mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-04 14:47:49 +00:00
Mark Aron Szulyovszky
6b26643ece
* feat(Transformations): removed feature-selection pre-processing step completely * fix(Core): removed unnecessary `original_X` * fix(Transformations): use the X_expanding_window to transform subsequent data * fix(RFE): should check for model correctly * fix(Config): only re-train the model every 40 timestamp * fix(MetaLabeling): pass in the correct X to meta-labeling step * fix(Transformation): PCA should at least keep as many features as sliding_window_size * feat(Transformations): cache transformations across the same asset * fix(Tests): missing preloaded_transformations arg * chore(Config): got rid of unnecessary 'classification_models' and 'regression_models' dictionary keys
92 lines
3.9 KiB
Python
92 lines
3.9 KiB
Python
import pandas as pd
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from typing import Literal, Union
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from training.walk_forward import walk_forward_train, walk_forward_inference
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from utils.evaluate import evaluate_predictions
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from models.base import Model
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from utils.scaler import get_scaler
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from utils.types import ScalerTypes
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from reporting.types import Reporting
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from transformations.rfe import RFETransformation
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from transformations.pca import PCATransformation
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def train_primary_model(
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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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expanding_window: bool,
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sliding_window_size: int,
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retrain_every: int,
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scaler: ScalerTypes,
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no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
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level: str,
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print_results: bool,
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preloaded_models: Union[list[Reporting.Single_Model], None] = None
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) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, list[Reporting.Single_Model]]:
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results = pd.DataFrame()
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predictions = pd.DataFrame(index=y.index)
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probabilities = pd.DataFrame(index=y.index)
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all_models_single_asset: list[Reporting.Single_Model] = []
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if preloaded_models is not None:
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models = preloaded_models
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transformations_over_time = None
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for model_name, model in models:
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if preloaded_models is None:
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model_over_time, transformations_over_time = walk_forward_train(
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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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expanding_window = expanding_window,
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window_size = sliding_window_size,
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retrain_every = retrain_every,
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transformations= [
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get_scaler(scaler),
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PCATransformation(ratio_components_to_keep=0.5, sliding_window_size=sliding_window_size),
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RFETransformation(n_feature_to_select=40, model=model)
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],
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preloaded_transformations=transformations_over_time,
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)
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preds, probs = walk_forward_inference(
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model_name = model_name,
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model_over_time= model_over_time if preloaded_models is None else pd.Series(model),
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transformations_over_time = transformations_over_time,
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X = X,
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expanding_window = expanding_window,
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window_size = sliding_window_size
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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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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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levelname=("_" + level) if level=='metalabeling' else ""
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column_name = "model_" + model_name + "_" + ticker_to_predict + levelname
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results[column_name] = result
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all_models_single_asset.append(Reporting.Single_Model(model_name=column_name, model_over_time=model_over_time.tolist()))
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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[column_name] = preds
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probs_column_name = "probs_" + ticker_to_predict + "_" + model_name + "_" + level
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probs.columns = [probs_column_name + "_" + c for c in probs.columns]
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probabilities = pd.concat([probabilities, probs], axis=1)
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return results, predictions, probabilities, all_models_single_asset |