def test_predicted_label_values():
"""
Tests whether exception is raised when predicted label values are differnt from positive label values
"""
df = pd.DataFrame(
[["a", "p", 1, "p"], ["b", "q", 1, "p"], ["b", "r", 1, "q"],
["c", "p", 0, "p"], ["c", "q", 0, "p"]],
columns=["x", "y", "z", "yhat"],
)
# when explicit label values are given for categorical data
# Pre training bias metrics
with pytest.raises(
ValueError,
match="Positive predicted label values or threshold should"
" be empty or same as label values or thresholds",
):
pretraining_report = bias_report(
df,
FacetColumn("x"),
LabelColumn("y", df["y"], ["p", "q"]),
StageType.PRE_TRAINING,
LabelColumn("yhat", df["yhat"], ["q"]),
metrics=["DPL", "CDDL"],
group_variable=df["z"],
)
def get_pretraining_bias_metrics(dataframe: pd.DataFrame,
facet_column: FacetColumn,
label_column: LabelColumn,
group_variable: Optional[pd.Series]) -> Dict:
# Measure pre-training bias for the ForeignWorker attribute
return bias_report(
dataframe,
facet_column,
label_column,
stage_type=StageType.PRE_TRAINING,
metrics=["all"],
group_variable=group_variable,
)
def test_report_category_data():
# test the bias_report function on the category data
#
# pre training bias metrics
df_cat = pd.DataFrame(
[["a", 1, 1, 1, "1"], ["b", 1, 1, 0, "0"], ["b", 0, 1, 0, "0"], ["b", 0, 0, 1, "1"]],
columns=["x", "y", "z", "yhat", "yhat_cat"],
)
pretraining_report = bias_report(
df_cat,
FacetColumn("x"),
LabelColumn("y", df_cat["y"], [0]),
StageType.PRE_TRAINING,
LabelColumn("yhat", df_cat["yhat"]),
group_variable=df_cat["z"],
)
pretraining_report_cat = bias_report(
df_cat,
FacetColumn("x"),
LabelColumn("y", df_cat["y"], [0]),
StageType.PRE_TRAINING,
LabelColumn("yhat", df_cat["yhat_cat"]),
group_variable=df_cat["z"],
)
assert isinstance(pretraining_report, list)
assert len(pretraining_report) > 0
assert pretraining_report == pretraining_report_cat
result = [
{
"metrics": [
{
"description": "Conditional Demographic Disparity in Labels " "(CDDL)",
"name": "CDDL",
"value": pytest.approx(-0.375),
},
{"description": "Class Imbalance (CI)", "name": "CI", "value": pytest.approx(0.5)},
{
"description": "Difference in Positive Proportions in Labels (DPL)",
"name": "DPL",
"value": pytest.approx(-0.6666666666666667),
},
{
"description": "Jensen-Shannon Divergence (JS)",
"name": "JS",
"value": pytest.approx(0.08720802396075798),
},
{
"description": "Kullback-Liebler Divergence (KL)",
"name": "KL",
"value": pytest.approx(-0.3662040962227032),
},
{
"description": "Kolmogorov-Smirnov Distance (KS)",
"name": "KS",
"value": pytest.approx(0.6666666666666667),
},
{"description": "L-p Norm (LP)", "name": "LP", "value": pytest.approx(0.6666666666666667)},
{
"description": "Total Variation Distance (TVD)",
"name": "TVD",
"value": pytest.approx(0.33333333333333337),
},
],
"value_or_threshold": "a",
},
{
"metrics": [
{
"description": "Conditional Demographic Disparity in Labels " "(CDDL)",
"name": "CDDL",
"value": pytest.approx(0.625),
},
{"description": "Class Imbalance (CI)", "name": "CI", "value": pytest.approx(-0.5)},
{
"description": "Difference in Positive Proportions in Labels (DPL)",
"name": "DPL",
"value": pytest.approx(0.6666666666666667),
},
{
"description": "Jensen-Shannon Divergence (JS)",
"name": "JS",
"value": pytest.approx(0.08720802396075798),
},
{
"description": "Kullback-Liebler Divergence (KL)",
"name": "KL",
"value": pytest.approx(1.0986122886681098),
},
{
"description": "Kolmogorov-Smirnov Distance (KS)",
"name": "KS",
"value": pytest.approx(0.6666666666666667),
},
{"description": "L-p Norm (LP)", "name": "LP", "value": pytest.approx(0.6666666666666667)},
{
"description": "Total Variation Distance (TVD)",
"name": "TVD",
"value": pytest.approx(0.33333333333333337),
},
],
"value_or_threshold": "b",
},
]
assert pretraining_report == result
# post training bias metrics
posttraining_report = bias_report(
df_cat,
FacetColumn("x"),
LabelColumn("y", df_cat["y"], [0]),
StageType.POST_TRAINING,
LabelColumn("yhat", df_cat["yhat"]),
metrics=["AD", "DI", "DPPL", "RD"],
group_variable=df_cat["z"],
)
posttraining_report_cat = bias_report(
df_cat,
FacetColumn("x"),
LabelColumn("y", df_cat["y"], [0]),
StageType.POST_TRAINING,
LabelColumn("yhat", df_cat["yhat_cat"]),
metrics=["AD", "DI", "DPPL", "RD"],
group_variable=df_cat["z"],
)
assert isinstance(posttraining_report, list)
assert len(posttraining_report) > 0
assert posttraining_report == posttraining_report_cat
expected_result_1 = [
{
"metrics": [
{"description": "Accuracy Difference (AD)", "name": "AD", "value": pytest.approx(-0.6666666666666667)},
{"description": "Disparate Impact (DI)", "name": "DI", "value": pytest.approx(3.0)},
{
"description": "Difference in Positive Proportions in Predicted " "Labels (DPPL)",
"name": "DPPL",
"value": pytest.approx(-0.6666666666666667),
},
{"description": "Recall Difference (RD)", "name": "RD", "value": pytest.approx(-1.0)},
],
"value_or_threshold": "a",
},
{
"metrics": [
{"description": "Accuracy Difference (AD)", "name": "AD", "value": pytest.approx(0.6666666666666667)},
{"description": "Disparate Impact (DI)", "name": "DI", "value": pytest.approx(0.3333333333333333)},
{
"description": "Difference in Positive Proportions in Predicted " "Labels (DPPL)",
"name": "DPPL",
"value": pytest.approx(0.6666666666666667),
},
{"description": "Recall Difference (RD)", "name": "RD", "value": pytest.approx(1.0)},
],
"value_or_threshold": "b",
},
]
assert posttraining_report == expected_result_1
def test_partial_bias_report():
"""
Test that bias report is generated in for partial metrics when errors occur to compute some metrics
"""
df = pd.DataFrame(
[[1, 1, 1, 1], [2, 1, 1, 0], [3, 0, 0, 0], [2, 0, 1, 1], [0, 0, 1, 1]], columns=["x", "y", "z", "yhat"]
)
# pre training bias metrics
pretraining_report = bias_report(
df,
FacetColumn("x", [2]),
LabelColumn("y", df["y"], [0]),
StageType.PRE_TRAINING,
LabelColumn("yhat", df["yhat"]),
metrics=["CI", "CDDL", "DPL", "KL"],
)
assert isinstance(pretraining_report, list)
expected_result_1 = [
{
"metrics": [
{
"description": "Conditional Demographic Disparity in Labels (CDDL)",
"error": "Group variable is empty or not provided",
"name": "CDDL",
"value": None,
},
{"description": "Class Imbalance (CI)", "name": "CI", "value": pytest.approx(0.6)},
{
"description": "Difference in Positive Proportions in Labels " "(DPL)",
"name": "DPL",
"value": pytest.approx(0.5),
},
{
"description": "Kullback-Liebler Divergence (KL)",
"name": "KL",
"value": pytest.approx(-0.34657359027997264),
},
],
"value_or_threshold": "(2, 3]",
}
]
assert pretraining_report == expected_result_1
# post training bias metrics
posttraining_report = bias_report(
df,
FacetColumn("x", [2]),
LabelColumn("y", df["y"], [0]),
StageType.POST_TRAINING,
LabelColumn("yhat", df["yhat"]),
metrics=["AD", "CDDPL", "DCA", "DI", "DPPL", "FT"],
)
assert isinstance(posttraining_report, list)
expected_result_2 = [
{
"metrics": [
{"description": "Accuracy Difference (AD)", "name": "AD", "value": pytest.approx(-0.75)},
{
"description": "Conditional Demographic Disparity in Predicted " "Labels (CDDPL)",
"error": "Group variable is empty or not provided",
"name": "CDDPL",
"value": None,
},
{
"description": "Difference in Conditional Acceptance (DCA)",
"name": "DCA",
"value": pytest.approx(0.6666666666666666),
},
{"description": "Disparate Impact (DI)", "name": "DI", "value": pytest.approx(0.0)},
{
"description": "Difference in Positive Proportions in Predicted " "Labels (DPPL)",
"name": "DPPL",
"value": pytest.approx(0.75),
},
{"description": "Flip Test (FT)", "name": "FT", "value": pytest.approx(-1.0)},
],
"value_or_threshold": "(2, 3]",
}
]
assert posttraining_report == expected_result_2
def test_label_values():
"""
Test bias metrics for multiple label values
"""
df = pd.DataFrame(
[["a", "p", 1, "p"], ["b", "q", 1, "p"], ["b", "r", 1, "q"], ["c", "p", 0, "p"], ["c", "q", 0, "p"]],
columns=["x", "y", "z", "yhat"],
)
# when explicit label values are given for categorical data
# Pre training bias metrics
pretraining_report = bias_report(
df,
FacetColumn("x"),
LabelColumn("y", df["y"], ["p", "q"]),
StageType.PRE_TRAINING,
LabelColumn("yhat", df["yhat"]),
metrics=["DPL", "CDDL"],
group_variable=df["z"],
)
assert isinstance(pretraining_report[0], dict)
expected_result_1 = [
{
"metrics": [
{
"description": "Conditional Demographic Disparity in Labels " "(CDDL)",
"name": "CDDL",
"value": pytest.approx(-0.3),
},
{
"description": "Difference in Positive Proportions in Labels " "(DPL)",
"name": "DPL",
"value": pytest.approx(-0.25),
},
],
"value_or_threshold": "a",
},
{
"metrics": [
{
"description": "Conditional Demographic Disparity in Labels " "(CDDL)",
"name": "CDDL",
"value": pytest.approx(0.3),
},
{
"description": "Difference in Positive Proportions in Labels " "(DPL)",
"name": "DPL",
"value": pytest.approx(0.5),
},
],
"value_or_threshold": "b",
},
{
"metrics": [
{
"description": "Conditional Demographic Disparity in Labels " "(CDDL)",
"name": "CDDL",
"value": pytest.approx(-0.4),
},
{
"description": "Difference in Positive Proportions in Labels (DPL)",
"name": "DPL",
"value": pytest.approx(-0.33333333333333337),
},
],
"value_or_threshold": "c",
},
]
assert pretraining_report == expected_result_1
# post training bias metrics
posttraining_report = bias_report(
df,
FacetColumn("x"),
LabelColumn("y", df["y"], ["p", "q"]),
StageType.POST_TRAINING,
LabelColumn("yhat", df["yhat"]),
metrics=["AD", "DI", "DPPL", "RD", "DAR", "DRR"],
group_variable=df["z"],
)
assert isinstance(posttraining_report[0], dict)
expected_result_2 = [
{
"metrics": [
{"description": "Accuracy Difference (AD)", "name": "AD", "value": pytest.approx(-0.25)},
{"description": "Difference in Acceptance Rates (DAR)", "name": "DAR", "value": pytest.approx(-0.25)},
{"description": "Disparate Impact (DI)", "name": "DI", "value": pytest.approx(1.0)},
{
"description": "Difference in Positive Proportions in Predicted " "Labels (DPPL)",
"name": "DPPL",
"value": pytest.approx(0.0),
},
{"description": "Difference in Rejection Rates (DRR)", "name": "DRR", "value": pytest.approx(0)},
{"description": "Recall Difference (RD)", "name": "RD", "value": pytest.approx(0.0)},
],
"value_or_threshold": "a",
},
{
"metrics": [
{"description": "Accuracy Difference (AD)", "name": "AD", "value": pytest.approx(0.5)},
{"description": "Difference in Acceptance Rates (DAR)", "name": "DAR", "value": pytest.approx(0.5)},
{"description": "Disparate Impact (DI)", "name": "DI", "value": pytest.approx(1.0)},
{
"description": "Difference in Positive Proportions in Predicted " "Labels (DPPL)",
"name": "DPPL",
"value": pytest.approx(0.0),
},
{"description": "Difference in Rejection Rates (DRR)", "name": "DRR", "value": pytest.approx(0)},
{"description": "Recall Difference (RD)", "name": "RD", "value": pytest.approx(0.0)},
],
"value_or_threshold": "b",
},
{
"metrics": [
{"description": "Accuracy Difference (AD)", "name": "AD", "value": pytest.approx(-0.33333333333333337)},
{
"description": "Difference in Acceptance Rates (DAR)",
"name": "DAR",
"value": pytest.approx(-0.33333333333333337),
},
{"description": "Disparate Impact (DI)", "name": "DI", "value": pytest.approx(1.0)},
{
"description": "Difference in Positive Proportions in Predicted " "Labels (DPPL)",
"name": "DPPL",
"value": pytest.approx(0.0),
},
{"description": "Difference in Rejection Rates (DRR)", "name": "DRR", "value": pytest.approx(0)},
{"description": "Recall Difference (RD)", "name": "RD", "value": pytest.approx(0.0)},
],
"value_or_threshold": "c",
},
]
assert posttraining_report == expected_result_2
def test_report_continuous_data_regression():
# test that we correctly apply thresholds for regression tasks.
#
df_cont_old = pd.DataFrame(
[
[0, 0, 0, 0, True, 1, 1, 1],
[3, 0, 0, 0, True, 0, 1, 1],
[3, 0, 1, 0, True, 0, 1, 1],
[0, 0, 0, 0, False, 1, 1, 0],
[4, 0, 0, 1, True, 0, 1, 1],
[0, 0, 1, 0, True, 1, 1, 1],
[3, 0, 0, 0, True, 1, 1, 1],
[3, 1, 0, 0, True, 1, 1, 1],
[0, 0, 1, 0, True, 1, 1, 1],
[3, 0, 1, 1, True, 1, 0, 1],
[4, 0, 0, 0, True, 1, 0, 1],
[3, 0, 1, 0, True, 1, 1, 1],
[3, 0, 0, 0, False, 1, 1, 0],
[0, 0, 0, 0, True, 1, 1, 1],
[0, 0, 1, 0, True, 0, 1, 1],
[0, 0, 1, 0, True, 1, 1, 1],
[0, 1, 0, 1, False, 0, 1, 0],
[3, 0, 0, 0, False, 1, 1, 0],
[0, 0, 1, 0, False, 1, 1, 1],
[3, 0, 0, 0, True, 1, 0, 1],
[3, 0, 1, 0, False, 1, 1, 0],
[0, 1, 0, 0, False, 1, 1, 0],
[3, 0, 1, 0, True, 0, 1, 1],
[0, 0, 0, 1, True, 1, 0, 1],
],
columns=["x", "y", "z", "a", "b", "c", "d", "yhat"],
)
df_cont = pd.DataFrame(
[
[0, 0.0, 0, 0, True, 1, 1, 11], # 11 is the highest among y and yhat
[3, 0.5, 0, 0, True, 0, 1, 6],
[3, 2, 1, 0, True, 0, 1, 6.6],
[0, 3, 0, 0, False, 1, 1, 0.3],
[4, 2.2, 0, 1, True, 0, 1, 6],
[0, 0.1, 1, 0, True, 1, 1, 6],
[3, 0, 0, 0, True, 1, 1, 6],
[3, 6, 0, 0, True, 1, 1, 6],
[0, 0, 1, 0, True, 1, 1, 6],
[3, 0, 1, 1, True, 1, 0, 6],
[4, 0, 0, 0, True, 1, 0, 6],
[3, 0, 1, 0, True, 1, 1, 6],
[3, 0, 0, 0, False, 1, 1, 0],
[0, 0, 0, 0, True, 1, 1, 6.2],
[0, 0, 1, 0, True, 0, 1, 6.6],
[0, 0, 1, 0, True, 1, 1, 6.6],
[0, 7, 0, 1, False, 0, 1, 0.1],
[3, 0, 0, 0, False, 1, 1, 2],
[0, 0, 1, 0, False, 1, 1, 8],
[3, 0, 0, 0, True, 1, 0, 9],
[3, 0, 1, 0, False, 1, 1, 0.1],
[0, 8, 0, 0, False, 1, 1, 2.2],
[3, 0, 1, 0, True, 0, 1, 10],
[0, 0, 0, 1, True, 1, 0, 9],
],
columns=["x", "y", "z", "a", "b", "c", "d", "yhat"],
)
# Old and new df should yield the same results if we use threshold 5 for the latter.
threshold_old = 0.5
threshold_new = 5
assert ((df_cont_old[["y"]] > threshold_old) == (df_cont[["y"]] > threshold_new)).all
assert ((df_cont_old[["yhat"]] > threshold_old) == (df_cont[["yhat"]] > threshold_new)).all
posttraining_report = bias_report(
df_cont,
FacetColumn("x", [2]),
LabelColumn("y", df_cont["y"], positive_label_values=[threshold_new]),
StageType.POST_TRAINING,
LabelColumn("yhat", df_cont["yhat"], positive_label_values=[threshold_new]),
group_variable=df_cont["z"],
)
posttraining_report_old = bias_report(
df_cont_old,
FacetColumn("x", [2]),
LabelColumn("y", df_cont_old["y"], positive_label_values=[threshold_old]),
StageType.POST_TRAINING,
LabelColumn("yhat", df_cont_old["yhat"], positive_label_values=[threshold_old]),
group_variable=df_cont["z"],
)
assert posttraining_report == posttraining_report_old
def test_report_continuous_data():
# test the bias_report function on the category data
#
# pre training bias metrics
df_cont = pd.DataFrame(
[
[0, 0, 0, 0, True, 1, 1, 1],
[3, 0, 0, 0, True, 0, 1, 1],
[3, 0, 1, 0, True, 0, 1, 1],
[0, 0, 0, 0, False, 1, 1, 0],
[4, 0, 0, 1, True, 0, 1, 1],
[0, 0, 1, 0, True, 1, 1, 1],
[3, 0, 0, 0, True, 1, 1, 1],
[3, 1, 0, 0, True, 1, 1, 1],
[0, 0, 1, 0, True, 1, 1, 1],
[3, 0, 1, 1, True, 1, 0, 1],
[4, 0, 0, 0, True, 1, 0, 1],
[3, 0, 1, 0, True, 1, 1, 1],
[3, 0, 0, 0, False, 1, 1, 0],
[0, 0, 0, 0, True, 1, 1, 1],
[0, 0, 1, 0, True, 0, 1, 1],
[0, 0, 1, 0, True, 1, 1, 1],
[0, 1, 0, 1, False, 0, 1, 0],
[3, 0, 0, 0, False, 1, 1, 0],
[0, 0, 1, 0, False, 1, 1, 1],
[3, 0, 0, 0, True, 1, 0, 1],
[3, 0, 1, 0, False, 1, 1, 0],
[0, 1, 0, 0, False, 1, 1, 0],
[3, 0, 1, 0, True, 0, 1, 1],
[0, 0, 0, 1, True, 1, 0, 1],
],
columns=["x", "y", "z", "a", "b", "c", "d", "yhat"],
)
pretraining_report = bias_report(
df_cont,
FacetColumn("x", [2]),
LabelColumn("y", df_cont["y"], [0]),
StageType.PRE_TRAINING,
LabelColumn("yhat", df_cont["yhat"]),
group_variable=df_cont["z"],
)
assert isinstance(pretraining_report, list)
assert len(pretraining_report) > 0
result = [
{
"metrics": [
{
"description": "Conditional Demographic Disparity in Labels " "(CDDL)",
"name": "CDDL",
"value": pytest.approx(0.3851010101010101),
},
{"description": "Class Imbalance (CI)", "name": "CI", "value": pytest.approx(-0.08333333333333333)},
{
"description": "Difference in Positive Proportions in Labels (DPL)",
"name": "DPL",
"value": pytest.approx(0.1048951048951049),
},
{
"description": "Jensen-Shannon Divergence (JS)",
"name": "JS",
"value": pytest.approx(0.01252420207928287),
},
{
"description": "Kullback-Liebler Divergence (KL)",
"name": "KL",
"value": pytest.approx(0.057704603668062765),
},
{
"description": "Kolmogorov-Smirnov Distance (KS)",
"name": "KS",
"value": pytest.approx(0.1048951048951049),
},
{"description": "L-p Norm (LP)", "name": "LP", "value": pytest.approx(0.14834407996920576)},
{
"description": "Total Variation Distance (TVD)",
"name": "TVD",
"value": pytest.approx(0.1048951048951049),
},
],
"value_or_threshold": "(2, 4]",
}
]
assert pretraining_report == result
posttraining_report = bias_report(
df_cont,
FacetColumn("x", [2]),
LabelColumn("y", df_cont["y"], [0]),
StageType.POST_TRAINING,
LabelColumn("yhat", df_cont["yhat"]),
group_variable=df_cont["z"],
)
assert isinstance(posttraining_report, list)
assert len(posttraining_report) > 0
expected_result_1 = [
{
"metrics": [
{"description": "Accuracy Difference (AD)", "name": "AD", "value": pytest.approx(-0.2167832167832168)},
{
"description": "Conditional Demographic Disparity in Predicted " "Labels (CDDPL)",
"name": "CDDPL",
"value": pytest.approx(0.07592592592592595),
},
{"description": "Difference in Acceptance Rates (DAR)", "name": "DAR", "value": pytest.approx(-0.1)},
{
"description": "Difference in Conditional Acceptance (DCA)",
"name": "DCA",
"value": pytest.approx(0.15),
},
{
"description": "Difference in Conditional Rejection (DCR)",
"name": "DCR",
"value": pytest.approx(1.0),
},
{"description": "Disparate Impact (DI)", "name": "DI", "value": pytest.approx(1.0576923076923077)},
{
"description": "Difference in Positive Proportions in Predicted " "Labels (DPPL)",
"name": "DPPL",
"value": pytest.approx(-0.04195804195804198),
},
{
"description": "Difference in Rejection Rates (DRR)",
"name": "DRR",
"value": pytest.approx(0.6666666666666667),
},
{"description": "Flip Test (FT)", "name": "FT", "value": pytest.approx(-0.23076923076923078)},
{"description": "Recall Difference (RD)", "name": "RD", "value": pytest.approx(-1.0)},
{"description": "Treatment Equality (TE)", "name": "TE", "value": pytest.approx(-0.25)},
],
"value_or_threshold": "(2, 4]",
}
]
assert posttraining_report == expected_result_1
def test_invalid_input():
df_cat = pd.DataFrame(
[["a", 0, 0, "n"], ["b", 0, 1, "y"], ["c", 1, 0, "n"]],
columns=["x", "y", "label", "predicted_label"],
)
for staging_type in StageType:
# facet not in dataset
with pytest.raises(ValueError):
bias_report(
df_cat,
FacetColumn("z"),
LabelColumn("Label", df_cat["label"]),
staging_type,
)
# no positive label value
with pytest.raises(ValueError):
bias_report(
df_cat,
FacetColumn("x"),
LabelColumn("Label", df_cat["label"]),
staging_type,
)
# incorrect stage type
with pytest.raises(ValueError):
# noinspection PyTypeChecker
bias_report(
df_cat,
FacetColumn("x"),
LabelColumn("Label", df_cat["label"], [1]),
"pre_training",
)
# post-training but no predicted label column
with pytest.raises(ValueError):
bias_report(
df_cat,
FacetColumn("x"),
LabelColumn("Label", df_cat["label"], [1]),
StageType.POST_TRAINING,
)
# positive label value of label and predicted label not the same
with pytest.raises(ValueError):
bias_report(
df_cat,
FacetColumn("x"),
LabelColumn("Label", df_cat["label"], [1]),
StageType.POST_TRAINING,
LabelColumn("Prediction", df_cat["predicted_label"], [1]),
)
# label and positive label have different data types.
with pytest.raises(ValueError):
bias_report(
df_cat,
FacetColumn("x"),
LabelColumn("Label", df_cat["label"], [1]),
StageType.POST_TRAINING,
LabelColumn("Prediction", df_cat["predicted_label"], [1]),
)
