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/141101-predict.5.csv'

updated probability file



In [3]:

    
FNAME_OUT = '../submissions/141101-predict.6.csv'



In [4]:

    
!head {FNAME_IN}









    



clip,preictal
Dog_1_test_segment_0001.mat,0.5161207276801631
Dog_1_test_segment_0002.mat,0.23896577961550508
Dog_1_test_segment_0003.mat,0.27128583613201696
Dog_1_test_segment_0004.mat,0.2684529016593175
Dog_1_test_segment_0005.mat,0.1620751884032815
Dog_1_test_segment_0006.mat,0.39919517273938215
Dog_1_test_segment_0007.mat,0.2602096865906257
Dog_1_test_segment_0008.mat,0.3606896830383452
Dog_1_test_segment_0009.mat,0.2415103948655313



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 0x11339c210>



In [7]:

    
scores['Dog_2_test_segment_0004.mat']









    Out[7]:





0.39420051309527732



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]:

    
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 > -0.7: # 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:
        p0 = 1.
        q0 = 1.
        p1 =0.
        p2 = 0.
        p3 = 1.
        p4 = 0.
        p4sum = 0.
        p5 = 0.
        p6 = 0.
        p7 = 0.
        U = 0.2 # chance of seizure detection event in a preictal segment
        V = 1-U
        for s in sequence:
            P = scores['%s_test_segment_%04d.mat'%(target,s+1)]
            Q = 1.-P
            p0 *= P
            q0 *= Q
            p1 += P
            p4 += (s+1)*P
            p4sum += (s+1)
            if P > p2:
                p2 = P
            p3 *= (U * P/Q + (1-U))
            p5 += P*P
            p6 += P*P*P
            p7 += P*P*P*P
        p0 = p0 / (p0+q0)
        p1 = p1 / len(sequence)
        p2 = p2
        p3 = 1./(1+1./p3)
        p4 /= p4sum
        p5 = np.sqrt(p5/len(sequence))
        p7 = np.sqrt(np.sqrt(p7))
#         print p0, p1, p2, p3
        sequences_prb.append(p2)
    # 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):
            #w = 0.6 * ((i+1.) * n )
            w=0.2
            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 201 %segments that was sequenced 0.975 longest sequence 6
[[11, 336, 161, 793, 310], [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], [92, 306, 455, 101, 994, 331], [93, 598, 695, 699, 638, 33], [94, 973], [97, 907, 142], [98, 223], [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], [141, 620, 573, 51, 930], [146, 634, 281, 498, 535, 674], [148, 762], [158, 198, 658, 652, 566, 113], [166, 105, 371, 197, 338], [169, 257, 73, 380], [173, 954, 761], [176, 662, 897, 117, 987, 539], [193, 38], [205, 322, 233, 292], [209, 509], [214, 419, 357, 221, 764, 252], [225, 384, 582, 251, 289, 616], [230, 402, 121, 267, 347, 46], [236, 783, 291], [237, 857, 515, 969, 266, 532], [238, 578, 112, 400, 139], [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], [319, 407, 819], [335, 467, 911, 103, 745, 561], [348, 240, 855, 836, 806, 503], [358, 164, 157, 731, 309, 723], [360, 413, 491], [363, 711, 328, 586, 991], [370, 608, 909], [374, 130, 10, 572, 60, 495], [376, 215, 789, 948, 327, 438], [394, 703, 19, 330, 269], [399, 259], [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], [425, 643, 647], [432, 307, 445, 474, 471], [433, 200, 271, 617, 568, 468], [434, 720, 521, 993], [436, 840, 856, 219, 536], [437, 245, 546, 514, 657, 323], [440, 839], [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], [475, 787], [484, 392], [486, 885, 805, 27, 378], [494, 147, 54, 850, 560, 929], [496, 992], [497, 900, 844, 738, 246, 625], [501, 278, 686, 718, 649, 726], [512, 401, 239, 122, 183, 967], [518, 659], [522, 809, 680], [528, 189, 460, 981, 155], [530, 778], [534, 477], [537, 995, 77, 442], [547, 366], [548, 6, 386], [551, 629, 31, 614, 361, 192], [553, 64, 435, 818, 165], [554, 153], [563, 224, 162, 213, 5], [579, 144, 852, 986], [584, 549, 916, 285], [585, 261, 43, 754, 441, 687], [591, 903, 187, 48, 552, 542], [601, 262, 653, 688, 747], [603, 963, 757, 0], [610, 621], [628, 500, 997, 811, 485], [632, 623, 691, 908, 741, 156], [633, 273, 807, 150], [642, 126], [644, 375, 190, 427, 481, 120], [646, 195], [648, 453, 431, 824, 82, 132], [650, 983], [654, 186, 487, 395, 272], [655, 631, 125], [656, 627], [664, 232, 792], [666, 177, 127, 998, 170, 32], [667, 950, 443, 354], [671, 694, 67, 254], [678, 635, 208, 286, 575, 682], [681, 41, 167, 896, 780], [689, 492, 975, 364, 211, 96], [693, 705, 590, 15, 571, 599], [697, 339, 846, 937], [706, 692, 556, 607], [708, 3, 226, 140, 838, 301], [709, 290, 849, 88, 947, 827], [710, 624], [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], [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], [759, 84, 235, 118], [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], [821, 429, 887], [822, 40, 611, 210], [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], [854, 934, 231, 704], [861, 989, 513, 883, 587, 154], [867, 7, 595, 791, 773], [869, 264, 317, 939, 645, 478], [870, 212], [873, 564, 959], [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], [912, 151, 825, 543], [913, 557], [915, 134], [918, 966, 39, 463, 960, 613], [919, 52, 737, 268, 728], [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], [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], [985, 29, 801, 66, 938, 17], [996, 882, 782, 470]]

Dog_3 907
#sequences 178 %segments that was sequenced 0.943770672547 longest sequence 6
[[3, 81, 539, 270, 702, 582], [13, 226, 111, 33, 645], [14, 116, 537, 654], [22, 211, 854, 889, 176, 486], [32, 396, 59, 569, 594], [36, 714, 218, 630], [44, 456, 906, 820, 521, 538], [45, 453, 480, 215, 688, 150], [48, 728, 665, 363, 802, 762], [56, 302, 766], [63, 432, 669, 664, 739, 483], [67, 708, 241, 715], [70, 368, 672, 600, 96], [76, 450, 704, 120], [78, 513, 187], [83, 170], [85, 497, 308, 718, 5, 751], [87, 847, 661, 720, 519, 327], [88, 16, 238, 581], [95, 19, 818, 458], [97, 49, 850], [101, 230], [107, 851, 246, 683, 596, 485], [118, 72, 635, 481, 874, 89], [121, 34], [131, 599, 39, 434, 367, 52], [135, 603, 852, 873, 780, 243], [137, 656, 564, 696], [138, 348, 168, 359], [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], [169, 779, 42, 588, 53, 275], [172, 355], [174, 82, 283, 188, 310, 811], [179, 303, 724, 489], [184, 35, 445, 580, 366, 51], [192, 631, 551, 12, 345, 786], [193, 145, 837], [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], [235, 625, 540, 252, 619, 735], [244, 161], [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], [269, 541, 807, 864, 790], [271, 298, 38, 428, 388, 426], [273, 508, 721, 425, 717, 379], [286, 555, 680, 525, 41], [289, 608, 689, 148, 447, 272], [293, 733, 198, 297, 418, 319], [300, 612, 305, 23], [306, 897, 361, 265], [313, 499, 18, 667, 755, 758], [329, 572, 217, 268, 203, 511], [331, 191, 260, 501, 369, 552], [338, 205, 559, 406, 333, 804], [339, 20, 143, 459, 846, 311], [343, 896, 741, 402, 640, 216], [346, 576, 587, 662], [354, 413], [357, 707, 636, 583], [358, 92, 431, 890, 105, 813], [362, 841, 798, 561, 810, 189], [365, 532], [371, 376, 185, 250, 886, 77], [373, 606, 208, 787, 782, 71], [374, 115, 341, 639, 793], [378, 644, 797], [380, 417], [386, 557, 765, 274, 647, 287], [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], [454, 119], [457, 106], [461, 291], [462, 370, 527], [466, 424, 139, 194, 549], [473, 254], [474, 822], [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], [517, 206, 114, 452, 794, 439], [522, 598, 37, 618, 54], [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], [558, 240], [567, 455, 643, 515, 141, 566], [579, 113], [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], [634, 219, 295, 335, 446, 181], [641, 349, 451, 322, 247], [653, 7, 420, 173, 73, 865], [657, 467, 66], [658, 878, 475, 404, 578, 278], [666, 153, 605], [668, 700, 502, 312], [670, 323], [673, 465, 109, 242, 838, 849], [685, 213, 175, 675, 674, 377], [691, 875, 330, 405, 124], [694, 528], [705, 692, 533, 4, 734, 292], [706, 570, 470], [719, 144, 740, 746, 884, 90], [722, 713, 86, 155], [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], [761, 523], [771, 768, 232, 225], [778, 60, 281, 554, 801, 221], [781, 507, 493, 763, 1, 573], [784, 441, 249], [785, 356, 726], [799, 862, 806, 845, 646], [800, 398, 317, 320, 699], [803, 817, 687, 449, 261, 195], [814, 47, 870, 609, 58, 887], [816, 792, 783, 435, 61, 352], [819, 843, 671, 239, 128, 29], [823, 869, 592], [825, 903], [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], [858, 360, 65], [859, 494, 276, 79, 381, 332], [860, 679, 17, 684], [861, 156], [867, 737, 880, 133, 676, 809], [876, 375, 344, 94, 132], [877, 547, 316, 621, 491, 725], [879, 80, 553, 196, 437, 894], [891, 129, 750, 529], [902, 108, 482, 723, 593, 419], [904, 448], [905, 214]]

Dog_1 502
#sequences 101 %segments that was sequenced 0.972111553785 longest sequence 6
[[3, 493, 475, 178], [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], [28, 124, 88, 215, 197, 242], [29, 398, 12, 224, 31, 0], [33, 390, 383, 309, 119], [40, 256, 262, 361, 428], [41, 357, 339, 153, 174, 364], [47, 211, 117, 479, 185, 246], [53, 126], [56, 24, 138], [68, 406, 282, 161, 468, 427], [71, 382, 192, 190, 463, 328], [72, 323, 351, 496, 385, 275], [73, 384, 444], [74, 290], [76, 127, 146, 106, 389, 349], [77, 204, 347, 214, 380, 462], [78, 375, 227, 109, 202, 405], [79, 48, 244, 114], [93, 321, 186, 473, 210, 298], [95, 452, 407, 445, 115, 358], [103, 171, 252, 175, 412, 154], [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], [205, 187, 207, 82, 472], [216, 155], [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], [238, 492, 58, 371, 222], [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], [274, 100], [278, 36, 198, 387, 69, 11], [289, 470, 330], [294, 341, 176, 245, 142, 292], [299, 478, 90, 353], [301, 366, 342, 30], [303, 19], [304, 230, 410, 471, 286, 201], [306, 421, 1], [308, 121, 404], [311, 212], [315, 163, 131, 80], [320, 57, 413, 75, 196, 27], [325, 436, 165, 279], [340, 281, 229, 333, 217, 233], [348, 213, 360, 441, 4, 220], [354, 181, 431, 338, 200], [355, 83, 39], [359, 409, 491], [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], [401, 250, 9, 172], [414, 459, 45, 363], [415, 273], [418, 208, 239, 67, 345], [419, 195, 5, 420], [423, 465, 151, 386], [450, 395, 455, 34, 307, 376], [451, 17, 454, 314, 254, 433], [457, 460, 310, 456, 372], [461, 193, 91, 167, 188, 259], [464, 135, 89, 264, 368], [466, 209, 263, 346, 26, 170], [467, 243, 101, 54, 240, 394], [469, 51, 497, 43, 20], [481, 489, 430], [482, 241, 440, 49, 111, 59], [487, 206, 25, 157, 435, 166], [494, 63, 189, 458], [498, 305, 403], [499, 446, 182, 392, 164]]

Dog_4 990
#sequences 172 %segments that was sequenced 0.987878787879 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], [36, 964, 75, 141, 790, 775], [37, 340, 532, 632, 883, 518], [38, 978, 678], [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], [93, 80], [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], [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], [308, 54], [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], [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], [445, 633], [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], [522, 182, 29], [524, 850, 670, 797, 525, 945], [535, 278, 475, 677, 205, 601], [536, 836, 869, 115, 503, 166], [537, 248, 572, 905, 886, 477], [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], [692, 83, 245, 695, 746, 541], [697, 871, 100], [699, 622, 666, 307, 753, 862], [710, 386, 708, 911, 856, 792], [712, 671], [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], [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], [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], [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 36 %segments that was sequenced 0.905759162304 longest sequence 6
[[1, 131, 105, 139, 147, 154], [3, 59, 30, 121], [11, 100, 79, 119, 55, 2], [12, 184], [17, 155, 28, 125, 98, 185], [18, 63, 137], [29, 122], [34, 49, 27, 25, 165], [38, 84, 107], [41, 21], [50, 179, 187, 57, 133, 126], [56, 170, 13, 58, 144, 14], [60, 80, 96, 15, 152, 5], [65, 111], [67, 108, 52, 90, 159], [70, 37, 64], [71, 35, 22, 112], [77, 113, 114, 135], [86, 82, 157, 24, 32, 74], [87, 183], [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], [175, 81, 172, 186, 83, 20], [177, 26, 143, 68, 62, 51]]

Patient_2 150
#sequences 34 %segments that was sequenced 0.88 longest sequence 6
[[3, 68], [4, 99, 78, 87, 0], [5, 61, 9], [6, 34, 129, 100, 10], [15, 52, 80], [28, 81, 82, 103, 25, 139], [29, 31], [37, 16], [42, 79, 66], [44, 119, 20], [48, 149, 108, 67, 58, 35], [51, 105, 140, 57], [53, 63, 71, 120, 125, 40], [55, 12], [60, 96], [65, 106, 21, 56, 14, 1], [69, 91, 75], [84, 93, 45], [85, 36], [92, 33, 86, 97], [95, 27, 141, 115, 89], [102, 24], [109, 43, 136, 138, 64, 18], [112, 98, 114, 143, 83, 147], [113, 126, 30, 101, 39, 7], [116, 41], [117, 62, 121, 49, 88], [124, 22, 118, 137], [127, 70, 110, 11], [128, 131, 72, 74, 13], [133, 2, 8, 17], [142, 32, 26], [146, 76, 59], [148, 130, 145, 50, 144]]

Patient_1 195
#sequences 44 %segments that was sequenced 0.738461538462 longest sequence 6
[[1, 104, 158], [3, 54, 27], [10, 118, 25, 83], [21, 112], [22, 170], [26, 44, 65], [30, 157, 92], [32, 193], [36, 61], [42, 194, 64], [43, 148, 46, 35, 79], [49, 129, 37, 48, 23, 154], [56, 66, 53, 87], [67, 47], [68, 51], [73, 159, 156, 183, 160], [76, 171], [78, 77, 147, 172], [82, 121, 4, 12], [86, 15], [89, 110, 117, 13, 179, 19], [90, 133, 167], [94, 165], [97, 72, 93], [99, 31, 136, 128, 185, 2], [102, 191, 137, 20, 145, 162], [106, 180, 105, 164], [107, 11], [119, 192, 181, 184, 91], [123, 18, 176, 115], [124, 127, 131], [132, 14, 5], [135, 186, 140, 101, 155, 80], [138, 81, 62], [139, 126], [141, 182], [149, 111], [150, 45], [151, 74, 69], [152, 29, 96], [173, 7], [175, 169, 134, 168], [178, 33], [189, 113, 109]]



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,0.5161207276801631	Dog_1_test_segment_0001.mat,0.6687720994416649
Dog_1_test_segment_0002.mat,0.23896577961550508	Dog_1_test_segment_0002.mat,0.4111389241163735
Dog_1_test_segment_0003.mat,0.27128583613201696	Dog_1_test_segment_0003.mat,0.3645571609049678
Dog_1_test_segment_0004.mat,0.2684529016593175	Dog_1_test_segment_0004.mat,0.3030257442692569
Dog_1_test_segment_0005.mat,0.1620751884032815	Dog_1_test_segment_0005.mat,0.31133894192320966
Dog_1_test_segment_0006.mat,0.39919517273938215	Dog_1_test_segment_0006.mat,0.44178806872767684
Dog_1_test_segment_0007.mat,0.2602096865906257	Dog_1_test_segment_0007.mat,0.2626526196604596
Dog_1_test_segment_0008.mat,0.3606896830383452	Dog_1_test_segment_0008.mat,0.3700109387907077
Dog_1_test_segment_0009.mat,0.2415103948655313	Dog_1_test_segment_0009.mat,0.24377777290996921



In [13]:

    
out_scores['Dog_2_test_segment_0004.mat']









    Out[13]:





0.39790139557280402



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)



In [15]:

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



In [16]:

    
w









    Out[16]:





0.2



In [16]: