diff --git a/.github/ISSUE_TEMPLATE/issue.yml b/.github/ISSUE_TEMPLATE/issue.yml index 84be072..1b1d469 100644 --- a/.github/ISSUE_TEMPLATE/issue.yml +++ b/.github/ISSUE_TEMPLATE/issue.yml @@ -1,5 +1,5 @@ name: Feature Request or Bug Report -description: Report a bug or request a new feature for VastOrbit +description: Report a bug or request a new feature for VAST Orbit labels: ["needs-triage"] body: - type: markdown @@ -62,7 +62,7 @@ body: description: | Please provide information about your environment: placeholder: | - - VastOrbit version: + - VAST Orbit version: - Python version: - Operating System: - Trino version (if applicable): diff --git a/NOTICE.txt b/NOTICE.txt index 6ed83a6..3b30720 100644 --- a/NOTICE.txt +++ b/NOTICE.txt @@ -10,5 +10,5 @@ This project is a derivative work based on VerticaPy: Original project: https://github.com/vertica/VerticaPy VAST Orbit represents a substantial fork and redesign of VerticaPy, adapted for use -with VAST Database and incorporating significant new features, modifications, and +with VAST DataBase and incorporating significant new features, modifications, and enhancements. diff --git a/README.md b/README.md index e68453c..64f9306 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). @@ -18,13 +18,13 @@ ## Trailer Video

- + VAST Orbit — Trailer (click to watch)

-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. +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. ## Table of Contents - [Introduction](#introduction) @@ -44,24 +44,24 @@ VAST Orbit is a Python library with scikit-learn-like functionality for conducti ## Introduction -VAST Database combines enterprise-grade storage with powerful analytics capabilities. Today, VAST Orbit leverages Trino as the SQL engine to deliver exceptional performance for data science workloads at scale. Soon, we will support the VAST SQL Engine (currently in development), which will provide even greater speed through VAST's columnar-optimized format. However, SQL alone isn't flexible enough to meet the evolving needs of modern data scientists. +VAST DataBase combines enterprise-grade storage with powerful analytics capabilities. Today, VAST Orbit leverages Trino as the SQL engine to deliver exceptional performance for data science workloads at scale. Soon, we will support the VAST SQL Engine (currently in development), which will provide even greater speed through VAST's columnar-optimized format. However, SQL alone isn't flexible enough to meet the evolving needs of modern data scientists. Python has become the lingua franca of data science, offering unparalleled flexibility through its high-level abstraction and an extensive ecosystem of libraries. The accessibility of Python has led to the development of powerful APIs like pandas and scikit-learn, supported by a vibrant community of data scientists worldwide. Unfortunately, traditional Python tools operate in-memory as single-node processes, creating fundamental limitations when working with large-scale data. While distributed computing frameworks attempt to address these constraints, they still require moving data for processing—an approach that is prohibitively expensive and increasingly impractical in the modern data landscape. On top of these challenges, data scientists face additional complexity in deploying and operationalizing their models. The entire workflow is time-consuming and inefficient. -**VAST Orbit solves these problems**. The concept is elegant: instead of moving data to compute, VAST Orbit brings the compute logic to where the data lives—in VAST Database. Train your models using familiar scikit-learn syntax in Python, then deploy them directly in the database for high-performance predictions at scale. +**VAST Orbit solves these problems**. The concept is elegant: instead of moving data to compute, VAST Orbit brings the compute logic to where the data lives—in VAST DataBase. Train your models using familiar ``scikit-learn`` syntax in Python, then deploy them directly in the database for high-performance predictions at scale. ### Main Advantages - **Easy Data Exploration**: Interactive exploration of massive datasets without memory constraints - **Fast Data Preparation**: Leverage Trino's distributed processing for rapid data transformation -- **Familiar Scikit-learn API**: Train models using the scikit-learn interface you already know -- **In-Database Deployment**: Deploy trained models directly in VAST Database for production workloads _(with some current limitations — see [Project Status & Roadmap](#project-status--roadmap))_ +- **Familiar Scikit-learn API**: Train models using the ``scikit-learn`` interface you already know +- **In-Database Deployment**: Deploy trained models directly in VAST DataBase for production workloads _(with some current limitations — see [Project Status & Roadmap](#project-status--roadmap))_ - **Easy Model Evaluation**: Comprehensive model evaluation tools with visual insights - **Seamless SQL Integration**: Use Python or SQL interchangeably based on your preference and use case

- +

## Project Status & Roadmap @@ -97,7 +97,7 @@ python3 setup.py install ## Connecting to the Database -VAST Orbit currently connects to VAST Database through Trino. Ensure you have Trino set up and configured to access your VAST Database instance. +VAST Orbit currently connects to VAST DataBase through Trino. Ensure you have Trino set up and configured to access your VAST DataBase instance. Connection example: @@ -144,14 +144,14 @@ 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 +185,7 @@ SELECT version();

- +

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

- +

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

- +

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

- +

### Complete Machine Learning Pipeline @@ -286,7 +286,7 @@ 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 +300,7 @@ titanic.corr(method="spearman")

- +

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

- +

#### Data Preparation @@ -338,14 +338,14 @@ data.outliers_plot(columns="Heights")

- +

#### Machine Learning -VAST Orbit's machine learning capabilities let you build models using the familiar scikit-learn API and evaluate them with rich, visual tooling. VAST Orbit supports a wide array of algorithms including time series forecasting, clustering, regression, and classification. +VAST Orbit's machine learning capabilities let you build models using the familiar ``scikit-learn`` API and evaluate them with rich, visual tooling. VAST Orbit supports a wide array of algorithms including time series forecasting, clustering, regression, and classification. -**Key idea: build with scikit-learn syntax, then deploy in-database for predictions.** +**Key idea: build with ``scikit-learn`` syntax, then deploy in-database for predictions.** > **Note (beta):** As of now, **most models are trained using scikit-learn** (in-memory) and can then be **deployed in-database** for prediction. In-database deployment is available for many algorithms but carries **some limitations depending on the model**, and a number of **in-database ML features are still beta**. Native in-database training is planned. See [Project Status & Roadmap](#project-status--roadmap). @@ -372,7 +372,7 @@ cross_validate(

- +

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

- +

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

- +

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

- +

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

- +

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

- +

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

- +

ROC Curve: @@ -544,7 +544,7 @@ 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/examples/business/base_station/shanghai_cdr.csv b/assets/data/business/base_station/shanghai_cdr.csv similarity index 100% rename from examples/business/base_station/shanghai_cdr.csv rename to assets/data/business/base_station/shanghai_cdr.csv diff --git a/examples/business/base_station/shanghai_districts.csv b/assets/data/business/base_station/shanghai_districts.csv similarity index 100% rename from examples/business/base_station/shanghai_districts.csv rename to assets/data/business/base_station/shanghai_districts.csv diff --git a/examples/business/battery/data.zip b/assets/data/business/battery/data.zip similarity index 100% rename from examples/business/battery/data.zip rename to assets/data/business/battery/data.zip diff --git a/examples/business/churn/customers.csv b/assets/data/business/churn/customers.csv similarity index 100% rename from examples/business/churn/customers.csv rename to assets/data/business/churn/customers.csv diff --git a/examples/business/football/games.csv b/assets/data/business/football/games.csv similarity index 100% rename from examples/business/football/games.csv rename to assets/data/business/football/games.csv diff --git a/examples/business/movies/movies.csv b/assets/data/business/movies/movies.csv similarity index 100% rename from examples/business/movies/movies.csv rename to assets/data/business/movies/movies.csv diff --git a/examples/business/smart_meters/sm_consumption.csv b/assets/data/business/smart_meters/sm_consumption.csv similarity index 100% rename from examples/business/smart_meters/sm_consumption.csv rename to assets/data/business/smart_meters/sm_consumption.csv diff --git a/examples/business/smart_meters/sm_meters.csv b/assets/data/business/smart_meters/sm_meters.csv similarity index 100% rename from examples/business/smart_meters/sm_meters.csv rename to assets/data/business/smart_meters/sm_meters.csv diff --git a/examples/business/smart_meters/sm_weather.csv b/assets/data/business/smart_meters/sm_weather.csv similarity index 100% rename from examples/business/smart_meters/sm_weather.csv rename to assets/data/business/smart_meters/sm_weather.csv diff --git a/examples/business/spam/spam.csv b/assets/data/business/spam/spam.csv similarity index 100% rename from examples/business/spam/spam.csv rename to assets/data/business/spam/spam.csv diff --git a/examples/learn/iris/iris.csv b/assets/data/learn/iris/iris.csv similarity index 100% rename from examples/learn/iris/iris.csv rename to assets/data/learn/iris/iris.csv diff --git a/examples/learn/pokemon/fights.csv b/assets/data/learn/pokemon/fights.csv similarity index 100% rename from examples/learn/pokemon/fights.csv rename to assets/data/learn/pokemon/fights.csv diff --git a/examples/learn/pokemon/pokemons.csv b/assets/data/learn/pokemon/pokemons.csv similarity index 100% rename from examples/learn/pokemon/pokemons.csv rename to assets/data/learn/pokemon/pokemons.csv diff --git a/examples/understand/africa_education/students.csv b/assets/data/understand/africa_education/students.csv similarity index 100% rename from examples/understand/africa_education/students.csv rename to assets/data/understand/africa_education/students.csv diff --git a/examples/understand/covid19/deaths.csv b/assets/data/understand/covid19/deaths.csv similarity index 100% rename from examples/understand/covid19/deaths.csv rename to assets/data/understand/covid19/deaths.csv diff --git a/docs/refresh.sh b/docs/refresh.sh index a45c41a..8c354ae 100755 --- a/docs/refresh.sh +++ b/docs/refresh.sh @@ -1,6 +1,6 @@ #!/bin/bash # ============================================================================= -# VastOrbit docs refresh — four gears: +# VAST Orbit docs refresh — four gears: # # ./refresh.sh FULL : reinstall package, clean, execute all # code, post-process. Release-quality. diff --git a/docs/source/_static/cicd_pipeline.svg b/docs/source/_static/cicd_pipeline.svg index 35b3540..6cc118c 100644 --- a/docs/source/_static/cicd_pipeline.svg +++ b/docs/source/_static/cicd_pipeline.svg @@ -1 +1 @@ -VAST ORBIT · CI/CD PIPELINE1Code FormattingBlack · isortDeterministic formattingPEP 8 enforcedZero stylistic driftAUTOMATED2Quality AnalysisPylintStatic scoring on every PRThreshold: score ≥ 9.5Fails gate → no mergeMERGE-BLOCKING3Unit Testingpytest · multi-versionPython version matrixParallel executionDeterministic resultsMERGE-BLOCKING495%Coverage95%+ enforcedLine + branch coverageGated upload via CICI ENFORCED5DocumentationSphinx → websiteDocs build on every mergePublished automaticallyAUTOMATED \ No newline at end of file +VAST Orbit · CI/CD PIPELINE1Code FormattingBlack · isortDeterministic formattingPEP 8 enforcedZero stylistic driftAUTOMATED2Quality AnalysisPylintStatic scoring on every PRThreshold: score ≥ 9.5Fails gate → no mergeMERGE-BLOCKING3Unit Testingpytest · multi-versionPython version matrixParallel executionDeterministic resultsMERGE-BLOCKING495%Coverage95%+ enforcedLine + branch coverageGated upload via CICI ENFORCED5DocumentationSphinx → websiteDocs build on every mergePublished automaticallyAUTOMATED \ No newline at end of file diff --git a/docs/source/_static/css/custom_styling.css b/docs/source/_static/css/custom_styling.css index 3813910..e160d7c 100644 --- a/docs/source/_static/css/custom_styling.css +++ b/docs/source/_static/css/custom_styling.css @@ -1271,7 +1271,7 @@ body[data-theme="dark"] h1 { } /* ========================================================================== - VAST ORBIT HERO LOGO (logo_include.rst) — theme-aware coloring + VAST Orbit HERO LOGO (logo_include.rst) — theme-aware coloring The SVG asset is the monochrome "light" variant; fills are controlled here. ========================================================================== */ diff --git a/docs/source/_static/css/vast_brand.css b/docs/source/_static/css/vast_brand.css index b443b47..9d568d3 100644 --- a/docs/source/_static/css/vast_brand.css +++ b/docs/source/_static/css/vast_brand.css @@ -1,7 +1,7 @@ /* ========================================================================== VAST DATA — BRAND TOKENS Source: brand.vastdata.com/document/4 (Brand Identity + Brand Elements) - Applied to: VastOrbit documentation (Furo / Sphinx) + Applied to: VAST Orbit documentation (Furo / Sphinx) -------------------------------------------------------------------------- Color Palette VAST Navy #03142C — primary dark (site background / theme color) diff --git a/docs/source/_static/js/ai_assistant.js b/docs/source/_static/js/ai_assistant.js index 77175af..04bf459 100644 --- a/docs/source/_static/js/ai_assistant.js +++ b/docs/source/_static/js/ai_assistant.js @@ -56,7 +56,7 @@ class VAST OrbitAI { 💡 Try asking:
• "How do I get started?"
• "Show me vDataFrame examples"
- • "How do I connect to VAST Database?" + • "How do I connect to VAST DataBase?" diff --git a/docs/source/_static/js/theme_dark_default.js b/docs/source/_static/js/theme_dark_default.js index ba9a9a1..e81b784 100644 --- a/docs/source/_static/js/theme_dark_default.js +++ b/docs/source/_static/js/theme_dark_default.js @@ -1,4 +1,4 @@ -/* theme_dark_default.js — VastOrbit docs theme policy: +/* theme_dark_default.js — VAST Orbit docs theme policy: * 1. Default theme is DARK (first visit, or any stale "auto" state). * 2. The sun/moon toggle switches strictly dark <-> light. No "auto". * diff --git a/docs/source/chart_gallery.rst b/docs/source/chart_gallery.rst index 3c30b9f..d520a0a 100644 --- a/docs/source/chart_gallery.rst +++ b/docs/source/chart_gallery.rst @@ -6,13 +6,13 @@ Chart Gallery .. include:: logo_include.rst -VastOrbit offers an extensive selection of interactive visualizations that play a pivotal role in extracting valuable business insights. These versatile visualization tools empower you to effectively analyze and communicate data trends, enabling informed decision-making and enhancing data-driven strategies. +VAST Orbit offers an extensive selection of interactive visualizations that play a pivotal role in extracting valuable business insights. These versatile visualization tools empower you to effectively analyze and communicate data trends, enabling informed decision-making and enhancing data-driven strategies. **Visualization Backends:** -- **Plotly**: Interactive web-based visualizations with hover effects and zoom capabilities -- **Matplotlib**: Publication-quality static plots with extensive customization -- **Graphviz**: Specialized tree and graph visualizations for ML models + - **Plotly**: Interactive web-based visualizations with hover effects and zoom capabilities + - **Matplotlib**: Publication-quality static plots with extensive customization + - **Graphviz**: Specialized tree and graph visualizations for ML models .. tip:: @@ -21,10 +21,10 @@ VastOrbit offers an extensive selection of interactive visualizations that play **Key Features:** -✓ **In-Database Execution**: Charts render from VAST DataBase without moving data to Python -✓ **Smart Sampling**: Automatically samples large datasets for responsive visualizations -✓ **Federated Queries**: Visualize data from multiple sources (VAST, S3, PostgreSQL) in one chart -✓ **Export Options**: Save as PNG, SVG, HTML, or interactive web dashboards + - ✓ **In-Database Execution**: Charts render from VAST DataBase without moving data to Python + - ✓ **Smart Sampling**: Automatically samples large datasets for responsive visualizations + - ✓ **Federated Queries**: Visualize data from multiple sources (VAST, S3, PostgreSQL) in one chart + - ✓ **Export Options**: Save as PNG, SVG, HTML, or interactive web dashboards .. note:: @@ -504,7 +504,7 @@ Quick Start Examples model.roc_curve() # Plot classification boundaries - model.plot_classification() + model.plot() **Federated Query Visualization:** diff --git a/docs/source/chart_gallery_geo.rst b/docs/source/chart_gallery_geo.rst index 11c0e3a..417753d 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 @@ -57,7 +57,7 @@ vastorbit provides the option to create various types of geospatial plots, inclu .. note:: - Currently, vastorbit provides geospatial capabilities using Matplotlib and GeoPandas. We plan to expand these functionalities in the future by incorporating Plotly. + Currently, vastorbit provides geospatial capabilities using Matplotlib and Geopandas. We plan to expand these functionalities in the future by incorporating Plotly. .. ipython:: python :suppress: diff --git a/docs/source/contribution_guidelines_code_setting_up.rst b/docs/source/contribution_guidelines_code_setting_up.rst index bdc84a6..d6f7dfc 100644 --- a/docs/source/contribution_guidelines_code_setting_up.rst +++ b/docs/source/contribution_guidelines_code_setting_up.rst @@ -66,12 +66,12 @@ Step 4: Get the test suite running (Under development) We appreciate any and all contributions to the test suite! These tests use a Python module: `pytest `__. You might want to check out the pytest documentation for more details. -You must have access to a VAST database to run the tests. We recommend using a non-production database, because some tests may need the superuser permission to manipulate global settings and potentially break that database. Here's one way to go about it: +You must have access to a VAST DataBase to run the tests. We recommend using a non-production database, because some tests may need the superuser permission to manipulate global settings and potentially break that database. Here's one way to go about it: - Download docker kitematic: https://kitematic.com/ - Spin up a VAST container (e.g. sumitchawla/VAST) -Spin up your VAST database for tests and then config test settings: +Spin up your VAST DataBase for tests and then config test settings: **Default settings:** diff --git a/docs/source/contribution_guidelines_code_unit_tests.rst b/docs/source/contribution_guidelines_code_unit_tests.rst index 8eac244..843f549 100644 --- a/docs/source/contribution_guidelines_code_unit_tests.rst +++ b/docs/source/contribution_guidelines_code_unit_tests.rst @@ -93,6 +93,6 @@ Testing Guidelines Add appropriate tests for the bugs or features behavior, run the test suite again, and ensure that all tests pass. Here are additional guidelines for writing tests: -- Tests should be easy for any contributor to run. Contributors may not get complete access to their VAST database. For example, they may only have a non-admin user with write privileges to a single schema, and the database may not be the latest version. We encourage tests to use only what they need and nothing more. +- Tests should be easy for any contributor to run. Contributors may not get complete access to their VAST DataBase. For example, they may only have a non-admin user with write privileges to a single schema, and the database may not be the latest version. We encourage tests to use only what they need and nothing more. - If there are requirements to the database for running a test, the test should adapt to different situations and never report a failure. For example, if a test depends on a multi-node database, it should check the number of DB nodes first, and skip itself when it connects to a single-node database (see helper function ``require_DB_nodes_at_least()`` in ``vastorbit/tests/integration_tests/base.py``). \ No newline at end of file diff --git a/docs/source/examples_business_battery.rst b/docs/source/examples_business_battery.rst index 02cf8f4..099e037 100644 --- a/docs/source/examples_business_battery.rst +++ b/docs/source/examples_business_battery.rst @@ -22,9 +22,6 @@ This dataset includes information on Li-ion batteries over several charge and di Discharge was at a constant current (CC) level of 2A until the battery voltage fell to 2.7V. -You can download the Jupyter Notebook of the study -`here `__. - The dataset includes the following: - **Voltage_measured:** Battery's terminal voltage (Volts) for charging and discharging cycles. diff --git a/docs/source/examples_business_booking.rst b/docs/source/examples_business_booking.rst index 9fa7376..d384a68 100644 --- a/docs/source/examples_business_booking.rst +++ b/docs/source/examples_business_booking.rst @@ -3,7 +3,7 @@ Booking ======== -This example uses the ``expedia`` dataset to predict, based on site activity, whether a user is likely to make a booking. You can download the Jupyter Notebook of the study `here `__ and the dataset `here `__. +This example uses the ``expedia`` dataset to predict, based on site activity, whether a user is likely to make a booking. You can download the dataset `here `__. - **cnt:** Number of similar events in the context of the same user session. - **user_location_city:** The ID of the city in which the customer is located. @@ -395,7 +395,7 @@ We're left with an excellent model. With this, we can predict whether a user wil Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_business_churn.rst b/docs/source/examples_business_churn.rst index ffd9854..fb7b340 100644 --- a/docs/source/examples_business_churn.rst +++ b/docs/source/examples_business_churn.rst @@ -3,7 +3,7 @@ Telco Churn ============ -This example uses the Telco Churn dataset to predict which Telco user is likely to churn; that is, customers that will likely stop using Telco. You can download the Jupyter Notebook of the study `here `__. +This example uses the Telco Churn dataset to predict which Telco user is likely to churn; that is, customers that will likely stop using Telco. - **Churn:** customers that left within the last month. - **Services:** services of each customer (phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies). @@ -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 @@ -358,7 +358,7 @@ By targeting less than ``10%`` of the entire distribution, our predictions will Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_business_credit_card_fraud.rst b/docs/source/examples_business_credit_card_fraud.rst index d577d9f..c0b68e5 100644 --- a/docs/source/examples_business_credit_card_fraud.rst +++ b/docs/source/examples_business_credit_card_fraud.rst @@ -3,7 +3,7 @@ Credit Card Fraud ================== -In this example, we use vastorbit to detect fraudulent credit card transactions. You can download the Jupyter notebook `here `__ and the dataset `here `__. +In this example, we use vastorbit to detect fraudulent credit card transactions. You can download the dataset `here `__. The Credit Card Fraud Detection dataset contains credit card transactions from September 2013 by European cardholders. It contains numerical input variables from a principal component analysis (:py:mod:`~vastorbit.machine_learning.vast.decomposition.PCA`) transformation. @@ -641,7 +641,7 @@ Other scalable techniques that can solve this problem are robust :py:mod:`~vasto Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_business_football.rst b/docs/source/examples_business_football.rst index 33b3794..512d4a2 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 Jupyter Notebook of the study `here `__ and 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. @@ -409,7 +409,7 @@ We can compute all the number of cup-wins by team. As expected, Brazil and Germa .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_football_groupby_3.html -Let's export the result to our VAST database. +Let's export the result to our VAST DataBase. .. code-block:: python @@ -538,7 +538,7 @@ We can decode the previous label encoding. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_football_confederation_8.html -Let's export the result to our VAST database. +Let's export the result to our VAST DataBase. .. code-block:: python @@ -726,7 +726,7 @@ Now we can compute each team's KPI. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_football_clean_12.html -We can join the different information about the cup winners to enrich our dataset. We'll be using this later, so let's export it to our VAST database. +We can join the different information about the cup winners to enrich our dataset. We'll be using this later, so let's export it to our VAST DataBase. .. code-block:: python @@ -883,7 +883,7 @@ Let's impute the missing values by 0. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_football_clean_kpi_final_4.html -Let's export the result to our VAST database. +Let's export the result to our VAST DataBase. .. code-block:: python @@ -1141,7 +1141,7 @@ Note that the best team in a weaker confederation might not be particularly stro .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_football_boxplot_2.html -Let's export the KPIs to our VAST database. +Let's export the KPIs to our VAST DataBase. .. code-block:: python @@ -1872,7 +1872,7 @@ We need to impute these missing values. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_football_clean_kpi_all_matchs_final_1.html -Let's export the result to our VAST database using the variable ``match_sample`` to avoid counting the same game twice. +Let's export the result to our VAST DataBase using the variable ``match_sample`` to avoid counting the same game twice. .. code-block:: python @@ -2139,7 +2139,7 @@ Fantastic: we built a very efficient model which predicted that France will win Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_business_insurance.rst b/docs/source/examples_business_insurance.rst index f26ebca..b620e94 100644 --- a/docs/source/examples_business_insurance.rst +++ b/docs/source/examples_business_insurance.rst @@ -4,8 +4,6 @@ Health Insurance Costs ======================= In this example, we use a `dataset of personal medical costs `__ to create a model to estimate treatment costs. - -You can download the Jupyter notebook `here `__. The columns provided include: diff --git a/docs/source/examples_business_movies.rst b/docs/source/examples_business_movies.rst index 1ef03da..e25b18e 100644 --- a/docs/source/examples_business_movies.rst +++ b/docs/source/examples_business_movies.rst @@ -3,8 +3,7 @@ Movies Scoring and Clustering ============================== -This example uses the ``filmtv_movies`` dataset to evaluate the quality of the movies and create clusters of similar movies. -You can download the Jupyter notebook `here `__. +This example uses the ``filmtv_movies`` dataset to evaluate the quality of the movies and create clusters of similar movies. The columns provided include: @@ -351,7 +350,7 @@ Let's join our notoriety metrics for actors and directors with the main dataset. ], ) -As we did many operation, it can be nice to save the :py:mod:`~vastorbit.VastFrame` as a table in the VAST database. +As we did many operation, it can be nice to save the :py:mod:`~vastorbit.VastFrame` as a table in the VAST DataBase. .. code-block:: python @@ -683,7 +682,7 @@ Before we export the data, we should normalize the numerical columns to get the for elem in ["category", "period", "language_area"]: filmtv_movies_complete[elem].one_hot_encode(drop_first = True) -We can export the results to our VAST database. +We can export the results to our VAST DataBase. .. code-block:: python @@ -1119,7 +1118,7 @@ Each cluster consists of similar movies. These clusters can be used to give movi Conclusion ---------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_business_smart_meters.rst b/docs/source/examples_business_smart_meters.rst index b6ab01a..3a892a3 100644 --- a/docs/source/examples_business_smart_meters.rst +++ b/docs/source/examples_business_smart_meters.rst @@ -3,21 +3,21 @@ Smart Meters ============= -This example uses the following datasets to predict peoples' electricity consumption. You can download the Jupyter Notebook of the study `here `__. We'll use the following datasets: +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. @@ -388,7 +388,7 @@ Interpolation & Aggregations Since power outages seem relatively common in each area, and the ``value`` represents the electricity consumed during 30 minute intervals (in ``kWh``), it'd be a good idea to interpolate and aggregate the data to get a monthly average in electricity consumption per region. -Let's save our new dataset in the VAST database. +Let's save our new dataset in the VAST DataBase. .. ipython:: python @@ -440,7 +440,7 @@ To get an equally-sliced dataset, we can then interpolate to fill any gaps. This .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_sm_consumption_weather_region_clean_1.html -Let's aggregate the data to figure out the monthly energy consumption for each smart meter. We can then save the result in the VAST database. +Let's aggregate the data to figure out the monthly energy consumption for each smart meter. We can then save the result in the VAST DataBase. .. code-block:: python @@ -873,7 +873,7 @@ With this model, we can make predictions about the energy consumption of househo Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_business_spam.rst b/docs/source/examples_business_spam.rst index 28a4518..c9cbf6d 100644 --- a/docs/source/examples_business_spam.rst +++ b/docs/source/examples_business_spam.rst @@ -3,7 +3,7 @@ Spam ===== -This example uses the ``spam`` dataset to detect SMS spam. You can download the Jupyter Notebook of the study `here `__. +This example uses the ``spam`` dataset to detect SMS spam. - **v1:** the SMS type (spam or ham). - **v2:** SMS content. @@ -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 @@ -322,7 +322,7 @@ Our model can reliably identify spam. Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_business_spotify.rst b/docs/source/examples_business_spotify.rst index d3116ce..ac3f91f 100644 --- a/docs/source/examples_business_spotify.rst +++ b/docs/source/examples_business_spotify.rst @@ -5,8 +5,6 @@ Predicting Popularity on Spotify This example uses the publicly-available Spotify from Kaggle to predict the popularity of Polish songs and artists on Spotify. We'll also use a model to group artists together based on how similar their songs are. -You can download the Jupyter notebook of this study `here `__. - .. note:: We are only using polish artists and a subset of the tracks dataset filtered by a handful of artists. The ``tracks`` dataset (``tracks.csv``) have the following features: @@ -62,7 +60,7 @@ This example uses the following version of vastorbit: vo.__version__ -Start by importing vastorbit and loading the SQL extension, which allows you to query the VAST database with SQL. +Start by importing vastorbit and loading the SQL extension, which allows you to query the VAST DataBase with SQL. .. ipython:: python diff --git a/docs/source/examples_learn_commodities.rst b/docs/source/examples_learn_commodities.rst index 2fa7890..f853221 100644 --- a/docs/source/examples_learn_commodities.rst +++ b/docs/source/examples_learn_commodities.rst @@ -3,7 +3,7 @@ Commodities ============ -This example uses the ``commodities`` dataset to predict the price of different commodities. You can download the Jupyter Notebook of the study `here `__. +This example uses the ``commodities`` dataset to predict the price of different commodities. - **date:** Date of the record. - **Gold:** Price per ounce of Gold. @@ -459,7 +459,7 @@ The models may be somewhat unstable. To improve them, we could apply data prepar Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_learn_iris.rst b/docs/source/examples_learn_iris.rst index 00724ef..b00bb31 100644 --- a/docs/source/examples_learn_iris.rst +++ b/docs/source/examples_learn_iris.rst @@ -3,7 +3,7 @@ Iris ===== -This example uses the ``iris`` dataset to predict the species of various flowers based on their physical features. You can download the Jupyter Notebook of the study `here `__. +This example uses the ``iris`` dataset to predict the species of various flowers based on their physical features. - **PetalLengthCm:** Petal Length in cm - **PetalWidthCm:** Petal Width in cm @@ -326,7 +326,7 @@ We have a great model with an accuracy of ``96%`` on an entirely balanced datase Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_learn_pokemon.rst b/docs/source/examples_learn_pokemon.rst index c954eba..7ed3d07 100644 --- a/docs/source/examples_learn_pokemon.rst +++ b/docs/source/examples_learn_pokemon.rst @@ -3,7 +3,7 @@ Pokemon ======== -This example uses the ``pokemons`` and ``combats`` datasets to predict the winner of a 1-on-1 Pokemon battle. You can download the Jupyter Notebook of the study here and two datasets: +This example uses the ``pokemons`` and ``combats`` datasets to predict the winner of a 1-on-1 Pokemon battle. You can download the two datasets: `pokemons `__ @@ -366,7 +366,7 @@ Based on our model, it seems that a Pokemon's speed and attack stats are the str Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_learn_titanic.rst b/docs/source/examples_learn_titanic.rst index 80da35f..38e0623 100644 --- a/docs/source/examples_learn_titanic.rst +++ b/docs/source/examples_learn_titanic.rst @@ -3,7 +3,7 @@ Titanic ======== -This example uses the ``titanic`` dataset to predict the survival of passengers on the Titanic. You can download the Jupyter Notebook of the study `here `__. +This example uses the ``titanic`` dataset to predict the survival of passengers on the Titanic. We will follow the data science cycle (Data Exploration - Data Preparation - Data Modeling - Model Evaluation - Model Deployment) to solve this problem. @@ -302,7 +302,7 @@ Survival correlates strongly with whether or not a passenger has a lifeboat (the - Passengers with a lifeboat - Passengers without a lifeboat -Before we move on: we did a lot of work to clean up this data, but we haven't saved anything to our VAST database! Let's look at the modifications we've made to the :py:mod:`~vastorbit.VastFrame`. +Before we move on: we did a lot of work to clean up this data, but we haven't saved anything to our VAST DataBase! Let's look at the modifications we've made to the :py:mod:`~vastorbit.VastFrame`. .. ipython:: python @@ -322,7 +322,7 @@ vastorbit dynamically generates SQL code whenever you make modifications to your vo.set_option("sql_on", False) print(titanic.info()) -Let's move on to modeling our data. Save the :py:mod:`~vastorbit.VastFrame` to your VAST database. +Let's move on to modeling our data. Save the :py:mod:`~vastorbit.VastFrame` to your VAST DataBase. .. ipython:: python :okwarning: @@ -487,7 +487,7 @@ As expected, the passenger's age and title are the most important predictors of Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_learn_winequality.rst b/docs/source/examples_learn_winequality.rst index c433496..7f3e251 100644 --- a/docs/source/examples_learn_winequality.rst +++ b/docs/source/examples_learn_winequality.rst @@ -3,8 +3,7 @@ Wine Quality ============= -This example uses the Wine Quality dataset to predict the quality of white wine. -You can download the Jupyter Notebook of the study `here `__. +This example uses the Wine Quality dataset to predict the quality of white wine. - fixed acidity - volatile acidity @@ -226,7 +225,7 @@ If we want to improve this model, we'll probably need more relevant features. Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_understand_africa_education.rst b/docs/source/examples_understand_africa_education.rst index cf5cf67..df127c6 100644 --- a/docs/source/examples_understand_africa_education.rst +++ b/docs/source/examples_understand_africa_education.rst @@ -3,8 +3,7 @@ Africa Education ================= -This example uses the 'Africa Education' dataset to predict student performance. -You can can download the Jupyter Notebook of the study `here `__. +This example uses the 'Africa Education' dataset to predict student performance. - **COUNTRY:** COUNTRY ID. - **REGION:** REGION ID. @@ -327,7 +326,7 @@ information to predict the students' scores. from vastorbit.machine_learning.vast import RandomForestRegressor from vastorbit.machine_learning.model_selection import cross_validate - # RandomForest is backed by scikit-learn and needs numeric inputs, so we + # RandomForest is backed by ``scikit-learn`` and needs numeric inputs, so we # train on the numeric predictors only (the categorical text columns are # left out). Shallow trees keep the generated prediction SQL compact. @@ -920,7 +919,7 @@ We can see that the best students tend to be young, speak English at home, come Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_understand_amazon.rst b/docs/source/examples_understand_amazon.rst index eea3a3a..6bd0d67 100644 --- a/docs/source/examples_understand_amazon.rst +++ b/docs/source/examples_understand_amazon.rst @@ -3,7 +3,7 @@ Amazon ======= -This example uses the ``amazon`` dataset to predict the number of forest fires in Brazil. You can download a copy of the Jupyter Notebook of the study `here `__. +This example uses the ``amazon`` dataset to predict the number of forest fires in Brazil. - **date:** Date of the record - **number:** Number of forest fires @@ -297,7 +297,7 @@ The plot shows that our model has successfully captured the seasonality present Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/examples_understand_covid19.rst b/docs/source/examples_understand_covid19.rst index 1873dae..f820439 100644 --- a/docs/source/examples_understand_covid19.rst +++ b/docs/source/examples_understand_covid19.rst @@ -3,7 +3,7 @@ COVID-19 ========= -This example uses the ``covid19`` dataset to predict the number of deaths and cases one day in advance. You can download the Jupyter Notebook of the study `here `__. +This example uses the ``covid19`` dataset to predict the number of deaths and cases one day in advance. - **date:** Date of the record. - **cases:** Number of people infected. @@ -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 @@ -440,7 +440,7 @@ The model performs well but may be somewhat unstable. To improve it, we could ap Conclusion ----------- -We've solved our problem in a Pandas-like way, all without ever loading data into memory! +We've solved our problem in a pandas-like way, all without ever loading data into memory! .. ipython:: python :suppress: diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst index e0a624f..2053319 100644 --- a/docs/source/getting_started.rst +++ b/docs/source/getting_started.rst @@ -21,16 +21,16 @@ the work executes where the data lives, the same code runs unchanged whether you holding a few megabytes or many petabytes, and nothing leaves the platform except the results you ask for. -What is VAST Database? +What is VAST DataBase? ^^^^^^^^^^^^^^^^^^^^^^^ -VAST Database is a unified transactional and analytical database built for AI. It +VAST DataBase is a unified transactional and analytical database built for AI. It combines ACID transactions with analytics in a single system, stores data in a columnar format tuned for data-science workloads, and delivers flash-native, sub-millisecond latency that scales linearly from gigabytes to exabytes. Crucially, it lets you query tables and files through the same interface — which is exactly what lets VAST Orbit treat your whole estate as one surface. Learn more in the -`VAST Database documentation `__. +`VAST DataBase documentation `__. Installation ------------ @@ -81,7 +81,7 @@ Quick start The fastest way to understand VAST Orbit is to run a short workflow end to end. Each step below executes inside VAST. -**1. Connect to VAST Database** +**1. Connect to VAST DataBase** .. code-block:: python @@ -225,7 +225,7 @@ inference all happen in the database, and only the results come back. | v +--------------------------------+ - | VAST Database | + | VAST DataBase | | - Data preparation | | - Analytics | | - ML inference | diff --git a/docs/source/index.rst b/docs/source/index.rst index aded7ed..06d6d6e 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -77,8 +77,8 @@ off differently for everyone who touches the data. Your tools and your syntax, at any scale. - Keep the pandas and scikit-learn habits you already have. ``VastFrame`` behaves - like a DataFrame and the models follow the scikit-learn API, so the notebook you + Keep the pandas and ``scikit-learn`` habits you already have. ``VastFrame`` behaves + like a DataFrame and the models follow the ``scikit-learn`` API, so the notebook you write against a sample becomes production code against the full table — no rewrite, no new language, and no waiting on an export before you can think. @@ -138,7 +138,7 @@ Key features :text-align: center ``VastFrame`` behaves like a pandas DataFrame and the models follow the - scikit-learn API, so notebook code becomes production code unchanged. + ``scikit-learn`` API, so notebook code becomes production code unchanged. .. grid-item-card:: |i-multisource| One query, every source :text-align: center @@ -220,7 +220,7 @@ What you can do with it Train flexibly, then score billions of rows in place. VAST Orbit supports a hybrid workflow that matches how teams really work: - train quickly with the embedded algorithms or bring a scikit-learn model you + train quickly with the embedded algorithms or bring a ``scikit-learn`` model you trained locally, then deploy it for inference that runs as SQL inside VAST. Scoring happens where the data is, so you can refresh predictions on live data diff --git a/docs/source/parsers.rst b/docs/source/parsers.rst index b218b05..94035e1 100644 --- a/docs/source/parsers.rst +++ b/docs/source/parsers.rst @@ -34,7 +34,7 @@ JSON ____ -Pandas +pandas ------ .. currentmodule:: vastorbit diff --git a/docs/source/user_guide.rst b/docs/source/user_guide.rst index 89d7319..a3e9ce2 100644 --- a/docs/source/user_guide.rst +++ b/docs/source/user_guide.rst @@ -78,7 +78,7 @@ Follow this structured path to master VAST Orbit. Total time: ~4.5 hours. ⏱️ **20 minutes** - Load data from multiple sources into VAST DataBase. Query files directly (Parquet, CSV, JSON) without loading. Access external databases via VASTDB's federated queries. + Load data from multiple sources into VAST DataBase. Query files directly (Parquet, CSV, JSON) without loading. Access external databases via VAST DataBase's federated queries. **Key Topics:** @@ -182,11 +182,11 @@ Follow this structured path to master VAST Orbit. Total time: ~4.5 hours. **Key Topics:** - - Geospatial analytics with GeoPandas + - Geospatial analytics with Geopandas - Real-time streaming with Kafka - Production deployment patterns - :bdg-primary:`GeoPandas` :bdg-primary:`Production` :bdg-primary:`Advanced` + :bdg-primary:`Geopandas` :bdg-primary:`Production` :bdg-primary:`Advanced` +++ Start Learning → diff --git a/docs/source/user_guide_data_exploration_correlations.rst b/docs/source/user_guide_data_exploration_correlations.rst index 82bcee1..cd8be68 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 6810f30..fb11e33 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_data_ingestion.rst b/docs/source/user_guide_data_ingestion.rst index 09c91d8..651e497 100644 --- a/docs/source/user_guide_data_ingestion.rst +++ b/docs/source/user_guide_data_ingestion.rst @@ -4,7 +4,7 @@ Data Ingestion =============== -Load data into VAST database from multiple sources. +Load data into VAST DataBase from multiple sources. ____ diff --git a/docs/source/user_guide_full_stack.rst b/docs/source/user_guide_full_stack.rst index 32fd7d8..583d8dd 100644 --- a/docs/source/user_guide_full_stack.rst +++ b/docs/source/user_guide_full_stack.rst @@ -13,7 +13,7 @@ ____ .. grid-item:: - .. card:: |i-geo| GeoPandas Integration + .. card:: |i-geo| Geopandas Integration :link: user_guide.full_stack.geopandas :link-type: ref :text-align: center diff --git a/docs/source/user_guide_full_stack_geopandas.rst b/docs/source/user_guide_full_stack_geopandas.rst index ac14496..12f18bb 100644 --- a/docs/source/user_guide_full_stack_geopandas.rst +++ b/docs/source/user_guide_full_stack_geopandas.rst @@ -1,12 +1,12 @@ .. _user_guide.full_stack.geopandas: =========================== -Integrating with GeoPandas +Integrating with Geopandas =========================== -GeoPandas integration allows you to easily export a :py:mod:`~vastorbit.VastFrame` as a GeoPandas DataFrame, giving you more control over geospatial data. +Geopandas integration allows you to easily export a :py:mod:`~vastorbit.VastFrame` as a Geopandas DataFrame, giving you more control over geospatial data. -This example demonstrates the advantages of GeoPandas integration with the ``world`` dataset. +This example demonstrates the advantages of Geopandas integration with the ``world`` dataset. .. code-block:: python @@ -58,7 +58,7 @@ The :py:func:`~vastorbit.VastFrame.apply` function of the vastorbit stats module .. raw:: html :file: SPHINX_DIRECTORY/figures/ug_fs_table_gpd_2.html -We can now export our VastFrame as a GeoPandas DataFrame. +We can now export our VastFrame as a Geopandas DataFrame. .. ipython:: python diff --git a/docs/source/user_guide_full_stack_train_test_split.rst b/docs/source/user_guide_full_stack_train_test_split.rst index efd2e0f..a5b4023 100644 --- a/docs/source/user_guide_full_stack_train_test_split.rst +++ b/docs/source/user_guide_full_stack_train_test_split.rst @@ -13,7 +13,7 @@ In vastorbit, the :py:func:`~vastorbit.VastFrame.train_test_split` method uses a from vastorbit.datasets import load_titanic titanic = load_titanic() - # LinearRegression is scikit-learn backed and rejects NaN, so we drop rows + # LinearRegression is ``scikit-learn`` backed and rejects NaN, so we drop rows # with missing values in the columns we model on before splitting. titanic = titanic.dropna(columns = ["age", "fare", "survived"]) train, test = titanic.train_test_split() diff --git a/docs/source/user_guide_introduction.rst b/docs/source/user_guide_introduction.rst index 37d8b46..6b6f23b 100644 --- a/docs/source/user_guide_introduction.rst +++ b/docs/source/user_guide_introduction.rst @@ -15,7 +15,7 @@ ____ .. grid-item:: - .. card:: |i-connect| Connect to VAST Database + .. card:: |i-connect| Connect to VAST DataBase :link: user_guide.introduction.installation :link-type: ref :text-align: center diff --git a/docs/source/user_guide_introduction_best_practices.rst b/docs/source/user_guide_introduction_best_practices.rst index 2c1d62f..f18cbd0 100644 --- a/docs/source/user_guide_introduction_best_practices.rst +++ b/docs/source/user_guide_introduction_best_practices.rst @@ -11,7 +11,7 @@ ____ Restrict to Essential Columns ------------------------------ -VAST Orbit executes queries directly in the VAST database. Limiting operations to essential columns significantly improves performance, especially with large datasets. +VAST Orbit executes queries directly in the VAST DataBase. Limiting operations to essential columns significantly improves performance, especially with large datasets. **Load specific columns:** diff --git a/docs/source/user_guide_introduction_installation.rst b/docs/source/user_guide_introduction_installation.rst index f576a41..2ef8ba0 100644 --- a/docs/source/user_guide_introduction_installation.rst +++ b/docs/source/user_guide_introduction_installation.rst @@ -1,10 +1,10 @@ .. _user_guide.introduction.installation: ============================== -Connect to a VAST Database +Connect to a VAST DataBase ============================== -Quick guide to connecting VAST Orbit to your VAST database. +Quick guide to connecting VAST Orbit to your VAST DataBase. ____ @@ -13,7 +13,7 @@ Requirements Before connecting, ensure you have: -- **VAST Database** – Any version with Trino interface enabled +- **VAST DataBase** – Any version with Trino interface enabled - **Python 3.12+** – Latest Python installation - **VAST Orbit** – Installed via ``pip install vastorbit`` diff --git a/docs/source/user_guide_introduction_vdf.rst b/docs/source/user_guide_introduction_vdf.rst index 2d39ca3..5bb3075 100644 --- a/docs/source/user_guide_introduction_vdf.rst +++ b/docs/source/user_guide_introduction_vdf.rst @@ -11,7 +11,7 @@ ____ Overview -------- -The :py:mod:`~vastorbit.VastFrame` is the core object of VAST Orbit. It enables Python-based data manipulation without moving data from the VAST database to local memory. +The :py:mod:`~vastorbit.VastFrame` is the core object of VAST Orbit. It enables Python-based data manipulation without moving data from the VAST DataBase to local memory. **Key benefits:** diff --git a/docs/source/user_guide_machine_learning_regression.rst b/docs/source/user_guide_machine_learning_regression.rst index 352ebeb..b949ffa 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_v0_1_0.rst b/docs/source/whats_new_v0_1_0.rst index 7d8375a..aaf6e81 100644 --- a/docs/source/whats_new_v0_1_0.rst +++ b/docs/source/whats_new_v0_1_0.rst @@ -17,7 +17,7 @@ Version 0.1.0 (Beta) -Welcome to the first release of **VAST Orbit** - Python data science for VAST Database! +Welcome to the first release of **VAST Orbit** - Python data science for VAST DataBase! .. important:: @@ -29,7 +29,7 @@ Release Highlights |i-start| **Python Data Science for VAST** -VAST Orbit 0.1.0 brings complete data science workflows to VAST Database - prepare, explore, analyze, and build ML models with in-database execution. +VAST Orbit 0.1.0 brings complete data science workflows to VAST DataBase - prepare, explore, analyze, and build ML models with in-database execution. **Core Features:** @@ -38,7 +38,7 @@ VAST Orbit 0.1.0 brings complete data science workflows to VAST Database - prepa - **400+ Functions** - Complete analytics toolkit executing in VAST - **10 ML Algorithms** - Embedded models for training and inference - **Multi-Source Access** - Query VAST tables, files, and external databases -- **Zero Data Movement** - All processing in VAST Database +- **Zero Data Movement** - All processing in VAST DataBase What's Included @@ -84,7 +84,7 @@ What's Included - Python 3.12+ - Linux and macOS -- VAST Database 5.0.0-sp10 or later +- VAST DataBase 5.0.0-sp10 or later **Example Usage:** @@ -92,7 +92,7 @@ What's Included import vastorbit as vo - # Connect to VAST Database + # Connect to VAST DataBase vo.new_connection({ 'host': 'vast-cluster.com', 'catalog': 'vast_catalog' @@ -207,7 +207,7 @@ We're excited to hear your feedback as we work toward 1.0.0! Thank You --------- -Thank you for being an early adopter of VAST Orbit. Your feedback shapes the future of data science on VAST Database. +Thank you for being an early adopter of VAST Orbit. Your feedback shapes the future of data science on VAST DataBase. **Get Involved:** diff --git a/docs/vastorbit-ai-assistant/README.md b/docs/vastorbit-ai-assistant/README.md index 5d18a94..99886a8 100644 --- a/docs/vastorbit-ai-assistant/README.md +++ b/docs/vastorbit-ai-assistant/README.md @@ -136,7 +136,7 @@ cd YOUR_DOCS && make html - "How do I get started with VAST Orbit?" - "What's the syntax for creating a vDataFrame?" - "Show me examples of machine learning with VAST Orbit" -- "How do I connect to VAST Database?" +- "How do I connect to VAST DataBase?" ## ⚙️ Configuration diff --git a/docs/vastorbit-ai-assistant/backend/app.py b/docs/vastorbit-ai-assistant/backend/app.py index da307ab..0c2e519 100644 --- a/docs/vastorbit-ai-assistant/backend/app.py +++ b/docs/vastorbit-ai-assistant/backend/app.py @@ -251,9 +251,9 @@ def generate_answer( messages.append({"role": "user", "content": question}) # IMPROVED System prompt - VAST-focused, emphasizes data prep and exploration - system_prompt = f"""You are Astra, VAST Orbit's AI documentation assistant. VAST Orbit is a Python library that brings data science to VAST Database with in-database execution. + system_prompt = f"""You are Astra, VAST Orbit's AI documentation assistant. VAST Orbit is a Python library that brings data science to VAST DataBase with in-database execution. -Your role is to help users prepare data, explore it interactively, and build ML workflows - all directly in VAST Database. +Your role is to help users prepare data, explore it interactively, and build ML workflows - all directly in VAST DataBase. Key Messaging: - Emphasize "data preparation" - cleaning, transforming, feature engineering in VAST diff --git a/examples/README.md b/examples/README.md deleted file mode 100755 index 5207cf6..0000000 --- a/examples/README.md +++ /dev/null @@ -1,19 +0,0 @@ -# VAST Orbit Examples - -Welcome to the VAST Orbit examples! This directory showcases the capabilities of VAST Orbit, a Python library for data science and analytics with VAST Database. - -## Categories - -- **[Learning VAST Orbit](learn/)** - Basic examples and tutorials for getting started with VAST Orbit's core functionalities. - -- **[Real-World Applications](understand/)** - Advanced examples exploring practical data science scenarios and use cases. - -- **[Business Solutions](business/)** - Industry-focused applications demonstrating VAST Orbit in production environments. - -## Contributing - -We welcome community contributions! Share your VAST Orbit notebooks and examples by submitting a pull request. For contribution guidelines, see the main project documentation. - -## Support - -For questions and support, join our Slack channel: **vastsupport.slack.com** \ No newline at end of file diff --git a/examples/business/README.md b/examples/business/README.md deleted file mode 100755 index dafd876..0000000 --- a/examples/business/README.md +++ /dev/null @@ -1,13 +0,0 @@ -# Business Applications - -Industry-focused examples demonstrating how VAST Orbit can be applied to real-world business use cases across various sectors including retail, finance, healthcare, and more. - -These notebooks showcase data analysis, predictive modeling, and insight generation for informed business decision-making using VAST Orbit with VAST Database. - -## Examples - -*Examples will be added as they become available.* - -## Support - -For questions and support, join our Slack channel: **vastsupport.slack.com** \ No newline at end of file diff --git a/examples/learn/README.md b/examples/learn/README.md deleted file mode 100755 index eda4c53..0000000 --- a/examples/learn/README.md +++ /dev/null @@ -1,13 +0,0 @@ -# Learning VAST Orbit - -Tutorials and examples to help you get started with VAST Orbit's core features and functionalities. - -These notebooks cover essential topics including data preprocessing, visualization, exploratory data analysis, and basic machine learning tasks. Each example includes explanations and code to guide you through using VAST Orbit with VAST Database. - -## Examples - -*Examples will be added as they become available.* - -## Support - -For questions and support, join our Slack channel: **vastsupport.slack.com** \ No newline at end of file diff --git a/examples/understand/README.md b/examples/understand/README.md deleted file mode 100755 index f5d122c..0000000 --- a/examples/understand/README.md +++ /dev/null @@ -1,13 +0,0 @@ -# Real-World Applications - -Advanced examples exploring practical data science scenarios and complex real-world use cases. - -These notebooks demonstrate sophisticated data manipulation, feature engineering, and advanced machine learning techniques using VAST Orbit with VAST Database. Learn how to tackle real-world data science challenges at scale. - -## Examples - -*Examples will be added as they become available.* - -## Support - -For questions and support, join our Slack channel: **vastsupport.slack.com** \ No newline at end of file diff --git a/requirements-test.txt b/requirements-test.txt index 9e83ac4..b0260ab 100644 --- a/requirements-test.txt +++ b/requirements-test.txt @@ -5,7 +5,7 @@ pytest-cov>=4.0 # Database driver trino>=0.327 -# VastOrbit runtime dependencies (listed explicitly so the suite installs even +# VAST Orbit runtime dependencies (listed explicitly so the suite installs even # if the repo is checked out without a populated setup.py / pyproject.toml). # If your repo root is pip-installable, `pip install -e .` already covers these. numpy diff --git a/requirements.txt b/requirements.txt index 3a93be8..5e492df 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -# VastOrbit core runtime dependencies. +# VAST Orbit core runtime dependencies. # # These are the packages the library needs to import and run. Test and # development tooling live in requirements-dev.txt and requirements-testing.txt, diff --git a/setup.py b/setup.py index b8ad87a..db48d84 100644 --- a/setup.py +++ b/setup.py @@ -34,7 +34,7 @@ ], description=( "vastorbit simplifies data exploration, data cleaning, and machine " - "learning in VAST databases using in-database analytics." + "learning in VAST DataBases using in-database analytics." ), long_description=long_description, long_description_content_type="text/markdown", diff --git a/tests/README.md b/tests/README.md index be8df07..b789d0d 100644 --- a/tests/README.md +++ b/tests/README.md @@ -1,6 +1,6 @@ -# VastOrbit tests +# VAST Orbit tests -Fast, broad **smoke coverage** of the VastOrbit public API. Each test runs an +Fast, broad **smoke coverage** of the VAST Orbit public API. Each test runs an operation on a small built-in dataset and checks that it *executes and returns a sane shape/type* — not exact numbers. Heavy work is deliberately avoided: tiny tree ensembles, short synthetic series, `cv=3` on flat relations. diff --git a/tests/conftest.py b/tests/conftest.py index 092b392..9f90fd8 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,13 +1,13 @@ """ SPDX-License-Identifier: Apache-2.0 -Root fixtures for the VastOrbit test-suite. +Root fixtures for the VAST Orbit test-suite. Connection ---------- Tests connect to a **local Trino** by default (``localhost:8080``) using the ``memory`` connector, which needs no external database and supports -``CREATE TABLE`` / ``INSERT`` (what the loaders and VastOrbit's temp tables +``CREATE TABLE`` / ``INSERT`` (what the loaders and VAST Orbit's temp tables require). Every detail is overridable with environment variables so the same suite runs unchanged in CI: diff --git a/vastorbit.zip b/vastorbit.zip new file mode 100644 index 0000000..2360405 Binary files /dev/null and b/vastorbit.zip differ diff --git a/vastorbit/__init__.py b/vastorbit/__init__.py index dd3b300..5633c4e 100755 --- a/vastorbit/__init__.py +++ b/vastorbit/__init__.py @@ -6,7 +6,7 @@ __author_email__: str = "badr.ouali@outlook.fr" __description__: str = ( "vastorbit simplifies data exploration, data cleaning" - " and machine learning in VAST." + " AI and machine learning in VAST." ) __url__: str = "https://github.com/vastdata-dev/vastorbit/" __license__: str = "Apache License, Version 2.0" diff --git a/vastorbit/_utils/_object.py b/vastorbit/_utils/_object.py index 7ef98c7..e994fa9 100755 --- a/vastorbit/_utils/_object.py +++ b/vastorbit/_utils/_object.py @@ -63,7 +63,7 @@ def get_VAST_mllib() -> Literal["vml"]: def read_pd(*args, **kwargs) -> "VastFrame": """ - Reads a Pandas DataFrame into a vastorbit + Reads a pandas DataFrame into a vastorbit VastFrame. It uses the :py:func:`~vastorbit.pandas.read_pandas` function. diff --git a/vastorbit/_utils/_sql/_collect.py b/vastorbit/_utils/_sql/_collect.py index 05100c6..5f35403 100755 --- a/vastorbit/_utils/_sql/_collect.py +++ b/vastorbit/_utils/_sql/_collect.py @@ -121,7 +121,7 @@ def save_to_query_profile( """ Saves information about the specified vastorbit method to the QUERY_PROFILES - table in the VAST database. It is + table in the VAST DataBase. It is used to collect usage statistics on methods and their parameters. This function generates a JSON string. diff --git a/vastorbit/_utils/_sql/_sys.py b/vastorbit/_utils/_sql/_sys.py index 5eb2179..a56eacd 100755 --- a/vastorbit/_utils/_sql/_sys.py +++ b/vastorbit/_utils/_sql/_sys.py @@ -169,7 +169,7 @@ def purge_memory( """ Drops staging tables left behind in Trino's ``memory`` connector. - VastOrbit ingestion helpers (for example :py:func:`~vastorbit.read_csv`) + VAST Orbit ingestion helpers (for example :py:func:`~vastorbit.read_csv`) stage data in the ``memory`` connector before copying it into VAST. When a load raises before its cleanup step, the staging table survives and either fills the connector's memory budget (``MEMORY_LIMIT_EXCEEDED``) or collides @@ -203,7 +203,7 @@ def purge_memory( from vastorbit._utils._sql._sys import purge_memory purge_memory() # full reset before an example - purge_memory(like="_vastorbit_tmp_%") # only VastOrbit's own staging + purge_memory(like="_vastorbit_tmp_%") # only VAST Orbit's own staging """ # 1. Enumerate the staging tables currently in the memory connector. list_sql = ( diff --git a/vastorbit/connection/connect.py b/vastorbit/connection/connect.py index 5e14a5e..7b6bb1f 100644 --- a/vastorbit/connection/connect.py +++ b/vastorbit/connection/connect.py @@ -442,7 +442,7 @@ def vast_connection( ) -> Any: """ Reads the input DSN and - creates a VAST Database + creates a VAST DataBase connection using Trino. Parameters @@ -583,7 +583,7 @@ def vastorbitlab_connection() -> Any: Sets the vastorbit connection. | :py:func:`~vastorbit.connection.vast_connection` : Reads the input DSN and creates a - VAST Database connection. + VAST DataBase connection. """ if trino is None: raise ImportError( @@ -591,7 +591,7 @@ def vastorbitlab_connection() -> Any: ) conn_info = { - "host": "VASTdb", + "host": "VAST DataBase", "port": 8080, "user": "admin", "catalog": "vast", diff --git a/vastorbit/core/parsers/csv.py b/vastorbit/core/parsers/csv.py index e73b6b9..54fb939 100755 --- a/vastorbit/core/parsers/csv.py +++ b/vastorbit/core/parsers/csv.py @@ -29,7 +29,7 @@ def infer_trino_type(series: pd.Series, column_name: str) -> str: Parameters ---------- series : pd.Series - Pandas series to analyze + pandas series to analyze column_name : str Name of the column (for error messages) @@ -229,7 +229,7 @@ def read_csv( If True, return SQL statements without executing batch_size: int, optional Batch size specifies the number of rows inserted into the - VAST DB concurrently during a bulk operation. + VAST DataBase concurrently during a bulk operation. Returns ------- diff --git a/vastorbit/core/parsers/pandas.py b/vastorbit/core/parsers/pandas.py index e3a16c8..516ab94 100755 --- a/vastorbit/core/parsers/pandas.py +++ b/vastorbit/core/parsers/pandas.py @@ -33,7 +33,7 @@ def read_pandas( ) -> VastFrame: """ Ingests a ``pandas.DataFrame`` into - the VAST database via Trino by creating + the VAST DataBase via Trino by creating a temporary CSV file and using the CSV parser to load the data. @@ -114,7 +114,7 @@ def read_pandas( In this example, we will first create a ``pandas.DataFrame`` using ``VastFrame.``:py:meth:`~vastorbit.VastFrame.to_pandas` - and ingest it into VAST database. + and ingest it into VAST DataBase. We import :py:mod:`vastorbit`: @@ -188,7 +188,7 @@ def read_pandas( Now, we will ingest the ``pandas.DataFrame`` - into the VAST database. + into the VAST DataBase. .. code-block:: python @@ -297,9 +297,9 @@ def read_pandas( .. seealso:: | :py:func:`~vastorbit.read_csv` : - Ingests a CSV file into the VAST DB. + Ingests a CSV file into the VAST DataBase. | :py:func:`~vastorbit.read_json` : - Ingests a JSON file into the VAST DB. + Ingests a JSON file into the VAST DataBase. """ dtype = format_type(dtype, dtype=dict) if not schema: diff --git a/vastorbit/core/vastframe/_eval.py b/vastorbit/core/vastframe/_eval.py index 6bae783..070ad4d 100755 --- a/vastorbit/core/vastframe/_eval.py +++ b/vastorbit/core/vastframe/_eval.py @@ -150,7 +150,7 @@ def eval(self, name: str, expr: Union[str, StringSQL]) -> "VastFrame": .. note:: - You can also create a feature in a Pandas-like way by assigning + You can also create a feature in a pandas-like way by assigning a result to a VastColumn. For example, similar to the above, the ``eval`` operation can be expressed as: @@ -190,7 +190,7 @@ def eval(self, name: str, expr: Union[str, StringSQL]) -> "VastFrame": .. note:: - You can also create a feature in a Pandas-like way by assigning + You can also create a feature in a pandas-like way by assigning a result to a VastColumn. For example, similar to the above, the ``eval`` operation can be expressed as: diff --git a/vastorbit/core/vastframe/_filter.py b/vastorbit/core/vastframe/_filter.py index c4b0259..d7b89d6 100755 --- a/vastorbit/core/vastframe/_filter.py +++ b/vastorbit/core/vastframe/_filter.py @@ -1103,7 +1103,7 @@ def filter( .. note:: - Similarly, the same can be done in a Pandas-like way: + Similarly, the same can be done in a pandas-like way: .. code-block:: python @@ -1792,7 +1792,7 @@ def search( .. note:: - Similarly, the same can be done in a Pandas-like way: + Similarly, the same can be done in a pandas-like way: .. code-block:: python diff --git a/vastorbit/core/vastframe/_io.py b/vastorbit/core/vastframe/_io.py index e55f421..52bd2ae 100755 --- a/vastorbit/core/vastframe/_io.py +++ b/vastorbit/core/vastframe/_io.py @@ -572,7 +572,7 @@ def to_csv( | ``VastFrame.``:py:meth:`~vastorbit.VastFrame.to_db` : Saves the current structure of :py:class:`~VastFrame` to the - VAST Database. + VAST DataBase. | ``VastFrame.``:py:meth:`~vastorbit.VastFrame.to_json` : Creates a JSON file of the current :py:class:`~VastFrame` structure. @@ -1095,7 +1095,7 @@ def to_geopandas(self, geometry: str) -> "GeoDataFrame": | ``VastFrame.``:py:meth:`~vastorbit.VastFrame.to_db` : Saves the current structure of :py:class:`~VastFrame` to the - VAST Database. + VAST DataBase. | ``VastFrame.``:py:meth:`~vastorbit.VastFrame.to_json` : Creates a JSON file of the current :py:class:`~VastFrame` structure. @@ -1286,7 +1286,7 @@ def to_json( | ``VastFrame.``:py:meth:`~vastorbit.VastFrame.to_db` : Saves the current structure of :py:class:`~VastFrame` to the - VAST Database. + VAST DataBase. | ``VastFrame.``:py:meth:`~vastorbit.VastFrame.to_csv` : Creates a CSV file of the current :py:class:`~VastFrame` structure. diff --git a/vastorbit/core/vastframe/base.py b/vastorbit/core/vastframe/base.py index 106f335..64fe01e 100755 --- a/vastorbit/core/vastframe/base.py +++ b/vastorbit/core/vastframe/base.py @@ -54,7 +54,7 @@ class VastFrame(vDFAnimatedPlot): mutating the underlying data in VAST. When changes are made, the :py:class:`~VastFrame` queries - the VAST database, which aggregates + the VAST DataBase, which aggregates and returns the final result. The :py:class:`~VastFrame` creates, for each column of the relation, a Virtual @@ -133,7 +133,7 @@ class VastFrame(vDFAnimatedPlot): the mathematical operators that are available: - - Pandas-Like + - pandas-Like - SQL-Like Lastly, we will look at some @@ -223,14 +223,14 @@ class VastFrame(vDFAnimatedPlot): .. raw:: html :file: SPHINX_DIRECTORY/figures/core_VastFrame_base_2.html - Pandas DataFrame + pandas DataFrame ^^^^^^^^^^^^^^^^^ We can also use a ``pandas.DataFrame`` object: .. ipython:: python - # Import Pandas library + # Import pandas library import pandas as pd # Create the data dictionary @@ -240,7 +240,7 @@ class VastFrame(vDFAnimatedPlot): 'City': ['New York', 'Gaza', 'Los Angeles'], } - # Create the Pandas DataFrame object + # Create the pandas DataFrame object df = pd.DataFrame(data) # Create a VastFrame @@ -315,7 +315,7 @@ class VastFrame(vDFAnimatedPlot): We can use all the common mathematical operators on the :py:class:`~VastFrame`. - Pandas-Like + pandas-Like ^^^^^^^^^^^^ First let us re-create a simple diff --git a/vastorbit/datasets/loaders.py b/vastorbit/datasets/loaders.py index 9d21f1d..eb95c47 100644 --- a/vastorbit/datasets/loaders.py +++ b/vastorbit/datasets/loaders.py @@ -288,7 +288,7 @@ def load_dataset( catalog: Optional[str] = None, ) -> VastFrame: """ - Ingest a dataset into VASTDB via Trino. + Ingest a dataset into VAST DataBase via Trino. Trino Implementation Strategy: 1. Check if table already exists → return VastFrame @@ -513,7 +513,7 @@ def load_market(schema: Optional[str] = None, name: str = "market") -> VastFrame @save_vastorbit_logs def load_iris(schema: Optional[str] = None, name: str = "iris") -> VastFrame: """ - Ingests the iris dataset into the VAST database. + Ingests the iris dataset into the VAST DataBase. This dataset is ideal for classification and clustering models. If a table with the same name and schema already exists, @@ -578,7 +578,7 @@ def load_iris(schema: Optional[str] = None, name: str = "iris") -> VastFrame: @save_vastorbit_logs def load_titanic(schema: Optional[str] = None, name: str = "titanic") -> VastFrame: """ - Ingests the titanic dataset into the VAST database. + Ingests the titanic dataset into the VAST DataBase. This dataset is ideal for classification models. If a table with the same name and schema already exists, @@ -646,7 +646,7 @@ def load_africa_education( schema: Optional[str] = None, name: str = "africa_education" ) -> VastFrame: """ - Ingests the Africa Education dataset into the VAST database. + Ingests the Africa Education dataset into the VAST DataBase. This dataset is ideal for testing geospatial functions. If a table with the same name and schema already exists, this function creates a VastFrame from the input relation. @@ -762,7 +762,7 @@ def load_winequality( schema: Optional[str] = None, name: str = "winequality" ) -> VastFrame: """ - Ingests the winequality dataset into the VAST database. + Ingests the winequality dataset into the VAST DataBase. This dataset is ideal for regression and classification models. If a table with the same name and schema already exists, @@ -835,7 +835,7 @@ def load_airline_passengers( schema: Optional[str] = None, name: str = "airline_passengers" ) -> VastFrame: """ - Ingests the airline passengers dataset into the VAST database. + Ingests the airline passengers dataset into the VAST DataBase. This dataset is ideal for time series and regression models. If a table with the same name and schema already exists, @@ -886,7 +886,7 @@ def load_airline_passengers( @save_vastorbit_logs def load_amazon(schema: Optional[str] = None, name: str = "amazon") -> VastFrame: """ - Ingests the amazon dataset into the VAST database. + Ingests the amazon dataset into the VAST DataBase. This dataset is ideal for time series and regression models. If a table with the same name and schema already exists, @@ -939,7 +939,7 @@ def load_commodities( schema: Optional[str] = None, name: str = "commodities" ) -> VastFrame: """ - Ingests the commodities dataset into the VAST database. + Ingests the commodities dataset into the VAST DataBase. This dataset is ideal for time series and regression models. If a table with the same name and schema already exists, @@ -998,7 +998,7 @@ def load_commodities( @save_vastorbit_logs def load_gapminder(schema: Optional[str] = None, name: str = "gapminder") -> VastFrame: """ - Ingests the gapminder dataset into the VAST database. + Ingests the gapminder dataset into the VAST DataBase. This dataset is ideal for time series and regression models. If a table with the same name and schema already exists, @@ -1058,7 +1058,7 @@ def load_pop_growth( schema: Optional[str] = None, name: str = "pop_growth" ) -> VastFrame: """ - Ingests the population growth dataset into the VAST database. + Ingests the population growth dataset into the VAST DataBase. This dataset is ideal for time series and geospatial models. If a table with the same name and schema already exists, @@ -1119,7 +1119,7 @@ def load_smart_meters( schema: Optional[str] = None, name: str = "smart_meters" ) -> VastFrame: """ - Ingests the smart meters dataset into the VAST database. + Ingests the smart meters dataset into the VAST DataBase. This dataset is ideal for time series and regression models. If a table with the same name and schema already exists, @@ -1175,7 +1175,7 @@ def load_smart_meters( @save_vastorbit_logs def load_cities(schema: Optional[str] = None, name: str = "cities") -> VastFrame: """ - Ingests the Cities dataset into the VAST database. + Ingests the Cities dataset into the VAST DataBase. This dataset is ideal for geospatial models. If a table with the same name and schema already exists, @@ -1230,7 +1230,7 @@ def load_cities(schema: Optional[str] = None, name: str = "cities") -> VastFrame @save_vastorbit_logs def load_world(schema: Optional[str] = None, name: str = "world") -> VastFrame: """ - Ingests the World dataset into the VAST database. + Ingests the World dataset into the VAST DataBase. This dataset is ideal for geospatial models. If a table with the same name and schema already exists, @@ -1295,7 +1295,7 @@ def load_world(schema: Optional[str] = None, name: str = "world") -> VastFrame: @save_vastorbit_logs def load_laliga(schema: Optional[str] = None, name: str = "laliga") -> VastFrame: """ - Ingests the LaLiga dataset into the VAST database. + Ingests the LaLiga dataset into the VAST DataBase. This dataset is ideal for testing complex data types. If a table with the same name and schema already exists, diff --git a/vastorbit/jupyter/extensions/ai_magic.py b/vastorbit/jupyter/extensions/ai_magic.py index 2774c16..0127385 100644 --- a/vastorbit/jupyter/extensions/ai_magic.py +++ b/vastorbit/jupyter/extensions/ai_magic.py @@ -448,7 +448,7 @@ def _resolve_api_key(explicit: str | None = None) -> str | None: @magics_class -class VastOrbitAIMagics(Magics): +class VASTOrbitAIMagics(Magics): """ Jupyter magics for VAST Orbit AI code generation. @@ -720,7 +720,7 @@ def load_ipython_extension(ipython): Registers all magics and auto-detects the API key from env vars or config file. """ - magics = VastOrbitAIMagics(ipython) + magics = VASTOrbitAIMagics(ipython) ipython.register_magics(magics) key_status = ( diff --git a/vastorbit/machine_learning/memmodel/cluster.py b/vastorbit/machine_learning/memmodel/cluster.py index 195cb22..664ebf9 100755 --- a/vastorbit/machine_learning/memmodel/cluster.py +++ b/vastorbit/machine_learning/memmodel/cluster.py @@ -661,7 +661,7 @@ class KMeans(Clustering): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. """ @@ -811,7 +811,7 @@ class NearestCentroid(Clustering): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. """ @@ -1033,7 +1033,7 @@ class BisectingKMeans(Clustering, Tree): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. """ @@ -1630,7 +1630,7 @@ class KPrototypes(Clustering): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. """ diff --git a/vastorbit/machine_learning/memmodel/decomposition.py b/vastorbit/machine_learning/memmodel/decomposition.py index d4077b1..f1b4bfa 100755 --- a/vastorbit/machine_learning/memmodel/decomposition.py +++ b/vastorbit/machine_learning/memmodel/decomposition.py @@ -159,7 +159,7 @@ class PCA(InMemoryModel): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. """ @@ -592,7 +592,7 @@ class SVD(InMemoryModel): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. """ diff --git a/vastorbit/machine_learning/memmodel/ensemble.py b/vastorbit/machine_learning/memmodel/ensemble.py index 354f8ac..d208c0d 100755 --- a/vastorbit/machine_learning/memmodel/ensemble.py +++ b/vastorbit/machine_learning/memmodel/ensemble.py @@ -262,7 +262,7 @@ class RandomForestRegressor(Ensemble): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. **Drawing Trees** @@ -612,7 +612,7 @@ class RandomForestClassifier(Ensemble, MulticlassClassifier): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. **Drawing Trees** @@ -1000,7 +1000,7 @@ class GradientBoostingRegressor(Ensemble): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. **Drawing Trees** @@ -1399,7 +1399,7 @@ class GradientBoostingClassifier(Ensemble, MulticlassClassifier): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. **Drawing Trees** @@ -1798,7 +1798,7 @@ class IsolationForest(Ensemble): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. **Drawing Trees** diff --git a/vastorbit/machine_learning/memmodel/linear_model.py b/vastorbit/machine_learning/memmodel/linear_model.py index 70e7c78..8cdb1c3 100755 --- a/vastorbit/machine_learning/memmodel/linear_model.py +++ b/vastorbit/machine_learning/memmodel/linear_model.py @@ -107,7 +107,7 @@ class LinearModel(InMemoryModel): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. """ @@ -523,7 +523,7 @@ class LinearModelClassifier(LinearModel): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. """ diff --git a/vastorbit/machine_learning/memmodel/naive_bayes.py b/vastorbit/machine_learning/memmodel/naive_bayes.py index 9a0c72e..60c3346 100644 --- a/vastorbit/machine_learning/memmodel/naive_bayes.py +++ b/vastorbit/machine_learning/memmodel/naive_bayes.py @@ -286,7 +286,7 @@ class NaiveBayes(MulticlassClassifier): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. """ diff --git a/vastorbit/machine_learning/memmodel/preprocessing.py b/vastorbit/machine_learning/memmodel/preprocessing.py index f2dcf0e..87bb3ef 100755 --- a/vastorbit/machine_learning/memmodel/preprocessing.py +++ b/vastorbit/machine_learning/memmodel/preprocessing.py @@ -289,7 +289,7 @@ class StandardScaler(Scaler): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. """ @@ -403,7 +403,7 @@ class MinMaxScaler(Scaler): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. """ @@ -541,7 +541,7 @@ class OneHotEncoder(InMemoryModel): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. """ diff --git a/vastorbit/machine_learning/memmodel/tree.py b/vastorbit/machine_learning/memmodel/tree.py index 128ad3b..16d50c6 100755 --- a/vastorbit/machine_learning/memmodel/tree.py +++ b/vastorbit/machine_learning/memmodel/tree.py @@ -1018,7 +1018,7 @@ class BinaryTreeRegressor(Tree): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. **Drawing Tree** @@ -1249,7 +1249,7 @@ class BinaryTreeAnomaly(Tree): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. **Drawing Tree** @@ -1501,7 +1501,7 @@ class BinaryTreeClassifier(Tree): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. **Drawing Tree** @@ -1754,7 +1754,7 @@ class NonBinaryTree(Tree): This object can be pickled and used in any in-memory environment, just like - `SKLEARN `__ + `scikit-learn `__ models. **Drawing Tree** diff --git a/vastorbit/machine_learning/vast/automl/dataprep.py b/vastorbit/machine_learning/vast/automl/dataprep.py index 242e0e5..f19c51f 100755 --- a/vastorbit/machine_learning/vast/automl/dataprep.py +++ b/vastorbit/machine_learning/vast/automl/dataprep.py @@ -31,7 +31,7 @@ class AutoDataPrep(VASTModel): ---------- name: str, optional Name of the model in which to store the output - relation in the VAST database. + relation in the VAST DataBase. overwrite_model: bool, optional If set to ``True``, training a model with the same name as an @@ -183,7 +183,7 @@ def __init__( def drop(self) -> bool: """ - Drops the model from the VAST database. + Drops the model from the VAST DataBase. """ # it could be stored as a model or a table dropped_model = super().drop() diff --git a/vastorbit/machine_learning/vast/base.py b/vastorbit/machine_learning/vast/base.py index d8b2a87..98c0cff 100644 --- a/vastorbit/machine_learning/vast/base.py +++ b/vastorbit/machine_learning/vast/base.py @@ -78,14 +78,14 @@ def _sklearn_model(self) -> Literal[None]: def _get_sklearn_params(self) -> dict: """ Returns the model parameters restricted (and translated) to those - accepted by the backing scikit-learn estimator. + accepted by the backing ``scikit-learn`` estimator. - VastOrbit wrappers expose a native parameter API (e.g. ``method`` on + VAST Orbit wrappers expose a native parameter API (e.g. ``method`` on ``Scaler``, ``init='k-means++'`` on ``KMeans``, ``drop_first`` on ``OneHotEncoder``). Some of those parameters are consumed by the wrapper's own SQL / transform logic and have no scikit-learn equivalent, and a few use different value names. This forwards only - the parameters scikit-learn actually accepts, translating known value + the parameters ``scikit-learn`` actually accepts, translating known value mismatches, so building ``self._sklearn_model(**...)`` never fails on an unexpected keyword. """ @@ -97,14 +97,14 @@ def _get_sklearn_params(self) -> dict: out = {} for key, value in params.items(): if key not in accepted: - continue # native-only parameter; not a scikit-learn argument + continue # native-only parameter; not a ``scikit-learn`` argument if key == "init" and value == "k-means++": - value = "k-means++" # VastOrbit -> scikit-learn naming + value = "k-means++" # VAST Orbit -> ``scikit-learn`` naming if key == "max_features" and value == "max": - # VastOrbit "max" (use all features) -> scikit-learn None. + # VAST Orbit "max" (use all features) -> ``scikit-learn`` None. value = None if key == "class_weight" and isinstance(value, (list, tuple)): - # VastOrbit accepts a per-class weight list; scikit-learn wants + # VAST Orbit accepts a per-class weight list; ``scikit-learn`` wants # a {class_index: weight} mapping. value = {i: w for i, w in enumerate(value)} out[key] = value @@ -244,7 +244,7 @@ def __repr__(self) -> str: def drop(self) -> bool: """ Drops the model from - the VAST database. + the VAST DataBase. Examples -------- @@ -1627,7 +1627,7 @@ def fit( # Split into X and y. Fetching X and y in one query preserves row # order, but if the response is categorical the combined array is # promoted to a string dtype; cast the predictors back to float so - # the scikit-learn estimator receives numeric X. + # the ``scikit-learn`` estimator receives numeric X. # CategoricalNB operates on (possibly string) categories rather than # floats. Ordinal-encode the predictors to integer codes and remember # the encoder so the in-database SQL can reference the original labels. @@ -1640,7 +1640,7 @@ def fit( X = data[:, :-1].astype(float) # All columns except last y = data[:, -1] # Last column - # scikit-learn estimators reject NaN/Inf. Drop rows with missing + # ``scikit-learn`` estimators reject NaN/Inf. Drop rows with missing # values in the predictors (and in the response when it is numeric) # so models fit on data containing nulls — e.g. lagged/differenced # series or columns with blanks — instead of raising. A categorical @@ -2239,7 +2239,7 @@ def get_tree(self, tree_id: int = 0) -> TableSample: cols["node_id"].append(i) cols["node_depth"].append(node_depth[i]) cols["is_leaf"].append(bool(is_leaf)) - # scikit-learn splits are always numeric (no native categorical + # ``scikit-learn`` splits are always numeric (no native categorical # split); leaf nodes have no split at all and are marked None so the # JSON/tree export routes them through the leaf-value branch instead # of trying to float() a non-existent numeric threshold. @@ -6918,7 +6918,7 @@ def fit( self.input_relation = input_relation # Per the docstring, an empty X means "use all numerical columns". - # Without this, self.X stays empty while scikit-learn is still fit on + # Without this, self.X stays empty while ``scikit-learn`` is still fit on # the numerical columns, so the fitted parameter vectors and self.X end # up with different lengths and transform()/deploySQL() later raise # "length of parameter 'X' must be equal to the length of the vector". diff --git a/vastorbit/machine_learning/vast/cluster.py b/vastorbit/machine_learning/vast/cluster.py index 93bca9d..038258b 100755 --- a/vastorbit/machine_learning/vast/cluster.py +++ b/vastorbit/machine_learning/vast/cluster.py @@ -311,8 +311,8 @@ def plot( class KMeans(Clustering): """ Creates an ``KMeans`` object - using SKLEARN for training and - the scalability of VASTDB for + using ``scikit-learn`` for training and + the scalability of VAST DataBase for the inferences. Parameters @@ -325,7 +325,7 @@ class KMeans(Clustering): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -950,7 +950,7 @@ class BisectingKMeans(KMeans, Tree): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -1381,7 +1381,7 @@ def _compute_attributes(self) -> None: self.p_ = 2 # 3. Tree structure. - # scikit-learn does not expose the bisecting hierarchy as flat arrays, + # ``scikit-learn`` does not expose the bisecting hierarchy as flat arrays, # but it keeps it in the private ``_bisecting_tree``. The memmodel # (predict_sql / get_tree / to_graphviz) needs ``children_left_`` / # ``children_right_`` arrays indexed by node id (root = node 0, ``None`` @@ -2091,7 +2091,7 @@ def __init__( def drop(self) -> bool: """ Drops the model from - the VAST database. + the VAST DataBase. Examples -------- @@ -3275,7 +3275,7 @@ def __init__( def drop(self) -> bool: """ ``NearestCentroid`` models are - not stored in the VAST DB. + not stored in the VAST DataBase. The method will always return ``False``. diff --git a/vastorbit/machine_learning/vast/decomposition.py b/vastorbit/machine_learning/vast/decomposition.py index 0df61ab..1ec17de 100755 --- a/vastorbit/machine_learning/vast/decomposition.py +++ b/vastorbit/machine_learning/vast/decomposition.py @@ -882,8 +882,8 @@ def plot_scree( class PCA(Decomposition): """ Creates an ``PCA`` object - using SKLEARN for training and - the scalability of VASTDB for + using ``scikit-learn`` for training and + the scalability of VAST DataBase for the inferences. Parameters @@ -896,7 +896,7 @@ class PCA(Decomposition): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -2107,8 +2107,8 @@ def plot_var( class SVD(Decomposition): """ Creates an ``SVD`` object - using SKLEARN for training and - the scalability of VASTDB for + using ``scikit-learn`` for training and + the scalability of VAST DataBase for the inferences. Parameters @@ -2121,7 +2121,7 @@ class SVD(Decomposition): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- diff --git a/vastorbit/machine_learning/vast/ensemble.py b/vastorbit/machine_learning/vast/ensemble.py index e1bf955..5f07c17 100755 --- a/vastorbit/machine_learning/vast/ensemble.py +++ b/vastorbit/machine_learning/vast/ensemble.py @@ -437,8 +437,8 @@ def to_json(self, path: Optional[str] = None) -> Optional[str]: class RandomForestRegressor(Regressor, RandomForest): """ Creates an ``RandomForestRegressor`` - object using SKLEARN for training - and the scalability of VASTDB for + object using ``scikit-learn`` for training + and the scalability of VAST DataBase for the inferences. Parameters @@ -452,7 +452,7 @@ class RandomForestRegressor(Regressor, RandomForest): existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -1002,8 +1002,8 @@ def to_memmodel(self) -> Union[mm.RandomForestRegressor, mm.BinaryTreeRegressor] class GradientBoostingRegressor(Regressor, GradientBoosting): """ Creates an ``GradientBoostingRegressor`` object - using SKLEARN for training and - the scalability of VASTDB for + using ``scikit-learn`` for training and + the scalability of VAST DataBase for the inferences. Parameters @@ -1017,7 +1017,7 @@ class GradientBoostingRegressor(Regressor, GradientBoosting): existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -1501,7 +1501,7 @@ def _sklearn_model(self) -> Literal[sklearn.ensemble.GradientBoostingRegressor]: def _compute_attributes(self) -> None: """ Computes the model's attributes from the - fitted scikit-learn gradient boosting model. + fitted ``scikit-learn`` gradient boosting model. """ self.eta_ = self._model.learning_rate self.n_estimators_ = self._model.n_estimators_ @@ -1590,8 +1590,8 @@ def to_memmodel(self) -> mm.GradientBoostingRegressor: class RandomForestClassifier(MulticlassClassifier, RandomForest): """ Creates an ``RandomForestClassifier`` - object using SKLEARN for training - and the scalability of VASTDB for + object using ``scikit-learn`` for training + and the scalability of VAST DataBase for the inferences. Parameters @@ -1605,7 +1605,7 @@ class RandomForestClassifier(MulticlassClassifier, RandomForest): existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -2407,8 +2407,8 @@ def to_memmodel(self) -> Union[mm.RandomForestClassifier, mm.BinaryTreeClassifie class GradientBoostingClassifier(MulticlassClassifier, GradientBoosting): """ Creates an ``GradientBoostingClassifier`` object - using SKLEARN for training and - the scalability of VASTDB for + using ``scikit-learn`` for training and + the scalability of VAST DataBase for the inferences. Parameters @@ -2422,7 +2422,7 @@ class GradientBoostingClassifier(MulticlassClassifier, GradientBoosting): existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -3161,7 +3161,7 @@ def _sklearn_model(self) -> Literal[sklearn.ensemble.GradientBoostingClassifier] def _compute_attributes(self) -> None: """ Computes the model's attributes from the - fitted scikit-learn gradient boosting model. + fitted ``scikit-learn`` gradient boosting model. """ self.eta_ = self._model.learning_rate self.n_estimators_ = self._model.n_estimators_ @@ -3292,8 +3292,8 @@ def to_memmodel(self) -> mm.GradientBoostingClassifier: class IsolationForest(Clustering, Tree): """ Creates an ``IsolationForest`` object - using SKLEARN for training and - the scalability of VASTDB for + using ``scikit-learn`` for training and + the scalability of VAST DataBase for the inferences. Parameters @@ -3307,7 +3307,7 @@ class IsolationForest(Clustering, Tree): existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- diff --git a/vastorbit/machine_learning/vast/linear_model.py b/vastorbit/machine_learning/vast/linear_model.py index d6d9590..b64a1f8 100755 --- a/vastorbit/machine_learning/vast/linear_model.py +++ b/vastorbit/machine_learning/vast/linear_model.py @@ -589,8 +589,8 @@ def plot( class ElasticNet(LinearModel, Regressor): """ Creates an ``ElasticNet`` object - using SKLEARN for training and - the scalability of VASTDB for + using ``scikit-learn`` for training and + the scalability of VAST DataBase for the inferences. Parameters @@ -603,7 +603,7 @@ class ElasticNet(LinearModel, Regressor): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -1026,8 +1026,8 @@ def __init__( class Lasso(LinearModel, Regressor): """ Creates a ``Lasso`` object - using SKLEARN for training and - the scalability of VASTDB for + using ``scikit-learn`` for training and + the scalability of VAST DataBase for the inferences. Parameters @@ -1040,7 +1040,7 @@ class Lasso(LinearModel, Regressor): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -1471,8 +1471,8 @@ def _sklearn_model(self) -> Literal[sklearn.linear_model.Lasso]: class LinearRegression(LinearModel, Regressor): """ Creates a ``LinearRegression`` - object using SKLEARN for training - and the scalability of VASTDB for + object using ``scikit-learn`` for training + and the scalability of VAST DataBase for the inferences. Parameters @@ -1485,7 +1485,7 @@ class LinearRegression(LinearModel, Regressor): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -1934,8 +1934,8 @@ def _sklearn_model(self) -> Literal[sklearn.linear_model.LinearRegression]: class PLSRegression(LinearModel, Regressor): """ Creates an ``PLSRegression`` - object using SKLEARN for training - and the scalability of VASTDB for + object using ``scikit-learn`` for training + and the scalability of VAST DataBase for the inferences. Parameters @@ -1948,7 +1948,7 @@ class PLSRegression(LinearModel, Regressor): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -2350,8 +2350,8 @@ def _sklearn_model(self) -> Literal[sklearn.cross_decomposition.PLSRegression]: class PoissonRegressor(LinearModel, Regressor): """ Creates an ``PoissonRegressor`` - object using SKLEARN for training - and the scalability of VASTDB for + object using ``scikit-learn`` for training + and the scalability of VAST DataBase for the inferences. Parameters @@ -2364,7 +2364,7 @@ class PoissonRegressor(LinearModel, Regressor): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -2776,8 +2776,8 @@ def _sklearn_model(self) -> Literal[sklearn.linear_model.PoissonRegressor]: class Ridge(LinearModel, Regressor): """ Creates a ``Ridge`` object - using SKLEARN for training - and the scalability of VASTDB + using ``scikit-learn`` for training + and the scalability of VAST DataBase for the inferences. Parameters @@ -2790,7 +2790,7 @@ class Ridge(LinearModel, Regressor): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -3252,8 +3252,8 @@ def _sklearn_model(self) -> Literal[sklearn.linear_model.Ridge]: class LogisticRegression(LinearModelClassifier, BinaryClassifier): """ Creates a ``LogisticRegression`` - object using SKLEARN for training and - the scalability of VASTDB for the inferences. + object using ``scikit-learn`` for training and + the scalability of VAST DataBase for the inferences. Parameters ---------- @@ -3265,7 +3265,7 @@ class LogisticRegression(LinearModelClassifier, BinaryClassifier): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- diff --git a/vastorbit/machine_learning/vast/naive_bayes.py b/vastorbit/machine_learning/vast/naive_bayes.py index dbcdee3..d914d1a 100644 --- a/vastorbit/machine_learning/vast/naive_bayes.py +++ b/vastorbit/machine_learning/vast/naive_bayes.py @@ -21,8 +21,8 @@ class NaiveBayes(MulticlassClassifier): """ Creates an ``NaiveBayes`` object - using SKLEARN for training and - the scalability of VASTDB for + using ``scikit-learn`` for training and + the scalability of VAST DataBase for the inferences. Parameters @@ -35,7 +35,7 @@ class NaiveBayes(MulticlassClassifier): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -645,7 +645,7 @@ def _attributes(self) -> list[str]: @property def _sklearn_model(self): """ - Selects the backing scikit-learn estimator from ``nbtype``. scikit-learn + Selects the backing ``scikit-learn`` estimator from ``nbtype``. scikit-learn has no mixed-distribution Naive Bayes, so ``"auto"`` maps to ``GaussianNB``. ``alpha``/``nbtype`` that the chosen estimator does not accept are dropped by :py:meth:`_get_sklearn_params`. @@ -682,7 +682,7 @@ def __init__( def _compute_attributes(self) -> None: """ Computes the model's attributes from the - fitted scikit-learn naive bayes model. + fitted ``scikit-learn`` naive bayes model. """ self.classes_ = self._array_to_int(np.asarray(self._model.classes_)) # Bernoulli/Multinomial/Categorical/Complement expose log priors; @@ -698,9 +698,9 @@ def _get_nb_attributes(self) -> list[dict]: Builds, for each input feature, a dictionary describing its per-class naive bayes parameters in the format expected by the memmodel. Parameters are read directly from the fitted - scikit-learn estimator (``self._model``). + ``scikit-learn`` estimator (``self._model``). - Unlike the database engine, scikit-learn uses a single + Unlike the database engine, ``scikit-learn`` uses a single distribution for every feature, determined by the estimator class, so the distribution type is taken from the model rather than detected column by column. @@ -711,7 +711,7 @@ def _get_nb_attributes(self) -> list[dict]: attributes = [] if model_name == "GaussianNB": - # var_ replaced sigma_ in scikit-learn 1.0. + # var_ replaced sigma_ in ``scikit-learn`` 1.0. variances = getattr(model, "var_", None) if variances is None: variances = model.sigma_ diff --git a/vastorbit/machine_learning/vast/neighbors.py b/vastorbit/machine_learning/vast/neighbors.py index 5b488d8..e6a97c7 100755 --- a/vastorbit/machine_learning/vast/neighbors.py +++ b/vastorbit/machine_learning/vast/neighbors.py @@ -406,7 +406,7 @@ def __init__( def drop(self) -> bool: """ ``KNeighborsRegressor`` models - are not stored in the VAST DB. + are not stored in the VAST DataBase. The method will always return ``False``. @@ -1299,7 +1299,7 @@ def __init__( def drop(self) -> bool: """ ``KNeighborsClassifier`` models - are not stored in the VAST DB. + are not stored in the VAST DataBase. The method will always return ``False``. diff --git a/vastorbit/machine_learning/vast/pipeline.py b/vastorbit/machine_learning/vast/pipeline.py index 68ff3ed..f02be3f 100755 --- a/vastorbit/machine_learning/vast/pipeline.py +++ b/vastorbit/machine_learning/vast/pipeline.py @@ -110,7 +110,7 @@ def __getitem__(self, index) -> VASTModel: def drop(self) -> None: """ - Drops the model from the VAST database. + Drops the model from the VAST DataBase. """ for step in self.steps: step[1].drop() diff --git a/vastorbit/machine_learning/vast/preprocessing.py b/vastorbit/machine_learning/vast/preprocessing.py index 8ad1e11..50c86eb 100644 --- a/vastorbit/machine_learning/vast/preprocessing.py +++ b/vastorbit/machine_learning/vast/preprocessing.py @@ -1339,7 +1339,7 @@ class OneHotEncoder(Preprocessing): existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. .. rubric:: Attributes @@ -1648,7 +1648,7 @@ def _compute_attributes(self) -> None: """ Computes the model's attributes from the fitted ``scikit-learn`` model. """ - # scikit-learn stores one array of category levels per input column, + # ``scikit-learn`` stores one array of category levels per input column, # aligned with the order of self.X. categories = [list(levels) for levels in self._model.categories_] diff --git a/vastorbit/machine_learning/vast/svm.py b/vastorbit/machine_learning/vast/svm.py index 61d8087..0392ff8 100755 --- a/vastorbit/machine_learning/vast/svm.py +++ b/vastorbit/machine_learning/vast/svm.py @@ -24,8 +24,8 @@ class LinearSVR(LinearModel, Regressor): """ Creates an ``LinearSVR`` object - using SKLEARN for training and - the scalability of VASTDB for + using ``scikit-learn`` for training and + the scalability of VAST DataBase for the inferences. Parameters @@ -38,7 +38,7 @@ class LinearSVR(LinearModel, Regressor): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ---------- @@ -493,8 +493,8 @@ def _sklearn_model(self) -> Literal[sklearn.svm.LinearSVR]: class LinearSVC(LinearModelClassifier, BinaryClassifier): """ Creates an ``LinearSVC`` object - using SKLEARN for training and - the scalability of VASTDB for + using ``scikit-learn`` for training and + the scalability of VAST DataBase for the inferences. Parameters @@ -507,7 +507,7 @@ class LinearSVC(LinearModelClassifier, BinaryClassifier): model with the same name as an existing model overwrites the existing model. - ``**kwargs``: SKLEARN model parameters. + ``**kwargs``: ``scikit-learn`` model parameters. Attributes ----------