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ensure signatures match, reduce verbosity

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robcaulk 2022-09-09 19:17:15 +02:00 committed by Robert Caulk
parent acb410a0de
commit a826c0eb83
4 changed files with 35 additions and 35 deletions
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8
freqtrade/freqai/freqai_interface.py
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@ -673,12 +673,12 @@ class IFreqaiModel(ABC):
# See freqai/prediction_models/CatboostPredictionModel.py for an example. # See freqai/prediction_models/CatboostPredictionModel.py for an example.
@abstractmethod @abstractmethod
def train(self, unfiltered_dataframe: DataFrame, pair: str, def train(self, unfiltered_df: DataFrame, pair: str,
dk: FreqaiDataKitchen, **kwargs) -> Any: dk: FreqaiDataKitchen, **kwargs) -> Any:
""" """
Filter the training data and train a model to it. Train makes heavy use of the datahandler Filter the training data and train a model to it. Train makes heavy use of the datahandler
for storing, saving, loading, and analyzing the data. for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period :param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy. :param metadata: pair metadata from strategy.
:return: Trained model which can be used to inference (self.predict) :return: Trained model which can be used to inference (self.predict)
""" """
@ -697,11 +697,11 @@ class IFreqaiModel(ABC):
@abstractmethod @abstractmethod
def predict( def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True, **kwargs self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, NDArray[np.int_]]: ) -> Tuple[DataFrame, NDArray[np.int_]]:
""" """
Filter the prediction features data and predict with it. Filter the prediction features data and predict with it.
:param unfiltered_dataframe: Full dataframe for the current backtest period. :param unfiltered_df: Full dataframe for the current backtest period.
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only :param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:param first: boolean = whether this is the first prediction or not. :param first: boolean = whether this is the first prediction or not.
:return: :return:

26
freqtrade/freqai/prediction_models/BaseClassifierModel.py
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@ -21,12 +21,12 @@ class BaseClassifierModel(IFreqaiModel):
""" """
def train( def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any: ) -> Any:
""" """
Filter the training data and train a model to it. Train makes heavy use of the datakitchen Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data. for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period :param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy. :param metadata: pair metadata from strategy.
:return: :return:
:model: Trained model which can be used to inference (self.predict) :model: Trained model which can be used to inference (self.predict)
@ -36,14 +36,14 @@ class BaseClassifierModel(IFreqaiModel):
# filter the features requested by user in the configuration file and elegantly handle NaNs # filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features( features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe, unfiltered_df,
dk.training_features_list, dk.training_features_list,
dk.label_list, dk.label_list,
training_filter=True, training_filter=True,
) )
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d") start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d") end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to " logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------") f"{end_date}--------------------")
# split data into train/test data. # split data into train/test data.
@ -68,25 +68,25 @@ class BaseClassifierModel(IFreqaiModel):
return model return model
def predict( def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False, **kwargs self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]: ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
""" """
Filter the prediction features data and predict with it. Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period. :param: unfiltered_df: Full dataframe for the current backtest period.
:return: :return:
:pred_df: dataframe containing the predictions :pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index) data (NaNs) or felt uncertain about data (PCA and DI index)
""" """
dk.find_features(dataframe) dk.find_features(unfiltered_df)
filtered_dataframe, _ = dk.filter_features( filtered_df, _ = dk.filter_features(
dataframe, dk.training_features_list, training_filter=False unfiltered_df, dk.training_features_list, training_filter=False
) )
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe) filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_dataframe dk.data_dictionary["prediction_features"] = filtered_df
self.data_cleaning_predict(dk, filtered_dataframe) self.data_cleaning_predict(dk, filtered_df)
predictions = self.model.predict(dk.data_dictionary["prediction_features"]) predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list) pred_df = DataFrame(predictions, columns=dk.label_list)

26
freqtrade/freqai/prediction_models/BaseRegressionModel.py
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@ -20,12 +20,12 @@ class BaseRegressionModel(IFreqaiModel):
""" """
def train( def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any: ) -> Any:
""" """
Filter the training data and train a model to it. Train makes heavy use of the datakitchen Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data. for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period :param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy. :param metadata: pair metadata from strategy.
:return: :return:
:model: Trained model which can be used to inference (self.predict) :model: Trained model which can be used to inference (self.predict)
@ -35,14 +35,14 @@ class BaseRegressionModel(IFreqaiModel):
# filter the features requested by user in the configuration file and elegantly handle NaNs # filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features( features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe, unfiltered_df,
dk.training_features_list, dk.training_features_list,
dk.label_list, dk.label_list,
training_filter=True, training_filter=True,
) )
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d") start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d") end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to " logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------") f"{end_date}--------------------")
# split data into train/test data. # split data into train/test data.
@ -67,26 +67,26 @@ class BaseRegressionModel(IFreqaiModel):
return model return model
def predict( def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False, **kwargs self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]: ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
""" """
Filter the prediction features data and predict with it. Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period. :param: unfiltered_df: Full dataframe for the current backtest period.
:return: :return:
:pred_df: dataframe containing the predictions :pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index) data (NaNs) or felt uncertain about data (PCA and DI index)
""" """
dk.find_features(dataframe) dk.find_features(unfiltered_df)
filtered_dataframe, _ = dk.filter_features( filtered_df, _ = dk.filter_features(
dataframe, dk.training_features_list, training_filter=False unfiltered_df, dk.training_features_list, training_filter=False
) )
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe) filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_dataframe dk.data_dictionary["prediction_features"] = filtered_df
# optional additional data cleaning/analysis # optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_dataframe) self.data_cleaning_predict(dk, filtered_df)
predictions = self.model.predict(dk.data_dictionary["prediction_features"]) predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list) pred_df = DataFrame(predictions, columns=dk.label_list)

10
freqtrade/freqai/prediction_models/BaseTensorFlowModel.py
View File

@ -17,12 +17,12 @@ class BaseTensorFlowModel(IFreqaiModel):
""" """
def train( def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any: ) -> Any:
""" """
Filter the training data and train a model to it. Train makes heavy use of the datakitchen Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data. for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period :param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy. :param metadata: pair metadata from strategy.
:return: :return:
:model: Trained model which can be used to inference (self.predict) :model: Trained model which can be used to inference (self.predict)
@ -32,14 +32,14 @@ class BaseTensorFlowModel(IFreqaiModel):
# filter the features requested by user in the configuration file and elegantly handle NaNs # filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features( features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe, unfiltered_df,
dk.training_features_list, dk.training_features_list,
dk.label_list, dk.label_list,
training_filter=True, training_filter=True,
) )
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d") start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d") end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to " logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------") f"{end_date}--------------------")
# split data into train/test data. # split data into train/test data.