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Rehaul organization of return values

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This commit is contained in:
robcaulk
2022-07-02 18:09:38 +02:00
parent 93e1410ed9
commit 106131ff0f
7 changed files with 429 additions and 292 deletions
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70
freqtrade/freqai/prediction_models/CatboostPredictionModel.py
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@@ -18,18 +18,16 @@ class CatboostPredictionModel(IFreqaiModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def return_values(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
dataframe["prediction"] = dh.full_predictions
dataframe["do_predict"] = dh.full_do_predict
dataframe["target_mean"] = dh.full_target_mean
dataframe["target_std"] = dh.full_target_std
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
dataframe["DI"] = dh.full_DI_values
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
"""
User uses this function to add any additional return values to the dataframe.
e.g.
dataframe['volatility'] = dk.volatility_values
"""
return dataframe
def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
def make_labels(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
"""
User defines the labels here (target values).
:params:
@@ -48,7 +46,7 @@ class CatboostPredictionModel(IFreqaiModel):
return dataframe["s"]
def train(self, unfiltered_dataframe: DataFrame,
pair: str, dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
"""
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
for storing, saving, loading, and analyzing the data.
@@ -62,27 +60,25 @@ class CatboostPredictionModel(IFreqaiModel):
logger.info('--------------------Starting training '
f'{pair} --------------------')
# create the full feature list based on user config info
dh.training_features_list = dh.find_features(unfiltered_dataframe)
unfiltered_labels = self.make_labels(unfiltered_dataframe, dh)
# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dh.filter_features(
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
dh.training_features_list,
unfiltered_labels,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
# split data into train/test data.
data_dictionary = dh.make_train_test_datasets(features_filtered, labels_filtered)
dh.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
# normalize all data based on train_dataset only
data_dictionary = dh.normalize_data(data_dictionary)
data_dictionary = dk.normalize_data(data_dictionary)
# optional additional data cleaning/analysis
self.data_cleaning_train(dh)
self.data_cleaning_train(dk)
logger.info(f'Training model on {len(dh.data_dictionary["train_features"].columns)}'
logger.info(f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
' features')
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
@@ -121,34 +117,32 @@ class CatboostPredictionModel(IFreqaiModel):
return model
def predict(self, unfiltered_dataframe: DataFrame,
dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
dk: FreqaiDataKitchen, first: bool = False) -> Tuple[DataFrame, DataFrame]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
:return:
:predictions: np.array of predictions
: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)
"""
# logger.info("--------------------Starting prediction--------------------")
original_feature_list = dh.find_features(unfiltered_dataframe)
filtered_dataframe, _ = dh.filter_features(
unfiltered_dataframe, original_feature_list, training_filter=False
dk.find_features(unfiltered_dataframe)
filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, dk.training_features_list, training_filter=False
)
filtered_dataframe = dh.normalize_data_from_metadata(filtered_dataframe)
dh.data_dictionary["prediction_features"] = filtered_dataframe
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
# optional additional data cleaning/analysis
self.data_cleaning_predict(dh, filtered_dataframe)
self.data_cleaning_predict(dk, filtered_dataframe)
predictions = self.model.predict(dh.data_dictionary["prediction_features"])
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)
# compute the non-normalized predictions
dh.predictions = (predictions + 1) * (dh.data["labels_max"] -
dh.data["labels_min"]) / 2 + dh.data["labels_min"]
for label in dk.label_list:
pred_df[label] = ((pred_df[label] + 1) *
(dk.data["labels_max"][label] -
dk.data["labels_min"][label]) / 2) + dk.data["labels_min"][label]
# logger.info("--------------------Finished prediction--------------------")
return (dh.predictions, dh.do_predict)
return (pred_df, dk.do_predict)