adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

fix docstrings

Browse Source
This commit is contained in:
robcaulk 2022-11-13 17:43:52 +01:00
parent c8d3e57712
commit 6394ef4558
3 changed files with 17 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -130,13 +130,12 @@ class BaseReinforcementLearningModel(IFreqaiModel):
dk: FreqaiDataKitchen):
"""
User can override this if they are using a custom MyRLEnv
:params:
data_dictionary: dict = common data dictionary containing train and test
:param data_dictionary: dict = common data dictionary containing train and test
features/labels/weights.
prices_train/test: DataFrame = dataframe comprised of the prices to be used in the
:param prices_train/test: DataFrame = dataframe comprised of the prices to be used in the
environment during training
or testing
dk: FreqaiDataKitchen = the datakitchen for the current pair
:param dk: FreqaiDataKitchen = the datakitchen for the current pair
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
@ -229,10 +228,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
dk: FreqaiDataKitchen, model: Any) -> DataFrame:
"""
A helper function to make predictions in the Reinforcement learning module.
:params:
dataframe: DataFrame = the dataframe of features to make the predictions on
dk: FreqaiDatakitchen = data kitchen for the current pair
model: Any = the trained model used to inference the features.
:param dataframe: DataFrame = the dataframe of features to make the predictions on
:param dk: FreqaiDatakitchen = data kitchen for the current pair
:param model: Any = the trained model used to inference the features.
"""
output = pd.DataFrame(np.zeros(len(dataframe)), columns=dk.label_list)
@ -322,9 +320,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
"""
An example reward function. This is the one function that users will likely
wish to inject their own creativity into.
:params:
action: int = The action made by the agent for the current candle.
:returns:
:param action: int = The action made by the agent for the current candle.
:return:
float = the reward to give to the agent for current step (used for optimization
of weights in NN)
"""

14
freqtrade/freqai/prediction_models/ReinforcementLearner.py
View File

@ -20,12 +20,11 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
"""
User customizable fit method
:params:
data_dictionary: dict = common data dictionary containing all train/test
:param data_dictionary: dict = common data dictionary containing all train/test
features/labels/weights.
dk: FreqaiDatakitchen = data kitchen for current pair.
:returns:
model: Any = trained model to be used for inference in dry/live/backtesting
:param dk: FreqaiDatakitchen = data kitchen for current pair.
:return:
model Any = trained model to be used for inference in dry/live/backtesting
"""
train_df = data_dictionary["train_features"]
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
@ -69,9 +68,8 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
"""
An example reward function. This is the one function that users will likely
wish to inject their own creativity into.
:params:
action: int = The action made by the agent for the current candle.
:returns:
:param action: int = The action made by the agent for the current candle.
:return:
float = the reward to give to the agent for current step (used for optimization
of weights in NN)
"""

7
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@ -61,13 +61,12 @@ class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
dk: FreqaiDataKitchen):
"""
User can override this if they are using a custom MyRLEnv
:params:
data_dictionary: dict = common data dictionary containing train and test
:param data_dictionary: dict = common data dictionary containing train and test
features/labels/weights.
prices_train/test: DataFrame = dataframe comprised of the prices to be used in
:param prices_train/test: DataFrame = dataframe comprised of the prices to be used in
the environment during training
or testing
dk: FreqaiDataKitchen = the datakitchen for the current pair
:param dk: FreqaiDataKitchen = the datakitchen for the current pair
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]