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first step toward cleaning output and enabling multimodel training per pair

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This commit is contained in:
robcaulk
2022-07-01 14:00:30 +02:00
parent 6c7d02cb18
commit 93e1410ed9
3 changed files with 63 additions and 144 deletions
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114
freqtrade/freqai/freqai_interface.py
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@@ -68,6 +68,8 @@ class IFreqaiModel(ABC):
self.scanning = False
self.ready_to_scan = False
self.first = True
self.keras = self.freqai_info.get('keras', False)
self.CONV_WIDTH = self.freqai_info.get('conv_width', 2)
def assert_config(self, config: Dict[str, Any]) -> None:
@@ -122,30 +124,28 @@ class IFreqaiModel(ABC):
time.sleep(1)
for pair in self.config.get('exchange', {}).get('pair_whitelist'):
(model_filename,
trained_timestamp,
_, _) = self.data_drawer.get_pair_dict_info(pair)
(_, trained_timestamp, _, _) = self.data_drawer.get_pair_dict_info(pair)
if self.data_drawer.pair_dict[pair]['priority'] != 1:
continue
dh = FreqaiDataKitchen(self.config, self.data_drawer,
self.live, pair)
file_exists = False
# file_exists = False
dh.set_paths(pair, trained_timestamp)
file_exists = self.model_exists(pair,
dh,
trained_timestamp=trained_timestamp,
model_filename=model_filename,
scanning=True)
# file_exists = self.model_exists(pair,
# dh,
# trained_timestamp=trained_timestamp,
# model_filename=model_filename,
# scanning=True)
(retrain,
new_trained_timerange,
data_load_timerange) = dh.check_if_new_training_required(trained_timestamp)
dh.set_paths(pair, new_trained_timerange.stopts)
if retrain or not file_exists:
if retrain: # or not file_exists:
self.train_model_in_series(new_trained_timerange, pair,
strategy, dh, data_load_timerange)
@@ -199,9 +199,9 @@ class IFreqaiModel(ABC):
self.data_drawer.pair_dict[metadata['pair']][
'trained_timestamp'] = trained_timestamp.stopts
dh.set_new_model_names(metadata['pair'], trained_timestamp)
dh.save_data(self.model, metadata['pair'])
dh.save_data(self.model, metadata['pair'], keras=self.keras)
else:
self.model = dh.load_data(metadata['pair'])
self.model = dh.load_data(metadata['pair'], keras=self.keras)
self.check_if_feature_list_matches_strategy(dataframe_train, dh)
@@ -278,7 +278,7 @@ class IFreqaiModel(ABC):
f'using { self.identifier }')
# load the model and associated data into the data kitchen
self.model = dh.load_data(coin=metadata['pair'])
self.model = dh.load_data(coin=metadata['pair'], keras=self.keras)
if not self.model:
logger.warning('No model ready, returning null values to strategy.')
@@ -297,14 +297,16 @@ class IFreqaiModel(ABC):
trained_timestamp: int) -> None:
# hold the historical predictions in memory so we are sending back
# correct array to strategy FIXME currently broken, but only affecting
# Frequi reporting. Signals remain unaffeted.
# correct array to strategy
if pair not in self.data_drawer.model_return_values:
preds, do_preds = self.predict(dataframe, dh)
dh.append_predictions(preds, do_preds, len(dataframe))
dh.fill_predictions(len(dataframe))
self.data_drawer.set_initial_return_values(pair, dh)
# mypy doesnt like the typing in else statement, so we need to explicitly add to
# dataframe separately
dataframe['prediction'], dataframe['do_predict'] = preds, do_preds
# dh.append_predictions(preds, do_preds, len(dataframe))
# dh.fill_predictions(len(dataframe))
self.data_drawer.set_initial_return_values(pair, dh, dataframe)
return
elif self.dh.check_if_model_expired(trained_timestamp):
preds, do_preds, dh.DI_values = np.zeros(2), np.ones(2) * 2, np.zeros(2)
@@ -312,7 +314,8 @@ class IFreqaiModel(ABC):
'construction should take care to consider this event with '
'prediction == 0 and do_predict == 2')
else:
preds, do_preds = self.predict(dataframe.iloc[-2:], dh)
# Only feed in the most recent candle for prediction in live scenario
preds, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dh, first=False)
self.data_drawer.append_model_predictions(pair, preds, do_preds,
dh.data["target_mean"],
@@ -426,71 +429,6 @@ class IFreqaiModel(ABC):
if not col.startswith('%') or col.startswith('%%')]
return dataframe[to_keep]
@threaded
def retrain_model_on_separate_thread(self, new_trained_timerange: TimeRange, pair: str,
strategy: IStrategy, dh: FreqaiDataKitchen,
data_load_timerange: TimeRange):
"""
Retreive data and train model on separate thread. Always called if the model folder already
contains a full set of trained models.
:params:
new_trained_timerange: TimeRange = the timerange to train the model on
metadata: dict = strategy provided metadata
strategy: IStrategy = user defined strategy object
dh: FreqaiDataKitchen = non-persistent data container for current coin/loop
data_load_timerange: TimeRange = the amount of data to be loaded for populate_any_indicators
(larger than new_trained_timerange so that new_trained_timerange does not contain any NaNs)
"""
# with nostdout():
# dh.download_new_data_for_retraining(data_load_timerange, metadata, strategy)
# corr_dataframes, base_dataframes = dh.load_pairs_histories(data_load_timerange,
# metadata)
corr_dataframes, base_dataframes = dh.get_base_and_corr_dataframes(data_load_timerange,
pair)
# protecting from common benign errors associated with grabbing new data from exchange:
try:
unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy,
corr_dataframes,
base_dataframes,
pair)
unfiltered_dataframe = dh.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
except Exception as err:
logger.exception(err)
self.training_on_separate_thread = False
self.retrain = False
return
try:
model = self.train(unfiltered_dataframe, pair, dh)
except ValueError:
logger.warning('Value error encountered during training')
self.training_on_separate_thread = False
self.retrain = False
return
self.data_drawer.pair_dict[pair][
'trained_timestamp'] = new_trained_timerange.stopts
dh.set_new_model_names(pair, new_trained_timerange)
# logger.info('Training queue'
# f'{sorted(self.data_drawer.pair_dict.items(), key=lambda item: item[1])}')
if self.data_drawer.pair_dict[pair]['priority'] == 1:
with self.lock:
self.data_drawer.pair_to_end_of_training_queue(pair)
dh.save_data(model, coin=pair)
# self.training_on_separate_thread = False
# self.retrain = False
# each time we finish a training, we check the directory to purge old models.
if self.freqai_info.get('purge_old_models', False):
self.data_drawer.purge_old_models()
return
def train_model_in_series(self, new_trained_timerange: TimeRange, pair: str,
strategy: IStrategy, dh: FreqaiDataKitchen,
data_load_timerange: TimeRange):
@@ -505,9 +443,7 @@ class IFreqaiModel(ABC):
data_load_timerange: TimeRange = the amount of data to be loaded for populate_any_indicators
(larger than new_trained_timerange so that new_trained_timerange does not contain any NaNs)
"""
# dh.download_new_data_for_retraining(data_load_timerange, metadata, strategy)
# corr_dataframes, base_dataframes = dh.load_pairs_histories(data_load_timerange,
# metadata)
corr_dataframes, base_dataframes = dh.get_base_and_corr_dataframes(data_load_timerange,
pair)
@@ -527,7 +463,7 @@ class IFreqaiModel(ABC):
if self.data_drawer.pair_dict[pair]['priority'] == 1 and self.scanning:
with self.lock:
self.data_drawer.pair_to_end_of_training_queue(pair)
dh.save_data(model, coin=pair)
dh.save_data(model, coin=pair, keras=self.keras)
if self.freqai_info.get('purge_old_models', False):
self.data_drawer.purge_old_models()
@@ -563,7 +499,7 @@ class IFreqaiModel(ABC):
@abstractmethod
def predict(self, dataframe: DataFrame,
dh: FreqaiDataKitchen) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
dh: FreqaiDataKitchen, first: bool = True) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
"""
Filter the prediction features data and predict with it.
:param: