Add XGBoostRegressor for freqAI, fix mypy errors

This commit is contained in:
Emre 2022-09-08 14:12:19 +03:00 committed by Robert Caulk
parent 4c9ac6b7c0
commit 1b6410d7d1
6 changed files with 87 additions and 9 deletions

View File

@ -21,7 +21,7 @@ class BaseClassifierModel(IFreqaiModel):
""" """
def train( def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any: ) -> Any:
""" """
Filter the training data and train a model to it. Train makes heavy use of the datakitchen Filter the training data and train a model to it. Train makes heavy use of the datakitchen
@ -68,7 +68,7 @@ class BaseClassifierModel(IFreqaiModel):
return model return model
def predict( def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]: ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
""" """
Filter the prediction features data and predict with it. Filter the prediction features data and predict with it.
@ -79,9 +79,9 @@ class BaseClassifierModel(IFreqaiModel):
data (NaNs) or felt uncertain about data (PCA and DI index) data (NaNs) or felt uncertain about data (PCA and DI index)
""" """
dk.find_features(unfiltered_dataframe) dk.find_features(dataframe)
filtered_dataframe, _ = dk.filter_features( filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, dk.training_features_list, training_filter=False dataframe, dk.training_features_list, training_filter=False
) )
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe) filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe dk.data_dictionary["prediction_features"] = filtered_dataframe

View File

@ -20,7 +20,7 @@ class BaseRegressionModel(IFreqaiModel):
""" """
def train( def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any: ) -> Any:
""" """
Filter the training data and train a model to it. Train makes heavy use of the datakitchen Filter the training data and train a model to it. Train makes heavy use of the datakitchen
@ -67,7 +67,7 @@ class BaseRegressionModel(IFreqaiModel):
return model return model
def predict( def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]: ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
""" """
Filter the prediction features data and predict with it. Filter the prediction features data and predict with it.
@ -78,9 +78,9 @@ class BaseRegressionModel(IFreqaiModel):
data (NaNs) or felt uncertain about data (PCA and DI index) data (NaNs) or felt uncertain about data (PCA and DI index)
""" """
dk.find_features(unfiltered_dataframe) dk.find_features(dataframe)
filtered_dataframe, _ = dk.filter_features( filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, dk.training_features_list, training_filter=False dataframe, dk.training_features_list, training_filter=False
) )
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe) filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe dk.data_dictionary["prediction_features"] = filtered_dataframe

View File

@ -17,7 +17,7 @@ class BaseTensorFlowModel(IFreqaiModel):
""" """
def train( def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any: ) -> Any:
""" """
Filter the training data and train a model to it. Train makes heavy use of the datakitchen Filter the training data and train a model to it. Train makes heavy use of the datakitchen

View File

@ -0,0 +1,46 @@
import logging
from typing import Any, Dict
import xgboost as xgb
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
logger = logging.getLogger(__name__)
class XGBoostRegressor(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
xgb.set_config(verbosity=2)
xgb.config_context(verbosity=2)
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
else:
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
sample_weight = data_dictionary["train_weights"]
xgb_model = self.get_init_model(dk.pair)
model = xgb.XGBRegressor(**self.model_training_parameters)
model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set, xgb_model=xgb_model)
return model

View File

@ -6,3 +6,4 @@ scikit-learn==1.1.2
joblib==1.1.0 joblib==1.1.0
catboost==1.0.6; platform_machine != 'aarch64' catboost==1.0.6; platform_machine != 'aarch64'
lightgbm==3.3.2 lightgbm==3.3.2
xgboost==1.6.2

View File

@ -172,6 +172,37 @@ def test_train_model_in_series_LightGBMClassifier(mocker, freqai_conf):
shutil.rmtree(Path(freqai.dk.full_path)) shutil.rmtree(Path(freqai.dk.full_path))
def test_train_model_in_series_XGBoostRegressor(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "XGBoostRegressor"})
freqai_conf.update({"strategy": "freqai_test_strat"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.train_model_in_series(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
shutil.rmtree(Path(freqai.dk.full_path))
def test_start_backtesting(mocker, freqai_conf): def test_start_backtesting(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180120-20180130"}) freqai_conf.update({"timerange": "20180120-20180130"})
freqai_conf.get("freqai", {}).update({"save_backtest_models": True}) freqai_conf.get("freqai", {}).update({"save_backtest_models": True})