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   "source": [
    "# Simple example: data engineering\n",
    "\n",
    "Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best-practices. In short, we train a domain classifier on the [semantic scholar dataset](https://api.semanticscholar.org/corpus) by taking full advantage of `great-ai`. Subsequently, we create a production-ready deployment.\n",
    "\n",
    "![position of this step in the lifecycle](/media/scope-data.svg)\n",
    "> The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook.\n",
    "\n",
    "Since the true scope of `great-ai` is the phase between proof-of-concept code and production-ready service, it is predominantly used in the [deployment notebook](/examples/simple/deploy). Feel free to skip there, or continue reading if you'd like to see the full picture.\n",
    "\n",
    "### Extract\n",
    "\n",
    "This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data.\n",
    "\n",
    "In this example, we download the semantic scholar dataset from a public S3 bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_CHUNK_COUNT = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Processing 4 out of the 6002 available chunks'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import urllib.request\n",
    "from random import shuffle\n",
    "\n",
    "manifest = (\n",
    "    urllib.request.urlopen(\n",
    "        \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt\"\n",
    "    )\n",
    "    .read()\n",
    "    .decode()\n",
    ")  # a list of available chunks separated by '\\n' characters\n",
    "\n",
    "lines = manifest.split()\n",
    "shuffle(lines)\n",
    "chunks = lines[:MAX_CHUNK_COUNT]\n",
    "\n",
    "f\"Processing {len(chunks)} out of the {len(manifest.split())} available chunks\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform\n",
    "\n",
    "- Filter out non-English abstracts using `great_ai.utilities.predict_language`\n",
    "- Project it to only keep the necessary components (text and labels), clean the textual content using `great_ai.utilities.clean`\n",
    "- We will speed up processing using `great_ai.utilities.simple_parallel_map`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4/4 [04:42<00:00, 70.62s/it] \n"
     ]
    }
   ],
   "source": [
    "from typing import List, Tuple\n",
    "import json\n",
    "import gzip\n",
    "from great_ai.utilities import (\n",
    "    simple_parallel_map,\n",
    "    clean,\n",
    "    is_english,\n",
    "    predict_language,\n",
    "    unchunk,\n",
    ")\n",
    "\n",
    "\n",
    "def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]:\n",
    "    # Extract\n",
    "    response = urllib.request.urlopen(\n",
    "        f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/{chunk_key}\"\n",
    "    )  # a gzipped JSON Lines file\n",
    "\n",
    "    decompressed = gzip.decompress(response.read())\n",
    "    decoded = decompressed.decode()\n",
    "    chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line]\n",
    "\n",
    "    # Transform\n",
    "    return [\n",
    "        (\n",
    "            clean(\n",
    "                f'{c[\"title\"]} {c[\"paperAbstract\"]} {c[\"journalName\"]} {c[\"venue\"]}',\n",
    "                convert_to_ascii=True,\n",
    "            ),  # The text is cleaned to remove PDF extraction, web scraping, and other common artifacts\n",
    "            c[\"fieldsOfStudy\"],\n",
    "        )  # Create pairs of `(text, [...domains])`\n",
    "        for c in chunk\n",
    "        if c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))\n",
    "    ]\n",
    "\n",
    "\n",
    "preprocessed_data = unchunk(\n",
    "    simple_parallel_map(preprocess_chunk, chunks, concurrency=4)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = zip(*preprocessed_data)  # X is the input, y is the expected output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load\n",
    "\n",
    "Upload the dataset (or a part of it) to a central repository using `great_ai.add_ground_truth`. This step automatically tags each datapoint with a split label according to the ratios we set. Additional tags can be also given.\n",
    "\n",
    "#### Production-ready backend\n",
    "\n",
    "The MongoDB driver is automatically configured if  `mongo.ini` exists with the following scheme:\n",
    "\n",
    "```ini\n",
    "mongo_connection_string=mongodb://localhost:27017/\n",
    "mongo_database=my_great_ai_db\n",
    "```\n",
    "> You can install MongoDB from [here](https://www.mongodb.com/docs/manual/installation) or [use it as a service](https://www.mongodb.com/cloud/atlas/register)\n",
    "\n",
    "Otherwise, TinyDB is used which is just a local JSON file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from great_ai import add_ground_truth\n",
    "\n",
    "add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next: [Part 2](/examples/simple/train)"
   ]
  }
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