Commit f4fb438122075786f8945e6a2fb505580697e694
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e3ec1f74
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mdata/ILSVRC.py
| ... | ... | @@ -321,12 +321,17 @@ class DataILSVRC(DataDumperBase): |
| 321 | 321 | |
| 322 | 322 | tables = self.connection.tables() |
| 323 | 323 | if self.table_name not in tables: |
| 324 | + families_compressed = {'cf_pic': dict(compression='LZO'), | |
| 325 | + 'cf_info': dict(max_versions=10,compression='LZO'), | |
| 326 | + 'cf_tag': dict(compression='LZO'), | |
| 327 | + 'cf_feat': dict(compression='LZO'), | |
| 328 | + } | |
| 324 | 329 | families = {'cf_pic': dict(), |
| 325 | 330 | 'cf_info': dict(max_versions=10), |
| 326 | 331 | 'cf_tag': dict(), |
| 327 | 332 | 'cf_feat': dict(), |
| 328 | 333 | } |
| 329 | - self.connection.create_table(name=self.table_name, families=families) | |
| 334 | + self.connection.create_table(name=self.table_name, families=families_compressed) | |
| 330 | 335 | |
| 331 | 336 | table = self.connection.table(name=self.table_name) |
| 332 | 337 | ... | ... |
mdata/ILSVRC_S.py
| ... | ... | @@ -52,7 +52,8 @@ class DataILSVRC_S(DataDumperBase): |
| 52 | 52 | if self.category != None: |
| 53 | 53 | self.table_name += ('-' + self.category) |
| 54 | 54 | |
| 55 | - self.sparker = None | |
| 55 | + self.sparker = SC.Sparker(host='HPC-server', appname='ImageILSVRC-S', | |
| 56 | + master='spark://HPC-server:7077') | |
| 56 | 57 | |
| 57 | 58 | self.steger = F5.F5(sample_key, 1) |
| 58 | 59 | ... | ... |
mmodel/svm/SVM.py
| ... | ... | @@ -146,7 +146,7 @@ class ModelSVM(ModelBase): |
| 146 | 146 | def _train_opencv(self, X, Y): |
| 147 | 147 | svm_params = dict(kernel_type=cv2.SVM_LINEAR, |
| 148 | 148 | svm_type=cv2.SVM_C_SVC, |
| 149 | - C=2.67, gamma=5.383) | |
| 149 | + C=4) | |
| 150 | 150 | |
| 151 | 151 | X, Y = np.array(X, dtype=np.float32), np.array(Y, dtype=np.float32) |
| 152 | 152 | |
| ... | ... | @@ -183,9 +183,10 @@ class ModelSVM(ModelBase): |
| 183 | 183 | |
| 184 | 184 | X, Y = np.array(X, dtype=np.float32), np.array(Y, dtype=np.float32) |
| 185 | 185 | |
| 186 | - # result_Y = np.array([self._predict_cv(x, model) for x in X]) | |
| 186 | + # result_Y = np.array([self._predict_opencv(x, model) for x in X]) | |
| 187 | 187 | result_Y = np.array(model.predict_all(X)).ravel() |
| 188 | - | |
| 188 | + # print X[0] | |
| 189 | + # print result_Y,Y | |
| 189 | 190 | return np.mean(Y == result_Y) |
| 190 | 191 | |
| 191 | 192 | ... | ... |
mspark/SC.py
| ... | ... | @@ -368,7 +368,7 @@ class Sparker(object): |
| 368 | 368 | |
| 369 | 369 | self.model = None |
| 370 | 370 | |
| 371 | - def read_hbase(self, table_name, func=None, collect=False, parallelism=20): | |
| 371 | + def read_hbase(self, table_name, func=None, collect=False, parallelism=30): | |
| 372 | 372 | """ |
| 373 | 373 | ref - http://happybase.readthedocs.org/en/latest/user.html#retrieving-data |
| 374 | 374 | |
| ... | ... | @@ -453,7 +453,7 @@ class Sparker(object): |
| 453 | 453 | # for feat, tag in zip(X, Y): |
| 454 | 454 | # data.append(LabeledPoint(tag, feat)) |
| 455 | 455 | # svm = SVMWithSGD.train(self.sc.parallelize(data)) |
| 456 | - hdd_data = self.sc.parallelize(zip(X, Y), 20).map(lambda x: LabeledPoint(x[1], x[0])) | |
| 456 | + hdd_data = self.sc.parallelize(zip(X, Y), 30).map(lambda x: LabeledPoint(x[1], x[0])) | |
| 457 | 457 | svm = SVMWithSGD.train(hdd_data) |
| 458 | 458 | self.model = svm |
| 459 | 459 | # with open('res/svm_spark.model', 'wb') as modelfile: | ... | ... |
| ... | ... | @@ -0,0 +1,2194 @@ |
| 1 | +svm_type c_svc | |
| 2 | +kernel_type linear | |
| 3 | +nr_class 2 | |
| 4 | +total_sv 2184 | |
| 5 | +rho 6.263 | |
| 6 | +label 0 1 | |
| 7 | +probA -2.27795 | |
| 8 | +probB -0.123511 | |
| 9 | +nr_sv 1088 1096 | |
| 10 | +SV | |
| 11 | +4 1:0.49258688 2:0.50224322 3:0.44610134 4:0.46328944 5:0.10203416 6:0.099711798 7:0.082678861 8:0.0808493 9:0.035019571 10:0.034613899 11:0.033104682 12:0.029680875 13:0.016219903 14:0.018094304 15:0.016502634 16:0.016099723 17:0.033685209 18:0.036069763 19:0.033436061 20:0.032167565 21:0.13702408 22:0.13191907 23:0.20707824 24:0.18235725 25:0.027398885 26:0.029562944 27:0.029890314 28:0.031784997 29:0.048689361 30:0.047003595 31:0.048016701 32:0.050084484 33:0.10734195 34:0.10078141 35:0.10319117 36:0.11368636 37:0.27145892 38:0.26909301 39:0.27078458 40:0.22455984 41:0.11477292 42:0.11617562 43:0.1050622 44:0.089684103 45:0.050238273 46:0.05389948 47:0.045572115 48:0.043793301 49:0.024803353 50:0.028249567 51:0.025249415 52:0.023263205 53:0.060285928 54:0.058116696 55:0.055487129 56:0.057331723 57:0.19768043 58:0.19780474 59:0.27423328 60:0.28144664 61:0.048573233 62:0.047718082 63:0.040522232 64:0.054662175 65:0.077453063 66:0.074870017 67:0.063677793 68:0.079005911 69:0.15473388 70:0.15407279 71:0.11941126 72:0.1462531 73:0.18669148 74:0.18198247 75:0.19016891 76:0.16091612 77:0.11261052 78:0.11376163 79:0.10789524 80:0.096866944 81:0.060865519 82:0.061353574 83:0.056841905 84:0.049177077 85:0.030618893 86:0.038492816 87:0.032264187 88:0.028951021 89:0.080037227 90:0.086202223 91:0.079616626 92:0.074162205 93:0.22019544 94:0.23583627 95:0.29692541 96:0.31499108 97:0.06030712 98:0.053673082 99:0.048965648 100:0.059389252 101:0.092787343 102:0.079425319 103:0.068229266 104:0.085167559 105:0.15588646 106:0.14927261 107:0.11909281 108:0.13037874 109:0.16388365 110:0.15308579 111:0.16180302 112:0.1375139 113:0.11311608 114:0.10809783 115:0.11685774 116:0.094410456 117:0.059385941 118:0.065897858 119:0.066831683 120:0.052975528 121:0.038109346 122:0.040552528 123:0.037258989 124:0.031562848 125:0.10449771 126:0.10645039 127:0.095752996 128:0.088153504 129:0.23471586 130:0.24762388 131:0.28348098 132:0.32703003 133:0.055211419 134:0.057280446 135:0.046508598 136:0.061179088 137:0.091300835 138:0.085793942 139:0.070870245 140:0.087041157 141:0.13977915 142:0.13521734 143:0.12063575 144:0.12013348 145:0.085048238 146:0.083380164 147:0.1032456 148:0.12951314 149:0.069003354 150:0.065605162 151:0.086610971 152:0.089088734 153:0.049832305 154:0.049869914 155:0.06376608 156:0.0590307 157:0.039066705 158:0.038172547 159:0.050661689 160:0.042329332 161:0.2846673 162:0.26674992 163:0.3076852 164:0.20269791 165:0.28566105 166:0.30981372 167:0.20344152 168:0.26917113 169:0.04919051 170:0.046685399 171:0.041678989 172:0.049129491 173:0.060991263 174:0.061879488 175:0.059182316 176:0.063881628 177:0.076539274 178:0.077843688 179:0.083727636 180:0.095157932 181:0.020292208 182:0.021848915 183:0.015345708 184:0.022008015 185:0.02764977 186:0.025721538 187:0.019446081 188:0.027358388 189:0.032415165 190:0.035141429 191:0.026468277 192:0.030954186 193:0.039222874 194:0.044665986 195:0.033416814 196:0.035611394 197:0.68569334 198:0.68791376 199:0.73514535 200:0.61689592 201:0.15443025 202:0.14048722 203:0.13771852 204:0.21923535 205:0.010735233 206:0.011827197 207:0.0090109998 208:0.013863316 209:0.012882279 210:0.014391501 211:0.010852485 212:0.015401278 213:0.016678886 214:0.01800246 215:0.012595757 216:0.018672154 217:0.071980603 218:0.069281635 219:0.060044605 220:0.073040978 221:0.09546901 222:0.11346558 223:0.088866015 224:0.088281812 225:0.12547356 226:0.14066727 227:0.12420655 228:0.11545651 229:0.12820124 230:0.13525699 231:0.12506433 232:0.11876348 233:0.26231247 234:0.26871055 235:0.23897753 236:0.23249461 237:0.20457645 238:0.16486324 239:0.26574026 240:0.24989216 241:0.025306865 242:0.026901112 243:0.021787614 244:0.028037383 245:0.035611456 246:0.033513676 247:0.030880082 248:0.040258807 249:0.051068344 250:0.047339946 251:0.044433007 252:0.053774263 253:0.10449625 254:0.10452666 255:0.08703271 256:0.091452543 257:0.15226894 258:0.15965228 259:0.13563084 260:0.13015207 261:0.1608035 262:0.15986431 263:0.15093458 264:0.13780808 265:0.091174022 266:0.09254744 267:0.082126168 268:0.087467226 269:0.14592007 270:0.16421075 271:0.14018692 272:0.1363398 273:0.20462115 274:0.17205555 275:0.28469626 276:0.26219192 277:0.029766861 278:0.034135482 279:0.026752336 280:0.039328789 281:0.041527893 282:0.045796671 283:0.039252336 284:0.048662821 285:0.069421316 286:0.067210856 287:0.05338785 288:0.066596749 289:0.18373054 290:0.17364612 291:0.13685422 292:0.16359432 293:0.22173499 294:0.22657361 295:0.21168513 296:0.19532404 297:0.10009533 298:0.10385162 299:0.086487264 300:0.087738455 301:0.045948522 302:0.048016027 303:0.044970499 304:0.037510437 305:0.081665078 306:0.090151221 307:0.078428551 308:0.077204702 309:0.18836988 310:0.17552023 311:0.28630019 312:0.25197508 313:0.033492215 314:0.037740726 315:0.031443373 316:0.038795041 317:0.055799174 318:0.054090733 319:0.050151101 320:0.057999872 321:0.089164283 322:0.09040972 323:0.073679666 324:0.089858051 | |
| 12 | +4 1:0.63187236 2:0.62280011 3:0.50294301 4:0.53070973 5:0.096693579 6:0.10265981 7:0.095750048 8:0.081085304 9:0.014265768 10:0.015226661 11:0.013814146 12:0.013181245 13:0.0045832575 14:0.0050823712 15:0.0042883913 16:0.0048550733 17:0.0073352354 18:0.0072400781 19:0.0057659043 20:0.0074556068 21:0.099050972 22:0.098497786 23:0.19214876 24:0.14679063 25:0.015075174 26:0.015666384 27:0.025454065 28:0.023125774 29:0.028238127 30:0.02852059 31:0.041310302 32:0.042858578 33:0.10288553 34:0.10430621 35:0.11852537 36:0.14993806 37:0.53723835 38:0.53062768 39:0.54488952 40:0.46669309 41:0.11663005 42:0.12018598 43:0.12088273 44:0.082466396 45:0.027350526 46:0.029325267 47:0.024422966 48:0.022655966 49:0.010494969 50:0.01047531 51:0.0091175424 52:0.0092473331 53:0.018850468 54:0.019158054 55:0.013580484 56:0.019221242 57:0.093182607 58:0.10845027 59:0.1395559 60:0.1297599 61:0.012056204 62:0.013304204 63:0.018426745 64:0.028402523 65:0.02740835 66:0.028625046 67:0.029050188 68:0.053700585 69:0.15678848 70:0.13984819 71:0.10007393 72:0.18785297 73:0.36904559 74:0.36340998 75:0.45646419 76:0.38120206 77:0.16493085 78:0.17423425 79:0.19532284 80:0.11612778 81:0.059074612 82:0.061509089 83:0.051551699 84:0.046994854 85:0.025354277 86:0.026147589 87:0.020738344 88:0.023594524 89:0.053184224 90:0.050219972 91:0.031769378 92:0.048548403 93:0.088441181 94:0.094463352 95:0.10222091 96:0.12544907 97:0.016646747 98:0.017929775 99:0.010810413 100:0.033789688 101:0.042683968 102:0.042085166 103:0.023018091 104:0.063404214 105:0.18063855 106:0.17000083 107:0.10810413 108:0.16088941 109:0.28265959 110:0.28897567 111:0.39362539 112:0.36487639 113:0.16747308 114:0.17032142 115:0.21366737 116:0.12429668 117:0.068830966 118:0.079152899 119:0.076469045 120:0.061040068 121:0.040268456 122:0.036647642 123:0.029905561 124:0.027791986 125:0.086780084 126:0.089966957 127:0.052203568 128:0.077749361 129:0.085063212 130:0.095524181 131:0.085650577 132:0.12276215 133:0.025753083 134:0.021477921 135:0.0087880378 136:0.032907076 137:0.058373654 138:0.050615801 139:0.020855194 140:0.0571185 141:0.18479788 142:0.16731751 143:0.11883526 144:0.1314578 145:0.12733664 146:0.12148368 147:0.17917858 148:0.34878415 149:0.098631304 150:0.10072381 151:0.15386661 152:0.11919203 153:0.065239389 154:0.066017976 155:0.092736662 156:0.048856491 157:0.047238643 158:0.049447199 159:0.061351597 160:0.029964944 161:0.33964425 162:0.34347907 163:0.31713101 164:0.20023927 165:0.15479044 166:0.16037861 167:0.061661901 168:0.11020533 169:0.031448048 170:0.029217594 171:0.015847685 172:0.019587113 173:0.049715077 174:0.045072514 175:0.030143848 176:0.030910912 177:0.085956223 178:0.084179548 179:0.088082098 180:0.092259752 181:0.018506725 182:0.019634548 183:0.0093228656 184:0.018498246 185:0.02431349 186:0.024363485 187:0.015210991 188:0.022736842 189:0.033491927 190:0.032648583 191:0.025729882 192:0.02725614 193:0.043119919 194:0.045095146 195:0.041830226 196:0.03365614 197:0.73292624 198:0.73847085 199:0.83163641 200:0.66152982 201:0.1129135 202:0.10502024 203:0.056611874 204:0.19351579 205:0.010452178 206:0.0088147392 207:0.0050907753 208:0.013108772 209:0.010414715 210:0.010479325 211:0.0062868008 212:0.013810526 213:0.013861312 214:0.015473083 215:0.0082801766 216:0.015887719 217:0.066682487 218:0.072152794 219:0.048769266 220:0.076683938 221:0.12126246 222:0.13770337 223:0.09615827 224:0.10086356 225:0.22069293 226:0.20820561 227:0.25511847 228:0.15906736 229:0.20977693 230:0.20254657 231:0.29100529 232:0.16183074 233:0.22235406 234:0.22023108 235:0.15619968 236:0.20880829 237:0.081395349 238:0.06979486 239:0.10190936 240:0.1775475 241:0.016373991 242:0.018627682 243:0.011502185 244:0.029360967 245:0.02301851 246:0.026880453 247:0.013802622 248:0.034196891 249:0.038443284 250:0.043857581 251:0.025534852 252:0.05164076 253:0.11297126 254:0.11568365 255:0.079066575 256:0.11563169 257:0.24584761 258:0.25790885 259:0.248593 260:0.20648516 261:0.26865278 262:0.26769437 263:0.34262183 264:0.20495564 265:0.089243343 266:0.094906166 267:0.078792038 268:0.089833792 269:0.11218033 270:0.11420912 271:0.074262183 272:0.095849903 273:0.085947799 274:0.054155496 275:0.11393274 276:0.14724177 277:0.01410493 278:0.018096515 279:0.011118737 280:0.032629754 281:0.022146059 282:0.023994638 283:0.01674674 284:0.04547772 285:0.048905879 286:0.053351206 287:0.034866163 288:0.061894565 289:0.22843097 290:0.23346969 291:0.17216219 292:0.24329861 293:0.45473167 294:0.46418434 295:0.45894523 296:0.37798611 297:0.076894715 298:0.083319419 299:0.073289 300:0.067465278 301:0.022040147 302:0.025171147 303:0.02022721 304:0.022881944 305:0.033838591 306:0.033269327 307:0.024845294 308:0.026944444 309:0.093281442 310:0.068041409 311:0.16357255 312:0.11336806 313:0.01274068 314:0.013608282 315:0.013900434 316:0.022708333 317:0.022859484 318:0.021873435 319:0.024337305 320:0.038854167 321:0.055182302 322:0.057062949 323:0.048720791 324:0.086493056 | |
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