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feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192)
* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba * fix(WalkForward): inference mini-batch parallelization * fix(WalkForward): don't use the parallel version of any of the functions * feat(CI): download the data required * fix(Project): 5min_crypto folder added * fix(Evaluate): make sure we have numerical stability in returns * feat(Models): use SKLearn models directly to enable composability * feat(Inference): batched inference now working, added forecasting_horizon * fix(Inference): works again * fix(Inference) * chore(Models): remove unused Ensemble model * fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then * Update test.yml
This commit is contained in:
Mark Aron Szulyovszky
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GitHub
GitHub
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5c94af8b01
commit
9d47ee942d
+16
-60
@@ -3,7 +3,7 @@ from sklearnex.ensemble import RandomForestClassifier
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from sklearnex.ensemble import RandomForestRegressor
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from .base import Model
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default_feature_selector_classification = SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification')
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default_feature_selector_classification = SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1))
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def get_model(model_name: str) -> Model:
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@@ -11,85 +11,41 @@ def get_model(model_name: str) -> Model:
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model.name = model_name
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return model
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if model_name == 'LinearRegression':
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from sklearn.linear_model import LinearRegression
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return set_name(SKLearnModel(LinearRegression(n_jobs=-1), 'regression'))
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elif model_name == 'Lasso':
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from sklearn.linear_model import Lasso
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return set_name(SKLearnModel(Lasso(alpha=100, random_state=1), 'regression'))
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elif model_name == 'Ridge':
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from sklearn.linear_model import Ridge
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return set_name(SKLearnModel(Ridge(alpha=0.1), 'regression'))
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elif model_name == 'BayesianRidge':
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from sklearn.linear_model import BayesianRidge
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return set_name(SKLearnModel(BayesianRidge(), 'regression'))
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elif model_name == 'KNN':
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from sklearnex.neighbors import KNeighborsRegressor
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return set_name(SKLearnModel(KNeighborsRegressor(n_neighbors=25), 'regression'))
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elif model_name == 'AB':
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from sklearn.ensemble import AdaBoostRegressor
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return set_name(SKLearnModel(AdaBoostRegressor(random_state=1), 'regression'))
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elif model_name == 'MLP':
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from sklearn.neural_network import MLPRegressor
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return set_name(SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000), 'regression'))
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elif model_name == 'RFR':
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return set_name(SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1), 'regression'))
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elif model_name == 'SVR':
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from sklearnex.svm import SVR
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return set_name(SKLearnModel(SVR(kernel='rbf', C=1e3, gamma=0.1), 'regression'))
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elif model_name == 'StaticNaive':
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from models.naive import StaticNaiveModel
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return set_name(StaticNaiveModel())
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elif model_name == 'DNN':
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from models.neural import LightningNeuralNetModel
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from models.pytorch.neural_nets import MultiLayerPerceptron
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import torch.nn.functional as F
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return set_name(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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elif model_name == 'LogisticRegression_two_class':
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if model_name == 'LogisticRegression_two_class':
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from sklearn.linear_model import LogisticRegression
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return set_name(SKLearnModel(LogisticRegression(C=10, random_state=1, solver='liblinear', max_iter=1000), 'classification'))
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return set_name(SKLearnModel(LogisticRegression(C=10, random_state=1, solver='liblinear', max_iter=1000)))
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elif model_name == 'LogisticRegression_three_class':
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from sklearnex.linear_model import LogisticRegression as LogisticRegression_EX
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return set_name(SKLearnModel(LogisticRegression_EX(C=10, random_state=1, max_iter=1000, n_jobs=-1), 'classification'))
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return set_name(SKLearnModel(LogisticRegression_EX(C=10, random_state=1, max_iter=1000, n_jobs=-1)))
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elif model_name == 'LDA':
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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return set_name(SKLearnModel(LinearDiscriminantAnalysis(), 'classification'))
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return set_name(SKLearnModel(LinearDiscriminantAnalysis()))
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elif model_name == 'KNN':
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from sklearn.neighbors import KNeighborsClassifier
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return set_name(SKLearnModel(KNeighborsClassifier(), 'classification'))
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return set_name(SKLearnModel(KNeighborsClassifier()))
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elif model_name == 'CART':
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from sklearn.tree import DecisionTreeClassifier
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return set_name(SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1), 'classification'))
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return set_name(SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1)))
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elif model_name == 'NB':
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from sklearn.naive_bayes import GaussianNB
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return set_name(SKLearnModel(GaussianNB(), 'classification'))
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return set_name(SKLearnModel(GaussianNB()))
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elif model_name == 'AB':
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from sklearn.ensemble import AdaBoostClassifier
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return set_name(SKLearnModel(AdaBoostClassifier(n_estimators=15), 'classification'))
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return set_name(SKLearnModel(AdaBoostClassifier(n_estimators=15)))
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elif model_name == 'RFC':
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return set_name(SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification'))
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return set_name(SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1)))
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elif model_name == 'SVC':
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from sklearn.svm import SVC
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return set_name(SKLearnModel(SVC(kernel='rbf', C=1e3, probability=True, random_state=1), 'classification'))
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elif model_name == 'XGB_two_class':
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from xgboost import XGBClassifier
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from models.xgboost import XGBoostModel
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return set_name(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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return set_name(SKLearnModel(SVC(kernel='rbf', C=1e3, probability=True, random_state=1)))
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# elif model_name == 'XGB_two_class':
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# from xgboost import XGBClassifier
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# from models.xgboost import XGBoostModel
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# return set_name(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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elif model_name == 'LGBM':
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from lightgbm import LGBMClassifier
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return set_name(SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification'))
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return set_name(SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1)))
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elif model_name == 'StaticMom':
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from models.momentum import StaticMomentumModel
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return set_name(StaticMomentumModel(allow_short=True))
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elif model_name == 'Average':
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from models.average import StaticAverageModel
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return set_name(StaticAverageModel())
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else:
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raise Exception(f'Model {model_name} not found')
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