Error in `infer_feature_types` for seoul bike dataset

Running infer_feature_types on the seoul bike dataset errors out. Based on the stack trace, I think this might be related to the partial schema PR (#2774) but haven’t gotten a chance to dig too deeply.

SeoulBikeData.csv

import pandas as pd
import woodwork as ww
data_set = pd.read_csv("Regression/SeoulBikeData.csv")
infer_feature_types(data_set)

Produces:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~/Desktop/evalml_venv/lib/python3.7/site-packages/pandas/core/tools/datetimes.py in _to_datetime_with_format(arg, orig_arg, name, tz, fmt, exact, errors, infer_datetime_format)
    508         try:
--> 509             values, tz = conversion.datetime_to_datetime64(arg)
    510             dta = DatetimeArray(values, dtype=tz_to_dtype(tz))

~/Desktop/evalml_venv/lib/python3.7/site-packages/pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.datetime_to_datetime64()

TypeError: Unrecognized value type: <class 'str'>

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
~/Desktop/evalml_venv/lib/python3.7/site-packages/woodwork/logical_types.py in transform(self, series)
    212                 else:
--> 213                     series = pd.to_datetime(series, format=self.datetime_format)
    214             except (TypeError, ValueError):

~/Desktop/evalml_venv/lib/python3.7/site-packages/pandas/core/tools/datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
    882     elif isinstance(arg, ABCSeries):
--> 883         cache_array = _maybe_cache(arg, format, cache, convert_listlike)
    884         if not cache_array.empty:

~/Desktop/evalml_venv/lib/python3.7/site-packages/pandas/core/tools/datetimes.py in _maybe_cache(arg, format, cache, convert_listlike)
    194         if len(unique_dates) < len(arg):
--> 195             cache_dates = convert_listlike(unique_dates, format)
    196             cache_array = Series(cache_dates, index=unique_dates)

~/Desktop/evalml_venv/lib/python3.7/site-packages/pandas/core/tools/datetimes.py in _convert_listlike_datetimes(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)
    393         res = _to_datetime_with_format(
--> 394             arg, orig_arg, name, tz, format, exact, errors, infer_datetime_format
    395         )

~/Desktop/evalml_venv/lib/python3.7/site-packages/pandas/core/tools/datetimes.py in _to_datetime_with_format(arg, orig_arg, name, tz, fmt, exact, errors, infer_datetime_format)
    512         except (ValueError, TypeError):
--> 513             raise err
    514 

~/Desktop/evalml_venv/lib/python3.7/site-packages/pandas/core/tools/datetimes.py in _to_datetime_with_format(arg, orig_arg, name, tz, fmt, exact, errors, infer_datetime_format)
    500         res = _array_strptime_with_fallback(
--> 501             arg, name, tz, fmt, exact, errors, infer_datetime_format
    502         )

~/Desktop/evalml_venv/lib/python3.7/site-packages/pandas/core/tools/datetimes.py in _array_strptime_with_fallback(arg, name, tz, fmt, exact, errors, infer_datetime_format)
    435     try:
--> 436         result, timezones = array_strptime(arg, fmt, exact=exact, errors=errors)
    437         if "%Z" in fmt or "%z" in fmt:

~/Desktop/evalml_venv/lib/python3.7/site-packages/pandas/_libs/tslibs/strptime.pyx in pandas._libs.tslibs.strptime.array_strptime()

ValueError: time data '13/12/2017' does not match format '%m/%d/%Y' (match)

During handling of the above exception, another exception occurred:

TypeConversionError                       Traceback (most recent call last)
<ipython-input-17-0cd25bce426e> in <module>
      1 # X_train, X_holdout, y_train, y_holdout = evalml.preprocessing.split_data(X, y, problem_type='regression', test_size=0.25, random_seed=0)
----> 2 infer_feature_types(data_set)

~/Desktop/evalml/evalml/utils/woodwork_utils.py in infer_feature_types(data, feature_types)
    110     else:
    111         ww_data = data.copy()
--> 112         ww_data.ww.init(logical_types=feature_types)
    113         return convert_all_nan_unknown_to_double(ww_data)
    114 

~/Desktop/evalml_venv/lib/python3.7/site-packages/woodwork/table_accessor.py in init(self, **kwargs)
     96                 Any errors resulting from skipping validation with invalid inputs may not be easily understood.
     97         """
---> 98         self.init_with_partial_schema(**kwargs)
     99 
    100     def init_with_full_schema(self, schema: TableSchema, validate: bool = True, **kwargs) -> None:

~/Desktop/evalml_venv/lib/python3.7/site-packages/woodwork/table_accessor.py in init_with_partial_schema(self, schema, index, time_index, logical_types, already_sorted, name, semantic_tags, table_metadata, column_metadata, use_standard_tags, column_descriptions, column_origins, validate, **kwargs)
    203 
    204         # overwrite schema parameters with specified kwargs
--> 205         logical_types = _infer_missing_logical_types(self._dataframe, logical_types, existing_logical_types)
    206         column_descriptions = {**existing_col_descriptions, **(column_descriptions or {})}
    207         column_metadata = {**existing_col_metadata, **(column_metadata or {})}

~/Desktop/evalml_venv/lib/python3.7/site-packages/woodwork/table_accessor.py in _infer_missing_logical_types(dataframe, force_logical_types, existing_logical_types)
   1060         logical_type = force_logical_types.get(name) if name in force_logical_types else existing_logical_types.get(name)
   1061         parsed_logical_types[name] = _get_column_logical_type(series, logical_type, name)
-> 1062         updated_series = parsed_logical_types[name].transform(series)
   1063         if updated_series is not series:
   1064             dataframe[name] = updated_series

~/Desktop/evalml_venv/lib/python3.7/site-packages/woodwork/logical_types.py in transform(self, series)
    213                     series = pd.to_datetime(series, format=self.datetime_format)
    214             except (TypeError, ValueError):
--> 215                 raise TypeConversionError(series, new_dtype, type(self))
    216         return super().transform(series)
    217 

TypeConversionError: Error converting datatype for Date from type object to type datetime64[ns]. Please confirm the underlying data is consistent with logical type Datetime.

[thehomebrewnerd]

Created Woodwork Issue 1152 to resolve this problem.

[thehomebrewnerd]

@freddyaboulton @angela97lin I’m pretty sure I know what is happening here. Let me dig a little deeper to confirm, and then I will see if there is a workaround or if we need to update Woodwork to resolve this.