add files

.gitattributes

@@ -53,3 +53,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+plasticc_train_augmented_4.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_train_augmented_5.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_train_augmented_6.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_train_augmented_8.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_train_augmented_2.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_train_augmented_1.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_train_augmented_3.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_train_augmented_7.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_train_augmented_9.jsonl filter=lfs diff=lfs merge=lfs -text
+train.jsonl filter=lfs diff=lfs merge=lfs -text
+val.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_train_augmented_0.jsonl filter=lfs diff=lfs merge=lfs -text

plasticc_train_augmented_0.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:00927ace4c4c221e9f88cff6b897cc0b81f8fb5db954c4ff4510fc738a34c08d
+size 19034864

plasticc_train_augmented_1.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d068dcba34ab478f25987e95c69f643019b38c9bc1056ad79f3e75f9802d10c6
+size 860899559

plasticc_train_augmented_2.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de29275cac4393eadf6cdcb03fbf340a9a7de5f009a73ed23630c2a8e7bc2d28
+size 828566728

plasticc_train_augmented_3.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6578db232c69171d8bc63ad03079c948ebcf6c95331f4f57a053c41042c32a6e
+size 760126858

plasticc_train_augmented_4.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3837873be6ce76d81f2dd3cfb3c6dd24f3c611062d97af731fa30e196d1d7747
+size 754818230

plasticc_train_augmented_5.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06eb167b47e2903e7df2b739a04f8cd31e3f7c59696ee044075bdc8774cee864
+size 764785764

plasticc_train_augmented_6.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e7ba3baea3691d92171226b655eed03067e036d2ccb0526d4f6dab4c6859c1a
+size 759301257

plasticc_train_augmented_7.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:422e10308af0aeb34c654378c6cfc0b90272ab30fd3e498291d98376cf1626ea
+size 757834113

plasticc_train_augmented_8.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb02dfad3698c6f23ae49fa6db3e429c181fb4fea43e87183280183a8477c477
+size 761242767

plasticc_train_augmented_9.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:637b692a6d1a7e218d0e57690f445b9f970e5ceca86f47699273ace12a6a835d
+size 747819889

train_augmented_dataset.py

@@ -0,0 +1,138 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+import jsonlines
+import pandas as pd
+from pathlib import Path
+from connect_later.split_dataset_into_files import split_augmented_jsonl_dataset
+from connect_later.constants import PLASTICC_CLASS_MAPPING, INT_LABELS
+
+import datasets
+import pdb
+
+RAW_DATA_PATH = "/pscratch/sd/h/helenqu/plasticc/raw"
+DATASET_PATH = "/pscratch/sd/h/helenqu/plasticc/train_augmented_dataset"
+ORIG_DATASET_PATH = "/pscratch/sd/h/helenqu/plasticc/raw_train_with_labels"
+
+# PLASTICC_CLASS_MAPPING = {
+#     90: "SNIa",
+#     67: "SNIa-91bg",
+#     52: "SNIax",
+#     42: "SNII",
+#     62: "SNIbc",
+#     95: "SLSN-I",
+#     15: "TDE",
+#     64: "KN",
+#     88: "AGN",
+#     92: "RRL",
+#     65: "M-dwarf",
+#     16: "EB",
+#     53: "Mira",
+#     6: "$\mu$-Lens-Single",
+# }
+# INT_LABELS = sorted(PLASTICC_CLASS_MAPPING.keys())
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        # if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        features = datasets.Features(
+            {
+                "objid": datasets.Value("string"),
+                "times_wv": datasets.Array2D(shape=(300, 2), dtype='float64'), # ith row is [time, central wv of band]
+                "target": datasets.Array2D(shape=(300, 2), dtype='float64'), # the time series data, ith row is [flux, flux_err]
+                "label": datasets.ClassLabel(
+                    num_classes=len(PLASTICC_CLASS_MAPPING),
+                    names=[PLASTICC_CLASS_MAPPING[int_label] for int_label in INT_LABELS]
+                ),
+                "redshift": datasets.Value("float32"),
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+        )
+
+    def _split_generators(self, dl_manager):
+        dataset_path = Path(DATASET_PATH)
+        if not (dataset_path / 'train.jsonl').exists():
+            print('Splitting dataset into files...')
+            split_augmented_jsonl_dataset(DATASET_PATH, Path(ORIG_DATASET_PATH) / "plasticc_train_lightcurves.csv.jsonl", "*.jsonl", 0.8)
+        print(f"int index to label mapping: {INT_LABELS}")
+        print(f"label to class name mapping: {PLASTICC_CLASS_MAPPING}")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": dataset_path / "train.jsonl",
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": dataset_path / "val.jsonl",
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        metadata = pd.read_csv(Path(RAW_DATA_PATH) / 'plasticc_train_metadata.csv.gz')
+
+        with jsonlines.open(filepath) as reader:
+            for obj in reader:
+                objid = int(obj['object_id'].split('_')[1]) if type(obj['object_id']) == str else obj['object_id'] # avocado objids are of the form 'plasticc_id{_aug_hash}'
+                metadata_obj = metadata[metadata['object_id'] == objid]
+                label = list(INT_LABELS).index(metadata_obj.true_target.values[0])
+                redshift = metadata_obj.true_z.values[0]
+                yield obj['object_id'], {
+                    "objid": obj['object_id'],
+                    "times_wv": obj['times_wv'],
+                    "target": obj['lightcurve'],
+                    "label": label,
+                    "redshift": redshift
+                }