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test: add test for impure function correlation behavior
Need to fix the a few broken cases. Related to #8921, trying to write down exactly what the expected behavior is.
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from __future__ import annotations | ||
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import pandas.testing as tm | ||
import pytest | ||
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import ibis | ||
import ibis.common.exceptions as com | ||
from ibis import _ | ||
from ibis.backends.tests.errors import ( | ||
PsycoPg2InternalError, | ||
PyDruidProgrammingError, | ||
) | ||
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no_random = [ | ||
pytest.mark.notimpl( | ||
["dask", "pandas", "polars"], raises=com.OperationNotDefinedError | ||
), | ||
pytest.mark.notimpl(["druid"], raises=PyDruidProgrammingError), | ||
pytest.mark.notimpl( | ||
["risingwave"], | ||
raises=PsycoPg2InternalError, | ||
reason="function random() does not exist", | ||
), | ||
] | ||
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no_udfs = [ | ||
pytest.mark.notyet(["datafusion"], raises=NotImplementedError), | ||
pytest.mark.notimpl( | ||
[ | ||
"bigquery", | ||
"clickhouse", | ||
"dask", | ||
"druid", | ||
"exasol", | ||
"impala", | ||
"mssql", | ||
"mysql", | ||
"oracle", | ||
"pandas", | ||
"trino", | ||
"risingwave", | ||
] | ||
), | ||
] | ||
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no_uuid = pytest.mark.notimpl( | ||
["druid", "exasol", "oracle", "polars", "pyspark", "risingwave", "pandas", "dask"], | ||
raises=com.OperationNotDefinedError, | ||
) | ||
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@ibis.udf.scalar.python(side_effects=True) | ||
def my_random(x: float) -> float: | ||
# need to make the whole UDF self-contained for postgres to work | ||
import random | ||
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return random.random() | ||
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mark_impures = pytest.mark.parametrize( | ||
"impure", | ||
[ | ||
pytest.param( | ||
lambda _: ibis.random(), | ||
marks=no_random, | ||
id="random", | ||
), | ||
pytest.param( | ||
lambda _: ibis.uuid().cast(str).contains("a").ifelse(1, 0), | ||
marks=no_uuid, | ||
id="uuid", | ||
), | ||
pytest.param( | ||
lambda table: my_random(table.float_col), | ||
marks=[ | ||
# once this is fixed, can we unify these params with the other params? | ||
pytest.mark.broken( | ||
["postgres" "flink"], reason="instances are uncorrelated" | ||
), | ||
*no_udfs, | ||
], | ||
id="udf", | ||
), | ||
], | ||
) | ||
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@pytest.mark.broken("sqlite", reason="instances are uncorrelated") | ||
@mark_impures | ||
def test_impure_correlated(alltypes, impure): | ||
df = ( | ||
alltypes.select(common=impure(alltypes)) | ||
.select(x=_.common, y=_.common) | ||
.execute() | ||
) | ||
tm.assert_series_equal(df.x, df.y, check_names=False) | ||
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@pytest.mark.broken("sqlite", reason="instances are uncorrelated") | ||
@mark_impures | ||
def test_chained_selections(alltypes, impure): | ||
# https://github.com/ibis-project/ibis/issues/8921#issue-2234327722 | ||
t = alltypes.mutate(num=impure(alltypes)) | ||
t = t.mutate(isbig=(t.num > 0.5)) | ||
df = t.select("num", "isbig").execute() | ||
df["expected"] = df.num > 0.5 | ||
tm.assert_series_equal(df.isbig, df.expected, check_names=False) | ||
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@pytest.mark.broken(["clickhouse"], reason="instances are correlated") | ||
@pytest.mark.parametrize( | ||
"impure", | ||
[ | ||
pytest.param( | ||
lambda _: ibis.random(), | ||
marks=[ | ||
*no_random, | ||
pytest.mark.broken(["trino"], reason="instances are correlated"), | ||
], | ||
id="random", | ||
), | ||
pytest.param( | ||
# make this a float so we can compare to .5 | ||
lambda _: ibis.uuid().cast(str).contains("a").ifelse(1, 0), | ||
marks=[ | ||
no_uuid, | ||
pytest.mark.broken(["trino"], reason="instances are correlated"), | ||
], | ||
id="uuid", | ||
), | ||
pytest.param( | ||
lambda table: my_random(table.float_col), | ||
# once this is fixed, can we unify these params with the other params? | ||
marks=[ | ||
*no_udfs, | ||
pytest.mark.broken(["duckdb"], reason="instances are correlated"), | ||
], | ||
id="udf", | ||
), | ||
], | ||
) | ||
def test_impure_uncorrelated(alltypes, impure): | ||
df = alltypes.select(x=impure(alltypes), y=impure(alltypes)).execute() | ||
assert (df.x == df.y).mean() < 1 | ||
# Even if the two expressions have the exact same ID, they should still be | ||
# uncorrelated | ||
common = impure(alltypes) | ||
df = alltypes.select(x=common, y=common).execute() | ||
assert (df.x == df.y).mean() < 1 |