Orange (software)

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Orange

Developer(s)	University of Ljubljana
Initial release	10 October 1996; 27 years ago (1996-10-10)[1]
Stable release	3.36.2[2] / 31 October 2023; 5 months ago (31 October 2023)
GPLv3 or later[3][4]
Website	orangedatamining.com

Orange is an

Description

Orange is a component-based

Visual programming is implemented through an interface in which workflows are created by linking predefined or user-designed widgets, while advanced users can use Orange as a Python library for data manipulation and widget alteration.^[5]

Software

Orange is an open-source software package released under

The default installation includes a number of machine learning, preprocessing and data visualization algorithms in 6 widget sets (data, transform, visualize, model, evaluate and unsupervised). Additional functionalities are available as add-ons (text-mining, image analytics, bioinformatics, etc.).

Orange is supported on macOS, Windows and Linux and can also be installed from the Python Package Index repository (pip install Orange3).

Features

Orange consists of a canvas interface onto which the user places widgets and creates a data analysis workflow. Widgets offer basic functionalities such as reading the data, showing a data table, selecting features, training predictors, comparing learning algorithms, visualizing data elements, etc. The user can interactively explore visualizations or feed the selected subset into other widgets.

• Canvas: graphical front-end for data analysis
• Widgets:
  • Data: widgets for data input, data filtering, sampling, imputation, feature manipulation and feature selection
  • Visualize: widgets for common visualization (box plot, histograms, scatter plot) and multivariate visualization (mosaic display, sieve diagram).
  • Classify: a set of supervised machine learning algorithms for classification
  • Regression: a set of supervised machine learning algorithms for regression
  • Evaluate: cross-validation, sampling-based procedures, reliability estimation and scoring of prediction methods
  • Unsupervised: unsupervised learning algorithms for clustering (k-means, hierarchical clustering) and data projection techniques (multidimensional scaling, principal component analysis, correspondence analysis).

Add-ons

Orange users can extend their core set of components with components in the add-ons. Supported add-ons include:

• Associate: components for mining frequent itemsets and association rule learning.
• Bioinformatics: components for gene expression analysis,
  enrichment, and access to expression databases (e.g., Gene Expression Omnibus
  ) and pathway libraries.
• Data fusion: components for fusing different data sets, collective matrix factorization, and exploration of latent factors.
• Educational: components for teaching machine learning concepts, such as k-means clustering, polynomial regression, stochastic gradient descent, ...
• Explain: provides an extension with components for the model explanation, including Shapley value analysis
• Geo: components for working with
  geospatial data
  .
• Image analytics: components for working with images and ImageNet embeddings
• Network: components for graph and network analysis.
• Text mining: components for natural language processing and text mining.
• Time series: widget components for time series analysis and modeling.
• Single-cell: support for single-cell gene expression analysis, including components for loading single-cell data, filtering and batch effect removal, marker genes discovery, scoring of cells and genes, and cell type prediction.

  

• Spectroscopy: components for analyzing and visualization of (hyper)spectral datasets.^[6]
• Survival analysis: add-on for data analysis dealing with survival data. It includes widgets for standard survival analysis techniques, such as the
  Kaplan-Meier plot, the Cox regression model
  , and several derivative widgets.
• World Happiness: support for downloading socioeconomic data from a database, including OECD and World Development Indicators. Provides access to thousands of country indicators from various economic databases.

Objectives

The program provides a platform for experiment selection,

• OASYS — ORange SYnchrotron Suite[7]
• scOrange — single cell biostatistics
• Quasar — data analysis in natural sciences

History

In 1996, the University of Ljubljana and Jožef Stefan Institute started development of ML*, a machine learning framework in C++, and Python bindings were developed for this framework in 1997, which, together with emerging Python modules, formed a joint framework called Orange. Over the following years, most contemporary major algorithms for data mining and machine learning were implemented in C++ (Orange's core) or Python modules.

• In 2002, first prototypes to create a flexible graphical user interface were designed using
  Pmw Python megawidgets
  .
• In 2003, the graphical user interface was redesigned and re-developed for Qt framework using PyQt Python bindings. The visual programming framework was defined, and the development of widgets (graphical components of the data analysis pipeline) began.
• In 2005, extensions for data analysis in bioinformatics was created.
• In 2008, Mac OS X DMG and Fink-based installation packages were developed.
• In 2009, over 100 widgets were created and maintained.
• Since 2009, Orange is in 2.0 beta, and web site offers installation packages based on the daily compiling cycle.
• In 2012, a new object hierarchy was imposed, replacing the old module-based structure.
• In 2013, a significant redesign of the graphical user interface included a new toolbox and depiction of workflows.
• In 2015, Orange 3.0 was released. Orange stores the data in the NumPy arrays; machine learning algorithms mostly use scikit-learn.
• In 2015, a text analysis add-on for Orange3 was released.
• In 2016, Orange is in version 3.3. The development uses a monthly stable release cycle.
• In 2016, Start of development and release of Image Analytics add-on, with server-site deep neural networks for image embedding ^[8]
• In 2017, Spectroscopy add-on for the analysis of spectral data was introduced.^[9]
• In 2017, Geo, an add-on for dealing with geo-location data and visualisation of geo maps was introduced ^[10]
• In 2018, the development and release of add-on for single-cell data analysis was started.^[11]
• In 2019, Orange's graphical interface is developed as a separate project, orange-canvas-core^[12]
• In 2020, Explain add-on with widgets for explaining
  regression model
  is introduced. It explains which features contribute the most and how they contribute towards predicting a specific class.
• In 2022 World Happiness, an add-on for the Orange3 data mining suite, is introduced. It provides widgets for accessing socioeconomic data from various databases such as World Happiness Report, World Development Indicators, OECD.
• In 2022, Explain add-on is extended with an Individual Conditional Expectation plot and the Permutation Feature Importance technique.

References