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update docs, improve the interaction with define_data_pipeline

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robcaulk
2023-06-07 18:26:49 +02:00
parent dc577d2a1a
commit 135aaa2be2
7 changed files with 114 additions and 108 deletions
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37
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@@ -110,40 +110,37 @@ class BaseReinforcementLearningModel(IFreqaiModel):
training_filter=True,
)
d: Dict[str, Any] = dk.make_train_test_datasets(
dd: Dict[str, Any] = dk.make_train_test_datasets(
features_filtered, labels_filtered)
self.df_raw = copy.deepcopy(d["train_features"])
self.df_raw = copy.deepcopy(dd["train_features"])
dk.fit_labels() # FIXME useless for now, but just satiating append methods
# normalize all data based on train_dataset only
prices_train, prices_test = self.build_ohlc_price_dataframes(dk.data_dictionary, pair, dk)
self.define_data_pipeline(dk)
self.define_label_pipeline(dk)
dk.feature_pipeline = self.define_data_pipeline()
dk.label_pipeline = self.define_label_pipeline()
# d["train_labels"], _, _ = dk.label_pipeline.fit_transform(d["train_labels"])
# d["test_labels"], _, _ = dk.label_pipeline.transform(d["test_labels"])
(dd["train_features"],
dd["train_labels"],
dd["train_weights"]) = dk.feature_pipeline.fit_transform(dd["train_features"],
dd["train_labels"],
dd["train_weights"])
(d["train_features"],
d["train_labels"],
d["train_weights"]) = dk.feature_pipeline.fit_transform(d["train_features"],
d["train_labels"],
d["train_weights"])
(d["test_features"],
d["test_labels"],
d["test_weights"]) = dk.feature_pipeline.transform(d["test_features"],
d["test_labels"],
d["test_weights"])
(dd["test_features"],
dd["test_labels"],
dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"],
dd["test_labels"],
dd["test_weights"])
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
f' features and {len(d["train_features"])} data points'
f' features and {len(dd["train_features"])} data points'
)
self.set_train_and_eval_environments(d, prices_train, prices_test, dk)
self.set_train_and_eval_environments(dd, prices_train, prices_test, dk)
model = self.fit(d, dk)
model = self.fit(dd, dk)
logger.info(f"--------------------done training {pair}--------------------")