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ensure raw features match when PCA is employed

This commit is contained in:
robcaulk 2022-10-01 13:14:59 +02:00
parent cdc01a0781
commit f2b875483f
2 changed files with 5 additions and 12 deletions
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4
freqtrade/freqai/data_kitchen.py
View File

@ -1,6 +1,5 @@
import copy import copy
import logging import logging
import re
import shutil import shutil
from datetime import datetime, timezone from datetime import datetime, timezone
from math import cos, sin from math import cos, sin
@ -882,9 +881,6 @@ class FreqaiDataKitchen:
""" """
column_names = dataframe.columns column_names = dataframe.columns
features = [c for c in column_names if "%" in c] features = [c for c in column_names if "%" in c]
pca_features = [c for c in column_names if re.search(r"^PC\d+$", c)]
if not features and pca_features:
features = pca_features
if not features: if not features:
raise OperationalException("Could not find any features!") raise OperationalException("Could not find any features!")

13
freqtrade/freqai/freqai_interface.py
View File

@ -275,7 +275,8 @@ class IFreqaiModel(ABC):
if dk.check_if_backtest_prediction_exists(): if dk.check_if_backtest_prediction_exists():
self.dd.load_metadata(dk) self.dd.load_metadata(dk)
self.check_if_feature_list_matches_strategy(dataframe_train, dk) dk.find_features(dataframe_train)
self.check_if_feature_list_matches_strategy(dk)
append_df = dk.get_backtesting_prediction() append_df = dk.get_backtesting_prediction()
dk.append_predictions(append_df) dk.append_predictions(append_df)
else: else:
@ -296,7 +297,6 @@ class IFreqaiModel(ABC):
else: else:
self.model = self.dd.load_data(pair, dk) self.model = self.dd.load_data(pair, dk)
# self.check_if_feature_list_matches_strategy(dataframe_train, dk)
pred_df, do_preds = self.predict(dataframe_backtest, dk) pred_df, do_preds = self.predict(dataframe_backtest, dk)
append_df = dk.get_predictions_to_append(pred_df, do_preds) append_df = dk.get_predictions_to_append(pred_df, do_preds)
dk.append_predictions(append_df) dk.append_predictions(append_df)
@ -420,7 +420,7 @@ class IFreqaiModel(ABC):
return return
def check_if_feature_list_matches_strategy( def check_if_feature_list_matches_strategy(
self, dataframe: DataFrame, dk: FreqaiDataKitchen self, dk: FreqaiDataKitchen
) -> None: ) -> None:
""" """
Ensure user is passing the proper feature set if they are reusing an `identifier` pointing Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
@ -429,15 +429,12 @@ class IFreqaiModel(ABC):
:param dk: FreqaiDataKitchen = non-persistent data container/analyzer for :param dk: FreqaiDataKitchen = non-persistent data container/analyzer for
current coin/bot loop current coin/bot loop
""" """
dk.find_features(dataframe)
if "training_features_list_raw" in dk.data: if "training_features_list_raw" in dk.data:
feature_list = dk.data["training_features_list_raw"] feature_list = dk.data["training_features_list_raw"]
else: else:
feature_list = dk.data['training_features_list'] feature_list = dk.data['training_features_list']
if self.ft_params.get('principal_component_analysis', False):
feature_list = dk.data['training_features_list']
if dk.training_features_list != feature_list: if dk.training_features_list != feature_list:
raise OperationalException( raise OperationalException(
"Trying to access pretrained model with `identifier` " "Trying to access pretrained model with `identifier` "
@ -510,7 +507,7 @@ class IFreqaiModel(ABC):
dk.use_DBSCAN_to_remove_outliers(predict=True) dk.use_DBSCAN_to_remove_outliers(predict=True)
# ensure user is feeding the correct indicators to the model # ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dk.data_dictionary['prediction_features'], dk) self.check_if_feature_list_matches_strategy(dk)
def model_exists(self, dk: FreqaiDataKitchen) -> bool: def model_exists(self, dk: FreqaiDataKitchen) -> bool:
""" """