Create difraud.py

difraud.py

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+import csv
+import json
+import os
+import sys
+import datasets
+from datasets.tasks import TextClassification
+
+
+# TODO: Add BibTeX citation
+_CITATION = """
+TODO: Add citation here
+"""
+
+_DESCRIPTION = """
+DIFrauD -- (Domain Independent Fraud Detection) is a corpus of deceptive and truthful texts from 7 domains:
+
+"fake_news",
+"job_scams",
+"phishing",
+"political_statements",
+"product_reviews",
+"sms",
+"twitter_rumours"
+
+To load a specific domain, pass it as the "name" parameter to load_dataset()
+"""
+
+class DIFrauD(datasets.GeneratorBasedBuilder):
+    """Domain Independent Fraud Detection benchmarks -- a Large multi-domain english corpus of truthful and deceptive texts"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="fake_news", description="Fake News domain"),
+        datasets.BuilderConfig(name="job_scams", description="Online Job Scams"),
+        datasets.BuilderConfig(name="phishing", description="Email phishing attacks"),
+        datasets.BuilderConfig(name="political_statements", description="Statements by various politicians"),
+        datasets.BuilderConfig(name="product_reviews", description="Amazon product reviews"),
+        datasets.BuilderConfig(name="sms", description="SMS spam and phishing attacks"),
+        datasets.BuilderConfig(name="twitter_rumours", 
+                               description="Collection of rumours from twitter spanning several years and topics"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "phishing"
+
+    def _info(self):
+        self.features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.ClassLabel(num_classes=2, names=['non-deceptive', 'deceptive']),
+                }
+        )
+        return datasets.DatasetInfo(
+            config_name=self.config.name,
+            # 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=self.features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            supervised_keys=("text", "label"),
+            # specify standard binary classification task for datasets to setup easier
+            task_templates=[TextClassification(text_column="text", label_column="label")],
+            # Homepage of the dataset for documentation
+            # homepage=_HOMEPAGE,
+            # License for the dataset if available
+            # license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = {
+            "train": self.config.name+"/train.jsonl",
+            "test": self.config.name+"/test.jsonl",
+            "validation": self.config.name+"/validation.jsonl",
+        }
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir['train']),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir['validation']),
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir['test']),
+                    "split": "test"
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # 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.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                yield key, {
+                    "text": data["text"],
+                    "label": int(data["label"]),
+                }
+