VerticaPy Versions Save

VerticaPy is a Python library that exposes sci-kit like functionality to conduct data science projects on data stored in Vertica, thus taking advantage Vertica’s speed and built-in analytics and machine learning capabilities.

1.0.3

1 month ago

This minor release has some chagnes that focus on improving the QueryProfiler and QueryProfilerInterface.

Some main changes are:

  • Improving the speed of the function
  • Enhancing the UI
  • Adding limitations to prevent overwriting of data when importing a file

Release Notes

Changelogs

Installation

The release will be on available on the defaults and can be installed using:

python3 -m pip install verticapy

If you want to install extra features, use:

python3 -m pip install verticapy[all]

Please report any issues on our GitHub page

Contributors

We would like to extend our thanks to all the contributors who made this release possible:

  • Badr Ouali @oualib
  • Umar Farooq Ghumman @mail4umar
  • Daniel Mickens @DMickens

If you would like to contribute then please visit our updated contributing guidelines.

1.0.2

2 months ago

This minor release has some significant feature additions with other changes. Some salient ones are listed below:

:warning: Please note that this list may be incomplete, and for a comprehensive overview, including additional features, refer to the changelogs.

Pipelines (Beta)

  • Pipelines is a YAML-based configuration for defining machine learning workflows, simplifying the process of setting up and managing machine learning pipelines.
  • For beginners, it provides an easy-to-learn alternative to Python and SQL reducing the initial barriers to entry for creating models.
  • For more experienced users, it offers templating features to enhance modularity, minimize errors, and promote efficient code reuse in machine learning projects.

Performance

  • We have enhanced the QueryProfiler to improve its robustness.
  • Introducing a completely new Query Profiler Interface, enabling users to navigate through various queries and access them without the need to re-enter all the code. All of this can be accomplished using only your mouse within Jupyter Notebook environments.

These updates significantly enhance the accessibility, debugging, and enhancement capabilities of your queries.

OAuth Refresh Tokens (Beta)

  • We have updated the connector to accept OAuth refresh tokens.
  • Additioanlly we have added a prompt option for verticapy.connection.new_connection. This allows the user to enter the secrets discretly with a masked display.

Multi-TimeSeries (Beta)

We added a new Time Series class: TimeSeriesByCategory. This allows the users to build multiple models based off on a category. The number of models created are equal to the categories. This saves users time to create multiple models separately. For more information please see verticapy.machine_learning.vertica.tsa.ensemble.TimeSeriesByCategory.

Plots

  • Two new plots have been added for plotly that were previously missing:

    • verticapy.machine_learning.vertica.decomposition.plot_scree
    • verticapy.machine_learning.vertica.decomposition.plot_var

Unit Tests

  • We continue to shift our old tests to the new more robust format.

Examples

  • Most of the examples <https://github.com/vertica/VerticaPy/tree/master/examples>_ have been updated with the latest verticapy format.

Release Notes

Changelogs

Installation

The release will be on available on the defaults and can be installed using:

python3 -m pip install verticapy

If you want to install extra features, use:

python3 -m pip install verticapy[all]

Please report any issues on our GitHub page

Contributors

We would like to extend our thanks to all the contributors who made this release possible:

  • Badr Ouali @oualib
  • Umar Farooq Ghumman @mail4umar
  • Vikash Singh @vikash018
  • Zachary Nowak @zacandcheese
  • Arash Fard @afard
  • Abhishek Sharma @abhsharma2
  • Tyler Consigny @tconsigny
  • Jason Slaunwhite @jslaunwhite-microfocus

If you would like to contribute then please visit our updated contributing guidelines.

1.0.1

4 months ago

This release includes numerous modifications to the previous code aimed at enhancing the user experience.

:warning: Please note that this list may be incomplete, and for a comprehensive overview, including additional features, refer to the changelogs.

New Options

The verticapy.set_option() function now allows you to set the following options:

  • max_cellwidth: Maximum width of VerticaPy table cells.
  • max_tableheight: Maximum height of VerticaPy tables.
  • theme: Set the display theme for VerticaPy objects to 'light' or 'dark'.

QueryProfiler

The verticapy.performance.vertica.qprof.QueryProfiler class offers an extended set of functionalities, enabling the creation of complex trees with multiple metrics. This can help in finding ways to improve the performance of slow-running queries.

Website

A new website is now available. It includes all the important information about the different changes and a totally new documentation generated by Sphinx. Check it out here

Docstrings

Docstrings have been further enriched with relevant examples for more functions.

Release Notes

Changelogs

Installation

The release will be on available on the defaults and can be installed using:

python3 -m pip install verticapy

If you want to install extra features, use:

python3 -m pip install verticapy[all]

Please report any issues on our GitHub page

Contributors

We would like to extend our thanks to all the contributors who made this release possible:

  • Badr Ouali @oualib
  • Umar Farooq Ghumman @mail4umar
  • Arash Fard @afard
  • Abhishek Sharma @abhsharma2
  • Vikash Singh @vikash018
  • Tyler Consigny @tconsigny
  • Jason Slaunwhite @jslaunwhite-microfocus

If you would like to contribute then please visit our updated contributing guidelines.

1.0.0

5 months ago

This is our very first major release, which includes numerous modifications to the previous code aimed at enhancing the user experience.

:warning: Please note that this list may be incomplete, and for a comprehensive overview, including additional features, refer to the changelogs.

CI/CD

The CI/CD pipeline is now more comprehensive, incorporating the following:

  • Integration of Black for code formatting.
  • Inclusion of Pylint to assess code robustness with a minimal threshold, ensuring code can be merged.
  • Implementation of Pytest for optimized Unit Tests, with results compared to standard libraries such as sklearn or scipy.
  • Integration of Codecov to monitor code coverage.
  • Implementation of GitHub actions across different Python versions (3.9-3.12) to ensure support for all of them.

New Features

Some of the highlights of this release are:

Algorithms

  • IsolationForest
  • KPrototypes
  • Poisson Regression
  • AutoRegressive (AR)
  • MovingAverages (MA)
  • AutoRegressive Moving Averages (ARMA)
  • AutoRegressive Integrated Moving Averages (ARIMA)
  • Term Frequency * Inverse Document Frequency (TFIDF). It is still beta.

ML New features

  • New function for finding the feature importance for XGBoost models.
  • Classification metrics are now available for multiclass data/model using three methods: micro, macro, weighted, score and none. - average_precision_score is another new metric that is added to classification metrics. - roc_auc and prc_auc now work for multi-class classification using different averaging techniques stated above.
  • Model names are now optional
  • Model Tracking and Versioning now supported. Check out notebooks/ml/model_tracking_versioning/index.ipynb for more details.
  • Model Export and Import: Now models can be exported to pmml, tensorflow, and binary. They can now be exported to another User Defined Location.
  • Added Model Versioning / Tracking (MLOps)

SQL

  • vDataFramesSQL is deprecated. Now, vDataFrame can be used directly to create vDataFrames from SQL.

Plotting

  • Flexibility to select from the three renowned plotting libraries for professional plots: Matplotlib, Highcharts, Plotly.

Library Hierarchy

  • Import structures have changed. The code has been completely restructured, which means that going forward all imports will be done differently. Currently, we still allow the previous structure of import, but it will gradually be deprecated.

Website

A new website will be soon available. It will include all the important information about the different changes and a totally new documentation generated by Sphinx.

Release Notes

Changelogs

Installation

The release will be on available on the defaults and can be installed using:

python3 -m pip install verticapy

If you want to install extra features, use:

python3 -m pip install verticapy[all]

Please report any issues on our GitHub page

Contributors

We would like to extend our thanks to all the contributors who made this release possible:

  • Badr Ouali @oualib
  • Umar Farooq Ghumman @mail4umar
  • Arash Fard @afard
  • Tyler Consigny @tconsigny
  • Vikash Singh @vikash018
  • Abhishek Sharma @abhsharma2
  • Matteo Monaldi @mat-shyR
  • Abdelhak Zabour @VerticaDataScientist

If you would like to contribute then please visit our updated contributing guidelines.

1.0.0-beta.2

10 months ago

This is our very first beta release, which includes numerous modifications to the previous code aimed at enhancing the user experience.

Some of the highlights of this release are:

  • Flexibility to select from the three renowned potting libraries for professional plots.
  • The introduction of a wide range of classification metrics.
  • Support for KPrototypes.
  • Furthermore, due to code restructuring, the import syntax has changed and is now more intuitive.

For a comprehensive list of all the changes, please refer to the change log.

The release will be on available on the defaults and can be installed using:

python3 -m pip install verticapy==1.0.0b2

Please report any issues on our GitHub page

We would like to extend our thanks to all the contributors who made this release possible:

  • Badr Ouali @oualib
  • Umar Farooq Ghumman @mail4umar
  • Arash Fard @afard
  • Tyler Consigny @tconsigny
  • Vikash Singh @vikash018

If you would like to contribute then please visit our updated contributing guidelines.

What's Changed

Full Changelog: https://github.com/vertica/VerticaPy/compare/1.0.0-beta.1...1.0.0-beta.2

1.0.0-beta.1

10 months ago

This is our very first beta release, which includes numerous modifications to the previous code aimed at enhancing the user experience.

Some of the highlights of this release are:

Flexibility to select from the three renowned potting libraries for professional plots. The introduction of a wide range of classification metrics. Support for KPrototypes. Furthermore, due to code restructuring, the import syntax has changed and is now more intuitive.

For a comprehensive list of all the changes, please refer to the change log.

The release will be on available on the defaults and can be install using:

python3 -m pip install verticapy==1.0.0b1

Please report any issues on our GitHub page

We would like to extend our thanks to all the contributors who made this release possible:

Badr Ouali @oualib Umar Farooq Ghumman @mail4umar Arash Fard @afard Tyler Consigny @tconsigny Vikash Singh @vikash018 If you would like to contribute then please visit our updated contributing guidelines.

1.0.0-beta

11 months ago

This is our very first beta release, which includes numerous modifications to the previous code aimed at enhancing the user experience.

Some of the highlights of this release are:

  • Flexibility to select from the three renowned potting libraries for professional plots.
  • The introduction of a wide range of classification metrics.
  • Support for KPrototypes.

Furthermore, due to code restructuring, the import syntax has changed and is now more intuitive.

For a comprehensive list of all the changes, please refer to the change log.

The release will be on available on the defaults and can be install using:

python3 -m pip install verticapy

Please report any issues on our GitHub page

We would like to extend our thanks to all the contributors who made this release possible:

  • Badr Ouali @oualib
  • Umar Farooq Ghumman @mail4umar
  • Arash Fard @afard
  • Tyler Consigny @tconsigny
  • Vikash Singh @vikash018

If you would like to contribute then please visit our updated contributing guidelines.

0.12.0

1 year ago

Documentation: https://www.vertica.com/python/documentation-0.12.x/

0.11.0

1 year ago

Documentation: https://www.vertica.com/python/documentation-0.11.x/

0.10.2

1 year ago

Documentation: https://www.vertica.com/python/documentation-0.10.x/