Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10M - 100M
ArXiv:
License:
File size: 8,618 Bytes
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{
"cells": [
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 7440/7440 [02:42<00:00, 45.75it/s] \n"
]
}
],
"source": [
"import json\n",
"import os\n",
"from tqdm import tqdm\n",
"from pathlib import Path\n",
"\n",
"\n",
"split = \"train\" # \"test\", \"eval\"\n",
"\n",
"def load_json_files(filepath):\n",
" with open(filepath, 'r') as f:\n",
" data = json.load(f)\n",
" return data\n",
"\n",
"\n",
"folders = [os.path.join('dataset', split, f) for f in os.listdir('dataset/'+ split )]\n",
"data = []\n",
"for folderpath in tqdm(folders):\n",
" if not os.path.isdir(folderpath):\n",
" continue\n",
" files = [os.path.join(folderpath, f) for f in os.listdir(folderpath)]\n",
" for filepath in files:\n",
" if filepath.endswith('.json'):\n",
" \n",
" data.append({\"id\": str(Path(filepath).stem), **load_json_files(filepath)})\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 624022/624022 [00:11<00:00, 53122.61it/s]\n"
]
}
],
"source": [
"\n",
"results = []\n",
"for item in tqdm(data):\n",
" # Focal class\n",
" focal_class = \"class\" + \" \" + item[\"focal_class\"][\"identifier\"]\n",
"\n",
" if item[\"focal_class\"][\"superclass\"]:\n",
" focal_class += \" \" + item[\"focal_class\"][\"superclass\"]\n",
"\n",
" if item[\"focal_class\"][\"interfaces\"]:\n",
" focal_class += \" \" + item[\"focal_class\"][\"interfaces\"]\n",
"\n",
" focal_class += \" {\"\n",
"\n",
" indent = item[\"focal_method\"][\"body\"].split(\"\\n\")[-1][:-1]\n",
"\n",
" # Focal method\n",
" focal_method = indent + item[\"focal_method\"][\"body\"]\n",
"\n",
" # Constructors\n",
" constructors = []\n",
" for method in item[\"focal_class\"][\"methods\"]:\n",
" if method[\"constructor\"]:\n",
" constructor = indent + method[\"full_signature\"] + \";\"\n",
" constructors.append(constructor)\n",
"\n",
" # Methods\n",
" methods = []\n",
" for method in item[\"focal_class\"][\"methods\"]:\n",
" if item[\"focal_method\"][\"full_signature\"] == method[\"full_signature\"]:\n",
" continue\n",
" #if method[\"testcase\"]:\n",
" # continue\n",
"\n",
" if not method[\"constructor\"]:\n",
" method_code = indent + method[\"full_signature\"] + \";\"\n",
" methods.append(method_code)\n",
"\n",
" # Fields\n",
" fields = []\n",
" for field in item[\"focal_class\"][\"fields\"]:\n",
" field_code = indent\n",
" field_code += field[\"modifier\"] + \" \" + field[\"type\"] + \" \" + field[\"var_name\"] + \";\"\n",
" fields.append(field_code)\n",
"\n",
"\n",
" # TEST\n",
" # Test class\n",
" test_class = \"class\" + \" \" + item[\"test_class\"][\"identifier\"]\n",
"\n",
" if item[\"test_class\"][\"superclass\"]:\n",
" test_class += \" \" + item[\"focal_class\"][\"superclass\"]\n",
"\n",
" if item[\"test_class\"][\"interfaces\"]:\n",
" test_class += \" \" + item[\"focal_class\"][\"interfaces\"]\n",
"\n",
" test_class += \" {\"\n",
"\n",
" indent = item[\"test_case\"][\"body\"].split(\"\\n\")[-1][:-1]\n",
" # Test case\n",
" fields = []\n",
" for field in item[\"test_class\"][\"fields\"]:\n",
" field_code = indent\n",
" field_code += field[\"modifier\"] + \" \" + field[\"type\"] + \" \" + field[\"var_name\"] + \";\"\n",
" fields.append(field_code)\n",
" test_case = indent + item[\"test_case\"][\"body\"]\n",
"\n",
" d = {\n",
" 'id': item['id'],\n",
" 't': test_case,\n",
" 't_tc': \"\\n\\n\".join(filter(None, [test_class, \"\\n\".join(fields), test_case, \"}\"])),\n",
" 'fm': focal_method,\n",
" 'fm_fc': \"\\n\\n\".join(filter(None, [focal_class, focal_method, \"}\"])),\n",
" 'fm_fc_c': \"\\n\\n\".join(filter(None, [focal_class, focal_method, \"\\n\".join(constructors), \"}\"])),\n",
" 'fm_fc_c_m': \"\\n\\n\".join(filter(None, [focal_class, focal_method, \"\\n\".join(constructors), \"\\n\".join(methods), \"}\"])),\n",
" 'fm_fc_c_m_f': \"\\n\\n\".join(filter(None, [focal_class, focal_method, \"\\n\".join(constructors), \"\\n\".join(methods), \"\\n\".join(fields), \"}\"])),\n",
" }\n",
" results.append(d)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# sort by id\n",
"results_sorted = sorted(results, key=lambda k: int(k['id']))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"from datasets import Dataset\n",
"train_dataset = Dataset.from_list(results_sorted)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"from datasets import DatasetDict\n",
"\n",
"dataset_dict = DatasetDict({'train': train_dataset, 'test': test_dataset, 'validation': eval_dataset})"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Creating parquet from Arrow format: 100%|██████████| 57/57 [00:00<00:00, 134.38ba/s]\n",
"Creating parquet from Arrow format: 100%|██████████| 57/57 [00:00<00:00, 143.75ba/s]\n",
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"Creating parquet from Arrow format: 100%|██████████| 57/57 [00:00<00:00, 145.44ba/s]\n",
"Uploading the dataset shards: 100%|██████████| 11/11 [01:26<00:00, 7.90s/it]\n",
"Creating parquet from Arrow format: 100%|██████████| 40/40 [00:00<00:00, 155.49ba/s]\n",
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"Uploading the dataset shards: 100%|██████████| 2/2 [00:11<00:00, 5.58s/it]\n",
"Creating parquet from Arrow format: 100%|██████████| 40/40 [00:00<00:00, 145.22ba/s]\n",
"Creating parquet from Arrow format: 100%|██████████| 40/40 [00:00<00:00, 139.08ba/s]\n",
"Uploading the dataset shards: 100%|██████████| 2/2 [00:12<00:00, 6.28s/it]\n",
"README.md: 100%|██████████| 21.0/21.0 [00:00<00:00, 8.60kB/s]\n"
]
}
],
"source": [
"dataset_dict.push_to_hub('andstor/methods2test')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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