Skip to main content

Библиотека для расчета минимального определяемого эффекта (MDE) в A/B тестах.

Project description

medoed

medoed

Библиотека для расчета минимального определяемого эффекта (MDE) в A/B тестах.

Установка

pip install medoed

Пример использования

from medoed import MDECalculator

mde_calculator = MDECalculator(
    df=pre_experiment_data,
    date_field='install_date',
    metrics=['revenue', 'retention'],
    df_historical=historical_data,
    strata=['geo', 'os'],
    alpha=0.05,
    power=0.8,
    outliers_handling_method='replace_threshold',
    outliers_threshold_quantile=0.995,
    outlier_type='upper',
    test_days=30,
    nobs=10000
)

df_mde = mde_calculator.calculate(n_processes=8)
fig = mde_calculator.create_mde_plot(df_mde)
fig.show()

Требования

  • Python 3.8+
  • pandas 1.3+
  • numpy 1.20+
  • scipy 1.7+
  • statsmodels 0.13+
  • otvertka 0.1.6+
  • tqdm 4.65+

Лицензия

MIT

Project details


Download files

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

Source Distribution

medoed-0.1.0.tar.gz (270.1 kB view details)

Uploaded Source

Built Distribution

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

medoed-0.1.0-py3-none-any.whl (8.2 kB view details)

Uploaded Python 3

File details

Details for the file medoed-0.1.0.tar.gz.

File metadata

  • Download URL: medoed-0.1.0.tar.gz
  • Upload date:
  • Size: 270.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.13.0

File hashes

Hashes for medoed-0.1.0.tar.gz
Algorithm Hash digest
SHA256 fcd84c9de59b09582b77dd3f51b5c8ed3a2a8d8994e839a6d3c4454c6e76bc23
MD5 b80d63296d7c97f98e24133e34840502
BLAKE2b-256 a395a1d877bf12bc895ecfe047fba79613c543808868d48aec5b09851864edda

See more details on using hashes here.

File details

Details for the file medoed-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: medoed-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 8.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.13.0

File hashes

Hashes for medoed-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 7bb3f237437474270f5d46dfc251af6522616896503ce4807efa6b5f776e708f
MD5 d5c7c170e4e957666f86c265b30c20a5
BLAKE2b-256 01eb0295a6c425ebf6b805a448f4d4d3181865a3c49091767e84009ccd4dc300

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page