Difference between revisions of "Parallel Enclosing Circle Algorithm"
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{{Project | {{Project | ||
− | |Has project output= | + | |Has project output=Tool |
|Has sponsor=McNair Center | |Has sponsor=McNair Center | ||
|Has title=Parallel Enclosing Circle Algorithm | |Has title=Parallel Enclosing Circle Algorithm |
Latest revision as of 13:47, 21 September 2020
Parallel Enclosing Circle Algorithm | |
---|---|
Project Information | |
Has title | Parallel Enclosing Circle Algorithm |
Has owner | Oliver Chang |
Has start date | July 31, 2017 |
Has deadline date | |
Has project status | Complete |
Is dependent on | Enclosing Circle Algorithm |
Has sponsor | McNair Center |
Has project output | Tool |
Copyright © 2019 edegan.com. All Rights Reserved. |
A thin-wrapper around the enclosing circle algorithm which allows for instance-level parallelization.
This project consists of the python files in E:\McNair\Projects\OliverLovesCircles\src\python
. There is another version of the project with plotting functionality that uses a slightly different approach (removes duplicate points and uses their counts before running the algorithm) in E:\McNair\Projects\KyranLovesCircles\src\python
.
Parallelization is implemented via Python2's subprocess.open()
which is non-blocking and available in the standard library.
Contents
The Problem
Note that this is not the classical enclosing circle algorithm.
Rather, we seek to minimize the sum of enclosing circles containing at least n
points.
Thus, multiple circles are allowed and inclusion in multiple circles is possible.
This algorithm has terrible time-performance characteristics, so we make the assumption that we can divide a large number of points with k-means and then solve those subproblems. In other words, we make the simplifying assumption that the Enclosing Circle Algorithm has Optimal Substructure.
Parameters
- in
circles.py
:PATH_SEPARATOR
: the string that separates parts of the filename for both input and output files. For example, an input could look like "St. Louis#MO#2017#0.tsv" for PATH_SEPARATOR = '#'ITERATIONS
: the number of iterations to attempt for eachk
to find minimum for thatk
MIN_POINTS_PER_CIRCLE
(AKAn
): the minimum number of data points that must be included in a circle
- in
vc_circles.py
NUMBER_INSTANCES
: number of parallel instances to run; assume no data-races between instancesSWEEP_CYCLE_SECONDS
: amount of time before removing completed jobs from the current job and adding new jobs if any files are left to processTIMEOUT_MINUTES
: maximum running time of a parallel instance of the algorithmSPLIT_THRESHOLD
: if a dataset has more than this threshold of data points, it will be split via k-meansEXECUTABLE_INSTANCE_PATH
: the path to circles.pyOUTJOINER_INSTANCE_PATH
: the path to outjoiner.pyDATA_DIRECTORY
: the input directoryOUTPUT_DIRECTORY
: the directory to write the outputs of circle.py toGENERATE_REPORTS
: whether or not to call outjoiner.py (writes reports on the output of circles.py)REPORT_DIRECTORY
: the directory to write reports to
Structure and Usage
vc_circles.py
- What it does
- If given a "master file" through argument infile, splits it into constituent data files, and stores them in DATA_DIRECTORY
- Takes data files in DATA_DIRECTORY and calls circles.py in parallel for each of these data files, which writes its output files to OUTPUT_DIRECTORY
- Takes output files in OUTPUT_DIRECTORY and calls outjoiner.py, which writes its report files to REPORT_DIRECTORY
- Command Line Arguments
- --sweep-time overwrites
SWEEP_CYCLE_SECONDS
- --instances overwrites
NUMBER_INSTANCES
- --min_points overwrites
MIN_POINTS_PER_CIRCLE
- --infile: Path to large master file, e.g. CirclesTestData.txt
- --split-out overwrites
DATA_DIRECTORY
- --out overwrites
OUTPUT_DIRECTORY
- --report overwrites
REPORT_DIRECTORY
- --sweep-time overwrites
circles.py
- What it does
- Called with two command line arguments, the input path and the output path
- Calculates points and circles for input and writes it to output
outjoiner.py
- What it does
- Using a given output directory, generates three files: circles.tsv, points.tsv, and summary.tsv, and stores them in a given reports directory
DATA_DIRECTORY
- The format of the filenames in this directory are
{city}{sep}{state}{sep}{year}{sep}{num}.tsv
wherenum
is a 0-indexed integer of a split city/state/yearinfile
that has greater thanSPLIT_THRESHOLD
. - These are files created when vc_circles.py splits up a master file.
OUTPUT_DIRECTORY
- The format of the filenames in this directory are
{city}{sep}{state}{sep}{year}{sep}{num}.tsv
wherenum
is a 0-indexed integer of a split city/state/yearinfile
that has greater thanSPLIT_THRESHOLD
. - These are files created when circles.py processes a file from DATA_DIRECTORY.
REPORT_DIRECTORY
- There are three files in this directory: circles.tsv, points.tsv, and summary.tsv.
Example Usage
Splitting a master file and running
$ python vc_circles.py --infile E:/McNair/Projects/OliverLovesCircles/CoLevelForCirclesNotRunGTE200.txt
where CoLevelForCirclesNotRunGTE200.txt
is a tab-separated values file with the columns
placestate, place, statecode, year, latitude, longitude, coname, datefirstinv, placens, geoid, city
This command will populate (and overwrite) any files in data/
, out/
, and reports/
.
Running on already split files
$ python vc_circles.py
This command will populate (and overwrite) any files in out/
and reports/
.
Bugs/Issues
- "St. Paul" and "St. Louis" have un-enclosed points--speculate because of weird file path issues
- Some place/state/year combinations do not run to completion regardless of how tractable the number of points
- How to merge small enclosing circles? This is a better measure of agglomeration regardless
- How to separate outliers?
- Sometimes circles with 0 radius are created
- enclosingcirclealg() returns None sometimes
Makeshift way to plot circles
- Connect to database with command
psql -U postgres arc
- password is tabspaceenter I think
\d
lists tables- Now run SQL script LoadCircles.sql in OliverLovesCircles
- Open ArcMap
- Add data -> Top of file tree -> Database connection -> localhost for instance, database arc -> connect to localhost and table testcirclegeom
- Add points from local files, make sure they are txt or tab files, not tsv, or they won't be found
- Points -> Properties -> Source -> Set data source -> x field: long, y field: lat
St. Louis bug
This image shows a rendering of the results of running St. Louis. There are four circles (the centers of circles are green dots), but two have radii of 0.0.
Progress on the bug
- Removing duplicate points from the data actually removes all of the errors, but this doesn't give you the solution with the smallest area.
- I tried removing duplicates but keeping track of a "count."
- I narrowed down the bug to the constrained_kmeans method in ckmeans.py (paper here)
- For some reason, this returns clusters with smaller numbers of points than n
- This is a good overview of the algorithm
- I wrote a plotter, the plot method in circles.py