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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2021/04/15 14:26:51 UTC
[GitHub] [spark] Yikun commented on a change in pull request #32177: [WIP][SPARK-34999][PYTHON] Consolidate PySpark testing utils
Yikun commented on a change in pull request #32177:
URL: https://github.com/apache/spark/pull/32177#discussion_r614085656
##########
File path: python/pyspark/testing/utils.py
##########
@@ -39,6 +54,29 @@
# No NumPy, but that's okay, we'll skip those tests
pass
+tabulate_requirement_message = None
+try:
+ from tabulate import tabulate # noqa: F401
+except ImportError as e:
+ # If tabulate requirement is not satisfied, skip related tests.
+ tabulate_requirement_message = str(e)
+have_tabulate = tabulate_requirement_message is None
Review comment:
I'm not sure why we need introduce the an extra `tabulate_requirement_message`?
I would like the below code:
```suggestion
have_tabulate = True
try:
from tabulate import tabulate # noqa: F401
except ImportError:
# If tabulate requirement is not satisfied, skip related tests.
have_tabulate = False
```
or just keep the above style:
```suggestion
have_tabulate = True
try:
from tabulate import tabulate # noqa: F401
except ImportError:
# If tabulate requirement is not satisfied, skip related tests.
have_tabulate = False
```
##########
File path: python/pyspark/pandas/tests/test_dataframe_conversion.py
##########
@@ -26,7 +26,7 @@
from pyspark import pandas as pp
Review comment:
unreltaed: we do the rename on pp --> ps in https://github.com/apache/spark/pull/32108 ,
- `from pyspark import pandas as pd` <-- this is the panda develper style to name pandas.
- `from pyspark import pandas as ps` <-- does it mean `panda on spark`
I know it's unrelated but would mind also do it in your patch? Or I'm also okay to submited in a separated patch.
##########
File path: python/pyspark/testing/utils.py
##########
@@ -39,6 +54,29 @@
# No NumPy, but that's okay, we'll skip those tests
Review comment:
unrelated, but better to change `except` to `except ImportError`
##########
File path: python/pyspark/testing/utils.py
##########
@@ -171,3 +209,393 @@ def search_jar(project_relative_path, sbt_jar_name_prefix, mvn_jar_name_prefix):
raise Exception("Found multiple JARs: %s; please remove all but one" % (", ".join(jars)))
else:
return jars[0]
+
+
+# Utilities below are used mainly in pyspark/pandas
+class SQLTestUtils(object):
+ """
+ This util assumes the instance of this to have 'spark' attribute, having a spark session.
+ It is usually used with 'ReusedSQLTestCase' class but can be used if you feel sure the
+ the implementation of this class has 'spark' attribute.
+ """
+
+ @contextmanager
+ def sql_conf(self, pairs):
+ """
+ A convenient context manager to test some configuration specific logic. This sets
+ `value` to the configuration `key` and then restores it back when it exits.
+ """
+ assert hasattr(self, "spark"), "it should have 'spark' attribute, having a spark session."
+
+ with sqlc(pairs, spark=self.spark):
+ yield
+
+ @contextmanager
+ def database(self, *databases):
+ """
+ A convenient context manager to test with some specific databases. This drops the given
+ databases if it exists and sets current database to "default" when it exits.
+ """
+ assert hasattr(self, "spark"), "it should have 'spark' attribute, having a spark session."
+
+ try:
+ yield
+ finally:
+ for db in databases:
+ self.spark.sql("DROP DATABASE IF EXISTS %s CASCADE" % db)
+ self.spark.catalog.setCurrentDatabase("default")
+
+ @contextmanager
+ def table(self, *tables):
+ """
+ A convenient context manager to test with some specific tables. This drops the given tables
+ if it exists.
+ """
+ assert hasattr(self, "spark"), "it should have 'spark' attribute, having a spark session."
+
+ try:
+ yield
+ finally:
+ for t in tables:
+ self.spark.sql("DROP TABLE IF EXISTS %s" % t)
+
+ @contextmanager
+ def tempView(self, *views):
+ """
+ A convenient context manager to test with some specific views. This drops the given views
+ if it exists.
+ """
+ assert hasattr(self, "spark"), "it should have 'spark' attribute, having a spark session."
+
+ try:
+ yield
+ finally:
+ for v in views:
+ self.spark.catalog.dropTempView(v)
+
+ @contextmanager
+ def function(self, *functions):
+ """
+ A convenient context manager to test with some specific functions. This drops the given
+ functions if it exists.
+ """
+ assert hasattr(self, "spark"), "it should have 'spark' attribute, having a spark session."
+
+ try:
+ yield
+ finally:
+ for f in functions:
+ self.spark.sql("DROP FUNCTION IF EXISTS %s" % f)
+
+
+class ReusedSQLTestCase(unittest.TestCase, SQLTestUtils):
+ @classmethod
+ def setUpClass(cls):
+ cls.spark = default_session()
+ cls.spark.conf.set(SPARK_CONF_ARROW_ENABLED, True)
+
+ @classmethod
+ def tearDownClass(cls):
+ # We don't stop Spark session to reuse across all tests.
+ # The Spark session will be started and stopped at PyTest session level.
+ # Please see databricks/koalas/conftest.py.
+ pass
+
+ def assertPandasEqual(self, left, right, check_exact=True):
+ if isinstance(left, pd.DataFrame) and isinstance(right, pd.DataFrame):
+ try:
+ if LooseVersion(pd.__version__) >= LooseVersion("1.1"):
+ kwargs = dict(check_freq=False)
+ else:
+ kwargs = dict()
+
+ assert_frame_equal(
+ left,
+ right,
+ check_index_type=("equiv" if len(left.index) > 0 else False),
+ check_column_type=("equiv" if len(left.columns) > 0 else False),
+ check_exact=check_exact,
+ **kwargs
+ )
+ except AssertionError as e:
+ msg = (
+ str(e)
+ + "\n\nLeft:\n%s\n%s" % (left, left.dtypes)
+ + "\n\nRight:\n%s\n%s" % (right, right.dtypes)
+ )
+ raise AssertionError(msg) from e
+ elif isinstance(left, pd.Series) and isinstance(right, pd.Series):
+ try:
+ if LooseVersion(pd.__version__) >= LooseVersion("1.1"):
+ kwargs = dict(check_freq=False)
+ else:
+ kwargs = dict()
+
+ assert_series_equal(
+ left,
+ right,
+ check_index_type=("equiv" if len(left.index) > 0 else False),
+ check_exact=check_exact,
+ **kwargs
+ )
+ except AssertionError as e:
+ msg = (
+ str(e)
+ + "\n\nLeft:\n%s\n%s" % (left, left.dtype)
+ + "\n\nRight:\n%s\n%s" % (right, right.dtype)
+ )
+ raise AssertionError(msg) from e
+ elif isinstance(left, pd.Index) and isinstance(right, pd.Index):
+ try:
+ assert_index_equal(left, right, check_exact=check_exact)
+ except AssertionError as e:
+ msg = (
+ str(e)
+ + "\n\nLeft:\n%s\n%s" % (left, left.dtype)
+ + "\n\nRight:\n%s\n%s" % (right, right.dtype)
+ )
+ raise AssertionError(msg) from e
+ else:
+ raise ValueError("Unexpected values: (%s, %s)" % (left, right))
+
+ def assertPandasAlmostEqual(self, left, right):
+ """
+ This function checks if given pandas objects approximately same,
+ which means the conditions below:
+ - Both objects are nullable
+ - Compare floats rounding to the number of decimal places, 7 after
+ dropping missing values (NaN, NaT, None)
+ """
+ if isinstance(left, pd.DataFrame) and isinstance(right, pd.DataFrame):
+ msg = (
+ "DataFrames are not almost equal: "
+ + "\n\nLeft:\n%s\n%s" % (left, left.dtypes)
+ + "\n\nRight:\n%s\n%s" % (right, right.dtypes)
+ )
+ self.assertEqual(left.shape, right.shape, msg=msg)
+ for lcol, rcol in zip(left.columns, right.columns):
+ self.assertEqual(lcol, rcol, msg=msg)
+ for lnull, rnull in zip(left[lcol].isnull(), right[rcol].isnull()):
+ self.assertEqual(lnull, rnull, msg=msg)
+ for lval, rval in zip(left[lcol].dropna(), right[rcol].dropna()):
+ self.assertAlmostEqual(lval, rval, msg=msg)
+ self.assertEqual(left.columns.names, right.columns.names, msg=msg)
+ elif isinstance(left, pd.Series) and isinstance(right, pd.Series):
+ msg = (
+ "Series are not almost equal: "
+ + "\n\nLeft:\n%s\n%s" % (left, left.dtype)
+ + "\n\nRight:\n%s\n%s" % (right, right.dtype)
+ )
+ self.assertEqual(left.name, right.name, msg=msg)
+ self.assertEqual(len(left), len(right), msg=msg)
+ for lnull, rnull in zip(left.isnull(), right.isnull()):
+ self.assertEqual(lnull, rnull, msg=msg)
+ for lval, rval in zip(left.dropna(), right.dropna()):
+ self.assertAlmostEqual(lval, rval, msg=msg)
+ elif isinstance(left, pd.MultiIndex) and isinstance(right, pd.MultiIndex):
+ msg = (
+ "MultiIndices are not almost equal: "
+ + "\n\nLeft:\n%s\n%s" % (left, left.dtype)
+ + "\n\nRight:\n%s\n%s" % (right, right.dtype)
+ )
+ self.assertEqual(len(left), len(right), msg=msg)
+ for lval, rval in zip(left, right):
+ self.assertAlmostEqual(lval, rval, msg=msg)
+ elif isinstance(left, pd.Index) and isinstance(right, pd.Index):
+ msg = (
+ "Indices are not almost equal: "
+ + "\n\nLeft:\n%s\n%s" % (left, left.dtype)
+ + "\n\nRight:\n%s\n%s" % (right, right.dtype)
+ )
+ self.assertEqual(len(left), len(right), msg=msg)
+ for lnull, rnull in zip(left.isnull(), right.isnull()):
+ self.assertEqual(lnull, rnull, msg=msg)
+ for lval, rval in zip(left.dropna(), right.dropna()):
+ self.assertAlmostEqual(lval, rval, msg=msg)
+ else:
+ raise ValueError("Unexpected values: (%s, %s)" % (left, right))
+
+ def assert_eq(self, left, right, check_exact=True, almost=False):
+ """
+ Asserts if two arbitrary objects are equal or not. If given objects are Koalas DataFrame
+ or Series, they are converted into pandas' and compared.
+
+ :param left: object to compare
+ :param right: object to compare
+ :param check_exact: if this is False, the comparison is done less precisely.
+ :param almost: if this is enabled, the comparison is delegated to `unittest`'s
+ `assertAlmostEqual`. See its documentation for more details.
+ """
+ lobj = self._to_pandas(left)
+ robj = self._to_pandas(right)
+ if isinstance(lobj, (pd.DataFrame, pd.Series, pd.Index)):
+ if almost:
+ self.assertPandasAlmostEqual(lobj, robj)
+ else:
+ self.assertPandasEqual(lobj, robj, check_exact=check_exact)
+ elif is_list_like(lobj) and is_list_like(robj):
+ self.assertTrue(len(left) == len(right))
+ for litem, ritem in zip(left, right):
+ self.assert_eq(litem, ritem, check_exact=check_exact, almost=almost)
+ elif (lobj is not None and pd.isna(lobj)) and (robj is not None and pd.isna(robj)):
+ pass
+ else:
+ if almost:
+ self.assertAlmostEqual(lobj, robj)
+ else:
+ self.assertEqual(lobj, robj)
+
+ @staticmethod
+ def _to_pandas(obj):
+ if isinstance(obj, (DataFrame, Series, Index)):
+ return obj.to_pandas()
+ else:
+ return obj
+
+
+class TestUtils(object):
+ @contextmanager
+ def temp_dir(self):
+ tmp = tempfile.mkdtemp()
+ try:
+ yield tmp
+ finally:
+ shutil.rmtree(tmp)
+
+ @contextmanager
+ def temp_file(self):
+ with self.temp_dir() as tmp:
+ yield tempfile.mktemp(dir=tmp)
+
+
+class ComparisonTestBase(ReusedSQLTestCase):
+ @property
+ def kdf(self):
+ return ps.from_pandas(self.pdf)
+
+ @property
+ def pdf(self):
+ return self.kdf.to_pandas()
+
+
+def compare_both(f=None, almost=True):
+
+ if f is None:
+ return functools.partial(compare_both, almost=almost)
+ elif isinstance(f, bool):
+ return functools.partial(compare_both, almost=f)
+
+ @functools.wraps(f)
+ def wrapped(self):
+ if almost:
+ compare = self.assertPandasAlmostEqual
+ else:
+ compare = self.assertPandasEqual
+
+ for result_pandas, result_spark in zip(f(self, self.pdf), f(self, self.kdf)):
+ compare(result_pandas, result_spark.to_pandas())
+
+ return wrapped
+
+
+@contextmanager
+def assert_produces_warning(
+ expected_warning=Warning,
+ filter_level="always",
+ check_stacklevel=True,
+ raise_on_extra_warnings=True,
+):
+ """
+ Context manager for running code expected to either raise a specific
+ warning, or not raise any warnings. Verifies that the code raises the
+ expected warning, and that it does not raise any other unexpected
+ warnings. It is basically a wrapper around ``warnings.catch_warnings``.
+
+ Notes
+ -----
+ Replicated from pandas._testing.
Review comment:
This notes is outdate.
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