mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
95573eb9dd
* feat(Data): add option to predict 3 classes * feat(Evaluation): added ability to evaluate 3 class predictions * chore(Config): set sensible config for regression models * feat(Data): added option to use balanced or imbalanced three-class data * feat(Evaluate): correctly track "no_of_samples" now that we have three classes * chore(Sweep): remove probably not useful scaler values from sweep
58 lines
2.8 KiB
Python
58 lines
2.8 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
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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
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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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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, 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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),
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"classification_models": dict(
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LR= SKLearnModel(LogisticRegression(C=10, random_state=1, max_iter=1000)),
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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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StaticMom= StaticMomentumModel(allow_short=True),
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),
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"classification_ensemble_models": dict(
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Ensemble_CART = SKLearnModel(DecisionTreeClassifier()),
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Ensemble_Average = StaticAverageModel(),
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),
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"regression_ensemble_models": dict(
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Ensemble_Ridge = SKLearnModel(Ridge(alpha=0.1)),
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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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def map_model_name_to_function(model_config:dict, method:str) -> dict:
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for level in ['level_1_models', 'level_2_models']:
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model_category = method + '_models' if level=='level_1_models' else method + '_ensemble_models'
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model_config[level] = [(model_name, model_map[model_category][model_name]) for model_name in model_config[level]]
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return model_config |