mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
b1c04afb13
* refactor(Naming): use `primary_models` & `meta_labeling_models` * refactor(Naming): using primary * meta_labeling across config and in pipeline * feat(Pipeline): added back Ensemble models * fix(Pipeline): compiler error * fix(Config): typo * chore(Pipeline): removed unused averaging step * revert the changes in discretizing * chore(Pipeline): remove sharpe improvement logging * fix(Pipeline): ensemble predictions should be a pd.Series instead of a DataFrame * fix(Pipeline): discard unnecessary ensemble_probabilities * fix(Pipeline): fixes regarding various meta-labeling ensemble bugs * fix(Reporting): use the new naming convention * fix(Reporting): use the right variable * feat(Sweep): new sweep for ensemble models * fix(Sweep): config reference * fix(Config): simplified dev config * fix(Models): use the faster LR model * fix(Models): use LGBM in the meta-labeling model for speed * fix(Selection): always use the first model for feature selection, commented out caching from select_features() as it's close to redundant in terms of speed
73 lines
3.6 KiB
Python
73 lines
3.6 KiB
Python
from sklearn.linear_model import LinearRegression, Lasso, BayesianRidge, Ridge
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from sklearn.linear_model import LogisticRegression
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from sklearnex.linear_model import LogisticRegression as LogisticRegression_EX
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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_two_class= SKLearnModel(LogisticRegression(C=10, random_state=1, solver='liblinear', max_iter=1000)),
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LR_three_class= SKLearnModel(LogisticRegression_EX(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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# ExpSmoothing = SKLearnModel(ExponentialSmoothing(trend='add', seasonal='add', seasonal_periods=30)),
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),
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"ensemble_models": dict(
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Average= StaticAverageModel(),
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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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