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fix: Make XgboostMulti an alias of Xgboost since it supports multiple targets implicitly now

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Robert Caulk
2025-04-04 13:53:44 +02:00
parent 91df1257e7
commit fae7198bb4
1 changed files with 22 additions and 35 deletions
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57
freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py
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@@ -4,8 +4,8 @@ from typing import Any
from xgboost import XGBRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.tensorboard import TBCallback
logger = logging.getLogger(__name__)
@@ -29,45 +29,32 @@ class XGBoostRegressorMultiTarget(BaseRegressionModel):
:param dk: The datakitchen object for the current coin/model
"""
xgb = XGBRegressor(**self.model_training_parameters)
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
eval_set = None
eval_weights = None
else:
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"]), (X, y)]
eval_weights = [data_dictionary["test_weights"], data_dictionary["train_weights"]]
sample_weight = data_dictionary["train_weights"]
eval_weights = None
eval_sets = [None] * y.shape[1]
xgb_model = self.get_init_model(dk.pair)
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
eval_weights = [data_dictionary["test_weights"]]
for i in range(data_dictionary["test_labels"].shape[1]):
eval_sets[i] = [ # type: ignore
(
data_dictionary["test_features"],
data_dictionary["test_labels"].iloc[:, i],
)
]
model = XGBRegressor(**self.model_training_parameters)
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{
"eval_set": eval_sets[i],
"sample_weight_eval_set": eval_weights,
"xgb_model": init_models[i],
}
)
model = FreqaiMultiOutputRegressor(estimator=xgb)
thread_training = self.freqai_info.get("multitarget_parallel_training", False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
model.set_params(callbacks=[TBCallback(dk.data_path)])
model.fit(
X=X,
y=y,
sample_weight=sample_weight,
eval_set=eval_set,
sample_weight_eval_set=eval_weights,
xgb_model=xgb_model,
)
# set the callbacks to empty so that we can serialize to disk later
model.set_params(callbacks=[])
return model