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Feature(Speed): Python launches faster by conditionally importing models. (#169)
* feat: Added optional import of models. * fix: Models weren't wrapped into abstract class, fixed it. * chore: Deleted leftover comments. * fix: Same merge commit as on remote. * fix: System wasn't putting in RF because there was no differentiation between RF as regressor and RF as classificator. * fix(Models): use the XGBoostModel wrapper Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com> Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
This commit is contained in:
Daniel Szemerey
co-authored by
Daniel Szemerey
Mark Aron Szulyovszky
Daniel Szemerey
Mark Aron Szulyovszky
parent
797d45d036
commit
31dc847be1
+104
-65
@@ -1,72 +1,111 @@
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from sklearn.linear_model import LinearRegression, Lasso, BayesianRidge, Ridge
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from sklearn.linear_model import LogisticRegression
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from sklearnex.linear_model import LogisticRegression as LogisticRegression_EX
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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, SVC
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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.sklearn import SKLearnModel
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from models.neural import LightningNeuralNetModel
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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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from models.pytorch.neural_nets import MultiLayerPerceptron
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from models.xgboost import XGBoostModel
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from models.statsmodels import StatsModel
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from xgboost import XGBClassifier
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import torch.nn.functional as F
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from lightgbm import LGBMClassifier
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from statsmodels.tsa.api import ExponentialSmoothing
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.ensemble import RandomForestRegressor
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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, max_depth=20, 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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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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),
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"classification_models": dict(
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LR_two_class= SKLearnModel(LogisticRegression(C=10, random_state=1, solver='liblinear', max_iter=1000)),
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LR_three_class= SKLearnModel(LogisticRegression_EX(C=10, random_state=1, max_iter=1000, n_jobs=-1)),
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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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SVC = SKLearnModel(SVC(kernel='rbf', C=1e3, probability=True, random_state=1)),
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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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LGBM = SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1)),
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StaticMom= StaticMomentumModel(allow_short=True),
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# ExpSmoothing = SKLearnModel(ExponentialSmoothing(trend='add', seasonal='add', seasonal_periods=30)),
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),
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"ensemble_models": dict(
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Average= StaticAverageModel(),
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)
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"regression_models": dict(),
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"classification_models": dict(),
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"ensemble_models": dict()
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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 get_model_map(config:dict):
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if len(config['primary_models']) > 0 and isinstance(config['primary_models'][0], str):
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print("Going to Load models")
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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 == 'LR':
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from sklearn.linear_model import LinearRegression
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model_map['regression_models']['LR'] = SKLearnModel(LinearRegression(n_jobs=-1))
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elif model_name == 'Lasso':
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from sklearn.linear_model import Lasso
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model_map['regression_models']['Lasso'] = SKLearnModel(Lasso(alpha=100, random_state=1))
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elif model_name == 'Ridge':
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from sklearn.linear_model import Ridge
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model_map['regression_models']['Ridge'] = SKLearnModel(Ridge(alpha=0.1))
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elif model_name == 'BayesianRidge':
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from sklearn.linear_model import BayesianRidge
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model_map['regression_models']['BayesianRidge'] = SKLearnModel(BayesianRidge())
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elif model_name == 'KNN':
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from sklearnex.neighbors import KNeighborsRegressor
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model_map['regression_models']['KNN'] = SKLearnModel(KNeighborsRegressor(n_neighbors=25))
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elif model_name == 'AB':
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from sklearn.ensemble import AdaBoostRegressor
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model_map['regression_models']['AB'] = SKLearnModel(AdaBoostRegressor(random_state=1))
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elif model_name == 'MLP':
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from sklearn.neural_network import MLPRegressor
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model_map['regression_models']['MLP'] = SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000))
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elif model_name == 'RFR':
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# from sklearn.ensemble import RandomForestRegressor
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model_map['regression_models']['RFR'] = SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1))
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elif model_name == 'SVR':
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from sklearnex.svm import SVR
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model_map['regression_models']['SVR'] = SKLearnModel(SVR(kernel='rbf', C=1e3, gamma=0.1))
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elif model_name == 'StaticNaive':
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from models.naive import StaticNaiveModel
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model_map['regression_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['regression_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 == 'LR_two_class':
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from sklearn.linear_model import LogisticRegression
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model_map['classification_models']['LR_two_class'] = SKLearnModel(LogisticRegression(C=10, random_state=1, solver='liblinear', max_iter=1000))
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elif model_name == 'LR_three_class':
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from sklearnex.linear_model import LogisticRegression as LogisticRegression_EX
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model_map['classification_models']['LR_three_class'] = 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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model_map['classification_models']['LDA'] = SKLearnModel(LinearDiscriminantAnalysis())
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elif model_name == 'KNN':
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from sklearn.neighbors import KNeighborsClassifier
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model_map['classification_models']['KNN'] = SKLearnModel(KNeighborsClassifier())
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elif model_name == 'CART':
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from sklearn.tree import DecisionTreeClassifier
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model_map['classification_models']['CART'] = 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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model_map['classification_models']['NB'] = SKLearnModel(GaussianNB())
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elif model_name == 'AB':
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from sklearn.ensemble import AdaBoostClassifier
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model_map['classification_models']['AB'] = SKLearnModel(AdaBoostClassifier(n_estimators=15))
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elif model_name == 'RFC':
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# from sklearn.ensemble import RandomForestClassifier
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model_map['classification_models']['RFC'] = 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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model_map['classification_models']['SVC'] = 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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model_map['classification_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['classification_models']['LGBM'] = 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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model_map['classification_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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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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default_feature_selector_regression = model_map['regression_models']['RF']
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default_feature_selector_classification = model_map['classification_models']['RF']
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default_feature_selector_classification = SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1))
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default_feature_selector_regression = SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1))
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return model_map, model_names_classification, model_names_regression, default_feature_selector_regression, default_feature_selector_classification
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