info.log

2021-07-02 12-52-52 wgcpy >>> wgcpy log save path: D:\DZPackage\wgcpy\info.log! 
2021-07-02 12-53-01 wgcpy >>> start detect dataframe >>>>>>>>>
2021-07-02 12-53-01 wgcpy >>> [detect dataframe] done in 0.11 s
2021-07-02 12-53-01 wgcpy >>> start calculate IV: duration.in.month
2021-07-02 12-53-01 wgcpy >>> calculate iv complete! bins cnt:11, IV value: 0.3548
2021-07-02 12-53-01 wgcpy >>> start calculate IV: credit.amount
2021-07-02 12-53-01 wgcpy >>> calculate iv complete! bins cnt:11, IV value: 0.3694
2021-07-02 12-53-01 wgcpy >>> start calculate IV: installment.rate.in.percentage.of.disposable.income
2021-07-02 12-53-01 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.0263
2021-07-02 12-53-01 wgcpy >>> start calculate IV: present.residence.since
2021-07-02 12-53-01 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.0036
2021-07-02 12-53-01 wgcpy >>> start calculate IV: age.in.years
2021-07-02 12-53-01 wgcpy >>> calculate iv complete! bins cnt:11, IV value: 0.1981
2021-07-02 12-53-01 wgcpy >>> start calculate IV: number.of.existing.credits.at.this.bank
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.0114
2021-07-02 12-53-02 wgcpy >>> start calculate IV: number.of.people.being.liable.to.provide.maintenance.for
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:3, IV value: 0.0
2021-07-02 12-53-02 wgcpy >>> start calculate IV: credit.history
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.2932
2021-07-02 12-53-02 wgcpy >>> start calculate IV: housing
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:3, IV value: 0.0833
2021-07-02 12-53-02 wgcpy >>> start calculate IV: property
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.1126
2021-07-02 12-53-02 wgcpy >>> start calculate IV: other.installment.plans
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:3, IV value: 0.0576
2021-07-02 12-53-02 wgcpy >>> start calculate IV: foreign.worker
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:2, IV value: 0.0439
2021-07-02 12-53-02 wgcpy >>> start calculate IV: savings.account.and.bonds
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.196
2021-07-02 12-53-02 wgcpy >>> start calculate IV: purpose
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:10, IV value: 0.1678
2021-07-02 12-53-02 wgcpy >>> start calculate IV: personal.status.and.sex
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.0088
2021-07-02 12-53-02 wgcpy >>> start calculate IV: other.debtors.or.guarantors
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:3, IV value: 0.032
2021-07-02 12-53-02 wgcpy >>> start calculate IV: telephone
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:2, IV value: 0.0064
2021-07-02 12-53-02 wgcpy >>> start calculate IV: present.employment.since
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.0864
2021-07-02 12-53-02 wgcpy >>> start calculate IV: job
2021-07-02 12-53-02 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.0088
2021-07-02 12-53-02 wgcpy >>> start calculate IV: status.of.existing.checking.account
2021-07-02 12-53-03 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.666
2021-07-02 12-53-03 wgcpy >>> [cal iv] done in 1.98 s
2021-07-02 12-53-03 wgcpy >>> start calculate psi >>>>>
2021-07-02 12-53-03 wgcpy >>> calclate psi complete, psi value: 0.006869865786904775
2021-07-02 12-53-03 wgcpy >>> [cal psi] done in 0.06 s
2021-07-02 12-53-03 wgcpy >>> 0 features with greater than 0.95 missing values.

2021-07-02 12-53-03 wgcpy >>> 0 features with a single unique value.

2021-07-02 12-53-03 wgcpy >>> 1 features with a correlation magnitude greater than 0.60.

2021-07-02 12-53-03 wgcpy >>> detect the total feats, drop feats: 1
2021-07-02 12-53-03 wgcpy >>> detect the total feats, userful feats: 19
2021-07-02 12-53-03 wgcpy >>> start cv calulate, Fold: 0
2021-07-02 12-53-03 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 12-53-04 wgcpy >>> start cv calulate, Fold: 1
2021-07-02 12-53-04 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 12-53-04 wgcpy >>> start cv calulate, Fold: 2
2021-07-02 12-53-04 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 12-53-04 wgcpy >>> start cv calulate, Fold: 3
2021-07-02 12-53-04 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 12-53-04 wgcpy >>> start cv calulate, Fold: 4
2021-07-02 12-53-04 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 12-53-05 wgcpy >>> cross validation complete!
2021-07-02 12-53-05 wgcpy >>> After filtering cumsum importance above 95.00%, The number of seleted features is: 12 !
2021-07-02 12-53-05 wgcpy >>> step:0, feature cnt:1
2021-07-02 12-53-05 wgcpy >>> add the step features, val_auc: 0.7066190476190476, val_std: 0.027922527052664896
2021-07-02 12-53-05 wgcpy >>> step:1, feature cnt:2
2021-07-02 12-53-05 wgcpy >>> add the step features, val_auc: 0.7499761904761904, val_std: 0.02764841931653505
2021-07-02 12-53-05 wgcpy >>> step:2, feature cnt:3
2021-07-02 12-53-06 wgcpy >>> delete the step features: ['age.in.years'], auc diff: -0.005523809523809375
2021-07-02 12-53-06 wgcpy >>> step:3, feature cnt:3
2021-07-02 12-53-06 wgcpy >>> add the step features, val_auc: 0.7574761904761904, val_std: 0.03196528552129717
2021-07-02 12-53-06 wgcpy >>> step:4, feature cnt:4
2021-07-02 12-53-06 wgcpy >>> add the step features, val_auc: 0.7591666666666667, val_std: 0.032495560485198034
2021-07-02 12-53-06 wgcpy >>> step:5, feature cnt:5
2021-07-02 12-53-07 wgcpy >>> add the step features, val_auc: 0.7651785714285714, val_std: 0.03659612493890871
2021-07-02 12-53-07 wgcpy >>> step:6, feature cnt:6
2021-07-02 12-53-07 wgcpy >>> add the step features, val_auc: 0.7746547619047619, val_std: 0.03659612493890871
2021-07-02 12-53-07 wgcpy >>> step:7, feature cnt:7
2021-07-02 12-53-07 wgcpy >>> delete the step features: ['installment.rate.in.percentage.of.disposable.income'], auc diff: -0.002142857142857113
2021-07-02 12-53-07 wgcpy >>> step:8, feature cnt:7
2021-07-02 12-53-08 wgcpy >>> add the step features, val_auc: 0.7867142857142857, val_std: 0.03659612493890871
2021-07-02 12-53-08 wgcpy >>> step:9, feature cnt:8
2021-07-02 12-53-08 wgcpy >>> delete the step features: ['present.employment.since'], auc diff: -0.0006666666666665932
2021-07-02 12-53-08 wgcpy >>> step:10, feature cnt:8
2021-07-02 12-53-09 wgcpy >>> delete the step features: ['housing'], auc diff: 0.0006666666666665932
2021-07-02 12-53-09 wgcpy >>> step:11, feature cnt:8
2021-07-02 12-53-09 wgcpy >>> delete the step features: ['present.residence.since'], auc diff: -0.003702380952380846
2021-07-02 12-53-10 wgcpy >>> [cal cv score] done in 7.12 s
2021-07-02 12-53-10 wgcpy >>> numeric features cnt: 7, 	category features cnt: 13
2021-07-02 12-53-10 wgcpy >>> train data shape: (800, 20), 	target data shape: (800,)
2021-07-02 12-53-11 wgcpy >>> auc:0.7891666666666667, ks:0.46904761904761905
2021-07-02 12-53-15 wgcpy >>> [PMML model build] done in 5.15 s
2021-07-02 13-04-41 wgcpy >>> wgcpy log save path: D:\DZPackage\wgcpy\info.log! 
2021-07-02 13-04-49 wgcpy >>> start detect dataframe >>>>>>>>>
2021-07-02 13-04-49 wgcpy >>> [detect dataframe] done in 0.07 s
2021-07-02 13-04-49 wgcpy >>> start calculate IV: duration.in.month
2021-07-02 13-04-49 wgcpy >>> calculate iv complete! bins cnt:11, IV value: 0.3548
2021-07-02 13-04-49 wgcpy >>> start calculate IV: credit.amount
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:11, IV value: 0.3694
2021-07-02 13-04-50 wgcpy >>> start calculate IV: installment.rate.in.percentage.of.disposable.income
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.0263
2021-07-02 13-04-50 wgcpy >>> start calculate IV: present.residence.since
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.0036
2021-07-02 13-04-50 wgcpy >>> start calculate IV: age.in.years
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:11, IV value: 0.1981
2021-07-02 13-04-50 wgcpy >>> start calculate IV: number.of.existing.credits.at.this.bank
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.0114
2021-07-02 13-04-50 wgcpy >>> start calculate IV: number.of.people.being.liable.to.provide.maintenance.for
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:3, IV value: 0.0
2021-07-02 13-04-50 wgcpy >>> start calculate IV: other.debtors.or.guarantors
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:3, IV value: 0.032
2021-07-02 13-04-50 wgcpy >>> start calculate IV: present.employment.since
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.0864
2021-07-02 13-04-50 wgcpy >>> start calculate IV: personal.status.and.sex
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.0088
2021-07-02 13-04-50 wgcpy >>> start calculate IV: telephone
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:2, IV value: 0.0064
2021-07-02 13-04-50 wgcpy >>> start calculate IV: savings.account.and.bonds
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.196
2021-07-02 13-04-50 wgcpy >>> start calculate IV: property
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.1126
2021-07-02 13-04-50 wgcpy >>> start calculate IV: job
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.0088
2021-07-02 13-04-50 wgcpy >>> start calculate IV: purpose
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:10, IV value: 0.1678
2021-07-02 13-04-50 wgcpy >>> start calculate IV: other.installment.plans
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:3, IV value: 0.0576
2021-07-02 13-04-50 wgcpy >>> start calculate IV: housing
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:3, IV value: 0.0833
2021-07-02 13-04-50 wgcpy >>> start calculate IV: foreign.worker
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:2, IV value: 0.0439
2021-07-02 13-04-50 wgcpy >>> start calculate IV: credit.history
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.2932
2021-07-02 13-04-50 wgcpy >>> start calculate IV: status.of.existing.checking.account
2021-07-02 13-04-50 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.666
2021-07-02 13-04-51 wgcpy >>> [cal iv] done in 1.15 s
2021-07-02 13-04-51 wgcpy >>> start calculate psi >>>>>
2021-07-02 13-04-51 wgcpy >>> calclate psi complete, psi value: 0.006869865786904775
2021-07-02 13-04-51 wgcpy >>> [cal psi] done in 0.04 s
2021-07-02 13-04-51 wgcpy >>> 0 features with greater than 0.95 missing values.

2021-07-02 13-04-51 wgcpy >>> 0 features with a single unique value.

2021-07-02 13-04-51 wgcpy >>> 1 features with a correlation magnitude greater than 0.60.

2021-07-02 13-04-51 wgcpy >>> detect the total feats, drop feats: 1
2021-07-02 13-04-51 wgcpy >>> detect the total feats, userful feats: 19
2021-07-02 13-04-51 wgcpy >>> start cv calulate, Fold: 0
2021-07-02 13-04-51 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 13-04-51 wgcpy >>> start cv calulate, Fold: 1
2021-07-02 13-04-51 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 13-04-51 wgcpy >>> start cv calulate, Fold: 2
2021-07-02 13-04-51 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 13-04-51 wgcpy >>> start cv calulate, Fold: 3
2021-07-02 13-04-51 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 13-04-52 wgcpy >>> start cv calulate, Fold: 4
2021-07-02 13-04-52 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 13-04-52 wgcpy >>> cross validation complete!
2021-07-02 13-04-52 wgcpy >>> After filtering cumsum importance above 95.00%, The number of seleted features is: 12 !
2021-07-02 13-04-52 wgcpy >>> step:0, feature cnt:1
2021-07-02 13-04-52 wgcpy >>> add the step features, val_auc: 0.7066190476190476, val_std: 0.027922527052664896
2021-07-02 13-04-52 wgcpy >>> step:1, feature cnt:2
2021-07-02 13-04-52 wgcpy >>> add the step features, val_auc: 0.7499761904761904, val_std: 0.02764841931653505
2021-07-02 13-04-52 wgcpy >>> step:2, feature cnt:3
2021-07-02 13-04-52 wgcpy >>> delete the step features: ['age.in.years'], auc diff: -0.005523809523809375
2021-07-02 13-04-52 wgcpy >>> step:3, feature cnt:3
2021-07-02 13-04-52 wgcpy >>> add the step features, val_auc: 0.7574761904761904, val_std: 0.03196528552129717
2021-07-02 13-04-52 wgcpy >>> step:4, feature cnt:4
2021-07-02 13-04-53 wgcpy >>> add the step features, val_auc: 0.7591666666666667, val_std: 0.032495560485198034
2021-07-02 13-04-53 wgcpy >>> step:5, feature cnt:5
2021-07-02 13-04-53 wgcpy >>> add the step features, val_auc: 0.7651785714285714, val_std: 0.03659612493890871
2021-07-02 13-04-53 wgcpy >>> step:6, feature cnt:6
2021-07-02 13-04-53 wgcpy >>> add the step features, val_auc: 0.7746547619047619, val_std: 0.03659612493890871
2021-07-02 13-04-53 wgcpy >>> step:7, feature cnt:7
2021-07-02 13-04-54 wgcpy >>> delete the step features: ['installment.rate.in.percentage.of.disposable.income'], auc diff: -0.002142857142857113
2021-07-02 13-04-54 wgcpy >>> step:8, feature cnt:7
2021-07-02 13-04-54 wgcpy >>> add the step features, val_auc: 0.7867142857142857, val_std: 0.03659612493890871
2021-07-02 13-04-54 wgcpy >>> step:9, feature cnt:8
2021-07-02 13-04-54 wgcpy >>> delete the step features: ['present.employment.since'], auc diff: -0.0006666666666665932
2021-07-02 13-04-54 wgcpy >>> step:10, feature cnt:8
2021-07-02 13-04-55 wgcpy >>> delete the step features: ['housing'], auc diff: 0.0006666666666665932
2021-07-02 13-04-55 wgcpy >>> step:11, feature cnt:8
2021-07-02 13-04-55 wgcpy >>> delete the step features: ['present.residence.since'], auc diff: -0.003702380952380846
2021-07-02 13-04-56 wgcpy >>> [cal cv score] done in 4.98 s
2021-07-02 13-04-56 wgcpy >>> numeric features cnt: 7, 	category features cnt: 13
2021-07-02 13-04-56 wgcpy >>> train data shape: (800, 20), 	target data shape: (800,)
2021-07-02 13-04-56 wgcpy >>> auc:0.7389641988182134, ks:0.4464140887498552
2021-07-02 13-05-00 wgcpy >>> [PMML model build] done in 4.23 s
2021-07-02 14-30-08 wgcpy >>> wgcpy log save path: D:\DZPackage\wgcpy\info.log! 
2021-07-02 14-30-15 wgcpy >>> start detect dataframe >>>>>>>>>
2021-07-02 14-30-15 wgcpy >>> [detect dataframe] done in 0.06 s
2021-07-02 14-30-15 wgcpy >>> start calculate IV: duration.in.month
2021-07-02 14-30-15 wgcpy >>> calculate iv complete! bins cnt:11, IV value: 0.3548
2021-07-02 14-30-15 wgcpy >>> start calculate IV: credit.amount
2021-07-02 14-30-15 wgcpy >>> calculate iv complete! bins cnt:11, IV value: 0.3694
2021-07-02 14-30-15 wgcpy >>> start calculate IV: installment.rate.in.percentage.of.disposable.income
2021-07-02 14-30-15 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.0263
2021-07-02 14-30-15 wgcpy >>> start calculate IV: present.residence.since
2021-07-02 14-30-15 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.0036
2021-07-02 14-30-15 wgcpy >>> start calculate IV: age.in.years
2021-07-02 14-30-15 wgcpy >>> calculate iv complete! bins cnt:11, IV value: 0.1981
2021-07-02 14-30-15 wgcpy >>> start calculate IV: number.of.existing.credits.at.this.bank
2021-07-02 14-30-15 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.0114
2021-07-02 14-30-15 wgcpy >>> start calculate IV: number.of.people.being.liable.to.provide.maintenance.for
2021-07-02 14-30-15 wgcpy >>> calculate iv complete! bins cnt:3, IV value: 0.0
2021-07-02 14-30-15 wgcpy >>> start calculate IV: savings.account.and.bonds
2021-07-02 14-30-15 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.196
2021-07-02 14-30-15 wgcpy >>> start calculate IV: property
2021-07-02 14-30-15 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.1126
2021-07-02 14-30-15 wgcpy >>> start calculate IV: other.installment.plans
2021-07-02 14-30-16 wgcpy >>> calculate iv complete! bins cnt:3, IV value: 0.0576
2021-07-02 14-30-16 wgcpy >>> start calculate IV: telephone
2021-07-02 14-30-16 wgcpy >>> calculate iv complete! bins cnt:2, IV value: 0.0064
2021-07-02 14-30-16 wgcpy >>> start calculate IV: foreign.worker
2021-07-02 14-30-16 wgcpy >>> calculate iv complete! bins cnt:2, IV value: 0.0439
2021-07-02 14-30-16 wgcpy >>> start calculate IV: housing
2021-07-02 14-30-16 wgcpy >>> calculate iv complete! bins cnt:3, IV value: 0.0833
2021-07-02 14-30-16 wgcpy >>> start calculate IV: status.of.existing.checking.account
2021-07-02 14-30-16 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.666
2021-07-02 14-30-16 wgcpy >>> start calculate IV: credit.history
2021-07-02 14-30-16 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.2932
2021-07-02 14-30-16 wgcpy >>> start calculate IV: present.employment.since
2021-07-02 14-30-16 wgcpy >>> calculate iv complete! bins cnt:5, IV value: 0.0864
2021-07-02 14-30-16 wgcpy >>> start calculate IV: job
2021-07-02 14-30-16 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.0088
2021-07-02 14-30-16 wgcpy >>> start calculate IV: personal.status.and.sex
2021-07-02 14-30-16 wgcpy >>> calculate iv complete! bins cnt:4, IV value: 0.0088
2021-07-02 14-30-16 wgcpy >>> start calculate IV: purpose
2021-07-02 14-30-16 wgcpy >>> calculate iv complete! bins cnt:10, IV value: 0.1678
2021-07-02 14-30-16 wgcpy >>> start calculate IV: other.debtors.or.guarantors
2021-07-02 14-30-16 wgcpy >>> calculate iv complete! bins cnt:3, IV value: 0.032
2021-07-02 14-30-16 wgcpy >>> [cal iv] done in 1.06 s
2021-07-02 14-30-16 wgcpy >>> start calculate psi >>>>>
2021-07-02 14-30-16 wgcpy >>> calclate psi complete, psi value: 0.006869865786904775
2021-07-02 14-30-16 wgcpy >>> [cal psi] done in 0.03 s
2021-07-02 14-30-16 wgcpy >>> 0 features with greater than 0.95 missing values.

2021-07-02 14-30-16 wgcpy >>> 0 features with a single unique value.

2021-07-02 14-30-16 wgcpy >>> 1 features with a correlation magnitude greater than 0.60.

2021-07-02 14-30-16 wgcpy >>> detect the total feats, drop feats: 1
2021-07-02 14-30-16 wgcpy >>> detect the total feats, userful feats: 19
2021-07-02 14-30-16 wgcpy >>> start cv calulate, Fold: 0
2021-07-02 14-30-16 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 14-30-17 wgcpy >>> start cv calulate, Fold: 1
2021-07-02 14-30-17 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 14-30-17 wgcpy >>> start cv calulate, Fold: 2
2021-07-02 14-30-17 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 14-30-17 wgcpy >>> start cv calulate, Fold: 3
2021-07-02 14-30-17 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 14-30-17 wgcpy >>> start cv calulate, Fold: 4
2021-07-02 14-30-17 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-07-02 14-30-17 wgcpy >>> cross validation complete!
2021-07-02 14-30-17 wgcpy >>> After filtering cumsum importance above 95.00%, The number of seleted features is: 12 !
2021-07-02 14-30-17 wgcpy >>> step:0, feature cnt:1
2021-07-02 14-30-18 wgcpy >>> add the step features, val_auc: 0.7066190476190476, val_std: 0.027922527052664896
2021-07-02 14-30-18 wgcpy >>> step:1, feature cnt:2
2021-07-02 14-30-18 wgcpy >>> add the step features, val_auc: 0.7499761904761904, val_std: 0.02764841931653505
2021-07-02 14-30-18 wgcpy >>> step:2, feature cnt:3
2021-07-02 14-30-18 wgcpy >>> delete the step features: ['age.in.years'], auc diff: -0.005523809523809375
2021-07-02 14-30-18 wgcpy >>> step:3, feature cnt:3
2021-07-02 14-30-18 wgcpy >>> add the step features, val_auc: 0.7574761904761904, val_std: 0.03196528552129717
2021-07-02 14-30-18 wgcpy >>> step:4, feature cnt:4
2021-07-02 14-30-18 wgcpy >>> add the step features, val_auc: 0.7591666666666667, val_std: 0.032495560485198034
2021-07-02 14-30-18 wgcpy >>> step:5, feature cnt:5
2021-07-02 14-30-19 wgcpy >>> add the step features, val_auc: 0.7651785714285714, val_std: 0.03659612493890871
2021-07-02 14-30-19 wgcpy >>> step:6, feature cnt:6
2021-07-02 14-30-19 wgcpy >>> add the step features, val_auc: 0.7746547619047619, val_std: 0.03659612493890871
2021-07-02 14-30-19 wgcpy >>> step:7, feature cnt:7
2021-07-02 14-30-19 wgcpy >>> delete the step features: ['installment.rate.in.percentage.of.disposable.income'], auc diff: -0.002142857142857113
2021-07-02 14-30-19 wgcpy >>> step:8, feature cnt:7
2021-07-02 14-30-19 wgcpy >>> add the step features, val_auc: 0.7867142857142857, val_std: 0.03659612493890871
2021-07-02 14-30-19 wgcpy >>> step:9, feature cnt:8
2021-07-02 14-30-20 wgcpy >>> delete the step features: ['present.employment.since'], auc diff: -0.0006666666666665932
2021-07-02 14-30-20 wgcpy >>> step:10, feature cnt:8
2021-07-02 14-30-20 wgcpy >>> delete the step features: ['housing'], auc diff: 0.0006666666666665932
2021-07-02 14-30-20 wgcpy >>> step:11, feature cnt:8
2021-07-02 14-30-20 wgcpy >>> delete the step features: ['present.residence.since'], auc diff: -0.003702380952380846
2021-07-02 14-30-21 wgcpy >>> [cal cv score] done in 4.30 s
2021-07-02 14-30-21 wgcpy >>> numeric features cnt: 7, 	category features cnt: 13
2021-07-02 14-30-21 wgcpy >>> train data shape: (800, 20), 	target data shape: (800,)
2021-07-02 14-30-21 wgcpy >>> auc:0.7286485063824187, ks:0.3388603763653112
2021-07-02 14-30-24 wgcpy >>> [PMML model build] done in 3.84 s

2021-09-16 10-08-07 wgcpy >>> wgcpy log save path: D:\DZPackage\wgcpy\info.log! 
2021-09-16 10-08-14 wgcpy >>> start detect dataframe >>>>>>>>>
2021-09-16 10-08-14 wgcpy >>> [detect dataframe] done in 0.08 s
2021-09-16 10-08-14 wgcpy >>> calculate IV: duration.in.month, bins cnt:11, IV value: 0.3548
2021-09-16 10-08-14 wgcpy >>> calculate IV: credit.amount, bins cnt:11, IV value: 0.3694
2021-09-16 10-08-14 wgcpy >>> calculate IV: installment.rate.in.percentage.of.disposable.income, bins cnt:5, IV value: 0.0263
2021-09-16 10-08-14 wgcpy >>> calculate IV: present.residence.since, bins cnt:5, IV value: 0.0036
2021-09-16 10-08-14 wgcpy >>> calculate IV: age.in.years, bins cnt:11, IV value: 0.1981
2021-09-16 10-08-14 wgcpy >>> calculate IV: number.of.existing.credits.at.this.bank, bins cnt:4, IV value: 0.0114
2021-09-16 10-08-14 wgcpy >>> calculate IV: number.of.people.being.liable.to.provide.maintenance.for, bins cnt:3, IV value: 0.0
2021-09-16 10-08-14 wgcpy >>> calculate IV: foreign.worker, bins cnt:2, IV value: 0.0439
2021-09-16 10-08-14 wgcpy >>> calculate IV: job, bins cnt:4, IV value: 0.0088
2021-09-16 10-08-14 wgcpy >>> calculate IV: property, bins cnt:4, IV value: 0.1126
2021-09-16 10-08-14 wgcpy >>> calculate IV: other.debtors.or.guarantors, bins cnt:3, IV value: 0.032
2021-09-16 10-08-14 wgcpy >>> calculate IV: present.employment.since, bins cnt:5, IV value: 0.0864
2021-09-16 10-08-14 wgcpy >>> calculate IV: housing, bins cnt:3, IV value: 0.0833
2021-09-16 10-08-14 wgcpy >>> calculate IV: credit.history, bins cnt:5, IV value: 0.2932
2021-09-16 10-08-14 wgcpy >>> calculate IV: telephone, bins cnt:2, IV value: 0.0064
2021-09-16 10-08-14 wgcpy >>> calculate IV: purpose, bins cnt:10, IV value: 0.1678
2021-09-16 10-08-14 wgcpy >>> calculate IV: status.of.existing.checking.account, bins cnt:4, IV value: 0.666
2021-09-16 10-08-14 wgcpy >>> calculate IV: savings.account.and.bonds, bins cnt:5, IV value: 0.196
2021-09-16 10-08-14 wgcpy >>> calculate IV: other.installment.plans, bins cnt:3, IV value: 0.0576
2021-09-16 10-08-15 wgcpy >>> calculate IV: personal.status.and.sex, bins cnt:4, IV value: 0.0088
2021-09-16 10-08-15 wgcpy >>> [cal iv] done in 0.99 s
2021-09-16 10-08-15 wgcpy >>> calclate psi complete, psi value: 0.006869865786904775
2021-09-16 10-08-15 wgcpy >>> [cal psi] done in 0.04 s
2021-09-16 10-08-15 wgcpy >>> 0 features with greater than 0.95 missing values.

2021-09-16 10-08-15 wgcpy >>> 0 features with a single unique value.

2021-09-16 10-08-15 wgcpy >>> 1 features with a correlation magnitude greater than 0.60.

2021-09-16 10-08-15 wgcpy >>> detect the total feats, drop feats: 1
2021-09-16 10-08-15 wgcpy >>> detect the total feats, userful feats: 19
2021-09-16 10-08-15 wgcpy >>> start cv calulate, Fold: 0
2021-09-16 10-08-15 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-09-16 10-08-15 wgcpy >>> start cv calulate, Fold: 1
2021-09-16 10-08-15 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-09-16 10-08-15 wgcpy >>> start cv calulate, Fold: 2
2021-09-16 10-08-15 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-09-16 10-08-15 wgcpy >>> start cv calulate, Fold: 3
2021-09-16 10-08-15 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-09-16 10-08-16 wgcpy >>> start cv calulate, Fold: 4
2021-09-16 10-08-16 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-09-16 10-08-16 wgcpy >>> cross validation complete!
2021-09-16 10-08-16 wgcpy >>> After filtering cumsum importance above 95.00%, The number of seleted features is: 12 !
2021-09-16 10-08-16 wgcpy >>> step:0, feature cnt:1
2021-09-16 10-08-16 wgcpy >>> add the step features, val_auc: 0.7066190476190476, val_std: 0.027922527052664896
2021-09-16 10-08-16 wgcpy >>> step:1, feature cnt:2
2021-09-16 10-08-16 wgcpy >>> add the step features, val_auc: 0.7499761904761904, val_std: 0.02764841931653505
2021-09-16 10-08-16 wgcpy >>> step:2, feature cnt:3
2021-09-16 10-08-16 wgcpy >>> delete the step features: ['age.in.years'], auc diff: -0.005523809523809375
2021-09-16 10-08-16 wgcpy >>> step:3, feature cnt:3
2021-09-16 10-08-16 wgcpy >>> add the step features, val_auc: 0.7574761904761904, val_std: 0.03196528552129717
2021-09-16 10-08-16 wgcpy >>> step:4, feature cnt:4
2021-09-16 10-08-17 wgcpy >>> add the step features, val_auc: 0.7591666666666667, val_std: 0.032495560485198034
2021-09-16 10-08-17 wgcpy >>> step:5, feature cnt:5
2021-09-16 10-08-17 wgcpy >>> add the step features, val_auc: 0.7651785714285714, val_std: 0.03659612493890871
2021-09-16 10-08-17 wgcpy >>> step:6, feature cnt:6
2021-09-16 10-08-17 wgcpy >>> add the step features, val_auc: 0.7746547619047619, val_std: 0.03659612493890871
2021-09-16 10-08-17 wgcpy >>> step:7, feature cnt:7
2021-09-16 10-08-17 wgcpy >>> delete the step features: ['installment.rate.in.percentage.of.disposable.income'], auc diff: -0.002142857142857113
2021-09-16 10-08-17 wgcpy >>> step:8, feature cnt:7
2021-09-16 10-08-18 wgcpy >>> add the step features, val_auc: 0.7867142857142857, val_std: 0.03659612493890871
2021-09-16 10-08-18 wgcpy >>> step:9, feature cnt:8
2021-09-16 10-08-18 wgcpy >>> delete the step features: ['present.employment.since'], auc diff: -0.0006666666666665932
2021-09-16 10-08-18 wgcpy >>> step:10, feature cnt:8
2021-09-16 10-08-18 wgcpy >>> delete the step features: ['housing'], auc diff: 0.0006666666666665932
2021-09-16 10-08-18 wgcpy >>> step:11, feature cnt:8
2021-09-16 10-08-18 wgcpy >>> delete the step features: ['present.residence.since'], auc diff: -0.003702380952380846
2021-09-16 10-08-19 wgcpy >>> [cal cv score] done in 4.05 s
2021-09-16 10-08-19 wgcpy >>> numeric features cnt: 7, 	category features cnt: 13
2021-09-16 10-08-19 wgcpy >>> train data shape: (800, 20), 	target data shape: (800,)
2021-09-16 10-08-20 wgcpy >>> auc:0.7585572322414428, ks:0.4292724819040608
2021-09-16 10-08-22 wgcpy >>> [PMML model build] done in 2.73 s
2021-09-30 17-22-15 wgcpy >>> wgcpy log save path: D:\DZPackage\wgcpy\info.log! 
2021-09-30 17-22-23 wgcpy >>> start detect dataframe >>>>>>>>>
2021-09-30 17-22-23 wgcpy >>> [detect dataframe] done in 0.07 s
2021-09-30 17-22-23 wgcpy >>> calculate IV: duration.in.month, bins cnt:11, IV value: 0.3548
2021-09-30 17-22-23 wgcpy >>> calculate IV: credit.amount, bins cnt:11, IV value: 0.3694
2021-09-30 17-22-23 wgcpy >>> calculate IV: installment.rate.in.percentage.of.disposable.income, bins cnt:5, IV value: 0.0263
2021-09-30 17-22-23 wgcpy >>> calculate IV: present.residence.since, bins cnt:5, IV value: 0.0036
2021-09-30 17-22-24 wgcpy >>> calculate IV: age.in.years, bins cnt:11, IV value: 0.1981
2021-09-30 17-22-24 wgcpy >>> calculate IV: number.of.existing.credits.at.this.bank, bins cnt:4, IV value: 0.0114
2021-09-30 17-22-24 wgcpy >>> calculate IV: number.of.people.being.liable.to.provide.maintenance.for, bins cnt:3, IV value: 0.0
2021-09-30 17-22-24 wgcpy >>> calculate IV: job, bins cnt:4, IV value: 0.0088
2021-09-30 17-22-24 wgcpy >>> calculate IV: property, bins cnt:4, IV value: 0.1126
2021-09-30 17-22-24 wgcpy >>> calculate IV: other.debtors.or.guarantors, bins cnt:3, IV value: 0.032
2021-09-30 17-22-24 wgcpy >>> calculate IV: present.employment.since, bins cnt:5, IV value: 0.0864
2021-09-30 17-22-24 wgcpy >>> calculate IV: housing, bins cnt:3, IV value: 0.0833
2021-09-30 17-22-24 wgcpy >>> calculate IV: personal.status.and.sex, bins cnt:4, IV value: 0.0088
2021-09-30 17-22-24 wgcpy >>> calculate IV: savings.account.and.bonds, bins cnt:5, IV value: 0.196
2021-09-30 17-22-24 wgcpy >>> calculate IV: telephone, bins cnt:2, IV value: 0.0064
2021-09-30 17-22-24 wgcpy >>> calculate IV: status.of.existing.checking.account, bins cnt:4, IV value: 0.666
2021-09-30 17-22-24 wgcpy >>> calculate IV: foreign.worker, bins cnt:2, IV value: 0.0439
2021-09-30 17-22-24 wgcpy >>> calculate IV: credit.history, bins cnt:5, IV value: 0.2932
2021-09-30 17-22-24 wgcpy >>> calculate IV: other.installment.plans, bins cnt:3, IV value: 0.0576
2021-09-30 17-22-24 wgcpy >>> calculate IV: purpose, bins cnt:10, IV value: 0.1678
2021-09-30 17-22-24 wgcpy >>> [cal iv] done in 1.08 s
2021-09-30 17-22-24 wgcpy >>> calclate psi complete, psi value: 0.006869865786904775
2021-09-30 17-22-24 wgcpy >>> [cal psi] done in 0.04 s
2021-09-30 17-22-24 wgcpy >>> 0 features with greater than 0.95 missing values.

2021-09-30 17-22-24 wgcpy >>> 0 features with a single unique value.

2021-09-30 17-22-24 wgcpy >>> 1 features with a correlation magnitude greater than 0.60.

2021-09-30 17-22-24 wgcpy >>> detect the total feats, drop feats: 1
2021-09-30 17-22-24 wgcpy >>> detect the total feats, userful feats: 19
2021-09-30 17-22-24 wgcpy >>> start cv calulate, Fold: 0
2021-09-30 17-22-24 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-09-30 17-22-25 wgcpy >>> start cv calulate, Fold: 1
2021-09-30 17-22-25 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-09-30 17-22-25 wgcpy >>> start cv calulate, Fold: 2
2021-09-30 17-22-25 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-09-30 17-22-25 wgcpy >>> start cv calulate, Fold: 3
2021-09-30 17-22-25 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-09-30 17-22-25 wgcpy >>> start cv calulate, Fold: 4
2021-09-30 17-22-25 wgcpy >>> train shape: (800, 19), train badRate: 0.3, valid shape: (200, 19), valid badRate: 0.3
2021-09-30 17-22-26 wgcpy >>> cross validation complete!
2021-09-30 17-22-26 wgcpy >>> After filtering cumsum importance above 95.00%, The number of seleted features is: 12 !
2021-09-30 17-22-26 wgcpy >>> step:0, feature cnt:1
2021-09-30 17-22-26 wgcpy >>> add the step features, val_auc: 0.7066190476190476, val_std: 0.027922527052664896
2021-09-30 17-22-26 wgcpy >>> step:1, feature cnt:2
2021-09-30 17-22-26 wgcpy >>> add the step features, val_auc: 0.7499761904761904, val_std: 0.02764841931653505
2021-09-30 17-22-26 wgcpy >>> step:2, feature cnt:3
2021-09-30 17-22-26 wgcpy >>> delete the step features: ['age.in.years'], auc diff: -0.005523809523809375
2021-09-30 17-22-26 wgcpy >>> step:3, feature cnt:3
2021-09-30 17-22-26 wgcpy >>> add the step features, val_auc: 0.7574761904761904, val_std: 0.03196528552129717
2021-09-30 17-22-26 wgcpy >>> step:4, feature cnt:4
2021-09-30 17-22-27 wgcpy >>> add the step features, val_auc: 0.7591666666666667, val_std: 0.032495560485198034
2021-09-30 17-22-27 wgcpy >>> step:5, feature cnt:5
2021-09-30 17-22-27 wgcpy >>> add the step features, val_auc: 0.7651785714285714, val_std: 0.03659612493890871
2021-09-30 17-22-27 wgcpy >>> step:6, feature cnt:6
2021-09-30 17-22-27 wgcpy >>> add the step features, val_auc: 0.7746547619047619, val_std: 0.03659612493890871
2021-09-30 17-22-27 wgcpy >>> step:7, feature cnt:7
2021-09-30 17-22-27 wgcpy >>> delete the step features: ['installment.rate.in.percentage.of.disposable.income'], auc diff: -0.002142857142857113
2021-09-30 17-22-27 wgcpy >>> step:8, feature cnt:7
2021-09-30 17-22-28 wgcpy >>> add the step features, val_auc: 0.7867142857142857, val_std: 0.03659612493890871
2021-09-30 17-22-28 wgcpy >>> step:9, feature cnt:8
2021-09-30 17-22-28 wgcpy >>> delete the step features: ['present.employment.since'], auc diff: -0.0006666666666665932
2021-09-30 17-22-28 wgcpy >>> step:10, feature cnt:8
2021-09-30 17-22-28 wgcpy >>> delete the step features: ['housing'], auc diff: 0.0006666666666665932
2021-09-30 17-22-28 wgcpy >>> step:11, feature cnt:8
2021-09-30 17-22-28 wgcpy >>> delete the step features: ['present.residence.since'], auc diff: -0.003702380952380846
2021-09-30 17-22-29 wgcpy >>> [cal cv score] done in 4.56 s
2021-09-30 17-22-29 wgcpy >>> numeric features cnt: 7, 	category features cnt: 13
2021-09-30 17-22-29 wgcpy >>> train data shape: (800, 20), 	target data shape: (800,)
2021-09-30 17-22-30 wgcpy >>> auc:0.766042780748663, ks:0.429144385026738
2021-09-30 17-22-34 wgcpy >>> [PMML model build] done in 4.68 s