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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"id": "a87fe5f3",
"metadata": {
"id": "a87fe5f3"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import torch\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, BitsAndBytesConfig, EarlyStoppingCallback, PreTrainedTokenizer\n",
"from torch.utils.data import DataLoader\n",
"import sys\n",
"from peft import LoraConfig, get_peft_model, TaskType\n",
"from huggingface_hub import snapshot_download\n",
"import os\n",
"import re\n",
"import contextlib #helps make pip silent\n",
"import sys\n",
"import os\n",
"import numpy as np\n",
"\n",
"with contextlib.redirect_stdout(sys.__stdout__), contextlib.redirect_stderr(sys.__stderr__):\n",
" %pip install datasets\n",
" %pip install sql_metadata\n",
"\"\"\"\"\n",
"with contextlib.redirect_stdout(sys.__stdout__), contextlib.redirect_stderr(sys.__stderr__):\n",
" %pip install datasets\n",
" %pip install sql_metadata\n",
"\"\"\"\n",
"from datasets import Dataset\n",
"from sql_metadata import Parser\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
"from rag_metadata import SQLMetadataRetriever"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4ec432b2",
"metadata": {
"id": "4ec432b2"
},
"outputs": [],
"source": [
"is_google_colab = False\n",
"use_bnb = False"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "47577a7f",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 170,
"referenced_widgets": [
"9200f1303f124bddaa6114cdf0f5f878",
"17ddbb74e1764f37b8d34c311fae200c",
"ef732739334b4ac593fd665e01cd83c1",
"949ee3d1a9cd4060864dec5d4283ef2c",
"b98629e053674527aacca899ab7f11a9",
"84cc47dc70864bf3aa7599c06eb13c51",
"5d711bb927024d8d9f9b8bb685d6f388",
"3b80c66e0f384c45ab4187301599fab2",
"db6a23e658a34722a8f22505c6ace7b4",
"7751defbc4534d518d9e923b9019aa8b",
"fe6352bce22a40e7a936e7f90313bd02"
]
},
"id": "47577a7f",
"outputId": "999c4e88-3f89-49b1-9e21-abac91703bf3"
},
"outputs": [],
"source": [
"current_read_path = \"./\"\n",
"current_write_path = \"./\"\n",
"\n",
"def read_path(rel_path):\n",
" return os.path.join(current_read_path, rel_path)\n",
"\n",
"def write_path(rel_path):\n",
" return os.path.join(current_write_path, rel_path)\n",
"\n",
"if is_google_colab:\n",
" from google.colab import drive\n",
" drive.mount('/content/drive')\n",
" current_write_path = \"/content/drive/MyDrive/sql_gen\"\n",
"\n",
" hugging_face_path = snapshot_download(\n",
" repo_id=\"USC-Applied-NLP-Group/SQL-Generation\",\n",
" repo_type=\"model\",\n",
" allow_patterns=[\"train-data/*\", \"deepseek-coder-1.3b-instruct/*\", \"src/*\", \"nba-data/*\"],\n",
" )\n",
" sys.path.append(hugging_face_path)\n",
" current_read_path = hugging_face_path\n",
"else:\n",
" base_path = os.getcwd() # Use current working directory in notebooks\n",
" sys.path.append(os.path.abspath(os.path.join(base_path, '../..')))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "de7c3cd3",
"metadata": {
"id": "de7c3cd3"
},
"outputs": [],
"source": [
"MODEL_DIR = write_path(\"rag-rank-16\")"
]
},
{
"cell_type": "markdown",
"id": "4b7eb12a",
"metadata": {
"id": "4b7eb12a"
},
"source": [
"## Prepare Model"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "3d0c0e3b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3d0c0e3b",
"outputId": "a64bc20b-a33f-453e-e445-cd08109ed43b"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Dean\\AppData\\Local\\Temp\\ipykernel_22484\\3262638624.py:6: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
" df_train.applymap(lambda x: re.sub(r'\\s+', ' ', x) if isinstance(x, str) else x)\n",
"C:\\Users\\Dean\\AppData\\Local\\Temp\\ipykernel_22484\\3262638624.py:7: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
" df_test.applymap(lambda x: re.sub(r'\\s+', ' ', x) if isinstance(x, str) else x)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total train dataset examples: 2143\n",
" natural_query \\\n",
"0 Which NBA teams were established after the yea... \n",
"1 What is the most points the Los Angeles Lakers... \n",
"2 What is the second-highest number of points th... \n",
"3 How many home games did the Golden State Warri... \n",
"4 What is the average number of assists by the B... \n",
"\n",
" sql_query result \n",
"0 SELECT full_name FROM team WHERE year_founded ... New Orleans Pelicans \n",
"1 SELECT MAX(pts_home) FROM game WHERE team_nam... 162 \n",
"2 SELECT pts_home FROM game WHERE team_name_home... 156 \n",
"3 SELECT COUNT(*) FROM game WHERE team_abbrevi... 29 \n",
"4 SELECT AVG(ast_home) FROM game WHERE team_ab... 26.51355662 \n",
"Total test dataset examples: 150\n",
" natural_query \\\n",
"0 What is the average number of fg_pct in home g... \n",
"1 How many lead changes occurred in games where ... \n",
"2 Which team had the most away games where they ... \n",
"3 What is the maximum number of team rebounds re... \n",
"4 What was the average margin of victory for the... \n",
"\n",
" sql_query result \n",
"0 SELECT AVG(fg_pct_home) FROM game WHERE team_n... 0.4636694306246544 \n",
"1 SELECT SUM(lead_changes) as total_lead_changes... 5828.0 \n",
"2 SELECT team_abbreviation_away FROM game WHERE ... ATL \n",
"3 SELECT MAX(o.team_rebounds_away) FROM game g J... 16 \n",
"4 SELECT AVG(victory_margin) AS avg_victory_marg... 11.48148148 \n"
]
}
],
"source": [
"# Load dataset\n",
"df_train = pd.read_csv(read_path(\"train-data/train_set.tsv\"), sep='\\t')\n",
"df_test = pd.read_csv(read_path(\"train-data/test_set.tsv\"), sep='\\t')\n",
"\n",
"# Fix any spacing issues\n",
"df_train.applymap(lambda x: re.sub(r'\\s+', ' ', x) if isinstance(x, str) else x)\n",
"df_test.applymap(lambda x: re.sub(r'\\s+', ' ', x) if isinstance(x, str) else x)\n",
"\n",
"# Display dataset info\n",
"print(f\"Total train dataset examples: {len(df_train)}\")\n",
"print(df_train.head())\n",
"print(f\"Total test dataset examples: {len(df_test)}\")\n",
"print(df_test.head())\n",
"# Load tokenizer\n",
"model_name = read_path(\"deepseek-coder-1.3b-instruct\")\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
"\n",
"# Enable 8-bit quantization for lower memory usage\n",
"bnb_config = None\n",
"if use_bnb:\n",
" bnb_config = BitsAndBytesConfig(\n",
" load_in_8bit=True,\n",
" bnb_8bit_compute_dtype=torch.float16\n",
" )\n",
"\n",
"# Load model with quantization\n",
"#device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"device_name = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
"device = torch.device(device_name)\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" model_name,\n",
" quantization_config=bnb_config,\n",
" device_map=device\n",
")\n",
"\n",
"special_token = \"<|endofsql|>\"\n",
"\n",
"# Only add if it doesn’t already exist\n",
"#if special_token not in tokenizer.get_vocab():\n",
"#print(\"adding!\")\n",
"#print(len(tokenizer))\n",
"tokenizer.add_special_tokens({\"additional_special_tokens\": [special_token]})\n",
"tokenizer.eos_token = special_token\n",
"model.resize_token_embeddings(len(tokenizer))\n",
"\n",
"tokenizer.truncation_side = \"left\"\n"
]
},
{
"cell_type": "markdown",
"id": "3f6248fe",
"metadata": {},
"source": [
"## Prepare RAG"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ccbb122b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Dean\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:440: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\native\\transformers\\cuda\\sdp_utils.cpp:555.)\n",
" attn_output = torch.nn.functional.scaled_dot_product_attention(\n"
]
}
],
"source": [
"retriever = SQLMetadataRetriever()\n",
"\n",
"metadata_docs = [\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",
"\n",
"retriever.add_documents(metadata_docs)"
]
},
{
"cell_type": "markdown",
"id": "f87be471",
"metadata": {},
"source": [
"## Tokenize and Mask Training Examples"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7f8b1acf",
"metadata": {
"id": "7f8b1acf"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Map: 100%|██████████| 2143/2143 [00:54<00:00, 39.03 examples/s]\n",
"Map: 100%|██████████| 150/150 [00:04<00:00, 37.49 examples/s]\n"
]
}
],
"source": [
"\n",
"\n",
"def format_deepseek_chat(example, tokenizer, special_token=\"<|endofsql|>\"):\n",
" # Manually build the prompt as one flat string\n",
" relevant_schemas = retriever.retrieve(example[\"natural_query\"], top_k=1)\n",
" schema_block = \"\\n\\n\".join(relevant_schemas)\n",
"\n",
" input_prompt = f\"\"\"\n",
"You are an AI assistant that generates SQLite queries for an NBA database based on user questions.\n",
"\n",
"### Relevant Schema:\n",
"{schema_block}\n",
"\n",
"### Instructions:\n",
"- Generate a valid SQLite query to retrieve relevant data from the database.\n",
"- Use column names correctly based on the provided schema.\n",
"- Output only the SQLite query as plain text.\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. Now generate an SQLite query for the following user request.\n",
"Request:\n",
"\"\"\"\n",
" prompt = f\"{input_prompt}{example['natural_query']}\\n\"\n",
" completion = f\"SQLite:\\n{example['sql_query']}{special_token}\"\n",
"\n",
" full_text = prompt + completion\n",
" tokenized = tokenizer(\n",
" full_text,\n",
" truncation=True,\n",
" padding=\"max_length\",\n",
" max_length=3156, # or whatever your model can handle\n",
" )\n",
"\n",
" # Mask out prompt tokens in the labels\n",
" prompt_len = len(tokenizer(prompt, truncation=True)[\"input_ids\"])\n",
" labels = tokenized[\"input_ids\"][:]\n",
" labels[:prompt_len] = [-100] * prompt_len\n",
" tokenized[\"labels\"] = labels\n",
"\n",
" return tokenized\n",
"\n",
"# Build dataset dict\n",
"train_dataset_dict = {\n",
" \"natural_query\": df_train[\"natural_query\"].tolist(),\n",
" \"sql_query\": df_train[\"sql_query\"].tolist(),\n",
"}\n",
"\n",
"val_dataset_dict = {\n",
" \"natural_query\": df_test[\"natural_query\"].tolist(),\n",
" \"sql_query\": df_test[\"sql_query\"].tolist(),\n",
"}\n",
"\n",
"# Create HuggingFace Dataset\n",
"train_dataset = Dataset.from_dict(train_dataset_dict)\n",
"val_dataset = Dataset.from_dict(val_dataset_dict)\n",
"\n",
"\n",
"# Apply formatting\n",
"train_dataset = train_dataset.map(\n",
" lambda x: format_deepseek_chat(x, tokenizer),\n",
" remove_columns=[\"natural_query\", \"sql_query\"]\n",
")\n",
"\n",
"val_dataset = val_dataset.map(\n",
" lambda x: format_deepseek_chat(x, tokenizer),\n",
" remove_columns=[\"natural_query\", \"sql_query\"]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "43562f78",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 121,
"referenced_widgets": [
"68ff2fc00bd041e7b79a811e3de1e596",
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"e11d04a9d22a4229922e3eb4e3eb6466",
"5d89a5574a3d4a8993e6dca78d406d2d",
"dd24270dc07942a6972fbfaf58129989",
"643903cd7a5b4a52a4687ec38eb8c4dc",
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"1ec5329ea0434df4b74d0f311e016c3e"
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"name": "stdout",
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"\n",
"You are an AI assistant that generates SQLite queries for an NBA database based on user questions.\n",
"\n",
"### Relevant Schema:\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",
"\n",
"### Instructions:\n",
"- Generate a valid SQLite query to retrieve relevant data from the database.\n",
"- Use column names correctly based on the provided schema.\n",
"- Output only the SQLite query as plain text.\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. Now generate an SQLite query for the following user request.\n",
"Request:\n",
"What is the average number of fg_pct in home games by the Chicago Bulls?\n",
"SQLite:\n",
"SELECT AVG(fg_pct_home) FROM game WHERE team_name_home = 'Chicago Bulls';\n"
]
}
],
"source": [
"for v in val_dataset:\n",
" print(v)\n",
" print(tokenizer.decode(v['input_ids'], skip_special_tokens=True))\n",
" break\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "8890a657",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8890a657",
"outputId": "95b9971d-d446-432b-9faa-baa1c060d66a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 14,991,360 || all params: 1,361,463,296 || trainable%: 1.1011\n"
]
}
],
"source": [
"# Define LoRA configuration\n",
"lora_config = LoraConfig(\n",
" r=16, # Rank of LoRA matrices (adjust for memory vs. accuracy)\n",
" lora_alpha=32, # Scaling factor\n",
" lora_dropout=0.0, # Dropout for regularization\n",
" bias=\"none\",\n",
" task_type=TaskType.CAUSAL_LM,\n",
" target_modules=[\n",
" \"q_proj\",\n",
" \"k_proj\",\n",
" \"v_proj\",\n",
" \"o_proj\",\n",
" \"gate_proj\",\n",
" \"up_proj\",\n",
" \"down_proj\"\n",
" ]\n",
")\n",
"\n",
"# Wrap model with LoRA adapters\n",
"model = get_peft_model(model, lora_config)\n",
"model = model.to(device)\n",
"model.print_trainable_parameters() # Show trainable parameters count"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d9508451",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d9508451",
"outputId": "d004fa38-78a0-49ee-eed5-bbc6373ccae2"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-11-319f42a4ed7b>:21: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
" trainer = Trainer(\n",
"No label_names provided for model class `PeftModelForCausalLM`. Since `PeftModel` hides base models input arguments, if label_names is not given, label_names can't be set automatically within `Trainer`. Note that empty label_names list will be used instead.\n"
]
}
],
"source": [
"training_args = TrainingArguments(\n",
" output_dir=MODEL_DIR,\n",
" eval_strategy=\"epoch\", # Evaluate at the end of each epoch\n",
" save_strategy=\"epoch\", # Save model every epoch\n",
" per_device_train_batch_size=1, # LoRA allows higher batch size\n",
" per_device_eval_batch_size=1,\n",
" gradient_accumulation_steps=16,\n",
" num_train_epochs=10, # Increase if needed\n",
" learning_rate=5e-5, # Higher LR since we're only training LoRA layers\n",
" weight_decay=0.001,\n",
" logging_steps=50, # Print loss every 50 steps\n",
" save_total_limit=2, # Keep last 4 checkpoints\n",
" bf16=True if torch.cuda.is_available() else False,\n",
" push_to_hub=False,\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"eval_loss\",\n",
" greater_is_better=False\n",
")\n",
"\n",
"# Trainer setup\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=val_dataset,\n",
" tokenizer=tokenizer,\n",
" callbacks=[EarlyStoppingCallback(early_stopping_patience=2)]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b0ff5278",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 214
},
"id": "b0ff5278",
"outputId": "07e6446f-c680-4532-caad-d62a7d3edd6d"
},
"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",
"\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[33mlicesma\u001b[0m (\u001b[33mlicesma-usc\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
]
},
{
"data": {
"text/html": [
"Tracking run with wandb version 0.19.9"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Run data is saved locally in <code>/content/wandb/run-20250420_174906-5ypbflqe</code>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
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{
"data": {
"text/html": [
"Syncing run <strong><a href='https://wandb.ai/licesma-usc/huggingface/runs/5ypbflqe' target=\"_blank\">/content/drive/MyDrive/sql_gen/dyn_rag_test</a></strong> to <a href='https://wandb.ai/licesma-usc/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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"<IPython.core.display.HTML object>"
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},
"metadata": {},
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{
"data": {
"text/html": [
" View project at <a href='https://wandb.ai/licesma-usc/huggingface' target=\"_blank\">https://wandb.ai/licesma-usc/huggingface</a>"
],
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"<IPython.core.display.HTML object>"
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" View run at <a href='https://wandb.ai/licesma-usc/huggingface/runs/5ypbflqe' target=\"_blank\">https://wandb.ai/licesma-usc/huggingface/runs/5ypbflqe</a>"
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]
},
"metadata": {},
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{
"data": {
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='4' max='580' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [ 4/580 00:11 < 54:56, 0.17 it/s, Epoch 0.05/10]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table><p>"
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"source": [
"# Run training\n",
"trainer.train()\n",
"\n",
"# Merge LoRA adapters with the base model before saving\n",
"model = model.merge_and_unload()\n",
"model.save_pretrained(MODEL_DIR)\n",
"tokenizer.save_pretrained(MODEL_DIR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "J7qO7FE73i40",
"metadata": {
"id": "J7qO7FE73i40"
},
"outputs": [],
"source": [
"\n",
"# Prepare query with the same prompt\n",
"input_text = \"How many points do the Los Angeles Lakers average at home?\"\n",
"message = [{'role': 'user', 'content': input_prompt + input_text}]\n",
"inputs = tokenizer.apply_chat_template(message, add_generation_prompt=True, return_tensors=\"pt\").to(model.device)\n",
"\n",
"# Generate Tables\n",
"outputs = model.generate(\n",
" inputs,\n",
" max_new_tokens=256,\n",
")\n",
"model_output = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)\n",
"\n",
"print(\"Generated Tables:\", model_output)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "kwHMVyQa3n89",
"metadata": {
"id": "kwHMVyQa3n89"
},
"outputs": [],
"source": [
"import sqlite3 as sql\n",
"\n",
"prompt_length = len(input_prompt)\n",
"\n",
"print(prompt_length)\n",
"\n",
"# Create connection to sqlite3 database\n",
"connection = sql.connect(read_path('nba-data/nba.sqlite'))\n",
"cursor = connection.cursor()\n",
"\n",
"for v in val_dataset:\n",
" full_example = tokenizer.decode(v[\"input_ids\"], skip_special_tokens=True)\n",
" user_prompt = full_example[:prompt_length]\n",
" question, tables = full_example[prompt_length:].split(\"Tables:\\n\")\n",
" print(question)\n",
" print(tables)\n",
" break\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "LhiHqAaB9uE4",
"metadata": {
"id": "LhiHqAaB9uE4"
},
"outputs": [],
"source": [
"def extract_tables_from_string(s):\n",
" keywords = {\"game\", \"team\", \"other_stats\"}\n",
" found = {k for k in keywords if k in s}\n",
" return found"
]
},
{
"cell_type": "markdown",
"id": "Kdd8nxWD9txh",
"metadata": {
"id": "Kdd8nxWD9txh"
},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "KjAXaUgp4TfY",
"metadata": {
"id": "KjAXaUgp4TfY"
},
"outputs": [],
"source": [
"def compare_table_lists(actual_tables, generated_tables):\n",
" actual_set = extract_tables_from_string(actual_tables)\n",
" generated_set = extract_tables_from_string(generated_tables)\n",
"\n",
" # Check if they match\n",
" return generated_set == actual_set"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8h7bpMML6G6v",
"metadata": {
"id": "8h7bpMML6G6v"
},
"outputs": [],
"source": [
"\n",
"num_sql_matched = 0\n",
"\n",
"first_actual = []\n",
"first_model = []\n",
"print(\"Evaluating...\")\n",
"for v in val_dataset:\n",
" full_example = tokenizer.decode(v[\"input_ids\"], skip_special_tokens=True)\n",
" user_prompt = full_example[:prompt_length]\n",
" question, training_tables = full_example[prompt_length:].split(\"Tables:\\n\")\n",
" #print(question)\n",
" #print(sql_query)\n",
"\n",
" # Obtain model output\n",
" message = [{'role': 'user', 'content': input_prompt + question}]\n",
" inputs = tokenizer.apply_chat_template(message, add_generation_prompt=True, return_tensors=\"pt\").to(model.device)\n",
"\n",
" # Generate SQL query\n",
" outputs = model.generate(\n",
" inputs,\n",
" max_new_tokens=256,\n",
" pad_token_id=tokenizer.eos_token_id,\n",
" )\n",
" model_output = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)\n",
" after_last_colon = model_output.rsplit(\":\", 1)[-1]\n",
" tables_string = after_last_colon.replace('\\n', '').replace('\\r', '')\n",
" #print(\"Training tables:\", training_tables)\n",
" #print(\"Model tables:\", tables_string.split(\" \"))\n",
" first_actual = training_tables\n",
" first_model = tables_string\n",
" result = compare_table_lists(training_tables, tables_string)\n",
" if result:\n",
" num_sql_matched += 1\n",
"\n",
"print(\"Accuracy :\", num_sql_matched/len(val_dataset))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "CoJeZ4FoUMp_",
"metadata": {
"id": "CoJeZ4FoUMp_"
},
"outputs": [],
"source": [
"\n",
"num_sql_matched = 0\n",
"\n",
"first_actual = []\n",
"first_model = []\n",
"print(\"Evaluating...\")\n",
"for v in val_dataset:\n",
" full_example = tokenizer.decode(v[\"input_ids\"], skip_special_tokens=True)\n",
" user_prompt = full_example[:prompt_length]\n",
" question, training_tables = full_example[prompt_length:].split(\"Tables:\\n\")\n",
" #print(question)\n",
" #print(sql_query)\n",
"\n",
" # Obtain model output\n",
" message = [{'role': 'user', 'content': input_prompt + question}]\n",
" inputs = tokenizer.apply_chat_template(message, add_generation_prompt=True, return_tensors=\"pt\").to(model.device)\n",
"\n",
" # Generate SQL query\n",
" outputs = model.generate(\n",
" inputs,\n",
" max_new_tokens=256,\n",
" pad_token_id=tokenizer.eos_token_id,\n",
" )\n",
" model_output = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)\n",
" after_last_colon = model_output.rsplit(\":\", 1)[-1]\n",
" tables_string = after_last_colon.replace('\\n', '').replace('\\r', '')\n",
" #print(\"Training tables:\", training_tables)\n",
" #print(\"Model tables:\", tables_string.split(\" \"))\n",
" first_actual = training_tables\n",
" first_model = tables_string\n",
" result = compare_table_lists(training_tables, tables_string)\n",
" if result:\n",
" num_sql_matched += 1\n",
"\n",
"print(\"Accuracy :\", num_sql_matched/len(val_dataset))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "lNG1joS3T8DN",
"metadata": {
"id": "lNG1joS3T8DN"
},
"outputs": [],
"source": [
"model = AutoModelForCausalLM.from_pretrained(MODEL_DIR, torch_dtype=torch.bfloat16, device_map=device)\n",
"tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)\n"
]
}
],
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