def test_validate_unique(self):
unique = pd.DataFrame(
[["abc", "def"], ["ghi", "jkl"], ["mnop", "qrs"]],
index=["X", "Y", "Z"],
columns=["col1", "col2"])
duped = pd.DataFrame([["abc", "def"], ["ghi", "qrs"], ["abc", "qrs"]],
index=["X", "Y", "X"],
columns=["col1", "col2"])
schema_def = {"unique": True}
errors = validate._validate_column(unique.index, "index", "unique_df",
schema_def)
self.assertFalse(errors)
errors = validate._validate_column(duped.index, "index", "duped_df",
schema_def)
self.assertEqual(len(errors), 1)
self.assertIn("is not unique", errors[0])
errors = validate._validate_column(unique["col1"], "col1", "unique_df",
schema_def)
self.assertFalse(errors)
errors = validate._validate_column(duped["col1"], "col1", "duped_df",
schema_def)
self.assertEqual(len(errors), 1)
self.assertIn("is not unique", errors[0])
schema_def = {"unique": False}
errors = validate._validate_column(duped["col1"], "col1", "duped_df",
schema_def)
self.assertFalse(errors)
def test_human_readable(self):
hr_df = pd.DataFrame(
[["for you, a human", "UBERON:12345", "UBERON:1234 (thundercat)"],
["hope you're well", "bit of lungs", "brain"]],
index=["ENSG00001", "ENSG00002"],
columns=["good", "curie", "suffixed_curie"])
schema_def = {"type": "human-readable string"}
errors = validate._validate_column(hr_df["good"], "good", "hr",
schema_def)
self.assertFalse(errors)
errors = validate._validate_column(hr_df["curie"], "curie", "hr",
schema_def)
self.assertEqual(len(errors), 1)
self.assertIn("non-human-readable", errors[0])
errors = validate._validate_column(hr_df["suffixed_curie"],
"suffixed_curie", "hr", schema_def)
self.assertEqual(len(errors), 1)
self.assertIn("non-human-readable", errors[0])
errors = validate._validate_column(hr_df.index, "ensg", "hr",
schema_def)
self.assertEqual(len(errors), 1)
self.assertIn("non-human-readable", errors[0])
def test_validate_nullable(self):
non_null = pd.DataFrame(
[["abc", "def"], ["ghi", "jkl"], ["mnop", "qrs"]],
index=["X", "Y", "Z"],
columns=["col1", "col2"])
has_null = pd.DataFrame(
[["abc", "", None], ["ghi", "jkl", 1], ["mnop", "qrs", 2]],
index=["X", "Y", "Z"],
columns=["col1", "col2", "col3"])
schema_def = {"nullable": False}
errors = validate._validate_column(non_null["col1"], "col1",
"nonnull_df", schema_def)
self.assertFalse(errors)
errors = validate._validate_column(has_null["col1"], "col1",
"hasnull_df", schema_def)
self.assertFalse(errors)
errors = validate._validate_column(has_null["col2"], "col2",
"hasnull_df", schema_def)
self.assertEqual(len(errors), 1)
self.assertIn("contains empty values", errors[0])
errors = validate._validate_column(has_null["col3"], "col3",
"hasnull_df", schema_def)
self.assertEqual(len(errors), 1)
self.assertIn("contains empty values", errors[0])
schema_def = {"nullable": True}
errors = validate._validate_column(has_null["col2"], "col2",
"hasnull_df", schema_def)
self.assertFalse(errors)
def test_curie(self):
curie_df = pd.DataFrame(
[[
"EFO:00001", "HsapDv:00001 (cell culture)", "EFO:",
"MONDO:0001 cell culture"
],
[
"UBERON:00002", "HsapDv:00002 (organoid)", "EFO:12345",
"MONDO:0002 (baba yaga)"
],
[
"EFO:0000000005", "HsapDv:000004 (humanzee)", "EFO:000002",
"MONDO:0004 (TMNT)"
]],
index=["X", "Y", "Z"],
columns=["good", "good_suffix", "bad", "bad_suffix"])
# Good
schema_def = {"type": "curie", "prefixes": ["EFO", "UBERON"]}
errors = validate._validate_column(curie_df["good"], "good",
"curie_df", schema_def)
self.assertFalse(errors)
# Good suffix
schema_def = {
"type": "suffixed curie",
"prefixes": ["HsapDv", "WHATEVER"]
}
errors = validate._validate_column(curie_df["good_suffix"],
"good_suffix", "curie_df",
schema_def)
self.assertFalse(errors)
# Bad prefix
schema_def = {"type": "curie", "prefixes": ["EFO"]}
errors = validate._validate_column(curie_df["good"], "good",
"curie_df", schema_def)
self.assertEqual(len(errors), 1)
self.assertIn("invalid ontology", errors[0])
self.assertIn("must be curies from one of these", errors[0])
# Bad curies
schema_def = {"type": "curie", "prefixes": ["EFO"]}
errors = validate._validate_column(curie_df["bad"], "bad", "curie_df",
schema_def)
self.assertEqual(len(errors), 1)
self.assertIn("invalid ontology", errors[0])
# Bad suffixes
schema_def = {"type": "suffixed curie", "prefixes": ["EFO"]}
errors = validate._validate_column(curie_df["bad_suffix"],
"bad_suffix", "curie_df",
schema_def)
self.assertEqual(len(errors), 1)
self.assertIn("invalid ontology", errors[0])
def test_enum(self):
enum_df = pd.DataFrame([["abc", "ghi"], ["def", "jkl"]],
index=["X", "Y"],
columns=["col1", "col2"])
# All match
schema_def = {"type": "string", "enum": ["abc", "def", "xyz"]}
errors = validate._validate_column(enum_df["col1"], "col1", "enum_df",
schema_def)
self.assertFalse(errors)
# Missing value
schema_def = {"type": "string", "enum": ["abc", "xyz"]}
errors = validate._validate_column(enum_df["col1"], "col1", "enum_df",
schema_def)
self.assertEqual(len(errors), 1)
self.assertIn("unpermitted values", errors[0])