mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
fc5eba4e2d
* 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
69 lines
3.4 KiB
Python
69 lines
3.4 KiB
Python
from sklearn.linear_model import LinearRegression, Lasso, BayesianRidge, Ridge
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from sklearnex.linear_model import LogisticRegression
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from sklearn.tree import DecisionTreeClassifier
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from sklearnex.neighbors import KNeighborsRegressor, KNeighborsClassifier
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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from sklearnex.svm import SVR, SVC
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from sklearn.naive_bayes import GaussianNB
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from sklearn.neural_network import MLPRegressor, MLPClassifier
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from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor, ExtraTreesRegressor, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier
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from sklearnex.ensemble import RandomForestClassifier
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from models.sklearn import SKLearnModel
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from models.neural import LightningNeuralNetModel
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from models.momentum import StaticMomentumModel
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from models.average import StaticAverageModel
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from models.naive import StaticNaiveModel
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from models.pytorch.neural_nets import MultiLayerPerceptron
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from models.xgboost import XGBoostModel
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from models.statsmodels import StatsModel
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from xgboost import XGBClassifier
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import torch.nn.functional as F
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from lightgbm import LGBMClassifier
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from statsmodels.tsa.api import ExponentialSmoothing
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model_map = {
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"regression_models": dict(
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LR= SKLearnModel(LinearRegression(n_jobs=-1)),
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Lasso= SKLearnModel(Lasso(alpha=100, random_state=1)),
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Ridge= SKLearnModel(Ridge(alpha=0.1)),
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BayesianRidge= SKLearnModel(BayesianRidge()),
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KNN= SKLearnModel(KNeighborsRegressor(n_neighbors=25)),
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AB= SKLearnModel(AdaBoostRegressor(random_state=1)),
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MLP= SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000)),
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RF= SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1)),
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SVR= SKLearnModel(SVR(kernel='rbf', C=1e3, gamma=0.1)),
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StaticNaive= StaticNaiveModel(),
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DNN = LightningNeuralNetModel(
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MultiLayerPerceptron(
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hidden_layers_ratio = [1.0],
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probabilities = False,
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loss_function = F.mse_loss),
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max_epochs=15
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)
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),
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"classification_models": dict(
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LR= SKLearnModel(LogisticRegression(C=10, random_state=1, max_iter=1000, n_jobs=-1)),
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LDA= SKLearnModel(LinearDiscriminantAnalysis()),
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KNN= SKLearnModel(KNeighborsClassifier()),
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CART= SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1)),
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NB= SKLearnModel(GaussianNB()),
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AB= SKLearnModel(AdaBoostClassifier(n_estimators=15)),
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RF= SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1)),
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SVC = SKLearnModel(SVC(kernel='rbf', C=1e3, probability=True)),
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XGB_two_class= XGBoostModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, objective='binary:logistic', use_label_encoder= False, eval_metric='mlogloss')),
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LGBM = SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1)),
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StaticMom= StaticMomentumModel(allow_short=True),
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Ensemble_Average= StaticAverageModel(),
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# ExpSmoothing = SKLearnModel(ExponentialSmoothing(trend='add', seasonal='add', seasonal_periods=30)),
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),
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}
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model_names_classification = list(model_map["classification_models"].keys())
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model_names_regression = list(model_map["regression_models"].keys())
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default_feature_selector_regression = model_map['regression_models']['RF']
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default_feature_selector_classification = model_map['classification_models']['RF']
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