ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity
spark-instrumented-optimizer/python/pyspark/pandas/tests/test_expanding.py
xinrong-databricks 58feb85145 [SPARK-35034][PYTHON] Port Koalas miscellaneous unit tests into PySpark 
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas miscellaneous unit tests to PySpark.

### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested fully. We should enable miscellaneous unit tests.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Enable miscellaneous unit tests.

Closes #32152 from xinrong-databricks/port.misc_tests.

Lead-authored-by: xinrong-databricks <47337188+xinrong-databricks@users.noreply.github.com>
Co-authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-15 11:45:15 +09:00

307 lines
13 KiB
Python
Raw Blame History

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pyspark.pandas as ps
from pyspark.pandas.testing.utils import ReusedSQLTestCase, TestUtils
from pyspark.pandas.window import Expanding
class ExpandingTest(ReusedSQLTestCase, TestUtils):
def _test_expanding_func(self, f):
pser = pd.Series([1, 2, 3], index=np.random.rand(3))
kser = ps.from_pandas(pser)
self.assert_eq(getattr(kser.expanding(2), f)(), getattr(pser.expanding(2), f)())
self.assert_eq(getattr(kser.expanding(2), f)().sum(), getattr(pser.expanding(2), f)().sum())
# Multiindex
pser = pd.Series(
[1, 2, 3], index=pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z")])
)
kser = ps.from_pandas(pser)
self.assert_eq(getattr(kser.expanding(2), f)(), getattr(pser.expanding(2), f)())
pdf = pd.DataFrame(
{"a": [1.0, 2.0, 3.0, 2.0], "b": [4.0, 2.0, 3.0, 1.0]}, index=np.random.rand(4)
)
kdf = ps.from_pandas(pdf)
self.assert_eq(getattr(kdf.expanding(2), f)(), getattr(pdf.expanding(2), f)())
self.assert_eq(getattr(kdf.expanding(2), f)().sum(), getattr(pdf.expanding(2), f)().sum())
# Multiindex column
columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
pdf.columns = columns
kdf.columns = columns
self.assert_eq(getattr(kdf.expanding(2), f)(), getattr(pdf.expanding(2), f)())
def test_expanding_error(self):
with self.assertRaisesRegex(ValueError, "min_periods must be >= 0"):
ps.range(10).expanding(-1)
with self.assertRaisesRegex(
TypeError, "kdf_or_kser must be a series or dataframe; however, got:.*int"
):
Expanding(1, 2)
def test_expanding_repr(self):
self.assertEqual(repr(ps.range(10).expanding(5)), "Expanding [min_periods=5]")
def test_expanding_count(self):
# The behaviour of Expanding.count are different between pandas>=1.0.0 and lower,
# and we're following the behaviour of latest version of pandas.
if LooseVersion(pd.__version__) >= LooseVersion("1.0.0"):
self._test_expanding_func("count")
else:
# Series
idx = np.random.rand(3)
kser = ps.Series([1, 2, 3], index=idx, name="a")
expected_result = pd.Series([None, 2.0, 3.0], index=idx, name="a")
self.assert_eq(kser.expanding(2).count().sort_index(), expected_result.sort_index())
self.assert_eq(kser.expanding(2).count().sum(), expected_result.sum())
# MultiIndex
midx = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z")])
kser = ps.Series([1, 2, 3], index=midx, name="a")
expected_result = pd.Series([None, 2.0, 3.0], index=midx, name="a")
self.assert_eq(kser.expanding(2).count().sort_index(), expected_result.sort_index())
# DataFrame
kdf = ps.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]})
expected_result = pd.DataFrame({"a": [None, 2.0, 3.0, 4.0], "b": [None, 2.0, 3.0, 4.0]})
self.assert_eq(kdf.expanding(2).count().sort_index(), expected_result.sort_index())
self.assert_eq(kdf.expanding(2).count().sum(), expected_result.sum())
# MultiIndex columns
idx = np.random.rand(4)
kdf = ps.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]}, index=idx)
kdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
expected_result = pd.DataFrame(
{("a", "x"): [None, 2.0, 3.0, 4.0], ("a", "y"): [None, 2.0, 3.0, 4.0]}, index=idx,
)
self.assert_eq(kdf.expanding(2).count().sort_index(), expected_result.sort_index())
def test_expanding_min(self):
self._test_expanding_func("min")
def test_expanding_max(self):
self._test_expanding_func("max")
def test_expanding_mean(self):
self._test_expanding_func("mean")
def test_expanding_sum(self):
self._test_expanding_func("sum")
def test_expanding_std(self):
self._test_expanding_func("std")
def test_expanding_var(self):
self._test_expanding_func("var")
def _test_groupby_expanding_func(self, f):
pser = pd.Series([1, 2, 3, 2], index=np.random.rand(4), name="a")
kser = ps.from_pandas(pser)
self.assert_eq(
getattr(kser.groupby(kser).expanding(2), f)().sort_index(),
getattr(pser.groupby(pser).expanding(2), f)().sort_index(),
)
self.assert_eq(
getattr(kser.groupby(kser).expanding(2), f)().sum(),
getattr(pser.groupby(pser).expanding(2), f)().sum(),
)
# Multiindex
pser = pd.Series(
[1, 2, 3, 2],
index=pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z"), ("c", "z")]),
name="a",
)
kser = ps.from_pandas(pser)
self.assert_eq(
getattr(kser.groupby(kser).expanding(2), f)().sort_index(),
getattr(pser.groupby(pser).expanding(2), f)().sort_index(),
)
pdf = pd.DataFrame({"a": [1.0, 2.0, 3.0, 2.0], "b": [4.0, 2.0, 3.0, 1.0]})
kdf = ps.from_pandas(pdf)
self.assert_eq(
getattr(kdf.groupby(kdf.a).expanding(2), f)().sort_index(),
getattr(pdf.groupby(pdf.a).expanding(2), f)().sort_index(),
)
self.assert_eq(
getattr(kdf.groupby(kdf.a).expanding(2), f)().sum(),
getattr(pdf.groupby(pdf.a).expanding(2), f)().sum(),
)
self.assert_eq(
getattr(kdf.groupby(kdf.a + 1).expanding(2), f)().sort_index(),
getattr(pdf.groupby(pdf.a + 1).expanding(2), f)().sort_index(),
)
self.assert_eq(
getattr(kdf.b.groupby(kdf.a).expanding(2), f)().sort_index(),
getattr(pdf.b.groupby(pdf.a).expanding(2), f)().sort_index(),
)
self.assert_eq(
getattr(kdf.groupby(kdf.a)["b"].expanding(2), f)().sort_index(),
getattr(pdf.groupby(pdf.a)["b"].expanding(2), f)().sort_index(),
)
self.assert_eq(
getattr(kdf.groupby(kdf.a)[["b"]].expanding(2), f)().sort_index(),
getattr(pdf.groupby(pdf.a)[["b"]].expanding(2), f)().sort_index(),
)
# Multiindex column
columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
pdf.columns = columns
kdf.columns = columns
self.assert_eq(
getattr(kdf.groupby(("a", "x")).expanding(2), f)().sort_index(),
getattr(pdf.groupby(("a", "x")).expanding(2), f)().sort_index(),
)
self.assert_eq(
getattr(kdf.groupby([("a", "x"), ("a", "y")]).expanding(2), f)().sort_index(),
getattr(pdf.groupby([("a", "x"), ("a", "y")]).expanding(2), f)().sort_index(),
)
def test_groupby_expanding_count(self):
# The behaviour of ExpandingGroupby.count are different between pandas>=1.0.0 and lower,
# and we're following the behaviour of latest version of pandas.
if LooseVersion(pd.__version__) >= LooseVersion("1.0.0"):
self._test_groupby_expanding_func("count")
else:
# Series
kser = ps.Series([1, 2, 3, 2], index=np.random.rand(4))
midx = pd.MultiIndex.from_tuples(
list(zip(kser.to_pandas().values, kser.index.to_pandas().values))
)
expected_result = pd.Series([np.nan, np.nan, np.nan, 2], index=midx)
self.assert_eq(
kser.groupby(kser).expanding(2).count().sort_index(), expected_result.sort_index()
)
self.assert_eq(kser.groupby(kser).expanding(2).count().sum(), expected_result.sum())
# MultiIndex
kser = ps.Series(
[1, 2, 3, 2],
index=pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z"), ("a", "y")]),
)
midx = pd.MultiIndex.from_tuples(
[(1, "a", "x"), (2, "a", "y"), (3, "b", "z"), (2, "a", "y")]
)
expected_result = pd.Series([np.nan, np.nan, np.nan, 2], index=midx)
self.assert_eq(
kser.groupby(kser).expanding(2).count().sort_index(), expected_result.sort_index()
)
# DataFrame
kdf = ps.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]})
midx = pd.MultiIndex.from_tuples([(1, 0), (2, 1), (2, 3), (3, 2)], names=["a", None])
expected_result = pd.DataFrame(
{"a": [None, None, 2.0, None], "b": [None, None, 2.0, None]}, index=midx
)
self.assert_eq(
kdf.groupby(kdf.a).expanding(2).count().sort_index(), expected_result.sort_index()
)
self.assert_eq(kdf.groupby(kdf.a).expanding(2).count().sum(), expected_result.sum())
expected_result = pd.DataFrame(
{"a": [None, None, 2.0, None], "b": [None, None, 2.0, None]},
index=pd.MultiIndex.from_tuples(
[(2, 0), (3, 1), (3, 3), (4, 2)], names=["a", None]
),
)
self.assert_eq(
kdf.groupby(kdf.a + 1).expanding(2).count().sort_index(),
expected_result.sort_index(),
)
expected_result = pd.Series([None, None, 2.0, None], index=midx, name="b")
self.assert_eq(
kdf.b.groupby(kdf.a).expanding(2).count().sort_index(), expected_result.sort_index()
)
self.assert_eq(
kdf.groupby(kdf.a)["b"].expanding(2).count().sort_index(),
expected_result.sort_index(),
)
expected_result = pd.DataFrame({"b": [None, None, 2.0, None]}, index=midx)
self.assert_eq(
kdf.groupby(kdf.a)[["b"]].expanding(2).count().sort_index(),
expected_result.sort_index(),
)
# MultiIndex column
kdf = ps.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]})
kdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
midx = pd.MultiIndex.from_tuples(
[(1, 0), (2, 1), (2, 3), (3, 2)], names=[("a", "x"), None]
)
expected_result = pd.DataFrame(
{"a": [None, None, 2.0, None], "b": [None, None, 2.0, None]}, index=midx
)
expected_result.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
self.assert_eq(
kdf.groupby(("a", "x")).expanding(2).count().sort_index(),
expected_result.sort_index(),
)
midx = pd.MultiIndex.from_tuples(
[(1, 4.0, 0), (2, 1.0, 3), (2, 2.0, 1), (3, 3.0, 2)],
names=[("a", "x"), ("a", "y"), None],
)
expected_result = pd.DataFrame(
{
("a", "x"): [np.nan, np.nan, np.nan, np.nan],
("a", "y"): [np.nan, np.nan, np.nan, np.nan],
},
index=midx,
)
self.assert_eq(
kdf.groupby([("a", "x"), ("a", "y")]).expanding(2).count().sort_index(),
expected_result.sort_index(),
)
def test_groupby_expanding_min(self):
self._test_groupby_expanding_func("min")
def test_groupby_expanding_max(self):
self._test_groupby_expanding_func("max")
def test_groupby_expanding_mean(self):
self._test_groupby_expanding_func("mean")
def test_groupby_expanding_sum(self):
self._test_groupby_expanding_func("sum")
def test_groupby_expanding_std(self):
self._test_groupby_expanding_func("std")
def test_groupby_expanding_var(self):
self._test_groupby_expanding_func("var")
if __name__ == "__main__":
import unittest
from pyspark.pandas.tests.test_expanding import * # noqa: F401
try:
import xmlrunner # type: ignore[import]
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)
Reference in a new issue View git blame Copy permalink