diff --git a/README.md b/README.md index fa22590..c1e00eb 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@

- +

> **Beta:** VAST Orbit `0.1.x` is the first beta release series. The API and features will change as we work toward a stable `1.0.0`. See [Project Status & Roadmap](#project-status--roadmap). @@ -13,16 +13,12 @@ [![codecov](https://codecov.io/gh/vast-data/vastorbit/branch/main/graph/badge.svg?token=a6GiFYI9at)](https://codecov.io/gh/vast-data/vastorbit) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![linting: pylint](https://img.shields.io/badge/linting-pylint-yellowgreen)](https://github.com/pylint-dev/pylint) +[![Docs](https://img.shields.io/badge/docs-vast--data.github.io-1FD9FE)](https://vast-data.github.io/Orbit/) [![Slack](https://img.shields.io/badge/Slack-VAST%20Support-4A154B?logo=slack&logoColor=white)](https://vastsupport.slack.com) ## Trailer Video -

- - VAST Orbit — Trailer (click to watch) - -

+

▶ Watch the trailer

VAST Orbit is a Python library with scikit-learn-like functionality for conducting data science projects on data stored in VAST DataBase. Train models using familiar ``scikit-learn`` syntax and deploy them directly in the database, leveraging VAST's high-performance analytics capabilities. VAST Orbit offers robust support for the entire data science life cycle, uses a 'pipeline' mechanism to sequentialize data transformation operations, and provides beautiful graphical options. @@ -61,7 +57,7 @@ Python has become the lingua franca of data science, offering unparalleled flexi

- +

## Project Status & Roadmap @@ -132,7 +128,7 @@ help(vo.VastFrame) Documentation can be generated locally. Refer to the documentation generation guide in the `docs/` directory. -Official documentation will be available soon at a dedicated documentation site. +Full documentation, API reference, and examples are available at [vast-data.github.io/Orbit](https://vast-data.github.io/Orbit/). ## Highlighted Features @@ -144,14 +140,14 @@ Dark mode, ideal for extended coding sessions, features a sleek and stylish dark

- +

On the other hand, Light mode serves as the default theme, offering a clean and bright interface for users who prefer a traditional coding ambiance.

- +

Theme can be easily switched by: @@ -185,7 +181,7 @@ SELECT version();

- +

### SQL Plots @@ -203,7 +199,7 @@ To create plots, simply provide the type of plot along with the SQL command.

- +

### Python and SQL Combo @@ -223,7 +219,7 @@ selected_titanic.groupby(columns=["pclass"], expr=["AVG(survived) AS avg_survive

- +

### Charts @@ -234,7 +230,7 @@ A gallery of VAST Orbit-generated charts will be available in the documentation.

- +

### Complete Machine Learning Pipeline @@ -286,7 +282,7 @@ iris_data.scatter(

- +

The **Correlation Matrix** is fast and convenient to compute. Users can choose from a wide variety of correlations, including Cramer, Spearman, Pearson, etc. @@ -300,7 +296,7 @@ titanic.corr(method="spearman")

- +

By turning on the SQL print option, users can see and copy SQL queries: @@ -319,7 +315,7 @@ titanic.corr(method="spearman", focus="survived")

- +

#### Data Preparation @@ -338,7 +334,7 @@ data.outliers_plot(columns="Heights")

- +

#### Machine Learning @@ -372,7 +368,7 @@ cross_validate(

- +

### Loading Predefined Datasets @@ -391,7 +387,7 @@ iris_data = load_iris()

- +

(2) Use the standard name of the dataset from the schema: @@ -404,7 +400,7 @@ iris_data = vo.VastFrame(input_relation="public.iris")

- +

## Quickstart @@ -452,7 +448,7 @@ vdf = load_titanic()

- +

Examine your data: @@ -463,7 +459,7 @@ vdf.describe()

- +

Print the SQL query with `set_option`: @@ -513,7 +509,7 @@ cross_validate(

- +

Train and deploy the model: @@ -532,7 +528,7 @@ model.features_importance()

- +

ROC Curve: @@ -544,7 +540,7 @@ model.roc_curve()

- +

Once trained, the model can be deployed in the database for high-performance predictions _(in-database deployment availability and limitations vary by algorithm — see [Project Status & Roadmap](#project-status--roadmap))_. diff --git a/docs/source/_static/website/about_us/team/member1.jpg b/docs/source/_static/website/about_us/team/member1.jpg index 5111995..15fc783 100644 Binary files a/docs/source/_static/website/about_us/team/member1.jpg and b/docs/source/_static/website/about_us/team/member1.jpg differ diff --git a/docs/source/_static/website/about_us/team/member2.jpg b/docs/source/_static/website/about_us/team/member2.jpg index 1bcd215..bc15844 100644 Binary files a/docs/source/_static/website/about_us/team/member2.jpg and b/docs/source/_static/website/about_us/team/member2.jpg differ diff --git a/docs/source/_static/website/about_us/team/member3.jpg b/docs/source/_static/website/about_us/team/member3.jpg index 4e163fe..1e46fc5 100644 Binary files a/docs/source/_static/website/about_us/team/member3.jpg and b/docs/source/_static/website/about_us/team/member3.jpg differ diff --git a/docs/source/_static/website/about_us/team/member4.jpg b/docs/source/_static/website/about_us/team/member4.jpg index b7a9832..7025dde 100644 Binary files a/docs/source/_static/website/about_us/team/member4.jpg and b/docs/source/_static/website/about_us/team/member4.jpg differ diff --git a/docs/source/_static/website/about_us/team/member5.jpg b/docs/source/_static/website/about_us/team/member5.jpg index 2dabf2b..f823b51 100644 Binary files a/docs/source/_static/website/about_us/team/member5.jpg and b/docs/source/_static/website/about_us/team/member5.jpg differ diff --git a/docs/source/_static/website/about_us/team/member6.jpg b/docs/source/_static/website/about_us/team/member6.jpg index 9cd6416..4cdd0e5 100644 Binary files a/docs/source/_static/website/about_us/team/member6.jpg and b/docs/source/_static/website/about_us/team/member6.jpg differ diff --git a/docs/source/_static/website/about_us/team/member7.jpg b/docs/source/_static/website/about_us/team/member7.jpg new file mode 100644 index 0000000..96ee15e Binary files /dev/null and b/docs/source/_static/website/about_us/team/member7.jpg differ diff --git a/docs/source/_static/website/about_us/team/old/member1.jpg b/docs/source/_static/website/about_us/team/old/member1.jpg new file mode 100644 index 0000000..5111995 Binary files /dev/null and b/docs/source/_static/website/about_us/team/old/member1.jpg differ diff --git a/docs/source/_static/website/about_us/team/old/member2.jpg b/docs/source/_static/website/about_us/team/old/member2.jpg new file mode 100644 index 0000000..1bcd215 Binary files /dev/null and b/docs/source/_static/website/about_us/team/old/member2.jpg differ diff --git a/docs/source/_static/website/about_us/team/old/member3.jpg b/docs/source/_static/website/about_us/team/old/member3.jpg new file mode 100644 index 0000000..4e163fe Binary files /dev/null and b/docs/source/_static/website/about_us/team/old/member3.jpg differ diff --git a/docs/source/_static/website/about_us/team/old/member4.jpg b/docs/source/_static/website/about_us/team/old/member4.jpg new file mode 100644 index 0000000..b7a9832 Binary files /dev/null and b/docs/source/_static/website/about_us/team/old/member4.jpg differ diff --git a/docs/source/_static/website/about_us/team/old/member5.jpg b/docs/source/_static/website/about_us/team/old/member5.jpg new file mode 100644 index 0000000..2dabf2b Binary files /dev/null and b/docs/source/_static/website/about_us/team/old/member5.jpg differ diff --git a/docs/source/_static/website/about_us/team/old/member6.jpg b/docs/source/_static/website/about_us/team/old/member6.jpg new file mode 100644 index 0000000..9cd6416 Binary files /dev/null and b/docs/source/_static/website/about_us/team/old/member6.jpg differ diff --git a/docs/source/_templates/page.html b/docs/source/_templates/page.html index d5aa144..22798dc 100644 --- a/docs/source/_templates/page.html +++ b/docs/source/_templates/page.html @@ -45,7 +45,7 @@ VAST Orbit

In-database analytics for Python — explore, model and visualize data - at scale, directly on the VAST Data Platform. + at scale, directly on the VAST AI OS.

diff --git a/docs/source/about_us.rst b/docs/source/about_us.rst index a183fe8..fcb3112 100644 --- a/docs/source/about_us.rst +++ b/docs/source/about_us.rst @@ -31,7 +31,7 @@ The story behind VAST Orbit thinks. Meanwhile the data spread across databases, lakes, and files, and reaching all of it meant building yet another pipeline. - The VAST Data Platform removed the usual reason for all that movement by + The VAST AI OS removed the usual reason for all that movement by unifying transactional and analytical processing, all-flash performance at data-lake economics, and linear scale from gigabytes to exabytes. What was still missing was a way for data scientists to use that power with the Python @@ -130,6 +130,16 @@ The team Principal Systems Engineer + .. grid-item:: + + .. card:: Maciej Paliwoda + :img-top: _static/website/about_us/team/member7.jpg + :link: https://www.linkedin.com/in/maciejpaliwoda/ + :text-align: center + :class-card: member-pics-card + + Senior Systems Engineer + .. grid-item:: .. card:: Kuldeep Venati @@ -187,5 +197,5 @@ tracker above. .. note:: - VAST Orbit brings Python data science to the VAST Data Platform: prepare, + VAST Orbit brings Python data science to the VAST AI OS: prepare, explore, analyze, and build AI — all with in-database execution at any scale. \ No newline at end of file diff --git a/docs/source/api.rst b/docs/source/api.rst index 1913382..6da59f1 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -17,7 +17,7 @@ API Reference
-Welcome to the VAST Orbit API Reference. This comprehensive guide covers all public objects, functions, and methods available in VAST Orbit for data science at scale on the VAST Data Platform. +Welcome to the VAST Orbit API Reference. This comprehensive guide covers all public objects, functions, and methods available in VAST Orbit for data science at scale on the VAST AI OS. .. tip:: diff --git a/docs/source/chart_gallery_geo.rst b/docs/source/chart_gallery_geo.rst index c0bb0c8..5893af9 100644 --- a/docs/source/chart_gallery_geo.rst +++ b/docs/source/chart_gallery_geo.rst @@ -44,7 +44,7 @@ Let's utilize the World dataset to demonstrate geospatial capabilities. africa = world[world["continent"] == "Africa"] Let's use Africa Education dataset from the vastorbit datasets. -Data is also available `here `__. +Data is also available `here `__. .. code-block:: python diff --git a/docs/source/cicd.rst b/docs/source/cicd.rst index 9bb705f..d0eea71 100644 --- a/docs/source/cicd.rst +++ b/docs/source/cicd.rst @@ -213,7 +213,7 @@ For Contributors .. tip:: - Set up pre-commit hooks to run checks automatically before each commit. See our `contributing guide `__ for setup instructions. + Set up pre-commit hooks to run checks automatically before each commit. See our `contributing guide `__ for setup instructions. ____ diff --git a/docs/source/connection.rst b/docs/source/connection.rst index 992cd03..72f66f7 100644 --- a/docs/source/connection.rst +++ b/docs/source/connection.rst @@ -6,9 +6,9 @@ Connection .. include:: logo_include.rst -**Connecting to VAST Data Platform with VAST Orbit** +**Connecting to VAST AI OS with VAST Orbit** -VAST Orbit connects to the VAST Data Platform through Trino today, a powerful distributed SQL query engine (VAST's own engine is coming). This connection unlocks access to VAST DataBase tables, data lake files, and any other Trino-supported data source - all through one unified Python API. +VAST Orbit connects to the VAST AI OS through Trino today, a powerful distributed SQL query engine (VAST's own engine is coming). This connection unlocks access to VAST DataBase tables, data lake files, and any other Trino-supported data source - all through one unified Python API. .. important:: @@ -625,4 +625,4 @@ power of VAST Orbit for federated analytics and AI development. - :ref:`getting_started` - Quick start guide - :ref:`user_guide` - VastFrame operations and federated queries - `Trino Documentation `__ - Trino reference - - `VAST Data Platform `__ - VAST overview \ No newline at end of file + - `VAST AI OS `__ - VAST overview \ No newline at end of file diff --git a/docs/source/examples.rst b/docs/source/examples.rst index c533607..5313881 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -60,7 +60,7 @@ ____ .. tip:: - All notebooks run on the VAST Data Platform. Download and execute them with + All notebooks run on the VAST AI OS. Download and execute them with your own data! .. toctree:: diff --git a/docs/source/examples_business.rst b/docs/source/examples_business.rst index 0c9d440..0ed72f2 100644 --- a/docs/source/examples_business.rst +++ b/docs/source/examples_business.rst @@ -6,7 +6,7 @@ Business Examples .. include:: logo_include.rst -Real-world ML applications on VAST Data Platform - from fraud detection to predictive maintenance. +Real-world ML applications on VAST AI OS - from fraud detection to predictive maintenance. ____ diff --git a/docs/source/examples_business_churn.rst b/docs/source/examples_business_churn.rst index 7ec7ce2..1c33435 100644 --- a/docs/source/examples_business_churn.rst +++ b/docs/source/examples_business_churn.rst @@ -31,7 +31,7 @@ You can skip the below cell if you already have an established connection. vo.connect("VASTDSN") -Let's create a VastFrame of the dataset. The dataset is available `here `__. +Let's create a VastFrame of the dataset. The dataset is available `here `__. .. code-block:: ipython diff --git a/docs/source/examples_business_football.rst b/docs/source/examples_business_football.rst index 3148cec..492d564 100644 --- a/docs/source/examples_business_football.rst +++ b/docs/source/examples_business_football.rst @@ -3,7 +3,7 @@ Football ========= -In this example, we use the ``football`` dataset to predict the outcomes of games between various teams. You can download the dataset `here `__. +In this example, we use the ``football`` dataset to predict the outcomes of games between various teams. You can download the dataset `here `__. - **date:** Date of the game. - **home_team:** Home Team. diff --git a/docs/source/examples_business_smart_meters.rst b/docs/source/examples_business_smart_meters.rst index ff7931a..2a7d649 100644 --- a/docs/source/examples_business_smart_meters.rst +++ b/docs/source/examples_business_smart_meters.rst @@ -5,19 +5,19 @@ Smart Meters This example uses the following datasets to predict peoples' electricity consumption. We'll use the following datasets: -`sm_consumption `__ +`sm_consumption `__ - **dateUTC:** Date and time of the record. - **meterID:** Smart meter ID. - **value:** Electricity consumed during 30 minute interval (in kWh). -`sm_weather `__ +`sm_weather `__ - **dateUTC:** Date and time of the record. - **temperature:** Temperature. - **humidity:** Humidity. -`sm_meters `__ +`sm_meters `__ - **longitude:** Longitude. - **latitude:** Latitude. diff --git a/docs/source/examples_business_spam.rst b/docs/source/examples_business_spam.rst index debceb4..4e221b9 100644 --- a/docs/source/examples_business_spam.rst +++ b/docs/source/examples_business_spam.rst @@ -29,7 +29,7 @@ You can skip the below cell if you already have an established connection. vo.connect("VASTDSN") -Let's create a VastFrame of the dataset. The dataset is available `here `__. +Let's create a VastFrame of the dataset. The dataset is available `here `__. .. code-block:: ipython diff --git a/docs/source/examples_understand_covid19.rst b/docs/source/examples_understand_covid19.rst index ad4e2ea..828c9d3 100644 --- a/docs/source/examples_understand_covid19.rst +++ b/docs/source/examples_understand_covid19.rst @@ -33,7 +33,7 @@ You can skip the below cell if you already have an established connection. vo.connect("VASTDSN") -Let's create a VastFrame of the dataset. The dataset is available `here `__. +Let's create a VastFrame of the dataset. The dataset is available `here `__. .. code-block:: python diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst index c0bdbe0..7c3287e 100644 --- a/docs/source/getting_started.rst +++ b/docs/source/getting_started.rst @@ -7,7 +7,7 @@ Getting Started .. include:: logo_include.rst VAST Orbit is the open-source Python library for in-database data science on the -VAST Data Platform. This guide takes you from an empty environment to your first +VAST AI OS. This guide takes you from an empty environment to your first in-database query: install the library, connect to VAST, and prepare, explore, and model your data without ever moving it. @@ -275,5 +275,5 @@ library. .. note:: - VAST Orbit brings Python data science to the VAST Data Platform: prepare, explore, + VAST Orbit brings Python data science to the VAST AI OS: prepare, explore, analyze, and build AI - all with in-database execution at any scale. \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index 06d6d6e..8a5cf92 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -43,7 +43,7 @@ In-database data science & AI VAST Orbit lets data scientists, analysts, and ML engineers run a complete workflow — preparing, exploring, analyzing, and modeling data — directly inside -the VAST Data Platform, using the pandas- and scikit-learn-style API they already +the VAST AI OS, using the pandas- and scikit-learn-style API they already know. Instead of copying data out to a notebook, VAST Orbit pushes the work down to where the data sits and brings only the answers back, so the same code runs on a kilobyte or a petabyte, against a table or a Parquet file, without a rewrite. @@ -331,10 +331,10 @@ VAST: model.fit(enriched, ["age", "tenure"], "churn") predictions = model.predict(enriched) # runs inside VAST -Built on the VAST Data Platform +Built on the VAST AI OS ------------------------------- -VAST Orbit is only as capable as the foundation beneath it. The VAST Data Platform +VAST Orbit is only as capable as the foundation beneath it. The VAST AI OS unifies storage, database, and compute into one consistent system, so every asset — transactional tables, data-lake files, streaming events, and vector embeddings — lives in one place and speaks one language. When the infrastructure is that @@ -344,7 +344,7 @@ second system to reconcile, and no scale ceiling to design around. VAST Orbit turns that consistency into a single queryable surface for Python, where one ``VastFrame`` reaches a table or a file, a gigabyte or an exabyte, all the same way. It works with **VAST 4.5 and later**. Learn more about the foundation it builds -on at the `VAST Data Platform `__. +on at the `VAST AI OS `__. Today that single query runs on Trino; VAST's own query engine is on the way and will become the default. Because you work through one ``VastFrame`` API, your code stays @@ -434,7 +434,7 @@ Explore the documentation .. note:: - VAST Orbit brings Python data science to the VAST Data Platform — query + VAST Orbit brings Python data science to the VAST AI OS — query anywhere, analyze everything, and build AI at any scale, all with in-database execution and zero data movement. diff --git a/docs/source/machine_learning.rst b/docs/source/machine_learning.rst index beecaa9..1ad51e3 100644 --- a/docs/source/machine_learning.rst +++ b/docs/source/machine_learning.rst @@ -6,7 +6,7 @@ Machine Learning .. include:: logo_include.rst -Build, train, and deploy ML models at scale on VAST Data Platform. +Build, train, and deploy ML models at scale on VAST AI OS. ____ diff --git a/docs/source/user_guide.rst b/docs/source/user_guide.rst index a3e9ce2..e17173c 100644 --- a/docs/source/user_guide.rst +++ b/docs/source/user_guide.rst @@ -17,7 +17,7 @@ User Guide -Welcome to the VAST Orbit User Guide! This comprehensive tutorial series takes you from basics to advanced topics, teaching you how to leverage VAST Data Platform for data science at scale. +Welcome to the VAST Orbit User Guide! This comprehensive tutorial series takes you from basics to advanced topics, teaching you how to leverage VAST AI OS for data science at scale. **What You'll Learn:** diff --git a/docs/source/user_guide_data_exploration_correlations.rst b/docs/source/user_guide_data_exploration_correlations.rst index 094351d..69c4139 100644 --- a/docs/source/user_guide_data_exploration_correlations.rst +++ b/docs/source/user_guide_data_exploration_correlations.rst @@ -8,7 +8,7 @@ Finding links between variables is a very important task. The main purpose of da Machine learning models are also sensitive to the number of variables and how they relate and affect each other, so finding correlations and dependencies can help us make better use of our machine learning algorithms. -Let's use the `Telco Churn dataset `__ to understand how we can find links between different variables in vastorbit. +Let's use the `Telco Churn dataset `__ to understand how we can find links between different variables in vastorbit. .. code-block:: ipython diff --git a/docs/source/user_guide_data_exploration_descriptive_statistics.rst b/docs/source/user_guide_data_exploration_descriptive_statistics.rst index b18d7b9..e225a46 100644 --- a/docs/source/user_guide_data_exploration_descriptive_statistics.rst +++ b/docs/source/user_guide_data_exploration_descriptive_statistics.rst @@ -30,7 +30,7 @@ The :py:func:`~vastorbit.VastFrame.aggregate` method is the best way to compute help(vo.VastFrame.agg) This is a tremendously useful function for understanding your data. -Let's use the `churn dataset `__ +Let's use the `churn dataset `__ .. code-block:: diff --git a/docs/source/user_guide_machine_learning_regression.rst b/docs/source/user_guide_machine_learning_regression.rst index fc6c9d3..02a2235 100644 --- a/docs/source/user_guide_machine_learning_regression.rst +++ b/docs/source/user_guide_machine_learning_regression.rst @@ -15,7 +15,7 @@ You must always verify that all the assumptions of a given algorithm are met bef Most of regression models are sensitive to unnormalized data, so it's important to normalize and decompose your data before using them (though some models like random forest can handle unnormalized and correlated data). If we don't follow the assumptions, we might get unexpected results (example: negative ``R2``). -Let's predict the total charges of the Telco customers using their tenure. We will start by importing `the telco dataset `__. +Let's predict the total charges of the Telco customers using their tenure. We will start by importing `the telco dataset `__. .. code-block:: ipython diff --git a/docs/source/whats_new.rst b/docs/source/whats_new.rst index 8c3b872..32b4989 100644 --- a/docs/source/whats_new.rst +++ b/docs/source/whats_new.rst @@ -35,7 +35,7 @@ Latest Release :class-card: custom-card The first public release of **VAST Orbit** — the open-source Python - library that brings data science directly to the **VAST Data Platform**. + library that brings data science directly to the **VAST AI OS**. Everything runs through the **VastFrame**, a familiar pandas-like interface that pushes each transformation, aggregation, and model down to the database. diff --git a/pyproject.toml b/pyproject.toml index 2344f61..5683c81 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta" [project] name = "vastorbit" # Keep this in sync with vastorbit/__init__.py (__version__). -version = "0.1.0b2" +version = "0.1.0" description = "VAST Orbit simplifies data exploration, data cleaning, and machine learning in the VAST DataBase using in-database analytics." readme = "README.md" requires-python = ">=3.12" diff --git a/vastorbit/__init__.py b/vastorbit/__init__.py index aa8acca..f7c74b2 100755 --- a/vastorbit/__init__.py +++ b/vastorbit/__init__.py @@ -10,8 +10,8 @@ ) __url__: str = "https://github.com/vast-data/Orbit/" __license__: str = "Apache License, Version 2.0" -__version__: str = "0.1.0b2" -__codecov__: float = 0.5 +__version__: str = "0.1.0" +__codecov__: float = 0.4725 from vastorbit._config.config import get_option, set_option from vastorbit._utils._sql._vast_version import vast_version diff --git a/vastorbit/machine_learning/memmodel/naive_bayes.py b/vastorbit/machine_learning/memmodel/naive_bayes.py index 199dc5c..5a3943f 100644 --- a/vastorbit/machine_learning/memmodel/naive_bayes.py +++ b/vastorbit/machine_learning/memmodel/naive_bayes.py @@ -155,7 +155,7 @@ class NaiveBayes(MulticlassClassifier): Here we will be using attributes of model trained on well known - `titanic dataset `__. + `titanic dataset `__. It tries to predict the port of embarkation (C = Cherbourg,