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

fix docstrings

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

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

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