Jim Leek leek2@llnl.gov
Symbolic Regression (SR) is a genetic programming algorithm for finding mathematical functions that match a given set of data. It works by evolving an approximation function from a set of primitives. This makes it able to discover the form of the function, in addition to it's coefficients. It has even been shown to be able to discover natural physical laws from experimental data 1.
Given that SR is one of the biggest success stories in genetic programming, it's surprising that there doesn't seem to be an easy-to-use, open-source SR program available.
SoRa attempts to fill that gap. With SoRa any user should easily be able to provide experimental data and write a simple configuration file such that SoRa can find a set of good mathematical functions to match the data.
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Python 2.7.5 - 2.7.11. SoRa doesn't currently support Python 3.
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DEAP 1.1.0 (or newer). Distributed Evolutionary Algorithms in Python is the Genetic Programming framework SoRa is built around. In many ways SoRa is just a front-end for DEAP. Get DEAP at https://github.com/DEAP/deap.
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numpy 1.9.2 (or newer): Numpy is mostly used to speed up function evaluation.
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SymPy: Symbolic Python is a symbolic mathematics library for python. Currently SoRa only uses it to simplify and pretty print the functions that come out of the evolution. Without it the output is a bit uglier:
- Without SymPy: mul(0.234, mul(X, X)
- With SymPy : 0.234 * X**2
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mpi4py: mpi4py is an MPI (Message Passing Interface) python library. It is used to handle multiple islands (demes) for greater parallelism. You cannot use islands without mpi4py.
SoRa is mostly configured by a JSON configuration file, but there are also some important command line options:
usage: sora.py [options] configFile
Options:
- -h, --help show this help message and exit
- -i INPUTFILE, --input-file=INPUTFILE
The name of the input file - -t NUMTHREADS, --num-threads=NUMTHREADS
The number of threads (actually processes) to use for evaluation - -p PRINTLEVEL, --print-level=PRINTLEVEL
Print level defines the verbosity of the output. Lower numbers give less output.
0: Errors and hall of fame only
1: Warning and Errors
2: Errors, Warnings, and generational statistics
3: Info
4: High Info
5: All debugging output - -r, --all-ranks-print
All Ranks Print describes how the printlevel is applied on different ranks. By default only rank 0 outputs anything. - -c LOADCHECKPOINT, --load-checkpoint=LOADCHECKPOINT
To restart a run pass the checkpoint file basename to load. The Resulting filename will be "..check". For example "TF.0.check". All ranks that find a checkpoint file will load it, otherwise they will generate a new population.
SoRa is primarily configured with a JSON configuration file. If a configuration item has an oddly named argument, it probably matches a named argument in DEAP. See: https://deap.readthedocs.io/en/master.
You can find example configuration files in the test_sr directory. test_sr/HARM2Dconfig.json the data is sin(x^2 + y^2) so, unlike my original testing data, a correct answer is possible.
Below is the default configuration file. Most of the items in the configuration file have defaults, which, amounts more or less to the below. Each item and section will get a more complete explanation further on in the documentation. Also note that the hash (#) comments are not allowed in actual JSON files, so you would have to remove them to actually run the file below.
{
"infile" : "foo", #The base input filename
"infileExtension" : "dat", #The input filename extension. foo.dat
"inVars" : [ "x" ], #The axis variables
"targetVar" : "target", #The variable SoRa is trying to match
"seed" : 0, #Random Seed
"depthLimit" : 17, #Maximum depth of any function
"prettyPrint" : true, #Use SymPy to pretty print functions
"errorfunc": "avgAbsErrorSquared", #Error function guides evolution
"primitives" : ["add", "sub", #The primitives to make functions
"mul", "div", #out of. These are the defaults
"neg", "sqrt"],
"HallOfFame" : { #Type of Hall Of Fame to use
"type" : "pareto"
},
"select" : [ #Selection algorithm and args
{
"type" : "selTournament",
"tournsize" : 4
} ],
"expr" : { "type" : "genHalfAndHalf", #Initial function generation
"min_" : 1,
"max_" : 4
},
"expr_mut": { "type" : "genHalfAndHalf", #Mutation expressions
"min_" : 0,
"max_" : 3
},
"islands" : { #Control of islands and migration
"migrationFreq" : 50,
"numMigrants" : 10,
"emmigrantSelect" : [ {
"type" : "selRandom"
} ],
"NOreplacementSelect" : [ { "type" : "selWorst" } ]
},
"algo" : { #Evolution arguments and configuration
"type" : "eaMuCommaLambda",
"initialPopulationSize" : 400,
"stopFrequency" : 100, #How often to stop and migrate/checkpoint
"numGenerations" : 400, #How many generation to run for
"populationSize" : 400, #Population to select each generation
"children" : 600, #Children generated per generation
"cxpb" : 0.7, #Crossover probability
"mutpb" : 0.2 #Mutation probability
},
"mate" : "cxOnePoint", #Crossover algorithm
"mutator" : { #mutation operation and args
"type" : "mutUniform",
},
"checkpoints" : { #Checkpointing configuration
"filenamebase" : "None", #If no filename, don't write checkpoints
"allRanks" : false, #If false only rank 0 checkpoints
"frequency" : 100 #Checkpoint every 100 generations
},
"constants" : [ { #How to generate constants
"type" : "uniform", #A uniform distribution (-3,3)
"min" : -3,
"max" : 3
} ]
}
SoRa allows two datafile formats. Comma Seperated Values format (CSV) and ASCII column format (AKA "dat" format).
2c.1: CSV format. If "infileExtension" : "csv" in the configuration file, then SoRa attempts to read the file as a CSV. CSV is a standard spreadsheet format. SoRa sees a CSV file as a set of columns, where the first value in each column is the name of the column, then every other value in the column should be a double. Comments are not allowed.
This example has 3 columns:
temp, rho, SabsErr
1.000000000000000E-06,1.000000000000000E-09,-9.215922916587857E-02
1.000000000000000E-06,1.467800000000000E-09,-9.215925486108226E-02
1.000000000000000E-06,2.154430000000000E-09,-9.215929362427140E-02
1.000000000000000E-06,3.162280000000000E-09,-9.215935277990283E-02
This comes from a 2D function SabsErr(temp,rho). As you can see, the first column is named temp, the second rho, and the third SabsErr. In the config file you would have this:
"infile" : "foo",
"infileExtension" : "csv",
"inVars" : [ "temp", "rho" ],
"targetVar" : "SabsErr",
2c.2: DAT format. DAT is a white space separated ASCII data column format. Unfortunately it is not nearly as standardized as CSV format, but variations of it are commonly used. Currently SoRa assumes DAT format if the infileExtension is NOT .csv. So: .dat, .in, .foo, etc. will all result in SoRa trying to read DAT format. DAT as implemented by SoRa allows # at the beginning of a line to designate a comment. Aside from that it is very similar to CSV, but with white space separators instead of commas. The first row is expected to be the column variable names. Like this:
\# Experimental cold energy data 7.2003
T rho E S
9.999999999999999E-10 1.000000000000000E-15 0.000000000000000E+00 5.136477167200000E-02
9.999999999999999E-10 1.122018000000000E-15 0.000000000000000E+00 5.950234464700000E-02
9.999999999999999E-10 2.818383000000000E-15 0.000000000000000E+00 5.457897012000000E-02
9.999999999999999E-10 7.079458000000000E-15 0.000000000000000E+00 6.013146606200000E-02
And in the config file:
"infile" : "foo",
"infileExtension" : "dat",
"inVars" : [ "T", "rho" ],
"targetVar" : "E",
Note that this configuration will ignore the S column completely! SoRa will only try to match the function E(T,rho).
If you look in the test directory, there are multiple tests ready to go. Let's try HARMconfig.json, which uses the HARM-GP algorithm, the best in DEAP for symbolic regression.
2d.1: A simple serial run: From the test directory: ../sora.py HARM2Dconfig.json
fitness size
----------------------------- -------------------
gen nevals avg max min avg max min
0 1000 -inf 0.89588 -1.79769e+308 5.263 28 2
1 1000 -inf 0.885123 -1.79769e+308 4.138 28 1
2 1000 -inf 0.885123 -1.79769e+308 3.894 20 1
3 1000 -inf 0.885123 -1.79769e+308 3.497 19 1
4 1000 -inf 0.896556 -1.79769e+308 2.857 21 1
Well, the output could be prettier, but it looks OK, and you can see that the best fitness is improving. But the whole thing runs a bit slowly.
2d.2: A multiprocessor run DEAP allows us to speed up the evaluation of each function by parallelizing the evaluations across multiple processes with the python multiprocessing library. If our computer has 4 processors, we can use them all with:
../sora.py -t3 HARM2Dconfig.json
fitness size
------------------------------------- -------------------
gen nevals avg max min avg max min
0 1000 -inf 0.885123 -1.79769e+308 4.879 25 2
1 1000 -inf 0.885123 -1.79769e+308 3.994 20 2
2 1000 -inf 0.890786 -1.79769e+308 3.721 23 1
3 1000 -inf 0.908352 -1.79769e+308 3.278 18 1
Well, that's going a lot faster. Let's see how well the CPUS are being used.
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
89946 leek2 20 0 502m 32m 7488 S 62.5 0.1 0:04.42 python
89947 leek2 20 0 299m 22m 1944 R 42.0 0.0 0:02.46 python
89948 leek2 20 0 299m 22m 1936 R 42.0 0.0 0:02.47 python
89949 leek2 20 0 299m 22m 1936 R 41.3 0.0 0:02.45 python
We can see that the main thread is using more CPU, but they are fairly even.
2d.3: An MPI run However SoRa can allow us to do even more paralleism by having MPI processes that run their own islands of evolution, where the best candidates migrate between islands occasionally. For this you will need an "islands" section in your configuration file, which HARM2Dconfig.json has. So let's try it. Our MPI cluster has 20 processors per node, so we can run a lot on one node. We'll run 6 islands on one node, with 4 sub-evaluators. (This is a bit of an overload, but should be fine.)
srun -N1 -n6 ../sora.py -t4 HARM2Dconfig.json
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
60746 leek2 20 0 528m 29m 8284 R 69.6 0.0 0:19.67 python
60742 leek2 20 0 528m 29m 8284 R 61.2 0.0 0:19.76 python
60747 leek2 20 0 528m 29m 8284 S 59.1 0.0 0:16.59 python
60744 leek2 20 0 528m 29m 8288 R 57.0 0.0 0:19.77 python
60743 leek2 20 0 529m 30m 8284 R 54.9 0.0 0:20.36 python
60745 leek2 20 0 528m 29m 8284 S 52.7 0.0 0:16.15 python
60869 leek2 20 0 325m 18m 1948 R 46.4 0.0 0:08.20 python
60866 leek2 20 0 325m 18m 1948 S 44.3 0.0 0:06.59 python
That's all the basics of doing a SoRa run.
SoRa has a lot of options. This section has documentation on each major section and the choices therein.
"infile" : "foo", "infileExtension" : "dat",
This will cause SoRa to look for a file named foo.dat. Which will assume ASCII white-space seperated column (DAT) format. Any extension other than CSV will be read as a DAT file. infileExtension may also be CSV, which will open the file as a comma-separated value file.
SoRa needs to know which column in the input file are important variables. There are two types of variables, the input variables define the dimensionality of the problem, and that target variable is the variable we're trying to match with the function. So for a function E(rho, T), we would use the following in the configuration file:
"inVars" : [ "rho", "T" ], "targetVar" : "E",
The user may supply a random seed, but that's not usually very useful, and there isn't any way to set a different one for each island, which would make multiple islands useless if you use just on seed between them. Defining the seed as "0" will result in the seed being pulled from os.urandom() which should be purely random.
"seed" : 0,
DEAP includes a simple bloat control. A simple "Depth Limit". Each function generated by symbolic regression is actually a parse tree internally. So, for example, 3 * (x + 4) is:
*
/ \
3 +
/ \
x 4
This tree has a depth of "3". The depth limit is enforced by throwing away any individual that has a depth greater than the limit. Python cannot handle a tree deeper than "90" because it hits a recursion limit. So the depth limit must be <= 90. 17 was originally suggested by Koza.
"depthLimit" : 30,
Currently this doesn't actually pretty print, it simplifies the
output of the individuals. Internally all calls are functions, so:
3*(x+4) is internally represented as mul(3, add(x, 4)).
That can be difficult to read, so SymPy may be used to simplify it.
This also results in simplifying silly expressions like:
add( 1, cos(0)) -> "2"
This is pretty useful, but it does require SymPy to be installed, and it does cause the output to happen more slowly.
"prettyPrint" : true,
"errorfunc": "rSquared",
The error function is probably the most important determinant of how your symbolic regression goes. It determines how well the function fits the data, and therefore guides how the evolution progresses. There are currently 7 choices:
3f.1: rSquared RSquared is a measure of error vs the error from the mean line. (Wikipedia has an OK explanation.) It resembles a percentage. 1.0 would be a perfect match.
Generally RSquared works well early on in the run, finding a good coarse fit, but error can start to creep in later when .99994 still isn't as good a fit as you might like.
3f.2: Absolute error: Absolute Error is simply the approximation function minus the actual value, so: |(Value - Approx)|
There are three Absolute Error functions:
- avgAbsErrorSquared : Average Absolute Error
- totalAbsErrorSquared : Total Absolute Error
- maxAbsErrorSquared : Max absolute error
Average and Total give very similar evolutionary results. Max concentrates on just improving the currently worst point. They have their places, but I don't generally find these to work well for symbolic regression as they concentrate very strongly on the points of greatest magnitude.
These are minimization functions. "0.0" would be perfect approximation.
3f.3: Relative Error: Relative error incorporates the magnitude of the value being matched into the calculation. It is: |(Value - Approx) / Value)|. This avoids the problem absolute error has with ignoring the points of small magnitude. All points have equal value to the result, but it may actually make the tiny points TOO important.
WARNING: No zeros are allowed as values in relative error, because that results in a divide by zero error.
There are three Relative Error functions:
- avgRelError : Average Relative Error
- totRelError : Total Relative Error
- maxRelError : Maximum Relative Error
maxRelativeError tends to not be very effective, because it concentrates too much on improving the currently worst point, rather than the overall fit.
The Hall of Fame records the best functions from every generation. There are two choices for a hall of fame, "hof" and "pareto."
3g.1: hof The hof Hall of Fame is very simple. It simply records the X best functions generated. So you have say how big you want the hall of fame to be:
"HallOfFame" : {
"type" : "hof",
"size" : 10
}
This type of hall of fame has the serious disadvantage that you usually end up with X functions with only trivial differences.
3g.2: Pareto A "Pareto Front" gives the dominant solutions according to multiple dimensions of metrics. In SoRa's case, that means "fitness vs function size." This provides a nice trade off between function complexity and fitness. This is the recommended type. The pareto front grows as large as necessary to hold all the "dominant" functions.
"HallOfFame" : {
"type" : "pareto"
},
R Squared error size function
0 : (0.99981390380331492, 9.0) : 0.144495201928092*sqrt(T**2*atan(atan(T)))
1 : (0.99980927751092796, 8.0) : 0.144758631974528*T - 0.000266440720473395
2 : (0.99896867989805338, 3.0) : 0.140651393141985*T
3 : (0.88512316341642705, 2.0) : sqrt(T)
4 : (-5.7872080461152109e-09, 1.0) : 0.727431686266
Selection defines how the next generations population is chosen. Generally many more children will be generated per generation than are allowed in the population. Somehow SoRa must choose which of these children will go on to breed. That's defined by the selection algorithm. All of which currently come from DEAP, so you can learn more about the choices in the DEAP documentation: https://deap.readthedocs.io/en/master/api/tools.html#deap.tools.selTournament All of the argument names to selection functions purposely match the DEAP arguments, so DEAP really has the best documentation for this.
NOTE: One oddity is that in the config file, "select" takes an ARRAY of selectors. The reason is that you can define a different selector for different islands. This didn't turn out to be terribly useful, but there it is. See 3h.2 for an example
3h.1: selRandom: Select survivors randomly. Makes an OK replacement selector.
3h.2: selTournament: The most traditional selector. Selects k individuals randomly and holds a fitness tournament. The winner survives. Works surprisingly well, despite lacking elitism.
"select" : [
{
"type" : "selTournament",
"tournsize" : 4
} ],
3h.3: selBest: Selects the X individuals with best fitness. This discourages diversity too much. Mostly used as a baseline for experiments I think.
3h.4: selWorst: Selects the X individals with the worst fitness. Only useful as a replacement selector, and not even great for that.
3h.5: selRoulette: Selects k individuals via k spins of a roulette, weighted by fitness somehow. Doesn't work with minimization problems or where fitness may be 0.
3h.6: selDoubleTournament: Holds 2 tournaments, one on fitness and one on SIZE, preferring the smaller individuals. This produces minimization pressure on the size, which is good. The arguments are a bit odd though, look them up in the DEAP docs. http://deap.readthedocs.io/en/master/api/tools.html#deap.tools.selDoubleTournament
"select" : [
{
"type" : "selDoubleTournament",
"fitness_size" : 4,
"parsimony_size" : 1.4,
"fitness_first" : false
} ],
3h.7: SPEA2 SPEA2 is a multi-objective optimizer, which, in this case, means it tries to select both for fitness and size. It works well, but is slow, so using the HARM-GP algorithm seems to work better for keeping size down.
3h.8: NSGA2 Like SPEA2, NSGA2 is a multi-objective optimizer, which in SoRa, tries to select both for fitness and size. It's faster than SPEA2, but has really big problems with maintaining diversity. So the results aren't very good.
3i: Initial function generation
expr defines how the initial population of functions gets generated. (expr is short for "expression").
There are three choices for how to make the functions: genFull, genGrow, and genHalfAndHalf. All of them take "min_" and "max_" which define the minimum and maximum initial tree depth.
"expr" : { "type" : "genHalfAndHalf",
"min_" : 1,
"max_" : 4
},
Generally genHalfAndHalf is the best one, but if you want to learn about the other choices see the DEAP documentation: https://deap.readthedocs.io/en/master/api/tools.html#deap.gp.genFull
If you decide to use MPI to increase the parallelism of the evolution, you need to have an islands section. Each MPI process is an island that has it's own population and evolution and what-not. Then every so often, migration happens and a few selected individuals move from rankX to rankX+1. All of that is defined in the islands section. Here's an example:
"islands" : {
"migrationFreq" : 50,
"numMigrants" : 10,
"emmigrantSelect" : [ {
"type" : "selRandom"
} ],
"replacementSelect" : [ { "type" : "selWorst" } ]
},
3j.1: migrationFreq (migration frequency) defines how many generations pass before migrants are sent on and immigrants received. Note that this number is only checked which "stopFrequency" in the algorithm section (3k) comes up. So, even if migrationFreq is 5, if stopFrequency is 50, migrants will only move every 50 generations.
3j.2: numMigrants. The number of individuals to send and receive.
3j.3: emmigrantSelect selects the individuals who are copied to the next island. Generally good migrants should be selected.
3j.4: replacementSelect determines which individuals will be eliminated to make room for the immigrants. This is OPTIONAL. If it is not defined, the immigrants simply replace the emigrants.
The Algorithm is the main loop of the evolution. It determines the size of the population, how many generations, how much crossover and mutation occur, etc. There are current 3 algorithms supported in SoRa (all from DEAP).
3k.1: HARM-GP HARM-GP is an algorithm that runs a fairly normal evolution, except that it tries to limit the size of the answers. This is the best algorithm for Symbolic Regression in my opinion. The following paper explains the details. 2
"algo" : {
"type" : "harm", #Algorithm name
"initialPopulationSize" : 1000, #Size of the population
"numGenerations" : 1000, #Number of generations to run
"stopFrequency" : 50, #stop and maybe checkpoint or migrate
"cxpb" : 0.7, #cross over probability
"mutpb" : 0.3, #mutation probability
"alpha" : 0.05, #see the paper above
"beta" : 10, #see the paper above
"gamma" : 0.25, #see the paper above
"rho" : 0.9, #see the paper above
"nbrindsmodel" : -1, #Hard to explain. See DEAP docs.
"mincutoff" : 20 #The absolute minimum value for the cutoff point.
},
3k.2: eaMuPlusLambda This is a very basic algorithm where crossover and mutation are computed separately. See the DEAP docs. This one works well if you don't want to use HARM.
"algo" : {
"type" : "eaMuPlusLambda",
"initialPopulationSize" : 1000, #Initial population size
"populationSize" : 200, #Population normally
"numGenerations" : 400, #Number of generations to run
"children" : 400, #Children generated per gen
"cxpb" : 0.7, #cross over probability
"mutpb" : 0.3 #mutation probability
},
3k.2: eaMuCommaLambda This is a very basic algorithm where only crossover OR mutation may happen to an individual. See the DEAP docs.
"algo" : {
"type" : "eaMuCommaLambda",
"initialPopulationSize" : 1000, #Initial population size
"populationSize" : 200, #Population normally
"numGenerations" : 400, #Number of generations to run
"children" : 400, #Children generated per gen
"cxpb" : 0.7, #cross over probability
"mutpb" : 0.3 #mutation probability
},
Actually, currently there is only one use-able crossover operator in DEAP, so this doesn't actually do anything.
"mate" : "cxOnePoint",
DEAP comes with multiple mutators that will work with symbolic regressions, and SoRa adds the "multiMutOr", which allows the user to use multiple mutators.
The selected mutator will only be called with the probability of "mutpb" in the "algo" configuration block. In the above case, that's on .3 or 30% of reproduction.
3m.1: mutUniform Randomly select a point in the tree individual, then replaces the subtree at that point as a root by the expression generated using expr_mut from the config file. This is the most powerful mutator.
3m.2: mutNodeReplacement Replaces a randomly chosen primitive from individual by a randomly chosen primitive with the same number of arguments from the pset attribute of the individual.
3m.3: mutShrink Shrinks the individual by randomly choosing a branch and replacing it with one of the branch~s arguments .
3m.4: mutEphemeral This operator works on the constants of the tree individual, it replaces the chosen constant with a newly generated one. mutEphemeral takes one argument: "mode" all / one. In mode "one", it will change the value of one of the individual ephemeral constants by calling its generator function. In mode "all", it will change the value of all the ephemeral constants.
{
"type" : "mutEphemeral",
"mode" : "one"
}
3m.5: mutInsert Inserts a new branch at a random position in individual. The subtree at the chosen position is used as child node of the created subtree, in that way, it is really an insertion rather than a replacement. Note that the original subtree will become one of the children of the new primitive inserted, but not perforce the first (its position is randomly selected if the new primitive has more than one child).
3m.6: multiMutOr Allows the user to select multiple mutators to use, each having it's own probability. These probability must sum <= 1.0. If they don't add to 1.0, the remaining probability simply results in reproduction.
Note that this internal probability is applied AFTER the overall mutpb found in the "algo" configuration block.
So, if mutpb = .3, and multiMurOr has mutUniform with prob = 0.3, mutUniform will only be applied to .09 children generated per generation.
"mutator" : {
"type" : "multiMutOr",
"submutators" : [
{
"type" : "mutUniform",
"prob" : 0.3
},
{
"type" : "mutShrink",
"prob" : 0.1
},
{
"type" : "mutEphemeral",
"prob" : 0.6,
"mode" : "one"
}
]
},
In Symbolic Regression, constants in the function are call "Ephemeral Constants.". Constants are generated as terminals. A terminal is either a variable or a constant, so there are a lot of constant generated.
SoRa allows the user to define distributation and ranges for constant generation, and many can be defined. For example, if you think you may have one kind of constant around zero, and another kind that an integer between (3,8), instead of generating a random number between (0,8), it would probably be better two have two constants, one (-1,1) and one randint (3,8).
Currently all of these come from DEAP, so see DEAP docs for more information.
Example:
"constants" : [ {
"type" : "uniform",
"min" : -1,
"max" : 1
},
"type" : "randint",
"min" : 3,
"max" : 8
},
]
3n.1: randint. A random integer between (min, max) 3n.2: uniform. A random double from the uniform distribution (min,max) 3n.3: normal. A normal distribution, arguments: mu, sigma 3n.4: gamma. A gamma distribution, arguments: alpha, beta 3n.5: constant Just a value. argument: value
When reading the input file data, SoRa can apply filters on the data to make it easier to find a regression.
However, while you can do that, it's fairly rare that a user would want to. More normally you want to modify the data first, then sanity check it, THEN do the symbolic regression run. That's what modifydata.py is for, it just applies the data filters and outputs them to a CSV or DAT file for reading by SoRa.
Example modifydata.py config file:
{
"infile" : "TF_xpthomas_March2013",
"infileExtension" : "dat",
"outfile" : "TF_xpthomas_March2013_noCold_gridReg",
"outfileExtension" : "csv",
"filters" : [
{ "type" : "removeCold",
"removeZero" : 0,
"functions" : [ "E" ]
},
{ "type" : "gridRegular",
"xAxisName" : "rho",
"yAxisName" : "T",
"xRemainingPoints" : 50,
"yRemainingPoints" : 40
}
]
}
As you can see, the modifydata.py configuration file is nearly the same as the SoRa configuration, but SoRa doesn't require an outfile or outfileExtension.
Either SoRa or modifydata.py can take the list of filters. There are currently 4 filters, with more to be added as required:
removeCold solves a particular problem with equation of state data sets. In many EOS data-sets, there are two functions combined into one. One is a energy at 0K, or the "cold energy". The other is the thermal component, which goes up with temperature. The cold energy runs throughout the data set, but at the lowest possible temperature in the data set, generally that's just the cold energy. It can make some data sets significantly easier to match if the cold term is removed. So remove cold does that, such that the cold term is subtracted from the whole data set. Of course this means the lowest temperature row becomes completely 0.
{ "type" : "removeCold", #The type of the filter
"removeZero" : false, #Delete the lowest T, or leave it as zeros
"functions" : [ "E" ] #The column to act on in the data.
},
fullRandom removes points from the data set completely randomly. (For DAT and CSV files, this is the equivalent of removing rows or lines randomly.)
fullRandom doesn't know anything about the grid or the underlying data, so if there was some order to the points, it may not be preserved.
{ "type" : "fullRandom",
"remainingPoints" : 50, #Leave 50 points
}
{ "type" : "fullRandom",
"removalFraction" : .4, #Leave 40% of the points
}
gridRandom assumes a 2D grid of points. So it needs to know which column in the data is the X axis, and which is the Y axis. Then it eliminates whole rows and columns from the 2D data randomly.
The boundary points always remain though, the lowest and greatest X and Y values will remain to define the edge of the grid.
{ "type" : "gridRandom",
"seed" : 0, #Optional argument, 0 gets seed from os.urandom
"xAxisName" : "rho", #The x Axis is called "rho"
"yAxisName" : "T", #The y Axis is called "T"
"xRemainingPoints" : 50, #Leave 50 points in rho
"yRemainingPoints" : 40 #Leave 40 points in T
}
{ "type" : "gridRandom",
"xAxisName" : "rho", #The x Axis is called "rho"
"yAxisName" : "T", #The y Axis is called "T"
"xRemovalFraction" : .5, #Leave 50% of the points in rho
"yRemovalFraction" : .4 #Leave 40% of the points in T
}
gridRandom assumes a 2D grid of points. So it needs to know which column in the data is the X axis, and which is the Y axis. Then it eliminates whole rows and columns from the 2D data at regular intervals. It doesn't really understand the grid, so it doesn't really keep the grid regular according whatever values are actually in the axes, it just eliminates "every 5th X point" or something like that.
The boundary points always remain though, the lowest and greatest X and Y values will remain to define the edge of the grid.
{ "type" : "gridRegular",
"xAxisName" : "rho", #The x Axis is called "rho"
"yAxisName" : "T", #The y Axis is called "T"
"xRemainingPoints" : 50, #Leave 50 points in rho
"yRemainingPoints" : 40 #Leave 40 points in T
}
{ "type" : "gridRegular",
"xAxisName" : "rho", #The x Axis is called "rho"
"yAxisName" : "T", #The y Axis is called "T"
"xRemovalFraction" : .5, #Leave 50% of the points in rho
"yRemovalFraction" : .4 #Leave 40% of the points in T
}
Footnotes
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[Schmidt, M., Lipson, H. Distilling Free-Form Natural Laws from Experimental Data. Science 324, 5923 (2009), 81-85.] ↩
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Gardner, M. A., Gagn\E9, C., & Parizeau, M. (2015). Controlling code growth by dynamically shaping the genotype size distribution. Genetic Programming and Evolvable Machines, 16(4), 455-498. ↩