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Experiment scripts

In the directory "new-scripts" you find some scripts that facilitate conducting experiments. An experiment is conducted in three stages: Generation of experiments, fetching of results and production of reports. Each stage has its own generic main module: experiments.py, resultfetcher.py and reports.py. These modules provide useful classes and methods and can be imported by scripts that actually define concrete actions. For the Fast Downward planning system the example scripts that use these modules are downward_experiments.py, downward-resultfetcher.py and downward-reports.py. Together they can be used to conduct Fast Downward experiments. Passing -h on the command line gives you an overview of each script's commands. Be sure to check out the available options as they may be more up to date than the instructions here.

An even easier, but more restricted way is to use experiment.sh that is a wrapper around the above-mentioned python scripts. In the following we will give instructions for both options.

Note for gkigrid users: If you submit an experiment to the gkigrid from a shared account, please let the experiment name begin with your initials. In the examples below e.g. Jane Doe would write downward_experiments.py --path jd-expname --suite ALL --configs lama gkigrid

Running experiments with `experiment.sh`

For your convenience the shell script experiment.sh has been created. It facilitates conducting experiments in the following way: You write your own little shell script, e.g. lama.sh, setup some options like the problem suite and the desired configuration and invoke experiment.sh from your script. Then you only have to call lama.sh 1, lama.sh 2 etc. to iterate over the experiment steps. Let's have a look at the annotated lama.sh:

   1 #! /bin/bash
   2 
   3 # Use any config name from downward_configs.py or from you own module (mymodule.py:myconfig)
   4 CONFIGS=lama
   5 
   6 # Select a cluster queue (Only applicable for experiments run on University of Freiburg's gkigrid cluster)
   7 QUEUE=athlon_core.q
   8 
   9 # Select a problem suite from downward_suites.py
  10 SUITE=IPC08_SAT_STRIPS
  11 
  12 # Available options are local and gkigrid
  13 EXPTYPE=gkigrid
  14 
  15 # Lets experiment.sh prepare and run the actual experiment
  16 source experiment.sh

Running a local experiment directly with the Python scripts

   1 # Preprocessing
   2 ./downward_experiments.py --path expname --suite ALL --preprocess local
   3 ./expname-p/run
   4 ./resultfetcher.py expname-p
   5 
   6 # Build and run the experiment
   7 ./downward_experiments.py --path expname --configs downward_configs.py:lama --suite ALL local
   8 ./expname/run
   9 
  10 # Make reports
  11 ./downward-resultfetcher.py expname
  12 ./downward-reports.py expname-eval

Below you find a detailed description of the above steps.

Generate an experiment

./downward_experiments.py --path expname --configs downward_configs.py:lama --suite ALL local

Generates a planning experiment for the suite ALL in the directory "expname". The planner will use the configuration string lama found in the file downward_configs.py. When you invoke the script you can specify on the command line whether you want the experiment to be run local or on the gkigrid. You can also directly set the timeout, memory limit, number of processes, etc.

Before you can execute that command however you have to run it once with the --preprocess parameter. This will generate a preprocessing experiment. After you have run this preprocessing experiment and fetched the results with ./resultfetcher.py you can generate the search experiment. If you want to do the preprocessing and search in a single experiment you can pass the --complete parameter.

Local experiments can be started by running

./expname/run

Gkigrid experiments are submitted to the queue by running

qsub expname/expname.q

Fetch and parse results

./downward-resultfetcher.py expname

Traverses the directory tree under "expname" and parses each run's experiment files. The results are written into a dictionary-like file at "expname-eval/properties". This file maps each run's id to its properties (search_time, problem_file, etc.). If additionally you want each run's files to be copied into a new directory structure organized by the ids you can pass the "--copy-all" option.

Partial fetching

If you don't need the results of the preprocessing or search phase, you can pass the options --no-preprocess and --no-search to downward-resultfetcher.py. This obviously speeds up parsing, too.

Combine results of multiple experiments

It is possible to combine the results of multiple experiments by running the above command on all the experiment directories while specifying the target evaluation directory. If you don't specify the evaluation directory it defaults to "expname-eval". An example would be

./downward-resultfetcher.py exp1 --dest my-eval-dir
./downward-resultfetcher.py exp2 --dest my-eval-dir

Make reports

./downward-reports.py expname-eval

Reads all properties files found under "expname-eval" and generates a report based on the given commandline parameters. By default this report contains absolute numbers, writes a HTML file and analyzes all numeric attributes found in the dataset. You can however choose only a subset of attributes and filter by configurations or suites, too. A detailed description of the available parameters can be obtained by invoking downward-reports.py -h.

Making a problem suite from results

The downward-reports.py also gives you the possibility to create a new problem suite based on the results of an experiment. To select a subset of problems you can specify filters for the set of the runs. E.g. to get a list of problems that had more than 1000 states expanded in the lama config, you could issue the following command:

   1 ./downward-reports.py expname-eval --filter config:eq:lama expanded:gt:1000 --report suite

(Remember to pass the correct name for config, it might not be just its nickname) As you can see the format of a filter is <attribute_name>:<operator from the operator module>:<value>. If the expression operator(run[attribute], value) evaluates to True, the run's planning problem is not removed from the result list.

Comparing different revisions

If you want to compare different revisions of Fast Downward, you can write your own script that imports python module downward_experiments.py. This module provides an easy way to select and compare specific revisions of the three subsystems (translate, preprocess and search). It does so by using the experiments.py module. The usage is pretty simple. As an example we will look at the code that has been used to get some information about issue69 from the issue tracker (The code resides in issue69.py):

   1 #! /usr/bin/env python
   2 
   3 import downward_experiments
   4 import checkouts
   5 
   6 combinations = [
   7     (checkouts.TranslatorSvnCheckout(rev=3613),
   8      checkouts.PreprocessorSvnCheckout(rev='HEAD'),
   9      checkouts.PlannerSvnCheckout(rev=3612)),
  10     (checkouts.TranslatorSvnCheckout(rev=3613),
  11      checkouts.PreprocessorSvnCheckout(rev='HEAD'),
  12      checkouts.PlannerSvnCheckout(rev=3613)),
  13     (checkouts.TranslatorSvnCheckout(rev=3613),
  14      checkouts.PreprocessorSvnCheckout(rev='HEAD'),
  15      checkouts.PlannerSvnCheckout(rev='HEAD')),
  16                ]
  17 
  18 downward_experiments.build_experiment(combinations)

This code builds an experiment that compares three revisions of the search component; rev 3612, rev 3613 and the latest (HEAD) revision. The different Checkout classes also have another keyword parameter called repo that can be used when you don't want to checkout a subsystem from trunk.

One combination of three checkouts results in one run of the fast-downward system (translate -> preprocess -> search) for each problem and configuration. Obviously you should checkout different revisions of the subsystems you want to compare and let the other subsystems have the same revisions in all runs.

As another example, if you want to compare your modified translator in your own branch with the one from trunk, you could do:

   1 combinations = [
   2     (TranslatorSvnCheckout(repo='svn+ssh://downward/branches/my-own-translator/downward/translate', rev=1234), PreprocessorSvnCheckout(), PlannerSvnCheckout()),
   3     (TranslatorSvnCheckout(), PreprocessorSvnCheckout(), PlannerSvnCheckout()),
   4                ]

When running your script, you'll be prompted to specify the suites and configurations. You have the same options here as for the downward_experiments.py script.

Example: Issue 7

You don't have to supply new Checkout instances for each combination. See the comparison experiment for issue7 as an example. This code compares three different revisions of the translator in an external branch with the checked-in translator from trunk. The revisions for the preprocessor and the search component remain the same for all combinations.

   1 from downward_experiments import *
   2 
   3 branch = 'svn+ssh://downward-svn/branches/translate-andrew/downward/translate'
   4 
   5 preprocessor = PreprocessorSvnCheckout()
   6 planner = PlannerSvnCheckout(rev=3842)
   7 
   8 combinations = [
   9     (TranslatorSvnCheckout(repo=branch, rev=3827), preprocessor, planner),
  10     (TranslatorSvnCheckout(repo=branch, rev=3829), preprocessor, planner),
  11     (TranslatorSvnCheckout(repo=branch, rev=3840), preprocessor, planner),
  12     (TranslatorSvnCheckout(rev=4283), preprocessor, planner),
  13                ]
  14                
  15 build_experiment(combinations)

Example: Checking impacts of your changes

If you want to check the impacts of your changes in your working copy with the latest checked-in revision you could pass the following combinations to the function build_experiment() in downward_experiments.py.

   1 from downward_experiments import *
   2 
   3 combinations = [
   4     (TranslatorHgCheckout(rev='tip'), PreprocessorHgCheckout(rev='tip'), PlannerHgCheckout(rev='tip')),
   5     (TranslatorHgCheckout(rev='WORK'), PreprocessorHgCheckout(rev='WORK'), PlannerHgCheckout(rev='WORK')),
   6     ]
   7                
   8 build_experiment(combinations)

This command creates an experiment that compares the subsystems in the working copy with the versions of the subsystems in the tip revision. You can also see that you can set rev to 'WORK' to use the working copy.