BERNN-MSMS

Minimal README for quick usage.

Longer historical content is kept in LEGACY_README.md.

Install

pip install bernn

Basic usage

from bernn import TrainAEClassifierHoldout

trainer_cls = TrainAEClassifierHoldout
trainer = trainer_cls(config=bernn_config, log_metrics=True, keep_models=False)

# Train and predict in one call
preds_encoded = trainer.fit_predict(
    X_train,
    y_train,
    X_test=X_test,
    y_test=y_test,
    groups_train=batches_train,
    groups_test=batches_test,
    cross_validation=False,
    cross_test=False,
)

# Decode predictions back to original labels
preds = trainer.predict(X_test)

Important runtime contract:

  • groups_train is mandatory.
  • If X_test is provided, groups_test is mandatory.

Important parameters

Focus on these first:

  • optimize_hyperparams: enable/disable Ax optimization.
  • n_trials: number of optimization trials.
  • fixed_hyperparams: force values and remove them from search.
  • n_repeats: number of holdout repeats.
  • n_layers, layer1: classifier depth and width seed.
  • dloss: domain loss mode.
  • warmup, n_epochs: core training schedule.
  • device: cpu/cuda target.
  • scaler, bs: preprocessing and batch size.

Official documentation

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.0.6)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for spell00 from gravatar.com spell00

Credits

Author: Simon Pelletier

License

MIT License (MIT)

Requires

Python >=3.11

Provides Extra

analysis analysis-with-ax core-extended deep-learning dev-tools development experiment-tracking external-tools full full-no-ax full-safe ide-tools minimal ml-full modern-web notebooks py311-plus py312-plus py313-plus python311-plus python313-minimal-safe python313-ml-minimal python313-ml-stable python313-safe r-integration special tools tools-with-ax typing web web-dev

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bernn-0.6.0.tar.gz (264.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

bernn-0.6.0-py3-none-any.whl (296.4 kB view details)

Uploaded Python 3

File details

Details for the file bernn-0.6.0.tar.gz.

File metadata

  • Download URL: bernn-0.6.0.tar.gz
  • Upload date:
  • Size: 264.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.12

File hashes

Hashes for bernn-0.6.0.tar.gz
Algorithm Hash digest
SHA256 2ce5e8397745f1655ffdd9790d9b5c4310d18aa6c8fa8dad0b983c0019e05da7
MD5 1e9cfe1551143ee1dd961314a9283f7e
BLAKE2b-256 5aa4975dfc03b87b42e1d4d6446227bde00b886af5e59c25afb9052c49791335

See more details on using hashes here.

File details

Details for the file bernn-0.6.0-py3-none-any.whl.

File metadata

  • Download URL: bernn-0.6.0-py3-none-any.whl
  • Upload date:
  • Size: 296.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.12

File hashes

Hashes for bernn-0.6.0-py3-none-any.whl
Algorithm Hash digest
SHA256 4a083d739b324d2b02b7175086f8c611d5b8913215368147c1ac660f2f9e3105
MD5 876b9bfad4cda8dbde0757680a3076ae
BLAKE2b-256 5873c500463eaeb35cfa5b06cd056891ee4f8ebdee5cf0efee7034ddc273ecd3

See more details on using hashes here.

Release history Release notifications | RSS feed

1.0.6

2 files

1.0.5

2 files

1.0.4

2 files

1.0.3

2 files

1.0.2

2 files

0.6.28

2 files

0.6.27

2 files

0.6.26

2 files

0.6.25

2 files

0.6.24

2 files

0.6.23

2 files

0.6.22

2 files

0.6.21

2 files

0.6.20

2 files

0.6.19

2 files

0.6.18

2 files

0.6.17

2 files

0.6.16

2 files

0.6.15

2 files

0.6.14

2 files

0.6.13

2 files

0.6.12

2 files

0.6.11

2 files

0.6.10

2 files

0.6.9

2 files

0.6.8

2 files

0.6.7

2 files

0.6.4

2 files

0.6.3

2 files

0.6.2

2 files

0.6.1

2 files

This release

0.6.0 This release

2 files

0.5.8

2 files

0.5.7

2 files

0.5.6

2 files

0.5.5

2 files

0.5.4

2 files

0.5.3

2 files

0.5.2

2 files

0.5.1

2 files

0.5.0

2 files

0.4.12

2 files

0.4.10

2 files

0.4.9

2 files

0.4.8

2 files

0.4.7

2 files

0.4.6

2 files

0.4.5

2 files

0.4.4

2 files

0.4.3

2 files

0.4.2

2 files

0.4.1

2 files

0.4.0

2 files

0.3.23

2 files

0.3.22

2 files

0.3.21

2 files

0.3.20

2 files

0.3.19

2 files

0.3.18

2 files

0.3.17

2 files

0.3.16

2 files

0.3.15

2 files

0.3.14

2 files

0.3.13

2 files

0.3.12

2 files

0.3.11

2 files

0.3.10

2 files

0.3.9

2 files

0.3.8

2 files

0.3.7

2 files

0.3.6

2 files

0.3.5

2 files

0.3.4

2 files

0.3.3

2 files

0.3.2

2 files

0.3.1

2 files

0.3.0

2 files

0.2.21

2 files

0.2.20

2 files

0.2.19

2 files

0.2.18

2 files

0.2.17

2 files

0.2.16

2 files

0.2.15

2 files

0.2.14

2 files

0.2.13

2 files

0.2.12

2 files

0.2.11

2 files

0.2.10

2 files

0.2.9

2 files

0.2.8

2 files

0.2.7

2 files

0.2.6

2 files

0.2.5

2 files

0.2.4

2 files

0.2.3

2 files

0.2.2

2 files

0.2.1

2 files

0.2.0

2 files

0.1.18

2 files

0.1.17

2 files

0.1.16

2 files

0.1.15

2 files

0.1.14

2 files

0.1.13

2 files

0.1.12

2 files

0.1.11

2 files

0.1.10

2 files

0.1.9

2 files

0.1.8

2 files

0.1.7

2 files

0.1.6

2 files

0.1.5

2 files

0.1.4

1 file

0.1.3

2 files

0.1.2

2 files

0.1.1

1 file

0.1.0

1 file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page