mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +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
61 lines
3.1 KiB
Python
61 lines
3.1 KiB
Python
import pandas as pd
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from training.types import ModelOverTime, TransformationsOverTime, PredictionsSeries, ProbabilitiesDataFrame
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from utils.helpers import get_first_valid_return_index
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from tqdm import tqdm
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from typing import Optional
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from data_loader.types import XDataFrame
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from utils.helpers import get_last_non_na_index
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def walk_forward_inference(
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model_name: str,
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model_over_time: ModelOverTime,
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transformations_over_time: TransformationsOverTime,
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X: XDataFrame,
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expanding_window: bool,
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window_size: int,
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retrain_every: int,
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from_index: Optional[pd.Timestamp],
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) -> tuple[PredictionsSeries, ProbabilitiesDataFrame]:
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predictions = pd.Series(index=X.index, dtype='object').rename(model_name)
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probabilities = pd.DataFrame(index=X.index)
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inference_from = max(get_first_valid_return_index(model_over_time), get_first_valid_return_index(X.iloc[:,0])) if from_index is None else X.index.to_list().index(from_index)
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inference_till = X.shape[0]
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model_index_offset = get_last_non_na_index(model_over_time, inference_from) if pd.isna(model_over_time[inference_from]) else 0
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first_model = model_over_time[inference_from - model_index_offset] if pd.isna(model_over_time[inference_from]) else model_over_time[inference_from]
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if first_model.only_column is not None:
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X = X[[column for column in X.columns if first_model.only_column in column]]
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if first_model.data_transformation == 'original':
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transformations_over_time = []
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for index in tqdm(range(inference_from, inference_till)):
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last_model_index = index - ((index - inference_from) % retrain_every) - model_index_offset
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train_window_start = X.index[inference_from] if expanding_window else X.index[index - window_size - 1]
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current_model = model_over_time[X.index[last_model_index]]
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current_transformations = [transformation_over_time[X.index[last_model_index]] for transformation_over_time in transformations_over_time]
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if current_model.predict_window_size == 'window_size':
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next_timestep = X.loc[train_window_start:X.index[index]]
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else:
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# we need to get a Dataframe out of it, since the transformation step always expects a 2D array, but it's equivalent to X.iloc[index]
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next_timestep = X.loc[X.index[index]:X.index[index]]
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for transformation in current_transformations:
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next_timestep = transformation.transform(next_timestep)
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next_timestep = next_timestep.to_numpy()
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prediction = current_model.predict(next_timestep)
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probs = current_model.predict_proba(next_timestep)
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predictions[X.index[index]] = prediction
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if inference_from == index and len(probabilities.columns) != len(probs):
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probabilities = probabilities.reindex(columns = ["prob_" + str(num) for num in range(0, len(probs.T))])
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probabilities.loc[X.index[index]] = probs
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return predictions, probabilities
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