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Releases: analokmaus/kuma_utils

v0.7.0

13 Jan 07:10
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What's new

  • Renewed implementation of TorchLogger and LGBMLogger. The new implementation uses Python's logging module to handle stream output and file writing. The arguments and usage remain the same as before.

v0.7.0b

13 Jan 05:49
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v0.7.0b Pre-release
Pre-release
update settings related to rye

v0.6.2

12 Nov 07:35
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What's new

  • Updated pytorch dependencies for security reasons

v0.6.1

12 Nov 06:44
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What's new

  • Updated dependencies

v0.6.0

31 Oct 03:57
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What's New

  • TorchTrainer can now accept models in which loss functions are included (e.g., ArcFace, LLMs). You must pass criterion = None to the TorchTrainer.train() and calculate the loss inside your training hook.

Sample code

def forward_train(self, trainer, inputs):
    embeddings, loss = trainer.model.*inputs)
    return loss, embeddings.detach()

v0.5.1

24 Oct 05:42
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What's new

  • fixed the implementation of make_demographic_table() in kuma_utils.stats to hide the first category when the variable is binary.
  • added loveplot() to visualize the balance of variables before/after the propensity score matching.
  • Examples

v0.5.0

20 May 10:06
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What's new

  • The backpropagation part can now be freely customized, enabling support for a wider range of learning tasks.
  • See implementation of TrainHook

v0.4.2

25 Feb 06:58
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What's new

  • Wandb run name will be set to TorchTrainer.serial when it is not specified.

v0.4.1

21 Feb 00:37
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What's new

  • Bugfixes for TorchTrainer
  • Wandb run name will be TorchTrainer.serial by default. Specify name in wandb_params to override.

v0.4.0

14 Feb 06:30
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Update

LGBMImputer

  • Cross validation support (see tutorial notebook)
  • New argument target_cols, in case you want to fit columns with no missing values.
  • Internal label encoder is added. Now you don't have to encode categorical variables in advance.

TemperatureScaler

  • Moved to kuma_utils.torch.modules.calibration

Others

  • Bugfixes
0