freqtrade_origin/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py
lolong ed0f8b1189
Improve FreqAI documentation (#7072)
Improve doc + some other small fixes

Co-authored-by: robcaulk <rob.caulk@gmail.com>
2022-07-18 11:57:52 +02:00

47 lines
1.6 KiB
Python

import logging
from typing import Any, Dict
from catboost import CatBoostRegressor # , Pool
from sklearn.multioutput import MultiOutputRegressor
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
logger = logging.getLogger(__name__)
class CatboostPredictionMultiModel(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
cbr = CatBoostRegressor(
allow_writing_files=False,
gpu_ram_part=0.5,
verbose=100,
early_stopping_rounds=400,
**self.model_training_parameters,
)
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
sample_weight = data_dictionary["train_weights"]
model = MultiOutputRegressor(estimator=cbr)
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
train_score = model.score(X, y)
test_score = model.score(*eval_set)
logger.info(f"Train score {train_score}, Test score {test_score}")
return model