mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Daniel Szemerey
3084f5e271
* refr: Took out main primary and secondary loops and data processing. * feat: Tidied the code up. * feat: Saving models and results now works in a type safe way. * fix: There was error in the saving function. * chore: Took out some remaining comments. * fix: Fixed the previous data checking process. * feat: Fixed model selection method. I will continue the inference after we merged. Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>
85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
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import pandas as pd
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from data_loader.load_data import load_data
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from typing import Optional, Union
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import warnings
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from utils.encapsulation import Asset, Single_Model, Training_Step
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def run_inference_pipeline(data_config:dict, training_config:dict, all_models_all_assets:list[Asset]):
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data_params = data_config.copy()
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data_params['target_asset'] = data_params['assets'][0]
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X, y, _ = load_data(**data_params)
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input_features = __select_data(X, training_config)
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primary_step, secondary_step = __select_models(data_params, all_models_all_assets)
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result = __inference(input_features, primary_step, secondary_step)
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return result
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def __inference(data:pd.DataFrame, primary_step:Union[Training_Step,None], secondary_step:Union[Training_Step,None]) -> pd.DataFrame:
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assert primary_step is not None, "No primary models found. Cancelling Inference."
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data = __primary_models(data, primary_step)
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if secondary_step is not None:
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warnings.warn("Secondary models are not specified.")
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data = __secondary_models(data, secondary_step)
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return data
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def __select_models( data_params:dict, all_models_all_assets:list[Asset])-> tuple[Union[Training_Step,None], Union[Training_Step, None]]:
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target_asset_name = data_params['target_asset'][1]
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primary_step, secondary_step, = None, None
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target_asset_models = next((x for x in all_models_all_assets if x.name == target_asset_name), None)
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if target_asset_models is not None:
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if len(target_asset_models.primary.base)>0:
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primary_step = target_asset_models.primary
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else: warnings.warn("No primary models found for {}.".format(target_asset_name))
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if len(target_asset_models.secondary.base)>0:
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secondary_step = target_asset_models.secondary
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else: warnings.warn("No secondary models found for {}.".format(target_asset_name))
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else:
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assert("No models found for asset: " + target_asset_name)
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return primary_step, secondary_step
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def __select_data(X:pd.DataFrame, training_config:dict)-> pd.DataFrame:
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window_size = training_config['sliding_window_size_primary']
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num_rows = X.shape[0]
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if num_rows <= window_size:
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return X.copy()
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else:
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return X.truncate(before=int(num_rows-window_size), after=num_rows, copy=True)
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def __primary_models(data:pd.DataFrame, models:dict)-> pd.DataFrame:
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for k, model in models:
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last_model = model[-1]
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prediction = last_model.predict(data.to_numpy())
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# result = evaluate_predictions(
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# model_name = model_name,
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# target_returns = target_returns,
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# y_pred = preds,
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# y_true = y,
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# method = method,
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# no_of_classes=no_of_classes,
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# print_results = print_results,
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# discretize=True
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# )
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return data
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def __secondary_models(data:pd.DataFrame, model:dict)-> pd.DataFrame:
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return data
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