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Fix bug with null replication metrics when row is all null #706

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merged 6 commits into from
Nov 8, 2022
Merged

Fix bug with null replication metrics when row is all null #706

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bee16d3
Fix bug when row is all null
tonywu315 Nov 4, 2022
7644b79
Improve test
tonywu315 Nov 4, 2022
a3b0a16
Remove unnecessary type
tonywu315 Nov 4, 2022
1d94e12
Merge branch 'main' into row_null_bug
tonywu315 Nov 7, 2022
f6bcf32
Merge branch 'main' into row_null_bug
tonywu315 Nov 8, 2022
00ee95a
Improve efficiency
tonywu315 Nov 8, 2022
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6 changes: 5 additions & 1 deletion dataprofiler/profilers/profile_builder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2287,7 +2287,11 @@ def _update_null_replication_metrics(self, clean_samples: Dict) -> None:
# Partition data based on whether target column value is null or not
# Calculate sum, mean of each partition without including current column
# in calculation
sum_null = data.iloc[null_indices, data.columns != col_id].sum().to_numpy()
sum_null = (
data.loc[data.index.intersection(null_indices), data.columns != col_id]
.sum()
.to_numpy()
)
Comment on lines +2290 to +2294
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This code does not error anymore if the entire DataFrame is null.

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this is much cleaner!

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beautiful!


# Add old sum_null if exists
if col_id in self._null_replication_metrics:
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15 changes: 15 additions & 0 deletions dataprofiler/tests/profilers/test_profile_builder.py
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Expand Up @@ -2081,6 +2081,21 @@ def test_null_replication_metrics_calculation(self):
np.testing.assert_array_almost_equal([[np.nan], [18]], column["class_sum"])
np.testing.assert_array_almost_equal([[np.nan], [9]], column["class_mean"])

# Test with all null in a row
data_4 = pd.DataFrame(
[[10, 20], [9999999, 9999999], [30, 9999999], [9999999, 9999999]]
)

profiler = dp.StructuredProfiler(data_4, options=profile_options)
report = profiler.report()

self.assertTrue("null_replication_metrics" in report["data_stats"][0])
column = report["data_stats"][0]["null_replication_metrics"]

np.testing.assert_array_almost_equal([0.5, 0.5], column["class_prior"])
np.testing.assert_array_almost_equal([[20], [0]], column["class_sum"])
np.testing.assert_array_almost_equal([[10], [0]], column["class_mean"])

def test_column_level_invalid_values(self):
data = pd.DataFrame([[1, 1], [9999999, 2], [3, 3]])

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