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test_classification_metric.py
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test_classification_metric.py
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import numpy as np
import pandas as pd
from aif360.datasets import BinaryLabelDataset
from aif360.datasets.multiclass_label_dataset import MulticlassLabelDataset
from aif360.metrics import ClassificationMetric
def test_generalized_entropy_index():
data = np.array([[0, 1],
[0, 0],
[1, 0],
[1, 1],
[1, 0],
[1, 0],
[2, 1],
[2, 0],
[2, 1],
[2, 1]])
pred = data.copy()
pred[[3, 9], -1] = 0
pred[[4, 5], -1] = 1
df = pd.DataFrame(data, columns=['feat', 'label'])
df2 = pd.DataFrame(pred, columns=['feat', 'label'])
bld = BinaryLabelDataset(df=df, label_names=['label'],
protected_attribute_names=['feat'])
bld2 = BinaryLabelDataset(df=df2, label_names=['label'],
protected_attribute_names=['feat'])
cm = ClassificationMetric(bld, bld2)
assert cm.generalized_entropy_index() == 0.2
pred = data.copy()
pred[:, -1] = np.array([0, 1, 1, 0, 0, 0, 0, 1, 1, 1])
df2 = pd.DataFrame(pred, columns=['feat', 'label'])
bld2 = BinaryLabelDataset(df=df2, label_names=['label'],
protected_attribute_names=['feat'])
cm = ClassificationMetric(bld, bld2)
assert cm.generalized_entropy_index() == 0.3
def test_theil_index():
data = np.array([[0, 1],
[0, 0],
[1, 0],
[1, 1],
[1, 0],
[1, 0],
[2, 1],
[2, 0],
[2, 1],
[2, 1]])
pred = data.copy()
pred[[3, 9], -1] = 0
pred[[4, 5], -1] = 1
df = pd.DataFrame(data, columns=['feat', 'label'])
df2 = pd.DataFrame(pred, columns=['feat', 'label'])
bld = BinaryLabelDataset(df=df, label_names=['label'],
protected_attribute_names=['feat'])
bld2 = BinaryLabelDataset(df=df2, label_names=['label'],
protected_attribute_names=['feat'])
cm = ClassificationMetric(bld, bld2)
assert cm.theil_index() == 4*np.log(2)/10
def test_between_all_groups():
data = np.array([[0, 1],
[0, 0],
[1, 0],
[1, 1],
[1, 0],
[1, 0],
[2, 1],
[2, 0],
[2, 1],
[2, 1]])
pred = data.copy()
pred[[3, 9], -1] = 0
pred[[4, 5], -1] = 1
df = pd.DataFrame(data, columns=['feat', 'label'])
df2 = pd.DataFrame(pred, columns=['feat', 'label'])
bld = BinaryLabelDataset(df=df, label_names=['label'],
protected_attribute_names=['feat'])
bld2 = BinaryLabelDataset(df=df2, label_names=['label'],
protected_attribute_names=['feat'])
cm = ClassificationMetric(bld, bld2)
b = np.array([1, 1, 1.25, 1.25, 1.25, 1.25, 0.75, 0.75, 0.75, 0.75])
assert cm.between_all_groups_generalized_entropy_index() == 1/20*np.sum(b**2 - 1)
def test_between_group():
data = np.array([[0, 0, 1],
[0, 1, 0],
[1, 1, 0],
[1, 1, 1],
[1, 0, 0],
[1, 0, 0]])
pred = data.copy()
pred[[0, 3], -1] = 0
pred[[4, 5], -1] = 1
df = pd.DataFrame(data, columns=['feat', 'feat2', 'label'])
df2 = pd.DataFrame(pred, columns=['feat', 'feat2', 'label'])
bld = BinaryLabelDataset(df=df, label_names=['label'],
protected_attribute_names=['feat', 'feat2'])
bld2 = BinaryLabelDataset(df=df2, label_names=['label'],
protected_attribute_names=['feat', 'feat2'])
cm = ClassificationMetric(bld, bld2, unprivileged_groups=[{'feat': 0}],
privileged_groups=[{'feat': 1}])
b = np.array([0.5, 0.5, 1.25, 1.25, 1.25, 1.25])
assert cm.between_group_generalized_entropy_index() == 1/12*np.sum(b**2 - 1)
def test_multiclass_confusion_matrix():
data = np.array([[0, 1],
[0, 0],
[1, 0],
[1, 1],
[1, 0],
[1, 2],
[2, 1],
[2, 0],
[2, 2],
[2, 1]])
pred = data.copy()
pred[3,1] = 0
pred[4,1] = 2
df = pd.DataFrame(data, columns=['feat', 'label'])
df2 = pd.DataFrame(pred, columns=['feat', 'label'])
favorable_values = [0,1]
unfavorable_values = [2]
mcld = MulticlassLabelDataset(favorable_label = favorable_values, unfavorable_label = unfavorable_values , df = df , label_names=['label'],
protected_attribute_names=['feat'])
mcld2 = MulticlassLabelDataset(favorable_label = favorable_values, unfavorable_label = unfavorable_values , df=df2, label_names=['label'],
protected_attribute_names=['feat'])
cm = ClassificationMetric(mcld, mcld2, unprivileged_groups=[{'feat': 2}],
privileged_groups=[{'feat': 0},{'feat': 1}])
confusion_matrix = cm.binary_confusion_matrix()
actual_labels_df = df[['label']].values
actual_labels_df2 = df2[['label']].values
assert np.all(actual_labels_df == mcld.labels)
assert np.all(actual_labels_df2 == mcld2.labels)
assert confusion_matrix == {'TP': 7.0, 'FN': 1.0, 'TN': 2.0, 'FP': 0.0}
fnr = cm.false_negative_rate_difference()
assert fnr == -0.2
def test_generalized_binary_confusion_matrix():
data = np.array([[0, 1],
[0, 0],
[1, 0],
[1, 1],
[1, 0],
[1, 0],
[1, 2],
[0, 0],
[0, 0],
[1, 2]])
pred = np.array([[0, 1, 0.8],
[0, 0, 0.6],
[1, 0, 0.7],
[1, 1, 0.8],
[1, 2, 0.36],
[1, 0, 0.82],
[1, 1, 0.79],
[0, 2, 0.42],
[0, 1, 0.81],
[1, 2, 0.3]])
df = pd.DataFrame(data, columns=['feat', 'label'])
df2 = pd.DataFrame(pred, columns=['feat', 'label', 'score'])
favorable_values = [0,1]
unfavorable_values = [2]
mcld = MulticlassLabelDataset(df=df, label_names=['label'],
protected_attribute_names=['feat'],favorable_label = favorable_values, unfavorable_label = unfavorable_values)
mcld2 = MulticlassLabelDataset(df=df2, label_names=['label'], scores_names=['score'],
protected_attribute_names=['feat'],favorable_label = favorable_values, unfavorable_label = unfavorable_values)
cm = ClassificationMetric(mcld, mcld2, unprivileged_groups=[{'feat': 0}],
privileged_groups=[{'feat': 1}])
gen_confusion_matrix = cm.generalized_binary_confusion_matrix()
gtp = cm.num_generalized_true_positives()
assert round(gtp,2) == 5.31
gfp = cm.num_generalized_false_positives()
assert gfp == 1.09