mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Mark Aron Szulyovszky
b5ddee8dce
* feat(HPO): added `run_hpo` script * fix(Linter): ran * feat(HPO): removed any reference to sweep (superseeded by optuna) * fix(HPO): optimize for sharpe * fix(Config): removed glassnode data, save trials from hpo * feat(Labelling): added three-balanced method works again * fix(BetSizing): set the correct class labels * fix(HPO): powerset should return what's expected, added two new normalization methods * fix(Linter): ran * fix(DataLoader): sort the dataframe when fetching data * fix(Config): only take z-score of other assets
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
import pandas as pd
|
|
from training.types import (
|
|
ModelOverTime,
|
|
TransformationsOverTime,
|
|
PredictionsSeries,
|
|
ProbabilitiesDataFrame,
|
|
)
|
|
from utils.helpers import get_first_valid_return_index
|
|
from tqdm import tqdm
|
|
from typing import Optional
|
|
from data_loader.types import XDataFrame
|
|
from utils.helpers import get_last_non_na_index
|
|
|
|
|
|
def walk_forward_inference(
|
|
model_name: str,
|
|
model_over_time: ModelOverTime,
|
|
transformations_over_time: TransformationsOverTime,
|
|
X: XDataFrame,
|
|
expanding_window: bool,
|
|
window_size: int,
|
|
retrain_every: int,
|
|
class_labels: list[int],
|
|
from_index: Optional[pd.Timestamp],
|
|
) -> tuple[PredictionsSeries, ProbabilitiesDataFrame]:
|
|
predictions = pd.Series(index=X.index, dtype="object").rename(model_name)
|
|
probabilities = pd.DataFrame(
|
|
index=X.index, columns=[str(label) for label in class_labels]
|
|
)
|
|
|
|
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)
|
|
)
|
|
inference_till = X.shape[0]
|
|
model_index_offset = (
|
|
get_last_non_na_index(model_over_time, inference_from)
|
|
if pd.isna(model_over_time[inference_from])
|
|
else 0
|
|
)
|
|
first_model = (
|
|
model_over_time[inference_from - model_index_offset]
|
|
if pd.isna(model_over_time[inference_from])
|
|
else model_over_time[inference_from]
|
|
)
|
|
|
|
if first_model.only_column is not None:
|
|
X = X[[column for column in X.columns if first_model.only_column in column]]
|
|
|
|
if first_model.data_transformation == "original":
|
|
transformations_over_time = []
|
|
|
|
for index in tqdm(range(inference_from, inference_till)):
|
|
|
|
last_model_index = (
|
|
index - ((index - inference_from) % retrain_every) - model_index_offset
|
|
)
|
|
train_window_start = (
|
|
X.index[inference_from]
|
|
if expanding_window
|
|
else X.index[index - window_size - 1]
|
|
)
|
|
|
|
current_model = model_over_time[X.index[last_model_index]]
|
|
current_transformations = [
|
|
transformation_over_time[X.index[last_model_index]]
|
|
for transformation_over_time in transformations_over_time
|
|
]
|
|
|
|
if current_model.predict_window_size == "window_size":
|
|
next_timestep = X.loc[train_window_start : X.index[index]]
|
|
else:
|
|
# 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]
|
|
next_timestep = X.loc[X.index[index] : X.index[index]]
|
|
|
|
for transformation in current_transformations:
|
|
next_timestep = transformation.transform(next_timestep)
|
|
|
|
next_timestep = next_timestep.to_numpy()
|
|
|
|
prediction = current_model.predict(next_timestep)
|
|
probs = current_model.predict_proba(next_timestep)
|
|
|
|
predictions[X.index[index]] = prediction
|
|
if inference_from == index and len(probabilities.columns) != len(probs):
|
|
probabilities = probabilities.reindex(
|
|
columns=["prob_" + str(num) for num in range(0, len(probs.T))]
|
|
)
|
|
probabilities.loc[X.index[index]] = probs
|
|
|
|
return predictions, probabilities
|