predict(
X, sensitive_features=sensitive_features_transform(sensitive_features[:-1]))
with pytest.raises(ValueError, match=DIFFERENT_INPUT_LENGTH_ERROR_MESSAGE
.format("X and sensitive_features")):
adjusted_predictor.predict(X_transform(_format_as_list_of_lists(sensitive_features))[:-1],
sensitive_features=sensitive_features_)
def create_adjusted_predictor(threshold_optimization_method, sensitive_features, labels, scores):
post_processed_predictor_by_sensitive_feature = threshold_optimization_method(
sensitive_features, labels, scores)
return lambda sensitive_features_, scores: _vectorized_prediction(
post_processed_predictor_by_sensitive_feature, sensitive_features_, scores)
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