Rehaul organization of return values
This commit is contained in:
robcaulk
@@ -18,18 +18,16 @@ 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 return_values(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
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dataframe["prediction"] = dh.full_predictions
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dataframe["do_predict"] = dh.full_do_predict
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dataframe["target_mean"] = dh.full_target_mean
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dataframe["target_std"] = dh.full_target_std
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
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dataframe["DI"] = dh.full_DI_values
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def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
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"""
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User uses this function to add any additional return values to the dataframe.
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e.g.
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dataframe['volatility'] = dk.volatility_values
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"""
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return dataframe
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def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
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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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@@ -48,7 +46,7 @@ class CatboostPredictionModel(IFreqaiModel):
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return dataframe["s"]
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def train(self, unfiltered_dataframe: DataFrame,
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pair: str, dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
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pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
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"""
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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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@@ -62,27 +60,25 @@ class CatboostPredictionModel(IFreqaiModel):
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logger.info('--------------------Starting training '
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f'{pair} --------------------')
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# create the full feature list based on user config info
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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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# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
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# filter the features requested by user in the configuration file and elegantly handle NaNs
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features_filtered, labels_filtered = dh.filter_features(
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features_filtered, labels_filtered = dk.filter_features(
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unfiltered_dataframe,
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dh.training_features_list,
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unfiltered_labels,
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dk.training_features_list,
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dk.label_list,
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training_filter=True,
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)
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# split data into train/test data.
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data_dictionary = dh.make_train_test_datasets(features_filtered, labels_filtered)
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dh.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
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data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
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dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
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# normalize all data based on train_dataset only
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data_dictionary = dh.normalize_data(data_dictionary)
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data_dictionary = dk.normalize_data(data_dictionary)
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# optional additional data cleaning/analysis
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self.data_cleaning_train(dh)
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self.data_cleaning_train(dk)
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logger.info(f'Training model on {len(dh.data_dictionary["train_features"].columns)}'
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logger.info(f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
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' features')
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logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
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@@ -121,34 +117,32 @@ class CatboostPredictionModel(IFreqaiModel):
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return model
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def predict(self, unfiltered_dataframe: DataFrame,
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dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
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dk: FreqaiDataKitchen, first: bool = False) -> Tuple[DataFrame, DataFrame]:
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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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:return:
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:predictions: np.array of predictions
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:pred_df: dataframe containing the predictions
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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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# logger.info("--------------------Starting prediction--------------------")
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original_feature_list = dh.find_features(unfiltered_dataframe)
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filtered_dataframe, _ = dh.filter_features(
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unfiltered_dataframe, original_feature_list, training_filter=False
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dk.find_features(unfiltered_dataframe)
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filtered_dataframe, _ = dk.filter_features(
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unfiltered_dataframe, dk.training_features_list, training_filter=False
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)
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filtered_dataframe = dh.normalize_data_from_metadata(filtered_dataframe)
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dh.data_dictionary["prediction_features"] = filtered_dataframe
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filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
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dk.data_dictionary["prediction_features"] = filtered_dataframe
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# optional additional data cleaning/analysis
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self.data_cleaning_predict(dh, filtered_dataframe)
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self.data_cleaning_predict(dk, filtered_dataframe)
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predictions = self.model.predict(dh.data_dictionary["prediction_features"])
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predictions = self.model.predict(dk.data_dictionary["prediction_features"])
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pred_df = DataFrame(predictions, columns=dk.label_list)
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# compute the non-normalized predictions
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dh.predictions = (predictions + 1) * (dh.data["labels_max"] -
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dh.data["labels_min"]) / 2 + dh.data["labels_min"]
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for label in dk.label_list:
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pred_df[label] = ((pred_df[label] + 1) *
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(dk.data["labels_max"][label] -
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dk.data["labels_min"][label]) / 2) + dk.data["labels_min"][label]
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# logger.info("--------------------Finished prediction--------------------")
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return (dh.predictions, dh.do_predict)
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return (pred_df, dk.do_predict)
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@@ -0,0 +1,126 @@
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import logging
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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 sklearn.multioutput import MultiOutputRegressor
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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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logger = logging.getLogger(__name__)
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class CatboostPredictionMultiModel(IFreqaiModel):
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"""
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User created prediction model. The class needs to override three necessary
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functions, predict(), train(), fit(). The class inherits ModelHandler which
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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 return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
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"""
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User uses this function to add any additional return values to the dataframe.
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e.g.
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dataframe['volatility'] = dk.volatility_values
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"""
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return dataframe
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def train(self, unfiltered_dataframe: DataFrame,
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pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
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"""
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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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:params:
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:unfiltered_dataframe: Full dataframe for the current training period
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:metadata: pair metadata from strategy.
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:returns:
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:model: Trained model which can be used to inference (self.predict)
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"""
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logger.info('--------------------Starting training '
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f'{pair} --------------------')
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# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
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# filter the features requested by user in the configuration file and elegantly handle NaNs
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features_filtered, labels_filtered = dk.filter_features(
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unfiltered_dataframe,
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dk.training_features_list,
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dk.label_list,
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training_filter=True,
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)
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# split data into train/test data.
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data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
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dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
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# normalize all data based on train_dataset only
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data_dictionary = dk.normalize_data(data_dictionary)
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# optional additional data cleaning/analysis
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self.data_cleaning_train(dk)
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logger.info(f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
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' features')
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logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
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model = self.fit(data_dictionary)
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logger.info(f'--------------------done training {pair}--------------------')
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return model
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def fit(self, data_dictionary: Dict) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:params:
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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cbr = CatBoostRegressor(
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allow_writing_files=False, gpu_ram_part=0.5,
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verbose=100, early_stopping_rounds=400, **self.model_training_parameters
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)
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X = data_dictionary["train_features"]
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y = data_dictionary["train_labels"]
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# eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
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sample_weight = data_dictionary['train_weights']
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model = MultiOutputRegressor(estimator=cbr)
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model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
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return model
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def predict(self, unfiltered_dataframe: DataFrame,
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dk: FreqaiDataKitchen, first: bool = False) -> Tuple[DataFrame, DataFrame]:
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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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:return:
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:pred_df: dataframe containing the predictions
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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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dk.find_features(unfiltered_dataframe)
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filtered_dataframe, _ = dk.filter_features(
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unfiltered_dataframe, dk.training_features_list, training_filter=False
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)
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filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
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dk.data_dictionary["prediction_features"] = filtered_dataframe
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# optional additional data cleaning/analysis
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self.data_cleaning_predict(dk, filtered_dataframe)
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predictions = self.model.predict(dk.data_dictionary["prediction_features"])
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pred_df = DataFrame(predictions, columns=dk.label_list)
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for label in dk.label_list:
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pred_df[label] = ((pred_df[label] + 1) *
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(dk.data["labels_max"][label] -
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dk.data["labels_min"][label]) / 2) + dk.data["labels_min"][label]
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return (pred_df, dk.do_predict)
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@@ -18,37 +18,17 @@ class LightGBMPredictionModel(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 return_values(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
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dataframe["prediction"] = dh.full_predictions
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dataframe["do_predict"] = dh.full_do_predict
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dataframe["target_mean"] = dh.full_target_mean
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dataframe["target_std"] = dh.full_target_std
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
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dataframe["DI"] = dh.full_DI_values
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def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
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"""
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User uses this function to add any additional return values to the dataframe.
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e.g.
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dataframe['volatility'] = dk.volatility_values
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"""
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return 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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:dataframe: the full dataframe for the present training period
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"""
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dataframe["s"] = (
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dataframe["close"]
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.shift(-self.feature_parameters["period"])
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.rolling(self.feature_parameters["period"])
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.mean()
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/ dataframe["close"]
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- 1
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)
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return dataframe["s"]
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def train(self, unfiltered_dataframe: DataFrame,
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pair: str, dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
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pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
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"""
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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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@@ -62,27 +42,25 @@ class LightGBMPredictionModel(IFreqaiModel):
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logger.info('--------------------Starting training '
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f'{pair} --------------------')
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# create the full feature list based on user config info
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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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# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
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# filter the features requested by user in the configuration file and elegantly handle NaNs
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features_filtered, labels_filtered = dh.filter_features(
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features_filtered, labels_filtered = dk.filter_features(
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unfiltered_dataframe,
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dh.training_features_list,
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unfiltered_labels,
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dk.training_features_list,
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dk.label_list,
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training_filter=True,
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)
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# split data into train/test data.
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data_dictionary = dh.make_train_test_datasets(features_filtered, labels_filtered)
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dh.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
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data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
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dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
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# normalize all data based on train_dataset only
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data_dictionary = dh.normalize_data(data_dictionary)
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data_dictionary = dk.normalize_data(data_dictionary)
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# optional additional data cleaning/analysis
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self.data_cleaning_train(dh)
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self.data_cleaning_train(dk)
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logger.info(f'Training model on {len(dh.data_dictionary["train_features"].columns)}'
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logger.info(f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
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' features')
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logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
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@@ -112,7 +90,7 @@ class LightGBMPredictionModel(IFreqaiModel):
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return model
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def predict(self, unfiltered_dataframe: DataFrame,
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dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
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dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
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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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@@ -124,22 +102,22 @@ class LightGBMPredictionModel(IFreqaiModel):
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# logger.info("--------------------Starting prediction--------------------")
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original_feature_list = dh.find_features(unfiltered_dataframe)
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filtered_dataframe, _ = dh.filter_features(
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original_feature_list = dk.find_features(unfiltered_dataframe)
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filtered_dataframe, _ = dk.filter_features(
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unfiltered_dataframe, original_feature_list, training_filter=False
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)
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filtered_dataframe = dh.normalize_data_from_metadata(filtered_dataframe)
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dh.data_dictionary["prediction_features"] = filtered_dataframe
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filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
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dk.data_dictionary["prediction_features"] = filtered_dataframe
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# optional additional data cleaning/analysis
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self.data_cleaning_predict(dh, filtered_dataframe)
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self.data_cleaning_predict(dk, filtered_dataframe)
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predictions = self.model.predict(dh.data_dictionary["prediction_features"])
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predictions = self.model.predict(dk.data_dictionary["prediction_features"])
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pred_df = DataFrame(predictions, columns=dk.label_list)
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# compute the non-normalized predictions
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dh.predictions = (predictions + 1) * (dh.data["labels_max"] -
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dh.data["labels_min"]) / 2 + dh.data["labels_min"]
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for label in dk.label_list:
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pred_df[label] = ((pred_df[label] + 1) *
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(dk.data["labels_max"][label] -
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dk.data["labels_min"][label]) / 2) + dk.data["labels_min"][label]
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# logger.info("--------------------Finished prediction--------------------")
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return (dh.predictions, dh.do_predict)
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return (pred_df, dk.do_predict)
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