mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
9d47ee942d
* 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
51 lines
2.6 KiB
Python
51 lines
2.6 KiB
Python
from models.sklearn import SKLearnModel
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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))
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def get_model(model_name: str) -> Model:
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def set_name(model: Model) -> Model:
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model.name = model_name
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return model
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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)))
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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)))
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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()))
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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()))
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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)))
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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()))
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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)))
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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)))
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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)))
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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)))
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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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else:
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raise Exception(f'Model {model_name} not found') |