def test_two_sample_ttest_hypothesis_relationships():
"""Check allowable relationships in two-sample ttest."""
for relationship in Hypothesis.RELATIONSHIPS:
schema = DataFrameSchema({
"height_in_feet": Column(Float, [
Hypothesis.two_sample_ttest(
sample1="M",
sample2="F",
groupby="sex",
relationship=relationship,
alpha=0.5),
]),
"sex": Column(String)
})
assert isinstance(schema, DataFrameSchema)
for relationship in ["foo", "bar", 1, 2, 3, None]:
with pytest.raises(errors.SchemaInitError):
DataFrameSchema({
"height_in_feet": Column(Float, [
Hypothesis.two_sample_ttest(
sample1="M",
sample2="F",
groupby="sex",
relationship=relationship,
alpha=0.5),
]),
"sex": Column(String)
})
def hypothesis_accident_probability(feature, increases=True):
relationship = "greater_than" if increases else "less_than"
return {
feature: Column(checks=Check.isin([1, 0])),
f"{feature}_shap": Column(
pa.Float,
checks=Hypothesis.two_sample_ttest(
sample1=1,
sample2=0,
groupby=feature,
relationship=relationship,
alpha=0.01,
)
),
}
def test_hypothesis():
"""Tests the different API calls of Hypothesis."""
# Example df for tests:
df = pd.DataFrame({
"height_in_feet": [6.5, 7, 6.1, 5.1, 4],
"sex": ["M", "M", "F", "F", "F"],
})
# Initialise the different ways of calling a test:
schema_pass_ttest_on_alpha_val_1 = DataFrameSchema({
"height_in_feet":
Column(
Float,
[
Hypothesis.two_sample_ttest(
sample1="M",
sample2="F",
groupby="sex",
relationship="greater_than",
alpha=0.5,
),
],
),
"sex":
Column(String),
})
schema_pass_ttest_on_alpha_val_2 = DataFrameSchema({
"height_in_feet":
Column(
Float,
[
Hypothesis(
test=stats.ttest_ind,
samples=["M", "F"],
groupby="sex",
relationship="greater_than",
relationship_kwargs={"alpha": 0.5},
),
],
),
"sex":
Column(String),
})
schema_pass_ttest_on_alpha_val_3 = DataFrameSchema({
"height_in_feet":
Column(
Float,
[
Hypothesis.two_sample_ttest(
sample1="M",
sample2="F",
groupby="sex",
relationship="greater_than",
alpha=0.5,
),
],
),
"sex":
Column(String),
})
schema_pass_ttest_on_custom_relationship = DataFrameSchema({
"height_in_feet":
Column(
Float,
[
Hypothesis(
test=stats.ttest_ind,
samples=["M", "F"],
groupby="sex",
relationship=lambda stat, pvalue, alpha=0.01:
(stat > 0 and pvalue / 2 < alpha),
relationship_kwargs={"alpha": 0.5},
)
],
),
"sex":
Column(String),
})
# Check the 3 happy paths are successful:
schema_pass_ttest_on_alpha_val_1.validate(df)
schema_pass_ttest_on_alpha_val_2.validate(df)
schema_pass_ttest_on_alpha_val_3.validate(df)
schema_pass_ttest_on_custom_relationship.validate(df)
schema_fail_ttest_on_alpha_val_1 = DataFrameSchema({
"height_in_feet":
Column(
Float,
[
Hypothesis.two_sample_ttest(
sample1="M",
sample2="F",
groupby="sex",
relationship="greater_than",
alpha=0.05,
),
],
),
"sex":
Column(String),
})
schema_fail_ttest_on_alpha_val_2 = DataFrameSchema({
"height_in_feet":
Column(
Float,
[
Hypothesis(
test=stats.ttest_ind,
samples=["M", "F"],
groupby="sex",
relationship="greater_than",
relationship_kwargs={"alpha": 0.05},
),
],
),
"sex":
Column(String),
})
schema_fail_ttest_on_alpha_val_3 = DataFrameSchema({
"height_in_feet":
Column(
Float,
[
Hypothesis.two_sample_ttest(
sample1="M",
sample2="F",
groupby="sex",
relationship="greater_than",
alpha=0.05,
),
],
),
"sex":
Column(String),
})
with pytest.raises(errors.SchemaError):
schema_fail_ttest_on_alpha_val_1.validate(df)
with pytest.raises(errors.SchemaError):
schema_fail_ttest_on_alpha_val_2.validate(df)
with pytest.raises(errors.SchemaError):
schema_fail_ttest_on_alpha_val_3.validate(df)