In [1]:
import graphlab
A newer version of GraphLab Create (v1.7.1) is available! Your current version is v1.6.1.
You can use pip to upgrade the graphlab-create package. For more information see https://dato.com/products/create/upgrade.
In [3]:
pr=graphlab.SFrame('data/data_balanced.csv')
PROGRESS: Finished parsing file C:\Users\brahul\data\data_balanced.csv
PROGRESS: Parsing completed. Parsed 100 lines in 1.05906 secs.
------------------------------------------------------
Inferred types from first line of file as
column_type_hints=[str,str,str,str,str,str,str,long,long,str,str,str,str,str,str,str,str,str,str,str,str,long,str,long,long,long,str,float,str,long]
If parsing fails due to incorrect types, you can correct
the inferred type list above and pass it to read_csv in
the column_type_hints argument
------------------------------------------------------
PROGRESS: Finished parsing file C:\Users\brahul\data\data_balanced.csv
PROGRESS: Parsing completed. Parsed 107232 lines in 1.02706 secs.
In [6]:
model=graphlab.logistic_classifier.create(train_data,target='Outcome',features=['TrafficType','PublisherId','AppSiteId','AppSiteCategory','DeviceType','Country','CampaignId','CreativeCategory','ExchangeBid'],validation_set=test_data,max_iterations=500)
PROGRESS: Logistic regression:
PROGRESS: --------------------------------------------------------
PROGRESS: Number of examples : 85764
PROGRESS: Number of classes : 3
PROGRESS: Number of feature columns : 9
PROGRESS: Number of unpacked features : 9
PROGRESS: Number of coefficients : 5850
PROGRESS: Starting L-BFGS
PROGRESS: --------------------------------------------------------
PROGRESS: +-----------+----------+-----------+--------------+-------------------+---------------------+
PROGRESS: | Iteration | Passes | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |
PROGRESS: +-----------+----------+-----------+--------------+-------------------+---------------------+
PROGRESS: | 1 | 3 | 0.000012 | 1.160002 | 0.874434 | 0.872089 |
PROGRESS: | 2 | 5 | 1.000000 | 1.394007 | 0.755772 | 0.753400 |
PROGRESS: | 3 | 6 | 1.000000 | 1.626018 | 0.862891 | 0.858720 |
PROGRESS: | 4 | 7 | 1.000000 | 1.847027 | 0.871613 | 0.868129 |
PROGRESS: | 5 | 8 | 1.000000 | 2.058035 | 0.872336 | 0.867896 |
PROGRESS: | 6 | 9 | 1.000000 | 2.274043 | 0.873572 | 0.869620 |
PROGRESS: | 10 | 14 | 1.000000 | 3.170079 | 0.873280 | 0.869108 |
PROGRESS: | 11 | 15 | 1.000000 | 3.388086 | 0.873490 | 0.868362 |
PROGRESS: | 15 | 19 | 1.000000 | 4.238123 | 0.882783 | 0.877399 |
PROGRESS: | 20 | 24 | 1.000000 | 5.290167 | 0.883040 | 0.878284 |
PROGRESS: | 25 | 29 | 1.000000 | 6.336213 | 0.883471 | 0.878098 |
PROGRESS: | 30 | 34 | 1.000000 | 7.376259 | 0.883238 | 0.877399 |
PROGRESS: | 35 | 39 | 1.000000 | 8.442306 | 0.883611 | 0.877911 |
PROGRESS: | 40 | 44 | 1.000000 | 9.520346 | 0.883611 | 0.877911 |
PROGRESS: | 45 | 49 | 1.000000 | 10.630399 | 0.883331 | 0.877166 |
PROGRESS: | 50 | 54 | 1.000000 | 11.791449 | 0.883284 | 0.876887 |
PROGRESS: | 51 | 55 | 1.000000 | 12.022459 | 0.883191 | 0.877026 |
PROGRESS: | 55 | 59 | 1.000000 | 12.892500 | 0.883179 | 0.877306 |
PROGRESS: | 60 | 64 | 1.000000 | 13.984548 | 0.883273 | 0.877259 |
PROGRESS: | 65 | 69 | 1.000000 | 15.107593 | 0.883378 | 0.876840 |
PROGRESS: | 70 | 75 | 1.000000 | 16.252651 | 0.883343 | 0.877213 |
PROGRESS: | 75 | 80 | 1.000000 | 17.319698 | 0.883191 | 0.877213 |
PROGRESS: | 80 | 85 | 1.000000 | 18.383747 | 0.883308 | 0.876980 |
PROGRESS: | 85 | 90 | 1.000000 | 19.424792 | 0.883354 | 0.876980 |
PROGRESS: | 90 | 95 | 1.000000 | 20.522836 | 0.883226 | 0.877352 |
PROGRESS: | 95 | 100 | 1.000000 | 21.657882 | 0.883308 | 0.877026 |
PROGRESS: | 100 | 105 | 1.000000 | 22.712927 | 0.883296 | 0.876980 |
PROGRESS: | 101 | 106 | 1.000000 | 22.926932 | 0.883261 | 0.876980 |
PROGRESS: | 105 | 110 | 1.000000 | 23.757974 | 0.883354 | 0.877119 |
PROGRESS: | 110 | 115 | 1.000000 | 24.817018 | 0.883319 | 0.876933 |
PROGRESS: | 115 | 120 | 1.000000 | 25.873076 | 0.883284 | 0.876980 |
PROGRESS: | 120 | 125 | 1.000000 | 26.945119 | 0.883319 | 0.877073 |
PROGRESS: | 125 | 130 | 1.000000 | 27.994165 | 0.883296 | 0.876933 |
PROGRESS: | 130 | 135 | 1.000000 | 29.131218 | 0.883389 | 0.876933 |
PROGRESS: | 135 | 140 | 1.000000 | 30.397270 | 0.883401 | 0.877166 |
PROGRESS: | 140 | 145 | 1.000000 | 31.732320 | 0.883494 | 0.877026 |
PROGRESS: | 145 | 150 | 1.000000 | 33.087380 | 0.883599 | 0.877073 |
PROGRESS: | 150 | 155 | 1.000000 | 34.405427 | 0.883611 | 0.877073 |
PROGRESS: | 155 | 160 | 1.000000 | 35.711479 | 0.883588 | 0.877166 |
PROGRESS: | 160 | 165 | 1.000000 | 37.080526 | 0.883576 | 0.876933 |
PROGRESS: | 165 | 170 | 1.000000 | 38.457576 | 0.883541 | 0.876980 |
PROGRESS: | 170 | 176 | 1.000000 | 39.870628 | 0.883599 | 0.877259 |
PROGRESS: | 175 | 181 | 1.000000 | 41.095679 | 0.883553 | 0.876980 |
PROGRESS: | 180 | 186 | 1.000000 | 42.348726 | 0.883622 | 0.876933 |
PROGRESS: | 185 | 191 | 1.000000 | 43.727777 | 0.883681 | 0.877166 |
PROGRESS: | 190 | 196 | 1.000000 | 45.032830 | 0.883657 | 0.877213 |
PROGRESS: | 195 | 201 | 1.000000 | 46.410881 | 0.883576 | 0.876887 |
PROGRESS: | 200 | 206 | 1.000000 | 47.682930 | 0.883599 | 0.877026 |
PROGRESS: | 205 | 211 | 1.000000 | 48.937984 | 0.883669 | 0.877166 |
PROGRESS: | 210 | 216 | 1.000000 | 50.017025 | 0.883692 | 0.876933 |
PROGRESS: | 215 | 222 | 1.000000 | 51.105069 | 0.883681 | 0.877026 |
PROGRESS: | 220 | 227 | 1.000000 | 52.176120 | 0.883669 | 0.876980 |
PROGRESS: | 225 | 232 | 1.000000 | 53.256166 | 0.883681 | 0.877119 |
PROGRESS: | 230 | 237 | 1.000000 | 54.373213 | 0.883634 | 0.876933 |
PROGRESS: | 235 | 242 | 1.000000 | 55.479261 | 0.883692 | 0.876980 |
PROGRESS: | 240 | 247 | 1.000000 | 56.577310 | 0.883622 | 0.877073 |
PROGRESS: | 245 | 252 | 1.000000 | 57.668360 | 0.883692 | 0.876933 |
PROGRESS: | 250 | 257 | 1.000000 | 58.711412 | 0.883657 | 0.877026 |
PROGRESS: | 255 | 262 | 1.000000 | 59.794461 | 0.883692 | 0.877119 |
PROGRESS: | 260 | 267 | 1.000000 | 60.836511 | 0.883681 | 0.877119 |
PROGRESS: | 265 | 272 | 1.000000 | 61.906558 | 0.883692 | 0.876933 |
PROGRESS: | 270 | 277 | 1.000000 | 62.944599 | 0.883739 | 0.877026 |
PROGRESS: | 275 | 282 | 1.000000 | 64.034645 | 0.883692 | 0.877166 |
PROGRESS: | 280 | 287 | 1.000000 | 65.092691 | 0.883739 | 0.877119 |
PROGRESS: | 285 | 292 | 1.000000 | 66.156738 | 0.883692 | 0.876980 |
PROGRESS: | 290 | 297 | 1.000000 | 67.220785 | 0.883704 | 0.877026 |
PROGRESS: | 295 | 302 | 1.000000 | 68.281823 | 0.883716 | 0.877119 |
PROGRESS: | 300 | 307 | 1.000000 | 69.330872 | 0.883681 | 0.876980 |
PROGRESS: | 305 | 312 | 1.000000 | 70.408919 | 0.883739 | 0.877119 |
PROGRESS: | 310 | 317 | 1.000000 | 71.488972 | 0.883716 | 0.877026 |
PROGRESS: | 315 | 322 | 1.000000 | 72.605012 | 0.883727 | 0.876980 |
PROGRESS: | 320 | 327 | 1.000000 | 73.662069 | 0.883681 | 0.876980 |
PROGRESS: | 325 | 332 | 1.000000 | 74.751109 | 0.883704 | 0.877073 |
PROGRESS: | 330 | 337 | 1.000000 | 75.825158 | 0.883704 | 0.877119 |
PROGRESS: | 335 | 342 | 1.000000 | 76.866208 | 0.883669 | 0.876980 |
PROGRESS: | 340 | 347 | 1.000000 | 77.953253 | 0.883739 | 0.877026 |
PROGRESS: | 345 | 352 | 1.000000 | 79.079297 | 0.883669 | 0.877026 |
PROGRESS: | 350 | 358 | 1.000000 | 80.244339 | 0.883716 | 0.877026 |
PROGRESS: | 355 | 364 | 1.000000 | 81.457393 | 0.883751 | 0.877026 |
PROGRESS: | 360 | 369 | 1.000000 | 82.697440 | 0.883692 | 0.877073 |
PROGRESS: | 365 | 374 | 1.000000 | 83.939490 | 0.883692 | 0.877026 |
PROGRESS: | 370 | 379 | 1.000000 | 85.133541 | 0.883716 | 0.876980 |
PROGRESS: | 375 | 384 | 1.000000 | 86.209579 | 0.883727 | 0.877026 |
PROGRESS: | 380 | 389 | 1.000000 | 87.289622 | 0.883727 | 0.877073 |
PROGRESS: | 385 | 394 | 1.000000 | 88.374665 | 0.883739 | 0.877073 |
PROGRESS: | 390 | 399 | 1.000000 | 89.453711 | 0.883704 | 0.876980 |
PROGRESS: | 395 | 404 | 1.000000 | 90.653758 | 0.883716 | 0.877026 |
PROGRESS: | 400 | 409 | 1.000000 | 91.994810 | 0.883692 | 0.877026 |
PROGRESS: | 405 | 414 | 1.000000 | 93.362867 | 0.883739 | 0.877026 |
PROGRESS: | 410 | 420 | 1.000000 | 94.723922 | 0.883716 | 0.877026 |
PROGRESS: | 415 | 426 | 1.000000 | 96.062973 | 0.883727 | 0.876980 |
PROGRESS: | 420 | 431 | 1.000000 | 97.388027 | 0.883692 | 0.876980 |
PROGRESS: | 425 | 436 | 1.000000 | 98.784079 | 0.883716 | 0.876980 |
PROGRESS: | 430 | 441 | 1.000000 | 100.096134 | 0.883692 | 0.877026 |
PROGRESS: | 435 | 447 | 1.000000 | 101.510190 | 0.883692 | 0.877026 |
PROGRESS: | 440 | 452 | 1.000000 | 102.819240 | 0.883716 | 0.876980 |
PROGRESS: | 445 | 457 | 1.000000 | 104.132295 | 0.883704 | 0.876980 |
PROGRESS: | 450 | 462 | 1.000000 | 105.473347 | 0.883704 | 0.876980 |
PROGRESS: | 455 | 468 | 1.000000 | 106.931395 | 0.883727 | 0.877026 |
PROGRESS: | 460 | 473 | 1.000000 | 108.227452 | 0.883716 | 0.877026 |
PROGRESS: | 465 | 478 | 1.000000 | 109.479506 | 0.883692 | 0.877026 |
PROGRESS: | 470 | 483 | 1.000000 | 110.629552 | 0.883716 | 0.876980 |
PROGRESS: | 475 | 488 | 1.000000 | 111.668603 | 0.883727 | 0.876980 |
PROGRESS: | 480 | 493 | 1.000000 | 112.760656 | 0.883692 | 0.876980 |
PROGRESS: | 485 | 498 | 1.000000 | 113.910704 | 0.883692 | 0.877026 |
PROGRESS: | 490 | 503 | 1.000000 | 115.009746 | 0.883716 | 0.877026 |
PROGRESS: | 495 | 508 | 1.000000 | 116.059785 | 0.883716 | 0.876980 |
PROGRESS: | 500 | 513 | 1.000000 | 117.147836 | 0.883704 | 0.876980 |
PROGRESS: +-----------+----------+-----------+--------------+-------------------+---------------------+
In [5]:
train_data,test_data = pr.random_split(.8)
In [7]:
model
Out[7]:
Class : LogisticClassifier
Schema
------
Number of coefficients : 5850
Number of examples : 85764
Number of classes : 3
Number of feature columns : 9
Number of unpacked features : 9
Hyperparameters
---------------
L1 penalty : 0.0
L2 penalty : 0.01
Training Summary
----------------
Solver : auto
Solver iterations : 500
Solver status : TERMINATED: Iteration limit reached.
Training time (sec) : 117.3298
Settings
--------
Log-likelihood : 23898.9743
Highest Positive Coefficients
-----------------------------
Country[MDA] : 29.4726
AppSiteId[66f161f9d43df83109c8c2e710194b2d695c169c]: 27.3681
AppSiteId[4551a2608684d6b66bf8eaa55b8819e7866c65d5]: 22.7308
AppSiteId[f8ed3d18079e62af1263f632a6f91af777bf9b3a]: 21.7491
CreativeCategory[American Cuisine#Barbecues & Grilling]: 18.8337
Lowest Negative Coefficients
----------------------------
AppSiteId[9c76a5c7ebeaa1adbf4be04bf03417c604f7a5c1]: -25.4386
AppSiteId[970f66f8cdbbc54a2eefd432617325cebb903d5d]: -24.3822
AppSiteId[9f9742baac7e7d247f50dab42f62f25ac5a9e47a]: -21.8575
AppSiteId[3673bce9e7fe67199f46e77e2679af0480a91c4b]: -21.5934
AppSiteId[ea7852ea7b123ab849c673171871d73c222c830b]: -20.5113
In [8]:
model=graphlab.logistic_classifier.create(train_data,target='Outcome',features=['TrafficType','PublisherId','AppSiteId','AppSiteCategory','DeviceType','Country','CampaignId','CreativeCategory','ExchangeBid'],validation_set=test_data,max_iterations=500)
PROGRESS: Logistic regression:
PROGRESS: --------------------------------------------------------
PROGRESS: Number of examples : 85764
PROGRESS: Number of classes : 2
PROGRESS: Number of feature columns : 15
PROGRESS: Number of unpacked features : 15
PROGRESS: Number of coefficients : 61226
PROGRESS: Starting L-BFGS
PROGRESS: --------------------------------------------------------
PROGRESS: +-----------+----------+-----------+--------------+-------------------+---------------------+
PROGRESS: | Iteration | Passes | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |
PROGRESS: +-----------+----------+-----------+--------------+-------------------+---------------------+
PROGRESS: | 1 | 3 | 0.000012 | 0.170004 | 0.711756 | 0.680688 |
PROGRESS: | 2 | 5 | 1.000000 | 0.306008 | 0.905473 | 0.815819 |
PROGRESS: | 3 | 6 | 1.000000 | 0.412011 | 0.950807 | 0.858394 |
PROGRESS: | 4 | 7 | 1.000000 | 0.521015 | 0.967702 | 0.862679 |
PROGRESS: | 5 | 8 | 1.000000 | 0.602015 | 0.973322 | 0.874697 |
PROGRESS: | 6 | 9 | 1.000000 | 0.704017 | 0.973765 | 0.862120 |
PROGRESS: | 10 | 13 | 1.000000 | 1.134030 | 0.978452 | 0.871902 |
PROGRESS: | 11 | 14 | 1.000000 | 1.256032 | 0.978511 | 0.872881 |
PROGRESS: | 20 | 23 | 1.000000 | 2.256062 | 0.980283 | 0.864915 |
PROGRESS: | 30 | 33 | 1.000000 | 3.322088 | 0.982592 | 0.858580 |
PROGRESS: | 40 | 43 | 1.000000 | 4.424117 | 0.983233 | 0.846702 |
PROGRESS: | 50 | 53 | 1.000000 | 5.489140 | 0.983373 | 0.837013 |
PROGRESS: | 51 | 54 | 1.000000 | 5.590143 | 0.983361 | 0.836454 |
PROGRESS: | 60 | 63 | 1.000000 | 6.508171 | 0.983501 | 0.832262 |
PROGRESS: | 70 | 73 | 1.000000 | 7.544197 | 0.983723 | 0.838597 |
PROGRESS: | 80 | 83 | 1.000000 | 8.559230 | 0.983909 | 0.843208 |
PROGRESS: | 90 | 93 | 1.000000 | 9.585251 | 0.984061 | 0.843488 |
PROGRESS: | 100 | 103 | 1.000000 | 10.654278 | 0.984026 | 0.844885 |
PROGRESS: | 101 | 104 | 1.000000 | 10.748282 | 0.983944 | 0.844932 |
PROGRESS: | 110 | 113 | 1.000000 | 11.663308 | 0.984038 | 0.845864 |
PROGRESS: | 120 | 123 | 1.000000 | 12.737333 | 0.983874 | 0.846516 |
PROGRESS: | 130 | 133 | 1.000000 | 13.729362 | 0.983933 | 0.846655 |
PROGRESS: | 140 | 144 | 1.000000 | 14.776393 | 0.984154 | 0.845538 |
PROGRESS: | 150 | 154 | 1.000000 | 15.782417 | 0.984084 | 0.845491 |
PROGRESS: | 160 | 164 | 1.000000 | 16.709442 | 0.984154 | 0.845398 |
PROGRESS: | 170 | 174 | 1.000000 | 17.557460 | 0.984084 | 0.844187 |
PROGRESS: | 180 | 184 | 1.000000 | 18.405974 | 0.984154 | 0.843721 |
PROGRESS: | 190 | 194 | 1.000000 | 19.263995 | 0.983991 | 0.846516 |
PROGRESS: | 200 | 204 | 1.000000 | 20.144016 | 0.983933 | 0.849730 |
PROGRESS: | 210 | 215 | 1.000000 | 21.147042 | 0.983979 | 0.852758 |
PROGRESS: | 220 | 225 | 1.000000 | 22.133062 | 0.984049 | 0.854015 |
PROGRESS: | 230 | 235 | 1.000000 | 23.058085 | 0.984014 | 0.854248 |
PROGRESS: | 240 | 245 | 1.000000 | 24.006108 | 0.984073 | 0.854900 |
PROGRESS: | 250 | 255 | 1.000000 | 24.896129 | 0.984259 | 0.855785 |
PROGRESS: | 260 | 265 | 1.000000 | 25.819153 | 0.984294 | 0.855413 |
PROGRESS: | 270 | 275 | 1.000000 | 26.714174 | 0.984259 | 0.856344 |
PROGRESS: | 280 | 285 | 1.000000 | 27.582194 | 0.984178 | 0.856950 |
PROGRESS: | 290 | 295 | 1.000000 | 28.450214 | 0.984201 | 0.857649 |
PROGRESS: | 300 | 305 | 1.000000 | 29.375238 | 0.984236 | 0.857881 |
PROGRESS: | 310 | 315 | 1.000000 | 30.352266 | 0.984143 | 0.858953 |
PROGRESS: | 320 | 325 | 1.000000 | 31.288291 | 0.984247 | 0.859791 |
PROGRESS: | 330 | 336 | 1.000000 | 32.206312 | 0.984212 | 0.860676 |
PROGRESS: | 340 | 346 | 1.000000 | 33.228340 | 0.984247 | 0.860956 |
PROGRESS: | 350 | 356 | 1.000000 | 34.268359 | 0.984259 | 0.861235 |
PROGRESS: | 360 | 366 | 1.000000 | 35.166387 | 0.984201 | 0.861515 |
PROGRESS: | 370 | 376 | 1.000000 | 36.034408 | 0.984224 | 0.862679 |
PROGRESS: | 380 | 386 | 1.000000 | 36.944434 | 0.984236 | 0.862912 |
PROGRESS: | 390 | 396 | 1.000000 | 37.822454 | 0.984178 | 0.863331 |
PROGRESS: | 400 | 406 | 1.000000 | 38.682476 | 0.984189 | 0.863099 |
PROGRESS: | 410 | 416 | 1.000000 | 39.568493 | 0.984271 | 0.862167 |
PROGRESS: | 420 | 426 | 1.000000 | 40.486517 | 0.984236 | 0.861748 |
PROGRESS: | 430 | 436 | 1.000000 | 41.388537 | 0.984236 | 0.861655 |
PROGRESS: | 440 | 446 | 1.000000 | 42.258557 | 0.984259 | 0.861142 |
PROGRESS: | 450 | 457 | 1.000000 | 43.163580 | 0.984236 | 0.860537 |
PROGRESS: | 460 | 467 | 1.000000 | 44.072604 | 0.984259 | 0.860537 |
PROGRESS: | 470 | 477 | 1.000000 | 44.958624 | 0.984294 | 0.860956 |
PROGRESS: | 480 | 487 | 1.000000 | 45.856645 | 0.984282 | 0.860583 |
PROGRESS: | 490 | 497 | 1.000000 | 46.754664 | 0.984282 | 0.860816 |
PROGRESS: | 500 | 507 | 1.000000 | 47.647684 | 0.984271 | 0.861096 |
PROGRESS: +-----------+----------+-----------+--------------+-------------------+---------------------+
In [ ]:
model=graphlab.logistic_classifier.create(train_data,target='Outcome',features=['TrafficType','PublisherId','AppSiteId','AppSiteCategory','DeviceType','Country','CampaignId','CreativeCategory','ExchangeBid'],validation_set=test_data,max_iterations=500)
In [10]:
model=graphlab.logistic_classifier.create(train_data,target='sentiment',features=['TrafficType','PublisherId','AppSiteId','AppSiteCategory','DeviceType','Country','CampaignId','CreativeCategory','ExchangeBid'],validation_set=test_data,max_iterations=500)
PROGRESS: Logistic regression:
PROGRESS: --------------------------------------------------------
PROGRESS: Number of examples : 85764
PROGRESS: Number of classes : 2
PROGRESS: Number of feature columns : 9
PROGRESS: Number of unpacked features : 9
PROGRESS: Number of coefficients : 2925
PROGRESS: Starting L-BFGS
PROGRESS: --------------------------------------------------------
PROGRESS: +-----------+----------+-----------+--------------+-------------------+---------------------+
PROGRESS: | Iteration | Passes | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |
PROGRESS: +-----------+----------+-----------+--------------+-------------------+---------------------+
PROGRESS: | 1 | 3 | 0.000012 | 0.090001 | 0.681580 | 0.678126 |
PROGRESS: | 2 | 5 | 1.000000 | 0.174001 | 0.767571 | 0.763695 |
PROGRESS: | 3 | 6 | 1.000000 | 0.222002 | 0.873059 | 0.869061 |
PROGRESS: | 4 | 7 | 1.000000 | 0.282002 | 0.873338 | 0.869387 |
PROGRESS: | 5 | 8 | 1.000000 | 0.337004 | 0.875379 | 0.871390 |
PROGRESS: | 6 | 10 | 1.000000 | 0.414006 | 0.874609 | 0.870552 |
PROGRESS: | 11 | 15 | 1.000000 | 0.696011 | 0.875286 | 0.868688 |
PROGRESS: | 25 | 29 | 1.000000 | 1.476021 | 0.885360 | 0.878983 |
PROGRESS: | 50 | 54 | 1.000000 | 2.880049 | 0.884847 | 0.878470 |
PROGRESS: | 51 | 55 | 1.000000 | 2.940049 | 0.884917 | 0.878377 |
PROGRESS: | 75 | 80 | 1.000000 | 4.310066 | 0.884987 | 0.877865 |
PROGRESS: | 100 | 107 | 1.000000 | 5.750084 | 0.885045 | 0.878051 |
PROGRESS: | 101 | 108 | 1.000000 | 5.796086 | 0.885068 | 0.878051 |
PROGRESS: | 125 | 132 | 1.000000 | 7.090102 | 0.884987 | 0.877818 |
PROGRESS: | 150 | 157 | 1.000000 | 8.424127 | 0.885162 | 0.878004 |
PROGRESS: | 175 | 183 | 1.000000 | 9.798145 | 0.885371 | 0.878237 |
PROGRESS: | 200 | 208 | 1.000000 | 11.236166 | 0.885418 | 0.878237 |
PROGRESS: | 225 | 233 | 1.000000 | 12.719188 | 0.885383 | 0.878144 |
PROGRESS: | 250 | 259 | 1.000000 | 14.520216 | 0.885371 | 0.878098 |
PROGRESS: | 275 | 284 | 1.000000 | 16.234234 | 0.885348 | 0.878098 |
PROGRESS: | 300 | 309 | 1.000000 | 17.986263 | 0.885360 | 0.878098 |
PROGRESS: | 325 | 334 | 1.000000 | 19.890296 | 0.885348 | 0.878098 |
PROGRESS: | 350 | 359 | 1.000000 | 21.634314 | 0.885337 | 0.878098 |
PROGRESS: | 375 | 384 | 1.000000 | 23.448335 | 0.885337 | 0.878098 |
PROGRESS: | 400 | 409 | 1.000000 | 25.244369 | 0.885337 | 0.878098 |
PROGRESS: | 425 | 434 | 1.000000 | 27.015398 | 0.885337 | 0.878098 |
PROGRESS: | 450 | 460 | 1.000000 | 28.716420 | 0.885337 | 0.878098 |
PROGRESS: | 475 | 486 | 1.000000 | 30.504445 | 0.885337 | 0.878051 |
PROGRESS: | 500 | 513 | 1.000000 | 32.094463 | 0.885337 | 0.878144 |
PROGRESS: +-----------+----------+-----------+--------------+-------------------+---------------------+
In [12]:
model
Out[12]:
Class : LogisticClassifier
Schema
------
Number of coefficients : 2925
Number of examples : 85764
Number of classes : 2
Number of feature columns : 9
Number of unpacked features : 9
Hyperparameters
---------------
L1 penalty : 0.0
L2 penalty : 0.01
Training Summary
----------------
Solver : auto
Solver iterations : 500
Solver status : TERMINATED: Iteration limit reached.
Training time (sec) : 29.0924
Settings
--------
Log-likelihood : 23197.0483
Highest Positive Coefficients
-----------------------------
Country[MDA] : 24.7432
AppSiteId[66f161f9d43df83109c8c2e710194b2d695c169c]: 23.8477
AppSiteId[f8ed3d18079e62af1263f632a6f91af777bf9b3a]: 22.2534
AppSiteId[c1952009179b2073b96040d98111c22ab89c544b]: 17.409
CreativeCategory[Financial Planning#Options]: 15.8339
Lowest Negative Coefficients
----------------------------
AppSiteId[813a85a06f66bd5cc64a065c9a92cfe974d85346]: -24.0515
AppSiteId[6adc879c3e8b6056688381ba06a14c4d0290ad17]: -23.0711
AppSiteId[93c5f967739b16d9e154b3399970e44c7070ded5]: -22.4148
CreativeCategory[Beauty] : -22.0531
CreativeCategory[Adventure Travel#Television]: -21.3993
In [14]:
model=graphlab.logistic_classifier.create(train_data,target='sentiment',features=['TrafficType','DeviceType','CampaignId','CreativeCategory','ExchangeBid'],validation_set=test_data,max_iterations=500)
PROGRESS: Logistic regression:
PROGRESS: --------------------------------------------------------
PROGRESS: Number of examples : 85764
PROGRESS: Number of classes : 2
PROGRESS: Number of feature columns : 5
PROGRESS: Number of unpacked features : 5
PROGRESS: Number of coefficients : 87
PROGRESS: Starting Newton Method
PROGRESS: --------------------------------------------------------
PROGRESS: +-----------+----------+--------------+-------------------+---------------------+
PROGRESS: | Iteration | Passes | Elapsed Time | Training-accuracy | Validation-accuracy |
PROGRESS: +-----------+----------+--------------+-------------------+---------------------+
PROGRESS: | 1 | 2 | 0.083002 | 0.879775 | 0.882756 |
PROGRESS: | 2 | 3 | 0.142004 | 0.880673 | 0.882663 |
PROGRESS: | 3 | 4 | 0.194005 | 0.880416 | 0.882337 |
PROGRESS: | 4 | 5 | 0.256006 | 0.879961 | 0.881917 |
PROGRESS: | 5 | 6 | 0.318007 | 0.879950 | 0.881917 |
PROGRESS: | 6 | 7 | 0.390009 | 0.879950 | 0.881917 |
PROGRESS: | 11 | 12 | 0.686018 | 0.879950 | 0.881917 |
PROGRESS: +-----------+----------+--------------+-------------------+---------------------+
In [15]:
model
Out[15]:
Class : LogisticClassifier
Schema
------
Number of coefficients : 87
Number of examples : 85764
Number of classes : 2
Number of feature columns : 5
Number of unpacked features : 5
Hyperparameters
---------------
L1 penalty : 0.0
L2 penalty : 0.01
Training Summary
----------------
Solver : auto
Solver iterations : 13
Solver status : SUCCESS: Optimal solution found.
Training time (sec) : 0.844
Settings
--------
Log-likelihood : 24218.683
Highest Positive Coefficients
-----------------------------
CreativeCategory[Investing] : 10.0249
CreativeCategory[Board Games/Puzzles#Card Games]: 9.9306
CreativeCategory[American Cuisine#Barbecues & Grilling]: 9.7159
CreativeCategory[Financial Planning#Options]: 9.7068
CreativeCategory[Buying/Selling Cars]: 9.6643
Lowest Negative Coefficients
----------------------------
CreativeCategory[American Cuisine#Barbecues & Grilling#Board Games/Puzzles]: -16.7284
CreativeCategory[Pickup#Air Travel]: -15.7733
CreativeCategory[Beauty] : -15.6791
CreativeCategory[Adventure Travel#Television]: -15.1703
CreativeCategory[Video & Computer Games#Roleplaying Games]: -15.1591
In [16]:
model.evaluate(test_data)
Out[16]:
{'accuracy': 0.8819172722191169, 'confusion_matrix': Columns:
target_label int
predicted_label int
count int
Rows: 4
Data:
+--------------+-----------------+-------+
| target_label | predicted_label | count |
+--------------+-----------------+-------+
| 1 | 0 | 785 |
| 1 | 1 | 6345 |
| 0 | 1 | 1750 |
| 0 | 0 | 12588 |
+--------------+-----------------+-------+
[4 rows x 3 columns]}
In [19]:
model.save('mymodel')
In [20]:
model.evaluate(test_data, metric='roc_curve')
Out[20]:
{'roc_curve': Columns:
threshold float
fpr float
tpr float
p int
n int
Rows: 1001
Data:
+------------------+----------------+------------------+------+-------+
| threshold | fpr | tpr | p | n |
+------------------+----------------+------------------+------+-------+
| 0.0 | 0.442381517415 | 0.00294654132173 | 7127 | 14327 |
| 0.0010000000475 | 0.557618482585 | 0.997053458678 | 7127 | 14327 |
| 0.00200000009499 | 0.539959516996 | 0.996913147187 | 7127 | 14327 |
| 0.00300000002608 | 0.539959516996 | 0.996913147187 | 7127 | 14327 |
| 0.00400000018999 | 0.539959516996 | 0.996913147187 | 7127 | 14327 |
| 0.00499999988824 | 0.539959516996 | 0.996913147187 | 7127 | 14327 |
| 0.00600000005215 | 0.539959516996 | 0.996913147187 | 7127 | 14327 |
| 0.00700000021607 | 0.539959516996 | 0.996913147187 | 7127 | 14327 |
| 0.00800000037998 | 0.539959516996 | 0.996913147187 | 7127 | 14327 |
| 0.00899999961257 | 0.539959516996 | 0.996913147187 | 7127 | 14327 |
+------------------+----------------+------------------+------+-------+
[1001 rows x 5 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}
In [21]:
model.show(view='Evaluation')
Canvas is accessible via web browser at the URL: http://localhost:62135/index.html
Opening Canvas in default web browser.
In [ ]:
Content source: arcolife/applift-hack-team-vicarious
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