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improve flexibility of user defined prediction dataframe

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This commit is contained in:
Robert Caulk
2022-08-06 13:51:19 +02:00
parent fdc82f8302
commit c172ce1011
6 changed files with 42 additions and 31 deletions
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37
freqtrade/freqai/data_drawer.py
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@@ -245,7 +245,7 @@ class FreqaiDataDrawer:
logger.info(f'Setting initial FreqUI plots from historical data for {pair}.')
else:
for label in dk.label_list:
for label in pred_df.columns:
mrv_df[label] = pred_df[label]
if mrv_df[label].dtype == object:
continue
@@ -278,15 +278,16 @@ class FreqaiDataDrawer:
# strat seems to feed us variable sized dataframes - and since we are trying to build our
# own return array in the same shape, we need to figure out how the size has changed
# and adapt our stored/returned info accordingly.
length_difference = len(self.model_return_values[pair]) - len_df
i = 0
if length_difference == 0:
i = 1
elif length_difference > 0:
i = length_difference + 1
# length_difference = len(self.model_return_values[pair]) - len_df
# i = 0
df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i)
# if length_difference == 0:
# i = 1
# elif length_difference > 0:
# i = length_difference + 1
df = self.model_return_values[pair] = self.model_return_values[pair].shift(-1)
if pair in self.historic_predictions:
hp_df = self.historic_predictions[pair]
@@ -296,7 +297,8 @@ class FreqaiDataDrawer:
hp_df = pd.concat([hp_df, nan_df], ignore_index=True, axis=0)
self.historic_predictions[pair] = hp_df[:-1]
for label in dk.label_list:
# incase user adds additional "predictions" e.g. predict_proba output:
for label in predictions.columns:
df[label].iloc[-1] = predictions[label].iloc[-1]
if df[label].dtype == object:
continue
@@ -318,11 +320,11 @@ class FreqaiDataDrawer:
for key in df.keys():
self.historic_predictions[pair][key].iloc[-1] = df[key].iloc[-1]
if length_difference < 0:
prepend_df = pd.DataFrame(
np.zeros((abs(length_difference) - 1, len(df.columns))), columns=df.columns
)
df = pd.concat([prepend_df, df], axis=0)
# if length_difference < 0:
# prepend_df = pd.DataFrame(
# np.zeros((abs(length_difference) - 1, len(df.columns))), columns=df.columns
# )
# df = pd.concat([prepend_df, df], axis=0)
def attach_return_values_to_return_dataframe(
self, pair: str, dataframe: DataFrame) -> DataFrame:
@@ -343,7 +345,12 @@ class FreqaiDataDrawer:
dk.find_features(dataframe)
for label in dk.label_list:
if self.freqai_info.get('predict_proba', []):
full_labels = dk.label_list + self.freqai_info['predict_proba']
else:
full_labels = dk.label_list
for label in full_labels:
dataframe[label] = 0
dataframe[f"{label}_mean"] = 0
dataframe[f"{label}_std"] = 0