Datasets:
File size: 16,541 Bytes
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "501d0d55-8d15-463d-95cb-1f70d72de7fb",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2023-12-30 12:56:11-- https://raw.githubusercontent.com/askplatypus/wikidata-simplequestions/master/annotated_wd_data_train_answerable.txt\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1193868 (1,1M) [text/plain]\n",
"Saving to: ‘annotated_wd_data_train_answerable.txt’\n",
"\n",
"annotated_wd_data_t 100%[===================>] 1,14M 6,45MB/s in 0,2s \n",
"\n",
"2023-12-30 12:56:11 (6,45 MB/s) - ‘annotated_wd_data_train_answerable.txt’ saved [1193868/1193868]\n",
"\n",
"--2023-12-30 12:56:11-- https://raw.githubusercontent.com/askplatypus/wikidata-simplequestions/master/annotated_wd_data_valid_answerable.txt\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 173187 (169K) [text/plain]\n",
"Saving to: ‘annotated_wd_data_valid_answerable.txt’\n",
"\n",
"annotated_wd_data_v 100%[===================>] 169,13K --.-KB/s in 0,09s \n",
"\n",
"2023-12-30 12:56:12 (1,86 MB/s) - ‘annotated_wd_data_valid_answerable.txt’ saved [173187/173187]\n",
"\n",
"--2023-12-30 12:56:12-- https://raw.githubusercontent.com/askplatypus/wikidata-simplequestions/master/annotated_wd_data_test_answerable.txt\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.110.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 345052 (337K) [text/plain]\n",
"Saving to: ‘annotated_wd_data_test_answerable.txt’\n",
"\n",
"annotated_wd_data_t 100%[===================>] 336,96K --.-KB/s in 0,1s \n",
"\n",
"2023-12-30 12:56:12 (2,70 MB/s) - ‘annotated_wd_data_test_answerable.txt’ saved [345052/345052]\n",
"\n"
]
}
],
"source": [
"!wget -nc https://raw.githubusercontent.com/askplatypus/wikidata-simplequestions/master/annotated_wd_data_train_answerable.txt\n",
"!wget -nc https://raw.githubusercontent.com/askplatypus/wikidata-simplequestions/master/annotated_wd_data_valid_answerable.txt\n",
"!wget -nc https://raw.githubusercontent.com/askplatypus/wikidata-simplequestions/master/annotated_wd_data_test_answerable.txt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "73af4417-0637-4848-9d35-e734a685ebc4",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/salnikov/.cache/pypoetry/virtualenvs/kgqa-signatures-J3ZJKtLx-py3.10/lib/python3.10/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"
]
}
],
"source": [
"import pandas as pd\n",
"import datasets \n",
"import numpy as np\n",
"import random\n",
"import logging\n",
"\n",
"np.random.seed(8)\n",
"random.seed(8)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6ffea0dd-3d28-416c-b7c3-c8dd73d5e304",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['subject', 'property', 'object', 'question'],\n",
" num_rows: 19481\n",
" })\n",
" valid: Dataset({\n",
" features: ['subject', 'property', 'object', 'question'],\n",
" num_rows: 2821\n",
" })\n",
" test: Dataset({\n",
" features: ['subject', 'property', 'object', 'question'],\n",
" num_rows: 5622\n",
" })\n",
"})"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset = datasets.DatasetDict()\n",
"for split, data_path in [\n",
" (\"train\", \"annotated_wd_data_train_answerable.txt\"),\n",
" (\"valid\", \"annotated_wd_data_valid_answerable.txt\"),\n",
" (\"test\", \"annotated_wd_data_test_answerable.txt\"),\n",
"]:\n",
" df = pd.read_csv(data_path, names=['subject', 'property', 'object', 'question'], sep='\\t')\n",
" dataset[split] = datasets.Dataset.from_pandas(df)\n",
"\n",
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f7e71241",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import os.path\n",
"import pickle\n",
"import warnings\n",
"\n",
"from joblib import register_store_backend, numpy_pickle\n",
"from joblib._store_backends import FileSystemStoreBackend, CacheWarning\n",
"\n",
"\n",
"class FileSystemStoreBackendNoNumpy(FileSystemStoreBackend):\n",
" NAME = \"no_numpy\"\n",
"\n",
" def load_item(self, path, verbose=1, msg=None):\n",
" \"\"\"Load an item from the store given its path as a list of\n",
" strings.\"\"\"\n",
" full_path = os.path.join(self.location, *path)\n",
"\n",
" if verbose > 1:\n",
" if verbose < 10:\n",
" print('{0}...'.format(msg))\n",
" else:\n",
" print('{0} from {1}'.format(msg, full_path))\n",
"\n",
" mmap_mode = (None if not hasattr(self, 'mmap_mode')\n",
" else self.mmap_mode)\n",
"\n",
" filename = os.path.join(full_path, 'output.pkl')\n",
" if not self._item_exists(filename):\n",
" raise KeyError(\"Non-existing item (may have been \"\n",
" \"cleared).\\nFile %s does not exist\" % filename)\n",
"\n",
" # file-like object cannot be used when mmap_mode is set\n",
" if mmap_mode is None:\n",
" with self._open_item(filename, \"rb\") as f:\n",
" item = pickle.load(f)\n",
" else:\n",
" item = numpy_pickle.load(filename, mmap_mode=mmap_mode)\n",
" return item\n",
"\n",
" def dump_item(self, path, item, verbose=1):\n",
" \"\"\"Dump an item in the store at the path given as a list of\n",
" strings.\"\"\"\n",
" try:\n",
" item_path = os.path.join(self.location, *path)\n",
" if not self._item_exists(item_path):\n",
" self.create_location(item_path)\n",
" filename = os.path.join(item_path, 'output.pkl')\n",
" if verbose > 10:\n",
" print('Persisting in %s' % item_path)\n",
"\n",
" def write_func(to_write, dest_filename):\n",
" mmap_mode = (None if not hasattr(self, 'mmap_mode')\n",
" else self.mmap_mode)\n",
" with self._open_item(dest_filename, \"wb\") as f:\n",
" try:\n",
" if mmap_mode is None:\n",
" pickle.dump(to_write, f)\n",
" else:\n",
" numpy_pickle.dump(to_write, f, compress=self.compress)\n",
" except pickle.PicklingError as e:\n",
" # TODO(1.5) turn into error\n",
" warnings.warn(\n",
" \"Unable to cache to disk: failed to pickle \"\n",
" \"output. In version 1.5 this will raise an \"\n",
" f\"exception. Exception: {e}.\",\n",
" FutureWarning\n",
" )\n",
"\n",
" self._concurrency_safe_write(item, filename, write_func)\n",
" except Exception as e: # noqa: E722\n",
" warnings.warn(\n",
" \"Unable to cache to disk. Possibly a race condition in the \"\n",
" f\"creation of the directory. Exception: {e}.\",\n",
" CacheWarning\n",
" )\n",
"\n",
"\n",
"register_store_backend(FileSystemStoreBackendNoNumpy.NAME, FileSystemStoreBackendNoNumpy)\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "41eb3bbd-19bb-4c0d-892c-35478eabc00b",
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"from http.client import RemoteDisconnected\n",
"\n",
"import requests\n",
"from joblib import Memory\n",
"from urllib3.exceptions import ProtocolError\n",
"\n",
"\n",
"SPARQL_API_URL = \"http://127.0.0.1:7001\"\n",
"CACHE_DIRECTORY = \"wikidata/cache\"\n",
"\n",
"logger = logging.getLogger()\n",
"memory = Memory(CACHE_DIRECTORY, verbose=0, backend=FileSystemStoreBackendNoNumpy.NAME)\n",
"\n",
"\n",
"def execute_wiki_request_with_delays(api_url, params, headers):\n",
" response = requests.get(\n",
" api_url,\n",
" params=params,\n",
" headers=headers,\n",
" )\n",
" to_sleep = 0.2\n",
" while response.status_code == 429:\n",
" logger.warning(\n",
" {\n",
" \"msg\": f\"Request to wikidata endpoint failed. Retry.\",\n",
" \"params\": params,\n",
" \"endpoint\": api_url,\n",
" \"response\": {\n",
" \"status_code\": response.status_code,\n",
" \"headers\": dict(response.headers),\n",
" },\n",
" \"retry_after\": to_sleep,\n",
" }\n",
" )\n",
" if \"retry-after\" in response.headers:\n",
" to_sleep += int(response.headers[\"retry-after\"])\n",
" to_sleep += 0.5\n",
" time.sleep(to_sleep)\n",
" response = requests.get(\n",
" api_url,\n",
" params=params,\n",
" headers=headers,\n",
" )\n",
"\n",
" return response\n",
"\n",
"\n",
"@memory.cache(ignore=['api_url'])\n",
"def execute_sparql_request(request: str, api_url: str = SPARQL_API_URL):\n",
" params = {\"format\": \"json\", \"query\": request}\n",
" headers = {\n",
" \"Accept\": \"application/sparql-results+json\",\n",
" \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36\",\n",
" }\n",
" logger.info(\n",
" {\n",
" \"msg\": \"Send request to Wikidata\",\n",
" \"params\": params,\n",
" \"endpoint\": api_url,\n",
" \"request\": request\n",
" }\n",
" )\n",
" try:\n",
" response = execute_wiki_request_with_delays(api_url, params, headers)\n",
" except (ProtocolError, RemoteDisconnected, requests.exceptions.ConnectionError) as e:\n",
" logger.error(\n",
" {\n",
" \"msg\": str(e),\n",
" \"request\": request,\n",
" \"endpoint\": api_url,\n",
" }\n",
" )\n",
" return None\n",
"\n",
" try:\n",
" response = response.json()[\"results\"][\"bindings\"]\n",
" logger.debug(\n",
" {\n",
" \"msg\": \"Received response from Wikidata\",\n",
" \"params\": params,\n",
" \"endpoint\": api_url,\n",
" \"request\": request,\n",
" \"response\": response\n",
" }\n",
" )\n",
" return response\n",
" except Exception as e:\n",
" logger.error(\n",
" {\n",
" \"msg\": str(e),\n",
" \"params\": params,\n",
" \"endpoint\": api_url,\n",
" \"response\": {\n",
" \"status_code\": response.status_code,\n",
" \"headers\": dict(response.headers),\n",
" },\n",
" }\n",
" )\n",
" raise e\n",
"\n",
"def get_label(entity_id):\n",
" query = \"\"\"\n",
" PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> \n",
" PREFIX wd: <http://www.wikidata.org/entity/> \n",
" SELECT DISTINCT ?label\n",
" WHERE {\n",
" wd:<ENTITY> rdfs:label ?label\n",
" } \n",
" \"\"\".replace(\n",
" \"<ENTITY>\", entity_id\n",
" )\n",
" \n",
" for lbl_obj in execute_sparql_request(query):\n",
" if lbl_obj['label']['xml:lang'] == 'en':\n",
" return lbl_obj['label']['value']"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "2568fa09",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Map: 0%| | 0/19481 [00:00<?, ? examples/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Map: 100%|██████████| 19481/19481 [01:44<00:00, 185.89 examples/s]\n",
"Map: 100%|██████████| 2821/2821 [00:08<00:00, 334.13 examples/s]\n",
"Map: 100%|██████████| 5622/5622 [00:15<00:00, 352.59 examples/s]\n"
]
}
],
"source": [
"dataset = dataset.map(\n",
" lambda record: {'object_label': get_label(record['object'])}\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "222d50c1",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Creating parquet from Arrow format: 100%|██████████| 20/20 [00:00<00:00, 474.06ba/s]\n",
"Uploading the dataset shards: 100%|██████████| 1/1 [00:02<00:00, 2.07s/it]\n",
"Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00, 921.29ba/s]\n",
"Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00, 1.63s/it]\n",
"Creating parquet from Arrow format: 100%|██████████| 6/6 [00:00<00:00, 1000.75ba/s]\n",
"Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00, 1.73s/it]\n"
]
},
{
"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/datasets/s-nlp/sqwd/commit/680b8199969fc0389fc96feb4f3b8be15b2674d0', commit_message='Upload dataset', commit_description='', oid='680b8199969fc0389fc96feb4f3b8be15b2674d0', pr_url=None, pr_revision=None, pr_num=None)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.push_to_hub('s-nlp/sqwd', 'answerable', set_default=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2652ed4d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|