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 [16]:

    
%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 [17]:

    
FNAME_IN = '../submissions/141001-predict.4.csv'

updated probability file



In [18]:

    
FNAME_OUT = '../submissions/141103-predict.16.csv'



In [19]:

    
!head {FNAME_IN}









    



clip,preictal
Dog_1_test_segment_0001.mat,0.453492852917729
Dog_1_test_segment_0002.mat,0.18780412648274436
Dog_1_test_segment_0003.mat,0.2349091166108111
Dog_1_test_segment_0004.mat,0.24158823967531529
Dog_1_test_segment_0005.mat,0.21239788897808465
Dog_1_test_segment_0006.mat,0.2755428892524271
Dog_1_test_segment_0007.mat,0.1615542284053599
Dog_1_test_segment_0008.mat,0.3077380630360851
Dog_1_test_segment_0009.mat,0.15654659718030256



In [20]:

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



In [21]:

    
scores.hist()









    Out[21]:





<matplotlib.axes._subplots.AxesSubplot at 0x11a5fcb90>



In [22]:

    
scores['Dog_2_test_segment_0004.mat']









    Out[22]:





0.27785087175573631



In [23]:

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









    Out[23]:





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



In [24]:

    
W=0.25
T=0.1
D=-0.5

for target in targets:
    print
    d = np.load('/Users/udi/Downloads/kaggle/seizure-prediction/distance/%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 30 %segments that was sequenced 0.94 longest sequence 6
[[4, 99, 78, 87, 0, 107], [6, 34, 129, 100, 10], [15, 52, 80], [28, 81, 82, 103, 25, 139], [29, 31], [37, 16, 54], [38, 127, 70, 110, 11], [42, 79, 66], [47, 146, 76, 59], [48, 149, 108, 67, 58, 35], [51, 105, 140, 57], [53, 63, 71, 120, 125, 40], [60, 96], [65, 106, 21, 56, 14, 1], [69, 91, 75], [77, 44, 119, 20, 55, 12], [84, 93, 45, 111, 94, 122], [85, 36], [92, 33, 86, 97], [95, 27, 141, 115, 89], [102, 24, 124, 22, 118, 137], [109, 43, 136, 138, 64, 18], [112, 98, 114, 143, 83, 147], [113, 126, 30, 101, 39, 7], [117, 62, 121, 49, 88], [123, 5, 61, 9, 116, 41], [128, 131, 72, 74, 13], [133, 2, 8, 17, 3, 68], [142, 32, 26], [148, 130, 145, 50, 144]]

Patient_1 195
#sequences 41 %segments that was sequenced 0.979487179487 longest sequence 6
[[1, 104, 158, 50, 166, 28], [3, 54, 27, 70, 161], [22, 170, 6, 57, 0], [24, 142], [26, 44, 65], [30, 157, 92, 21, 112], [32, 193, 189, 113, 109], [36, 61, 63, 84, 94, 165], [40, 42, 194, 64, 174], [49, 129, 37, 48, 23, 154], [56, 66, 53, 87], [58, 8, 143], [59, 139, 126, 88, 177], [60, 132, 14, 5, 71, 120], [67, 47, 149, 111], [73, 159, 156, 183, 160], [82, 121, 4, 12, 173, 7], [85, 16], [86, 15, 68, 51], [89, 110, 117, 13, 179, 19], [90, 133, 167], [97, 72, 93, 125], [99, 31, 136, 128, 185, 2], [102, 191, 137, 20, 145, 162], [106, 180, 105, 164], [107, 11, 41, 122, 108, 130], [116, 43, 148, 46, 35, 79], [119, 192, 181, 184, 91, 114], [123, 18, 176, 115], [124, 127, 131, 55, 39], [135, 186, 140, 101, 155, 80], [138, 81, 62], [141, 182, 10, 118, 25, 83], [144, 163], [146, 38, 153], [150, 45, 98, 17], [152, 29, 96, 76, 171, 75], [175, 169, 134, 168, 34], [178, 33], [187, 190, 78, 77, 147, 172], [188, 9, 151, 74, 69]]



In [25]:

    
out_scores = out_scores/out_scores.max()



In [26]:

    
out_scores.to_csv(FNAME_OUT, header=True)



In [27]:

    
!paste {FNAME_IN} {FNAME_OUT} | head









    



clip,preictal	clip,preictal
Dog_1_test_segment_0001.mat,0.453492852917729	Dog_1_test_segment_0001.mat,0.6320223455017411
Dog_1_test_segment_0002.mat,0.18780412648274436	Dog_1_test_segment_0002.mat,0.293134900834414
Dog_1_test_segment_0003.mat,0.2349091166108111	Dog_1_test_segment_0003.mat,0.35879033123773435
Dog_1_test_segment_0004.mat,0.24158823967531529	Dog_1_test_segment_0004.mat,0.3060291634428786
Dog_1_test_segment_0005.mat,0.21239788897808465	Dog_1_test_segment_0005.mat,0.24988527896795
Dog_1_test_segment_0006.mat,0.2755428892524271	Dog_1_test_segment_0006.mat,0.3789214364035274
Dog_1_test_segment_0007.mat,0.1615542284053599	Dog_1_test_segment_0007.mat,0.3118441623674342
Dog_1_test_segment_0008.mat,0.3077380630360851	Dog_1_test_segment_0008.mat,0.43377328087580735
Dog_1_test_segment_0009.mat,0.15654659718030256	Dog_1_test_segment_0009.mat,0.17017281221414676



In [28]:

    
out_scores['Dog_2_test_segment_0004.mat']









    Out[28]:





0.32674201164875716



In [29]:

    
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/141103-predict.1.csv', index_col='clip', squeeze=True)



In [30]:

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