75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
import logging
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from typing import Any, Dict, Tuple
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import pandas as pd
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from pandas import DataFrame
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from catboost import CatBoostClassifier, Pool
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import numpy.typing as npt
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import numpy as np
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from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class CatboostClassifier(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 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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train_data = Pool(
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data=data_dictionary["train_features"],
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label=data_dictionary["train_labels"],
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weight=data_dictionary["train_weights"],
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)
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cbr = CatBoostClassifier(
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allow_writing_files=False,
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loss_function='MultiClass',
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**self.model_training_parameters,
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)
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cbr.fit(train_data)
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return cbr
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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, npt.NDArray[np.int_]]:
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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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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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predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"])
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pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_)
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pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
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return (pred_df, dk.do_predict)
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