check

boiler_plate_check_functions.ipynb

@@ -0,0 +1,330 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "\"\"\"This script's purpose is to re-define the datset loading functions to better suit this specific\n",
+    "reddit posts dataset.\"\"\"\n",
+    "\n",
+    "import csv\n",
+    "import json\n",
+    "import os"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "import csv\n",
+    "import json\n",
+    "import os"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    ""
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "c:\\Users\\ericr\\miniconda3\\envs\\sta663C\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "ds = load_dataset('rotten_tomatoes')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Downloading data: 100%|██████████| 699k/699k [00:00<00:00, 2.88MB/s]\n",
+      "Downloading data: 100%|██████████| 90.0k/90.0k [00:00<00:00, 90.7kB/s]\n",
+      "Downloading data: 100%|██████████| 92.2k/92.2k [00:00<00:00, 1.56MB/s]\n",
+      "Generating train split: 100%|██████████| 8530/8530 [00:00<00:00, 448827.83 examples/s]\n",
+      "Generating validation split: 100%|██████████| 1066/1066 [00:00<00:00, 94627.05 examples/s]\n",
+      "Generating test split: 100%|██████████| 1066/1066 [00:00<00:00, 127328.15 examples/s]\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "ds"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['text', 'label'],\n",
+       "        num_rows: 8530\n",
+       "    })\n",
+       "    validation: Dataset({\n",
+       "        features: ['text', 'label'],\n",
+       "        num_rows: 1066\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['text', 'label'],\n",
+       "        num_rows: 1066\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "ds.citation"
+   ],
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "AttributeError",
+     "evalue": "'DatasetDict' object has no attribute 'citation'",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m ds\u001b[39m.\u001b[39;49mcitation\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'DatasetDict' object has no attribute 'citation'"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "ds.citation()"
+   ],
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "AttributeError",
+     "evalue": "'DatasetDict' object has no attribute 'citation'",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m ds\u001b[39m.\u001b[39;49mcitation()\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'DatasetDict' object has no attribute 'citation'"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "ds.column_names"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "{'train': ['text', 'label'],\n",
+       " 'validation': ['text', 'label'],\n",
+       " 'test': ['text', 'label']}"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "ds['train']"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Dataset({\n",
+       "    features: ['text', 'label'],\n",
+       "    num_rows: 8530\n",
+       "})"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "ds.license"
+   ],
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "AttributeError",
+     "evalue": "'DatasetDict' object has no attribute 'license'",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[9], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m ds\u001b[39m.\u001b[39;49mlicense\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'DatasetDict' object has no attribute 'license'"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "ds.dataset_size"
+   ],
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "AttributeError",
+     "evalue": "'DatasetDict' object has no attribute 'dataset_size'",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m ds\u001b[39m.\u001b[39;49mdataset_size\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'DatasetDict' object has no attribute 'dataset_size'"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "ds._info"
+   ],
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "AttributeError",
+     "evalue": "'DatasetDict' object has no attribute '_info'",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[11], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m ds\u001b[39m.\u001b[39;49m_info\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'DatasetDict' object has no attribute '_info'"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "ds['train']._info"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "DatasetInfo(description=\"Movie Review Dataset.\\nThis is a dataset of containing 5,331 positive and 5,331 negative processed\\nsentences from Rotten Tomatoes movie reviews. This data was first used in Bo\\nPang and Lillian Lee, ``Seeing stars: Exploiting class relationships for\\nsentiment categorization with respect to rating scales.'', Proceedings of the\\nACL, 2005.\\n\", citation='@InProceedings{Pang+Lee:05a,\\n  author =       {Bo Pang and Lillian Lee},\\n  title =        {Seeing stars: Exploiting class relationships for sentiment\\n                  categorization with respect to rating scales},\\n  booktitle =    {Proceedings of the ACL},\\n  year =         2005\\n}\\n', homepage='http://www.cs.cornell.edu/people/pabo/movie-review-data/', license='', features={'text': Value(dtype='string', id=None), 'label': ClassLabel(names=['neg', 'pos'], id=None)}, post_processed=None, supervised_keys=SupervisedKeysData(input='', output=''), task_templates=[TextClassification(task='text-classification', text_column='text', label_column='label')], builder_name='parquet', dataset_name='rotten_tomatoes', config_name='default', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=1075873, num_examples=8530, shard_lengths=None, dataset_name='rotten_tomatoes'), 'validation': SplitInfo(name='validation', num_bytes=134809, num_examples=1066, shard_lengths=None, dataset_name='rotten_tomatoes'), 'test': SplitInfo(name='test', num_bytes=136102, num_examples=1066, shard_lengths=None, dataset_name='rotten_tomatoes')}, download_checksums={'hf://datasets/rotten_tomatoes@cab0f883b39cfb510c34e41db874679b3e2bafa3/default/train/0000.parquet': {'num_bytes': 698845, 'checksum': None}, 'hf://datasets/rotten_tomatoes@cab0f883b39cfb510c34e41db874679b3e2bafa3/default/validation/0000.parquet': {'num_bytes': 90001, 'checksum': None}, 'hf://datasets/rotten_tomatoes@cab0f883b39cfb510c34e41db874679b3e2bafa3/default/test/0000.parquet': {'num_bytes': 92206, 'checksum': None}}, download_size=881052, post_processing_size=None, dataset_size=1346784, size_in_bytes=2227836)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "print(ds['train']._info)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "DatasetInfo(description=\"Movie Review Dataset.\\nThis is a dataset of containing 5,331 positive and 5,331 negative processed\\nsentences from Rotten Tomatoes movie reviews. This data was first used in Bo\\nPang and Lillian Lee, ``Seeing stars: Exploiting class relationships for\\nsentiment categorization with respect to rating scales.'', Proceedings of the\\nACL, 2005.\\n\", citation='@InProceedings{Pang+Lee:05a,\\n  author =       {Bo Pang and Lillian Lee},\\n  title =        {Seeing stars: Exploiting class relationships for sentiment\\n                  categorization with respect to rating scales},\\n  booktitle =    {Proceedings of the ACL},\\n  year =         2005\\n}\\n', homepage='http://www.cs.cornell.edu/people/pabo/movie-review-data/', license='', features={'text': Value(dtype='string', id=None), 'label': ClassLabel(names=['neg', 'pos'], id=None)}, post_processed=None, supervised_keys=SupervisedKeysData(input='', output=''), task_templates=[TextClassification(task='text-classification', text_column='text', label_column='label')], builder_name='parquet', dataset_name='rotten_tomatoes', config_name='default', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=1075873, num_examples=8530, shard_lengths=None, dataset_name='rotten_tomatoes'), 'validation': SplitInfo(name='validation', num_bytes=134809, num_examples=1066, shard_lengths=None, dataset_name='rotten_tomatoes'), 'test': SplitInfo(name='test', num_bytes=136102, num_examples=1066, shard_lengths=None, dataset_name='rotten_tomatoes')}, download_checksums={'hf://datasets/rotten_tomatoes@cab0f883b39cfb510c34e41db874679b3e2bafa3/default/train/0000.parquet': {'num_bytes': 698845, 'checksum': None}, 'hf://datasets/rotten_tomatoes@cab0f883b39cfb510c34e41db874679b3e2bafa3/default/validation/0000.parquet': {'num_bytes': 90001, 'checksum': None}, 'hf://datasets/rotten_tomatoes@cab0f883b39cfb510c34e41db874679b3e2bafa3/default/test/0000.parquet': {'num_bytes': 92206, 'checksum': None}}, download_size=881052, post_processing_size=None, dataset_size=1346784, size_in_bytes=2227836)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "source": [
+    "print(ds['train']._info.description)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Movie Review Dataset.\n",
+      "This is a dataset of containing 5,331 positive and 5,331 negative processed\n",
+      "sentences from Rotten Tomatoes movie reviews. This data was first used in Bo\n",
+      "Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for\n",
+      "sentiment categorization with respect to rating scales.'', Proceedings of the\n",
+      "ACL, 2005.\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  }
+ }
+}

reddit_dataset_loader.py

@@ -47,7 +47,7 @@ which have risen in response to the clashes between traditional gender roles and
 _HOMEPAGE = "https://huggingface.co/datasets/steamcyclone/Pill_Ideologies-Post_Titles"
 
 # TODO: Add the licence for the dataset here if you can find it
-_LICENSE = "Creative Commons"
+_LICENSE = "Creative Commons"  # cc
 
 # TODO: Add link to the official dataset URLs here
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
@@ -94,99 +94,99 @@ class SubRedditPosts(datasets.GeneratorBasedBuilder):
 
     DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
-    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(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option1": datasets.Value("string"),
-                    "answer": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option2": datasets.Value("string"),
-                    "second_domain_answer": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        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
-            # 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=("sentence", "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 = _URLS[self.config.name]
-        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.jsonl"),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev.jsonl"),
-                    "split": "dev",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "test.jsonl"),
-                    "split": "test",
-                },
-            ),
-        ]
-
-    # 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.
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                if self.config.name == "first_domain":
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option1": data["option1"],
-                        "answer": "" if split == "test" else data["answer"],
-                    }
-                else:
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option2": data["option2"],
-                        "second_domain_answer": (
-                            "" if split == "test" else data["second_domain_answer"]
-                        ),
-                    }
+    # 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(
+    #             {
+    #                 "sentence": datasets.Value("string"),
+    #                 "option1": datasets.Value("string"),
+    #                 "answer": datasets.Value("string"),
+    #                 # These are the features of your dataset like images, labels ...
+    #             }
+    #         )
+    #     else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
+    #         features = datasets.Features(
+    #             {
+    #                 "sentence": datasets.Value("string"),
+    #                 "option2": datasets.Value("string"),
+    #                 "second_domain_answer": datasets.Value("string"),
+    #                 # These are the features of your dataset like images, labels ...
+    #             }
+    #         )
+    #     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
+    #         # 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=("sentence", "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 = _URLS[self.config.name]
+    #     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.jsonl"),
+    #                 "split": "train",
+    #             },
+    #         ),
+    #         datasets.SplitGenerator(
+    #             name=datasets.Split.VALIDATION,
+    #             # These kwargs will be passed to _generate_examples
+    #             gen_kwargs={
+    #                 "filepath": os.path.join(data_dir, "dev.jsonl"),
+    #                 "split": "dev",
+    #             },
+    #         ),
+    #         datasets.SplitGenerator(
+    #             name=datasets.Split.TEST,
+    #             # These kwargs will be passed to _generate_examples
+    #             gen_kwargs={
+    #                 "filepath": os.path.join(data_dir, "test.jsonl"),
+    #                 "split": "test",
+    #             },
+    #         ),
+    #     ]
+
+    # # 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.
+    #     with open(filepath, encoding="utf-8") as f:
+    #         for key, row in enumerate(f):
+    #             data = json.loads(row)
+    #             if self.config.name == "first_domain":
+    #                 # Yields examples as (key, example) tuples
+    #                 yield key, {
+    #                     "sentence": data["sentence"],
+    #                     "option1": data["option1"],
+    #                     "answer": "" if split == "test" else data["answer"],
+    #                 }
+    #             else:
+    #                 yield key, {
+    #                     "sentence": data["sentence"],
+    #                     "option2": data["option2"],
+    #                     "second_domain_answer": (
+    #                         "" if split == "test" else data["second_domain_answer"]
+    #                     ),
+    #                 }