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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
View File

@ -673,12 +673,12 @@ class IFreqaiModel(ABC):
# See freqai/prediction_models/CatboostPredictionModel.py for an example.
@abstractmethod
def train(self, unfiltered_dataframe: DataFrame, pair: str,
def train(self, unfiltered_df: DataFrame, pair: str,
dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
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.
: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.
:return: Trained model which can be used to inference (self.predict)
"""
@ -697,11 +697,11 @@ class IFreqaiModel(ABC):
@abstractmethod
def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True, **kwargs
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, NDArray[np.int_]]:
"""
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 first: boolean = whether this is the first prediction or not.
:return:

26
freqtrade/freqai/prediction_models/BaseClassifierModel.py
View File

@ -21,12 +21,12 @@ class BaseClassifierModel(IFreqaiModel):
"""
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
"""
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.
: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.
:return:
: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
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
unfiltered_df,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
start_date = unfiltered_df["date"].iloc[0].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 "
f"{end_date}--------------------")
# split data into train/test data.
@ -68,25 +68,25 @@ class BaseClassifierModel(IFreqaiModel):
return model
def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False, **kwargs
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
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:
:pred_df: dataframe containing the predictions
: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)
"""
dk.find_features(dataframe)
filtered_dataframe, _ = dk.filter_features(
dataframe, dk.training_features_list, training_filter=False
dk.find_features(unfiltered_df)
filtered_df, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
filtered_df = dk.normalize_data_from_metadata(filtered_df)
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"])
pred_df = DataFrame(predictions, columns=dk.label_list)

26
freqtrade/freqai/prediction_models/BaseRegressionModel.py
View File

@ -20,12 +20,12 @@ class BaseRegressionModel(IFreqaiModel):
"""
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
"""
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.
: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.
:return:
: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
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
unfiltered_df,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
start_date = unfiltered_df["date"].iloc[0].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 "
f"{end_date}--------------------")
# split data into train/test data.
@ -67,26 +67,26 @@ class BaseRegressionModel(IFreqaiModel):
return model
def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False, **kwargs
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
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:
:pred_df: dataframe containing the predictions
: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)
"""
dk.find_features(dataframe)
filtered_dataframe, _ = dk.filter_features(
dataframe, dk.training_features_list, training_filter=False
dk.find_features(unfiltered_df)
filtered_df, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_df
# 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"])
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(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
"""
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.
: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.
:return:
: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
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
unfiltered_df,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
start_date = unfiltered_df["date"].iloc[0].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 "
f"{end_date}--------------------")
# split data into train/test data.