set shuffling of time series in file and list dataset to false by default

pts/dataset/artificial.py

@@ -72,7 +72,7 @@ class ArtificialDataset:
         return TrainDatasets(
             metadata=self.metadata,
             train=ListDataset(self.train, self.freq),
-            test=ListDataset(self.test, self.freq, is_train=False),
+            test=ListDataset(self.test, self.freq, shuffle=False),
         )
 
 
@@ -776,7 +776,6 @@ def constant_dataset() -> Tuple[DatasetInfo, Dataset, Dataset]:
             for i in range(10)
         ],
         freq=metadata.freq,
-        is_train=False
     )
 
     info = DatasetInfo(

pts/dataset/file_dataset.py

@@ -37,14 +37,14 @@ class JsonLinesFile:
         JSON Lines file.
     """
 
-    def __init__(self, path: Path, is_train: bool = True) -> None:
+    def __init__(self, path: Path, shuffle: bool = True) -> None:
         self.path = path
-        self.is_train = is_train
+        self.shuffle = shuffle
 
     def __iter__(self):
         with open(self.path) as jsonl_file:
             lines = jsonl_file.read().splitlines()
-            if self.is_train:
+            if self.shuffle:
                 random.shuffle(lines)
 
             for line_number, raw in enumerate(lines, start=1):
@@ -81,9 +81,9 @@ class FileDataset(Dataset):
     """
 
     def __init__(
-        self, path: Path, freq: str, one_dim_target: bool = True, is_train: bool = True
+        self, path: Path, freq: str, one_dim_target: bool = True, shuffle: bool = False
     ) -> None:
-        self.is_train = is_train
+        self.shuffle = shuffle
         self.path = path
         self.process = ProcessDataEntry(freq, one_dim_target=one_dim_target)
         if not self.files():
@@ -91,7 +91,7 @@ class FileDataset(Dataset):
 
     def __iter__(self) -> Iterator[DataEntry]:
         for path in self.files():
-            for line in JsonLinesFile(path, self.is_train):
+            for line in JsonLinesFile(path, self.shuffle):
                 data = self.process(line.content)
                 data["source"] = SourceContext(
                     source=line.span.path, row=line.span.line
@@ -110,4 +110,8 @@ class FileDataset(Dataset):
         List[Path]
             List of the paths of all files composing the dataset.
         """
-        return glob.glob(str(self.path))
+        files = glob.glob(str(self.path))
+        if self.shuffle:
+            random.shuffle(files)
+        return files
+

pts/dataset/list_dataset.py

@@ -11,14 +11,13 @@ class ListDataset(Dataset):
         data_iter: Iterable[DataEntry],
         freq: str,
         one_dim_target: bool = True,
-        is_train: bool = True,
+        shuffle: bool = False,
     ) -> None:
         process = ProcessDataEntry(freq, one_dim_target)
         self.list_data = [process(data) for data in data_iter]
-        if is_train:
+        if shuffle:
             random.shuffle(self.list_data)
 
-
     def __iter__(self):
         source_name = "list_data"
         for row_number, data in enumerate(self.list_data, start=1):

pts/dataset/multivariate_grouper.py

@@ -122,9 +122,7 @@ class MultivariateGrouper:
         grouped_data[FieldName.START] = self.first_timestamp
         grouped_data[FieldName.FEAT_STATIC_CAT] = [0]
 
-        return ListDataset(
-            [grouped_data], freq=self.frequency, one_dim_target=False, is_train=False
-        )
+        return ListDataset([grouped_data], freq=self.frequency, one_dim_target=False)
 
     def _prepare_test_data(self, dataset: Dataset) -> ListDataset:
         logging.info("group test time-series to datasets")

pts/dataset/repository/_gp_copula_2019.py

@@ -137,9 +137,7 @@ def save_metadata(dataset_path: Path, ds_info: GPCopulaDataset):
 
 
 def save_dataset(dataset_path: Path, ds_info: GPCopulaDataset):
-    dataset = list(
-        FileDataset(dataset_path / "*.json", freq=ds_info.freq, is_train=False)
-    )
+    dataset = list(FileDataset(dataset_path / "*.json", freq=ds_info.freq))
     shutil.rmtree(dataset_path)
     train_file = dataset_path / "data.json"
     save_to_file(

pts/dataset/utils.py

@@ -42,7 +42,7 @@ def to_pandas(instance: dict, freq: str = None) -> pd.Series:
     return pd.Series(target, index=index)
 
 
-def load_datasets(metadata, train, test) -> TrainDatasets:
+def load_datasets(metadata, train, test, shuffle: bool = False) -> TrainDatasets:
     """
     Loads a dataset given metadata, train and test path.
     Parameters
@@ -59,8 +59,8 @@ def load_datasets(metadata, train, test) -> TrainDatasets:
         An object collecting metadata, training data, test data.
     """
     meta = MetaData.parse_file(metadata)
-    train_ds = FileDataset(train, meta.freq)
-    test_ds = FileDataset(test, meta.freq, is_train=False) if test else None
+    train_ds = FileDataset(train, meta.freq, shuffle=shuffle)
+    test_ds = FileDataset(test, meta.freq) if test else None
 
     return TrainDatasets(metadata=meta, train=train_ds, test=test_ds)
 

test/dataset/test_multivariate_grouper.py

@@ -67,10 +67,8 @@ TRAIN_FILL_RULE = [np.mean, np.mean, np.mean, np.mean, lambda x: 0.0]
 def test_multivariate_grouper_train(
     univariate_ts, multivariate_ts, train_fill_rule
 ) -> None:
-    univariate_ds = ListDataset(univariate_ts, freq="1D", is_train=False)
-    multivariate_ds = ListDataset(
-        multivariate_ts, freq="1D", one_dim_target=False, is_train=False
-    )
+    univariate_ds = ListDataset(univariate_ts, freq="1D")
+    multivariate_ds = ListDataset(multivariate_ts, freq="1D", one_dim_target=False)
 
     grouper = MultivariateGrouper(train_fill_rule=train_fill_rule)
     assert (
@@ -117,10 +115,9 @@ MAX_TARGET_DIM = [2, 1]
 def test_multivariate_grouper_test(
     univariate_ts, multivariate_ts, test_fill_rule, max_target_dim
 ) -> None:
-    univariate_ds = ListDataset(univariate_ts, freq="1D", is_train=False)
+    univariate_ds = ListDataset(univariate_ts, freq="1D")
     multivariate_ds = ListDataset(
-        multivariate_ts, freq="1D", one_dim_target=False, is_train=False
-    )
+        multivariate_ts, freq="1D", one_dim_target=False)
 
     grouper = MultivariateGrouper(
         test_fill_rule=test_fill_rule, num_test_dates=2, max_target_dim=max_target_dim,