mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
6b26643ece
* feat(Transformations): removed feature-selection pre-processing step completely * fix(Core): removed unnecessary `original_X` * fix(Transformations): use the X_expanding_window to transform subsequent data * fix(RFE): should check for model correctly * fix(Config): only re-train the model every 40 timestamp * fix(MetaLabeling): pass in the correct X to meta-labeling step * fix(Transformation): PCA should at least keep as many features as sliding_window_size * feat(Transformations): cache transformations across the same asset * fix(Tests): missing preloaded_transformations arg * chore(Config): got rid of unnecessary 'classification_models' and 'regression_models' dictionary keys
101 lines
6.1 KiB
Python
101 lines
6.1 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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default_feature_selector_classification = SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification')
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default_feature_selector_regression = SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1), 'regression')
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def get_model_map(config:dict):
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model_map = {
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"primary_models": dict(),
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"ensemble_models": dict(),
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}
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combined_list = config['primary_models'] + config['meta_labeling_models'] + [config['ensemble_model']]
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for model_name in combined_list:
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if model_name == 'LinearRegression':
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from sklearn.linear_model import LinearRegression
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model_map['primary_models']['LR'] = 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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model_map['primary_models']['Lasso'] = 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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model_map['primary_models']['Ridge'] = 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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model_map['primary_models']['BayesianRidge'] = SKLearnModel(BayesianRidge(), 'regression')
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elif model_name == 'KNN':
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from sklearnex.neighbors import KNeighborsRegressor
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model_map['primary_models']['KNN'] = 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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model_map['primary_models']['AB'] = 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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model_map['primary_models']['MLP'] = SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000), 'regression')
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elif model_name == 'RFR':
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# from sklearn.ensemble import RandomForestRegressor
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model_map['primary_models']['RFR'] = 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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model_map['primary_models']['SVR'] = 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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model_map['primary_models']['StaticNaive'] = 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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model_map['primary_models']['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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elif model_name == 'LogisticRegression_two_class':
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from sklearn.linear_model import LogisticRegression
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model_map['primary_models']['LogisticRegression_two_class'] = SKLearnModel(LogisticRegression(C=10, random_state=1, solver='liblinear', max_iter=1000), 'classification')
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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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model_map['primary_models']['LogisticRegression_three_class'] = SKLearnModel(LogisticRegression_EX(C=10, random_state=1, max_iter=1000, n_jobs=-1), 'classification')
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elif model_name == 'LDA':
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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model_map['primary_models']['LDA'] = SKLearnModel(LinearDiscriminantAnalysis(), 'classification')
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elif model_name == 'KNN':
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from sklearn.neighbors import KNeighborsClassifier
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model_map['primary_models']['KNN'] = SKLearnModel(KNeighborsClassifier(), 'classification')
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elif model_name == 'CART':
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from sklearn.tree import DecisionTreeClassifier
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model_map['primary_models']['CART'] = SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1), 'classification')
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elif model_name == 'NB':
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from sklearn.naive_bayes import GaussianNB
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model_map['primary_models']['NB'] = SKLearnModel(GaussianNB(), 'classification')
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elif model_name == 'AB':
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from sklearn.ensemble import AdaBoostClassifier
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model_map['primary_models']['AB'] = SKLearnModel(AdaBoostClassifier(n_estimators=15), 'classification')
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elif model_name == 'RFC':
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model_map['primary_models']['RFC'] = SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification')
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elif model_name == 'SVC':
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from sklearn.svm import SVC
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model_map['primary_models']['SVC'] = 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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model_map['primary_models']['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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elif model_name == 'LGBM':
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from lightgbm import LGBMClassifier
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model_map['primary_models']['LGBM'] = SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification')
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elif model_name == 'StaticMom':
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from models.momentum import StaticMomentumModel
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model_map['primary_models']['StaticMom'] = 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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model_map['ensemble_models']['Average'] = StaticAverageModel()
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return model_map
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