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first step toward cleaning output and enabling multimodel training per pair

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This commit is contained in:
robcaulk
2022-07-01 14:00:30 +02:00
parent 6c7d02cb18
commit 93e1410ed9
3 changed files with 63 additions and 144 deletions
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20
freqtrade/freqai/data_kitchen.py
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@@ -88,7 +88,7 @@ class FreqaiDataKitchen:
return
def save_data(self, model: Any, coin: str = '') -> None:
def save_data(self, model: Any, coin: str = '', keras_model=False) -> None:
"""
Saves all data associated with a model for a single sub-train time range
:params:
@@ -102,7 +102,10 @@ class FreqaiDataKitchen:
save_path = Path(self.data_path)
# Save the trained model
dump(model, save_path / str(self.model_filename + "_model.joblib"))
if not keras_model:
dump(model, save_path / str(self.model_filename + "_model.joblib"))
else:
model.save(save_path / str(self.model_filename + "_model.h5"))
if self.svm_model is not None:
dump(self.svm_model, save_path / str(self.model_filename + "_svm_model.joblib"))
@@ -144,7 +147,7 @@ class FreqaiDataKitchen:
return
def load_data(self, coin: str = '') -> Any:
def load_data(self, coin: str = '', keras_model=False) -> Any:
"""
loads all data required to make a prediction on a sub-train time range
:returns:
@@ -190,8 +193,11 @@ class FreqaiDataKitchen:
# try to access model in memory instead of loading object from disk to save time
if self.live and self.model_filename in self.data_drawer.model_dictionary:
model = self.data_drawer.model_dictionary[self.model_filename]
else:
elif not keras_model:
model = load(self.data_path / str(self.model_filename + "_model.joblib"))
else:
from tensorflow import keras
model = keras.models.load_model(self.data_path / str(self.model_filename + "_model.h5"))
if Path(self.data_path / str(self.model_filename +
"_svm_model.joblib")).resolve().exists():
@@ -287,7 +293,11 @@ class FreqaiDataKitchen:
training_filter
): # we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs
drop_index_labels = pd.isnull(labels)
# if labels has multiple columns (user wants to train multiple models), we detect here
if labels.shape[1] == 1:
drop_index_labels = pd.isnull(labels)
else:
drop_index_labels = pd.isnull(labels).any(1)
drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0)
filtered_dataframe = filtered_dataframe[
(drop_index == 0) & (drop_index_labels == 0)