Example: Deploy and load the Booster model trained outside Vantage using train() - Teradata Package for Python

>>> from lightgbm import Dataset, train

>>> pdf_dataset = Dataset(params={},data=pdf_x, label=pdf_y)
>>> pdf_dataset
<lightgbm.basic.Dataset at 0x29d...a0>

>>> local_train = train(train_set=pdf_dataset, params={})
>>> local_train
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 532
[LightGBM] [Info] Number of data points in the train set: 400, number of used features: 4
[LightGBM] [Info] Start training from score 0.507500
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
...
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

<lightgbm.basic.Booster at 0x29d19f446d0>

>>> type(local_train)
lightgbm.basic.Booster

>>> opt_outside = td_lightgbm.deploy(model_name="model_trained_outside_vantage_using_train", model=local_train)

Model is saved.

>>> type(opt_outside)
teradataml.opensource._lightgbm._LightgbmBoosterWrapper

>>> opt_load_outside = td_lightgbm.load("model_trained_outside_vantage_using_train")

>>> type(opt_load_outside)
teradataml.opensource._lightgbm._LightgbmBoosterWrapper

>>> opt_load_outside.predict(data=df_x_classif, label=df_y_classif, pred_contrib=True)

			col1			col2			col3  			col4 label	booster_predict_1  ...   booster_predict_5
0.19124073041977  1.971001330153  -0.29853017845  0.890194110648	 1	0.031404602776514  ...  0.2040997952197996
-1.1699913889726  0.354732459083  -0.19356318494  -0.35802804454	 1	0.092682932810633  ...  0.2532883087965086
-0.8449702490257  1.453101117091  -0.33456086466  0.232041220971	 1	0.046136151499138  ...  0.2164065785284724
1.61885814289279  0.530672471352  0.101599080962  0.914093399898	 0	-0.10520322255265  ...  0.1027900245523291
-1.0093122519523  1.498390036418  -0.36101107471  0.179890827548	 1	0.062801639713840  ...  0.2107812341392544
1.49413777147502  0.172801814430  0.145353695535  0.713738112186	 0	-0.16550849715057  ...  0.1549907235100593
-0.8103072656328  -0.95301356540  0.061001667799  -0.73930084696	 0	-0.00069855457829  ...  -0.171764014056180
-0.8156136865920  -1.23797225905  0.106755845337  -0.85838759227	 0	0.001558571995085  ...  -0.195691235260052
-1.0993936064538  0.871762881447  -0.26950104137  -0.11572200547	 1	0.065010292043574  ...  0.2115849205082161
-1.2173132357157  -0.75099738722  -0.01912607171  -0.83162437457	 0	0.021267893372609  ...  -0.244580217753899