added constant dataset

pts/dataset/__init__.py

@@ -19,4 +19,5 @@ from .artificial import (
     RecipeDataset,
     constant_dataset,
     default_synthetic,
+    generate_sf2,
 )

pts/dataset/artificial.py

@@ -1,9 +1,11 @@
+import os
 import math
 import random
 from typing import Callable, List, NamedTuple, Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd
+import rapidjson as json
 
 from .common import (
     MetaData,
@@ -785,3 +787,34 @@ def constant_dataset() -> Tuple[DatasetInfo, Dataset, Dataset]:
     )
 
     return info, train_ds, test_ds
+
+
+def generate_sf2(
+    filename: str, time_series: List, is_missing: bool, num_missing: int
+) -> None:
+    #  This function generates the test and train json files which will be converted to csv format
+    if not os.path.exists(os.path.dirname(filename)):
+        os.makedirs(os.path.dirname(filename))
+    with open(filename, "w") as json_file:
+        for ts in time_series:
+            if is_missing:
+                target = []  # type: List
+                # For Forecast don't output feat_static_cat and feat_static_real
+                for j, val in enumerate(ts[FieldName.TARGET]):
+                    # only add ones that are not missing
+                    if j != 0 and j % num_missing == 0:
+                        target.append(None)
+                    else:
+                        target.append(val)
+                ts[FieldName.TARGET] = target
+            ts.pop(FieldName.FEAT_STATIC_CAT, None)
+            ts.pop(FieldName.FEAT_STATIC_REAL, None)
+            # Chop features in training set
+            if FieldName.FEAT_DYNAMIC_REAL in ts.keys() and "train" in filename:
+                # TODO: Fix for missing values
+                for i, feat_dynamic_real in enumerate(ts[FieldName.FEAT_DYNAMIC_REAL]):
+                    ts[FieldName.FEAT_DYNAMIC_REAL][i] = feat_dynamic_real[
+                        : len(ts[FieldName.TARGET])
+                    ]
+            json.dump(ts, json_file)
+            json_file.write("\n")

pts/dataset/repository/_artificial.py

@@ -0,0 +1,48 @@
+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
+
+# Standard library imports
+import json
+from pathlib import Path
+
+# First-party imports
+from pts.dataset import ArtificialDataset, generate_sf2, serialize_data_entry
+
+
+def generate_artificial_dataset(dataset_path: Path, dataset: ArtificialDataset) -> None:
+    dataset_path_train = dataset_path / "train"
+    dataset_path_test = dataset_path / "test"
+
+    dataset_path.mkdir(exist_ok=True)
+    dataset_path_train.mkdir(exist_ok=False)
+    dataset_path_test.mkdir(exist_ok=False)
+
+    ds = dataset.generate()
+    assert ds.test is not None
+
+    with (dataset_path / "metadata.json").open("w") as fp:
+        json.dump(ds.metadata.dict(), fp, indent=2, sort_keys=True)
+
+    generate_sf2(
+        filename=str(dataset_path_train / "train.json"),
+        time_series=list(map(serialize_data_entry, ds.train)),
+        is_missing=False,
+        num_missing=0,
+    )
+
+    generate_sf2(
+        filename=str(dataset_path_test / "test.json"),
+        time_series=list(map(serialize_data_entry, ds.test)),
+        is_missing=False,
+        num_missing=0,
+    )

pts/dataset/repository/datasets.py

@@ -18,6 +18,7 @@ from pathlib import Path
 
 from pts.dataset import ConstantDataset, TrainDatasets, load_datasets
 
+from ._artificial import generate_artificial_dataset
 from ._lstnet import generate_lstnet_dataset
 from ._m4 import generate_m4_dataset
 from ._gp_copula_2019 import generate_gp_copula_dataset
@@ -30,6 +31,8 @@ prediction_length = 48
 
 dataset_recipes = OrderedDict(
     {
+        # each recipe generates a dataset given a path
+        "constant": partial(generate_artificial_dataset, dataset=ConstantDataset()),
         "exchange_rate": partial(generate_lstnet_dataset, dataset_name="exchange_rate"),
         "solar-energy": partial(generate_lstnet_dataset, dataset_name="solar-energy"),
         "electricity": partial(generate_lstnet_dataset, dataset_name="electricity"),