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https://issues.apache.org/jira/browse/BEAM-9547?focusedWorklogId=610301&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-610301
]
ASF GitHub Bot logged work on BEAM-9547:
----------------------------------------
Author: ASF GitHub Bot
Created on: 14/Jun/21 07:37
Start Date: 14/Jun/21 07:37
Worklog Time Spent: 10m
Work Description: TheNeuralBit commented on a change in pull request
#15002:
URL: https://github.com/apache/beam/pull/15002#discussion_r650241149
##########
File path: sdks/python/apache_beam/dataframe/frames_test.py
##########
@@ -1607,6 +1607,30 @@ def test_sample_with_missing_weights(self):
self.assertEqual(series_result.name, "GDP")
self.assertEqual(set(series_result.index), set(["Nauru", "Iceland"]))
+ def test_sample_with_weights_distribution(self):
+ num_other_elements = 100
+ num_runs = 20
+
+ def sample_many_times(s, weights):
+ all = None
+ for _ in range(num_runs):
+ sampled = s.sample(weights=weights)
+ if all is None:
+ all = sampled
+ else:
+ all = all.append(sampled)
+ return all.sum()
+
+ result = self._run_test(
+ sample_many_times,
+ # The first element is 1, the rest are all 0. This means that when
+ # we sum all the sampled elements (above), the result should be the
+ # number of times the first element was sampled.
+ pd.Series([1] + [0] * num_other_elements),
+ # Pick the first element about 20% of the time.
+ pd.Series([0.2] + [.8 / num_other_elements] * num_other_elements))
+
+ self.assertTrue(0 < result < num_runs / 2, result)
Review comment:
Discussed this some offline, I think the probability of flake here is
too high. result should follow a binomial distribution with n=20, p=0.2
Then P(result=0) alone is 0.8**20 ~= 1e-2. I don't think this approach can
totally eliminate the possibility of a flake, but we should probably get it at
least below 1e-3. I'm not sure if we can tweak the constants enough to make
that work while still getting a signal from this test, given that we also don't
want to make `n` too high to keep from slowing it down.
##########
File path: sdks/python/apache_beam/dataframe/frames_test.py
##########
@@ -1607,6 +1608,29 @@ def test_sample_with_missing_weights(self):
self.assertEqual(series_result.name, "GDP")
self.assertEqual(set(series_result.index), set(["Nauru", "Iceland"]))
+ def test_sample_with_weights_distribution(self):
+ target_prob = 0.25
+ num_samples = 100
+ num_targets = 200
+ num_other_elements = 10000
+
+ target_weight = target_prob / num_targets
+ other_weight = (1 - target_prob) / num_other_elements
+ self.assertTrue(target_weight > other_weight * 10, "weights too close")
+
+ result = self._run_test(
+ lambda s,
+ weights: s.sample(n=num_samples, weights=weights).sum(),
+ # The first elements are 1, the rest are all 0. This means that when
+ # we sum all the sampled elements (above), the result should be the
+ # number of times the first elements (aka targets) were sampled.
+ pd.Series([1] * num_targets + [0] * num_other_elements),
+ pd.Series([target_weight] * num_targets +
+ [other_weight] * num_other_elements))
+
+ expected = num_samples * target_prob
+ self.assertTrue(expected / 3 < result < expected * 2, (expected, result))
Review comment:
```suggestion
# Note the probabilistic nature of this test means that it will flake
for ~1 in every 100,000 runs
expected = num_samples * target_prob
self.assertTrue(expected / 3 < result < expected * 2, (expected, result))
```
Let's add a note about the (low) flake probability here
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Issue Time Tracking
-------------------
Worklog Id: (was: 610301)
Time Spent: 119h (was: 118h 50m)
> Implement all pandas operations (or raise WontImplementError)
> -------------------------------------------------------------
>
> Key: BEAM-9547
> URL: https://issues.apache.org/jira/browse/BEAM-9547
> Project: Beam
> Issue Type: Improvement
> Components: sdk-py-core
> Reporter: Brian Hulette
> Assignee: Robert Bradshaw
> Priority: P2
> Labels: dataframe-api
> Time Spent: 119h
> Remaining Estimate: 0h
>
> We should have an implementation for every DataFrame, Series, and GroupBy
> method. Everything that's not possible to implement should get a default
> implementation that raises WontImplementError
> See https://github.com/apache/beam/pull/10757#discussion_r389132292
> Progress at the individual operation level is tracked in a
> [spreadsheet|https://docs.google.com/spreadsheets/d/1hHAaJ0n0k2tw465ORs5tfdy4Lg0DnGWIQ53cLjAhel0/edit],
> consider requesting edit access if you'd like to help out.
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