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Classif_with_raw_train.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import itertools
",
"
",
"import shelve
",
"
",
"import pickle
",
"
",
"import pandas
",
"
",
"import numpy as np
",
"
",
"import nltk
",
"
",
"import codecs
",
"
",
"import gensim
",
"
",
"import scipy
",
"
",
"from scipy import sparse
",
"
",
"import scipy.sparse
",
"
",
"import scipy.io
",
"
",
"import sklearn
",
"
",
"from sklearn.feature_extraction.text import CountVectorizer
",
"
",
"import sklearn.metrics
",
"
",
"from sklearn.neighbors import NearestNeighbors
",
"
",
"from sklearn.metrics import confusion_matrix
",
"
",
"from sklearn import preprocessing
",
"from keras.models import Sequential
",
"from keras.layers.core import Dense, Dropout, Activation,AutoEncoder
",
"from keras.optimizers import SGD,Adam
",
"from keras.layers import containers
",
"from mlp import *
",
"import mlp
",
"import sys
",
"import utils
",
"from sklearn.preprocessing import LabelBinarizer"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"corps=shelve.open(\"models/DECODA_AE_TANH_1060_TFIDF.shelve\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lb=LabelBinarizer()
",
"y_train=lb.fit_transform([utils.select(ligneid) for ligneid in corps[\"LABEL\"][\"TRAIN\"]])
",
"y_dev=lb.transform([utils.select(ligneid) for ligneid in corps[\"LABEL\"][\"DEV\"]])
",
"y_test=lb.transform([utils.select(ligneid) for ligneid in corps[\"LABEL\"][\"TEST\"]])
",
"keys = corps.keys()
",
"if \"LABEL\" in keys:
",
" keys.remove(\"LABEL\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"keys=[\"TRS_SPARSE\",\"ASR_SPARSE\",\"ASR_H2_TRANFORMED_OUT\",\"ASR_H1_TRANFORMED_OUT\",\"TRS_AE_OUT\"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"out_db=shelve.open(\"scores/RAW_TRS_TRAIN.shelve\",writeback=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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",
"Save 136
",
"Save 138
",
"Save 139
",
"Save 142
",
"Save 144
",
"Save 148
",
"Save 150
",
"Save 152
",
"Save 155
",
"Save 158
",
"Save 161
",
"Save 162
",
"Save 165
",
"Save 168
",
"Save 169
",
"Save 171
",
"Save 173
",
"Save 176
",
"Save 178
",
"Save 179
",
"Save 182
",
"Save 185
",
"Save 189
",
"Save 190
",
"Save 191
",
"Save 196
",
"Save 197
",
"Save 198
",
"Save 202
",
"Save 203
",
"Save 208
",
"Save 212
",
"Save 214
",
"Save 216
",
"Save 218
",
"Save 225
",
"Save 226
",
"Save 228
",
"Save 229
",
"Save 231
",
"Save 234
",
"Save 235
",
"Save 238
",
"Save 242
",
"Save 250
",
"Save 254
",
"Save 257
",
"Save 265
",
"Save 268
",
"Save 269
",
"Save 271
",
"Save 273
",
"Save 275
",
"Save 276
",
"Save 278
",
"Save 279
",
"Save 282
",
"Save 283
",
"Save 284
",
"Save 286
",
"Save 292
",
"Save 293
",
"Save 296
",
"Save 297
",
"Save 299
",
"Save 301
",
"Save 307
",
"Save 310
",
"Save 311
",
"Save 312
",
"Save 313
",
"Save 318
",
"Save 321
",
"Save 324
",
"Save 325
",
"Save 326
",
"Save 329
",
"Save 330
",
"Save 332
",
"Save 333
",
"Save 335
",
"Save 337
",
"Save 340
",
"Save 349
",
"Save 351
",
"Save 352
",
"Save 353
",
"Save 355
",
"Save 359
",
"Save 362
",
"Save 365
",
"Save 366
",
"Save 368
",
"Save 370
",
"Save 372
",
"Save 373
",
"Save 378
",
"Save 387
",
"Save 395
",
"Save 401
",
"Save 402
",
"Save 403
",
"Save 407
",
"Save 408
",
"Save 413
",
"Save 415
",
"Save 418
",
"Save 420
",
"Save 421
",
"Save 422
",
"Save 423
",
"Save 426
",
"Save 431
",
"Save 432
",
"Save 433
",
"Save 435
",
"Save 436
",
"Save 438
",
"Save 440
",
"Save 441
",
"Save 442
",
"Save 444
",
"Save 445
",
"Save 448
",
"Save 453
",
"Save 459
",
"Save 460
",
"Save 467
",
"Save 469
",
"Save 472
",
"Save 474
",
"Save 476
",
"Save 481
",
"Save 484
",
"Save 488
",
"Save 491
",
"Save 495
",
"Save 499
",
"TRS_AE_OUT
",
"Save 0
",
"Save 3
",
"Save 4
",
"Save 5
",
"Save 6
",
"Save 7
",
"Save 8
",
"Save 9
",
"Save 13
",
"Save 15
",
"Save 16
",
"Save 17
",
"Save 18
",
"Save 23
",
"Save 24
",
"Save 25
",
"Save 26
",
"Save 29
",
"Save 30
",
"Save 31
",
"Save 34
",
"Save 37
",
"Save 39
",
"Save 42
",
"Save 44
",
"Save 45
",
"Save 46
",
"Save 48
",
"Save 50
",
"Save 51
",
"Save 52
",
"Save 56
",
"Save 59
",
"Save 60
",
"Save 66
",
"Save 68
",
"Save 71
",
"Save 72
",
"Save 74
",
"Save 78
",
"Save 81
",
"Save 82
",
"Save 84
",
"Save 86
",
"Save 89
",
"Save 91
",
"Save 94
",
"Save 95
",
"Save 99
",
"Save 102
",
"Save 104
",
"Save 105
",
"Save 109
",
"Save 111
",
"Save 117
",
"Save 120
",
"Save 122
",
"Save 124
",
"Save 126
",
"Save 130
",
"Save 131
",
"Save 135
",
"Save 147
",
"Save 152
",
"Save 154
",
"Save 163
",
"Save 164
",
"Save 166
",
"Save 171
",
"Save 173
",
"Save 177
",
"Save 186
",
"Save 189
",
"Save 193
",
"Save 194
",
"Save 196
",
"Save 200
",
"Save 207
",
"Save 209
",
"Save 216
",
"Save 219
",
"Save 221
",
"Save 223
",
"Save 225
",
"Save 226
",
"Save 236
",
"Save 237
",
"Save 239
",
"Save 242
",
"Save 249
",
"Save 250
",
"Save 256
",
"Save 257
",
"Save 260
",
"Save 262
",
"Save 267
",
"Save 275
",
"Save 277
",
"Save 278
",
"Save 279
",
"Save 282
",
"Save 284
",
"Save 285
",
"Save 286
",
"Save 289
",
"Save 301
",
"Save 313
",
"Save 317
",
"Save 322
",
"Save 326
",
"Save 327
",
"Save 334
",
"Save 345
",
"Save 350
",
"Save 353
",
"Save 359
",
"Save 362
",
"Save 364
",
"Save 365
",
"Save 369
",
"Save 373
",
"Save 376
",
"Save 382
",
"Save 384
",
"Save 388
",
"Save 401
",
"Save 404
",
"Save 409
",
"Save 415
",
"Save 418
",
"Save 420
",
"Save 421
",
"Save 432
",
"Save 436
",
"Save 450
",
"Save 454
",
"Save 455
",
"Save 459
",
"Save 467
",
"Save 480
",
"Save 484
",
"Save 496
"
]
}
],
"source": [
"nb_epochs=500
",
"for key in keys:
",
" print key
",
" try:
",
" x_train=corps[\"TRS_SPARSE\"][\"TRAIN\"].todense()
",
" x_dev=corps[key][\"DEV\"].todense()
",
" x_test=corps[key][\"TEST\"].todense()
",
" except :
",
" x_train=corps[\"TRS_SPARSE\"][\"TRAIN\"].todense()
",
" x_dev=corps[key][\"DEV\"]
",
" x_test=corps[key][\"TEST\"]
",
"
",
" out_db[key]=mlp.train_mlp(x_train,y_train,x_dev,y_dev,x_test,y_test,[256,128,256],dropouts=[0.5,0,0],sgd=Adam(lr=0.0001),epochs=nb_epochs,batch_size=8,save_pred=True,keep_histo=True,fit_verbose=0)
",
"out_db.close()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TRS_SPARSE
",
"Save 0
",
"Save 4
",
"Save 5
",
"Save 6
",
"Save 7
",
"Save 9
",
"Save 10
",
"Save 12
",
"Save 16
",
"Save 17
",
"Save 18
",
"Save 19
",
"Save 22
",
"Save 24
",
"Save 28
",
"Save 29
",
"Save 33
",
"Save 34
",
"Save 35
",
"Save 36
",
"Save 38
",
"Save 39
",
"Save 42
",
"Save 44
",
"Save 46
",
"Save 48
",
"Save 50
",
"Save 57
",
"Save 60
",
"Save 64
",
"Save 65
",
"Save 74
",
"Save 76
",
"Save 78
",
"Save 83
",
"Save 88
",
"Save 92
",
"Save 102
",
"Save 104
",
"Save 106
",
"Save 108
",
"Save 115
",
"Save 119
",
"Save 121
",
"Save 123
",
"Save 128
",
"Save 133
",
"Save 134
",
"Save 136
",
"Save 141
",
"Save 146
",
"Save 153
",
"Save 162
",
"Save 165
",
"Save 169
",
"Save 171
",
"Save 173
",
"Save 175
",
"Save 177
",
"Save 182
",
"Save 189
",
"Save 193
",
"Save 197
",
"Save 202
",
"Save 205
",
"Save 211
",
"Save 215
",
"Save 216
",
"Save 233
",
"Save 238
",
"Save 251
",
"Save 254
",
"Save 262
",
"Save 266
",
"Save 269
",
"Save 274
",
"Save 277
",
"Save 280
",
"Save 282
",
"Save 286
",
"Save 290
",
"Save 291
",
"Save 294
",
"Save 298
",
"Save 301
",
"Save 303
",
"Save 310
",
"Save 314
",
"Save 319
",
"Save 323
",
"Save 334
",
"Save 339
",
"Save 359
",
"Save 363
",
"Save 365
",
"Save 375
",
"Save 380
",
"Save 382
",
"Save 388
",
"Save 390
",
"Save 393
",
"Save 396
",
"Save 406
",
"Save 412
",
"Save 422
",
"Save 433
",
"Save 438
",
"Save 452
",
"Save 459
",
"Save 464
",
"Save 475
",
"Save 485
",
"Save 493
",
"Save 494
",
"ASR_SPARSE
",
"Save 0
",
"Save 3
",
"Save 4
",
"Save 5
",
"Save 6
",
"Save 7
",
"Save 8
",
"Save 10
",
"Save 16
",
"Save 17
",
"Save 19
",
"Save 26
",
"Save 28
",
"Save 31
",
"Save 32
",
"Save 34
",
"Save 35
",
"Save 39
",
"Save 41
",
"Save 43
",
"Save 46
",
"Save 47
",
"Save 51
",
"Save 52
",
"Save 53
",
"Save 55
",
"Save 57
",
"Save 60
",
"Save 61
",
"Save 63
",
"Save 66
",
"Save 68
",
"Save 71
",
"Save 73
",
"Save 75
",
"Save 77
",
"Save 79
",
"Save 83
",
"Save 85
",
"Save 90
",
"Save 93
",
"Save 94
",
"Save 96
",
"Save 99
",
"Save 101
",
"Save 102
",
"Save 104
",
"Save 106
",
"Save 109
",
"Save 112
",
"Save 113
",
"Save 117
",
"Save 123
",
"Save 126
",
"Save 127
",
"Save 129
",
"Save 130
",
"Save 133
",
"Save 137
",
"Save 142
",
"Save 145
",
"Save 146
",
"Save 147
",
"Save 148
",
"Save 149
",
"Save 151
",
"Save 152
",
"Save 155
",
"Save 157
",
"Save 160
",
"Save 164
",
"Save 167
",
"Save 173
",
"Save 176
",
"Save 177
",
"Save 184
",
"Save 189
",
"Save 193
",
"Save 195
",
"Save 196
",
"Save 204
",
"Save 209
",
"Save 212
",
"Save 215
",
"Save 218
",
"Save 219
",
"Save 221
",
"Save 223
",
"Save 226
",
"Save 229
",
"Save 231
",
"Save 237
",
"Save 239
",
"Save 242
",
"Save 244
",
"Save 246
",
"Save 249
",
"Save 250
",
"Save 255
",
"Save 258
",
"Save 259
",
"Save 261
",
"Save 263
",
"Save 267
",
"Save 271
",
"Save 274
",
"Save 276
",
"Save 277
",
"Save 279
",
"Save 283
",
"Save 284
",
"Save 286
",
"Save 288
",
"Save 289
",
"Save 298
",
"Save 301
",
"Save 304
",
"Save 310
",
"Save 314
",
"Save 318
",
"Save 321
",
"Save 331
",
"Save 338
",
"Save 340
",
"Save 342
",
"Save 347
",
"Save 351
",
"Save 352
",
"Save 353
",
"Save 357
",
"Save 367
",
"Save 379
",
"Save 384
",
"Save 388
",
"Save 390
",
"Save 392
",
"Save 394
",
"Save 398
",
"Save 399
",
"Save 401
",
"Save 405
",
"Save 409
",
"Save 411
",
"Save 414
",
"Save 415
",
"Save 419
",
"Save 427
",
"Save 431
",
"Save 436
",
"Save 438
",
"Save 440
",
"Save 445
",
"Save 454
",
"Save 456
",
"Save 466
",
"Save 469
",
"Save 481
",
"Save 484
",
"Save 492
",
"Save 494
",
"Save 498
",
"ASR_H2_TRANFORMED_OUT
",
"Save 0
",
"Save 4
",
"Save 5
",
"Save 6
",
"Save 7
",
"Save 8
",
"Save 9
",
"Save 11
",
"Save 12
",
"Save 13
",
"Save 15
",
"Save 18
",
"Save 20
",
"Save 24
",
"Save 27
",
"Save 29
",
"Save 33
",
"Save 34
",
"Save 36
",
"Save 39
",
"Save 42
",
"Save 44
",
"Save 46
",
"Save 47
",
"Save 48
",
"Save 50
",
"Save 55
",
"Save 58
",
"Save 59
",
"Save 61
",
"Save 63
",
"Save 64
",
"Save 65
",
"Save 66
",
"Save 70
",
"Save 71
",
"Save 74
",
"Save 75
",
"Save 77
",
"Save 79
",
"Save 81
",
"Save 86
",
"Save 90
",
"Save 93
",
"Save 96
",
"Save 98
",
"Save 100
",
"Save 103
",
"Save 107
",
"Save 108
",
"Save 109
",
"Save 110
",
"Save 112
",
"Save 114
",
"Save 117
",
"Save 118
",
"Save 119
",
"Save 122
",
"Save 123
",
"Save 125
",
"Save 127
",
"Save 129
",
"Save 131
",
"Save 133
",
"Save 135
",
"Save 138
",
"Save 139
",
"Save 142
",
"Save 143
",
"Save 149
",
"Save 151
",
"Save 159
",
"Save 161
",
"Save 162
",
"Save 164
",
"Save 166
",
"Save 168
",
"Save 172
",
"Save 174
",
"Save 178
",
"Save 181
",
"Save 182
",
"Save 184
",
"Save 192
",
"Save 193
",
"Save 194
",
"Save 196
",
"Save 201
",
"Save 206
",
"Save 207
",
"Save 211
",
"Save 214
",
"Save 215
",
"Save 218
",
"Save 220
",
"Save 221
",
"Save 225
",
"Save 226
",
"Save 230
",
"Save 232
",
"Save 233
",
"Save 235
",
"Save 236
",
"Save 241
",
"Save 242
",
"Save 244
",
"Save 245
",
"Save 247
",
"Save 256
",
"Save 261
",
"Save 262
",
"Save 263
",
"Save 266
",
"Save 267
",
"Save 269
",
"Save 277
",
"Save 280
",
"Save 282
",
"Save 287
",
"Save 290
",
"Save 294
",
"Save 295
",
"Save 296
",
"Save 298
",
"Save 303
",
"Save 306
",
"Save 308
",
"Save 309
",
"Save 312
",
"Save 319
",
"Save 323
",
"Save 329
",
"Save 332
",
"Save 335
",
"Save 345
",
"Save 348
",
"Save 349
",
"Save 353
",
"Save 355
",
"Save 363
",
"Save 364
",
"Save 365
",
"Save 371
",
"Save 372
",
"Save 373
",
"Save 374
",
"Save 375
",
"Save 376
",
"Save 378
",
"Save 381
",
"Save 385
",
"Save 386
",
"Save 388
",
"Save 391
",
"Save 396
",
"Save 400
",
"Save 405
",
"Save 409
",
"Save 411
",
"Save 418
",
"Save 421
",
"Save 427
",
"Save 428
",
"Save 431
",
"Save 432
",
"Save 434
",
"Save 437
",
"Save 442
",
"Save 443
",
"Save 445
",
"Save 447
",
"Save 448
",
"Save 451
",
"Save 452
",
"Save 453
",
"Save 457
",
"Save 460
",
"Save 461
",
"Save 465
",
"Save 466
",
"Save 477
",
"Save 484
",
"Save 486
",
"Save 489
",
"Save 496
",
"Save 498
",
"ASR_H1_TRANFORMED_OUT
",
"Save 0
",
"Save 4
",
"Save 5
",
"Save 6
",
"Save 7
",
"Save 9
",
"Save 11
",
"Save 14
",
"Save 15
",
"Save 24
",
"Save 26
",
"Save 30
",
"Save 32
",
"Save 35
",
"Save 38
",
"Save 39
",
"Save 42
",
"Save 46
",
"Save 48
",
"Save 50
",
"Save 51
",
"Save 54
",
"Save 55
",
"Save 57
",
"Save 59
",
"Save 62
",
"Save 65
",
"Save 66
",
"Save 70
",
"Save 71
",
"Save 73
",
"Save 75
",
"Save 76
",
"Save 79
",
"Save 81
",
"Save 86
",
"Save 89
",
"Save 91
",
"Save 93
",
"Save 94
",
"Save 95
",
"Save 99
",
"Save 101
",
"Save 102
",
"Save 103
",
"Save 106
",
"Save 107
",
"Save 109
",
"Save 111
",
"Save 113
",
"Save 115
",
"Save 117
",
"Save 118
",
"Save 120
",
"Save 126
",
"Save 127
",
"Save 129
",
"Save 133
",
"Save 134
",
"Save 135
",
"Save 139
",
"Save 140
",
"Save 144
",
"Save 148
",
"Save 150
",
"Save 153
",
"Save 155
",
"Save 156
",
"Save 157
",
"Save 159
",
"Save 160
",
"Save 162
",
"Save 164
",
"Save 168
",
"Save 169
",
"Save 171
",
"Save 175
",
"Save 177
",
"Save 180
",
"Save 182
",
"Save 183
",
"Save 186
",
"Save 189
",
"Save 191
",
"Save 195
",
"Save 198
",
"Save 200
",
"Save 201
",
"Save 202
",
"Save 206
",
"Save 208
",
"Save 209
",
"Save 213
",
"Save 214
",
"Save 215
",
"Save 217
",
"Save 219
",
"Save 220
",
"Save 234
",
"Save 236
",
"Save 237
",
"Save 239
",
"Save 241
",
"Save 244
",
"Save 249
",
"Save 250
",
"Save 251
",
"Save 254
",
"Save 256
",
"Save 257
",
"Save 261
",
"Save 263
",
"Save 264
",
"Save 265
",
"Save 269
",
"Save 271
",
"Save 273
",
"Save 276
",
"Save 277
",
"Save 279
",
"Save 280
",
"Save 287
",
"Save 288
",
"Save 290
",
"Save 291
",
"Save 294
",
"Save 296
",
"Save 301
",
"Save 307
",
"Save 309
",
"Save 310
",
"Save 315
",
"Save 318
",
"Save 322
",
"Save 325
",
"Save 328
",
"Save 330
",
"Save 331
",
"Save 332
",
"Save 334
",
"Save 339
",
"Save 341
",
"Save 342
",
"Save 343
",
"Save 352
",
"Save 357
",
"Save 361
",
"Save 364
",
"Save 369
",
"Save 371
",
"Save 374
",
"Save 381
",
"Save 384
",
"Save 385
",
"Save 389
",
"Save 392
",
"Save 393
",
"Save 397
",
"Save 399
",
"Save 401
",
"Save 403
",
"Save 407
",
"Save 409
",
"Save 410
",
"Save 411
",
"Save 413
",
"Save 416
",
"Save 421
",
"Save 425
",
"Save 427
",
"Save 435
",
"Save 438
",
"Save 440
",
"Save 441
",
"Save 446
",
"Save 447
",
"Save 449
",
"Save 450
",
"Save 451
",
"Save 452
",
"Save 454
",
"Save 458
",
"Save 460
",
"Save 461
",
"Save 463
",
"Save 470
",
"Save 475
",
"Save 480
",
"Save 481
",
"Save 482
",
"Save 483
",
"Save 488
",
"Save 494
",
"Save 497
",
"Save 499
",
"TRS_AE_OUT
",
"Save 0
",
"Save 3
",
"Save 4
",
"Save 5
",
"Save 6
",
"Save 7
",
"Save 8
",
"Save 10
",
"Save 12
",
"Save 15
",
"Save 17
",
"Save 18
",
"Save 20
",
"Save 23
",
"Save 28
",
"Save 29
",
"Save 30
",
"Save 33
",
"Save 34
",
"Save 37
",
"Save 40
",
"Save 42
",
"Save 46
",
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]
}
],
"source": [
"out_db=shelve.open(\"scores/RAW_ASR_TRAIN.shelve\",writeback=True)
",
"nb_epochs=500
",
"for key in keys:
",
" print key
",
" try:
",
" x_train=corps[\"ASR_SPARSE\"][\"TRAIN\"].todense()
",
" x_dev=corps[key][\"DEV\"].todense()
",
" x_test=corps[key][\"TEST\"].todense()
",
" except :
",
" x_train=corps[\"ASR_SPARSE\"][\"TRAIN\"].todense()
",
" x_dev=corps[key][\"DEV\"]
",
" x_test=corps[key][\"TEST\"]
",
"
",
" out_db[key]=mlp.train_mlp(x_train,y_train,x_dev,y_dev,x_test,y_test,[256,128,256],dropouts=[0.5,0,0],sgd=Adam(lr=0.0001),epochs=nb_epochs,batch_size=8,save_pred=True,keep_histo=True,fit_verbose=0)
",
"out_db.close()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['ASR_H1_TRANFORMED_OUT', 'ASR_H2_TRANFORMED_OUT', 'TRS_AE_OUT', 'TRS_SPARSE', 'ASR_SPARSE']
"
]
}
],
"source": [
"out_db=shelve.open(\"scores/RAW_ASR_TRAIN.shelve\")
",
"print out_db.keys()
",
"out_db.close()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ASR_H1_TRANFORMED_OUT 0.697
",
"ASR_H2_TRANFORMED_OUT 0.682
",
"TRS_AE_OUT 0.838
",
"TRS_SPARSE 0.841
",
"ASR_SPARSE 0.78
"
]
}
],
"source": [
"data=shelve.open(\"scores/RAW_ASR_TRAIN.shelve\")
",
"scores={}
",
"#del scores_ordoned
",
"for key,table in data.iteritems():
",
" scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
",
" print key,scores[key]
",
" pandas.DataFrame(zip([x[0] for x in data[key][0] ],[x[0] for x in data[key][1] ])).plot()
",
"data.close()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ASR_H1_TRANFORMED_OUT 0.688
",
"ASR_H2_TRANFORMED_OUT 0.654
",
"TRS_AE_OUT 0.832
",
"TRS_SPARSE 0.832
",
"ASR_SPARSE 0.734
"
]
}
],
"source": [
"data=shelve.open(\"scores/RAW_TRS_TRAIN.shelve\")
",
"scores={}
",
"#del scores_ordoned
",
"for key,table in data.iteritems():
",
" scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
",
" print key,scores[key]
",
" pandas.DataFrame(zip([x[0] for x in data[key][0] ],[x[0] for x in data[key][1] ])).plot()
",
"data.close()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ASR_H1_TRANFORMED_OUT 0.697
",
"ASR_H2_TRANFORMED_OUT 0.682
",
"TRS_AE_OUT 0.838
",
"TRS_SPARSE 0.841
",
"ASR_SPARSE 0.78
"
]
},
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc258328890>"
]
},
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},
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"text/plain": [
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]
},
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},
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",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc251f4ddd0>"
]
},
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},
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",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc251dff390>"
]
},
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},
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",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc252bb85d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data=shelve.open(\"scores/RAW_ASR_TRAIN.shelve\")
",
"scores={}
",
"#del scores_ordoned
",
"for key,table in data.iteritems():
",
" scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
",
" print key,scores[key]
",
" pandas.DataFrame(zip([x[0] for x in data[key][0] ],[x[0] for x in data[key][1] ])).plot()
",
"data.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|