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%% Cell type:code id:9d54cd9a tags: %% Cell type:code id:9d54cd9a tags:
``` python ``` python
path = "./" path = "./"
datasets = ['assist09_tkde', 'assist17_tkde', 'algebra','math_1', 'math_2'] datasets = ['assist09_tkde', 'assist17_tkde', 'algebra','math_1', 'math_2']
``` ```
%% Cell type:markdown id:1da92e4f tags: %% Cell type:markdown id:1da92e4f tags:
## Table 2: compute ACC, AUC and RMSE ## Table 2 and 3: compute ACC, AUC and RMSE and DOA
%% Cell type:code id:61c53cb1 tags: %% Cell type:code id:61c53cb1 tags:
``` python ``` python
# can be long to compute. # can be long to compute.
# You need to unzip data.zip and results.zip # You need to unzip data.zip and results.zip
# All results are stored in ../../results/table_2 # All results are stored in ../../results/table_2
import os import os
print(os.getcwd()) print(os.getcwd())
cmd = 'cd code/binary_bpr && python ./script.py ' cmd = 'cd code/binary_bpr && python ./script.py '
!{cmd} !{cmd}
``` ```
%% Cell type:markdown id:6b1c6b4d tags:
## Table 3: compute DOA
%% Cell type:markdown id:b72eb27b tags:
import os
embDirPath = "results/table_2/users/"
i = 3 # dataset index
print(os.getcwd())
cmd = 'python code/binary_bpr_ablation/compute_doa.py --data '+embDirPath+datasets[i]+'_0_BPR.csv'
!{cmd}
#doa = compute_doa(path+datasets[i]+'/train_embed.csv')
#print("DOA:", doa)
%% Cell type:markdown id:8d5630b5 tags: %% Cell type:markdown id:8d5630b5 tags:
## Table 4: ablation ## Table 4: ablation
%% Cell type:code id:a9e32954 tags: %% Cell type:code id:a9e32954 tags:
``` python ``` python
import os import os
print(os.getcwd()) print(os.getcwd())
cmd = 'cd code/binary_bpr_ablation && python script_ablation.py ' cmd = 'cd code/binary_bpr_ablation && python script_ablation.py '
!{cmd} !{cmd}
``` ```
%% Cell type:markdown id:c5960372 tags: %% Cell type:markdown id:c5960372 tags:
## Table 6 ## Table 6
%% Cell type:code id:e46ff5d4 tags: %% Cell type:code id:e46ff5d4 tags:
``` python ``` python
# pour all # pour all
cmd = 'python code/nary_model/main_nary_cv.py --data '+ path+'data/covid/initsurvey.csv' cmd = 'python code/nary_model/main_nary_cv.py --data '+ path+'data/covid/initsurvey.csv'
print(os.system(cmd)) print(os.system(cmd))
cmd = 'python code/nary_model/main_nary_cv.py --data '+path+'data/covid/psysurvey.csv' cmd = 'python code/nary_model/main_nary_cv.py --data '+path+'data/covid/psysurvey.csv'
print(os.system(cmd)) print(os.system(cmd))
``` ```
%% Cell type:markdown id:e586f5cb tags: %% Cell type:markdown id:e586f5cb tags:
## Figure 1 and Table 7 ## Figure 1 and Table 7
%% Cell type:code id:4c4b8106 tags: %% Cell type:code id:4c4b8106 tags:
``` python ``` python
cmd = 'python code/unsupervised_DT/decision_tree.py --lower 0' cmd = 'python code/unsupervised_DT/decision_tree.py --lower 0'
print(os.system(cmd)) print(os.system(cmd))
cmd = 'python code/unsupervised_DT/decision_tree.py --lower 1' cmd = 'python code/unsupervised_DT/decision_tree.py --lower 1'
print(os.system(cmd)) print(os.system(cmd))
``` ```
%% Cell type:markdown id:f39f6de3 tags: %% Cell type:markdown id:f39f6de3 tags:
## Figure 2: radar plots -- ## Figure 2: radar plots --
%% Cell type:code id:ceee0ec4 tags: %% Cell type:code id:ceee0ec4 tags:
``` python ``` python
def fromDFtoArray(name, vector, type_value): def fromDFtoArray(name, vector, type_value):
# Read dataframe and generate a matrix or # Read dataframe and generate a matrix or
# a vector of appropriate type # a vector of appropriate type
df = pd.read_csv(name,index_col=None, header=None) df = pd.read_csv(name,index_col=None, header=None)
cols = df.columns cols = df.columns
if(type_value == "f"): if(type_value == "f"):
for col in cols: for col in cols:
df[col] = df[col].astype(float) df[col] = df[col].astype(float)
if(type_value == 'i'): if(type_value == 'i'):
for col in cols: for col in cols:
df[col] = df[col].astype(int) df[col] = df[col].astype(int)
r = df.values r = df.values
if(vector): if(vector):
r = r.reshape(-1,) r = r.reshape(-1,)
return r return r
``` ```
%% Cell type:code id:801e2ab1 tags: %% Cell type:code id:801e2ab1 tags:
``` python ``` python
from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import MinMaxScaler
import plotly.graph_objects as go import plotly.graph_objects as go
import pandas as pd import pandas as pd
import numpy as np import numpy as np
import csv import csv
from matplotlib import gridspec from matplotlib import gridspec
import seaborn as sns import seaborn as sns
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
``` ```
%% Cell type:code id:5a8b764b tags: %% Cell type:code id:5a8b764b tags:
``` python ``` python
H_users = fromDFtoArray(path+ "code/unsupervised_DT/files_for_dt/train_embed.csv",False,'f') H_users = fromDFtoArray(path+ "code/unsupervised_DT/files_for_dt/train_embed.csv",False,'f')
classPsy = fromDFtoArray(path+ "code/unsupervised_DT/files_for_dt/train_user_quest_label.csv",False,'f') classPsy = fromDFtoArray(path+ "code/unsupervised_DT/files_for_dt/train_user_quest_label.csv",False,'f')
``` ```
%% Cell type:code id:33643887 tags: %% Cell type:code id:33643887 tags:
``` python ``` python
d = [151,111,57,415,110,73] d = [151,111,57,415,110,73]
g = [[7, 14, 20, 22, 23, 39, 44, 53, 56, 57, 62, 71, 95, 100, 106, 119, 122, 133, 141, 142, 152, 154, 156, 160, 179, 182, 183, 187, 188, 191, 194, 198, 199, 201, 203, 207, 208, 214, 219, 221, 224, 233, 235, 241, 243, 246, 252, 259, 261, 265, 266, 267, 271, 282, 289, 293, 299, 302, 303, 304, 307, 308, 309, 310, 312, 313, 325, 327, 351, 358, 385, 390, 393, 396, 406, 409, 415, 419, 431, 435, 445, 447, 448, 450, 468, 479, 486, 488, 490, 494, 510, 516, 517, 525, 546, 547, 550, 551, 553, 557, 559, 565, 567, 578, 580, 597, 600, 604, 608, 611, 612, 615, 640, 641, 644, 656, 662, 677, 679, 682, 686, 688, 690, 703, 709, 714, 723, 732, 745, 752, 776, 782, 785, 786, 787, 789, 792, 798, 810, 829, 839, 842, 844, 847, 854, 866, 869, 870, 881, 883, 900] g = [[7, 14, 20, 22, 23, 39, 44, 53, 56, 57, 62, 71, 95, 100, 106, 119, 122, 133, 141, 142, 152, 154, 156, 160, 179, 182, 183, 187, 188, 191, 194, 198, 199, 201, 203, 207, 208, 214, 219, 221, 224, 233, 235, 241, 243, 246, 252, 259, 261, 265, 266, 267, 271, 282, 289, 293, 299, 302, 303, 304, 307, 308, 309, 310, 312, 313, 325, 327, 351, 358, 385, 390, 393, 396, 406, 409, 415, 419, 431, 435, 445, 447, 448, 450, 468, 479, 486, 488, 490, 494, 510, 516, 517, 525, 546, 547, 550, 551, 553, 557, 559, 565, 567, 578, 580, 597, 600, 604, 608, 611, 612, 615, 640, 641, 644, 656, 662, 677, 679, 682, 686, 688, 690, 703, 709, 714, 723, 732, 745, 752, 776, 782, 785, 786, 787, 789, 792, 798, 810, 829, 839, 842, 844, 847, 854, 866, 869, 870, 881, 883, 900]
, [4, 8, 15, 28, 40, 41, 42, 50, 59, 84, 85, 88, 89, 109, 112, 123, 138, 145, 146, 150, 157, 158, 166, 181, 186, 190, 232, 234, 242, 247, 248, 249, 260, 272, 274, 277, 281, 296, 301, 338, 339, 345, 352, 359, 365, 388, 403, 404, 412, 418, 441, 443, 461, 464, 478, 481, 489, 491, 495, 497, 500, 508, 524, 531, 569, 576, 587, 589, 609, 610, 613, 617, 620, 622, 627, 637, 642, 647, 673, 678, 711, 712, 715, 720, 726, 729, 730, 758, 759, 760, 788, 797, 811, 815, 819, 820, 821, 830, 831, 840, 845, 849, 862, 878, 880, 884, 891, 895, 898, 904, 906 ] , [4, 8, 15, 28, 40, 41, 42, 50, 59, 84, 85, 88, 89, 109, 112, 123, 138, 145, 146, 150, 157, 158, 166, 181, 186, 190, 232, 234, 242, 247, 248, 249, 260, 272, 274, 277, 281, 296, 301, 338, 339, 345, 352, 359, 365, 388, 403, 404, 412, 418, 441, 443, 461, 464, 478, 481, 489, 491, 495, 497, 500, 508, 524, 531, 569, 576, 587, 589, 609, 610, 613, 617, 620, 622, 627, 637, 642, 647, 673, 678, 711, 712, 715, 720, 726, 729, 730, 758, 759, 760, 788, 797, 811, 815, 819, 820, 821, 830, 831, 840, 845, 849, 862, 878, 880, 884, 891, 895, 898, 904, 906 ]
,[58, 75, 80, 103, 114, 161, 205, 210, 211, 226, 227, 236, 244, 257, 258, 328, 347, 356, 357, 372, 380, 391, 399, 400, 402, 410, 455, 457, 526, 558, 583, 606, 614, 616, 618, 624, 643, 645, 660, 664, 676, 691, 717, 738, 755, 756, 763, 771, 796, 812, 817, 832, 850, 861, 868, 882, 912] ,[58, 75, 80, 103, 114, 161, 205, 210, 211, 226, 227, 236, 244, 257, 258, 328, 347, 356, 357, 372, 380, 391, 399, 400, 402, 410, 455, 457, 526, 558, 583, 606, 614, 616, 618, 624, 643, 645, 660, 664, 676, 691, 717, 738, 755, 756, 763, 771, 796, 812, 817, 832, 850, 861, 868, 882, 912]
,[0, 2, 3, 5, 6, 9, 10, 11, 12, 17, 18, 24, 29, 30, 31, 32, 33, 34, 35, 36, 38, 43, 46, 48, 52, 54, 55, 63, 64, 66, 68, 69, 72, 74, 76, 77, 78, 79, 81, 82, 87, 90, 91, 92, 93, 94, 98, 99, 101, 102, 104, 105, 107, 108, 110, 111, 115, 117, 120, 121, 124, 126, 127, 129, 130, 131, 132, 134, 135, 136, 137, 139, 143, 144, 148, 149, 151, 155, 163, 167, 171, 172, 173, 175, 176, 177, 178, 184, 189, 192, 193, 195, 196, 197, 200, 202, 204, 206, 215, 218, 222, 223, 225, 229, 230, 237, 238, 240, 251, 253, 254, 256, 262, 263, 264, 268, 273, 278, 279, 280, 283, 284, 287, 288, 290, 291, 292, 294, 295, 305, 306, 315, 317, 318, 319, 320, 321, 322, 323, 324, 330, 331, 332, 333, 334, 336, 337, 340, 342, 343, 346, 350, 353, 354, 355, 360, 361, 363, 364, 366, 367, 368, 370, 371, 373, 374, 376, 382, 383, 386, 389, 392, 394, 397, 401, 407, 411, 413, 414, 416, 417, 423, 424, 425, 426, 427, 428, 429, 432, 434, 437, 438, 440, 442, 444, 446, 449, 453, 454, 456, 458, 459, 462, 467, 469, 471, 472, 473, 475, 477, 480, 482, 483, 484, 485, 492, 493, 496, 498, 499, 502, 504, 506, 507, 509, 513, 515, 518, 519, 520, 521, 522, 523, 527, 528, 529, 530, 532, 533, 534, 535, 536, 537, 538, 539, 541, 542, 543, 544, 545, 549, 552, 556, 560, 561, 562, 563, 566, 570, 571, 573, 574, 575, 577, 581, 582, 585, 588, 591, 592, 593, 594, 595, 596, 598, 599, 601, 602, 603, 605, 607, 619, 623, 625, 626, 628, 629, 630, 631, 632, 635, 638, 639, 648, 649, 650, 651, 652, 654, 657, 663, 667, 668, 669, 670, 672, 675, 681, 683, 684, 692, 693, 694, 695, 697, 698, 700, 704, 705, 706, 707, 708, 710, 713, 716, 718, 719, 722, 724, 727, 728, 731, 733, 734, 735, 736, 737, 740, 741, 744, 747, 748, 749, 750, 751, 754, 757, 762, 764, 765, 767, 768, 770, 772, 773, 774, 775, 777, 778, 779, 780, 781, 783, 791, 793, 795, 799, 800, 801, 802, 803, 804, 806, 808, 816, 818, 822, 823, 824, 825, 826, 833, 836, 837, 838, 841, 846, 848, 851, 852, 856, 857, 864, 873, 875, 877, 885, 886, 887, 888, 889, 890, 893, 894, 896, 897, 899, 901, 902, 903, 905, 907, 909, 911, 914] ,[0, 2, 3, 5, 6, 9, 10, 11, 12, 17, 18, 24, 29, 30, 31, 32, 33, 34, 35, 36, 38, 43, 46, 48, 52, 54, 55, 63, 64, 66, 68, 69, 72, 74, 76, 77, 78, 79, 81, 82, 87, 90, 91, 92, 93, 94, 98, 99, 101, 102, 104, 105, 107, 108, 110, 111, 115, 117, 120, 121, 124, 126, 127, 129, 130, 131, 132, 134, 135, 136, 137, 139, 143, 144, 148, 149, 151, 155, 163, 167, 171, 172, 173, 175, 176, 177, 178, 184, 189, 192, 193, 195, 196, 197, 200, 202, 204, 206, 215, 218, 222, 223, 225, 229, 230, 237, 238, 240, 251, 253, 254, 256, 262, 263, 264, 268, 273, 278, 279, 280, 283, 284, 287, 288, 290, 291, 292, 294, 295, 305, 306, 315, 317, 318, 319, 320, 321, 322, 323, 324, 330, 331, 332, 333, 334, 336, 337, 340, 342, 343, 346, 350, 353, 354, 355, 360, 361, 363, 364, 366, 367, 368, 370, 371, 373, 374, 376, 382, 383, 386, 389, 392, 394, 397, 401, 407, 411, 413, 414, 416, 417, 423, 424, 425, 426, 427, 428, 429, 432, 434, 437, 438, 440, 442, 444, 446, 449, 453, 454, 456, 458, 459, 462, 467, 469, 471, 472, 473, 475, 477, 480, 482, 483, 484, 485, 492, 493, 496, 498, 499, 502, 504, 506, 507, 509, 513, 515, 518, 519, 520, 521, 522, 523, 527, 528, 529, 530, 532, 533, 534, 535, 536, 537, 538, 539, 541, 542, 543, 544, 545, 549, 552, 556, 560, 561, 562, 563, 566, 570, 571, 573, 574, 575, 577, 581, 582, 585, 588, 591, 592, 593, 594, 595, 596, 598, 599, 601, 602, 603, 605, 607, 619, 623, 625, 626, 628, 629, 630, 631, 632, 635, 638, 639, 648, 649, 650, 651, 652, 654, 657, 663, 667, 668, 669, 670, 672, 675, 681, 683, 684, 692, 693, 694, 695, 697, 698, 700, 704, 705, 706, 707, 708, 710, 713, 716, 718, 719, 722, 724, 727, 728, 731, 733, 734, 735, 736, 737, 740, 741, 744, 747, 748, 749, 750, 751, 754, 757, 762, 764, 765, 767, 768, 770, 772, 773, 774, 775, 777, 778, 779, 780, 781, 783, 791, 793, 795, 799, 800, 801, 802, 803, 804, 806, 808, 816, 818, 822, 823, 824, 825, 826, 833, 836, 837, 838, 841, 846, 848, 851, 852, 856, 857, 864, 873, 875, 877, 885, 886, 887, 888, 889, 890, 893, 894, 896, 897, 899, 901, 902, 903, 905, 907, 909, 911, 914]
,[13, 16, 19, 27, 37, 47, 49, 60, 61, 65, 67, 70, 73, 83, 86, 125, 128, 140, 147, 153, 159, 162, 165, 168, 170, 185, 209, 228, 239, 245, 250, 269, 270, 275, 285, 300, 311, 314, 316, 329, 341, 348, 349, 362, 369, 375, 377, 378, 379, 384, 395, 398, 408, 433, 439, 452, 465, 466, 470, 487, 501, 503, 514, 540, 548, 554, 555, 564, 568, 572, 584, 590, 634, 653, 655, 658, 659, 661, 665, 666, 671, 674, 696, 699, 702, 721, 739, 742, 746, 753, 769, 784, 790, 794, 809, 813, 827, 828, 843, 853, 855, 858, 859, 860, 865, 867, 874, 908, 915, 916] ,[13, 16, 19, 27, 37, 47, 49, 60, 61, 65, 67, 70, 73, 83, 86, 125, 128, 140, 147, 153, 159, 162, 165, 168, 170, 185, 209, 228, 239, 245, 250, 269, 270, 275, 285, 300, 311, 314, 316, 329, 341, 348, 349, 362, 369, 375, 377, 378, 379, 384, 395, 398, 408, 433, 439, 452, 465, 466, 470, 487, 501, 503, 514, 540, 548, 554, 555, 564, 568, 572, 584, 590, 634, 653, 655, 658, 659, 661, 665, 666, 671, 674, 696, 699, 702, 721, 739, 742, 746, 753, 769, 784, 790, 794, 809, 813, 827, 828, 843, 853, 855, 858, 859, 860, 865, 867, 874, 908, 915, 916]
,[1, 21, 25, 26, 45, 51, 96, 97, 113, 116, 118, 164, 169, 174, 180, 212, 213, 216, 217, 220, 231, 255, 276, 286, 297, 298, 326, 335, 344, 381, 387, 405, 420, 421, 422, 430, 436, 451, 460, 463, 474, 476, 505, 511, 512, 579, 586, 621, 633, 636, 646, 680, 685, 687, 689, 701, 725, 743, 761, 766, 805, 807, 814, 834, 835, 863, 871, 872, 876, 879, 892, 910, 913] ,[1, 21, 25, 26, 45, 51, 96, 97, 113, 116, 118, 164, 169, 174, 180, 212, 213, 216, 217, 220, 231, 255, 276, 286, 297, 298, 326, 335, 344, 381, 387, 405, 420, 421, 422, 430, 436, 451, 460, 463, 474, 476, 505, 511, 512, 579, 586, 621, 633, 636, 646, 680, 685, 687, 689, 701, 725, 743, 761, 766, 805, 807, 814, 834, 835, 863, 871, 872, 876, 879, 892, 910, 913]
] ]
``` ```
%% Cell type:code id:d190bd89 tags: %% Cell type:code id:d190bd89 tags:
``` python ``` python
titre = ['agency', 'anxiety','avoiding', 'depression','ext. control', 'fatigue', 'hyper vigilance', titre = ['agency', 'anxiety','avoiding', 'depression','ext. control', 'fatigue', 'hyper vigilance',
'trauma','int. control', 'memory', 'problem focused', 'quality of life', 'trauma','int. control', 'memory', 'problem focused', 'quality of life',
'sadness', 'self efficacy','sleep','social','stress'] 'sadness', 'self efficacy','sleep','social','stress']
dim = len(titre) dim = len(titre)
seuils = [ 0.38646466, 0.13056517 , 0.16311094 , 0.5522181 , 0.4068393 , 0.4961518, seuils = [ 0.38646466, 0.13056517 , 0.16311094 , 0.5522181 , 0.4068393 , 0.4961518,
-0.12576449, -0.16419357 , 0.34581536, -0.06974197, -0.25050393 , 0.17414908, -0.12576449, -0.16419357 , 0.34581536, -0.06974197, -0.25050393 , 0.17414908,
-0.09691429, 0.39290804, 0.37336868, -0.30328006 , 0.23517847] -0.09691429, 0.39290804, 0.37336868, -0.30328006 , 0.23517847]
print(len(seuils)) print(len(seuils))
indices = [] indices = []
for i in range(17): for i in range(17):
indices.append([i]) indices.append([i])
print(H_users.shape) print(H_users.shape)
radar = [] radar = []
for k in range(len(g)): for k in range(len(g)):
rows = H_users[g[k]] rows = H_users[g[k]]
v = np.full(dim, 0.0) v = np.full(dim, 0.0)
for i in range(len(rows)): for i in range(len(rows)):
#print(len(rows[i])) #print(len(rows[i]))
for j in range(len(rows[i])): for j in range(len(rows[i])):
if(rows[i][j] > seuils[j]): if(rows[i][j] > seuils[j]):
v[j] = v[j] + 1 #rows[i][j] v[j] = v[j] + 1 #rows[i][j]
if(rows[i][j] < seuils[j]): if(rows[i][j] < seuils[j]):
v[j] = v[j] - 1#+ rows[i][j] v[j] = v[j] - 1#+ rows[i][j]
v = v / len(rows) v = v / len(rows)
radar.append(v) radar.append(v)
print(radar) print(radar)
scaler = MinMaxScaler() scaler = MinMaxScaler()
radar = scaler.fit_transform(np.array(radar)) radar = scaler.fit_transform(np.array(radar))
``` ```
%% Cell type:code id:34d43f1e tags: %% Cell type:code id:34d43f1e tags:
``` python ``` python
print(classPsy.shape) print(classPsy.shape)
radar2 = [] radar2 = []
for k in range(len(g)): for k in range(len(g)):
rows = classPsy[g[k]] rows = classPsy[g[k]]
v = np.full(dim, 0.0) v = np.full(dim, 0.0)
for i in range(len(rows)): for i in range(len(rows)):
for j in range(len(rows[i])): for j in range(len(rows[i])):
if(rows[i][j] == 2): if(rows[i][j] == 2):
v[j] = v[j] + 1 #rows[i][j] v[j] = v[j] + 1 #rows[i][j]
if(rows[i][j] == 0): if(rows[i][j] == 0):
v[j] = v[j] - 1#+ rows[i][j] v[j] = v[j] - 1#+ rows[i][j]
v = v / len(rows) v = v / len(rows)
radar2.append(v) radar2.append(v)
print(radar2) print(radar2)
scaler = MinMaxScaler() scaler = MinMaxScaler()
radar2 = scaler.fit_transform(np.array(radar2)) radar2 = scaler.fit_transform(np.array(radar2))
print(radar2) print(radar2)
``` ```
%% Cell type:code id:acb7d4df tags: %% Cell type:code id:acb7d4df tags:
``` python ``` python
name_cluster=['Trauma', 'Sadness, stress and memory','Trauma and memory','Memory','Depression and associated symptoms', 'Depression'] name_cluster=['Trauma', 'Sadness, stress and memory','Trauma and memory','Memory','Depression and associated symptoms', 'Depression']
categories = ['processing cost','mechanical properties','chemical stability', categories = ['processing cost','mechanical properties','chemical stability',
'thermal stability', 'device integration'] 'thermal stability', 'device integration']
titleGraph = [] titleGraph = []
for i in range(len(d)): for i in range(len(d)):
#titleGraph.append('DTCluster ' + str(i)) #titleGraph.append('DTCluster ' + str(i))
titleGraph.append('DTCluster: ' + name_cluster[i]) titleGraph.append('DTCluster: ' + name_cluster[i])
def radar_fig(i): def radar_fig(i):
fig = go.Figure() fig = go.Figure()
fig.add_trace(go.Scatterpolar( fig.add_trace(go.Scatterpolar(
r=radar[i], r=radar[i],
theta=titre, theta=titre,
fill='toself', fill='toself',
name='H' name='H'
)) ))
fig.add_trace(go.Scatterpolar( fig.add_trace(go.Scatterpolar(
r=radar2[i], r=radar2[i],
theta=titre, theta=titre,
fill='toself', fill='toself',
name='Class psy' name='Class psy'
#text = 'r' #text = 'r'
)) ))
fig.update_layout( fig.update_layout(
polar=dict( polar=dict(
radialaxis=dict( radialaxis=dict(
visible=True visible=True
#range=[0, 5] #range=[0, 5]
)), )),
showlegend=False showlegend=False
) )
fig.update_layout(title_text = titleGraph[i]) fig.update_layout(title_text = titleGraph[i])
fig.update_layout( fig.update_layout(
font=dict( font=dict(
#family="Courier New, monospace", #family="Courier New, monospace",
size=18, # Set the font size here size=18, # Set the font size here
#color="RebeccaPurple" #color="RebeccaPurple"
) )
) )
fig.show() fig.show()
fig.write_image(path+"radar"+str(i)+"_fig.png") fig.write_image(path+"radar"+str(i)+"_fig.png")
for i in range(len(d)): for i in range(len(d)):
radar_fig(i) radar_fig(i)
``` ```
%% Cell type:code id:c03ac5f8 tags: %% Cell type:code id:c03ac5f8 tags:
``` python ``` python
``` ```
%% Cell type:code id:39b0792d tags: %% Cell type:code id:39b0792d tags:
``` python ``` python
``` ```
%% Cell type:code id:fe44084e tags: %% Cell type:code id:fe44084e tags:
``` python ``` python
``` ```
......
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