test_pretrained.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run pre-trained DeepSeek Coder 1.3B Model on Chat-GPT 4o generated dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First load dataset into pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total dataset examples: 1044\n",
      "\n",
      "\n",
      "Which team committed the fewest total turnovers in an away game that resulted in a win?\n",
      "SELECT team_abbreviation_away FROM other_stats WHERE game_id IN (SELECT game_id FROM game WHERE wl_away = 'W') ORDER BY total_turnovers_away ASC LIMIT 1;\n",
      "PHX\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Load dataset and check length\n",
    "df = pd.read_csv(\"./train-data/sql_train.tsv\", sep='\\t')\n",
    "print(\"Total dataset examples: \" + str(len(df)))\n",
    "print(\"\\n\")\n",
    "\n",
    "# Test sampling\n",
    "sample = df.sample(n=1)\n",
    "print(sample[\"natural_query\"].values[0])\n",
    "print(sample[\"sql_query\"].values[0])\n",
    "print(sample[\"result\"].values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load pre-trained DeepSeek model using transformers and pytorch packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "import torch\n",
    "\n",
    "# Set device to cuda if available, otherwise CPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Load model and tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"./deepseek-coder-1.3b-instruct\")\n",
    "model = AutoModelForCausalLM.from_pretrained(\"./deepseek-coder-1.3b-instruct\", torch_dtype=torch.bfloat16, device_map=device) \n",
    "model.generation_config.pad_token_id = tokenizer.pad_token_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create prompt to setup the model for better performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_text = \"\"\"You are an AI assistant that converts natural language queries into valid SQLite queries.\n",
    "Database Schema and Explanations\n",
    "\n",
    "team Table\n",
    "Stores information about NBA teams.\n",
    "CREATE TABLE IF NOT EXISTS \"team\" (\n",
    "  \"id\" TEXT PRIMARY KEY,      -- Unique identifier for the team\n",
    "  \"full_name\" TEXT,           -- Full official name of the team (e.g., \"Los Angeles Lakers\")\n",
    "  \"abbreviation\" TEXT,        -- Shortened team name (e.g., \"LAL\")\n",
    "  \"nickname\" TEXT,            -- Commonly used nickname for the team (e.g., \"Lakers\")\n",
    "  \"city\" TEXT,                -- City where the team is based\n",
    "  \"state\" TEXT,               -- State where the team is located\n",
    "  \"year_founded\" REAL         -- Year the team was established\n",
    ");\n",
    "\n",
    "game Table\n",
    "Contains detailed statistics for each NBA game, including home and away team performance.\n",
    "CREATE TABLE IF NOT EXISTS \"game\" (\n",
    "  \"season_id\" TEXT,            -- Season identifier, formatted as \"2YYYY\" (e.g., \"21970\" for the 1970 season)\n",
    "  \"team_id_home\" TEXT,         -- ID of the home team (matches \"id\" in team table)\n",
    "  \"team_abbreviation_home\" TEXT, -- Abbreviation of the home team\n",
    "  \"team_name_home\" TEXT,       -- Full name of the home team\n",
    "  \"game_id\" TEXT PRIMARY KEY,  -- Unique identifier for the game\n",
    "  \"game_date\" TIMESTAMP,       -- Date the game was played (YYYY-MM-DD format)\n",
    "  \"matchup_home\" TEXT,         -- Matchup details including opponent (e.g., \"LAL vs. BOS\")\n",
    "  \"wl_home\" TEXT,              -- \"W\" if the home team won, \"L\" if they lost\n",
    "  \"min\" INTEGER,               -- Total minutes played in the game\n",
    "  \"fgm_home\" REAL,             -- Field goals made by the home team\n",
    "  \"fga_home\" REAL,             -- Field goals attempted by the home team\n",
    "  \"fg_pct_home\" REAL,          -- Field goal percentage of the home team\n",
    "  \"fg3m_home\" REAL,            -- Three-point field goals made by the home team\n",
    "  \"fg3a_home\" REAL,            -- Three-point attempts by the home team\n",
    "  \"fg3_pct_home\" REAL,         -- Three-point field goal percentage of the home team\n",
    "  \"ftm_home\" REAL,             -- Free throws made by the home team\n",
    "  \"fta_home\" REAL,             -- Free throws attempted by the home team\n",
    "  \"ft_pct_home\" REAL,          -- Free throw percentage of the home team\n",
    "  \"oreb_home\" REAL,            -- Offensive rebounds by the home team\n",
    "  \"dreb_home\" REAL,            -- Defensive rebounds by the home team\n",
    "  \"reb_home\" REAL,             -- Total rebounds by the home team\n",
    "  \"ast_home\" REAL,             -- Assists by the home team\n",
    "  \"stl_home\" REAL,             -- Steals by the home team\n",
    "  \"blk_home\" REAL,             -- Blocks by the home team\n",
    "  \"tov_home\" REAL,             -- Turnovers by the home team\n",
    "  \"pf_home\" REAL,              -- Personal fouls by the home team\n",
    "  \"pts_home\" REAL,             -- Total points scored by the home team\n",
    "  \"plus_minus_home\" INTEGER,   -- Plus/minus rating for the home team\n",
    "  \"video_available_home\" INTEGER, -- Indicates whether video is available (1 = Yes, 0 = No)\n",
    "  \"team_id_away\" TEXT,         -- ID of the away team\n",
    "  \"team_abbreviation_away\" TEXT, -- Abbreviation of the away team\n",
    "  \"team_name_away\" TEXT,       -- Full name of the away team\n",
    "  \"matchup_away\" TEXT,         -- Matchup details from the away team’s perspective\n",
    "  \"wl_away\" TEXT,              -- \"W\" if the away team won, \"L\" if they lost\n",
    "  \"fgm_away\" REAL,             -- Field goals made by the away team\n",
    "  \"fga_away\" REAL,             -- Field goals attempted by the away team\n",
    "  \"fg_pct_away\" REAL,          -- Field goal percentage of the away team\n",
    "  \"fg3m_away\" REAL,            -- Three-point field goals made by the away team\n",
    "  \"fg3a_away\" REAL,            -- Three-point attempts by the away team\n",
    "  \"fg3_pct_away\" REAL,         -- Three-point field goal percentage of the away team\n",
    "  \"ftm_away\" REAL,             -- Free throws made by the away team\n",
    "  \"fta_away\" REAL,             -- Free throws attempted by the away team\n",
    "  \"ft_pct_away\" REAL,          -- Free throw percentage of the away team\n",
    "  \"oreb_away\" REAL,            -- Offensive rebounds by the away team\n",
    "  \"dreb_away\" REAL,            -- Defensive rebounds by the away team\n",
    "  \"reb_away\" REAL,             -- Total rebounds by the away team\n",
    "  \"ast_away\" REAL,             -- Assists by the away team\n",
    "  \"stl_away\" REAL,             -- Steals by the away team\n",
    "  \"blk_away\" REAL,             -- Blocks by the away team\n",
    "  \"tov_away\" REAL,             -- Turnovers by the away team\n",
    "  \"pf_away\" REAL,              -- Personal fouls by the away team\n",
    "  \"pts_away\" REAL,             -- Total points scored by the away team\n",
    "  \"plus_minus_away\" INTEGER,   -- Plus/minus rating for the away team\n",
    "  \"video_available_away\" INTEGER, -- Indicates whether video is available (1 = Yes, 0 = No)\n",
    "  \"season_type\" TEXT           -- Regular season or playoffs\n",
    ");\n",
    "\n",
    "other_stats Table\n",
    "Stores additional statistics, linked to the game table via game_id.\n",
    "CREATE TABLE IF NOT EXISTS \"other_stats\" (\n",
    "  \"game_id\" TEXT,             -- Unique game identifier, matches id column from game table\n",
    "  \"league_id\" TEXT,           -- League identifier\n",
    "  \"team_id_home\" TEXT,        -- Home team identifier\n",
    "  \"team_abbreviation_home\" TEXT, -- Home team abbreviation\n",
    "  \"team_city_home\" TEXT,      -- Home team city\n",
    "  \"pts_paint_home\" INTEGER,   -- Points in the paint by the home team\n",
    "  \"pts_2nd_chance_home\" INTEGER, -- Second chance points by the home team\n",
    "  \"pts_fb_home\" INTEGER,      -- Fast break points by the home team\n",
    "  \"largest_lead_home\" INTEGER,-- Largest lead by the home team\n",
    "  \"lead_changes\" INTEGER,     -- Number of lead changes \n",
    "  \"times_tied\" INTEGER,       -- Number of times the score was tied\n",
    "  \"team_turnovers_home\" INTEGER, -- Home team turnovers\n",
    "  \"total_turnovers_home\" INTEGER, -- Total turnovers by the home team\n",
    "  \"team_rebounds_home\" INTEGER, -- Home team rebounds\n",
    "  \"pts_off_to_home\" INTEGER,  -- Points off turnovers by the home team\n",
    "  \"team_id_away\" TEXT,        -- Away team identifier\n",
    "  \"team_abbreviation_away\" TEXT,  -- Away team abbreviation\n",
    "  \"pts_paint_away\" INTEGER,   -- Points in the paint by the away team\n",
    "  \"pts_2nd_chance_away\" INTEGER, -- Second chance points by the away team\n",
    "  \"pts_fb_away\" INTEGER,      -- Fast break points by the away team\n",
    "  \"largest_lead_away\" INTEGER,-- Largest lead by the away team\n",
    "  \"team_turnovers_away\" INTEGER, -- Away team turnovers\n",
    "  \"total_turnovers_away\" INTEGER, -- Total turnovers by the away team\n",
    "  \"team_rebounds_away\" INTEGER, -- Away team rebounds\n",
    "  \"pts_off_to_away\" INTEGER   -- Points off turnovers by the away team\n",
    ");\n",
    "\n",
    "\n",
    "Team Name Information\n",
    "In the plaintext user questions, only the full team names will be used, but in the queries you may use the full team names or the abbreviations. \n",
    "The full team names can be used with the game table, while the abbreviations should be used with the other_stats table.\n",
    "Notice they are separated by the | character in the following list:\n",
    "\n",
    "Atlanta Hawks|ATL\n",
    "Boston Celtics|BOS\n",
    "Cleveland Cavaliers|CLE\n",
    "New Orleans Pelicans|NOP\n",
    "Chicago Bulls|CHI\n",
    "Dallas Mavericks|DAL\n",
    "Denver Nuggets|DEN\n",
    "Golden State Warriors|GSW\n",
    "Houston Rockets|HOU\n",
    "Los Angeles Clippers|LAC\n",
    "Los Angeles Lakers|LAL\n",
    "Miami Heat|MIA\n",
    "Milwaukee Bucks|MIL\n",
    "Minnesota Timberwolves|MIN\n",
    "Brooklyn Nets|BKN\n",
    "New York Knicks|NYK\n",
    "Orlando Magic|ORL\n",
    "Indiana Pacers|IND\n",
    "Philadelphia 76ers|PHI\n",
    "Phoenix Suns|PHX\n",
    "Portland Trail Blazers|POR\n",
    "Sacramento Kings|SAC\n",
    "San Antonio Spurs|SAS\n",
    "Oklahoma City Thunder|OKC\n",
    "Toronto Raptors|TOR\n",
    "Utah Jazz|UTA\n",
    "Memphis Grizzlies|MEM\n",
    "Washington Wizards|WAS\n",
    "Detroit Pistons|DET\n",
    "Charlotte Hornets|CHA\n",
    "\n",
    "Query Guidelines\n",
    "Use team_name_home and team_name_away to match teams to the game table. Use team_abbreviation_home and team_abbreviation away to match teams to the other_stats table.\n",
    "\n",
    "To filter by season, use season_id = '2YYYY'.\n",
    "\n",
    "Example: To get statistics from 2005, use a statement like: season_id = '22005'. To get statistics from 1972, use a statement like: season_id = \"21972\". To get statistics from 2015, use a statement like: season_id = \"22015\".\n",
    "\n",
    "Ensure queries return relevant columns and avoid unnecessary joins.\n",
    "\n",
    "Example User Requests and SQLite Queries\n",
    "Request:\n",
    "\"What is the most points the Los Angeles Lakers have ever scored at home?\"\n",
    "SQLite:\n",
    "SELECT MAX(pts_home) \n",
    "FROM game \n",
    "WHERE team_name_home = 'Los Angeles Lakers';\n",
    "\n",
    "Request:\n",
    "\"Which teams are located in the state of California?\"\n",
    "SQLite:\n",
    "SELECT full_name FROM team WHERE state = 'California';\n",
    "\n",
    "Request:\n",
    "\"Which team had the highest number of team turnovers in an away game?\"\n",
    "SQLite:\n",
    "SELECT team_abbreviation_away FROM other_stats ORDER BY team_turnovers_away DESC LIMIT 1;\n",
    "\n",
    "Request:\n",
    "\"Which teams were founded before 1979?\"\n",
    "SQLite:\n",
    "SELECT full_name FROM team WHERE year_founded < 1979;\n",
    "\n",
    "Request:\n",
    "\"Find the Boston Celtics largest home victory margin in the 2008 season.\"\n",
    "SQLite:\n",
    "SELECT MAX(pts_home - pts_away) AS biggest_win\n",
    "FROM game\n",
    "WHERE team_name_home = 'Boston Celtics' AND season_id = '22008';\n",
    "\n",
    "Generate only the SQLite query prefaced by SQLite: and no other text, do not output an explanation of the query. Now generate an SQLite query for the following user request. Request:\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test model performance on a single example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SQLite:\n",
      "SELECT team_abbreviation_away \n",
      "FROM other_stats \n",
      "WHERE wl_away = 'W' AND total_turnovers_away < (SELECT MIN(total_turnovers_away) FROM other_stats WHERE wl_away = 'L');\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Create message with sample query and run model\n",
    "message=[{ 'role': 'user', 'content': input_text + sample[\"natural_query\"].values[0]}]\n",
    "inputs = tokenizer.apply_chat_template(message, add_generation_prompt=True, return_tensors=\"pt\").to(model.device)\n",
    "outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)\n",
    "\n",
    "# Print output\n",
    "query_output = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)\n",
    "print(query_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test sample output on sqlite3 database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaned\n"
     ]
    }
   ],
   "source": [
    "import sqlite3 as sql\n",
    "\n",
    "# Create connection to sqlite3 database\n",
    "connection = sql.connect('./nba-data/nba.sqlite')\n",
    "cursor = connection.cursor()\n",
    "\n",
    "# Execute query from model output and print result\n",
    "if query_output[0:7] == \"SQLite:\":\n",
    "    print(\"cleaned\")\n",
    "    query = query_output[7:]\n",
    "elif query_output[0:4] == \"SQL:\":\n",
    "    query = query_output[4:]\n",
    "else:\n",
    "    query = query_output\n",
    "\n",
    "try:\n",
    "    cursor.execute(query)\n",
    "    rows = cursor.fetchall()\n",
    "    for row in rows:\n",
    "        print(row)\n",
    "except:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create function to compare output to ground truth result from examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the largest lead the Minnesota Timberwolves had at home?\n",
      "SELECT MAX(largest_lead_home) as max_lead  FROM other_stats  WHERE team_abbreviation_home = 'MIN';\n",
      "48.0\n",
      "SQLite:\n",
      "SELECT MAX(largest_lead_home) \n",
      "FROM other_stats \n",
      "WHERE team_name_home = 'Minnesota Timberwolves';\n",
      "\n",
      "Statement valid? False\n",
      "SQLite matched? False\n",
      "Result matched? False\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "def compare_result(sample_query, sample_result, query_output):\n",
    "    # Clean model output to only have the query output\n",
    "    if query_output[0:7] == \"SQLite:\":\n",
    "        query = query_output[7:]\n",
    "    elif query_output[0:4] == \"SQL:\":\n",
    "        query = query_output[4:]\n",
    "    else:\n",
    "        query = query_output\n",
    "    \n",
    "    # Try to execute query, if it fails, then this is a failure of the model\n",
    "    try:\n",
    "        # Execute query and obtain result\n",
    "        cursor.execute(query)\n",
    "        rows = cursor.fetchall()\n",
    "\n",
    "        # Strip all whitespace before comparing queries since there may be differences in spacing, newlines, tabs, etc.\n",
    "        query = query.replace(\" \", \"\").replace(\"\\n\", \"\").replace(\"\\t\", \"\")\n",
    "        sample_query = sample_query.replace(\" \", \"\").replace(\"\\n\", \"\").replace(\"\\t\", \"\")\n",
    "        query_match = (query == sample_query)\n",
    "\n",
    "        # If the queries match, the results clearly also match\n",
    "        if query_match:\n",
    "            return True, True, True\n",
    "\n",
    "        # Check if this is a multi-line query\n",
    "        if \"|\" in sample_result or \"(\" in sample_result:\n",
    "            #print(rows)\n",
    "            # Create list of results by stripping separators and splitting on them\n",
    "            if \"(\" in sample_result:\n",
    "                sample_result = sample_result.replace(\"(\", \"\").replace(\")\", \"\")\n",
    "                result_list = sample_result.split(\",\") \n",
    "            else:\n",
    "                result_list = sample_result.split(\"|\") \n",
    "\n",
    "            # Strip all results in list\n",
    "            for i in range(len(result_list)):\n",
    "                result_list[i] = str(result_list[i]).strip()\n",
    "            \n",
    "            # Loop through model result and see if it matches training example\n",
    "            result = False\n",
    "            for row in rows:\n",
    "                for r in row:\n",
    "                    for res in result_list:\n",
    "                        try:\n",
    "                            if math.isclose(float(r), float(res), abs_tol=0.5):\n",
    "                                return True, query_match, True\n",
    "                        except:\n",
    "                            if r in res or res in r:\n",
    "                                return True, query_match, True\n",
    "                    \n",
    "            # Check if the model returned a sum of examples as opposed to the whole thing\n",
    "            if len(rows) == 1:\n",
    "                for r in rows[0]:\n",
    "                    if r == str(len(result_list)):\n",
    "                        return True, query_match, True\n",
    "                    \n",
    "            return True, query_match, result\n",
    "        # Else the sample result is a single value or string\n",
    "        else:\n",
    "            #print(rows)\n",
    "            result = False\n",
    "            # Loop through model result and see if it contains the sample result\n",
    "            for row in rows:\n",
    "                for r in row:\n",
    "                    # Check by string\n",
    "                    if str(r) in str(sample_result):\n",
    "                        try:\n",
    "                            if math.isclose(float(r), float(sample_result), abs_tol=0.5):\n",
    "                                return True, query_match, True\n",
    "                        except:\n",
    "                            return True, query_match, True\n",
    "                    # Check by number, using try incase the cast as float fails\n",
    "                    try:\n",
    "                        if math.isclose(float(r), float(sample_result), abs_tol=0.5):\n",
    "                            return True, query_match, True\n",
    "                    except:\n",
    "                        pass\n",
    "\n",
    "            # Check if the model returned a list of examples instead of a total sum (both acceptable)\n",
    "            try:\n",
    "                if len(rows) > 1 and len(rows) == int(sample_result):\n",
    "                    return True, query_match, True\n",
    "                if len(rows[0]) > 1 and rows[0][1] is not None and  len(rows[0]) == int(sample_result):\n",
    "                    return True, query_match, True\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "            # Compare results and return\n",
    "            return True, query_match, result\n",
    "    except:\n",
    "        return False, False, False\n",
    "\n",
    "# Obtain sample\n",
    "sample = df.sample(n=1)\n",
    "print(sample[\"natural_query\"].values[0])\n",
    "print(sample[\"sql_query\"].values[0])\n",
    "print(sample[\"result\"].values[0])\n",
    "\n",
    "# Create message with sample query and run model\n",
    "message=[{ 'role': 'user', 'content': input_text + sample[\"natural_query\"].values[0]}]\n",
    "inputs = tokenizer.apply_chat_template(message, add_generation_prompt=True, return_tensors=\"pt\").to(model.device)\n",
    "outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)\n",
    "\n",
    "# Print output\n",
    "query_output = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)\n",
    "print(query_output)\n",
    "\n",
    "result = compare_result(sample[\"sql_query\"].values[0], sample[\"result\"].values[0], query_output)\n",
    "print(\"Statement valid? \" + str(result[0]))\n",
    "print(\"SQLite matched? \" + str(result[1]))\n",
    "print(\"Result matched? \" + str(result[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create function to evaluate pretrained model on full datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_evaluation(nba_df, title):\n",
    "    counter = 0\n",
    "    num_valid = 0\n",
    "    num_sql_matched = 0\n",
    "    num_result_matched = 0\n",
    "    for index, row in nba_df.iterrows():\n",
    "        # Create message with sample query and run model\n",
    "        message=[{ 'role': 'user', 'content': input_text + row[\"natural_query\"]}]\n",
    "        inputs = tokenizer.apply_chat_template(message, add_generation_prompt=True, return_tensors=\"pt\").to(model.device)\n",
    "        outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)\n",
    "\n",
    "        # Obtain output\n",
    "        query_output = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)\n",
    "\n",
    "        # Evaluate model result\n",
    "        valid, sql_matched, result_matched = compare_result(row[\"sql_query\"], row[\"result\"], query_output)\n",
    "        if valid:\n",
    "            num_valid += 1\n",
    "        if sql_matched:\n",
    "            num_sql_matched += 1\n",
    "        if result_matched:\n",
    "            num_result_matched += 1\n",
    "\n",
    "        # Break after predefined number of examples\n",
    "        counter += 1\n",
    "        if counter % 50 == 0:\n",
    "            print(\"Completed \" + str(counter))\n",
    "\n",
    "    # Print evaluation results\n",
    "    print(\"\\n\" + title + \" results:\")\n",
    "    print(\"Percent valid: \" + str(num_valid / len(nba_df)))\n",
    "    print(\"Percent SQLite matched: \" + str(num_sql_matched / len(nba_df)))\n",
    "    print(\"Percent result matched: \" + str(num_result_matched / len(nba_df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate on less than 90 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 50\n",
      "Completed 100\n",
      "Completed 150\n",
      "Completed 200\n",
      "\n",
      "Less than 90 results:\n",
      "Percent valid: 0.8448979591836735\n",
      "Percent SQLite matched: 0.43673469387755104\n",
      "Percent result matched: 0.6530612244897959\n",
      "Dataset length: 245\n"
     ]
    }
   ],
   "source": [
    "less_than_90_df = pd.read_csv(\"./train-data/less_than_90.tsv\", sep='\\t')\n",
    "run_evaluation(less_than_90_df, \"Less than 90\")\n",
    "print(\"Dataset length: \" + str(len(less_than_90_df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate on game table queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 50\n",
      "Completed 100\n",
      "Completed 150\n",
      "Completed 200\n",
      "Completed 250\n",
      "Completed 300\n",
      "Completed 350\n",
      "Completed 400\n",
      "Completed 450\n",
      "Completed 500\n",
      "Completed 550\n",
      "Completed 600\n",
      "Completed 650\n",
      "Completed 700\n",
      "Completed 750\n",
      "Completed 800\n",
      "\n",
      "Queries from game results:\n",
      "Percent valid: 0.7613365155131265\n",
      "Percent SQLite matched: 0.13842482100238662\n",
      "Percent result matched: 0.383054892601432\n",
      "Dataset length: 838\n"
     ]
    }
   ],
   "source": [
    "game_queries = pd.read_csv(\"./train-data/queries_from_game.tsv\", sep='\\t')\n",
    "run_evaluation(game_queries, \"Queries from game\")\n",
    "print(\"Dataset length: \" + str(len(game_queries)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on other stats queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 50\n",
      "Completed 100\n",
      "Completed 150\n",
      "\n",
      "Queries from other stats results:\n",
      "Percent valid: 0.21428571428571427\n",
      "Percent SQLite matched: 0.01948051948051948\n",
      "Percent result matched: 0.07142857142857142\n",
      "Dataset length: 154\n"
     ]
    }
   ],
   "source": [
    "other_stats_queries = pd.read_csv(\"./train-data/queries_from_other_stats.tsv\", sep='\\t')\n",
    "run_evaluation(other_stats_queries, \"Queries from other stats\")\n",
    "print(\"Dataset length: \" + str(len(other_stats_queries)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on team queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 50\n",
      "\n",
      "Queries from team results:\n",
      "Percent valid: 0.8653846153846154\n",
      "Percent SQLite matched: 0.5961538461538461\n",
      "Percent result matched: 0.7884615384615384\n",
      "Dataset length: 52\n"
     ]
    }
   ],
   "source": [
    "team_queries = pd.read_csv(\"./train-data/queries_from_team.tsv\", sep='\\t')\n",
    "run_evaluation(team_queries, \"Queries from team\")\n",
    "print(\"Dataset length: \" + str(len(team_queries)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on queries requiring join statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 50\n",
      "Completed 100\n",
      "Completed 150\n",
      "\n",
      "Queries with join results:\n",
      "Percent valid: 0.1945945945945946\n",
      "Percent SQLite matched: 0.0\n",
      "Percent result matched: 0.04864864864864865\n",
      "Dataset length: 185\n"
     ]
    }
   ],
   "source": [
    "join_queries = pd.read_csv(\"./train-data/with_join.tsv\", sep='\\t')\n",
    "run_evaluation(join_queries, \"Queries with join\")\n",
    "print(\"Dataset length: \" + str(len(join_queries)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on queries not requiring join statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 50\n",
      "Completed 100\n",
      "Completed 150\n",
      "Completed 200\n",
      "Completed 250\n",
      "Completed 300\n",
      "Completed 350\n",
      "Completed 400\n",
      "Completed 450\n",
      "Completed 500\n",
      "Completed 550\n",
      "Completed 600\n",
      "Completed 650\n",
      "Completed 700\n",
      "Completed 750\n",
      "Completed 800\n",
      "Completed 850\n",
      "\n",
      "Queries without join results:\n",
      "Percent valid: 0.7916181606519208\n",
      "Percent SQLite matched: 0.17462165308498254\n",
      "Percent result matched: 0.42374854481955765\n",
      "Dataset length: 859\n"
     ]
    }
   ],
   "source": [
    "no_join_queries = pd.read_csv(\"./train-data/without_join.tsv\", sep='\\t')\n",
    "run_evaluation(no_join_queries, \"Queries without join\")\n",
    "print(\"Dataset length: \" + str(len(no_join_queries)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on full training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 50\n",
      "Completed 100\n",
      "Completed 150\n",
      "Completed 200\n",
      "Completed 250\n",
      "Completed 300\n",
      "Completed 350\n",
      "Completed 400\n",
      "Completed 450\n",
      "Completed 500\n",
      "Completed 550\n",
      "Completed 600\n",
      "Completed 650\n",
      "Completed 700\n",
      "Completed 750\n",
      "Completed 800\n",
      "Completed 850\n",
      "Completed 900\n",
      "Completed 950\n",
      "Completed 1000\n",
      "\n",
      "All training data results:\n",
      "Percent valid: 0.685823754789272\n",
      "Percent SQLite matched: 0.14367816091954022\n",
      "Percent result matched: 0.35823754789272033\n",
      "Dataset length: 1044\n"
     ]
    }
   ],
   "source": [
    "# Run evaluation on all training data\n",
    "run_evaluation(df, \"All training data\")\n",
    "print(\"Dataset length: \" + str(len(df)))"
   ]
  }
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