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+ "2 5.4526 49.0385 55.5263 \n",
354
+ "3 5.6604 19.2308 46.6667 \n",
355
+ "4 0.3784 37.5405 38.4709 \n",
356
+ "... ... ... ... \n",
357
+ "39138 4.3779 44.6429 47.2831 \n",
358
+ "39139 0.9901 27.7372 30.4878 \n",
359
+ "39140 2.4476 68.5185 64.7754 \n",
360
+ "39141 0.7042 64.1935 62.5000 \n",
361
+ "39142 6.3559 43.8776 37.7778 \n",
362
+ "\n",
363
+ " l6m_called_cnt_rate contact_ratio ... rcn_chnl_id rcn_chnl_typ \\\n",
364
+ "0 84.3023 1 ... 30507 3 \n",
365
+ "1 67.8191 3 ... GZ_GZPY0612038 NaN \n",
366
+ "2 56.4885 7 ... 218568 NaN \n",
367
+ "3 52.8302 0 ... 2.25E+18 NaN \n",
368
+ "4 40.6486 15 ... BASS1_ST 9999 \n",
369
+ "... ... ... ... ... ... \n",
370
+ "39138 49.9078 20 ... 112000259 3 \n",
371
+ "39139 30.5831 9 ... 27031001 1002 \n",
372
+ "39140 63.4615 5 ... NX.01.06.03.100 2104 \n",
373
+ "39141 63.8833 14 ... HB.TS.13.01.G1 NaN \n",
374
+ "39142 35.5932 8 ... 10191125 2103 \n",
375
+ "\n",
376
+ " rcn_mode user_star_val star_evalu_tm is_fam_v_ntw is_camp_user \\\n",
377
+ "0 4.0 198.0 202110 0 0 \n",
378
+ "1 4.0 91.0 202207 0 0 \n",
379
+ "2 4.0 NaN -3 1 1 \n",
380
+ "3 4.0 133.0 202112 1 1 \n",
381
+ "4 99.0 NaN -3 0 0 \n",
382
+ "... ... ... ... ... ... \n",
383
+ "39138 99.0 311.0 202202 0 0 \n",
384
+ "39139 4.0 203.0 202110 0 0 \n",
385
+ "39140 4.0 119.0 202107 0 0 \n",
386
+ "39141 4.0 163.0 202111 0 0 \n",
387
+ "39142 99.0 250.0 202111 1 0 \n",
388
+ "\n",
389
+ " is_camp_area_user row_num label \n",
390
+ "0 0 29833 0 \n",
391
+ "1 0 18121 0 \n",
392
+ "2 0 8372 0 \n",
393
+ "3 0 32780 0 \n",
394
+ "4 0 3509 0 \n",
395
+ "... ... ... ... \n",
396
+ "39138 0 3193 0 \n",
397
+ "39139 0 46717 0 \n",
398
+ "39140 0 46379 0 \n",
399
+ "39141 0 12670 0 \n",
400
+ "39142 0 43570 0 \n",
401
+ "\n",
402
+ "[39143 rows x 85 columns]"
403
+ ]
404
+ },
405
+ "execution_count": 1,
406
+ "metadata": {},
407
+ "output_type": "execute_result"
408
+ }
409
+ ],
410
+ "source": [
411
+ "import numpy as np\n",
412
+ "import pandas as pd\n",
413
+ "import os\n",
414
+ "import warnings\n",
415
+ "warnings.filterwarnings('ignore')\n",
416
+ "\n",
417
+ "data1=pd.read_csv('csvdata/train_a_label.csv', encoding='gbk')\n",
418
+ "data2=pd.read_csv('csvdata/train_b.csv')\n",
419
+ "data3=pd.read_csv('csvdata/test_a.csv', encoding='gbk')\n",
420
+ "data4=pd.read_csv('csvdata/test_b.csv')\n",
421
+ "data1"
422
+ ]
423
+ },
424
+ {
425
+ "cell_type": "code",
426
+ "execution_count": 2,
427
+ "id": "deadly-torture",
428
+ "metadata": {
429
+ "scrolled": true
430
+ },
431
+ "outputs": [],
432
+ "source": [
433
+ "#补0\n",
434
+ "data1=data1.fillna(-1)\n",
435
+ "data2=data2.fillna(-1)\n",
436
+ "data3=data3.fillna(-1)\n",
437
+ "data4=data4.fillna(-1)\n",
438
+ "\n",
439
+ "data1=data1[['row_num'] + data1.drop(labels=['row_num'],axis=1).columns.tolist() ] \n",
440
+ "data2=data2[['row_num'] + data2.drop(labels=['row_num'],axis=1).columns.tolist() ] \n",
441
+ "data3=data3[['row_num'] + data3.drop(labels=['row_num'],axis=1).columns.tolist() ] \n",
442
+ "data4=data4[['row_num'] + data4.drop(labels=['row_num'],axis=1).columns.tolist() ] \n"
443
+ ]
444
+ },
445
+ {
446
+ "cell_type": "code",
447
+ "execution_count": 3,
448
+ "id": "narrow-feedback",
449
+ "metadata": {},
450
+ "outputs": [
451
+ {
452
+ "data": {
453
+ "text/html": [
454
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+ " text-align: right;\n",
466
+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
469
+ " <thead>\n",
470
+ " <tr style=\"text-align: right;\">\n",
471
+ " <th></th>\n",
472
+ " <th>row_num</th>\n",
473
+ " <th>label</th>\n",
474
+ " <th>user_star_val</th>\n",
475
+ " <th>night_call_dura_rate</th>\n",
476
+ " <th>night_call_cnt_rate</th>\n",
477
+ " <th>age</th>\n",
478
+ " <th>cust_star</th>\n",
479
+ " <th>opp_belo_cnt</th>\n",
480
+ " <th>rcn_mode</th>\n",
481
+ " <th>l6m_night_call_cnt_rate</th>\n",
482
+ " <th>night_percent_six</th>\n",
483
+ " <th>brand_id</th>\n",
484
+ " </tr>\n",
485
+ " </thead>\n",
486
+ " <tbody>\n",
487
+ " <tr>\n",
488
+ " <th>0</th>\n",
489
+ " <td>29833</td>\n",
490
+ " <td>0</td>\n",
491
+ " <td>198.0</td>\n",
492
+ " <td>1.2726</td>\n",
493
+ " <td>3.1250</td>\n",
494
+ " <td>2</td>\n",
495
+ " <td>4.0</td>\n",
496
+ " <td>5</td>\n",
497
+ " <td>4.0</td>\n",
498
+ " <td>4.6512</td>\n",
499
+ " <td>5.11</td>\n",
500
+ " <td>2</td>\n",
501
+ " </tr>\n",
502
+ " <tr>\n",
503
+ " <th>1</th>\n",
504
+ " <td>18121</td>\n",
505
+ " <td>0</td>\n",
506
+ " <td>91.0</td>\n",
507
+ " <td>8.4315</td>\n",
508
+ " <td>6.9767</td>\n",
509
+ " <td>1</td>\n",
510
+ " <td>6.0</td>\n",
511
+ " <td>3</td>\n",
512
+ " <td>4.0</td>\n",
513
+ " <td>1.3298</td>\n",
514
+ " <td>17.64</td>\n",
515
+ " <td>2</td>\n",
516
+ " </tr>\n",
517
+ " <tr>\n",
518
+ " <th>2</th>\n",
519
+ " <td>8372</td>\n",
520
+ " <td>0</td>\n",
521
+ " <td>-1.0</td>\n",
522
+ " <td>5.0105</td>\n",
523
+ " <td>7.6923</td>\n",
524
+ " <td>4</td>\n",
525
+ " <td>-1.0</td>\n",
526
+ " <td>3</td>\n",
527
+ " <td>4.0</td>\n",
528
+ " <td>5.4526</td>\n",
529
+ " <td>12.70</td>\n",
530
+ " <td>2</td>\n",
531
+ " </tr>\n",
532
+ " <tr>\n",
533
+ " <th>3</th>\n",
534
+ " <td>32780</td>\n",
535
+ " <td>0</td>\n",
536
+ " <td>133.0</td>\n",
537
+ " <td>1.4925</td>\n",
538
+ " <td>3.8462</td>\n",
539
+ " <td>2</td>\n",
540
+ " <td>7.0</td>\n",
541
+ " <td>3</td>\n",
542
+ " <td>4.0</td>\n",
543
+ " <td>5.6604</td>\n",
544
+ " <td>2.83</td>\n",
545
+ " <td>1</td>\n",
546
+ " </tr>\n",
547
+ " <tr>\n",
548
+ " <th>4</th>\n",
549
+ " <td>3509</td>\n",
550
+ " <td>0</td>\n",
551
+ " <td>-1.0</td>\n",
552
+ " <td>0.7424</td>\n",
553
+ " <td>0.6472</td>\n",
554
+ " <td>6</td>\n",
555
+ " <td>-1.0</td>\n",
556
+ " <td>3</td>\n",
557
+ " <td>99.0</td>\n",
558
+ " <td>0.3784</td>\n",
559
+ " <td>6.18</td>\n",
560
+ " <td>1</td>\n",
561
+ " </tr>\n",
562
+ " <tr>\n",
563
+ " <th>...</th>\n",
564
+ " <td>...</td>\n",
565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
+ " <th>39138</th>\n",
579
+ " <td>3193</td>\n",
580
+ " <td>0</td>\n",
581
+ " <td>311.0</td>\n",
582
+ " <td>0.6517</td>\n",
583
+ " <td>2.5510</td>\n",
584
+ " <td>5</td>\n",
585
+ " <td>4.0</td>\n",
586
+ " <td>3</td>\n",
587
+ " <td>99.0</td>\n",
588
+ " <td>4.3779</td>\n",
589
+ " <td>11.52</td>\n",
590
+ " <td>2</td>\n",
591
+ " </tr>\n",
592
+ " <tr>\n",
593
+ " <th>39139</th>\n",
594
+ " <td>46717</td>\n",
595
+ " <td>0</td>\n",
596
+ " <td>203.0</td>\n",
597
+ " <td>0.3208</td>\n",
598
+ " <td>0.7299</td>\n",
599
+ " <td>3</td>\n",
600
+ " <td>5.0</td>\n",
601
+ " <td>4</td>\n",
602
+ " <td>4.0</td>\n",
603
+ " <td>0.9901</td>\n",
604
+ " <td>11.19</td>\n",
605
+ " <td>1</td>\n",
606
+ " </tr>\n",
607
+ " <tr>\n",
608
+ " <th>39140</th>\n",
609
+ " <td>46379</td>\n",
610
+ " <td>0</td>\n",
611
+ " <td>119.0</td>\n",
612
+ " <td>1.3945</td>\n",
613
+ " <td>1.8519</td>\n",
614
+ " <td>3</td>\n",
615
+ " <td>5.0</td>\n",
616
+ " <td>4</td>\n",
617
+ " <td>4.0</td>\n",
618
+ " <td>2.4476</td>\n",
619
+ " <td>30.93</td>\n",
620
+ " <td>1</td>\n",
621
+ " </tr>\n",
622
+ " <tr>\n",
623
+ " <th>39141</th>\n",
624
+ " <td>12670</td>\n",
625
+ " <td>0</td>\n",
626
+ " <td>163.0</td>\n",
627
+ " <td>1.3134</td>\n",
628
+ " <td>0.3226</td>\n",
629
+ " <td>6</td>\n",
630
+ " <td>5.0</td>\n",
631
+ " <td>2</td>\n",
632
+ " <td>4.0</td>\n",
633
+ " <td>0.7042</td>\n",
634
+ " <td>36.48</td>\n",
635
+ " <td>1</td>\n",
636
+ " </tr>\n",
637
+ " <tr>\n",
638
+ " <th>39142</th>\n",
639
+ " <td>43570</td>\n",
640
+ " <td>0</td>\n",
641
+ " <td>250.0</td>\n",
642
+ " <td>3.2597</td>\n",
643
+ " <td>7.1429</td>\n",
644
+ " <td>4</td>\n",
645
+ " <td>5.0</td>\n",
646
+ " <td>5</td>\n",
647
+ " <td>99.0</td>\n",
648
+ " <td>6.3559</td>\n",
649
+ " <td>24.18</td>\n",
650
+ " <td>1</td>\n",
651
+ " </tr>\n",
652
+ " </tbody>\n",
653
+ "</table>\n",
654
+ "<p>39143 rows × 12 columns</p>\n",
655
+ "</div>"
656
+ ],
657
+ "text/plain": [
658
+ " row_num label user_star_val night_call_dura_rate \\\n",
659
+ "0 29833 0 198.0 1.2726 \n",
660
+ "1 18121 0 91.0 8.4315 \n",
661
+ "2 8372 0 -1.0 5.0105 \n",
662
+ "3 32780 0 133.0 1.4925 \n",
663
+ "4 3509 0 -1.0 0.7424 \n",
664
+ "... ... ... ... ... \n",
665
+ "39138 3193 0 311.0 0.6517 \n",
666
+ "39139 46717 0 203.0 0.3208 \n",
667
+ "39140 46379 0 119.0 1.3945 \n",
668
+ "39141 12670 0 163.0 1.3134 \n",
669
+ "39142 43570 0 250.0 3.2597 \n",
670
+ "\n",
671
+ " night_call_cnt_rate age cust_star opp_belo_cnt rcn_mode \\\n",
672
+ "0 3.1250 2 4.0 5 4.0 \n",
673
+ "1 6.9767 1 6.0 3 4.0 \n",
674
+ "2 7.6923 4 -1.0 3 4.0 \n",
675
+ "3 3.8462 2 7.0 3 4.0 \n",
676
+ "4 0.6472 6 -1.0 3 99.0 \n",
677
+ "... ... ... ... ... ... \n",
678
+ "39138 2.5510 5 4.0 3 99.0 \n",
679
+ "39139 0.7299 3 5.0 4 4.0 \n",
680
+ "39140 1.8519 3 5.0 4 4.0 \n",
681
+ "39141 0.3226 6 5.0 2 4.0 \n",
682
+ "39142 7.1429 4 5.0 5 99.0 \n",
683
+ "\n",
684
+ " l6m_night_call_cnt_rate night_percent_six brand_id \n",
685
+ "0 4.6512 5.11 2 \n",
686
+ "1 1.3298 17.64 2 \n",
687
+ "2 5.4526 12.70 2 \n",
688
+ "3 5.6604 2.83 1 \n",
689
+ "4 0.3784 6.18 1 \n",
690
+ "... ... ... ... \n",
691
+ "39138 4.3779 11.52 2 \n",
692
+ "39139 0.9901 11.19 1 \n",
693
+ "39140 2.4476 30.93 1 \n",
694
+ "39141 0.7042 36.48 1 \n",
695
+ "39142 6.3559 24.18 1 \n",
696
+ "\n",
697
+ "[39143 rows x 12 columns]"
698
+ ]
699
+ },
700
+ "execution_count": 3,
701
+ "metadata": {},
702
+ "output_type": "execute_result"
703
+ }
704
+ ],
705
+ "source": [
706
+ "#data1=data1.drop(labels=['pretty_num_typ','rcn_chnl_id','rcn_chnl_typ','star_evalu_tm','star_evalu_tm', 'top5_call_dura_rate', 'l6m_once_numbers_rate', 'once_numbers_rate', 'l6m_contact_ratio', 'l3m_top10_call_cnt_rate', 'l3m_top5_call_cnt_rate', 'l6m_top10_call_dura_rate', 'l3m_is_same_imei_msisdn_cnt_gtr2', 'top10_call_dura_rate', 'ocpn_code', 'top5_call_cnt_rate', 'top10_opp_belo_cnt', 'educat_degree_code', 'l6m_top10_opp_belo_cnt', 'l3m_once_numbers_rate', 'l3m_top10_opp_belo_cnt', 'l3m_contact_ratio', 'l6m_top5_call_dura_rate', 'pre3m_top10_diff_num', 'age_lvl', 'top10_diff_num', 'same_idcard_msisdn_cnt', 'pre3m_top5_diff_num', 'l3m_id_cnt', 'l3m_top10_call_dura_rate', 'pre2m_top10_diff_num', 'top10_call_cnt_rate', 'contact_ratio', 'top5_opp_belo_cnt', 'is_pretty_num', 'l3m_top5_opp_belo_cnt', 'pre2m_top5_diff_num', 'l1m_is_same_imei_msisdn_cnt_gtr2', 'sex', 'same_imei_msisdn_hr_cnt', 'l6m_top5_opp_belo_cnt', 'is_fam_v_ntw', 'top5_diff_num', 'is_camp_user', 'idty_typ', 'zuche_six_total_dur', 'vip_lvl'],axis=1)\n",
707
+ "data1=data1[['row_num','label','user_star_val','night_call_dura_rate', 'night_call_cnt_rate', 'age', 'cust_star', 'opp_belo_cnt', 'rcn_mode', 'l6m_night_call_cnt_rate', 'night_percent_six', 'brand_id']]\n",
708
+ "#, 'star_evalu_tm', 'l3m_called_cnt_rate', 'night_percent_current', 'l6m_called_cnt_rate', 'l3m_night_call_dura_rate'\n",
709
+ "data1['age']=data1['age']//10\n",
710
+ "data1.to_csv(path_or_buf='csvclear/train_a_label.csv', index=None) \n",
711
+ "\n",
712
+ "\n",
713
+ "#data3=data3.drop(labels=['pretty_num_typ','rcn_chnl_id','rcn_chnl_typ','star_evalu_tm', 'top5_call_dura_rate', 'l6m_once_numbers_rate', 'once_numbers_rate', 'l6m_contact_ratio', 'l3m_top10_call_cnt_rate', 'l3m_top5_call_cnt_rate', 'l6m_top10_call_dura_rate', 'l3m_is_same_imei_msisdn_cnt_gtr2', 'top10_call_dura_rate', 'ocpn_code', 'top5_call_cnt_rate', 'top10_opp_belo_cnt', 'educat_degree_code', 'l6m_top10_opp_belo_cnt', 'l3m_once_numbers_rate', 'l3m_top10_opp_belo_cnt', 'l3m_contact_ratio', 'l6m_top5_call_dura_rate', 'pre3m_top10_diff_num', 'age_lvl', 'top10_diff_num', 'same_idcard_msisdn_cnt', 'pre3m_top5_diff_num', 'l3m_id_cnt', 'l3m_top10_call_dura_rate', 'pre2m_top10_diff_num', 'top10_call_cnt_rate', 'contact_ratio', 'top5_opp_belo_cnt', 'is_pretty_num', 'l3m_top5_opp_belo_cnt', 'pre2m_top5_diff_num', 'l1m_is_same_imei_msisdn_cnt_gtr2', 'sex', 'same_imei_msisdn_hr_cnt', 'l6m_top5_opp_belo_cnt', 'is_fam_v_ntw', 'top5_diff_num', 'is_camp_user', 'idty_typ', 'zuche_six_total_dur', 'vip_lvl'],axis=1)\n",
714
+ "data3=data3[['row_num','user_star_val', 'star_evalu_tm','night_call_dura_rate', 'night_call_cnt_rate', 'age', 'cust_star', 'opp_belo_cnt', 'rcn_mode', 'l6m_night_call_cnt_rate', 'night_percent_six', 'brand_id']]\n",
715
+ "#data3=data3[['row_num'] + data3.drop(labels=['row_num'],axis=1).columns.tolist() ] \n",
716
+ "data3['age']=data3['age']//10\n",
717
+ "\n",
718
+ "data3.to_csv(path_or_buf='csvclear/test_a.csv', index=None) \n",
719
+ "data1"
720
+ ]
721
+ },
722
+ {
723
+ "cell_type": "code",
724
+ "execution_count": 4,
725
+ "id": "supposed-composer",
726
+ "metadata": {},
727
+ "outputs": [
728
+ {
729
+ "data": {
730
+ "text/html": [
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732
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+ " <thead>\n",
747
+ " <tr style=\"text-align: right;\">\n",
748
+ " <th></th>\n",
749
+ " <th>row_num</th>\n",
750
+ " <th>label</th>\n",
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+ " <th>user_star_val</th>\n",
752
+ " <th>night_call_dura_rate</th>\n",
753
+ " <th>night_call_cnt_rate</th>\n",
754
+ " <th>age</th>\n",
755
+ " <th>cust_star</th>\n",
756
+ " <th>opp_belo_cnt</th>\n",
757
+ " <th>rcn_mode</th>\n",
758
+ " <th>l6m_night_call_cnt_rate</th>\n",
759
+ " <th>night_percent_six</th>\n",
760
+ " <th>brand_id</th>\n",
761
+ " </tr>\n",
762
+ " </thead>\n",
763
+ " <tbody>\n",
764
+ " <tr>\n",
765
+ " <th>0</th>\n",
766
+ " <td>29833</td>\n",
767
+ " <td>0</td>\n",
768
+ " <td>198.0</td>\n",
769
+ " <td>1.2726</td>\n",
770
+ " <td>3.1250</td>\n",
771
+ " <td>2</td>\n",
772
+ " <td>4.0</td>\n",
773
+ " <td>5</td>\n",
774
+ " <td>4.0</td>\n",
775
+ " <td>4.6512</td>\n",
776
+ " <td>5.11</td>\n",
777
+ " <td>2</td>\n",
778
+ " </tr>\n",
779
+ " <tr>\n",
780
+ " <th>1</th>\n",
781
+ " <td>18121</td>\n",
782
+ " <td>0</td>\n",
783
+ " <td>91.0</td>\n",
784
+ " <td>8.4315</td>\n",
785
+ " <td>6.9767</td>\n",
786
+ " <td>1</td>\n",
787
+ " <td>6.0</td>\n",
788
+ " <td>3</td>\n",
789
+ " <td>4.0</td>\n",
790
+ " <td>1.3298</td>\n",
791
+ " <td>17.64</td>\n",
792
+ " <td>2</td>\n",
793
+ " </tr>\n",
794
+ " <tr>\n",
795
+ " <th>2</th>\n",
796
+ " <td>8372</td>\n",
797
+ " <td>0</td>\n",
798
+ " <td>-1.0</td>\n",
799
+ " <td>5.0105</td>\n",
800
+ " <td>7.6923</td>\n",
801
+ " <td>4</td>\n",
802
+ " <td>-1.0</td>\n",
803
+ " <td>3</td>\n",
804
+ " <td>4.0</td>\n",
805
+ " <td>5.4526</td>\n",
806
+ " <td>12.70</td>\n",
807
+ " <td>2</td>\n",
808
+ " </tr>\n",
809
+ " <tr>\n",
810
+ " <th>3</th>\n",
811
+ " <td>32780</td>\n",
812
+ " <td>0</td>\n",
813
+ " <td>133.0</td>\n",
814
+ " <td>1.4925</td>\n",
815
+ " <td>3.8462</td>\n",
816
+ " <td>2</td>\n",
817
+ " <td>7.0</td>\n",
818
+ " <td>3</td>\n",
819
+ " <td>4.0</td>\n",
820
+ " <td>5.6604</td>\n",
821
+ " <td>2.83</td>\n",
822
+ " <td>1</td>\n",
823
+ " </tr>\n",
824
+ " <tr>\n",
825
+ " <th>4</th>\n",
826
+ " <td>3509</td>\n",
827
+ " <td>0</td>\n",
828
+ " <td>-1.0</td>\n",
829
+ " <td>0.7424</td>\n",
830
+ " <td>0.6472</td>\n",
831
+ " <td>6</td>\n",
832
+ " <td>-1.0</td>\n",
833
+ " <td>3</td>\n",
834
+ " <td>99.0</td>\n",
835
+ " <td>0.3784</td>\n",
836
+ " <td>6.18</td>\n",
837
+ " <td>1</td>\n",
838
+ " </tr>\n",
839
+ " <tr>\n",
840
+ " <th>...</th>\n",
841
+ " <td>...</td>\n",
842
+ " <td>...</td>\n",
843
+ " <td>...</td>\n",
844
+ " <td>...</td>\n",
845
+ " <td>...</td>\n",
846
+ " <td>...</td>\n",
847
+ " <td>...</td>\n",
848
+ " <td>...</td>\n",
849
+ " <td>...</td>\n",
850
+ " <td>...</td>\n",
851
+ " <td>...</td>\n",
852
+ " <td>...</td>\n",
853
+ " </tr>\n",
854
+ " <tr>\n",
855
+ " <th>39138</th>\n",
856
+ " <td>3193</td>\n",
857
+ " <td>0</td>\n",
858
+ " <td>311.0</td>\n",
859
+ " <td>0.6517</td>\n",
860
+ " <td>2.5510</td>\n",
861
+ " <td>5</td>\n",
862
+ " <td>4.0</td>\n",
863
+ " <td>3</td>\n",
864
+ " <td>99.0</td>\n",
865
+ " <td>4.3779</td>\n",
866
+ " <td>11.52</td>\n",
867
+ " <td>2</td>\n",
868
+ " </tr>\n",
869
+ " <tr>\n",
870
+ " <th>39139</th>\n",
871
+ " <td>46717</td>\n",
872
+ " <td>0</td>\n",
873
+ " <td>203.0</td>\n",
874
+ " <td>0.3208</td>\n",
875
+ " <td>0.7299</td>\n",
876
+ " <td>3</td>\n",
877
+ " <td>5.0</td>\n",
878
+ " <td>4</td>\n",
879
+ " <td>4.0</td>\n",
880
+ " <td>0.9901</td>\n",
881
+ " <td>11.19</td>\n",
882
+ " <td>1</td>\n",
883
+ " </tr>\n",
884
+ " <tr>\n",
885
+ " <th>39140</th>\n",
886
+ " <td>46379</td>\n",
887
+ " <td>0</td>\n",
888
+ " <td>119.0</td>\n",
889
+ " <td>1.3945</td>\n",
890
+ " <td>1.8519</td>\n",
891
+ " <td>3</td>\n",
892
+ " <td>5.0</td>\n",
893
+ " <td>4</td>\n",
894
+ " <td>4.0</td>\n",
895
+ " <td>2.4476</td>\n",
896
+ " <td>30.93</td>\n",
897
+ " <td>1</td>\n",
898
+ " </tr>\n",
899
+ " <tr>\n",
900
+ " <th>39141</th>\n",
901
+ " <td>12670</td>\n",
902
+ " <td>0</td>\n",
903
+ " <td>163.0</td>\n",
904
+ " <td>1.3134</td>\n",
905
+ " <td>0.3226</td>\n",
906
+ " <td>6</td>\n",
907
+ " <td>5.0</td>\n",
908
+ " <td>2</td>\n",
909
+ " <td>4.0</td>\n",
910
+ " <td>0.7042</td>\n",
911
+ " <td>36.48</td>\n",
912
+ " <td>1</td>\n",
913
+ " </tr>\n",
914
+ " <tr>\n",
915
+ " <th>39142</th>\n",
916
+ " <td>43570</td>\n",
917
+ " <td>0</td>\n",
918
+ " <td>250.0</td>\n",
919
+ " <td>3.2597</td>\n",
920
+ " <td>7.1429</td>\n",
921
+ " <td>4</td>\n",
922
+ " <td>5.0</td>\n",
923
+ " <td>5</td>\n",
924
+ " <td>99.0</td>\n",
925
+ " <td>6.3559</td>\n",
926
+ " <td>24.18</td>\n",
927
+ " <td>1</td>\n",
928
+ " </tr>\n",
929
+ " </tbody>\n",
930
+ "</table>\n",
931
+ "<p>39143 rows × 12 columns</p>\n",
932
+ "</div>"
933
+ ],
934
+ "text/plain": [
935
+ " row_num label user_star_val night_call_dura_rate \\\n",
936
+ "0 29833 0 198.0 1.2726 \n",
937
+ "1 18121 0 91.0 8.4315 \n",
938
+ "2 8372 0 -1.0 5.0105 \n",
939
+ "3 32780 0 133.0 1.4925 \n",
940
+ "4 3509 0 -1.0 0.7424 \n",
941
+ "... ... ... ... ... \n",
942
+ "39138 3193 0 311.0 0.6517 \n",
943
+ "39139 46717 0 203.0 0.3208 \n",
944
+ "39140 46379 0 119.0 1.3945 \n",
945
+ "39141 12670 0 163.0 1.3134 \n",
946
+ "39142 43570 0 250.0 3.2597 \n",
947
+ "\n",
948
+ " night_call_cnt_rate age cust_star opp_belo_cnt rcn_mode \\\n",
949
+ "0 3.1250 2 4.0 5 4.0 \n",
950
+ "1 6.9767 1 6.0 3 4.0 \n",
951
+ "2 7.6923 4 -1.0 3 4.0 \n",
952
+ "3 3.8462 2 7.0 3 4.0 \n",
953
+ "4 0.6472 6 -1.0 3 99.0 \n",
954
+ "... ... ... ... ... ... \n",
955
+ "39138 2.5510 5 4.0 3 99.0 \n",
956
+ "39139 0.7299 3 5.0 4 4.0 \n",
957
+ "39140 1.8519 3 5.0 4 4.0 \n",
958
+ "39141 0.3226 6 5.0 2 4.0 \n",
959
+ "39142 7.1429 4 5.0 5 99.0 \n",
960
+ "\n",
961
+ " l6m_night_call_cnt_rate night_percent_six brand_id \n",
962
+ "0 4.6512 5.11 2 \n",
963
+ "1 1.3298 17.64 2 \n",
964
+ "2 5.4526 12.70 2 \n",
965
+ "3 5.6604 2.83 1 \n",
966
+ "4 0.3784 6.18 1 \n",
967
+ "... ... ... ... \n",
968
+ "39138 4.3779 11.52 2 \n",
969
+ "39139 0.9901 11.19 1 \n",
970
+ "39140 2.4476 30.93 1 \n",
971
+ "39141 0.7042 36.48 1 \n",
972
+ "39142 6.3559 24.18 1 \n",
973
+ "\n",
974
+ "[39143 rows x 12 columns]"
975
+ ]
976
+ },
977
+ "execution_count": 4,
978
+ "metadata": {},
979
+ "output_type": "execute_result"
980
+ }
981
+ ],
982
+ "source": [
983
+ "data1"
984
+ ]
985
+ },
986
+ {
987
+ "cell_type": "code",
988
+ "execution_count": 5,
989
+ "id": "handy-custom",
990
+ "metadata": {},
991
+ "outputs": [],
992
+ "source": [
993
+ "def fun_flux(x):\n",
994
+ " x=x/1024\n",
995
+ " if x <= 100:\n",
996
+ " return 1\n",
997
+ " if x <= 500:\n",
998
+ " return 2\n",
999
+ " if x <= 1024:\n",
1000
+ " return 3\n",
1001
+ " if x <= 2048:\n",
1002
+ " return 4\n",
1003
+ " else:\n",
1004
+ " return 5"
1005
+ ]
1006
+ },
1007
+ {
1008
+ "cell_type": "code",
1009
+ "execution_count": 6,
1010
+ "id": "linear-divorce",
1011
+ "metadata": {},
1012
+ "outputs": [],
1013
+ "source": [
1014
+ "df2 = data2[['row_num','innet_dura','basic_package_prc','package_flux','in_set_voice_minu','pri_package_lvl','cur_eff_sale_cmpn_cnt','mon_stp_cnt','gsm_user_lvl','is_join_busi_typ_contr','user_status']]\n",
1015
+ "#,'become_group_user_memb_tm','term_os','mage_status','user_area_belo','group_indus_typ_code'\n",
1016
+ "#df2 = data2.drop(labels=['belo_camp_id','become_group_user_memb_tm', 'belo_group_cust_id', 'term_brand', 'term_mdl', 'charge_package_unify_code', 'befo_pri_package_code', 'basic_package_id', 'basic_package_eff_date', 'package_stp_date','is_exempt_prest_open_inter_roam ','is_hnet_bind','camp_lvl','cm_nadd_mkcase_cnt','pretty_num_typ_code','term_typ','is_mkcase_user','is_warnt_boot','is_fuse_brd','year_nadd_id','is_group_user','exit_typ','is_main_card_user','is_cm_ord_nolimit_and_eff','is_give_card','this_is_replace_package','is_join_term_contr','memb_typ','is_use_backup_sim_svc','owe_stp_days','become_group_user_memb_tm','mon_stp_cnt','gsm_user_lvl','is_join_busi_typ_contr','user_status','term_os','mage_status','user_area_belo','group_indus_typ_code','nation','ord_4g_package_user_id','stp_dura','last_one_stp_tm','is_ord_nolimit','brd_bdwth','msisdn_owe_stp_freq','innet_dura_lvl_code','is_urgent_boot','is_gsm_user','is_ass_card','in_set_sms_cnt','package_typ','ass_card_cnt','gsm_user_src','unique_flag','is_indv_cust_posb','is_fnet_bind','is_group_v_ntw_bind','is_bind_pay','cancl_date'],axis=1)\n",
1017
+ "#df2['pretty_num_typ_name'] = df2['pretty_num_typ_name'].str.replace('非靓号', '0')\n",
1018
+ "#if df2['pretty_num_typ_name'].str != '0':\n",
1019
+ "# df2['pretty_num_typ_name']='1'\n",
1020
+ " \n",
1021
+ "#df2['pretty_num_typ_name']=df2['pretty_num_typ_name'].astype(int) \n",
1022
+ "#if df2['stp_typ'].str != '9999':\n",
1023
+ "# df2['stp_typ']='0' \n",
1024
+ "#df2['stp_typ']=df2['stp_typ'].astype(int) \n",
1025
+ "#df2['cm_nadd_mkcase_cnt'] = df2['cm_nadd_mkcase_cnt'].str.replace(r'\\\\N','-1')\n",
1026
+ "#df2['cm_nadd_mkcase_cnt']=df2['cm_nadd_mkcase_cnt'].astype(int)\n",
1027
+ "df2['cur_eff_sale_cmpn_cnt'] = df2['cur_eff_sale_cmpn_cnt'].str.replace(r'\\\\N','-1')\n",
1028
+ "df2['cur_eff_sale_cmpn_cnt']=df2['cur_eff_sale_cmpn_cnt'].astype(int) \n",
1029
+ "df2['basic_package_prc'] = df2['basic_package_prc'].str.replace(r'\\\\N','-1')\n",
1030
+ "df2['basic_package_prc']=df2['basic_package_prc'].astype(int)\n",
1031
+ "\n",
1032
+ "#df2['user_status']=df2['user_status']-1000\n",
1033
+ "#df2['package_flux'] = df2['package_flux'].apply(lambda x: fun_flux(x))\n",
1034
+ "\n",
1035
+ "df2.to_csv(path_or_buf='csvclear/train_b.csv', index=None) \n"
1036
+ ]
1037
+ },
1038
+ {
1039
+ "cell_type": "code",
1040
+ "execution_count": 7,
1041
+ "id": "detected-teddy",
1042
+ "metadata": {},
1043
+ "outputs": [],
1044
+ "source": [
1045
+ "df4 = data4[['row_num','innet_dura','basic_package_prc','package_flux','in_set_voice_minu','pri_package_lvl','cur_eff_sale_cmpn_cnt','mon_stp_cnt','gsm_user_lvl','is_join_busi_typ_contr','user_status']]\n",
1046
+ "\n",
1047
+ "#df4 = data4.drop(labels=['belo_camp_id','become_group_user_memb_tm', 'belo_group_cust_id', 'term_brand', 'term_mdl', 'charge_package_unify_code', 'befo_pri_package_code', 'basic_package_id', 'basic_package_eff_date', 'package_stp_date','is_exempt_prest_open_inter_roam ','is_hnet_bind','camp_lvl','cm_nadd_mkcase_cnt','pretty_num_typ_code','term_typ','is_mkcase_user','is_warnt_boot','is_fuse_brd','year_nadd_id','is_group_user','exit_typ','is_main_card_user','is_cm_ord_nolimit_and_eff','is_give_card','this_is_replace_package','is_join_term_contr','memb_typ','is_use_backup_sim_svc','owe_stp_days','become_group_user_memb_tm','mon_stp_cnt','gsm_user_lvl','is_join_busi_typ_contr','user_status','term_os','mage_status','user_area_belo','group_indus_typ_code','nation','ord_4g_package_user_id','stp_dura','last_one_stp_tm','is_ord_nolimit','brd_bdwth','msisdn_owe_stp_freq','innet_dura_lvl_code','is_urgent_boot','is_gsm_user','is_ass_card','in_set_sms_cnt','package_typ','ass_card_cnt','gsm_user_src','unique_flag','is_indv_cust_posb','is_fnet_bind','is_group_v_ntw_bind','is_bind_pay','cancl_date'],axis=1)\n",
1048
+ "#df4['pretty_num_typ_name'] = df4['pretty_num_typ_name'].str.replace('非靓号', '0')\n",
1049
+ "#if df4['pretty_num_typ_name'].str != '0':\n",
1050
+ "# df4['pretty_num_typ_name']='1'\n",
1051
+ " \n",
1052
+ "#df4['pretty_num_typ_name']=df4['pretty_num_typ_name'].astype(int) \n",
1053
+ "#if df4['stp_typ'].str != '9999':\n",
1054
+ "# df4['stp_typ']='0' \n",
1055
+ "#df4['stp_typ']=df2['stp_typ'].astype(int) \n",
1056
+ "#df4['cm_nadd_mkcase_cnt'] = df4['cm_nadd_mkcase_cnt'].str.replace(r'\\\\N','-1')\n",
1057
+ "#df4['cm_nadd_mkcase_cnt']=df4['cm_nadd_mkcase_cnt'].astype(int)\n",
1058
+ "df4['cur_eff_sale_cmpn_cnt'] = df4['cur_eff_sale_cmpn_cnt'].str.replace(r'\\\\N','-1')\n",
1059
+ "df4['cur_eff_sale_cmpn_cnt']=df4['cur_eff_sale_cmpn_cnt'].astype(int) \n",
1060
+ "df4['basic_package_prc'] = df4['basic_package_prc'].str.replace(r'\\\\N','-1')\n",
1061
+ "df4['basic_package_prc']=df4['basic_package_prc'].astype(int)\n",
1062
+ "\n",
1063
+ "#df4['user_status']=df4['user_status']-1000\n",
1064
+ "#df4['package_flux'] = df4['package_flux'].apply(lambda x: fun_flux(x))\n",
1065
+ "\n",
1066
+ "df4.to_csv(path_or_buf='csvclear/test_b.csv', index=None)\n"
1067
+ ]
1068
+ },
1069
+ {
1070
+ "cell_type": "code",
1071
+ "execution_count": 8,
1072
+ "id": "chronic-cisco",
1073
+ "metadata": {},
1074
+ "outputs": [
1075
+ {
1076
+ "data": {
1077
+ "text/html": [
1078
+ "<div>\n",
1079
+ "<style scoped>\n",
1080
+ " .dataframe tbody tr th:only-of-type {\n",
1081
+ " vertical-align: middle;\n",
1082
+ " }\n",
1083
+ "\n",
1084
+ " .dataframe tbody tr th {\n",
1085
+ " vertical-align: top;\n",
1086
+ " }\n",
1087
+ "\n",
1088
+ " .dataframe thead th {\n",
1089
+ " text-align: right;\n",
1090
+ " }\n",
1091
+ "</style>\n",
1092
+ "<table border=\"1\" class=\"dataframe\">\n",
1093
+ " <thead>\n",
1094
+ " <tr style=\"text-align: right;\">\n",
1095
+ " <th></th>\n",
1096
+ " <th>row_num</th>\n",
1097
+ " <th>innet_dura</th>\n",
1098
+ " <th>basic_package_prc</th>\n",
1099
+ " <th>package_flux</th>\n",
1100
+ " <th>in_set_voice_minu</th>\n",
1101
+ " <th>pri_package_lvl</th>\n",
1102
+ " <th>cur_eff_sale_cmpn_cnt</th>\n",
1103
+ " <th>mon_stp_cnt</th>\n",
1104
+ " <th>gsm_user_lvl</th>\n",
1105
+ " <th>is_join_busi_typ_contr</th>\n",
1106
+ " <th>user_status</th>\n",
1107
+ " </tr>\n",
1108
+ " </thead>\n",
1109
+ " <tbody>\n",
1110
+ " <tr>\n",
1111
+ " <th>0</th>\n",
1112
+ " <td>5239</td>\n",
1113
+ " <td>7</td>\n",
1114
+ " <td>-1</td>\n",
1115
+ " <td>0</td>\n",
1116
+ " <td>0</td>\n",
1117
+ " <td>0.0</td>\n",
1118
+ " <td>-1</td>\n",
1119
+ " <td>0</td>\n",
1120
+ " <td>-1.0</td>\n",
1121
+ " <td>0</td>\n",
1122
+ " <td>9000</td>\n",
1123
+ " </tr>\n",
1124
+ " <tr>\n",
1125
+ " <th>1</th>\n",
1126
+ " <td>5220</td>\n",
1127
+ " <td>53</td>\n",
1128
+ " <td>99</td>\n",
1129
+ " <td>66560</td>\n",
1130
+ " <td>1300</td>\n",
1131
+ " <td>6.0</td>\n",
1132
+ " <td>1</td>\n",
1133
+ " <td>1</td>\n",
1134
+ " <td>7.0</td>\n",
1135
+ " <td>0</td>\n",
1136
+ " <td>1010</td>\n",
1137
+ " </tr>\n",
1138
+ " <tr>\n",
1139
+ " <th>2</th>\n",
1140
+ " <td>37261</td>\n",
1141
+ " <td>36</td>\n",
1142
+ " <td>128</td>\n",
1143
+ " <td>40960</td>\n",
1144
+ " <td>200</td>\n",
1145
+ " <td>0.0</td>\n",
1146
+ " <td>4</td>\n",
1147
+ " <td>0</td>\n",
1148
+ " <td>-1.0</td>\n",
1149
+ " <td>0</td>\n",
1150
+ " <td>1010</td>\n",
1151
+ " </tr>\n",
1152
+ " <tr>\n",
1153
+ " <th>3</th>\n",
1154
+ " <td>32634</td>\n",
1155
+ " <td>257</td>\n",
1156
+ " <td>98</td>\n",
1157
+ " <td>20480</td>\n",
1158
+ " <td>500</td>\n",
1159
+ " <td>0.0</td>\n",
1160
+ " <td>-1</td>\n",
1161
+ " <td>1</td>\n",
1162
+ " <td>1.0</td>\n",
1163
+ " <td>0</td>\n",
1164
+ " <td>1010</td>\n",
1165
+ " </tr>\n",
1166
+ " <tr>\n",
1167
+ " <th>4</th>\n",
1168
+ " <td>39782</td>\n",
1169
+ " <td>215</td>\n",
1170
+ " <td>0</td>\n",
1171
+ " <td>0</td>\n",
1172
+ " <td>0</td>\n",
1173
+ " <td>0.0</td>\n",
1174
+ " <td>20</td>\n",
1175
+ " <td>0</td>\n",
1176
+ " <td>7.0</td>\n",
1177
+ " <td>1</td>\n",
1178
+ " <td>1010</td>\n",
1179
+ " </tr>\n",
1180
+ " <tr>\n",
1181
+ " <th>...</th>\n",
1182
+ " <td>...</td>\n",
1183
+ " <td>...</td>\n",
1184
+ " <td>...</td>\n",
1185
+ " <td>...</td>\n",
1186
+ " <td>...</td>\n",
1187
+ " <td>...</td>\n",
1188
+ " <td>...</td>\n",
1189
+ " <td>...</td>\n",
1190
+ " <td>...</td>\n",
1191
+ " <td>...</td>\n",
1192
+ " <td>...</td>\n",
1193
+ " </tr>\n",
1194
+ " <tr>\n",
1195
+ " <th>9781</th>\n",
1196
+ " <td>9304</td>\n",
1197
+ " <td>69</td>\n",
1198
+ " <td>0</td>\n",
1199
+ " <td>0</td>\n",
1200
+ " <td>0</td>\n",
1201
+ " <td>0.0</td>\n",
1202
+ " <td>4</td>\n",
1203
+ " <td>0</td>\n",
1204
+ " <td>-1.0</td>\n",
1205
+ " <td>0</td>\n",
1206
+ " <td>1010</td>\n",
1207
+ " </tr>\n",
1208
+ " <tr>\n",
1209
+ " <th>9782</th>\n",
1210
+ " <td>38713</td>\n",
1211
+ " <td>48</td>\n",
1212
+ " <td>128</td>\n",
1213
+ " <td>30720</td>\n",
1214
+ " <td>500</td>\n",
1215
+ " <td>0.0</td>\n",
1216
+ " <td>1</td>\n",
1217
+ " <td>1</td>\n",
1218
+ " <td>6.0</td>\n",
1219
+ " <td>0</td>\n",
1220
+ " <td>1010</td>\n",
1221
+ " </tr>\n",
1222
+ " <tr>\n",
1223
+ " <th>9783</th>\n",
1224
+ " <td>41809</td>\n",
1225
+ " <td>215</td>\n",
1226
+ " <td>28</td>\n",
1227
+ " <td>5120</td>\n",
1228
+ " <td>80</td>\n",
1229
+ " <td>0.0</td>\n",
1230
+ " <td>8</td>\n",
1231
+ " <td>0</td>\n",
1232
+ " <td>-1.0</td>\n",
1233
+ " <td>0</td>\n",
1234
+ " <td>1010</td>\n",
1235
+ " </tr>\n",
1236
+ " <tr>\n",
1237
+ " <th>9784</th>\n",
1238
+ " <td>2165</td>\n",
1239
+ " <td>97</td>\n",
1240
+ " <td>8</td>\n",
1241
+ " <td>100</td>\n",
1242
+ " <td>0</td>\n",
1243
+ " <td>0.0</td>\n",
1244
+ " <td>2</td>\n",
1245
+ " <td>0</td>\n",
1246
+ " <td>6.0</td>\n",
1247
+ " <td>0</td>\n",
1248
+ " <td>1010</td>\n",
1249
+ " </tr>\n",
1250
+ " <tr>\n",
1251
+ " <th>9785</th>\n",
1252
+ " <td>48929</td>\n",
1253
+ " <td>225</td>\n",
1254
+ " <td>18</td>\n",
1255
+ " <td>1024</td>\n",
1256
+ " <td>100</td>\n",
1257
+ " <td>0.0</td>\n",
1258
+ " <td>-1</td>\n",
1259
+ " <td>0</td>\n",
1260
+ " <td>1.0</td>\n",
1261
+ " <td>0</td>\n",
1262
+ " <td>1010</td>\n",
1263
+ " </tr>\n",
1264
+ " </tbody>\n",
1265
+ "</table>\n",
1266
+ "<p>9786 rows × 11 columns</p>\n",
1267
+ "</div>"
1268
+ ],
1269
+ "text/plain": [
1270
+ " row_num innet_dura basic_package_prc package_flux in_set_voice_minu \\\n",
1271
+ "0 5239 7 -1 0 0 \n",
1272
+ "1 5220 53 99 66560 1300 \n",
1273
+ "2 37261 36 128 40960 200 \n",
1274
+ "3 32634 257 98 20480 500 \n",
1275
+ "4 39782 215 0 0 0 \n",
1276
+ "... ... ... ... ... ... \n",
1277
+ "9781 9304 69 0 0 0 \n",
1278
+ "9782 38713 48 128 30720 500 \n",
1279
+ "9783 41809 215 28 5120 80 \n",
1280
+ "9784 2165 97 8 100 0 \n",
1281
+ "9785 48929 225 18 1024 100 \n",
1282
+ "\n",
1283
+ " pri_package_lvl cur_eff_sale_cmpn_cnt mon_stp_cnt gsm_user_lvl \\\n",
1284
+ "0 0.0 -1 0 -1.0 \n",
1285
+ "1 6.0 1 1 7.0 \n",
1286
+ "2 0.0 4 0 -1.0 \n",
1287
+ "3 0.0 -1 1 1.0 \n",
1288
+ "4 0.0 20 0 7.0 \n",
1289
+ "... ... ... ... ... \n",
1290
+ "9781 0.0 4 0 -1.0 \n",
1291
+ "9782 0.0 1 1 6.0 \n",
1292
+ "9783 0.0 8 0 -1.0 \n",
1293
+ "9784 0.0 2 0 6.0 \n",
1294
+ "9785 0.0 -1 0 1.0 \n",
1295
+ "\n",
1296
+ " is_join_busi_typ_contr user_status \n",
1297
+ "0 0 9000 \n",
1298
+ "1 0 1010 \n",
1299
+ "2 0 1010 \n",
1300
+ "3 0 1010 \n",
1301
+ "4 1 1010 \n",
1302
+ "... ... ... \n",
1303
+ "9781 0 1010 \n",
1304
+ "9782 0 1010 \n",
1305
+ "9783 0 1010 \n",
1306
+ "9784 0 1010 \n",
1307
+ "9785 0 1010 \n",
1308
+ "\n",
1309
+ "[9786 rows x 11 columns]"
1310
+ ]
1311
+ },
1312
+ "execution_count": 8,
1313
+ "metadata": {},
1314
+ "output_type": "execute_result"
1315
+ }
1316
+ ],
1317
+ "source": [
1318
+ "df4"
1319
+ ]
1320
+ },
1321
+ {
1322
+ "cell_type": "code",
1323
+ "execution_count": 9,
1324
+ "id": "invalid-parameter",
1325
+ "metadata": {},
1326
+ "outputs": [
1327
+ {
1328
+ "name": "stdout",
1329
+ "output_type": "stream",
1330
+ "text": [
1331
+ "数据清洗完成\n"
1332
+ ]
1333
+ }
1334
+ ],
1335
+ "source": [
1336
+ "print('数据清洗完成')"
1337
+ ]
1338
+ },
1339
+ {
1340
+ "cell_type": "code",
1341
+ "execution_count": 10,
1342
+ "id": "timely-judges",
1343
+ "metadata": {},
1344
+ "outputs": [
1345
+ {
1346
+ "ename": "SyntaxError",
1347
+ "evalue": "Missing parentheses in call to 'print'. Did you mean print(cccc)? (<ipython-input-10-541b57d62b0e>, line 1)",
1348
+ "output_type": "error",
1349
+ "traceback": [
1350
+ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-10-541b57d62b0e>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m print cccc\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(cccc)?\n"
1351
+ ]
1352
+ }
1353
+ ],
1354
+ "source": [
1355
+ "print cccc"
1356
+ ]
1357
+ },
1358
+ {
1359
+ "cell_type": "code",
1360
+ "execution_count": null,
1361
+ "id": "broadband-pitch",
1362
+ "metadata": {},
1363
+ "outputs": [],
1364
+ "source": []
1365
+ },
1366
+ {
1367
+ "cell_type": "code",
1368
+ "execution_count": 11,
1369
+ "id": "cordless-intelligence",
1370
+ "metadata": {},
1371
+ "outputs": [
1372
+ {
1373
+ "name": "stdout",
1374
+ "output_type": "stream",
1375
+ "text": [
1376
+ "{\r\n",
1377
+ " \"retcode\": 0,\r\n",
1378
+ " \"retmsg\": \"Fate Flow CLI has been initialized successfully.\"\r\n",
1379
+ "}\r\n",
1380
+ "\r\n"
1381
+ ]
1382
+ }
1383
+ ],
1384
+ "source": [
1385
+ "!flow init --ip 10.43.159.182 --port 9380"
1386
+ ]
1387
+ },
1388
+ {
1389
+ "cell_type": "code",
1390
+ "execution_count": 12,
1391
+ "id": "dffd4c06",
1392
+ "metadata": {},
1393
+ "outputs": [
1394
+ {
1395
+ "name": "stdout",
1396
+ "output_type": "stream",
1397
+ "text": [
1398
+ "{\r\n",
1399
+ " \"data\": {\r\n",
1400
+ " \"board_url\": \"http://board:8080/index.html#/dashboard?job_id=20230424161428388177448&role=local&party_id=0\",\r\n",
1401
+ " \"job_dsl_path\": \"/data/projects/fate/jobs/20230424161428388177448/job_dsl.json\",\r\n",
1402
+ " \"job_id\": \"20230424161428388177448\",\r\n",
1403
+ " \"job_runtime_conf_on_party_path\": \"/data/projects/fate/jobs/20230424161428388177448/local/job_runtime_on_party_conf.json\",\r\n",
1404
+ " \"job_runtime_conf_path\": \"/data/projects/fate/jobs/20230424161428388177448/job_runtime_conf.json\",\r\n",
1405
+ " \"logs_directory\": \"/data/projects/fate/logs/20230424161428388177448\",\r\n",
1406
+ " \"model_info\": {\r\n",
1407
+ " \"model_id\": \"local-0#model\",\r\n",
1408
+ " \"model_version\": \"20230424161428388177448\"\r\n",
1409
+ " },\r\n",
1410
+ " \"namespace\": \"experiment\",\r\n",
1411
+ " \"pipeline_dsl_path\": \"/data/projects/fate/jobs/20230424161428388177448/pipeline_dsl.json\",\r\n",
1412
+ " \"table_name\": \"train_a_label\",\r\n",
1413
+ " \"train_runtime_conf_path\": \"/data/projects/fate/jobs/20230424161428388177448/train_runtime_conf.json\"\r\n",
1414
+ " },\r\n",
1415
+ " \"jobId\": \"20230424161428388177448\",\r\n",
1416
+ " \"retcode\": 0,\r\n",
1417
+ " \"retmsg\": \"success\"\r\n",
1418
+ "}\r\n",
1419
+ "\r\n"
1420
+ ]
1421
+ }
1422
+ ],
1423
+ "source": [
1424
+ "!flow data upload -c /Examples/csvclear/upload_conf.json --drop"
1425
+ ]
1426
+ },
1427
+ {
1428
+ "cell_type": "code",
1429
+ "execution_count": 13,
1430
+ "id": "34470baf",
1431
+ "metadata": {},
1432
+ "outputs": [
1433
+ {
1434
+ "name": "stdout",
1435
+ "output_type": "stream",
1436
+ "text": [
1437
+ "{\r\n",
1438
+ " \"data\": {\r\n",
1439
+ " \"board_url\": \"http://board:8080/index.html#/dashboard?job_id=20230424161429367145449&role=local&party_id=0\",\r\n",
1440
+ " \"job_dsl_path\": \"/data/projects/fate/jobs/20230424161429367145449/job_dsl.json\",\r\n",
1441
+ " \"job_id\": \"20230424161429367145449\",\r\n",
1442
+ " \"job_runtime_conf_on_party_path\": \"/data/projects/fate/jobs/20230424161429367145449/local/job_runtime_on_party_conf.json\",\r\n",
1443
+ " \"job_runtime_conf_path\": \"/data/projects/fate/jobs/20230424161429367145449/job_runtime_conf.json\",\r\n",
1444
+ " \"logs_directory\": \"/data/projects/fate/logs/20230424161429367145449\",\r\n",
1445
+ " \"model_info\": {\r\n",
1446
+ " \"model_id\": \"local-0#model\",\r\n",
1447
+ " \"model_version\": \"20230424161429367145449\"\r\n",
1448
+ " },\r\n",
1449
+ " \"namespace\": \"experiment\",\r\n",
1450
+ " \"pipeline_dsl_path\": \"/data/projects/fate/jobs/20230424161429367145449/pipeline_dsl.json\",\r\n",
1451
+ " \"table_name\": \"train_b\",\r\n",
1452
+ " \"train_runtime_conf_path\": \"/data/projects/fate/jobs/20230424161429367145449/train_runtime_conf.json\"\r\n",
1453
+ " },\r\n",
1454
+ " \"jobId\": \"20230424161429367145449\",\r\n",
1455
+ " \"retcode\": 0,\r\n",
1456
+ " \"retmsg\": \"success\"\r\n",
1457
+ "}\r\n",
1458
+ "\r\n"
1459
+ ]
1460
+ }
1461
+ ],
1462
+ "source": [
1463
+ "!flow data upload -c /Examples/csvclear/upload_conf_host.json --drop"
1464
+ ]
1465
+ },
1466
+ {
1467
+ "cell_type": "code",
1468
+ "execution_count": 14,
1469
+ "id": "filled-princess",
1470
+ "metadata": {},
1471
+ "outputs": [
1472
+ {
1473
+ "name": "stdout",
1474
+ "output_type": "stream",
1475
+ "text": [
1476
+ "{\r\n",
1477
+ " \"data\": {\r\n",
1478
+ " \"count\": 39143,\r\n",
1479
+ " \"exist\": 1,\r\n",
1480
+ " \"namespace\": \"experiment\",\r\n",
1481
+ " \"partition\": 4,\r\n",
1482
+ " \"schema\": {\r\n",
1483
+ " \"header\": \"label,user_star_val,night_call_dura_rate,night_call_cnt_rate,age,cust_star,opp_belo_cnt,rcn_mode,l6m_night_call_cnt_rate,night_percent_six,brand_id\",\r\n",
1484
+ " \"sid\": \"row_num\"\r\n",
1485
+ " },\r\n",
1486
+ " \"table_name\": \"train_a_label\"\r\n",
1487
+ " },\r\n",
1488
+ " \"retcode\": 0,\r\n",
1489
+ " \"retmsg\": \"success\"\r\n",
1490
+ "}\r\n",
1491
+ "\r\n"
1492
+ ]
1493
+ }
1494
+ ],
1495
+ "source": [
1496
+ "#查看表信息\n",
1497
+ "!flow table info -t train_a_label -n experiment\n",
1498
+ "#!flow table info -t train_a_label -n experiment"
1499
+ ]
1500
+ },
1501
+ {
1502
+ "cell_type": "code",
1503
+ "execution_count": 15,
1504
+ "id": "mighty-health",
1505
+ "metadata": {},
1506
+ "outputs": [
1507
+ {
1508
+ "name": "stdout",
1509
+ "output_type": "stream",
1510
+ "text": [
1511
+ "{\r\n",
1512
+ " \"data\": {\r\n",
1513
+ " \"board_url\": \"http://board:8080/index.html#/dashboard?job_id=20230424161430596799450&role=local&party_id=0\",\r\n",
1514
+ " \"job_dsl_path\": \"/data/projects/fate/jobs/20230424161430596799450/job_dsl.json\",\r\n",
1515
+ " \"job_id\": \"20230424161430596799450\",\r\n",
1516
+ " \"job_runtime_conf_on_party_path\": \"/data/projects/fate/jobs/20230424161430596799450/local/job_runtime_on_party_conf.json\",\r\n",
1517
+ " \"job_runtime_conf_path\": \"/data/projects/fate/jobs/20230424161430596799450/job_runtime_conf.json\",\r\n",
1518
+ " \"logs_directory\": \"/data/projects/fate/logs/20230424161430596799450\",\r\n",
1519
+ " \"model_info\": {\r\n",
1520
+ " \"model_id\": \"local-0#model\",\r\n",
1521
+ " \"model_version\": \"20230424161430596799450\"\r\n",
1522
+ " },\r\n",
1523
+ " \"namespace\": \"experiment\",\r\n",
1524
+ " \"pipeline_dsl_path\": \"/data/projects/fate/jobs/20230424161430596799450/pipeline_dsl.json\",\r\n",
1525
+ " \"table_name\": \"test_a\",\r\n",
1526
+ " \"train_runtime_conf_path\": \"/data/projects/fate/jobs/20230424161430596799450/train_runtime_conf.json\"\r\n",
1527
+ " },\r\n",
1528
+ " \"jobId\": \"20230424161430596799450\",\r\n",
1529
+ " \"retcode\": 0,\r\n",
1530
+ " \"retmsg\": \"success\"\r\n",
1531
+ "}\r\n",
1532
+ "\r\n"
1533
+ ]
1534
+ }
1535
+ ],
1536
+ "source": [
1537
+ "#上传预测数据\n",
1538
+ "!flow data upload -c /Examples/csvclear/upload_testa_conf.json --drop"
1539
+ ]
1540
+ },
1541
+ {
1542
+ "cell_type": "code",
1543
+ "execution_count": 16,
1544
+ "id": "similar-teaching",
1545
+ "metadata": {},
1546
+ "outputs": [
1547
+ {
1548
+ "name": "stdout",
1549
+ "output_type": "stream",
1550
+ "text": [
1551
+ "{\r\n",
1552
+ " \"data\": {\r\n",
1553
+ " \"board_url\": \"http://board:8080/index.html#/dashboard?job_id=20230424161431222943451&role=local&party_id=0\",\r\n",
1554
+ " \"job_dsl_path\": \"/data/projects/fate/jobs/20230424161431222943451/job_dsl.json\",\r\n",
1555
+ " \"job_id\": \"20230424161431222943451\",\r\n",
1556
+ " \"job_runtime_conf_on_party_path\": \"/data/projects/fate/jobs/20230424161431222943451/local/job_runtime_on_party_conf.json\",\r\n",
1557
+ " \"job_runtime_conf_path\": \"/data/projects/fate/jobs/20230424161431222943451/job_runtime_conf.json\",\r\n",
1558
+ " \"logs_directory\": \"/data/projects/fate/logs/20230424161431222943451\",\r\n",
1559
+ " \"model_info\": {\r\n",
1560
+ " \"model_id\": \"local-0#model\",\r\n",
1561
+ " \"model_version\": \"20230424161431222943451\"\r\n",
1562
+ " },\r\n",
1563
+ " \"namespace\": \"experiment\",\r\n",
1564
+ " \"pipeline_dsl_path\": \"/data/projects/fate/jobs/20230424161431222943451/pipeline_dsl.json\",\r\n",
1565
+ " \"table_name\": \"test_b\",\r\n",
1566
+ " \"train_runtime_conf_path\": \"/data/projects/fate/jobs/20230424161431222943451/train_runtime_conf.json\"\r\n",
1567
+ " },\r\n",
1568
+ " \"jobId\": \"20230424161431222943451\",\r\n",
1569
+ " \"retcode\": 0,\r\n",
1570
+ " \"retmsg\": \"success\"\r\n",
1571
+ "}\r\n",
1572
+ "\r\n"
1573
+ ]
1574
+ }
1575
+ ],
1576
+ "source": [
1577
+ "!flow data upload -c /Examples/csvclear/upload_testb_conf.json --drop"
1578
+ ]
1579
+ },
1580
+ {
1581
+ "cell_type": "code",
1582
+ "execution_count": 17,
1583
+ "id": "yellow-luxury",
1584
+ "metadata": {},
1585
+ "outputs": [
1586
+ {
1587
+ "name": "stdout",
1588
+ "output_type": "stream",
1589
+ "text": [
1590
+ "{\r\n",
1591
+ " \"data\": {\r\n",
1592
+ " \"count\": 9786,\r\n",
1593
+ " \"exist\": 1,\r\n",
1594
+ " \"namespace\": \"experiment\",\r\n",
1595
+ " \"partition\": 4,\r\n",
1596
+ " \"schema\": {\r\n",
1597
+ " \"header\": \"user_star_val,star_evalu_tm,night_call_dura_rate,night_call_cnt_rate,age,cust_star,opp_belo_cnt,rcn_mode,l6m_night_call_cnt_rate,night_percent_six,brand_id\",\r\n",
1598
+ " \"sid\": \"row_num\"\r\n",
1599
+ " },\r\n",
1600
+ " \"table_name\": \"test_a\"\r\n",
1601
+ " },\r\n",
1602
+ " \"retcode\": 0,\r\n",
1603
+ " \"retmsg\": \"success\"\r\n",
1604
+ "}\r\n",
1605
+ "\r\n"
1606
+ ]
1607
+ }
1608
+ ],
1609
+ "source": [
1610
+ "#查看表信息\n",
1611
+ "!flow table info -t test_a -n experiment\n",
1612
+ "#!flow table info -t train_a_label -n experiment"
1613
+ ]
1614
+ },
1615
+ {
1616
+ "cell_type": "code",
1617
+ "execution_count": 18,
1618
+ "id": "artistic-drilling",
1619
+ "metadata": {},
1620
+ "outputs": [],
1621
+ "source": [
1622
+ "!"
1623
+ ]
1624
+ },
1625
+ {
1626
+ "cell_type": "code",
1627
+ "execution_count": 29,
1628
+ "id": "hungry-eagle",
1629
+ "metadata": {},
1630
+ "outputs": [],
1631
+ "source": [
1632
+ "train_conf = {\n",
1633
+ " \"dsl_version\": 2,\n",
1634
+ " \"initiator\": {\n",
1635
+ " \"role\": \"guest\",\n",
1636
+ " \"party_id\": 9999\n",
1637
+ " },\n",
1638
+ " \"job_parameters\": {\n",
1639
+ " \"common\": {\n",
1640
+ " \"work_mode\": 0 \n",
1641
+ " }\n",
1642
+ "},\n",
1643
+ " \"role\": {\n",
1644
+ " \"host\": [10000],\n",
1645
+ " \"guest\": [9999]\n",
1646
+ " },\n",
1647
+ " \"component_parameters\": {\n",
1648
+ " \"common\": {\n",
1649
+ " \"hetero_secure_boost_0\": {\n",
1650
+ " \"task_type\": \"classification\",\n",
1651
+ " \"objective_param\": {\n",
1652
+ " \"objective\": \"cross_entropy\"\n",
1653
+ " },\n",
1654
+ " \"num_trees\": 1,\n",
1655
+ " \"validation_freqs\": 1,\n",
1656
+ " \"encrypt_param\": {\n",
1657
+ " \"method\": \"Paillier\"\n",
1658
+ " },\n",
1659
+ " \"tree_param\": {\n",
1660
+ " \"max_depth\": 8\n",
1661
+ " },\n",
1662
+ " \"use_missing\": True\n",
1663
+ " },\n",
1664
+ " \"evaluation_0\": {\n",
1665
+ " \"eval_type\": \"binary\"\n",
1666
+ " }\n",
1667
+ " },\n",
1668
+ " \"role\": {\n",
1669
+ " \"guest\": {\n",
1670
+ " \"0\": {\n",
1671
+ " \n",
1672
+ " \"data_transform_0\": {\n",
1673
+ " \"with_label\": True,\n",
1674
+ " \"label_name\": \"label\",\n",
1675
+ " \"label_type\": \"int\",\n",
1676
+ " \"output_format\": \"dense\"\n",
1677
+ " },\n",
1678
+ " \"reader_0\": {\n",
1679
+ " \"table\": {\n",
1680
+ " \"name\": \"train_a_label\",\n",
1681
+ " \"namespace\": \"experiment\"\n",
1682
+ " }\n",
1683
+ " }\n",
1684
+ " }\n",
1685
+ " },\n",
1686
+ " \"host\": {\n",
1687
+ " \"0\": {\n",
1688
+ " \"data_transform_0\": {\n",
1689
+ " \"with_label\": False\n",
1690
+ " },\n",
1691
+ " \"reader_0\": {\n",
1692
+ " \"table\": {\n",
1693
+ " \"name\": \"train_b\",\n",
1694
+ " \"namespace\": \"experiment\"\n",
1695
+ " }\n",
1696
+ " }\n",
1697
+ " }\n",
1698
+ " }\n",
1699
+ " }\n",
1700
+ " }\n",
1701
+ "}"
1702
+ ]
1703
+ },
1704
+ {
1705
+ "cell_type": "code",
1706
+ "execution_count": 30,
1707
+ "id": "loaded-ferry",
1708
+ "metadata": {},
1709
+ "outputs": [],
1710
+ "source": [
1711
+ "train_dsl = {\n",
1712
+ " \"components\": {\n",
1713
+ " \"reader_0\": {\n",
1714
+ " \"module\": \"Reader\",\n",
1715
+ " \"output\": {\n",
1716
+ " \"data\": [\"data\"]\n",
1717
+ " }\n",
1718
+ " },\n",
1719
+ " \"data_transform_0\": {\n",
1720
+ " \"module\": \"DataTransform\",\n",
1721
+ " \"input\": {\n",
1722
+ " \"data\": {\n",
1723
+ " \"data\": [\"reader_0.data\"]\n",
1724
+ " }\n",
1725
+ " },\n",
1726
+ " \"output\": {\n",
1727
+ " \"data\": [\"data\"],\n",
1728
+ " \"model\": [\"model\"]\n",
1729
+ " }\n",
1730
+ " },\n",
1731
+ " \"intersection_0\": {\n",
1732
+ " \"module\": \"Intersection\",\n",
1733
+ " \"input\": {\n",
1734
+ " \"data\": {\n",
1735
+ " \"data\": [\"data_transform_0.data\"]\n",
1736
+ " }\n",
1737
+ " },\n",
1738
+ " \"output\": {\n",
1739
+ " \"data\": [\"data\"]\n",
1740
+ " }\n",
1741
+ " }, \n",
1742
+ " \"hetero_secure_boost_0\": {\n",
1743
+ " \"module\": \"HeteroSecureBoost\",\n",
1744
+ " \"input\": {\n",
1745
+ " \"data\": {\n",
1746
+ " \"train_data\": [\"intersection_0.data\"]\n",
1747
+ " }\n",
1748
+ " },\n",
1749
+ " \"output\": {\n",
1750
+ " \"data\": [\"data\"],\n",
1751
+ " \"model\": [\"model\"]\n",
1752
+ " }\n",
1753
+ " },\n",
1754
+ " \"evaluation_0\": {\n",
1755
+ " \"module\": \"Evaluation\",\n",
1756
+ " \"input\": {\n",
1757
+ " \"data\": {\n",
1758
+ " \"data\": [\"hetero_secure_boost_0.data\"]\n",
1759
+ " }\n",
1760
+ " },\n",
1761
+ " \"output\": {\n",
1762
+ " \"data\": [\"data\"]\n",
1763
+ " }\n",
1764
+ " }\n",
1765
+ " }\n",
1766
+ "}"
1767
+ ]
1768
+ },
1769
+ {
1770
+ "cell_type": "code",
1771
+ "execution_count": 31,
1772
+ "id": "blind-bachelor",
1773
+ "metadata": {
1774
+ "scrolled": true
1775
+ },
1776
+ "outputs": [
1777
+ {
1778
+ "name": "stdout",
1779
+ "output_type": "stream",
1780
+ "text": [
1781
+ "{'data': {'board_url': 'http://board:8080/index.html#/dashboard?job_id=20230424164733674031455&role=guest&party_id=9999', 'job_dsl_path': '/data/projects/fate/jobs/20230424164733674031455/job_dsl.json', 'job_id': '20230424164733674031455', 'job_runtime_conf_on_party_path': '/data/projects/fate/jobs/20230424164733674031455/guest/job_runtime_on_party_conf.json', 'job_runtime_conf_path': '/data/projects/fate/jobs/20230424164733674031455/job_runtime_conf.json', 'logs_directory': '/data/projects/fate/logs/20230424164733674031455', 'model_info': {'model_id': 'guest-9999#host-10000#model', 'model_version': '20230424164733674031455'}, 'pipeline_dsl_path': '/data/projects/fate/jobs/20230424164733674031455/pipeline_dsl.json', 'train_runtime_conf_path': '/data/projects/fate/jobs/20230424164733674031455/train_runtime_conf.json'}, 'jobId': '20230424164733674031455', 'retcode': 0, 'retmsg': 'success'}\n"
1782
+ ]
1783
+ }
1784
+ ],
1785
+ "source": [
1786
+ "#开始训练\n",
1787
+ "import requests\n",
1788
+ "post_data = {'job_dsl': train_dsl, 'job_runtime_conf': train_conf}\n",
1789
+ "response = requests.post(\"http://10.43.159.182:9380/v1/job/submit\", json=post_data)\n",
1790
+ "print(response.json())"
1791
+ ]
1792
+ },
1793
+ {
1794
+ "cell_type": "code",
1795
+ "execution_count": null,
1796
+ "id": "apparent-treasure",
1797
+ "metadata": {},
1798
+ "outputs": [],
1799
+ "source": [
1800
+ "#登录board_url查看模型训练结果"
1801
+ ]
1802
+ },
1803
+ {
1804
+ "cell_type": "code",
1805
+ "execution_count": null,
1806
+ "id": "divine-smith",
1807
+ "metadata": {},
1808
+ "outputs": [],
1809
+ "source": [
1810
+ "#模型部署\n",
1811
+ "import requests\n",
1812
+ "config_data = {\n",
1813
+ " \"job_parameters\": {\n",
1814
+ " \"model_id\": \"guest-9999#host-10000#model\",\n",
1815
+ " \"model_version\": \"20230424085041481989420\"\n",
1816
+ " }\n",
1817
+ " }\n",
1818
+ "response = requests.post(\"http://10.43.159.182:9380/v1/model/deploy\", json=config_data)\n",
1819
+ "\n",
1820
+ "print(response.json())\n"
1821
+ ]
1822
+ },
1823
+ {
1824
+ "cell_type": "code",
1825
+ "execution_count": null,
1826
+ "id": "global-navigation",
1827
+ "metadata": {},
1828
+ "outputs": [],
1829
+ "source": [
1830
+ "predict_conf = {\n",
1831
+ " \"dsl_version\": 2,\n",
1832
+ " \"initiator\": {\n",
1833
+ " \"role\": \"guest\",\n",
1834
+ " \"party_id\": 9999\n",
1835
+ " },\n",
1836
+ " \"role\": {\n",
1837
+ " \"host\": [10000],\n",
1838
+ " \"guest\": [9999]\n",
1839
+ " },\n",
1840
+ " \"job_parameters\": {\n",
1841
+ " \"common\": {\n",
1842
+ " \"work_mode\": 0, \n",
1843
+ " \"job_type\": \"predict\",\n",
1844
+ " \"model_id\": \"guest-9999#host-10000#model\",\n",
1845
+ " \"model_version\": \"20230424085832841569421\"\n",
1846
+ " }\n",
1847
+ " },\n",
1848
+ " \"component_parameters\": {\n",
1849
+ " \"role\": {\n",
1850
+ " \"guest\": {\n",
1851
+ " \"0\": {\n",
1852
+ " \"reader_0\": {\n",
1853
+ " \"table\": {\n",
1854
+ " \"name\": \"test_a\",\n",
1855
+ " \"namespace\": \"experiment\"\n",
1856
+ " }\n",
1857
+ " }\n",
1858
+ " }\n",
1859
+ " },\n",
1860
+ " \"host\": {\n",
1861
+ " \"0\": {\n",
1862
+ " \"reader_0\": {\n",
1863
+ " \"table\": {\n",
1864
+ " \"name\": \"test_b\",\n",
1865
+ " \"namespace\": \"experiment\"\n",
1866
+ " }\n",
1867
+ " }\n",
1868
+ " }\n",
1869
+ " }\n",
1870
+ " }\n",
1871
+ " }\n",
1872
+ "}"
1873
+ ]
1874
+ },
1875
+ {
1876
+ "cell_type": "code",
1877
+ "execution_count": null,
1878
+ "id": "affecting-accordance",
1879
+ "metadata": {},
1880
+ "outputs": [],
1881
+ "source": [
1882
+ "#预测数据\n",
1883
+ "post_data = {'job_runtime_conf': predict_conf}\n",
1884
+ "response = requests.post(\"http://10.43.159.182:9380/v1/job/submit\", json=post_data)\n",
1885
+ "print(response.json())"
1886
+ ]
1887
+ },
1888
+ {
1889
+ "cell_type": "code",
1890
+ "execution_count": null,
1891
+ "id": "thermal-guatemala",
1892
+ "metadata": {},
1893
+ "outputs": [],
1894
+ "source": [
1895
+ "#登录board_url查看模型预测结果"
1896
+ ]
1897
+ },
1898
+ {
1899
+ "cell_type": "code",
1900
+ "execution_count": null,
1901
+ "id": "fatal-palestine",
1902
+ "metadata": {},
1903
+ "outputs": [],
1904
+ "source": [
1905
+ "!flow component output-data -j 20230424085846677853422 -r guest -p 9999 -cpn hetero_secure_boost_0 --output-path ./"
1906
+ ]
1907
+ },
1908
+ {
1909
+ "cell_type": "code",
1910
+ "execution_count": null,
1911
+ "id": "dental-million",
1912
+ "metadata": {},
1913
+ "outputs": [],
1914
+ "source": []
1915
+ },
1916
+ {
1917
+ "cell_type": "code",
1918
+ "execution_count": null,
1919
+ "id": "detected-adventure",
1920
+ "metadata": {},
1921
+ "outputs": [],
1922
+ "source": []
1923
+ }
1924
+ ],
1925
+ "metadata": {
1926
+ "kernelspec": {
1927
+ "display_name": "Python 3",
1928
+ "language": "python",
1929
+ "name": "python3"
1930
+ },
1931
+ "language_info": {
1932
+ "codemirror_mode": {
1933
+ "name": "ipython",
1934
+ "version": 3
1935
+ },
1936
+ "file_extension": ".py",
1937
+ "mimetype": "text/x-python",
1938
+ "name": "python",
1939
+ "nbconvert_exporter": "python",
1940
+ "pygments_lexer": "ipython3",
1941
+ "version": "3.7.10"
1942
+ }
1943
+ },
1944
+ "nbformat": 4,
1945
+ "nbformat_minor": 5
1946
+ }