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/141106-predict.2.csv'

updated probability file



In [3]:

    
FNAME_OUT = '../submissions/141112-predict.1.csv'



In [4]:

    
!head {FNAME_IN}









    



clip,preictal
Dog_1_test_segment_0001.mat,0.4102004206021832
Dog_1_test_segment_0002.mat,0.17851398941000557
Dog_1_test_segment_0003.mat,0.20815645275767747
Dog_1_test_segment_0004.mat,0.21869018865655465
Dog_1_test_segment_0005.mat,0.21518199742988148
Dog_1_test_segment_0006.mat,0.26476291723231554
Dog_1_test_segment_0007.mat,0.15555049205016958
Dog_1_test_segment_0008.mat,0.31624411544152164
Dog_1_test_segment_0009.mat,0.14910462670287136



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 0x11370e390>



In [7]:

    
scores['Dog_2_test_segment_0004.mat']









    Out[7]:





0.29629604410156579



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,0.4102004206021832	Dog_1_test_segment_0001.mat,0.5894641037445011
Dog_1_test_segment_0002.mat,0.17851398941000557	Dog_1_test_segment_0002.mat,0.33580068408866953
Dog_1_test_segment_0003.mat,0.20815645275767747	Dog_1_test_segment_0003.mat,0.3972657748278185
Dog_1_test_segment_0004.mat,0.21869018865655465	Dog_1_test_segment_0004.mat,0.29975872601696707
Dog_1_test_segment_0005.mat,0.21518199742988148	Dog_1_test_segment_0005.mat,0.286501184644189
Dog_1_test_segment_0006.mat,0.26476291723231554	Dog_1_test_segment_0006.mat,0.3443907725164791
Dog_1_test_segment_0007.mat,0.15555049205016958	Dog_1_test_segment_0007.mat,0.3302145475028642
Dog_1_test_segment_0008.mat,0.31624411544152164	Dog_1_test_segment_0008.mat,0.3977182063113663
Dog_1_test_segment_0009.mat,0.14910462670287136	Dog_1_test_segment_0009.mat,0.16751649273067404



In [13]:

    
out_scores['Dog_2_test_segment_0004.mat']









    Out[13]:





0.35866521637822468



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/141106-predict.3.csv', index_col='clip', squeeze=True)



In [15]:

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



In [15]: