86 lines
3.1 KiB
Python
86 lines
3.1 KiB
Python
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import logging
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from typing import Any, Dict, Tuple
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import numpy as np
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import numpy.typing as npt
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import pandas as pd
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from pandas import DataFrame
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from pandas.api.types import is_integer_dtype
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from sklearn.preprocessing import LabelEncoder
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from xgboost import XGBRFClassifier
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from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class XGBoostRFClassifier(BaseClassifierModel):
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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, dk: FreqaiDataKitchen, **kwargs) -> 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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X = data_dictionary["train_features"].to_numpy()
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y = data_dictionary["train_labels"].to_numpy()[:, 0]
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le = LabelEncoder()
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if not is_integer_dtype(y):
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y = pd.Series(le.fit_transform(y), dtype="int64")
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if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
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eval_set = None
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else:
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test_features = data_dictionary["test_features"].to_numpy()
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test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
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if not is_integer_dtype(test_labels):
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test_labels = pd.Series(le.transform(test_labels), dtype="int64")
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eval_set = [(test_features, test_labels)]
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train_weights = data_dictionary["train_weights"]
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init_model = self.get_init_model(dk.pair)
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model = XGBRFClassifier(**self.model_training_parameters)
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model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
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xgb_model=init_model)
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return model
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def predict(
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self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
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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_df: 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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(pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
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le = LabelEncoder()
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label = dk.label_list[0]
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labels_before = list(dk.data['labels_std'].keys())
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labels_after = le.fit_transform(labels_before).tolist()
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pred_df[label] = le.inverse_transform(pred_df[label])
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pred_df = pred_df.rename(
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columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))})
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return (pred_df, dk.do_predict)
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