Baseline_SVM_v3.py

import sys
sys.path.append('../Utils/')
import random
import time
import pickle
import numpy as np
import pandas as pd
from sklearn import *


from sklearn.svm import *
from sklearn.model_selection import *
from sklearn.metrics import *
#from keras.utils import np_utils
#import seaborn as sns
from statistics import mean
import concurrent.futures
from sklearn.model_selection import cross_val_score
from concurrent.futures import ProcessPoolExecutor, as_completed
import datetime
from functools import partial
import argparse
from concurrent.futures import ThreadPoolExecutor


from multiprocessing import Process, Queue
import os
# manually tune svm model hyperparameters using skopt on the ionosphere dataset

from pathlib import Path
from skopt.space import Integer
from skopt.space import Real
from skopt.space import Categorical
from skopt.utils import use_named_args
from skopt import gp_minimize
from joblib import Parallel, delayed
import matplotlib.pyplot as plt
from skopt import BayesSearchCV
# import required module

from tqdm import tqdm

OPTIMIZED_RESULTS_FOLDER = "optimized_results"



# nohup python3 Baseline_SVM_v3.py --dataset_name taffc_final_base > nohup_output_taffc_final_base.out

# python3 -m demucs --two-stems=drums /home/shared/merge/datasets/audio/final_audio/Q1/MT0036368550.mp3 -d cuda -o /home/shared/merge/datasets/audio/final_audio/demucs/Q1


curr_path = Path('.').absolute().parent.__str__()
# Path to the directory with all necessary data
ptd = 'New_MERGE_Complete_Data/'
# Location of the .wav files converted from .mp3 as the intermediate step to generate Mel-spectrograms
#location = r'C:\Users\Red\Desktop\hugo'
# Name of the file with the song ids and respective quadrants
#csv_name = r'merged_squared_balanced_audio_not_sorted_annotations.csv'
#csv_name = r"panda_dataset_taffc_annotations.csv"
# Name of the file containing all samples. The program expects a pickle file
# with the structure: (num_samples, (song_id, waveform, target))
#samples_file = r'all_features.csv'
#samples_file = r"merged_squared_full_bimodal_not_sorted.csv"
###############
## Utils ######
###############

def print_with_time(message, flush = False):
    current_time = datetime.datetime.now().strftime("%H:%M:%S")
    print(f"[{current_time}] {message}", flush = flush)

def write_to_file_with_time(message, file):
    current_time = datetime.datetime.now().strftime("%H:%M:%S")
    file.write(f"[{current_time}] {message}")

def get_information(dataset_name):
    ##########################
    ## Baseline ##
    ##########################
    if dataset_name == "bimodal_full_baseline":
        csv_normal = "merged_squared_full_bimodal_not_sorted.csv"
        csv_annots = "merged_squared_full_bimodal_not_sorted_annotations.csv"
        top_features = [1152,1755,1747,525,1151,1166,1750,945,1089,927,1077,507,2091,2092,2105,1441,2106,1475,2055,1497,909,1071,10,133,1059,1065,946,361,923,1090,529,1765,1455,13,917,1603,1489,772,407,1053,1753,1768,1537,136,929,246,911,21,1574,526,2075,2085,1481,1748,1550,2068,1604,1154,2086,958,1952,2061,406,1096,1078,2069,1772,1940,109,252,2597,2074,1539,905,1101,2087,1757,1760,404,1594,1652,2056,1761,778,776,1084,1102,1764,247,770,1839,899,357,1726,2066,497,1194,1469,1792,2090,935,2598,1609,2083,891,235,1982,2089,503,1072,2081,431,9,2299,315,121,1350,1637,2289,1047,2073,236,1649,1863,1472,2302,897,784,115,1909,879,1108,2405,438,146,2072,1650,1791,893,753,2277,251,764,1798,1485,8,2107,1874,2603,2057,469,84,2084,1643,885,2599,1607,669,2076,2136,1035,2297,509,2142,2064,2298,1447,521,1419,1590,1153,782,1406,1928,1800,1845,1506,1581,1407,1483,1806,681,2071,1756,277,1448,1460,1107,513,2082,2268,1155,1644,881,1802,11,66,1066,1568,1055,1988,71,1790,468,1378,790,2065,941,1149,964,1842,928,887,2094,152,466,1638,1857,448,2631,362,2404,1189,465,2067,131,1353,1427,1161,766,1640,2070,1023,281,2077,960,2627,1897,2625,1582,1041,2334,1461,1965,358,1516,1635,363,1862,1787,1474,1364,1984,963,1775,1552,3,1767,1518,1634,767,1393,80,1639,2088,1150,447,1470,2274,1647,1887,1231,2010,1433,1641,464,1821,1955,956,692,321,2028,1234,1953,873,1487,413,950,1596,2364,1653,922,1789,1900,1451,2600,1777,1486,916,1060,86,1114,2303,166,975,1468,437,434,1029,1538,1605,2445,1349,954,1770,1080,1859,1507,1912,1915,1428,418,1865,2058,2399,1422,2079,64,1444,1195,2628,90,2672,417,1646,2194,2009,2335,1377,794,79,2292,1803,2109,1754,1971,1941,527,435,1805,1435,2108,137,788,562,2051,1502,796,2319,240,2317,2188,1168,1878,1025,1345,2271,2306,58,1729,1633,1774,445,2311,101,1412,1264,415,2078,520,2452,2683,763,1334,1885,2626,2363,1735,1356,1898,910,2451,757,1371,2670,2269,2411,556,1645,1876,1903,2677,446,1210,1017,1357,2049,1125,976,1508,1981,1434,2310,1530,1834,1651,1980,2307,1943,2099,1733,1476,1420,1967,1906,1449,685,2507,2632,1664,414,1079,532,1542,416,1113,1973,2337,1216,130,2172,688,793,1529,754,981,2634,443,408,1739,1569,1352,2648,1910,1543,1822,2454,2633,2647,1778,1068,1727,1869,85,1904,1549,1440,1482,1801,1191,1970,892,2166,775,2513,1479,2407,409,1126,1933,761,2483,1793,1532,1595,2145,1807,962,1985,2620,1804,977,1119,70,2348,1690,410,2027,480,2678,412,1824,944,2406,1098,538,2551,2329,2376,1929,697,474,2606,522,223,1092,1120,1864,1825,875,938,1056,1833,2524,140,1061,2278,1931,904,1648,2063,7,1073,1416,2313,2130,439,2512,1546,1667,1587,2684,2442,2617,2275,285,2629,2624,440,2316,1049,1630,254,308,2050,476,2132,2615,1467,479,352,2401,1795,1235,505,59,1986,1836,241,34,1211,2623,1763,444,2137,1037,2295,524,2111,898,1868,894,477,411,94,314,1624,2610,1170,1964,493,918,2485,874,2080,869,280,2146,1026,2580,1974,1786,1514,867,1217,1372,2552,516,442,802,1642,755,1054,53,1031,2570,2192,1987,968,1327,161,1019,2614,855,1544,1730,2176,1629,1373,1776,2102,2294,2021,1358,2331,673,920,2037,2616,333,817,2618,682,2579,969,2286,2143,2179,2611,812,1856,1322,1085,882,1734,2473,1105,1086,1048,1991,2006,244,1454,1111,1886,671,2515,209,25,2475,91,806,2440,2506,932,2320,2457,2381,327,104,162,2296,1178,2015,2185,475,229,1365,1465,1989,2366,547,1074,29,760,1769,1011,1632,2127,851,1024,769,1075,953,1608,2630,2375,2410,253,818,773,340,35,2193,959,2550,1968,506,2612,217,2622,1099,779,2643,1619,1621,335,970,2189,478,829,1457,811,1036,2161,168,2644,1104,1832,683,2427,174,211,309,287,2038,2619,1888,256,1490,27,832,1205,1545,1329,530,471,1879,2448,2430,1013,880,39,2428,2003,16,2641,1997,2175,1175,758,900,329,1588,800,850,1014,780,1872,470,2098,199,689,243,1654,1671,948,1920,2676,2367,405,886,2608,1589,2043,2177,1043,876,2384,40,1553,1093,838,2378,1843,2594,2416,605,2096,2008,1771,2566,26,550,303,856,204,808,544,173,1192,304,906,1442,24,1109,814,845,364,980,2592,2149,888,1873,1922,1762,2093,2682,2591,242,2144,870,1548,2613,1784,1736,1837,2429,2502,2395,2342,2191,2180,840,2104,1218,860,647,2674,2110,2344,134,1018,2196,2147,854,2446,328,914,2655,288,63,1742,317,641,659,2195,820,2662,2533,2548,2663,826,2340,1012,472,2593,2232,853,785,868,1732,1700,844,112,2022,983,1511,686,1998,1826,2004,2572,294,1881,839,2138,2530,2153,221,433,2343,155,1741,1087,2569,226,2333,2120,18,2052,2121,1779,982,2518,1877,310,2640,2187,1956,833,2345,250,2183,2171,205,2113,2414,2163,1976,345,912,1042,1611,2129,274,2413,342,1882,441,896,2681,1177,665,861,1020,1005,95,2387,1979,28,2276,147,2607,2290,1846,599,541,341,1045,295,2300,128,1960,821,2519,1303,2332,2272,2115,2649,1977,2568,566,1227,302,202,297,347,1027,2539,301,272,2471,1421,2186,1062,2639,653,1880,884,1030,1752,1117,2347,611,1050,926,2522,1618,902,2328,2346,380,1081,286,1744,1841,1975,924,2128,2141,2100,695,842,307,151,2097,2675,289,2415,216,623,836,82,2283,263,1317,2190,1531,635,2664,1963,848,300,2305,1318,339,617,596,2370,1844,2312,517,797,2135,1463,2398,2523,57,2412,1894,324,2685,791,2284,847,1785,1129,473,1555,2531,1559,2318,311,827,2509,1883,23,2495,1946,1032,719,1930,518,351,971,320,2397,2016,1069,862,536,1321,292,864,1992,2000,1203,629,355,1456,2101,1731,1934,1273,374,185,1969,1884,595,1039,2351,1917,1996,2526,704,701,2532,2536,553,2642,2182,2173,2420,2160,2281,1870,1745,846,1737,306,2528,2527,2124,859,385,1942,296,1038,1838,1828,305,1853,2563,228,908,974,1855,593,2181,890,4,1296,2557,1890,1860,2669,76,1617,232,2279,1990,1994,319,2666,2679,572,1871,2309,2358,2665,2046,1517,2505,337,2280,834,2585,2321,2030,334,2538,2119,74,1196,2379,2390,2668,2322,141,878,1583,2425,1616,2426,841,1274,290,1033,2546,2439,2462,2002,2287,293,1781,1057,1796,1954,1379,54,336,1226,22,299,1926,1923,1260,1628,278,271,1063,258,1123,259,1809,1115,1458,2545,2650,2604,828,2486,1554,2361,2469,312,857,17,2421,2474,1816,2667,809,2432,2169,1957,1535,164,1939,2438,2349,2424,2529,1044,1232,275,1224,2273,2125,1443,2024,803,1462,67,214,2609,198,291,1221,815,2494,323,1051,284,2459,2031,60,2158,2389,2516,1015,2385,2464,2394,1847,2510,2042,672,1797,2369,2621,2423,1938,587,1891,1627,1021,1818,2368,1623,2418,2184,1631,2408,1831,559,2170,2154,1499,298,2661,1464,2386,467,2637,1259,2501,208,2525,1612,88,1783,1819,261,2391,678,1799,2517,2206,348,2103,359,349,330,1740,381,2383,1927,72,2372,225,189,331,2565,265,1246,2497,666,2155,1622,343,1937,1866,2646,1162,583,206,269,2359,2117,2455,1198,2118,1849,1212,1006,2660,1895,576,1949,1861,2651,1780,2054,2460,2449,338,1944,2053,584,2151,2419,1615,1932,594,1746,539,2282,2461,2270,1896,2588,1848,1919,2500,930,2547,111,1498,2487,2036,2352,191,2044,1814,15,1867,2400,2466,2039,2041,2468,2377,2123,360,2584,73,2059,264,2060,267,2564,1916,273,2045,1902,1935,2314,2026,325,2122,382,2521,2114,1914,558,1889,2540,171,2558,2534,2542,1620,266,2388,1901,2431,1854,1682,2636,1908,1830,222,1907,188,2673,1127,986,2032,1758,569,2458,972,270,1613,2304,548,2470,2511,1945,2341,2450,684,2576,354,2605,2338,2126,2537,318,2671,432,2602,154,268,2167,1686,1921,2140,2503,2047,2062,2396,2472,2402,1812,1220,2535,1685,512,2336,316,1681,135,2589,2134,545,2436,2164,2380,618,1561,792,1436,589,1687,2586,1817,119,2456,1297,346,2288,2393,2327,563,1851,2437,2653,2159,1827,279,110,552,350,2463,2433,2544,1892,1918,2575,588,2365,2482,1190,2339,2447,2573,61,2040,2293,2139,2360,1688,6,2353,1999,1913,2150,159,1680,822,2465,1625,1983,2435,551,1911,872,326,1738,75,2581,19,2508,1788,1331,1541,2324,2326,2116,612,1343,2645,2434,165,2409,2514,1684,564,1893,207,2556,2657,2574,716,2422,2374,624,1689,260,2162,220,965,2493,2350,2555,565,2178,1679,2168,2492,2578,2453,195,2488,56,810,2498,149,2583,332,2481,68,1121,2157,2541,1560,581,219,2131,36,2554,105,1924,2582,2403,1504,2152,1852,2156,2018,1008,852,786,2520,2308,2242,322,83,2680,1751,93,728,2362,1811,1993,1614,1199,143,2315,2656,1813,1678,2444,113,679,2165,129,2499,1340,858,2325,2174,2686,2023,2357,1850,2590,984,365,1683,1808,233,2371,1728,201,690,798,2443,1951,1875,1858,1676,2577,2596,2480,2323,1660,2048,2567,2561,123,1950,2356,1533,1661,2553,2601,2005,2213,2212,2571,2211,2204,2203,2210,2496,2224,2202,2231,2201,2223,2200,2467,2218,2230,2222,2199,2197,2198,2214,2215,2216,2217,2219,2220,2221,2225,2226,2227,2228,2229,768,2207,966,816,2261,2262,2263,2264,2265,2266,2267,117,2209,2208,196,1659,1708,1713,55,1961,77,89,92,116,122,230,231,248,276,282,356,1773,2392,249,1691,1606,2239,2240,2595,1709,1710,1714,1692,1693,1694,1695,1696,1697,1698,1711,1712,1715,1716,1717,1718,1722,1723,1724,1725,2249,1721,1820,2133,2246,2248,2490,1247,2238,2247,1662,1677,2254,1128,2233,2205,255,2237,2234,2236,2235,2253,69,49,1701,2484,2543,2252,804,2478,2251,1835,2250,1519,2255,2256,2257,2258,2259,2260,2491,774,1223,1719,2504,1658,1947,1704,2244,2245,2635,2654,2549,1720,528,2559,2638,2243,1276,570,1702,1703,2241,762,1699,37,1610,1766,2587,1336,2095,2373,2441,1665,740,2035,2659,756,1925,1122,344,1666,582,2658,1225,1705,213,1657,1636,20,1707,367,866,1491,1197,2489,1959,1958,2355,1905,1706,1656,2354,2034,2285,2560,2382,752,153,1663,1670,2013,1655,2033,2330,2476,546,1840,1669,257,1668,2014,62,1823,1995,44,47,2479,1899,1972,1675,2652,2019,2011,48,38,2001,2025,1009,2012,2417,2562,50,30,31,1962,2477,1794,1815,2007,1626,41,1782,43,1978,2020,32,2017,1674,1810,46,1743,1672,1673,52,1829,45,1966]

        bad_iteration_list = []
    elif dataset_name == "bimodal_balanced_baseline":
        csv_normal = "merged_squared_balanced_bimodal_not_sorted.csv"
        csv_annots = "merged_squared_balanced_bimodal_not_sorted_annotations.csv"
        top_features = 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        bad_iteration_list = []
    elif dataset_name == "audio_full_baseline":
        csv_normal = "merged_squared_final_not_sorted.csv"
        csv_annots = "merged_squared_final_not_sorted_annotations.csv"
        top_features = [1747,1152,1755,525,2055,1151,1166,507,1750,2091,2092,2105,2106,945,927,361,10,2056,1441,1497,529,1089,13,21,1765,909,1768,1071,1475,1077,407,1550,1065,133,404,772,1537,1489,2085,946,1757,2057,1760,1753,406,526,1059,2068,2061,1053,1154,136,109,1455,2086,1574,121,1090,917,753,923,2066,246,776,2074,1761,911,2083,770,2075,1748,1839,1764,357,778,1194,252,2069,9,891,1603,497,2087,958,2597,2107,1469,1539,1594,2090,1078,2299,1485,897,1982,784,1652,469,1604,1096,1419,1427,1047,2073,466,893,2277,438,247,2302,1447,465,1649,1609,1084,468,1406,1874,2599,236,1101,1155,2289,1102,2084,2072,2136,1637,1350,2142,2089,2081,1153,235,2603,11,905,8,1798,2071,521,115,1842,2598,764,1845,1481,1483,879,1590,1650,431,464,2298,1472,1407,899,1607,434,1643,1644,513,782,885,1353,362,3,1581,2064,1772,2065,1887,84,1735,1234,315,146,766,1364,66,681,1072,790,887,2067,928,358,1928,1035,1791,1149,1756,935,251,1486,503,2109,1775,1638,1800,1433,1582,2297,1435,1640,1434,1460,1487,2094,1552,1952,2076,1150,1474,881,2627,277,1568,1634,1444,1161,1635,1802,1988,435,1108,1733,152,1378,692,1806,1451,1506,2077,1516,1334,2082,166,1448,2625,131,1470,2070,2274,1984,1647,2631,1595,1231,754,1066,437,1596,1023,1653,1412,1770,1264,1641,2028,2405,281,767,2058,1915,71,1461,1041,2051,922,64,1777,1981,1468,2303,1980,1055,79,408,788,1885,90,58,1605,1878,2049,1941,2404,321,1754,1440,2010,975,80,86,1538,1803,130,669,413,2632,873,757,1801,1170,916,793,2268,1518,409,2683,796,964,527,685,1420,1542,2306,137,1357,1371,240,1778,2130,1940,1210,418,1790,2677,794,2009,2600,1476,446,1569,697,415,1428,1774,1767,763,1953,2310,981,1125,2399,1349,1356,910,2088,1865,1126,1530,1929,941,1416,524,1029,1345,1107,417,2292,363,1639,2079,976,1529,532,1025,2307,2317,1805,775,2678,1060,855,2319,2194,414,2626,2275,1189,538,516,1909,562,520,956,2335,1482,950,1646,1955,2684,7,904,509,892,416,867,1664,1645,2137,1532,1327,1054,1931,1393,1454,1080,1017,101,2132,1587,1377,1690,1235,2628,963,443,2145,869,505,2475,1804,977,2037,1787,2364,2473,688,1648,433,85,1876,94,59,1933,2311,2363,1739,1079,898,411,1422,556,412,1329,2111,671,440,241,530,944,1912,1358,1863,2634,2606,1322,1216,70,1549,2188,2269,2411,1352,2648,2647,1467,2633,2063,1786,161,1365,2579,447,1985,2427,2295,1217,1763,352,2428,761,829,1056,140,2027,2278,1211,410,2021,1825,2483,1514,2050,2457,2337,162,2108,2551,1479,2078,875,2127,2143,1372,1553,1178,856,2672,2524,802,1092,1667,1965,817,439,2038,2348,444,53,1205,769,1734,2175,1168,2623,1886,1762,1807,1727,1869,250,1026,1822,1903,2552,1114,874,760,2098,1792,1191,682,445,1011,91,1619,2172,1651,2430,2271,2591,168,242,35,493,2381,2445,2015,1897,2629,1085,2670,480,1872,243,1989,2166,2624,2146,448,2320,800,920,2592,2294,1630,1987,953,811,880,1049,1986,522,1642,1789,870,851,2334,104,442,1068,1910,1545,2296,1061,1105,938,474,2179,689,1589,285,1073,479,1588,263,1546,2286,683,1784,477,779,2416,2580,547,1014,1633,818,471,959,1793,405,1373,850,2196,174,2610,280,2316,2313,2620,1732,1904,962,550,755,2485,868,1769,2614,2192,1621,773,886,1465,1726,2329,308,2676,860,39,1048,40,1834,209,1043,1013,1457,340,2120,2121,2113,1075,2594,1632,2195,223,932,1037,1880,506,2003,1086,806,812,1974,1195,2161,1098,1111,1736,1019,605,1873,1104,173,253,470,1654,2375,2570,2185,1888,2096,1624,1119,351,327,2608,1868,2410,2442,1543,673,1730,244,2507,1490,27,544,1968,2611,876,832,1449,1671,1967,329,333,1943,2513,2452,541,1120,2630,2193,476,134,2043,2176,1843,1608,838,2102,2153,28,2099,226,472,686,1879,1031,1113,758,2128,2407,969,24,2406,1611,2471,2682,2366,2617,914,441,2618,983,2384,1821,2189,1844,2401,2097,2022,2440,2149,888,2612,1321,854,2616,1920,814,1877,2187,204,112,599,1012,2110,968,1074,1922,2104,1629,2141,475,1024,1036,1997,2008,2290,845,2506,695,2177,287,2004,2163,128,256,836,808,1175,2613,2615,1093,2135,63,2662,2655,2663,2276,1898,314,1742,25,1841,1109,185,2186,95,1507,1042,2171,2147,2129,1785,647,155,826,2387,1833,309,1062,844,2530,2080,2429,1559,1018,26,2328,2115,229,286,665,1964,1129,199,641,1511,2454,2512,820,478,2644,1744,595,1227,1826,1963,1741,2340,841,1442,2550,1881,2284,2232,259,611,840,2144,1776,2191,1832,982,2343,1099,2593,1977,317,902,701,34,1729,1508,970,342,18,719,1045,1020,926,1998,151,1824,1218,1087,57,1991,2006,2448,566,635,1960,842,1882,623,2622,617,1618,1837,1005,2681,861,864,2572,304,2643,2342,355,2183,2138,2486,2016,1517,1051,2674,2052,2675,2533,2548,2522,2333,659,74,839,274,2331,1926,202,1846,335,2566,1548,272,2619,1081,517,1531,1555,833,884,473,2518,82,1855,629,853,2376,847,1554,1853,1971,380,2346,1296,862,848,2030,2367,2398,211,2502,2668,785,1317,1050,221,2607,328,311,254,23,1117,653,1752,2180,797,385,2344,896,2100,258,960,1934,2395,2667,275,301,1771,1203,16,303,912,1894,2557,2451,1906,374,2523,2469,596,1796,1809,1027,2569,1883,1779,22,2160,2432,216,536,2413,1781,908,2318,2345,1616,518,297,1318,1303,217,2446,205,2526,1273,1917,2173,1862,310,265,1870,791,971,1969,294,1033,2046,1828,2190,2685,2641,2661,208,2412,2170,878,341,2420,2158,288,1057,2093,2182,337,339,292,2154,821,1192,2300,1623,2287,704,1816,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        # new with percussion
        bad_iteration_list = []
    elif dataset_name == "audio_balanced_baseline":
        csv_normal = "merged_squared_balanced_audio_not_sorted.csv"
        csv_annots = "merged_squared_balanced_audio_not_sorted_annotations.csv"
        top_features = [1152,525,1747,1755,1166,1151,2055,507,361,927,1750,2091,10,2105,2092,2106,945,1497,1441,13,1475,529,21,2056,1089,133,909,1065,1071,1077,1550,407,1765,1768,1537,1489,404,1760,772,1757,2057,946,1455,2085,136,1059,406,526,1753,2061,1574,1154,923,753,917,1053,2068,109,2086,246,1761,911,1090,1194,2074,1481,252,1764,776,121,2075,1748,770,1603,2083,2066,1539,778,357,1469,9,891,2597,2087,1839,897,497,2069,2107,247,1350,2299,2090,958,1594,1485,1604,469,1078,893,1609,1047,1406,1096,2289,2302,2277,1982,2136,466,236,2603,2599,784,438,899,1652,1419,2142,2073,235,1427,1084,2089,115,879,465,1101,521,1447,11,1874,1637,905,1153,2598,8,885,468,2084,2081,764,1102,84,2072,1607,1472,431,146,1798,1155,1650,1649,66,1581,1590,887,1842,434,2071,2298,1072,1483,1887,1407,513,1353,782,1928,1149,2064,1772,1845,1364,362,935,928,464,2297,1506,251,1952,681,1035,315,1775,1735,1582,1234,2627,1791,881,1756,3,1644,766,503,1487,1378,1643,358,277,790,152,1568,1634,2094,1486,2625,1460,1638,2067,1640,2631,2109,1448,2065,1470,2405,1108,71,1596,1800,1635,1231,1435,1552,1474,1433,166,1150,1023,281,80,131,1161,1066,435,1434,1041,2076,1777,1461,64,1516,1444,86,2082,1451,1915,692,1334,2303,437,1802,1264,1653,2404,767,2070,2051,1647,1806,1770,922,79,1988,1941,2058,2632,2077,2274,1055,1595,1733,1468,788,1885,1641,1412,58,1440,754,2268,2049,1538,1357,2399,321,1518,1984,669,916,1605,2306,527,1371,873,2010,1107,1940,964,418,1767,1530,1865,1542,1420,1803,1189,2683,2028,975,1774,1349,1428,1778,413,1929,1356,1953,1569,2319,90,794,2292,793,981,2677,2194,1345,1029,1393,2317,2310,2009,446,130,2600,1422,1878,685,409,1754,1639,910,950,796,1210,363,2626,1646,408,757,697,240,2275,1790,520,1025,137,763,2130,2307,417,1125,1805,1416,1909,2678,101,976,963,516,415,1955,2335,1126,1981,956,1060,509,1933,443,1801,2088,2363,1787,775,1645,1664,1377,85,2137,562,59,904,2037,524,898,2628,2684,1931,892,1476,414,1980,867,855,2079,1017,2132,2364,140,977,1529,1080,941,1170,532,70,869,1690,7,688,1454,2188,2606,2475,1327,2311,1532,2483,411,1079,505,2672,2269,2411,241,1054,1739,1912,1482,538,1235,433,1587,1863,1648,444,1216,671,447,440,2473,94,1352,448,1365,2143,416,2634,439,556,1553,1217,2648,1467,2647,2108,2633,1549,2337,2145,1372,412,1479,1965,442,2552,1358,2579,352,162,530,2295,1651,1804,2278,2348,2445,802,1114,2620,161,1876,1168,2111,2271,944,410,445,1322,2050,1667,1763,2551,2021,2038,2623,1734,1985,1903,761,1056,53,2629,1514,2063,2078,875,1822,474,938,1727,1869,2127,2670,1886,829,874,2172,480,2457,1205,2427,1178,1092,2334,2524,2166,1807,1987,522,1061,1329,1211,1026,250,1789,1897,2614,1792,1786,1619,870,1068,2624,682,1011,1546,1624,769,773,2428,683,920,1726,2320,242,800,479,856,755,168,1642,760,2175,2316,1910,1014,2286,2313,1043,1073,285,550,2027,1630,2591,1191,2485,779,817,886,1943,280,2146,243,547,2442,1588,405,932,850,1589,1793,2381,2430,605,477,1834,493,1457,2610,1825,1545,91,104,25,1019,35,1633,851,689,2179,1085,1037,1373,1967,1048,2098,263,2375,1621,1490,2580,1904,1465,1195,506,1872,2416,223,1013,812,2296,174,811,953,1986,2294,329,2015,2410,1989,1111,2329,1113,2592,2192,806,959,2120,673,2121,2113,1671,880,28,2507,1105,2161,818,1974,441,2401,2513,2630,40,1075,2440,1736,2407,868,1868,327,24,2676,1098,962,758,1049,1543,134,1086,2452,1629,1119,599,27,1449,253,1654,1120,2290,1769,476,1762,876,1104,26,2611,2185,209,2406,1632,2176,471,860,244,333,914,2617,2196,39,2570,1888,888,2043,1920,340,686,308,2366,470,256,2608,544,1784,2195,969,1608,832,204,983,2506,2618,1880,1821,2622,2193,2616,1074,2171,287,2471,2189,2615,2099,472,155,1742,2128,814,840,475,2096,611,647,1732,808,2512,2594,1042,1873,18,1877,1922,2384,968,173,2141,1175,1611,2613,838,1879,845,665,1843,2003,854,1321,1898,902,2135,2682,1012,2102,2276,351,1036,1776,541,185,1031,2612,226,1062,641,1218,2644,2110,1507,1227,1024,2662,309,317,229,1005,2663,2448,1833,2655,617,1744,820,2163,826,1093,2177,623,1618,1997,63,659,286,2022,112,1741,864,2153,1109,1045,861,199,1442,2429,844,2104,1511,314,1559,926,2080,1964,841,2643,2454,2129,1018,1837,695,862,2186,2619,1020,2593,2149,1882,1129,2144,635,2572,2284,151,2232,2530,1832,629,128,2097,2343,1824,517,2387,1729,1730,1963,982,82,2008,839,836,2328,2004,1555,653,1099,2187,254,970,304,16,1508,478,1841,797,566,1844,2191,1971,2376,2183,1771,1548,1517,34,335,274,2342,2138,2331,2398,2340,719,536,2052,2147,1881,1752,1960,272,473,2533,2548,842,1968,884,2566,1785,1977,896,2446,301,1855,328,2115,259,847,355,595,342,2550,1117,1998,57,785,2395,960,1317,303,1027,853,2413,2502,2318,2345,221,95,2518,1926,23,701,1051,380,1894,1087,912,2674,2451,2681,2607,1081,2685,1826,1273,2160,297,2668,791,385,2344,1906,2367,1554,2300,2346,2522,848,1828,1934,1853,833,217,1991,2006,1050,704,2486,310,2305,1779,2030,1303,1870,211,1192,2557,294,374,1296,295,1203,341,1846,292,311,1030,878,288,2523,2526,265,22,216,553,74,2333,2180,1177,1544,2287,821,2016,2046,2679,2641,205,258,2412,2675,1033,1318,2667,147,2519,275,908,846,202,596,1836,1862,1809,2182,2173,971,572,2312,2279,2665,2462,2569,339,1531,1917,2469,1900,296,381,1069,2158,827,2509,2100,929,518,1890,1781,2432,1260,2154,2420,1463,1115,289,1057,882,2650,1421,980,1956,2515,1259,1970,2190,890,17,1975,1583,2495,2649,780,307,208,1458,2283,2093,2585,1627,1816,1930,347,2280,1039,1944,1443,2378,2332,2640,2664,2439,300,1883,323,2181,2347,2124,302,284,359,2122,1996,2505,337,2461,2370,2472,29,2531,298,1456,305,2466,2281,290,1973,2459,1612,67,1226,1795,228,1990,1994,2666,2474,1857,1622,809,2164,1946,2170,2642,2155,1032,1631,2349,1819,319,2464,2103,1628,334,2351,1379,1908,2568,2044,1196,141,859,214,2134,1871,1818,293,2272,828,834,299,2563,1895,291,678,2321,1274,60,225,1831,1462,672,2586,206,164,198,2455,2140,2604,306,2661,1038,2397,559,1797,2042,1623,2421,2468,1044,1123,88,2414,1891,2379,2527,1927,269,1617,1783,2002,1796,312,1830,1021,1992,2000,1907,2639,2492,2390,593,2358,1932,2660,2423,1856,2588,1731,1838,1860,2119,1464,1814,68,1935,1745,1939,2460,2669,2637,1937,271,2341,1969,2491,336,1740,2206,76,2309,324,154,1006,320,1945,1758,2437,1737,1923,1063,188,2389,72,326,1246,2041,1615,2314,2438,382,1859,2545,2576,273,594,1780,2415,2539,2169,278,2418,467,261,539,1896,2595,2529,2385,331,2059,2184,2322,330,2391,2487,1221,2060,267,75,2456,54,232,191,1976,360,1921,803,569,584,4,2424,1616,1848,2501,2369,1854,2139,2621,1938,1799,2534,2542,576,2636,2036,2026,815,2517,684,1919,1916,2463,1614,563,1212,2157,345,548,2510,2525,2609,2516,2434,974,1901,583,2054,318,2528,2386,135,268,2488,348,2431,2646,2400,2532,2536,1867,2167,2546,2493,2350,2489,2433,15,325,1812,2361,61,2425,2288,894,2125,565,2581,1224,264,1613,2031,1849,270,1861,2497,1851,1892,2408,1127,551,349,1535,219,1232,2159,1884,1924,716,2368,2582,316,1847,1636,2282,2444,354,222,2558,1343,972,1902,207,581,2359,2544,111,338,2419,2273,1738,260,2494,2118,2377,332,2541,2521,587,558,2388,1015,2573,2383,19,2394,1866,1850,1957,2435,73,666,2039,2360,552,2293,1949,2657,2605,266,2024,2101,110,857,588,2436,2500,343,1502,589,2133,1561,2503,545,2117,2426,1162,165,1498,279,1827,2470,1541,1700,2537,1918,1297,2596,1766,1893,83,2365,2443,189,2372,1811,2560,2602,1560,2482,1746,1817,918,2450,2362,220,2538,2565,618,2575,1682,2651,1788,1813,171,2578,1198,346,350,2126,1889,1121,2504,1864,1913,2040,322,1751,2053,1499,2151,1190,2680,2422,2338,1914,822,2270,2165,2326,2339,2671,564,2114,2564,195,965,2458,2653,2514,2480,2686,2047,2638,1331,954,612,2547,1983,2336,2577,1681,1436,792,432,1979,2352,2168,2449,196,367,56,364,2508,2467,1336,2511,2062,1925,1008,2018,2380,872,2324,2409,2555,2556,1220,986,2162,2673,2353,2574,2396,119,924,2374,2152,1504,2304,624,1840,2393,810,2330,2357,149,2481,2308,2659,2315,6,2116,2156,2535,2543,2590,679,2589,1942,2447,113,512,2498,2584,2327,1961,1954,36,143,69,1999,1875,1852,2131,2587,365,2601,2178,1680,2562,2048,93,1625,2465,900,1199,2453,774,2645,233,105,201,582,2583,2540,213,798,2499,1687,2559,2571,2392,1951,1340,123,966,1672,2243,2403,2249,2248,2247,984,2246,2520,906,1686,1701,2210,2402,2245,858,2214,2215,2216,2217,2218,1688,2250,2251,2252,2253,2254,2219,2220,1620,2211,2212,2221,2213,2225,2226,2228,2229,117,2227,1658,2230,2222,2261,2231,2223,2244,2255,2256,2224,2197,2198,2199,2200,2201,2202,2203,2204,1659,2263,2264,2238,2235,2236,2237,2265,2266,2267,1683,2239,1703,1655,1657,1656,55,2234,1708,77,89,92,116,122,230,231,248,276,282,356,948,1684,1773,1677,2262,2233,1685,2240,2207,249,2553,1709,1678,2208,2209,1710,2242,1606,1689,1711,1712,1714,1713,31,852,2561,1715,1660,1679,1661,930,1662,1716,1702,1717,1718,1519,2257,816,1722,1723,1724,1725,1691,1720,1699,2285,62,1698,1694,1695,1696,1697,1247,1719,1721,1693,1707,2258,1276,2259,2260,528,2174,762,2490,1692,1704,255,1533,2441,728,1706,49,1993,768,153,2241,2033,1128,2496,2095,37,1959,1676,2005,1610,2635,1705,2656,2654,1958,44,2554,159,2356,1808,2484,2478,2023,2323,2032,740,690,129,2150,2549,2205,2371,2123,1491,1950,1911,1197,2035,2382,2025,786,1947,2034,1122,1009,1823,257,804,2567,1820,2658,570,1728,30,344,1668,756,1794,2007,546,1626,1666,2652,1858,2325,1835,1663,2013,1670,2045,1962,1225,1223,38,1995,1905,48,1669,2479,752,50,1899,2011,2001,2019,32,2417,1815,46,1673,1665,45,2355,1972,20,1743,2373,2476,2477,2354,2014,1966,2020,41,47,866,1675,2012,1978,2017,1829,1810,1782,52,1674,43]
        bad_iteration_list = []

    elif dataset_name == "taffc_baseline":
        csv_normal = "merged_taffc_not_sorted.csv"
        csv_annots = "merged_taffc_not_sorted_annotations.csv"
        #top_features = [1152,525,1755,1747,1166,133,136,945,1750,927,2056,1497,1089,246,507,2055,21,1151,1489,1071,247,151,772,1077,2068,909,529,1194,2074,404,2086,1550,121,2105,2106,9,526,2092,2091,946,2069,1065,1760,2066,753,13,1053,1441,109,1059,1757,113,10,958,2087,1469,2090,407,1475,778,891,2075,917,406,2083,1537,1940,1637,770,1090,893,923,2061,1101,155,2081,911,899,1753,905,361,1096,1765,251,110,1768,1603,1195,1652,357,776,1461,2084,1643,2085,116,1574,1764,1350,328] 
        # all features
        top_features = [1152,525,1755,1747,1166,133,136,945,1750,927,2056,1497,1089,246,507,2055,21,1151,1489,1071,247,151,772,1077,2068,909,529,1194,2074,404,2086,1550,121,2105,2106,9,526,2092,2091,946,2069,1065,1760,2066,753,13,1053,1441,109,1059,1757,113,10,958,2087,1469,2090,407,1475,778,891,2075,917,406,2083,1537,1940,1637,770,1090,893,923,2061,1101,155,2081,911,899,1753,905,361,1096,1765,251,110,1768,1603,1195,1652,357,776,1461,2084,1643,2085,116,1574,1764,1350,328,2057,352,1653,1486,1419,2089,1792,1447,1761,362,1539,1748,1649,897,438,2072,235,1638,1650,90,469,2073,1604,1102,1078,782,2064,1108,1072,2065,1644,2071,1460,1952,346,879,2597,1455,315,1839,1647,1047,1084,358,1607,466,443,766,1798,86,2603,464,497,2299,764,1407,1502,334,1154,1442,2302,885,80,236,1035,223,166,887,117,1406,1508,1334,1487,881,149,205,446,411,1874,928,1149,1833,2277,2082,1596,1775,363,229,556,1153,1635,1483,784,1552,2599,1634,1982,1609,1633,2067,1485,2404,1803,340,1842,146,2077,248,669,788,1594,1772,1800,1023,2268,1868,1863,2136,1472,1778,1915,418,131,2632,431,790,321,1364,413,1433,71,1107,562,1060,1516,1941,2070,442,1645,1943,532,1451,1641,408,1595,1444,1836,414,1639,910,1988,2142,1726,1735,3,115,1756,550,199,11,867,2364,1605,2399,1845,2627,252,465,754,1506,2269,2411,417,1054,1822,1729,1802,1470,211,1727,1869,2405,84,2625,1590,1834,1518,412,1791,217,1955,1981,1542,1787,1080,72,1231,1150,521,79,415,513,1965,2598,2303,922,1490,1155,1640,1859,1092,692,1878,416,1790,2137,935,8,1824,2306,240,1481,1807,1933,1953,2363,1763,152,2079,681,1353,1170,2175,503,1507,2078,509,1648,249,1903,2335,530,1379,2274,1777,1029,1448,963,916,1795,2317,1416,2076,409,2406,1984,254,2606,2143,316,1651,1793,2194,1161,2289,1789,1085,2410,1767,1754,1821,137,1581,1086,950,1435,1217,1786,1928,2631,697,1733,1066,447,2626,161,1025,209,441,767,2145,475,1865,66,671,2094,2063,964,1971,444,1041,130,1870,1806,445,1393,1857,1549,938,1476,1482,505,2473,433,2107,58,892,2088,1568,1434,154,2028,1529,2329,869,1582,1358,250,2683,1428,1642,480,2195,1931,1322,868,527,1468,1770,2010,1011,763,2319,322,977,140,1427,162,1841,2320,941,1538,1530,1929,794,873,2506,439,1912,2628,757,2271,242,448,351,1887,1904,2401,2678,2100,1964,2677,1825,2109,1545,1073,672,1079,2608,2337,1774,2684,2334,1420,1327,355,348,2111,2457,1970,2375,1690,473,1909,796,2579,904,544,1805,898,1105,470,2193,2471,793,1061,1897,1967,1646,2307,2445,1372,2611,1898,2030,434,685,2310,1977,1619,476,2051,704,2146,1234,2149,2297,2009,1804,975,2475,2102,2620,2623,1532,880,479,1762,18,474,829,1098,2108,769,1630,1017,173,2398,1730,221,263,1543,2381,2629,2366,1454,2617,761,112,2614,336,64,330,410,1843,1973,1125,2328,2163,1474,2188,1074,1906,623,2513,882,73,1816,468,1801,2610,886,538,1624,1569,2179,1013,1440,855,2451,2416,2171,435,817,2376,962,2348,2275,1985,1871,2172,981,506,2512,932,688,1621,2384,1553,1853,2015,689,1980,944,1876,775,1175,226,440,1357,1191,2331,1378,1114,1371,605,953,327,1479,1910,478,959,1700,520,2313,2624,870,1119,477,1819,2021,1329,1352,1055,595,241,900,2147,1831,800,2524,641,956,760,2412,243,356,277,1920,1037,1989,1900,1732,1976,1832,2192,16,874,1168,1126,280,2316,862,2292,2428,1062,1192,1877,122,1544,147,1178,92,1514,2161,2187,2097,2298,1465,233,7,1210,253,91,647,2153,629,1608,896,2633,2647,976,2622,2648,1113,902,2634,1856,2185,1862,1189,2349,2160,1784,811,524,2058,856,673,547,1588,1068,611,1979,1264,319,1922,1587,1104,134,758,2580,1611,333,1049,2662,2607,2663,185,960,2295,599,1844,1872,2043,2132,920,1048,204,2655,1232,968,683,1422,1026,1987,1589,2600,1019,2612,1796,1014,493,350,2613,2196,1546,1111,2333,2342,2430,2294,1618,888,2668,2099,716,2427,2667,437,2027,516,1227,659,2318,331,1974,2098,966,969,1457,2593,2591,665,1120,1881,802,1758,1615,27,1093,861,320,2515,1031,1613,1449,85,541,1855,2507,2177,53,1075,2144,653,1365,2378,77,2181,1099,29,1742,2309,2572,360,1734,1781,983,2592,2170,1736,2429,2180,2016,785,2080,1927,1636,2311,1997,2644,2312,908,1012,864,2619,1024,2176,806,2413,1818,1043,2166,1826,2287,914,850,594,2158,635,101,1664,1117,2630,1321,2618,1627,2186,2206,2037,2472,1045,34,2395,168,2407,1885,755,2483,841,2387,2509,2173,281,522,1880,94,1349,1986,1809,1345,1412,2049,1211,1739,325,2439,2038,566,2616,2279,472,1769,2157,259,596,326,1467,2485,1632,982,2518,818,83,682,1828,1745,773,2008,617,338,1203,471,208,1511,2278,256,912,1235,1039,2189,174,2452,2676,965,2164,1033,1056,701,1926,2130,1216,1883,929,2191,76,1081,2138,860,2519,232,1886,188,1873,971,231,2666,2672,2003,2183,559,1846,1030,1623,1616,2340,1771,70,1020,1221,57,894,104,826,1464,2060,926,1356,1005,2141,59,2643,848,2552,1776,1631,1377,191,337,686,2332,2682,1882,719,1006,141,618,584,285,884,324,1218,1737,695,225,2675,1109,1224,553,1968,2530,1196,1629,536,2685,1991,2006,1827,1042,1628,26,1456,1205,2674,1443,1744,1848,2113,244,2351,1785,1998,374,2665,2615,2296,295,63,1373,845,906,1087,2469,2486,2641,1177,1129,119,2182,2104,2557,265,1051,2290,1879,1622,2679,339,2386,2154,2120,2621,1018,812,2423,28,1617,40,2121,2344,2127,1560,1531,359,329,82,851,287,840,317,354,2343,2096,342,2563,728,740,2151,2455,797,2022,2551,2280,2150,1069,2482,1783,1036,2050,846,1554,1830,1458,612,382,297,2495,1956,1975,2456,842,2448,35,808,2131,364,593,1667,1032,1847,2461,1741,2418,2046,1421,2442,2383,17,2661,2454,2155,878,2494,1797,2134,1498,37,1837,2650,1851,2570,405,1535,890,1583,2036,323,286,23,1050,202,258,343,1944,2103,135,189,2664,875,2284,1226,1814,820,2135,1548,2595,2550,275,2642,2004,385,349,853,1612,2594,274,2462,1884,2669,2474,1122,347,206,838,2345,2681,128,1671,95,2018,2601,2585,309,2190,2276,284,2305,75,1517,814,1960,2446,1919,2522,821,1963,2169,558,1894,1027,666,1891,318,847,2480,548,308,2321,2369,2165,2420,2539,68,1942,844,24,267,2466,2425,2468,1932,307,1296,1121,2569,1917,272,854,1992,2000,545,2286,89,809,1654,2459,1738,2637,1740,1731,1799,2581,2491,2391,2159,1815,876,1038,2526,1559,2463,539,970,2093,341,292,303,2002,2346,832,1780,2497,2529,1127,2534,2542,1057,31,220,2031,678,2232,304,2140,1896,1752,2646,2653,467,61,2502,1504,2533,2548,517,1849,2052,1259,2440,22,2609,2370,1945,827,273,1555,1888,332,836,2604,1813,381,2168,1957,1246,2115,1918,2481,2358,310,1063,39,2487,2470,2670,2560,1990,1994,74,1123,2356,1969,2558,2293,69,2330,2288,839,2059,2680,1854,2521,2541,2360,2415,1746,779,572,294,2433,1852,2432,2501,288,207,972,268,2110,165,2024,2605,1115,2489,2460,1343,2367,1044,269,2544,2128,1817,380,2531,1779,1260,2402,2508,123,833,88,2464,791,38,345,2421,2352,2347,228,4,2660,1901,2488,624,589,2436,2546,1934,2576,2528,298,576,1890,2388,1864,2602,2178,582,2540,2272,300,2543,1436,2673,1812,1838,1015,222,1766,551,2133,2044,2434,2527,2365,2523,2408,2041,2505,1939,111,2380,518,1318,2034,296,2686,684,201,2426,2119,2300,2035,25,1892,219,2005,2574,279,2573,289,2350,2419,1860,2545,552,2368,1999,291,32,36,2017,2324,50,1336,1925,301,1938,1811,2467,1893,1895,2640,564,1930,2400,918,1788,2520,15,311,432,48,1625,55,213,1867,2362,2649,2510,2139,2450,2422,1866,335,60,2645,2389,1850,1954,569,264,2492,2414,2281,834,2040,679,306,1993,19,2588,803,2020,2577,2535,299,2449,2652,1951,859,2053,2499,270,2636,2282,1212,1913,2438,583,1946,2371,2308,1924,2379,857,1908,276,2409,2042,2525,2039,216,2353,2117,2532,2536,2431,2283,2314,2397,2184,2023,2564,2114,314,581,1273,1491,1751,198,2538,2326,1561,2341,2537,2361,214,2124,828,1902,1921,1914,587,1274,67,780,1297,2589,2493,2357,143,54,2377,1983,1996,2435,1626,2417,2322,2453,1021,93,62,2479,2156,2374,2437,260,302,2559,2390,2359,171,2126,1008,2516,2424,2385,2562,2122,565,2273,2336,954,1340,290,2596,20,815,1303,266,305,2575,1907,2447,2261,344,2118,278,2054,2587,2638,2243,2270,1840,984,2262,2586,872,1861,271,312,45,2555,293,2263,1198,690,164,2659,30,52,924,2101,2392,2116,2496,1950,1614,752,563,2032,2561,2514,153,2651,2315,2026,1331,852,2197,2198,2199,2200,2201,2202,2203,2204,49,1916,261,2233,1672,2234,2504,2393,2582,2241,1162,512,2264,804,195,2062,570,1875,2373,2152,1794,1823,2267,2265,1687,2583,2266,822,2216,2394,2217,2218,774,1958,2011,1199,2215,1658,2242,2209,1678,2248,2249,2252,2253,2254,1657,588,1655,1656,1677,1683,1684,2304,2403,2246,255,2245,2250,2251,2247,2219,2220,2221,2222,2223,2224,2225,2226,2227,2228,2229,2230,2231,2547,1935,2125,2244,2025,2260,2259,1685,1773,2258,2210,2257,2255,2458,2256,792,546,2567,1190,1659,2441,2382,1716,1717,1718,1715,1499,2208,2553,2549,2235,2236,2237,1686,786,2238,2239,2240,2013,1714,2657,2211,2511,2048,2214,6,2207,2566,1276,2212,2671,1712,2443,1711,2047,930,2129,1937,2213,282,1710,948,2565,1709,2167,2477,2012,196,1220,105,1519,1691,1692,1693,1694,1695,1696,1697,1698,1688,2490,2654,1713,2571,1708,2465,1689,2500,2372,1699,1706,1707,1719,1720,1721,1722,1723,1724,1725,1533,1705,257,1462,798,2338,1995,756,2568,762,1703,230,2095,2033,2639,2517,1662,2590,858,1197,1889,1225,1728,2578,159,1541,1702,2498,1223,2658,2339,2556,1676,2014,1317,1961,1808,1704,2396,986,1679,365,1959,1701,2327,1829,1660,2007,2478,2656,1949,1966,2484,2174,1682,1835,1911,1661,2323,1610,1463,2354,2285,1905,1858,980,2045,2444,1947,1820,2635,2162,1962,2001,2503,1978,367,1620,1128,41,2355,2584,1247,2476,1663,1670,56,768,2205,129,1674,1899,2554,528,2019,1009,810,2325,44,1675,1673,1681,1782,1810,46,43,1680,1669,1743,974,1668,47,2123,1923,1972,1666,1665,816,866]


        bad_iteration_list = []
    ##########################
    ## Percussion Only ##
    ##########################

    elif dataset_name == "audio_full_percussion": # percussion only
        #csv_normal = "full_audio_percussion_features.csv"
        csv_normal = "full_audio_percussion_features.csv"
        csv_annots = "full_audio_annotations.csv"
        #top_features = [1765, 1152, 1773, 525, 2073, 1166, 1151, 507, 1768, 2109, 2123, 2110, 2124, 945, 927, 361, 10, 2074, 529, 1441, 1089, 1497, 13, 21, 1783, 1786, 909, 1071, 1475, 1077, 407, 1550, 1065, 133, 404, 772, 2103, 1537, 1489, 1775, 1778, 1771, 946, 2075, 406, 1059, 2086, 526, 1053, 2079, 1154, 136, 2104, 1455, 109, 1574, 1090, 917, 753, 121, 923, 2092, 2084, 246, 1779, 2101, 911, 776, 2093, 770, 1857, 1766, 1782, 357, 1194, 778, 252, 2087, 891, 9, 958, 1605, 2105, 497, 2631, 2125, 1469, 1539, 1078, 1594, 2108, 2333, 1485, 897, 1654, 2000, 784, 469, 1427, 1606]
        #top_features = [1,8,2,5,11,29,9,27,3,26,46,17,20,52,14,41,23,55,38,30,16,12,22,32,39,45,44,35,15,21,31,53,50,40,49,54,6,47,28,33,51,24,18,13,36,42,34,56,25,37,10,7,19,57,4,43]

        top_features = [1,8,2,5,11,29,9,27,3,26,46,17,20,52,14,41,23,55,38,30,16,12,22,32,39,45,44,35,15,21,31,53,50,40,49,54,6,47,28,33,51,24,18,13,36,42,34,56,25,37,10,7,19,57,4,43]

        bad_iteration_list = []

    elif dataset_name == "audio_balanced_percussion": # percussion only
        csv_normal = "balanced_audio_final_percussion_features.csv"
        csv_annots = "balanced_audio_annotations.csv"
        #top_features = [1765, 1152, 1773, 525, 2073, 1166, 1151, 507, 1768, 2109, 2123, 2110, 2124, 945, 927, 361, 10, 2074, 529, 1441, 1089, 1497, 13, 21, 1783, 1786, 909, 1071, 1475, 1077, 407, 1550, 1065, 133, 404, 772, 2103, 1537, 1489, 1775, 1778, 1771, 946, 2075, 406, 1059, 2086, 526, 1053, 2079, 1154, 136, 2104, 1455, 109, 1574, 1090, 917, 753, 121, 923, 2092, 2084, 246, 1779, 2101, 911, 776, 2093, 770, 1857, 1766, 1782, 357, 1194, 778, 252, 2087, 891, 9, 958, 1605, 2105, 497, 2631, 2125, 1469, 1539, 1078, 1594, 2108, 2333, 1485, 897, 1654, 2000, 784, 469, 1427, 1606]
        top_features = [1,8,2,5,11,29,9,27,3,26,46,17,20,52,14,41,23,55,38,30,22,16,32,12,39,35,45,44,15,53,21,31,50,40,49,54,6,47,33,28,24,51,18,13,42,36,34,56,25,37,10,19,7,57,43,4]

        bad_iteration_list = [4,5,20,23, 29, 36,40,42, 50, 67, 69]

    elif dataset_name == "bimodal_full_percussion": # percussion only
        csv_normal = "full_bimodal_final_percussion_features.csv"
        csv_annots = "full_bimodal_annotations.csv"
        #top_features = [1765, 1152, 1773, 525, 2073, 1166, 1151, 507, 1768, 2109, 2123, 2110, 2124, 945, 927, 361, 10, 2074, 529, 1441, 1089, 1497, 13, 21, 1783, 1786, 909, 1071, 1475, 1077, 407, 1550, 1065, 133, 404, 772, 2103, 1537, 1489, 1775, 1778, 1771, 946, 2075, 406, 1059, 2086, 526, 1053, 2079, 1154, 136, 2104, 1455, 109, 1574, 1090, 917, 753, 121, 923, 2092, 2084, 246, 1779, 2101, 911, 776, 2093, 770, 1857, 1766, 1782, 357, 1194, 778, 252, 2087, 891, 9, 958, 1605, 2105, 497, 2631, 2125, 1469, 1539, 1078, 1594, 2108, 2333, 1485, 897, 1654, 2000, 784, 469, 1427, 1606]
        top_features = [8,1,2,5,11,3,9,29,27,26,17,46,14,55,23,20,41,30,22,6,12,52,45,44,38,39,35,32,16,15,31,53,21,50,49,40,42,28,54,24,51,33,47,18,13,34,36,7,56,25,37,10,43,19,57,4]

        bad_iteration_list = [4,5]
    elif dataset_name == "bimodal_balanced_percussion": # percussion only
        csv_normal = "balanced_bimodal_final_percussion_features.csv"
        csv_annots = "balanced_bimodal_annotations.csv"
        #top_features = [1765, 1152, 1773, 525, 2073, 1166, 1151, 507, 1768, 2109, 2123, 2110, 2124, 945, 927, 361, 10, 2074, 529, 1441, 1089, 1497, 13, 21, 1783, 1786, 909, 1071, 1475, 1077, 407, 1550, 1065, 133, 404, 772, 2103, 1537, 1489, 1775, 1778, 1771, 946, 2075, 406, 1059, 2086, 526, 1053, 2079, 1154, 136, 2104, 1455, 109, 1574, 1090, 917, 753, 121, 923, 2092, 2084, 246, 1779, 2101, 911, 776, 2093, 770, 1857, 1766, 1782, 357, 1194, 778, 252, 2087, 891, 9, 958, 1605, 2105, 497, 2631, 2125, 1469, 1539, 1078, 1594, 2108, 2333, 1485, 897, 1654, 2000, 784, 469, 1427, 1606]
        top_features = [8,2,1,5,11,3,29,9,27,26,17,55,46,20,14,6,12,23,30,52,22,41,45,44,38,39,16,35,32,15,53,31,21,50,49,28,54,24,42,40,18,33,51,13,47,36,34,7,56,25,37,10,43,19,57,4]

        bad_iteration_list = [14, 15, 16, 17,20,28]
    elif dataset_name == "taffc_percussion": # percussion only
        csv_normal = "taffc_final_percussion_features.csv"
        csv_annots = "taffc_annotations.csv"
        #top_features = [1765, 1152, 1773, 525, 2073, 1166, 1151, 507, 1768, 2109, 2123, 2110, 2124, 945, 927, 361, 10, 2074, 529, 1441, 1089, 1497, 13, 21, 1783, 1786, 909, 1071, 1475, 1077, 407, 1550, 1065, 133, 404, 772, 2103, 1537, 1489, 1775, 1778, 1771, 946, 2075, 406, 1059, 2086, 526, 1053, 2079, 1154, 136, 2104, 1455, 109, 1574, 1090, 917, 753, 121, 923, 2092, 2084, 246, 1779, 2101, 911, 776, 2093, 770, 1857, 1766, 1782, 357, 1194, 778, 252, 2087, 891, 9, 958, 1605, 2105, 497, 2631, 2125, 1469, 1539, 1078, 1594, 2108, 2333, 1485, 897, 1654, 2000, 784, 469, 1427, 1606]
        top_features = [1,8,2,5,9,3,11,27,29,17,23,26,55,12,30,14,20,46,22,41,44,45,39,6,52,15,38,53,31,16,35,32,33,21,49,40,18,24,50,13,54,51,47,28,42,34,36,25,7,56,10,19,37,43,57,4]

        bad_iteration_list = []

    ##########################
    ## MIDI Only ##
    ##########################
    elif dataset_name == "audio_balanced_midi": # midi only
        csv_normal = "balanced_audio_final_midi_features.csv"
        csv_annots = "balanced_audio_annotations.csv"
        top_features = [378,416,413,387,390,291,377,384,383,286,41,399,712,660,375,427,317,487,421,417,289,420,509,443,38,412,405,449,62,465,515,271,493,406,364,409,325,494,668,516,407,403,10,450,504,2,526,495,471,491,8,376,508,447,444,374,661,400,318,278,438,429,517,456,442,283,559,482,681,666,530,338,425,524,428,323,488,478,502,460,513,510,274,100,363,706,434,453,473,518,522,16,368,422,451,472,436,500,373,457,711,469,12,303,439,505,431,475,432,520,173,379,610,496,501,220,523,707,281,466,479,480,519,498,527,497,182,435,408,275,280,270,60,372,180,476,4,414,181,54,36,309,277,430,369,664,169,321,563,525,570,503,454,304,92,474,486,340,683,571,572,410,529,507,538,437,499,521,464,461,433,370,68,483,365,618,477,419,415,670,708,327,481,5,663,296,1,52,322,320,172,665,381,7,673,329,672,53,562,3,148,669,326,330,44,411,45,228,441,467,458,273,423,282,11,452,511,489,351,418,694,506,164,485,528,279,554,459,58,662,319,324,484,440,333,667,156,288,676,166,64,401,20,293,556,671,28,9,445,578,710,295,328,55,350,284,59,693,332,85,675,380,315,196,580,297,294,356,699,272,679,298,586,285,336,362,596,455,343,686,705,78,341,684,190,357,700,287,307,73,674,331,335,678,345,310,290,76,577,144,334,688,358,701,677,311,463,187,206,208,342,200,685,697,354,704,80,446,344,314,687,361,186,598,300,576,590,546,603,468,302,213,299,183,211,192,312,601,594,346,349,301,347,702,359,692,690,204,313,587,682,292,339,689,188,367,703,69,305,360,680,490,573,512,197,306,695,352,600,337,210,353,82,696,424,402,72,348,691,404,492,81,599,209,462,198,591,70,588,6,597,276,698,534,355,308,79,83,207,201,582]
        
        bad_iteration_list = []

    elif dataset_name == "bimodal_full_midi": # midi only
        csv_normal = "full_bimodal_final_midi_features.csv"
        csv_annots = "full_bimodal_annotations.csv"
        top_features = [378,387,390,377,384,383,416,291,413,286,712,375,399,289,317,660,487,526,427,504,41,421,509,417,478,38,493,405,515,456,438,465,420,283,471,62,412,508,442,443,373,494,495,403,407,406,434,453,516,271,325,530,668,522,482,409,429,475,500,374,449,400,559,274,523,501,338,681,364,444,517,488,502,524,2,450,439,435,277,505,431,447,270,491,510,422,460,519,379,472,497,16,711,278,323,666,280,527,457,428,479,8,513,436,275,473,425,432,100,376,54,518,707,466,664,480,321,469,303,486,661,499,368,281,318,520,498,496,60,372,279,521,10,169,451,173,454,369,610,414,220,563,430,7,706,477,53,483,92,408,476,322,572,665,461,11,433,182,464,474,363,309,1,52,5,4,708,381,467,3,437,562,365,44,410,12,418,411,506,340,683,172,36,528,538,9,304,507,370,554,670,327,529,525,673,333,669,676,326,296,330,503,484,672,293,329,164,282,571,419,489,156,273,288,511,423,320,663,180,570,181,148,272,85,45,440,441,452,481,284,356,485,699,415,68,618,401,28,667,324,458,166,228,671,332,662,55,319,58,675,20,328,59,445,190,546,580,315,459,295,380,351,294,578,710,679,694,336,556,287,276,455,362,586,196,705,674,297,350,693,213,331,285,343,686,335,678,603,64,700,357,310,446,596,187,183,298,78,200,342,144,76,685,290,344,687,334,307,314,311,299,211,677,358,208,83,701,186,600,80,684,697,341,573,354,82,598,590,210,704,345,587,594,688,361,601,72,534,197,577,188,367,204,468,300,337,576,680,463,69,73,682,292,339,512,490,206,302,597,313,347,207,690,360,424,703,349,692,79,305,306,352,695,353,696,346,312,702,359,402,462,81,689,599,209,70,198,588,591,698,355,308,301,6,691,348,192,201,582]
        bad_iteration_list = []
    elif dataset_name == "bimodal_balanced_midi": # midi only
        csv_normal = "balanced_bimodal_final_midi_features.csv"
        csv_annots = "balanced_bimodal_annotations.csv"
        top_features = [378,390,387,377,384,383,291,712,416,375,413,286,399,317,660,526,504,289,487,427,421,438,41,456,509,417,478,465,508,471,420,38,515,442,530,453,493,405,482,443,407,494,364,406,373,501,523,516,62,403,283,495,400,460,559,2,449,435,274,434,475,488,505,325,374,524,668,429,412,502,522,439,457,444,8,510,500,338,422,681,491,447,517,527,271,277,270,280,472,450,275,409,479,666,379,323,16,431,711,10,376,425,664,513,436,321,486,428,466,473,480,519,278,499,497,432,518,521,60,372,707,281,469,451,173,414,477,496,303,483,610,169,461,454,464,100,92,54,12,661,369,220,318,520,706,498,279,430,182,433,7,418,408,363,506,563,53,474,411,368,528,4,572,665,11,322,44,562,476,5,1,437,172,309,381,708,673,507,484,3,529,293,554,670,52,330,538,327,282,273,410,467,36,365,181,571,440,525,180,164,419,272,340,683,304,370,570,148,296,326,669,503,452,9,288,441,458,85,156,489,45,320,485,284,401,663,511,415,423,672,333,676,481,329,356,618,68,699,671,328,166,55,190,228,546,319,662,578,710,20,667,324,315,276,58,580,459,455,28,380,295,679,445,294,336,362,705,586,59,556,351,332,196,213,675,674,297,694,603,331,200,350,693,183,335,678,298,64,343,144,285,686,357,700,310,187,342,446,685,290,211,600,287,596,82,78,311,358,573,76,590,701,188,367,72,208,186,307,210,299,83,534,80,697,587,354,314,344,601,598,684,341,687,346,345,334,689,577,197,204,463,594,704,300,468,682,292,339,688,677,69,597,361,576,206,337,512,490,207,313,680,462,302,360,703,347,352,695,305,306,692,349,353,696,73,690,79,424,402,6,312,702,359,81,599,209,198,70,588,591,698,355,308,301,691,348,192,582,201]
        bad_iteration_list = []

    elif dataset_name == "audio_full_midi": # midi only
        csv_normal = "full_audio_midi_features.csv"
        csv_annots = "full_audio_annotations.csv"
 
        top_features = [378,416,413,390,387,291,383,377,384,41,286,399,712,660,317,375,487,443,417,289,271,38,427,420,421,509,412,364,405,406,449,465,62,10,409,515,494,493,450,516,2,407,325,668,471,8,376,495,444,504,403,666,374,526,323,559,661,517,400,318,283,491,100,447,681,338,429,278,508,460,488,438,473,482,524,428,478,456,510,502,530,513,442,472,363,434,451,425,16,274,368,453,518,522,711,457,500,706,373,422,220,436,303,520,173,475,610,466,469,496,431,498,707,501,182,523,505,476,12,432,36,479,519,439,497,408,480,281,180,379,435,4,181,280,414,60,372,169,454,527,664,321,275,277,525,369,304,503,270,54,538,430,92,474,309,570,340,486,683,572,563,410,571,464,437,481,529,507,433,172,461,562,415,370,663,499,320,521,670,365,148,327,708,52,483,68,419,296,228,322,618,665,53,672,329,7,1,477,44,5,452,45,381,669,326,3,288,458,467,673,330,441,411,423,459,273,511,662,20,319,489,440,282,11,351,164,694,554,418,333,506,324,528,676,667,485,166,293,284,156,58,401,64,484,671,59,315,295,85,279,328,445,297,350,55,693,556,578,710,294,332,675,28,9,580,196,298,362,380,679,705,336,455,678,285,335,343,310,686,356,699,700,341,357,684,596,586,345,577,78,290,463,688,190,674,358,331,311,701,307,272,334,187,677,344,287,687,73,200,144,603,206,314,446,208,546,704,80,76,697,354,361,213,342,685,598,590,300,468,186,302,312,183,576,72,359,702,349,490,512,211,692,601,299,313,301,192,680,347,682,292,339,690,703,360,337,402,346,305,204,82,600,587,306,69,210,689,424,695,352,188,367,594,353,197,462,696,573,404,492,81,599,209,348,691,198,70,588,597,6,79,698,355,591,308,207,582,83,201,276,534]
        bad_iteration_list = []


    ##########################
    ## Percussion and MIDI Only ##
    ##########################

    elif dataset_name == "audio_balanced_perc_midi_only": # midi only
        csv_normal = "balanced_audio_final_perc_midi_only_features.csv"
        csv_annots = "balanced_audio_annotations.csv"
        top_features = [1,8,2,5,11,9,473,435,3,470,29,27,46,348,343,444,447,98,456,440,434,441,20,484,717,374,769,477,544,95,474,478,38,432,469,566,23,346,17,382,26,45,725,328,30,44,500,462,119,506,522,718,572,375,550,421,466,548,14,460,41,723,380,463,464,59,65,335,551,561,528,67,39,583,552,433,573,504,507,738,431,581,55,565,395,12,559,482,574,570,501,499,157,486,587,457,495,513,545,539,420,763,616,360,340,425,493,535,485,567,530,517,491,21,510,489,331,430,73,52,575,577,69,277,526,479,579,562,496,529,768,508,557,667,555,239,230,553,537,117,429,488,514,533,584,536,471,366,237,558,580,532,35,238,523,764,15,465,576,554,93,361,32,61,620,582,492,560,531,436,721,31,595,338,378,487,494,426,22,511,332,543,556,467,327,578,226,627,629,334,111,53,337,149,586,628,397,16,740,490,720,729,386,377,6,564,534,33,521,518,49,36,50,125,205,540,472,538,675,422,765,353,476,468,427,28,726,383,730,54,619,515,379,408,102,722,727,438,62,384,387,285,751,229,58,339,18,498,60,475,101,110,509,480,109,64,330,563,585,541,345,524,568,546,542,611,221,213,407,34,77,750,68,516,42,350,223,341,497,376,719,390,352,733,381,13,724,121,47,613,458,24,372,635,767,351,637,502,115,116,336,413,37,728,354,389,756,732,85,385,419,355,253,762,342,66,400,40,743,731,388,437,398,741,112,142,653,736,344,757,414,393,643,347,56,512,135,392,735,367,247,402,51,634,399,257,364,742,371,745,415,391,734,368,758,520,401,761,137,744,265,130,647,418,655,329,244,754,503,411,263,133,201,357,243,249,603,10,359,245,424,129,19,525,356,369,759,416,404,737,406,370,747,358,749,126,644,394,268,362,651,760,240,417,658,547,633,569,403,752,739,349,396,254,409,363,57,270,261,660,459,746,657,481,267,410,648,753,519,139,405,461,549,748,138,656,266,4,255,43,127,645,591,755,654,412,365,7,264,63,630,136,333,258,140,25,639] 
        
        bad_iteration_list = []

    elif dataset_name == "bimodal_full_perc_midi_only": # midi only
        csv_normal = "full_bimodal_final_perc_midi_only_features.csv"
        csv_annots = "full_bimodal_annotations.csv"
        top_features = [1,8,2,5,435,11,444,447,473,9,3,440,434,441,470,343,348,29,27,769,456,432,46,346,374,717,544,484,478,98,95,583,561,566,45,44,474,26,477,513,522,462,535,469,38,550,572,495,119,20,382,725,340,499,528,430,30,565,460,17,500,510,466,23,457,328,587,551,491,395,464,738,463,552,431,486,545,579,506,581,14,559,573,557,532,539,616,41,421,496,380,567,723,580,548,39,558,59,504,562,479,335,12,331,501,574,488,73,493,55,492,768,507,6,576,554,334,570,482,157,529,584,65,718,536,327,485,375,489,436,517,530,556,537,337,575,360,543,433,523,721,578,526,378,332,514,277,764,117,429,553,230,111,21,577,425,555,667,620,67,511,534,226,487,508,540,471,763,426,149,336,490,22,518,465,366,52,31,531,379,338,521,722,533,110,64,35,765,36,420,239,42,494,595,15,61,422,629,467,68,109,32,93,50,563,101,69,62,619,397,475,740,524,611,58,729,468,582,438,585,386,229,60,361,53,727,730,726,384,560,383,720,221,49,377,353,427,350,205,387,213,541,564,390,733,586,345,66,16,18,339,476,33,54,480,546,102,509,413,28,238,330,675,237,756,568,472,628,538,37,498,341,515,542,497,627,85,724,285,142,458,381,125,77,223,329,376,24,719,603,372,13,728,637,635,767,385,351,47,408,344,352,389,419,751,516,732,112,407,762,115,750,247,502,116,731,388,613,400,743,437,342,121,512,392,736,354,735,333,393,355,253,643,757,367,414,257,660,347,399,742,270,503,51,40,56,371,391,244,734,34,240,368,401,43,653,364,201,135,744,415,758,268,754,411,657,133,647,402,761,265,356,398,418,745,741,267,129,137,10,140,139,655,7,591,644,243,357,394,737,254,261,630,525,651,739,349,396,245,424,520,25,359,370,654,569,404,634,130,547,126,658,264,747,417,760,648,406,362,363,749,409,752,369,481,410,753,519,759,416,136,19,459,255,57,127,645,755,412,263,138,633,656,266,403,4,365,746,358,748,405,258,249,63,639]
        
        bad_iteration_list = []
    elif dataset_name == "bimodal_balanced_perc_midi_only": # midi only
        csv_normal = "balanced_bimodal_final_perc_midi_only_features.csv"
        csv_annots = "balanced_bimodal_annotations.csv"
        top_features = [1,8,2,5,11,435,447,444,434,441,440,473,3,9,470,769,348,432,343,29,27,456,374,717,561,583,46,544,484,478,45,44,346,98,513,566,495,26,474,95,477,522,535,510,565,499,457,572,550,528,462,587,500,119,38,460,421,430,545,539,558,551,20,464,580,463,382,725,581,506,340,573,395,738,616,559,567,562,59,552,496,469,492,491,486,548,431,532,14,479,39,517,23,17,579,30,723,504,328,65,380,12,557,584,466,501,536,331,529,73,493,55,482,574,768,543,570,67,556,514,41,6,537,335,488,433,507,578,332,334,523,721,575,117,429,327,378,489,576,485,337,540,554,230,436,530,157,526,718,667,277,375,534,553,508,360,471,764,518,521,21,511,149,69,490,487,226,763,577,620,555,239,338,111,426,465,420,531,563,475,722,379,425,585,494,595,468,61,101,336,533,366,52,110,64,619,36,42,730,15,68,629,765,541,611,32,93,22,229,387,62,31,467,582,727,422,438,58,205,350,586,35,384,564,221,49,60,50,524,109,560,330,361,339,726,427,509,238,345,383,476,353,237,740,397,497,102,28,53,498,213,377,720,628,18,515,542,472,729,16,33,458,386,54,627,66,546,341,142,675,568,538,413,480,329,285,756,37,77,603,390,733,635,767,24,372,728,223,385,125,13,376,85,719,724,247,637,381,512,516,419,112,762,333,731,408,407,750,352,388,751,115,351,392,735,121,502,437,355,613,47,257,736,389,393,253,400,354,116,732,643,743,367,757,342,414,660,344,201,347,270,399,51,240,742,56,503,244,34,129,657,40,415,368,647,43,371,758,268,139,402,245,424,754,364,411,591,267,391,644,745,265,734,398,761,7,741,137,133,653,356,403,418,135,10,140,243,746,520,655,401,254,357,630,25,261,744,739,349,396,394,737,525,126,634,569,370,519,547,658,654,359,404,409,752,362,363,648,130,651,459,417,760,410,753,19,264,749,747,406,369,759,416,481,255,127,57,645,138,656,266,755,412,136,4,263,365,633,63,358,748,405,249,258,639]
        bad_iteration_list = []

    elif dataset_name == "audio_full_perc_midi_only": # midi only
        csv_normal = "full_audio_perc_midi_only_features.csv"
        csv_annots = "full_audio_annotations.csv"

        top_features = [1,8,2,5,11,9,435,473,29,470,3,27,46,343,98,447,444,348,456,440,434,441,20,717,374,328,484,477,95,544,474,769,469,346,45,17,38,478,432,500,44,566,30,421,23,723,380,382,718,725,26,375,522,466,462,119,67,506,572,59,463,41,550,14,433,65,464,39,551,528,157,460,548,507,431,552,738,335,561,395,501,583,573,12,504,574,581,55,616,457,559,565,570,486,530,545,340,499,495,539,587,420,513,425,482,360,567,535,485,21,517,493,763,577,52,491,430,277,529,73,508,768,575,510,533,526,489,667,579,239,479,555,230,331,557,117,429,69,553,537,237,93,35,471,514,488,562,536,532,238,580,361,558,496,32,523,15,595,764,582,560,465,31,576,511,554,721,378,366,61,531,22,226,584,492,487,426,620,467,494,627,149,338,543,720,436,332,377,629,334,397,337,53,740,538,628,111,6,556,490,327,586,578,50,16,205,472,729,521,386,564,518,33,28,619,49,353,427,422,476,765,534,540,125,229,54,675,515,727,36,726,383,384,102,285,379,509,468,722,345,109,110,408,77,438,101,62,751,498,60,58,330,516,339,376,64,475,719,524,480,221,730,563,497,18,585,387,341,611,568,350,546,407,213,542,750,34,541,352,372,390,24,68,223,733,13,381,724,351,121,458,47,354,116,502,42,613,355,728,637,635,767,385,85,419,389,732,762,342,735,392,115,400,743,40,367,66,413,37,756,731,388,757,142,253,414,51,634,736,398,741,402,112,347,393,344,745,512,368,437,247,415,391,653,336,734,758,56,520,257,135,401,744,643,371,137,647,265,364,399,742,244,655,761,503,418,603,754,130,411,263,129,357,201,10,359,249,525,329,356,133,369,660,416,759,245,424,270,240,406,243,737,370,547,569,749,19,394,404,268,760,358,747,417,7,658,362,739,349,396,459,363,752,409,126,481,657,644,267,410,261,57,403,753,651,139,746,254,519,461,549,138,656,266,255,4,127,645,648,405,633,748,43,25,755,412,654,630,365,136,63,264,591,258,333,140,639]
        
        bad_iteration_list = []


    ##########################
    ## Old features with Percussion and MIDI##
    ##########################
    elif dataset_name == "audio_full_with_perc_midi": # midi only
        csv_normal = "full_audio_with_perc_midi_features.csv"
        csv_annots = "full_audio_annotations.csv"
        top_features = 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       
        bad_iteration_list = []

    elif dataset_name == "bimodal_full_with_perc_midi": # midi only
        csv_normal = "full_bimodal_final_with_perc_midi_features.csv"
        csv_annots = "full_bimodal_annotations.csv"
        top_features = [1152,1755,525,1747,1151,2687,1166,1750,2694,2688,945,1497,1441,1089,927,507,1077,10,133,2691,2055,2091,2092,1475,2105,2106,13,1765,946,3163,2697,772,529,1059,909,1065,1768,407,1071,1537,1489,136,406,1455,361,1090,526,246,3160,2689,1603,1574,917,923,1753,3159,21,1053,2075,1550,2068,1154,1096,1604,109,2085,2086,958,247,2061,1952,2074,1078,2069,911,778,2695,1539,1748,2597,1652,2087,9,1469,776,1594,770,252,1940,929,3156,357,1084,905,404,236,1101,1102,2056,1609,1194,497,1072,431,2598,121,1481,891,2066,3403,1761,1726,3066,3060,2090,1764,2805,2083,1839,3409,1792,3029,251,1772,899,3207,2081,235,784,1350,2289,2405,503,3455,1757,2089,893,1982,2072,3118,879,764,1649,753,438,469,1637,1760,1650,897,3142,3014,521,3204,115,8,885,1863,1485,2299,3126,3120,3127,2073,11,315,3130,3133,2057,3032,935,1035,2136,3251,3034,1047,3121,1153,3230,669,2064,1568,1791,3306,1406,2084,2603,2916,1447,2082,2277,1643,513,3185,3353,782,2076,3267,3273,146,1066,1419,1607,2142,2302,2297,1460,3269,1909,766,2107,2268,3247,468,1108,1448,3252,3245,881,790,3454,681,1928,1590,1552,131,2071,2065,1472,2963,1874,928,1798,3179,84,2599,3195,362,3261,3151,2298,2803,3115,1407,3248,1800,66,1644,3164,3201,2781,1806,1538,2404,3239,1155,3199,1470,509,1427,358,466,1353,3223,3270,1378,1581,3116,887,3217,3182,465,1988,2703,448,2713,1506,2334,2715,1845,2070,1965,1364,2727,1055,2709,956,3046,1023,1802,3068,922,1842,3411,1790,1953,1518,1461,1149,1955,3026,3221,1638,3181,1640,1857,873,3208,3,1474,3229,166,2631,1195,2067,1897,1887,1767,2627,1641,1107,1393,941,1231,1516,2010,3225,916,1041,3203,1647,1161,692,3173,1468,3063,1582,1029,3157,3406,3196,1486,1984,464,2445,1150,277,413,2028,1483,1635,964,80,152,2077,3186,1234,1487,2600,2625,1596,1775,1821,2274,960,1189,1653,1433,3155,2364,3020,2088,3226,71,447,1862,975,363,1941,950,3244,1060,417,1634,1639,3190,2732,418,3266,796,2784,910,2194,562,281,3231,3200,2399,2843,3024,2271,434,1787,3021,2009,1859,101,240,2712,437,1444,1915,1777,963,2094,3023,1428,415,2303,3165,1900,954,435,3222,2051,2797,1865,1756,2335,3265,1770,3194,1789,3177,2058,90,520,2363,1451,3017,3187,794,767,137,79,1435,2079,1885,532,1422,1729,1605,3237,981,1549,1449,1646,3253,1754,3064,1125,321,1971,3407,527,2269,2411,1543,1633,1651,1017,2049,86,2759,3143,1080,1943,2628,1377,1349,3170,3243,3047,2632,445,1025,1805,3404,1264,1371,1664,1967,64,2188,3178,1912,2109,2787,3281,556,1114,2317,2319,788,1803,2683,1412,1334,3061,2677,408,3152,1774,414,2306,1645,685,2670,1434,3218,1508,3016,1420,977,58,1168,2451,2672,3214,416,2452,1903,976,3259,1834,2166,2706,3258,130,2307,2620,2724,2310,2407,1981,2376,1357,757,2311,1530,1876,2507,1507,1878,3424,3081,875,1595,1822,1532,3234,538,3451,1529,446,410,3215,2626,1980,1727,1869,1735,1345,2751,2130,476,2606,2078,522,944,1569,688,793,439,2731,763,1906,3146,2725,2172,140,2337,1824,1210,2730,85,3236,2137,962,1037,1973,1793,3426,2292,2454,1733,2108,3083,2483,7,2700,409,241,3148,3117,480,3321,3453,1216,1898,1352,2513,1933,412,867,775,3119,2442,2406,1739,1825,904,1440,2050,1502,898,443,3111,2348,1542,1068,3018,3168,479,223,1079,1690,3209,2329,1092,493,1126,3039,2747,2512,411,2313,1667,2132,1801,70,2634,1910,1804,1985,2648,1191,3174,938,2678,1467,869,2647,874,2633,1904,2027,1970,2143,1217,1356,2145,1778,1544,1514,34,1019,1931,1098,2610,892,3256,1929,2684,2099,1868,2401,477,3212,1795,1476,1763,2192,2624,3036,2580,474,2629,1546,1836,505,754,2614,1630,3192,440,1105,3108,1833,1119,2615,918,2485,1056,1113,1964,1054,3171,2524,2278,254,2579,1864,2189,1235,2551,2617,1886,882,516,920,1587,3262,855,3323,3263,352,2021,1416,2063,2146,1372,478,3025,1031,2275,1807,2316,682,3450,524,3197,475,2366,3250,761,697,2616,2440,2618,3240,1482,3022,2891,244,2623,442,1465,285,1629,3150,1011,1358,1026,2515,1327,3031,2176,1991,2006,673,29,3107,2294,1730,199,217,1049,59,1211,308,174,253,161,3149,2795,1986,2745,1987,1075,2506,880,547,1648,2707,1036,2708,2552,211,2015,229,2037,2381,3297,932,3015,1111,209,162,2080,1621,760,894,2296,2570,2410,506,1974,2331,3184,1322,25,1061,444,806,2193,94,1619,2127,1968,802,2622,2457,1479,2611,470,2473,671,1454,2179,550,1024,471,1545,91,1624,1734,817,1120,314,2741,812,27,1771,3272,2375,1073,1013,1373,2550,3069,1205,35,1989,53,850,968,280,851,818,1786,2448,2641,1856,1014,1776,800,1170,2475,333,2185,1192,3172,861,2619,1178,2003,3175,3302,1642,755,2286,1005,832,1048,256,2378,2630,773,1365,544,969,405,1632,758,2427,2320,888,2111,2175,1837,168,3412,3219,811,1457,2295,829,1099,1085,876,243,1608,3241,886,1442,1490,856,3162,959,2177,40,2644,948,1329,2612,845,605,39,3072,3415,1997,226,840,2161,104,1218,900,2102,2796,970,1511,2736,340,814,2008,953,2643,1888,1086,1872,983,1109,1671,3238,3013,779,1104,3093,683,1922,204,3436,1588,906,2149,914,134,309,472,2416,2428,641,826,868,2701,2430,155,2716,769,838,808,2384,173,2098,3449,1548,250,2342,1042,2038,2144,3260,870,2429,1654,1589,2533,2548,2907,2120,659,1769,2191,1074,1832,530,2121,2518,2043,820,151,63,3027,2113,1879,242,2344,221,1012,980,1043,2702,780,2367,2593,864,686,1920,1421,2663,2662,1553,2340,647,327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      
        

        bad_iteration_list = []
       
    elif dataset_name == "taffc_with_perc_midi":
        csv_normal = "taffc_final_with_perc_midi_features.csv"
        csv_annots = "taffc_annotations.csv"
        


        bad_iteration_list = []

    elif dataset_name == "bimodal_balanced_with_perc_midi": # midi only
        csv_normal = "balanced_bimodal_final_with_perc_midi_features.csv"
        csv_annots = "balanced_bimodal_annotations.csv"
        top_features = 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        bad_iteration_list = []

    elif dataset_name == "audio_balanced_with_perc_midi": # midi only
        csv_normal = "balanced_audio_final_with_perc_midi_features.csv"
        csv_annots = "balanced_audio_annotations.csv"
        top_features = 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        bad_iteration_list = []

    ##########################
    ## Standard + Original + Demucs Vocals ##
    ##########################
    elif dataset_name == "audio_full_std_org_demucs_vocals": 
        csv_normal = "full_audio_std_org_demucs_vocals_features.csv"
        csv_annots = "full_audio_annotations.csv"
        top_features = 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

        bad_iteration_list = []

    elif dataset_name == "bimodal_full_std_org_demucs_vocals": 
        csv_normal = "full_bimodal_final_std_org_demucs_vocals_features.csv"
        csv_annots = "full_bimodal_annotations.csv"
        top_features = 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        bad_iteration_list = []
       
    elif dataset_name == "taffc_std_org_demucs_vocals":
        csv_normal = "taffc_final_std_org_demucs_vocals_features.csv"
        csv_annots = "taffc_annotations.csv"
        top_features = 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        bad_iteration_list = []

    elif dataset_name == "bimodal_balanced_std_org_demucs_vocals": 
        csv_normal = "balanced_bimodal_final_std_org_demucs_vocals_features.csv"
        csv_annots = "balanced_bimodal_annotations.csv"
        top_features = 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        bad_iteration_list = []

    elif dataset_name == "audio_balanced_std_org_demucs_vocals": 
        csv_normal = "balanced_audio_final_std_org_demucs_vocals_features.csv"
        csv_annots = "balanced_audio_annotations.csv"
        top_features = 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        bad_iteration_list = []   

    ##########################
    ## Standard + Original + Spleeter Vocals + No Drums Demucs ##
    ##########################
    elif dataset_name == "audio_full_std_org_vocsplt_nodrums_demucs": 
        csv_normal = "full_audio_std_org_vocsplt_nodrums_demucs_features.csv"
        csv_annots = "full_audio_annotations.csv"
        top_features = 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       
        bad_iteration_list = []

    elif dataset_name == "bimodal_full_std_org_vocsplt_nodrums_demucs": 
        csv_normal = "full_bimodal_final_std_org_vocsplt_nodrums_demucs_features.csv"
        csv_annots = "full_bimodal_annotations.csv"
        top_features = 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       
        bad_iteration_list = []
       
    elif dataset_name == "bimodal_balanced_std_org_vocsplt_nodrums_demucs": 
        csv_normal = "balanced_bimodal_final_std_org_vocsplt_nodrums_demucs_features.csv"
        csv_annots = "balanced_bimodal_annotations.csv"
        top_features = 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       
        bad_iteration_list = []

    elif dataset_name == "audio_balanced_std_org_vocsplt_nodrums_demucs": 
        csv_normal = "balanced_audio_final_std_org_vocsplt_nodrums_demucs_features.csv"
        csv_annots = "balanced_audio_annotations.csv"
        top_features = 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        bad_iteration_list = []   

    ##########################
    ## Standard + Original + Spleeter Vocals + Drums Demucs ##
    ##########################

    elif dataset_name == "audio_full_std_org_vocsplt_drums_demucs": 
        csv_normal = "full_audio_std_org_vocsplt_drums_demucs_features.csv"
        csv_annots = "full_audio_annotations.csv"
        top_features = 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       
        bad_iteration_list = []

    elif dataset_name == "bimodal_full_std_org_vocsplt_drums_demucs": 
        csv_normal = "full_bimodal_final_std_org_vocsplt_drums_demucs_features.csv"
        csv_annots = "full_bimodal_annotations.csv"
        top_features = 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       
        bad_iteration_list = []
       
    elif dataset_name == "bimodal_balanced_std_org_vocsplt_drums_demucs": 
        csv_normal = "balanced_bimodal_final_std_org_vocsplt_drums_demucs_features.csv"
        csv_annots = "balanced_bimodal_annotations.csv"
        top_features = 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       
        bad_iteration_list = []

    elif dataset_name == "audio_balanced_std_org_vocsplt_drums_demucs": 
        csv_normal = "balanced_audio_final_std_org_vocsplt_drums_demucs_features.csv"
        csv_annots = "balanced_audio_annotations.csv"
        top_features = 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        bad_iteration_list = []   

    ##########################
    ## All ##
    ##########################

    elif dataset_name == "audio_full_all": 
        csv_normal = "full_audio_all_features.csv"
        csv_annots = "full_audio_annotations.csv"
        top_features = 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       
        bad_iteration_list = []

    elif dataset_name == "bimodal_full_all": 
        csv_normal = "full_bimodal_final_all_features.csv"
        csv_annots = "full_bimodal_annotations.csv"
        
        top_features = top_features = 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        bad_iteration_list = []
       
    elif dataset_name == "bimodal_balanced_all": 
        csv_normal = "balanced_bimodal_final_all_features.csv"
        csv_annots = "balanced_bimodal_annotations.csv"
       
        top_features = 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       
        bad_iteration_list = []

    elif dataset_name == "audio_balanced_all": 
        csv_normal = "balanced_audio_final_all_features.csv"
        csv_annots = "balanced_audio_annotations.csv"
        top_features = 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      
        bad_iteration_list = []   

    elif dataset_name == "taffc_all":
        csv_normal = "taffc_final_all_features.csv"
        csv_annots = "taffc_annotations.csv"

        top_features = 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       
        bad_iteration_list = []

    elif dataset_name == "baseline_lyrics_609":
        csv_normal = r"Ricardo_Letra/Mixed(C6)_NomesRevistos_Reorder_Best.csv"
        csv_annots = r"Ricardo_Letra/Mixed(C6)_NomesRevistos_Reorder_Best_annotations.csv"
        top_features = [i for i in range(1, 610)]

        bad_iteration_list = []
    elif dataset_name == "180_letras":
        csv_normal = "180_features_good_features.csv"
        csv_annots = "180_annotations.csv"
        top_features = 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
        bad_iteration_list = []
    elif dataset_name == "bimodal_balanced_letras":
        csv_normal = "bimodal_balanced_features_good_features.csv"
        csv_annots = "bimodal_balanced_annotations.csv"
        top_features = 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
        bad_iteration_list = []
    


    return csv_normal, csv_annots, top_features, bad_iteration_list

def load_data(dataset_name):
    # carregar .csv com top 100
    samples_file, csv_name, new_top, bad_iteration_list = get_information(dataset_name)
    data = pd.read_csv(
#        curr_path + ptd + samples_file,
        samples_file,
        sep=',', header=0)
    
    print(data.shape)
    print(samples_file)
    print(csv_name)

    data.sort_values(by=['IdSong'])
    
    #X = data.iloc[:, 1:101]  # cabeçalho, 1a coluna -> nome, label no fim
    #print(len(data))
    #data = data.dropna(axis=1, how='any') # we dont need to do thsi when we arleady have the top 100 ids

    #size_data = data.shape[1]
    size_data_1 = len(data)

    #data = data.dropna(axis=1, how='any')
    #data = data.fillna(value=0)
    print(data.shape)


    #new_top = [1152, 525, 1765, 1773, 1166, 2073, 1151, 507, 361, 927, 1768, 2109, 2123, 2110, 2124, 10, 945, 1441, 1497, 13, 1475, 529, 21, 2074, 1089, 133, 1065, 909, 1077, 1071, 1550, 1783, 407, 1786, 404, 1537, 1489, 1778, 1775, 772, 2075, 946, 1455, 2103, 136, 1771, 1059, 406, 526, 2079, 1574, 1154, 923, 917, 753, 1053, 2086, 2104, 109, 246, 911, 1779, 1481, 2092, 1090, 1194, 252, 1782, 121, 776, 2093, 1766, 2101, 770, 1605, 2084, 1539, 357, 778, 891, 1469, 2631, 1857, 9, 2105, 897, 497, 2125, 2087, 958, 247, 2108, 2333, 1485, 1350, 1078, 469, 1606, 1594, 893]

    print("Feature list is: ")
    print(new_top)

    #new_top = [i for i in range(1,541)]

    X = data.iloc[:,new_top]

    print("Data length: %d with %d features" % (size_data_1, len(X.columns)))
    #print(X)
    #exit(-1)

    target = pd.read_csv(
 #       curr_path + ptd + csv_name,
        csv_name,
        sep=',', header=0)
    target.sort_values(by=['Song'])
    Y = target.iloc[:, 1]

    # normalizar dataset
    X = preprocessing.scale(X.to_numpy())


    # now we want to save X to a file, where the header is the Y

    data_columns = data.columns
    columns = []
    for i in new_top:
        columns.append(data_columns[i])
        

    # save X
    pd.DataFrame(X).to_csv("X_new.csv", header=columns, index=None)



    return X, Y



def avg(lst):
    return sum(lst) / len(lst)


def map_targets(Y):
    # converter para inteiros
    mapping = {}
    classes = ['Q1', 'Q2', 'Q3', 'Q4']
    for x in range(len(classes)):
        mapping[classes[x]] = x
    Y_hot_encoded = [mapping[temp] for temp in Y]
    # one hot enconding
    Y_hot_encoded = np_utils.to_categorical(Y_hot_encoded)
    return Y_hot_encoded

    


##########################
## Find Hyperparameters ##
##########################

def process_one_fold(train_index, test_index, fold, total_folds, X, y, model, verbose):
    if verbose > 0:
        print(f"Running fold {fold}/{total_folds}")

    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]

    try:
        model.fit(X_train, y_train)  # Assuming model.fit() is thread-safe
    except Exception as e:
        print(f"Exception occurred while fitting model on fold {fold}: {e}")
        return None

    y_pred = model.predict(X_test)
    score = accuracy_score(y_test, y_pred)

    return score


def custom_cross_val_score_new(model, X, y, cv, scoring='accuracy', verbose=1, timeout=10, n_jobs=1):
    scores = []

    fold = 0
    total_folds = cv.get_n_splits() * cv.n_repeats
    futures = []

    with ProcessPoolExecutor(max_workers=n_jobs) as executor:
        for train_index, test_index in cv.split(X, y):
            fold += 1
            future = executor.submit(process_one_fold, train_index, test_index, fold, total_folds, X, y, model, verbose)
            futures.append(future)

        for future in as_completed(futures):
            try:
                score = future.result(timeout=timeout)
                if score is not None:
                    scores.append(score)
            except concurrent.futures.TimeoutError:
                print(f"A fold took too long to complete and was skipped.")
            except Exception as e:
                print(f"An unexpected error occurred: {e}")

    #print(np.array(scores))

    return np.array(scores)



def find_optimal_hyperparameters(X, y, dataset_name, file_name=""):
    # define the space of hyperparameters to search
    search_space = list()
    search_space.append(Real(1e-6, 100.0, 'log-uniform', name='C'))
    search_space.append(Categorical(['linear', 'poly', 'rbf', 'sigmoid'], name='kernel'))
    search_space.append(Integer(1, 5, name='degree'))
    search_space.append(Real(1e-6, 100.0, 'log-uniform', name='gamma'))

    #print(search_space)
    #exit
    iteration = 0

    #if file_name != ":
        #file_write = open(file_name, "w")



    # define the function used to evaluate a given configuration
    @use_named_args(search_space)
    def evaluate_model(**params):
        # configure the model with specific hyperparameters
        model = SVC()
        model.set_params(**params)
        current_minimum = 1.0
        nonlocal iteration, dataset_name
        iteration += 1
        # define test harness
        cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=10, random_state=1)

        print_with_time(f"Starting iteration {iteration}\n", flush=True)
        #write_to_file_with_time(f"Starting iteration {iteration}\n", file_write)
        #file_write.flush()

        # for relief we need to skip iteration 13. it bugs out
        _, _, _, bad_iteration_list = get_information(dataset_name)
        

        #print_with_time(bad_iteration_list)
        if iteration in bad_iteration_list:
            print_with_time(f"Iteration {iteration}, skiping\n", flush=True)
            #write_to_file_with_time(f"Iteration {iteration}, skiping\n", file_write)
            #file_write.flush()
            return 1

        # n_jobs has to be 1, or this never completes
        
        timeout_for_iteration = 120  # 60 seconds for the entire iteration

        def run_custom_cross_val_score(q):
            # Make sure result is accessible outside this function
            res = custom_cross_val_score_new(model, X, y, cv=cv, timeout=10, verbose=0, n_jobs=10)
            q.put(res)  # Put result in queue

        result_queue = Queue()

        # Start the process
        process = Process(target=run_custom_cross_val_score, args=(result_queue,))
        process.start()

        # Wait for the thread to finish or timeout
        process.join(timeout_for_iteration)

        if process.is_alive():
            print_with_time(f"Iteration {iteration} timed out\n", flush=True)
            #write_to_file_with_time(f"Iteration {iteration} timed out\n", file_write)
            #file_write.flush()
            # make it so that result is an np.array with 1 elemnt of value 1
            result = np.array([])
            process.terminate()  # Optional: wait for thread to finish anyway
        else:
            result = result_queue.get()  # Get result from queue

        #print(result)

         # Existing code to handle `result`
        if len(result) != 0:
            estimate = np.mean(result)
            result = 1.0 - estimate
            if result < current_minimum:
                current_minimum = result
                #file_write.flush()
        else:
            result = np.float64(1)



        print_with_time(f"Ending iteration {iteration} with result: {result}\n", flush=True)
        #write_to_file_with_time(f"Ending iteration {iteration} with result: {result} {1-result}\n", file_write)
        #write_to_file_with_time(f"Curren minimum: {current_minimum} {1-current_minimum}\n", file_write)
        #file_write.flush()

        # if we time out we need a result
        if not isinstance(result, np.float64):
            result = np.float64(1)
        
        # convert from a maximizing score to a minimizing score
        return result

    # load dataset
    print_with_time(X.shape, y.shape)
    #write_to_file_with_time(f"{X.shape}, {y.shape}\n", file_write)
    #file_write.flush()
    # Create a new version of evaluate_model with dataset_name fixed
    #evaluate_model_with_dataset = partial(evaluate_model, dataset_name=dataset_name)

    # perform optimization
    result = gp_minimize(evaluate_model, search_space, verbose=1, random_state=1, n_jobs=20)
    # summarizing finding:
    print_with_time('Best Accuracy: %.3f' % (1.0 - result.fun))
    print_with_time('Best Parameters: %s' % result.x)

    #write_to_file_with_time(f"Best Accuracy: %.3f\n" % (1.0 - result.fun), file_write)
    #write_to_file_with_time(f"Best Parameters: %s\n" % result.x, file_write)
    #file_write.flush()

    return result.x


#######################################
## Run SVM with found Hyperparamters ##
#######################################

# this is the function that has the old code
def run_SVM_old(X, y, Y_string, c, kernel, degree, gamma, file_write=None):
    kfold = RepeatedStratifiedKFold(n_splits=10, n_repeats=10)
    macro_f1_score, f1_each_all, conf_mat_global, accuracy_list = [], [], np.zeros((4, 4)), []

    start_time = time.time()

    for train, test in tqdm(list(kfold.split(X, Y_string))):
        svc = SVC(C=c, kernel=kernel, degree=degree, gamma=gamma)
        svc.fit(X[train], y[train])

        ypred = svc.predict(X[test])
        f1_temp = f1_score(y[test], ypred, average='weighted')
        accuracy = accuracy_score(y[test], ypred)
        conf_mat = confusion_matrix(y[test], ypred)

        macro_f1_score.append(f1_temp)
        accuracy_list.append(accuracy)
        conf_mat_global += conf_mat

    print_with_time(conf_mat_global)

    if file_write != None:
        file_write.write(str(conf_mat_global) + "\n")

    # Calculate accuracy for all classes and F1-score per class
    class_acc = {}
    class_f1 = {}

    list_to_print_f1 = []

    for i in range(4):
        TP = conf_mat_global[i, i]
        FN = conf_mat_global[i, :].sum() - TP
        FP = conf_mat_global[:, i].sum() - TP
        TN = conf_mat_global.sum() - (TP + FN + FP)
        class_f1[f'Q{i+1}'] = 2 * TP / (2 * TP + FN + FP)
        class_acc[f'Q{i+1}'] = TP / (TP + FN)
        print_with_time(f'Accuracy for Q{i+1}: {class_acc[f"Q{i+1}"]:.3f}')
        if file_write != None:
            file_write.write(f'Accuracy for Q{i+1}: {class_acc[f"Q{i+1}"]:.3f}\n')

        list_to_print_f1.append(f'F1-score for Q{i+1}: {class_f1[f"Q{i+1}"]:.3f}')

    # Calculate global accuracy and F1-score
    global_acc = np.mean(accuracy_list)
    global_f1 = np.mean(macro_f1_score)
    std_acc = np.std(accuracy_list)  # Calculate standard deviation of accuracy
    sdt_f1 = np.std(macro_f1_score)
    print_with_time(f'Global accuracy: {global_acc:.3f}')
    if file_write != None:
        file_write.write(f'Global accuracy: {global_acc:.3f}\n')
    list_to_print_f1.append(f'Global F1-score: {global_f1:.3f}')
    print_with_time(f'Standard deviation (accuracy): {std_acc:.3f}')
    if file_write != None:
        file_write.write(f'Standard deviation (accuracy): {std_acc:.3f}\n')

    list_to_print_f1.append(f'Standard deviation (F1-score): {sdt_f1:.3f}')

    print("-----------------------------","\n")

    if file_write != None:
        file_write.write("-----------------------------\n")
        file_write.write("\n")

    for i in list_to_print_f1:
        print_with_time(i)
        if file_write != None:
            file_write.write(i + "\n")

    end_time = time.time() - start_time

    # Save metrics
    with open("f1_score_svm_100_features.pickle", "wb") as file_results:
        pickle.dump(macro_f1_score, file_results)
    with open("conf_mat_svm_100_features.pickle", "wb") as conf_mat_results:
        pickle.dump(conf_mat_global, conf_mat_results)
    with open("time_svm_100_features.txt", "w") as time_file:
        time_file.write('Took {} minutes to run.'.format(end_time / 60))

def run_SVM(X, y, Y_string, c, kernel, degree, gamma, file_write=None, file_name=None):
    kfold = RepeatedStratifiedKFold(n_splits=10, n_repeats=10)
    macro_f1_score, f1_each_all, conf_mat_global, accuracy_list, conf_matrices_percentages = [], [], np.zeros((4, 4)), [], []

    start_time = time.time()

    for train, test in tqdm(list(kfold.split(X, Y_string))):
        svc = SVC(C=c, kernel=kernel, degree=degree, gamma=gamma)
        svc.fit(X[train], y[train])

        ypred = svc.predict(X[test])
        f1_temp = f1_score(y[test], ypred, average='weighted')
        accuracy = accuracy_score(y[test], ypred)
        conf_mat = confusion_matrix(y[test], ypred)

        macro_f1_score.append(f1_temp)
        accuracy_list.append(accuracy)
        conf_mat_global += conf_mat
        
        # Calculate percentage confusion matrix for this fold
        fold_conf_percentages = (conf_mat / conf_mat.sum(axis=1, keepdims=True)) * 100
        conf_matrices_percentages.append(fold_conf_percentages)

    # Print the old confusion matrix format
    print_with_time(conf_mat_global)

    avg_conf_mat = np.mean(conf_matrices_percentages, axis=0)
    std_conf_mat = np.std(conf_matrices_percentages, axis=0)

    # Print the formatted 4x4 confusion matrix
    print_with_time("Confusion Matrix (in %):")
    for i in range(4):
        row = " | ".join([f"{avg_conf_mat[i, j]:.2f}% ± {std_conf_mat[i, j]:.2f}%" for j in range(4)])
        print_with_time(row)
        if file_write is not None:
            file_write.write(row + "\n")

    class_f1 = {}
    class_f1_std = {}
    list_to_print_f1 = []
    list_to_print_std = []

    for i in range(4):
        TP = conf_mat_global[i, i]
        FN = conf_mat_global[i, :].sum() - TP
        FP = conf_mat_global[:, i].sum() - TP
        TN = conf_mat_global.sum() - (TP + FN + FP)
        
        # Calculate F1-score and its standard deviation for each class
        class_f1[f'Q{i+1}'] = 2 * TP / (2 * TP + FN + FP)
        class_f1_std[f'Q{i+1}'] = np.std([2 * cm[i, i] / (2 * cm[i, i] + cm[i, :].sum() - cm[i, i] + cm[:, i].sum() - cm[i, i]) for cm in conf_matrices_percentages])
        
        list_to_print_f1.append(f'F1-score for Q{i+1}: {class_f1[f"Q{i+1}"]:.3f}')
        list_to_print_std.append(f'\tStandard deviation for Q{i+1} (F1-score): {class_f1_std[f"Q{i+1}"]:.3f}')

    global_acc = np.mean(accuracy_list)
    global_f1 = np.mean(macro_f1_score)
    std_acc = np.std(accuracy_list)
    sdt_f1_global = np.std(macro_f1_score)

    print("-----------------------------","\n")    

    if file_write is not None:
        file_write.write("-----------------------------\n")
        file_write.write("\n")

    for i, j in zip(list_to_print_f1, list_to_print_std):
        print_with_time(i)
        print_with_time(j)
        if file_write is not None:
            file_write.write(i + "\n")
            file_write.write(j + "\n")

    print("-----------------------------","\n")    
    if file_write is not None:
        file_write.write("-----------------------------\n")
        file_write.write("\n")

    print_with_time(f'Global F1-score: {global_f1:.3f}')
    if file_write is not None:
        file_write.write(f'Global F1-score: {global_f1:.3f}\n')

    print_with_time(f'\tStandard deviation (Global F1-score): {sdt_f1_global:.3f}')
    if file_write is not None:
        file_write.write(f'\tStandard deviation (Global F1-score): {sdt_f1_global:.3f}\n')

    print("-----------------------------","\n")        
    if file_write is not None:
        file_write.write("-----------------------------\n")
        file_write.write("\n")

    # now I want to dump the 100 folds of f1-score onto a pickle file

    if file_name != None:
        with open(file_name, "wb") as file_results:
            pickle.dump(macro_f1_score, file_results)


def main(dataset_name,num_features):
    # load data
    #X, Y = load_data(dataset_name)
    X, Y = load_data_features(dataset_name, num_features)
    
    print_with_time("Dados carregados com sucesso!")
    # hot one encoding

    start = time.time()
    
    #result = [1.7769934685278082, 'rbf', 1, 0.00976460455797839]

    #result = find_optimal_hyperparameters(X, Y, dataset_name)
    
    svc = SVC()

    search_space = {
        'C': Real(1e-6, 100.0, 'log-uniform'),
        'kernel': Categorical(['rbf']),  # Fixed to 'rbf'
        'gamma': Real(1e-6, 100.0, 'log-uniform')
    }

    # Define the cross-validation strategy
    cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=10, random_state=1)

    # Define the weighted F1-score as a custom scoring metric
    weighted_f1_scorer = make_scorer(f1_score, average='weighted')

    # Perform Bayesian optimization for hyperparameter tuning
    opt = BayesSearchCV(
        svc,
        search_space,
        n_iter=100,  # Number of optimization steps
        cv=cv,  # Cross-validation strategy
        verbose=0,  # Set verbose level to 2 to display iteration information
        n_jobs=10,  # Number of CPU cores to use (set to -1 to use all available cores)
        scoring=weighted_f1_scorer  # Use the weighted F1-score as the scoring metric
    )

    print("Fitting to data")

    # Fit the optimizer to the data
    opt.fit(X, Y)

    # Print the best hyperparameters
    print("Best hyperparameters found:")
    print(opt.best_params_)
    print("Weighted F1-score:", opt.best_score_)

    best_gamma = opt.best_params_['gamma']
    best_cost = opt.best_params_['C']
    best_kernel = 'rbf'
    best_degree = 1
    run_SVM(X,Y,Y, best_cost, best_kernel, best_degree, best_gamma)
    
    #result = find_optimal_hyperparameters(X, Y, dataset_name)

    #opt = BayesSearchCV()

    #result = [1, 'poly', 2, 0.012222834595883997]

    #result = [1.722848544277722, 'rbf', 1, 0.00619566461617553]

    #result = [2.133525900222659, 'rbf', 3, 0.0036661347842258853]

    #with open('best_hyper_for_corrcted.pickle', 'wb') as hype_file:
    #   pickle.dump(result, hype_file)
    #result = [0.0002883146929196109, 'poly', 1, 90.38625210972366]
    #result = [8, "rbf", 1, 0.001953125]
    #result = [1.7059670315046978, 'rbf', 1, 0.010983162294952793]
    #result = [1.1162069057291064, 'rbf', 1, 0.010709892505221284]

    # ... other result comments

    #result = [100.0, 'rbf', 1, 0.002439324358513435]

    end = time.time()
    time_elapsed = round(end - start)
    print_with_time("Minutos passados: " + str(time_elapsed / 60))

    #run_SVM(X, Y, Y,
                         #result[0], result[1],
                         #result[2], result[3])

    print_with_time('Finished')

def run_svm_returns_f1_score(X, y, c, kernel, degree, gamma):
    kfold = RepeatedStratifiedKFold(n_splits=10, n_repeats=10)
    macro_f1_score, f1_each_all, conf_mat_global, accuracy_list = [], [], np.zeros((4, 4)), []

    for train, test in kfold.split(X, y):
        svc = SVC(C=c, kernel=kernel, degree=degree, gamma=gamma)
        svc.fit(X[train], y[train])

        ypred = svc.predict(X[test])
        f1_temp = f1_score(y[test], ypred, average='macro')
        f1_each = f1_score(y[test], ypred, average=None)
        accuracy = accuracy_score(y[test], ypred)
        conf_mat = confusion_matrix(y[test], ypred)

        macro_f1_score.append(f1_temp)
        f1_each_all.append(f1_each)
        accuracy_list.append(accuracy)
        conf_mat_global += conf_mat

    return macro_f1_score, f1_each_all, conf_mat_global, accuracy_list

def load_data_features(dataset_name, feature_amount):
    # Load information and data
    samples_file, csv_name, new_top, _ = get_information(dataset_name)
    data = pd.read_csv(samples_file, sep=',', header=0)



    print_with_time(f"Data Shape: {data.shape}")
    print_with_time(f"Sample File: {samples_file}")
    print_with_time(f"CSV Name: {csv_name}")
    print_with_time(f"Feature amount: {feature_amount}")

    # Sort by 'IdSong' (assumed to be a column in your data)
    data.sort_values(by=['IdSong'])

    # Extract relevant features
    X = data.iloc[:, new_top]

    #print(X)

    # Now just keep the amount of features we want

    X = X.iloc[:, :feature_amount]

    #print(X)

    #print_with_time(f"Data length: {len(data)} with {len(X.columns)} features")

    # Load target labels
    target = pd.read_csv(csv_name, sep=',', header=0)
    target.sort_values(by=['Song'])
    Y = target.iloc[:, 1]

    X = X.replace("?", 0)

    # now we fill nas with 0 (important for lyrics)

    X = X.fillna(value=0)

    X.to_csv("X_new.csv")

    # Normalize dataset
    X = preprocessing.scale(X.to_numpy())



    exit(-1)


    return X, Y





if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Process some arguments.')
    parser.add_argument('--dataset_name', type=str, required=True, help='Name of the dataset')
    parser.add_argument('--num_feat',type=int,required= True, help='Number of features to use')

    #print(args.dataset_name)

    args = parser.parse_args()
    #get_values_and_save()

    #datasets = ["audio_full_percussion","audio_balanced_percussion","bimodal_full_percussion","bimodal_balanced_percussion","taffc_percussion"]

    #for dataset in datasets:
        #print_with_time(f"Running for dataset {dataset}")
        #test_various_features(dataset)

    main(args.dataset_name, args.num_feat)
    #test_various_features(args.dataset_name)