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https://issues.apache.org/jira/browse/BEAM-12169?focusedWorklogId=727460&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-727460
]
ASF GitHub Bot logged work on BEAM-12169:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 15/Feb/22 18:52
Start Date: 15/Feb/22 18:52
Worklog Time Spent: 10m
Work Description: yeandy commented on a change in pull request #16615:
URL: https://github.com/apache/beam/pull/16615#discussion_r806222736
##########
File path: sdks/python/apache_beam/dataframe/transforms_test.py
##########
@@ -73,29 +74,52 @@ def df_equal_to(expected):
class TransformTest(unittest.TestCase):
- def run_scenario(self, input, func):
+ def run_scenario(self, input, func, check_subset=False):
+ """
+ In order for us to perform non-deferred operations, we have
+ to enumerate all possible categories, even if they are
+ unobserved. The default Pandas implementation on the other hand
+ does not produce unobserved columns. This means when conducting
+ tests, we need to account for the fact that the Beam result may
+ be a superset of that of the Pandas result.
+
+ When ``check_subset`` is set to ``True``, we will only
+ check that all of the columns in the Pandas implementation is
+ contained in that of the Beam implementation.
Review comment:
No worries! The logic I added can support verifying if they are all 0s
or NaNs (or other values), depending on the expected operation behavior.
##########
File path: sdks/python/apache_beam/dataframe/frames_test.py
##########
@@ -1950,9 +1950,55 @@ class BeamSpecificTest(unittest.TestCase):
"""Tests for functionality that's specific to the Beam DataFrame API.
These features don't exist in pandas so we must verify them independently."""
- def assert_frame_data_equivalent(self, actual, expected):
+ def assert_frame_data_equivalent(
+ self, actual, expected, check_subset=False, extra_col_value=0):
"""Verify that actual is the same as expected, ignoring the index and order
- of the data."""
+ of the data.
+
+ Note: In order for us to perform non-deferred operations in Beam, we have
Review comment:
Fixed
##########
File path: sdks/python/apache_beam/dataframe/frames_test.py
##########
@@ -1950,9 +1950,55 @@ class BeamSpecificTest(unittest.TestCase):
"""Tests for functionality that's specific to the Beam DataFrame API.
These features don't exist in pandas so we must verify them independently."""
- def assert_frame_data_equivalent(self, actual, expected):
+ def assert_frame_data_equivalent(
+ self, actual, expected, check_subset=False, extra_col_value=0):
"""Verify that actual is the same as expected, ignoring the index and order
- of the data."""
+ of the data.
+
+ Note: In order for us to perform non-deferred operations in Beam, we have
+ to enumerate all possible categories of data, even if they are ultimately
+ unobserved. The default Pandas implementation on the other hand does not
+ produce unobserved columns. This means when conducting tests, we need to
+ account for the fact that the Beam result may be a superset of that of the
+ Pandas result.
+
+ If ``check_subset`` is `True`, we verify that all of the columns in the
Review comment:
Fixed
##########
File path: sdks/python/apache_beam/dataframe/frames_test.py
##########
@@ -1950,9 +1950,55 @@ class BeamSpecificTest(unittest.TestCase):
"""Tests for functionality that's specific to the Beam DataFrame API.
These features don't exist in pandas so we must verify them independently."""
- def assert_frame_data_equivalent(self, actual, expected):
+ def assert_frame_data_equivalent(
+ self, actual, expected, check_subset=False, extra_col_value=0):
"""Verify that actual is the same as expected, ignoring the index and order
- of the data."""
+ of the data.
+
+ Note: In order for us to perform non-deferred operations in Beam, we have
+ to enumerate all possible categories of data, even if they are ultimately
+ unobserved. The default Pandas implementation on the other hand does not
+ produce unobserved columns. This means when conducting tests, we need to
+ account for the fact that the Beam result may be a superset of that of the
+ Pandas result.
+
+ If ``check_subset`` is `True`, we verify that all of the columns in the
+ Dataframe returned from the Pandas implementation is contained in the
+ Dataframe created from the Beam implementation.
+
+ We also check if all columns that exist in the Beam implementation but
+ not in the Pandas implementation are all equal to the ``extra_col_value``
+ to ensure that they were not erroneously populated.
+ """
+ if check_subset:
+ if isinstance(expected, pd.DataFrame):
+ expected_cols = set(expected.columns)
+ actual_cols = set(actual.columns)
+ # Verifying that expected columns is a subset of the actual columns
+ if not set(expected_cols).issubset(set(actual_cols)):
+ raise AssertionError(
+ f"Expected columns:\n{expected.columns}\n is not a"
+ f"subset of {actual.columns}.")
+
+ # Verifying that columns that don't exist in expected
+ # are all NaN in actual
Review comment:
No worries; I forgot to change the wording in this comment. The logic
I've added supports configurable values, with `0` as the default value to which
the extra columns should be compared.
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Issue Time Tracking
-------------------
Worklog Id: (was: 727460)
Time Spent: 6h 40m (was: 6.5h)
> Allow non-deferred column operations on categorical columns
> -----------------------------------------------------------
>
> Key: BEAM-12169
> URL: https://issues.apache.org/jira/browse/BEAM-12169
> Project: Beam
> Issue Type: Improvement
> Components: dsl-dataframe, sdk-py-core
> Reporter: Brian Hulette
> Assignee: Andy Ye
> Priority: P3
> Labels: dataframe-api
> Time Spent: 6h 40m
> Remaining Estimate: 0h
>
> There are several operations that we currently disallow because they produce
> a variable set of columns in the output based on the data
> (non-deferred-columns). However, for some dtypes (categorical, boolean) we
> can easily enumerate all the possible values that will be seen at execution
> time, so we can predict the columns that will be seen.
> Note we still can't implement these operations 100% correctly, as pandas will
> typically only create columns for the values that are _observed_, while we'd
> have to create a column for every possible value.
> We should allow these operations in these special cases.
> Operations in this category:
> - DataFrame.unstack (can work if unstacked level is a categorical or boolean
> column)
> - Series.str.get_dummies
> - Series.str.split
> - Series.str.rsplit
> - DataFrame.pivot
> - DataFrame.pivot_table
> - len(GroupBy) and ngroups
> ** if groupers are all categorical _and_ observed=False or all boolean
> ** Note these two may not actually be equivalent in all cases:
> [https://github.com/pandas-dev/pandas/issues/26326]
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