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Make check constant pred labels agnostic

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This commit is contained in:
th0rntwig 2022-10-18 12:55:47 +02:00
parent 20fc521771
commit 033c5bd441
1 changed files with 17 additions and 20 deletions
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25
freqtrade/freqai/data_kitchen.py
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@ -71,6 +71,7 @@ class FreqaiDataKitchen:
self.data_path = Path()
self.label_list: List = []
self.training_features_list: List = []
self.constant_features_list: List = []
self.model_filename: str = ""
self.backtesting_results_path = Path()
self.backtest_predictions_folder: str = "backtesting_predictions"
@ -206,15 +207,14 @@ class FreqaiDataKitchen:
drop_index = pd.isnull(filtered_df).any(axis=1) # get the rows that have NaNs,
drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
ft_params = self.freqai_config["feature_parameters"]
if (training_filter):
if not ft_params.get(
"principal_component_analysis", False
):
const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
if const_cols:
filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
self.constant_features_list = const_cols
logger.warning(f"Removed features {const_cols} with constant values.")
else:
self.constant_features_list = []
# we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple modelEs), we detect here
@ -245,9 +245,7 @@ class FreqaiDataKitchen:
self.data["filter_drop_index_training"] = drop_index
else:
if not ft_params.get(
"principal_component_analysis", False
):
if len(self.constant_features_list):
filtered_df = self.check_pred_labels(filtered_df)
# we are backtesting so we need to preserve row number to send back to strategy,
# so now we use do_predict to avoid any prediction based on a NaN
@ -474,14 +472,13 @@ class FreqaiDataKitchen:
:params:
:df_predictions: incoming predictions
"""
train_labels = self.data_dictionary["train_features"].columns
pred_labels = df_predictions.columns
num_diffs = len(pred_labels.difference(train_labels))
if num_diffs != 0:
df_predictions = df_predictions[train_labels]
constant_labels = self.constant_features_list
df_predictions = df_predictions.filter(
df_predictions.columns.difference(constant_labels)
)
logger.warning(
f"Removed {num_diffs} features from prediction features, "
f"these were likely considered constant values during most recent training."
f"Removed {len(constant_labels)} features from prediction features, "
f"these were considered constant values during most recent training."
)
return df_predictions