paying closer attention to managing live retraining on separate thread without affecting prediction of other coins on master thread
This commit is contained in:
robcaulk
parent
b0d2d13eb1
commit
059c285425
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@ -24,6 +24,7 @@ class FreqaiDataDrawer:
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self.pair_dict: Dict[str, Any] = {}
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# dictionary holding all actively inferenced models in memory given a model filename
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self.model_dictionary: Dict[str, Any] = {}
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self.pair_data_dict: Dict[str, Any] = {}
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self.full_path = full_path
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self.load_drawer_from_disk()
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@ -91,14 +91,15 @@ class FreqaiDataKitchen:
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assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai feature_parameters"
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"found in config file.")
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def set_paths(self, trained_timestamp: int = None) -> None:
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def set_paths(self, metadata: dict, trained_timestamp: int = None,) -> None:
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self.full_path = Path(self.config['user_data_dir'] /
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"models" /
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str(self.freqai_config.get('live_full_backtestrange') +
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self.freqai_config.get('identifier')))
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self.data_path = Path(self.full_path / str("sub-train" + "-" + self.pair.split("/")[0] +
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str(trained_timestamp)))
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self.data_path = Path(self.full_path / str("sub-train" + "-" +
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metadata['pair'].split("/")[0] +
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str(trained_timestamp)))
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return
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@ -108,14 +108,22 @@ class IFreqaiModel(ABC):
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self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
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# FreqaiDataKitchen is reinstantiated for each coin
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self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"])
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if self.live:
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# logger.info('testing live')
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self.start_live(dataframe, metadata, strategy)
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if not self.training_on_separate_thread:
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self.dh = FreqaiDataKitchen(self.config, self.data_drawer,
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self.live, metadata["pair"])
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dh = self.start_live(dataframe, metadata, strategy, self.dh)
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else:
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# we will have at max 2 separate instances of the kitchen at once.
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self.dh_fg = FreqaiDataKitchen(self.config, self.data_drawer,
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self.live, metadata["pair"])
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dh = self.start_live(dataframe, metadata, strategy, self.dh_fg)
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return (self.dh.full_predictions, self.dh.full_do_predict,
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self.dh.full_target_mean, self.dh.full_target_std)
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return (dh.full_predictions, dh.full_do_predict,
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dh.full_target_mean, dh.full_target_std)
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# Backtesting only
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self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"])
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logger.info(f'Training {len(self.dh.training_timeranges)} timeranges')
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@ -138,8 +146,9 @@ class IFreqaiModel(ABC):
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self.dh.data_path = Path(self.dh.full_path /
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str("sub-train" + "-" + metadata['pair'].split("/")[0] +
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str(int(trained_timestamp.stopts))))
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if not self.model_exists(metadata["pair"], trained_timestamp=trained_timestamp.stopts):
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self.model = self.train(dataframe_train, metadata)
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if not self.model_exists(metadata["pair"], self.dh,
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trained_timestamp=trained_timestamp.stopts):
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self.model = self.train(dataframe_train, metadata, self.dh)
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self.dh.save_data(self.model)
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else:
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self.model = self.dh.load_data()
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@ -150,7 +159,7 @@ class IFreqaiModel(ABC):
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# self.model = self.train(dataframe_train, metadata)
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# self.dh.save_data(self.model)
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preds, do_preds = self.predict(dataframe_backtest, metadata)
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preds, do_preds = self.predict(dataframe_backtest, self.dh)
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self.dh.append_predictions(preds, do_preds, len(dataframe_backtest))
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print('predictions', len(self.dh.full_predictions),
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@ -161,7 +170,8 @@ class IFreqaiModel(ABC):
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return (self.dh.full_predictions, self.dh.full_do_predict,
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self.dh.full_target_mean, self.dh.full_target_std)
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def start_live(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> None:
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def start_live(self, dataframe: DataFrame, metadata: dict,
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strategy: IStrategy, dh: FreqaiDataKitchen) -> FreqaiDataKitchen:
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"""
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The main broad execution for dry/live. This function will check if a retraining should be
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performed, and if so, retrain and reset the model.
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@ -172,52 +182,49 @@ class IFreqaiModel(ABC):
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trained_timestamp,
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coin_first) = self.data_drawer.get_pair_dict_info(metadata)
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if trained_timestamp != 0:
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self.dh.set_paths(trained_timestamp)
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# data_drawer thinks the file eixts, verify here
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file_exists = self.model_exists(metadata['pair'],
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trained_timestamp=trained_timestamp,
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model_filename=model_filename)
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if not self.training_on_separate_thread:
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file_exists = False
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if trained_timestamp != 0:
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dh.set_paths(metadata, trained_timestamp)
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# data_drawer thinks the file eixts, verify here
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file_exists = self.model_exists(metadata['pair'],
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dh,
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trained_timestamp=trained_timestamp,
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model_filename=model_filename)
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# if not self.training_on_separate_thread:
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# this will also prevent other pairs from trying to train simultaneously.
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(self.retrain,
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new_trained_timerange) = self.dh.check_if_new_training_required(
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trained_timestamp)
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self.dh.set_paths(new_trained_timerange.stopts)
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new_trained_timerange) = dh.check_if_new_training_required(trained_timestamp)
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dh.set_paths(metadata, new_trained_timerange.stopts)
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# if self.training_on_separate_thread:
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# logger.info("FreqAI training a new model on background thread.")
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# self.retrain = False
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if self.retrain or not file_exists:
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if coin_first:
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self.train_model_in_series(new_trained_timerange, metadata, strategy, dh)
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else:
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self.training_on_separate_thread = True # acts like a lock
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self.retrain_model_on_separate_thread(new_trained_timerange,
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metadata, strategy, dh)
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else:
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logger.info("FreqAI training a new model on background thread.")
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self.retrain = False
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if self.retrain or not file_exists:
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if coin_first:
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self.train_model_in_series(new_trained_timerange, metadata, strategy)
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else:
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self.training_on_separate_thread = True # acts like a lock
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self.retrain_model_on_separate_thread(new_trained_timerange,
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metadata, strategy)
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self.model = dh.load_data(coin=metadata['pair'])
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self.model = self.dh.load_data(coin=metadata['pair'])
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# strategy_provided_features = dh.find_features(dataframe)
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# if strategy_provided_features != dh.training_features_list:
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# self.train_model_in_series(new_trained_timerange, metadata, strategy)
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strategy_provided_features = self.dh.find_features(dataframe)
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if strategy_provided_features != self.dh.training_features_list:
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self.train_model_in_series(new_trained_timerange, metadata, strategy)
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preds, do_preds = self.predict(dataframe, dh)
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dh.append_predictions(preds, do_preds, len(dataframe))
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preds, do_preds = self.predict(dataframe, metadata)
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self.dh.append_predictions(preds, do_preds, len(dataframe))
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return dh
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return
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def make_labels(self, dataframe: DataFrame) -> DataFrame:
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"""
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User defines the labels here (target values).
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:params:
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:dataframe: the full dataframe for the present training period
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"""
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return
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def data_cleaning_train(self) -> None:
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def data_cleaning_train(self, dh: FreqaiDataKitchen) -> None:
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"""
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Base data cleaning method for train
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Any function inside this method should drop training data points from the filtered_dataframe
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@ -225,23 +232,23 @@ class IFreqaiModel(ABC):
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of how outlier data points are dropped from the dataframe used for training.
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"""
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if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
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self.dh.principal_component_analysis()
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dh.principal_component_analysis()
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# if self.feature_parameters["determine_statistical_distributions"]:
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# self.dh.determine_statistical_distributions()
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# dh.determine_statistical_distributions()
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# if self.feature_parameters["remove_outliers"]:
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# self.dh.remove_outliers(predict=False)
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# dh.remove_outliers(predict=False)
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if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
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self.dh.use_SVM_to_remove_outliers(predict=False)
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dh.use_SVM_to_remove_outliers(predict=False)
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
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self.dh.data["avg_mean_dist"] = self.dh.compute_distances()
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dh.data["avg_mean_dist"] = dh.compute_distances()
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def data_cleaning_predict(self, filtered_dataframe: DataFrame) -> None:
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def data_cleaning_predict(self, dh: FreqaiDataKitchen) -> None:
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"""
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Base data cleaning method for predict.
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These functions each modify self.dh.do_predict, which is a dataframe with equal length
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These functions each modify dh.do_predict, which is a dataframe with equal length
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to the number of candles coming from and returning to the strategy. Inside do_predict,
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1 allows prediction and < 0 signals to the strategy that the model is not confident in
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the prediction.
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@ -250,20 +257,20 @@ class IFreqaiModel(ABC):
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for buy signals.
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"""
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if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
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self.dh.pca_transform()
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dh.pca_transform()
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# if self.feature_parameters["determine_statistical_distributions"]:
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# self.dh.determine_statistical_distributions()
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# dh.determine_statistical_distributions()
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# if self.feature_parameters["remove_outliers"]:
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# self.dh.remove_outliers(predict=True) # creates dropped index
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# dh.remove_outliers(predict=True) # creates dropped index
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if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
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self.dh.use_SVM_to_remove_outliers(predict=True)
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dh.use_SVM_to_remove_outliers(predict=True)
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
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self.dh.check_if_pred_in_training_spaces() # sets do_predict
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dh.check_if_pred_in_training_spaces() # sets do_predict
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def model_exists(self, pair: str, trained_timestamp: int = None,
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def model_exists(self, pair: str, dh: FreqaiDataKitchen, trained_timestamp: int = None,
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model_filename: str = '') -> bool:
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"""
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Given a pair and path, check if a model already exists
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@ -272,17 +279,17 @@ class IFreqaiModel(ABC):
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"""
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coin, _ = pair.split("/")
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if self.live and trained_timestamp is None:
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self.dh.model_filename = model_filename
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else:
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self.dh.model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
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# if self.live and trained_timestamp == 0:
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# dh.model_filename = model_filename
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if not self.live:
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dh.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
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path_to_modelfile = Path(self.dh.data_path / str(self.dh.model_filename + "_model.joblib"))
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path_to_modelfile = Path(dh.data_path / str(model_filename + "_model.joblib"))
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file_exists = path_to_modelfile.is_file()
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if file_exists:
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logger.info("Found model at %s", self.dh.data_path / self.dh.model_filename)
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logger.info("Found model at %s", dh.data_path / dh.model_filename)
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else:
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logger.info("Could not find model at %s", self.dh.data_path / self.dh.model_filename)
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logger.info("Could not find model at %s", dh.data_path / dh.model_filename)
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return file_exists
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def set_full_path(self) -> None:
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@ -293,58 +300,58 @@ class IFreqaiModel(ABC):
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@threaded
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def retrain_model_on_separate_thread(self, new_trained_timerange: TimeRange, metadata: dict,
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strategy: IStrategy):
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strategy: IStrategy, dh: FreqaiDataKitchen):
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# with nostdout():
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self.dh.download_new_data_for_retraining(new_trained_timerange, metadata)
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corr_dataframes, base_dataframes = self.dh.load_pairs_histories(new_trained_timerange,
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metadata)
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dh.download_new_data_for_retraining(new_trained_timerange, metadata)
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corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange,
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metadata)
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unfiltered_dataframe = self.dh.use_strategy_to_populate_indicators(strategy,
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corr_dataframes,
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base_dataframes,
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metadata)
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unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy,
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corr_dataframes,
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base_dataframes,
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metadata)
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self.model = self.train(unfiltered_dataframe, metadata)
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self.model = self.train(unfiltered_dataframe, metadata, dh)
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self.data_drawer.pair_dict[metadata['pair']][
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'trained_timestamp'] = new_trained_timerange.stopts
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self.dh.set_new_model_names(metadata, new_trained_timerange)
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dh.set_new_model_names(metadata, new_trained_timerange)
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self.dh.save_data(self.model, coin=metadata['pair'])
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dh.save_data(self.model, coin=metadata['pair'])
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self.training_on_separate_thread = False
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self.retrain = False
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def train_model_in_series(self, new_trained_timerange: TimeRange, metadata: dict,
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strategy: IStrategy):
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strategy: IStrategy, dh: FreqaiDataKitchen):
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self.dh.download_new_data_for_retraining(new_trained_timerange, metadata)
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corr_dataframes, base_dataframes = self.dh.load_pairs_histories(new_trained_timerange,
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metadata)
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dh.download_new_data_for_retraining(new_trained_timerange, metadata)
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corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange,
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metadata)
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unfiltered_dataframe = self.dh.use_strategy_to_populate_indicators(strategy,
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corr_dataframes,
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base_dataframes,
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metadata)
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unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy,
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corr_dataframes,
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base_dataframes,
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metadata)
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self.model = self.train(unfiltered_dataframe, metadata)
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self.model = self.train(unfiltered_dataframe, metadata, dh)
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self.data_drawer.pair_dict[metadata['pair']][
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'trained_timestamp'] = new_trained_timerange.stopts
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self.dh.set_new_model_names(metadata, new_trained_timerange)
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dh.set_new_model_names(metadata, new_trained_timerange)
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self.data_drawer.pair_dict[metadata['pair']]['first'] = False
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self.dh.save_data(self.model, coin=metadata['pair'])
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dh.save_data(self.model, coin=metadata['pair'])
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self.retrain = False
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# Methods which are overridden by user made prediction models.
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# See freqai/prediction_models/CatboostPredictionModlel.py for an example.
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@abstractmethod
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def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Any:
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def train(self, unfiltered_dataframe: DataFrame, metadata: dict, dh: FreqaiDataKitchen) -> Any:
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datahandler
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for storing, saving, loading, and analyzing the data.
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@ -369,7 +376,8 @@ class IFreqaiModel(ABC):
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return
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@abstractmethod
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def predict(self, dataframe: DataFrame, metadata: dict) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
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def predict(self, dataframe: DataFrame,
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dh: FreqaiDataKitchen) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
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"""
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Filter the prediction features data and predict with it.
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:param: unfiltered_dataframe: Full dataframe for the current backtest period.
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@ -378,3 +386,13 @@ class IFreqaiModel(ABC):
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:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
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data (NaNs) or felt uncertain about data (PCA and DI index)
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"""
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@abstractmethod
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def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
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"""
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User defines the labels here (target values).
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:params:
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:dataframe: the full dataframe for the present training period
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"""
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return
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@ -4,6 +4,7 @@ from typing import Any, Dict, Tuple
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from catboost import CatBoostRegressor, Pool
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from pandas import DataFrame
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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@ -17,7 +18,7 @@ class CatboostPredictionModel(IFreqaiModel):
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has its own DataHandler where data is held, saved, loaded, and managed.
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"""
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def make_labels(self, dataframe: DataFrame) -> DataFrame:
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def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
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"""
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User defines the labels here (target values).
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:params:
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@ -32,14 +33,15 @@ class CatboostPredictionModel(IFreqaiModel):
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/ dataframe["close"]
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- 1
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)
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self.dh.data["s_mean"] = dataframe["s"].mean()
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self.dh.data["s_std"] = dataframe["s"].std()
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dh.data["s_mean"] = dataframe["s"].mean()
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dh.data["s_std"] = dataframe["s"].std()
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# logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"])
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# logger.info("label mean", dh.data["s_mean"], "label std", dh.data["s_std"])
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return dataframe["s"]
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|
||||
def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]:
|
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def train(self, unfiltered_dataframe: DataFrame,
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metadata: dict, dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
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for storing, saving, loading, and analyzing the data.
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|
|
@ -52,25 +54,25 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
logger.info("--------------------Starting training--------------------")
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||||
|
||||
# create the full feature list based on user config info
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||||
self.dh.training_features_list = self.dh.find_features(unfiltered_dataframe)
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unfiltered_labels = self.make_labels(unfiltered_dataframe)
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dh.training_features_list = dh.find_features(unfiltered_dataframe)
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unfiltered_labels = self.make_labels(unfiltered_dataframe, dh)
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||||
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = self.dh.filter_features(
|
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features_filtered, labels_filtered = dh.filter_features(
|
||||
unfiltered_dataframe,
|
||||
self.dh.training_features_list,
|
||||
dh.training_features_list,
|
||||
unfiltered_labels,
|
||||
training_filter=True,
|
||||
)
|
||||
|
||||
# split data into train/test data.
|
||||
data_dictionary = self.dh.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
data_dictionary = dh.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
# standardize all data based on train_dataset only
|
||||
data_dictionary = self.dh.standardize_data(data_dictionary)
|
||||
data_dictionary = dh.standardize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_train()
|
||||
self.data_cleaning_train(dh)
|
||||
|
||||
logger.info(f'Training model on {len(self.dh.training_features_list)} features')
|
||||
logger.info(f'Training model on {len(dh.training_features_list)} features')
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
|
@ -107,8 +109,8 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
|
||||
return model
|
||||
|
||||
def predict(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame,
|
||||
DataFrame]:
|
||||
def predict(self, unfiltered_dataframe: DataFrame,
|
||||
dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
|
||||
|
|
@ -120,23 +122,22 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
|
||||
# logger.info("--------------------Starting prediction--------------------")
|
||||
|
||||
original_feature_list = self.dh.find_features(unfiltered_dataframe)
|
||||
filtered_dataframe, _ = self.dh.filter_features(
|
||||
original_feature_list = dh.find_features(unfiltered_dataframe)
|
||||
filtered_dataframe, _ = dh.filter_features(
|
||||
unfiltered_dataframe, original_feature_list, training_filter=False
|
||||
)
|
||||
filtered_dataframe = self.dh.standardize_data_from_metadata(filtered_dataframe)
|
||||
self.dh.data_dictionary["prediction_features"] = filtered_dataframe
|
||||
filtered_dataframe = dh.standardize_data_from_metadata(filtered_dataframe)
|
||||
dh.data_dictionary["prediction_features"] = filtered_dataframe
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_predict(filtered_dataframe)
|
||||
self.data_cleaning_predict(dh)
|
||||
|
||||
predictions = self.model.predict(self.dh.data_dictionary["prediction_features"])
|
||||
predictions = self.model.predict(dh.data_dictionary["prediction_features"])
|
||||
|
||||
# compute the non-standardized predictions
|
||||
self.dh.predictions = (predictions + 1) * (self.dh.data["labels_max"] -
|
||||
self.dh.data["labels_min"]) / 2 + self.dh.data[
|
||||
"labels_min"]
|
||||
dh.predictions = (predictions + 1) * (dh.data["labels_max"] -
|
||||
dh.data["labels_min"]) / 2 + dh.data["labels_min"]
|
||||
|
||||
# logger.info("--------------------Finished prediction--------------------")
|
||||
|
||||
return (self.dh.predictions, self.dh.do_predict)
|
||||
return (dh.predictions, dh.do_predict)
|
||||
|
|
|
|||
Loading…
Reference in New Issue