Checking Baseline with AutoML

%config InlineBackend.figure_format='retina'
from ekorpkit import eKonf

eKonf.setLogger("WARNING")
print("version:", eKonf.__version__)
print("is notebook?", eKonf.is_notebook())

version: 0.1.38+40.geb12c43.dirty
is notebook? True

data_dir = "../data/fomc"

Load a feature set

fs_cfg = eKonf.compose(config_group="dataset=feature")
fs_cfg.name = "fomc_features_small"
fs_cfg.data_dir = data_dir
fs_fomc = eKonf.instantiate(fs_cfg)

INFO:ekorpkit.base:Loaded .env from /workspace/projects/ekorpkit-book/config/.env
INFO:ekorpkit.base:setting environment variable CACHED_PATH_CACHE_ROOT to /workspace/.cache/cached_path
INFO:ekorpkit.base:setting environment variable KMP_DUPLICATE_LIB_OK to TRUE

Auto ML

model_cfg = eKonf.compose(config_group='model/automl=classification')
model_cfg.dataset = fs_cfg
model_cfg.config.time_budget = 60
model_cfg.verbose = False
model = eKonf.instantiate(model_cfg)

INFO:ekorpkit.base:No method defined to call

model.fit()

Best ML leaner: lgbm
Best hyperparmeter config: {'n_estimators': 10, 'num_leaves': 4, 'min_child_samples': 18, 'learning_rate': 0.2293009676418639, 'log_max_bin': 9, 'colsample_bytree': 0.9086551727646448, 'reg_alpha': 0.0015561782752413472, 'reg_lambda': 0.33127416269768944}
Best accuracy on validation data: 0.672
Training duration of best run: 0.03197 s

model.best_estimator

LGBMClassifier(colsample_bytree=0.9086551727646448,
               learning_rate=0.2293009676418639, max_bin=511,
               min_child_samples=18, n_estimators=10, num_leaves=4,
               reg_alpha=0.0015561782752413472, reg_lambda=0.33127416269768944,
               verbose=-1)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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model.save()

model.load()

model.show_results()

Best ML leaner: lgbm
Best hyperparmeter config: {'n_estimators': 10, 'num_leaves': 4, 'min_child_samples': 18, 'learning_rate': 0.2293009676418639, 'log_max_bin': 9, 'colsample_bytree': 0.9086551727646448, 'reg_alpha': 0.0015561782752413472, 'reg_lambda': 0.33127416269768944}
Best accuracy on validation data: 0.672
Training duration of best run: 0.03197 s

model.plot_learning_curve()

model.eval()

r2: -0.3066542577943232
mse: 0.7788461538461539
mae: 0.47115384615384615
Accuracy:  0.6826923076923077
Precison:  0.6514423076923077
Recall:  0.6826923076923077
F1 Score:  0.6251761059453366
Model Report: 
___________________________________________________
              precision    recall  f1-score   support

         Cut       0.62      0.28      0.38        18
        Hike       0.50      0.12      0.19        17
        Hold       0.70      0.93      0.80        69

    accuracy                           0.68       104
   macro avg       0.61      0.44      0.46       104
weighted avg       0.65      0.68      0.63       104

model.plot_feature_importance()

model.get_feature_importance()

	columns	importances
2	PMI	28
1	GDP_diff_prev	17
3	EMP_diff_prev	16
5	UNEMP_diff_prev	12
0	prev_decision	7
4	RSALES_diff_year	5
6	HSALES_diff_year	5
7	Inertia_diff	0
8	Balanced_diff	0