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Orange Canvas

To run

  • python -m Orange.canvas

Blog posts & documentation

Containing recent changes to Orange canvas explaining conflicts with some of the tutorials available

Python script widget to access external python libraries

Main python script support for

For Python Script

Example 1:

One can, for example, do batch filtering by attributes. We used zoo.tab for the example and we filtered out all the attributes that have more than 5 discrete values. This in our case removed only ‘leg’ attribute, but imagine an example where one would have many such attributes.

from Orange.data import Domain, Table

domain = Domain([attr for attr in in_data.domain.attributes

if attr.is_continuous or len(attr.values) <= 5],

in_data.domain.class_vars)

out_data = Table(domain, in_data)

Example 2:

The second example shows how to round all the values in a few lines of code. This time we used wine.tab and rounded all the values to whole numbers.

import numpy as np

out_data = in_data.copy() #copy, otherwise input data will be overwritten np.round(out_data.X, 0, out_data.X)

Example 3:

The third example introduces some gaussian noise to the data. Again we make a copy of the input data, then walk through all the values with a double for loop and add random noise.

import random from Orange.data import Domain, Table

new_data = in_data.copy() for inst in new_data: for f in inst.domain.attributes: inst[f] += random.gauss(0, 0.02) out_data = new_data

Example 4:

The final example uses Orange3-Text add-on. Python Script is very useful for custom preprocessing in text mining, extracting new features from strings, or utilizing advanced nltk or gensim functions. Below, we simply tokenized our input data from deerwester.tab by splitting them by whitespace.

print('Running Preprocessing ...') tokens = [doc.split(' ') for doc in in_data.documents] print('Tokens:', tokens) out_object = in_data out_object.store_tokens(tokens)

github

Network Module

  • http://orange3-network.readthedocs.io/en/latest/widgets/networkfile.html

    1. Graph File. Loads network file and (optionally) constructs a data table from the graph. A dropdown menu provides access to documentation data sets with Browse documentation networks.... The folder icon provides access to local data files. If Build graph data table automatically is checked, the widget will not output an inferred data table (no Items output will be available).
    2. Vertices Data File. Information on the network nodes. Reads standard Orange data files. he folder icon provides access to local data files.
    3. Information on the constructed network. Reports on the type of graph, number of nodes and edges and the provided vertices data file

Educational

Contains:

  • K-Means
  • Polynomial Regression
  • Gradient Descent
  • Polynomial Classification

conda environment

The system cannot find the path specified.

(C:pythonNew folder) C:pythonNew folderetccondaactivate.d>set "GDAL_DRIVER_PATH="

C:pythonNew folder) C:UsersMarkus.Walden>conda env --help usage: conda-env-script.py [-h] {attach,create,export,list,remove,upload,update} ...

positional arguments:
{attach,create,export,list,remove,upload,update}
attach Embeds information describing your conda environment

into the notebook metadata

create Create an environment based on an environment file export Export a given environment list List the Conda environments remove Remove an environment upload Upload an environment to anaconda.org update Update the current environment based on environment file

optional arguments:

-h, --help Show this help message and exit.