Kaggle competition: Titanic - Machine Learning from Disaster
<SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<define_variables>
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
labels = {np.nan: -999, 'nan': -999, 't': 1, 'f': 2, 'm2': 3, 'm0': 4, 'm1': 5, 'gmail.com': 6, 'outlook.com': 7, 'yahoo.com': 8, 'mail.com': 9, 'anonymous.com': 10, 'hotmail.com': 11, 'verizon.net': 12, 'aol.com': 13, 'me.com': 14, 'comcast.net': 15, 'optonline.net': 16, 'cox.net': 17, 'charter.net': 18, 'rocketmail.com': 19, 'prodigy.net.mx': 20, 'embarqmail.com': 21, 'icloud.com': 22, 'live.com.mx': 23, 'gmail': 24, 'live.com': 25, 'att.net': 26, 'juno.com': 27, 'ymail.com': 28, 'sbcglobal.net': 29, 'bellsouth.net': 30, 'msn.com': 31, 'q.com': 32, 'yahoo.com.mx': 33, 'centurylink.net': 34, 'servicios-ta.com': 35, 'earthlink.net': 36, 'hotmail.es': 37, 'cfl.rr.com': 38, 'roadrunner.com': 39, 'netzero.net': 40, 'gmx.de': 41, 'suddenlink.net': 42, 'frontiernet.net': 43, 'windstream.net': 44, 'frontier.com': 45, 'outlook.es': 46, 'mac.com': 47, 'netzero.com': 48, 'aim.com': 49, 'web.de': 50, 'twc.com': 51, 'cableone.net': 52, 'yahoo.fr': 53, 'yahoo.de': 54, 'yahoo.es': 55, 'sc.rr.com': 56, 'ptd.net': 57, 'live.fr': 58, 'yahoo.co.uk': 59, 'hotmail.fr': 60, 'hotmail.de': 61, 'hotmail.co.uk': 62, 'protonmail.com': 63, 'yahoo.co.jp': 64, 'scranton.edu': 65, 'w': 66, 'h': 67, 'c': 68, 's': 69, 'r': 70, 'discover': 71, 'mastercard': 72, 'visa': 73, 'american express': 74, 'credit': 75, 'debit': 76, 'debit or credit': 77, 'charge card': 78, 'notfound': 81, 'found': 82, 'new': 83, 'unknown': 84, 'ip_proxy:transparent': 85, 'ip_proxy:anonymous': 86, 'ip_proxy:hidden': 87, 'android 7.0': 88, 'ios 11.1.2': 89, 'mac os x 10_11_6': 90, 'windows 10': 91, 'android': 284, 'linux': 93, 'ios 11.0.3': 94, 'mac os x 10_7_5': 95, 'mac os x 10_12_6': 96, 'mac os x 10_13_1': 97, 'ios 11.1.0': 98, 'mac os x 10_9_5': 99, 'windows 7': 100, 'windows 8.1': 101, 'mac': 102, 'ios 10.3.3': 103, 'mac os x 10.12': 104, 'mac os x 10_10_5': 105, 'mac os x 10_11_5': 106, 'ios 9.3.5': 107, 'android 5.1.1': 108, 'android 7.1.1': 109, 'android 6.0': 110, 'ios 10.3.1': 111, 'mac os x 10.9': 112, 'ios 11.1.1': 113, 'windows vista': 114, 'ios 10.3.2': 115, 'ios 11.0.2': 116, 'mac os x 10.11': 117, 'android 8.0.0': 118, 'ios 10.2.0': 119, 'ios 10.2.1': 120, 'ios 11.0.0': 121, 'mac os x 10.10': 122, 'mac os x 10_12_3': 123, 'mac os x 10_12': 124, 'android 6.0.1': 125, 'ios': 126, 'mac os x 10.13': 127, 'mac os x 10_12_5': 128, 'mac os x 10_8_5': 129, 'ios 11.0.1': 130, 'ios 10.0.2': 131, 'android 5.0.2': 132, 'windows xp': 133, 'ios 11.2.0': 134, 'mac os x 10.6': 135, 'windows 8': 136, 'mac os x 10_6_8': 137, 'mac os x 10_11_4': 138, 'mac os x 10_12_1': 139, 'ios 10.1.1': 140, 'mac os x 10_11_3': 141, 'mac os x 10_12_4': 142, 'mac os x 10_13_2': 143, 'android 4.4.2': 144, 'mac os x 10_12_2': 145, 'android 5.0': 146, 'func': 147, 'android 7.1.2': 148, 'android 8.1.0': 149, 'other': 150, 'mac os x 10_13_3': 151, 'ios 11.2.1': 152, 'ios 11.2.5': 153, 'windows': 154, 'ios 11.2.2': 155, 'ios 11.3.0': 156, 'ios 11.2.6': 157, 'mac os x 10_13_4': 158, 'mac os x 10_13_5': 159, 'ios 11.4.0': 160, 'ios 11.3.1': 161, 'ios 11.4.1': 162, 'android 9': 163, 'ios 12.0.0': 164, 'mac os x 10_13_6': 165, 'mac os x 10.14': 166, 'mac os x 10_14_0': 167, 'mac os x 10_14': 168, 'ios 12.1.0': 169, 'mac os x 10_14_1': 170, 'ios 12.0.1': 171, 'ios 12.1.1': 172, 'mac os x 10_14_2': 173, 'ios 12.1.2': 174, 'samsung browser 6.2': 175, 'mobile safari 11.0': 176, 'chrome 62.0': 177, 'chrome 62.0 for android': 178, 'edge 15.0': 179, 'mobile safari generic': 180, 'chrome 49.0': 181, 'chrome 61.0': 182, 'edge 16.0': 183, 'safari generic': 184, 'edge 14.0': 185, 'chrome 56.0 for android': 186, 'firefox 57.0': 187, 'chrome 54.0 for android': 188, 'mobile safari uiwebview': 189, 'chrome': 190, 'chrome 62.0 for ios': 191, 'firefox': 192, 'chrome 60.0 for android': 193, 'mobile safari 10.0': 194, 'chrome 61.0 for android': 195, 'ie 11.0 for desktop': 196, 'ie 11.0 for tablet': 197, 'mobile safari 9.0': 198, 'chrome generic': 199, 'chrome 59.0 for android': 200, 'firefox 56.0': 201, 'android webview 4.0': 202, 'chrome 55.0': 203, 'opera 49.0': 204, 'ie': 205, 'chrome 55.0 for android': 206, 'firefox 52.0': 207, 'chrome 57.0 for android': 208, 'chrome 56.0': 209, 'chrome 46.0 for android': 210, 'chrome 58.0': 211, 'firefox 48.0': 212, 'chrome 59.0': 213, 'samsung browser 4.0': 214, 'edge 13.0': 215, 'chrome 53.0 for android': 216, 'chrome 58.0 for android': 217, 'chrome 60.0': 218, 'mobile safari 8.0': 219, 'firefox generic': 220, 'generic/android 7.0': 221, 'mobile': 222, 'samsung/sm-g532m': 223, 'chrome 50.0 for android': 224, 'chrome 51.0 for android': 225, 'chrome 63.0': 226, 'chrome 52.0 for android': 227, 'chrome 51.0': 228, 'firefox 55.0': 229, 'edge': 230, 'opera': 231, 'chrome generic for android': 232, 'aol': 233, 'samsung browser 5.4': 234, 'samsung/sch': 235, 'silk': 236, 'chrome 57.0': 237, 'firefox 47.0': 238, 'chrome 63.0 for android': 239, 'samsung/sm-g531h': 240, 'chrome 43.0 for android': 241, 'waterfox': 242, 'nokia/lumia': 243, 'chrome 63.0 for ios': 244, 'puffin': 245, 'microsoft/windows': 246, 'cyberfox': 247, 'generic/android': 248, 'samsung': 849, 'opera generic': 250, 'chrome 49.0 for android': 251, 'zte/blade': 252, 'safari': 253, 'android browser 4.0': 254, 'samsung browser 5.2': 255, 'palemoon': 256, 'maxthon': 257, 'line': 258, 'lg/k-200': 259, 'iron': 260, 'blu/dash': 261, 'seamonkey': 262, 'firefox 58.0': 263, 'chrome 64.0 for android': 264, 'chrome 64.0': 265, 'firefox 59.0': 266, 'chrome 64.0 for ios': 267, 'm4tel/m4': 268, 'comodo': 269, 'lanix/ilium': 270, 'samsung browser generic': 271, 'chromium': 272, 'opera 51.0': 273, 'inco/minion': 274, 'samsung browser 7.0': 275, 'mozilla/firefox': 276, 'samsung browser 4.2': 277, 'samsung browser 6.4': 278, 'chrome 65.0': 279, 'chrome 65.0 for android': 280, 'chrome 65.0 for ios': 281, 'cherry': 282, 'icedragon': 283, 'edge 17.0': 285, 'chrome 66.0': 286, 'chrome 66.0 for android': 287, 'safari 11.0': 288, 'safari 9.0': 289, 'safari 10.0': 290, 'google': 291, 'chrome 66.0 for ios': 292, 'google search application 48.0': 293, 'opera 52.0': 294, 'firefox 60.0': 295, 'opera 53.0': 296, 'samsung browser 3.3': 297, 'google search application 49.0': 298, 'facebook': 299, 'firefox mobile 61.0': 300, 'chrome 67.0': 301, 'chrome 69.0': 302, 'chrome 67.0 for android': 303, 'firefox 61.0': 304, 'samsung browser 7.2': 305, 'chrome 67.0 for ios': 306, 'google search application 52.0': 307, 'firefox 62.0': 308, 'mobile safari 12.0': 309, 'chrome 68.0': 310, 'opera 54.0': 311, 'firefox mobile 62.0': 312, 'google search application 54.0': 313, 'safari 12.0': 314, 'chrome 68.0 for android': 315, 'chrome 68.0 for ios': 316, 'chrome 39.0 for android': 317, 'chrome 70.0': 318, 'rim': 2752, 'chrome 69.0 for android': 320, 'google search application 56.0': 321, 'samsung browser 7.4': 322, 'opera 55.0': 323, 'firefox 63.0': 324, 'google search application 58.0': 325, 'chrome 69.0 for ios': 326, 'chrome 70.0 for android': 327, 'uc': 328, 'google search application 59.0': 329, 'opera 56.0': 330, 'chrome 71.0': 331, 'google search application 60.0': 332, 'edge 18.0': 333, 'chrome 70.0 for ios': 334, 'firefox mobile 63.0': 335, 'google search application 61.0': 336, 'firefox 64.0': 337, 'google search application 62.0': 338, 'chrome 71.0 for android': 339, 'google search application 63.0': 340, 'chrome 71.0 for ios': 341, 'google search application 64.0': 342, 'samsung browser 8.2': 343, 'google search application 65.0': 344, 'blackberry': 345, '2220x1080': 346, '1334x750': 347, '1280x800': 348, '1366x768': 349, '1920x1080': 350, '1680x1050': 351, '1136x640': 352, '5120x2880': 353, '2880x1800': 354, '1920x1200': 355, '2560x1600': 356, '2048x1536': 357, '1024x768': 358, '1280x720': 359, '2560x1440': 360, '2208x1242': 361, '2001x1125': 362, '1440x900': 363, '1600x900': 364, '2672x1440': 365, '1280x1024': 366, '960x540': 367, '2732x2048': 368, '2436x1125': 369, '2048x1152': 370, '2960x1440': 371, '1024x600': 372, '855x480': 373, '4096x2304': 374, '2160x1440': 375, '2562x1442': 376, '801x480': 377, '2736x1824': 378, '3441x1440': 379, '2880x1620': 380, '3840x2160': 381, '1638x922': 382, '1280x768': 383, '1360x768': 384, '1280x960': 385, '3440x1440': 386, '1152x720': 387, '1280x1025': 388, '3360x2100': 389, '2304x1296': 390, '1152x864': 391, '3200x1800': 392, '2112x1188': 393, '2224x1668': 394, '2400x1350': 395, '2000x1125': 396, '1600x1000': 397, '2560x1080': 398, '1728x972': 399, '3000x2000': 400, '1024x640': 401, '3840x2400': 402, '2304x1440': 403, '1280x600': 404, '1400x1050': 405, '1600x1200': 406, '3201x1800': 407, '1356x900': 408, '1344x756': 409, '1624x1080': 410, '1536x864': 411, '1800x1125': 412, '1920x1281': 413, '2961x1442': 414, '1366x1024': 415, '1344x840': 416, '3360x1890': 417, '1536x1152': 418, '1200x675': 419, '1480x720': 420, '2400x1600': 421, '3200x2000': 422, '1281x801': 423, '960x640': 424, '1776x1000': 425, '2048x1280': 426, '2049x1152': 427, '1138x640': 428, '2160x1215': 429, '2880x1440': 430, '0x0': 431, '2520x1575': 432, '5760x3240': 433, '3843x2163': 434, '1184x720': 435, '1440x810': 436, '2076x1080': 437, '1600x837': 438, '1093x615': 439, '1281x721': 440, '1152x648': 441, '2392x1440': 442, '2048x1080': 443, '2735x1825': 444, '1680x945': 445, '1805x1015': 446, '5760x1080': 447, '2816x1584': 448, '4500x3000': 449, '1684x947': 450, '1440x960': 451, '1364x768': 452, '3072x1728': 453, '5040x3150': 454, '7500x5000': 455, '768x576': 456, '1768x992': 457, '1658x946': 458, '1200x720': 459, '1239x697': 460, '1188x720': 461, '1232x800': 462, '1920x1280': 463, '1264x924': 464, '1400x900': 465, '3240x2160': 466, '2961x1440': 467, '1422x889': 468, '1848x1155': 469, '3360x1050': 470, '3840x1080': 471, '2010x1080': 472, '2160x1350': 473, '1440x720': 474, '1280x712': 475, '1512x945': 476, '1296x774': 477, '1368x768': 478, '3520x1980': 479, '800x600': 480, '1700x960': 481, '2560x1800': 482, '6400x3600': 483, '2368x1440': 484, '1824x1026': 485, '1912x1025': 486, '600x450': 487, '3840x1600': 488, '1760x990': 489, '2700x1800': 490, '1371x857': 491, '1776x1080': 492, '2552x1337': 493, '3600x2250': 494, '2560x1700': 495, '2816x1760': 496, '1440x800': 497, '1440x803': 498, '1920x1018': 499, '6016x3384': 500, '1280x620': 501, '1281x720': 502, '1720x1440': 503, '1408x880': 504, '640x360': 505, '1920x975': 506, '976x600': 507, '1062x630': 508, '2800x1575': 509, '6720x3780': 510, '1440x759': 511, '1120x700': 512, '1921x1081': 513, '1280x1023': 514, '1279x1023': 515, '1441x901': 516, '1679x1049': 517, '1680x1051': 518, '2220x1081': 519, '1920x1079': 520, '1919x1199': 521, '1680x1049': 522, '1365x768': 523, '1919x1079': 524, '1919x1200': 525, '1919x1080': 526, '1366x767': 527, '1584x990': 528, '2880x1442': 529, '1281x800': 530, '1229x691': 531, '1600x1024': 532, '1600x899': 533, '1536x960': 534, '1502x844': 535, '1920x1201': 536, '1439x809': 537, '1408x792': 538, '1279x1024': 539, '1599x900': 540, '1920x1081': 541, '921x691': 542, '3841x2161': 543, '1921x1080': 544, '480x320': 545, '1888x941': 546, '2049x1536': 547, '2160x1439': 548, '1707x960': 549, '1024x767': 550, '1365x767': 551, '3001x2000': 552, '3839x2160': 553, '1916x901': 554, '3838x2158': 555, '1599x899': 556, '3199x1800': 557, '1511x944': 558, '2737x1825': 559, '2736x1823': 560, '2735x1823': 561, '2559x1439': 562, '2400x1500': 563, '2882x1442': 564, '1729x973': 565, '1727x971': 566, '1023x767': 567, '1918x1080': 568, '1439x900': 569, '4499x2999': 570, '1280x740': 571, '2999x2000': 572, '1024x552': 573, '1440x899': 574, '2255x1503': 575, '1025x768': 576, '1280x732': 577, '3839x2159': 578, '3840x2162': 579, '3696x2310': 580, '2159x1439': 581, '2256x1504': 582, '1439x899': 583, '2159x1440': 584, '1359x768': 585, '1092x614': 586, '2048x1278': 587, '2591x1619': 588, '4200x2625': 589, '2710x1440': 590, '1272x960': 591, '1023x768': 592, '3838x2160': 593, '2100x1312': 594, '1360x767': 595, '1024x819': 596, '1502x845': 597, '2561x1442': 598, '2559x1440': 599, '2160x1081': 600, '1920x1279': 601, '2160x1080': 602, '1596x710': 603, '1496x844': 604, '1280x900': 605, '2047x1152': 606, '2094x1080': 607, '1800x1440': 608, '993x664': 609, '1622x1081': 610, '1360x765': 611, '1280x799': 612, '1279x800': 613, '1920x983': 614, '888x540': 615, '1722x1440': 616, '1679x1050': 617, '2791x1440': 618, '1921x1200': 619, '6144x3456': 620, '2699x1799': 621, '4096x3072': 622, '4608x2592': 623, '910x512': 624, '2257x1505': 625, '1536x1024': 626, '1208x720': 627, '1842x1047': 628, '2462x1641': 629, '1920x1199': 630, '1601x900': 631, '1368x912': 632, '2112x1320': 633, '1265x948': 634, '1359x767': 635, '1920x1017': 636, '1681x1050': 637, '3241x2161': 638, '3011x2007': 639, '2188x1459': 640, '1599x1200': 641, '1500x1000': 642, '2560x1313': 643, '1920x1440': 644, '2703x1441': 645, '1151x921': 646, '3000x1687': 647, '1151x863': 648, '1480x721': 649, '792x480': 650, '3521x1980': 651, '1279x767': 652, '3200x1801': 653, '2560x1439': 654, '1272x952': 655, '1600x901': 656, '2255x1504': 657, '1862x1048': 658, '3840x2159': 659, '3168x1980': 660, '3439x1440': 661, '2560x1441': 662, '800x480': 663, '3200x1799': 664, '3840x2161': 665, '1364x767': 666, '3000x1999': 667, '2688x1242': 668, '1080x675': 669, '2735x1824': 670, '2303x1295': 671, '1592x828': 672, '3122x1442': 673, '2560x1599': 674, '3239x2159': 675, '1281x1024': 676, '1466x403': 677, '4096x2160': 678, '1727x1079': 679, '1231x358': 680, '3584x2016': 681, '2390x1344': 682, '3839x2158': 683, '2281x1081': 684, '1152x672': 685, '2401x1350': 686, '2560x1079': 687, '2161x1080': 688, '1366x766': 689, '683x384': 690, '1720x947': 691, '1024x576': 692, '4320x2700': 693, '2341x1081': 694, '1537x865': 695, '1792x828': 696, '960x480': 697, '3280x2048': 698, '1408x844': 699, '1021x669': 700, '1800x1200': 701, '3120x1440': 702, '1919x1279': 703, '1279x799': 704, '2556x1248': 705, '1000x798': 706, '1600x1280': 707, '1296x810': 708, '3199x1799': 709, '2562x1314': 710, '1624x750': 711, '1416x805': 712, '1920x1128': 713, '2399x1598': 714, '3840x1599': 715, '1918x1078': 716, '2388x1668': 717, '2592x1728': 718, '2562x1441': 719, '1792x768': 720, '3201x1799': 721, '1679x987': 722, '3841x2401': 723, '1794x1080': 724, '1279x720': 725, '2960x1442': 726, '1501x844': 727, '1918x1347': 728, '2950x1440': 729, '1024x820': 730, '1366x769': 731, '2401x1351': 732, '1600x868': 733, '1280x801': 734, '1264x945': 735, '1279x719': 736, '4800x2700': 737, '1151x864': 738, '2880x1920': 739, '960x600': 740, '2696x1440': 741, '5960x1080': 742, '2241x1081': 743, '7680x4320': 744, '2303x1439': 745, '1025x640': 746, '2399x1348': 747, '8640x1620': 748, '2048x1151': 749, '1399x1050': 750, '1920x974': 751, '4225x2377': 752, '2680x1440': 753, '1223x691': 754, '2047x1279': 755, '1279x768': 756, '1621x1081': 757, '1152x922': 758, '1260x787': 759, '1669x942': 760, '3123x1440': 761, '2463x1642': 762, '1361x768': 763, '2048x1537': 764, '8960x5040': 765, '1500x840': 766, '2559x1080': 767, '1408x1127': 768, '1416x758': 769, '2560x1418': 770, '1926x921': 771, '1268x862': 772, '3784x1584': 773, 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2932, 'samsung-sm-g870a build/lrx21t': 2933, 'l-03k': 2934, 'h8314': 2935, 'sm-g532mt': 2936, 'z9': 2937, 'samsung sm-j810m build/r16nw': 2938, 'samsung-sm-g530a': 2939, 'rct6103w46': 2941, 'frd-l19': 2942, 'nokia 2 build/nmf26f': 2943, 'sm-j727t': 2944, 'sm-j250f': 2945, 'pixel 2 build/ppr2.181005.003': 2946, 'sm-g900f': 2947, 'gt-i9195 build/kot49h': 2948, 'ane-lx3': 2949, 'g8231': 2950, 'ilium m3 build/nrd90m': 2951, 's60 lite build/nrd90m': 2952, 'mix build/opr1.170623.032': 2953, 'lg-m700': 2954, 'sm-t350': 2955, 'nx591j': 2956, 'lg-v530': 2957, 'pra-lx3': 2958, 'moto x play': 2959, 'redmi note 4': 2960, 'sm-j700m': 2961, 'trt-l53': 2962, 'lgmp260': 2963, 'samsung sm-t377w build/nmf26x': 2964, 'sm-g950f': 2965, 'moto g(6)play': 2966, 'moto z3 play': 2967, 'sm-j600fn': 2968, 'rv:63.0': 2969, 'sm-j730gm': 2970, 'moto g(4)': 2971, 'huawei vns-l23': 2972, 'c6506 build/10.7.a.0.222': 2973, 'blade v8 se': 2974, 'fig-lx3': 2975, 'rv:21.0': 2976, 'moto z2 play': 2977, 's9+': 2978, '5056a': 2979, 'g24027k': 2980, 'sm-n9600': 2981, 'm4_b2': 2982, 'sm-g610m': 2983, 'bla-a09': 2984, 'sm-a510m': 2985, 'blade v6 plus': 2986, 'rne-l03': 2987, 'h8216': 2988, 'ultra': 2989, 'ldn-lx3': 2990, 'htc one': 2991, 'samsung sm-a530f build/r16nw': 2992, 'moto g(6)plus': 2993, 'sm-g930f': 2994, '5026a': 2995, 'moto e(4)': 2996, 'r505': 2997, 'moto g(5)': 2998, 'lm-x210': 2999, 'sm-a605gn': 3000, 'uniq': 3001, 'c6506': 3002, 'atu-lx3': 3003, 'huawei rio-l03': 3004, 'bac-l03': 3005, 'sm-n960u': 3006, 'lg-h840': 3007, 'cam-l03': 3008, 'sla-l03': 3009, 'blade v8q': 3010, 'lg-m400': 3011, '5052y': 3012, 'sm-g570m build/r16nw': 3013, 'sm-a720f': 3014, 'qmv7b': 3015, 'huawei tag-l13': 3016, 'sm-g930r4 build/r16nw': 3017, '5080a': 3018, 'sm-t113nu': 3019, 'redmi 6a': 3020, 'sm-a600fn': 3021, 'was-lx3': 3022, 'eva-l09': 3023, 'moto g(6)': 3024, 'moto g(5)plus': 3025, 'asus_a007': 3026, 'p008': 3027, 'nokia_x': 3028, 'm4 ss4453': 3029, 'sm-j400m': 3030, 'blade a602': 3031, 'd5303': 3032, 'bll-l23': 3033, 'sgh-t999l': 3034, 'moto e(4)plus': 3035, 'moto g play': 3036, 'sm-g570m': 3037, 'ags-l03': 3038, 'alumini_3_plus': 3039, 'sm-g965f': 3040, 'fig-lx1': 3041, 'sm-j120h': 3042, '4047a': 3043, 'motorola': 3044, 'sm-j320f': 3045, 'hisense f23': 3046, 'huawei vns-l53': 3047, 'htc desire 10 lifestyle': 3048, 'mi a1': 3049, 'sm-j727v': 3050, 'sm-t560': 3051, 'lg-x180g': 3052, 'mya-l13': 3053, 'moto e5': 3054, 'pixel 2': 3055, 'sc-03k': 3056, 'mha-l09': 3057, 'sm-g960u': 3058, 'sm-j530gm': 3059, 'hisense f24': 3060, 'asus_x00hd': 3061, 'infinit_1': 3062, 'sm-a530f': 3063, 'sm-g925v': 3064, 'sm-j600g': 3065, 'sm-j730g': 3066, 'dra-lx3': 3067, 'lg-k530': 3068, 'fedora': 3069, 'vky-l09': 3070, 'lg-k410': 3071, 'f3111': 3072, 'lenovo a2016b30': 3073, 'pixel xl': 3074, 'lg-k200': 3075, 'sm-n950u': 3076, 'blade v6': 3077, 'clt-l04': 3078, 'redmi note 5a': 3079, 'lg-k120': 3080, 'moto c': 3081, 'sm-g800m': 3082, 'rv:64.0': 3083, 'sm-a320fl': 3084, 'gxq6580_weg_l': 3085, 'nexus 5x build/opm7.181005.003': 3086, 'ale-l23': 3087, '5080x': 3088, 'lg-k520': 3089, 'sm-g950u': 3090, 'sm-t813': 3091, 'lg-k580': 3092, 'asus_z01md': 3093, 'mya-l03': 3094, 'shv39': 3095, 'm4 ss4456': 3096, 'samsung sm-n910f/n910fxxu1dql2 build/mmb29m': 3097, 'vs987': 3098, 'sm-g955u': 3099, '5011a': 3100, 'pixel 2 xl': 3101, 'huawei vns-l31': 3102, 'sm-g960n': 3103, 'htc desire 626s': 3104, 'redmi note 5': 3105, 'lg-ls993': 3106, 'sm-g532m': 3107, 'sm-p580': 3108, 'sm-a530w': 3109, 'lg-h873': 3110, 'sm-j500m': 3111, 'sm-j727u': 3112, 'bln-l24': 3113, 'blade v580': 3114, 'sm-a520f': 3115, 'sm-g965u': 3116, 'rne-l23': 3117, 'sm-g960f': 3118, 'gt-i9505': 3119, 'lg-m700 build/opm1.171019.026': 3120, 'ale-l21': 3121, 'sm-g9600': 3122, 'b1-850': 3123, 'redmi 4x': 3124, 'fla-lx3': 3125, 'oneplus a3000': 3126, 'hisense f8 mini': 3127, 'sm-g950u1': 3128, 'rne-l21': 3129, 'sm-j810m': 3130, 'alcatel_5044r': 3131, 'huawei cun-l03': 3132, 'eml-l09': 3133, 'moto g(6)play build/opp27.91-143': 3134, '4047g': 3135, 'sm-g531h': 3136, 'gt-i9060m': 3137, 'sm-j250m': 3138, 'ilium x520': 3139, 'was-lx1a': 3140, 'sm-n920g': 3141, 'sm-g900m': 3142, 'mi max 3': 3143, 'aw790': 3144, 'redmi note 3': 3145, 'sm-n900': 3146, 'sm-g850f': 3147, 'bnd-l21': 3148, 'kfdowi': 3149, 'cmr-w09': 3150, 'sm-t280': 3151, 'kfauwi': 3152, 'lg-x240': 3153, 'ilium l1120': 3154, 'neffos x1 max': 3155, 'ilium x710': 3156, 'moto x4': 3157, 'hisense u963': 3158, 'pra-lx1': 3159, 'nem-l51': 3160, 'sm-g930w8': 3161, 'gt-i9300i': 3162, 'lld-l31': 3163, 'cmr-w19': 3164, 'h3223': 3165, 'sm-j530f': 3166, 'lm-q610.fg': 3167, 'dra-l21': 3168, 'lg-k220': 3169, 'lg-k500': 3170, 'htc one a9s': 3171, 'nexus 5x': 3172, 'pixel 3': 3173, 'cph1725': 3174, 'kfgiwi': 3175, 'sm-j727t1': 3176, 'htc desire 530': 3177, 'lg-tp450': 3178, 'sm-n920c': 3179, 'oneplus a5000': 3180, 'moto c plus': 3181, 'sm-g930v': 3182, 'samsung sm-p900 build/lrx22g': 3183, 'oneplus a6003': 3184, 'p5047a': 3185, 'sm-a310f': 3186, 'sm-g955u1': 3187, 'lg-h831': 3188, 'oneplus a3003': 3189, 'lg-m320': 3190, 'motorola one': 3191, 'lml413dl': 3192, 'sm-j327t1': 3193, 'sm-n950f': 3194, 'eml-l29': 3195, 'oneplus a5010': 3196, 'ane-lx1': 3197, 'sm-j510mn': 3198, 'sm-s727vl': 3199, 'sm-n910v': 3200, 'sm-j320m': 3201, 'oneplus a3010': 3202, 'sm-g955n': 3203, 'nexus 5': 3204, '9003a': 3205, 'sm-t560nu': 3206, 'ane-lx2': 3207, 'kfsuwi': 3208, 'vtr-l09': 3209, 'sm-t713': 3210, 'redmi 5 plus': 3211, 'sm-g920f': 3212, 'lg-m250': 3213, 'blade l7': 3214, 'tr10rs1': 3215, 'sm-j700t': 3216, 'sm-g900f build/njh47f': 3217, 'sm-t385m': 3218, '6045o': 3219, 'hisense t963': 3220, 'redmi 6': 3221, 'm4_b3': 3222, 'clt-l29': 3223, 'zte blade v8 mini': 3224, 'lenovo p2a42': 3225, 'sm-n960f': 3226, 'alp-l09': 3227, 'atu-l11': 3228, 'asus_a009': 3229, 'mi a2': 3230, 'lg-h650': 3231, 'm5': 3232, 'sm-j200g build/lmy47x': 3233, '5012g': 3234, 'htc desire 650': 3235, '5059a': 3236, 'rne-l22': 3237, 'alcatel_6060c': 3238, 'hisense hi 3 build/nrd90m': 3239, 'kfaswi': 3240, 'sm-t700': 3241, 'sm-g550t1': 3242, 'g3121': 3243, 'sm-j610g': 3244, 'lenovo k33b36': 3245, 'frd-l04': 3246, 'sm-n910c': 3247, 'moto e5 plus': 3248, 'zte blade a6': 3249, 'blade l2 plus': 3250, 'lg-x210': 3251, 'sm-g925f': 3253, 'dli-l22': 3254, 'lenovoa3300-gv': 3255, 'sm-n900w8': 3256, 'lgl84vl': 3257, '5033a': 3258, 'lg-k240': 3259, 'lg-h634': 3260, 'lg-us998': 3261, 'moto e5 play': 3262, 'z982': 3263, 'sm-t710': 3264, 'iphone 6plus': 3265, 'lg-x150': 3266, 'm4 ss4458': 3267, 'lg-h870ds': 3268, 'd6653': 3269, 'ta-1039': 3270, 'ta-1033': 3271, 'lm-g710vm': 3272, 'y635-l03': 3273, 'z981': 3274, 'moto e5 play build/opg28.54-19': 3275, 'samsung-sm-j320a': 3276, 'cro-l03': 3277, 'sne-lx3': 3278, 'sm-g900t build/lmy47x': 3279, 'eva-l19': 3280, 'vs990': 3281, 'lgms330': 3282, 'ta-1028': 3283, 'blade a520': 3284, 'lgls775': 3285, 'sm-n7505': 3286, 'shift4.2': 3287, 'e6883': 3288, '5049w': 3289, 'alpha 950': 3290, 'sm-j400f': 3291, 'ilium x510': 3292, 'g3412': 3293, 'lg-m430': 3294, 'max10 build/lmy47i': 3295, '5054s': 3296, 'chc-u03': 3297, 'lenovo pb2-650y': 3298, '5015a': 3299, 'hi6210sft': 3300, '5025g': 3301, 'sm-g530h': 3302, 's60': 3303, 'htc 10': 3304, 'lg-h700': 3305, 'lm-q610(fgn': 3306, 'sm-j700h': 3307, 'lg-x230': 3308, 'sm-j320fn': 3309, 'samsung sm-t580 build/m1ajq': 3310, 'blade a6 max build/n2g47h': 3311, 'sm-t530nu': 3312, 'ldn-l21': 3313, 'lg-d280': 3314, 'sm-a500m': 3315, 'sm-j337t': 3316, 'm4 ss4457': 3317, 'sm-a300fu': 3318, 'blade v9': 3319, 'lg-h918': 3320, 'hisense f20': 3321, 'sm-j111m': 3322, 'huawei g7-l03': 3323, 'ridge': 3324, 'hs20502-16': 3325, 'lenovo pb2-670y': 3326, 'lg-x165g': 3327, 'kffowi': 3328, 'asus_x00lda': 3329, 'cmr-al09': 3330, 'bluboo': 3331, 'lg-d852g': 3332, '2ps64': 3333, 'lm-q710.fg': 3334, 'samsung-sm-j327a': 3335, 'xt1585': 3336, 'mi a2 lite': 3337, 'lg-m151': 3338, '5010g': 3339, 'sm-j327v': 3340, 'sm-j710mn': 3341, 'vs988': 3342, 'lgls676': 3343, 'z988': 3344, 'lgl158vl': 3345, 'moto e5 cruise': 3346, 'sm-g892u': 3347, 'lg-h500': 3348, 'rv:27.0': 3349, 'lm-v350': 3350, 'sm-j200m': 3351, 'samsung-sm-g920a': 3352, '8_plus': 3353, 'kfarwi': 3354, '5044t': 3355, 'lld-l21': 3356, 'sm-s337tl': 3357, 'sm-j727r4': 3358, 'onix': 3359, 'sm-t900': 3360, 'redmi s2': 3361, 'a502dl': 3362, 'lg-m153': 3363, '4009a': 3364, 'vs995': 3365, 'lg-k425': 3366, 'moto g(6)build/opss27.82-87-3': 3367, 'bln-l21': 3368, 'h3123': 3369, 'p5525a': 3370, '5041c': 3371, 'sm-g9500': 3372, 'gt-i9060i': 3373, 'gt-n8013': 3374, 'lg-h910': 3375, 'gravity': 3376, 'sm-t357w': 3377, 'lg-x220': 3378, 'shift': 3379, 'bla-l29': 3380, 'moto g(5)plus build/opss28.85-13-3': 3381, '5099a': 3382, 'pocophone': 3383, 'sm-c5010': 3384, 'lya-l09': 3385, '8050g': 3386, 'rex build/mra58k': 3387, 'lg-ls777': 3388, 'sm-g930u': 3389, 'pixel 3 xl': 3390, 'p5526a': 3391, 'lg-d120 build/kot49i.v10a': 3392, 'frd-l09': 3393, 'hisense l675': 3394, 'sm-t110': 3395, 'kfapwi': 3396, 'gt-i8190l': 3397, 'moto e(4)plus build/nma26.42-167': 3398, 'sm-t378v': 3399, 'a10': 3400, 'sm-g925t': 3401, 'sm-j710f': 3402, 'n9517': 3403, 'ax921': 3404, 'mxg401': 3405, '6016a': 3406, 'lg-tp260': 3407, 'sm-g355m': 3408, 'alp-l29': 3409, 'lgms550': 3410, 'lg-d415': 3411, 'e6782': 3412, 'k81': 3413, 'sm-n900p': 3414, 'lg-h542': 3415, 'sm-j327u': 3416, 'lg-h811': 3417, '2014819': 3418, 'ghia_axis7': 3419, 'm4 ss4458-r': 3420, 'samsung-sm-g928a': 3421, 'z916bl': 3422, 'lg-ls997': 3423, 'lgmp450': 3424, 'sm-g950n': 3425, 'c5': 3426, 'lgms210': 3427, 'rocket': 3428, 'sm-j720f': 3429, 'sm-g930r4': 3430, 'sch-i545': 3431, 'samsung sm-j610g build/m1ajq': 3432, 'lg-m710': 3433, 'sm-n960u1': 3434, 'sgp311': 3435, 'vk410': 3436, 'hi6250': 3437, 'vs986': 3438, 'sm-j327p': 3439, 'nexus 6p': 3441, 'kfthwi': 3442, 'sm-j337v': 3443, 'htc one m9plus': 3444, 'kftbwi': 3445, 'sm-j737t': 3446, 'sm-t337t': 3447, 'sm-n960w': 3448, 'im6': 3449, 'sm-j737v': 3450, 'kfkawi': 3451, 'huawei y560-l03': 3452, 'sm-j415g': 3453, 'asus_a006': 3454, 'vie-l29': 3455, 'sm-g610m build/m1ajq': 3456, 'moto e5 play build/opgs28.54-19-2': 3457, 'lg-d690': 3458, 'moto e5 play build/ocps27.91-51-3-3': 3459, 'lg-d631': 3460, 'redmi note 6 pro': 3461, 'kyocera-c6742': 3462, 'gt-i9060l': 3463, 'moto z3': 3464, 'vs501': 3465, 'g3311': 3466, 'htl23': 3467, 'q01a': 3468, 'samsung sm-t820 build/r16nw': 3469, 'sm-g930vl': 3470, 'sm-a750g': 3471, '6062w': 3472, 'redmi 4a': 3473, 'p10': 3474, 'infinit_lite_2': 3475, 'sm-t827v': 3476, 'sm-j737p': 3477, 'moto e(4)build/nma26.42-167': 3478, 'a622gl': 3479, 'sm-j327vpp': 3480, 'sm-a600t1': 3481, 'e2306': 3482, 'lm-q710.fgn': 3483, 'jkm-lx3': 3484, 'sm-a700k': 3485, 'slate': 3486, 'sm-j327t': 3487, 'moto g(5)build/opps28.85-13-2': 3488, 'hma-l09': 3489, 'ale-l02': 3490, 'rv:65.0': 3491, 'rct6513w87': 3492, 'cph1835': 3493, '5085b': 3494, 'lgl164vl': 3495, 'asus_x00pd': 3496, 'rct6303w87m7': 3497, 'ghia_axis7p': 3498, 'samsung-sm-t337a': 3499, 'mha-l29': 3500, 'sm-g532g': 3501, 'samsung-sm-g530az': 3502, 'mb520': 3503, 'lg-h990': 3504, 'lg-m327': 3505, 'lifesize': 3506, 'pspc550': 3507, '8080': 3508, 'oneplus a6013': 3509, '7048a': 3510, 'sm-n950u1': 3511, 'tab8': 3512, 'sm-n9200': 3513, 'c2105': 3514, 'sm-j320v': 3515, 'kftt': 3516, 'samsung sm-g610m build/m1ajq': 3517, 'verykools5702': 3518, 'sm-j105b': 3519, 'max10': 3520, 'sm-g600s': 3521, 'cpu': 3522, 'lm-v405': 3523, '4027a': 3524, 'sm-g360p': 3525, 'n9560': 3526, 'z956': 3527, 'c6902': 3528, 'z818l': 3529, 'sm-p350': 3530, 'a462c': 3531, 'sm-j600gt': 3532, 'sm-t837v': 3533, 'e6910': 3534, 'z851m': 3535, 'sm-a920f': 3536, 'asus_a002a': 3537, 'pic-lx9': 3538, 'bla-al00': 3539, 'neffos_c9a': 3540, 'lenovo tb-7104f build/o11019': 3541, 'sgp621': 3542, 'sm-g903f': 3543, '5086a': 3544, 'sm-t387v': 3545, 'sm-g611f': 3546, 'lg-d325': 3547, 'sm-t597v': 3548, 'neffos_c7': 3549, 'sm-g925p': 3550, 'lg-m210': 3551, 'zte-z999': 3552, 'p002': 3553, 'lm-x410pm': 3554, 'che2-l23': 3555, 'q05a': 3556, 'azumi_doshi_a55_ql': 3557, 'lg-k430': 3558, 'hk-914581': 3559, 'sm-t817v': 3560, 'verykools5036': 3561, 'rct6b03w13': 3562, 'sm-j810y': 3563, 'z899vl': 3564, 'sm-s367vl': 3565, 'sm-p905v': 3566, 'infinity': 3567, 'sm-g935r4': 3568, '6043a': 3569, 'atu-l21': 3570, 'd2303': 3571, 'lgl157bl': 3572, 'phone': 3573, 'lg-q710al': 3574, 'n5702l': 3575, 'so-02h': 3576, 'verykools5027': 3577, 'bah-l09': 3578, 'sm-j120fn': 3579, 'asus_x008dc': 3580, 'ags-l09': 3581, 'techpad_10y': 3582, 'dig-l03': 3583, 'sm-j530l': 3584, 'asus_z017dc': 3585, 'motoe2': 3586, 'f3116': 3587, 'lava_r1': 3588, 'sm-j260m': 3589, '8082': 3590, 'lg-k540': 3591, 'zebra': 3592, 'armor': 3593, 'sm-t320': 3594, 'lm-q850': 3595, 'pspcl20a0': 3596, 'aura': 3597, 'azumi_iro_a5_q': 3598, 'kyocera-c6742a': 3599, 'alcatel_5098o': 3600, 'centurylink': 3601, 'spectrum': 3602, 'us': 3603, 'mx': 3604, 'yahoo': 3605, 'de': 3606, 'jp': 3607, 'uk': 3608, 'fr': 3609, 'com': 3610, 'es': 3611, 'apple': 3612,'0':0}<data_type_conversions>
train_data = pd.read_csv('.. /input/train.csv')
 test_data = pd.read_csv('.. /input/test.csv' )
for c1, c2 in train.dtypes.reset_index().values:
if c2=='O':
train[c1] = train[c1].map(lambda x: labels[str(x ).lower() ])
 test[c1] = test[c1].map(lambda x: labels[str(x ).lower() ])
 train.fillna(-999, inplace=True)
 test.fillna(-999, inplace=True )<categorify>
train_data, dev_data = train_test_split(train_data, test_size=0.25 )
train = train.fillna(-999)
 test = test.fillna(-999)
 for f in test.columns:
if train[f].dtype=='object' or test[f].dtype=='object':
print(f)
 lbl = preprocessing.LabelEncoder()
lbl.fit(list(train[f].values)+ list(test[f].values))
train[f] = lbl.transform(list(train[f].values))
test[f] = lbl.transform(list(test[f].values))<feature_engineering>
test_data[test_data.Fare.isnull() ]
train['TransactionAmt']=np.log(train['TransactionAmt'])
 test['TransactionAmt']=np.log(test['TransactionAmt'] )<filter>
test_data[["Pclass", "Fare", "Embarked"]].groupby(["Pclass", "Embarked"] ).mean()
<train_model>
X_train = train_data.drop(["PassengerId", "Survived", "Name", "Age", "Ticket", "Cabin"], axis=1)
 Y_train = train_data["Survived"]
X_dev = dev_data.drop(["PassengerId", "Survived", "Name", "Age", "Ticket", "Cabin"], axis=1)
 Y_dev = dev_data["Survived"]
X_test = test_data.drop(["PassengerId", "Name", "Age", "Ticket", "Cabin"], axis=1)
 X_train.shape, Y_train.shape, X_dev.shape, Y_dev.shape, X_test.shape
<create_dataframe>
X_full = [X_train, X_dev, X_test]
for dataset in X_full:
dataset["Sex"] = dataset["Sex"].map({"female": "1", "male": "0"} ).astype("int")
 dataset["Embarked"] = dataset["Embarked"].fillna("S" ).map({"S": "0", "C": "1", "Q": "2"} ).astype("int")
 X_test["Fare"] = X_test["Fare"].fillna(13.9 )
<prepare_x_and_y>
logit = LogisticRegression()
logit.fit(X_train, Y_train)
 acc_logit_train = round(logit.score(X_train, Y_train)*100, 2)
 acc_logit_dev = round(logit.score(X_dev, Y_dev)*100, 2)
 print(f"logit: train accuracy = {acc_logit_train}, dev accuracy = {acc_logit_dev}" )
y_train=train['isFraud']
train=train.drop('isFraud',axis=1)
 train=train.drop('month',axis=1)
 test=test.drop('month',axis=1)
 X_test = test.copy()
del test; gc.collect()<drop_column>
svc = SVC(C=1.0, kernel='rbf', gamma='auto')
 svc.fit(X_train, Y_train)
 acc_svc_train = round(svc.score(X_train, Y_train)* 100, 2)
 acc_svc_dev = round(svc.score(X_dev, Y_dev)* 100, 2)
 print(f"svc: train accuracy = {acc_svc_train}, dev accuracy = {acc_svc_dev}" )
cols_to_drop=['V108', 'id_22', 'id_26', 'id_25', 'C3', 'V14', 'id_24', 'id_07', 'V121', 'V65', 'V305', 'V311', 'V286', 'V111', 'V300', 'V115', 'V113', 'V301', 'V25', 'V123', 'V118', 'V109', 'id_23', 'V112', 'V114', 'V120', 'V88', 'V107', 'V117', 'V122', 'V116', 'id_08', 'id_27', 'id_21', 'V110', 'V119']
train = train.drop(cols_to_drop, axis=1)
 X_test = X_test.drop(cols_to_drop, axis=1 )<compute_test_metric>
combine_data = [train_data, dev_data, test_data]
for dataset in combine_data:
dataset["Title"] = dataset.Name.str.extract("([A-Za-z]+)\.", expand=False)
 pd.crosstab(train_data['Title'], train_data['Sex'] )
def fast_auc(y_true, y_prob):

 y_true = np.asarray(y_true)
 y_true = y_true[np.argsort(y_prob)]
nfalse = 0
auc = 0
n = len(y_true)
 for i in range(n):
y_i = y_true[i]
nfalse +=(1 - y_i)
 auc += y_i * nfalse
auc /=(nfalse *(n - nfalse))
return auc
def eval_auc(y_true, y_pred):

 return 'auc', fast_auc(y_true, y_pred), True
def group_mean_log_mae(y_true, y_pred, types, floor=1e-9):

 maes =(y_true-y_pred ).abs().groupby(types ).mean()
return np.log(maes.map(lambda x: max(x, floor)) ).mean()
def train_model_classification(X, X_test, y, params, folds, model_type='lgb', eval_metric='auc', columns=None, plot_feature_importance=False, model=None,
verbose=10000, early_stopping_rounds=200, n_estimators=50000, splits=None, n_folds=3, averaging='usual', n_jobs=-1):

 columns = X.columns if columns is None else columns
n_splits = folds.n_splits if splits is None else n_folds
X_test = X_test[columns]
metrics_dict = {'auc': {'lgb_metric_name': eval_auc,
'catboost_metric_name': 'AUC',
'sklearn_scoring_function': metrics.roc_auc_score},
}
result_dict = {}
if averaging == 'usual':
oof = np.zeros(( len(X), 1))
prediction = np.zeros(( len(X_test), 1))
elif averaging == 'rank':
oof = np.zeros(( len(X), 1))
prediction = np.zeros(( len(X_test), 1))
scores = []
feature_importance = pd.DataFrame()
for fold_n,(train_index, valid_index)in enumerate(folds.split(X)) :
print(f'Fold {fold_n + 1} started at {time.ctime() }')
 if type(X)== np.ndarray:
X_train, X_valid = X[columns][train_index], X[columns][valid_index]
y_train, y_valid = y[train_index], y[valid_index]
else:
X_train, X_valid = X[columns].iloc[train_index], X[columns].iloc[valid_index]
y_train, y_valid = y.iloc[train_index], y.iloc[valid_index]
if model_type == 'lgb':
model = lgb.LGBMClassifier(**params, n_estimators=n_estimators, n_jobs = n_jobs)
 model.fit(X_train, y_train,
eval_set=[(X_train, y_train),(X_valid, y_valid)], eval_metric=metrics_dict[eval_metric]['lgb_metric_name'],
verbose=verbose, early_stopping_rounds=early_stopping_rounds)
 y_pred_valid = model.predict_proba(X_valid)[:, 1]
y_pred = model.predict_proba(X_test, num_iteration=model.best_iteration_)[:, 1]
if model_type == 'xgb':
train_data = xgb.DMatrix(data=X_train, label=y_train, feature_names=X.columns)
 valid_data = xgb.DMatrix(data=X_valid, label=y_valid, feature_names=X.columns)
 watchlist = [(train_data, 'train'),(valid_data, 'valid_data')]
model = xgb.train(dtrain=train_data, num_boost_round=n_estimators, evals=watchlist, early_stopping_rounds=early_stopping_rounds, verbose_eval=verbose, params=params)
 y_pred_valid = model.predict(xgb.DMatrix(X_valid, feature_names=X.columns), ntree_limit=model.best_ntree_limit)
 y_pred = model.predict(xgb.DMatrix(X_test, feature_names=X.columns), ntree_limit=model.best_ntree_limit)
 if model_type == 'sklearn':
model = model
model.fit(X_train, y_train)
 y_pred_valid = model.predict(X_valid ).reshape(-1,)
 score = metrics_dict[eval_metric]['sklearn_scoring_function'](y_valid, y_pred_valid)
 print(f'Fold {fold_n}.{eval_metric}: {score:.4f}.')
 print('')
 y_pred = model.predict_proba(X_test)
 if model_type == 'cat':
model = CatBoostClassifier(iterations=n_estimators, eval_metric=metrics_dict[eval_metric]['catboost_metric_name'], **params,
loss_function=metrics_dict[eval_metric]['catboost_metric_name'])
 model.fit(X_train, y_train, eval_set=(X_valid, y_valid), cat_features=[], use_best_model=True, verbose=False)
 y_pred_valid = model.predict(X_valid)
 y_pred = model.predict(X_test)
 if averaging == 'usual':
oof[valid_index] = y_pred_valid.reshape(-1, 1)
 scores.append(metrics_dict[eval_metric]['sklearn_scoring_function'](y_valid, y_pred_valid))
prediction += y_pred.reshape(-1, 1)
 elif averaging == 'rank':
oof[valid_index] = y_pred_valid.reshape(-1, 1)
 scores.append(metrics_dict[eval_metric]['sklearn_scoring_function'](y_valid, y_pred_valid))
prediction += pd.Series(y_pred ).rank().values.reshape(-1, 1)
 if model_type == 'lgb' and plot_feature_importance:
fold_importance = pd.DataFrame()
fold_importance["feature"] = columns
fold_importance["importance"] = model.feature_importances_
fold_importance["fold"] = fold_n + 1
feature_importance = pd.concat([feature_importance, fold_importance], axis=0)
 prediction /= n_splits
print('CV mean score: {0:.4f}, std: {1:.4f}.'.format(np.mean(scores), np.std(scores)))
 result_dict['oof'] = oof
result_dict['prediction'] = prediction
result_dict['scores'] = scores
if model_type == 'lgb':
if plot_feature_importance:
feature_importance["importance"] /= n_splits
cols = feature_importance[["feature", "importance"]].groupby("feature" ).mean().sort_values(
 by="importance", ascending=False)[:50].index
best_features = feature_importance.loc[feature_importance.feature.isin(cols)]
plt.figure(figsize=(16, 12)) ;
sns.barplot(x="importance", y="feature", data=best_features.sort_values(by="importance", ascending=False)) ;
plt.title('LGB Features(avg over folds)');
result_dict['feature_importance'] = feature_importance
result_dict['top_columns'] = cols
return result_dict<string_transform>
for dataset in combine_data:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',
'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
 dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
 dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
 dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
 train_data[['Title', 'Survived']].groupby(['Title'], as_index=False ).mean()
n_fold = 7
folds = TimeSeriesSplit(n_splits=n_fold)
 folds = KFold(n_splits=5 )<init_hyperparams>
for dataset in combine_data:
dataset['Title'] = dataset['Title'].map({"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5})
 dataset['Title'] = dataset['Title'].fillna(0 )
params = {'num_leaves': 256,
'min_child_samples': 79,
'objective': 'binary',
'max_depth': 13,
'learning_rate': 0.03,
"boosting_type": "gbdt",
"subsample_freq": 3,
"subsample": 0.9,
"bagging_seed": 11,
"metric": 'auc',
"verbosity": -1,
'reg_alpha': 0.3,
'reg_lambda': 0.3,
'colsample_bytree': 0.9,
'tree_method':'gpu_hist'
}
result_dict_lgb = train_model_classification(X=train, X_test=X_test, y=y_train, params=params, folds=folds, model_type='lgb', eval_metric='auc', plot_feature_importance=True,
verbose=500, early_stopping_rounds=200, n_estimators=5000, averaging='usual', n_jobs=-1 )<save_to_csv>
X_train["Title"] = train_data["Title"]
X_dev["Title"] = dev_data["Title"]
X_test["Title"] = test_data["Title"]
X_train.shape, Y_train.shape, X_dev.shape, Y_dev.shape, X_test.shape
sample_submission = pd.read_csv('.. /input/sample_submission.csv')
 sample_submission['isFraud'] = result_dict_lgb['prediction']
sample_submission.to_csv('submission_IEEE_.csv',index=False )<load_from_csv>
logit.fit(X_train, Y_train)
 acc_logit_train = round(logit.score(X_train, Y_train)*100, 2)
 acc_logit_dev = round(logit.score(X_dev, Y_dev)*100, 2)
 print(f"logit: train accuracy = {acc_logit_train}, dev accuracy = {acc_logit_dev}" )
sub1 = pd.read_csv('.. /input/stacking/stack_median.csv')
 sub2 = pd.read_csv('.. /input/stacking-higher-and-higher/stack_median.csv')
 sub3 = pd.read_csv('.. /input/ensemble/submission_p2_1.csv')
 sub4 = pd.read_csv('.. /input/safe-box/blend02.csv')
 sample = pd.read_csv('.. /input/ieee-fraud-detection/sample_submission.csv' )<save_to_csv>
svc.fit(X_train, Y_train)
 acc_svc_train = round(svc.score(X_train, Y_train)* 100, 2)
 acc_svc_dev = round(svc.score(X_dev, Y_dev)* 100, 2)
 print(f"svc: train accuracy = {acc_svc_train}, dev accuracy = {acc_svc_dev}" )
sample['isFraud'] =(0.35*sub1['isFraud'] + 0.30*sub2['isFraud'] + 0.25*sub3['isFraud'] + 0.10*sub4['isFraud'])
 sample.to_csv('All_Blends.csv', index=False )<install_modules>
mu = X_train["Fare"].mean()
sigma =(((X_train["Fare"]-mu)**2 ).mean())**0.5
for subset in X_full:
subset["Fare"] =(subset["Fare"] - mu)/sigma
print(mu, sigma )
!pip install xgbfir<set_options>
logit.fit(X_train, Y_train)
 acc_logit_train = round(logit.score(X_train, Y_train)*100, 2)
 acc_logit_dev = round(logit.score(X_dev, Y_dev)*100, 2)
 print(f"logit: train accuracy = {acc_logit_train}, dev accuracy = {acc_logit_dev}" )
warnings.filterwarnings('ignore' )<load_from_csv>
svc.fit(X_train, Y_train)
 acc_svc_train = round(svc.score(X_train, Y_train)* 100, 2)
 acc_svc_dev = round(svc.score(X_dev, Y_dev)* 100, 2)
 print(f"svc: train accuracy = {acc_svc_train}, dev accuracy = {acc_svc_dev}" )
<merge><EOS> 
Y_pred = svc.predict(X_test)
 submission = pd.DataFrame({"PassengerId": test_data["PassengerId"], "Survived": Y_pred} ).sort_values(by="PassengerId")
 submission.to_csv("submission.csv", index=False )