mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +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
|
|
from sklearnex.ensemble import RandomForestClassifier
|
|
from sklearnex.ensemble import RandomForestRegressor
|
|
|
|
default_feature_selector_classification = SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification')
|
|
default_feature_selector_regression = SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1), 'regression')
|
|
|
|
def get_model_map(config:dict):
|
|
|
|
model_map = {
|
|
"primary_models": dict(),
|
|
"ensemble_models": dict(),
|
|
}
|
|
|
|
combined_list = config['primary_models'] + config['meta_labeling_models'] + [config['ensemble_model']]
|
|
for model_name in combined_list:
|
|
if model_name == 'LinearRegression':
|
|
from sklearn.linear_model import LinearRegression
|
|
model_map['primary_models']['LR'] = SKLearnModel(LinearRegression(n_jobs=-1), 'regression')
|
|
elif model_name == 'Lasso':
|
|
from sklearn.linear_model import Lasso
|
|
model_map['primary_models']['Lasso'] = SKLearnModel(Lasso(alpha=100, random_state=1), 'regression')
|
|
elif model_name == 'Ridge':
|
|
from sklearn.linear_model import Ridge
|
|
model_map['primary_models']['Ridge'] = SKLearnModel(Ridge(alpha=0.1), 'regression')
|
|
elif model_name == 'BayesianRidge':
|
|
from sklearn.linear_model import BayesianRidge
|
|
model_map['primary_models']['BayesianRidge'] = SKLearnModel(BayesianRidge(), 'regression')
|
|
elif model_name == 'KNN':
|
|
from sklearnex.neighbors import KNeighborsRegressor
|
|
model_map['primary_models']['KNN'] = SKLearnModel(KNeighborsRegressor(n_neighbors=25), 'regression')
|
|
elif model_name == 'AB':
|
|
from sklearn.ensemble import AdaBoostRegressor
|
|
model_map['primary_models']['AB'] = SKLearnModel(AdaBoostRegressor(random_state=1), 'regression')
|
|
elif model_name == 'MLP':
|
|
from sklearn.neural_network import MLPRegressor
|
|
model_map['primary_models']['MLP'] = SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000), 'regression')
|
|
elif model_name == 'RFR':
|
|
# from sklearn.ensemble import RandomForestRegressor
|
|
model_map['primary_models']['RFR'] = SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1), 'regression')
|
|
elif model_name == 'SVR':
|
|
from sklearnex.svm import SVR
|
|
model_map['primary_models']['SVR'] = SKLearnModel(SVR(kernel='rbf', C=1e3, gamma=0.1), 'regression')
|
|
elif model_name == 'StaticNaive':
|
|
from models.naive import StaticNaiveModel
|
|
model_map['primary_models']['StaticNaive'] = StaticNaiveModel()
|
|
elif model_name == 'DNN':
|
|
from models.neural import LightningNeuralNetModel
|
|
from models.pytorch.neural_nets import MultiLayerPerceptron
|
|
import torch.nn.functional as F
|
|
model_map['primary_models']['DNN'] = LightningNeuralNetModel(
|
|
MultiLayerPerceptron(
|
|
hidden_layers_ratio = [1.0],
|
|
probabilities = False,
|
|
loss_function = F.mse_loss),
|
|
max_epochs=15
|
|
)
|
|
|
|
elif model_name == 'LogisticRegression_two_class':
|
|
from sklearn.linear_model import LogisticRegression
|
|
model_map['primary_models']['LogisticRegression_two_class'] = SKLearnModel(LogisticRegression(C=10, random_state=1, solver='liblinear', max_iter=1000), 'classification')
|
|
elif model_name == 'LogisticRegression_three_class':
|
|
from sklearnex.linear_model import LogisticRegression as LogisticRegression_EX
|
|
model_map['primary_models']['LogisticRegression_three_class'] = SKLearnModel(LogisticRegression_EX(C=10, random_state=1, max_iter=1000, n_jobs=-1), 'classification')
|
|
elif model_name == 'LDA':
|
|
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
|
|
model_map['primary_models']['LDA'] = SKLearnModel(LinearDiscriminantAnalysis(), 'classification')
|
|
elif model_name == 'KNN':
|
|
from sklearn.neighbors import KNeighborsClassifier
|
|
model_map['primary_models']['KNN'] = SKLearnModel(KNeighborsClassifier(), 'classification')
|
|
elif model_name == 'CART':
|
|
from sklearn.tree import DecisionTreeClassifier
|
|
model_map['primary_models']['CART'] = SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1), 'classification')
|
|
elif model_name == 'NB':
|
|
from sklearn.naive_bayes import GaussianNB
|
|
model_map['primary_models']['NB'] = SKLearnModel(GaussianNB(), 'classification')
|
|
elif model_name == 'AB':
|
|
from sklearn.ensemble import AdaBoostClassifier
|
|
model_map['primary_models']['AB'] = SKLearnModel(AdaBoostClassifier(n_estimators=15), 'classification')
|
|
elif model_name == 'RFC':
|
|
model_map['primary_models']['RFC'] = SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification')
|
|
elif model_name == 'SVC':
|
|
from sklearn.svm import SVC
|
|
model_map['primary_models']['SVC'] = SKLearnModel(SVC(kernel='rbf', C=1e3, probability=True, random_state=1), 'classification')
|
|
elif model_name == 'XGB_two_class':
|
|
from xgboost import XGBClassifier
|
|
from models.xgboost import XGBoostModel
|
|
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'))
|
|
elif model_name == 'LGBM':
|
|
from lightgbm import LGBMClassifier
|
|
model_map['primary_models']['LGBM'] = SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification')
|
|
elif model_name == 'StaticMom':
|
|
from models.momentum import StaticMomentumModel
|
|
model_map['primary_models']['StaticMom'] = StaticMomentumModel(allow_short=True)
|
|
elif model_name == 'Average':
|
|
from models.average import StaticAverageModel
|
|
model_map['ensemble_models']['Average'] = StaticAverageModel()
|
|
|
|
return model_map
|
|
|