def test_get_compute_domain_with_multicolumn_domain():
engine = PandasExecutionEngine()
df = pd.DataFrame({
"a": [1, 2, 3, 4],
"b": [2, 3, 4, None],
"c": [1, 2, 2, 3],
"d": [2, 7, 9, 2]
})
expected_identity = df.drop(columns=["d"])
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={"columns": ["a", "b", "c"]}, domain_type="multicolumn")
assert data.equals(df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {
"columns": ["a", "b", "c"]
}, "Accessor kwargs have been modified"
# Trying same test with enum form of table domain - should work the same way
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={"columns": ["a", "b", "c"]}, domain_type="identity")
assert data.equals(
expected_identity), "Data does not match after getting compute domain"
assert compute_kwargs == {
"columns": ["a", "b", "c"]
}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {}, "Accessor kwargs have been modified"
def test_get_compute_domain_with_column_domain():
engine = PandasExecutionEngine()
df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [2, 3, 4, None]})
expected_identity = df.drop(columns=["b"])
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={"column": "a"}, domain_type=MetricDomainTypes.COLUMN)
assert data.equals(df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {
"column": "a"
}, "Accessor kwargs have been modified"
# Doing this using identity domain should yield different results
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={"column": "a"}, domain_type=MetricDomainTypes.IDENTITY)
assert data.equals(
expected_identity), "Data does not match after getting compute domain"
assert compute_kwargs == {
"column": "a"
}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {}, "Accessor kwargs have been modified"
def test_get_compute_domain_with_unmeetable_row_condition():
engine = PandasExecutionEngine()
df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [2, 3, 4, None]})
expected_df = df[df["b"] > 24]
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"column": "a",
"row_condition": "b > 24",
"condition_parser": "pandas",
},
domain_type="column",
)
# Ensuring data has been properly queried
assert data["b"].equals(
expected_df["b"]), "Data does not match after getting compute domain"
# Ensuring compute kwargs have not been modified
assert ("row_condition" in compute_kwargs.keys()
), "Row condition should be located within compute kwargs"
assert accessor_kwargs == {
"column": "a"
}, "Accessor kwargs have been modified"
def test_get_compute_domain_with_no_domain_kwargs():
engine = PandasExecutionEngine()
df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [2, 3, 4, None]})
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={}, domain_type="identity")
assert data.equals(df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {}, "Accessor kwargs have been modified"
# Trying same test with enum form of table domain - should work the same way
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={}, domain_type=MetricDomainTypes.TABLE)
assert data.equals(df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {}, "Accessor kwargs have been modified"
def test_get_compute_domain_with_column_domain():
engine = PandasExecutionEngine()
df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [2, 3, 4, None]})
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={"column": "a"}, domain_type=MetricDomainTypes.COLUMN)
assert data.equals(df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {
"column": "a"
}, "Accessor kwargs have been modified"
def test_get_compute_domain_with_column_pair_domain():
engine = PandasExecutionEngine()
df = pd.DataFrame({
"a": [1, 2, 3, 4],
"b": [2, 3, 4, 5],
"c": [1, 2, 3, 4]
})
expected_column_pair_df = df.drop(columns=["c"])
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"column_A": "a",
"column_B": "b"
},
domain_type="column_pair")
assert data.equals(df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {
"column_A": "a",
"column_B": "b",
}, "Accessor kwargs have been modified"
