Florida Investment Network


Recent Blogs


Pitching Help Desk


Testimonials

"I wish to thank the Dealflow Investment Network for their splendid service on listing our project summary. Our entire fund raise was achieved within 5-months from China. Long flight, but well worth it. I am happy to give a recommendation."
James E. Mack


 BLOG >> Recent

Layout, Weights, and Highlighting with Graphviz [Decision Trees
Posted on August 8, 2013 @ 04:56:00 AM by Paul Meagher

I introduced the Graphviz program in my last blog. In today's blog I want to go a little deeper into the DOT language to show how you can achieve three useful effects using the DOT language. The three effects are:

  • Change the overall layout of the graph. Instead of starting our decision tree from the top, I would prefer to start it from the left side of the canvas and expand it towards the right side of the canvas (i.e., left-to-right reading order). I can do this by adding the command rankdir=LR; to my dot file.
  • Would be nice to show probability values on links going into event nodes. For example, the probability of high rain fall this season. We do this by adding a bracket next to link commands and specifying the value for the "label" attribute (e.g., Action -> HighRainFall[label="0.6"];).
  • If you are trying to highlight a path through a decision tree, then there are ways to highlight a path in graphviz. One way would be to thicken the line and add red coloration to each link in the path (e.g., Action -> LowRainFall[label="0.4",color=red,penwidth=3.0]; ).

If we put all these elements together in one dot program file, it would look like this:

digraph { 
  
  rankdir=LR;

  Action -> LowRainFall[label="0.4",color=red,penwidth=3.0]; 
  Action -> HighRainFall[label="0.6"]; 

}

If we load this dot file into the graphviz program "dot", it will generate this graph:

What we have here is a fragment of a graph. A fragment like this might appear in your decision tree leading from an action node to an event node. This is how we can get probabilities to appear on our graphical representations of a decision problem. Also, I like to orient the tree from left-to-right because if you have a large branchy tree it can more easily be printed off whereas top-to-bottom trees are hard to print off and involve alot of horizontal scrolling to view. Finally, when you make a decision to pursue a particular course of action, you can highlight that course of action graphically with a thick red pen effect.

Permalink 

 Archive 
 

Archive


 November 2019 [2]
 October 2019 [2]
 September 2019 [1]
 July 2019 [1]
 June 2019 [2]
 May 2019 [2]
 April 2019 [5]
 March 2019 [4]
 February 2019 [3]
 January 2019 [3]
 December 2018 [4]
 November 2018 [2]
 September 2018 [2]
 August 2018 [1]
 July 2018 [1]
 June 2018 [1]
 May 2018 [5]
 April 2018 [4]
 March 2018 [2]
 February 2018 [4]
 January 2018 [4]
 December 2017 [2]
 November 2017 [6]
 October 2017 [6]
 September 2017 [6]
 August 2017 [2]
 July 2017 [2]
 June 2017 [5]
 May 2017 [7]
 April 2017 [6]
 March 2017 [8]
 February 2017 [7]
 January 2017 [9]
 December 2016 [7]
 November 2016 [7]
 October 2016 [5]
 September 2016 [5]
 August 2016 [4]
 July 2016 [6]
 June 2016 [5]
 May 2016 [10]
 April 2016 [12]
 March 2016 [10]
 February 2016 [11]
 January 2016 [12]
 December 2015 [6]
 November 2015 [8]
 October 2015 [12]
 September 2015 [10]
 August 2015 [14]
 July 2015 [9]
 June 2015 [9]
 May 2015 [10]
 April 2015 [10]
 March 2015 [9]
 February 2015 [8]
 January 2015 [5]
 December 2014 [11]
 November 2014 [10]
 October 2014 [10]
 September 2014 [8]
 August 2014 [7]
 July 2014 [6]
 June 2014 [7]
 May 2014 [6]
 April 2014 [3]
 March 2014 [8]
 February 2014 [6]
 January 2014 [5]
 December 2013 [5]
 November 2013 [3]
 October 2013 [4]
 September 2013 [11]
 August 2013 [4]
 July 2013 [8]
 June 2013 [10]
 May 2013 [14]
 April 2013 [12]
 March 2013 [11]
 February 2013 [19]
 January 2013 [20]
 December 2012 [5]
 November 2012 [1]
 October 2012 [3]
 September 2012 [1]
 August 2012 [1]
 July 2012 [1]
 June 2012 [2]


Categories


 Agriculture [71]
 Bayesian Inference [14]
 Books [15]
 Business Models [24]
 Causal Inference [2]
 Creativity [7]
 Decision Making [15]
 Decision Trees [8]
 Design [37]
 Eco-Green [4]
 Economics [12]
 Education [10]
 Energy [0]
 Entrepreneurship [61]
 Events [2]
 Farming [20]
 Finance [25]
 Future [15]
 Growth [18]
 Investing [24]
 Lean Startup [10]
 Leisure [5]
 Lens Model [9]
 Making [1]
 Management [9]
 Motivation [3]
 Nature [22]
 Patents & Trademarks [1]
 Permaculture [34]
 Psychology [1]
 Real Estate [2]
 Robots [1]
 Selling [11]
 Site News [15]
 Startups [12]
 Statistics [3]
 Systems Thinking [3]
 Trends [7]
 Useful Links [3]
 Valuation [1]
 Venture Capital [5]
 Video [2]
 Writing [2]