mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Mark Aron Szulyovszky
9d47ee942d
* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba * fix(WalkForward): inference mini-batch parallelization * fix(WalkForward): don't use the parallel version of any of the functions * feat(CI): download the data required * fix(Project): 5min_crypto folder added * fix(Evaluate): make sure we have numerical stability in returns * feat(Models): use SKLearn models directly to enable composability * feat(Inference): batched inference now working, added forecasting_horizon * fix(Inference): works again * fix(Inference) * chore(Models): remove unused Ensemble model * fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then * Update test.yml
69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
import pandas as pd
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from typing import Literal, Optional
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from training.walk_forward import walk_forward_train, walk_forward_inference, walk_forward_inference_batched
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from utils.evaluate import evaluate_predictions
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from models.base import Model
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from .types import ModelOverTime, TransformationsOverTime, TrainingOutcome
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def train_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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forward_returns: pd.Series,
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model: Model,
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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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from_index: Optional[pd.Timestamp],
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no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
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level: str,
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output_stats: bool,
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transformations_over_time: TransformationsOverTime,
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model_over_time: Optional[ModelOverTime]
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) -> TrainingOutcome:
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if model_over_time is None:
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print("Train model")
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model_over_time = walk_forward_train(
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model = model,
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X = X,
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y = y,
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forward_returns = forward_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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from_index = from_index,
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transformations_over_time = transformations_over_time,
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)
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levelname = ("_" + level) if level == 'meta' else ""
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if model_over_time is None:
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model_id = "model_" + model.name + "_" + ticker_to_predict + levelname
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else:
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model_id = model_over_time.name
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inference_function = walk_forward_inference if from_index is not None else walk_forward_inference_batched
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predictions, probabilities = inference_function(
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model_name = model_id,
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model_over_time= model_over_time,
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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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retrain_every = retrain_every,
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from_index = from_index,
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)
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assert len(predictions) == len(y)
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if output_stats:
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stats = evaluate_predictions(
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forward_returns = forward_returns,
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y_pred = predictions,
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y_true = y,
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no_of_classes=no_of_classes,
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discretize=True
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)
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else:
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stats = None
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return TrainingOutcome(model_id, predictions, probabilities, stats, model_over_time) |