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feat(Sweep): separated level-1 and level-2 sweep configs, skip assets with too few samples to train on, simplified model mapping (#84)

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* feat: Added ensemble models to sweep and configured naming convention.

* fix: Default value was misconfigured.

* feat(Sweep): separated level-1 and level-2 sweep configs, skip assets with too few samples to train on, simplified model mapping

* fix(Sweep): syntax error

* chore(Sweep): set sweep names accordingly

* fix(Sweep): set sliding window

* fix(Sweep): adjusted sweep config

* fix(Sweep): removed invalid feature extractor preset

Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
This commit is contained in:
Daniel Szemerey
2021-12-23 23:48:59 +01:00
committed by GitHub
co-authored by Mark Aron Szulyovszky
parent eea88103f4
commit fc4e59a7d2
7 changed files with 76 additions and 32 deletions
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run_pipeline.py
+10 -3
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@@ -5,6 +5,7 @@ from reporting.wandb import launch_wandb, send_report_to_wandb, register_config_
from models.model_map import map_model_name_to_function
from feature_extractors.feature_extractor_presets import preprocess_feature_extractors_config
from config import get_default_config, validate_config, get_model_name
from utils.helpers import get_first_valid_return_index
def setup_pipeline(project_name:str, with_wandb: bool, sweep: bool):
model_config, training_config, data_config = get_default_config()
@@ -33,6 +34,11 @@ def pipeline(project_name:str, wandb, sweep:bool, model_config:dict, training_co
data_params['target_asset'] = asset
X, y, target_returns = load_data(**data_params)
first_valid_index = get_first_valid_return_index(X.iloc[:,0])
samples_to_train = len(y) - first_valid_index
if samples_to_train < training_config['sliding_window_size'] * 2.6:
print("Not enough samples to train")
continue
# 2. Train Level-1 models
current_result, current_predictions = run_single_asset_trainig(
@@ -46,14 +52,14 @@ def pipeline(project_name:str, wandb, sweep:bool, model_config:dict, training_co
sliding_window_size = training_config['sliding_window_size'],
retrain_every = training_config['retrain_every'],
scaler = training_config['scaler'],
no_of_classes = data_config['no_of_classes']
no_of_classes = data_config['no_of_classes'],
level = 1
)
results = pd.concat([results, current_result], axis=1)
all_predictions = pd.concat([all_predictions, current_predictions], axis=1)
if len(model_config['level_2_models']) > 0:
# 3. Train Level-2 (Ensemble) model
ensemble_X = all_predictions
if training_config['include_original_data_in_ensemble']:
ensemble_X = pd.concat([ensemble_X, X], axis=1)
@@ -69,7 +75,8 @@ def pipeline(project_name:str, wandb, sweep:bool, model_config:dict, training_co
sliding_window_size = training_config['sliding_window_size'],
retrain_every = training_config['retrain_every'],
scaler = training_config['scaler'],
no_of_classes = data_config['no_of_classes']
no_of_classes = data_config['no_of_classes'],
level = 2
)
results = pd.concat([results, ensemble_result], axis=1)