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