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Fix pandas deprecation warnings from freqAI

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This commit is contained in:
Matthias 2022-09-30 15:43:05 +02:00
parent 0d8dfc1a92
commit cc06c60fd8
1 changed files with 4 additions and 4 deletions
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8
freqtrade/freqai/data_kitchen.py
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@ -210,7 +210,7 @@ class FreqaiDataKitchen:
filtered_df = unfiltered_df.filter(training_feature_list, axis=1) filtered_df = unfiltered_df.filter(training_feature_list, axis=1)
filtered_df = filtered_df.replace([np.inf, -np.inf], np.nan) filtered_df = filtered_df.replace([np.inf, -np.inf], np.nan)
drop_index = pd.isnull(filtered_df).any(1) # get the rows that have NaNs, 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. drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
if (training_filter): if (training_filter):
const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index) const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
@ -221,7 +221,7 @@ class FreqaiDataKitchen:
# 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
labels = unfiltered_df.filter(label_list, axis=1) labels = unfiltered_df.filter(label_list, axis=1)
drop_index_labels = pd.isnull(labels).any(1) drop_index_labels = pd.isnull(labels).any(axis=1)
drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0) drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0)
dates = unfiltered_df['date'] dates = unfiltered_df['date']
filtered_df = filtered_df[ filtered_df = filtered_df[
@ -249,7 +249,7 @@ class FreqaiDataKitchen:
else: else:
# 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
drop_index = pd.isnull(filtered_df).any(1) drop_index = pd.isnull(filtered_df).any(axis=1)
self.data["filter_drop_index_prediction"] = drop_index self.data["filter_drop_index_prediction"] = drop_index
filtered_df.fillna(0, inplace=True) filtered_df.fillna(0, inplace=True)
# replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction # replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction
@ -808,7 +808,7 @@ class FreqaiDataKitchen:
:, :no_prev_pts :, :no_prev_pts
] ]
distances = distances.replace([np.inf, -np.inf], np.nan) distances = distances.replace([np.inf, -np.inf], np.nan)
drop_index = pd.isnull(distances).any(1) drop_index = pd.isnull(distances).any(axis=1)
distances = distances[drop_index == 0] distances = distances[drop_index == 0]
inliers = pd.DataFrame(index=distances.index) inliers = pd.DataFrame(index=distances.index)