Kaggle competition: IEEE-CIS Fraud Detection
<SOS> metric: AUC Kaggle data source: ieee-cis-fraud-detection<create_dataframe>
warnings.filterwarnings("ignore")
 %matplotlib inline
pd.DataFrame(results, columns=['Model', 'Train RMSE', 'Test RMSE'] )<predict_on_test>
%%time
train_transaction = pd.read_csv('.. /input/train_transaction.csv', index_col='TransactionID')
 test_transaction = pd.read_csv('.. /input/test_transaction.csv', index_col='TransactionID')
 train_identity = pd.read_csv('.. /input/train_identity.csv', index_col='TransactionID')
 test_identity = pd.read_csv('.. /input/test_identity.csv', index_col='TransactionID')
 sample_submission = pd.read_csv('.. /input/sample_submission.csv', index_col='TransactionID' )
predicted_revenue = voting_reg.predict(X_transformed)
 overall_data = X.copy()
overall_data['revenue'] = y.copy()
overall_data['predicted_revenue'] = np.expm1(predicted_revenue )<train_model>
train_df = train_transaction.merge(train_identity, how='left', left_index=True, right_index=True)
 test_df = test_transaction.merge(test_identity, how='left', left_index=True, right_index=True)
 print("Train shape : "+str(train_df.shape))
print("Test shape : "+str(test_df.shape))
final_model = Pipeline([('pre_process', pre_process), 
('voting_reg', voting_reg)])
 final_model.fit(X, np.log1p(y))<load_from_csv>
pd.set_option('display.max_columns', 500 )
test_data = pd.read_csv('.. /input/tmdb-box-office-prediction/test.csv')
 print(test_data.shape)
 test_data.head(n=3 )<count_missing_values>
def missing_data(data):
total = data.isnull().sum()
percent =(data.isnull().sum() /data.isnull().count() *100)
 tt = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
 types = []
for col in data.columns:
dtype = str(data[col].dtype)
 types.append(dtype)
 tt['Types'] = types
return(np.transpose(tt))
test_data.isna().sum()<predict_on_test>
display(missing_data(train_df), missing_data(test_df))
predictions = final_model.predict(test_data)
 predictions = np.expm1(predictions )<create_dataframe>
del train_transaction, train_identity, test_transaction, test_identity
output = pd.DataFrame(test_data['id'])
 output['revenue'] = predictions.copy()<save_to_csv>
train_df['nulls1'] = train_df.isna().sum(axis=1)
 test_df['nulls1'] = test_df.isna().sum(axis=1 )
output.to_csv("./submission.csv", index=False )<set_options>
for c in ['P_emaildomain', 'R_emaildomain']:
train_df[c + '_bin'] = train_df[c].map(emails)
 test_df[c + '_bin'] = test_df[c].map(emails)
 train_df[c + '_suffix'] = train_df[c].map(lambda x: str(x ).split('.')[-1])
 test_df[c + '_suffix'] = test_df[c].map(lambda x: str(x ).split('.')[-1])
 train_df[c + '_suffix'] = train_df[c + '_suffix'].map(lambda x: x if str(x)not in us_emails else 'us')
 test_df[c + '_suffix'] = test_df[c + '_suffix'].map(lambda x: x if str(x)not in us_emails else 'us' )
pd.set_option('max_columns', None)
 %matplotlib inline
plt.style.use('ggplot')
 stop = set(stopwords.words('english'))
py.init_notebook_mode(connected=True)
<load_from_csv>
labels = {np.nan: np.nan, 'nan': np.nan, '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': 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'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}
trainAdditionalFeatures = pd.read_csv('.. /input/tmdb-competition-additional-features/TrainAdditionalFeatures.csv')
 testAdditionalFeatures = pd.read_csv('.. /input/tmdb-competition-additional-features/TestAdditionalFeatures.csv')
 train = pd.read_csv('.. /input/tmdb-box-office-prediction/train.csv')
 test = pd.read_csv('.. /input/tmdb-box-office-prediction/test.csv')
 train = pd.merge(train, trainAdditionalFeatures, how='left', on=['imdb_id'])
 test = pd.merge(test, testAdditionalFeatures, how='left', on=['imdb_id'])
 test['revenue'] = -np.inf
train.loc[train['id'] == 16,'revenue'] = 192864
train.loc[train['id'] == 90,'budget'] = 30000000
train.loc[train['id'] == 118,'budget'] = 60000000
train.loc[train['id'] == 149,'budget'] = 18000000
train.loc[train['id'] == 313,'revenue'] = 12000000
train.loc[train['id'] == 451,'revenue'] = 12000000
train.loc[train['id'] == 464,'budget'] = 20000000
train.loc[train['id'] == 470,'budget'] = 13000000
train.loc[train['id'] == 513,'budget'] = 930000
train.loc[train['id'] == 797,'budget'] = 8000000
train.loc[train['id'] == 819,'budget'] = 90000000
train.loc[train['id'] == 850,'budget'] = 90000000
train.loc[train['id'] == 1007,'budget'] = 2
train.loc[train['id'] == 1112,'budget'] = 7500000
train.loc[train['id'] == 1131,'budget'] = 4300000
train.loc[train['id'] == 1359,'budget'] = 10000000
train.loc[train['id'] == 1542,'budget'] = 1
train.loc[train['id'] == 1570,'budget'] = 15800000
train.loc[train['id'] == 1571,'budget'] = 4000000
train.loc[train['id'] == 1714,'budget'] = 46000000
train.loc[train['id'] == 1721,'budget'] = 17500000
train.loc[train['id'] == 1865,'revenue'] = 25000000
train.loc[train['id'] == 1885,'budget'] = 12
train.loc[train['id'] == 2091,'budget'] = 10
train.loc[train['id'] == 2268,'budget'] = 17500000
train.loc[train['id'] == 2491,'budget'] = 6
train.loc[train['id'] == 2602,'budget'] = 31000000
train.loc[train['id'] == 2612,'budget'] = 15000000
train.loc[train['id'] == 2696,'budget'] = 10000000
train.loc[train['id'] == 2801,'budget'] = 10000000
train.loc[train['id'] == 335,'budget'] = 2
train.loc[train['id'] == 348,'budget'] = 12
train.loc[train['id'] == 470,'budget'] = 13000000
train.loc[train['id'] == 513,'budget'] = 1100000
train.loc[train['id'] == 640,'budget'] = 6
train.loc[train['id'] == 696,'budget'] = 1
train.loc[train['id'] == 797,'budget'] = 8000000
train.loc[train['id'] == 850,'budget'] = 1500000
train.loc[train['id'] == 1199,'budget'] = 5
train.loc[train['id'] == 1282,'budget'] = 9
train.loc[train['id'] == 1347,'budget'] = 1
train.loc[train['id'] == 1755,'budget'] = 2
train.loc[train['id'] == 1801,'budget'] = 5
train.loc[train['id'] == 1918,'budget'] = 592
train.loc[train['id'] == 2033,'budget'] = 4
train.loc[train['id'] == 2118,'budget'] = 344
train.loc[train['id'] == 2252,'budget'] = 130
train.loc[train['id'] == 2256,'budget'] = 1
train.loc[train['id'] == 2696,'budget'] = 10000000
test.loc[test['id'] == 6733,'budget'] = 5000000
test.loc[test['id'] == 3889,'budget'] = 15000000
test.loc[test['id'] == 6683,'budget'] = 50000000
test.loc[test['id'] == 5704,'budget'] = 4300000
test.loc[test['id'] == 6109,'budget'] = 281756
test.loc[test['id'] == 7242,'budget'] = 10000000
test.loc[test['id'] == 7021,'budget'] = 17540562
test.loc[test['id'] == 5591,'budget'] = 4000000
test.loc[test['id'] == 4282,'budget'] = 20000000
test.loc[test['id'] == 3033,'budget'] = 250
test.loc[test['id'] == 3051,'budget'] = 50
test.loc[test['id'] == 3084,'budget'] = 337
test.loc[test['id'] == 3224,'budget'] = 4
test.loc[test['id'] == 3594,'budget'] = 25
test.loc[test['id'] == 3619,'budget'] = 500
test.loc[test['id'] == 3831,'budget'] = 3
test.loc[test['id'] == 3935,'budget'] = 500
test.loc[test['id'] == 4049,'budget'] = 995946
test.loc[test['id'] == 4424,'budget'] = 3
test.loc[test['id'] == 4460,'budget'] = 8
test.loc[test['id'] == 4555,'budget'] = 1200000
test.loc[test['id'] == 4624,'budget'] = 30
test.loc[test['id'] == 4645,'budget'] = 500
test.loc[test['id'] == 4709,'budget'] = 450
test.loc[test['id'] == 4839,'budget'] = 7
test.loc[test['id'] == 3125,'budget'] = 25
test.loc[test['id'] == 3142,'budget'] = 1
test.loc[test['id'] == 3201,'budget'] = 450
test.loc[test['id'] == 3222,'budget'] = 6
test.loc[test['id'] == 3545,'budget'] = 38
test.loc[test['id'] == 3670,'budget'] = 18
test.loc[test['id'] == 3792,'budget'] = 19
test.loc[test['id'] == 3881,'budget'] = 7
test.loc[test['id'] == 3969,'budget'] = 400
test.loc[test['id'] == 4196,'budget'] = 6
test.loc[test['id'] == 4221,'budget'] = 11
test.loc[test['id'] == 4222,'budget'] = 500
test.loc[test['id'] == 4285,'budget'] = 11
test.loc[test['id'] == 4319,'budget'] = 1
test.loc[test['id'] == 4639,'budget'] = 10
test.loc[test['id'] == 4719,'budget'] = 45
test.loc[test['id'] == 4822,'budget'] = 22
test.loc[test['id'] == 4829,'budget'] = 20
test.loc[test['id'] == 4969,'budget'] = 20
test.loc[test['id'] == 5021,'budget'] = 40
test.loc[test['id'] == 5035,'budget'] = 1
test.loc[test['id'] == 5063,'budget'] = 14
test.loc[test['id'] == 5119,'budget'] = 2
test.loc[test['id'] == 5214,'budget'] = 30
test.loc[test['id'] == 5221,'budget'] = 50
test.loc[test['id'] == 4903,'budget'] = 15
test.loc[test['id'] == 4983,'budget'] = 3
test.loc[test['id'] == 5102,'budget'] = 28
test.loc[test['id'] == 5217,'budget'] = 75
test.loc[test['id'] == 5224,'budget'] = 3
test.loc[test['id'] == 5469,'budget'] = 20
test.loc[test['id'] == 5840,'budget'] = 1
test.loc[test['id'] == 5960,'budget'] = 30
test.loc[test['id'] == 6506,'budget'] = 11
test.loc[test['id'] == 6553,'budget'] = 280
test.loc[test['id'] == 6561,'budget'] = 7
test.loc[test['id'] == 6582,'budget'] = 218
test.loc[test['id'] == 6638,'budget'] = 5
test.loc[test['id'] == 6749,'budget'] = 8
test.loc[test['id'] == 6759,'budget'] = 50
test.loc[test['id'] == 6856,'budget'] = 10
test.loc[test['id'] == 6858,'budget'] = 100
test.loc[test['id'] == 6876,'budget'] = 250
test.loc[test['id'] == 6972,'budget'] = 1
test.loc[test['id'] == 7079,'budget'] = 8000000
test.loc[test['id'] == 7150,'budget'] = 118
test.loc[test['id'] == 6506,'budget'] = 118
test.loc[test['id'] == 7225,'budget'] = 6
test.loc[test['id'] == 7231,'budget'] = 85
test.loc[test['id'] == 5222,'budget'] = 5
test.loc[test['id'] == 5322,'budget'] = 90
test.loc[test['id'] == 5350,'budget'] = 70
test.loc[test['id'] == 5378,'budget'] = 10
test.loc[test['id'] == 5545,'budget'] = 80
test.loc[test['id'] == 5810,'budget'] = 8
test.loc[test['id'] == 5926,'budget'] = 300
test.loc[test['id'] == 5927,'budget'] = 4
test.loc[test['id'] == 5986,'budget'] = 1
test.loc[test['id'] == 6053,'budget'] = 20
test.loc[test['id'] == 6104,'budget'] = 1
test.loc[test['id'] == 6130,'budget'] = 30
test.loc[test['id'] == 6301,'budget'] = 150
test.loc[test['id'] == 6276,'budget'] = 100
test.loc[test['id'] == 6473,'budget'] = 100
test.loc[test['id'] == 6842,'budget'] = 30
dict_columns = ['belongs_to_collection', 'genres', 'production_companies',
'production_countries', 'spoken_languages', 'Keywords', 'cast', 'crew']
def text_to_dict(df):
for column in dict_columns:
df[column] = df[column].apply(lambda x: {} if pd.isna(x)else ast.literal_eval(x))
return df
train = text_to_dict(train)
 test = text_to_dict(test )<feature_engineering>
for c1, c2 in train_df.dtypes.reset_index().values:
if c2=='O':
train_df[c1] = train_df[c1].map(lambda x: labels[str(x ).lower() ])
 test_df[c1] = test_df[c1].map(lambda x: labels[str(x ).lower() ] )
def fix_date(x):

 if not isinstance(x, str): return x
year = x.split('/')[2]
if int(year)<= 19:
return x[:-2] + '20' + year
else:
return x[:-2] + '19' + year
train.loc[train['release_date'].isnull() == True, 'release_date'] = '01/01/19'
test.loc[test['release_date'].isnull() == True, 'release_date'] = '01/01/19'
train['release_date'] = train['release_date'].apply(lambda x: fix_date(x))
test['release_date'] = test['release_date'].apply(lambda x: fix_date(x))
train['release_date'] = pd.to_datetime(train['release_date'])
 test['release_date'] = pd.to_datetime(test['release_date'])
 train['year'] = train['release_date'].dt.year
train['month'] = train['release_date'].dt.month
train['day'] = train['release_date'].dt.day
train['weekday'] = train['release_date'].dt.weekday
test['year'] = test['release_date'].dt.year
test['month'] = test['release_date'].dt.month
test['day'] = test['release_date'].dt.day
test['weekday'] = test['release_date'].dt.weekday
train_cp = train.copy() 
<feature_engineering>
def nan2mean(df):
for x in list(df.columns.values):
df[x] = df[x].fillna(df[x].mean())
 return df
features_to_num = ['production_companies', 'production_countries', 'Keywords', 'cast', 'crew']
for f in features_to_num:
train_cp['num_'+f] = train_cp[f].apply(lambda x : len(x))
features = ['year','budget', 'revenue', 'popularity', 'num_production_companies', 'num_production_countries', 'runtime', 'num_Keywords', 'num_cast', 'num_crew']
means = train_cp[features].groupby('year' ).mean()
means['budget'] = np.log1p(means['budget'])
 means['revenue'] = np.log1p(means['revenue'])
 medians = train_cp[features].groupby('year' ).median()
medians['budget'] = np.log1p(medians['budget'])
 medians['revenue'] = np.log1p(medians['revenue'])
 for f in features_to_num:
train_cp['median_num_'+f] = train_cp[f]
train_cp['mean_num_'+f] = train_cp[f] 
<categorify>
train_df=nan2mean(train_df )
def get_dictionary(s):
try:
d = ''
d_l = [k['name'] for k in s]
for l in d_l:
d += l + ','
d = d[:-1]
except:
d = ''
return d
data = pd.concat([train,test], axis=0, sort=False)
 genres = data['genres'].apply(get_dictionary)
 genres = genres.str.get_dummies(sep=',')
 data = pd.concat([data, genres], axis=1, sort=False)
 train = data[data['revenue'] != -np.inf]
test = data[data['revenue'] == -np.inf]
print("Action Genres Movie ", train[train.Action == 1].shape[0])
 print("Adventure Genres Movie ", train[train.Adventure == 1].shape[0])
 print("Animation Genres Movie ", train[train.Animation == 1].shape[0])
 print("Comedy Genres Movie ", train[train.Comedy == 1].shape[0])
 print("Crime Genres Movie ", train[train.Crime == 1].shape[0])
 print("Documentary Genres Movie ", train[train.Documentary == 1].shape[0])
 print("Drama Genres Movie ", train[train.Drama == 1].shape[0])
 print("Family Genres Movie ", train[train.Family == 1].shape[0])
 print("Fantasy Genres Movie ", train[train.Fantasy == 1].shape[0])
 print("Foreign Genres Movie ", train[train.Foreign == 1].shape[0])
 print("History Genres Movie ", train[train.History == 1].shape[0])
 print("Music Genres Movie ", train[train.Music == 1].shape[0])
 print("Mystery Genres Movie ", train[train.Mystery == 1].shape[0])
 print("Romance Genres Movie ", train[train.Romance == 1].shape[0])
 print("Science Fiction Genres Movie ", train[train['Science Fiction'] == 1].shape[0])
 print("TV Movie Genres Movie ", train[train['TV Movie'] == 1].shape[0])
 print("Thriller Genres Movie ", train[train.Thriller == 1].shape[0])
 print("War Genres Movie ", train[train.War == 1].shape[0])
 print("Western Genres Movie ", train[train.Western == 1].shape[0] )<feature_engineering>
test_df=nan2mean(test_df )
def fill(df, col):
return random.choice(df[df[col] != np.nan][col])
 train['rating'] = train['rating'].map(lambda x: fill(train, 'rating')if np.isnan(x)else x)
 train['totalVotes'] = train['totalVotes'].map(lambda x: fill(train, 'totalVotes')if np.isnan(x)else x)
 test['rating'] = test['rating'].map(lambda x: fill(test, 'rating')if np.isnan(x)else x)
 test['totalVotes'] = test['totalVotes'].map(lambda x: fill(test, 'totalVotes')if np.isnan(x)else x)
 plt.figure(figsize=(20,12))
sns.countplot(train['rating'].sort_values())
 plt.title("Train Rating Count",fontsize=20)
 plt.show()
plt.figure(figsize=(20,12))
sns.countplot(train['totalVotes'].sort_values())
 plt.title("Train Votes Count",fontsize=20)
 plt.show() 
<feature_engineering>
for f in train_df.columns:
if train_df[f].dtype=='object':
lbl = preprocessing.LabelEncoder()
lbl.fit(list(train_df[f].values)+ list(test_df[f].values))
train_df[f] = lbl.transform(list(train_df[f].values))
test_df[f] = lbl.transform(list(test_df[f].values))
train_df = train_df.reset_index()
test_df = test_df.reset_index()
df_all = pd.concat([train,test], sort=False)
 rating_null = df_all.groupby(["year","original_language"])['rating'].mean().reset_index()
df_all[df_all.rating.isna() ]['rating'] = df_all.merge(rating_null, how = 'left' ,on = ["year","original_language"])
 train = df_all[df_all['revenue'] != -np.inf]
test = df_all[df_all['revenue'] == -np.inf]
train['Producer'] = train['crew'].apply(lambda x : [str(i['name'])for i in x if i['job']=='Producer'])
 test['Producer'] = test['crew'].apply(lambda x : [str(i['name'])for i in x if i['job']=='Producer'])
 train['ProducerT'] = train['Producer'].apply(lambda x: str(x)[2:-2])
 test['ProducerT'] = test['Producer'].apply(lambda x: str(x)[2:-2])
 train['Director'] = train['crew'].apply(lambda x : [i['name'] for i in x if i['job']=='Director'])
 test['Director'] = test['crew'].apply(lambda x : [i['name'] for i in x if i['job']=='Director'])
 train['DirectorT'] = train['Director'].apply(lambda x: str(x)[2:-2])
 test['DirectorT'] = test['Director'].apply(lambda x: str(x)[2:-2])
 directors_list = np.concatenate(( train['DirectorT'].unique() ,test['DirectorT'].unique()))
unique_directors = np.unique(directors_list ).tolist()
df = pd.DataFrame(unique_directors, columns = ['name' ])
 df['movies_num'] = 0
df['score'] = 0
df['popularity'] = 0.0
df['director_avg_score'] = 0.0
df['director_avg_popularity'] = 0.0
df.set_index('name', inplace=True)
 producers_list = np.concatenate(( train['ProducerT'].unique() ,test['ProducerT'].unique()))
unique_producers = np.unique(producers_list ).tolist()
df2 = pd.DataFrame(unique_producers, columns = ['name' ])
 df2['movies_num'] = 0
df2['score'] = 0
df2['popularity'] = 0.0
df2['producer_avg_score'] = 0.0
df2['producer_avg_popularity'] = 0.0
df2.set_index('name', inplace=True)
 for i in tqdm(range(len(train))):
df.at[train['DirectorT'][i],'score'] += train['rating'][i]
df.at[train['DirectorT'][i],'popularity'] += train['popularity'][i]
df.at[train['DirectorT'][i],'movies_num'] += 1
df2.at[train['ProducerT'][i],'score'] += train['rating'][i]
df2.at[train['ProducerT'][i],'popularity'] += train['popularity'][i]
df2.at[train['ProducerT'][i],'movies_num'] += 1
for i in tqdm(range(len(test))):
try:
df.at[test['DirectorT'][i],'score'] += test['rating'][i]
df.at[test['DirectorT'][i],'popularity'] += test['popularity'][i]
df.at[test['DirectorT'][i],'movies_num'] += 1
except:
pass
try:
df2.at[test['ProducerT'][i],'score'] += test['rating'][i]
df2.at[test['ProducerT'][i],'popularity'] += test['popularity'][i]
df2.at[test['ProducerT'][i],'movies_num'] += 1
except:
pass
for i in tqdm(range(len(unique_directors))):
df.at[unique_directors[i],'director_avg_score'] = df.at[unique_directors[i],'score']/df.at[unique_directors[i],'movies_num']
df.at[unique_directors[i],'director_avg_popularity'] = df.at[unique_directors[i],'popularity']/df.at[unique_directors[i],'movies_num']
for i in tqdm(range(len(unique_producers))):
df2.at[unique_producers[i],'producer_avg_score'] = df2.at[unique_producers[i],'score']/df2.at[unique_producers[i],'movies_num']
df2.at[unique_producers[i],'producer_avg_popularity'] = df2.at[unique_producers[i],'popularity']/df2.at[unique_producers[i],'movies_num']
test['director_avg_score'] = 0.0
test['director_avg_popularity'] = 0.0
train['director_avg_score'] = 0.0
train['director_avg_popularity'] = 0.0
test['producer_avg_score'] = 0.0
test['producer_avg_popularity'] = 0.0
train['producer_avg_score'] = 0.0
train['producer_avg_popularity'] = 0.0
lunch = False
if lunch:
for i in tqdm(range(len(train))):
director, ld = df.loc[train['DirectorT'][i]][['director_avg_popularity', 'director_avg_score']],len(train['Director'][i])
 producer, lp = df2.loc[train['ProducerT'][i]][['producer_avg_popularity', 'producer_avg_score']],len(train['Producer'][i])
 if ld==0:
train['director_avg_popularity'][i] = 0.0
train['director_avg_score'][i] = 0.0
else :
train['director_avg_popularity'][i] = director[0]/ld
train['director_avg_score'][i] = director[1]/ld
if lp==0:
train['producer_avg_popularity'][i] = 0.0
train['producer_avg_score'][i] = 0.0
else :
train['producer_avg_popularity'][i] = producer[0]/lp
train['producer_avg_score'][i] = producer[1]/lp
for i in tqdm(range(len(test))):
director, ld = df.loc[test['DirectorT'][i]][['director_avg_popularity', 'director_avg_score']],len(test['Director'][i])
 producer, lp = df2.loc[test['ProducerT'][i]][['producer_avg_popularity', 'producer_avg_score']],len(test['Producer'][i])
 if ld==0:
test['director_avg_popularity'][i] = 0.0
test['director_avg_score'][i] = 0.0
else :
test['director_avg_popularity'][i] = director[0]/ld
test['director_avg_score'][i] = director[1]/ld
if lp==0:
test['producer_avg_popularity'][i] = 0.0
test['producer_avg_score'][i] = 0.0
else :
test['producer_avg_popularity'][i] = producer[0]/lp
test['producer_avg_score'][i] = producer[1]/lp
for i in tqdm(range(len(unique_directors))):
df.loc[unique_directors[i]]['score'] = df.loc[unique_directors[i]]['score']/df.loc[unique_directors[i]]['movies_num']
train['crew_popularity'] = train['producer_avg_popularity'] + train['director_avg_popularity']
test['crew_popularity'] = test['producer_avg_popularity'] + test['director_avg_popularity']
drop_colums = ['Producer', 'Director', 'DirectorT', 'ProducerT']
for i in drop_colums:
train = train.drop([i], axis=1)
 test = test.drop([i], axis=1 )<create_dataframe>
features = list(train_df)
 features.remove('isFraud')
 target = 'isFraud'
df = pd.concat([train,test], sort=False)
 def get_all_names(col):
all_names = []
for col_list in df[col]:
for value in col_list:
if not value['name'] in all_names:
all_names.append(value['name'])
 return all_names
actors_names = get_all_names('cast')
 print('number of actors: ',len(actors_names))
actors = pd.DataFrame(0, index=actors_names, columns=['num_movies', 'mean_revenue'])
 for index, row in tqdm(df.iterrows() , total=df.shape[0]):
movie = row['title']
cast = row['cast']
genres = row['genres']
rev = row['revenue']
actors_in_movie = [a['name'] for a in cast]
genres_in_movie = [a['name'] for a in genres]
for a in actors_in_movie:
if rev != -np.inf:
actors.loc[a]['num_movies'] += 1
actors.loc[a]['mean_revenue'] += rev
actors['num_movies'][actors['num_movies'] == 0] = 1
actors['mean_revenue'] = actors['mean_revenue']/actors['num_movies']
actors['mean_revenue'] = actors.apply(lambda row: np.nan if row['num_movies'] < 2 else row['mean_revenue'], axis=1)
 q = list(actors.mean_revenue.quantile(q=[0.25, 0.5, 0.75]))
bins = [-np.inf]
bins.extend(q)
 bins.append(np.inf)
 lables = [0, 1, 2, 3]
actors['level'] = pd.cut(actors['mean_revenue'], bins=bins, labels=lables, include_lowest=False)
 actors['level'] = actors['level'].cat.add_categories(4 ).fillna(4)
<feature_engineering>
bayesian_tr_idx, bayesian_val_idx = train_test_split(train_df, test_size = 0.3, random_state = 42, stratify = train_df[target])
 bayesian_tr_idx = bayesian_tr_idx.index
bayesian_val_idx = bayesian_val_idx.index
def prepare(train_df):
df = train_df.copy()
df['inflationBudget'] = df['budget'] + df['budget']*1.8/100*(2018-df['year'])
 df['log_budget'] = np.log1p(df['budget'])
 df['log_inflationBudget'] = np.log1p(df['inflationBudget'])
 df['genders_0_crew'] = df['crew'].apply(lambda x: sum([1 for i in x if i['gender'] == 0]))
df['genders_1_crew'] = df['crew'].apply(lambda x: sum([1 for i in x if i['gender'] == 1]))
df['genders_2_crew'] = df['crew'].apply(lambda x: sum([1 for i in x if i['gender'] == 2]))
df['is_in_collection'] = df['belongs_to_collection'].apply(lambda x: 1 if x != {} else 0)
 df['movies_in_collection'] = df['belongs_to_collection'].apply(lambda x: len(x)if x != {} else 0)
 df['num_genres'] = df['genres'].apply(lambda x: len(x)if x != {} else 0)
 df['num_spoken_langs'] = df['spoken_languages'].apply(lambda x: len(x)if x != {} else 0)
 df['num_Keywords'] = df['Keywords'].apply(lambda x: len(x)if x != {} else 0)
 df['num_cast'] = df['cast'].apply(lambda x: len(x)if x != {} else 0)
 df['popularity_mean_year'] = df['popularity'] / df.groupby("year")["popularity"].transform('mean')
 df['runtime_mean_year'] = df['runtime'] / df.groupby("year")["runtime"].transform('mean')
 df['log_budget_mean_year'] = df['log_budget']/ df.groupby("year")["log_budget"].transform('mean')
 df['totalVotes_mean_year'] = df['totalVotes'] / df.groupby("year")["totalVotes"].transform('mean')
 df['rating_mean_year'] = df['rating'] / df.groupby("year")["rating"].transform('mean')
 df['log_budget_runtime_ratio'] = df['log_budget']/df['runtime']
df['log_budget_popularity_ratio'] = df['log_budget']/df['popularity']
df['popularity_totalVotes_ratio'] = df['totalVotes']/df['popularity']
df['rating_popularity_ratio'] = df['rating']/df['popularity']
df['rating_totalVotes_ratio'] = df['totalVotes']/df['rating']
df['budget_rating_ratio'] = df['log_budget']/df['rating']
df['runtime_rating_ratio'] = df['runtime']/df['rating']
df['budget_totalVotes_ratio'] = df['log_budget']/df['totalVotes']
df['has_homepage'] = 1
df.loc[pd.isnull(df['homepage']),"has_homepage"] = 0
df['has_tagline'] = 0
df.loc[df['tagline'] == 0 ,"has_tagline"] = 1
df['is_eng'] = 0
df.loc[ df['original_language'] == "en" ,"is_eng"] = 1
df['isTitleDifferent'] = 1
df.loc[ df['original_title'] == df['title'] ,"isTitleDifferent"] = 0
df['original_title_letter_count'] = df['original_title'].str.len()
df['original_title_word_count'] = df['original_title'].str.split().str.len()
df['title_word_count'] = df['title'].str.split().str.len()
df['overview_word_count'] = df['overview'].str.split().str.len()
df['tagline_word_count'] = df['tagline'].str.split().str.len()
df['production_countries_count'] = df['production_countries'].apply(lambda x : len(x))
df['production_companies_count'] = df['production_companies'].apply(lambda x : len(x))
df['cast_count'] = df['cast'].apply(lambda x : len(x))
df['crew_count'] = df['crew'].apply(lambda x : len(x))
levels = np.zeros(( len(df),5))
for i, row in tqdm(df.iterrows() , total=df.shape[0]):
cast = row['cast']
actors_in_movie = [a['name'] for a in cast]
for a in actors_in_movie:
levels[i, actors.loc[a]['level']] += 1
df[["level_0", "level_1", "level_2", "level_3", "level_-1"]] = levels
df[["level_0", "level_1", "level_2", "level_3", "level_-1"]] /= df['cast_count']
df = df.drop(['id','belongs_to_collection','genres','homepage','imdb_id','overview','runtime'
,'poster_path','production_companies','production_countries','release_date','spoken_languages'
,'status','title','Keywords','cast','crew','original_language','original_title','tagline', 'budget'
],axis=1)
 df.fillna(value=0.0, inplace = True)
 return df 
<prepare_x_and_y>
def LGB_bayesian(
 num_leaves,
bagging_fraction,
feature_fraction,
min_child_weight,
min_data_in_leaf,
max_depth,
reg_alpha,
reg_lambda 
):
num_leaves = int(num_leaves)
 min_data_in_leaf = int(min_data_in_leaf)
 max_depth = int(max_depth)
 assert type(num_leaves)== int
assert type(min_data_in_leaf)== int
assert type(max_depth)== int
param = {
'num_leaves': num_leaves,
'min_data_in_leaf': min_data_in_leaf,
'min_child_weight': min_child_weight,
'bagging_fraction' : bagging_fraction,
'feature_fraction' : feature_fraction,
'max_depth': max_depth,
'reg_alpha': reg_alpha,
'reg_lambda': reg_lambda,
'objective': 'binary',
'save_binary': True,
'seed': 1337,
'feature_fraction_seed': 1337,
'bagging_seed': 1337,
'drop_seed': 1337,
'data_random_seed': 1337,
'boosting_type': 'gbdt',
'verbose': 1,
'is_unbalance': False,
'boost_from_average': True,
'metric':'auc'}
oof = np.zeros(len(train_df))
trn_data= lgb.Dataset(train_df.iloc[bayesian_tr_idx][features].values, label=train_df.iloc[bayesian_tr_idx][target].values)
 val_data= lgb.Dataset(train_df.iloc[bayesian_val_idx][features].values, label=train_df.iloc[bayesian_val_idx][target].values)
 clf = lgb.train(param, trn_data, num_boost_round=50, valid_sets = [trn_data, val_data], verbose_eval=0, early_stopping_rounds = 50)
 oof[bayesian_val_idx] = clf.predict(train_df.iloc[bayesian_val_idx][features].values, num_iteration=clf.best_iteration)
 score = roc_auc_score(train_df.iloc[bayesian_val_idx][target].values, oof[bayesian_val_idx])
 return score
def intersection(lst1, lst2):
lst3 = [value for value in lst1 if not value in lst2]
return lst3
print(len(set(test.columns.values)))
 print(len(set(train.columns.values)))
 print(intersection(list(test.columns.values), list(train.columns.values)))
 print(intersection(list(train.columns.values), list(test.columns.values)))
 print(test.shape, train.shape)
 df = pd.concat([train,test], sort=False)
 data_df = prepare(df)
 train_df = data_df[data_df['revenue'] != -np.inf]
test_df = data_df[data_df['revenue'] == -np.inf]
X, y = train_df.drop(['revenue'],axis=1), np.log1p(train_df['revenue'])
 Xtest = test_df.drop(['revenue'],axis=1)
 random_seed = 42
k = 5
fold = list(KFold(k, shuffle = True, random_state = random_seed ).split(X,y))
np.random.seed(random_seed )<import_modules>
bounds_LGB = {
'num_leaves':(31, 500),
'min_data_in_leaf':(20, 200),
'bagging_fraction' :(0.1, 0.9),
'feature_fraction' :(0.1, 0.9),
'min_child_weight':(0.00001, 0.01),
'reg_alpha':(1, 2),
'reg_lambda':(1, 2),
'max_depth':(-1,50),
}
def cat_model(trn_x, trn_y, val_x, val_y, test, verbose):
model = CatBoostRegressor(iterations=100000,
learning_rate=0.004,
depth=5,
eval_metric='RMSE',
colsample_bylevel=0.8,
random_seed = random_seed,
bagging_temperature = 0.2,
metric_period = None,
early_stopping_rounds=200 
)
 model.fit(trn_x, trn_y,
eval_set=(val_x, val_y),
use_best_model=True,
verbose=False)
 val_pred = model.predict(val_x)
 test_pred = model.predict(test)
 return {'val':val_pred,
'test':test_pred,
'error':model.get_best_score() ['validation']['RMSE']}
def xgb_model(trn_x, trn_y, val_x, val_y, test, verbose):
params = {'objective': 'reg:linear',
'eta': 0.01,
'max_depth': 6,
'subsample': 0.6,
'colsample_bytree': 0.7,
'eval_metric': 'rmse',
'seed': random_seed,
'silent': True,
}
record = dict()
model = xgb.train(params
, xgb.DMatrix(trn_x, trn_y)
 , 100000
, [(xgb.DMatrix(trn_x, trn_y), 'train'),(xgb.DMatrix(val_x, val_y), 'valid')]
, verbose_eval=verbose
, early_stopping_rounds=500
, callbacks = [xgb.callback.record_evaluation(record)])
 best_idx = np.argmin(np.array(record['valid']['rmse']))
val_pred = model.predict(xgb.DMatrix(val_x), ntree_limit=model.best_ntree_limit)
 test_pred = model.predict(xgb.DMatrix(test), ntree_limit=model.best_ntree_limit)
 return {'val':val_pred, 'test':test_pred, 'error':record['valid']['rmse'][best_idx], 'importance':[i for k, i in model.get_score().items() ]}
def lgb_model(trn_x, trn_y, val_x, val_y, test, verbose):
params = {'objective':'regression',
'num_leaves' : 30,
'min_data_in_leaf' : 20,
'max_depth' : 9,
'learning_rate': 0.004,
'feature_fraction':0.9,
"bagging_freq": 1,
"bagging_fraction": 0.9,
'lambda_l1': 0.2,
"bagging_seed": random_seed,
"metric": 'rmse',
"random_state" : random_seed,
"verbosity": -1}
record = dict()
model = lgb.train(params
, lgb.Dataset(trn_x, trn_y)
 , num_boost_round = 100000
, valid_sets = [lgb.Dataset(val_x, val_y)]
, verbose_eval = verbose
, early_stopping_rounds = 500
, callbacks = [lgb.record_evaluation(record)] 
)
 best_idx = np.argmin(np.array(record['valid_0']['rmse']))
val_pred = model.predict(val_x, num_iteration = model.best_iteration)
 test_pred = model.predict(test, num_iteration = model.best_iteration)
 return {'val':val_pred, 'test':test_pred, 'error':record['valid_0']['rmse'][best_idx], 'importance':model.feature_importance('gain')}<prepare_x_and_y>
LGB_BO = BayesianOptimization(LGB_bayesian, bounds_LGB, random_state=42 )
result_dict = dict()
val_pred = np.zeros(train.shape[0])
 test_pred = np.zeros(test.shape[0])
 final_err = 0
verbose = False
def now() :
return datetime.datetime.fromtimestamp(time.time())
 for i,(trn, val)in enumerate(fold):
print(i+1, "fold.RMSE")
 trn_x = X.loc[trn, :]
trn_y = y[trn]
val_x = X.loc[val, :]
val_y = y[val]
fold_val_pred = []
fold_test_pred = []
fold_err = []
start = now()
result = xgb_model(trn_x, trn_y, val_x, val_y, Xtest, verbose)
 fold_val_pred.append(result['val']*0.2)
 fold_test_pred.append(result['test']*0.2)
 fold_err.append(result['error'])
 print("xgb model.", "{0:.5f}".format(result['error']), '(' + str(int(( now() -start ).seconds/60)) + 'm)')
 start = now()
result = lgb_model(trn_x, trn_y, val_x, val_y, Xtest, verbose)
 fold_val_pred.append(result['val']*0.4)
 fold_test_pred.append(result['test']*0.4)
 fold_err.append(result['error'])
 print("lgb model.", "{0:.5f}".format(result['error']), '(' + str(int(( now() -start ).seconds/60)) + 'm)')
 start = now()
result = cat_model(trn_x, trn_y, val_x, val_y, Xtest, verbose)
 fold_val_pred.append(result['val']*0.4)
 fold_test_pred.append(result['test']*0.4)
 fold_err.append(result['error'])
 print("cat model.", "{0:.5f}".format(result['error']), '(' + str(int(( now() -start ).seconds/60)) + 'm)')
 val_pred[val] += np.mean(np.array(fold_val_pred), axis = 0)
 test_pred += np.mean(np.array(fold_test_pred), axis = 0)/ k
final_err +=(sum(fold_err)/ len(fold_err)) / k
print("---------------------------")
 print("avg err.", "{0:.5f}".format(sum(fold_err)/ len(fold_err)))
 print("blend err.", "{0:.5f}".format(np.sqrt(np.mean(( np.mean(np.array(fold_val_pred), axis = 0)- val_y)**2))))
print('')
 print("fianl avg err.", final_err)
 print("fianl blend err.", np.sqrt(np.mean(( val_pred - y)**2)) )<save_to_csv>
init_points = 10
n_iter = 15
sub = pd.read_csv('.. /input/tmdb-box-office-prediction/sample_submission.csv')
 df_sub = pd.DataFrame()
df_sub['id'] = sub['id']
df_sub['revenue'] = np.expm1(test_pred*3)
 df_sub.to_csv("submission.csv", index=False )<set_options>
print('-' * 130)
 with warnings.catch_warnings() :
warnings.filterwarnings('ignore')
 LGB_BO.maximize(init_points=init_points, n_iter=n_iter, acq='ucb', xi=0.0, alpha=1e-6 )
plt.style.use('ggplot')
 %config InlineBackend.figure_format = 'svg'<install_modules>
param_lgb = {
'min_data_in_leaf': int(LGB_BO.max['params']['min_data_in_leaf']),
'num_leaves': int(LGB_BO.max['params']['num_leaves']),
'min_child_weight': LGB_BO.max['params']['min_child_weight'],
'bagging_fraction': LGB_BO.max['params']['bagging_fraction'],
'feature_fraction': LGB_BO.max['params']['feature_fraction'],
'reg_lambda': LGB_BO.max['params']['reg_lambda'],
'reg_alpha': LGB_BO.max['params']['reg_alpha'],
'max_depth': int(LGB_BO.max['params']['max_depth']),
'objective': 'binary',
'save_binary': True,
'seed': 1337,
'feature_fraction_seed': 1337,
'bagging_seed': 1337,
'drop_seed': 1337,
'data_random_seed': 1337,
'boosting_type': 'gbdt',
'verbose': 1,
'is_unbalance': False,
'boost_from_average': True,
'metric':'auc'
}
!pip install bert-serving-server
!pip install bert-serving-client<import_modules>
plt.rcParams["axes.grid"] = True
nfold = 5
skf = StratifiedKFold(n_splits=nfold, shuffle=True, random_state=42)
 oof = np.zeros(len(train_df))
mean_fpr = np.linspace(0,1,100)
 cms= []
tprs = []
aucs = []
y_real = []
y_proba = []
recalls = []
roc_aucs = []
f1_scores = []
accuracies = []
precisions = []
predictions = np.zeros(len(test_df))
feature_importance_df = pd.DataFrame()
i = 1
for train_idx, valid_idx in skf.split(train_df, train_df.isFraud.values):
print("
fold {}".format(i))
trn_data = lgb.Dataset(train_df.iloc[train_idx][features].values,
label=train_df.iloc[train_idx][target].values 
)
 val_data = lgb.Dataset(train_df.iloc[valid_idx][features].values,
label=train_df.iloc[valid_idx][target].values 
)
 clf = lgb.train(param_lgb, trn_data, num_boost_round = 500, valid_sets = [trn_data, val_data], verbose_eval = 100, early_stopping_rounds = 100)
 oof[valid_idx] = clf.predict(train_df.iloc[valid_idx][features].values)
 predictions += clf.predict(test_df[features])/ nfold
roc_aucs.append(roc_auc_score(train_df.iloc[valid_idx][target].values, oof[valid_idx]))
accuracies.append(accuracy_score(train_df.iloc[valid_idx][target].values, oof[valid_idx].round()))
recalls.append(recall_score(train_df.iloc[valid_idx][target].values, oof[valid_idx].round()))
precisions.append(precision_score(train_df.iloc[valid_idx][target].values ,oof[valid_idx].round()))
f1_scores.append(f1_score(train_df.iloc[valid_idx][target].values, oof[valid_idx].round()))
f = plt.figure(1)
 fpr, tpr, t = roc_curve(train_df.iloc[valid_idx][target].values, oof[valid_idx])
 tprs.append(interp(mean_fpr, fpr, tpr))
roc_auc = auc(fpr, tpr)
 aucs.append(roc_auc)
 plt.plot(fpr, tpr, lw=2, alpha=0.3, label='ROC fold %d(AUC = %0.4f)' %(i,roc_auc))
g = plt.figure(2)
 precision, recall, _ = precision_recall_curve(train_df.iloc[valid_idx][target].values, oof[valid_idx])
 y_real.append(train_df.iloc[valid_idx][target].values)
 y_proba.append(oof[valid_idx])
 plt.plot(recall, precision, lw=2, alpha=0.3, label='P|R fold %d' %(i))
i= i+1
cms.append(confusion_matrix(train_df.iloc[valid_idx][target].values, oof[valid_idx].round()))
fold_importance_df = pd.DataFrame()
fold_importance_df["Feature"] = features
fold_importance_df["importance"] = clf.feature_importance()
fold_importance_df["fold"] = nfold + 1
feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
 print(
 '
CV roc score : {0:.4f}, std: {1:.4f}.'.format(np.mean(roc_aucs), np.std(roc_aucs)) ,
'
CV accuracy score : {0:.4f}, std: {1:.4f}.'.format(np.mean(accuracies), np.std(accuracies)) ,
'
CV recall score : {0:.4f}, std: {1:.4f}.'.format(np.mean(recalls), np.std(recalls)) ,
'
CV precision score : {0:.4f}, std: {1:.4f}.'.format(np.mean(precisions), np.std(precisions)) ,
'
CV f1 score : {0:.4f}, std: {1:.4f}.'.format(np.mean(f1_scores), np.std(f1_scores)) 
)
 f = plt.figure(1)
 plt.plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'grey')
 mean_tpr = np.mean(tprs, axis=0)
 mean_auc = auc(mean_fpr, mean_tpr)
 plt.plot(mean_fpr, mean_tpr, color='blue',
label=r'Mean ROC(AUC = %0.4f)' %(np.mean(roc_aucs)) ,lw=2, alpha=1)
 plt.xlabel('False Positive Rate')
 plt.ylabel('True Positive Rate')
 plt.title('LGB ROC curve by folds')
 plt.legend(loc="lower right")
 g = plt.figure(2)
 plt.plot([0,1],[1,0],linestyle = '--',lw = 2,color = 'grey')
 y_real = np.concatenate(y_real)
 y_proba = np.concatenate(y_proba)
 precision, recall, _ = precision_recall_curve(y_real, y_proba)
 plt.plot(recall, precision, color='blue',
label=r'Mean P|R')
 plt.xlabel('Recall')
 plt.ylabel('Precision')
 plt.title('P|R curve by folds')
 plt.legend(loc="lower left")
 plt.rcParams["axes.grid"] = False
cm = np.average(cms, axis=0)
 class_names = [0,1]
plt.figure()
plot_confusion_matrix(cm,
classes=class_names,
title= 'LGB Confusion matrix [averaged/folds]')
 plt.show()
<define_variables><EOS> 
sample_submission['isFraud'] = predictions
sample_submission.to_csv('submission_IEEE.csv' )