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

merge in inference timer and historic predictions handling improvements.

Browse Source
This commit is contained in:
robcaulk
2022-08-14 16:41:50 +02:00
parent ad846cdb76
commit 8961b8d560
2 changed files with 46 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

25
freqtrade/freqai/data_drawer.py
View File

@@ -85,6 +85,7 @@ class FreqaiDataDrawer:
self.training_queue: Dict[str, int] = {}
self.history_lock = threading.Lock()
self.save_lock = threading.Lock()
self.pair_dict_lock = threading.Lock()
self.old_DBSCAN_eps: Dict[str, float] = {}
self.empty_pair_dict: pair_info = {
"model_filename": "", "trained_timestamp": 0,
@@ -228,10 +229,11 @@ class FreqaiDataDrawer:
def pair_to_end_of_training_queue(self, pair: str) -> None:
# march all pairs up in the queue
for p in self.pair_dict:
self.pair_dict[p]["priority"] -= 1
# send pair to end of queue
self.pair_dict[pair]["priority"] = len(self.pair_dict)
with self.pair_dict_lock:
for p in self.pair_dict:
self.pair_dict[p]["priority"] -= 1
# send pair to end of queue
self.pair_dict[pair]["priority"] = len(self.pair_dict)
def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:
"""
@@ -261,13 +263,14 @@ class FreqaiDataDrawer:
the strategy originally. Doing this allows FreqUI to always display the correct
historic predictions.
"""
df = self.historic_predictions[pair]
# here are some pandas hula hoops to accommodate the possibility of a series
# or dataframe depending number of labels requested by user
nan_df = pd.DataFrame(np.nan, index=df.index[-2:] + 2, columns=df.columns)
df = pd.concat([df, nan_df], ignore_index=True, axis=0)
df = self.historic_predictions[pair] = df[:-1]
index = self.historic_predictions[pair].index[-1:]
columns = self.historic_predictions[pair].columns
nan_df = pd.DataFrame(np.nan, index=index, columns=columns)
self.historic_predictions[pair] = pd.concat(
[self.historic_predictions[pair], nan_df], ignore_index=True, axis=0)
df = self.historic_predictions[pair]
# model outputs and associated statistics
for label in predictions.columns:
@@ -523,7 +526,7 @@ class FreqaiDataDrawer:
history_data[pair][tf] = pd.concat(
[
history_data[pair][tf],
strategy.dp.get_pair_dataframe(pair, tf).iloc[index:],
df_dp.iloc[index:],
],
ignore_index=True,
axis=0,