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tft gap detection on each time series independently #770

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tft gap detection on each time series independently #770

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de9c30e
update automl.py - tft gap detection on each time series independently
int-chaos Oct 19, 2022
5fc6255
update automl.py
int-chaos Oct 19, 2022
99b7c1b
update automl.py - fix bugs
int-chaos Oct 19, 2022
31b326f
update automl.py - use groupby
int-chaos Oct 19, 2022
5fdd8fa
update automl.py - fix issue
int-chaos Oct 19, 2022
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25 changes: 19 additions & 6 deletions flaml/automl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1026,12 +1026,25 @@ def _validate_ts_data(
assert (
dataframe[[dataframe.columns[0]]].duplicated() is None
), "Duplicate timestamp values with different values for other columns."
ts_series = pd.to_datetime(dataframe[dataframe.columns[0]])
inferred_freq = pd.infer_freq(ts_series)
if inferred_freq is None:
logger.warning(
"Missing timestamps detected. To avoid error with estimators, set estimator list to ['prophet']. "
)
if self._state.task == TS_FORECASTPANEL:
# check for each time series independently
group_ids = self._state.fit_kwargs.get("group_ids")
group_df = dataframe.groupby(group_ids)
for group in group_df:
ts = group[1]
ts_series = pd.to_datetime(ts[ts.columns[0]])
inferred_freq = pd.infer_freq(ts_series)
if inferred_freq is None:
logger.warning(
"Missing timestamps detected."
)
else:
ts_series = pd.to_datetime(dataframe[dataframe.columns[0]])
inferred_freq = pd.infer_freq(ts_series)
if inferred_freq is None:
logger.warning(
"Missing timestamps detected. To avoid error with estimators, set estimator list to ['prophet']. "
)
if y_train_all is not None:
return dataframe.iloc[:, :-1], dataframe.iloc[:, -1]
return dataframe
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