6181253459

Committed 08 Sep 2023 06:04AM UTC coverage: 94.614% (+0.06%) from 94.556%

Build # 6181253459

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push

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Committed by

web-flow

Commit Message

Merge pull request #9159 from stash86/fix-adjust

remove old codes when we only can do partial entries

Run Details

2 of 2 new or added lines in 1 file covered. (100.0%)

19114 of 20202 relevant lines covered (94.61%)

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91.3

/freqtrade/freqai/prediction_models/XGBoostRFRegressor.py

import logging
from typing import Any, Dict

from xgboost import XGBRFRegressor

from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.tensorboard import TBCallback


logger = logging.getLogger(__name__)


class XGBoostRFRegressor(BaseRegressionModel):
    """
    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"]
        y = data_dictionary["train_labels"]

        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
            eval_set = None
            eval_weights = None
        else:
            eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
            eval_weights = [data_dictionary['test_weights']]

        sample_weight = data_dictionary["train_weights"]

        xgb_model = self.get_init_model(dk.pair)

        model = XGBRFRegressor(**self.model_training_parameters)

        model.set_params(callbacks=[TBCallback(dk.data_path)], activate=self.activate_tensorboard)
        model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
                  sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
        # set the callbacks to empty so that we can serialize to disk later
        model.set_params(callbacks=[])

        return model

1	import logging	1✔
2	from typing import Any, Dict	1✔
3
4	from xgboost import XGBRFRegressor	1✔
5
6	from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel	1✔
7	from freqtrade.freqai.data_kitchen import FreqaiDataKitchen	1✔
8	from freqtrade.freqai.tensorboard import TBCallback	1✔
9
10
11	logger = logging.getLogger(__name__)	1✔
12
13
14	class XGBoostRFRegressor(BaseRegressionModel):	1✔
15	"""
16	User created prediction model. The class inherits IFreqaiModel, which
17	means it has full access to all Frequency AI functionality. Typically,
18	users would use this to override the common `fit()`, `train()`, or
19	`predict()` methods to add their custom data handling tools or change
20	various aspects of the training that cannot be configured via the
21	top level config.json file.
22	"""
23
24	def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:	1✔
25	"""
26	User sets up the training and test data to fit their desired model here
27	:param data_dictionary: the dictionary holding all data for train, test,
28	labels, weights
29	:param dk: The datakitchen object for the current coin/model
30	"""
31
32	X = data_dictionary["train_features"]	1✔
33	y = data_dictionary["train_labels"]	1✔
34
35	if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:	1✔
36	eval_set = None	×
37	eval_weights = None	×
38	else:
39	eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]	1✔
40	eval_weights = [data_dictionary['test_weights']]	1✔
41
42	sample_weight = data_dictionary["train_weights"]	1✔
43
44	xgb_model = self.get_init_model(dk.pair)	1✔
45
46	model = XGBRFRegressor(**self.model_training_parameters)	1✔
47
48	model.set_params(callbacks=[TBCallback(dk.data_path)], activate=self.activate_tensorboard)	1✔
49	model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,	1✔
50	sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
51	# set the callbacks to empty so that we can serialize to disk later
52	model.set_params(callbacks=[])	1✔
53
54	return model	1✔

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