mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-01 13:17:47 +00:00
Daniel Szemerey
1c1b8b2e54
* feat: Parametricized model selection works now. * feat: Fixed errors. Sweep generates and you can run it, but it gives an error for model.only_columns attribute. * feat: Factored the wandb management, default config managment and the model_dictionary out of the run_pipeline to a seperate file. * fix: Took out prints and fixed the mismatch of ensemble models when classifing. * fix(Models): added StaticMomentum model to the dictionary, hopefully fixed sklearn-ex RandomForestRegressor problem * fix(Dependencies): pin scikit-learn-ex's version, moved map_model_name_to_function to `models` * feat(Sweep): added `run_sweep.py` shortcut * feat(Pipeline): skip training a meta model if array is empty Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
import pandas as pd
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from typing import Literal
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from training.walk_forward import walk_forward_train_test
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from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler
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from utils.evaluate import evaluate_predictions
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from models.base import Model
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def __get_scaler(type: Literal['normalize', 'minmax', 'standardize', 'none']):
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if type == 'normalize':
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return Normalizer()
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elif type == 'minmax':
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return MinMaxScaler(feature_range= (-1, 1))
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elif type == 'standardize':
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return StandardScaler()
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else:
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return None
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def run_single_asset_trainig(
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ticker_to_predict: str,
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X: pd.DataFrame,
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y: pd.Series,
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target_returns: pd.Series,
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models: list[tuple[str, Model]],
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method: Literal['regression', 'classification'],
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sliding_window_size: int,
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retrain_every: int,
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scaler: Literal['normalize', 'minmax', 'standardize', 'none'],
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wandb,
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project_name:str,
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sweep:bool
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) -> tuple[pd.DataFrame, pd.DataFrame]:
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scaler = __get_scaler(scaler)
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results = pd.DataFrame()
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predictions = pd.DataFrame()
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wandb_active = type(wandb) is not type(None)
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for model_name, model in models:
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model_over_time, preds = walk_forward_train_test(
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model_name=model_name,
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model = model,
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X = X,
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y = y,
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target_returns = target_returns,
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window_size = sliding_window_size,
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retrain_every = retrain_every,
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scaler = scaler
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)
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assert len(preds) == len(y)
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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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method = method,
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)
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column_name = ticker_to_predict + "_" + model_name
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results[column_name] = result
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# column names for model outputs should be different, so we can differentiate between original data and model predictions later, where necessary
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predictions["model_" + column_name] = preds
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if wandb_active and not sweep:
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run = wandb.init(project=project_name, config={"model_type": model_name, "ticker": ticker_to_predict}, reinit=True)
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wandb.run.name = ticker_to_predict + "-" + model_name+ "-" + wandb.run.id
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wandb.run.save()
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for rownum,(indx,val) in enumerate(result.iteritems()):
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run.log({"model_type": model_name, indx:val })
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run.finish()
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if wandb_active and sweep:
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mean_results = results.mean()
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wandb.log({"model_type": 'avarage_model', 'results':results })
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for rownum,(indx,val) in enumerate(mean_results.iteritems()):
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wandb.log({"model_type": 'avarage_model', indx:val })
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return results, predictions |