def test_target_leakage_data_check_warnings_pearson():
y = pd.Series([1, 0, 1, 1])
X = pd.DataFrame()
X["a"] = y * 3
X["b"] = y - 1
X["c"] = y / 10
X["d"] = ~y
X["e"] = [0, 0, 0, 0]
y = y.astype(bool)
leakage_check = TargetLeakageDataCheck(pct_corr_threshold=0.5,
method='pearson')
assert leakage_check.validate(X, y) == {
"warnings": [
DataCheckWarning(
message=
"Column 'a' is 50.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "a"
}).to_dict(),
DataCheckWarning(
message=
"Column 'b' is 50.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "b"
}).to_dict(),
DataCheckWarning(
message=
"Column 'c' is 50.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "c"
}).to_dict(),
DataCheckWarning(
message=
"Column 'd' is 50.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "d"
}).to_dict()
],
"errors": [],
"actions": []
}
y = ["a", "b", "a", "a"]
leakage_check = TargetLeakageDataCheck(pct_corr_threshold=0.5,
method='pearson')
assert leakage_check.validate(X, y) == {
"warnings": [],
"errors": [],
"actions": []
}
def test_target_leakage_multi():
leakage_check = TargetLeakageDataCheck(pct_corr_threshold=0.8)
# test empty pd.DataFrame, empty pd.Series
assert leakage_check.validate(pd.DataFrame(), pd.Series()) == {
"warnings": [],
"errors": [],
"actions": []
}
y = pd.Series([1, 0, 2, 1, 2, 0])
X = pd.DataFrame()
X["a"] = y * 3
X["b"] = y - 1
X["c"] = y / 10
X["d"] = [0, 0, 0, 0, 0, 0]
X["e"] = ["a", "b", "c", "a", "b", "c"]
expected_messages = {
"warnings": [
DataCheckWarning(
message=
"Column 'a' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "a"
}).to_dict(),
DataCheckWarning(
message=
"Column 'b' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "b"
}).to_dict(),
DataCheckWarning(
message=
"Column 'c' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "c"
}).to_dict()
],
"errors": [],
"actions": []
}
# test X as ww.DataTable, y as ww.DataColumn
assert leakage_check.validate(ww.DataTable(X),
ww.DataColumn(y)) == expected_messages
# test y as list
assert leakage_check.validate(X, y.values) == expected_messages
def test_target_leakage_types():
leakage_check = TargetLeakageDataCheck(pct_corr_threshold=0.8)
y = pd.Series([1, 0, 1, 1])
X = pd.DataFrame()
X["a"] = ["a", "b", "a", "a"]
X["b"] = y - 1
X["c"] = [
datetime.strptime("2015", "%Y"),
datetime.strptime("2016", "%Y"),
datetime.strptime("2015", "%Y"),
datetime.strptime("2015", "%Y")
]
X["d"] = ~y
X["e"] = [0, 0, 0, 0]
y = y.astype(bool)
expected_messages = {
"warnings": [
DataCheckWarning(
message=
"Column 'a' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "a"
}).to_dict(),
DataCheckWarning(
message=
"Column 'b' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "b"
}).to_dict(),
DataCheckWarning(
message=
"Column 'c' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "c"
}).to_dict(),
DataCheckWarning(
message=
"Column 'd' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "d"
}).to_dict()
],
"errors": [],
"actions": []
}
assert leakage_check.validate(X, y) == expected_messages
def test_target_leakage_data_check_empty(data_type, make_data_type):
X = make_data_type(data_type, pd.DataFrame())
y = make_data_type(data_type, pd.Series())
leakage_check = TargetLeakageDataCheck(pct_corr_threshold=0.8,
method='mutual')
assert leakage_check.validate(X, y) == {
"warnings": [],
"errors": [],
"actions": []
}
def test_target_leakage_none():
leakage_check = TargetLeakageDataCheck(pct_corr_threshold=0.8)
y = pd.Series([1, 0, 1, 1])
X = pd.DataFrame()
X["a"] = [1, 1, 1, 1]
X["b"] = [0, 0, 0, 0]
y = y.astype(bool)
expected = {"warnings": [], "errors": [], "actions": []}
assert leakage_check.validate(X, y) == expected
def test_target_leakage_data_check_input_formats_pearson():
leakage_check = TargetLeakageDataCheck(pct_corr_threshold=0.8,
method='pearson')
# test empty pd.DataFrame, empty pd.Series
assert leakage_check.validate(pd.DataFrame(), pd.Series()) == {
"warnings": [],
"errors": [],
"actions": []
}
y = pd.Series([1, 0, 1, 1])
X = pd.DataFrame()
X["a"] = y * 3
X["b"] = y - 1
X["c"] = y / 10
X["d"] = ~y
X["e"] = [0, 0, 0, 0]
y = y.astype(bool)
expected = {
"warnings": [
DataCheckWarning(
message=
"Column 'a' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "a"
}).to_dict(),
DataCheckWarning(
message=
"Column 'b' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "b"
}).to_dict(),
DataCheckWarning(
message=
"Column 'c' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "c"
}).to_dict(),
DataCheckWarning(
message=
"Column 'd' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "d"
}).to_dict()
],
"errors": [],
"actions": [
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": 'a'
}).to_dict(),
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": 'b'
}).to_dict(),
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": 'c'
}).to_dict(),
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": 'd'
}).to_dict()
]
}
# test X as np.array
assert leakage_check.validate(X.values, y) == {
"warnings": [
DataCheckWarning(
message=
"Column '0' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": 0
}).to_dict(),
DataCheckWarning(
message=
"Column '1' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": 1
}).to_dict(),
DataCheckWarning(
message=
"Column '2' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": 2
}).to_dict(),
DataCheckWarning(
message=
"Column '3' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": 3
}).to_dict()
],
"errors": [],
"actions": [
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": 0
}).to_dict(),
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": 1
}).to_dict(),
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": 2
}).to_dict(),
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": 3
}).to_dict()
]
}
# test X as ww.DataTable, y as ww.DataColumn
assert leakage_check.validate(ww.DataTable(X),
ww.DataColumn(y)) == expected
# test y as list
assert leakage_check.validate(X, y.values) == expected
def test_target_leakage_regression():
leakage_check = TargetLeakageDataCheck(pct_corr_threshold=0.8)
# test empty pd.DataFrame, empty pd.Series
assert leakage_check.validate(pd.DataFrame(), pd.Series()) == {
"warnings": [],
"errors": [],
"actions": []
}
y = pd.Series([
0.4, 0.1, 2.3, 4.3, 2.2, 1.8, 3.7, 3.6, 2.4, 0.9, 3.1, 2.8, 4.1, 1.6,
1.2
])
X = pd.DataFrame()
X["a"] = y * 3
X["b"] = y - 1
X["c"] = y / 10
X["d"] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
X["e"] = [
"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n",
"o"
]
expected = {
"warnings": [
DataCheckWarning(
message=
"Column 'a' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "a"
}).to_dict(),
DataCheckWarning(
message=
"Column 'b' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "b"
}).to_dict(),
DataCheckWarning(
message=
"Column 'c' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "c"
}).to_dict(),
DataCheckWarning(
message=
"Column 'e' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "e"
}).to_dict()
],
"errors": [],
"actions": [
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": 'a'
}).to_dict(),
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": 'b'
}).to_dict(),
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": 'c'
}).to_dict(),
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": 'e'
}).to_dict()
]
}
# test X as ww.DataTable, y as ww.DataColumn
assert leakage_check.validate(ww.DataTable(X),
ww.DataColumn(y)) == expected
# test y as list
assert leakage_check.validate(X, y.values) == expected
def test_target_leakage_data_check_input_formats():
leakage_check = TargetLeakageDataCheck(pct_corr_threshold=0.8)
y = pd.Series([1, 0, 1, 1])
X = pd.DataFrame()
X["a"] = y * 3
X["b"] = y - 1
X["c"] = y / 10
X["d"] = ~y
X["e"] = [0, 0, 0, 0]
y = y.astype(bool)
expected_messages = {
"warnings": [
DataCheckWarning(
message=
"Column 'a' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "a"
}).to_dict(),
DataCheckWarning(
message=
"Column 'b' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "b"
}).to_dict(),
DataCheckWarning(
message=
"Column 'c' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "c"
}).to_dict(),
DataCheckWarning(
message=
"Column 'd' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": "d"
}).to_dict()
],
"errors": []
}
# test X as ww.DataTable, y as ww.DataColumn
assert leakage_check.validate(ww.DataTable(X),
ww.DataColumn(y)) == expected_messages
# test y as list
assert leakage_check.validate(X, y.values) == expected_messages
# test X as np.array
assert leakage_check.validate(X.to_numpy().astype(float), y) == {
"warnings": [
DataCheckWarning(
message=
"Column '0' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": 0
}).to_dict(),
DataCheckWarning(
message=
"Column '1' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": 1
}).to_dict(),
DataCheckWarning(
message=
"Column '2' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": 2
}).to_dict(),
DataCheckWarning(
message=
"Column '3' is 80.0% or more correlated with the target",
data_check_name=target_leakage_data_check_name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": 3
}).to_dict()
],
"errors": []
}