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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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models/model_map.py
+3 -3
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@@ -22,18 +22,18 @@ model_map = {
KNN = SKLearnModel(KNeighborsRegressor(n_neighbors=25)),
AB = SKLearnModel(AdaBoostRegressor(random_state=1)),
MLP = SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000)),
RF = SKLearnModel(RandomForestRegressor(n_jobs=-1)),
RF = SKLearnModel(RandomForestRegressor(n_jobs=-1, random_state=1)),
SVR = SKLearnModel(SVR(kernel='rbf', C=1e3, gamma=0.1)),
StaticNaive = StaticNaiveModel(),
),
"classification_models": dict(
LR= SKLearnModel(LogisticRegression(solver='liblinear', C=10, max_iter=1000)),
LR= SKLearnModel(LogisticRegression(C=10, random_state=1, max_iter=1000)),
LDA= SKLearnModel(LinearDiscriminantAnalysis()),
KNN= SKLearnModel(KNeighborsClassifier()),
CART= SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1)),
NB= SKLearnModel(GaussianNB()),
AB= SKLearnModel(AdaBoostClassifier(n_estimators=15)),
RF= SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20)),
RF= SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1)),
StaticMom= StaticMomentumModel(allow_short=True),
),
"classification_ensemble_models": dict(