mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
59 lines
2.6 KiB
Python
59 lines
2.6 KiB
Python
from data_loader.load_data import load_data
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from data_loader.process_data import check_data
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from reporting.saving import load_models
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from run_pipeline import run_pipeline
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from config.config import Config, get_dev_config, get_default_ensemble_config, get_lightweight_ensemble_config
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from typing import Callable, Optional
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from reporting.types import Reporting
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from training.training_steps import primary_step, secondary_step
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import pandas as pd
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import warnings
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def run_inference(preload_models:bool, get_config:Callable):
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if preload_models:
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all_models, config = load_models(None)
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else:
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all_models, config, _, _, _ = run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, get_config=get_config)
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__inference(config, all_models.primary, all_models.secondary)
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def __inference(config: Config, primary_models: Optional[Reporting.Training_Step], secondary_models: Optional[Reporting.Training_Step]):
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reporting = Reporting()
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asset = config.target_asset
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# 1. Load data, check for validity and process data
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X, y, target_returns = load_data(
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assets = config.assets,
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other_assets = config.other_assets,
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exogenous_data = config.exogenous_data,
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target_asset = config.target_asset,
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load_non_target_asset = config.load_non_target_asset,
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log_returns = config.log_returns,
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forecasting_horizon = config.forecasting_horizon,
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own_features = config.own_features,
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other_features = config.other_features,
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exogenous_features = config.exogenous_features,
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no_of_classes = config.no_of_classes,
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)
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assert check_data(X, y, config) == True, "Data is not valid. Cancelling Inference."
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inference_from: pd.Timestamp = X.index[len(X.index) - 2]
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# 2. Train a Primary model with optional metalabeling for each asset
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training_step_primary, current_predictions = primary_step(X, y, target_returns, config, reporting, from_index = inference_from, preloaded_training_step = primary_models)
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# 3. Train an Ensemble model with optional metalabeling for each asset
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if secondary_models is not None:
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warnings.warn("Secondary models are not specified.")
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training_step_secondary = secondary_step(X, y, current_predictions, target_returns, config, reporting, from_index = inference_from, preloaded_training_step = secondary_models)
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# 4. Save the models
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reporting.asset = Reporting.Asset(ticker=asset[1], primary=training_step_primary, secondary=training_step_secondary)
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return reporting
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if __name__ == '__main__':
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run_inference(preload_models=True, get_config=get_lightweight_ensemble_config) |