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feat(Models): added lightGBM, moved other models to separate files (#128)

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* feat(Models): added lightGBM, moved other models to separate files

* feat(Models): added non-working statsmodel wrapper

* fix(Models): added work-in-progress comment to StatsModels
This commit is contained in:
Mark Aron Szulyovszky
2022-01-08 12:02:42 +01:00
committed by GitHub
parent 6982187872
commit fc5eba4e2d
12 changed files with 165 additions and 73 deletions
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models/model_map.py
+10 -3
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@@ -8,13 +8,19 @@ from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPRegressor, MLPClassifier
from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor, ExtraTreesRegressor, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier
from sklearnex.ensemble import RandomForestClassifier
from models.base import SKLearnModel, LightningNeuralNetModel
from models.sklearn import SKLearnModel
from models.neural import LightningNeuralNetModel
from models.momentum import StaticMomentumModel
from models.average import StaticAverageModel
from models.naive import StaticNaiveModel
from models.pytorch.neural_nets import MultiLayerPerceptron
from models.xgboost import XGBoostModel
from models.statsmodels import StatsModel
from xgboost import XGBClassifier
import torch.nn.functional as F
from lightgbm import LGBMClassifier
from statsmodels.tsa.api import ExponentialSmoothing
model_map = {
@@ -46,10 +52,11 @@ model_map = {
AB= SKLearnModel(AdaBoostClassifier(n_estimators=15)),
RF= SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1)),
SVC = SKLearnModel(SVC(kernel='rbf', C=1e3, probability=True)),
XGB_three_class= SKLearnModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, use_label_encoder=True, objective='multi:softprob', eval_metric='mlogloss')),
XGB_two_class= SKLearnModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, objective='binary:logistic', eval_metric='mlogloss')),
XGB_two_class= XGBoostModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, objective='binary:logistic', use_label_encoder= False, eval_metric='mlogloss')),
LGBM = SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1)),
StaticMom= StaticMomentumModel(allow_short=True),
Ensemble_Average= StaticAverageModel(),
# ExpSmoothing = SKLearnModel(ExponentialSmoothing(trend='add', seasonal='add', seasonal_periods=30)),
),
}