Tuning with optuna (Experimental)

To run this code, you need to have optuna installed.

Installation

# To install the required package, use the following command:
# !pip install modeva

Authentication

# To get authentication, use the following command: (To get full access please replace the token to your own token)
# from modeva.utils.authenticate import authenticate
# authenticate(auth_code='eaaa4301-b140-484c-8e93-f9f633c8bacb')

Import required modules

from modeva import DataSet
from modeva import TestSuite
from modeva.models import MoLGBMClassifier
from modeva.models import ModelTuneOptuna
from scipy.stats import uniform, randint

Load Dataset

ds = DataSet()
ds.load(name="SimuCredit")
ds.set_random_split()

Run HPO

param_distributions = {"max_depth": [1, 2, 3],
                       "learning_rate": uniform(0.01, 0.3),
                       "n_estimators": randint(1, 100),
                      }

model = MoLGBMClassifier(verbose=-1)
hpo = ModelTuneOptuna(dataset=ds, model=model)
result = hpo.run(param_distributions=param_distributions,
                 sampler="tpe", # "grid", "random", "tpe", "gs", "cma-es", "qmc"
                 metric=("AUC", "ACC", "LogLoss"),
                 cv=5)
result.table

	max_depth	learning_rate	n_estimators	AUC	ACC	LogLoss	AUC_rank	ACC_rank	LogLoss_rank	mean_fit_time
4	3	0.1184	64	0.8384	0.7568	0.4908	1	1	1	0.0601
5	3	0.0702	53	0.8341	0.7540	0.5004	2	2	2	0.0548
8	2	0.1490	34	0.8301	0.7529	0.5091	3	3	3	0.0345
9	2	0.0478	74	0.8260	0.7474	0.5188	4	5	5	0.0590
6	1	0.1442	99	0.8243	0.7500	0.5151	5	4	4	0.0550
7	1	0.1502	32	0.8129	0.7417	0.5411	6	6	6	0.0285
3	2	0.0517	21	0.8039	0.7346	0.5669	7	7	7	0.0277
1	1	0.0278	79	0.8004	0.7174	0.5744	8	8	8	0.0471
0	1	0.1570	10	0.7949	0.7172	0.5855	9	9	9	0.0197
2	2	0.0422	7	0.7885	0.7157	0.6311	10	10	10	0.0190

result.plot("parallel", figsize=(8, 6))

result.plot(("max_depth", "AUC"))

result.plot(("learning_rate", "AUC"))

result.plot(("n_estimators", "AUC"))

Retrain model with best hyperparameter

model_tuned = MoLGBMClassifier(**result.value["params"][0],
                               name="LGBM-Tuned",
                               verbose=-1)
model_tuned.fit(ds.train_x, ds.train_y)
model_tuned

MoLGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
                 importance_type='split', learning_rate=0.15704454364210055,
                 max_depth=1, min_child_samples=20, min_child_weight=0.001,
                 min_split_gain=0.0, n_estimators=10, n_jobs=None,
                 num_leaves=31, objective=None, random_state=None,
                 reg_alpha=0.0, reg_lambda=0.0, subsample=1.0,
                 subsample_for_bin=200000, subsample_freq=0, verbose=-1)

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Diagnose the tuned model

ts = TestSuite(ds, model_tuned)
result = ts.diagnose_accuracy_table()
result.table

	AUC	ACC	F1	LogLoss	Precision	Recall	Brier
train	0.7964	0.7174	0.7494	0.5848	0.7344	0.7650	0.1980
test	0.7973	0.7205	0.7487	0.5839	0.7380	0.7596	0.1975
GAP	0.0009	0.0031	-0.0008	-0.0010	0.0036	-0.0054	-0.0005