mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-02 21:57:46 +00:00
Daniel Szemerey
516c8bcc87
* fix, feat: Fixed inference processing data. Add transformation attribute. * feat: Added transformations step, refractored the loop to make more sense (divided the train and inference loop). * feat: Truncated models over time and transformations over time. Fixed some typing aswell. * fix: Fixed a number of out of array problems. * feat: Inference now works! * fix(Steps): runtime error not checking for None * fix(Steps): preloaded transformers are not optional anymore, sped up training by temporary increasing the retrain_every * fix(CI): disable ray memory monitoring * refactor(Inference): removed truncate_models and replaced it with filling X with NaN until inference should start * feat(Inference): added index_from parameter * fix(Tests): walk_forward test * refactor(Pipeline): only predict one asset * refactor(Inference): removed select_models step, inference code moved to run_inference.py so it matches convention (similar to run_pipeline.py) * fix(Evaluation): adjust transaction costs * fix(Config): adjusted retrain_every Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com> Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
71 lines
3.0 KiB
Python
71 lines
3.0 KiB
Python
from utils.evaluate import discretize_threeway_threshold, evaluate_predictions
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from utils.helpers import equal_except_nan
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from training.primary_model import train_primary_model
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import pandas as pd
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from models.model_map import default_feature_selector_classification, default_feature_selector_regression
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from models.base import Model
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from reporting.types import Reporting
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from typing import Union, Optional
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def train_meta_labeling_model(
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target_asset: str,
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X: pd.DataFrame,
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input_predictions: pd.Series,
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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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data_config: dict,
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model_config: dict,
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training_config: dict,
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model_suffix: str,
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from_index: Optional[int],
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preloaded_models: Optional[list[tuple[str, pd.Series, list[pd.Series]]]] = None
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) -> tuple[pd.Series, pd.Series, pd.DataFrame, list[Reporting.Single_Model]]:
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discretize = discretize_threeway_threshold(0.33)
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discretized_predictions = input_predictions.apply(discretize)
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meta_y: pd.Series = pd.concat([discretized_predictions, y], axis=1).apply(equal_except_nan, axis = 1)
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meta_X = pd.concat([X, input_predictions, discretized_predictions], axis = 1)
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_, meta_preds, meta_probabilities, all_models_single_asset = train_primary_model(
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ticker_to_predict = "prediction_correct",
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X = meta_X,
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y = meta_y,
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target_returns = target_returns,
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models = models,
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method = 'classification',
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expanding_window = training_config['expanding_window_meta_labeling'],
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sliding_window_size = training_config['sliding_window_size_meta_labeling'],
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retrain_every = training_config['retrain_every'],
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from_index = from_index,
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scaler = training_config['scaler'],
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no_of_classes = 'two',
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level = 'meta_labeling',
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print_results = False,
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preloaded_models = preloaded_models
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)
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if len(models) > 1:
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meta_preds = meta_preds.mean(axis = 1)
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bet_size = meta_probabilities[meta_probabilities.columns[1::2]].mean(axis = 1)
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else:
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bet_size = meta_probabilities.iloc[:,1]
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avg_predictions_with_sizing = input_predictions * bet_size
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avg_predictions_with_sizing.rename("model_" + target_asset + "_" + model_suffix, inplace=True)
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meta_result = evaluate_predictions(
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model_name = "Meta",
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target_returns = target_returns,
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y_pred = avg_predictions_with_sizing,
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y_true = y,
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method = 'classification',
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no_of_classes = 'two',
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print_results = True,
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discretize=False
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)
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meta_result.rename("model_" + target_asset + "_" + model_suffix, inplace=True)
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return meta_result, avg_predictions_with_sizing, meta_probabilities, all_models_single_asset
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