mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
9488e92597
* feature(MetaLabeling): added hacky prototype * fix(MetaLabeling): drop index until first valid X & y * fix(MetaLabeling): transform both X & y before feature selection * fix(MetaLabeling): got feature selection to work * fix(MetaLabeling): correct values for meta_y * feat(MetaLabeling): created predictions multiplied by bet sizes * feat(Pipeline): print out averaged result * fix(Evaluation): correctly deal with non-discretized data * fix(Pipeline): use the right column names * refactor(Pipeline): move out meta-labeling * refactor(Pipeline): complete refactoring * feat(CI): post results to PR * fix(Pipeline): use the correct filename * chore(Config): removed now redundant feature_selection flag * feat(Models): added SVC * fix(Pipeline): accidentally switched two return values * feat(Sweep): prepared sweep_meta.yaml, moved report_results() into a separate file * fix(Pipeline): wrong function name * fix(Sweep): yaml + run_sweep * fix(Sweep): typo in name * fix(Reporting): only save averaged results * feat(MetaLabeling): use optional meta-labeling step for every lvl1 models, before averaging * feat(Reporting): print out sharpe improvement in meta-labeling step * fix(Sweep): adjusted config, defaulted to good defaults * fix(Sweep): adjusted sweep
61 lines
3.1 KiB
Python
61 lines
3.1 KiB
Python
from sklearn.linear_model import LinearRegression, Lasso, BayesianRidge, LogisticRegression, Ridge
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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.base import SKLearnModel, 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 xgboost import XGBClassifier
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import torch.nn.functional as F
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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_three_class= SKLearnModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, use_label_encoder=True, objective='multi:softprob', eval_metric='mlogloss')),
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XGB_two_class= SKLearnModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, objective='binary:logistic', eval_metric='mlogloss')),
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StaticMom= StaticMomentumModel(allow_short=True),
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Ensemble_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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