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Add ground work for TensorFlow models, add protections from common mistakes

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This commit is contained in:
robcaulk
2022-07-12 18:09:17 +02:00
parent fea63fba12
commit ef409dd345
4 changed files with 44 additions and 21 deletions
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32
freqtrade/freqai/freqai_interface.py
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@@ -69,6 +69,9 @@ class IFreqaiModel(ABC):
self.ready_to_scan = False
self.first = True
self.keras = self.freqai_info.get("keras", False)
if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
self.freqai_info["feature_parameters"]["DI_threshold"] = 0
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
def assert_config(self, config: Dict[str, Any]) -> None:
@@ -197,9 +200,9 @@ class IFreqaiModel(ABC):
self.model = self.train(dataframe_train, metadata["pair"], dk)
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = trained_timestamp.stopts
dk.set_new_model_names(metadata["pair"], trained_timestamp)
dk.save_data(self.model, metadata["pair"], keras_model=self.keras)
dk.save_data(self.model, metadata["pair"])
else:
self.model = dk.load_data(metadata["pair"], keras_model=self.keras)
self.model = dk.load_data(metadata["pair"])
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
@@ -276,7 +279,7 @@ class IFreqaiModel(ABC):
)
# load the model and associated data into the data kitchen
self.model = dk.load_data(coin=metadata["pair"], keras_model=self.keras)
self.model = dk.load_data(coin=metadata["pair"])
if not self.model:
logger.warning(
@@ -353,13 +356,15 @@ class IFreqaiModel(ABC):
of how outlier data points are dropped from the dataframe used for training.
"""
if self.freqai_info.get("feature_parameters", {}).get("principal_component_analysis"):
if self.freqai_info.get("feature_parameters", {}).get(
"principal_component_analysis", False
):
dk.principal_component_analysis()
if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers"):
if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=False)
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold"):
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
dk.data["avg_mean_dist"] = dk.compute_distances()
# if self.feature_parameters["determine_statistical_distributions"]:
@@ -378,13 +383,15 @@ class IFreqaiModel(ABC):
of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
for buy signals.
"""
if self.freqai_info.get("feature_parameters", {}).get("principal_component_analysis"):
if self.freqai_info.get("feature_parameters", {}).get(
"principal_component_analysis", False
):
dk.pca_transform(dataframe)
if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers"):
if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=True)
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold"):
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
dk.check_if_pred_in_training_spaces()
# if self.feature_parameters["determine_statistical_distributions"]:
@@ -479,14 +486,15 @@ class IFreqaiModel(ABC):
if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning:
with self.lock:
self.dd.pair_to_end_of_training_queue(pair)
dk.save_data(model, coin=pair, keras_model=self.keras)
dk.save_data(model, coin=pair)
if self.freqai_info.get("purge_old_models", False):
self.dd.purge_old_models()
# self.retrain = False
def set_initial_historic_predictions(self, df: DataFrame, model: Any,
dk: FreqaiDataKitchen, pair: str) -> None:
def set_initial_historic_predictions(
self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
) -> None:
trained_predictions = model.predict(df)
pred_df = DataFrame(trained_predictions, columns=dk.label_list)
for label in dk.label_list: