In [1]:

    

%matplotlib inline

import configparser
import os

import requests
from tqdm import tqdm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import sparse, stats, spatial
import scipy.sparse.linalg
from sklearn import preprocessing, decomposition
import librosa
import IPython.display as ipd
import json
import tmdbsimple as tmdb
from itertools import chain
from pygsp import graphs, filters, plotting

plt.rcParams['figure.figsize'] = (17, 5)
plotting.BACKEND = 'matplotlib'


    

In [172]:

    

dataset = pd.read_csv('Saved_Datasets/NewFeaturesDataset.csv')


    

In [173]:

    

dataset.head(10)


    

    
Out[173]:







  

    

      

      
id
      
budget
      
genres
      
imdb_id
      
overview
      
production_companies
      
release_date
      
revenue
      
tagline
      
title
      
director_name
      
director_id
      
actor_names
      
actor_ids
      
Metacritic
      
Normed_Metacritic
      
ROI
      
Normed_ROI
      
success
    
  
  

    

      
0
      
12
      
94000000
      
Animation|Family
      
266543
      
Nemo, an adventurous young clownfish, is unexp...
      
Pixar Animation Studios
      
2003-05-30
      
940335536
      
There are 3.7 trillion fish in the ocean, they...
      
Finding Nemo
      
Andrew Stanton
      
0
      
['Albert Brooks', 'Ellen DeGeneres', 'Alexande...
      
[0, 908, 2000, 772, 3304]
      
90
      
0.943
      
9.003570
      
0.47
      
1
    
    

      
1
      
16
      
12800000
      
Drama|Crime|Music
      
168629
      
Selma, a Czech immigrant on the verge of blind...
      
Fine Line Features
      
2000-05-17
      
40031879
      
You don't need eyes to see.
      
Dancer in the Dark
      
Lars von Trier
      
1
      
['Björk', 'Catherine Deneuve', 'David Morse', ...
      
[1, 434, 2001, 1630, 3993]
      
61
      
0.731
      
2.127491
      
0.14
      
1
    
    

      
2
      
22
      
140000000
      
Adventure|Fantasy|Action
      
325980
      
Jack Sparrow, a freewheeling 17th-century pira...
      
Walt Disney Pictures
      
2003-09-07
      
655011224
      
Prepare to be blown out of the water.
      
Pirates of the Caribbean: The Curse of the Bla...
      
Gore Verbinski
      
2
      
['Johnny Depp', 'Geoffrey Rush', 'Orlando Bloo...
      
[2, 412, 136, 71, 3143]
      
63
      
0.754
      
3.678652
      
0.23
      
1
    
    

      
3
      
24
      
30000000
      
Action|Crime
      
266697
      
An assassin is shot at the altar by her ruthle...
      
Miramax Films
      
2003-10-10
      
180949000
      
Go for the kill.
      
Kill Bill: Vol. 1
      
Quentin Tarantino
      
3
      
['Uma Thurman', 'Lucy Liu', 'Vivica A. Fox', '...
      
[3, 1071, 2002, 1684, 1097]
      
69
      
0.814
      
5.031633
      
0.30
      
1
    
    

      
4
      
25
      
72000000
      
Drama|War
      
418763
      
Jarhead is a film about a US Marine Anthony Sw...
      
Universal Pictures
      
2005-04-11
      
96889998
      
Welcome to the suck.
      
Jarhead
      
Sam Mendes
      
4
      
['Jamie Foxx', 'Scott MacDonald', 'Lucas Black...
      
[4, 1072, 328, 1293]
      
58
      
0.695
      
0.345694
      
0.02
      
1
    
    

      
5
      
27
      
1000000
      
Drama|Music|Romance
      
411705
      
Matt, a young glaciologist, soars across the v...
      
Revolution Films
      
2004-07-16
      
1574623
      
2 lovers, one summer, and the 9 songs that def...
      
9 Songs
      
Michael Winterbottom
      
5
      
["Kieran O'Brien", 'Margo Stilley', 'Courtney ...
      
[5, 1073, 2003, 2983, 3994]
      
43
      
0.480
      
0.574623
      
0.04
      
1
    
    

      
6
      
35
      
75000000
      
Animation|Comedy|Family
      
462538
      
After Homer accidentally pollutes the town's w...
      
Gracie Films
      
2007-07-25
      
527068851
      
See our family. And feel better about yours.
      
The Simpsons Movie
      
David Silverman
      
6
      
['Dan Castellaneta', 'Julie Kavner', 'Nancy Ca...
      
[6, 1074, 2004, 2984, 719]
      
80
      
0.896
      
6.027585
      
0.34
      
1
    
    

      
7
      
38
      
20000000
      
Science Fiction|Drama|Romance
      
338013
      
Joel Barish, heartbroken that his girlfriend u...
      
Anonymous Content
      
2004-03-19
      
72258126
      
You can erase someone from your mind. Getting ...
      
Eternal Sunshine of the Spotless Mind
      
Michel Gondry
      
7
      
['Jim Carrey', 'Kate Winslet', 'Kirsten Dunst'...
      
[7, 131, 147, 652, 20]
      
89
      
0.939
      
2.612906
      
0.17
      
1
    
    

      
8
      
58
      
200000000
      
Adventure|Fantasy|Action
      
383574
      
Captain Jack Sparrow works his way out of a bl...
      
Walt Disney Pictures
      
2006-06-20
      
1065659812
      
Jack is back!
      
Pirates of the Caribbean: Dead Man's Chest
      
Gore Verbinski
      
2
      
['Johnny Depp', 'Orlando Bloom', 'Keira Knight...
      
[2, 136, 71, 421, 472]
      
53
      
0.629
      
4.328299
      
0.26
      
1
    
    

      
9
      
59
      
32000000
      
Drama|Thriller|Crime
      
399146
      
An average family is thrust into the spotlight...
      
New Line Cinema
      
2005-09-23
      
60740827
      
Tom Stall had the perfect life... until he bec...
      
A History of Violence
      
David Cronenberg
      
8
      
['Viggo Mortensen', 'Maria Bello', 'Heidi Haye...
      
[8, 1075, 2005, 2985, 636]
      
81
      
0.901
      
0.898151
      
0.06
      
1
    
  

In [174]:

    

#number of movies per directors
all_director = list(dataset['director_name'])
print(len(all_director))
diff_all_director = list(set(all_director))
print(len(diff_all_director))
#print(diff_all_director)

freqDir = []
for i in diff_all_director:
    freqDir.append(all_director.count(i))
#print(freqDir)    

#distribution nb movies per director
movies_per_dir = []
diff_freq = list(set(freqDir))
print(diff_freq)
for i in diff_freq:
    movies_per_dir.append(freqDir.count(i))
print(movies_per_dir)


    

    




2621
1294
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14]
[742, 237, 131, 73, 46, 26, 14, 11, 6, 3, 3, 2]

In [175]:

    

moy = ((np.array(movies_per_dir) @ np.array(diff_freq)))/(len(diff_all_director))
print(moy)


    

    




2.02550231839

In [176]:

    

plt.bar(diff_freq, movies_per_dir, align='center');
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right');
plt.xlabel('Number of movies per director');
plt.ylabel('Number of Director');
plt.savefig('images/nb_movie_per_dir.png', dpi=300, bbox_inches='tight')


    

In [178]:

    

top_director = {}

for j in diff_freq[6:]:
    dir_name_top = []
    for i in range(0,len(freqDir)):
        if freqDir[i] == j:
            dir_name_top.append(diff_all_director[i])
    top_director[j] = dir_name_top

print(top_director)


    

    




{7: ['Kevin Smith', 'James Wan', 'Louis Leterrier', 'Danny Boyle', 'Martin Scorsese', 'Tim Story', 'Robert Luketic', 'Rob Cohen', 'Zack Snyder', 'Bryan Singer', 'Renny Harlin', 'Brett Ratner', 'Lasse Hallström', 'Dennis Dugan'], 8: ['Jon Favreau', 'Robert Zemeckis', 'Adam Shankman', 'Peter Jackson', 'Roland Emmerich', 'Gore Verbinski', 'Christopher Nolan', 'M. Night Shyamalan', 'Marc Forster', 'Peter Berg', 'Paul W.S. Anderson'], 9: ['Antoine Fuqua', 'Bobby Farrelly', 'Michael Bay', 'Todd Phillips', 'Ron Howard', 'Richard Linklater'], 10: ['Tim Burton', 'Woody Allen', 'Shawn Levy'], 12: ['Robert Rodriguez', 'Steven Spielberg', 'Ridley Scott'], 14: ['Clint Eastwood', 'Steven Soderbergh']}

In [179]:

    

director_list = list(set(dataset['director_id']))
nb_directors = len(director_list)

director_dic = {}
for i in range(0,len(dataset)):
    director_dic[i]=dataset['director_id'][i]
print(director_dic)


    

    




{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 2, 9: 8, 10: 9, 11: 10, 12: 11, 13: 12, 14: 13, 15: 14, 16: 15, 17: 16, 18: 17, 19: 18, 20: 19, 21: 20, 22: 21, 23: 22, 24: 22, 25: 22, 26: 23, 27: 24, 28: 25, 29: 26, 30: 14, 31: 27, 32: 27, 33: 28, 34: 29, 35: 30, 36: 31, 37: 13, 38: 27, 39: 32, 40: 33, 41: 33, 42: 34, 43: 35, 44: 36, 45: 36, 46: 13, 47: 37, 48: 38, 49: 39, 50: 40, 51: 22, 52: 41, 53: 14, 54: 42, 55: 2, 56: 7, 57: 43, 58: 27, 59: 44, 60: 45, 61: 46, 62: 47, 63: 48, 64: 14, 65: 49, 66: 11, 67: 50, 68: 51, 69: 52, 70: 53, 71: 54, 72: 55, 73: 3, 74: 56, 75: 57, 76: 58, 77: 59, 78: 60, 79: 41, 80: 61, 81: 62, 82: 63, 83: 7, 84: 64, 85: 65, 86: 27, 87: 66, 88: 67, 89: 68, 90: 69, 91: 70, 92: 71, 93: 20, 94: 72, 95: 73, 96: 74, 97: 75, 98: 75, 99: 75, 100: 76, 101: 2, 102: 67, 103: 77, 104: 21, 105: 78, 106: 12, 107: 64, 108: 13, 109: 79, 110: 79, 111: 80, 112: 13, 113: 81, 114: 82, 115: 83, 116: 13, 117: 84, 118: 13, 119: 69, 120: 36, 121: 85, 122: 86, 123: 87, 124: 88, 125: 89, 126: 90, 127: 91, 128: 92, 129: 93, 130: 88, 131: 94, 132: 95, 133: 96, 134: 97, 135: 98, 136: 99, 137: 59, 138: 59, 139: 100, 140: 90, 141: 101, 142: 42, 143: 102, 144: 18, 145: 103, 146: 21, 147: 104, 148: 105, 149: 64, 150: 106, 151: 107, 152: 108, 153: 109, 154: 110, 155: 112, 156: 113, 157: 14, 158: 114, 159: 66, 160: 39, 161: 115, 162: 116, 163: 117, 164: 118, 165: 11, 166: 119, 167: 103, 168: 120, 169: 121, 170: 122, 171: 106, 172: 29, 173: 124, 174: 125, 175: 126, 176: 127, 177: 103, 178: 82, 179: 128, 180: 84, 181: 129, 182: 130, 183: 131, 184: 132, 185: 133, 186: 33, 187: 54, 188: 134, 189: 135, 190: 136, 191: 137, 192: 138, 193: 139, 194: 140, 195: 18, 196: 141, 197: 142, 198: 143, 199: 144, 200: 16, 201: 145, 202: 42, 203: 146, 204: 56, 205: 147, 206: 81, 207: 148, 208: 15, 209: 149, 210: 150, 211: 151, 212: 90, 213: 152, 214: 89, 215: 153, 216: 154, 217: 155, 218: 156, 219: 157, 220: 158, 221: 159, 222: 159, 223: 160, 224: 161, 225: 162, 226: 163, 227: 164, 228: 165, 229: 166, 230: 167, 231: 51, 232: 134, 233: 168, 234: 169, 235: 91, 236: 170, 237: 171, 238: 172, 239: 173, 240: 174, 241: 85, 242: 175, 243: 89, 244: 146, 245: 26, 246: 176, 247: 176, 248: 27, 249: 96, 250: 146, 251: 29, 252: 177, 253: 25, 254: 178, 255: 179, 256: 180, 257: 55, 258: 181, 259: 182, 260: 183, 261: 184, 262: 185, 263: 175, 264: 186, 265: 186, 266: 189, 267: 5, 268: 190, 269: 191, 270: 192, 271: 102, 272: 193, 273: 194, 274: 195, 275: 51, 276: 62, 277: 196, 278: 197, 279: 167, 280: 198, 281: 75, 282: 199, 283: 200, 284: 201, 285: 202, 286: 203, 287: 204, 288: 205, 289: 206, 290: 207, 291: 208, 292: 209, 293: 210, 294: 27, 295: 211, 296: 212, 297: 213, 298: 169, 299: 69, 300: 214, 301: 215, 302: 216, 303: 217, 304: 218, 305: 219, 306: 80, 307: 220, 308: 221, 309: 222, 310: 8, 311: 137, 312: 40, 313: 223, 314: 224, 315: 225, 316: 226, 317: 226, 318: 28, 319: 227, 320: 228, 321: 229, 322: 134, 323: 230, 324: 59, 325: 231, 326: 97, 327: 232, 328: 232, 329: 233, 330: 132, 331: 234, 332: 235, 333: 236, 334: 237, 335: 238, 336: 239, 337: 227, 338: 208, 339: 240, 340: 46, 341: 241, 342: 242, 343: 243, 344: 244, 345: 245, 346: 245, 347: 246, 348: 10, 349: 247, 350: 194, 351: 248, 352: 249, 353: 250, 354: 132, 355: 170, 356: 252, 357: 253, 358: 254, 359: 255, 360: 15, 361: 43, 362: 256, 363: 11, 364: 170, 365: 257, 366: 11, 367: 258, 368: 259, 369: 21, 370: 148, 371: 260, 372: 261, 373: 162, 374: 262, 375: 263, 376: 4, 377: 4, 378: 264, 379: 203, 380: 265, 381: 265, 382: 266, 383: 266, 384: 267, 385: 73, 386: 268, 387: 268, 388: 269, 389: 270, 390: 271, 391: 272, 392: 273, 393: 274, 394: 275, 395: 239, 396: 276, 397: 277, 398: 278, 399: 60, 400: 182, 401: 279, 402: 280, 403: 281, 404: 282, 405: 283, 406: 284, 407: 285, 408: 286, 409: 92, 410: 287, 411: 24, 412: 288, 413: 223, 414: 289, 415: 290, 416: 253, 417: 182, 418: 253, 419: 291, 420: 276, 421: 133, 422: 292, 423: 293, 424: 294, 425: 18, 426: 295, 427: 20, 428: 296, 429: 207, 430: 297, 431: 298, 432: 299, 433: 299, 434: 300, 435: 227, 436: 301, 437: 302, 438: 303, 439: 304, 440: 305, 441: 11, 442: 306, 443: 307, 444: 308, 445: 309, 446: 310, 447: 311, 448: 312, 449: 313, 450: 313, 451: 314, 452: 155, 453: 303, 454: 2, 455: 315, 456: 316, 457: 317, 458: 318, 459: 319, 460: 320, 461: 76, 462: 321, 463: 224, 464: 322, 465: 323, 466: 324, 467: 203, 468: 87, 469: 280, 470: 163, 471: 309, 472: 240, 473: 292, 474: 325, 475: 2, 476: 148, 477: 101, 478: 154, 479: 23, 480: 165, 481: 327, 482: 328, 483: 329, 484: 330, 485: 331, 486: 332, 487: 333, 488: 334, 489: 335, 490: 336, 491: 337, 492: 338, 493: 339, 494: 246, 495: 340, 496: 341, 497: 342, 498: 343, 499: 344, 500: 345, 501: 166, 502: 346, 503: 347, 504: 348, 505: 349, 506: 197, 507: 350, 508: 351, 509: 352, 510: 353, 511: 354, 512: 143, 513: 355, 514: 356, 515: 115, 516: 357, 517: 62, 518: 358, 519: 359, 520: 121, 521: 360, 522: 361, 523: 362, 524: 103, 525: 22, 526: 99, 527: 363, 528: 339, 529: 364, 530: 273, 531: 12, 532: 270, 533: 103, 534: 365, 535: 366, 536: 367, 537: 368, 538: 369, 539: 370, 540: 371, 541: 97, 542: 372, 543: 373, 544: 374, 545: 375, 546: 67, 547: 376, 548: 377, 549: 189, 550: 378, 551: 107, 552: 227, 553: 379, 554: 89, 555: 374, 556: 380, 557: 279, 558: 381, 559: 382, 560: 383, 561: 384, 562: 385, 563: 386, 564: 16, 565: 309, 566: 387, 567: 388, 568: 389, 569: 240, 570: 390, 571: 386, 572: 391, 573: 230, 574: 392, 575: 393, 576: 394, 577: 209, 578: 270, 579: 395, 580: 396, 581: 397, 582: 398, 583: 399, 584: 401, 585: 64, 586: 402, 587: 403, 588: 404, 589: 405, 590: 406, 591: 407, 592: 269, 593: 89, 594: 408, 595: 409, 596: 410, 597: 411, 598: 230, 599: 412, 600: 413, 601: 414, 602: 249, 603: 371, 604: 415, 605: 416, 606: 417, 607: 418, 608: 419, 609: 420, 610: 421, 611: 422, 612: 169, 613: 310, 614: 423, 615: 424, 616: 425, 617: 265, 618: 277, 619: 308, 620: 426, 621: 427, 622: 205, 623: 428, 624: 429, 625: 430, 626: 172, 627: 381, 628: 431, 629: 432, 630: 433, 631: 434, 632: 162, 633: 435, 634: 436, 635: 437, 636: 368, 637: 438, 638: 50, 639: 439, 640: 440, 641: 142, 642: 294, 643: 441, 644: 428, 645: 338, 646: 442, 647: 344, 648: 443, 649: 60, 650: 73, 651: 444, 652: 445, 653: 197, 654: 118, 655: 446, 656: 33, 657: 447, 658: 172, 659: 143, 660: 448, 661: 303, 662: 80, 663: 429, 664: 299, 665: 226, 666: 87, 667: 449, 668: 450, 669: 221, 670: 197, 671: 11, 672: 451, 673: 452, 674: 453, 675: 445, 676: 109, 677: 96, 678: 452, 679: 194, 680: 454, 681: 279, 682: 455, 683: 456, 684: 457, 685: 458, 686: 459, 687: 242, 688: 205, 689: 460, 690: 461, 691: 462, 692: 463, 693: 86, 694: 240, 695: 464, 696: 112, 697: 465, 698: 460, 699: 466, 700: 393, 701: 467, 702: 18, 703: 188, 704: 18, 705: 240, 706: 468, 707: 469, 708: 50, 709: 333, 710: 322, 711: 470, 712: 471, 713: 150, 714: 161, 715: 271, 716: 247, 717: 472, 718: 408, 719: 473, 720: 474, 721: 475, 722: 166, 723: 476, 724: 204, 725: 477, 726: 478, 727: 330, 728: 479, 729: 232, 730: 170, 731: 480, 732: 428, 733: 481, 734: 284, 735: 287, 736: 482, 737: 169, 738: 220, 739: 252, 740: 483, 741: 484, 742: 485, 743: 55, 744: 486, 745: 487, 746: 488, 747: 373, 748: 28, 749: 140, 750: 335, 751: 143, 752: 61, 753: 153, 754: 316, 755: 489, 756: 490, 757: 491, 758: 492, 759: 493, 760: 494, 761: 495, 762: 496, 763: 497, 764: 374, 765: 498, 766: 499, 767: 321, 768: 446, 769: 500, 770: 264, 771: 501, 772: 194, 773: 385, 774: 140, 775: 162, 776: 502, 777: 503, 778: 166, 779: 46, 780: 191, 781: 33, 782: 343, 783: 504, 784: 505, 785: 469, 786: 506, 787: 333, 788: 507, 789: 508, 790: 509, 791: 499, 792: 163, 793: 91, 794: 184, 795: 510, 796: 511, 797: 512, 798: 269, 799: 247, 800: 513, 801: 514, 802: 515, 803: 516, 804: 517, 805: 518, 806: 519, 807: 520, 808: 240, 809: 146, 810: 417, 811: 521, 812: 389, 813: 150, 814: 194, 815: 435, 816: 194, 817: 522, 818: 523, 819: 283, 820: 524, 821: 60, 822: 253, 823: 516, 824: 525, 825: 526, 826: 371, 827: 226, 828: 193, 829: 528, 830: 529, 831: 530, 832: 531, 833: 410, 834: 332, 835: 360, 836: 490, 837: 532, 838: 278, 839: 56, 840: 533, 841: 534, 842: 499, 843: 175, 844: 108, 845: 19, 846: 535, 847: 536, 848: 537, 849: 366, 850: 387, 851: 158, 852: 538, 853: 539, 854: 540, 855: 541, 856: 299, 857: 542, 858: 543, 859: 366, 860: 544, 861: 188, 862: 209, 863: 371, 864: 170, 865: 532, 866: 545, 867: 546, 868: 547, 869: 256, 870: 548, 871: 549, 872: 194, 873: 550, 874: 551, 875: 426, 876: 552, 877: 553, 878: 332, 879: 259, 880: 554, 881: 555, 882: 163, 883: 150, 884: 556, 885: 164, 886: 557, 887: 558, 888: 559, 889: 195, 890: 103, 891: 560, 892: 332, 893: 561, 894: 562, 895: 367, 896: 365, 897: 563, 898: 209, 899: 256, 900: 564, 901: 565, 902: 566, 903: 567, 904: 568, 905: 569, 906: 570, 907: 280, 908: 571, 909: 572, 910: 164, 911: 151, 912: 353, 913: 408, 914: 490, 915: 385, 916: 188, 917: 574, 918: 575, 919: 373, 920: 312, 921: 543, 922: 576, 923: 577, 924: 578, 925: 579, 926: 284, 927: 580, 928: 446, 929: 581, 930: 73, 931: 238, 932: 32, 933: 321, 934: 582, 935: 583, 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2386: 1000, 2387: 433, 2388: 433, 2389: 159, 2390: 1298, 2391: 364, 2392: 1299, 2393: 1300, 2394: 1301, 2395: 1146, 2396: 391, 2397: 377, 2398: 268, 2399: 1104, 2400: 162, 2401: 1303, 2402: 13, 2403: 161, 2404: 322, 2405: 906, 2406: 1304, 2407: 23, 2408: 108, 2409: 171, 2410: 1306, 2411: 292, 2412: 403, 2413: 1307, 2414: 1308, 2415: 545, 2416: 3, 2417: 1240, 2418: 1179, 2419: 1309, 2420: 62, 2421: 684, 2422: 1310, 2423: 948, 2424: 341, 2425: 1221, 2426: 729, 2427: 1313, 2428: 445, 2429: 279, 2430: 1314, 2431: 417, 2432: 1315, 2433: 1316, 2434: 1317, 2435: 163, 2436: 407, 2437: 1318, 2438: 66, 2439: 1319, 2440: 1320, 2441: 20, 2442: 1321, 2443: 1322, 2444: 21, 2445: 1323, 2446: 956, 2447: 1324, 2448: 389, 2449: 900, 2450: 1325, 2451: 1023, 2452: 227, 2453: 33, 2454: 18, 2455: 1327, 2456: 468, 2457: 1102, 2458: 724, 2459: 1328, 2460: 1329, 2461: 1284, 2462: 1330, 2463: 300, 2464: 1331, 2465: 685, 2466: 1332, 2467: 1333, 2468: 1334, 2469: 377, 2470: 1335, 2471: 885, 2472: 1338, 2473: 1339, 2474: 25, 2475: 1340, 2476: 90, 2477: 1341, 2478: 1342, 2479: 15, 2480: 935, 2481: 1343, 2482: 13, 2483: 1344, 2484: 700, 2485: 1345, 2486: 269, 2487: 121, 2488: 1346, 2489: 240, 2490: 1347, 2491: 1348, 2492: 904, 2493: 1140, 2494: 89, 2495: 324, 2496: 1349, 2497: 1350, 2498: 1035, 2499: 1351, 2500: 1043, 2501: 175, 2502: 585, 2503: 444, 2504: 657, 2505: 977, 2506: 1071, 2507: 1352, 2508: 623, 2509: 946, 2510: 197, 2511: 1354, 2512: 428, 2513: 1355, 2514: 1252, 2515: 1356, 2516: 883, 2517: 432, 2518: 447, 2519: 1357, 2520: 556, 2521: 1358, 2522: 1359, 2523: 1360, 2524: 1269, 2525: 1361, 2526: 201, 2527: 1088, 2528: 39, 2529: 1362, 2530: 1196, 2531: 1363, 2532: 393, 2533: 1364, 2534: 1365, 2535: 1366, 2536: 506, 2537: 29, 2538: 1225, 2539: 80, 2540: 727, 2541: 188, 2542: 1239, 2543: 158, 2544: 232, 2545: 81, 2546: 481, 2547: 1367, 2548: 974, 2549: 1368, 2550: 921, 2551: 1369, 2552: 387, 2553: 1370, 2554: 344, 2555: 1179, 2556: 1371, 2557: 930, 2558: 1372, 2559: 379, 2560: 1373, 2561: 505, 2562: 712, 2563: 569, 2564: 207, 2565: 1374, 2566: 197, 2567: 37, 2568: 1375, 2569: 939, 2570: 1376, 2571: 424, 2572: 1194, 2573: 1377, 2574: 1378, 2575: 38, 2576: 20, 2577: 1036, 2578: 1338, 2579: 1380, 2580: 1381, 2581: 989, 2582: 82, 2583: 298, 2584: 1382, 2585: 53, 2586: 1383, 2587: 1384, 2588: 971, 2589: 1385, 2590: 1222, 2591: 1386, 2592: 1258, 2593: 1387, 2594: 1174, 2595: 284, 2596: 314, 2597: 11, 2598: 1389, 2599: 307, 2600: 227, 2601: 45, 2602: 161, 2603: 1137, 2604: 1390, 2605: 1391, 2606: 1392, 2607: 1393, 2608: 1254, 2609: 240, 2610: 1394, 2611: 913, 2612: 1396, 2613: 269, 2614: 675, 2615: 1399, 2616: 382, 2617: 1401, 2618: 1403, 2619: 100, 2620: 1405}

In [180]:

    

directorArray = np.ndarray(shape=(len(dataset), nb_directors), dtype=int)
for i in range(0, len(dataset)):
    #genreDict.setdefault(i, [])
    
    vector = np.zeros(nb_directors)
    
    #add vectors of 1's and 0's to obtain vector of genres
    vector += (director_dic[i] == np.array(director_list)).astype(int)
    
    #genreDict[i].append(vector)
    directorArray[i] = vector


    

In [181]:

    

print(len(directorArray[0]))
print(directorArray.size)


    

    




1294
3391574

In [182]:

    

Directors = pd.DataFrame(directorArray, columns=director_list)
Directors.head(10)


    

    
Out[182]:







  

    

      

      
0
      
1
      
2
      
3
      
4
      
5
      
6
      
7
      
8
      
9
      
...
      
1390
      
1391
      
1392
      
1393
      
1394
      
1396
      
1399
      
1401
      
1403
      
1405
    
  
  

    

      
0
      
1
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
1
      
0
      
1
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
2
      
0
      
0
      
1
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
3
      
0
      
0
      
0
      
1
      
0
      
0
      
0
      
0
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
4
      
0
      
0
      
0
      
0
      
1
      
0
      
0
      
0
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
5
      
0
      
0
      
0
      
0
      
0
      
1
      
0
      
0
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
6
      
0
      
0
      
0
      
0
      
0
      
0
      
1
      
0
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
7
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
1
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
8
      
0
      
0
      
1
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
9
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
1
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
  

10 rows × 1294 columns

In [189]:

    

W = np.dot(directorArray,np.transpose(directorArray))


    

In [190]:

    

np.fill_diagonal(W, 0)


    

In [191]:

    

plt.spy(W)


    

    
Out[191]:





<matplotlib.image.AxesImage at 0x124dce7f0>






    

In [192]:

    

plt.hist(W[W > 0].reshape(-1), bins=50);


    

In [193]:

    

print('There are {} edges equal to zero '.format(np.sum(W == 0)/2))
print('There are {} edges equal to one '.format(np.sum(W == 1)/2))


    

    




There are 3431569.5 edges equal to zero 
There are 3251.0 edges equal to one 

In [194]:

    

#Compute degree distribution 
degrees = np.zeros(len(W)) 
for i in range(0, len(W)):
    degrees[i] = sum(W[i])

plt.hist(degrees, bins=50);


    

In [195]:

    

NormW = pd.DataFrame(W)
NormW.head()


    

    
Out[195]:







  

    

      

      
0
      
1
      
2
      
3
      
4
      
5
      
6
      
7
      
8
      
9
      
...
      
2611
      
2612
      
2613
      
2614
      
2615
      
2616
      
2617
      
2618
      
2619
      
2620
    
  
  

    

      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
1
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
2
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
1
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
3
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
    

      
4
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
...
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
      
0
    
  

5 rows × 2621 columns

In [24]:

    

W.to_csv('Saved_Datasets/NormalizedDirectorW.csv', index=False)


    

In [39]:

    

#Apply nb movie per director to the dataset
mov_per_dir_dataset = []
for i in range(0,len(dataset['director_name'])):
    for j in range(0,len(diff_all_director)):
        if dataset['director_name'][i] == diff_all_director[j]:
            mov_per_dir_dataset.append(freqDir[j])


    

In [83]:

    

#W = np.ndarray(shape=(10, 10), dtype=int)
W_diff = np.zeros(shape=(len(dataset), len(dataset)), dtype=int)
for i in range(0,len(dataset)):
    for j in range(i,len(dataset)):
        W_diff[i][j] = abs(mov_per_dir_dataset[i]-mov_per_dir_dataset[j])


    

In [84]:

    

print(W_diff)


    

    




[[0 2 5 ..., 2 1 2]
 [0 0 7 ..., 0 1 0]
 [0 0 0 ..., 7 6 7]
 ..., 
 [0 0 0 ..., 0 1 0]
 [0 0 0 ..., 0 0 1]
 [0 0 0 ..., 0 0 0]]

In [85]:

    

bigger = W_diff.transpose() > W_diff
W_diff = W_diff - W_diff*bigger + W_diff.transpose()*bigger
np.fill_diagonal(W_diff, 0)


    

In [86]:

    

plt.spy(W_diff)


    

    
Out[86]:





<matplotlib.image.AxesImage at 0x143437710>






    

In [101]:

    

plt.hist(W_diff.reshape(-1),bins=50);


    

In [1]:

    

val_75 = np.percentile(W_diff,75)
print(val_75)


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-d008b8b64128> in <module>()
----> 1 val_75 = np.percentile(W_diff,50)
      2 print(val_75)

NameError: name 'np' is not defined

In [136]:

    

W_diff_norm = np.zeros(shape=(len(dataset), len(dataset)), dtype=float)
for i in range(0,len(dataset)):
    for j in range(i,len(dataset)):
        if W_diff[i][j] == 0:
            W_diff_norm[i][j] = 1
        elif W_diff[i][j] <= 4:  
            W_diff_norm[i][j] = 1-(W_diff[i][j])/(val_75)
        else:
            W_diff_norm[i][j] = 0


    

In [137]:

    

plt.spy(W_diff_norm)


    

    
Out[137]:





<matplotlib.image.AxesImage at 0x11dc321d0>






    

In [138]:

    

bigger = W_diff_norm.transpose() > W_diff_norm
W_diff_norm = W_diff_norm - W_diff_norm*bigger + W_diff_norm.transpose()*bigger
np.fill_diagonal(W_diff_norm, 0)


    

In [139]:

    

plt.spy(W_diff_norm)


    

    
Out[139]:





<matplotlib.image.AxesImage at 0x1490e3470>






    

In [166]:

    

DiffNormW = pd.DataFrame(W_diff_norm)
DiffNormW.head()


    

    
Out[166]:







  

    

      

      
0
      
1
      
2
      
3
      
4
      
5
      
6
      
7
      
8
      
9
      
...
      
2611
      
2612
      
2613
      
2614
      
2615
      
2616
      
2617
      
2618
      
2619
      
2620
    
  
  

    

      
0
      
0.00
      
0.5
      
0.00
      
0.50
      
0.25
      
0.75
      
0.5
      
0.75
      
0.00
      
0.75
      
...
      
0.75
      
0.5
      
0.00
      
1.00
      
0.5
      
0.75
      
0.5
      
0.5
      
0.75
      
0.5
    
    

      
1
      
0.50
      
0.0
      
0.00
      
0.00
      
0.00
      
0.75
      
1.0
      
0.25
      
0.00
      
0.25
      
...
      
0.75
      
1.0
      
0.00
      
0.50
      
1.0
      
0.75
      
1.0
      
1.0
      
0.75
      
1.0
    
    

      
2
      
0.00
      
0.0
      
0.00
      
0.25
      
0.50
      
0.00
      
0.0
      
0.00
      
1.00
      
0.00
      
...
      
0.00
      
0.0
      
1.00
      
0.00
      
0.0
      
0.00
      
0.0
      
0.0
      
0.00
      
0.0
    
    

      
3
      
0.50
      
0.0
      
0.25
      
0.00
      
0.75
      
0.25
      
0.0
      
0.75
      
0.25
      
0.75
      
...
      
0.25
      
0.0
      
0.25
      
0.50
      
0.0
      
0.25
      
0.0
      
0.0
      
0.25
      
0.0
    
    

      
4
      
0.25
      
0.0
      
0.50
      
0.75
      
0.00
      
0.00
      
0.0
      
0.50
      
0.50
      
0.50
      
...
      
0.00
      
0.0
      
0.50
      
0.25
      
0.0
      
0.00
      
0.0
      
0.0
      
0.00
      
0.0
    
  

5 rows × 2621 columns

In [167]:

    

DiffNormW.to_csv('Saved_Datasets/DiffNormDirW.csv', index=False)


    

In [140]:

    

plt.hist(W_diff_norm.reshape(-1),bins=50);


    

In [141]:

    

G = graphs.Graph(W_diff_norm)
G.compute_laplacian('normalized')


    

In [134]:

    

len(W_diff_norm)


    

    
Out[134]:





2621

In [142]:

    

G.compute_fourier_basis(recompute=True)
plt.plot(G.e[0:10]);


    

In [152]:

    

genres = preprocessing.LabelEncoder().fit_transform(dataset['success'])
G.set_coordinates(G.U[:,1:3])


    

In [148]:

    

G.plot_signal(genres, vertex_size=20)


    

In [153]:

    

NEIGHBORS = 300

#sort the order of the weights
sort_order = np.argsort(W_diff_norm, axis = 1)

#declaration of a sorted weight matrix
sorted_weights = np.zeros((len(W_diff_norm), len(W_diff_norm)))

for i in range (0, len(W_diff_norm)):  
    for j in range(0, len(W_diff_norm)):
        if (j >= len(W_diff_norm) - NEIGHBORS):
            #copy the k strongest edges for each node
            sorted_weights[i, sort_order[i,j]] = W_diff_norm[i,sort_order[i,j]]
        else:
            #set the other edges to zero
            sorted_weights[i, sort_order[i,j]] = 0

#ensure the matrix is symmetric
bigger = sorted_weights.transpose() > sorted_weights
sorted_weights = sorted_weights - sorted_weights*bigger + sorted_weights.transpose()*bigger


    

In [154]:

    

plt.spy(sorted_weights)


    

    
Out[154]:





<matplotlib.image.AxesImage at 0x11d044b38>






    

In [155]:

    

G_s = graphs.Graph(sorted_weights)
G_s.compute_laplacian('normalized')


    

In [156]:

    

G_s.compute_fourier_basis(recompute=True)
plt.plot(G_s.e[0:10]);


    

In [160]:

    

G_s.set_coordinates(G_s.U[:,1:3])
G_s.plot_signal(genres, vertex_size=20)


    

In [161]:

    

labels_reg = preprocessing.LabelEncoder().fit_transform(dataset['Normed_ROI'])


    

In [163]:

    

G_s.plot_signal(labels_reg, vertex_size=20)


    

In [ ]: