Upload build_dataset.py

build_dataset.py

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+# 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.
+
+
+import json
+import os
+
+import datasets
+
+
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {Ember2018},
+author=Christian Williams
+},
+year={2023}
+}
+"""
+
+_DESCRIPTION = """\
+This dataset is from the EMBER 2018 Malware Analysis dataset
+"""
+_HOMEPAGE = "https://github.com/elastic/ember"
+_LICENSE = ""
+_URLS = {
+    "text_classification": "https://huggingface.co/datasets/cw1521/ember2018-malware/blob/main/data/"
+}
+
+
+class EMBERConfig(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.1.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="text_classification", 
+            version=VERSION, description="This part of my dataset covers text classification"
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = "text_classification" 
+
+    def _info(self):
+        if self.config.name == "text_classification": 
+            features = datasets.Features(
+                {
+                    "input": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+                    "x": datasets.features.Sequence(
+                            datasets.Value("float32")
+                    ),
+                    "y": datasets.Value("float32"),
+                    "appeared": datasets.Value("string"),
+                    "avclass": datasets.Value("string"),
+                    "subset": datasets.Value("string"),
+                    "sha256": datasets.Value("string")
+                }
+            )
+        else: 
+            features = datasets.Features(
+                {
+                    "input": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+                    "x": datasets.features.Sequence(
+                            datasets.Value("float32")
+                    ),
+                    "y": datasets.Value("float32"),
+                    "appeared": datasets.Value("string"),
+                    "avclass": datasets.Value("string"),
+                    "subset": datasets.Value("string"),
+                    "sha256": datasets.Value("string")
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features, 
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+        
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": os.path.join(data_dir, "ember2018_train_*.jsonl"),
+                    "split": "train",
+                },
+            ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.VALIDATION,
+            #     gen_kwargs={
+            #         "filepaths": os.path.join(data_dir, "*_valid_*.jsonl"),
+            #         "split": "valid",
+            #     },
+            # ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepaths": os.path.join(data_dir, "ember2018_test_*.jsonl"),
+                    "split": "test"
+                },
+            )
+        ]
+
+
+    def _generate_examples(self, filepaths, split):
+        key = 0
+        for id, filepath in enumerate(filepaths[split]): 
+            with open(filepath[id], encoding="utf-8") as f:
+                data_list = json.load(f)
+                for data in data_list:
+                    key += 1
+                    if self.config.name == "text_classification":
+                        yield key, {
+                            "input": data["input"],
+                            "label": data["label"],
+                            "x": data["x"],
+                            "y": data["y"],
+                            "appeared": data["appeared"],
+                            "avclass": data["avclass"],
+                            "subset": data["subset"],
+                            "sha256": data["sha256"]
+                        }
+                    else:
+                        yield key, {
+                            "input": data["input"],
+                            "label": data["label"],
+                            "x": data["x"],
+                            "y": data["y"],
+                            "appeared": data["appeared"],
+                            "avclass": data["avclass"],
+                            "subset": data["subset"],
+                            "sha256": data["sha256"]
+                        }