Train Models

Load PiML

from piml import Experiment
exp = Experiment()
exp.data_loader(data='CaliforniaHousing_raw')
exp.data_prepare(target='MedHouseVal', task_type='regression', random_state=0)

       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \
0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88
1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86
2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85
3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85
4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85
...       ...       ...       ...        ...         ...       ...       ...
20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48
20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49
20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43
20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43
20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37

       Longitude  MedHouseVal
0        -122.23        4.526
1        -122.22        3.585
2        -122.24        3.521
3        -122.25        3.413
4        -122.25        3.422
...          ...          ...
20635    -121.09        0.781
20636    -121.21        0.771
20637    -121.22        0.923
20638    -121.32        0.847
20639    -121.24        0.894

[20640 rows x 9 columns]
             Config        Value
0  Excluded columns           []
1   Target variable  MedHouseVal
2     Sample weight         None
3         Task type   regression
4      Split method       random
5        Test ratio          0.2
6      Random state            0

Train and Register Models using piml

from lightgbm import LGBMRegressor
lgbm2 = LGBMRegressor(max_depth=2)
exp.model_train(lgbm2, name='LGBM_2')

Save Fitted Models

exp.model_save("LGBM_2", "CH_LGBM_2.pkl")

Load model from file system, if not specified, the default train and test data will be used.

pipeline = exp.make_pipeline(model='CH_LGBM_2.pkl')
exp.register(pipeline, "LGBM_2_load")

Run post-hoc explanation using PDP.

exp.model_explain(model="LGBM_2_load", show="pdp", uni_feature="MedInc", figsize=(5, 4))

Estimated memory usage: 19 MB