9394559170

Committed 26 Apr 2024 06:36AM UTC coverage: 94.656% (-0.02%) from 94.674%

Build # 9394559170

Build Type

push

github

Committed by

xmatthias

Commit Message

Loader should be passed as kwarg for clarity

Run Details

20280 of 21425 relevant lines covered (94.66%)

0.95 hits per line

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92.68

/freqtrade/freqai/tensorboard/tensorboard.py

import logging
from pathlib import Path
from typing import Any

from torch.utils.tensorboard import SummaryWriter
from xgboost import callback

from freqtrade.freqai.tensorboard.base_tensorboard import (BaseTensorBoardCallback,
                                                           BaseTensorboardLogger)


logger = logging.getLogger(__name__)


class TensorboardLogger(BaseTensorboardLogger):
    def __init__(self, logdir: Path, activate: bool = True):
        self.activate = activate
        if self.activate:
            self.writer: SummaryWriter = SummaryWriter(f"{str(logdir)}/tensorboard")

    def log_scalar(self, tag: str, scalar_value: Any, step: int):
        if self.activate:
            self.writer.add_scalar(tag, scalar_value, step)

    def close(self):
        if self.activate:
            self.writer.flush()
            self.writer.close()


class TensorBoardCallback(BaseTensorBoardCallback):

    def __init__(self, logdir: Path, activate: bool = True):
        self.activate = activate
        if self.activate:
            self.writer: SummaryWriter = SummaryWriter(f"{str(logdir)}/tensorboard")

    def after_iteration(
        self, model, epoch: int, evals_log: callback.TrainingCallback.EvalsLog
    ) -> bool:
        if not self.activate:
            return False
        if not evals_log:
            return False

        evals = ["validation", "train"]
        for metric, eval in zip(evals_log.items(), evals):
            for metric_name, log in metric[1].items():
                score = log[-1][0] if isinstance(log[-1], tuple) else log[-1]
                self.writer.add_scalar(f"{eval}-{metric_name}", score, epoch)

        return False

    def after_training(self, model):
        if not self.activate:
            return model
        self.writer.flush()
        self.writer.close()

        return model

1	import logging	1✔
2	from pathlib import Path	1✔
3	from typing import Any	1✔
4
5	from torch.utils.tensorboard import SummaryWriter	1✔
6	from xgboost import callback	1✔
7
8	from freqtrade.freqai.tensorboard.base_tensorboard import (BaseTensorBoardCallback,	1✔
9	BaseTensorboardLogger)
10
11
12	logger = logging.getLogger(__name__)	1✔
13
14
15	class TensorboardLogger(BaseTensorboardLogger):	1✔
16	def __init__(self, logdir: Path, activate: bool = True):	1✔
17	self.activate = activate	1✔
18	if self.activate:	1✔
19	self.writer: SummaryWriter = SummaryWriter(f"{str(logdir)}/tensorboard")	1✔
20
21	def log_scalar(self, tag: str, scalar_value: Any, step: int):	1✔
22	if self.activate:	1✔
23	self.writer.add_scalar(tag, scalar_value, step)	1✔
24
25	def close(self):	1✔
26	if self.activate:	1✔
27	self.writer.flush()	1✔
28	self.writer.close()	1✔
29
30
31	class TensorBoardCallback(BaseTensorBoardCallback):	1✔
32
33	def __init__(self, logdir: Path, activate: bool = True):	1✔
34	self.activate = activate	1✔
35	if self.activate:	1✔
36	self.writer: SummaryWriter = SummaryWriter(f"{str(logdir)}/tensorboard")	1✔
37
38	def after_iteration(	1✔
39	self, model, epoch: int, evals_log: callback.TrainingCallback.EvalsLog
40	) -> bool:
41	if not self.activate:	1✔
42	return False	×
43	if not evals_log:	1✔
44	return False	×
45
46	evals = ["validation", "train"]	1✔
47	for metric, eval in zip(evals_log.items(), evals):	1✔
48	for metric_name, log in metric[1].items():	1✔
49	score = log[-1][0] if isinstance(log[-1], tuple) else log[-1]	1✔
50	self.writer.add_scalar(f"{eval}-{metric_name}", score, epoch)	1✔
51
52	return False	1✔
53
54	def after_training(self, model):	1✔
55	if not self.activate:	1✔
56	return model	×
57	self.writer.flush()	1✔
58	self.writer.close()	1✔
59
60	return model	1✔

freqtrade / freqtrade / 9394559170

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