Merge pull request #792 from mindsdb/fix_delta_ungrouped

Fix: temporal delta estimation for ungrouped series

lightwood/data/timeseries_analyzer.py

@@ -82,6 +82,7 @@ def get_delta(df: pd.DataFrame, ts_info: dict, group_combinations: list, order_c
     # get default delta for all data
     for col in order_cols:
         series = pd.Series([x[-1] for x in df[col]])
+        series = series.drop_duplicates()  # by this point df is ordered so duplicate timestamps are either because of non-handled groups or repeated data that, for mode delta estimation, should be ignored  # noqa
         rolling_diff = series.rolling(window=2).apply(lambda x: x.iloc[1] - x.iloc[0])
         delta = rolling_diff.value_counts(ascending=False).keys()[0]  # pick most popular
         deltas["__default"][col] = delta

lightwood/data/timeseries_transform.py

@@ -106,7 +106,7 @@ def transform_timeseries(
             df_arr.append(df.sort_values(by=ob_arr))
             group_lengths.append(len(df))
     else:
-        df_arr = [original_df]
+        df_arr = [original_df.sort_values(by=ob_arr)]
         group_lengths.append(len(original_df))
 
     n_groups = len(df_arr)

tests/integration/advanced/test_timeseries.py

@@ -137,9 +137,16 @@ def test_1_time_series_regression(self):
 
         # test inferring mode
         test_df['__mdb_make_predictions'] = False
+        test_df = test_df.sample(frac=1)  # shuffle to test internal ordering logic
         preds = pred.predict(test_df)
         self.check_ts_prediction_df(preds, nr_preds, [order_by])
 
+        # Additionally, check timestamps are further into the future than test dates
+        latest_timestamp = pd.to_datetime(test_df[order_by]).max().timestamp()
+        for idx, row in preds.iterrows():
+            for timestamp in row[f'order_{order_by}']:
+                assert timestamp > latest_timestamp
+
     def test_2_time_series_classification(self):
         from lightwood.api.high_level import predictor_from_problem