Windows

This page contains nightly builds of Orange from the code repository. These are typically stable and we recommend using them.

  • Full package: Snapshot of Orange with Python 3 and required libraries
    This package is recommended to those installing Orange for the first time. It includes all required libraries (Python, PythonWin, NumPy, PyQt, PyQwt ...), though it will not change any libraries you might already have.

Previous version

Orange 2.7 is still available.

Add-ons

Orange Bioinformatics extends Orange, a data mining software package, with common functionality for bioinformatics. The provided functionality can be accessed as a Python library or through a visual programming interface (Orange Canvas). The latter is also suitable for non-programmers.

In Orange Canvas the analyst connects basic computational units, called widgets, into data flow analytics schemas. Two units-widgets can be connected if they share a data type. Compared to other popular tools like Taverna, Orange widgets are high-level, integrated potentially complex tasks, but are specific enough to be used independently. Even elaborate analyses rarely consist of more than ten widgets; while tasks such as clustering and enrichment analysis could be executed with up to five widgets. While building the schema each widget is independently controlled with settings, the settings do not conceptually burden the analyst.

Orange Bioinformatics provides access to publicly available data, like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx database. As for the analytics, there is gene selection, quality control, scoring distances between experiments with multiple factors. All features can be combined with powerful visualization, network exploration and data mining techniques from the Orange data mining framework.

Documentation: http://orange-bioinformatics.readthedocs.org/

Orange add-on for enumerating frequent itemsets and association rules mining.

This is a data fusion add-on for [Orange3](http://orange.biolab.si). Add-on wraps [scikit-fusion](http://github.com/marinkaz/scikit-fusion), a Python library for data fusion, and implements a set of widgets for loading of the data, definition of data fusion schema, collective matrix factorization and exploration of latent factors.

Installation

To install the add-on, run

python setup.py install

To register this add-on with Orange, but keep the code in the development directory (do not copy it to Python's site-packages directory), run

python setup.py develop
Usage

Run Orange from the terminal by

python -m Orange.canvas

Data fusion widgets are in the toolbox bar under Data Fusion section.

Orange Network is an add-on for Orange data mining software package. It provides network visualization and analysis tools.

Documentation is found at:

http://orange-network.readthedocs.org/

Prototype Orange widgets. Only for the brave.

NOTE: This plug-in currently could NOT be installed. We are working on resolving the issue; until then if curious you can install it [from source](https://github.com/biolab/orange3-text) but beware that it is still in development.

Orange3 Text extends Orange with common functionality for text mining. It provides access to publicly available data, like NY Times, Twitter and PubMed. Further, it provides tools for preprocessing, constructing vector spaces (like bag-of-words, topic modeling and word2vec) and visualizations like word cloud end geo map. All features can be combined with powerful data mining techniques from the Orange data mining framework.

A set of widgets for Orange data mining suite to work with Apache Spark ML API.

Requirements
  • Python >= 3.4
  • Pandas
  • Orange 3

Please follow the instruction to install Orange 3 first.

The main Orange project is hosted at: https://github.com/biolab/orange3 Download from: http://orange.biolab.si

Features
  • A Spark Context.
  • A Hive Table.
  • A Dataframe from an SQL Query.
  • A Dataset Builder, basically a call to VectorAssembler, this is usefull before sending data to Estimators.
  • Transformers from the feature module.
  • Estimators from classification module.
  • Estimators from regression module.
  • Estimators from clustering module.
  • Evaluation from evaluator module.
  • A PySpark script executor + PySpark console.
  • DataFrame transformes for Pandas and Orangle Tables

... more coming soon!

Installing

First, you need to have Apache Spark installed. Follow the instructions here: http://spark.apache.org/docs/latest/

Then you can do:

pip install Orange3-spark

or install the add-on from the Orange's Options | Add-ons menu. Note, if installing from Add-ons menu, the installation may fail if not all requirements are satisfiable.

If you require ODBC connectivity, you need to install pyodbc (which requires sql.h available if built with pip – that's unixodbc-dev package on Linux).

If install is ok, you should see a new section in Orange containing a series of widgets from Spark ML API.

Installing add-ons

Add-ons should be installed in Orange canvas (menu Options, Add-ons...).