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feat(Data): add option to predict 3 classes (#79)

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* feat(Data): add option to predict 3 classes

* feat(Evaluation): added ability to evaluate 3 class predictions

* chore(Config): set sensible config for regression models

* feat(Data): added option to use balanced or imbalanced three-class data

* feat(Evaluate): correctly track "no_of_samples" now that we have three classes

* chore(Sweep): remove probably not useful scaler values from sweep
This commit is contained in:
Mark Aron Szulyovszky
2021-12-23 13:24:56 +01:00
committed by GitHub
parent b6cd6b14fe
commit 95573eb9dd
8 changed files with 134 additions and 96 deletions
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config.py
+5 -4
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@@ -6,7 +6,7 @@ from models.model_map import model_names_classification, model_names_regression
def get_default_config() -> tuple[dict, dict, dict]:
training_config = dict(
expanding_window = True,
expanding_window = False,
sliding_window_size = 200,
retrain_every = 100,
scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
@@ -23,13 +23,14 @@ def get_default_config() -> tuple[dict, dict, dict]:
other_features = [],
index_column= 'int',
method= 'classification',
no_of_classes= 'two'
)
# regression_models = ["Lasso", "Ridge", "BayesianRidge", "KNN", "AB", "LR", "MLP", "RF", "SVR"]
regression_models = model_names_regression
regression_models = ["Lasso", "KNN", "RF"]
regression_ensemble_models = ['Ensemble_Average']
# classification_models = ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
classification_models = model_names_classification
classification_models = ["LR", "LDA", "KNN", "CART", "RF"]
# classification_models = model_names_classification
classification_ensemble_models = ['Ensemble_Average']
model_config = dict(