adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Change storage loc and fix test fail

Browse Source
This commit is contained in:
th0rntwig 2022-10-23 16:24:02 +02:00
parent 033c5bd441
commit 49ff51f11f
2 changed files with 6 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
freqtrade/freqai/data_kitchen.py
View File

@ -71,7 +71,6 @@ class FreqaiDataKitchen:
self.data_path = Path() self.data_path = Path()
self.label_list: List = [] self.label_list: List = []
self.training_features_list: List = [] self.training_features_list: List = []
self.constant_features_list: List = []
self.model_filename: str = "" self.model_filename: str = ""
self.backtesting_results_path = Path() self.backtesting_results_path = Path()
self.backtest_predictions_folder: str = "backtesting_predictions" self.backtest_predictions_folder: str = "backtesting_predictions"
@ -211,10 +210,10 @@ class FreqaiDataKitchen:
const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index) const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
if const_cols: if const_cols:
filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols)) filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
self.constant_features_list = const_cols self.data['constant_features_list'] = const_cols
logger.warning(f"Removed features {const_cols} with constant values.") logger.warning(f"Removed features {const_cols} with constant values.")
else: else:
self.constant_features_list = [] self.data['constant_features_list'] = []
# we don't care about total row number (total no. datapoints) in training, we only care # we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs # about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple modelEs), we detect here # if labels has multiple columns (user wants to train multiple modelEs), we detect here
@ -245,7 +244,7 @@ class FreqaiDataKitchen:
self.data["filter_drop_index_training"] = drop_index self.data["filter_drop_index_training"] = drop_index
else: else:
if len(self.constant_features_list): if len(self.data['constant_features_list']):
filtered_df = self.check_pred_labels(filtered_df) filtered_df = self.check_pred_labels(filtered_df)
# we are backtesting so we need to preserve row number to send back to strategy, # 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 # so now we use do_predict to avoid any prediction based on a NaN
@ -472,7 +471,7 @@ class FreqaiDataKitchen:
:params: :params:
:df_predictions: incoming predictions :df_predictions: incoming predictions
""" """
constant_labels = self.constant_features_list constant_labels = self.data['constant_features_list']
df_predictions = df_predictions.filter( df_predictions = df_predictions.filter(
df_predictions.columns.difference(constant_labels) df_predictions.columns.difference(constant_labels)
) )

3
tests/freqai/test_freqai_datakitchen.py
View File

@ -125,7 +125,8 @@ def test_normalize_data(mocker, freqai_conf):
freqai = make_data_dictionary(mocker, freqai_conf) freqai = make_data_dictionary(mocker, freqai_conf)
data_dict = freqai.dk.data_dictionary data_dict = freqai.dk.data_dictionary
freqai.dk.normalize_data(data_dict) freqai.dk.normalize_data(data_dict)
assert len(freqai.dk.data) == 32 assert any('_max' in entry for entry in freqai.dk.data.keys())
assert any('_min' in entry for entry in freqai.dk.data.keys())
def test_filter_features(mocker, freqai_conf): def test_filter_features(mocker, freqai_conf):