Add problematic target data check #814
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@@ Coverage Diff @@
## master #814 +/- ##
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Coverage 99.67% 99.67%
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Files 186 188 +2
Lines 7295 7338 +43
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+ Hits 7271 7314 +43
Misses 24 24
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dsherry
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Jun 2, 2020
dsherry
reviewed
Jun 2, 2020
| y = pd.Series(y) | ||
| null_rows = y.isnull() | ||
| error_msg = "Row '{}' contains a null value" | ||
| return [DataCheckError(error_msg.format(row_index), self.name) for row_index, row_value in null_rows.items() if row_value] |
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I don't think we need an error for each row. Let's just return one error saying the target contains a missing value, yeah?
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what if we put the count and % of the null values in the error message? 1 row vs 50% of rows is a very different error
dsherry
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Jun 2, 2020
dsherry
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dsherry
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dsherry
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dsherry
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dsherry
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dsherry
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Closes #710 by adding
InvalidTargetsDataCheck, appending it to DefaultDataChecks run by AutoMLInvalidTargetsDataCheckcurrently only checks if there are any NaN/None values in the target labels.