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refactor(Core): small refactor in the pipeline to streamline classification/regression model handling (#60)

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This commit is contained in:
Mark Aron Szulyovszky
2021-12-21 09:23:06 +01:00
committed by GitHub
parent 52268d0141
commit 85ad937078
2 changed files with 44 additions and 55 deletions
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run_pipeline.py
+35 -35
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@@ -21,41 +21,12 @@ from typing import Tuple
def get_config()->Tuple[dict, dict, dict]:
# Parameters
model_config = dict(
regression_models = [
# ('Lasso', Lasso(alpha=0.1, max_iter=1000)),
('Ridge', Ridge(alpha=0.1)),
('BayesianRidge', BayesianRidge()),
# ('KNN', KNeighborsRegressor(n_neighbors=25)),
# ('AB', AdaBoostRegressor(random_state=1)),
# ('LR', LinearRegression(n_jobs=-1)),
# ('MLP', MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000)),
# ('RF', RandomForestRegressor(n_jobs=-1)),
# ('SVR', SVR(kernel='rbf', C=1e3, gamma=0.1))
],
regression_ensemble_model = [('Ensemble - Ridge', Ridge(alpha=0.1))],
classification_models = [
('LR', LogisticRegression(n_jobs=-1)),
# ('LDA', LinearDiscriminantAnalysis()),
# ('KNN', KNeighborsClassifier()),
# ('CART', DecisionTreeClassifier()),
# ('NB', GaussianNB()),
# ('AB', AdaBoostClassifier()),
# ('RF', RandomForestClassifier(n_jobs=-1))
],
classification_ensemble_model = [('Ensemble - CART', DecisionTreeClassifier())]
)
training_config = dict(
# path = 'data/',
sliding_window_size = 150,
retrain_every = 20,
scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
include_original_data_in_ensemble = True,
# method = 'regression',
# forecasting_horizon = 1
)
data_config = dict(
@@ -67,9 +38,38 @@ def get_config()->Tuple[dict, dict, dict]:
own_features= feature_extractor_presets.date + feature_extractor_presets.level1,
other_features= [],
index_column= 'int',
method= 'regression',
method= 'classification',
)
classification_models = [
('LR', LogisticRegression(n_jobs=-1)),
# ('LDA', LinearDiscriminantAnalysis()),
('KNN', KNeighborsClassifier()),
# ('CART', DecisionTreeClassifier()),
# ('NB', GaussianNB()),
# ('AB', AdaBoostClassifier()),
# ('RF', RandomForestClassifier(n_jobs=-1))
]
regression_models = [
# ('Lasso', Lasso(alpha=0.1, max_iter=1000)),
('Ridge', Ridge(alpha=0.1)),
('BayesianRidge', BayesianRidge()),
# ('KNN', KNeighborsRegressor(n_neighbors=25)),
# ('AB', AdaBoostRegressor(random_state=1)),
# ('LR', LinearRegression(n_jobs=-1)),
# ('MLP', MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000)),
# ('RF', RandomForestRegressor(n_jobs=-1)),
# ('SVR', SVR(kernel='rbf', C=1e3, gamma=0.1))
]
regression_ensemble_model = [('Ensemble - Ridge', Ridge(alpha=0.1))]
classification_ensemble_model = [('Ensemble - CART', DecisionTreeClassifier())]
model_config = dict(
level_1_models = regression_models if data_config['method'] == 'regression' else classification_models,
level_2_model = regression_ensemble_model if data_config['method'] == 'regression' else classification_ensemble_model,
)
return model_config, training_config, data_config
def launch_wandb(config, sweep=False):
@@ -86,7 +86,7 @@ def launch_wandb(config, sweep=False):
return wandb
def run_pipeline(with_wandb, sweep):
def run_pipeline(with_wandb: bool, sweep: bool):
model_config, training_config, data_config = get_config()
wandb = None
@@ -122,7 +122,7 @@ def pipeline(model_config:dict, training_config:dict, data_config:dict, wandb):
X = X,
y = y,
target_returns = target_returns,
models = model_config['regression_models'] if data_config['method'] == 'regression' else model_config['classification_models'],
models = model_config['level_1_models'],
method = data_config['method'],
sliding_window_size = training_config['sliding_window_size'],
retrain_every = training_config['retrain_every'],
@@ -142,7 +142,7 @@ def pipeline(model_config:dict, training_config:dict, data_config:dict, wandb):
X = ensemble_X,
y = y,
target_returns = target_returns,
models = model_config['regression_ensemble_model'] if data_config['method'] == 'regression' else model_config['classification_ensemble_model'],
models = model_config['level_2_model'],
method = data_config['method'],
sliding_window_size = training_config['sliding_window_size'],
retrain_every = training_config['retrain_every'],
@@ -165,4 +165,4 @@ def pipeline(model_config:dict, training_config:dict, data_config:dict, wandb):
if __name__ == '__main__':
run_pipeline(False, False)
run_pipeline(with_wandb = False, sweep = False)