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clean up code, add docstrings

This commit is contained in:
robcaulk 2022-08-12 16:12:28 +02:00
parent 3e38c1b0bd
commit 31be707cc8
2 changed files with 19 additions and 71 deletions
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86
freqtrade/freqai/data_drawer.py
View File

@ -232,85 +232,43 @@ class FreqaiDataDrawer:
# send pair to end of queue
self.pair_dict[pair]["priority"] = len(self.pair_dict)
def set_initial_return_values(self, pair: str, dk: FreqaiDataKitchen,
pred_df: DataFrame, do_preds: ArrayLike) -> None:
def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:
"""
Set the initial return values to a persistent dataframe. This avoids needing to repredict on
historical candles, and also stores historical predictions despite retrainings (so stored
predictions are true predictions, not just inferencing on trained data)
Set the initial return values to the historical predictions dataframe. This avoids needing
to repredict on historical candles, and also stores historical predictions despite
retrainings (so stored predictions are true predictions, not just inferencing on trained
data)
"""
# dynamic df returned to strategy and plotted in frequi
# mrv_df = self.model_return_values[pair] = pd.DataFrame()
# if user reused `identifier` in config and has historical predictions collected, load them
# so that frequi remains uninterrupted after a crash
hist_df = self.historic_predictions
# if pair in hist_df:
len_diff = len(hist_df[pair].index) - len(pred_df.index)
if len_diff < 0:
df_concat = pd.concat([pred_df.iloc[:abs(len_diff)], hist_df[pair]],
ignore_index=True, keys=hist_df[pair].keys())
ignore_index=True, keys=hist_df[pair].keys())
else:
df_concat = hist_df[pair].tail(len(pred_df.index)).reset_index(drop=True)
df_concat = df_concat.fillna(0)
self.model_return_values[pair] = df_concat
logger.info(f'Setting initial FreqUI plots from historical data for {pair}.')
# else:
# for label in pred_df.columns:
# mrv_df[label] = pred_df[label]
# if mrv_df[label].dtype == object:
# continue
# mrv_df[f"{label}_mean"] = dk.data["labels_mean"][label]
# mrv_df[f"{label}_std"] = dk.data["labels_std"][label]
# if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
# mrv_df["DI_values"] = dk.DI_values
# mrv_df["do_predict"] = do_preds
# if dk.data['extra_returns_per_train']:
# rets = dk.data['extra_returns_per_train']
# for return_str in rets:
# mrv_df[return_str] = rets[return_str]
# # for keras type models, the conv_window needs to be prepended so
# # viewing is correct in frequi
# if self.freqai_info.get('keras', False):
# n_lost_points = self.freqai_info.get('conv_width', 2)
# zeros_df = DataFrame(np.zeros((n_lost_points, len(mrv_df.columns))),
# columns=mrv_df.columns)
# self.model_return_values[pair] = pd.concat(
# [zeros_df, mrv_df], axis=0, ignore_index=True)
def append_model_predictions(self, pair: str, predictions: DataFrame,
do_preds: NDArray[np.int_],
dk: FreqaiDataKitchen, len_df: int) -> None:
# 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
# df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i)
# if pair in self.historic_predictions:
"""
Append model predictions to historic predictions dataframe, then set the
strategy return dataframe to the tail of the historic predictions. The length of
the tail is equivalent to the length of the dataframe that entered FreqAI from
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]
# incase user adds additional "predictions" e.g. predict_proba output:
# model outputs and associated statistics
for label in predictions.columns:
df[label].iloc[-1] = predictions[label].iloc[-1]
if df[label].dtype == object:
@ -318,11 +276,12 @@ class FreqaiDataDrawer:
df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label]
df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label]
# outlier indicators
df["do_predict"].iloc[-1] = do_preds[-1]
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
df["DI_values"].iloc[-1] = dk.DI_values[-1]
# extra values the user added within custom prediction model
if dk.data['extra_returns_per_train']:
rets = dk.data['extra_returns_per_train']
for return_str in rets:
@ -330,17 +289,6 @@ class FreqaiDataDrawer:
self.model_return_values[pair] = df.tail(len_df).reset_index(drop=True)
# # append the new predictions to persistent storage
# if pair in self.historic_predictions:
# 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)
def attach_return_values_to_return_dataframe(
self, pair: str, dataframe: DataFrame) -> DataFrame:
"""

4
freqtrade/freqai/freqai_interface.py
View File

@ -321,7 +321,7 @@ class IFreqaiModel(ABC):
pred_df, do_preds = self.predict(dataframe, dk)
if pair not in self.dd.historic_predictions:
self.set_initial_historic_predictions(pred_df, dk, pair)
self.dd.set_initial_return_values(pair, dk, pred_df, do_preds)
self.dd.set_initial_return_values(pair, pred_df)
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
return
@ -558,7 +558,7 @@ class IFreqaiModel(ABC):
n_lost_points = self.freqai_info.get('conv_width', 2)
zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))),
columns=hist_preds_df.columns)
self.model_return_values[pair] = pd.concat(
self.dd.historic_predictions[pair] = pd.concat(
[zeros_df, hist_preds_df], axis=0, ignore_index=True)
def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None: