mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
66 lines
2.8 KiB
Python
66 lines
2.8 KiB
Python
from utils.evaluate import discretize_threeway_threshold, evaluate_predictions
|
|
from utils.helpers import equal_except_nan
|
|
from training.primary_model import train_primary_model
|
|
import pandas as pd
|
|
from models.base import Model
|
|
from reporting.types import Reporting
|
|
from typing import Union, Optional
|
|
from config.types import Config
|
|
|
|
def train_meta_labeling_model(
|
|
target_asset: str,
|
|
X: pd.DataFrame,
|
|
input_predictions: pd.Series,
|
|
y: pd.Series,
|
|
forward_returns: pd.Series,
|
|
models: list[tuple[str, Model]],
|
|
config: Config,
|
|
model_suffix: str,
|
|
from_index: Optional[pd.Timestamp],
|
|
preloaded_models: Optional[list[tuple[str, pd.Series, list[pd.Series]]]] = None
|
|
) -> tuple[pd.Series, pd.Series, pd.DataFrame, list[Reporting.Single_Model]]:
|
|
|
|
discretize = discretize_threeway_threshold(0.33)
|
|
discretized_predictions = input_predictions.apply(discretize)
|
|
meta_y: pd.Series = pd.concat([discretized_predictions, y], axis=1).apply(equal_except_nan, axis = 1)
|
|
|
|
meta_X = pd.concat([X, input_predictions, discretized_predictions], axis = 1)
|
|
|
|
_, meta_preds, meta_probabilities, all_models_single_asset = train_primary_model(
|
|
ticker_to_predict = "prediction_correct",
|
|
X = meta_X,
|
|
y = meta_y,
|
|
forward_returns = forward_returns,
|
|
models = models,
|
|
expanding_window = config.expanding_window_meta_labeling,
|
|
sliding_window_size = config.sliding_window_size_meta_labeling,
|
|
retrain_every = config.retrain_every,
|
|
from_index = from_index,
|
|
scaler = config.scaler,
|
|
no_of_classes = 'two',
|
|
level = 'meta_labeling',
|
|
print_results = False,
|
|
preloaded_models = preloaded_models
|
|
)
|
|
if len(models) > 1:
|
|
meta_preds = meta_preds.mean(axis = 1)
|
|
bet_size = meta_probabilities[meta_probabilities.columns[1::2]].mean(axis = 1)
|
|
else:
|
|
bet_size = meta_probabilities.iloc[:,1]
|
|
avg_predictions_with_sizing = input_predictions * bet_size
|
|
avg_predictions_with_sizing.rename("model_" + target_asset + "_" + model_suffix, inplace=True)
|
|
|
|
meta_result = evaluate_predictions(
|
|
model_name = "Meta",
|
|
forward_returns = forward_returns,
|
|
y_pred = avg_predictions_with_sizing,
|
|
y_true = y,
|
|
no_of_classes = 'two',
|
|
print_results = True,
|
|
discretize=False
|
|
)
|
|
meta_result.rename("model_" + target_asset + "_" + model_suffix, inplace=True)
|
|
|
|
|
|
return meta_result, avg_predictions_with_sizing, meta_probabilities, all_models_single_asset
|