mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
import pandas as pd
|
|
from models.base import Model
|
|
from training.types import ModelOverTime, TransformationsOverTime
|
|
from utils.helpers import get_first_valid_return_index
|
|
from tqdm import tqdm
|
|
from typing import Optional
|
|
from data_loader.types import ForwardReturnSeries, XDataFrame, ySeries
|
|
from copy import deepcopy
|
|
|
|
|
|
def walk_forward_train(
|
|
model: Model,
|
|
X: XDataFrame,
|
|
y: ySeries,
|
|
forward_returns: ForwardReturnSeries,
|
|
window_size: int,
|
|
retrain_every: int,
|
|
from_index: Optional[pd.Timestamp],
|
|
transformations_over_time: TransformationsOverTime,
|
|
) -> ModelOverTime:
|
|
models_over_time = pd.Series(index=y.index, dtype="object").rename(model.name)
|
|
|
|
first_nonzero_return = max(
|
|
get_first_valid_return_index(forward_returns),
|
|
get_first_valid_return_index(X.iloc[:, 0]),
|
|
get_first_valid_return_index(y),
|
|
)
|
|
train_from = (
|
|
first_nonzero_return + window_size + 1
|
|
if from_index is None
|
|
else X.index.to_list().index(from_index)
|
|
)
|
|
train_till = len(y)
|
|
|
|
if model.only_column is not None:
|
|
X = X[[column for column in X.columns if model.only_column in column]]
|
|
|
|
if model.data_transformation == "original":
|
|
transformations_over_time = []
|
|
|
|
for index in tqdm(range(train_from, train_till, retrain_every)):
|
|
train_window_start = X.index[first_nonzero_return]
|
|
|
|
train_window_end = X.index[index - 1]
|
|
current_transformations = [
|
|
transformation_over_time[index]
|
|
for transformation_over_time in transformations_over_time
|
|
]
|
|
X_slice = X[train_window_start:train_window_end]
|
|
|
|
for transformation in current_transformations:
|
|
X_slice = transformation.transform(X_slice)
|
|
|
|
X_slice = X_slice.to_numpy()
|
|
y_slice = y[train_window_start:train_window_end].to_numpy()
|
|
|
|
current_model = deepcopy(model)
|
|
current_model.fit(X_slice, y_slice)
|
|
|
|
models_over_time[X.index[index]] = current_model
|
|
|
|
return models_over_time
|