DatetimeFormatDataCheck for equal interval and sorting #2603
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## main #2603 +/- ##
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+ Coverage 99.9% 99.9% +0.1%
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Files 295 297 +2
Lines 26895 27027 +132
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+ Hits 26851 26983 +132
Misses 44 44
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| y_train = infer_feature_types(y_train) | ||
| problem_type = handle_problem_types(problem_type) | ||
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| datetime_column = None |
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Depending on the outcome of our time series discussions, we can change this if we want to consider the index as the default placement of datetime data for time series. Until then, we'd need the user to pass in a minimum of date_index for time series default objectives.
| y = infer_feature_types(y) | ||
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| if self.datetime_column != "index": | ||
| datetime_values = X[self.datetime_column] |
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We're not trying to extract every datetime column, just the one that indexes the datetime information
| datetime_values = X.index | ||
| if not isinstance(datetime_values, pd.DatetimeIndex): | ||
| datetime_values = y.index | ||
| if not isinstance(datetime_values, pd.DatetimeIndex): |
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This allows the user to just specify index, and we can check the X index first followed by the y index.
| def __init__(self, problem_type, objective, n_splits=3): | ||
| def __init__(self, problem_type, objective, n_splits=3, datetime_column="index"): | ||
| default_checks = self._DEFAULT_DATA_CHECK_CLASSES | ||
| data_check_params = {} |
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Changed the layout of this to make it cleaner and more easily understandable as to what data checks and params are being passed across different problem_types.
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| @pytest.mark.parametrize("input_type", ["pd", "ww"]) | ||
| @pytest.mark.parametrize( | ||
| "uneven,type_errors", [(True, False), (False, True), (False, False)] |
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Checks across uneven frequencies and incorrect datetime types
evalml/tests/data_checks_tests/test_datetime_format_data_check.py
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ParthivNaresh
requested review from
angela97lin,
bchen1116,
christopherbunn,
chukarsten,
dsherry,
eccabay and
freddyaboulton
and removed request for
angela97lin and
dsherry
evalml/tests/data_checks_tests/test_datetime_format_data_check.py
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evalml/tests/data_checks_tests/test_datetime_format_data_check.py
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freddyaboulton
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freddyaboulton
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@ParthivNaresh This looks great! I think there are two changes I'd like to make before merge though. The first is disallowing monotonic decreasing columns, the second is returning DataCheckErrors rather than raising TypeErrors in the data check.
…yx/evalml into DataCheck-For-Equal-Interval
Fixes: #2124