mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Mark Aron Szulyovszky
84 lines
2.9 KiB
Python
84 lines
2.9 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(
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RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1)
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)
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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(
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SKLearnModel(
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LogisticRegression(
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C=10, random_state=1, solver="liblinear", max_iter=1000
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)
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)
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)
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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(
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SKLearnModel(
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LogisticRegression_EX(C=10, random_state=1, max_iter=1000, n_jobs=-1)
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)
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)
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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(
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SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1))
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)
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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(
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SKLearnModel(
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RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1)
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)
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)
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elif model_name == "SVC":
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from sklearn.svm import SVC
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return set_name(
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SKLearnModel(SVC(kernel="rbf", C=1e3, probability=True, random_state=1))
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)
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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(
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SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1))
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)
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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")
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