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https://github.com/freqtrade/freqtrade.git
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clean up obsolete comments, move remove_features_from_df to datakitchen
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robcaulk
parent
0b21750e76
commit
afcb0bec00
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@ -1116,6 +1116,16 @@ class FreqaiDataKitchen:
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# self.data["lower_quantile"] = lower_q
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return
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def remove_features_from_df(self, dataframe: DataFrame) -> DataFrame:
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"""
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Remove the features from the dataframe before returning it to strategy. This keeps it
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compact for Frequi purposes.
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"""
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to_keep = [
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col for col in dataframe.columns if not col.startswith("%") or col.startswith("%%")
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]
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return dataframe[to_keep]
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def np_encoder(self, object):
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if isinstance(object, np.generic):
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return object.item()
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@ -37,9 +37,7 @@ def threaded(fn):
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class IFreqaiModel(ABC):
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"""
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Class containing all tools for training and prediction in the strategy.
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User models should inherit from this class as shown in
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templates/ExamplePredictionModel.py where the user overrides
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train(), predict(), fit(), and make_labels().
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Base*PredictionModels inherit from this class.
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Author: Robert Caulk, rob.caulk@gmail.com
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"""
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@ -51,23 +49,15 @@ class IFreqaiModel(ABC):
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self.data_split_parameters = config.get("freqai", {}).get("data_split_parameters")
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self.model_training_parameters = config.get("freqai", {}).get("model_training_parameters")
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self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
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self.time_last_trained = None
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self.current_time = None
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self.model = None
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self.predictions = None
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self.training_on_separate_thread = False
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self.retrain = False
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self.first = True
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self.update_historic_data = 0
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self.set_full_path()
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self.follow_mode = self.freqai_info.get("follow_mode", False)
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self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
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self.lock = threading.Lock()
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self.follow_mode = self.freqai_info.get("follow_mode", False)
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self.identifier = self.freqai_info.get("identifier", "no_id_provided")
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self.scanning = False
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self.ready_to_scan = False
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self.first = True
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self.keras = self.freqai_info.get("keras", False)
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if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
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self.freqai_info["feature_parameters"]["DI_threshold"] = 0
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@ -114,7 +104,7 @@ class IFreqaiModel(ABC):
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)
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dk = self.start_backtesting(dataframe, metadata, self.dk)
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dataframe = self.remove_features_from_df(dk.return_dataframe)
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dataframe = dk.remove_features_from_df(dk.return_dataframe)
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return self.return_values(dataframe, dk)
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@threaded
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@ -260,9 +250,6 @@ class IFreqaiModel(ABC):
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dk.update_historic_data(strategy)
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logger.debug(f'Updating historic data on pair {metadata["pair"]}')
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# if trainable, check if model needs training, if so compute new timerange,
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# then save model and metadata.
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# if not trainable, load existing data
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if not self.follow_mode:
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(_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
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@ -320,6 +307,8 @@ class IFreqaiModel(ABC):
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# correct array to strategy
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if pair not in self.dd.model_return_values:
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# first predictions are made on entire historical candle set coming from strategy. This
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# allows FreqUI to show full return values.
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pred_df, do_preds = self.predict(dataframe, dk)
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self.dd.set_initial_return_values(pair, dk, pred_df, do_preds)
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dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
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@ -333,7 +322,8 @@ class IFreqaiModel(ABC):
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"prediction == 0 and do_predict == 2"
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)
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else:
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# Only feed in the most recent candle for prediction in live scenario
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# remaining predictions are made only on the most recent candles for performance and
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# historical accuracy reasons.
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pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dk, first=False)
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self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe))
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@ -384,11 +374,6 @@ class IFreqaiModel(ABC):
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if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
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dk.data["avg_mean_dist"] = dk.compute_distances()
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# if self.feature_parameters["determine_statistical_distributions"]:
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# dk.determine_statistical_distributions()
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# if self.feature_parameters["remove_outliers"]:
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# dk.remove_outliers(predict=False)
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def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
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"""
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Base data cleaning method for predict.
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@ -411,11 +396,6 @@ class IFreqaiModel(ABC):
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if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
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dk.check_if_pred_in_training_spaces()
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# if self.feature_parameters["determine_statistical_distributions"]:
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# dk.determine_statistical_distributions()
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# if self.feature_parameters["remove_outliers"]:
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# dk.remove_outliers(predict=True) # creates dropped index
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def model_exists(
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self,
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pair: str,
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@ -428,6 +408,8 @@ class IFreqaiModel(ABC):
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Given a pair and path, check if a model already exists
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:param pair: pair e.g. BTC/USD
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:param path: path to model
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:return:
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:boolean: whether the model file exists or not.
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"""
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coin, _ = pair.split("/")
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@ -452,16 +434,6 @@ class IFreqaiModel(ABC):
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Path(self.full_path, Path(self.config["config_files"][0]).name),
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)
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def remove_features_from_df(self, dataframe: DataFrame) -> DataFrame:
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"""
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Remove the features from the dataframe before returning it to strategy. This keeps it
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compact for Frequi purposes.
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"""
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to_keep = [
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col for col in dataframe.columns if not col.startswith("%") or col.startswith("%%")
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]
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return dataframe[to_keep]
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def train_model_in_series(
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self,
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new_trained_timerange: TimeRange,
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@ -507,7 +479,6 @@ class IFreqaiModel(ABC):
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if self.freqai_info.get("purge_old_models", False):
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self.dd.purge_old_models()
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# self.retrain = False
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def set_initial_historic_predictions(
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self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
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@ -567,16 +538,6 @@ class IFreqaiModel(ABC):
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data (NaNs) or felt uncertain about data (i.e. SVM and/or DI index)
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"""
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def make_labels(self, dataframe: DataFrame, dk: 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: DataFrame = the full dataframe for the present training period
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dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
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"""
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return
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@abstractmethod
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def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
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"""
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