mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 10:21:59 +00:00
89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
import logging
|
|
from typing import Any, Dict, Tuple
|
|
|
|
import numpy as np
|
|
import numpy.typing as npt
|
|
import pandas as pd
|
|
from pandas import DataFrame
|
|
from pandas.api.types import is_integer_dtype
|
|
from sklearn.preprocessing import LabelEncoder
|
|
from xgboost import XGBRFClassifier
|
|
|
|
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
|
|
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class XGBoostRFClassifier(BaseClassifierModel):
|
|
"""
|
|
User created prediction model. The class inherits IFreqaiModel, which
|
|
means it has full access to all Frequency AI functionality. Typically,
|
|
users would use this to override the common `fit()`, `train()`, or
|
|
`predict()` methods to add their custom data handling tools or change
|
|
various aspects of the training that cannot be configured via the
|
|
top level config.json file.
|
|
"""
|
|
|
|
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
|
|
"""
|
|
User sets up the training and test data to fit their desired model here
|
|
:param data_dictionary: the dictionary holding all data for train, test,
|
|
labels, weights
|
|
:param dk: The datakitchen object for the current coin/model
|
|
"""
|
|
|
|
X = data_dictionary["train_features"].to_numpy()
|
|
y = data_dictionary["train_labels"].to_numpy()[:, 0]
|
|
|
|
le = LabelEncoder()
|
|
if not is_integer_dtype(y):
|
|
y = pd.Series(le.fit_transform(y), dtype="int64")
|
|
|
|
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
|
|
eval_set = None
|
|
else:
|
|
test_features = data_dictionary["test_features"].to_numpy()
|
|
test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
|
|
|
|
if not is_integer_dtype(test_labels):
|
|
test_labels = pd.Series(le.transform(test_labels), dtype="int64")
|
|
|
|
eval_set = [(test_features, test_labels)]
|
|
|
|
train_weights = data_dictionary["train_weights"]
|
|
|
|
init_model = self.get_init_model(dk.pair)
|
|
|
|
model = XGBRFClassifier(**self.model_training_parameters)
|
|
|
|
model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights, xgb_model=init_model)
|
|
|
|
return model
|
|
|
|
def predict(
|
|
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
|
|
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
|
|
"""
|
|
Filter the prediction features data and predict with it.
|
|
:param unfiltered_df: Full dataframe for the current backtest period.
|
|
:return:
|
|
: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)
|
|
"""
|
|
|
|
(pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
|
|
|
|
le = LabelEncoder()
|
|
label = dk.label_list[0]
|
|
labels_before = list(dk.data["labels_std"].keys())
|
|
labels_after = le.fit_transform(labels_before).tolist()
|
|
pred_df[label] = le.inverse_transform(pred_df[label])
|
|
pred_df = pred_df.rename(
|
|
columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))}
|
|
)
|
|
|
|
return (pred_df, dk.do_predict)
|