File size: 13,552 Bytes
b0335cf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Augment data from TSV files to change team names and years"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create dictionary for mapping team names and abbreviations"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"30\n",
"30\n",
"30\n",
"30\n",
"0,Atlanta Hawks,0,ATL,0\n",
"1,Boston Celtics,1,BOS,1\n",
"2,Cleveland Cavaliers,2,CLE,2\n",
"3,New Orleans Pelicans,3,NOP,3\n",
"4,Chicago Bulls,4,CHI,4\n",
"5,Dallas Mavericks,5,DAL,5\n",
"6,Denver Nuggets,6,DEN,6\n",
"7,Golden State Warriors,7,GSW,7\n",
"8,Houston Rockets,8,HOU,8\n",
"9,Los Angeles Clippers,9,LAC,9\n",
"10,Los Angeles Lakers,10,LAL,10\n",
"11,Miami Heat,11,MIA,11\n",
"12,Milwaukee Bucks,12,MIL,12\n",
"13,Minnesota Timberwolves,13,MIN,13\n",
"14,Brooklyn Nets,14,BKN,14\n",
"15,New York Knicks,15,NYK,15\n",
"16,Orlando Magic,16,ORL,16\n",
"17,Indiana Pacers,17,IND,17\n",
"18,Philadelphia 76ers,18,PHI,18\n",
"19,Phoenix Suns,19,PHX,19\n",
"20,Portland Trail Blazers,20,POR,20\n",
"21,Sacramento Kings,21,SAC,21\n",
"22,San Antonio Spurs,22,SAS,22\n",
"23,Oklahoma City Thunder,23,OKC,23\n",
"24,Toronto Raptors,24,TOR,24\n",
"25,Utah Jazz,25,UTA,25\n",
"26,Memphis Grizzlies,26,MEM,26\n",
"27,Washington Wizards,27,WAS,27\n",
"28,Detroit Pistons,28,DET,28\n",
"29,Charlotte Hornets,29,CHA,29\n"
]
}
],
"source": [
"# Create team map and team array\n",
"team_map = {\n",
" \"Atlanta Hawks\": 0,\n",
" \"Boston Celtics\": 1,\n",
" \"Cleveland Cavaliers\": 2,\n",
" \"New Orleans Pelicans\": 3,\n",
" \"Chicago Bulls\": 4,\n",
" \"Dallas Mavericks\": 5,\n",
" \"Denver Nuggets\": 6,\n",
" \"Golden State Warriors\": 7,\n",
" \"Houston Rockets\": 8,\n",
" \"Los Angeles Clippers\": 9,\n",
" \"Los Angeles Lakers\": 10,\n",
" \"Miami Heat\": 11,\n",
" \"Milwaukee Bucks\": 12,\n",
" \"Minnesota Timberwolves\": 13,\n",
" \"Brooklyn Nets\": 14,\n",
" \"New York Knicks\": 15,\n",
" \"Orlando Magic\": 16,\n",
" \"Indiana Pacers\": 17,\n",
" \"Philadelphia 76ers\": 18,\n",
" \"Phoenix Suns\": 19,\n",
" \"Portland Trail Blazers\": 20,\n",
" \"Sacramento Kings\": 21,\n",
" \"San Antonio Spurs\": 22,\n",
" \"Oklahoma City Thunder\": 23,\n",
" \"Toronto Raptors\": 24,\n",
" \"Utah Jazz\": 25,\n",
" \"Memphis Grizzlies\": 26,\n",
" \"Washington Wizards\": 27,\n",
" \"Detroit Pistons\": 28,\n",
" \"Charlotte Hornets\": 29\n",
"}\n",
"\n",
"team_array = [\n",
"\"Atlanta Hawks\",\n",
"\"Boston Celtics\",\n",
"\"Cleveland Cavaliers\",\n",
"\"New Orleans Pelicans\",\n",
"\"Chicago Bulls\",\n",
"\"Dallas Mavericks\",\n",
"\"Denver Nuggets\",\n",
"\"Golden State Warriors\",\n",
"\"Houston Rockets\",\n",
"\"Los Angeles Clippers\",\n",
"\"Los Angeles Lakers\",\n",
"\"Miami Heat\",\n",
"\"Milwaukee Bucks\",\n",
"\"Minnesota Timberwolves\",\n",
"\"Brooklyn Nets\",\n",
"\"New York Knicks\",\n",
"\"Orlando Magic\",\n",
"\"Indiana Pacers\",\n",
"\"Philadelphia 76ers\",\n",
"\"Phoenix Suns\",\n",
"\"Portland Trail Blazers\",\n",
"\"Sacramento Kings\",\n",
"\"San Antonio Spurs\",\n",
"\"Oklahoma City Thunder\",\n",
"\"Toronto Raptors\",\n",
"\"Utah Jazz\",\n",
"\"Memphis Grizzlies\",\n",
"\"Washington Wizards\",\n",
"\"Detroit Pistons\",\n",
"\"Charlotte Hornets\"]\n",
"\n",
"# Check that array and dictionary are aligned properly\n",
"for i in range(len(team_array)):\n",
" if i != team_map[team_array[i]]:\n",
" print(\"Invalid!\")\n",
"\n",
"# Create abbreviation map and array\n",
"abbreviation_array = [\n",
"\"ATL\",\n",
"\"BOS\",\n",
"\"CLE\",\n",
"\"NOP\",\n",
"\"CHI\",\n",
"\"DAL\",\n",
"\"DEN\",\n",
"\"GSW\",\n",
"\"HOU\",\n",
"\"LAC\",\n",
"\"LAL\",\n",
"\"MIA\",\n",
"\"MIL\",\n",
"\"MIN\",\n",
"\"BKN\",\n",
"\"NYK\",\n",
"\"ORL\",\n",
"\"IND\",\n",
"\"PHI\",\n",
"\"PHX\",\n",
"\"POR\",\n",
"\"SAC\",\n",
"\"SAS\",\n",
"\"OKC\",\n",
"\"TOR\",\n",
"\"UTA\",\n",
"\"MEM\",\n",
"\"WAS\",\n",
"\"DET\",\n",
"\"CHA\"]\n",
"\n",
"abbreviation_map = {}\n",
"for i in range(len(abbreviation_array)):\n",
" abbreviation_map[abbreviation_array[i]] = i\n",
"\n",
"print(len(team_array))\n",
"print(len(team_map))\n",
"print(len(abbreviation_array))\n",
"print(len(abbreviation_map))\n",
"\n",
"# Check that team names, abbreviation and index maps all line up\n",
"for i in range(len(team_array)):\n",
" print(str(i) + \",\" + team_array[i] + \",\" + str(team_map[team_array[i]]) + \",\" + abbreviation_array[i] + \",\" + str(abbreviation_map[abbreviation_array[i]]))\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create function to augment data by updating team names in SQL queries"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"import pandas as pd\n",
"import sqlite3 as sql\n",
"\n",
"# Find team names in the sample\n",
"def find_teams_in_sample(sample, team_list):\n",
" result = []\n",
" for i in range(len(team_list)):\n",
" if team_list[i] in sample:\n",
" result.append(i)\n",
" return result\n",
"\n",
"# Get random number excluding the one already used\n",
"def get_random_excluding(floor, ceiling, excluded_number):\n",
" number = random.randint(floor, ceiling)\n",
" while number == excluded_number:\n",
" number = random.randint(floor, ceiling)\n",
" return number\n",
"\n",
"def augment_dataframe(df, team_list, abbreviation_list, database):\n",
" augmented_df = df.copy()\n",
" for _, row in df.iterrows():\n",
" team_idx = find_teams_in_sample(row[\"natural_query\"], team_list)\n",
" # Only do simple update if only one team detected \n",
" if len(team_idx) == 1:\n",
" team_idx = team_idx[0]\n",
"\n",
" # Check if team name is used in SQL query\n",
" if team_list[team_idx] in row[\"sql_query\"]:\n",
" # Create updated query with new team \n",
" new_team_name = team_list[get_random_excluding(0, 29, team_idx)]\n",
" new_natural_query = row[\"natural_query\"].replace(team_list[team_idx], new_team_name)\n",
" new_sql_query = row[\"sql_query\"].replace(team_list[team_idx], new_team_name)\n",
"\n",
" # Obtain result of running on sqlite database\n",
" try:\n",
" database.execute(new_sql_query)\n",
" rows = database.fetchall()\n",
" if len(rows) == 1:\n",
" if len(rows[0]) == 2 and rows[0][1] == None:\n",
" result = str(rows[0][0])\n",
" else:\n",
" result = \" | \".join(str(x) for x in rows[0]) \n",
" else:\n",
" result = \" | \".join(str(x) for x in rows)\n",
" # Append new row to augmented dataframe if result successful\n",
" new_row = pd.DataFrame([{'natural_query': new_natural_query, 'sql_query': new_sql_query, 'result': result}])\n",
" augmented_df = pd.concat([augmented_df, new_row], ignore_index=True)\n",
" except:\n",
" pass\n",
"\n",
" # Check if abbreviation is in SQL query used instead\n",
" elif abbreviation_list[team_idx] in row[\"sql_query\"]:\n",
" # Create updated query with new team \n",
" new_index = get_random_excluding(0, 29, team_idx)\n",
" new_team_name = team_list[new_index]\n",
" new_team_abbreviation = abbreviation_list[new_index]\n",
" new_natural_query = row[\"natural_query\"].replace(team_list[team_idx], new_team_name)\n",
" new_sql_query = row[\"sql_query\"].replace(abbreviation_list[team_idx], new_team_abbreviation)\n",
"\n",
" # Obtain result of running on sqlite database\n",
" try:\n",
" database.execute(new_sql_query)\n",
" rows = database.fetchall()\n",
" if len(rows) == 1:\n",
" \n",
" if len(rows[0]) == 2 and rows[0][1] == None:\n",
" result = str(rows[0][0])\n",
" else:\n",
" result = \" | \".join(str(x) for x in rows[0]) \n",
" else:\n",
" result = \" | \".join(str(x) for x in rows)\n",
" # Append new row to augmented dataframe if result successful\n",
" new_row = pd.DataFrame([{'natural_query': new_natural_query, 'sql_query': new_sql_query, 'result': result}])\n",
" augmented_df = pd.concat([augmented_df, new_row], ignore_index=True)\n",
" except:\n",
" pass\n",
" return augmented_df\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test functions on small dataframe sample"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total dataset examples: 1044\n",
"New Dataset Length:\n",
"6\n",
"\n",
"What is the total free throws made by the Indiana Pacers at home?\n",
"SELECT SUM(ftm_home) as total_ftm FROM game WHERE team_name_home = 'Indiana Pacers';\n",
"39545.0\n",
"\n",
"How many total rebounds did the Los Angeles Lakers grab in the 1985 season?\n",
"SELECT SUM(reb) AS total_rebounds FROM ( SELECT reb_home AS reb FROM game WHERE team_abbreviation_home = 'LAL' AND season_id = '21985' UNION ALL SELECT reb_away AS reb FROM game WHERE team_abbreviation_away = 'LAL' AND season_id = '21985' );\n",
"3655.0\n",
"\n",
"How many home games did the Orlando Magic play in the 2013 season?\n",
"SELECT COUNT(*) FROM game WHERE team_name_home = 'Orlando Magic' AND season_id = '22013';\n",
"41.0\n",
"\n",
"What is the total free throws made by the Denver Nuggets at home?\n",
"SELECT SUM(ftm_home) as total_ftm FROM game WHERE team_name_home = 'Denver Nuggets';\n",
"43821.0\n",
"\n",
"How many total rebounds did the Miami Heat grab in the 1985 season?\n",
"SELECT SUM(reb) AS total_rebounds FROM ( SELECT reb_home AS reb FROM game WHERE team_abbreviation_home = 'MIA' AND season_id = '21985' UNION ALL SELECT reb_away AS reb FROM game WHERE team_abbreviation_away = 'MIA' AND season_id = '21985' );\n",
"None\n",
"\n",
"How many home games did the Atlanta Hawks play in the 2013 season?\n",
"SELECT COUNT(*) FROM game WHERE team_name_home = 'Atlanta Hawks' AND season_id = '22013';\n",
"41\n",
"\n"
]
}
],
"source": [
"# Load dataset\n",
"train_df = pd.read_csv(\"./train-data/sql_train.tsv\", sep='\\t')\n",
"\n",
"# Display dataset info\n",
"print(f\"Total dataset examples: {len(train_df)}\")\n",
"#print(train_df.head())\n",
"\n",
"# Setup sqlite database connection\n",
"connection = sql.connect('./nba-data/nba.sqlite')\n",
"cursor = connection.cursor()\n",
"\n",
"# Test augmentation on sample of 3 rows\n",
"test_df = train_df.sample(n=3)\n",
"#for _, row in test_df.iterrows():\n",
" #print(row)\n",
"#print()\n",
"#print()\n",
"\n",
"# Run augmentation function and print output\n",
"augmented_df = augment_dataframe(test_df, team_array, abbreviation_array, cursor)\n",
"print(\"New Dataset Length:\")\n",
"print(len(augmented_df))\n",
"print()\n",
"for _, row in augmented_df.iterrows():\n",
" print(row[\"natural_query\"])\n",
" print(row[\"sql_query\"])\n",
" print(row[\"result\"])\n",
" print()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|