Create BaseRegression model - designed to reduce code duplication across currently available models.
This commit is contained in:
Matthias
parent
607455919e
commit
3fc92b1b21
104
freqtrade/freqai/prediction_models/BaseRegressionModel.py
Normal file
104
freqtrade/freqai/prediction_models/BaseRegressionModel.py
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@ -0,0 +1,104 @@
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import logging
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from typing import Tuple
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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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logger = logging.getLogger(__name__)
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class BaseRegressionModel(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(
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self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
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) -> 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 datakitchen
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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 " f"{pair} --------------------")
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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(
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f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
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)
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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 predict(
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self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
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) -> 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] = (
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(pred_df[label] + 1)
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* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
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/ 2
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) + dk.data["labels_min"][label]
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return (pred_df, dk.do_predict)
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@ -1,75 +1,21 @@
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import logging
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from typing import Any, Dict, Tuple
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from typing import Any, Dict
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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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from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
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logger = logging.getLogger(__name__)
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class CatboostPredictionModel(IFreqaiModel):
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class CatboostPredictionModel(BaseRegressionModel):
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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(
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self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
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) -> 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 datakitchen
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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 " f"{pair} --------------------")
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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(
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f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
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)
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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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@ -99,37 +45,3 @@ class CatboostPredictionModel(IFreqaiModel):
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model.fit(X=train_data, eval_set=test_data)
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return model
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def predict(
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self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
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) -> 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] = (
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(pred_df[label] + 1)
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* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
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/ 2
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) + dk.data["labels_min"][label]
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return (pred_df, dk.do_predict)
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@ -1,77 +1,22 @@
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import logging
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from typing import Any, Dict, Tuple
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from typing import Any, Dict
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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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from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
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logger = logging.getLogger(__name__)
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class CatboostPredictionMultiModel(IFreqaiModel):
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class CatboostPredictionMultiModel(BaseRegressionModel):
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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(
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self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
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) -> 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 " 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(
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f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
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)
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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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@ -97,37 +42,3 @@ class CatboostPredictionMultiModel(IFreqaiModel):
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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(
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self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
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) -> 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] = (
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(pred_df[label] + 1)
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* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
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/ 2
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) + dk.data["labels_min"][label]
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return (pred_df, dk.do_predict)
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@ -1,76 +1,21 @@
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import logging
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from typing import Any, Dict, Tuple
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from typing import Any, Dict
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from lightgbm import LGBMRegressor
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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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from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
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logger = logging.getLogger(__name__)
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class LightGBMPredictionModel(IFreqaiModel):
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class LightGBMPredictionModel(BaseRegressionModel):
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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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|
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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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|
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return dataframe
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def train(
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self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
|
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) -> 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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|
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logger.info("--------------------Starting training " f"{pair} --------------------")
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|
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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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|
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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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|
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# optional additional data cleaning/analysis
|
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self.data_cleaning_train(dk)
|
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|
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logger.info(
|
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f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
|
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)
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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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Most regressors use the same function names and arguments e.g. user
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@ -89,37 +34,3 @@ class LightGBMPredictionModel(IFreqaiModel):
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model.fit(X=X, y=y, eval_set=eval_set)
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return model
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def predict(
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self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
|
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) -> 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
|
||||
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
||||
data (NaNs) or felt uncertain about data (PCA and DI index)
|
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"""
|
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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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|
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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)
|
||||
* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
|
||||
/ 2
|
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) + dk.data["labels_min"][label]
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return (pred_df, dk.do_predict)
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||||
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