File size: 3,481 Bytes
fc0f7bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | # Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
from collections import defaultdict, namedtuple
import numpy as np
import pandas as pd
from fairlearn.postprocessing._threshold_operation import ThresholdOperation
from fairlearn.postprocessing._constants import SCORE_KEY, LABEL_KEY, SENSITIVE_FEATURE_KEY
sensitive_feature_names_ex1 = ["A", "B", "C"]
sensitive_features_ex1 = [x for x in 'AAAAAAA' 'BBBBBBB' 'CCCCCC']
sensitive_feature_names_ex2 = ["x", "Y"]
sensitive_features_ex2 = [x for x in 'xxxYYYY' 'xYYYYYx' 'YYYYYY']
labels_ex = [int(x) for x in '0110100' '0010111' '000111']
degenerate_labels_ex = [int(x) for x in '0000000' '0000000' '000000']
scores_ex = [int(x) for x in '0011233' '0001111' '011112']
LabelAndPrediction = namedtuple('LabelAndPrediction', 'label prediction')
class ExamplePredictor():
def predict(self, X):
return scores_ex
class ExampleNotPredictor():
pass
class ExampleEstimator():
def fit(self, X, Y):
pass
def predict(self, X):
return scores_ex
class ExampleNotEstimator1():
def fit(self, X, Y):
pass
class ExampleNotEstimator2():
def predict(self, X):
pass
def _get_grouped_data_and_base_points(sensitive_feature_value):
data = pd.DataFrame({
SENSITIVE_FEATURE_KEY: sensitive_features_ex1,
SCORE_KEY: scores_ex,
LABEL_KEY: labels_ex})
grouped_data = data.groupby(SENSITIVE_FEATURE_KEY).get_group(sensitive_feature_value) \
.sort_values(by=SCORE_KEY, ascending=False)
x_grid = np.linspace(0, 1, 100)
if sensitive_feature_value == "A":
expected_roc_points = pd.DataFrame({
"x": [0, 0.25, 0.5, 0.5, 1],
"y": [0, 1/3, 2/3, 1, 1],
"operation": [ThresholdOperation('>', np.inf),
ThresholdOperation('<', 0.5),
ThresholdOperation('<', 1.5),
ThresholdOperation('<', 2.5),
ThresholdOperation('>', -np.inf)]
})
ignore_for_base_points = [1, 2]
if sensitive_feature_value == "B":
expected_roc_points = pd.DataFrame({
"x": [0, 1/3, 1],
"y": [0, 3/4, 1],
"operation": [ThresholdOperation('>', np.inf),
ThresholdOperation('<', 0.5),
ThresholdOperation('>', -np.inf)]
})
ignore_for_base_points = []
if sensitive_feature_value == "C":
expected_roc_points = pd.DataFrame({
"x": [0, 0, 2/3, 1],
"y": [0, 1/3, 1, 1],
"operation": [ThresholdOperation('>', np.inf),
ThresholdOperation('<', 0.5),
ThresholdOperation('<', 1.5),
ThresholdOperation('>', -np.inf)]
})
ignore_for_base_points = [0]
return grouped_data, expected_roc_points, ignore_for_base_points, x_grid
def _get_predictions_by_sensitive_feature(adjusted_predictor, sensitive_features, scores, labels):
labels_and_predictions = defaultdict(list)
for i in range(len(sensitive_features)):
labels_and_predictions[sensitive_features[i]].append(
LabelAndPrediction(labels[i],
adjusted_predictor([sensitive_features[i]], [scores[i]])))
return labels_and_predictions
def _format_as_list_of_lists(lst):
return [[item] for item in lst]
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