Fine_tune_llama3_dora_extra10.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "nAMzy_0FtaUZ"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    LlamaForCausalLM,\n",
        "    LlamaTokenizer,\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    DataCollatorForLanguageModeling,\n",
        "    BitsAndBytesConfig,\n",
        "    HfArgumentParser,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from peft import LoraConfig, PeftModel\n",
        "from trl import SFTTrainer\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "print(torch.cuda.is_available())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CUY0B7esGGJU",
        "outputId": "c461231d-5073-41e3-bdaa-89c8aa9053c5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mdpopovvelasco\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "env: WANDB_PROJECT=sql-fine-tuning\n"
          ]
        }
      ],
      "source": [
        "import wandb # do from scipt later\n",
        "wandb.login()\n",
        "%env WANDB_PROJECT=sql-fine-tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "0fURKlxksPza"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dU9R34gZpnvj",
        "outputId": "1ed8db9b-a585-4d40-eedd-0fdcecfc474e"
      },
      "outputs": [],
      "source": [
        "output_dir = './sql_output_dir_llama3'\n",
        "logging_dir = './sql_logging_dir_llama3'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "PldOXgbdEukX"
      },
      "outputs": [],
      "source": [
        "import sys,gc,traceback\n",
        "import torch\n",
        "def clean_ipython_hist():\n",
        "    # Code in this function mainly copied from IPython source\n",
        "    if not 'get_ipython' in globals(): return\n",
        "    ip = get_ipython()\n",
        "    user_ns = ip.user_ns\n",
        "    ip.displayhook.flush()\n",
        "    pc = ip.displayhook.prompt_count + 1\n",
        "    for n in range(1, pc): user_ns.pop('_i'+repr(n),None)\n",
        "    user_ns.update(dict(_i='',_ii='',_iii=''))\n",
        "    hm = ip.history_manager\n",
        "    hm.input_hist_parsed[:] = [''] * pc\n",
        "    hm.input_hist_raw[:] = [''] * pc\n",
        "    hm._i = hm._ii = hm._iii = hm._i00 =  ''\n",
        "\n",
        "\n",
        "\n",
        "def clean_tb():\n",
        "    # h/t Piotr Czapla\n",
        "    if hasattr(sys, 'last_traceback'):\n",
        "        traceback.clear_frames(sys.last_traceback)\n",
        "        delattr(sys, 'last_traceback')\n",
        "    if hasattr(sys, 'last_type'): delattr(sys, 'last_type')\n",
        "    if hasattr(sys, 'last_value'): delattr(sys, 'last_value')\n",
        "\n",
        "def clean_mem():\n",
        "    clean_tb()\n",
        "    clean_ipython_hist()\n",
        "    gc.collect()\n",
        "    torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "6gVO4k2tE1IQ"
      },
      "outputs": [],
      "source": [
        "clean_mem()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gMLWysPxrXd-"
      },
      "source": [
        "## Load and Prepare the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<|begin▁of▁sentence|> Question: What is the total sales amount? Context: The sales table contains information about each sale including date, amount, and customer ID. Answer: SELECT SUM(amount) FROM sales; <|end▁of▁sentence|>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer\n",
        "from datasets import load_dataset\n",
        "\n",
        "def format_prompt(sample):\n",
        "    sample['text'] = f\"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
        "\n",
        "You are a helpful assistant who is a SQL expert. Your task is to write a SQL query based on the given context and question.<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
        "\n",
        "Context: {sample['context']}\n",
        "Question: {sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>SQL Query:{sample['answer']} <|end▁of▁sentence|>\"\"\"\n",
        "    return {\"text\":sample['text']}\n",
        "  \n",
        "\n",
        "# Load and format the data using the template TinyLLama is using\n",
        "dataset = (\n",
        "    load_dataset(\"dpv/exoplanets-sql2\")#, split=\"train\")\n",
        "      .shuffle(seed=42)\n",
        ")\n",
        "dataset = dataset.map(format_prompt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['context', 'question', 'answer', 'results', 'text'],\n",
              "        num_rows: 60\n",
              "    })\n",
              "    valid: Dataset({\n",
              "        features: ['context', 'question', 'answer', 'results', 'text'],\n",
              "        num_rows: 10\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nYou are a helpful assistant who is a SQL expert. Your task is to write a SQL query based on the given context and question.<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nContext: CREATE TABLE exoplanets (\\n    name TEXT,\\n    distance REAL,\\n    stellar_magnitude REAL,\\n    planet_type TEXT,\\n    discovery_year INTEGER,\\n    mass_multiplier REAL,\\n    mass_wrt TEXT,\\n    radius_multiplier REAL,\\n    radius_wrt TEXT,\\n    orbital_radius REAL,\\n    orbital_period REAL,\\n    eccentricity REAL,\\n    detection_method TEXT\\n); CREATE TABLE reference_planets (name TEXT, mass REAL);  'mass_wrt' in exoplanets table has a one-to-one match to 'name' in reference_planets table, and  'name' refers to either Earth or Jupyter, with Jupyter having 317.8 the mass of Earth.\\nQuestion: Identify the discovery year with the highest average eccentricity of discovered exoplanets.<|eot_id|><|start_header_id|>assistant<|end_header_id|>SQL Query:SELECT discovery_year, AVG(eccentricity) AS avg_eccentricity\\nFROM exoplanets\\nGROUP BY discovery_year\\nORDER BY avg_eccentricity DESC\\nLIMIT 1; <|end▁of▁sentence|>\""
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset['train']['text'][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Evaluate the original model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Loading checkpoint shards: 100%|██████████| 2/2 [00:02<00:00,  1.14s/it]\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "MODEL_ID='meta-llama/Llama-3.2-3B-Instruct'\n",
        "model_orig = AutoModelForCausalLM.from_pretrained(MODEL_ID)#.to(\"cuda:1\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "idx:  0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SELECT \n",
            "    discovery_year, \n",
            "    COUNT(*) as num_discoveries\n",
            "FROM \n",
            "    exoplanets\n",
            "GROUP BY \n",
            "    discovery_year\n",
            "ORDER BY \n",
            "    num_discoveries DESC;\n",
            "idx:  1\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SELECT detection_method\n",
            "FROM exoplanets\n",
            "GROUP BY detection_method\n",
            "ORDER BY SUM(mass_wrt) DESC\n",
            "LIMIT 1;\n",
            "idx:  2\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SELECT \n",
            "    discovery_year, \n",
            "    SUM(mass_wrt * mass_multiplier) AS total_mass\n",
            "FROM \n",
            "    exoplanets\n",
            "GROUP BY \n",
            "    discovery_year\n",
            "ORDER BY \n",
            "    total_mass DESC\n",
            "LIMIT 5;\n",
            "idx:  3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SELECT \n",
            "    e.name, \n",
            "    e.orbital_period, \n",
            "    e.discovery_year, \n",
            "    ROW_NUMBER() OVER (PARTITION BY e.discovery_year ORDER BY e.orbital_period DESC) as rank\n",
            "FROM \n",
            "    exoplanets e\n",
            "WHERE \n",
            "    e.name IN (\n",
            "        SELECT \n",
            "            name\n",
            "        FROM \n",
            "            reference_planets\n",
            "        WHERE \n",
            "            name = 'Earth'\n",
            "    )\n",
            "ORDER BY \n",
            "    rank;\n",
            "idx:  4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SELECT * \n",
            "FROM exoplanets \n",
            "WHERE discovery_year < 2000 \n",
            "AND eccentricity < 0.2;\n",
            "idx:  5\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SELECT DISTINCT planet_type \n",
            "FROM exoplanets \n",
            "WHERE discovery_year < 2020;\n",
            "idx:  6\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SELECT \n",
            "    e.name, \n",
            "    e.mass_wrt * r.mass AS mass\n",
            "FROM \n",
            "    exoplanets e\n",
            "JOIN \n",
            "    reference_planets r ON e.mass_wrt = r.name\n",
            "WHERE \n",
            "    e.discovery_year = (SELECT MIN(discovery_year) FROM exoplanets)\n",
            "ORDER BY \n",
            "    e.discovery_year;\n",
            "idx:  7\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SELECT e.name, e.discovery_year, e.detection_method\n",
            "FROM exoplanets e\n",
            "WHERE e.discovery_year >= (CURRENT_YEAR - 2)\n",
            "AND e.name NOT IN (\n",
            "  SELECT r.name\n",
            "  FROM reference_planets r\n",
            "  WHERE r.mass_wrt = 'Jupiter'\n",
            ");\n",
            "no such column: CURRENT_YEAR\n",
            "idx:  8\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SELECT \n",
            "    planet_type, \n",
            "    AVG(orbital_radius) AS average_orbital_radius\n",
            "FROM \n",
            "    exoplanets\n",
            "GROUP BY \n",
            "    planet_type\n",
            "ORDER BY \n",
            "    average_orbital_radius DESC;\n",
            "idx:  9\n",
            "SELECT \n",
            "    e.name, \n",
            "    e.mass_wrt / e.radius_wrt AS mass_to_radius_ratio\n",
            "FROM \n",
            "    exoplanets e\n",
            "WHERE \n",
            "    e.planet_type = 'Terrestrial' AND \n",
            "    e.mass_wrt > 0 AND \n",
            "    e.radius_wrt > 0\n",
            "ORDER BY \n",
            "    e.mass_wrt / e.radius_wrt DESC\n",
            "LIMIT 5;\n",
            "num_opp_errors: 1, num_other_exceptions:0,\n",
            "Error rate: 10.0\n"
          ]
        }
      ],
      "source": [
        "import sqlite3\n",
        "import json\n",
        "import pandas as pd\n",
        "import re\n",
        "pd.set_option('display.max_colwidth',1000)\n",
        "conn = sqlite3.connect('./databases/exoplanets_db.db')\n",
        "cursor = conn.cursor()\n",
        "cursor.execute(f\"SELECT * FROM exoplanets LIMIT 5;\")\n",
        "def extract_sql_statement(text):\n",
        "    # Regular expression to match the SQL statement\n",
        "    #pattern = re.compile(r'SQL Query:(.*?;)', re.DOTALL)\n",
        "    pattern = re.compile(r'SQL Query:\\s*```sql\\s*(SELECT.*?|WITH.*?)\\s*```', re.DOTALL | re.IGNORECASE)\n",
        "\n",
        "#    pattern = re.compile(r'SELECT.*?;', re.DOTALL)\n",
        "    match = pattern.search(text)\n",
        "    if match:\n",
        "        return match.group(1).strip()\n",
        "    return None\n",
        "\n",
        "def run_query(query, cursor):\n",
        "    cursor.execute(query)\n",
        "    rows = cursor.fetchall()\n",
        "    columns = [desc[0] for desc in cursor.description]\n",
        "    result_dict = [dict(zip(columns, row)) for row in rows]\n",
        "    return json.dumps(result_dict)\n",
        "def compare_results(query_results, expected_results):\n",
        "    \"\"\"Compare the query results with the expected results.\"\"\"\n",
        "    # Sort the results for comparison\n",
        "    query_results_sorted = sorted(query_results, key=lambda x: json.dumps(x, sort_keys=True))\n",
        "    expected_results_sorted = sorted(expected_results, key=lambda x: json.dumps(x, sort_keys=True))\n",
        "    return query_results_sorted == expected_results_sorted\n",
        "\n",
        "valid_loaded = load_dataset('json', data_files='./databases/valid.json', split='train')\n",
        "\n",
        "def generate_sql(context, question, max_new_tokens = 256, debug=False):\n",
        "    full_question = f\"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
        "\n",
        "You are a helpful assistant who is a SQL expert. Your task is to write a SQL query based on the given context and question.<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
        "\n",
        "Context: {context}\n",
        "Question: {question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>SQL Query:\"\"\"\n",
        "    model_input = tokenizer(full_question, return_tensors=\"pt\")#.to(\"cuda:1\")\n",
        "    model_orig.eval()\n",
        "    with torch.no_grad():\n",
        "        out = tokenizer.decode(model_orig.generate(**model_input,max_new_tokens=max_new_tokens)[0],skip_special_tokens=True)\n",
        "    #print('out: ',out)\n",
        "    #import sys; sys.exit()\n",
        "    #res = out[out.find(\"\"\"```sql```\"\"\")+3:out.find(\"\"\";\\n```\"\"\")]\n",
        "    #res = out[out.find('SQL Query:')+10:]\n",
        "    res = extract_sql_statement(out)\n",
        "    if debug: print(res)\n",
        "    run_query(res, cursor)\n",
        "    #return out\n",
        "    return res\n",
        "num_opp_errors = 0\n",
        "num_other_exceptions = 0\n",
        "op_errors = []\n",
        "exceptions  = []\n",
        "generations = []\n",
        "bad_idxs = []\n",
        "result_comparisons = [False]*len(valid_loaded)\n",
        "for idx,example in enumerate(valid_loaded):\n",
        "    generation = ''\n",
        "    try:\n",
        "        print('idx: ',idx)\n",
        "        generation=generate_sql(context=example['context'], question=example['question'],debug=True)\n",
        "        #print(generation); break\n",
        "    except sqlite3.OperationalError as op_err:\n",
        "        num_opp_errors += 1\n",
        "        op_errors.append((idx,op_err))\n",
        "        bad_idxs.append(idx)\n",
        "        print(op_err)\n",
        "    except Exception as e:\n",
        "        num_other_exceptions +=1\n",
        "        exceptions.append((idx,e))\n",
        "        bad_idxs.append(idx)\n",
        "        print(e)\n",
        "    else:\n",
        "        result_comparisons[idx]=compare_results(run_query(generation,cursor), example['results'])\n",
        "    finally:\n",
        "        generations.append(generation)\n",
        "print(f\"num_opp_errors: {num_opp_errors}, num_other_exceptions:{num_other_exceptions},\\nError rate: {(num_other_exceptions+num_opp_errors)*100/len(valid_loaded)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "([(7, sqlite3.OperationalError('no such column: CURRENT_YEAR'))],\n",
              " [],\n",
              " [False, False, False, False, False, False, False, False, False, False])"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "op_errors, exceptions, result_comparisons"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- OK, 80% execution rate out of the box.  Looks like query 7 used improper SQLite syntax -> consider fine tuning on umesh16071973/SQLite_Training_Dataset first to align syntax.  Still not getting correct answers, it seems, must dig deeper."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['SELECT \\n    discovery_year, \\n    COUNT(*) as num_discoveries\\nFROM \\n    exoplanets\\nGROUP BY \\n    discovery_year\\nORDER BY \\n    num_discoveries DESC;',\n",
              " 'SELECT detection_method\\nFROM exoplanets\\nGROUP BY detection_method\\nORDER BY SUM(mass_wrt) DESC\\nLIMIT 1;',\n",
              " 'SELECT \\n    discovery_year, \\n    SUM(mass_wrt * mass_multiplier) AS total_mass\\nFROM \\n    exoplanets\\nGROUP BY \\n    discovery_year\\nORDER BY \\n    total_mass DESC\\nLIMIT 5;',\n",
              " \"SELECT \\n    e.name, \\n    e.orbital_period, \\n    e.discovery_year, \\n    ROW_NUMBER() OVER (PARTITION BY e.discovery_year ORDER BY e.orbital_period DESC) as rank\\nFROM \\n    exoplanets e\\nWHERE \\n    e.name IN (\\n        SELECT \\n            name\\n        FROM \\n            reference_planets\\n        WHERE \\n            name = 'Earth'\\n    )\\nORDER BY \\n    rank;\",\n",
              " 'SELECT * \\nFROM exoplanets \\nWHERE discovery_year < 2000 \\nAND eccentricity < 0.2;',\n",
              " 'SELECT DISTINCT planet_type \\nFROM exoplanets \\nWHERE discovery_year < 2020;',\n",
              " 'SELECT \\n    e.name, \\n    e.mass_wrt * r.mass AS mass\\nFROM \\n    exoplanets e\\nJOIN \\n    reference_planets r ON e.mass_wrt = r.name\\nWHERE \\n    e.discovery_year = (SELECT MIN(discovery_year) FROM exoplanets)\\nORDER BY \\n    e.discovery_year;',\n",
              " '',\n",
              " 'SELECT \\n    planet_type, \\n    AVG(orbital_radius) AS average_orbital_radius\\nFROM \\n    exoplanets\\nGROUP BY \\n    planet_type\\nORDER BY \\n    average_orbital_radius DESC;',\n",
              " \"SELECT \\n    e.name, \\n    e.mass_wrt / e.radius_wrt AS mass_to_radius_ratio\\nFROM \\n    exoplanets e\\nWHERE \\n    e.planet_type = 'Terrestrial' AND \\n    e.mass_wrt > 0 AND \\n    e.radius_wrt > 0\\nORDER BY \\n    e.mass_wrt / e.radius_wrt DESC\\nLIMIT 5;\"]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "generations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['SELECT discovery_year, COUNT(*) AS count\\nFROM exoplanets\\nGROUP BY discovery_year\\nORDER BY count DESC\\nLIMIT 1;',\n",
              " 'SELECT detection_method,\\n       SUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\\nFROM exoplanets\\nGROUP BY detection_method\\nORDER BY total_mass DESC\\nLIMIT 1;',\n",
              " 'SELECT discovery_year, \\nSUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\\nFROM exoplanets\\nWHERE mass_multiplier IS NOT NULL\\nGROUP BY discovery_year\\nORDER BY total_mass DESC\\nLIMIT 5;',\n",
              " 'SELECT discovery_year, name, orbital_period FROM exoplanets WHERE (discovery_year, name, orbital_period) IN (   SELECT discovery_year, name, orbital_period    FROM exoplanets    ORDER BY discovery_year, orbital_period DESC) GROUP BY discovery_year LIMIT 3;',\n",
              " 'SELECT name, discovery_year, eccentricity\\nFROM exoplanets\\nWHERE discovery_year < 2000 AND eccentricity < 0.2;',\n",
              " 'SELECT DISTINCT \\n    planet_type\\nFROM \\n    exoplanets\\nWHERE \\n    planet_type NOT IN (\\n        SELECT DISTINCT \\n            planet_type\\n        FROM \\n            exoplanets\\n        WHERE \\n            discovery_year >= 2020\\n    );',\n",
              " 'WITH MinMassExoplanets AS (\\n    SELECT \\n        discovery_year,\\n        MIN(mass_multiplier * r.mass) AS min_mass\\n    FROM exoplanets e\\n    JOIN reference_planets r ON e.mass_wrt = r.name\\n    GROUP BY discovery_year\\n)\\nSELECT \\n    e.discovery_year,\\n    e.name,\\n    e.mass_multiplier * r.mass AS exoplanet_mass\\nFROM exoplanets e\\nJOIN reference_planets r ON e.mass_wrt = r.name\\nJOIN MinMassExoplanets m ON e.discovery_year = m.discovery_year\\nWHERE e.mass_multiplier * r.mass = m.min_mass;',\n",
              " \"SELECT name, discovery_year, detection_method\\nFROM exoplanets\\nWHERE discovery_year >= strftime('%Y', 'now') - 2\\nORDER BY discovery_year DESC;\",\n",
              " 'SELECT \\n    e.planet_type,\\n    AVG(e.orbital_radius) AS avg_orbital_radius\\nFROM exoplanets e\\nGROUP BY e.planet_type\\nORDER BY avg_orbital_radius DESC;  ',\n",
              " 'SELECT name, \\n       (mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) / radius_multiplier AS mass_radius_ratio\\nFROM exoplanets\\nORDER BY mass_radius_ratio DESC\\nLIMIT 5;']"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "valid_loaded['answer']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('[{\"name\": \"EPIC 201497682 b\", \"mass_to_radius_ratio\": null}, {\"name\": \"EPIC 201757695.02\", \"mass_to_radius_ratio\": null}, {\"name\": \"EPIC 201833600 c\", \"mass_to_radius_ratio\": null}, {\"name\": \"EPIC 206215704 b\", \"mass_to_radius_ratio\": null}, {\"name\": \"EPIC 206317286 b\", \"mass_to_radius_ratio\": null}]',\n",
              " '[{\"name\": \"HD 100546 b\", \"mass_radius_ratio\": 2.068544927536232e+29}, {\"name\": \"K2-52 b\", \"mass_radius_ratio\": 1.7018181818181818e+29}, {\"name\": \"Kepler-718 b\", \"mass_radius_ratio\": 1.683398781313473e+29}, {\"name\": \"PH2 b\", \"mass_radius_ratio\": 1.6815060908084162e+29}, {\"name\": \"Kepler-488 b\", \"mass_radius_ratio\": 1.670347764371895e+29}]')"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "run_query(generations[-1],cursor), valid_loaded['results'][-1]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'Find the exoplanets with the highest mass-to-radius ratio, showing their name and calculated ratio (return top 5).'"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "valid_loaded['question'][-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "compare_results(run_query(generations[-1],cursor), valid_loaded['results'][-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('[{\"discovery_year\": 2016, \"num_discoveries\": 1517}, {\"discovery_year\": 2014, \"num_discoveries\": 875}, {\"discovery_year\": 2021, \"num_discoveries\": 525}, {\"discovery_year\": 2022, \"num_discoveries\": 338}, {\"discovery_year\": 2018, \"num_discoveries\": 326}, {\"discovery_year\": 2020, \"num_discoveries\": 234}, {\"discovery_year\": 2019, \"num_discoveries\": 203}, {\"discovery_year\": 2015, \"num_discoveries\": 157}, {\"discovery_year\": 2017, \"num_discoveries\": 153}, {\"discovery_year\": 2012, \"num_discoveries\": 138}, {\"discovery_year\": 2011, \"num_discoveries\": 138}, {\"discovery_year\": 2013, \"num_discoveries\": 126}, {\"discovery_year\": 2010, \"num_discoveries\": 97}, {\"discovery_year\": 2009, \"num_discoveries\": 94}, {\"discovery_year\": 2008, \"num_discoveries\": 65}, {\"discovery_year\": 2007, \"num_discoveries\": 52}, {\"discovery_year\": 2005, \"num_discoveries\": 36}, {\"discovery_year\": 2006, \"num_discoveries\": 31}, {\"discovery_year\": 2002, \"num_discoveries\": 29}, {\"discovery_year\": 2004, \"num_discoveries\": 27}, {\"discovery_year\": 2003, \"num_discoveries\": 22}, {\"discovery_year\": 2000, \"num_discoveries\": 16}, {\"discovery_year\": 1999, \"num_discoveries\": 13}, {\"discovery_year\": 2001, \"num_discoveries\": 12}, {\"discovery_year\": 2023, \"num_discoveries\": 9}, {\"discovery_year\": 1998, \"num_discoveries\": 6}, {\"discovery_year\": 1996, \"num_discoveries\": 6}, {\"discovery_year\": 1992, \"num_discoveries\": 2}, {\"discovery_year\": 1997, \"num_discoveries\": 1}, {\"discovery_year\": 1995, \"num_discoveries\": 1}, {\"discovery_year\": 1994, \"num_discoveries\": 1}]',\n",
              " '[{\"discovery_year\": 2016, \"count\": 1517}]')"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "run_query(generations[0],cursor), valid_loaded['results'][0]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'Determine the number of exoplanets discovered each year, and show the year with the highest number of discoveries.'"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "valid_loaded['question'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "compare_results(run_query(generations[0],cursor), valid_loaded['results'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[7, 8]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bad_idxs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [],
      "source": [
        "for idx in bad_idxs:\n",
        "    print(run_query(generations[idx],cursor))\n",
        "    print(valid_loaded[idx]['results'])\n",
        "    print('_'*100)\n",
        "#generations[2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [],
      "source": [
        "#run_query(\"\"\"SELECT name, mass, discovery_year FROM (SELECT name, mass, discovery_year, ROW_NUMBER() OVER (PARTITION BY discovery_year ORDER BY mass ASC) as rn FROM exoplanets) t WHERE rn = 1;\"\"\",cursor)\n",
        "run_query(\"\"\"SELECT \n",
        "    p.planet_type,\n",
        "    AVG(exoplanets.orbital_radius) AS avg_orbital_radius\n",
        "FROM \n",
        "    exoplanets\n",
        "JOIN \n",
        "    reference_planets r ON exoplanets.mass_wrt = r.name\n",
        "GROUP BY \n",
        "    exoplanets.planet_type\n",
        "ORDER BY \n",
        "    avg_orbital_radius DESC;\"\"\",cursor)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHMMGlMSrx7y"
      },
      "source": [
        "## Configure Model, Data, Tokenizer, Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "m4yd_XJI4dQh"
      },
      "outputs": [],
      "source": [
        "clean_mem()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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          "output_type": "stream",
          "text": [
            "Loading checkpoint shards: 100%|██████████| 2/2 [00:02<00:00,  1.09s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
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            "base_model.model.model.layers.26.self_attn.v_proj.lora_magnitude_vector.default.weight: torch.float32\n",
            "base_model.model.model.layers.26.self_attn.o_proj.lora_A.default.weight: torch.float32\n",
            "base_model.model.model.layers.26.self_attn.o_proj.lora_B.default.weight: torch.float32\n",
            "base_model.model.model.layers.26.self_attn.o_proj.lora_magnitude_vector.default.weight: torch.float32\n",
            "base_model.model.model.layers.26.mlp.gate_proj.lora_A.default.weight: torch.float32\n",
            "base_model.model.model.layers.26.mlp.gate_proj.lora_B.default.weight: torch.float32\n",
            "base_model.model.model.layers.26.mlp.gate_proj.lora_magnitude_vector.default.weight: torch.float32\n",
            "base_model.model.model.layers.26.mlp.up_proj.lora_A.default.weight: torch.float32\n",
            "base_model.model.model.layers.26.mlp.up_proj.lora_B.default.weight: torch.float32\n",
            "base_model.model.model.layers.26.mlp.up_proj.lora_magnitude_vector.default.weight: torch.float32\n",
            "base_model.model.model.layers.26.mlp.down_proj.lora_A.default.weight: torch.float32\n",
            "base_model.model.model.layers.26.mlp.down_proj.lora_B.default.weight: torch.float32\n",
            "base_model.model.model.layers.26.mlp.down_proj.lora_magnitude_vector.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.self_attn.q_proj.lora_A.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.self_attn.q_proj.lora_B.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.self_attn.q_proj.lora_magnitude_vector.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.self_attn.k_proj.lora_A.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.self_attn.k_proj.lora_B.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.self_attn.k_proj.lora_magnitude_vector.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.self_attn.v_proj.lora_A.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.self_attn.v_proj.lora_B.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.self_attn.v_proj.lora_magnitude_vector.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.self_attn.o_proj.lora_A.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.self_attn.o_proj.lora_B.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.self_attn.o_proj.lora_magnitude_vector.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.mlp.gate_proj.lora_A.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.mlp.gate_proj.lora_B.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.mlp.gate_proj.lora_magnitude_vector.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.mlp.up_proj.lora_A.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.mlp.up_proj.lora_B.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.mlp.up_proj.lora_magnitude_vector.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.mlp.down_proj.lora_A.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.mlp.down_proj.lora_B.default.weight: torch.float32\n",
            "base_model.model.model.layers.27.mlp.down_proj.lora_magnitude_vector.default.weight: torch.float32\n"
          ]
        }
      ],
      "source": [
        "from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_config,get_peft_model\n",
        "MODEL_ID='meta-llama/Llama-3.2-3B-Instruct'\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_8bit=True,\n",
        ")\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    MODEL_ID,\n",
        "    device_map='auto',\n",
        "    # quantization_config = bnb_config\n",
        ")\n",
        "\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1\n",
        "model.gradient_checkpointing_enable()\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)\n",
        "# tokenizer.pad_token = \"<pad>\"\n",
        "# tokenizer.padding_side = \"left\"\n",
        "# if tokenizer.pad_token is None:\n",
        "#     tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})\n",
        "if tokenizer.pad_token is None:\n",
        "    tokenizer.add_special_tokens({'pad_token': '<pad>'})\n",
        "\n",
        "# Set the padding side\n",
        "tokenizer.padding_side = \"left\"\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=32,\n",
        "    lora_dropout=0.1,\n",
        "    r=128,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        "    target_modules=  [\"k_proj\", \"gate_proj\", \"v_proj\", \"up_proj\", \"q_proj\", \"o_proj\", \"down_proj\"],# Layers to target\n",
        "    use_dora=True\n",
        "   # dtype=\"float32\"                   \t# Keep LoRA parameters in float32 precision\n",
        "\n",
        ")\n",
        "\n",
        "#model = prepare_model_for_kbit_training(model)\n",
        "model = get_peft_model(model, peft_config)\n",
        "# Ensure LoRA weights are in full precision\n",
        "for name, param in model.named_parameters():\n",
        "    if \"lora\" in name or \"dora\" in name:\n",
        "        param.data = param.data.to(torch.float32)\n",
        "\n",
        "# Verify the precision of LoRA weights\n",
        "for name, param in model.named_parameters():\n",
        "    if \"lora\" in name:\n",
        "        print(f\"{name}: {param.dtype}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Pad token: <pad>\n",
            "Pad token ID: 128256\n",
            "Padding side: left\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)\n",
        "\n",
        "# Ensure the tokenizer has a padding token\n",
        "if tokenizer.pad_token is None:\n",
        "    tokenizer.add_special_tokens({'pad_token': '<pad>'})\n",
        "\n",
        "# Set the padding side\n",
        "tokenizer.padding_side = \"left\"\n",
        "\n",
        "# Verify the tokenizer configuration\n",
        "print(f\"Pad token: {tokenizer.pad_token}\")\n",
        "print(f\"Pad token ID: {tokenizer.pad_token_id}\")\n",
        "print(f\"Padding side: {tokenizer.padding_side}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "Changes to your `wandb` environment variables will be ignored because your `wandb` session has already started. For more information on how to modify your settings with `wandb.init()` arguments, please refer to <a href='https://wandb.me/wandb-init' target=\"_blank\">the W&B docs</a>."
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              "<IPython.core.display.HTML object>"
            ]
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          "metadata": {},
          "output_type": "display_data"
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        {
          "data": {
            "text/html": [
              "Tracking run with wandb version 0.18.3"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Run data is saved locally in <code>/home/mainuser/Desktop/sql-finetune/wandb/run-20241011_183132-1qa0k01q</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/dpopovvelasco/sql-fine-tuning/runs/1qa0k01q' target=\"_blank\">./sql_output_dir_llama3</a></strong> to <a href='https://wandb.ai/dpopovvelasco/sql-fine-tuning' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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              " View project at <a href='https://wandb.ai/dpopovvelasco/sql-fine-tuning' target=\"_blank\">https://wandb.ai/dpopovvelasco/sql-fine-tuning</a>"
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              " View run at <a href='https://wandb.ai/dpopovvelasco/sql-fine-tuning/runs/1qa0k01q' target=\"_blank\">https://wandb.ai/dpopovvelasco/sql-fine-tuning/runs/1qa0k01q</a>"
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            "{'loss': 1.5801, 'grad_norm': 0.35512882471084595, 'learning_rate': 0.00019659258262890683, 'epoch': 1.18}\n"
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            "{'loss': 0.0353, 'grad_norm': 0.0818667933344841, 'learning_rate': 1.339745962155613e-05, 'epoch': 11.76}\n"
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            "{'loss': 0.0326, 'grad_norm': 0.11110261082649231, 'learning_rate': 3.40741737109318e-06, 'epoch': 12.94}\n"
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        {
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          "text": [
            "{'loss': 0.0332, 'grad_norm': 0.08531470596790314, 'learning_rate': 0.0, 'epoch': 14.12}\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "100%|██████████| 120/120 [08:09<00:00,  4.08s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'train_runtime': 489.9195, 'train_samples_per_second': 2.051, 'train_steps_per_second': 0.245, 'train_loss': 0.2563798112173875, 'epoch': 14.12}\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=120, training_loss=0.2563798112173875, metrics={'train_runtime': 489.9195, 'train_samples_per_second': 2.051, 'train_steps_per_second': 0.245, 'total_flos': 4379557839765504.0, 'train_loss': 0.2563798112173875, 'epoch': 14.117647058823529})"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import DataCollatorForLanguageModeling\n",
        "# Step 3: Launch training with SFTTrainer\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    per_device_train_batch_size=2,\n",
        "    gradient_accumulation_steps=4,\n",
        "    optim=\"paged_adamw_32bit\",\n",
        "    learning_rate=2e-4,\n",
        "    lr_scheduler_type=\"cosine\",\n",
        "    num_train_epochs = 15.0,\n",
        "    logging_steps=10,\n",
        "    fp16=True,\n",
        "    gradient_checkpointing=True\n",
        ")\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset['train'],\n",
        "    eval_dataset=dataset['valid'],\n",
        "    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=256,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing=True,\n",
        ")\n",
        "\n",
        "trainer.train()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('./sql_output_dir_llama3/extra_dora_ckpt/tokenizer_config.json',\n",
              " './sql_output_dir_llama3/extra_dora_ckpt/special_tokens_map.json',\n",
              " './sql_output_dir_llama3/extra_dora_ckpt/tokenizer.json')"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = model.merge_and_unload()\n",
        "\n",
        "# Save the model\n",
        "model.save_pretrained(os.path.join(output_dir, \"extra_dora_ckpt\"))\n",
        "\n",
        "# Save the tokenizer\n",
        "tokenizer.save_pretrained(os.path.join(output_dir, \"extra_dora_ckpt\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00,  2.18it/s]\n"
          ]
        }
      ],
      "source": [
        "# trainer.model.save_pretrained(output_dir)\n",
        "# from peft import AutoPeftModelForCausalLM\n",
        "# model = AutoPeftModelForCausalLM.from_pretrained(output_dir, device_map=\"auto\", torch_dtype=torch.bfloat16)\n",
        "# model = model.merge_and_unload()\n",
        "\n",
        "# output_merged_dir = os.path.join(output_dir, \"final_merged_checkpoint\")\n",
        "# model.save_pretrained(output_merged_dir, safe_serialization=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "idx:  0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  SELECT discovery_year, COUNT(*) AS num_discoveries\n",
            "FROM exoplanets\n",
            "GROUP BY discovery_year\n",
            "ORDER BY num_discoveries DESC\n",
            "LIMIT 1;\n",
            "SELECT discovery_year, COUNT(*) AS num_discoveries\n",
            "FROM exoplanets\n",
            "GROUP BY discovery_year\n",
            "ORDER BY num_discoveries DESC\n",
            "LIMIT 1;\n",
            "idx:  1\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  SELECT detection_method, \n",
            "       SUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\n",
            "FROM exoplanets\n",
            "GROUP BY detection_method\n",
            "ORDER BY total_mass DESC\n",
            "LIMIT 1;\n",
            "SELECT detection_method, \n",
            "       SUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\n",
            "FROM exoplanets\n",
            "GROUP BY detection_method\n",
            "ORDER BY total_mass DESC\n",
            "LIMIT 1;\n",
            "idx:  2\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  WITH YearlyMass AS (\n",
            "    SELECT discovery_year, \n",
            "           SUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\n",
            "    FROM exoplanets\n",
            "    GROUP BY discovery_year\n",
            ")\n",
            "SELECT discovery_year, total_mass\n",
            "FROM YearlyMass\n",
            "ORDER BY total_mass DESC\n",
            "LIMIT 5;\n",
            "WITH YearlyMass AS (\n",
            "    SELECT discovery_year, \n",
            "           SUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\n",
            "    FROM exoplanets\n",
            "    GROUP BY discovery_year\n",
            ")\n",
            "SELECT discovery_year, total_mass\n",
            "FROM YearlyMass\n",
            "ORDER BY total_mass DESC\n",
            "LIMIT 5;\n",
            "idx:  3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  WITH RankedExoplanets AS (\n",
            "    SELECT \n",
            "        e.name,\n",
            "        e.orbital_period,\n",
            "        RANK() OVER (PARTITION BY e.discovery_year ORDER BY e.orbital_period DESC) AS period_rank\n",
            "    FROM exoplanets e\n",
            ")\n",
            "SELECT * \n",
            "FROM RankedExoplanets\n",
            "WHERE period_rank <= 3;\n",
            "WITH RankedExoplanets AS (\n",
            "    SELECT \n",
            "        e.name,\n",
            "        e.orbital_period,\n",
            "        RANK() OVER (PARTITION BY e.discovery_year ORDER BY e.orbital_period DESC) AS period_rank\n",
            "    FROM exoplanets e\n",
            ")\n",
            "SELECT * \n",
            "FROM RankedExoplanets\n",
            "WHERE period_rank <= 3;\n",
            "idx:  4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  SELECT \n",
            "    e.name,\n",
            "    e.eccentricity,\n",
            "    e.discovery_year\n",
            "FROM exoplanets e\n",
            "WHERE e.eccentricity < 0.2 AND e.discovery_year < 2000;\n",
            "SELECT \n",
            "    e.name,\n",
            "    e.eccentricity,\n",
            "    e.discovery_year\n",
            "FROM exoplanets e\n",
            "WHERE e.eccentricity < 0.2 AND e.discovery_year < 2000;\n",
            "idx:  5\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  WITH DiscoveredYears AS (\n",
            "    SELECT planet_type, MAX(discovery_year) AS last_discovered\n",
            "    FROM exoplanets\n",
            "    GROUP BY planet_type\n",
            ")\n",
            "SELECT planet_type\n",
            "FROM DiscoveredYears\n",
            "WHERE last_discovered < 2020;\n",
            "WITH DiscoveredYears AS (\n",
            "    SELECT planet_type, MAX(discovery_year) AS last_discovered\n",
            "    FROM exoplanets\n",
            "    GROUP BY planet_type\n",
            ")\n",
            "SELECT planet_type\n",
            "FROM DiscoveredYears\n",
            "WHERE last_discovered < 2020;\n",
            "idx:  6\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  WITH MinMassExoplanets AS (\n",
            "    SELECT discovery_year, \n",
            "           MIN(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS min_mass\n",
            "    FROM exoplanets\n",
            "    GROUP BY discovery_year\n",
            ")\n",
            "SELECT e.discovery_year, e.name, e.mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt) AS mass\n",
            "FROM exoplanets e\n",
            "JOIN MinMassExoplanets m ON e.discovery_year = m.discovery_year\n",
            "WHERE e.mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt) = m.min_mass;\n",
            "WITH MinMassExoplanets AS (\n",
            "    SELECT discovery_year, \n",
            "           MIN(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS min_mass\n",
            "    FROM exoplanets\n",
            "    GROUP BY discovery_year\n",
            ")\n",
            "SELECT e.discovery_year, e.name, e.mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt) AS mass\n",
            "FROM exoplanets e\n",
            "JOIN MinMassExoplanets m ON e.discovery_year = m.discovery_year\n",
            "WHERE e.mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt) = m.min_mass;\n",
            "idx:  7\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  SELECT \n",
            "    e.name,\n",
            "    e.discovery_year,\n",
            "    e.detection_method\n",
            "FROM exoplanets e\n",
            "WHERE e.discovery_year >= 2021 AND e.detection_method IS NOT NULL;\n",
            "SELECT \n",
            "    e.name,\n",
            "    e.discovery_year,\n",
            "    e.detection_method\n",
            "FROM exoplanets e\n",
            "WHERE e.discovery_year >= 2021 AND e.detection_method IS NOT NULL;\n",
            "idx:  8\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  SELECT planet_type, AVG(orbital_radius) AS avg_orbital_radius FROM exoplanets GROUP BY planet_type ORDER BY avg_orbital_radius DESC;\n",
            "SELECT planet_type, AVG(orbital_radius) AS avg_orbital_radius FROM exoplanets GROUP BY planet_type ORDER BY avg_orbital_radius DESC;\n",
            "idx:  9\n",
            "EXTRACTED:  SELECT \n",
            "    e.name,\n",
            "    e.mass_multiplier * r.mass / e.radius_multiplier AS mass_to_radius_ratio\n",
            "FROM exoplanets e\n",
            "JOIN reference_planets r ON e.mass_wrt = r.name\n",
            "ORDER BY mass_to_radius_ratio DESC\n",
            "LIMIT 5;\n",
            "SELECT \n",
            "    e.name,\n",
            "    e.mass_multiplier * r.mass / e.radius_multiplier AS mass_to_radius_ratio\n",
            "FROM exoplanets e\n",
            "JOIN reference_planets r ON e.mass_wrt = r.name\n",
            "ORDER BY mass_to_radius_ratio DESC\n",
            "LIMIT 5;\n",
            "num_opp_errors: 0, num_other_exceptions:0,\n",
            "Error rate: 0.0\n"
          ]
        }
      ],
      "source": [
        "import sqlite3\n",
        "import json\n",
        "import pandas as pd\n",
        "import re\n",
        "pd.set_option('display.max_colwidth',1000)\n",
        "conn = sqlite3.connect('./databases/exoplanets_db.db')\n",
        "cursor = conn.cursor()\n",
        "cursor.execute(f\"SELECT * FROM exoplanets LIMIT 5;\")\n",
        "def extract_sql_statement(text):\n",
        "    # Regular expression to match the SQL statement\n",
        "    #pattern = re.compile(r'SELECT.*?;', re.DOTALL)\n",
        "    #pattern = re.compile(r'(WITH.*?SELECT.*?;|SELECT.*?;)', re.DOTALL | re.IGNORECASE)\n",
        "    pattern = re.compile(r'SQL Query:(.*?;)', re.DOTALL)\n",
        "\n",
        "    match = pattern.search(text)\n",
        "    if match:\n",
        "        print('EXTRACTED: ', match.group(1).strip())\n",
        "        return match.group(1).strip()\n",
        "    return None\n",
        "\n",
        "def run_query(query, cursor):\n",
        "    cursor.execute(query)\n",
        "    rows = cursor.fetchall()\n",
        "    columns = [desc[0] for desc in cursor.description]\n",
        "    result_dict = [dict(zip(columns, row)) for row in rows]\n",
        "    return json.dumps(result_dict)\n",
        "def compare_results(query_results, expected_results):\n",
        "    \"\"\"Compare the query results with the expected results.\"\"\"\n",
        "    # Sort the results for comparison\n",
        "    query_results_sorted = sorted(query_results, key=lambda x: json.dumps(x, sort_keys=True))\n",
        "    expected_results_sorted = sorted(expected_results, key=lambda x: json.dumps(x, sort_keys=True))\n",
        "    return query_results_sorted == expected_results_sorted\n",
        "\n",
        "valid_loaded = load_dataset('json', data_files='./databases/valid.json', split='train')\n",
        "\n",
        "def generate_sql(context, question, max_new_tokens = 256, debug=False):\n",
        "    full_question = f\"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
        "\n",
        "You are a helpful assistant who is a SQL expert. Your task is to write a SQL query based on the given context and question.<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
        "\n",
        "Context: {context}\n",
        "Question: {question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>SQL Query:\"\"\"\n",
        "    model_input = tokenizer(full_question, return_tensors=\"pt\")#.to(\"cuda:1\")\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        out = tokenizer.decode(model.generate(**model_input,max_new_tokens=max_new_tokens)[0],skip_special_tokens=True)\n",
        "    #print('out: ',out)\n",
        "    #import sys; sys.exit()\n",
        "    #res = out[out.find(\"\"\"```sql```\"\"\")+3:out.find(\"\"\";\\n```\"\"\")]\n",
        "    #res = out[out.find('SQL Query:')+10:]\n",
        "    #print('out:\\n',out)\n",
        "    res = extract_sql_statement(out)\n",
        "    #if debug: print(res)\n",
        "    run_query(res, cursor)\n",
        "    #return out\n",
        "    return res\n",
        "num_opp_errors = 0\n",
        "num_other_exceptions = 0\n",
        "op_errors = []\n",
        "exceptions  = []\n",
        "generations = []\n",
        "bad_idxs = []\n",
        "result_comparisons = [False]*len(valid_loaded)\n",
        "for idx,example in enumerate(valid_loaded):\n",
        "    generation = '' # OK, had mistake here, query 9 is still wrong due to AS, though!\n",
        "    result = None\n",
        "    try:\n",
        "        print('idx: ',idx)\n",
        "        generation=generate_sql(context=example['context'], question=example['question'],debug=True)\n",
        "        result =  run_query(generation,cursor)\n",
        "        #print(\"generation: \")\n",
        "        print(generation)\n",
        "    except sqlite3.OperationalError as op_err:\n",
        "        num_opp_errors += 1\n",
        "        op_errors.append((idx,op_err))\n",
        "        bad_idxs.append(idx)\n",
        "        print(op_err)\n",
        "    except Exception as e:\n",
        "        num_other_exceptions +=1\n",
        "        exceptions.append((idx,e))\n",
        "        bad_idxs.append(idx)\n",
        "        print(e)\n",
        "    else:\n",
        "        if result: result_comparisons[idx]=compare_results(result, example['results'])\n",
        "    finally:\n",
        "        generations.append(generation)\n",
        "print(f\"num_opp_errors: {num_opp_errors}, num_other_exceptions:{num_other_exceptions},\\nError rate: {(num_other_exceptions+num_opp_errors)*100/len(valid_loaded)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- After 10 epochs of training with QLoRA set with default precision for it, got the following, execution rate down from 80% to 70%, still not getting correct queries.  Trying full precision for adaptors LoRA."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- Same deal with full precision adaptor training with QLoRA."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- Same full precision LoRA (not 'Q', comment out peft_config), get 90% execution rate.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "([], [], [False, True, True, False, True, True, False, False, True, False])"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "op_errors, exceptions,result_comparisons"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- Next steps: Dive into results divergence  + Possibly do preliminary fine tune on SQLite ds (umesh16071973/SQLite_Training_Dataset)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['SELECT discovery_year, COUNT(*) AS count\\nFROM exoplanets\\nGROUP BY discovery_year\\nORDER BY count DESC\\nLIMIT 1;',\n",
              " 'SELECT detection_method,\\n       SUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\\nFROM exoplanets\\nGROUP BY detection_method\\nORDER BY total_mass DESC\\nLIMIT 1;',\n",
              " 'SELECT discovery_year, \\nSUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\\nFROM exoplanets\\nWHERE mass_multiplier IS NOT NULL\\nGROUP BY discovery_year\\nORDER BY total_mass DESC\\nLIMIT 5;',\n",
              " 'SELECT discovery_year, name, orbital_period FROM exoplanets WHERE (discovery_year, name, orbital_period) IN (   SELECT discovery_year, name, orbital_period    FROM exoplanets    ORDER BY discovery_year, orbital_period DESC) GROUP BY discovery_year LIMIT 3;',\n",
              " 'SELECT name, discovery_year, eccentricity\\nFROM exoplanets\\nWHERE discovery_year < 2000 AND eccentricity < 0.2;',\n",
              " 'SELECT DISTINCT \\n    planet_type\\nFROM \\n    exoplanets\\nWHERE \\n    planet_type NOT IN (\\n        SELECT DISTINCT \\n            planet_type\\n        FROM \\n            exoplanets\\n        WHERE \\n            discovery_year >= 2020\\n    );',\n",
              " 'WITH MinMassExoplanets AS (\\n    SELECT \\n        discovery_year,\\n        MIN(mass_multiplier * r.mass) AS min_mass\\n    FROM exoplanets e\\n    JOIN reference_planets r ON e.mass_wrt = r.name\\n    GROUP BY discovery_year\\n)\\nSELECT \\n    e.discovery_year,\\n    e.name,\\n    e.mass_multiplier * r.mass AS exoplanet_mass\\nFROM exoplanets e\\nJOIN reference_planets r ON e.mass_wrt = r.name\\nJOIN MinMassExoplanets m ON e.discovery_year = m.discovery_year\\nWHERE e.mass_multiplier * r.mass = m.min_mass;',\n",
              " \"SELECT name, discovery_year, detection_method\\nFROM exoplanets\\nWHERE discovery_year >= strftime('%Y', 'now') - 2\\nORDER BY discovery_year DESC;\",\n",
              " 'SELECT \\n    e.planet_type,\\n    AVG(e.orbital_radius) AS avg_orbital_radius\\nFROM exoplanets e\\nGROUP BY e.planet_type\\nORDER BY avg_orbital_radius DESC;  ',\n",
              " 'SELECT name, \\n       (mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) / radius_multiplier AS mass_radius_ratio\\nFROM exoplanets\\nORDER BY mass_radius_ratio DESC\\nLIMIT 5;']"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "valid_loaded['answer']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['SELECT discovery_year, COUNT(*) AS num_discoveries\\nFROM exoplanets\\nGROUP BY discovery_year\\nORDER BY num_discoveries DESC\\nLIMIT 1;',\n",
              " 'SELECT detection_method, \\n       SUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\\nFROM exoplanets\\nGROUP BY detection_method\\nORDER BY total_mass DESC\\nLIMIT 1;',\n",
              " 'WITH YearlyMass AS (\\n    SELECT discovery_year, \\n           SUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\\n    FROM exoplanets\\n    GROUP BY discovery_year\\n)\\nSELECT discovery_year, total_mass\\nFROM YearlyMass\\nORDER BY total_mass DESC\\nLIMIT 5;',\n",
              " 'WITH RankedExoplanets AS (\\n    SELECT \\n        e.name,\\n        e.orbital_period,\\n        RANK() OVER (PARTITION BY e.discovery_year ORDER BY e.orbital_period DESC) AS period_rank\\n    FROM exoplanets e\\n)\\nSELECT * \\nFROM RankedExoplanets\\nWHERE period_rank <= 3;',\n",
              " 'SELECT \\n    e.name,\\n    e.eccentricity,\\n    e.discovery_year\\nFROM exoplanets e\\nWHERE e.eccentricity < 0.2 AND e.discovery_year < 2000;',\n",
              " 'WITH DiscoveredYears AS (\\n    SELECT planet_type, MAX(discovery_year) AS last_discovered\\n    FROM exoplanets\\n    GROUP BY planet_type\\n)\\nSELECT planet_type\\nFROM DiscoveredYears\\nWHERE last_discovered < 2020;',\n",
              " 'WITH MinMassExoplanets AS (\\n    SELECT discovery_year, \\n           MIN(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS min_mass\\n    FROM exoplanets\\n    GROUP BY discovery_year\\n)\\nSELECT e.discovery_year, e.name, e.mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt) AS mass\\nFROM exoplanets e\\nJOIN MinMassExoplanets m ON e.discovery_year = m.discovery_year\\nWHERE e.mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt) = m.min_mass;',\n",
              " 'SELECT \\n    e.name,\\n    e.discovery_year,\\n    e.detection_method\\nFROM exoplanets e\\nWHERE e.discovery_year >= 2021 AND e.detection_method IS NOT NULL;',\n",
              " 'SELECT planet_type, AVG(orbital_radius) AS avg_orbital_radius FROM exoplanets GROUP BY planet_type ORDER BY avg_orbital_radius DESC;',\n",
              " 'SELECT \\n    e.name,\\n    e.mass_multiplier * r.mass / e.radius_multiplier AS mass_to_radius_ratio\\nFROM exoplanets e\\nJOIN reference_planets r ON e.mass_wrt = r.name\\nORDER BY mass_to_radius_ratio DESC\\nLIMIT 5;']"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "generations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- All generations are valid!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "valid_result:\n",
            " [{\"discovery_year\": 2016, \"count\": 1517}]\n",
            "gen_result:\n",
            " [{\"discovery_year\": 2016, \"num_discoveries\": 1517}]\n",
            "How are the following two queries different? SELECT discovery_year, COUNT(*) AS num_discoveries\n",
            "FROM exoplanets\n",
            "GROUP BY discovery_year\n",
            "ORDER BY num_discoveries DESC\n",
            "LIMIT 1;, SELECT discovery_year, COUNT(*) AS count\n",
            "FROM exoplanets\n",
            "GROUP BY discovery_year\n",
            "ORDER BY count DESC\n",
            "LIMIT 1; and which one better answers the following question: Determine the number of exoplanets discovered each year, and show the year with the highest number of discoveries.?\n"
          ]
        }
      ],
      "source": [
        "i=0 # Different column naming/approach +1\n",
        "valid_result=valid_loaded['results'][i]\n",
        "print('valid_result:\\n',valid_result)\n",
        "gen_result=run_query(generations[i],cursor)\n",
        "print('gen_result:\\n', gen_result)\n",
        "compare_results(valid_result,gen_result)\n",
        "print(f\"\"\"How are the following two queries different? {generations[i]}, {valid_loaded['answer'][i]} and which one better answers the following question: {valid_loaded['question'][i]}?\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "valid_result:\n",
            " [{\"discovery_year\": 1992, \"name\": \"PSR B1257+12 c\", \"orbital_period\": 0.18206708}, {\"discovery_year\": 1994, \"name\": \"PSR B1257+12 b\", \"orbital_period\": 0.06926762}, {\"discovery_year\": 1995, \"name\": \"51 Pegasi b\", \"orbital_period\": 0.011498973}]\n",
            "gen_result:\n",
            " [{\"name\": \"PSR B1257+12 d\", \"orbital_period\": 0.26885694, \"period_rank\": 1}, {\"name\": \"PSR B1257+12 c\", \"orbital_period\": 0.18206708, \"period_rank\": 2}, {\"name\": \"PSR B1257+12 b\", \"orbital_period\": 0.06926762, \"period_rank\": 1}, {\"name\": \"51 Pegasi b\", \"orbital_period\": 0.011498973, \"period_rank\": 1}, {\"name\": \"47 Ursae Majoris b\", \"orbital_period\": 3.0, \"period_rank\": 1}, {\"name\": \"16 Cygni B b\", \"orbital_period\": 2.2, \"period_rank\": 2}, {\"name\": \"70 Virginis b\", \"orbital_period\": 0.31950718, \"period_rank\": 3}, {\"name\": \"Rho Coronae Borealis b\", \"orbital_period\": 0.10896646, \"period_rank\": 1}, {\"name\": \"HD 210277 b\", \"orbital_period\": 1.2106776, \"period_rank\": 1}, {\"name\": \"GJ 876 b\", \"orbital_period\": 0.16728269, \"period_rank\": 2}, {\"name\": \"HD 168443 b\", \"orbital_period\": 0.15906912, \"period_rank\": 3}, {\"name\": \"Upsilon Andromedae d\", \"orbital_period\": 3.5, \"period_rank\": 1}, {\"name\": \"HD 10697 b\", \"orbital_period\": 2.9, \"period_rank\": 2}, {\"name\": \"HD 222582 b\", \"orbital_period\": 1.6, \"period_rank\": 3}, {\"name\": \"Epsilon Eridani b\", \"orbital_period\": 7.3, \"period_rank\": 1}, {\"name\": \"HD 38529 c\", \"orbital_period\": 5.8, \"period_rank\": 2}, {\"name\": \"HD 168443 c\", \"orbital_period\": 4.8, \"period_rank\": 3}, {\"name\": \"47 Ursae Majoris c\", \"orbital_period\": 6.6, \"period_rank\": 1}, {\"name\": \"HD 39091 b\", \"orbital_period\": 5.7, \"period_rank\": 2}, {\"name\": \"HD 213240 b\", \"orbital_period\": 2.4, \"period_rank\": 3}, {\"name\": \"55 Cancri d\", \"orbital_period\": 15.3, \"period_rank\": 1}, {\"name\": \"HD 72659 b\", \"orbital_period\": 9.7, \"period_rank\": 2}, {\"name\": \"HD 30177 b\", \"orbital_period\": 6.9, \"period_rank\": 3}, {\"name\": \"PSR B1620-26 b\", \"orbital_period\": 95.0, \"period_rank\": 1}, {\"name\": \"HD 190360 b\", \"orbital_period\": 7.8, \"period_rank\": 2}, {\"name\": \"HD 74156 c\", \"orbital_period\": 6.7, \"period_rank\": 3}, {\"name\": \"DH Tauri b\", \"orbital_period\": 10441.5, \"period_rank\": 1}, {\"name\": \"2MASS J12073346-3932539 b\", \"orbital_period\": 2885.9, \"period_rank\": 2}, {\"name\": \"GQ Lupi b\", \"orbital_period\": 1195.9, \"period_rank\": 3}, {\"name\": \"AB Pictoris b\", \"orbital_period\": 4421.7, \"period_rank\": 1}, {\"name\": \"HD 217107 c\", \"orbital_period\": 11.7, \"period_rank\": 2}, {\"name\": \"OGLE-2005-BLG-071L b\", \"orbital_period\": 10.1, \"period_rank\": 3}, {\"name\": \"HN Pegasi b\", \"orbital_period\": 20692.2, \"period_rank\": 1}, {\"name\": \"Ophiuchi 11 b\", \"orbital_period\": 20000.0, \"period_rank\": 2}, {\"name\": \"HD 203030 b\", \"orbital_period\": 11094.6, \"period_rank\": 3}, {\"name\": \"HD 11506 b\", \"orbital_period\": 4.4, \"period_rank\": 1}, {\"name\": \"HD 196885 A b\", \"orbital_period\": 3.7, \"period_rank\": 2}, {\"name\": \"Kappa Coronae Borealis b\", \"orbital_period\": 3.4, \"period_rank\": 3}, {\"name\": \"FU Tauri b\", \"orbital_period\": 101250.8, \"period_rank\": 1}, {\"name\": \"USco CTIO 108 b\", \"orbital_period\": 70841.0, \"period_rank\": 2}, {\"name\": \"1RXS J160929.1-210524 b\", \"orbital_period\": 6505.9, \"period_rank\": 3}, {\"name\": \"HIP 70849 b\", \"orbital_period\": 47.5, \"period_rank\": 1}, {\"name\": \"47 Ursae Majoris d\", \"orbital_period\": 38.4, \"period_rank\": 2}, {\"name\": \"DP Leonis b\", \"orbital_period\": 28.0, \"period_rank\": 3}, {\"name\": \"Ross 458 c\", \"orbital_period\": 48780.2, \"period_rank\": 1}, {\"name\": \"HIP 78530 b\", \"orbital_period\": 12738.7, \"period_rank\": 2}, {\"name\": \"GSC 06214-00210 b\", \"orbital_period\": 6037.4, \"period_rank\": 3}, {\"name\": \"WD 0806-661 b\", \"orbital_period\": 158840.9, \"period_rank\": 1}, {\"name\": \"CFBDSIR J145829+101343 b\", \"orbital_period\": 27.5, \"period_rank\": 2}, {\"name\": \"HIP 5158 c\", \"orbital_period\": 24.7, \"period_rank\": 3}, {\"name\": \"Kappa Andromedae b\", \"orbital_period\": 253.1, \"period_rank\": 1}, {\"name\": \"WISEP J121756.91+162640.2 A b\", \"orbital_period\": 130.7, \"period_rank\": 2}, {\"name\": \"HD 142 c\", \"orbital_period\": 27.8, \"period_rank\": 3}, {\"name\": \"HD 106906 b\", \"orbital_period\": 13538.6, \"period_rank\": 1}, {\"name\": \"ROXs 12 b\", \"orbital_period\": 3264.5, \"period_rank\": 2}, {\"name\": \"ROXs 42 B b\", \"orbital_period\": 1968.3, \"period_rank\": 3}, {\"name\": \"GU Piscium b\", \"orbital_period\": 155788.8, \"period_rank\": 1}, {\"name\": \"HD 100546 b\", \"orbital_period\": 249.2, \"period_rank\": 2}, {\"name\": \"OGLE-2008-BLG-092L b\", \"orbital_period\": 69.0, \"period_rank\": 3}, {\"name\": \"2MASS J02192210-3925225 b\", \"orbital_period\": 5878.1, \"period_rank\": 1}, {\"name\": \"VHS J125601.92-125723.9 b\", \"orbital_period\": 3895.8, \"period_rank\": 2}, {\"name\": \"51 Eridani b\", \"orbital_period\": 32.0, \"period_rank\": 3}, {\"name\": \"2MASS J22362452+4751425 b\", \"orbital_period\": 4505.7, \"period_rank\": 1}, {\"name\": \"HR 2562 b\", \"orbital_period\": 80.3, \"period_rank\": 2}, {\"name\": \"GJ 676 A c\", \"orbital_period\": 38.1, \"period_rank\": 3}, {\"name\": \"HIP 65426 b\", \"orbital_period\": 630.7, \"period_rank\": 1}, {\"name\": \"MOA-2012-BLG-006L b\", \"orbital_period\": 46.6, \"period_rank\": 2}, {\"name\": \"OGLE-2016-BLG-0263L b\", \"orbital_period\": 34.8, \"period_rank\": 3}, {\"name\": \"PDS 70 b\", \"orbital_period\": 119.2, \"period_rank\": 1}, {\"name\": \"OGLE-2011-BLG-0173L b\", \"orbital_period\": 35.4, \"period_rank\": 2}, {\"name\": \"HD 75784 c\", \"orbital_period\": 21.6, \"period_rank\": 3}, {\"name\": \"USco1556 b\", \"orbital_period\": 360656.2, \"period_rank\": 1}, {\"name\": \"USco1621 b\", \"orbital_period\": 257742.3, \"period_rank\": 2}, {\"name\": \"HIP 79098 AB b\", \"orbital_period\": 3311.0, \"period_rank\": 3}, {\"name\": \"TYC 8998-760-1 c\", \"orbital_period\": 5727.6, \"period_rank\": 1}, {\"name\": \"TYC 8998-760-1 b\", \"orbital_period\": 2063.1, \"period_rank\": 2}, {\"name\": \"OGLE-2016-BLG-1227L b\", \"orbital_period\": 19.8, \"period_rank\": 3}, {\"name\": \"COCONUTS-2 b\", \"orbital_period\": 1101369.9, \"period_rank\": 1}, {\"name\": \"BD+60 1417 b\", \"orbital_period\": 67794.6, \"period_rank\": 2}, {\"name\": \"CFHTWIR-Oph 98 b\", \"orbital_period\": 22027.4, \"period_rank\": 3}, {\"name\": \"mu2 Scorpii b\", \"orbital_period\": 1251.8, \"period_rank\": 1}, {\"name\": \"AB Aurigae b\", \"orbital_period\": 587.7, \"period_rank\": 2}, {\"name\": \"HD 105618 c\", \"orbital_period\": 211.3, \"period_rank\": 3}, {\"name\": \"TOI-1669 b\", \"orbital_period\": 1.4, \"period_rank\": 1}, {\"name\": \"GJ 1151 c\", \"orbital_period\": 1.0669404, \"period_rank\": 2}, {\"name\": \"TOI-1694 c\", \"orbital_period\": 1.0655715, \"period_rank\": 3}]\n",
            "How are the following two queries different? WITH RankedExoplanets AS (\n",
            "    SELECT \n",
            "        e.name,\n",
            "        e.orbital_period,\n",
            "        RANK() OVER (PARTITION BY e.discovery_year ORDER BY e.orbital_period DESC) AS period_rank\n",
            "    FROM exoplanets e\n",
            ")\n",
            "SELECT * \n",
            "FROM RankedExoplanets\n",
            "WHERE period_rank <= 3;, SELECT discovery_year, name, orbital_period FROM exoplanets WHERE (discovery_year, name, orbital_period) IN (   SELECT discovery_year, name, orbital_period    FROM exoplanets    ORDER BY discovery_year, orbital_period DESC) GROUP BY discovery_year LIMIT 3; and which one better answers the following question: Rank exoplanets based on their orbital period and return the top 3 longest-period exoplanets discovered each year.?\n"
          ]
        }
      ],
      "source": [
        "i=3 # Incorrect\n",
        "valid_result=valid_loaded['results'][i]\n",
        "print('valid_result:\\n',valid_result)\n",
        "gen_result=run_query(generations[i],cursor)\n",
        "print('gen_result:\\n', gen_result)\n",
        "compare_results(valid_result,gen_result)\n",
        "print(f\"\"\"How are the following two queries different? {generations[i]}, {valid_loaded['answer'][i]} and which one better answers the following question: {valid_loaded['question'][i]}?\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "valid_result:\n",
            " [{\"discovery_year\": 2001, \"name\": \"47 Ursae Majoris c\", \"exoplanet_mass\": 1.02492e+27}, {\"discovery_year\": 1995, \"name\": \"51 Pegasi b\", \"exoplanet_mass\": 8.730800000000001e+26}, {\"discovery_year\": 2004, \"name\": \"55 Cancri e\", \"exoplanet_mass\": 4.771628000000001e+25}, {\"discovery_year\": 2019, \"name\": \"EPIC 201497682 b\", \"exoplanet_mass\": 1.5527200000000002e+24}, {\"discovery_year\": 2007, \"name\": \"GJ 581 c\", \"exoplanet_mass\": 3.2846e+25}, {\"discovery_year\": 2009, \"name\": \"GJ 581 e\", \"exoplanet_mass\": 1.01524e+25}, {\"discovery_year\": 1998, \"name\": \"HD 187123 b\", \"exoplanet_mass\": 9.926540000000001e+26}, {\"discovery_year\": 2003, \"name\": \"HD 3651 b\", \"exoplanet_mass\": 4.32744e+26}, {\"discovery_year\": 2000, \"name\": \"HD 46375 b\", \"exoplanet_mass\": 4.28948e+26}, {\"discovery_year\": 2002, \"name\": \"HD 49674 b\", \"exoplanet_mass\": 1.89808076e+26}, {\"discovery_year\": 2006, \"name\": \"HD 69830 b\", \"exoplanet_mass\": 6.09144e+25}, {\"discovery_year\": 1999, \"name\": \"HD 75289 b\", \"exoplanet_mass\": 9.300199999999999e+26}, {\"discovery_year\": 2017, \"name\": \"K2-116 b\", \"exoplanet_mass\": 1.534804e+24}, {\"discovery_year\": 2018, \"name\": \"K2-266 c\", \"exoplanet_mass\": 1.73265636e+24}, {\"discovery_year\": 2022, \"name\": \"K2-413 b\", \"exoplanet_mass\": 2.167836e+24}, {\"discovery_year\": 2010, \"name\": \"Kepler-11 b\", \"exoplanet_mass\": 1.13468e+25}, {\"discovery_year\": 2013, \"name\": \"Kepler-128 b\", \"exoplanet_mass\": 4.5984400000000005e+24}, {\"discovery_year\": 2016, \"name\": \"Kepler-1308 b\", \"exoplanet_mass\": 5.571876e+23}, {\"discovery_year\": 2021, \"name\": \"Kepler-1877 b\", \"exoplanet_mass\": 1.068988e+24}, {\"discovery_year\": 2012, \"name\": \"Kepler-32 f\", \"exoplanet_mass\": 2.848644e+24}, {\"discovery_year\": 2014, \"name\": \"Kepler-37 e\", \"exoplanet_mass\": 1.6423e+23}, {\"discovery_year\": 2011, \"name\": \"Kepler-42 d\", \"exoplanet_mass\": 7.763600000000001e+23}, {\"discovery_year\": 2015, \"name\": \"Kepler-444 b\", \"exoplanet_mass\": 2.233528e+23}, {\"discovery_year\": 2008, \"name\": \"MOA-2007-BLG-192L b\", \"exoplanet_mass\": 1.97076e+25}, {\"discovery_year\": 2005, \"name\": \"OGLE-2005-BLG-390L b\", \"exoplanet_mass\": 3.2846e+25}, {\"discovery_year\": 1994, \"name\": \"PSR B1257+12 b\", \"exoplanet_mass\": 1.1944000000000001e+23}, {\"discovery_year\": 1992, \"name\": \"PSR B1257+12 d\", \"exoplanet_mass\": 2.32908e+25}, {\"discovery_year\": 1997, \"name\": \"Rho Coronae Borealis b\", \"exoplanet_mass\": 1.9832202e+27}, {\"discovery_year\": 2020, \"name\": \"TOI-540 b\", \"exoplanet_mass\": 4.025128000000001e+24}, {\"discovery_year\": 2023, \"name\": \"TOI-700 e\", \"exoplanet_mass\": 4.885096e+24}, {\"discovery_year\": 1996, \"name\": \"Upsilon Andromedae b\", \"exoplanet_mass\": 1.3050648e+27}]\n",
            "gen_result:\n",
            " [{\"discovery_year\": 2001, \"name\": \"47 Ursae Majoris c\", \"mass\": 1.02492e+27}, {\"discovery_year\": 1995, \"name\": \"51 Pegasi b\", \"mass\": 8.730800000000001e+26}, {\"discovery_year\": 2004, \"name\": \"55 Cancri e\", \"mass\": 4.771628000000001e+25}, {\"discovery_year\": 2019, \"name\": \"EPIC 201497682 b\", \"mass\": 1.5527200000000002e+24}, {\"discovery_year\": 2007, \"name\": \"GJ 581 c\", \"mass\": 3.2846e+25}, {\"discovery_year\": 2009, \"name\": \"GJ 581 e\", \"mass\": 1.01524e+25}, {\"discovery_year\": 1998, \"name\": \"HD 187123 b\", \"mass\": 9.926540000000001e+26}, {\"discovery_year\": 2003, \"name\": \"HD 3651 b\", \"mass\": 4.32744e+26}, {\"discovery_year\": 2000, \"name\": \"HD 46375 b\", \"mass\": 4.28948e+26}, {\"discovery_year\": 2002, \"name\": \"HD 49674 b\", \"mass\": 1.89808076e+26}, {\"discovery_year\": 2006, \"name\": \"HD 69830 b\", \"mass\": 6.09144e+25}, {\"discovery_year\": 1999, \"name\": \"HD 75289 b\", \"mass\": 9.300199999999999e+26}, {\"discovery_year\": 2017, \"name\": \"K2-116 b\", \"mass\": 1.534804e+24}, {\"discovery_year\": 2018, \"name\": \"K2-266 c\", \"mass\": 1.73265636e+24}, {\"discovery_year\": 2022, \"name\": \"K2-413 b\", \"mass\": 2.167836e+24}, {\"discovery_year\": 2010, \"name\": \"Kepler-11 b\", \"mass\": 1.13468e+25}, {\"discovery_year\": 2013, \"name\": \"Kepler-128 b\", \"mass\": 4.5984400000000005e+24}, {\"discovery_year\": 2016, \"name\": \"Kepler-1308 b\", \"mass\": 5.571876e+23}, {\"discovery_year\": 2021, \"name\": \"Kepler-1877 b\", \"mass\": 1.068988e+24}, {\"discovery_year\": 2012, \"name\": \"Kepler-32 f\", \"mass\": 2.848644e+24}, {\"discovery_year\": 2014, \"name\": \"Kepler-37 e\", \"mass\": 1.6423e+23}, {\"discovery_year\": 2011, \"name\": \"Kepler-42 d\", \"mass\": 7.763600000000001e+23}, {\"discovery_year\": 2015, \"name\": \"Kepler-444 b\", \"mass\": 2.233528e+23}, {\"discovery_year\": 2008, \"name\": \"MOA-2007-BLG-192L b\", \"mass\": 1.97076e+25}, {\"discovery_year\": 2005, \"name\": \"OGLE-2005-BLG-390L b\", \"mass\": 3.2846e+25}, {\"discovery_year\": 1994, \"name\": \"PSR B1257+12 b\", \"mass\": 1.1944000000000001e+23}, {\"discovery_year\": 1992, \"name\": \"PSR B1257+12 d\", \"mass\": 2.32908e+25}, {\"discovery_year\": 1997, \"name\": \"Rho Coronae Borealis b\", \"mass\": 1.9832202e+27}, {\"discovery_year\": 2020, \"name\": \"TOI-540 b\", \"mass\": 4.025128000000001e+24}, {\"discovery_year\": 2023, \"name\": \"TOI-700 e\", \"mass\": 4.885096e+24}, {\"discovery_year\": 1996, \"name\": \"Upsilon Andromedae b\", \"mass\": 1.3050648e+27}]\n",
            "How are the following two queries different? WITH MinMassExoplanets AS (\n",
            "    SELECT discovery_year, \n",
            "           MIN(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS min_mass\n",
            "    FROM exoplanets\n",
            "    GROUP BY discovery_year\n",
            ")\n",
            "SELECT e.discovery_year, e.name, e.mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt) AS mass\n",
            "FROM exoplanets e\n",
            "JOIN MinMassExoplanets m ON e.discovery_year = m.discovery_year\n",
            "WHERE e.mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt) = m.min_mass;, WITH MinMassExoplanets AS (\n",
            "    SELECT \n",
            "        discovery_year,\n",
            "        MIN(mass_multiplier * r.mass) AS min_mass\n",
            "    FROM exoplanets e\n",
            "    JOIN reference_planets r ON e.mass_wrt = r.name\n",
            "    GROUP BY discovery_year\n",
            ")\n",
            "SELECT \n",
            "    e.discovery_year,\n",
            "    e.name,\n",
            "    e.mass_multiplier * r.mass AS exoplanet_mass\n",
            "FROM exoplanets e\n",
            "JOIN reference_planets r ON e.mass_wrt = r.name\n",
            "JOIN MinMassExoplanets m ON e.discovery_year = m.discovery_year\n",
            "WHERE e.mass_multiplier * r.mass = m.min_mass; and which one better answers the following question: What are the names and masses of the exoplanets that have the minimum mass for each discovery year??\n"
          ]
        }
      ],
      "source": [
        "i=6 # Both correct!\n",
        "valid_result=valid_loaded['results'][i]\n",
        "print('valid_result:\\n',valid_result)\n",
        "gen_result=run_query(generations[i],cursor)\n",
        "print('gen_result:\\n', gen_result)\n",
        "compare_results(valid_result,gen_result)\n",
        "print(f\"\"\"How are the following two queries different? {generations[i]}, {valid_loaded['answer'][i]} and which one better answers the following question: {valid_loaded['question'][i]}?\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "valid_result:\n",
            " [{\"name\": \"GJ 1151 c\", \"discovery_year\": 2023, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-1669 b\", \"discovery_year\": 2023, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-1694 b\", \"discovery_year\": 2023, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1694 c\", \"discovery_year\": 2023, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-4342 b\", \"discovery_year\": 2023, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-4342 c\", \"discovery_year\": 2023, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-4562 b\", \"discovery_year\": 2023, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-4582 b\", \"discovery_year\": 2023, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-700 e\", \"discovery_year\": 2023, \"detection_method\": \"Transit\"}, {\"name\": \"AB Aurigae b\", \"discovery_year\": 2022, \"detection_method\": \"Direct Imaging\"}, {\"name\": \"CoRoT-35 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"CoRoT-36 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"CoRoT-7 d\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"EPIC 206317286 c\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"Gaia-1 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"Gaia-2 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"GJ 1002 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"GJ 1002 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"GJ 2030 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"GJ 2030 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"GJ 3090 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"GJ 3222 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"GJ 3929 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"GJ 3929 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"GJ 514 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"GJ 680 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"GJ 896 A b\", \"discovery_year\": 2022, \"detection_method\": \"Astrometry\"}, {\"name\": \"GJ 9714 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 103891 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 105618 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 105618 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 105779 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 109833 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"HD 109833 c\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"HD 109988 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 110537 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 11112 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 111232 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 113337 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 11343 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 114082 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 11505 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 11506 d\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 115404 A b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 115404 A c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 12235 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 12235 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 122562 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 125271 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 127506 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 135872 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 136118 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 13724 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 140901 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 140901 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 142 A d\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 144899 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 149806 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 150010 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 151450 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 151450 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 153557 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"HD 153557 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            "gen_result:\n",
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b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"KOI-984 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"KOI-984 c\", \"discovery_year\": 2022, \"detection_method\": \"Transit Timing Variations\"}, {\"name\": \"L 98-59 e\", \"discovery_year\": 2021, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"LHS 1478 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"LHS 1678 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"LHS 1678 c\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"LP 890-9 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"LP 890-9 c\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"LTT 1445 A c\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"MOA-2019-BLG-008L b\", \"discovery_year\": 2022, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"MOA-2020-BLG-135L b\", \"discovery_year\": 2022, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"mu2 Scorpii b\", \"discovery_year\": 2022, \"detection_method\": \"Direct Imaging\"}, {\"name\": \"NGTS-13 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"NGTS-14 A b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"NGTS-15 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"NGTS-16 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"NGTS-17 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"NGTS-18 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"NGTS-20 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"NGTS-21 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"NGTS-23 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"NGTS-24 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"NGTS-25 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"OGLE-2014-BLG-0319L b\", \"discovery_year\": 2022, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2017-BLG-1099L b\", \"discovery_year\": 2021, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2017-BLG-1691L b\", \"discovery_year\": 2022, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2018-BLG-0383L b\", \"discovery_year\": 2022, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2018-BLG-0567L b\", \"discovery_year\": 2021, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2018-BLG-0962L b\", \"discovery_year\": 2021, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2018-BLG-1185L b\", \"discovery_year\": 2021, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2018-BLG-1428L b\", \"discovery_year\": 2021, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2019-BLG-0299L b\", \"discovery_year\": 2021, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2019-BLG-0304L b\", \"discovery_year\": 2021, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2019-BLG-0362L b\", \"discovery_year\": 2022, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2019-BLG-0468L b\", \"discovery_year\": 2022, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2019-BLG-0468L c\", \"discovery_year\": 2022, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2019-BLG-0954L b\", \"discovery_year\": 2021, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2019-BLG-0960L b\", \"discovery_year\": 2021, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"OGLE-2019-BLG-1470L AB c\", \"discovery_year\": 2022, \"detection_method\": \"Gravitational Microlensing\"}, {\"name\": \"Pi Mensae d\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"Ross 508 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TIC 172900988 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TIC 257060897 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1062 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1062 c\", \"discovery_year\": 2021, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-1064 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1064 c\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1075 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1107 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1136 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1136 c\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1136 d\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1136 e\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1136 f\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1136 g\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1181 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1201 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1227 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1231 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1246 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1246 c\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1246 d\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1246 e\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1259 A b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1260 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1260 c\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1260 d\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1268 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1272 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1272 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-1278 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1288 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1288 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-1296 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1298 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1333 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1411 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1422 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1431 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1442 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1444 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1444 c\", \"discovery_year\": 2021, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-1452 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1468 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1468 c\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1478 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1516 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1518 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1601 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1634 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1669 b\", \"discovery_year\": 2023, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-1670 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1670 c\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1685 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1693 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1694 b\", \"discovery_year\": 2023, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1694 c\", \"discovery_year\": 2023, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-1695 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1696 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1710 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1749 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1749 c\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1749 d\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1759 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1789 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-178 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-178 c\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-178 d\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-178 e\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-178 f\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-178 g\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1807 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1820 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1842 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-1860 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-201 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2025 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2046 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2048 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-206 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2076 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2076 c\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2076 d\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2081 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2109 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2136 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2158 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2180 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2184 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2193 A b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2196 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2202 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2202 c\", \"discovery_year\": 2021, \"detection_method\": \"Transit Timing Variations\"}, {\"name\": \"TOI-2207 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-220 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2236 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2257 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2260 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2285 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2337 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2406 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2411 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2421 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2427 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2445 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-251 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2545 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2567 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2570 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-257 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-2669 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-269 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-277 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-3331 A b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-3362 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-3540 A b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-3629 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-3693 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-3714 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-3757 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-3884 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-411 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-411 c\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-4137 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-431 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-431 c\", \"discovery_year\": 2021, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-431 d\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-4329 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-4342 b\", \"discovery_year\": 2023, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-4342 c\", \"discovery_year\": 2023, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-4479 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-4562 b\", \"discovery_year\": 2023, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-4582 b\", \"discovery_year\": 2023, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-500 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-500 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-500 d\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-500 e\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TOI-5153 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-5174 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-519 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-5238 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-530 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-532 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-5398 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-544 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-5542 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-558 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-559 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-620 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-628 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-640 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-674 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-700 e\", \"discovery_year\": 2023, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-712 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-712 c\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-712 d\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-833 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-969 b\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"TOI-969 c\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TYC 0434-04538-1 b\", \"discovery_year\": 2021, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"TYC 2187-512-1 b\", \"discovery_year\": 2022, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"Upsilon Leonis b\", \"discovery_year\": 2021, \"detection_method\": \"Radial Velocity\"}, {\"name\": \"WASP-110 b\", \"discovery_year\": 2021, \"detection_method\": \"Transit\"}, {\"name\": \"WASP-132 c\", \"discovery_year\": 2022, \"detection_method\": \"Transit\"}, {\"name\": \"YSES 2 b\", \"discovery_year\": 2021, \"detection_method\": \"Direct Imaging\"}]\n",
            "How are the following two queries different? SELECT \n",
            "    e.name,\n",
            "    e.discovery_year,\n",
            "    e.detection_method\n",
            "FROM exoplanets e\n",
            "WHERE e.discovery_year >= 2021 AND e.detection_method IS NOT NULL;, SELECT name, discovery_year, detection_method\n",
            "FROM exoplanets\n",
            "WHERE discovery_year >= strftime('%Y', 'now') - 2\n",
            "ORDER BY discovery_year DESC; and which one better answers the following question: Find exoplanets discovered in the last two years and list them along with their discovery year and detection method.?\n"
          ]
        }
      ],
      "source": [
        "i=7 # Not quite, close though,toss up\n",
        "valid_result=valid_loaded['results'][i]\n",
        "print('valid_result:\\n',valid_result)\n",
        "gen_result=run_query(generations[i],cursor)\n",
        "print('gen_result:\\n', gen_result)\n",
        "compare_results(valid_result,gen_result)\n",
        "print(f\"\"\"How are the following two queries different? {generations[i]}, {valid_loaded['answer'][i]} and which one better answers the following question: {valid_loaded['question'][i]}?\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "valid_result:\n",
            " [{\"name\": \"HD 100546 b\", \"mass_radius_ratio\": 2.068544927536232e+29}, {\"name\": \"K2-52 b\", \"mass_radius_ratio\": 1.7018181818181818e+29}, {\"name\": \"Kepler-718 b\", \"mass_radius_ratio\": 1.683398781313473e+29}, {\"name\": \"PH2 b\", \"mass_radius_ratio\": 1.6815060908084162e+29}, {\"name\": \"Kepler-488 b\", \"mass_radius_ratio\": 1.670347764371895e+29}]\n",
            "gen_result:\n",
            " [{\"name\": \"HD 100546 b\", \"mass_to_radius_ratio\": 2.068544927536232e+29}, {\"name\": \"K2-52 b\", \"mass_to_radius_ratio\": 1.7018181818181818e+29}, {\"name\": \"Kepler-718 b\", \"mass_to_radius_ratio\": 1.683398781313473e+29}, {\"name\": \"PH2 b\", \"mass_to_radius_ratio\": 1.6815060908084162e+29}, {\"name\": \"Kepler-488 b\", \"mass_to_radius_ratio\": 1.670347764371895e+29}]\n",
            "How are the following two queries different? SELECT \n",
            "    e.name,\n",
            "    e.mass_multiplier * r.mass / e.radius_multiplier AS mass_to_radius_ratio\n",
            "FROM exoplanets e\n",
            "JOIN reference_planets r ON e.mass_wrt = r.name\n",
            "ORDER BY mass_to_radius_ratio DESC\n",
            "LIMIT 5;, SELECT name, \n",
            "       (mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) / radius_multiplier AS mass_radius_ratio\n",
            "FROM exoplanets\n",
            "ORDER BY mass_radius_ratio DESC\n",
            "LIMIT 5; and which one better answers the following question: Find the exoplanets with the highest mass-to-radius ratio, showing their name and calculated ratio (return top 5).?\n"
          ]
        }
      ],
      "source": [
        "i=9 # Ok, both correct!\n",
        "valid_result=valid_loaded['results'][i]\n",
        "print('valid_result:\\n',valid_result)\n",
        "gen_result=run_query(generations[i],cursor)\n",
        "print('gen_result:\\n', gen_result)\n",
        "compare_results(valid_result,gen_result)\n",
        "print(f\"\"\"How are the following two queries different? {generations[i]}, {valid_loaded['answer'][i]} and which one better answers the following question: {valid_loaded['question'][i]}?\"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- Overall, 100% execution rate with DoRA, ~80+% accuracy post 8 minutes of fine tuning.  3 Minutes/10 queries generation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- Loading: don't need DoRA configs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Loading checkpoint shards: 100%|██████████| 3/3 [00:01<00:00,  1.92it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Pad token: <pad>\n",
            "Pad token ID: 128256\n",
            "Padding side: left\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
        "import os\n",
        "\n",
        "MODEL_ID = 'meta-llama/Llama-3.2-3B-Instruct'\n",
        "bnb_config = BitsAndBytesConfig(load_in_8bit=True)\n",
        "\n",
        "# Load the model with the same configuration\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    './sql_output_dir_llama3/extra_dora_ckpt',\n",
        "    device_map='auto',\n",
        "    quantization_config=bnb_config  # Ensure quantization is applied\n",
        ")\n",
        "\n",
        "# Ensure model config is the same as before\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1\n",
        "model.gradient_checkpointing_enable()\n",
        "\n",
        "# Load the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained('./sql_output_dir_llama3/extra_dora_ckpt')\n",
        "\n",
        "# Add padding token if needed\n",
        "if tokenizer.pad_token is None:\n",
        "    tokenizer.add_special_tokens({'pad_token': '<pad>'})\n",
        "tokenizer.padding_side = \"left\"\n",
        "\n",
        "# Optionally print tokenizer configuration\n",
        "print(f\"Pad token: {tokenizer.pad_token}\")\n",
        "print(f\"Pad token ID: {tokenizer.pad_token_id}\")\n",
        "print(f\"Padding side: {tokenizer.padding_side}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "idx:  0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  SELECT discovery_year, COUNT(*) AS num_discoveries\n",
            "FROM exoplanets\n",
            "GROUP BY discovery_year\n",
            "ORDER BY num_discoveries DESC\n",
            "LIMIT 1;\n",
            "SELECT discovery_year, COUNT(*) AS num_discoveries\n",
            "FROM exoplanets\n",
            "GROUP BY discovery_year\n",
            "ORDER BY num_discoveries DESC\n",
            "LIMIT 1;\n",
            "idx:  1\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  SELECT detection_method, \n",
            "       SUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\n",
            "FROM exoplanets\n",
            "GROUP BY detection_method\n",
            "ORDER BY total_mass DESC\n",
            "LIMIT 1;\n",
            "SELECT detection_method, \n",
            "       SUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\n",
            "FROM exoplanets\n",
            "GROUP BY detection_method\n",
            "ORDER BY total_mass DESC\n",
            "LIMIT 1;\n",
            "idx:  2\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  WITH TotalMassPerYear AS (\n",
            "    SELECT discovery_year, \n",
            "           SUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\n",
            "    FROM exoplanets\n",
            "    GROUP BY discovery_year\n",
            ")\n",
            "SELECT discovery_year, total_mass\n",
            "FROM TotalMassPerYear\n",
            "ORDER BY total_mass DESC\n",
            "LIMIT 5;\n",
            "WITH TotalMassPerYear AS (\n",
            "    SELECT discovery_year, \n",
            "           SUM(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS total_mass\n",
            "    FROM exoplanets\n",
            "    GROUP BY discovery_year\n",
            ")\n",
            "SELECT discovery_year, total_mass\n",
            "FROM TotalMassPerYear\n",
            "ORDER BY total_mass DESC\n",
            "LIMIT 5;\n",
            "idx:  3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  WITH RankedExoplanets AS (\n",
            "    SELECT \n",
            "        e.name,\n",
            "        e.orbital_period,\n",
            "        RANK() OVER (PARTITION BY e.discovery_year ORDER BY e.orbital_period DESC) AS period_rank\n",
            "    FROM exoplanets e\n",
            "    WHERE e.orbital_period > 200  -- Only consider long-period exoplanets\n",
            ")\n",
            "\n",
            "SELECT * \n",
            "FROM RankedExoplanets\n",
            "WHERE period_rank <= 3;\n",
            "WITH RankedExoplanets AS (\n",
            "    SELECT \n",
            "        e.name,\n",
            "        e.orbital_period,\n",
            "        RANK() OVER (PARTITION BY e.discovery_year ORDER BY e.orbital_period DESC) AS period_rank\n",
            "    FROM exoplanets e\n",
            "    WHERE e.orbital_period > 200  -- Only consider long-period exoplanets\n",
            ")\n",
            "\n",
            "SELECT * \n",
            "FROM RankedExoplanets\n",
            "WHERE period_rank <= 3;\n",
            "idx:  4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  SELECT \n",
            "    e.name,\n",
            "    e.eccentricity,\n",
            "    e.discovery_year\n",
            "FROM exoplanets e\n",
            "WHERE e.eccentricity < 0.2 AND e.discovery_year < 2000;\n",
            "SELECT \n",
            "    e.name,\n",
            "    e.eccentricity,\n",
            "    e.discovery_year\n",
            "FROM exoplanets e\n",
            "WHERE e.eccentricity < 0.2 AND e.discovery_year < 2000;\n",
            "idx:  5\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  WITH DistinctPlanetTypes AS (\n",
            "    SELECT planet_type\n",
            "    FROM exoplanets\n",
            "    GROUP BY planet_type\n",
            "    HAVING COUNT(DISTINCT discovery_year) == 1  -- Only planet types discovered in a single year\n",
            ")\n",
            "SELECT *\n",
            "FROM DistinctPlanetTypes\n",
            "WHERE discovery_year < 2020;\n",
            "no such column: discovery_year\n",
            "idx:  6\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  WITH MinMassExoplanets AS (\n",
            "    SELECT \n",
            "        discovery_year,\n",
            "        MIN(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS min_mass\n",
            "    FROM exoplanets\n",
            "    GROUP BY discovery_year\n",
            ")\n",
            "SELECT \n",
            "    e.discovery_year,\n",
            "    e.name,\n",
            "    e.mass_multiplier * (SELECT mass FROM reference_planets WHERE name = e.mass_wrt) AS mass\n",
            "FROM exoplanets e\n",
            "JOIN MinMassExoplanets m ON e.discovery_year = m.discovery_year\n",
            "WHERE e.mass_multiplier * (SELECT mass FROM reference_planets WHERE name = e.mass_wrt) = m.min_mass;\n",
            "WITH MinMassExoplanets AS (\n",
            "    SELECT \n",
            "        discovery_year,\n",
            "        MIN(mass_multiplier * (SELECT mass FROM reference_planets WHERE name = mass_wrt)) AS min_mass\n",
            "    FROM exoplanets\n",
            "    GROUP BY discovery_year\n",
            ")\n",
            "SELECT \n",
            "    e.discovery_year,\n",
            "    e.name,\n",
            "    e.mass_multiplier * (SELECT mass FROM reference_planets WHERE name = e.mass_wrt) AS mass\n",
            "FROM exoplanets e\n",
            "JOIN MinMassExoplanets m ON e.discovery_year = m.discovery_year\n",
            "WHERE e.mass_multiplier * (SELECT mass FROM reference_planets WHERE name = e.mass_wrt) = m.min_mass;\n",
            "idx:  7\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  WITH RecentDiscoveries AS (\n",
            "    SELECT \n",
            "        name,\n",
            "        discovery_year,\n",
            "        detection_method\n",
            "    FROM exoplanets\n",
            "    WHERE discovery_year >= CURRENT_YEAR() - 2  -- Last two years\n",
            ")\n",
            "SELECT * FROM RecentDiscoveries;\n",
            "no such function: CURRENT_YEAR\n",
            "idx:  8\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EXTRACTED:  SELECT planet_type, AVG(orbital_radius) AS avg_orbital_radius FROM exoplanets GROUP BY planet_type ORDER BY avg_orbital_radius DESC;\n",
            "SELECT planet_type, AVG(orbital_radius) AS avg_orbital_radius FROM exoplanets GROUP BY planet_type ORDER BY avg_orbital_radius DESC;\n",
            "idx:  9\n",
            "EXTRACTED:  SELECT \n",
            "    e.name,\n",
            "    e.mass_multiplier * r.mass / e.radius_multiplier AS mass_to_radius_ratio\n",
            "FROM exoplanets e\n",
            "JOIN reference_planets r ON e.mass_wrt = r.name\n",
            "ORDER BY mass_to_radius_ratio DESC\n",
            "LIMIT 5;\n",
            "SELECT \n",
            "    e.name,\n",
            "    e.mass_multiplier * r.mass / e.radius_multiplier AS mass_to_radius_ratio\n",
            "FROM exoplanets e\n",
            "JOIN reference_planets r ON e.mass_wrt = r.name\n",
            "ORDER BY mass_to_radius_ratio DESC\n",
            "LIMIT 5;\n",
            "num_opp_errors: 2, num_other_exceptions:0,\n",
            "Error rate: 20.0\n"
          ]
        }
      ],
      "source": [
        "import sqlite3\n",
        "import json\n",
        "import pandas as pd\n",
        "import re\n",
        "pd.set_option('display.max_colwidth',1000)\n",
        "conn = sqlite3.connect('./databases/exoplanets_db.db')\n",
        "cursor = conn.cursor()\n",
        "cursor.execute(f\"SELECT * FROM exoplanets LIMIT 5;\")\n",
        "def extract_sql_statement(text):\n",
        "    # Regular expression to match the SQL statement\n",
        "    #pattern = re.compile(r'SELECT.*?;', re.DOTALL)\n",
        "    #pattern = re.compile(r'(WITH.*?SELECT.*?;|SELECT.*?;)', re.DOTALL | re.IGNORECASE)\n",
        "    pattern = re.compile(r'SQL Query:(.*?;)', re.DOTALL)\n",
        "\n",
        "    match = pattern.search(text)\n",
        "    if match:\n",
        "        print('EXTRACTED: ', match.group(1).strip())\n",
        "        return match.group(1).strip()\n",
        "    return None\n",
        "\n",
        "def run_query(query, cursor):\n",
        "    cursor.execute(query)\n",
        "    rows = cursor.fetchall()\n",
        "    columns = [desc[0] for desc in cursor.description]\n",
        "    result_dict = [dict(zip(columns, row)) for row in rows]\n",
        "    return json.dumps(result_dict)\n",
        "def compare_results(query_results, expected_results):\n",
        "    \"\"\"Compare the query results with the expected results.\"\"\"\n",
        "    # Sort the results for comparison\n",
        "    query_results_sorted = sorted(query_results, key=lambda x: json.dumps(x, sort_keys=True))\n",
        "    expected_results_sorted = sorted(expected_results, key=lambda x: json.dumps(x, sort_keys=True))\n",
        "    return query_results_sorted == expected_results_sorted\n",
        "\n",
        "valid_loaded = load_dataset('json', data_files='./databases/valid.json', split='train')\n",
        "\n",
        "def generate_sql(context, question, max_new_tokens = 256, debug=False):\n",
        "    full_question = f\"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
        "\n",
        "You are a helpful assistant who is a SQL expert. Your task is to write a SQL query based on the given context and question.<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
        "\n",
        "Context: {context}\n",
        "Question: {question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>SQL Query:\"\"\"\n",
        "    model_input = tokenizer(full_question, return_tensors=\"pt\")#.to(\"cuda:1\")\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        out = tokenizer.decode(model.generate(**model_input,max_new_tokens=max_new_tokens)[0],skip_special_tokens=True)\n",
        "    #print('out: ',out)\n",
        "    #import sys; sys.exit()\n",
        "    #res = out[out.find(\"\"\"```sql```\"\"\")+3:out.find(\"\"\";\\n```\"\"\")]\n",
        "    #res = out[out.find('SQL Query:')+10:]\n",
        "    #print('out:\\n',out)\n",
        "    res = extract_sql_statement(out)\n",
        "    #if debug: print(res)\n",
        "    run_query(res, cursor)\n",
        "    #return out\n",
        "    return res\n",
        "num_opp_errors = 0\n",
        "num_other_exceptions = 0\n",
        "op_errors = []\n",
        "exceptions  = []\n",
        "generations = []\n",
        "bad_idxs = []\n",
        "result_comparisons = [False]*len(valid_loaded)\n",
        "for idx,example in enumerate(valid_loaded):\n",
        "    generation = '' # OK, had mistake here, query 9 is still wrong due to AS, though!\n",
        "    result = None\n",
        "    try:\n",
        "        print('idx: ',idx)\n",
        "        generation=generate_sql(context=example['context'], question=example['question'],debug=True)\n",
        "        result =  run_query(generation,cursor)\n",
        "        #print(\"generation: \")\n",
        "        print(generation)\n",
        "    except sqlite3.OperationalError as op_err:\n",
        "        num_opp_errors += 1\n",
        "        op_errors.append((idx,op_err))\n",
        "        bad_idxs.append(idx)\n",
        "        print(op_err)\n",
        "    except Exception as e:\n",
        "        num_other_exceptions +=1\n",
        "        exceptions.append((idx,e))\n",
        "        bad_idxs.append(idx)\n",
        "        print(e)\n",
        "    else:\n",
        "        if result: result_comparisons[idx]=compare_results(result, example['results'])\n",
        "    finally:\n",
        "        generations.append(generation)\n",
        "print(f\"num_opp_errors: {num_opp_errors}, num_other_exceptions:{num_other_exceptions},\\nError rate: {(num_other_exceptions+num_opp_errors)*100/len(valid_loaded)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "([(5, sqlite3.OperationalError('no such column: discovery_year')),\n",
              "  (7, sqlite3.OperationalError('no such function: CURRENT_YEAR'))],\n",
              " [],\n",
              " [False, True, True, False, True, False, False, False, True, False])"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "op_errors, exceptions,result_comparisons"
      ]
    }
  ],
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