We will use distance between test segments computed in 140926-test-signal-jump to find sequence of segments that were likely together. Armed with this fact we can take the individual proababilities of each segment and combine it to form one probability that will be used to update the probabilites of all the segments in the sequence

the sequences are found using a greedy algoirthm that stops when a conflict is detected

the probabilities of segments should be combined by multiplying them, however this did not work well. Probably because the probabilites are not well calibrated. Taking the mean had a better effect.

Suppose you have a chain of segments: $i \in 1 \ldots N $

Each segment predicts a seizure $P_i$ or not $Q_i=1-P_i$

if a chain is negative then the probability is $\prod Q_i$ if a chain is positive the situation is more complex. There is a chance $U$ that a seizure detection even has happened and $V=1-U$ it did not. I estimate $U$ to be around $0.2$. So the probability is $\prod ( U * P_i + V*Q_i)$

or $\prod Q_i \times \Pi ( U \frac{P_i}{Q_i} +V )$

the ratio of positive to negative probability is just $r = \prod ( U \frac{P_i}{Q_i} +V )$ and probability is $1/(1+1/r)$



In [1]:

    
%matplotlib inline
from matplotlib import pylab as pl
import cPickle as pickle
import pandas as pd
import numpy as np
import os

individual segment probablility file



In [2]:

    
FNAME_IN = '../submissions/141117-predict.4.csv'

updated probability file



In [3]:

    
FNAME_OUT = '../submissions/141117-predict.5.csv'



In [4]:

    
!head {FNAME_IN}









    



clip,preictal
Dog_1_test_segment_0001.mat,0.36422108564142136
Dog_1_test_segment_0002.mat,0.15354376708954037
Dog_1_test_segment_0003.mat,0.2311784685067122
Dog_1_test_segment_0004.mat,0.23395507538236127
Dog_1_test_segment_0005.mat,0.20919729376936513
Dog_1_test_segment_0006.mat,0.27385060377807313
Dog_1_test_segment_0007.mat,0.10813703226308491
Dog_1_test_segment_0008.mat,0.30873829630672683
Dog_1_test_segment_0009.mat,0.1967252194234564



In [5]:

    
scores = pd.read_csv(FNAME_IN, index_col='clip', squeeze=True)
out_scores = scores.copy()



In [6]:

    
scores.hist()









    Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x1134a7390>



In [7]:

    
scores['Dog_2_test_segment_0004.mat']









    Out[7]:





0.21150878853824528



In [8]:

    
targets = set(['_'.join(f.split('_')[:2]) for f in scores.index.values])
targets









    Out[8]:





{'Dog_1', 'Dog_2', 'Dog_3', 'Dog_4', 'Dog_5', 'Patient_1', 'Patient_2'}



In [9]:

    
W=0.25
T=0.1
D=-0.5

for target in targets:
    print
    d = np.load('/Users/udi/Downloads/kaggle/seizure-prediction/distance1/%s-test-jump-distance.npy'%target)
    N = d.shape[0]
    print target, N
    dord = np.unravel_index(d.ravel().argsort(),d.shape)
    Nsequences = N/6
    
    # find good pairs of segments that are likely to be paired in time
    next_segment = [-1]*N
    previous_segment = [-1]*N
    for i,(s1,s2) in enumerate(np.array(dord).T):
        dist = d[s1,s2]
        if dist > D: # from 140926-train-signal-jump
            break
        if next_segment[s1] != -1:
            #print i,'right conflict',dist
            continue
        if previous_segment[s2] != -1:
            #print i,'left conflict',dist
            continue
        c = 1
        j = s1
        while previous_segment[j] != -1:
            j = previous_segment[j]
            c += 1
        j = s2
        c += 1
        while next_segment[j] != -1:
            j = next_segment[j]
            c += 1
        if c > 6:
            continue
        next_segment[s1] = s2
        previous_segment[s2] = s1
#     if i < Nsequences:
#         print 'skip'
#         continue
    # check code
    for i in range(N):
        if next_segment[i] != -1:
            assert previous_segment[next_segment[i]] == i

    # find good sequences
    sequences = []
    for i in range(N):
        if previous_segment[i] == -1 and next_segment[i] != -1:
            j = i
            sequence = [j]
            while next_segment[j] != -1:
                j = next_segment[j]
                sequence.append(j)
            sequences.append(sequence)
    len_sequences = [len(sequence) for sequence in sequences]
    print '#sequences',len(sequences), '%segments that was sequenced',sum(len_sequences)/float(N), 'longest sequence', max(len_sequences)
    print sequences

    #compute probability for sequences
    sequences_prb = []
    for sequence in sequences:
        probs = np.array([scores['%s_test_segment_%04d.mat'%(target,s+1)] for s in sequence])
        wgts = np.exp(probs/T)
        wgts /= np.sum(wgts)
        p = np.dot(wgts, probs)
        sequences_prb.append(p)
    # fix probability for segments in sequences
    for p,sequence in zip(sequences_prb,sequences):
        # all segments in the same sequence will be assigned the same probability
        n = 1./len(sequence)
        for i, s in enumerate(sequence):
            out_scores['%s_test_segment_%04d.mat'%(target,s+1)] = W*scores['%s_test_segment_%04d.mat'%(target,s+1)] +(1.-W)*p









    



Dog_2 1000
#sequences 177 %segments that was sequenced 0.993 longest sequence 6
[[11, 336, 161, 793, 310, 204], [13, 179, 925, 636, 444], [16, 862, 329, 618], [35, 175, 472, 152, 340, 606], [36, 480, 880, 304, 421, 569], [44, 346, 660, 383, 626, 21], [49, 902, 944, 490, 488, 337], [56, 559, 980, 287, 414, 464], [59, 234, 352, 820, 14, 42], [61, 297, 45, 504, 760, 630], [62, 859, 858, 785, 133, 30], [65, 755, 465, 459, 550, 871], [69, 68, 351, 830], [74, 302, 775, 588, 673, 184], [80, 776, 108, 293, 9, 47], [81, 141, 620, 573, 51, 930], [92, 306, 455, 101, 994, 331], [93, 598, 695, 699, 638, 33], [98, 223, 651, 440, 839], [99, 359, 326, 879, 813, 333], [104, 889, 798, 733, 50, 72], [106, 217, 250, 892, 702, 555], [109, 63, 404, 447, 365, 763], [114, 79, 87, 216, 469, 977], [115, 57, 979, 594, 185, 341], [129, 722, 116, 640], [146, 634, 281, 498, 535, 674], [158, 198, 658, 652, 566, 113], [166, 105, 371, 197, 338], [173, 954, 761, 370, 608, 909], [176, 662, 897, 117, 987, 539], [193, 38, 821, 429, 887], [209, 509, 697, 339, 846, 937], [214, 419, 357, 221, 764, 252], [225, 384, 582, 251, 289, 616], [230, 402, 121, 267, 347, 46], [236, 783, 291, 548, 6, 386], [237, 857, 515, 969, 266, 532], [238, 578, 112, 400, 139], [242, 639, 205, 322, 233, 292], [249, 202, 600, 277, 196, 296], [256, 393, 2, 220, 482, 523], [258, 531, 201, 965, 835, 385], [260, 593, 22, 424, 312, 160], [270, 100, 507, 589, 602, 525], [284, 663, 716, 282, 826, 833], [294, 305, 283, 917, 136, 510], [295, 332, 466, 24, 558, 853], [299, 685, 831, 334, 298, 452], [303, 194, 877, 922, 984, 715], [313, 408, 707, 752], [335, 467, 911, 103, 745, 561], [348, 240, 855, 836, 806, 503], [358, 164, 157, 731, 309, 723], [363, 711, 328, 586, 991], [374, 130, 10, 572, 60, 495], [376, 215, 789, 948, 327, 438], [394, 703, 19, 330, 269], [403, 95, 131, 941, 955, 574], [406, 748, 318, 533, 89, 111], [415, 841, 832, 255, 612, 581], [416, 76, 502, 777], [417, 982, 300, 700, 665, 538], [420, 725, 369, 570, 924, 373], [422, 547, 366, 534, 477], [425, 643, 647, 360, 413, 491], [432, 307, 445, 474, 471], [433, 200, 271, 617, 568, 468], [434, 720, 521, 993, 135], [436, 840, 856, 219, 536, 577], [437, 245, 546, 514, 657, 323], [449, 458, 661, 349], [450, 83, 920, 794, 751], [451, 58, 315, 772, 124, 91], [456, 874, 517, 943, 637, 957], [461, 423, 769, 321, 784], [486, 885, 805, 27, 378], [494, 147, 54, 850, 560, 929], [497, 900, 844, 738, 246, 625], [501, 278, 686, 718, 649, 726], [512, 401, 239, 122, 183, 967], [522, 809, 680, 319, 407, 819], [528, 189, 460, 981, 155], [530, 778, 933, 664, 232, 792], [537, 995, 77, 442], [551, 629, 31, 614, 361, 192], [553, 64, 435, 818, 165], [554, 153, 843, 544], [579, 144, 852, 986], [585, 261, 43, 754, 441, 687], [591, 903, 187, 48, 552, 542], [596, 996, 882, 782, 470], [601, 262, 653, 688, 747, 597], [603, 963, 757, 0], [610, 621, 759, 84, 235, 118], [628, 500, 997, 811, 485], [632, 623, 691, 908, 741, 156], [633, 273, 807, 150], [644, 375, 190, 427, 481, 120], [648, 453, 431, 824, 82, 132], [650, 983, 584, 549, 916, 285], [654, 186, 487, 395, 272], [655, 631, 125, 712, 484, 392], [656, 627, 873, 564, 959], [666, 177, 127, 998, 170, 32], [667, 950, 443, 354], [671, 694, 67, 254, 350], [678, 635, 208, 286, 575, 682], [681, 41, 167, 896, 780], [689, 492, 975, 364, 211, 96], [693, 705, 590, 15, 571, 599], [706, 692, 556, 607], [708, 3, 226, 140, 838, 301], [709, 290, 849, 88, 947, 827], [710, 624, 97, 907, 142], [713, 342, 730, 397, 749, 834], [717, 968, 324, 675, 288], [719, 345, 454, 412, 765, 188], [721, 123, 280, 439, 817], [727, 25, 446, 520, 936, 229], [735, 390, 802, 398, 526], [736, 696, 381, 473, 949, 279], [739, 734, 970, 866, 85, 684], [742, 428, 23, 956, 935, 457], [743, 499, 479, 904, 609], [756, 753, 379, 567, 75, 53], [758, 462, 174, 163, 893, 516], [766, 714, 418, 815, 316, 851], [768, 396, 808], [771, 932, 972, 191, 138, 524], [786, 888, 128, 732, 906, 669], [788, 367, 86, 387, 641, 781], [790, 677, 26, 37, 276, 355], [795, 690, 344, 946, 847, 698], [797, 222, 990, 203, 275, 962], [799, 430, 910, 181, 263, 145], [803, 362, 562, 767, 508, 541], [804, 576, 182, 886, 28, 489], [812, 382, 529, 274, 248, 227], [814, 878, 988, 864, 527, 800], [822, 40, 611, 210, 399, 259], [823, 729, 343, 583, 107, 905], [828, 180, 20, 923, 228], [829, 895, 311, 178], [837, 953, 356], [842, 921, 770, 377, 672, 476], [845, 505, 746, 172, 724, 320], [848, 372, 592, 872, 206, 71], [860, 563, 224, 162, 213, 5], [861, 989, 513, 883, 587, 154], [867, 7, 595, 791, 773], [869, 264, 317, 939, 645, 478], [870, 212, 496, 992, 159], [876, 448, 619, 810, 18, 974], [881, 668, 483], [884, 898, 137, 615, 353, 410], [891, 171, 391, 890, 580, 927], [894, 12, 875, 999, 90, 265], [899, 368, 110, 779, 389, 241], [901, 701, 8, 914, 642, 126], [912, 151, 825, 543, 646, 195], [913, 557, 169, 257, 73, 380], [915, 134, 854, 934, 231, 704], [918, 966, 39, 463, 960, 613], [919, 52, 737, 268, 728, 70], [926, 961, 4, 774, 816, 868], [928, 409, 218, 622, 796, 744], [931, 34, 143, 605, 253, 405], [940, 244, 670, 740, 519], [942, 683, 475, 787, 94, 973], [945, 676, 243, 411, 750, 511], [951, 493, 1, 565, 976, 314], [952, 540, 679, 119, 325, 545], [958, 506, 199, 426, 168], [964, 78, 102, 865, 604, 247], [971, 863, 148, 762, 978], [985, 29, 801, 66, 938, 17]]

Dog_3 907
#sequences 167 %segments that was sequenced 0.991179713341 longest sequence 6
[[3, 81, 539, 270, 702, 582], [13, 226, 111, 33, 645], [15, 286, 555, 680, 525, 41], [22, 211, 854, 889, 176, 486], [32, 396, 59, 569, 594, 296], [36, 714, 218, 630], [44, 456, 906, 820, 521, 538], [45, 453, 480, 215, 688, 150], [48, 728, 665, 363, 802, 762], [63, 432, 669, 664, 739, 483], [68, 522, 598, 37, 618, 54], [70, 368, 672, 600, 96], [75, 579, 113, 784, 441, 249], [76, 450, 704, 120, 380, 417], [78, 513, 187, 56, 302, 766], [83, 170, 868], [85, 497, 308, 718, 5, 751], [87, 847, 661, 720, 519, 327], [88, 16, 238, 581], [95, 19, 818, 458, 694, 528], [97, 49, 850, 378, 644, 797], [107, 851, 246, 683, 596, 485], [118, 72, 635, 481, 874, 89], [121, 34, 860, 679, 17, 684], [131, 599, 39, 434, 367, 52], [135, 603, 852, 873, 780, 243], [151, 615, 415, 400, 423, 748], [152, 40, 154, 839, 98, 756], [159, 210, 648, 698, 534, 791], [162, 863, 202, 307, 546, 742], [164, 427, 279, 160], [166, 901, 468, 487, 245, 342], [167, 244, 161, 666, 153, 605], [169, 779, 42, 588, 53, 275], [172, 355, 137, 656, 564, 696], [174, 82, 283, 188, 310, 811], [184, 35, 445, 580, 366, 51], [192, 631, 551, 12, 345, 786], [193, 145, 837, 454, 119], [197, 832, 626, 104, 325, 277], [199, 147, 2, 171, 182, 347], [201, 372, 565, 893, 710, 828], [209, 177, 770, 776, 231], [224, 848, 727, 690, 55, 821], [227, 808, 524, 11, 760, 701], [228, 394, 280, 31, 709, 282], [235, 625, 540, 252, 619, 735], [248, 476, 474, 822, 835], [251, 321], [253, 383], [255, 774, 681, 234, 767, 21], [256, 122, 510, 130, 27, 50], [259, 102, 526, 764, 382, 872], [263, 389, 498, 110, 117, 207], [264, 433, 844, 757, 262, 655], [267, 650, 562, 299, 712, 827], [271, 298, 38, 428, 388, 426], [273, 508, 721, 425, 717, 379], [289, 608, 689, 148, 447, 272], [293, 733, 198, 297, 418, 319], [300, 612, 305, 23], [301, 351, 149, 649], [306, 897, 361, 265], [309, 876, 375, 344, 94, 132], [313, 499, 18, 667, 755, 758], [315, 100], [324, 179, 303, 724, 489], [326, 14, 116, 537, 654], [329, 572, 217, 268, 203, 511], [331, 191, 260, 501, 369, 552], [334, 730], [338, 205, 559, 406, 333, 804], [339, 20, 143, 459, 846, 311], [343, 896, 741, 402, 640, 216], [346, 576, 587, 662], [350, 891, 129, 750, 529], [354, 413, 138, 348, 168, 359], [358, 92, 431, 890, 105, 813], [362, 841, 798, 561, 810, 189], [365, 532, 461, 291], [371, 376, 185, 250, 886, 77], [373, 606, 208, 787, 782, 71], [374, 115, 341, 639, 793], [386, 557, 765, 274, 647, 287], [390, 101, 230, 473, 254], [393, 407, 146, 693], [397, 505, 318, 337], [409, 805, 421, 530, 10, 660], [411, 697, 899, 183, 340, 642], [412, 575, 290, 531, 401, 477], [440, 25, 732, 212, 866, 336], [443, 479, 200, 678, 233, 180], [444, 220, 91, 429, 157, 294], [462, 370, 527, 584, 651], [466, 424, 139, 194, 549], [478, 883, 258, 550, 703, 57], [490, 853, 577], [492, 834, 28, 613, 836, 422], [500, 682, 628, 123], [504, 620, 126, 882, 384], [509, 74, 610, 520, 623, 391], [512, 436, 403, 633, 186, 364], [514, 24, 457, 106, 663], [517, 206, 114, 452, 794, 439], [535, 892, 885, 545, 591], [542, 178, 0, 796, 304], [543, 190, 229, 590, 112, 410], [544, 536, 506], [556, 257, 460, 488, 46, 395], [567, 455, 643, 515, 141, 566], [574, 800, 398, 317, 320, 699], [585, 223, 125, 496], [602, 745, 288, 595, 142], [604, 140, 795, 548, 586, 62], [614, 716, 158, 93, 888, 824], [616, 759, 204, 833, 442, 8], [617, 632, 788, 43, 624, 484], [622, 387, 695, 840, 686], [627, 773, 266, 589, 856, 747], [629, 518], [634, 219, 295, 335, 446, 181], [637, 641, 349, 451, 322, 247], [653, 7, 420, 173, 73, 865], [657, 467, 66], [658, 878, 475, 404, 578, 278], [668, 700, 502, 312, 905, 214], [670, 323], [673, 465, 109, 242, 838, 849], [685, 213, 175, 675, 674, 377], [691, 875, 330, 405, 124], [705, 692, 533, 4, 734, 292], [706, 570, 470, 858, 360, 65], [719, 144, 740, 746, 884, 90], [722, 713, 86, 155, 558, 240], [729, 269, 541, 807, 864, 790], [731, 469, 871], [736, 597, 601, 495, 753, 285], [738, 769, 103, 136, 659, 6], [743, 30, 563, 237, 408, 754], [744, 777, 99, 638, 471, 284], [749, 857, 571, 503, 895, 568], [752, 464, 789, 414, 353, 677], [761, 523, 785, 356, 726], [771, 768, 232, 225], [772, 163, 26], [778, 60, 281, 554, 801, 221], [781, 507, 493, 763, 1, 573], [799, 862, 806, 845, 646], [803, 817, 687, 449, 261, 195], [814, 47, 870, 609, 58, 887], [815, 357, 707, 636, 583], [816, 792, 783, 435, 61, 352], [819, 843, 671, 239, 128, 29], [825, 903, 861, 156, 328], [826, 314, 64, 775], [829, 472, 399, 236, 812, 416], [830, 607, 711, 165, 127, 560], [831, 9, 842, 438], [855, 84, 430, 463, 652, 69], [859, 494, 276, 79, 381, 332], [867, 737, 880, 133, 676, 809], [877, 547, 316, 621, 491, 725], [879, 80, 553, 196, 437, 894], [898, 67, 708, 241, 715], [900, 385, 392], [902, 108, 482, 723, 593, 419], [904, 448]]

Dog_1 502
#sequences 95 %segments that was sequenced 0.998007968127 longest sequence 6
[[3, 493, 475, 178], [6, 73, 384, 444, 216, 155], [10, 177, 271, 255, 60], [13, 231, 159], [14, 317, 276, 15, 123], [16, 453, 425, 285, 191], [21, 32, 236, 199, 218], [22, 291, 381, 92, 268, 86], [23, 359, 409, 491], [28, 124, 88, 215, 197, 242], [29, 398, 12, 224, 31, 0], [33, 390, 383, 309, 119, 396], [40, 256, 262, 361, 428], [41, 357, 339, 153, 174, 364], [46, 464, 135, 89, 264, 368], [47, 211, 117, 479, 185, 246], [53, 126, 401, 250, 9, 172], [68, 406, 282, 161, 468, 427], [71, 382, 192, 190, 463, 328], [72, 323, 351, 496, 385, 275], [74, 290], [76, 127, 146, 106, 389, 349], [77, 204, 347, 214, 380, 462], [78, 375, 227, 109, 202, 405], [79, 48, 244, 114, 415, 273], [81, 418, 208, 239, 67, 345], [93, 321, 186, 473, 210, 298], [95, 452, 407, 445, 115, 358], [103, 171, 252, 175, 412, 154], [105, 205, 187, 207, 82, 472], [110, 85, 7, 258], [128, 373, 477, 356, 50, 500], [133, 288, 260, 490, 331, 424], [137, 162, 287, 129, 344], [139, 18, 313, 65], [149, 116, 97, 107, 219, 284], [150, 148, 112, 253, 283], [152, 179, 194, 377, 99, 87], [158, 140, 411, 143, 352, 474], [160, 434, 267, 293, 442, 221], [169, 302, 343, 232], [184, 173, 485, 96, 156, 480], [203, 238, 492, 58, 371, 222], [225, 397, 228, 448, 35, 318], [226, 66, 134, 476, 44, 108], [234, 329, 438, 147, 136], [235, 37, 84, 336, 295], [237, 426, 118], [247, 223, 249, 102, 62, 42], [251, 266, 483, 125, 61], [257, 122, 326, 432, 332], [261, 248, 416, 132, 70, 495], [265, 144], [269, 327, 270, 52, 8, 98], [278, 36, 198, 387, 69, 11], [289, 470, 330], [294, 341, 176, 245, 142, 292], [299, 478, 90, 353, 272], [301, 366, 342, 30], [303, 19], [304, 230, 410, 471, 286, 201], [306, 421, 1], [311, 212, 56, 24, 138], [315, 163, 131, 80], [320, 57, 413, 75, 196, 27], [340, 281, 229, 333, 217, 233], [348, 213, 360, 441, 4, 220], [354, 181, 431, 338, 200], [355, 83, 39, 402], [365, 277, 322, 130, 2, 378], [367, 484, 180, 335, 296, 486], [369, 439, 488, 362, 312, 422], [370, 316, 300, 120, 141, 400], [374, 408, 447, 104, 297, 429], [379, 443, 449, 38, 399, 417], [388, 113, 280, 437, 168], [391, 94, 337], [393, 145, 350, 319, 324, 334], [414, 459, 45, 363, 55], [419, 195, 5, 420], [423, 465, 151, 386, 64], [450, 395, 455, 34, 307, 376], [451, 17, 454, 314, 254, 433], [457, 460, 310, 456, 372], [461, 193, 91, 167, 188, 259], [466, 209, 263, 346, 26, 170], [467, 243, 101, 54, 240, 394], [469, 51, 497, 43, 20], [481, 489, 430, 308, 121, 404], [482, 241, 440, 49, 111, 59], [487, 206, 25, 157, 435, 166], [494, 63, 189, 458], [498, 305, 403, 274, 100], [499, 446, 182, 392, 164], [501, 325, 436, 165, 279]]

Dog_4 990
#sequences 170 %segments that was sequenced 0.994949494949 longest sequence 6
[[0, 399, 297, 940, 455, 284], [1, 260, 101, 478, 415, 236], [7, 759, 635, 264, 216, 848], [12, 184, 479, 829, 139, 724], [14, 581, 429, 714, 660, 582], [15, 24, 960, 519, 931, 965], [25, 315, 527, 323, 595, 133], [31, 328, 740, 637, 167, 942], [33, 631, 493, 561, 421, 74], [34, 38, 978, 678], [36, 964, 75, 141, 790, 775], [37, 340, 532, 632, 883, 518], [40, 387, 810, 690, 930, 679], [44, 875, 116, 405, 118, 565], [46, 402, 971, 446, 506, 901], [55, 170, 319, 767, 925, 252], [56, 431, 566, 815, 124, 296], [60, 127, 847, 634, 778, 285], [63, 528, 362, 359, 346, 332], [67, 937, 987, 148, 948, 72], [85, 959, 897, 388, 962, 580], [87, 584, 902, 99, 289, 50], [92, 643, 212, 240, 335, 501], [103, 737, 721, 709, 131, 440], [112, 827, 155, 106, 890, 244], [120, 122, 281, 727, 406, 820], [123, 383, 809, 970, 82, 333], [125, 647, 374, 624, 312, 730], [128, 134, 437], [136, 853, 417, 230, 494, 966], [138, 235, 613, 208, 349, 413], [140, 499, 604, 696, 379, 720], [142, 341, 860, 409, 655, 791], [159, 628, 261, 921, 375, 586], [163, 817, 648, 540, 39, 626], [172, 680, 295, 68, 214, 194], [175, 976, 16, 688, 924, 262], [176, 743, 196, 972, 702, 173], [177, 795, 520, 713, 439, 433], [197, 545, 531, 17, 756, 674], [204, 749, 876, 471, 114, 866], [211, 638, 376, 102, 373, 259], [220, 502, 770, 908, 985, 828], [225, 813, 344, 907, 465, 652], [242, 881, 207, 878, 253, 956], [256, 516, 473, 983, 597, 485], [263, 288, 186, 22, 444, 859], [276, 291, 313, 662, 272, 305], [279, 222, 327, 448, 729, 504], [282, 957, 497, 269, 656, 426], [294, 156, 357, 733, 698, 891], [299, 152, 183, 456, 861, 880], [300, 239, 926, 851, 392, 474], [303, 11, 10, 191, 89, 364], [309, 361, 187, 585, 728, 154], [310, 377, 783, 830, 672, 412], [314, 974, 542, 741, 832, 164], [320, 507, 824, 895, 715, 378], [329, 685, 523, 673, 686, 543], [330, 98, 144, 747, 988, 554], [338, 768, 703, 927, 511, 893], [343, 951, 943, 203, 760, 608], [350, 766, 835, 807, 837, 365], [353, 107, 664, 546, 18, 571], [372, 165, 923, 961, 66, 464], [389, 355, 337, 805, 583, 569], [390, 578, 599, 706, 910, 742], [394, 51, 564, 739, 316, 487], [395, 110, 419, 606, 977, 217], [396, 984, 257, 658, 954, 663], [403, 162, 659, 754, 798, 53], [404, 846, 58, 318, 229, 731], [411, 401, 286, 514, 398, 657], [424, 900, 251, 618, 232, 691], [425, 722, 258, 93, 80, 271], [430, 894, 711, 23, 45, 462], [434, 27, 748, 787, 290, 933], [436, 734, 842, 348, 562, 416], [441, 619, 121, 573, 324, 544], [443, 794, 843, 77, 234, 808], [451, 449, 935, 873, 19, 592], [458, 687, 950, 629, 917, 854], [461, 906, 489, 322, 292, 552], [469, 796, 146, 226, 147, 200], [472, 958, 834, 761, 968, 109], [481, 877, 826, 973, 969, 247], [490, 919, 354, 90, 625, 548], [492, 188, 2, 505, 306, 3], [496, 273, 932, 73, 418], [498, 755, 718, 453, 168, 223], [500, 301, 233, 231, 459, 612], [509, 423, 13, 321, 132, 889], [513, 630, 366, 454, 428, 438], [515, 547, 605, 553, 52, 683], [524, 850, 670, 797, 525, 945], [535, 278, 475, 677, 205, 601], [536, 836, 869, 115, 503, 166], [537, 248, 572, 905, 886, 477], [538, 522, 182, 29], [551, 28, 8, 589, 725, 801], [555, 920, 967, 654, 468, 470], [558, 484, 266, 979, 181, 852], [559, 903, 675, 936, 97, 145], [570, 385, 726, 526, 250], [575, 151, 71, 574, 283, 717], [577, 81, 653, 550, 704, 482], [588, 530, 407, 339, 457, 363], [596, 694, 735, 35, 466, 275], [598, 447, 645], [602, 265, 255, 224, 47, 195], [610, 667, 594, 342, 360, 169], [614, 137, 135, 311, 157, 111], [615, 369, 228, 892, 784, 368], [616, 517, 408, 215, 849, 280], [620, 178, 567, 161, 896, 76], [623, 975, 627, 593, 331, 947], [636, 867, 946, 707, 84, 367], [641, 65, 888, 644, 757, 336], [642, 96, 665, 414, 270, 317], [651, 668, 736, 833, 510, 149], [661, 61], [684, 914, 79, 126, 249, 119], [692, 83, 245, 695, 746, 541], [697, 871, 100], [699, 622, 666, 307, 753, 862], [710, 386, 708, 911, 856, 792], [712, 671, 210], [719, 928, 435, 922, 237, 804], [723, 206, 639, 752, 776, 20], [738, 48, 70, 483, 840, 986], [745, 351, 391, 4, 774], [750, 982, 352, 744, 108, 590], [751, 287, 274, 5, 370, 432], [762, 865, 870, 302, 189, 855], [763, 193, 885, 676, 649, 32], [764, 86, 789, 213, 872, 913], [765, 646, 650, 158, 427, 371], [771, 944, 909, 380, 160, 788], [777, 912, 486, 825, 129, 915], [779, 732, 64, 603, 591, 803], [786, 533, 410, 939, 617, 839], [806, 780, 549, 609, 293, 452], [811, 938, 347, 450, 221, 521], [812, 326, 219, 989, 62, 420], [814, 802, 793, 700, 772, 201], [816, 400, 246, 277, 192, 334], [818, 304, 397, 874, 693, 238], [821, 6, 104, 105, 382], [822, 781, 705, 467, 701, 95], [823, 480, 180, 199, 57, 841], [831, 463, 539, 952, 785, 91], [838, 576, 579, 916, 568, 9], [844, 393, 113, 78, 356, 782], [845, 981, 254, 884, 381, 198], [858, 202], [863, 460, 21, 799, 179, 495], [864, 512, 43, 882, 267, 769], [868, 185, 30, 560, 209, 143], [879, 227, 358, 773, 669, 689], [887, 819, 563, 26, 243, 325], [898, 445, 633], [899, 556, 174, 508, 488, 529], [904, 963, 117, 587, 600, 69], [918, 268, 442, 934, 171, 241], [929, 476, 534, 218, 94, 59], [941, 758, 345, 491, 800, 681], [949, 150, 153, 88], [953, 42, 298, 857, 607, 41], [955, 621, 611, 190, 682, 49], [980, 422, 716, 130]]

Dog_5 191
#sequences 33 %segments that was sequenced 0.931937172775 longest sequence 6
[[1, 131, 105, 139, 147, 154], [3, 59, 30, 121, 12, 184], [9, 156], [11, 100, 79, 119, 55, 2], [17, 155, 28, 125, 98, 185], [29, 122, 71, 35, 22, 112], [31, 174], [34, 49, 27, 25, 165, 110], [41, 21], [47, 45], [50, 179, 187, 57, 133, 126], [56, 170, 13, 58, 144, 14], [60, 80, 96, 15, 152, 5], [65, 111, 8, 38, 84, 107], [67, 108, 52, 90, 159], [86, 82, 157, 24, 32, 74], [87, 183, 77, 113, 114, 135], [94, 161, 171, 46, 176, 39], [99, 43, 129, 75, 178, 88], [104, 162, 118, 66, 97, 76], [124, 93, 148, 136, 169, 132], [128, 48, 42, 160, 0, 73], [134, 85, 78, 158, 102, 189], [138, 92, 109, 44, 10, 7], [141, 95, 91, 115, 117, 163], [145, 130, 103, 123, 120, 180], [146, 149, 6, 36, 140], [153, 40, 150, 164, 142, 101], [166, 182, 33, 23, 190, 106], [167, 127, 69, 188], [168, 173, 151, 18, 63, 137], [175, 81, 172, 186, 83, 20], [177, 26, 143, 68, 62, 51]]

Patient_2 150
#sequences 28 %segments that was sequenced 0.96 longest sequence 6
[[1, 84, 105, 134, 3, 138], [4, 0], [6, 145, 106, 33], [8, 63, 122], [12, 98, 50, 144, 85, 133], [21, 56, 16, 43, 27, 112], [25, 76], [29, 71, 75, 58, 10], [30, 49, 135, 57, 28, 24], [37, 14, 11, 36, 109, 136], [39, 80, 67, 86, 96, 119], [42, 47, 140, 72, 93, 38], [46, 130, 34, 40, 2, 22], [51, 44, 129, 115, 55], [60, 141, 7, 64, 95], [62, 31, 121, 137, 18], [73, 53, 20], [78, 91, 26, 100, 125, 61], [90, 48, 35], [92, 23, 68, 126, 101, 32], [94, 41, 79, 9, 17, 52], [108, 124, 77, 139, 45, 107], [110, 65, 69, 87, 88], [113, 15, 123, 5, 103, 66], [114, 81, 82, 131, 97, 59], [127, 102, 99, 128, 118, 149], [142, 147, 146, 74, 13, 54], [148, 19, 89, 117, 120, 116]]

Patient_1 195
#sequences 49 %segments that was sequenced 0.938461538462 longest sequence 6
[[1, 0], [3, 52], [8, 14, 27, 95, 5, 18], [9, 11, 40, 53, 28], [16, 30, 22, 74, 71, 81], [17, 63, 174, 125], [21, 64], [24, 113, 191, 137, 47, 99], [31, 15, 20], [33, 72, 80], [37, 48, 42], [46, 32, 44, 65, 123, 139], [49, 23, 87, 36, 61], [54, 19, 13], [55, 127], [57, 56, 66, 68, 51], [58, 128, 185], [59, 78, 77, 35, 109, 157], [60, 92, 101, 83, 143, 178], [62, 38], [67, 141, 182, 135, 129, 84], [73, 102, 96], [75, 70], [85, 171, 119, 94, 43, 41], [86, 45], [89, 110, 146, 170, 118], [90, 147], [93, 153], [107, 115], [108, 88], [116, 155, 98], [122, 91, 160], [130, 114, 117, 172], [132, 165], [133, 131, 151, 103], [138, 158, 97, 179, 112], [144, 120], [149, 111], [150, 193, 189, 181, 187, 177], [152, 190, 145, 162, 100], [156, 183, 104, 136, 167], [161, 173, 124, 186, 106], [166, 148, 159], [168, 164], [169, 134, 192, 121, 154, 142], [176, 163], [180, 50, 25, 29], [184, 82, 79, 105], [194, 140, 39, 76]]



In [10]:

    
out_scores = out_scores/out_scores.max()



In [11]:

    
out_scores.to_csv(FNAME_OUT, header=True)



In [12]:

    
!paste {FNAME_IN} {FNAME_OUT} | head









    



clip,preictal	clip,preictal
Dog_1_test_segment_0001.mat,0.36422108564142136	Dog_1_test_segment_0001.mat,0.626027118093895
Dog_1_test_segment_0002.mat,0.15354376708954037	Dog_1_test_segment_0002.mat,0.3140241563918862
Dog_1_test_segment_0003.mat,0.2311784685067122	Dog_1_test_segment_0003.mat,0.4609277951619965
Dog_1_test_segment_0004.mat,0.23395507538236127	Dog_1_test_segment_0004.mat,0.27315678195462245
Dog_1_test_segment_0005.mat,0.20919729376936513	Dog_1_test_segment_0005.mat,0.2775612482496556
Dog_1_test_segment_0006.mat,0.27385060377807313	Dog_1_test_segment_0006.mat,0.33628767427806566
Dog_1_test_segment_0007.mat,0.10813703226308491	Dog_1_test_segment_0007.mat,0.4674859607025775
Dog_1_test_segment_0008.mat,0.30873829630672683	Dog_1_test_segment_0008.mat,0.35146595710209094
Dog_1_test_segment_0009.mat,0.1967252194234564	Dog_1_test_segment_0009.mat,0.182577652460813



In [13]:

    
out_scores['Dog_2_test_segment_0004.mat']









    Out[13]:





0.2503607269094909



In [14]:

    
df = pd.DataFrame()
df['in'] = pd.read_csv(FNAME_IN, index_col='clip', squeeze=True) #64
df['out'] = pd.read_csv(FNAME_OUT, index_col='clip', squeeze=True)
df['best'] = pd.read_csv('../submissions/141116-predict.8.csv', index_col='clip', squeeze=True)



In [15]:

    
pd.scatter_matrix(df,figsize=(6, 6), diagonal='kde');



In [15]: