pebl-1.0.2/000755 000766 000024 00000000000 11377315471 013041 5ustar00shahadstaff000000 000000 pebl-1.0.2/docs/000755 000766 000024 00000000000 11377315471 013771 5ustar00shahadstaff000000 000000 pebl-1.0.2/ez_setup.py000644 000766 000024 00000020542 10556231652 015250 0ustar00shahadstaff000000 000000 #!python """Bootstrap setuptools installation If you want to use setuptools in your package's setup.py, just include this file in the same directory with it, and add this to the top of your setup.py:: from ez_setup import use_setuptools use_setuptools() If you want to require a specific version of setuptools, set a download mirror, or use an alternate download directory, you can do so by supplying the appropriate options to ``use_setuptools()``. This file can also be run as a script to install or upgrade setuptools. """ import sys DEFAULT_VERSION = "0.6c5" DEFAULT_URL = "http://cheeseshop.python.org/packages/%s/s/setuptools/" % sys.version[:3] md5_data = { 'setuptools-0.6b1-py2.3.egg': '8822caf901250d848b996b7f25c6e6ca', 'setuptools-0.6b1-py2.4.egg': 'b79a8a403e4502fbb85ee3f1941735cb', 'setuptools-0.6b2-py2.3.egg': '5657759d8a6d8fc44070a9d07272d99b', 'setuptools-0.6b2-py2.4.egg': '4996a8d169d2be661fa32a6e52e4f82a', 'setuptools-0.6b3-py2.3.egg': 'bb31c0fc7399a63579975cad9f5a0618', 'setuptools-0.6b3-py2.4.egg': '38a8c6b3d6ecd22247f179f7da669fac', 'setuptools-0.6b4-py2.3.egg': '62045a24ed4e1ebc77fe039aa4e6f7e5', 'setuptools-0.6b4-py2.4.egg': '4cb2a185d228dacffb2d17f103b3b1c4', 'setuptools-0.6c1-py2.3.egg': 'b3f2b5539d65cb7f74ad79127f1a908c', 'setuptools-0.6c1-py2.4.egg': 'b45adeda0667d2d2ffe14009364f2a4b', 'setuptools-0.6c2-py2.3.egg': 'f0064bf6aa2b7d0f3ba0b43f20817c27', 'setuptools-0.6c2-py2.4.egg': '616192eec35f47e8ea16cd6a122b7277', 'setuptools-0.6c3-py2.3.egg': 'f181fa125dfe85a259c9cd6f1d7b78fa', 'setuptools-0.6c3-py2.4.egg': 'e0ed74682c998bfb73bf803a50e7b71e', 'setuptools-0.6c3-py2.5.egg': 'abef16fdd61955514841c7c6bd98965e', 'setuptools-0.6c4-py2.3.egg': 'b0b9131acab32022bfac7f44c5d7971f', 'setuptools-0.6c4-py2.4.egg': '2a1f9656d4fbf3c97bf946c0a124e6e2', 'setuptools-0.6c4-py2.5.egg': '8f5a052e32cdb9c72bcf4b5526f28afc', 'setuptools-0.6c5-py2.3.egg': 'ee9fd80965da04f2f3e6b3576e9d8167', 'setuptools-0.6c5-py2.4.egg': 'afe2adf1c01701ee841761f5bcd8aa64', 'setuptools-0.6c5-py2.5.egg': 'a8d3f61494ccaa8714dfed37bccd3d5d', } import sys, os def _validate_md5(egg_name, data): if egg_name in md5_data: from md5 import md5 digest = md5(data).hexdigest() if digest != md5_data[egg_name]: print >>sys.stderr, ( "md5 validation of %s failed! (Possible download problem?)" % egg_name ) sys.exit(2) return data def use_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=os.curdir, download_delay=15 ): """Automatically find/download setuptools and make it available on sys.path `version` should be a valid setuptools version number that is available as an egg for download under the `download_base` URL (which should end with a '/'). `to_dir` is the directory where setuptools will be downloaded, if it is not already available. If `download_delay` is specified, it should be the number of seconds that will be paused before initiating a download, should one be required. If an older version of setuptools is installed, this routine will print a message to ``sys.stderr`` and raise SystemExit in an attempt to abort the calling script. """ try: import setuptools if setuptools.__version__ == '0.0.1': print >>sys.stderr, ( "You have an obsolete version of setuptools installed. Please\n" "remove it from your system entirely before rerunning this script." ) sys.exit(2) except ImportError: egg = download_setuptools(version, download_base, to_dir, download_delay) sys.path.insert(0, egg) import setuptools; setuptools.bootstrap_install_from = egg import pkg_resources try: pkg_resources.require("setuptools>="+version) except pkg_resources.VersionConflict, e: # XXX could we install in a subprocess here? print >>sys.stderr, ( "The required version of setuptools (>=%s) is not available, and\n" "can't be installed while this script is running. Please install\n" " a more recent version first.\n\n(Currently using %r)" ) % (version, e.args[0]) sys.exit(2) def download_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=os.curdir, delay = 15 ): """Download setuptools from a specified location and return its filename `version` should be a valid setuptools version number that is available as an egg for download under the `download_base` URL (which should end with a '/'). `to_dir` is the directory where the egg will be downloaded. `delay` is the number of seconds to pause before an actual download attempt. """ import urllib2, shutil egg_name = "setuptools-%s-py%s.egg" % (version,sys.version[:3]) url = download_base + egg_name saveto = os.path.join(to_dir, egg_name) src = dst = None if not os.path.exists(saveto): # Avoid repeated downloads try: from distutils import log if delay: log.warn(""" --------------------------------------------------------------------------- This script requires setuptools version %s to run (even to display help). I will attempt to download it for you (from %s), but you may need to enable firewall access for this script first. I will start the download in %d seconds. (Note: if this machine does not have network access, please obtain the file %s and place it in this directory before rerunning this script.) ---------------------------------------------------------------------------""", version, download_base, delay, url ); from time import sleep; sleep(delay) log.warn("Downloading %s", url) src = urllib2.urlopen(url) # Read/write all in one block, so we don't create a corrupt file # if the download is interrupted. data = _validate_md5(egg_name, src.read()) dst = open(saveto,"wb"); dst.write(data) finally: if src: src.close() if dst: dst.close() return os.path.realpath(saveto) def main(argv, version=DEFAULT_VERSION): """Install or upgrade setuptools and EasyInstall""" try: import setuptools except ImportError: egg = None try: egg = download_setuptools(version, delay=0) sys.path.insert(0,egg) from setuptools.command.easy_install import main return main(list(argv)+[egg]) # we're done here finally: if egg and os.path.exists(egg): os.unlink(egg) else: if setuptools.__version__ == '0.0.1': # tell the user to uninstall obsolete version use_setuptools(version) req = "setuptools>="+version import pkg_resources try: pkg_resources.require(req) except pkg_resources.VersionConflict: try: from setuptools.command.easy_install import main except ImportError: from easy_install import main main(list(argv)+[download_setuptools(delay=0)]) sys.exit(0) # try to force an exit else: if argv: from setuptools.command.easy_install import main main(argv) else: print "Setuptools version",version,"or greater has been installed." print '(Run "ez_setup.py -U setuptools" to reinstall or upgrade.)' def update_md5(filenames): """Update our built-in md5 registry""" import re from md5 import md5 for name in filenames: base = os.path.basename(name) f = open(name,'rb') md5_data[base] = md5(f.read()).hexdigest() f.close() data = [" %r: %r,\n" % it for it in md5_data.items()] data.sort() repl = "".join(data) import inspect srcfile = inspect.getsourcefile(sys.modules[__name__]) f = open(srcfile, 'rb'); src = f.read(); f.close() match = re.search("\nmd5_data = {\n([^}]+)}", src) if not match: print >>sys.stderr, "Internal error!" sys.exit(2) src = src[:match.start(1)] + repl + src[match.end(1):] f = open(srcfile,'w') f.write(src) f.close() if __name__=='__main__': if len(sys.argv)>2 and sys.argv[1]=='--md5update': update_md5(sys.argv[2:]) else: main(sys.argv[1:]) pebl-1.0.2/INSTALL.txt000644 000766 000024 00000017042 11130677031 014702 0ustar00shahadstaff000000 000000 Installation ============= Unfortunatly, installing pebl is not a one-stop process although it should take no longer than a few minutes. If you experience any problems, please contact me at abhikshah@gmail.com. Pebl is known to run on Linux and Mac OSX and should also run on Windows and any platform that supports Python 2.5 and numpy. Pebl depends on the following packages: * Numpy: tested with version 1.0.4 but should work with other recent versions * Pydot: tested with version 1.0.2 * Matplotlib (optional): tested with version 0.91.2 but should work with other recent versions * Graphviz (optional): any recent version of dot and neato * IPython1 (optional): curently, require r2815 from svn trunk * boto (optional): any recent version Install Python 2.5 ------------------- Check what version of Python you have with:: python --version You can download Python 2.5 from http://python.org/download .. note:: Pebl requires Python 2.5 or greater and will not run under earlier versions. Install easy_install -------------------- easy_install lets you install python packages and their requirements in one easy step. Unfortunately, easy_install is not distributed with python and needs to be installed separately. 1. Download ez_setup.py from http://peak.telecommunity.com/dist/ez_setup.py 2. Run :command:`python ez_setup.py` to install easy_install Run :command:`easy_install --help` to make sure that it is in your path. On unix-type systems, it is usually installed in /usr/bin/ and on Windows in C:\\Python2.5\\Scripts\\ Install Numpy ------------- Numpy can be tricky to install because it require C and Fortran compilers and several libraries. You can try installing it from source using easy_install:: easy_install numpy If that doesn't work or if you'd rather install using a binary package, consult http://www.scipy.org/Download. Install Pebl ------------ You can now install (or upgrade) pebl and it's required dependencies using easy_install:: easy_install pebl .. note:: The current version of Pebl is 1.0.1 If you have downloaded the source code for Pebl (or installing pebl from svn), you can run :command:`sudo python setup.py install` from pebl's root directory instead. Testing Pebl Installation ------------------------- To test pebl installation, you can run the automated unit tests by first install nose:: easy_install nose and then running nose from the command line, in any directory:: nosetests -v pebl.test If you haven't installed all the optional dependencies, certain tests will fail but the name of the tests should make it clear whether it's a normal feature or an optional one. Installing optional dependencies --------------------------------- Certain features of pebl require additional dependencies. You only need to install these if you need the optional features. Without the optional features, you can load data, learn networks and output networks in dot format, although not as an image. If installing pebl on a cluster, the optional features are only necessary on the controller or the machine used interactively by the user. For creating HTML reports of Pebl results ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Pebl uses Graphviz to visualize networks and matplotlib for plotting. If using Pebl on a cluster, these don't need to be installed on the worker nodes. 1. Install Graphviz from http://www.graphviz.org/Download.php 2. Install matplotlib from http://matplotlib.sourceforge.net/installing.html Finally, install simplejson using easy_install:: easy_install simplejson simplejson sometimes fails to install on Windows. In that case, you can use a binary package:: easy_install http://pebl-project.googlecode.com/files/simplejson-1.7.3-py2.5-win32.egg To test the html-report feature, run:: nosetests -v pebl.test.test_result:TestHtmlReport For the XGrid Task Controller ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Apple XGrid is a grid solution for Apple computers that lets desktops and dedicated servers be used in a computational grid. The Xgrid task controller only runs on platforms where XGrid is available (currently, only Mac OSX). Pebl uses the PyXG package to access the XGrid controller:: easy_install PyXg To test the XGrid feature, find the location where pebl was installed by easy_install and navigate to the src/pebl/test.manual directory. There, create a file called xgridconfig.txt and include the relevant configuration parameters. The file should look like:: [xgrid] controller = your.controller.com password = yourpassword grid = gridnumber pollinterval = 30 Then, execute the test_xgrid.py test:: python test_xgrid.py xgridconfig.txt Pebl will create and run 10 short learners over the XGrid. It will print some information about submitting and retrieving data from the Xgrid controller and finally print a list of machines where the tasks ran. For the IPython1 Task Controller ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ IPython1 is the next version of the popular IPython shell that also includes an interactive, clustering solution. Pebl can use IPython1 to execute learners in parallel. You will need to install both IPython and IPython1. IPython1 is in active development and pebl requires a specific version. Once, IPython1 is oficially released, we will use that package:: easy_install ipython svn co -r 2815 http://ipython.scipy.org/svn/ipython/ipython1/trunk ipython1-2815 cd ipython1-2815 sudo python setup.py install To test the IPython1 feature, make sure that IPython1's ipcluster is in the path and run:: nosetests -v pebl.test.test_taskcontroller:TestIPython1TC The test will create three local IPython1 engines and run tasks on them. The test cannot, unfortunately, terminate the engines and that needs to be done manually. Simply run 'ps' and terminate the appropriate processes. For the EC2 Task Controller ^^^^^^^^^^^^^^^^^^^^^^^^^^^ Amazon EC2 is an on-demand cloud computing solution from Amazon. It allows users to rent computing power on an as-needed basis. Pebl can reserve, create, use and terminate EC2 instances automatically. More information available at http://aws.amazon.com/ec2/ Before you can use Amazon's EC2 platform, you need to register with Amazon and create the required authentication credentials. Consult the `Getting Started Guide `_ at Amazon for further information. Pebl uses the boto package to interact with EC2:: easy_install boto Also see the instructions above for installing dependencies for the IPython1 Task Controller (which is required by the EC2 Task Controller). To test the EC2 feature, find the location where pebl was installed by easy_install and navigate to the src/pebl/test.manual directory. There, create a file called ec2config.txt and include the relevant configuration parameters. The file should look like:: [EC2] access_key = YOUR_AMAZON_ACCESSKEY secret_access_key = YOUR_AMAZON_SECRET_ACCESS_KEY ami = ami-66a3470f # or any AMI with pebl, IPython1 and svn installed key_name = keyname-to-use credential = ~/foo/private-key-to-use Then, execute the test_ec2.py test:: python test_ec2.py ec2config.txt Pebl will reserve and use 3 EC2 instances, upgrade the version of pebl installed, run 10 shorts tasks on them and terminate the instances when finished. If all works sucessfully, Pebl will print the hostnames of the machines used. For developing Pebl ^^^^^^^^^^^^^^^^^^^ Pebl developers will also need nose for testing and sphinx for generating html documentation:: easy_install nose easy_install sphinx pebl-1.0.2/LICENSE.txt000644 000766 000024 00000002117 11010425560 014646 0ustar00shahadstaff000000 000000 PEBL: Python Environment for Bayesian Learning Copyright (c) 2008, Abhik Shah. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. pebl-1.0.2/MANIFEST.in000644 000766 000024 00000000031 11331653572 014566 0ustar00shahadstaff000000 000000 include docs/build/dummy pebl-1.0.2/PKG-INFO000644 000766 000024 00000002555 11377315471 014145 0ustar00shahadstaff000000 000000 Metadata-Version: 1.0 Name: pebl Version: 1.0.2 Summary: Python Environment for Bayesian Learning Home-page: http://pebl-project.googlecode.com Author: Abhik Shah Author-email: abhikshah@gmail.comm License: MIT Download-URL: http://pypi.python.org/pypi/pebl Description: Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl includes the following features: * Can learn with observational and interventional data * Handles missing values and hidden variables using exact and heuristic methods * Provides several learning algorithms; makes creating new ones simple * Supports hard and soft structural priors * Has facilities for transparent parallel execution * Calculates edge marginals and consensus networks * Presents results in a variety of formats Platform: UNKNOWN Classifier: Development Status :: 5 - Production/Stable Classifier: Environment :: Console Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: MIT License Classifier: Operating System :: OS Independent Classifier: Programming Language :: Python Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence Classifier: Topic :: Scientific/Engineering :: Bio-Informatics pebl-1.0.2/README.txt000644 000766 000024 00000000207 11030024157 014516 0ustar00shahadstaff000000 000000 PEBL: Python Environment for Bayesian Learning by Abhik Shah (abhikshah@gmail.com) Extensive documentation in docs/src and docs/html. pebl-1.0.2/setup.cfg000644 000766 000024 00000000335 11377315471 014663 0ustar00shahadstaff000000 000000 [apydia] modules = pebl [build_sphinx] all_files = 1 build-dir = docs/build source-dir = docs/src [egg_info] tag_build = tag_date = 0 tag_svn_revision = 0 [nosetests] verbosity = 3 doctest-tests = 1 with-doctest = 1 pebl-1.0.2/setup.py000644 000766 000024 00000007025 11323353155 014547 0ustar00shahadstaff000000 000000 import ez_setup ez_setup.use_setuptools() from setuptools import setup, find_packages, Extension, Feature from distutils.command.build_ext import build_ext from distutils.errors import CCompilerError, DistutilsExecError, \ DistutilsPlatformError import numpy BUILD_EXT_WARNING = """ WARNING: The C extensions could not be compiled. Pebl will run normally but will be slower. Below is the output showing how the compilation failed: """ long_desc = """ Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl includes the following features: * Can learn with observational and interventional data * Handles missing values and hidden variables using exact and heuristic methods * Provides several learning algorithms; makes creating new ones simple * Supports hard and soft structural priors * Has facilities for transparent parallel execution * Calculates edge marginals and consensus networks * Presents results in a variety of formats """ # This class allows C extension building to fail. # from http://simplejson.googlecode.com/svn/trunk/setup.py class ve_build_ext(build_ext): def run(self): try: build_ext.run(self) except DistutilsPlatformError, x: self._unavailable(x) def build_extension(self, ext): try: build_ext.build_extension(self, ext) except (CCompilerError, DistutilsExecError), x: self._unavailable(x) def _unavailable(self, exc): print '*'*70 print BUILD_EXT_WARNING print exc print '*'*70 # C extension modules (as an optional feature) speedymodules = Feature( "Optional speedier C modules.", standard = True, ext_modules = [ Extension('pebl._network', sources=['src/pebl/_network.c']), Extension('pebl._cpd', sources=['src/pebl/_cpd.c'], include_dirs=[numpy.get_include()]), ] ) setup( name='pebl', version='1.0.2', description='Python Environment for Bayesian Learning', long_description = long_desc, package_dir={'': 'src'}, packages=find_packages('src'), ## Package metadata.. for upload to PyPI author='Abhik Shah', author_email='abhikshah@gmail.comm', url='http://pebl-project.googlecode.com', download_url='http://pypi.python.org/pypi/pebl', license='MIT', zip_safe=False, classifiers=[ 'Development Status :: 5 - Production/Stable', 'Environment :: Console', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Topic :: Scientific/Engineering :: Artificial Intelligence', 'Topic :: Scientific/Engineering :: Bio-Informatics', ], # required dependencies install_requires=[ # 'numpy >= 1.0.3', # matrices, linear algebra, etc 'pydot', # to output network as dot file 'pyparsing >= 1.4.7', # required by pydot but not specified in its setup ], # data files, resources, etc include_package_data = True, # tests test_suite = 'nose.collector', # scripts entry_points = { 'console_scripts': [ 'pebl = pebl.pebl_script:main' ] }, # C extension modules (as an optional feature) features = { 'speedups': speedymodules }, # build extensions optionally cmdclass={'build_ext': ve_build_ext}, ) pebl-1.0.2/src/000755 000766 000024 00000000000 11377315471 013630 5ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl/000755 000766 000024 00000000000 11377315471 014552 5ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl.egg-info/000755 000766 000024 00000000000 11377315471 016244 5ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl.egg-info/dependency_links.txt000644 000766 000024 00000000001 11377315470 022311 0ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl.egg-info/entry_points.txt000644 000766 000024 00000000060 11377315470 021535 0ustar00shahadstaff000000 000000 [console_scripts] pebl = pebl.pebl_script:main pebl-1.0.2/src/pebl.egg-info/not-zip-safe000644 000766 000024 00000000001 11032251054 020452 0ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl.egg-info/PKG-INFO000644 000766 000024 00000002555 11377315470 017347 0ustar00shahadstaff000000 000000 Metadata-Version: 1.0 Name: pebl Version: 1.0.2 Summary: Python Environment for Bayesian Learning Home-page: http://pebl-project.googlecode.com Author: Abhik Shah Author-email: abhikshah@gmail.comm License: MIT Download-URL: http://pypi.python.org/pypi/pebl Description: Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl includes the following features: * Can learn with observational and interventional data * Handles missing values and hidden variables using exact and heuristic methods * Provides several learning algorithms; makes creating new ones simple * Supports hard and soft structural priors * Has facilities for transparent parallel execution * Calculates edge marginals and consensus networks * Presents results in a variety of formats Platform: UNKNOWN Classifier: Development Status :: 5 - Production/Stable Classifier: Environment :: Console Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: MIT License Classifier: Operating System :: OS Independent Classifier: Programming Language :: Python Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence Classifier: Topic :: Scientific/Engineering :: Bio-Informatics pebl-1.0.2/src/pebl.egg-info/requires.txt000644 000766 000024 00000000030 11377315470 020634 0ustar00shahadstaff000000 000000 pydot pyparsing >= 1.4.7pebl-1.0.2/src/pebl.egg-info/SOURCES.txt000644 000766 000024 00000013765 11377315471 020144 0ustar00shahadstaff000000 000000 INSTALL.txt LICENSE.txt MANIFEST.in README.txt ez_setup.py setup.cfg setup.py docs/build/dummy docs/src/apiref.rst docs/src/conf.py docs/src/config.rst docs/src/cpd.rst docs/src/data.rst docs/src/devguide.rst docs/src/discretizer.rst docs/src/evaluator.rst docs/src/index.rst docs/src/install.rst docs/src/intro.rst docs/src/network.rst docs/src/paramref.rst docs/src/posterior.rst docs/src/prior.rst docs/src/result.rst docs/src/tutorial.rst docs/src/userguide.rst docs/src/util.rst docs/src/learner/exhaustive.rst docs/src/learner/greedy.rst docs/src/learner/simanneal.rst docs/src/static/tutorial/pebl-tutorial-data1.txt docs/src/static/tutorial/pebl-tutorial-data2.txt docs/src/static/tutorial/example1-result/index.html docs/src/static/tutorial/example1-result/data/0-common.png docs/src/static/tutorial/example1-result/data/0.png 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docs/src/static/tutorial/example1-result/data/consensus.0.png docs/src/static/tutorial/example1-result/data/consensus.1.png docs/src/static/tutorial/example1-result/data/consensus.2.png docs/src/static/tutorial/example1-result/data/consensus.3.png docs/src/static/tutorial/example1-result/data/consensus.4.png docs/src/static/tutorial/example1-result/data/consensus.5.png docs/src/static/tutorial/example1-result/data/consensus.6.png docs/src/static/tutorial/example1-result/data/consensus.7.png docs/src/static/tutorial/example1-result/data/consensus.8.png docs/src/static/tutorial/example1-result/data/consensus.9.png docs/src/static/tutorial/example1-result/data/result.data.js docs/src/static/tutorial/example1-result/data/scores.png docs/src/static/tutorial/example1-result/lib/jquery.js docs/src/static/tutorial/example1-result/lib/pebl-logo.png docs/src/static/tutorial/example1-result/lib/result.css docs/src/static/tutorial/example1-result/lib/result.js docs/src/static/tutorial/example1-result/lib/tab.png docs/src/static/tutorial/example1-result/lib/ui.base.js docs/src/static/tutorial/example1-result/lib/ui.tabs.css docs/src/static/tutorial/example1-result/lib/ui.tabs.js docs/src/taskcontroller/ec2.rst docs/src/taskcontroller/ipy1.rst docs/src/taskcontroller/multiprocess.rst docs/src/taskcontroller/serial.rst docs/src/taskcontroller/xgrid.rst src/pebl/__init__.py src/pebl/_cpd.c src/pebl/_network.c src/pebl/config.py src/pebl/cpd.py src/pebl/data.py src/pebl/discretizer.py src/pebl/evaluator.py src/pebl/network.py src/pebl/pebl_script.py src/pebl/posterior.py src/pebl/prior.py src/pebl/result.py src/pebl/util.py src/pebl.egg-info/PKG-INFO src/pebl.egg-info/SOURCES.txt src/pebl.egg-info/dependency_links.txt src/pebl.egg-info/entry_points.txt src/pebl.egg-info/not-zip-safe src/pebl.egg-info/requires.txt src/pebl.egg-info/top_level.txt src/pebl/learner/__init__.py src/pebl/learner/base.py src/pebl/learner/custom.py src/pebl/learner/exhaustive.py src/pebl/learner/greedy.py src/pebl/learner/simanneal.py src/pebl/resources/htmlresult/index.html src/pebl/resources/htmlresult/lib/jquery.js src/pebl/resources/htmlresult/lib/pebl-logo.png src/pebl/resources/htmlresult/lib/result.css src/pebl/resources/htmlresult/lib/result.js src/pebl/resources/htmlresult/lib/tab.png src/pebl/resources/htmlresult/lib/ui.base.js src/pebl/resources/htmlresult/lib/ui.tabs.css src/pebl/resources/htmlresult/lib/ui.tabs.js src/pebl/taskcontroller/__init__.py src/pebl/taskcontroller/base.py src/pebl/taskcontroller/ec2.py src/pebl/taskcontroller/ec2ipy1.py src/pebl/taskcontroller/ipy1.py src/pebl/taskcontroller/multiprocess.py src/pebl/taskcontroller/serial.py src/pebl/taskcontroller/xgrid.py src/pebl/test/__init__.py src/pebl/test/benchmark.py src/pebl/test/test_config.py src/pebl/test/test_cpd.py src/pebl/test/test_data.py src/pebl/test/test_evaluator.py src/pebl/test/test_network.py src/pebl/test/test_pebl_script.py src/pebl/test/test_posterior.py src/pebl/test/test_prior.py src/pebl/test/test_result.py src/pebl/test/test_taskcontroller.py src/pebl/test/test_tutorial.py src/pebl/test.manual/README src/pebl/test.manual/test_ec2.py src/pebl/test.manual/test_scale.py src/pebl/test.manual/test_xgrid.py src/pebl/test/benchdata/benchdata.10.20.txt src/pebl/test/benchdata/benchdata.10.50.txt src/pebl/test/benchdata/benchdata.100.100.txt src/pebl/test/benchdata/benchdata.20.100.txt src/pebl/test/benchdata/benchdata.20.50.txt src/pebl/test/learner/__init__.py src/pebl/test/learner/test_all.py src/pebl/test/learner/test_greedy.py src/pebl/test/learner/test_simanneal.py src/pebl/test/testfiles/config1.txt src/pebl/test/testfiles/config2.txt src/pebl/test/testfiles/greedytest1-200.txt src/pebl/test/testfiles/pebl-tutorial-data1.txt src/pebl/test/testfiles/pebl-tutorial-data2.txt src/pebl/test/testfiles/testdata1.txt src/pebl/test/testfiles/testdata10.txt src/pebl/test/testfiles/testdata11.txt src/pebl/test/testfiles/testdata12.txt src/pebl/test/testfiles/testdata13.txt src/pebl/test/testfiles/testdata2.txt src/pebl/test/testfiles/testdata3.txt src/pebl/test/testfiles/testdata4.txt src/pebl/test/testfiles/testdata5.txt src/pebl/test/testfiles/testdata6.txt src/pebl/test/testfiles/testdata7.txt src/pebl/test/testfiles/testdata8.txt src/pebl/test/testfiles/testdata9.txtpebl-1.0.2/src/pebl.egg-info/top_level.txt000644 000766 000024 00000000005 11377315470 020770 0ustar00shahadstaff000000 000000 pebl pebl-1.0.2/src/pebl/__init__.py000644 000766 000024 00000000642 11113040337 016645 0ustar00shahadstaff000000 000000 # import sub-modules so they're in the namespace and available to # tab-completion in ipython import config, cpd, data, discretizer, evaluator, network, posterior, prior, \ result, util import learner.base, learner.greedy, learner.custom, learner.simanneal, \ learner.exhaustive # BUT, don't import the taskcontroller sub-modules because some of them are # optional features with extra dependencies. pebl-1.0.2/src/pebl/_cpd.c000644 000766 000024 00000015621 11024451674 015623 0ustar00shahadstaff000000 000000 #include "Python.h" #include #include typedef struct { // Nijk and Nijk counts // Nij = counts[j][0] // Nijk = counts[j][k+1] // so, Nij in the first column, Nijk in the rest int **counts; int *offsets; int num_parents, qi, ri, max_qi; } CPT; // cpt that can be reused. like a pool of size=1 static CPT *_oldcpt = NULL; // get index into counts for given row of obs int cptindex(PyArrayObject *obs, int row, int *offsets, int num_parents) { register int i,ind=0; for (i=0; ioffsets = PyMem_Realloc(cpt->offsets, sizeof(int) * len_offsets); // qi > max_qi, so allocate more arrays if (qi > cpt->max_qi) { cpt->counts = PyMem_Realloc(cpt->counts, sizeof(int *) * qi); for (i=cpt->max_qi; i < qi; i++) { cpt->counts[i] = PyMem_Malloc(sizeof(int) * (ri+1)); } cpt->max_qi = qi; } // reallocate and initialize arrays we need for (i=0; i < qi; i++) { cpt->counts[i] = PyMem_Realloc(cpt->counts[i], sizeof(int) * (ri+1)); for (j=0; j < ri+1; j++) cpt->counts[i][j] = 0; } } else { // create new cpt cpt = (CPT*) PyMem_Malloc(sizeof(CPT)); cpt->max_qi = qi; cpt->offsets = PyMem_Malloc(sizeof(int) * len_offsets); cpt->counts = PyMem_Malloc(sizeof(int *) * qi); for (i=0; icounts[i] = PyMem_Malloc(sizeof(int) * (ri+1)); for (j=0; j < ri+1; j++) cpt->counts[i][j] = 0; } } // update cpt cpt->ri = ri; cpt->qi = qi; cpt->num_parents = num_parents; // create offsets cpt->offsets[0] = 1; for (i=1; ioffsets[i] = cpt->offsets[i-1]*PyInt_AsSsize_t(PyList_GET_ITEM(arities, i)); // adding to nij and nijk nx = PyArray_DIM(obs, 0); ny = PyArray_DIM(obs, 1); for (i=0; ioffsets, num_parents); cpt->counts[j][0]++; cpt->counts[j][*(int*)PyArray_GETPTR2(obs,i,0) + 1]++; } // return the cpt return cpt; } // calculate the loglikelihood of data double _loglikelihood(CPT *cpt, PyArrayObject *lnfac) { register int j,k; double score = 0.0; // score is calculated as follows: // 1) add log((ri-1)!) // 2) subtract log((Nij + ri -1)!) // 3) add sum of log(Nijk!) score += cpt->qi * *((double*)PyArray_GETPTR1(lnfac, cpt->ri - 1)); for (j=0; jqi; j++) { score -= *((double*)PyArray_GETPTR1(lnfac, cpt->counts[j][0] + cpt->ri - 1)); for (k=0; kri; k++) { score += *((double*)PyArray_GETPTR1(lnfac, cpt->counts[j][k+1])); } } return score; } // print CPT (useful for debugging) void print_cpt(CPT *cpt) { int j,k; printf("\n## CPT:\n"); printf("ri=%d, qi=%d\n", cpt->ri, cpt->qi); for (j=0; jqi; j++) { for (k=0; kri+1; k++) { printf("%d,", cpt->counts[j][k]); } printf("\n"); } } // delete/deallocate the cpt // if _oldcpt is null, we just set it to this cpt instead of freeing memory. void _dealloc_cpt(CPT *cpt) { // _oldcpt doesn't exist, so set it to current cpt if (_oldcpt == NULL) { _oldcpt = cpt; return; } // _oldcpt exists, so just free this one register int j; for (j=0; jmax_qi; j++) { PyMem_Free(cpt->counts[j]); } PyMem_Free(cpt->counts); PyMem_Free(cpt->offsets); PyMem_Free(cpt); } /*****************************************************************************/ PyObject * dealloc_cpt(PyObject *self, PyObject *args) { PyObject *pycpt; // addr of cpt as a python int if (!PyArg_ParseTuple(args, "O", &pycpt)) { return NULL; } CPT *cpt = (CPT*) PyInt_AsSsize_t(pycpt); _dealloc_cpt(cpt); Py_RETURN_NONE; } PyObject * replace_data(PyObject *self, PyObject *args) { int pycpt; PyArrayObject *oldrow, *newrow; if (!PyArg_ParseTuple(args, "iO!O!", &pycpt, &PyArray_Type, &oldrow, &PyArray_Type, &newrow)) { return NULL; } CPT *cpt = (CPT*) pycpt; int old_index = cptindex1(oldrow, cpt->offsets, cpt->num_parents); int new_index = cptindex1(newrow, cpt->offsets, cpt->num_parents); int oldval = *((int*)PyArray_GETPTR1(oldrow, 0)); int newval = *((int*)PyArray_GETPTR1(newrow, 0)); cpt->counts[old_index][0]--; cpt->counts[new_index][0]++; cpt->counts[old_index][oldval+1]--; cpt->counts[new_index][newval+1]++; Py_RETURN_NONE; } PyObject * loglikelihood(PyObject *self, PyObject *args) { PyArrayObject *lnfac; int pycpt; double score; if (!PyArg_ParseTuple(args, "iO!", &pycpt, &PyArray_Type, &lnfac)) { return NULL; } CPT *cpt = (CPT*) pycpt; score = _loglikelihood(cpt, lnfac); PyObject *pyscore = Py_BuildValue("d", score); Py_INCREF(pyscore); return pyscore; } PyObject * buildcpt(PyObject *self, PyObject *args) { PyArrayObject *obs; PyObject *arities; int num_parents; if (!PyArg_ParseTuple(args, "O!Oi", &PyArray_Type, &obs, &arities, &num_parents)) { return NULL; } // build the cpt CPT *cpt = _buildcpt(obs, (PyListObject *)arities, num_parents); // return address of cpt as python int PyObject *pycpt = PyInt_FromSsize_t((int)cpt); Py_INCREF(pycpt); return pycpt; } static PyMethodDef cpd_methods[] = { {"buildcpt", (PyCFunction)buildcpt, METH_VARARGS}, {"loglikelihood", (PyCFunction)loglikelihood, METH_VARARGS}, {"replace_data", (PyCFunction)replace_data, METH_VARARGS}, {"dealloc_cpt", (PyCFunction)dealloc_cpt, METH_VARARGS}, {NULL, NULL} /* sentinel */ }; PyMODINIT_FUNC init_cpd(void) { (void) Py_InitModule("_cpd", cpd_methods); import_array(); } pebl-1.0.2/src/pebl/_network.c000644 000766 000024 00000004707 11010410471 016532 0ustar00shahadstaff000000 000000 #include "Python.h" /* ****************************************************************************/ int internal_is_acyclic(PyListObject *adjlist, PyListObject *tovisit, PyListObject *visited, int *checked) { register PyObject *node; register PyListObject *children; register int i,j,nodeid,cycle; int len_visited = PyList_GET_SIZE(visited); int len_tovisit = PyList_GET_SIZE(tovisit); // have we already visited a node in tovisit?, if so, cycle! for (i=0; i < len_tovisit; i++){ for (j=0; j < len_visited; j++) { if (PyInt_AsSsize_t(PyList_GET_ITEM(tovisit, i)) == PyInt_AsSsize_t(PyList_GET_ITEM(visited, j))) return 0; } } // check each node in tovisit for cycles for (i=0; i < len_tovisit; i++) { // have we checked the node already? node = (PyObject *) PyList_GET_ITEM(tovisit, i); nodeid = PyInt_AsSsize_t(node); if (checked[nodeid]) { continue; } checked[nodeid] = 1; // children of node children = (PyListObject *) PyList_GET_ITEM(adjlist, nodeid); // update visited list (add node) Py_INCREF(node); PyList_Append((PyObject *)visited, node); // check node for cycle cycle = !internal_is_acyclic(adjlist, children, visited, checked); PySequence_DelItem((PyObject *)visited, len_visited); Py_DECREF(node); if (cycle) return 0; } return 1; } PyObject * is_acyclic(PyObject *self, PyObject *args) { PyObject *adjlist, *tovisit, *visited; int *checked; int i, num_nodes, ret; if (!PyArg_ParseTuple(args, "OOO", &adjlist, &tovisit, &visited)) return NULL; num_nodes = PyList_GET_SIZE((PyListObject *)adjlist); checked = PyMem_New(int, num_nodes); for (i=0; i < num_nodes; i++) checked[i] = 0; ret = internal_is_acyclic((PyListObject *) adjlist, (PyListObject *) tovisit, (PyListObject *) visited, checked); PyMem_Del((void *) checked); if (ret) Py_RETURN_TRUE; else Py_RETURN_FALSE; } /* ****************************************************************************/ static PyMethodDef network_methods[] = { {"is_acyclic", (PyCFunction)is_acyclic, METH_VARARGS, "is_acyclic doc"}, {NULL, NULL, 0, NULL} /* sentinel */ }; PyMODINIT_FUNC init_network(void) { (void) Py_InitModule("_network", network_methods); } pebl-1.0.2/src/pebl/config.py000644 000766 000024 00000016342 11013407146 016363 0ustar00shahadstaff000000 000000 """Classes and functions for specifying and working with parameters.""" from __future__ import with_statement import sys import os.path from ConfigParser import ConfigParser from itertools import groupby from time import asctime from operator import attrgetter from pebl.util import unzip, as_list # # Global list of parameters # _parameters = {} # # Validator Factories (they return validator functions) # def between(min, max): """Returns validator that checks whether value is between min and max.""" v = lambda x: x >= min and x <= max v.__doc__ = "Parameter value should be between %d and %d." % (min,max) return v def oneof(*values): """Returns validator that checks whether value is in approved list.""" v = lambda x: x in values v.__doc__ = "Parameter value should be one of {%s}." % ', '.join(values) return v def atleast(min): """Returns validator that checks whether value is > min.""" v = lambda x: x >= min v.__doc__ = "Parameter value should be at least %d." % min return v def atmost(max): """Returns validator that checks whether value is < max.""" v = lambda x: x <= max v.__doc__ = "Parameter value should be at most %d." % max return v def fileexists(): """Returns validator that checks whether value is an existing file.""" v = lambda x: os.path.exists(x) and os.path.isfile(x) v.__doc__ = "Parameter value should be a file that exists." return v # # Parameter classes # def default_validator(x): return True class Parameter: """Classes for configuration parameters.""" datatype = None def __init__(self, name, description, validator=default_validator, default=None): nameparts = name.split('.') if len(nameparts) is not 2: raise Exception("Parameter name has to be of the form 'section.name'") self.name = name.lower() self.section, self.option = nameparts self.description = description self.validator = validator self.value = self.default = default self.source = None # add self to the parameter registry _parameters[self.name] = self class StringParameter(Parameter): datatype=str class IntParameter(Parameter): datatype=int class FloatParameter(Parameter): datatype=float # # Functions for {get/set/read/write/list}ing parameters # def set(name, value, source='config.set'): """Set a parameter value. - name should be of the form "section.name". - value can be a string which will be casted to the required datatype. - source specifies where this parameter value was specified (config file, command line, interactive shell, etc). """ name = name.lower() if name not in _parameters: msg = "Parameter %s is unknown." % name raise KeyError(msg) param = _parameters[name] # try coercing value to required data type try: value = param.datatype(value) except ValueError, e: msg = "Cannot convert value to required datatype: %s" % e.message raise Exception(msg) # try validating try: valid = param.validator(value) except: msg = "Validator for parameter %s caused an error while validating" + \ "value %s" raise Exception(msg % (name, value)) if not valid: raise Exception("Value %s is not valid for parameter %s. %s" % \ (value, name, param.validator.__doc__ or 'error unknown')) param.value = value param.source = source def get(name): """Returns value of parameter. Raises KeyError if parameter not found. Examples:: from pebl import config config.get('data.filename') config.get('result.format') The value returned could be the default value or the latest value set using config.set or config.read """ name = name.lower() if name in _parameters: return _parameters[name].value else: raise KeyError("Parameter %s not found." % name) def read(filename): """Reads parameter from config file. Config files should conform to the format specified in the ConfigParser module from Python's standard library. A Parameter's name has two parts: the section and the option name. These correspond to 'section' and 'option' as defined in ConfigParser. For example, parameter 'foo.bar' would be specified in the config file as:: [foo] bar = 5 """ config = ConfigParser() config.read(filename) errors = [] for section in config.sections(): for option,value in config.items(section): name = "%s.%s" % (section, option) try: set(name, value, "config file %s" % filename) except Exception, e: errors.append(str(e)) if errors: errheader = "%d errors encountered:" % len(errors) raise Exception("\n".join([errheader] + errors)) def write(filename, include_defaults=False): """Writes parameters to config file. If include_default is True, writes all parameters. Else, only writes parameters that were specifically set (via config file, command line, etc). """ config = ConfigParser() params = _parameters.values() if include_defaults \ else [p for p in _parameters.values() if p.source] for param in params: config.set(param.section, param.option, param.value) with file(filename, 'w') as f: config.write(f) def parameters(section=None): """Returns list of parameters. If section is specified, returns parameters for that section only. section can be a section name or a search string to use with string.startswith(..) """ if section: return [p for p in _parameters.values() if p.name.startswith(section)] else: return _parameters.values() def paramdocs(section=None, section_header=False): lines = [] params = sorted(parameters(section), key=attrgetter('name')) lines += [".. Autogenerated by pebl.config.paramdocs at %s\n\n" % asctime()] for section, options in groupby(params, lambda p:p.section): if section_header: lines += ["%s\n%s\n\n" % (section, '-'*len(section))] lines += [".. confparam:: %s\n\n\t%s\n\tdefault=%s\n\n" % (o.name, o.description, 'None' if o.default is None else o.default) for o in options] return ''.join(lines) def configobj(params): """Given a list of parameters, returns a ConfigParser object. This function can be used to convert a list of parameters to a config object which can then be written to file. """ if isinstance(params, list): params = dict((p.name, p.value) for p in params) configobj = ConfigParser() for key,value in params.items(): section,name = key.strip().split('.') if section not in configobj.sections(): configobj.add_section(section) configobj.set(section, name, value) return configobj def setparams(obj, options): """Sets attributes of self based on options and pebl.config.""" for p in getattr(obj, '_params', []): setattr(obj, p.option, options.get(p.option, get(p.name))) pebl-1.0.2/src/pebl/cpd.py000644 000766 000024 00000012222 11065521115 015655 0ustar00shahadstaff000000 000000 """Classes for conditional probability distributions.""" import math from itertools import izip import numpy as N try: from pebl import _cpd except: _cpd = None # # CPD classes # class CPD(object): """Conditional probability distributions. Currently, pebl only includes multinomial cpds and there are two versions: a pure-python and a fast C implementation. The C implementation will be used if available. """ def __init__(self, data_): """Create a CPD. data_ should only contain data for the nodes involved in this CPD. The first column should be for the child node and the rest for its parents. The Dataset.subset method can be used to create the required dataset:: d = data.fromfile("somedata.txt") n = network.random_network(d.variables) d.subset([child] + n.edges.parents(child)) """ def loglikelihood(self): """Calculates the loglikelihood of the data. This method implements the log of the g function (equation 12) from: Cooper, Herskovitz. A Bayesian Method for the Induction of Probabilistic Networks from Data. """ pass def replace_data(self, oldrow, newrow): """Replaces a data row with a new one. Missing values are handled using some form of sampling over the possible values and this requires making small changes to the data. Instead of recreating a CPD after every change, it's far more efficient to simply make a small change in the CPD. """ pass class MultinomialCPD_Py(CPD): """Pure python implementation of Multinomial cpd. See MultinomialCPD for method documentation. """ # cache shared by all instances lnfactorial_cache = N.array([]) def __init__(self, data_): self.data = data_ arities = [v.arity for v in data_.variables] # ensure that there won't be a cache miss maxcount = data_.samples.size + max(arities) if len(self.__class__.lnfactorial_cache) < maxcount: self._prefill_lnfactorial_cache(maxcount) # create a Conditional Probability Table (cpt) qi = int(N.product(arities[1:])) self.counts = N.zeros((qi, arities[0] + 1), dtype=int) if data_.variables.size == 1: self.offsets = N.array([0]) else: multipliers = N.concatenate(([1], arities[1:-1])) offsets = N.multiply.accumulate(multipliers) self.offsets = N.concatenate(([0], offsets)) # add data to cpt self._change_counts(data_.observations, 1) # # Public methods # def replace_data(self, oldrow, newrow): add_index = sum(i*o for i,o in izip(newrow, self.offsets)) remove_index = sum(i*o for i,o in izip(oldrow, self.offsets)) self.counts[add_index][newrow[0]] += 1 self.counts[add_index][-1] += 1 self.counts[remove_index][oldrow[0]] -= 1 self.counts[remove_index][-1] -= 1 def loglikelihood(self): lnfac = self.lnfactorial_cache counts = self.counts ri = self.data.variables[0].arity part1 = lnfac[ri-1] result = N.sum( part1 # log((ri-1)!) - lnfac[counts[:,-1] + ri -1] # log((Nij + ri -1)!) + N.sum(lnfac[counts[:,:-1]], axis=1) # log(Product(Nijk!)) ) return result # # Private methods # def _change_counts(self, observations, change=1): indices = N.dot(observations, self.offsets) child_values = observations[:,0] for j,k in izip(indices, child_values): self.counts[j,k] += change self.counts[j,-1] += change def _prefill_lnfactorial_cache(self, size): # logs = log(x) for x in [0, 1, 2, ..., size+10] # * EXCEPT, log(0) = 0 instead of -inf. logs = N.concatenate(([0.0], N.log(N.arange(1, size+10, dtype=float)))) # add.accumulate does running sums.. self.__class__.lnfactorial_cache = N.add.accumulate(logs) class MultinomialCPD_C(MultinomialCPD_Py): """C implementation of Multinomial cpd.""" def __init__(self, data_): if not _cpd: raise Exception("_cpd C extension module not loaded.") self.data = data_ arities = [v.arity for v in data_.variables] num_parents = len(arities)-1 # ensure that there won't be a cache miss maxcount = data_.samples.size + max(arities) if len(self.__class__.lnfactorial_cache) < maxcount: self._prefill_lnfactorial_cache(maxcount) self.__cpt = _cpd.buildcpt(data_.observations, arities, num_parents) def loglikelihood(self): return _cpd.loglikelihood(self.__cpt, self.lnfactorial_cache) def replace_data(self, oldrow, newrow): _cpd.replace_data(self.__cpt, oldrow, newrow) def __del__(self): _cpd.dealloc_cpt(self.__cpt) # use the C implementation if possible, else the python one MultinomialCPD = MultinomialCPD_C if _cpd else MultinomialCPD_Py pebl-1.0.2/src/pebl/data.py000644 000766 000024 00000047466 11030205126 016033 0ustar00shahadstaff000000 000000 """Classes and functions for working with datasets.""" from __future__ import with_statement import re import copy from itertools import groupby import numpy as N from pebl.util import * from pebl import discretizer from pebl import config # # Module parameters # _pfilename = config.StringParameter( 'data.filename', 'File to read data from.', config.fileexists(), ) _ptext = config.StringParameter( 'data.text', 'The text of a dataset included in config file.', default='' ) _pdiscretize = config.IntParameter( 'data.discretize', 'Number of bins used to discretize data. Specify 0 to indicate that '+\ 'data should not be discretized.', default=0 ) # # Exceptions # class ParsingError(Exception): """Error encountered while parsing an ill-formed datafile.""" pass class IncorrectArityError(Exception): """Error encountered when the datafile speifies an incorrect variable arity. If variable arity is specified, it should be greater than the number of unique observation values for the variable. """ def __init__(self, errors): self.errors = errors def __repr__(self): msg = "Incorrect arity specified for some variables.\n" for v,uniquevals in errors: msg += "Variable %s has arity of %d but %d unique values.\n" % \ (v.name, v.arity, uniquevals) class ClassVariableError(Exception): """Error with a class variable.""" msg = """Data for class variables must include only the labels specified in the variable annotation.""" # # Variables and Samples # class Annotation(object): """Additional information about a sample or variable.""" def __init__(self, name, *args): # *args is for subclasses self.name = str(name) def __repr__(self): return "<%s: %s>" % (self.__class__.__name__, self.name) class Sample(Annotation): """Additional information about a sample.""" pass class Variable(Annotation): """Additional information about a variable.""" arity = -1 class ContinuousVariable(Variable): """A variable from a continuous domain.""" def __init__(self, name, param): self.name = str(name) class DiscreteVariable(Variable): """A variable from a discrete domain.""" def __init__(self, name, param): self.name = str(name) self.arity = int(param) class ClassVariable(DiscreteVariable): """A labeled, discrete variable.""" def __init__(self, name, param): self.name = str(name) self.labels = [l.strip() for l in param.split(',')] self.label2int = dict((l,i) for i,l in enumerate(self.labels)) self.arity = len(self.labels) # # Main class for dataset # class Dataset(object): def __init__(self, observations, missing=None, interventions=None, variables=None, samples=None, skip_stats=False): """Create a pebl Dataset instance. A Dataset consists of the following data structures which are all numpy.ndarray instances: * observations: a 2D matrix of observed values. - dimension 1 is over samples, dimension 2 is over variables. - observations[i,j] is the observed value for jth variable in the ith sample. * missing: a 2D binary mask for missing values - missing[i,j] = 1 IFF observation[i,j] is missing * interventions: a 2D binary mask for interventions - interventions[i,j] = 1 IFF the jth variable was intervened upon in the ith sample. * variables,samples: 1D array of variable or sample annotations This class provides a few public methods to manipulate datasets; one can also use numpy functions/methods directly. Required/Default values: * The only required argument is observations (a 2D numpy array). * If missing or interventions are not specified, they are assumed to be all zeros (no missing values and no interventions). * If variables or samples are not specified, appropriate Variable or Sample annotations are created with only the name attribute. Note: If you alter Dataset.interventions or Dataset.missing, you must call Dataset._calc_stats(). This is a terrible hack but it speeds up pebl when used with datasets without interventions or missing values (a common case). """ self.observations = observations self.missing = missing self.interventions = interventions self.variables = variables self.samples = samples # With a numpy array X, we can't do 'if not X' to check the # truth value because it raises an exception. So, we must use the # non-pythonic 'if X is None' obs = observations if missing is None: self.missing = N.zeros(obs.shape, dtype=bool) if interventions is None: self.interventions = N.zeros(obs.shape, dtype=bool) if variables is None: self.variables = N.array([Variable(str(i)) for i in xrange(obs.shape[1])]) self._guess_arities() if samples is None: self.samples = N.array([Sample(str(i)) for i in xrange(obs.shape[0])]) if not skip_stats: self._calc_stats() # # public methods # def subset(self, variables=None, samples=None): """Returns a subset of the dataset (and metadata). Specify the variables and samples for creating a subset of the data. variables and samples should be a list of ids. If not specified, it is assumed to be all variables or samples. Some examples: - d.subset([3], [4]) - d.subset([3,1,2]) - d.subset(samples=[5,2,7,1]) Note: order matters! d.subset([3,1,2]) != d.subset([1,2,3]) """ variables = variables if variables is not None else range(self.variables.size) samples = samples if samples is not None else range(self.samples.size) skip_stats = not (self.has_interventions or self.has_missing) d = Dataset( self.observations[N.ix_(samples,variables)], self.missing[N.ix_(samples,variables)], self.interventions[N.ix_(samples,variables)], self.variables[variables], self.samples[samples], skip_stats = skip_stats ) # if self does not have interventions or missing, the subset can't. if skip_stats: d._has_interventions = False d._has_missing = False return d def _subset_ni_fast(self, variables): ds = _FastDataset.__new__(_FastDataset) if not self.has_interventions: ds.observations = self.observations[:,variables] ds.samples = self.samples else: samples = N.where(self.interventions[:,variables[0]] == False)[0] ds.observations = self.observations[samples][:,variables] ds.samples = self.samples[samples] ds.variables = self.variables[variables] return ds # TODO: test def subset_byname(self, variables=None, samples=None): """Returns a subset of the dataset (and metadata). Same as Dataset.subset() except that variables and samples can be specified by their names. Some examples: - d.subset(variables=['shh', 'genex']) - s.subset(samples=["control%d" % i for i in xrange(10)]) """ vardict = dict((v.name, i) for i,v in enumerate(self.variables)) sampledict = dict((s.name, i) for i,s in enumerate(self.samples)) # if name not found, we let the KeyError be raised variables = [vardict[v] for v in variables] if variables else variables samples = [sampledict[s] for s in samples] if samples else samples return self.subset(variables, samples) def discretize(self, includevars=None, excludevars=[], numbins=3): """Discretize (or bin) the data in-place. This method is just an alias for pebl.discretizer.maximum_entropy_discretizer() See the module documentation for pebl.discretizer for more information. """ self.original_observations = self.observations.copy() self = discretizer.maximum_entropy_discretize( self, includevars, excludevars, numbins ) def tofile(self, filename, *args, **kwargs): """Write the data and metadata to file in a tab-delimited format.""" with file(filename, 'w') as f: f.write(self.tostring(*args, **kwargs)) def tostring(self, linesep='\n', variable_header=True, sample_header=True): """Return the data and metadata as a string in a tab-delimited format. If variable_header is True, include variable names and type. If sample_header is True, include sample names. Both are True by default. """ def dataitem(row, col): val = "X" if self.missing[row,col] else str(self.observations[row,col]) val += "!" if self.interventions[row,col] else '' return val def variable(v): name = v.name if isinstance(v, ClassVariable): return "%s,class(%s)" % (name, ','.join(v.labels)) elif isinstance(v, DiscreteVariable): return "%s,discrete(%d)" % (name, v.arity) elif isinstance(v, ContinuousVariable): return "%s,continuous" % name else: return v.name # --------------------------------------------------------------------- # python strings are immutable, so string concatenation is expensive! # preferred way is to make list of lines, then use one join. lines = [] # add variable annotations if sample_header: lines.append("\t".join([variable(v) for v in self.variables])) # format data nrows,ncols = self.shape d = [[dataitem(r,c) for c in xrange(ncols)] for r in xrange(nrows)] # add sample names if we have them if sample_header and hasattr(self.samples[0], 'name'): d = [[s.name] + row for row,s in zip(d,self.samples)] # add data to lines lines.extend(["\t".join(row) for row in d]) return linesep.join(lines) # # public propoerties # @property def shape(self): """The shape of the dataset as (number of samples, number of variables).""" return self.observations.shape @property def has_interventions(self): """Whether the dataset has any interventions.""" if hasattr(self, '_has_interventions'): return self._has_interventions else: self._has_interventions = self.interventions.any() return self._has_interventions @property def has_missing(self): """Whether the dataset has any missing values.""" if hasattr(self, '_has_missing'): return self._has_missing else: self._has_missing = self.missing.any() return self._has_missing # # private methods/properties # def _calc_stats(self): self._has_interventions = self.interventions.any() self._has_missing = self.missing.any() def _guess_arities(self): """Guesses variable arity by counting the number of unique observations.""" for col,var in enumerate(self.variables): var.arity = N.unique(self.observations[:,col]).size var.__class__ = DiscreteVariable def check_arities(self): """Checks whether the specified airty >= number of unique observations. The check is only performed for discrete variables. If this check fails, the CPT and other data structures would fail. So, we should raise error while loading the data. Fail Early and Explicitly! """ errors = [] for col,v in enumerate(self.variables): if isinstance(v, DiscreteVariable): uniquevals = N.unique(self.observations[:,col]).size if v.arity < uniquevals: errors.append((v, uniquevals)) if errors: raise IncorrectArityError(errors) class _FastDataset(Dataset): """A version of the Dataset class created by the _subset_ni_fast method. The Dataset._subset_ni_fast method creates a quick and dirty subset that skips many steps. It's a private method used by the evaluator module. Do not use this unless you know what you're doing. """ pass # # Factory Functions # def fromfile(filename): """Parse file and return a Dataset instance. The data file is expected to conform to the following format - comment lines begin with '#' and are ignored. - The first non-comment line *must* specify variable annotations separated by tab characters. - data lines specify the data values separated by tab characters. - data lines *can* include sample names A data value specifies the observed numeric value, whether it's missing and whether it represents an intervention: - An 'x' or 'X' indicate that the value is missing - A '!' before or after the numeric value indicates an intervention Variable annotations specify the name of the variable and, *optionally*, the data type. Examples include: - Foo : just variable name - Foo,continuous : Foo is a continuous variable - Foo,discrete(3) : Foo is a discrete variable with arity of 3 - Foo,class(normal,cancer): Foo is a class variable with arity of 2 and values of either normal or cancer. """ with file(filename) as f: return fromstring(f.read()) def fromstring(stringrep, fieldsep='\t'): """Parse the string representation of a dataset and return a Dataset instance. See the documentation for fromfile() for information about file format. """ # parse a data item (examples: '5' '2.5', 'X', 'X!', '5!') def dataitem(item, v): item = item.strip() intervention = False missing = False # intervention? if item[0] == "!": intervention = True item = item[1:] elif item[-1] == "!": intervention = True item = item[:-1] # missing value? if item[0] in ('x', 'X') or item[-1] in ('x', 'X'): missing = True item = "0" if not isinstance(v, ClassVariable) else v.labels[0] # convert to expected data type val = item if isinstance(v, ClassVariable): try: val = v.label2int[val] except KeyError: raise ClassVariableError() elif isinstance(v, DiscreteVariable): try: val = int(val) except ValueError: msg = "Invalid value for discrete variable %s: %s" % (v.name, val) raise ParsingError(msg) elif isinstance(v, ContinuousVariable): try: val = float(val) except ValueError: msg = "Invalid value for continuous variable %s: %s" % (v.name, val) raise ParsingError(msg) else: # if not specified, try parsing as float or int if '.' in val: try: val = float(val) except: msg = "Cannot convert value %s to a float." % val raise ParsingError(msg) else: try: val = int(val) except: msg = "Cannot convert value %s to an int." % val raise ParsingError(msg) return (val, missing, intervention) dtype_re = re.compile("([\w\d_-]+)[\(]*([\w\d\s,]*)[\)]*") def variable(v): # MS Excel encloses cells with punctuations in double quotes # and many people use Excel to prepare data v = v.strip("\"") parts = v.split(",", 1) if len(parts) is 2: # datatype specified? name,dtype = parts match = dtype_re.match(dtype) if not match: raise ParsingError("Error parsing variable header: %s" % v) dtype_name,dtype_param = match.groups() dtype_name = dtype_name.lower() else: name = parts[0] dtype_name, dtype_param = None,None vartypes = { None: Variable, 'continuous': ContinuousVariable, 'discrete': DiscreteVariable, 'class': ClassVariable } return vartypes[dtype_name](name, dtype_param) # ------------------------------------------------------------------------- # split on all known line seperators, ignoring blank and comment lines lines = (l.strip() for l in stringrep.splitlines() if l) lines = (l for l in lines if not l.startswith('#')) # parse variable annotations (first non-comment line) variables = lines.next().split(fieldsep) variables = N.array([variable(v) for v in variables]) # split data into cells d = [[c for c in row.split(fieldsep)] for row in lines] # does file contain sample names? samplenames = True if len(d[0]) == len(variables) + 1 else False samples = None if samplenames: samples = N.array([Sample(row[0]) for row in d]) d = [row[1:] for row in d] # parse data lines and separate into 3 numpy arrays # d is a 3D array where the inner dimension is over # (values, missing, interventions) transpose(2,0,1) makes the inner # dimension the outer one d = N.array([[dataitem(c,v) for c,v in zip(row,variables)] for row in d]) obs, missing, interventions = d.transpose(2,0,1) # pack observations into bytes if possible (they're integers and < 255) dtype = 'int' if obs.dtype.kind is 'i' else obs.dtype # x.astype() returns a casted *copy* of x # returning copies of observations, missing and interventions ensures that # they're contiguous in memory (should speedup future calculations) d = Dataset( obs.astype(dtype), missing.astype(bool), interventions.astype(bool), variables, samples, ) d.check_arities() return d def fromconfig(): """Create a Dataset from the configuration information. Loads data and discretizes (if requested) based on configuration parameters. """ fname = config.get('data.filename') text = config.get('data.text') if text: data_ = fromstring(text) else: if not fname: raise Exception("Filename (nor text) for dataset not specified.") data_ = fromfile(fname) numbins = config.get('data.discretize') if numbins > 0: data_.discretize(numbins=numbins) return data_ def merge(datasets, axis=None): """Merges multiple datasets. datasets should be a list of Dataset objects. axis should be either 'variables' or 'samples' and determines how the datasets are merged. """ if axis == 'variables': variables = N.hstack(tuple(d.variables for d in datasets)) samples = datasets[0].samples stacker = N.hstack else: samples = N.hstack(tuple(d.samples for d in datasets)) variables = datasets[0].variables stacker = N.vstack missing = stacker(tuple(d.missing for d in datasets)) interventions = stacker(tuple(d.interventions for d in datasets)) observations = stacker(tuple(d.observations for d in datasets)) return Dataset(observations, missing, interventions, variables, samples) pebl-1.0.2/src/pebl/discretizer.py000755 000766 000024 00000004347 11347743423 017465 0ustar00shahadstaff000000 000000 """ Collection of data discretization algorithms.""" import numpy as N from util import as_list import data def maximum_entropy_discretize(indata, includevars=None, excludevars=[], numbins=3): """Performs a maximum-entropy discretization of data in-place. Requirements for this implementation: 1. Try to make all bins equal sized (maximize the entropy) 2. If datum x==y in the original dataset, then disc(x)==disc(y) For example, all datapoints with value 3.245 discretize to 1 even if it violates requirement 1. 3. Number of bins reflects only the non-missing data. Example: input: [3,7,4,4,4,5] output: [0,1,0,0,0,1] Note that all 4s discretize to 0, which makes bin sizes unequal. Example: input: [1,2,3,4,2,1,2,3,1,x,x,x] output: [0,1,2,2,1,0,1,2,0,0,0,0] Note that the missing data ('x') gets put in the bin with 0.0. """ # includevars can be an atom or list includevars = as_list(includevars) # determine the variables to discretize includevars = includevars or range(indata.variables.size) includevars = [v for v in includevars if v not in excludevars] for v in includevars: # "_nm" means "no missing" vdata = indata.observations[:,v] vmiss = indata.missing[:,v] vdata_nm = vdata[-vmiss] argsorted = vdata_nm.argsort() if len(vdata_nm): # Find bin edges (cutpoints) using no-missing binsize = len(vdata_nm)//numbins binedges = [vdata_nm[argsorted[binsize*b - 1]] for b in range(numbins)][1:] # Discretize full data. Missings get added to bin with 0.0. indata.observations[:,v] = N.searchsorted(binedges, vdata) oldvar = indata.variables[v] newvar = data.DiscreteVariable(oldvar.name, numbins) newvar.__dict__.update(oldvar.__dict__) # copy any other data attached to variable newvar.arity = numbins indata.variables[v] = newvar # if discretized all variables, then cast observations to int if len(includevars) == indata.variables.size: indata.observations = indata.observations.astype(int) return indata pebl-1.0.2/src/pebl/evaluator.py000755 000766 000024 00000062023 11173461454 017131 0ustar00shahadstaff000000 000000 """Classes and functions for efficiently evaluating networks.""" from math import log import random import numpy as N from pebl import data, cpd, prior, config, network from pebl.util import * N.random.seed() # # Exceptions # class CyclicNetworkError(Exception): msg = "Network has cycle and is thus not a DAG." # # Localscore Cache # class LocalscoreCache(object): """ A LRU cache for local scores. Based on code from http://code.activestate.com/recipes/498245/ """ _params = ( config.IntParameter( 'localscore_cache.maxsize', "Max number of localscores to cache. Default=-1 means unlimited size.", default=-1 ) ) def __init__(self, evaluator, cachesize=None): self._cache = {} self._queue = deque() self._refcount = {} self.cachesize = cachesize or config.get('localscore_cache.maxsize') self.neteval = evaluator self.hits = 0 self.misses = 0 def __call__(self, node, parents): # make variables local _len = len _queue = self._queue _refcount = self._refcount _cache = self._cache _maxsize = self.cachesize index = tuple([node] + parents) # get from cache or compute try: score = _cache[index] self.hits += 1 except KeyError: score = _cache[index] = self.neteval._cpd(node, parents).loglikelihood() self.misses += 1 # if using LRU cache (maxsize != -1) if _maxsize > 0: # record that key was accessed _queue.append(index) _refcount[index] = _refcount.get(index, 0) + 1 # purge LRU entry while _len(_cache) > _maxsize: k = _queue.popleft() _refcount[k] -= 1 if not _refcount[k]: del _cache[k] del _refcount[k] # Periodically compact the queue by duplicate keys if _len(_queue) > _maxsize * 4: for i in xrange(_len(_queue)): k = _queue.popleft() if _refcount[k] == 1: _queue.append(k) else: _refcount[k] -= 1 return score # # Network Evaluators # class NetworkEvaluator(object): """Base Class for all Network Evaluators. Provides methods for scoring networks but does not eliminate any redundant computation or cache retrievals. """ def __init__(self, data_, network_, prior_=None, localscore_cache=None): self.network = network_ self.data = data_ self.prior = prior_ or prior.NullPrior() self.datavars = range(self.data.variables.size) self.score = None self._localscore = localscore_cache or LocalscoreCache(self) self.localscore_cache = self._localscore # # Private Interface # def _globalscore(self, localscores): # log(P(M|D)) + log(P(M)) == likelihood + prior return N.sum(localscores) + self.prior.loglikelihood(self.network) def _cpd(self, node, parents): #return cpd.MultinomialCPD( #self.data.subset( #[node] + parents, #N.where(self.data.interventions[:,node] == False)[0])) return cpd.MultinomialCPD( self.data._subset_ni_fast([node] + parents)) def _score_network_core(self): # in this implementation, we score all nodes (even if that means # redundant computation) parents = self.network.edges.parents self.score = self._globalscore( self._localscore(n, parents(n)) for n in self.datavars ) return self.score # # Public Interface # def score_network(self, net=None): """Score a network. If net is provided, scores that. Otherwise, score network previously set. """ self.network = net or self.network return self._score_network_core() def alter_network(self, add=[], remove=[]): """Alter network by adding and removing sets of edges.""" self.network.edges.add_many(add) self.network.edges.remove_many(remove) return self.score_network() def randomize_network(self): """Randomize the network edges.""" self.network = network.random_network(self.network.nodes) return self.score_network() def clear_network(self): """Clear all edges from the network.""" self.network.edges.clear() return self.score_network() class SmartNetworkEvaluator(NetworkEvaluator): def __init__(self, data_, network_, prior_=None, localscore_cache=None): """Create a 'smart' network evaluator. This network evaluator eliminates redundant computation by keeping track of changes to network and only rescoring the changes. This requires that all changes to the network are done through this evaluator's methods. The network can be altered by the following methods: * alter_network * score_network * randomize_network * clear_network The last change applied can be 'undone' with restore_network """ super(SmartNetworkEvaluator, self).__init__(data_, network_, prior_, localscore_cache) # can't use this with missing data #if self.data.missing.any(): # msg = "Cannot use the SmartNetworkEvaluator with missing data." #raise Exception(msg) # these represent that state that we intelligently manage self.localscores = N.zeros((self.data.variables.size), dtype=float) self.dirtynodes = set(self.datavars) self.saved_state = None # # Private Interface # def _backup_state(self, added, removed): self.saved_state = ( self.score, # saved score #[(n,self.localscores[n]) for n in self.dirtynodes], self.localscores.copy(), # saved localscores added, # edges added removed # edges removed ) def _restore_state(self): if self.saved_state: self.score, self.localscores, added, removed = self.saved_state #self.score, changedscores, added, removed = self.saved_state #for n,score in changedscores: #self.localscores[n] = score self.network.edges.add_many(removed) self.network.edges.remove_many(added) self.saved_state = None self.dirtynodes = set() def _score_network_core(self): # if no nodes are dirty, just return last score. if len(self.dirtynodes) == 0: return self.score # update localscore for dirtynodes, then re-calculate globalscore parents = self.network.edges.parents for node in self.dirtynodes: self.localscores[node] = self._localscore(node, parents(node)) self.dirtynodes = set() self.score = self._globalscore(self.localscores) return self.score def _update_dirtynodes(self, add, remove): # given the edges being added and removed, determine nodes to rescore # with fully observed data, only the parensets of edge destinations have changed self.dirtynodes.update(set(unzip(add+remove, 1))) # # Public Interface # def score_network(self, net=None): """Score a network. If net is provided, scores that. Otherwise, score network previously set. """ if net: add = [edge for edge in net.edges if edge not in self.network.edges] remove = [edge for edge in self.network.edges if edge not in net.edges] else: add = remove = [] return self.alter_network(add, remove) def alter_network(self, add=[], remove=[]): """Alter the network while retaining the ability to *quickly* undo the changes.""" # make the required changes # NOTE: remove existing edges *before* adding new ones. # if edge e is in `add`, `remove` and `self.network`, # it should exist in the new network. (the add and remove cancel out. self.network.edges.remove_many(remove) self.network.edges.add_many(add) # check whether changes lead to valid DAG (raise error if they don't) affected_nodes = set(unzip(add, 1)) if affected_nodes and not self.network.is_acyclic(affected_nodes): self.network.edges.remove_many(add) self.network.edges.add_many(remove) raise CyclicNetworkError() # accept changes: # 1) determine dirtynodes # 2) backup state # 3) score network (but only rescore dirtynodes) self._update_dirtynodes(add, remove) self._backup_state(add, remove) self.score = self._score_network_core() return self.score def randomize_network(self): """Randomize the network edges.""" newnet = network.random_network(self.network.nodes) return self.score_network(newnet) def clear_network(self): """Clear all edges from the network.""" return self.alter_network(remove=list(self.network.edges)) def restore_network(self): """Undo the last change to the network (and score). Undo the last change performed by any of these methods: * score_network * alter_network * randomize_network * clear_network """ self._restore_state() return self.score class GibbsSamplerState(object): """Represents the state of the Gibbs sampler. This state object can be used to resume the Gibbs sampler from a particaular point. Note that the state does not include the network or data and it's upto the caller to ensure that the Gibbs sampler is resumed with the same network and data. The following values are saved: - number of sampled scores (numscores) - average score (avgscore) - most recent value assignments for missing values (assignedvals) """ def __init__(self, avgscore, numscores, assignedvals): self.avgscore = avgscore self.numscores = numscores self.assignedvals = assignedvals @property def scoresum(self): """Log sum of scores.""" return self.avgscore + N.log(self.numscores) class MissingDataNetworkEvaluator(SmartNetworkEvaluator): # # Parameters # _params = ( config.IntParameter( 'gibbs.burnin', """Burn-in period for the gibbs sampler (specified as a multiple of the number of missing values)""", default=10 ), config.StringParameter( 'gibbs.max_iterations', """Stopping criteria for the gibbs sampler. The number of Gibb's sampler iterations to run. Should be a valid python expression using the variable n (number of missing values). Examples: * n**2 (for n-squared iterations) * 100 (for 100 iterations) """, default="n**2" ) ) def __init__(self, data_, network_, prior_=None, localscore_cache=None, **options): """Create a network evaluator for use with missing values. This evaluator uses a Gibb's sampler for sampling over the space of possible completions for the missing values. For more information about Gibb's sampling, consult: 1. http://en.wikipedia.org/wiki/Gibbs_sampling 2. D. Heckerman. A Tutorial on Learning with Bayesian Networks. Microsoft Technical Report MSR-TR-95-06, 1995. p.21-22. Any config param for 'gibbs' can be passed in via options. Use just the option part of the parameter name. """ super(MissingDataNetworkEvaluator, self).__init__(data_, network_, prior_) self._localscore = None # no cache w/ missing data config.setparams(self, options) def _init_state(self): parents = self.network.edges.parents self.cpds = [self._cpd(n, parents(n)) for n in self.datavars] self.localscores = N.array([cpd.loglikelihood() for cpd in self.cpds], dtype=float) self.data_dirtynodes = set(self.datavars) def _update_dirtynodes(self, add, remove): # With hidden nodes: # 1. dirtynode calculation is more expensive (need to look beyond # markov blanket). # 2. time spent rescoring observed nodes is insignificant compared # to scoring hidden/missing nodes. self.dirtynodes = set(self.datavars) def _score_network_with_tempdata(self): # update localscore for data_dirtynodes, then calculate globalscore. for n in self.data_dirtynodes: self.localscores[n] = self.cpds[n].loglikelihood() self.data_dirtynodes = set() self.score = self._globalscore(self.localscores) return self.score def _alter_data(self, row, col, value): oldrow = self.data.observations[row].copy() self.data.observations[row,col] = value # update data_dirtynodes affected_nodes = set(self.network.edges.children(col) + [col]) self.data_dirtynodes.update(affected_nodes) # update cpds for node in affected_nodes: datacols = [node] + self.network.edges.parents(node) if not self.data.interventions[row,node]: self.cpds[node].replace_data( oldrow[datacols], self.data.observations[row][datacols]) def _alter_data_and_score(self, row, col, value): self._alter_data(row, col, value) return self._score_network_with_tempdata() def _calculate_score(self, chosenscores, gibbs_state): # discard the burnin period scores and average the rest burnin_period = self.burnin * \ self.data.missing[self.data.missing==True].size if gibbs_state: # resuming from a previous gibbs run. so, no burnin required. scoresum = logsum(N.concatenate((chosenscores, [gibbs_state.scoresum]))) numscores = len(chosenscores) + gibbs_state.numscores elif len(chosenscores) > burnin_period: # remove scores from burnin period. nonburn_scores = chosenscores[burnin_period:] scoresum = logsum(nonburn_scores) numscores = len(nonburn_scores) else: # this occurs when gibbs iterations were less than burnin period. scoresum = chosenscores[-1] numscores = 1 score = scoresum - log(numscores) return score, numscores def _assign_missingvals(self, indices, gibbs_state): if gibbs_state: assignedvals = gibbs_state.assignedvals else: arities = [v.arity for v in self.data.variables] assignedvals = [random.randint(0, arities[col]-1) for row,col in indices] self.data.observations[unzip(indices)] = assignedvals def score_network(self, net=None, gibbs_state=None): """Score a network. If net is provided, scores that. Otherwise, score network previously set. The default stopping criteria is to run for n**2 iterations. gibbs_state is the state of a previous run of the Gibb's sampler. With this, one can do the following:: myeval = evaluator.MissingDataNetworkEvaluator(...) myeval.score_network(...) gibbs_state = myeval.gibbs_state cPickle.dump(gibbs_state, 'gibbs_state.txt') # look at results, do other analysis, etc # If we decide that we need further Gibb's sampler iterations, we # don't need to restart gibbs_state = cPickle.load(open('gibbs_state.txt')) myeval = evaluator.MissingDataNetworkEvaluator(...) # continue with the previous run of the Gibb's sampler myeval.score_network( gibbs_state=gibbs_state, stopping_criteria=lambda i,N: i>200*N**2 ) """ self.gibbs_state = gibbs_state return super(MissingDataNetworkEvaluator, self).score_network(net) def _score_network_core(self): # create some useful lists and local variables missing_indices = unzip(N.where(self.data.missing==True)) num_missingvals = len(missing_indices) n = num_missingvals max_iterations = eval(self.max_iterations) arities = [v.arity for v in self.data.variables] chosenscores = [] self._assign_missingvals(missing_indices, self.gibbs_state) self._init_state() # Gibbs Sampling: # For each missing value: # 1) score net with each possible value (based on node's arity) # 2) using a probability wheel, sample a value from the possible values iters = 0 while iters < max_iterations: for row,col in missing_indices: scores = [self._alter_data_and_score(row, col, val) \ for val in xrange(arities[col])] chosenval = logscale_probwheel(range(len(scores)), scores) self._alter_data(row, col, chosenval) chosenscores.append(scores[chosenval]) iters += num_missingvals self.chosenscores = N.array(chosenscores) self.score, numscores = self._calculate_score(self.chosenscores, self.gibbs_state) # save state of gibbs sampler self.gibbs_state = GibbsSamplerState( avgscore=self.score, numscores=numscores, assignedvals=self.data.observations[unzip(missing_indices)].tolist() ) return self.score class MissingDataExactNetworkEvaluator(MissingDataNetworkEvaluator): """MissingDataNEtworkEvaluator that does an exact enumeration. This network evaluator enumerates over all possible completions of the missing values. Since this is a combinatorial space, this class is only feasible with datasets with few missing values. """ def _score_network_core(self): """Score a network. If net is provided, scores that. Otherwise, score network previously set. Note: See MissingDataNetworkEvaluator.score_network for more information about arguments. """ # create some useful lists and local variables missing_indices = unzip(N.where(self.data.missing==True)) num_missingvals = len(missing_indices) possiblevals = [range(self.data.variables[col].arity) for row,col in missing_indices] self._init_state() # Enumerate through all possible values for the missing data (using # the cartesian_product function) and score. scores = [] for assignedvals in cartesian_product(possiblevals): for (row,col),val in zip(missing_indices, assignedvals): self._alter_data(row, col, val) scores.append(self._score_network_with_tempdata()) # average score (in log space) self.score = logsum(scores) - log(len(scores)) return self.score class MissingDataMaximumEntropyNetworkEvaluator(MissingDataNetworkEvaluator): """MissingDataNetworkEvaluator that uses a different space of completions. This evaluator only samples from missing value completions that result in a maximum entropy discretization for the variables with missing values. This is useful when the rest of the variables are maximum-entropy discretized because then all variables have the same entropy. """ def _do_maximum_entropy_assignment(self, var): """Assign values to the missing values for this variable such that it has a maximum entropy discretization. """ arity = self.data.variables[var].arity numsamples = self.data.samples.size missingvals = self.data.missing[:,var] missingsamples = N.where(missingvals == True)[0] observedsamples = N.where(missingvals == False)[0] # maximum entropy discretization for *all* samples for this variable numeach = numsamples/arity assignments = flatten([val]*numeach for val in xrange(arity)) for i in xrange(numsamples - len(assignments)): assignments.append(i) # remove the values of the observed samples for val in self.data.observations[observedsamples, var]: assignments.remove(val) N.random.shuffle(assignments) self.data.observations[missingsamples,var] = assignments def _assign_missingvals(self, missingvars, gibbs_state): if gibbs_state: assignedvals = gibbs_state.assignedvals self.data.observations[N.where(self.data.missing==True)] = assignedvals else: for var in missingvars: self._do_maximum_entropy_assignment(var) def _swap_data(self, var, sample1, choices_for_sample2): val1 = self.data.observations[sample1, var] # try swapping till we get a different value (but don't keep trying # forever) for i in xrange(len(choices_for_sample2)/2): sample2 = random.choice(choices_for_sample2) val2 = self.data.observations[sample2, var] if val1 != val2: break self._alter_data(sample1, var, val2) self._alter_data(sample2, var, val1) return (sample1, var, val1, sample2, var, val2) def _undo_swap(self, row1, col1, val1, row2, col2, val2): self._alter_data(row1, col1, val1) self._alter_data(row2, col2, val2) def _score_network_core(self): # create some useful lists and counts num_missingvals = self.data.missing[self.data.missing == True].shape[0] n = num_missingvals max_iterations = eval(self.max_iterations) chosenscores = [] # determine missing vars and samples missingvars = [v for v in self.datavars if self.data.missing[:,v].any()] missingsamples = [N.where(self.data.missing[:,v] == True)[0] \ for v in self.datavars] self._assign_missingvals(missingvars, self.gibbs_state) self._init_state() # iteratively swap data randomly amond samples of a var and score iters = 0 while iters < max_iterations: for var in missingvars: for sample in missingsamples[var]: score0 = self._score_network_with_tempdata() swap = self._swap_data(var, sample, missingsamples[var]) score1 = self._score_network_with_tempdata() chosenval = logscale_probwheel([0,1], [score0, score1]) if chosenval == 0: self._undo_swap(*swap) chosenscores.append(score0) else: chosenscores.append(score1) iters += num_missingvals self.chosenscores = N.array(chosenscores) self.score, numscores = self._calculate_score(self.chosenscores, self.gibbs_state) # save state of gibbs sampler self.gibbs_state = GibbsSamplerState( avgscore=self.score, numscores=numscores, assignedvals=self.data.observations[ N.where(self.data.missing==True) ].tolist() ) return self.score # # Parameters # _pmissingdatahandler = config.StringParameter( 'evaluator.missingdata_evaluator', """ Evaluator to use for handling missing data. Choices include: * gibbs: Gibb's sampling * maxentropy_gibbs: Gibbs's sampling over all completions of the missing values that result in maximum entropy discretization for the variables. * exact: exact enumeration of all possible missing values (only useable when there are few missing values) """, config.oneof('gibbs', 'exact', 'maxentropy_gibbs'), default='gibbs' ) _missingdata_evaluators = { 'gibbs': MissingDataNetworkEvaluator, 'exact': MissingDataExactNetworkEvaluator, 'maxentropy_gibbs': MissingDataMaximumEntropyNetworkEvaluator } def fromconfig(data_=None, network_=None, prior_=None): """Create an evaluator based on configuration parameters. This function will return the correct evaluator based on the relevant configuration parameters. """ data_ = data_ or data.fromconfig() network_ = network_ or network.fromdata(data_) prior_ = prior_ or prior.fromconfig() if data_.missing.any(): e = _missingdata_evaluators[config.get('evaluator.missingdata_evaluator')] return e(data_, network_, prior_) else: return SmartNetworkEvaluator(data_, network_, prior_) pebl-1.0.2/src/pebl/learner/000755 000766 000024 00000000000 11377315471 016202 5ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl/network.py000644 000766 000024 00000027060 11247032045 016607 0ustar00shahadstaff000000 000000 """Classes for representing networks and functions to create/modify them.""" import re import tempfile import os from copy import copy, deepcopy from itertools import chain from bisect import insort from collections import deque import pydot import numpy as N from pebl.util import * try: from pebl import _network except: _network = None class EdgeSet(object): """ Maintains a set of edges. Performance characteristics: - Edge insertion: O(1) - Edge retrieval: O(n) Uses adjacency lists but exposes an adjacency matrix interface via the adjacency_matrix property. """ def __init__(self, num_nodes=0): self._outgoing = [[] for i in xrange(num_nodes)] self._incoming = [[] for i in xrange(num_nodes)] def clear(self): """Clear the list of edges.""" self.__init__(len(self._outgoing)) def add(self, edge): """Add an edge to the list.""" self.add_many([edge]) def add_many(self, edges): """Add multiple edges.""" for src,dest in edges: if dest not in self._outgoing[src]: insort(self._outgoing[src], dest) insort(self._incoming[dest], src) def remove(self, edge): """Remove edges from edgelist. If an edge to be removed does not exist, fail silently (no exceptions). """ self.remove_many([edge]) def remove_many(self, edges): """Remove multiple edges.""" for src,dest in edges: try: self._incoming[dest].remove(src) self._outgoing[src].remove(dest) except KeyError, ValueError: pass def incoming(self, node): """Return list of nodes (as node indices) that have an edge to given node. The returned list is sorted. Method is also aliased as parents(). """ return self._incoming[node] def outgoing(self, node): """Return list of nodes (as node indices) that have an edge from given node. The returned list is sorted. Method is also aliased as children(). """ return self._outgoing[node] parents = incoming children = outgoing def __iter__(self): """Iterate through the edges in this edgelist. Sample usage: for edge in edgelist: print edge """ for src, dests in enumerate(self._outgoing): for dest in dests: yield (src, dest) def __eq__(self, other): for out1,out2 in zip(self._outgoing, other._outgoing): if out1 != out2: return False return True def __hash__(self): return hash(tuple(tuple(s) for s in self._outgoing)) def __copy__(self): other = EdgeSet.__new__(EdgeSet) other._outgoing = [[i for i in lst] for lst in self._outgoing] other._incoming = [[i for i in lst] for lst in self._incoming] return other def as_tuple(self): return tuple(tuple(s) for s in self._outgoing) @extended_property def adjacency_matrix(): """Set or get edges as an adjacency matrix. The adjacency matrix is a boolean numpy.ndarray instance. """ def fget(self): size = len(self._outgoing) adjmat = N.zeros((size, size), dtype=bool) selfedges = list(self) if selfedges: adjmat[unzip(selfedges)] = True return adjmat def fset(self, adjmat): self.clear() for edge in zip(*N.nonzero(adjmat)): self.add(edge) return locals() @extended_property def adjacency_lists(): """Set or get edges as two adjacency lists. Property returns/accepts two adjacency lists for outgoing and incoming edges respectively. Each adjacency list if a list of sets. """ def fget(self): return self._outgoing, self._incoming def fset(self, adjlists): if len(adjlists) is not 2: raise Exception("Specify both outgoing and incoming lists.") # adjlists could be any iterable. convert to list of lists _outgoing, _incoming = adjlists self._outgoing = [list(lst) for lst in _outgoing] self._incoming = [list(lst) for lst in _incoming] return locals() def __contains__(self, edge): """Check whether an edge exists in the edgelist. Sample usage: if (4,5) in edges: print "edge exists!" """ src, dest = edge try: return dest in self._outgoing[src] except IndexError: return False def __len__(self): return sum(len(out) for out in self._outgoing) class Network(object): """A network is a set of nodes and directed edges between nodes""" # # Public methods # def __init__(self, nodes, edges=None): """Creates a Network. nodes is a list of pebl.data.Variable instances. edges can be: * an EdgeSet instance * a list of edge tuples * an adjacency matrix (as boolean numpy.ndarray instance) * string representation (see Network.as_string() for format) """ self.nodes = nodes self.nodeids = range(len(nodes)) # add edges if isinstance(edges, EdgeSet): self.edges = edges elif isinstance(edges, N.ndarray): self.edges = EdgeSet(len(edges)) self.edges.adjacency_matrix = edges else: self.edges = EdgeSet(len(self.nodes)) if isinstance(edges, list): self.edges.add_many(edges) elif isinstance(edges, str) and edges: edges = edges.split(';') edges = [tuple([int(n) for n in e.split(',')]) for e in edges] self.edges.add_many(edges) def is_acyclic(self, roots=None): """Uses a depth-first search (dfs) to detect cycles.""" roots = list(roots) if roots else self.nodeids if _network: return _network.is_acyclic(self.edges._outgoing, roots, []) else: return self.is_acyclic_python(roots) def is_acyclic_python(self, roots=None): """Uses a depth-first search (dfs) to detect cycles.""" def _isacyclic(tovisit, visited): if tovisit.intersection(visited): # already visited a node we need to visit. thus, cycle! return False for n in tovisit: # check children for cycles if not _isacyclic(set(children(n)), visited.union([n])): return False # got here without returning false, so no cycles below rootnodes return True #--------------- children = self.edges.children roots = set(roots) if roots else set(range(len(self.nodes))) return _isacyclic(roots, set()) # TODO: test def copy(self): """Returns a copy of this network.""" newedges = EdgeSet(len(self.nodes)) newedges.adjacency_lists = deepcopy(self.edges.adjacency_lists) return Network(self.nodes, newedges) def layout(self, width=400, height=400, dotpath="dot"): """Determines network layout using Graphviz's dot algorithm. width and height are in pixels. dotpath is the path to the dot application. The resulting node positions are saved in network.node_positions. """ tempdir = tempfile.mkdtemp(prefix="pebl") dot1 = os.path.join(tempdir, "1.dot") dot2 = os.path.join(tempdir, "2.dot") self.as_dotfile(dot1) try: os.system("%s -Tdot -Gratio=fill -Gdpi=60 -Gfill=10,10 %s > %s" % (dotpath, dot1, dot2)) except: raise Exception("Cannot find the dot program at %s." % dotpath) dotgraph = pydot.graph_from_dot_file(dot2) nodes = (n for n in dotgraph.get_node_list() if n.get_pos()) self.node_positions = [[int(float(i)) for i in n.get_pos()[1:-1].split(',')] for n in nodes] def as_string(self): """Returns the sparse string representation of network. If network has edges (2,3) and (1,2), the sparse string representation is "2,3;1,2". """ return ";".join([",".join([str(n) for n in edge]) for edge in list(self.edges)]) def as_dotstring(self): """Returns network as a dot-formatted string""" def node(n, position): s = "\t\"%s\"" % n.name if position: x,y = position s += " [pos=\"%d,%d\"]" % (x,y) return s + ";" nodes = self.nodes positions = self.node_positions if hasattr(self, 'node_positions') \ else [None for n in nodes] return "\n".join( ["digraph G {"] + [node(n, pos) for n,pos in zip(nodes, positions)] + ["\t\"%s\" -> \"%s\";" % (nodes[src].name, nodes[dest].name) for src,dest in self.edges] + ["}"] ) def as_dotfile(self, filename): """Saves network as a dot file.""" f = file(filename, 'w') f.write(self.as_dotstring()) f.close() def as_pydot(self): """Returns a pydot instance for the network.""" return pydot.graph_from_dot_data(self.as_dotstring()) def as_image(self, filename, decorator=lambda x: x, prog='dot'): """Creates an image (PNG format) for the newtork. decorator is a function that accepts a pydot graph, modifies it and returns it. decorators can be used to set visual appearance of networks (color, style, etc). prog is the Graphviz program to use (default: dot). """ g = self.as_pydot() g = decorator(g) g.write_png(filename, prog=prog) # # Factory functions # def fromdata(data_): """Creates a network from the variables in the dataset.""" return Network(data_.variables) def random_network(nodes, required_edges=[], prohibited_edges=[]): """Creates a random network with the given set of nodes. Can specify required_edges and prohibited_edges to control the resulting random network. """ def _randomize(net, density=None): n_nodes = len(net.nodes) density = density or 1.0/n_nodes max_attempts = 50 for attempt in xrange(max_attempts): # create an random adjacency matrix with given density adjmat = N.random.rand(n_nodes, n_nodes) adjmat[adjmat >= (1.0-density)] = 1 adjmat[adjmat < 1] = 0 # add required edges for src,dest in required_edges: adjmat[src][dest] = 1 # remove prohibited edges for src,dest in prohibited_edges: adjmat[src][dest] = 0 # remove self-loop edges (those along the diagonal) adjmat = N.invert(N.identity(n_nodes).astype(bool))*adjmat # set the adjaceny matrix and check for acyclicity net.edges.adjacency_matrix = adjmat.astype(bool) if net.is_acyclic(): return net # got here without finding a single acyclic network. # so try with a less dense network return _randomize(density/2) # ----------------------- net = Network(nodes) _randomize(net) return net pebl-1.0.2/src/pebl/pebl_script.py000644 000766 000024 00000005513 11031064510 017414 0ustar00shahadstaff000000 000000 import sys import os, os.path import cPickle # import everything to make sure that all config parameters get registered from pebl import config, data, network, learner, taskcontroller, result, prior, result, posterior from pebl.learner import greedy, simanneal, exhaustive #from pebl.taskcontroller import serial, multiprocess, ec2, xgrid USAGE = """ Usage: %s [] Actions ------- run Runs pebl based on params in config file. runtask Unpickles the file and calls run() on it. should be a a pickled learner or task. viewhtml Creates a html report of the results. should be a pickled pebl.result. is where the html files will be placed. It will be created if it does not exist. """ % os.path.basename(sys.argv[0]) def usage(msg, exitcode=-1): print "Pebl: Python Environment for Bayesian Learning" print "----------------------------------------------" print "\n===============================================" print "ERROR:", msg print "===============================================\n" print USAGE sys.exit(exitcode) def main(): """The pebl script. This is installed by setuptools as /usr/local/bin/pebl. """ if len(sys.argv) < 2: usage("Please specify the action.") if sys.argv[1] in ('run', 'runtask', 'viewhtml'): action = eval(sys.argv[1]) action() else: usage("Action %s not found." % sys.argv[1]) def run(configfile=None): try: configfile = configfile or sys.argv[2] except: usage("Please specify a config file.") config.read(configfile) numtasks = config.get('learner.numtasks') tasks = learner.fromconfig().split(numtasks) controller = taskcontroller.fromconfig() results = controller.run(tasks) merged_result = result.merge(results) if config.get('result.format') == 'html': merged_result.tohtml() else: merged_result.tofile() def runtask(picklefile=None): try: picklefile = picklefile or sys.argv[2] except: usage("Please specify a pickled task file.") outfile = os.path.join(os.path.dirname(picklefile), 'result.pebl') picklestr = open(picklefile).read() result = runtask_picklestr(picklestr) result.tofile(outfile) def runtask_picklestr(picklestr): learntask = cPickle.loads(picklestr) result = learntask.run() return result def viewhtml(resultfile=None, outdir=None): try: resultfile = resultfile or sys.argv[2] outdir = outdir or sys.argv[3] except: usage("Please specify the result file and output directory.") cPickle.load(open(resultfile)).tohtml(outdir) # ----------------------------- if __name__ == '__main__': main() pebl-1.0.2/src/pebl/posterior.py000644 000766 000024 00000007756 11010410016 017140 0ustar00shahadstaff000000 000000 """Class for representing posterior distribution.""" import math from copy import deepcopy from itertools import izip import pydot import numpy as N from pebl import network from pebl.util import * class Posterior(object): """Class for representing posterior distribution. Except for trivial cases, we can only have an estimated posterior distribution. It is usually constructed as a list of the top N networks found during a search of the space of networks. The pebl posterior object supports a list-like interface. So, given a posterior object post, one can do the following: * Access the top-scoring network: post[0] * Access the top 10 networks as a new posterior object: post[0:10] * Calculate entropy of distribution: post.entropy * Iterate through networks: for net in post: print net.score Note: a posterior object is immutable. That is, you cannot add and remove networks once it is created. See result.Result for a mutable container for networks. """ def __init__(self, nodes, adjacency_matrices, scores, sortedscores=False): """Creates a posterior object. adjacency_matrices and scores can be lists or numpy arrays. If sorted is True, adjacency_matrices and scores should be sorted in descending order of score. """ if not sortedscores: mycmp = lambda x,y: cmp(x[1],y[1]) adjmat_and_scores = sorted(zip(adjacency_matrices, scores), cmp=mycmp, reverse=True) adjacency_matrices, scores = unzip(adjmat_and_scores) self.adjacency_matrices = N.array(adjacency_matrices) self.scores = N.array(scores) self.nodes = nodes # # Public interface # def consensus_network(self, threshold=.3): """Return a consensus network with the given threshold.""" features = self.consensus_matrix features[features >= threshold] = 1 features[features < threshold] = 0 features = features.astype(bool) return network.Network(self.nodes, features) @property def entropy(self): """The information entropy of the posterior distribution.""" # entropy = -scores*log(scores) # but since scores are in log, # entropy = -exp(scores)*scores lscores = rescale_logvalues(self.scores) return -N.sum(N.exp(lscores)*lscores) @property def consensus_matrix(self): norm_scores = normalize(N.exp(rescale_logvalues(self.scores))) return sum(n*s for n,s in zip(self.adjacency_matrices, norm_scores)) # # Special interfaces # def __iter__(self): """Iterate over the networks in the posterior in sorted order.""" for adjmat,score in zip(self.adjacency_matrices, self.scores): net = network.Network(self.nodes, adjmat) net.score = score yield net def __getitem__(self, key): """Retrieve a specific network (and score) from the posterior.""" if isinstance(key, slice): return self.__getslice__(self, key.start, key.stop) net = network.Network(self.nodes, self.adjacency_matrices[key]) net.score = self.scores[key] return net def __getslice__(self, i, j): """Retrieve a subset (as a new posterior object) of the networks.""" return Posterior( self.nodes, self.adjacency_matrices[i:j], self.scores[i:j] ) def __len__(self): """Return the number of networks in this posterior distribution.""" return len(self.scores) # # Factory functions # def from_sorted_scored_networks(nodes, networks): """Create a posterior object from a list of sorted, scored networks. networks should be sorted in descending order. """ return Posterior( nodes, [n.edges.adjacency_matrix for n in networks], [n.score for n in networks] ) pebl-1.0.2/src/pebl/prior.py000644 000766 000024 00000010477 11065521031 016251 0ustar00shahadstaff000000 000000 """Classes and functions for representing prior distributions and constraints.""" import numpy as N NEGINF = -N.inf # # Prior Models # class Prior(object): """Class for representing prior model. Priors have two aspects: * soft priors: weights for each possible edge. * hard priors: constraints that MUST be met. Soft priors are specified by an energy matrix, a weight matrix taking values over [0,1.0]. Hard priors are specified as: * required_edges: a list of edge-tuples that must be rpesent * prohibited_edges: a list of edge-tuples that must not be present * constraints: a list of functions that take adjacency matrix as input and return true if constraint met and false otherwise. For more information about calculating prior probabilities via energy matrices, consult: 1. Imoto, S. and Higuchi, T. and Goto, T. and Tashiro, K. and Kuhara, S. and Miyano, S. Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. Proc IEEE Comput Soc Bioinform Conf. 2003, p.104-113. """ def __init__(self, num_nodes, energy_matrix=None, required_edges=[], prohibited_edges=[], constraints=[], weight=1.0): self.energy_matrix = energy_matrix # mustexist are edges that must exist. They are set as zero and the # rest as one. We can then perfrom a bitwise-or with the adjacency # matrix and if the required edges are not in the adjacency matrix, the # result will not be all ones. self.mustexist = N.ones((num_nodes, num_nodes), dtype=bool) for src,dest in required_edges: self.mustexist[src,dest] = 0 # mustnotexist are edges that cannot be present. They are set as one # and the rest as zero. We can then perform a bitwise-and with the # adjacency matrix and if the specified edges are present, the result # will not be all zeros. self.mustnotexist = N.zeros((num_nodes, num_nodes), dtype=bool) for src,dest in prohibited_edges: self.mustnotexist[src,dest] = 1 self.constraints = constraints self.weight = weight # TODO: test @property def required_edges(self): return N.transpose(N.where(self.mustexist == 0)).tolist() # TODO: test @property def prohibited_edges(self): return N.transpose(N.where(self.mustnotexist == 1)).tolist() def loglikelihood(self, net): """Returns the log likelihood of the given network. Similar to the loglikelihood method of a Conditional Probability Distribution. """ adjmat = net.edges.adjacency_matrix # if any of the mustexist or mustnotexist constraints are violated, # return negative infinity if (not (adjmat | self.mustexist).all()) or \ (adjmat & self.mustnotexist).any(): return NEGINF # if any custom constraints are violated, return negative infinity if self.constraints and not all(c(adjmat) for c in self.constraints): return NEGINF loglike = 0.0 if self.energy_matrix != None: energy = N.sum(adjmat * self.energy_matrix) loglike = -self.weight * energy return loglike class UniformPrior(Prior): """A uniform prior -- that is, every edge is equally likely.""" def __init__(self, num_nodes, weight=1.0): energymat = N.ones((num_nodes, num_nodes)) * .5 super(UniformPrior, self).__init__(num_nodes, energymat, weight=weight) class NullPrior(Prior): """A null prior which returns 0.0 for the loglikelihood. The name for this object is a bit confusing because the UniformPrior is often considered the null prior in that it doesn't favor any edge more than another. It still favors smaller networks and takes time to calculate the loglikelihood. This class provides an implementation that simply returns 0.0 for the loglikelihood. It's a null prior in the sense that the resulting scores are the same as if you hadn't used a prior at all. """ def __init__(self, *args, **kwargs): pass def loglikelihood(self, net): return 0.0 def fromconfig(): # TODO: implement this return NullPrior() pebl-1.0.2/src/pebl/resources/000755 000766 000024 00000000000 11377315471 016564 5ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl/result.py000644 000766 000024 00000023766 11325162254 016450 0ustar00shahadstaff000000 000000 """Classes for learner results and statistics.""" from __future__ import with_statement import time import socket from bisect import insort, bisect from copy import deepcopy, copy import cPickle import os.path import shutil import tempfile from numpy import exp try: from matplotlib.backends.backend_agg import FigureCanvasAgg from matplotlib.figure import Figure import simplejson from pkg_resources import resource_filename _can_create_html = True except: _can_create_html = False from pebl import posterior, config from pebl.util import flatten, rescale_logvalues from pebl.network import Network class _ScoredNetwork(Network): """A class for representing scored networks. Supports comparision of networks based on score and equality based on first checking score equality (MUCH faster than checking network edges), then edges. Note: This is a private class used by LearnerResult. It's interface is not guaranteed to ramain stable. """ def __init__(self, edgelist, score): self.edges = edgelist self.score = score def __cmp__(self, other): return cmp(self.score, other.score) def __eq__(self, other): return self.score == other.score and self.edges == other.edges def __hash__(self): return hash(self.edges) class LearnerRunStats: def __init__(self, start): self.start = start self.end = None self.host = socket.gethostname() class LearnerResult: """Class for storing any and all output of a learner. This is a mutable container for networks and scores. In the future, it will also be the place to collect statistics related to the learning task. """ # # Parameters # _params = ( config.StringParameter( 'result.filename', 'The name of the result output file', default='result.pebl' ), config.StringParameter( 'result.format', 'The format for the pebl result file (pickle or html)', config.oneof('pickle', 'html'), default='pickle' ), config.StringParameter( 'result.outdir', 'Directory for html report.', default='result' ), config.IntParameter( 'result.size', """Number of top-scoring networks to save. Specify 0 to indicate that all scored networks should be saved.""", default=1000 ) ) def __init__(self, learner_=None, size=None): self.data = learner_.data if learner_ else None self.nodes = self.data.variables if self.data else None self.size = size or config.get('result.size') self.networks = [] self.nethashes = {} self.runs = [] def start_run(self): """Indicates that the learner is starting a new run.""" self.runs.append(LearnerRunStats(time.time())) def stop_run(self): """Indicates that the learner is stopping a run.""" self.runs[-1].end = time.time() def add_network(self, net, score): """Add a network and score to the results.""" nets = self.networks nethashes = self.nethashes nethash = hash(net.edges) if self.size == 0 or len(nets) < self.size: if nethash not in nethashes: snet = _ScoredNetwork(copy(net.edges), score) insort(nets, snet) nethashes[nethash] = 1 elif score > nets[0].score and nethash not in nethashes: nethashes.pop(hash(nets[0].edges)) nets.remove(nets[0]) snet = _ScoredNetwork(copy(net.edges), score) insort(nets, snet) nethashes[nethash] = 1 def tofile(self, filename=None): """Save the result to a python pickle file. The result can be later read using the result.fromfile function. """ filename = filename or config.get('result.filename') with open(filename, 'w') as fp: cPickle.dump(self, fp) def tohtml(self, outdir=None): """Create a html report of the result. outdir is a directory to create html files inside. """ if _can_create_html: HtmlFormatter().htmlreport( self, outdir or config.get('result.outdir') ) else: print "Cannot create html reports because some dependencies are missing." @property def posterior(self): """Returns a posterior object for this result.""" return posterior.from_sorted_scored_networks( self.nodes, list(reversed(self.networks)) ) class HtmlFormatter: def htmlreport(self, result_, outdir, numnetworks=10): """Create a html report for the given result.""" def jsonize_run(r): return { 'start': time.asctime(time.localtime(r.start)), 'end': time.asctime(time.localtime(r.end)), 'runtime': round((r.end - r.start)/60, 3), 'host': r.host } pjoin = os.path.join # make outdir if it does not exist if not os.path.exists(outdir): os.makedirs(outdir) # copy static files to outdir staticdir = resource_filename('pebl', 'resources/htmlresult') shutil.copy2(pjoin(staticdir, 'index.html'), outdir) shutil.copytree(pjoin(staticdir, 'lib'), pjoin(outdir, 'lib')) # change outdir to outdir/data outdir = pjoin(outdir, 'data') os.mkdir(outdir) # get networks and scores post = result_.posterior numnetworks = numnetworks if len(post) >= numnetworks else len(post) topscores = post.scores[:numnetworks] norm_topscores = exp(rescale_logvalues(topscores)) # create json-able datastructure resultsdata = { 'topnets_normscores': [round(s,3) for s in norm_topscores], 'topnets_scores': [round(s,3) for s in topscores], 'runs': [jsonize_run(r) for r in result_.runs], } # write out results related data (in json format) open(pjoin(outdir, 'result.data.js'), 'w').write( "resultdata=" + simplejson.dumps(resultsdata) ) # create network images top = post[0] top.layout() for i,net in enumerate(post[:numnetworks]): self.network_image( net, pjoin(outdir, "%s.png" % i), pjoin(outdir, "%s-common.png" % i), top.node_positions ) # create consensus network images cm = post.consensus_matrix for threshold in xrange(10): self.consensus_network_image( post.consensus_network(threshold/10.0), pjoin(outdir, "consensus.%s.png" % threshold), cm, top.node_positions ) # create score plot self.plot(post.scores, pjoin(outdir, "scores.png")) def plot(self, values, outfile): fig = Figure(figsize=(5,5)) ax = fig.add_axes([0.18, 0.15, 0.75, 0.75]) ax.scatter(range(len(values)), values, edgecolors='None',s=10) ax.set_title("Scores (in sorted order)") ax.set_xlabel("Networks") ax.set_ylabel("Log score") ax.set_xbound(-20, len(values)+20) canvas = FigureCanvasAgg(fig) canvas.print_figure(outfile, dpi=80) def network_image(self, net, outfile1, outfile2, node_positions, dot="dot", neato="neato"): # with network's optimal layout fd,fname = tempfile.mkstemp() net.as_dotfile(fname) os.system("%s -Tpng -o%s %s" % (dot, outfile1, fname)) os.remove(fname) # with given layout net.node_positions = node_positions fd,fname = tempfile.mkstemp() net.as_dotfile(fname) os.system("%s -n1 -Tpng -o%s %s" % (neato, outfile2, fname)) os.remove(fname) def consensus_network_image(self, net, outfile, cm, node_positions): def colorize_edge(weight): colors = "9876543210" breakpoints = [.1, .2, .3, .4, .5, .6, .7, .8, .9] return "#" + str(colors[bisect(breakpoints, weight)])*6 def node(n, position): s = "\t\"%s\"" % n.name if position: x,y = position s += " [pos=\"%d,%d\"]" % (x,y) return s + ";" nodes = net.nodes positions = node_positions dotstr = "\n".join( ["digraph G {"] + [node(n, pos) for n,pos in zip(nodes, positions)] + ["\t\"%s\" -> \"%s\" [color=\"%s\"];" % \ (nodes[src].name, nodes[dest].name, colorize_edge(cm[src][dest])) \ for src,dest in net.edges ] + ["}"] ) fd,fname = tempfile.mkstemp() open(fname, 'w').write(dotstr) os.system("neato -n1 -Tpng -o%s %s" % (outfile, fname)) os.remove(fname) # # Factory and other functions # def merge(*args): """Returns a merged result object. Example:: merge(result1, result2, result3) results = [result1, result2, result3] merge(results) merge(*results) """ results = flatten(args) if len(results) is 1: return results[0] # create new result object newresults = LearnerResult() newresults.data = results[0].data newresults.nodes = results[0].nodes # merge all networks, remove duplicates, then sort allnets = list(set([net for net in flatten(r.networks for r in results)])) allnets.sort() newresults.networks = allnets newresults.nethashes = dict([(net, 1) for net in allnets]) # merge run statistics if hasattr(results[0], 'runs'): newresults.runs = flatten([r.runs for r in results]) else: newresults.runs = [] return newresults def fromfile(filename): """Loads a learner result from file.""" return cPickle.load(open(filename)) pebl-1.0.2/src/pebl/taskcontroller/000755 000766 000024 00000000000 11377315471 017620 5ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl/test/000755 000766 000024 00000000000 11377315471 015531 5ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl/test.manual/000755 000766 000024 00000000000 11377315471 017005 5ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl/util.py000644 000766 000024 00000021521 11065525140 016070 0ustar00shahadstaff000000 000000 """Miscellaneous utility functions.""" import numpy as N import math import os.path from copy import copy from collections import deque def as_list(c): """Ensures that the result is a list. If input is a list/tuple/set, return it. If it's None, return empty list. Else, return a list with input as the only element. """ if isinstance(c, (list,tuple,set)): return c elif c is None: return [] else: return [c] def cond(condition, expr1, expr2): """Marked for deletion.. Python2.5 provides this.""" if condition: return expr1 else: return expr2 def flatten(seq): """Given a nested datastructure, flatten it.""" lst = [] for el in seq: if type(el) in [list, tuple, set]: lst.extend(flatten(el)) else: lst.append(el) return lst def normalize(lst): """Normalizes a list of numbers (sets sum to 1.0).""" if not isinstance(lst, N.ndarray): lst = N.array(lst) return lst/lst.sum() def rescale_logvalues(lst): """Rescales a list of log values by setting max value to 0.0 This function is necessary when working with list of log values. Without it, we could have overflows. This is a lot faster than using arbitrary precision math libraries. """ if not isinstance(lst, N.ndarray): lst = N.array(lst) return lst - lst.max() _LogZero = 1.0e-100 _MinLogExp = math.log(_LogZero); def logadd(x, y): """Adds two log values. Ensures accuracy even when the difference between values is large. """ if x < y: temp = x x = y y = temp z = math.exp(y - x) logProb = x + math.log(1.0 + z) if logProb < _MinLogExp: return _MinLogExp else: return logProb def logsum(lst): """Sums a list of log values, ensuring accuracy.""" if not isinstance(lst, N.ndarray): lst = N.array(lst) maxval = lst.max() lst = lst - maxval return reduce(logadd, lst) + maxval ## from webpy (webpy.org) def autoassign(self, locals): """ Automatically assigns local variables to `self`. Generally used in `__init__` methods, as in:: def __init__(self, foo, bar, baz=1): autoassign(self, locals()) """ for (key, value) in locals.iteritems(): if key == 'self': continue setattr(self, key, value) def unzip(l, *jj): """Opposite of zip(). jj is a tuple of list indexes (or keys) to extract or unzip. If not specified, all items are unzipped. """ if jj==(): jj=range(len(l[0])) rl = [[li[j] for li in l] for j in jj] # a list of lists if len(rl)==1: rl=rl[0] #convert list of 1 list to a list return rl def nestediter(lst1, lst2): """A syntactic shortform for doing nested loops.""" for i in lst1: for j in lst2: yield (i,j) def cartesian_product(list_of_lists): """Given n lists (or sets), generate all n-tuple combinations. >>> list(cartesian_product([[0,1], [0,1,"foo"]])) [(0, 0), (0, 1), (0, 'foo'), (1, 0), (1, 1), (1, 'foo')] >>> list(cartesian_product([[0,1], [0,1], [0,1]])) [(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)] """ head,rest = list_of_lists[0], list_of_lists[1:] if len(rest) is 0: for val in head: yield (val,) else: for val in head: for val2 in cartesian_product(rest): yield (val,) + val2 def probwheel(items, weights): """Randomly select an item from a weighted list of items.""" # convert to numpy array and normalize weights = normalize(N.array(weights)) # edges for bins binedges = weights.cumsum() randval = N.random.random() for item, edge in zip(items, binedges): if randval <= edge: return item # should never reach here.. but might due to rounding errors. return items[-1] def logscale_probwheel(items, logweights): """Randomly select an item from a [log] weighted list of items. Fucntion just rescale logweights and exponentiates before calling probwheel. """ return probwheel(items, N.exp(rescale_logvalues(logweights))) def entropy_of_list(lst): """Given a list of values, generate histogram and calculate the entropy.""" unique_values = N.unique(lst) unique_counts = N.array([float(len([i for i in lst if i == unique_val])) for unique_val in unique_values]) total = N.sum(unique_counts) probs = unique_counts/total # remove probabilities==0 because log(0) = -Inf and causes problems. # This is ok because p*log(p) == 0*log(0) == 0 so removing these doesn't affect the final sum. probs = probs[probs>0] return sum(-probs*N.log(probs)) def edit_distance(network1, network2): """Returns the edit distance between two networks. This is a good (but not the only one) metric for determining similarity between two networks. """ def inverse(edge): return (edge[1], edge[0]) edges1 = copy(list(network1.edges)) edges2 = copy(list(network2.edges)) # Calculating distance: # Add 1 to distance for every edge in one network but not in the other, # EXCEPT, if inverse of edge exists in the other network, distance is # 1 not 2 (this is because edit operations include add, remove and reverse) dist = 0 for edge in edges1: if edge in edges2: edges2.remove(edge) elif inverse(edge) in edges2: dist += 1 edges2.remove(inverse(edge)) else: dist += 1 # edges2 now contains all edges not in edges1. dist += len(edges2) return dist def levenshtein(a,b): """Calculates the Levenshtein distance between *strings* a and b. from http://hetland.org/coding/python/levenshtein.py """ n, m = len(a), len(b) if n > m: # Make sure n <= m, to use O(min(n,m)) space a,b = b,a n,m = m,n current = range(n+1) for i in range(1,m+1): previous, current = current, [i]+[0]*n for j in range(1,n+1): add, delete = previous[j]+1, current[j-1]+1 change = previous[j-1] if a[j-1] != b[i-1]: change = change + 1 current[j] = min(add, delete, change) return current[n] def extended_property(func): """Function decorator for defining property attributes The decorated function is expected to return a dictionary containing one or more of the following pairs: * fget - function for getting attribute value * fset - function for setting attribute value * fdel - function for deleting attribute """ return property(doc=func.__doc__, **func()) def lru_cache(maxsize): '''Decorator applying a least-recently-used cache with the given maximum size. Arguments to the cached function must be hashable. Cache performance statistics stored in f.hits and f.misses. from http://code.activestate.com/recipes/498245/ ''' def decorating_function(f): cache = {} # mapping of args to results queue = deque() # order that keys have been accessed refcount = {} # number of times each key is in the access queue def wrapper(*args): # localize variable access (ugly but fast) _cache=cache; _len=len; _refcount=refcount; _maxsize=maxsize queue_append=queue.append; queue_popleft = queue.popleft # get cache entry or compute if not found try: result = _cache[args] wrapper.hits += 1 except KeyError: result = _cache[args] = f(*args) wrapper.misses += 1 # record that this key was recently accessed queue_append(args) _refcount[args] = _refcount.get(args, 0) + 1 # Purge least recently accessed cache contents while _len(_cache) > _maxsize: k = queue_popleft() _refcount[k] -= 1 if not _refcount[k]: del _cache[k] del _refcount[k] # Periodically compact the queue by duplicate keys if _len(queue) > _maxsize * 4: for i in [None] * _len(queue): k = queue_popleft() if _refcount[k] == 1: queue_append(k) else: _refcount[k] -= 1 assert len(queue) == len(cache) == len(refcount) == sum(refcount.itervalues()) return result wrapper.__doc__ = f.__doc__ wrapper.__name__ = f.__name__ wrapper.hits = wrapper.misses = 0 return wrapper return decorating_function pebl-1.0.2/src/pebl/test.manual/README000644 000766 000024 00000000171 11024065506 017652 0ustar00shahadstaff000000 000000 This directory contains tests that either cannot be automated or that we don't want to run everytime we run unit tests. pebl-1.0.2/src/pebl/test.manual/test_ec2.py000644 000766 000024 00000001572 11026013376 021062 0ustar00shahadstaff000000 000000 import sys from pebl import data, result from pebl.learner import greedy from pebl.taskcontroller import ec2 from pebl.test import testfile help = """Test the EC2 TaskController. USAGE: test_ec2.py configfile You need to provide the configfile for use with EC2Controller. ############################################################################### WARNING for pebl devs: Do NOT put your configfile under svn. It contains sensitve information. ############################################################################### """ if len(sys.argv) < 2: print help sys.exit(1) d = data.fromfile(testfile("testdata5.txt")) d.discretize() tc = ec2.EC2Controller(config=sys.argv[1], min_count=3) results = tc.run([greedy.GreedyLearner(d, max_time=10) for i in xrange(10)]) results = result.merge(results) print results print [r.host for r in results.runs] pebl-1.0.2/src/pebl/test.manual/test_scale.py000644 000766 000024 00000001330 11066242431 021470 0ustar00shahadstaff000000 000000 """Testing the scale of problems that pebl can handle. How to use this --------------- Import into python shell and call test_pebl with different sets of arguments. """ import numpy as N from pebl import data, config from pebl.learner import greedy def test_pebl(numvars, numsamples, greedy_iters, cachesize): print "Testing with #vars=%d, #samples=%d, iters=%d, cachesize=%d" % ( numvars, numsamples, greedy_iters, cachesize) config.set('localscore_cache.maxsize', cachesize) d = data.Dataset(N.random.rand(numsamples, numvars)) d.discretize() g = greedy.GreedyLearner(d, max_iterations=greedy_iters) g.run() return g if __name__ == '__main__': test_pebl(1000, 1000, 1000000, 1000) pebl-1.0.2/src/pebl/test.manual/test_xgrid.py000644 000766 000024 00000001610 11074240755 021525 0ustar00shahadstaff000000 000000 import sys from pebl import data, result, config from pebl.learner import greedy from pebl.taskcontroller import xgrid from pebl.test import testfile help = """Test the Xgrid TaskController. USAGE: test_xgrid.py configfile You need to provide the configfile for use with XGridController. ############################################################################### WARNING for pebl devs: Do NOT put your configfile under svn. It contains sensitve information. ############################################################################### """ if len(sys.argv) < 2: print help sys.exit(1) config.read(sys.argv[1]) d = data.fromfile(testfile("testdata5.txt")) d.discretize() tc = xgrid.XgridController() results = tc.run([greedy.GreedyLearner(d, max_time=10) for i in xrange(10)]) results = result.merge(results) print results print [r.host for r in results.runs] pebl-1.0.2/src/pebl/test/__init__.py000644 000766 000024 00000001307 11010410017 017613 0ustar00shahadstaff000000 000000 import tempfile import os, os.path import inspect import doctest # make sure that any files created during running tests are created in a temp # directory (and later deleted) location = tempfile.mkdtemp() def setup(): os.chdir(location) def teardown(): os.system("rm -rf %s" % location) def testfile(fname): """Returns the full path for the specified file in the test directory.""" return os.path.join( os.path.dirname(inspect.getfile(testfile)), 'testfiles', fname ) # so nose doesn't think it's a test testfile.__test__ = False def run(module=None): failed,total = doctest.testmod(module) print "%d of %d failed." % (failed,total) pebl-1.0.2/src/pebl/test/benchdata/000755 000766 000024 00000000000 11377315471 017442 5ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl/test/benchmark.py000644 000766 000024 00000001200 11010410017 017776 0ustar00shahadstaff000000 000000 import os, os.path from pebl import data, network from pebl.learner import greedy def benchmark_datafiles(dir=None): benchdata_dir = dir or os.path.join(os.path.dirname(__file__), "benchdata") for filename in (f for f in os.listdir(benchdata_dir) if f.startswith('benchdata.')): yield os.path.join(benchdata_dir, filename) def run_benchmarks(datafile): print datafile dat = data.fromfile(datafile) l = greedy.GreedyLearner(dat) l.run() def run_all_benchmarks(dir=None): for datafile in benchmark_datafiles(dir): run_benchmarks(datafile) if __name__== '__main__': run_all_benchmarks() pebl-1.0.2/src/pebl/test/learner/000755 000766 000024 00000000000 11377315471 017161 5ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl/test/test_config.py000644 000766 000024 00000006076 11025550746 020414 0ustar00shahadstaff000000 000000 import os.path import sys from tempfile import NamedTemporaryFile from pebl import config from pebl.test import testfile def should_raise_exception(param, value): try: config.set(param, value) except: assert True else: assert False class TestConfig: def setUp(self): config.StringParameter('test.param0', 'a param', default='foo') config.StringParameter('test.param1', 'a param', config.oneof('foo', 'bar')) config.IntParameter('test.param2', 'a param', default=20) config.IntParameter('test.param3', 'a param', config.atmost(100)) config.IntParameter('test.param4', 'a param', config.atleast(100)) config.IntParameter('test.param5', 'a param', config.between(10,100)) config.IntParameter('test.param6', 'a param', lambda x: x == 50) config.FloatParameter('test.param7', 'a param', config.between(1.3, 2.7)) def test_get1(self): assert config.get('test.param0') == 'foo' def test_get2(self): assert config.get('tEST.paRam0') == 'foo' def test_get3(self): assert config.get('test.param2') == 20 def test_get4(self): config.set('test.param2', "10") assert isinstance(config.get('test.param2'), int) assert config.get('test.param2') == 10 def test_set1(self): should_raise_exception('test.param2', 'foo') def test_set2(self): should_raise_exception('test.param100', 50) def test_set3(self): config.set('test.param4', 150) # no exception should_raise_exception('test.param4', 50) def test_set4(self): config.set('test.param5', 50) # no exception should_raise_exception('test.param5', 5) assert config.get('test.param5') == 50 def test_set5(self): should_raise_exception('test.param6', 49) def test_set6(self): should_raise_exception('test.param7', .50) def test_set7(self): config.set('test.param7', 1.50) config.set('test.param7', "1.50") config.set('test.param7', "1.5e0") assert config.get('test.param7') == 1.5 assert isinstance(config.get('test.param7'), float) def test_set8(self): config.set('test.param1', 'foo') config.set('test.param1', 'bar') should_raise_exception('test.param1', 'foobar') def test_config1(self): config.read(testfile('config1.txt')) def test_config2(self): try: config.read(testfile('config2.txt')) except: assert True else: assert False def test_configobj1(self): expected = \ """[test] param1 = foo param0 = foo [test1] param1 = 5 """ config.IntParameter('test1.param1', 'a param', default=5) config.set('test.param1', 'foo') params = [config._parameters.get(x) for x in ('test.param0', 'test.param1', 'test1.param1')] tmpfile = NamedTemporaryFile(prefix="pebl.test") config.configobj(params).write(tmpfile) tmpfile.file.seek(0) actual = tmpfile.read() assert actual == expected pebl-1.0.2/src/pebl/test/test_cpd.py000644 000766 000024 00000017125 11024452761 017707 0ustar00shahadstaff000000 000000 from numpy import array, allclose from pebl import data, cpd from pebl.test import testfile def test_cextension(): try: from pebl import _cpd except: assert False class TestCPD_Py: """ Deriving loglikelihood manually ------------------------------- Below is the derived calculation for the loglikelihood of the parentset for node 0. Calculation done according to the g function from Cooper and Herskovits. This test is done with binary varaibles because doing more on paper is tedious. There are other tests that check for correct loglikelihood with more complicated data. data: 0110 parentset: {1,2,3} --> {0} 1001 1110 1110 0011 ri = child.arity = 2 parent config - (Nij+ri-1)! - Pi[Nijk!] ------------------------------------------- 000 (0+2-1)! 0!0! 001 (1+2-1)! 0!1! 010 (0+2-1)! 0!0! 011 (1+2-1)! 1!0! 100 (0+2-1)! 0!0! 101 (0+2-1)! 0!0! 110 (3+2-1)! 1!2! 111 (0+2-1)! 0!0! likelihood = Pi[[(ri-1)!/(Nij+ri-1)!] Pi[Nijk]) = 1!0!0!/1! x 1!0!1!/2! x 1!0!0!/1! x 1!1!0!/2! x 1!1!2!/4! x 1!0!0!/1! = 1 x 1/2 x 1 x 1/2 x 1/12 x 1 = 1/48 loglikelihood = ln(1/48) = -3.87120101107 """ cpdtype = cpd.MultinomialCPD_Py def setUp(self): self.data = data.Dataset(array([[0, 1, 1, 0], [1, 0, 0, 1], [1, 1, 1, 0], [1, 1, 1, 0], [0, 0, 1, 1]])) for v in self.data.variables: v.arity = 2 self.cpd = self.cpdtype(self.data) def test_lnfactorial_cache(self): expected = array([ 0. , 0. , 0.69314718, 1.79175947, 3.17805383, 4.78749174, 6.57925121, 8.52516136, 10.6046029, 12.80182748, 15.10441257, 17.50230785, 19.9872145, 22.55216385, 25.19122118, 27.89927138, 30.67186011]) assert allclose(self.cpd.lnfactorial_cache, expected) def test_offsets(self): assert (self.cpd.offsets == array([0,1,2,4])).all() def test_counts(self): expected = array([[0, 0, 0], [0, 0, 0], [0, 0, 0], [1, 2, 3], [0, 1, 1], [0, 0, 0], [1, 0, 1], [0, 0, 0]]) assert (self.cpd.counts == expected).all() def loglikelihood(self): assert allclose(self.cpd.loglikelihood(), -3.87120101091) def test_replace1_loglikelihood(self): # Do a noop replace. self.cpd.replace_data(array([0,1,1,0]), array([0,1,1,0])) assert allclose(self.cpd.loglikelihood(), -3.87120101091) def test_replace1_counts(self): self.cpd.replace_data(array([0,1,1,0]), array([0,1,1,0])) expected = array([[0, 0, 0], [0, 0, 0], [0, 0, 0], [1, 2, 3], [0, 1, 1], [0, 0, 0], [1, 0, 1], [0, 0, 0]]) assert (self.cpd.counts == expected).all() def test_replace2_loglikelihood(self): self.cpd.replace_data(self.data.observations[0], array([1,1,1,0])) assert allclose(self.cpd.loglikelihood(), -2.77258872224) def test_replace2_counts(self): self.cpd.replace_data(self.data.observations[0], array([1,1,1,0])) expected = array([[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 3, 3], [0, 1, 1], [0, 0, 0], [1, 0, 1], [0, 0, 0]]) assert (self.cpd.counts == expected).all() def test_undo_loglikelihood(self): self.cpd.replace_data(self.data.observations[0], array([1,1,1,0])) self.cpd.replace_data(array([1,1,1,0]),array([0,1,1,0])) assert allclose(self.cpd.loglikelihood(), -3.87120101091) def test_undo_counts(self): self.cpd.replace_data(self.data.observations[0], array([1,1,1,0])) self.cpd.replace_data(array([1,1,1,0]),array([0,1,1,0])) expected = array([[0, 0, 0], [0, 0, 0], [0, 0, 0], [1, 2, 3], [0, 1, 1], [0, 0, 0], [1, 0, 1], [0, 0, 0]]) assert (self.cpd.counts == expected).all() def test_replace_with_ndarray(self): self.cpd.replace_data(array([0,1,1,0]), array([1,1,1,0])) assert allclose(self.cpd.loglikelihood(), -2.77258872224) class TestCPD_C(TestCPD_Py): cpdtype = cpd.MultinomialCPD_C # The C version doesn't expose all datastructures to python def test_lnfactorial_cache(self): pass def test_offsets(self): pass def test_counts(self): pass def test_replace1_counts(self): pass def test_replace2_counts(self): pass def test_undo_counts(self): pass class TestCPD2_Py: """ Can we properly handle nodes with no parents? ---------------------------------------------- With data=[1,0,1,1,0] for a node with no parents: ri = child.arity = 2 parent config (Nij+ri-1)! Pi[Nijk!] ------------------------------------------- null set (5+2-1)! 3!2! likelihood = Pi[[(ri-1)!/(Nij+ri-1)!] Pi[Nijk]) = 1!3!2!/6! = 12/720 = 1/60 loglikelihood = ln(1/60) = -4.09434456 """ cpdtype = cpd.MultinomialCPD_Py def setUp(self): self.data = data.Dataset(array([[1], [0], [1], [1], [0]])) self.data.variables[0].arity = 2 self.cpd = self.cpdtype(self.data) def test_offsets(self): assert (self.cpd.offsets == array([0])).all() def test_counts(self): assert (self.cpd.counts == array([[2,3,5]])).all() def test_loglikelihood(self): assert allclose(self.cpd.loglikelihood(), -4.09434456) class TestCPD2_C(TestCPD2_Py): cpdtype = cpd.MultinomialCPD_C # The C version doesn't expose all datastructures to python def test_offsets(self): pass def test_counts(self): pass class TestMultinomialCPD_C: def setUp(self): self.data = data.fromfile(testfile("greedytest1-200.txt")) def test_cpt_reuse(self): # check that we don't have SegFault or BusError # instead of freeing memory, _cpd will reuse it for i in xrange(10000): c = cpd.MultinomialCPD_C(self.data) c.loglikelihood() del c def test_cpt_ceate_delete(self): # "del c" will reuse memory while "del c2" will free it for i in xrange(10000): c = cpd.MultinomialCPD_C(self.data) c2 = cpd.MultinomialCPD_C(self.data) c.loglikelihood() c2.loglikelihood() del c del c2 pebl-1.0.2/src/pebl/test/test_data.py000644 000766 000024 00000035571 11322714634 020060 0ustar00shahadstaff000000 000000 import numpy as N from pebl import data from pebl.test import testfile class TestFileParsing: def setUp(self): self.data = data.fromfile(testfile('testdata1.txt')) self.expected_observations = N.array([[ 2.5, 0. , 1.7], [ 1.1, 1.7, 2.3], [ 4.2, 999.3, 12. ]]) self.expected_dtype = N.dtype(float) self.expected_varnames = ['var1', 'var2', 'var3'] self.expected_missing = N.array([[False, True, False], [False, False, False], [False, False, False]], dtype=bool) self.expected_interventions = N.array([[ True, True, False], [False, True, False], [False, False, False]], dtype=bool) self.expected_arities = [-1,-1,-1] def test_observations(self): assert (self.data.observations == self.expected_observations).all() def test_dtype(self): assert self.data.observations.dtype == self.expected_dtype def test_varnames(self): assert [v.name for v in self.data.variables] == self.expected_varnames def test_missing(self): assert (self.data.missing == self.expected_missing).all() def test_interventions(self): assert (self.data.interventions == self.expected_interventions).all() def test_arities(self): assert [v.arity for v in self.data.variables] == self.expected_arities class TestComplexFileParsing(TestFileParsing): def setUp(self): self.data = data.fromfile(testfile('testdata2.txt')) self.expected_observations = N.array([[ 0. , 0. , 1.25, 0. ], [ 1. , 1. , 1.1 , 1. ], [ 1. , 2. , 0.45, 1. ]]) self.expected_dtype = N.dtype(float) # because one continuous variable self.expected_varnames = ['shh', 'ptchp', 'smo', 'outcome'] self.expected_interventions = N.array([[ True, True, False, False], [False, True, False, False], [False, False, False, False]], dtype=bool) self.expected_missing = N.array([[False, False, False, False], [False, False, False, False], [False, False, False, False]], dtype=bool) self.expected_arities = [2, 3, -1, 2] def test_classlabels(self): assert self.data.variables[3].labels == ['good', 'bad'] class TestFileParsing_WithSampleNames(TestFileParsing): def setUp(self): self.data = data.fromfile(testfile('testdata3.txt')) self.expected_observations = N.array([[0, 0], [1, 1], [1,2]]) self.expected_missing = N.array([[0, 0], [0, 0], [0, 0]], dtype=bool) self.expected_interventions = N.array([[1, 1], [0, 1], [0, 0]], dtype=bool) self.expected_varnames = ['shh', 'ptchp'] self.expected_samplenames = ['sample1', 'sample2', 'sample3'] self.expected_arities = [2,3] self.expected_dtype = N.dtype(int) def test_sample_names(self): assert [s.name for s in self.data.samples] == self.expected_samplenames class TestFileParsing_WithSampleNames2(TestFileParsing_WithSampleNames): def setUp(self): self.data = data.fromfile(testfile('testdata4.txt')) # no tab before variable names self.expected_observations = N.array([[0, 0], [1, 1], [1,2]]) self.expected_missing = N.array([[0, 0], [0, 0], [0, 0]], dtype=bool) self.expected_interventions = N.array([[1, 1], [0, 1], [0, 0]], dtype=bool) self.expected_varnames = ['shh', 'ptchp'] self.expected_samplenames = ['sample1', 'sample2', 'sample3'] self.expected_arities = [2,3] self.expected_dtype = N.dtype(int) class TestManualDataCreations: def setUp(self): obs = N.array([[1.2, 1.4, 2.1, 2.2, 1.1], [2.3, 1.1, 2.1, 3.2, 1.3], [3.2, 0.0, 2.2, 2.5, 1.6], [4.2, 2.4, 3.2, 2.1, 2.8], [2.7, 1.5, 0.0, 1.5, 1.1], [1.1, 2.3, 2.1, 1.7, 3.2] ]) interventions = N.array([[0,0,0,0,0], [0,1,0,0,0], [0,0,1,1,0], [0,0,0,0,0], [0,0,0,0,0], [0,0,0,1,0] ]) missing = N.array([[0,0,0,0,0], [0,0,0,0,0], [0,1,0,0,0], [0,1,0,0,0], [0,0,1,0,0], [0,0,0,0,0] ]) variablenames = ["gene A", "gene B", "receptor protein C", " receptor D", "E kinase protein"] samplenames = ["head.wt", "limb.wt", "head.shh_knockout", "head.gli_knockout", "limb.shh_knockout", "limb.gli_knockout"] self.data = data.Dataset( obs, missing.astype(bool), interventions.astype(bool), N.array([data.Variable(n) for n in variablenames]), N.array([data.Sample(n) for n in samplenames]) ) def test_missing(self): x,y = N.where(self.data.missing) assert (x == N.array([2, 3, 4])).all() and \ (y == N.array([1, 1, 2])).all() def test_missing2(self): assert self.data.missing[N.where(self.data.missing)].tolist() == [ True, True, True] def test_missing3(self): assert (N.transpose(N.where(self.data.missing)) == N.array([[2, 1],[3, 1],[4, 2]])).all() def test_subset1(self): expected = N.array([[ 1.2, 2.1, 1.1], [ 2.3, 2.1, 1.3], [ 3.2, 2.2, 1.6], [ 4.2, 3.2, 2.8], [ 2.7, 0. , 1.1], [ 1.1, 2.1, 3.2]]) assert (self.data.subset(variables=[0,2,4]).observations == expected).all() def test_subset2(self): expected = N.array([[ 1.2, 1.4, 2.1, 2.2, 1.1], [ 3.2, 0. , 2.2, 2.5, 1.6]]) assert (self.data.subset(samples=[0,2]).observations == expected).all() def test_subset3(self): subset = self.data.subset(variables=[0,2], samples=[1,2]) expected = N.array([[ 2.3, 2.1], [ 3.2, 2.2]]) assert (subset.observations == expected).all() def test_subset3_interventions(self): subset = self.data.subset(variables=[0,2], samples=[1,2]) expected = N.array([[False, False], [False, True]], dtype=bool) assert (subset.interventions == expected).all() def test_subset3_missing(self): subset = self.data.subset(variables=[0,2], samples=[1,2]) expected = N.array([[False, False], [False, False]], dtype=bool) assert (subset.missing == expected).all() def test_subset3_varnames(self): subset = self.data.subset(variables=[0,2], samples=[1,2]) expected = ['gene A', 'receptor protein C'] assert [v.name for v in subset.variables] == expected def test_subset3_samplenames(self): subset = self.data.subset(variables=[0,2], samples=[1,2]) expected = ['limb.wt', 'head.shh_knockout'] assert [s.name for s in subset.samples] == expected class TestDataDiscretization: def setUp(self): self.data = data.fromfile(testfile('testdata5.txt')) self.data.discretize() self.expected_original = \ N.array([[ 1.2, 1.4, 2.1, 2.2, 1.1], [ 2.3, 1.1, 2.1, 3.2, 1.3], [ 3.2, 0. , 1.2, 2.5, 1.6], [ 4.2, 2.4, 3.2, 2.1, 2.8], [ 2.7, 1.5, 0. , 1.5, 1.1], [ 1.1, 2.3, 2.1, 1.7, 3.2], [ 2.3, 1.1, 4.3, 2.3, 1.1], [ 3.2, 2.6, 1.9, 1.7, 1.1], [ 2.1, 1.5, 3. , 1.4, 1.1]]) self.expected_discretized = \ N.array([[0, 1, 1, 1, 0], [1, 0, 1, 2, 1], [2, 0, 0, 2, 2], [2, 2, 2, 1, 2], [1, 1, 0, 0, 0], [0, 2, 1, 0, 2], [1, 0, 2, 2, 0], [2, 2, 0, 0, 0], [0, 1, 2, 0, 0]]) self.expected_arities = [3,3,3,3,3] def test_orig_observations(self): assert (self.data.original_observations == self.expected_original).all() def test_disc_observations(self): assert (self.data.observations == self.expected_discretized).all() def test_arity(self): assert [v.arity for v in self.data.variables] == self.expected_arities class TestDataDiscretizationWithMissing: """Respond to Issue 32: Pebl should ignore the missing values when selecting bins for each data point. Discretization for this should be the same as if there were no missing data, as in TestDataDiscretization. """ def setUp(self): self.data = data.fromfile(testfile('testdata5m.txt')) self.data.discretize() self.expected_original = \ N.array([[ 1.2, 1.4, 2.1, 2.2, 1.1], [ 2.3, 1.1, 2.1, 3.2, 1.3], [ 3.2, 0. , 1.2, 2.5, 1.6], [ 4.2, 2.4, 3.2, 2.1, 2.8], [ 2.7, 1.5, 0. , 1.5, 1.1], [ 1.1, 2.3, 2.1, 1.7, 3.2], [ 2.3, 1.1, 4.3, 2.3, 1.1], [ 3.2, 2.6, 1.9, 1.7, 1.1], [ 2.1, 1.5, 3. , 1.4, 1.1], [ 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. ]]) self.expected_discretized = \ N.array([[0, 1, 1, 1, 0], [1, 0, 1, 2, 1], [2, 0, 0, 2, 2], [2, 2, 2, 1, 2], [1, 1, 0, 0, 0], [0, 2, 1, 0, 2], [1, 0, 2, 2, 0], [2, 2, 0, 0, 0], [0, 1, 2, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]) self.expected_arities = [3,3,3,3,3] self.expected_missing = N.array([[False, False, False, False, False], [False, False, False, False, False], [False, False, False, False, False], [False, False, False, False, False], [False, False, False, False, False], [False, False, False, False, False], [False, False, False, False, False], [False, False, False, False, False], [False, False, False, False, False], [True , True , True , True , True ], [True , True , True , True , True ], [True , True , True , True , True ]], dtype=bool) def test_orig_observations(self): assert (self.data.original_observations == self.expected_original).all() def test_disc_observations(self): assert (self.data.observations == self.expected_discretized).all() def test_arity(self): assert [v.arity for v in self.data.variables] == self.expected_arities def test_missing(self): assert (self.data.missing == self.expected_missing).all() class TestSelectiveDataDiscretization(TestDataDiscretization): def setUp(self): self.data = data.fromfile(testfile('testdata5.txt')) self.data.discretize(includevars=[0,2]) self.expected_original = \ N.array([[ 1.2, 1.4, 2.1, 2.2, 1.1], [ 2.3, 1.1, 2.1, 3.2, 1.3], [ 3.2, 0. , 1.2, 2.5, 1.6], [ 4.2, 2.4, 3.2, 2.1, 2.8], [ 2.7, 1.5, 0. , 1.5, 1.1], [ 1.1, 2.3, 2.1, 1.7, 3.2], [ 2.3, 1.1, 4.3, 2.3, 1.1], [ 3.2, 2.6, 1.9, 1.7, 1.1], [ 2.1, 1.5, 3. , 1.4, 1.1]]) self.expected_discretized = \ N.array([[ 0. , 1.4, 1. , 2.2, 1.1], [ 1. , 1.1, 1. , 3.2, 1.3], [ 2. , 0. , 0. , 2.5, 1.6], [ 2. , 2.4, 2. , 2.1, 2.8], [ 1. , 1.5, 0. , 1.5, 1.1], [ 0. , 2.3, 1. , 1.7, 3.2], [ 1. , 1.1, 2. , 2.3, 1.1], [ 2. , 2.6, 0. , 1.7, 1.1], [ 0. , 1.5, 2. , 1.4, 1.1]]) self.expected_arities = [3,-1,3,-1,-1] class TestSelectiveDataDiscretization2(TestDataDiscretization): def setUp(self): self.data = data.fromfile(testfile('testdata5.txt')) self.data.discretize(excludevars=[0,1]) self.expected_original = \ N.array([[ 1.2, 1.4, 2.1, 2.2, 1.1], [ 2.3, 1.1, 2.1, 3.2, 1.3], [ 3.2, 0. , 1.2, 2.5, 1.6], [ 4.2, 2.4, 3.2, 2.1, 2.8], [ 2.7, 1.5, 0. , 1.5, 1.1], [ 1.1, 2.3, 2.1, 1.7, 3.2], [ 2.3, 1.1, 4.3, 2.3, 1.1], [ 3.2, 2.6, 1.9, 1.7, 1.1], [ 2.1, 1.5, 3. , 1.4, 1.1]]) self.expected_discretized = \ N.array([[ 1.2, 1.4, 1. , 1. , 0. ], [ 2.3, 1.1, 1. , 2. , 1. ], [ 3.2, 0. , 0. , 2. , 2. ], [ 4.2, 2.4, 2. , 1. , 2. ], [ 2.7, 1.5, 0. , 0. , 0. ], [ 1.1, 2.3, 1. , 0. , 2. ], [ 2.3, 1.1, 2. , 2. , 0. ], [ 3.2, 2.6, 0. , 0. , 0. ], [ 2.1, 1.5, 2. , 0. , 0. ]]) self.expected_arities = [-1,-1,3,3,3] def test_arity_checking(): try: # arity specified is less than number of unique values!! dataset = data.fromfile(testfile('testdata6.txt')) except data.IncorrectArityError: assert True else: assert False def test_arity_checking2(): try: # arity specified is MORE than number of unique values. this is ok. dataset = data.fromfile(testfile('testdata7.txt')) except: assert False assert [v.arity for v in dataset.variables] == [3,4,3,6] pebl-1.0.2/src/pebl/test/test_evaluator.py000644 000766 000024 00000025244 11330321031 021125 0ustar00shahadstaff000000 000000 # set numpy.test to None so we don't run numpy's tests. from numpy import * test = None from pebl import data, network, evaluator, prior, config from pebl.test import testfile import os import copy import random class TestBaseNetworkEvaluator: def setUp(self): self.data = data.fromfile(testfile('testdata10.txt')) self.neteval = evaluator.NetworkEvaluator(self.data, network.fromdata(self.data)) self.neteval.network.edges.add_many([(1,0), (2,0), (3,0)]) # {1,2,3} --> 0 def test_localscore(self): assert allclose( self.neteval._localscore(0, self.neteval.network.edges.parents(0)), -3.87120101091 ) def test_cache1(self): self.neteval._localscore(0, self.neteval.network.edges.parents(0)) assert (self.neteval._localscore.hits, self.neteval._localscore.misses) == (0,1) def test_cache2(self): self.neteval._localscore(0, self.neteval.network.edges.parents(0)) self.neteval._localscore(0, self.neteval.network.edges.parents(0)) assert self.neteval._localscore.hits, self.neteval._localscore.misses == (1,1) def test_score1(self): assert allclose(self.neteval.score_network(), -15.461087517) def test_score2(self): assert allclose(self.neteval.clear_network(), -15.6842310683) def test_score3(self): self.neteval.clear_network() self.neteval.alter_network(add=[(1,0),(2,0),(3,0)]) assert allclose(self.neteval.score_network(), -15.461087517) class TestNetworkEvalWithPrior: def setUp(self): self.data = data.fromfile(testfile('testdata10.txt')) self.neteval = evaluator.NetworkEvaluator( self.data, network.fromdata(self.data), prior.UniformPrior(self.data.variables.size)) self.neteval.network.edges.add_many([(1,0),(2,0),(3,0)]) def test_localscore(self): assert allclose( self.neteval._localscore(0, self.neteval.network.edges.parents(0)), -3.87120101091 ) def test_scorenetwork(self): assert allclose(self.neteval.score_network(), -16.961087517) class TestSmartNetworkEvaluator: def setUp(self): self.data = data.fromfile(testfile('testdata10.txt')) self.ne = evaluator.SmartNetworkEvaluator( self.data, network.fromdata(self.data)) def test_alter1(self): assert allclose(self.ne.alter_network(add=[(1,0),(2,0),(3,0)]), -15.461087517) def test_cache1(self): self.ne.alter_network(add=[(1,0),(2,0),(3,0)]) assert (self.ne._localscore.hits, self.ne._localscore.misses) == (0,4) def test_alter2(self): self.ne.alter_network(add=[(1,0),(2,0),(3,0)]) assert allclose(self.ne.alter_network(add=[(1,2)]), -15.2379439657) def test_cache2(self): self.ne.alter_network(add=[(1,0),(2,0),(3,0)]) self.ne.alter_network(add=[(1,2)]) # NOTE: because nodes 0,1,3 didn't change, we don't even hit the cache for them! assert (self.ne._localscore.hits, self.ne._localscore.misses) == (0,5) def test_clear1(self): self.ne.alter_network(add=[(1,0),(2,0),(3,0)]) self.ne.alter_network(add=[(1,2)]) assert allclose(self.ne.clear_network(), -15.6842310683) def test_score1(self): self.ne.alter_network(add=[(1,0),(2,0),(3,0)]) self.ne.alter_network(add=[(1,2)]) assert allclose( self.ne.score_network(network.Network(self.data.variables, "1,0;2,0;3,0")), -15.461087517 ) def test_cyclic1(self): self.ne.alter_network(add=[(1,0),(2,0),(3,0)]) try: self.ne.alter_network(add=[(0,1)]) except evaluator.CyclicNetworkError, e: assert True else: assert False def test_cyclic2(self): self.ne.alter_network(add=[(1,0),(2,0),(3,0)]) try: self.ne.alter_network(add=[(0,1)]) except: pass # 0 --> 1 not added because it leads to cycle. assert list(self.ne.network.edges) == [(1, 0), (2, 0), (3, 0)] def test_alter3(self): self.ne.alter_network(add=[(1,0),(2,0),(3,0)]) assert allclose(self.ne.alter_network(add=[(2,3)]), -15.0556224089) assert allclose( evaluator.NetworkEvaluator( self.data, network.Network(self.data.variables, "1,0;2,0;3,0;2,3") ).score_network(), -15.0556224089 ) def test_alter4(self): self.ne.alter_network(add=[(1,0),(2,0),(3,0)]) self.ne.alter_network(add=[(2,3)]) assert allclose(self.ne.alter_network(add=[(1,2)], remove=[(1,0)]), -14.8324788576) def test_alter5(self): self.ne.alter_network(add=[(1,0),(2,0),(3,0)]) self.ne.alter_network(add=[(2,3)]) self.ne.alter_network(add=[(1,2)], remove=[(1,0)]) assert allclose(self.ne.restore_network(), -15.0556224089) assert list(self.ne.network.edges) == [(1, 0), (2, 0), (2, 3), (3, 0)] def test_alter6(self): self.ne.alter_network(add=[(1,0),(2,0),(3,0)]) self.ne.alter_network(add=[(2,3)]) self.ne.alter_network(add=[(1,2)], remove=[(1,0)]) self.ne.restore_network() assert allclose( self.ne.alter_network(add=[(1,2), (1,3)], remove=[(1,0), (3,0)]), -14.139331677 ) assert list(self.ne.network.edges) == [(1, 2), (1, 3), (2, 0), (2, 3)] def test_alter7(self): self.ne.alter_network(add=[(1,0),(2,0),(3,0)]) self.ne.alter_network(add=[(2,3)]) self.ne.alter_network(add=[(1,2)], remove=[(1,0)]) self.ne.restore_network() self.ne.alter_network(add=[(1,2), (1,3)], remove=[(1,0), (3,0)]) assert allclose(self.ne.restore_network(), -15.0556224089) assert list(self.ne.network.edges) == [(1, 0), (2, 0), (2, 3), (3, 0)] class TestMissingDataNetworkEvaluator: neteval_type = evaluator.MissingDataNetworkEvaluator def setUp(self): a,b,c,d,e = 0,1,2,3,4 self.data = data.fromfile(testfile('testdata9.txt')) self.net = network.fromdata(self.data) self.net.edges.add_many([(a,c), (b,c), (c,d), (c,e)]) self.neteval1 = self.neteval_type(self.data, self.net, max_iterations="10*n**2") def test_gibbs_scoring(self): # score two nets (correct one and bad one) with missing values. # ensure that correct one scores better. (can't check for exact score) # score network: {a,b}->c->{d,e} score1 = self.neteval1.score_network() # score network: {a,b}->{d,e} c a,b,c,d,e = 0,1,2,3,4 net2 = network.fromdata(self.data) net2.edges.add_many([(a,d), (a,e), (b,d), (b,e)]) neteval2 = self.neteval_type(self.data, net2) score2 = neteval2.score_network() # score1 should be better than score2 print score1, score2 assert score1 > score2, "Gibbs sampling can find goodhidden node." def test_gibbs_saving_state(self): score1 = self.neteval1.score_network() gibbs_state = self.neteval1.gibbs_state num_missing = len(self.data.missing[self.data.missing==True]) assert len(gibbs_state.assignedvals) == num_missing, "Can save state of Gibbs sampler." def test_gibbs_restoring_state(self): score1 = self.neteval1.score_network() gibbs_state = self.neteval1.gibbs_state score2 = self.neteval1.score_network(gibbs_state=gibbs_state) # no way to check if score is correct but we can check whether everything proceeds without any errors. assert True def test_alterdata_dirtynodes(self): # alter data. check dirtynodes. self.neteval1.score_network() # to initialize datastructures self.neteval1._alter_data(0, 2, 1) assert set(self.neteval1.data_dirtynodes) == set([2,3,4]), "Altering data dirties affected nodes." def test_alterdata_scoring(self): # score. alter data. score. alter data (back to original). score. # 1st and last scores should be same. self.neteval1.score_network() score1 = self.neteval1._score_network_with_tempdata() oldval = self.neteval1.data.observations[0][2] self.neteval1._alter_data(0, 2, 1) self.neteval1._score_network_with_tempdata() self.neteval1._alter_data(0, 2, oldval) score2 = self.neteval1._score_network_with_tempdata() assert score1 == score2, "Altering and unaltering data leaves score unchanged." class TestMissingDataMaximumEntropyNetworkEvaluator(TestMissingDataNetworkEvaluator): neteval_type = evaluator.MissingDataMaximumEntropyNetworkEvaluator """ Test out the smart network evaluator ------------------------------------- >>> ne3.clear_network() -15.6842310683 >>> ne3.score_network(network.Network(data10.variables, "1,0;2,0;3,0")) -15.461087517 >>> ne3.alter_network(add=[(0,1)]) Traceback (most recent call last): ... CyclicNetworkError >>> list(ne3.network.edges) # 0-->1 should not have been added [(1, 0), (2, 0), (3, 0)] >>> ne3.score # score should have remained the same -15.461087517 >>> ne3.alter_network(add=[(2,3)]) -15.0556224089 >>> -15.0556224089 >>> ne3.alter_network(add=[(1,2)], remove=[(1,0)]) -14.8324788576 >>> ne3.restore_network() -15.0556224089 >>> list(ne3.network.edges) [(1, 0), (2, 0), (2, 3), (3, 0)] >>> ne3.alter_network(add=[(1,2), (1,3)], remove=[(1,0), (3,0)]) -14.139331677 >>> list(ne3.network.edges) [(1, 2), (1, 3), (2, 0), (2, 3)] >>> ne3.restore_network() -15.0556224089 >>> list(ne3.network.edges) [(1, 0), (2, 0), (2, 3), (3, 0)] Test the exact enumerating missing data evaluator ------------------------------------------------- >>> data11 = data.fromfile(testfile("testdata11.txt")) >>> exactne = evaluator.MissingDataExactNetworkEvaluator(data11, network.Network(data11.variables, "0,1;2,1;1,3")) >>> exactne.score_network() -14.7473210493 >>> """ class TestLocalscoreCache: def setUp(self): self.data = data.fromfile(testfile('testdata10.txt')) self.evaluator = evaluator.NetworkEvaluator(self.data, network.fromdata(self.data)) def test_creating_cache(self): c = evaluator.LocalscoreCache(self.evaluator) nodes = range(len(self.data.variables)) for i in xrange(10): c(random.choice(nodes), [random.choice(nodes) for i in xrange(random.randrange(len(nodes)))]) def test_settingmaxsize(self): c = evaluator.LocalscoreCache(self.evaluator, 3) nodes = range(len(self.data.variables)) for i in xrange(10): c(random.choice(nodes), [random.choice(nodes) for i in xrange(random.randrange(len(nodes)))]) assert len(c._cache) <= 3 pebl-1.0.2/src/pebl/test/test_network.py000644 000766 000024 00000015345 11025776640 020642 0ustar00shahadstaff000000 000000 import os import numpy as N from pebl import network, data, config # # Testing edgesets (working with edges) # class TestEdgeSet: def setUp(self): self.edges = network.EdgeSet(num_nodes=6) self.tuplelist = [(0,2), (0,5), (1,2)] for edge in self.tuplelist: self.edges.add(edge) def test_add(self): self.edges.add((5,1)) assert set(self.edges) == set(self.tuplelist + [(5,1)]) def test_add_many(self): self.edges.add_many([(5,1), (5,2)]) assert set(self.edges) == set(self.tuplelist + [(5,1), (5,2)]) def test_remove(self): self.edges.remove((0,2)) assert set(self.edges) == set([(0,5), (1,2)]) def test_remove_many(self): self.edges.remove_many([(0,2), (0,5)]) assert set(self.edges) == set([(1,2)]) def test_edgeiter(self): assert set(self.edges) == set(self.tuplelist), "Can use edgelist as an iterable object." def test_len(self): assert len(self.edges) == len(self.tuplelist), "Can determine number of edges" def test_addedges1(self): self.edges.add((0, 3)) assert (0,3) in self.edges, "Can add edges to edgelist." def test_incoming(self): assert set(self.edges.incoming(0)) == set([]), "Testing edgelist.incoming" assert set(self.edges.incoming(2)) == set([0,1]), "Testing edgelist.incoming" def test_outgoing(self): assert set(self.edges.outgoing(2)) == set([]), "Testing edgelist.outgoing" assert set(self.edges.outgoing(0)) == set([2,5]), "Testing edgelist.outgoing" def test_parents(self): assert set(self.edges.parents(0)) == set([]), "Testing edgelist.parents" assert set(self.edges.parents(2)) == set([0,1]), "Testing edgelist.parents" def test_children(self): assert set(self.edges.children(2)) == set([]), "Testing edgelist.children" assert set(self.edges.children(0)) == set([2,5]), "Testing edgelist.children" def test_contains1(self): assert (0,2) in self.edges, "Can check if edge in edgelist." def test_contains2(self): # Should return false without throwing exception. assert (99,99) not in self.edges , "Edge not in edgelist." def test_remove(self): self.edges.remove((0,2)) assert set(self.edges) == set([(0,5), (1,2)]), "Can remove an edge." def test_clear(self): self.edges.clear() assert list(self.edges) == [], "Can clear edgelist." def test_adjacency_matrix(self): expected = [ [0,0,1,0,0,1], [0,0,1,0,0,0], [0,0,0,0,0,0], [0,0,0,0,0,0], [0,0,0,0,0,0], [0,0,0,0,0,0], ] assert (self.edges.adjacency_matrix == N.array(expected, dtype=bool)).all(), "Testing boolean matrix representation." # # Testing is_acyclic # class TestIsAcyclic: def setUp(self): self.net = network.Network([data.DiscreteVariable(i,3) for i in xrange(6)]) for edge in [(0,1), (0,3), (1,2)]: self.net.edges.add(edge) def test_loopchecking(self): assert self.net.is_acyclic(), "Should be acyclic" def test_loopchecking2(self): self.net.edges.add((2,0)) assert not self.net.is_acyclic(), "Should not be acyclic" # # Testing network features (misc methods) # class TestNetwork: expected_dotstring = """digraph G {\n\t"0";\n\t"1";\n\t"2";\n\t"3";\n\t"4";\n\t"5";\n\t"0" -> "1";\n\t"0" -> "3";\n\t"1" -> "2";\n}""" expected_string = '0,1;0,3;1,2' def setUp(self): self.net = network.Network([data.DiscreteVariable(i,3) for i in xrange(6)]) for edge in [(0,1), (0,3), (1,2)]: self.net.edges.add(edge) def test_as_pydot(self): assert len(self.net.as_pydot().get_edges()) == 3, "Can convert to pydot graph instance." # We can only check whether the image is created or not. Cannot check if it is correct. def test_as_image(self): filename = "testnet.png" self.net.as_image(filename=filename) file_exists = filename in os.listdir(".") if file_exists: os.remove("./" + filename) assert file_exists, "Can create image file." def test_as_dotstring(self): assert self.net.as_dotstring() == self.expected_dotstring, "Create dot-formatted string" def test_as_dotfile(self): self.net.as_dotfile('testdotfile.txt') assert open('testdotfile.txt').read() == self.expected_dotstring, "Create dotfile." def test_as_string(self): assert self.net.as_string() == self.expected_string, "Create string representation." def test_layout(self): self.net.layout() assert hasattr(self.net, 'node_positions'), "Has node_positions" assert len(self.net.node_positions[0]) == 2, "Node positions are 2 values (x and y)" assert isinstance(self.net.node_positions[0][0], (int, float)), "Positions are in floats or ints" # # Test constructor and factory funcs # class TestNetworkFromListOfEdges: def setUp(self): self.net = network.Network( [data.DiscreteVariable(str(i),3) for i in xrange(6)], [(0,1), (4,5), (2,3)] ) def test_number_of_edges(self): assert len(list(self.net.edges)) == 3 def test_edges_exist(self): assert (0,1) in self.net.edges and \ (4,5) in self.net.edges and \ (2,3) in self.net.edges class TestNetworkFromString(TestNetworkFromListOfEdges): def setUp(self): self.net = network.Network( [data.DiscreteVariable(str(i),3) for i in xrange(6)], "0,1;4,5;2,3" ) class TestRandomNetwork: def setUp(self): self.nodes = [data.DiscreteVariable(str(i),3) for i in xrange(6)] def test_acyclic(self): net = network.random_network(self.nodes) assert net.is_acyclic() == True, "Random network is acyclic." def test_required_edges(self): net = network.random_network(self.nodes, required_edges=[(0,1), (3,0)]) assert net.is_acyclic() == True and \ (0,1) in net.edges and \ (3,0) in net.edges def test_prohibited_edges(self): net = network.random_network(self.nodes, prohibited_edges=[(0,1), (3,0)]) assert net.is_acyclic() == True and \ (0,1) not in net.edges and \ (3,0) not in net.edges def test_required_and_prohibited_edges(self): net = network.random_network(self.nodes, required_edges=[(0,1), (3,0)], prohibited_edges=[(2,3), (1,4)]) assert net.is_acyclic() == True and \ (0,1) in net.edges and \ (3,0) in net.edges and \ (2,3) not in net.edges and \ (1,4) not in net.edges pebl-1.0.2/src/pebl/test/test_pebl_script.py000644 000766 000024 00000001576 11030107713 021441 0ustar00shahadstaff000000 000000 from __future__ import with_statement import textwrap import tempfile import os.path import shutil from pebl import pebl_script from pebl.test import testfile class TestHtmlReport: def setup(self): self.tempdir = tempfile.mkdtemp() self.outdir = os.path.join(self.tempdir, 'result') htmlreport_config = textwrap.dedent(""" [data] filename = %s [result] format = html outdir = %s """ % (testfile("testdata12.txt"), self.outdir)) configfile = os.path.join(self.tempdir, "config.txt") with file(configfile, 'w') as f: f.write(htmlreport_config) def teardown(self): shutil.rmtree(self.tempdir) def test_htmlreport(self): pebl_script.run(os.path.join(self.tempdir, 'config.txt')) os.path.exists(os.path.join(self.outdir, 'index.html')) pebl-1.0.2/src/pebl/test/test_posterior.py000644 000766 000024 00000000314 11013407146 021152 0ustar00shahadstaff000000 000000 """ ====================== Testing pebl.posterior ====================== >>> from pebl import posterior >>> import numpy as N """ if __name__ == '__main__': from pebl.test import run run() pebl-1.0.2/src/pebl/test/test_prior.py000644 000766 000024 00000007376 11025556260 020303 0ustar00shahadstaff000000 000000 import numpy as N from pebl import data, network, prior def test_null_prior(): net = network.Network( [data.DiscreteVariable(i,3) for i in xrange(5)], "0,1;3,2;2,4;1,4" ) p1 = prior.NullPrior() assert p1.loglikelihood(net) == 0.0 net.edges.add((1,4)) assert p1.loglikelihood(net) == 0.0 class TestUniformPrior: ## **Note:** The uniform prior assumes equal likelihood for each edge; ## thus, it penalizes networks with large number of edges. def setUp(self): self.net = network.Network( [data.DiscreteVariable(i,3) for i in xrange(5)], "0,1;3,2;2,4;1,4" ) self.p = prior.UniformPrior(len(self.net.nodes)) self.p2 = prior.UniformPrior(len(self.net.nodes), weight=2.0) def test_net1(self): assert self.p.loglikelihood(self.net) == -2.0 def test_net2(self): self.net.edges.remove((1,4)) assert self.p.loglikelihood(self.net) == -1.5 def test_weight1(self): assert self.p2.loglikelihood(self.net) == -4.0 def test_weight2(self): self.net.edges.remove((1,4)) assert self.p2.loglikelihood(self.net) == -3.0 class TestHardPriors: def setUp(self): self.net = network.Network( [data.DiscreteVariable(i,3) for i in xrange(5)], "0,1;3,2;2,4;1,4" ) self.p = prior.Prior( len(self.net.nodes), required_edges=[(1,4),(0,1)], prohibited_edges=[(3,4)], constraints=[lambda am: not am[0,4]] ) def test_net1(self): assert self.p.loglikelihood(self.net) == 0.0 def test_net2(self): self.net.edges.remove((1,4)) assert self.p.loglikelihood(self.net) == float('-inf') def test_net3(self): self.net.edges.add((3,4)) assert self.p.loglikelihood(self.net) == float('-inf') def test_net4(self): self.net.edges.add((0,4)) assert self.p.loglikelihood(self.net) == float('-inf') def test_net5(self): self.net.edges.add((3,2)) assert self.p.loglikelihood(self.net) == 0.0 class TestSoftPriors: def setUp(self): self.net = network.Network( [data.DiscreteVariable(i,3) for i in xrange(5)], "0,1;2,4;1,2" ) energymat = N.array([[ 0.5, 0. , 0.5, 0.5, 0.5], [ 0.5, 0.5, 0.5, 0.5, 0. ], [ 0.5, 0.5, 0.5, 0.5, 0.5], [ 0.5, 0.5, 0.5, 0.5, 5. ], [ 0.5, 0.5, 0.5, 0.5, 0.5]]) self.p = prior.Prior(len(self.net.nodes), energymat) def test_net1(self): assert self.p.loglikelihood(self.net) == -1.0 def test_net2(self): self.net.edges.remove((2,4)) self.net.edges.add((1,4)) assert self.p.loglikelihood(self.net) == -0.5 def test_net3(self): self.net.edges.add((3,4)) assert self.p.loglikelihood(self.net) == -6.0 class TestCombinedPriors: def setUp(self): self.net = network.Network( [data.DiscreteVariable(i,3) for i in xrange(5)], "0,1;1,3;1,2" ) energymat = N.array([[ 0.5, 0. , 0.5, 0.5, 0.5], [ 0.5, 0.5, 0.5, 0.5, 0. ], [ 0.5, 0.5, 0.5, 0.5, 0.5], [ 0.5, 0.5, 0.5, 0.5, 5. ], [ 0.5, 0.5, 0.5, 0.5, 0.5]]) self.p = prior.Prior(len(self.net.nodes), energymat, required_edges=[(1,2)]) def test_net1(self): assert self.p.loglikelihood(self.net) == -1.0 def test_net2(self): self.net.edges.remove((1,2)) assert self.p.loglikelihood(self.net) == float('-inf') pebl-1.0.2/src/pebl/test/test_result.py000644 000766 000024 00000012101 11325161365 020445 0ustar00shahadstaff000000 000000 from copy import deepcopy import tempfile import shutil import os.path from numpy import allclose from pebl import result from pebl import data, network from pebl.learner import greedy from pebl.test import testfile class TestScoredNetwork: def setUp(self): net1 = network.Network([data.Variable(x) for x in range(5)], "0,1") self.sn1 = result._ScoredNetwork(net1.edges, -11.15) net2 = network.Network([data.Variable(x) for x in range(5)], "1,0") self.sn2 = result._ScoredNetwork(net2.edges, -11.15) def test_equality(self): # score is same, but different network assert (self.sn1 == self.sn2) == False def test_comparison(self): self.sn2.score = -10.0 assert self.sn2 > self.sn1 def TestScoredNetworkHashing(): # test due to a bug encountered earlier net = network.Network([data.Variable(x) for x in range(5)], "0,1;1,2;4,3") snet = result._ScoredNetwork(net.edges, -10) for i in xrange(1000): if hash(snet) != hash(deepcopy(snet)): assert False class TestLearnerResult: size = 3 len = 3 zero_score = -11 scores = [-11, -10.5, -8.5] def setUp(self): nodes = [data.Variable(x) for x in range(5)] self.result = result.LearnerResult(size=self.size) self.result.nodes = nodes self.result.start_run() nets = ("0,1", "1,0" , "1,2;2,3", "4,1;1,2", "4,2;4,3") scores = (-10.5, -11, -8.5, -12, -13) for n,s in zip(nets, scores): self.result.add_network(network.Network(nodes, n), s) def test_sorting(self): assert self.result.networks[0].score == self.zero_score assert [n.score for n in self.result.networks] == self.scores def test_len(self): assert len(self.result.networks) == self.len class TestLearnerResult2(TestLearnerResult): size = 0 len = 5 zero_score = -13 scores = [-13, -12, -11, -10.5, -8.5] class TestMergingResults(object): def setUp(self): nodes = [data.Variable(x) for x in range(5)] nets = ("0,1", "1,0" , "1,2;2,3", "4,1;1,2", "4,2;4,3") scores = (-10.5, -11, -8.5, -12, -13) self.result1 = result.LearnerResult(size=0) self.result1.nodes = nodes self.result1.start_run() for n,s in zip(nets, scores): self.result1.add_network(network.Network(nodes, n), s) self.result2 = result.LearnerResult(size=0) self.result2.nodes = nodes self.result2.start_run() self.result2.add_network(network.Network(nodes, "1,2;2,3;3,4"), -6) self.result2.add_network(network.Network(nodes, "1,2;2,3;3,4;0,4;0,2"), -5.5) self.result2.add_network(network.Network(nodes, "0,1"), -10.5) def test_individual_sizes(self): assert len(self.result1.networks) == 5 assert len(self.result2.networks) == 3 def test_merged_size1(self): mr = result.merge(self.result1, self.result2) len(mr.networks) == (5+3-1) # 1 duplicate network def test_merged_scores(self): mr = result.merge([self.result1, self.result2]) assert [n.score for n in mr.networks] == [-13, -12, -11, -10.5, -8.5, -6, -5.5] class TestPosterior(TestMergingResults): def setUp(self): super(TestPosterior, self).setUp() self.merged = result.merge(self.result1, self.result2) self.posterior = self.merged.posterior def test_top_score(self): assert self.posterior[0].score == -5.5 def test_top_network(self): assert list(self.posterior[0].edges) == [(0, 2), (0, 4), (1, 2), (2, 3), (3, 4)] def test_len(self): assert len(self.posterior) == 7 def test_slicing(self): assert self.posterior[:2][1].score == -6.0 def test_entropy(self): assert allclose(self.posterior.entropy, 0.522714000397) def test_consensus_net1(self): expected = '0,2;0,4;1,2;2,3;3,4' assert self.posterior.consensus_network(.5).as_string() == expected def test_consensus_net1(self): expected = '1,2;2,3;3,4' assert self.posterior.consensus_network(.8).as_string() == expected class TestHtmlReport: def setUp(self): dat = data.fromfile(testfile("testdata5.txt")) dat.discretize() g = greedy.GreedyLearner(dat, max_iterations=100) g.run() self.result = g.result self.tempdir = tempfile.mkdtemp() self.result.tohtml(self.tempdir) def tearDown(self): shutil.rmtree(self.tempdir) def test_report_creation(self): assert os.path.exists(os.path.join(self.tempdir, 'index.html')) def test_json_datafile(self): assert os.path.exists(os.path.join(self.tempdir, 'data', 'result.data.js')) def test_scoresplot(self): assert os.path.exists(os.path.join(self.tempdir, 'data', 'scores.png')) def test_network_images(self): assert os.path.exists(os.path.join(self.tempdir, 'data', '0.png')) assert os.path.exists(os.path.join(self.tempdir, 'data', '0-common.png')) assert os.path.exists(os.path.join(self.tempdir, 'data', 'consensus.1.png')) pebl-1.0.2/src/pebl/test/test_taskcontroller.py000644 000766 000024 00000003730 11025646261 022205 0ustar00shahadstaff000000 000000 import os, sys, signal, os.path import subprocess import time from ipython1.kernel.scripts import ipcluster from ipython1.kernel import task, controllerservice as cs, engineservice as es from pebl import data, result from pebl.learner import greedy from pebl.taskcontroller import serial, multiprocess, ipy1 from pebl.test import testfile # NOTE: The EC2 task controller is not tested automatically because: # 1. it requires authentication credential that we can't put in svn # 2. don't want to spend $$ everytime we run pebl's unittest. # So, it's in pebl/test.manual/test_ec2.py class TestSerialTC: tctype = serial.SerialController args = () def setUp(self): d = data.fromfile(testfile("testdata5.txt")) d.discretize() self.tc = self.tctype(*self.args) self.tasks = [greedy.GreedyLearner(d, max_iterations=100) for i in xrange(6)] def test_tc(self): results = self.tc.run(self.tasks) results = result.merge(results) assert isinstance(results, result.LearnerResult) class TestMultiProcessTC(TestSerialTC): tctype = multiprocess.MultiProcessController args = (2,) class TestIPython1TC: # I've tried any ways of creating and terminating the cluster but the # terminating always fails.. So, for now, you have to kill the cluster # manually. def setUp(self): d = data.fromfile(testfile("testdata5.txt")) d.discretize() self.proc = subprocess.Popen("ipcluster -n 2 &0 2>&0", shell=True) time.sleep(5) def tearDown(self): os.kill(self.proc.pid, signal.SIGINT) time.sleep(5) def test_tc(self): d = data.fromfile(testfile("testdata5.txt")) d.discretize() tasks = [greedy.GreedyLearner(d) for x in range(5)] tc = ipy1.IPython1Controller("127.0.0.1:10113") results = tc.run(tasks) results = result.merge(results) assert isinstance(results, result.LearnerResult) pebl-1.0.2/src/pebl/test/test_tutorial.py000644 000766 000024 00000002603 11325161410 020767 0ustar00shahadstaff000000 000000 """tests all the code included in the pebl tutorial""" from __future__ import with_statement import tempfile, shutil, os.path import textwrap from pebl import data, pebl_script from pebl.learner import greedy, simanneal from pebl.test import testfile class TestTutorial: def setup(self): self.tmpdir = tempfile.mkdtemp() def teardown(self): shutil.rmtree(self.tmpdir) def test_example1(self): outdir = os.path.join(self.tmpdir, "example1-result") dataset = data.fromfile(testfile("pebl-tutorial-data1.txt")) dataset.discretize() learner = greedy.GreedyLearner(dataset) ex1result = learner.run() ex1result.tohtml(outdir) assert os.path.exists(os.path.join(outdir, 'index.html')) def test_example1_configfile(self): configfile = os.path.join(self.tmpdir, 'config1.txt') outdir = os.path.join(self.tmpdir, "example1-result-2") configstr = textwrap.dedent(""" [data] filename = %s discretize = 3 [learner] type = greedy.GreedyLearner [result] format = html outdir = %s """ % (testfile("pebl-tutorial-data1.txt"), outdir)) with file(configfile, 'w') as f: f.write(configstr) pebl_script.run(configfile) assert os.path.exists(os.path.join(outdir, 'data', 'result.data.js')) pebl-1.0.2/src/pebl/test/testfiles/000755 000766 000024 00000000000 11377315471 017533 5ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl/test/testfiles/config1.txt000644 000766 000024 00000000055 11322711102 021577 0ustar00shahadstaff000000 000000 [test] param1 = foo param5 = 50 param7 = 2.0 pebl-1.0.2/src/pebl/test/testfiles/config2.txt000644 000766 000024 00000000057 11322711102 021602 0ustar00shahadstaff000000 000000 [test] param1 = foo param5 = 150 param7 = 12.0 pebl-1.0.2/src/pebl/test/testfiles/greedytest1-200.txt000644 000766 000024 00000004027 11322711102 023013 0ustar00shahadstaff000000 000000 a,discrete(3) b,discrete(3) c,discrete(3) d,discrete(3) e,discrete(3) 1 2 1 2 1 1 0 1 1 0 0 0 2 0 2 1 1 0 1 0 2 1 1 1 0 2 2 1 2 1 2 1 1 1 0 2 2 0 2 1 2 1 1 1 0 1 1 0 1 0 1 2 0 2 1 0 1 2 1 0 2 0 1 0 2 2 2 1 2 1 1 2 0 2 1 0 1 2 1 0 1 1 1 2 1 1 2 2 2 0 1 1 0 1 0 2 0 1 0 2 2 2 1 2 1 2 0 1 0 1 1 0 2 0 2 0 1 2 1 0 0 1 1 1 2 2 0 1 0 2 2 0 2 0 2 0 0 1 2 1 1 1 0 1 0 1 2 2 0 2 0 1 2 1 0 0 1 2 2 1 0 1 0 1 0 2 0 1 1 2 0 0 0 0 2 0 2 2 2 1 0 1 2 1 1 1 2 2 2 1 0 2 2 2 1 1 1 2 0 2 0 1 0 1 0 1 1 1 1 0 0 0 1 0 2 1 1 0 2 0 2 0 0 2 1 0 2 2 2 1 0 1 2 1 0 1 0 0 0 2 2 0 1 1 0 2 0 2 0 2 0 0 0 0 1 1 1 0 1 0 1 0 0 2 1 2 2 1 2 1 2 2 1 2 1 2 1 2 1 0 1 2 2 2 1 0 2 2 2 0 0 1 2 0 2 2 0 1 0 2 2 0 1 0 2 0 2 2 2 1 0 0 2 0 2 1 2 2 1 1 1 1 0 1 0 1 0 2 0 2 2 0 1 0 0 2 1 1 2 1 0 1 2 1 0 0 1 2 1 0 0 2 0 2 1 2 2 1 0 2 0 0 2 1 0 0 1 2 0 2 1 1 0 1 0 2 0 1 0 2 1 2 0 2 1 2 1 1 1 0 0 1 2 1 1 2 0 1 0 2 1 2 2 2 0 1 1 0 2 1 2 2 2 2 0 2 2 1 2 1 2 2 1 1 0 1 2 2 1 0 2 1 0 2 1 0 2 2 2 1 1 2 0 2 1 0 2 2 2 1 2 2 1 2 1 0 1 2 2 1 2 0 0 0 1 1 0 0 0 2 2 2 1 2 1 1 1 0 0 2 1 2 1 1 0 2 0 1 0 2 0 1 2 0 0 0 0 0 0 2 1 2 2 0 2 2 2 1 2 1 1 1 0 1 0 1 1 0 0 2 2 2 1 2 1 1 2 1 2 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 2 0 1 2 1 0 2 0 1 2 1 1 1 2 1 0 0 1 2 1 0 0 2 2 2 1 2 0 1 0 1 1 2 0 2 1 1 1 1 1 0 2 2 0 2 1 2 0 1 0 2 1 2 2 2 2 2 0 1 1 0 0 1 2 0 2 2 1 1 0 2 1 1 1 1 0 0 2 0 2 0 0 0 0 0 2 2 0 1 0 2 1 1 0 1 2 1 0 0 0 2 1 2 2 2 1 0 0 2 0 2 0 1 2 1 0 2 1 1 0 2 0 2 2 2 0 2 1 0 0 0 2 1 1 1 1 1 1 0 1 0 2 1 1 1 0 1 1 0 0 2 0 0 0 0 1 1 0 0 1 0 0 0 0 0 2 1 0 0 0 2 0 0 2 0 1 0 2 1 2 1 0 1 2 0 2 2 2 0 2 1 1 0 0 0 2 1 2 2 2 1 0 2 2 2 1 0 1 2 2 1 1 1 2 0 2 0 2 2 1 1 2 2 1 2 1 0 0 1 0 0 2 2 1 2 1 2 0 1 0 2 0 1 2 1 0 1 2 2 2 1 0 2 2 2 1 0 2 1 2 1 2 0 1 1 2 0 1 2 1 0 2 0 1 1 2 2 0 1 0 1 0 2 2 2 1 2 2 1 2 2 1 2 2 1 0 0 1 0 1 0 0 0 0 0 2 2 0 1 0 2 1 2 2 2 2 1 2 1 0 2 0 1 1 0 2 2 1 1 2 1 0 2 2 2 0 2 1 0 1 1 0 0 0 0 2 2 0 1 0 2 1 2 0 0 2 2 2 1 1 0 0 2 2 2 1 0 1 2 1 0 0 2 2 2 1 2 0 1 0 2 0 1 2 1 0 0 2 2 2 1 0 0 1 0 2 2 0 2 0 2 0 0 0 0 2 2 2 1 1 0 1 2 2 2 1 0 1 2 0 1 1 1 0 1 0 2 2 1 2 1 1 2 2 2 1 1 0 0 0 2 1 1 0 1 0 1 2 2 2 1 0 2 2 1 0 pebl-1.0.2/src/pebl/test/testfiles/pebl-tutorial-data1.txt000644 000766 000024 00000011601 11322711102 024023 0ustar00shahadstaff000000 000000 CLN1 SRO4 RAD51 RNR3 CLN2 SVS1 MNN1 CDC5 CLB2 MYO1 ALK1 ACE2 1.17 1.61 2.17 1.58 1.98 0.19 -0.47 -0.12 -1.09 0.57 0.83 0.5 0.65 2.79 0.12 1.14 2.76 3.12 2.49 -0.32 -0.69 0.03 -0.38 -0.4 -2 -2.74 -0.42 -1.03 -2.74 -2.64 -1.6 2.47 2.49 0.03 2.14 -0.22 -1.32 -2.12 -0.49 -0.67 -2 -1.51 -0.15 2.41 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-1.42 0.25 -0.32 -0.39 -0.9 -0.29 -0.43 -0.4 0.1 0 -0.53 -0.3 -0.9 -1.29 -0.5 -1.3 -1.15 0.23 0.08 0.01 0.41 0.4 0.21 0.45 -0.8 -1.18 -0.32 -0.79 -0.6 0.65 0.52 0.52 0.76 0.68 0.58 0.68 -0.39 -0.55 -0.38 -0.39 -0.37 0.48 0.56 0.46 0.63 0.57 0.55 0.74 -0.12 0.63 -0.13 0.28 -0.32 0.54 0.71 0.66 0.42 0.6 0.61 0.34 0.77 0.54 0.29 0.52 0.52 0.59 0.01 0.19 0.21 0.27 0.22 0.03 0.79 0.37 0.6 0.88 0.72 0.53 0.57 0.19 0.56 0.6 0.46 0.44 1.15 0.95 0.61 0.7 0.89 0.24 0.08 0.18 0.16 0.4 0.28 0.53 0.67 1.08 0.54 0.73 0.86 0.27 -0.2 -0.19 -0.27 -0.3 0.13 -0.43 1.08 0.79 0.7 1.01 0.93 -0.03 -0.4 -0.01 -0.46 -0.55 -0.63 -1.05 0.87 0.85 -0.02 0.47 0.9 0.3 0.01 0.16 -0.13 0.68 0.15 -0.19 0.37 0.61 0.56 0.17 0.42 pebl-1.0.2/src/pebl/test/testfiles/pebl-tutorial-data2.txt000644 000766 000024 00000005374 11322711102 024036 0ustar00shahadstaff000000 000000 CLN1,discrete(3) SRO4,discrete(3) RAD51,discrete(3) RNR3,discrete(3) CLN2,discrete(3) SVS1,discrete(3) MNN1,discrete(3) CDC5,discrete(3) CLB2,discrete(3) MYO1,discrete(3) ALK1,discrete(3) ACE2,discrete(3) sample-0 2 2 2 2 2 1 0 1 0 2 2 2 sample-1 2 2 1 2 2 2 2 0 0 1 1 0 sample-2 0 0 0 0 0 0 0 2 2 1 2 1 sample-3 0 0 0 0 0 0 1 2 2 2 2 2 sample-4 0 0 0 0 0 0 0 0 0 0 0 0 sample-5 0 0 2 1 0 0 1 0 1 0 0 1 sample-6 2 1 2 2 2 2 2 0 0 0 0 0 sample-7 1 2 2 2 2 2 2 0 0 0 0 0 sample-8 2 2 2 2 2 2 2 0 0 0 0 0 sample-9 2 2 2 1 1 2 2 1 1 1 1 1 sample-10 1 1 0 0 0 1 1 2 2 2 2 2 sample-11 0 0 0 0 0 0 0 2 2 2 2 2 sample-12 0 0 0 0 0 0 0 2 2 2 2 2 sample-13 0 0 0 0 0 0 0 2 2 2 1 2 sample-14 2 1 1 2 1 1 1 1 1 1 1 1 sample-15 2 2 2 2 2 2 2 1 1 1 0 1 sample-16 2 2 2 2 2 2 2 0 0 0 0 2 sample-17 1 1 1 1 2 2 2 1 1 0 1 1 sample-18 2 1 1 2 1 2 1 1 1 2 1 1 sample-19 1 1 1 1 1 1 1 2 2 2 2 1 sample-20 0 1 0 0 0 0 0 2 2 1 2 2 sample-21 0 0 0 0 0 0 0 2 2 2 2 2 sample-22 0 0 0 0 0 0 0 1 1 0 0 1 sample-23 2 2 2 2 2 2 1 0 0 0 1 0 sample-24 2 2 2 2 2 2 2 0 0 0 0 0 sample-25 1 2 1 0 1 1 2 2 1 1 2 2 sample-26 1 1 1 0 1 1 2 1 2 1 2 1 sample-27 0 0 0 0 0 0 0 2 1 2 2 2 sample-28 0 0 0 0 0 2 0 1 2 2 2 2 sample-29 0 0 0 0 0 0 0 2 2 2 2 2 sample-30 0 0 0 0 0 1 0 1 2 1 1 1 sample-31 1 1 2 2 2 1 1 1 1 1 0 1 sample-32 2 2 2 2 2 1 2 0 0 1 0 0 sample-33 2 2 2 2 2 2 2 0 0 1 0 0 sample-34 2 2 2 2 2 1 2 0 0 1 0 0 sample-35 2 2 2 2 2 2 2 1 0 0 1 0 sample-36 1 2 2 2 2 1 2 0 1 0 0 1 sample-37 0 1 1 1 1 0 2 2 2 1 2 2 sample-38 2 1 1 1 1 1 1 1 1 1 1 1 sample-39 0 0 0 0 0 0 0 2 2 0 2 2 sample-40 0 0 0 1 0 1 0 2 2 2 1 0 sample-41 0 0 0 1 0 1 0 2 2 2 1 1 sample-42 1 2 2 1 1 1 0 0 1 2 1 1 sample-43 1 1 1 2 1 1 1 1 1 1 0 1 sample-44 1 1 1 1 1 0 1 0 0 1 0 0 sample-45 1 0 1 1 1 0 1 1 0 0 1 1 sample-46 1 1 0 2 1 0 0 0 0 0 0 0 sample-47 1 0 1 1 2 0 1 0 0 0 0 0 sample-48 2 2 2 1 2 2 2 0 0 0 0 0 sample-49 2 2 2 2 2 2 2 0 0 0 0 0 sample-50 1 2 1 1 1 2 2 1 1 1 1 1 sample-51 1 1 1 2 1 1 1 1 1 2 2 1 sample-52 1 0 0 1 1 1 1 2 2 2 2 2 sample-53 0 0 0 1 0 0 0 2 2 2 2 2 sample-54 0 0 0 1 0 0 0 2 2 2 2 2 sample-55 2 1 2 1 1 1 0 1 1 1 1 2 sample-56 2 2 2 1 2 2 1 1 1 1 0 1 sample-57 2 2 2 1 2 1 2 0 0 1 1 0 sample-58 1 2 2 0 1 2 2 1 1 0 1 1 sample-59 1 1 1 0 1 2 1 1 1 1 2 1 sample-60 1 1 1 1 0 1 1 1 2 2 1 1 sample-61 0 0 1 0 1 1 1 2 1 2 2 2 sample-62 0 0 0 0 0 0 0 2 1 2 1 1 sample-63 0 0 0 0 0 0 0 0 0 0 0 0 sample-64 0 0 0 0 0 2 1 0 0 0 0 0 sample-65 0 2 2 0 1 0 1 0 1 0 1 0 sample-66 1 1 0 1 1 0 1 0 0 0 0 0 sample-67 1 1 1 2 1 1 2 0 0 0 0 0 sample-68 2 2 2 2 2 2 2 0 0 0 1 0 sample-69 2 2 2 2 2 2 2 1 2 1 1 0 sample-70 2 2 2 2 2 2 1 2 1 1 2 2 sample-71 2 1 1 2 1 2 1 2 1 2 2 2 sample-72 2 2 1 2 2 2 2 2 2 2 2 2 sample-73 1 1 1 1 1 2 2 2 2 2 2 2 sample-74 1 1 1 0 1 1 0 2 2 2 2 2 sample-75 1 1 1 0 0 0 0 2 2 1 1 2 sample-76 1 1 1 1 2 1 1 1 2 2 1 2pebl-1.0.2/src/pebl/test/testfiles/testdata1.txt000644 000766 000024 00000000071 11322711102 022141 0ustar00shahadstaff000000 000000 var1 var2 var3 2.5! !X 1.7 1.1 !1.7 2.3 4.2 999.3 12 pebl-1.0.2/src/pebl/test/testfiles/testdata10.txt000644 000766 000024 00000000275 11322711102 022227 0ustar00shahadstaff000000 000000 # some calculations with this dataset derived manually in pebl/test/test_cpd.py "v1,discrete(2)" "v2,discrete(2)" "v3,discrete(2)" "v4,discrete(2)" 0 1 1 0 1 0 0 1 1 1 1 0 1 1 1 0 0 0 1 1 pebl-1.0.2/src/pebl/test/testfiles/testdata11.txt000644 000766 000024 00000000154 11322711102 022224 0ustar00shahadstaff000000 000000 "v1,discrete(2)" "v2,discrete(2)" "v3,discrete(2)" "v4,discrete(2)" 0 X 1 0 1 X 0 1 1 X 1 0 1 X 1 0 0 X 1 1 pebl-1.0.2/src/pebl/test/testfiles/testdata12.txt000644 000766 000024 00000000266 11322711102 022231 0ustar00shahadstaff000000 000000 v1,discrete(3) v2,discrete(3) v3,discrete(3) v4,discrete(3) v5,discrete(3) 0 0 1 1 1 0 1 1 0 1 2 1 2 2 0 0 2 2 3 2 2 2 1 2 4 1 1 0 0 0 5 0 2 1 0 2 6 1 0 2 2 0 7 2 2 0 0 0 8 0 1 2 0 0pebl-1.0.2/src/pebl/test/testfiles/testdata13.txt000644 000766 000024 00000000275 11322711102 022232 0ustar00shahadstaff000000 000000 a,discrete(3) b,discrete(3) c,discrete(3) d,discrete(3) e,discrete(3) 0 1 X 1 2 1 1 1 X 1 1 0 2 0 X 2 0 2 3 1 X 0 1 0 4 2 X 1 1 0 5 2 X 1 2 1 6 2 X 1 1 0 7 2 X 0 2 1 8 2 X 1 1 0 9 1 X 0 1 0pebl-1.0.2/src/pebl/test/testfiles/testdata2.txt000644 000766 000024 00000000257 11322711102 022150 0ustar00shahadstaff000000 000000 # comment line "shh,discrete(2)" "ptchp,discrete(3)" "smo,continuous" "outcome,class(good, bad)" 0! !0 1.25 good #comment in the data section 1 !1 1.1 bad 1 2 0.45 bad pebl-1.0.2/src/pebl/test/testfiles/testdata3.txt000644 000766 000024 00000000116 11322711102 022143 0ustar00shahadstaff000000 000000 "shh,discrete(2)" "ptchp,discrete(3)" sample1 0! !0 sample2 1 !1 sample3 1 2 pebl-1.0.2/src/pebl/test/testfiles/testdata4.txt000644 000766 000024 00000000116 11322711102 022144 0ustar00shahadstaff000000 000000 "shh,discrete(2)" "ptchp,discrete(3)" sample1 0! !0 sample2 1 !1 sample3 1 2 pebl-1.0.2/src/pebl/test/testfiles/testdata5.txt000644 000766 000024 00000000275 11322712131 022156 0ustar00shahadstaff000000 000000 v1 v2 v3 v4 v5 1.2 1.4 2.1 2.2 1.1 2.3 1.1 2.1 3.2 1.3 3.2 0 1.2 2.5 1.6 4.2 2.4 3.2 2.1 2.8 2.7 1.5 0 1.5 1.1 1.1 2.3 2.1 1.7 3.2 2.3 1.1 4.3 2.3 1.1 3.2 2.6 1.9 1.7 1.1 2.1 1.5 3 1.4 1.1 pebl-1.0.2/src/pebl/test/testfiles/testdata6.txt000644 000766 000024 00000000154 11322711102 022150 0ustar00shahadstaff000000 000000 "v1,discrete(2)" "v2,discrete(2)" "v3,discrete(2)" "v4,discrete(2)" 0 1 1 0 1 0 0 2 1 1 2 0 1 1 1 0 0 0 1 1 pebl-1.0.2/src/pebl/test/testfiles/testdata7.txt000644 000766 000024 00000000464 11322711102 022155 0ustar00shahadstaff000000 000000 "v1,discrete(3)" "v2,discrete(4)" "v3,discrete(3)" "v4,discrete(6)" 0 2 1 2 1 2 1 1 2 1 0 2 2 1 1 1 1 1 2 1 1 1 0 1 2 0 1 0 1 2 1 1 1 0 0 2 1 2 2 0 2 1 0 2 1 2 0 2 0 2 0 1 1 2 1 2 1 2 0 0 1 0 1 0 1 2 0 0 1 2 0 1 2 0 2 2 1 1 0 1 1 0 0 2 0 1 1 1 2 2 2 2 2 2 1 0 2 0 2 2 2 1 0 0 0 2 0 1 1 0 2 2 1 2 1 1 0 0 2 1 pebl-1.0.2/src/pebl/test/testfiles/testdata8.txt000644 000766 000024 00000000602 11322711102 022150 0ustar00shahadstaff000000 000000 "v1,discrete(3)," "v2,discrete(3)" "v3,discrete(3)" "v4,discrete(3)" "v5,discrete(3)" 0 0 X 1 2 0 0 X 1 1 2 1 X 0 0 2 1 X 1 1 1 1 X 2 0 1 1 X 0 0 2 0 X 1 0 1 2 X 1 1 1 0 X 0 2 1 2 X 2 0 2 1 X 0 2 1 2 X 0 2 0 2 X 2 1 1 2 X 1 2 1 2 X 0 0 0 0 X 1 0 0 1 X 0 0 0 1 X 0 1 2 0 X 2 2 2 1 X 2 1 1 0 X 0 2 0 1 X 1 1 2 2 X 2 2 2 2 X 1 0 2 0 X 2 2 2 1 X 2 0 0 2 X 0 1 0 0 X 2 2 1 2 X 1 1 0 0 X 2 1 pebl-1.0.2/src/pebl/test/testfiles/testdata9.txt000644 000766 000024 00000000416 11322711102 022154 0ustar00shahadstaff000000 000000 a,discrete(3) b,discrete(3) c,discrete(3) d,discrete(3) e,discrete(3) 2 2 X 2 2 2 1 X 2 2 1 2 X 1 1 0 1 X 0 0 2 1 X 2 2 1 2 X 2 2 0 1 X 0 0 0 2 X 1 1 2 2 X 2 2 0 0 X 0 0 2 1 X 1 1 1 1 X 1 1 2 2 X 2 2 0 0 X 0 0 1 2 X 2 2 1 0 X 0 1 2 0 X 1 0 1 2 X 2 2 2 0 X 1 1 0 0 X 0 0 pebl-1.0.2/src/pebl/test/learner/__init__.py000644 000766 000024 00000000000 11025565332 021251 0ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl/test/learner/test_all.py000644 000766 000024 00000003773 11173463661 021353 0ustar00shahadstaff000000 000000 import cPickle from pebl.test import testfile from pebl import data, result, config from pebl.learner import greedy, simanneal class TestGreedyLearner_Basic: learnertype = greedy.GreedyLearner def setUp(self): self.data = data.fromfile(testfile('testdata5.txt')).subset(samples=range(5)) self.data.discretize() self.learner = self.learnertype(self.data) def test_result(self): self.learner.run() assert hasattr(self.learner, 'result') def test_pickleable(self): try: x = cPickle.dumps(self.learner) self.learner.run() y = cPickle.dumps(self.learner) assert 1 == 1 except: assert 1 == 2 class TestSimAnnealLearner_Basic(TestGreedyLearner_Basic): learnertype = simanneal.SimulatedAnnealingLearner ## Missing data evaluators ## Added in response to issue #31 (on googlecode) class TestGreedyWithGibbs: learnertype = greedy.GreedyLearner missing_evaluator = 'gibbs' def setUp(self): config.set('evaluator.missingdata_evaluator', self.missing_evaluator) self.data = data.fromfile(testfile('testdata13.txt')) self.learner = self.learnertype(self.data) def test_learner_run(self): self.learner.run() assert hasattr(self.learner, 'result') class TestGreedyWithMaxEntGibbs(TestGreedyWithGibbs): missing_evaluator = 'maxentropy_gibbs' class TestSAWithGibbs(TestGreedyWithGibbs): learnertype = simanneal.SimulatedAnnealingLearner missing_evaluator = 'gibbs' class TestSAWithMaxEntGibbs(TestSAWithGibbs): missing_evaluator = 'maxentropy_gibbs' class TestExactMethodWithHiddenData: def setUp(self): config.set('evaluator.missingdata_evaluator', 'exact') self.data = data.fromfile(testfile('testdata13.txt')).subset(samples=range(5)) self.learner = greedy.GreedyLearner(self.data, max_iterations=10) def test_learner_run(self): self.learner.run() assert hasattr(self.learner, 'result') pebl-1.0.2/src/pebl/test/learner/test_greedy.py000644 000766 000024 00000001623 11025565263 022047 0ustar00shahadstaff000000 000000 from pebl.test import testfile from pebl import data, result from pebl.learner import greedy class TestGreedyLearner: def setUp(self): self.data = data.fromfile(testfile('testdata5.txt')) self.data.discretize() def test_max_iterations(self): g = greedy.GreedyLearner(self.data, max_iterations=100) g.run() assert g.stats.iterations == 100 def test_max_time(self): g = greedy.GreedyLearner(self.data, max_time=2) g.run() runtime = g.stats.runtime # time based stopping criteria is approximate.. assert runtime >= 2 and runtime < 3 def test_max_unimproved_iterations(self): g1 = greedy.GreedyLearner(self.data, max_unimproved_iterations=1) g1.run() g2 = greedy.GreedyLearner(self.data, max_unimproved_iterations=100) g2.run() assert g1.stats.restarts > g2.stats.restarts pebl-1.0.2/src/pebl/test/learner/test_simanneal.py000644 000766 000024 00000001210 11025565263 022527 0ustar00shahadstaff000000 000000 from pebl.test import testfile from pebl import data, result from pebl.learner import simanneal class TestGreedyLearner: def setUp(self): self.data = data.fromfile(testfile('testdata5.txt')) self.data.discretize() def test_default_params(self): s = simanneal.SimulatedAnnealingLearner(self.data) s.run() assert True def test_param_effect(self): s1 = simanneal.SimulatedAnnealingLearner(self.data) s1.run() s2 = simanneal.SimulatedAnnealingLearner( self.data, start_temp = 50) s2.run() assert s1.stats.iterations > s2.stats.iterations pebl-1.0.2/src/pebl/test/benchdata/benchdata.10.20.txt000644 000766 000024 00000000620 10671534051 022542 0ustar00shahadstaff000000 000000 2 0 0 2 0 0 2 1 2 0 2 1 2 0 2 0 2 1 0 0 0 2 1 0 0 1 0 1 0 0 2 0 1 2 2 0 0 0 2 2 0 2 0 1 0 1 0 1 0 0 0 1 2 0 0 1 0 1 2 0 2 1 0 2 0 1 0 0 2 1 0 0 1 0 0 0 0 2 1 2 0 0 2 0 2 0 0 0 0 0 1 0 2 0 0 1 2 0 0 2 1 1 0 2 0 0 0 1 2 2 2 1 1 1 2 1 0 2 1 0 0 2 1 0 0 1 2 1 0 2 1 1 1 2 2 1 0 1 2 0 1 0 0 2 0 0 1 1 0 0 1 1 2 1 2 0 0 1 2 1 1 1 0 2 0 1 1 1 1 1 2 1 0 2 1 0 0 0 0 0 0 2 0 1 0 1 2 1 0 2 1 0 1 1 1 2 0 1 0 2 pebl-1.0.2/src/pebl/test/benchdata/benchdata.10.50.txt000644 000766 000024 00000001750 10671534051 022552 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controller to use.', default = 'serial.SerialController' ) #TODO:test def fromconfig(): tctype = config.get('taskcontroller.type') tcmodule,tcclass = tctype.split('.') mymod = __import__("pebl.taskcontroller.%s" % tcmodule, fromlist=['pebl.taskcontroller']) mytc = getattr(mymod, tcclass) return mytc() pebl-1.0.2/src/pebl/taskcontroller/base.py000644 000766 000024 00000002236 11031005411 021060 0ustar00shahadstaff000000 000000 import copy # # Tasks and their results # class Task(object): def run(self): pass def split(self, count): return [self] + [copy.deepcopy(self) for i in xrange(count-1)] class DeferredResult(object): def __init__(self, **kw): self.__dict__.update(kw) @property def result(self): pass @property def finished(self): pass # # Base Controllers # class _BaseController(object): def __init__(self, *args, **kwargs): pass def run(self, tasks): pass # For synchronous task controllers (like serial and multiprocess), submit # simply runs (blocking). Since the result of submit is a real result and # not a deferred result, retrieve simply returns the results that were # passed in. This let's synchronous and asynchronous task controllers have # the same interface. def submit(self, tasks): return self.run(tasks) def retrieve(self, deferred_results): return deferred_results class _BaseSubmittingController(_BaseController): def submit(self, tasks): pass def retrieve(self, deferred_results): pass def run(self, tasks): return self.retrieve(self.submit(tasks)) pebl-1.0.2/src/pebl/taskcontroller/ec2.py000644 000766 000024 00000003410 11026014622 020622 0ustar00shahadstaff000000 000000 """Classes and functions for running tasks on Amazon's EC2""" import time import os.path import shutil import tempfile import sys from pebl import config, result from pebl.taskcontroller.ipy1 import IPython1Controller, IPython1DeferredResult from pebl.taskcontroller import ec2ipy1 class EC2DeferredResult(IPython1DeferredResult): pass class EC2Controller(IPython1Controller): _params = ( config.StringParameter( 'ec2.config', 'EC2 config file', default='' ), config.IntParameter( 'ec2.min_count', 'Minimum number of EC2 instances to create (default=1).', default=1 ), config.IntParameter( 'ec2.max_count', """Maximum number of EC2 instances to create (default=0 means the same number as ec2.min_count).""", default=0 ) ) def __init__(self, **options): config.setparams(self, options) self.ec2 = ec2ipy1.EC2Cluster(self.config) self.start() def __del__(self): self.stop() def start(self): self.ec2.create_instances(self.min_count, self.max_count) print "Updating pebl on worker nodes" self.ec2.remote_all("cd /usr/local/src/pebl; svn update; python setup.py install") self.ec2.start_ipython1(engine_on_controller=True) self.ipy1taskcontroller = IPython1Controller(self.ec2.task_controller_url) def stop(self): self.ec2.terminate_instances() def submit(self, tasks): return self.ipy1taskcontroller.submit(tasks) def retrieve(self, deferred_results): return self.ipy1taskcontroller.retrieve(deferred_results) def run(self, tasks): return self.ipy1taskcontroller.run(tasks) pebl-1.0.2/src/pebl/taskcontroller/ec2ipy1.py000755 000766 000024 00000022041 11026014610 021426 0ustar00shahadstaff000000 000000 import sys, os, time import ConfigParser from itertools import groupby import ipython1.kernel.api as kernel import boto # options required in the config file required_config_options = [ ('access_key', 'Please specify your AWS access key ID.'), ('secret_access_key', 'Please specify your AWS secret access key.'), ('ami', 'Please specify the AMI to use for the controller and engines.'), ('key_name', 'Please specify the key_name to use with the EC2 instances.'), ('credential', 'Please specify the ssh credential file.'), ] class EC2Cluster: """ * starts desired number of EC2 instances * starts controller on first instance * starts engines on all other instances * includes methods for: * creating and terminating cluster * creating RemoteController and TaskController from cluster states: * aws_connected: have connection to AWS * instances_reserved * instances_running * cluster_ready: instances are running and IPython1 controller/engines setup """ def __init__(self, configfile, instances=[]): self.config = self._check_config(configfile) self.conn = boto.connect_ec2( self.config['access_key'], self.config['secret_access_key'] ) self._state = ['aws_connected'] self.instances = instances if instances else [] def _check_config(self, configfile): configp = ConfigParser.SafeConfigParser() configp.read(configfile) config = dict(configp.items('EC2')) for key, error in required_config_options: if key not in config: print error sys.exit(1) return config def _wait_till_instances_in_state(self, waitingfor, resulting_state, sleepfor=10): print "Waiting till all instances are %s. Will check every %s seconds." % (waitingfor, sleepfor) print "Hit Ctrl-C to stop waiting." while True: statuses = [i.update() for i in self.instances] if all(status == waitingfor for status in statuses): print "All instances %s" % waitingfor self._state.append(resulting_state) return else: print "Not all instances are %s" % waitingfor statuses.sort() for statustype, statuses in groupby(statuses, lambda x: x): print "\t%s: %s instances" % (statustype, len(list(statuses))) time.sleep(sleepfor) def wait_till_instances_running(self, sleepfor=10): self._wait_till_instances_in_state('running', 'instances_running', sleepfor) def wait_till_instances_terminated(self, sleepfor=10): self._wait_till_instances_in_state('terminated', 'instances_terminated', sleepfor) def create_instances(self, min_count=1, max_count=None): # if max not specified, it's the same as the min max_count = max_count or min_count # reserve instances print "Reserving EC2 instances." self.reservation = self.conn.run_instances( self.config['ami'], min_count, max_count, self.config['key_name'], ) self._state.append('instances_reserved') self.instances = self.reservation.instances self.wait_till_instances_running() print "Waiting for firewall ports to open up (10 secs)" time.sleep(10) print "Trying to connect to worker nodes using ssh" self._check_ssh_connection() def _check_ssh_connection(self): instances = [i for i in self.instances] while instances: for i in instances: time.sleep(1) # so we're not bombarding the servers if 0 == self.remote(i, "ls /"): instances.remove(i) def start_ipython1(self, engine_on_controller=False): if not 'instances_running' in self._state: print "Not all instances are running." return False if not hasattr(self, 'instances'): print "Create EC2 instances before starting cluster." return False print "Starting ipython1 controller/engines on running instances" # redirect stdin, stdout and stderr on remote processes so ssh terminates. # we could use 'ssh -f' but that will fork ssh in the background # and on large clusters that could mean many ssh background procs cmd_postfix = "&0 2>&0 &" # run ipcontroller on the first controller instance controller_ip = self.instances[0].public_dns_name controller_port = kernel.defaultRemoteController[1] print "Starting controller on %s" % controller_ip self.remote( host = self.instances[0], cmd = "nohup /usr/local/bin/ipcontroller -l /mnt/ipcontroller_ %s" % cmd_postfix, ) print "Waiting for controller to start (6 secs)" time.sleep(6) # run engine on the same instance as controller? engine_instances = self.instances[1:] if not engine_on_controller else self.instances # run ipengine on selected instances for inst in engine_instances: print "Starting engine on %s" % inst.public_dns_name self.remote( host = inst, cmd = "nohup /usr/local/bin/ipengine --controller-ip=%s -l /mnt/ipengine_ %s" % (controller_ip, cmd_postfix), ) time.sleep(1) # so we don't bombard the controller.. print "-"*70 print "Ipython1 controller running on %s:%s" % (controller_ip, controller_port) print "Type the following to login to controller:" print "ssh -i %s root@%s" % (self.config['credential'], controller_ip) self._state.append('ipython1_running') time.sleep(6) # waiting for cluster to be setup return True def reboot_instances(self): print "Rebooting all instances" for inst in self.instances: inst.reboot() self._state = ['instances_reserved'] self.wait_till_instances_running() def terminate_instances(self): for i in self.instances: i.stop() self.wait_till_instances_terminated() def authorize_access_to_controller(self, from_ip): ports = [kernel.defaultRemoteController[1], kernel.defaultTaskController[1]] for port in ports: print "Authorizing access for group default for port %s from IP %s" % (port, from_ip) self.conn.authorize_security_group('default', ip_protocol='tcp', from_port=port, to_port=port, cidr_ip=from_ip) @property def remote_controller(self): return kernel.RemoteController(( self.instances[0].public_dns_name, kernel.defaultRemoteController[1] )) @property def task_controller(self): return kernel.TaskController(( self.instances[0].public_dns_name, kernel.defaultTaskController[1] )) @property def task_controller_url(self): return "%s:%s" % (self.instances[0].public_dns_name, kernel.defaultTaskController[1]) @property def remote_controller_url(self): return "%s:%s" % (self.instances[0].public_dns_name, kernel.defaultRemoteController[1]) # from Peter Skomoroch's ec2-mpi-config.py (see http://datawrangling.com) def remote(self, host, cmd='scp', src=None, dest=None, test=False): """ Run a command on remote machine (or copy files) using ssh. @param host: boto ec2 instance, ip address or dns name """ d = { 'cmd':cmd, 'src':src, 'dest':dest, 'host':getattr(host, 'public_dns_name', str(host)), 'switches': '' } d['switches'] += " -i %s " % self.config['credential'] if cmd == 'scp': template = '%(cmd)s %(switches)s -o "StrictHostKeyChecking no" %(src)s root@%(host)s:%(dest)s' else: template = 'ssh %(switches)s -o "StrictHostKeyChecking no" root@%(host)s "%(cmd)s" ' cmdline = template % d print "Trying: ", cmdline if not test: return os.system(cmdline) def remote_all(self, cmd='scp', src=None, dest=None, test=False): for i in self.instances: self.remote(i.public_dns_name, cmd, src, dest, test) def tofile(self, filename): f = file(filename, 'w') f.writelines(inst.id + "\n" for inst in self.instances) f.close() def fromfile(self, filename): def _instance(id): inst = boto.ec2.instance.Instance(self.conn) inst.id = id inst.update() return inst self.instances = [_instance(id[:-1]) for id in file(filename).readlines()] # USAGE #ec2 = EC2Cluster() #ec2.create_instances() #ec2.start_ipython1() #tc = ec2.task_controller #ec2.terminate_instances() pebl-1.0.2/src/pebl/taskcontroller/ipy1.py000644 000766 000024 00000004703 11031065174 021045 0ustar00shahadstaff000000 000000 """Classes and functions for running tasks using IPython1""" from __future__ import with_statement import sys import os, os.path import tempfile import cPickle from pebl import config, result from pebl.taskcontroller.base import _BaseSubmittingController, DeferredResult from pebl.learner import custom try: import ipython1.kernel.api as ipy1kernel except: ipy1kernel = False class IPython1DeferredResult(DeferredResult): def __init__(self, ipy1_taskcontroller, taskid): self.tc = ipy1_taskcontroller self.taskid = taskid @property def result(self): if not hasattr(self, 'ipy1result'): self.ipy1result = self.tc.getTaskResult(self.taskid, block=True) return self.ipy1result['result'] @property def finished(self): rst = self.tc.getTaskResult(self.taskid, block=False) if rst: self.ipy1result = rst return True return False class IPython1Controller(_BaseSubmittingController): _params = ( config.StringParameter( 'ipython1.controller', 'IPython1 TaskController (default is 127.0.0.1:10113)', default='127.0.0.1:10113' ) ) def __init__(self, tcserver=None): """Create a IPython1Controller instance. tcserver is the server and port of the Ipython1 TaskController. It should be of the form :. (default is "127.0.0.1:10113"). """ if not ipy1kernel: print "IPython1 not found." return None self.tcserver = tcserver or config.get('ipython1.controller') self.tc = ipy1kernel.TaskController(tuple(self.tcserver.split(':'))) def submit(self, tasks): drs = [] for task in tasks: # create an ipython1 task from pebl task ipy1task = ipy1kernel.Task( "from pebl.pebl_script import runtask_picklestr; result = runtask_picklestr(task)", resultNames = ['result'], setupNS = {'task': cPickle.dumps(task)} ) task.ipy1_taskid = self.tc.run(ipy1task) drs.append(IPython1DeferredResult(self.tc, task.ipy1_taskid)) return drs def retrieve(self, deferred_results): # block/wait for all tasks taskids = [dr.taskid for dr in deferred_results] self.tc.barrier(taskids) return [dr.result for dr in deferred_results] pebl-1.0.2/src/pebl/taskcontroller/multiprocess.py000644 000766 000024 00000004454 11031011760 022707 0ustar00shahadstaff000000 000000 """Module providing a taskcontroller than runs tasks over multiple processes.""" import os, os.path import cPickle import thread, time import shutil import tempfile from copy import copy from pebl import config, result from pebl.taskcontroller.base import _BaseController PEBL = "pebl" class MultiProcessController(_BaseController): # # Parameters # _params = ( config.IntParameter( 'multiprocess.poolsize', 'Number of processes to run concurrently (0 means no limit)', default=0 ) ) def __init__(self, poolsize=None): """Creates a task controller that runs taks on multiple processes. This task controller uses a pool of processes rather than spawning all processes concurrently. poolsize is the size of this pool and by default it is big enough to run all processes concurrently. """ self.poolsize = poolsize or config.get('multiprocess.poolsize') def run(self, tasks): """Run tasks by creating multiple processes. If poolsize was specified when creating this controller, additional tasks will be queued. """ tasks = copy(tasks) # because we do tasks.pop() below.. numtasks = len(tasks) poolsize = self.poolsize or numtasks running = {} done = [] opjoin = os.path.join while len(done) < numtasks: # submit tasks (if below poolsize and tasks remain) for i in xrange(min(poolsize-len(running), len(tasks))): task = tasks.pop() task.cwd = tempfile.mkdtemp() cPickle.dump(task, open(opjoin(task.cwd, 'task.pebl'), 'w')) pid = os.spawnlp(os.P_NOWAIT, PEBL, PEBL, "runtask", opjoin(task.cwd, "task.pebl")) running[pid] = task # wait for any child process to finish pid,status = os.wait() done.append(running.pop(pid, None)) results = [result.fromfile(opjoin(t.cwd, 'result.pebl')) for t in done] # to make the results look like deferred results for r in results: r.taskid = 0 # clean up for t in done: shutil.rmtree(t.cwd) return results pebl-1.0.2/src/pebl/taskcontroller/serial.py000644 000766 000024 00000000776 11031021615 021440 0ustar00shahadstaff000000 000000 """Module providing a taskcontroller than runs tasks serially.""" from pebl.taskcontroller.base import _BaseController class SerialController(_BaseController): """A simple taskcontroller that runs tasks in serial in one process. This is just the default, null task controller. """ def run(self, tasks): results = [t.run() for t in tasks] # to make the results look like deferred results for r in results: r.taskid = 0 return results pebl-1.0.2/src/pebl/taskcontroller/xgrid.py000644 000766 000024 00000006307 11065773631 021315 0ustar00shahadstaff000000 000000 import time import os.path import shutil import tempfile import cPickle try: import xg except: xg = False from pebl import config, result from pebl.taskcontroller.base import _BaseSubmittingController, DeferredResult class XgridDeferredResult(DeferredResult): def __init__(self, grid, task): self.grid = grid self.job = task.job self.taskid = self.job.jobID @property def result(self): tmpdir = tempfile.mkdtemp('pebl') self.job.results( stdout = os.path.join(tmpdir, 'stdout'), stderr = os.path.join(tmpdir, 'stderr'), outdir = tmpdir ) self.job.delete() rst = result.fromfile(os.path.join(tmpdir,'result.pebl')) shutil.rmtree(tmpdir) return rst @property def finished(self): return self.job.info(update=1).get('jobStatus') in ('Finished',) class XgridController(_BaseSubmittingController): # # Parameters # _params = ( config.StringParameter( 'xgrid.controller', 'Hostname or IP of the Xgrid controller.', default='' ), config.StringParameter( 'xgrid.password', 'Password for the Xgrid controller.', default='' ), config.StringParameter( 'xgrid.grid', 'Id of the grid to use at the Xgrid controller.', default='0' ), config.FloatParameter( 'xgrid.pollinterval', 'Time (in secs) to wait between polling the Xgrid controller.', default=60.0 ), config.StringParameter( 'xgrid.peblpath', 'Full path to the pebl script on Xgrid agents.', default='pebl' ) ) def __init__(self, **options): """Create a XGridController. Any config param for 'xgrid' can be passed in via options. Use just the option part of the parameter name. """ config.setparams(self, options) @property def _grid(self): if xg: cn = xg.Connection(self.controller, self.password) ct = xg.Controller(cn) return ct.grid(self.grid) return None # # Public interface # def submit(self, tasks): grid = self._grid drs = [] for task in tasks: task.cwd = tempfile.mkdtemp() cPickle.dump(task, open(os.path.join(task.cwd, 'task.pebl'), 'w')) task.job = grid.submit(self.peblpath, 'runtask task.pebl', indir=task.cwd) drs.append(XgridDeferredResult(grid, task)) return drs def retrieve(self, deferred_results): drs = deferred_results # poll for job results # i'd rather select() or wait() but xgrid doesn't offer that via the # xgrid command line app done = [] while drs: for i,dr in enumerate(drs): if dr.finished: done.append(drs.pop(i)) break # modified drs, so break and re-iterate else: time.sleep(self.pollinterval) return [dr.result for dr in done] pebl-1.0.2/src/pebl/resources/htmlresult/000755 000766 000024 00000000000 11377315471 020767 5ustar00shahadstaff000000 000000 pebl-1.0.2/src/pebl/resources/htmlresult/index.html000644 000766 000024 00000010255 11010410473 022745 0ustar00shahadstaff000000 000000 Pebl Results

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').addClass(name).css({position:'absolute', top:'-5000px', left:'-5000px', display:'block'}).appendTo('body'); //if (!$.browser.safari) //tmp.appendTo('body'); //Opera and Safari set width and height to 0px instead of auto //Safari returns rgba(0,0,0,0) when bgcolor is not set $.ui.cssCache[name] = !!( (!/auto|default/.test(tmp.css('cursor')) || (/^[1-9]/).test(tmp.css('height')) || (/^[1-9]/).test(tmp.css('width')) || !(/none/).test(tmp.css('backgroundImage')) || !(/transparent|rgba\(0, 0, 0, 0\)/).test(tmp.css('backgroundColor'))) ); try { $('body').get(0).removeChild(tmp.get(0)); } catch(e){} return $.ui.cssCache[name]; }, disableSelection: function(e) { e.unselectable = "on"; e.onselectstart = function() { return false; }; if (e.style) e.style.MozUserSelect = "none"; }, enableSelection: function(e) { e.unselectable = "off"; e.onselectstart = function() { return true; }; if (e.style) e.style.MozUserSelect = ""; }, hasScroll: function(e, a) { var scroll = /top/.test(a||"top") ? 'scrollTop' : 'scrollLeft', has = false; if (e[scroll] > 0) return true; e[scroll] = 1; has = e[scroll] > 0 ? true : false; e[scroll] = 0; return has; } }); /******* fn scope modifications ********/ $.each( ['Left', 'Top'], function(i, name) { if(!$.fn['scroll'+name]) $.fn['scroll'+name] = function(v) { return v != undefined ? this.each(function() { this == window || this == document ? window.scrollTo(name == 'Left' ? v : $(window)['scrollLeft'](), name == 'Top' ? v : $(window)['scrollTop']()) : this['scroll'+name] = v; }) : this[0] == window || this[0] == document ? self[(name == 'Left' ? 'pageXOffset' : 'pageYOffset')] || $.boxModel && document.documentElement['scroll'+name] || document.body['scroll'+name] : this[0][ 'scroll' + name ]; }; }); var _remove = $.fn.remove; $.fn.extend({ position: function() { var offset = this.offset(); var offsetParent = this.offsetParent(); var parentOffset = offsetParent.offset(); return { top: offset.top - num(this[0], 'marginTop') - parentOffset.top - num(offsetParent, 'borderTopWidth'), left: offset.left - num(this[0], 'marginLeft') - parentOffset.left - num(offsetParent, 'borderLeftWidth') }; }, offsetParent: function() { var offsetParent = this[0].offsetParent; while ( offsetParent && (!/^body|html$/i.test(offsetParent.tagName) && $.css(offsetParent, 'position') == 'static') ) offsetParent = offsetParent.offsetParent; return $(offsetParent); }, mouseInteraction: function(o) { return this.each(function() { new $.ui.mouseInteraction(this, o); }); }, removeMouseInteraction: function(o) { return this.each(function() { if($.data(this, "ui-mouse")) $.data(this, "ui-mouse").destroy(); }); }, remove: function() { jQuery("*", this).add(this).trigger("remove"); return _remove.apply(this, arguments ); } }); function num(el, prop) { return parseInt($.curCSS(el.jquery?el[0]:el,prop,true))||0; }; /********** Mouse Interaction Plugin *********/ $.ui.mouseInteraction = function(element, options) { var self = this; this.element = element; $.data(this.element, "ui-mouse", this); this.options = $.extend({}, options); $(element).bind('mousedown.draggable', function() { return self.click.apply(self, arguments); }); if($.browser.msie) $(element).attr('unselectable', 'on'); //Prevent text selection in IE // prevent draggable-options-delay bug #2553 $(element).mouseup(function() { if(self.timer) clearInterval(self.timer); }); }; $.extend($.ui.mouseInteraction.prototype, { destroy: function() { $(this.element).unbind('mousedown.draggable'); }, trigger: function() { return this.click.apply(this, arguments); }, click: function(e) { if( e.which != 1 //only left click starts dragging || $.inArray(e.target.nodeName.toLowerCase(), this.options.dragPrevention || []) != -1 // Prevent execution on defined elements || (this.options.condition && !this.options.condition.apply(this.options.executor || this, [e, this.element])) //Prevent execution on condition ) return true; var self = this; var initialize = function() { self._MP = { left: e.pageX, top: e.pageY }; // Store the click mouse position $(document).bind('mouseup.draggable', function() { return self.stop.apply(self, arguments); }); $(document).bind('mousemove.draggable', function() { return self.drag.apply(self, arguments); }); if(!self.initalized && Math.abs(self._MP.left-e.pageX) >= self.options.distance || Math.abs(self._MP.top-e.pageY) >= self.options.distance) { if(self.options.start) self.options.start.call(self.options.executor || self, e, self.element); if(self.options.drag) self.options.drag.call(self.options.executor || self, e, this.element); //This is actually not correct, but expected self.initialized = true; } }; if(this.options.delay) { if(this.timer) clearInterval(this.timer); this.timer = setTimeout(initialize, this.options.delay); } else { initialize(); } return false; }, stop: function(e) { var o = this.options; if(!this.initialized) return $(document).unbind('mouseup.draggable').unbind('mousemove.draggable'); if(this.options.stop) this.options.stop.call(this.options.executor || this, e, this.element); $(document).unbind('mouseup.draggable').unbind('mousemove.draggable'); this.initialized = false; return false; }, drag: function(e) { var o = this.options; if ($.browser.msie && !e.button) return this.stop.apply(this, [e]); // IE mouseup check if(!this.initialized && (Math.abs(this._MP.left-e.pageX) >= o.distance || Math.abs(this._MP.top-e.pageY) >= o.distance)) { if(this.options.start) this.options.start.call(this.options.executor || this, e, this.element); this.initialized = true; } else { if(!this.initialized) return false; } if(o.drag) o.drag.call(this.options.executor || this, e, this.element); return false; } }); })(jQuery); pebl-1.0.2/src/pebl/resources/htmlresult/lib/ui.tabs.css000644 000766 000024 00000007200 11010410474 023572 0ustar00shahadstaff000000 000000 /* Caution! Ensure accessibility in print and other media types... */ @media projection, screen { /* Use class for showing/hiding tab content, so that visibility can be better controlled in different media types... */ .ui-tabs-hide { display: none; } } /* Hide useless elements in print layouts... */ @media print { .ui-tabs-nav { display: none; } } /* Skin */ .ui-tabs-nav, .ui-tabs-panel { font-family: "Trebuchet MS", Trebuchet, Verdana, Helvetica, Arial, sans-serif; font-size: 12px; } .ui-tabs-nav { list-style: none; margin: 0; padding: 0 0 0 4px; } .ui-tabs-nav:after { /* clearing without presentational markup, IE gets extra treatment */ display: block; clear: both; content: " "; } .ui-tabs-nav li { float: left; margin: 0 0 0 1px; min-width: 84px; /* be nice to Opera */ } .ui-tabs-nav a, .ui-tabs-nav a span { display: block; padding: 0 10px; background: url(tab.png) no-repeat; } .ui-tabs-nav a { margin: 1px 0 0; /* position: relative makes opacity fail for disabled tab in IE */ padding-left: 0; color: #27537a; font-weight: bold; line-height: 1.2; text-align: center; text-decoration: none; white-space: nowrap; /* required in IE 6 */ outline: 0; /* prevent dotted border in Firefox */ } .ui-tabs-nav .ui-tabs-selected a { position: relative; top: 1px; z-index: 2; margin-top: 0; color: #000; } .ui-tabs-nav a span { width: 64px; /* IE 6 treats width as min-width */ min-width: 64px; height: 18px; /* IE 6 treats height as min-height */ min-height: 18px; padding-top: 6px; padding-right: 0; } *>.ui-tabs-nav a span { /* hide from IE 6 */ width: auto; height: auto; } .ui-tabs-nav .ui-tabs-selected a span { padding-bottom: 1px; } .ui-tabs-nav .ui-tabs-selected a, .ui-tabs-nav a:hover, .ui-tabs-nav a:focus, .ui-tabs-nav a:active { background-position: 100% -150px; } .ui-tabs-nav a, .ui-tabs-nav .ui-tabs-disabled a:hover, .ui-tabs-nav .ui-tabs-disabled a:focus, .ui-tabs-nav .ui-tabs-disabled a:active { background-position: 100% -100px; } .ui-tabs-nav .ui-tabs-selected a span, .ui-tabs-nav a:hover span, .ui-tabs-nav a:focus span, .ui-tabs-nav a:active span { background-position: 0 -50px; } .ui-tabs-nav a span, .ui-tabs-nav .ui-tabs-disabled a:hover span, .ui-tabs-nav .ui-tabs-disabled a:focus span, .ui-tabs-nav .ui-tabs-disabled a:active span { background-position: 0 0; } .ui-tabs-nav .ui-tabs-selected a:link, .ui-tabs-nav .ui-tabs-selected a:visited, .ui-tabs-nav .ui-tabs-disabled a:link, .ui-tabs-nav .ui-tabs-disabled a:visited { /* @ Opera, use pseudo classes otherwise it confuses cursor... */ cursor: text; } .ui-tabs-nav a:hover, .ui-tabs-nav a:focus, .ui-tabs-nav a:active, .ui-tabs-nav .ui-tabs-unselect a:hover, .ui-tabs-nav .ui-tabs-unselect a:focus, .ui-tabs-nav .ui-tabs-unselect a:active { /* @ Opera, we need to be explicit again here now... */ cursor: pointer; } .ui-tabs-disabled { opacity: .4; filter: alpha(opacity=40); } .ui-tabs-panel { border-top: 1px solid #97a5b0; padding: 1em 8px; background: #fff; /* declare background color for container to avoid distorted fonts in IE while fading */ } .ui-tabs-loading em { padding: 0 0 0 20px; background: url(loading.gif) no-repeat 0 50%; } /* Additional IE specific bug fixes... */ * html .ui-tabs-nav { /* auto clear, @ IE 6 & IE 7 Quirks Mode */ display: inline-block; } *:first-child+html .ui-tabs-nav { /* @ IE 7 Standards Mode - do not group selectors, otherwise IE 6 will ignore complete rule (because of the unknown + combinator)... */ display: inline-block; } pebl-1.0.2/src/pebl/resources/htmlresult/lib/ui.tabs.js000644 000766 000024 00000057601 11010410474 023430 0ustar00shahadstaff000000 000000 /* * jQuery UI Tabs * * Copyright (c) 2007 Klaus Hartl (stilbuero.de) * Dual licensed under the MIT (MIT-LICENSE.txt) * and GPL (GPL-LICENSE.txt) licenses. * * http://docs.jquery.com/UI/Tabs * * Depends: * ui.base.js * * Revision: $Id: ui.tabs.js 5149 2008-03-31 10:51:18Z rdworth $ */ ;(function($) { // tabs API methods $.fn.tabs = function() { var method = typeof arguments[0] == 'string' && arguments[0]; var args = method && Array.prototype.slice.call(arguments, 1) || arguments; return method == 'length' ? $.data(this[0], 'tabs').$tabs.length : this.each(function() { if (method) { var tabs = $.data(this, 'tabs'); if (tabs) tabs[method].apply(tabs, args); } else new $.ui.tabs(this, args[0] || {}); }); }; // tabs class $.ui.tabs = function(el, options) { var self = this; this.options = $.extend({}, $.ui.tabs.defaults, options); this.element = el; // doesn't extend with null if (options.selected === null) this.options.selected = null; this.options.event += '.tabs'; // namespace event $(el).bind('setData.tabs', function(event, key, value) { if ((/^selected/).test(key)) self.select(value); else { self.options[key] = value; self.tabify(); } }).bind('getData.tabs', function(event, key) { return self.options[key]; }); // save instance for later $.data(el, 'tabs', this); // create tabs this.tabify(true); }; $.ui.tabs.defaults = { // basic setup selected: 0, unselect: false, event: 'click', disabled: [], cookie: null, // e.g. { expires: 7, path: '/', domain: 'jquery.com', secure: true } // TODO history: false, // Ajax spinner: 'Loading…', cache: false, idPrefix: 'ui-tabs-', ajaxOptions: {}, // animations fx: null, // e.g. { height: 'toggle', opacity: 'toggle', duration: 200 } // templates tabTemplate: '
  • #{label}
    • ', panelTemplate: '
    ', // CSS classes navClass: 'ui-tabs-nav', selectedClass: 'ui-tabs-selected', unselectClass: 'ui-tabs-unselect', disabledClass: 'ui-tabs-disabled', panelClass: 'ui-tabs-panel', hideClass: 'ui-tabs-hide', loadingClass: 'ui-tabs-loading' }; // instance methods $.extend($.ui.tabs.prototype, { tabId: function(a) { return a.title && a.title.replace(/\s/g, '_').replace(/[^A-Za-z0-9\-_:\.]/g, '') || this.options.idPrefix + $.data(a); }, ui: function(tab, panel) { return { instance: this, options: this.options, tab: tab, panel: panel }; }, tabify: function(init) { this.$lis = $('li:has(a[href])', this.element); this.$tabs = this.$lis.map(function() { return $('a', this)[0]; }); this.$panels = $([]); var self = this, o = this.options; this.$tabs.each(function(i, a) { // inline tab if (a.hash && a.hash.replace('#', '')) // Safari 2 reports '#' for an empty hash self.$panels = self.$panels.add(a.hash); // remote tab else if ($(a).attr('href') != '#') { // prevent loading the page itself if href is just "#" $.data(a, 'href.tabs', a.href); // required for restore on destroy $.data(a, 'load.tabs', a.href); // mutable var id = self.tabId(a); a.href = '#' + id; var $panel = $('#' + id); if (!$panel.length) { $panel = $(o.panelTemplate).attr('id', id).addClass(o.panelClass) .insertAfter( self.$panels[i - 1] || self.element ); $panel.data('destroy.tabs', true); } self.$panels = self.$panels.add( $panel ); } // invalid tab href else o.disabled.push(i + 1); }); if (init) { // attach necessary classes for styling if not present $(this.element).hasClass(o.navClass) || $(this.element).addClass(o.navClass); this.$panels.each(function() { var $this = $(this); $this.hasClass(o.panelClass) || $this.addClass(o.panelClass); }); // Try to retrieve selected tab: // 1. from fragment identifier in url if present // 2. from cookie // 3. from selected class attribute on
  • // 4. otherwise use given "selected" option // 5. check if tab is disabled this.$tabs.each(function(i, a) { if (location.hash) { if (a.hash == location.hash) { o.selected = i; // prevent page scroll to fragment //if (($.browser.msie || $.browser.opera) && !o.remote) { if ($.browser.msie || $.browser.opera) { var $toShow = $(location.hash), toShowId = $toShow.attr('id'); $toShow.attr('id', ''); setTimeout(function() { $toShow.attr('id', toShowId); // restore id }, 500); } scrollTo(0, 0); return false; // break } } else if (o.cookie) { var index = parseInt($.cookie('ui-tabs' + $.data(self.element)),10); if (index && self.$tabs[index]) { o.selected = index; return false; // break } } else if ( self.$lis.eq(i).hasClass(o.selectedClass) ) { o.selected = i; return false; // break } }); // highlight selected tab this.$panels.addClass(o.hideClass); this.$lis.removeClass(o.selectedClass); if (o.selected !== null) { this.$panels.eq(o.selected).show().removeClass(o.hideClass); // use show and remove class to show in any case no matter how it has been hidden before this.$lis.eq(o.selected).addClass(o.selectedClass); } // load if remote tab var href = o.selected !== null && $.data(this.$tabs[o.selected], 'load.tabs'); if (href) this.load(o.selected); // Take disabling tabs via class attribute from HTML // into account and update option properly... o.disabled = $.unique(o.disabled.concat( $.map(this.$lis.filter('.' + o.disabledClass), function(n, i) { return self.$lis.index(n); } ) )).sort(); // clean up to avoid memory leaks in certain versions of IE 6 $(window).bind('unload', function() { self.$tabs.unbind('.tabs'); self.$lis = self.$tabs = self.$panels = null; }); } // disable tabs for (var i = 0, li; li = this.$lis[i]; i++) $(li)[$.inArray(i, o.disabled) != -1 && !$(li).hasClass(o.selectedClass) ? 'addClass' : 'removeClass'](o.disabledClass); // reset cache if switching from cached to not cached if (o.cache === false) this.$tabs.removeData('cache.tabs'); // set up animations var hideFx, showFx, baseFx = { 'min-width': 0, duration: 1 }, baseDuration = 'normal'; if (o.fx && o.fx.constructor == Array) hideFx = o.fx[0] || baseFx, showFx = o.fx[1] || baseFx; else hideFx = showFx = o.fx || baseFx; // reset some styles to maintain print style sheets etc. var resetCSS = { display: '', overflow: '', height: '' }; if (!$.browser.msie) // not in IE to prevent ClearType font issue resetCSS.opacity = ''; // Hide a tab, animation prevents browser scrolling to fragment, // $show is optional. function hideTab(clicked, $hide, $show) { $hide.animate(hideFx, hideFx.duration || baseDuration, function() { // $hide.addClass(o.hideClass).css(resetCSS); // maintain flexible height and accessibility in print etc. if ($.browser.msie && hideFx.opacity) $hide[0].style.filter = ''; if ($show) showTab(clicked, $show, $hide); }); } // Show a tab, animation prevents browser scrolling to fragment, // $hide is optional. function showTab(clicked, $show, $hide) { if (showFx === baseFx) $show.css('display', 'block'); // prevent occasionally occuring flicker in Firefox cause by gap between showing and hiding the tab panels $show.animate(showFx, showFx.duration || baseDuration, function() { $show.removeClass(o.hideClass).css(resetCSS); // maintain flexible height and accessibility in print etc. if ($.browser.msie && showFx.opacity) $show[0].style.filter = ''; // callback $(self.element).triggerHandler('tabsshow', [self.ui(clicked, $show[0])], o.show); }); } // switch a tab function switchTab(clicked, $li, $hide, $show) { /*if (o.bookmarkable && trueClick) { // add to history only if true click occured, not a triggered click $.ajaxHistory.update(clicked.hash); }*/ $li.addClass(o.selectedClass) .siblings().removeClass(o.selectedClass); hideTab(clicked, $hide, $show); } // attach tab event handler, unbind to avoid duplicates from former tabifying... this.$tabs.unbind('.tabs').bind(o.event, function() { //var trueClick = e.clientX; // add to history only if true click occured, not a triggered click var $li = $(this).parents('li:eq(0)'), $hide = self.$panels.filter(':visible'), $show = $(this.hash); // If tab is already selected and not unselectable or tab disabled or // or is already loading or click callback returns false stop here. // Check if click handler returns false last so that it is not executed // for a disabled or loading tab! if (($li.hasClass(o.selectedClass) && !o.unselect) || $li.hasClass(o.disabledClass) || $(this).hasClass(o.loadingClass) || $(self.element).triggerHandler('tabsselect', [self.ui(this, $show[0])], o.select) === false ) { this.blur(); return false; } self.options.selected = self.$tabs.index(this); // if tab may be closed if (o.unselect) { if ($li.hasClass(o.selectedClass)) { self.options.selected = null; $li.removeClass(o.selectedClass); self.$panels.stop(); hideTab(this, $hide); this.blur(); return false; } else if (!$hide.length) { self.$panels.stop(); var a = this; self.load(self.$tabs.index(this), function() { $li.addClass(o.selectedClass).addClass(o.unselectClass); showTab(a, $show); }); this.blur(); return false; } } if (o.cookie) $.cookie('ui-tabs' + $.data(self.element), self.options.selected, o.cookie); // stop possibly running animations self.$panels.stop(); // show new tab if ($show.length) { // prevent scrollbar scrolling to 0 and than back in IE7, happens only if bookmarking/history is enabled /*if ($.browser.msie && o.bookmarkable) { var showId = this.hash.replace('#', ''); $show.attr('id', ''); setTimeout(function() { $show.attr('id', showId); // restore id }, 0); }*/ var a = this; self.load(self.$tabs.index(this), $hide.length ? function() { switchTab(a, $li, $hide, $show); } : function() { $li.addClass(o.selectedClass); showTab(a, $show); } ); // Set scrollbar to saved position - need to use timeout with 0 to prevent browser scroll to target of hash /*var scrollX = window.pageXOffset || document.documentElement && document.documentElement.scrollLeft || document.body.scrollLeft || 0; var scrollY = window.pageYOffset || document.documentElement && document.documentElement.scrollTop || document.body.scrollTop || 0; setTimeout(function() { scrollTo(scrollX, scrollY); }, 0);*/ } else throw 'jQuery UI Tabs: Mismatching fragment identifier.'; // Prevent IE from keeping other link focussed when using the back button // and remove dotted border from clicked link. This is controlled in modern // browsers via CSS, also blur removes focus from address bar in Firefox // which can become a usability and annoying problem with tabsRotate. if ($.browser.msie) this.blur(); //return o.bookmarkable && !!trueClick; // convert trueClick == undefined to Boolean required in IE return false; }); // disable click if event is configured to something else if (!(/^click/).test(o.event)) this.$tabs.bind('click.tabs', function() { return false; }); }, add: function(url, label, index) { if (index == undefined) index = this.$tabs.length; // append by default var o = this.options; var $li = $(o.tabTemplate.replace(/#\{href\}/, url).replace(/#\{label\}/, label)); $li.data('destroy.tabs', true); var id = url.indexOf('#') == 0 ? url.replace('#', '') : this.tabId( $('a:first-child', $li)[0] ); // try to find an existing element before creating a new one var $panel = $('#' + id); if (!$panel.length) { $panel = $(o.panelTemplate).attr('id', id) .addClass(o.panelClass).addClass(o.hideClass); $panel.data('destroy.tabs', true); } if (index >= this.$lis.length) { $li.appendTo(this.element); $panel.appendTo(this.element.parentNode); } else { $li.insertBefore(this.$lis[index]); $panel.insertBefore(this.$panels[index]); } o.disabled = $.map(o.disabled, function(n, i) { return n >= index ? ++n : n }); this.tabify(); if (this.$tabs.length == 1) { $li.addClass(o.selectedClass); $panel.removeClass(o.hideClass); var href = $.data(this.$tabs[0], 'load.tabs'); if (href) this.load(index, href); } // callback $(this.element).triggerHandler('tabsadd', [this.ui(this.$tabs[index], this.$panels[index])], o.add ); }, remove: function(index) { var o = this.options, $li = this.$lis.eq(index).remove(), $panel = this.$panels.eq(index).remove(); // If selected tab was removed focus tab to the right or // in case the last tab was removed the tab to the left. if ($li.hasClass(o.selectedClass) && this.$tabs.length > 1) this.select(index + (index + 1 < this.$tabs.length ? 1 : -1)); o.disabled = $.map($.grep(o.disabled, function(n, i) { return n != index; }), function(n, i) { return n >= index ? --n : n }); this.tabify(); // callback $(this.element).triggerHandler('tabsremove', [this.ui($li.find('a')[0], $panel[0])], o.remove ); }, enable: function(index) { var o = this.options; if ($.inArray(index, o.disabled) == -1) return; var $li = this.$lis.eq(index).removeClass(o.disabledClass); if ($.browser.safari) { // fix disappearing tab (that used opacity indicating disabling) after enabling in Safari 2... $li.css('display', 'inline-block'); setTimeout(function() { $li.css('display', 'block'); }, 0); } o.disabled = $.grep(o.disabled, function(n, i) { return n != index; }); // callback $(this.element).triggerHandler('tabsenable', [this.ui(this.$tabs[index], this.$panels[index])], o.enable ); }, disable: function(index) { var self = this, o = this.options; if (index != o.selected) { // cannot disable already selected tab this.$lis.eq(index).addClass(o.disabledClass); o.disabled.push(index); o.disabled.sort(); // callback $(this.element).triggerHandler('tabsdisable', [this.ui(this.$tabs[index], this.$panels[index])], o.disable ); } }, select: function(index) { if (typeof index == 'string') index = this.$tabs.index( this.$tabs.filter('[href$=' + index + ']')[0] ); this.$tabs.eq(index).trigger(this.options.event); }, load: function(index, callback) { // callback is for internal usage only var self = this, o = this.options, $a = this.$tabs.eq(index), a = $a[0], bypassCache = callback == undefined || callback === false, url = $a.data('load.tabs'); callback = callback || function() {}; // no remote or from cache - just finish with callback if (!url || ($.data(a, 'cache.tabs') && !bypassCache)) { callback(); return; } // load remote from here on if (o.spinner) { var $span = $('span', a); $span.data('label.tabs', $span.html()).html('' + o.spinner + ''); } var finish = function() { self.$tabs.filter('.' + o.loadingClass).each(function() { $(this).removeClass(o.loadingClass); if (o.spinner) { var $span = $('span', this); $span.html($span.data('label.tabs')).removeData('label.tabs'); } }); self.xhr = null; }; var ajaxOptions = $.extend({}, o.ajaxOptions, { url: url, success: function(r, s) { $(a.hash).html(r); finish(); // This callback is required because the switch has to take // place after loading has completed. callback(); if (o.cache) $.data(a, 'cache.tabs', true); // if loaded once do not load them again // callback $(self.element).triggerHandler('tabsload', [self.ui(self.$tabs[index], self.$panels[index])], o.load ); o.ajaxOptions.success && o.ajaxOptions.success(r, s); } }); if (this.xhr) { // terminate pending requests from other tabs and restore tab label this.xhr.abort(); finish(); } $a.addClass(o.loadingClass); setTimeout(function() { // timeout is again required in IE, "wait" for id being restored self.xhr = $.ajax(ajaxOptions); }, 0); }, url: function(index, url) { this.$tabs.eq(index).removeData('cache.tabs').data('load.tabs', url); }, destroy: function() { var o = this.options; $(this.element).unbind('.tabs') .removeClass(o.navClass).removeData('tabs'); this.$tabs.each(function() { var href = $.data(this, 'href.tabs'); if (href) this.href = href; var $this = $(this).unbind('.tabs'); $.each(['href', 'load', 'cache'], function(i, prefix) { $this.removeData(prefix + '.tabs'); }); }); this.$lis.add(this.$panels).each(function() { if ($.data(this, 'destroy.tabs')) $(this).remove(); else $(this).removeClass([o.selectedClass, o.unselectClass, o.disabledClass, o.panelClass, o.hideClass].join(' ')); }); } }); /* * Tabs Extensions */ /* * Rotate */ $.extend($.ui.tabs.prototype, { rotation: null, rotate: function(ms, continuing) { continuing = continuing || false; var self = this, t = this.options.selected; function start() { self.rotation = setInterval(function() { t = ++t < self.$tabs.length ? t : 0; self.select(t); }, ms); } function stop(e) { if (!e || e.clientX) { // only in case of a true click clearInterval(self.rotation); } } // start interval if (ms) { start(); if (!continuing) this.$tabs.bind(this.options.event, stop); else this.$tabs.bind(this.options.event, function() { stop(); t = self.options.selected; start(); }); } // stop interval else { stop(); this.$tabs.unbind(this.options.event, stop); } } }); })(jQuery); pebl-1.0.2/src/pebl/learner/__init__.py000755 000766 000024 00000001130 11065772726 020315 0ustar00shahadstaff000000 000000 from pebl import config, data, prior #TODO: test def fromconfig(data_=None, prior_=None): learnertype = config.get('learner.type') if ':' in learnertype: CustomLearner( data_ or data.fromconfig(), prior_ or prior.fromconfig(), learnerurl=learnertype ) else: learnermodule,learnerclass = learnertype.split('.') mymod = __import__("pebl.learner.%s" % learnermodule, fromlist=['pebl.learner']) mylearner = getattr(mymod, learnerclass) return mylearner(data_ or data.fromconfig(), prior_ or prior.fromconfig()) pebl-1.0.2/src/pebl/learner/base.py000644 000766 000024 00000005462 11065772615 017476 0ustar00shahadstaff000000 000000 import numpy as N from pebl import network, config, evaluator, data, prior from pebl.taskcontroller.base import Task # # Exceptions # class CannotAlterNetworkException(Exception): pass # # Module parameters # _plearnertype = config.StringParameter( 'learner.type', """Type of learner to use. The following learners are included with pebl: * greedy.GreedyLearner * simanneal.SimulatedAnnealingLearner * exhaustive.ListLearner """, default = 'greedy.GreedyLearner' ) _ptasks = config.IntParameter( 'learner.numtasks', "Number of learner tasks to run.", config.atleast(0), default=1 ) class Learner(Task): def __init__(self, data_=None, prior_=None, **kw): self.data = data_ or data.fromconfig() self.prior = prior_ or prior.fromconfig() self.__dict__.update(kw) # parameters self.numtasks = config.get('learner.numtasks') # stats self.reverse = 0 self.add = 0 self.remove = 0 def _alter_network_randomly_and_score(self): net = self.evaluator.network n_nodes = self.data.variables.size max_attempts = n_nodes**2 # continue making changes and undoing them till we get an acyclic network for i in xrange(max_attempts): node1, node2 = N.random.random_integers(0, n_nodes-1, 2) if (node1, node2) in net.edges: # node1 -> node2 exists, so reverse it. add,remove = [(node2, node1)], [(node1, node2)] elif (node2, node1) in net.edges: # node2 -> node1 exists, so remove it add,remove = [], [(node2, node1)] else: # node1 and node2 unconnected, so connect them add,remove = [(node1, node2)], [] try: score = self.evaluator.alter_network(add=add, remove=remove) except evaluator.CyclicNetworkError: continue # let's try again! else: if add and remove: self.reverse += 1 elif add: self.add += 1 else: self.remove += 1 return score # Could not find a valid network raise CannotAlterNetworkException() def _all_changes(self): net = self.evaluator.network changes = [] # edge removals changes.extend((None, edge) for edge in net.edges) # edge reversals reverse = lambda edge: (edge[1], edge[0]) changes.extend((reverse(edge), edge) for edge in net.edges) # edge additions nz = N.nonzero(invert(net.edges.adjacency_matrix)) changes.extend( ((src,dest), None) for src,dest in zip(*nz) ) return changes pebl-1.0.2/src/pebl/learner/custom.py000644 000766 000024 00000005365 11065772657 020106 0ustar00shahadstaff000000 000000 from __future__ import with_statement import sys, os, os.path import tempfile from functools import partial import shutil import cPickle import numpy as N from pebl import network, config, evaluator, data, prior from pebl.learner.base import Learner #TODO: test class CustomLearner(Learner): def __init__(self, data_, prior_=None, learnerurl=':', **kw): """Create a CustomLearner wrapper. If you don't use a TaskController, you can simply create a custom learner (as a Learner subclass) and run it. With a TaskController, however, the learner might run on a different machine and so its code needs to be copied over to any worker machine(s). This is what CustomLearner does. learnerurl is your custom learner class specified as ":" (for example, "/Users/shahad/mycode.py:SuperLearner"). Example:: dataset = data.fromfile("data.txt") tc = taskcontroller.XgridController() mylearner = CustomLearner( dataset, learnerurl="/Users/shahad/mycode.py:SuperLearner" ) # learner will run on the Xgrid (where mycode.py doesn't exist) results = tc.run([mylearner]) """ # save info so custom learner can be recreated at run (possibly on a # different machine) self.learner_filepath, self.learner_class = learnerurl.split(':') self.learner_filename = os.path.basename(self.learner_filepath) self.learner_source = open(self.learner_filepath).read() self.data = data_ self.prior = prior_ self.kw = kw def run(self): # re-create the custom learner tempdir = tempfile.mkdtemp() with file(os.path.join(tempdir, self.learner_filename), 'w') as f: f.write(self.learner_source) sys.path.insert(0, tempdir) modname = self.learner_filename.split('.')[0] mod = __import__(modname, fromlist=['*']) reload(mod) # to load the latest if an older version exists custlearner = getattr(mod, self.learner_class) # run the custom learner clearn = custlearner( self.data or data.fromconfig(), self.prior or prior.fromconfig(), **self.kw ) self.result = clearn.run() # cleanup sys.path.remove(tempdir) shutil.rmtree(tempdir) return self.result class CustomResult: def __init__(self, **kw): for k,v in kw.iteritems(): setattr(self, k, v) def tofile(self, filename=None): filename = filename or config.get('result.filename') with open(filename, 'w') as fp: cPickle.dump(self, fp) pebl-1.0.2/src/pebl/learner/exhaustive.py000644 000766 000024 00000004205 11013407146 020726 0ustar00shahadstaff000000 000000 """Classes and functions for doing exhaustive learning.""" from pebl import prior, config, evaluator, result, network from pebl.learner.base import Learner from pebl.taskcontroller.base import Task class ListLearner(Learner): # # Parameter # _params = ( config.StringParameter( 'listlearner.networks', """List of networks, one per line, in network.Network.as_string() format.""", default='' ) ) def __init__(self, data_=None, prior_=None, networks=None): """Create a ListLearner learner. networks should be a list of networks (as network.Network instances). """ super(ListLearner, self).__init__(data_, prior_) self.networks = networks if not networks: variables = self.data.variables _net = lambda netstr: network.Network(variables, netstr) netstrings = config.get('listlearner.networks').splitlines() self.networks = (_net(s) for s in netstrings if s) def run(self): self.result = result.LearnerResult(self) self.evaluator = evaluator.fromconfig(self.data, prior_=self.prior) self.result.start_run() for net in self.networks: self.result.add_network(net, self.evaluator.score_network(net)) self.result.stop_run() return self.result def split(self, count): """Split the learner into multiple learners. Splits self.networks into `count` parts. This is similar to MPI's scatter functionality. """ nets = list(self.networks) numnets = len(nets) netspertask = numnets/count # divide list into parts indices = [[i,i+netspertask] for i in xrange(0,numnets,netspertask)] if len(indices) > count: indices.pop(-1) indices[-1][1] = numnets-1 return [ListLearner(self.data, self.prior, nets[i:j])for i,j in indices] def __getstate__(self): # convert self.network from iterators or generators to a list d = self.__dict__ d['networks'] = list(d['networks']) return d pebl-1.0.2/src/pebl/learner/._greedy.py000755 000766 000024 00000000270 11065774016 020250 0ustar00shahadstaff000000 000000 Mac OS X  2ATTR)  com.macromates.caretxR<[k0?'3/«pebl-1.0.2/src/pebl/learner/greedy.py000755 000766 000024 00000011235 11065774016 020036 0ustar00shahadstaff000000 000000 """Learner that implements a greedy learning algorithm""" import time from pebl import network, result, evaluator from pebl.util import * from pebl.learner.base import * class GreedyLearnerStatistics: def __init__(self): self.restarts = -1 self.iterations = 0 self.unimproved_iterations = 0 self.best_score = 0 self.start_time = time.time() @property def runtime(self): return time.time() - self.start_time class GreedyLearner(Learner): # # Parameters # _params = ( config.IntParameter( 'greedy.max_iterations', """Maximum number of iterations to run.""", default=1000 ), config.IntParameter( 'greedy.max_time', """Maximum learner runtime in seconds.""", default=0 ), config.IntParameter( 'greedy.max_unimproved_iterations', """Maximum number of iterations without score improvement before a restart.""", default=500 ), config.StringParameter( 'greedy.seed', 'Starting network for a greedy search.', default='' ) ) def __init__(self, data_=None, prior_=None, **options): """ Create a learner that uses a greedy learning algorithm. The algorithm works as follows: 1. start with a random network 2. Make a small, local change and rescore network 3. If new network scores better, accept it, otherwise reject. 4. Steps 2-3 are repeated till the restarting_criteria is met, at which point we begin again with a new random network (step 1) Any config param for 'greedy' can be passed in via options. Use just the option part of the parameter name. For more information about greedy learning algorithms, consult: 1. http://en.wikipedia.org/wiki/Greedy_algorithm 2. D. Heckerman. A Tutorial on Learning with Bayesian Networks. Microsoft Technical Report MSR-TR-95-06, 1995. p.35. """ super(GreedyLearner, self).__init__(data_, prior_) self.options = options config.setparams(self, options) if not isinstance(self.seed, network.Network): self.seed = network.Network(self.data.variables, self.seed) def run(self): """Run the learner. Returns a LearnerResult instance. Also sets self.result to that instance. """ # max_time and max_iterations are mutually exclusive stopping critera if 'max_time' not in self.options: _stop = self._stop_after_iterations else: _stop = self._stop_after_time self.stats = GreedyLearnerStatistics() self.result = result.LearnerResult(self) self.evaluator = evaluator.fromconfig(self.data, self.seed, self.prior) self.evaluator.score_network(self.seed.copy()) first = True self.result.start_run() while not _stop(): self._run_without_restarts(_stop, self._restart, randomize_net=(not first)) first = False self.result.stop_run() return self.result def _run_without_restarts(self, _stop, _restart, randomize_net=True): self.stats.restarts += 1 self.stats.unimproved_iterations = 0 if randomize_net: self.evaluator.randomize_network() # set the default best score self.stats.best_score = self.evaluator.score_network() # continue learning until time to stop or restart while not (_restart() or _stop()): self.stats.iterations += 1 try: curscore = self._alter_network_randomly_and_score() except CannotAlterNetworkException: return self.result.add_network(self.evaluator.network, curscore) if curscore <= self.stats.best_score: # score did not improve, undo network alteration self.stats.unimproved_iterations += 1 self.evaluator.restore_network() else: self.stats.best_score = curscore self.stats.unimproved_iterations = 0 # # Stopping and restarting criteria # def _stop_after_time(self): return self.stats.runtime >= self.max_time def _stop_after_iterations(self): return self.stats.iterations >= self.max_iterations def _restart(self): return self.stats.unimproved_iterations >= self.max_unimproved_iterations pebl-1.0.2/src/pebl/learner/simanneal.py000755 000766 000024 00000007700 11173462771 020532 0ustar00shahadstaff000000 000000 """Classes and functions for Simulated Annealing learner""" from math import exp import random from pebl import network, result, evaluator, config from pebl.learner.base import * class SALearnerStatistics: def __init__(self, starting_temp, delta_temp, max_iterations_at_temp): self.temp = starting_temp self.iterations_at_temp = 0 self.max_iterations_at_temp = max_iterations_at_temp self.delta_temp = delta_temp self.iterations = 0 self.best_score = 0 self.current_score = 0 def update(self): self.iterations += 1 self.iterations_at_temp += 1 if self.iterations_at_temp >= self.max_iterations_at_temp: self.temp *= self.delta_temp self.iterations_at_temp = 0 class SimulatedAnnealingLearner(Learner): # # Parameters # _params = ( config.FloatParameter( 'simanneal.start_temp', "Starting temperature for a run.", config.atleast(0.0), default=100.0 ), config.FloatParameter( 'simanneal.delta_temp', 'Change in temp between steps.', config.atleast(0.0), default=0.5 ), config.IntParameter( 'simanneal.max_iters_at_temp', 'Max iterations at any temperature.', config.atleast(0), default=100 ), config.StringParameter( 'simanneal.seed', 'Starting network for a greedy search.', default='' ) ) def __init__(self, data_=None, prior_=None, **options): """Create a Simulated Aneaaling learner. For more information about Simulated Annealing algorithms, consult: 1. http://en.wikipedia.org/wiki/Simulated_annealing 2. D. Heckerman. A Tutorial on Learning with Bayesian Networks. Microsoft Technical Report MSR-TR-95-06, 1995. p.35-36. Any config param for 'simanneal' can be passed in via options. Use just the option part of the parameter name. """ super(SimulatedAnnealingLearner,self).__init__(data_, prior_) config.setparams(self, options) if not isinstance(self.seed, network.Network): self.seed = network.Network(self.data.variables, self.seed) def run(self): """Run the learner.""" self.stats = SALearnerStatistics(self.start_temp, self.delta_temp, self.max_iters_at_temp) self.result = result.LearnerResult(self) self.evaluator = evaluator.fromconfig(self.data, self.seed, self.prior) self.evaluator.score_network(self.seed.copy()) self.result.start_run() curscore = self.evaluator.score_network() # temperature decays exponentially, so we'll never get to 0. # So, we continue until temp < 1 while self.stats.temp >= 1: try: newscore = self._alter_network_randomly_and_score() except CannotAlterNetworkException: return self.result.add_network(self.evaluator.network, newscore) if self._accept(newscore): # set current score self.stats.current_score = newscore if self.stats.current_score > self.stats.best_score: self.stats.best_score = self.stats.current_score else: # undo network alteration self.evaluator.restore_network() # temp not updated EVERY iteration. just whenever criteria met. self.stats.update() self.result.stop_run() return self.result def _accept(self, newscore): oldscore = self.stats.current_score if newscore >= oldscore: return True elif random.random() < exp((newscore - oldscore)/self.stats.temp): return True else: return False pebl-1.0.2/docs/build/000755 000766 000024 00000000000 11377315471 015070 5ustar00shahadstaff000000 000000 pebl-1.0.2/docs/src/000755 000766 000024 00000000000 11377315471 014560 5ustar00shahadstaff000000 000000 pebl-1.0.2/docs/src/apiref.rst000644 000766 000024 00000000611 11030531025 016533 0ustar00shahadstaff000000 000000 .. _apiref: API Reference ============= Modules: .. toctree:: :maxdepth: 0 config cpd data discretizer evaluator learner/exhaustive learner/greedy learner/simanneal network posterior prior result taskcontroller/multiprocess taskcontroller/serial taskcontroller/xgrid taskcontroller/ipy1 taskcontroller/ec2 util pebl-1.0.2/docs/src/conf.py000644 000766 000024 00000010234 11153614433 016047 0ustar00shahadstaff000000 000000 # -*- coding: utf-8 -*- # # Pebl documentation build configuration file, created by # sphinx-quickstart on Wed Apr 2 08:36:21 2008. # # This file is execfile()d with the current directory set to its containing dir. # # The contents of this file are pickled, so don't put values in the namespace # that aren't pickleable (module imports are okay, they're removed automatically). # # All configuration values have a default value; values that are commented out # serve to show the default value. import sys, os # If your extensions are in another directory, add it here. #sys.path.append('some/directory') # General configuration # --------------------- # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc'] autoclass_content = 'both' # Add any paths that contain templates here, relative to this directory. templates_path = ['.templates'] # The suffix of source filenames. source_suffix = '.rst' # The master toctree document. master_doc = 'index' # General substitutions. project = 'Pebl' copyright = '2008, Abhik Shah' # The default replacements for |version| and |release|, also used in various # other places throughout the built documents. # # The short X.Y version. version = '1.0.1' # The full version, including alpha/beta/rc tags. release = '1.0.1' # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. today_fmt = '%B %d, %Y' # List of documents that shouldn't be included in the build. #unused_docs = [] # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # Options for HTML output # ----------------------- # The style sheet to use for HTML and HTML Help pages. A file of that name # must exist either in Sphinx' static/ path, or in one of the custom paths # given in html_static_path. html_style = 'default.css' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Content template for the index page. #html_index = '' # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_use_modindex = True # If true, the reST sources are included in the HTML build as _sources/. #html_copy_source = True # Output file base name for HTML help builder. htmlhelp_basename = 'Pebldoc' # Options for LaTeX output # ------------------------ # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, document class [howto/manual]). #latex_documents = [] # Additional stuff for the LaTeX preamble. #latex_preamble = '' # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_use_modindex = True # Custom extension for configuration parameters # --------------------------------------------- def setup(app): app.add_description_unit('confparam', 'confparam', '%s; configuration parameter') pebl-1.0.2/docs/src/config.rst000644 000766 000024 00000005635 11010413005 016540 0ustar00shahadstaff000000 000000 :mod:`config` -- Pebl's configuration system ============================================= .. module:: pebl.config :synopsis: Pebl's configuration system The config module defines a set of parameter types and functions to validate user input. A module or class can define a parameter by creating an instance of one of the Parameter classes -- it is automatically registered with the config module. Many modules provide a fromconfig() factory function that create and return a parametrized object. Specifying Parameters --------------------- A Parameter definition requires the following: * a name of the form section.option (examples: data.filename, result.format) * a short description * a validator function The function has one argument: the string value of the parameter. It should return true if the value is valid and false otherwise. The config module includes several factory functions that create validator functions. See section on validator factories. * a default value specified as a string Example parameter definitions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ From the pebl.data module:: _pfilename = config.StringParameter( 'data.filename', 'File to read data from.', config.fileexists(), ) In the above example, the config.fileexists() function returns a validator function that checks whether the file exists. From the pebl.result module:: _psize = config.IntParameter( 'result.numnetworks', \"\"\"Number of top-scoring networks to save. Specify 0 to indicate that all scored networks should be saved.\"\"\", default=1000 ) In the above example, The IntParameter subclass of Parameter automatically checks whether the parameter value is an integer and raises an error if it is not. It thus does not require an additional validator. Parameter Types --------------- .. autoclass:: StringParameter .. autoclass:: IntParameter .. autoclass:: FloatParameter Validator Factories ------------------- The following functions create validator functions. .. autofunction:: between .. autofunction:: oneof .. autofunction:: atleast .. autofunction:: atmost .. autofunction:: fileexists Using Parameters ---------------- Parameters can be set with the config.set function or in a configuration file. The config file should be in a format compatible with Python's ConfigParser module. Specifying parameter values using a config file:: [data] filename = example_data.txt [result] format=html Specifying parameter values using config.set:: from pebl import config config.set('data.filename', 'example_data.txt') config.set('result.format', 'html') .. autofunction:: set The following functions are used for reading, getting, writing and listing parameters. .. autofunction:: read .. autofunction:: get .. autofunction:: write .. autofunction:: parameters .. autofunction:: configobj pebl-1.0.2/docs/src/cpd.rst000644 000766 000024 00000000676 11010413005 016041 0ustar00shahadstaff000000 000000 :mod:`cpd` -- Conditional probability distributions ==================================================== .. module:: pebl.cpd :synopsis: Conditional probability distributions A cpd (conditional probability distribution) determines the probability of a variable given its parents. Currently, pebl inbcludes a pure-python and a C implementation of a multinomial cpd. A cpd has only three public methods: .. autoclass:: CPD :members: pebl-1.0.2/docs/src/data.rst000644 000766 000024 00000002302 11024275005 016203 0ustar00shahadstaff000000 000000 :mod:`data` -- Pebl Dataset =========================== .. module:: pebl.data :synopsis: Pebl dataset This module provides classes and functions for working with datasets. Configuration Parameters ------------------------ .. :r !python -c "from pebl import data, config; print config.paramdocs('data')" .. Autogenerated by pebl.config.paramdocs at Wed Apr 23 17:19:08 2008 .. confparam:: data.discretize Number of bins used to discretize data. Specify 0 to indicate that data should not be discretized. default=0 .. confparam:: data.filename File to read data from. default=None .. confparam:: data.text The text of a dataset included in config file. default='' Dataset class ------------- .. autoclass:: Dataset :members: Functions --------- .. autofunction:: fromstring .. autofunction:: fromfile .. autofunction:: fromconfig .. autofunction:: merge Variable and sample annotations ------------------------------- .. autoclass:: Variable .. autoclass:: DiscreteVariable .. autoclass:: ContinuousVariable .. autoclass:: ClassVariable .. autoclass:: Sample Exceptions ---------- .. autoexception:: ParsingError .. autoexception:: IncorrectArityError .. autoexception:: ClassVariableError pebl-1.0.2/docs/src/devguide.rst000644 000766 000024 00000005146 11026026031 017072 0ustar00shahadstaff000000 000000 .. _devguide: Developer's Guide ================== This page contains information useful for developing pebl (adding code to it), not for using it. For instructions on using pebl, consult the :ref:`tutorial`. Setting up a development environment for Pebl ---------------------------------------------- 1. Checkout pebl code from the subversion repository:: svn co http://pebl-project.googlecode.com/svn/trunk pebl 2. Install development-related dependencies. Install nose (:command:`easy_install nose`) for unit tests and sphinx (:command:`easy_install sphinx`) for creating documentation. 3. Install pebl in development mode. This allows you to make changes to the source code and have it reflected in the "installed" version of pebl immediately.:: sudo python setup.py build develop You can now modify pebl source code and use the changes simply by importing pebl. If you modify C or C++ code, you must run :command:`python setup.py build` again. Coding guildelines ------------------ 1. Style guidelines We follow the python community standard :pep:`8` for coding style although in-project consistency is more important that strict compliance with :pep:`8`. 2. Using pebl.config Use the :mod:`pebl.config` module to parameterize your code. Configuration should be read during object initialization (in __init__) even if it is not used until later. Any changes to configuration parameters after object intialization should not affect your object. 3. Testing We're not religious about following strict unit testing methodologies but ALL critical code should be accompanied by tests (unit tests are preferred but doctests are also accepted). Run the unit tests by running :command:`nosetests -v pebl.test` from any directory. Run unit tests frequently during development and especially before commiting any code. You can place test to be run manually in pebl/test.manual but automated unit tests are strongly preferred. Bug Reports ----------- Use the Issues tab on the Google code site for all bug reports and proposed enhancements. When closing a bug, include the subversion revison that resolves the bug in the comments. Committing code --------------- Before you can commit code, I will have to you to the developers list in Google code. Please email me for this. Thanks dude ----------- And finally, thank you for considering contributing code to Pebl. I envision pebl to be a community resource that can serve as a good base for further research into Bayesian networks. Feel free to contact me with any questions or comments (abhikshah@gmail.com). pebl-1.0.2/docs/src/discretizer.rst000644 000766 000024 00000000613 11010413004 017610 0ustar00shahadstaff000000 000000 :mod:`discretizer` -- Discretization algorithms =============================================== .. module:: pebl.discretizer :synopsis: Discretization algorithms Currently, Pebl only includes one discretization implementation but more may come. Discretization and other data pre-processing steps can have a big impact on the final results. .. autofunction:: maximum_entropy_discretize pebl-1.0.2/docs/src/evaluator.rst000644 000766 000024 00000006615 11074234637 017322 0ustar00shahadstaff000000 000000 :mod:`evaluator` -- Network evaluators ====================================== .. module:: pebl.evaluator :synopsis: Network evaluators Greedy learning algorithms work by scoring small, local changes to existing networks. To do this efficiently, one must maintain state to eliminate redundant computation or unnecessary cache retrievals -- that is, we should only rescore nodes that have changed. Maintaining this state can make implementing efficient versions of learning algorithms more difficult than a naive implementation. The classes in this module provide helpers that encapsulate all the state-management complexities required for efficient scoring. As long as callers make changes to networks in a transactional manner (using the provided methods), networks will be scored efficiently without redendant computation. The main evaluator is the SmartNetworkEvaluator class. It's interface is described below. Note: Most user's shouldn't need to use this module directly. All included learners encapsulate this functionality. This is really only for writing custom learners. LocalscoreCache --------------- Although most users will never use the localscore cache directly, using pebl with large datasets will require setting the maximum size of the cache to avoid memory issues. There is only one relevant configuration parameter. .. confparam:: localscore_cache.maxsize Max number of localscores to cache. Default=-1 means unlimited size. default=-1 SmartNetworkEvaluator --------------------- .. autoclass:: SmartNetworkEvaluator :members: Network Evaluators for use with Missing Values ---------------------------------------------- Scoring networks with missing values requires use of sampling algorithms to sample over the space of possible completions for the missing values. Pebl provides a few algorithms for this. Configuration Parameters ^^^^^^^^^^^^^^^^^^^^^^^^ .. Autogenerated by pebl.config.paramdocs at Tue Apr 29 10:52:50 2008 .. confparam:: evaluator.missingdata_evaluator Evaluator to use for handling missing data. Choices include: * gibbs: Gibb's sampling * maxentropy_gibbs: Gibbs's sampling over all completions of the missing values that result in maximum entropy discretization for the variables. * exact: exact enumeration of all possible missing values (only useable when there are few missing values) default=gibbs .. Autogenerated by pebl.config.paramdocs at Tue Apr 29 10:33:33 2008 .. confparam:: gibbs.burnin Burn-in period for the gibbs sampler (specified as a multiple of the number of missing values) default=10 .. confparam:: gibbs.stopping_criteria Stopping criteria for the gibbs sampler. Should be a valid python expression that evaluates to true when gibbs should stop. It can use the following variables: * iters: number of iterations * n: number of missing values Examples: * iters > n**2 (for n-squared iterations) * iters > 100 (for 100 iterations) default=iters > n**2 MissingDataNetworkEvaluator ^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: MissingDataNetworkEvaluator :members: .. autoclass:: MissingDataMaximumEntropyNetworkEvaluator :members: .. autoclass:: MissingDataExactNetworkEvaluator :members: Factory Functions ------------------ .. autofunction:: fromconfig pebl-1.0.2/docs/src/index.rst000644 000766 000024 00000000714 11026002030 016372 0ustar00shahadstaff000000 000000 .. Pebl documentation master file, created by sphinx-quickstart on Wed Apr 2 08:36:21 2008. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Pebl Documentation! =================== Contents: .. toctree:: :maxdepth: 1 intro install tutorial devguide paramref apiref Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` pebl-1.0.2/docs/src/install.rst000644 000766 000024 00000000000 11130677031 022160 1pebl-1.0.2/INSTALL.txtustar00shahadstaff000000 000000 pebl-1.0.2/docs/src/intro.rst000644 000766 000024 00000004503 11322705645 016443 0ustar00shahadstaff000000 000000 .. _intro: Pebl Introduction ================== Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl includes the following features: * Can learn with observational and interventional data * Handles missing values and hidden variables using exact and heuristic methods * Provides several learning algorithms; makes creating new ones simple * Has facilities for transparent parallel execution * Calculates edge marginals and consensus networks * Presents results in a variety of formats Availability ------------ Pebl is licensed under a permissive `MIT-style license `_ and can be downloaded from its `Google code site `_ or from the `Python Package Index `_. Concepts -------- All Pebl analysis include :term:`data`, a :term:`learner` and a :term:`result`. They may also include :term:`prior models` and :term:`task controllers`. .. glossary:: Data This is the set of observations that is used to score a given network. The data can include missing values and hidden/unobserved variables and observations can be marked as being the result of specific interventions. Data can be read from a file or created programatically. Learner A learner implements a specific learning algorithm. It is given some data, prior model and a stopping criteria and returns a :term:`result` object. Result A result object contains a list of the top-scoring networks found during a learner run and some statistics about the analysis. Results from different learning runs with the same data can be merged and visualized in various formats. Prior Models A key strength of Bayesian analysis is the ability to integrate knowledge with observations. A Pebl prior model specifies the prior belief about the set of possible networks and can include hard and soft constraints. Task Controllers Pebl uses task controllers to run analyses in parallel. Users can utilize multiple CPU cores or computational clusters without managing any of the details related to parallel programming. pebl-1.0.2/docs/src/learner/000755 000766 000024 00000000000 11377315471 016210 5ustar00shahadstaff000000 000000 pebl-1.0.2/docs/src/network.rst000644 000766 000024 00000001621 11010413004 016752 0ustar00shahadstaff000000 000000 :mod:`network` -- Directed Acyclic Graphs ========================================= .. module:: pebl.network :synopsis: datastructures and functions for dealing with networks A pebl network is a collection of nodes and directed edges between nodes. Nodes are a list of pebl.data.Variable instances. Edges are stored in EdgeList instances. This module provides two implementations, MatrixEdgeList for small networks and SparseEdgeList for large, sparse networks. Both offer the same functionality with different performance characteristics. Functions and methods accept and return nodes as numbers representing their indices and edges as tuples of integers corresponding to (srcnode, destnode). Edges ----- .. autoclass:: EdgeSet :members: Network ------- .. autoclass:: Network :members: Factory functions ------------------ .. autofunction:: fromdata .. autofunction:: random_network pebl-1.0.2/docs/src/paramref.rst000644 000766 000024 00000010021 11153615315 017071 0ustar00shahadstaff000000 000000 Configuration Parameters Reference ================================== Here are the configuration parameters supported by pebl. These can set in a configuration file or via the config.set() function. data ---- .. confparam:: data.discretize Number of bins used to discretize data. Specify 0 to indicate that data should not be discretized. default=0 .. confparam:: data.filename File to read data from. default=None .. confparam:: data.text The text of a dataset included in config file. default= learner ------- .. confparam:: learner.numtasks Number of learner tasks to run. default=1 .. confparam:: learner.type Type of learner to use. The following learners are included with pebl: * greedy.GreedyLearner * simanneal.SimulatedAnnealingLearner * exhaustive.ListLearner default=greedy.GreedyLearner greedy ------ .. Autogenerated by pebl.config.paramdocs at Wed Mar 4 19:01:35 2009 .. confparam:: greedy.max_iterations Maximum number of iterations to run. default=1000 .. confparam:: greedy.max_time Maximum learner runtime in seconds. default=0 .. confparam:: greedy.max_unimproved_iterations Maximum number of iterations without score improvement before a restart. default=500 .. confparam:: greedy.seed Starting network for a greedy search. default= simanneal --------- .. confparam:: simanneal.delta_temp Change in temp between steps. default=0.5 .. confparam:: simanneal.max_iters_at_temp Max iterations at any temperature. default=100 .. confparam:: simanneal.seed Starting network for a greedy search. default= .. confparam:: simanneal.start_temp Starting temperature for a run. default=100.0 listlearner ----------- .. confparam:: listlearner.networks List of networks to score. default= localscore_cache ---------------- .. confparam:: localscore_cache.maxsize Max number of localscores to cache. Default=-1 means unlimited size. default=-1 result ------ .. confparam:: result.filename The name of the result output file default=result.pebl .. confparam:: result.format The format for the pebl result file (pickle or html). default=pickle .. confparam:: result.size Number of top-scoring networks to save. Specify 0 to indicate that all scored networks should be saved. default=1000 .. confparam:: gibbs.burnin Burn-in period for the gibbs sampler (specified as a multiple of the number of missing values) default=10 .. confparam:: gibbs.max_iterations Stopping criteria for the gibbs sampler. The number of Gibb's sampler iterations to run. Should be a valid python expression using the variable n (number of missing values). Examples: * n**2 (for n-squared iterations) * 100 (for 100 iterations) default=n**2 taskcontroller -------------- .. confparam:: taskcontroller.type The task controller to use. default=serial multiprocess ------------ .. confparam:: multiprocess.poolsize Number of processes to run concurrently (0 means no limit) default=0 xgrid ----- .. confparam:: xgrid.controller Hostname or IP of the Xgrid controller. default= .. confparam:: xgrid.grid Id of the grid to use at the Xgrid controller. default=0 .. confparam:: xgrid.password Password for the Xgrid controller. default= .. confparam:: xgrid.peblpath Full path to the pebl script on Xgrid agents default=pebl .. confparam:: xgrid.pollinterval Time (in secs) to wait between polling the Xgrid controller. default=60.0 ipython1 -------- .. confparam:: ipython1.controller IPython1 TaskController (default is 127.0.0.1:10113) default=127.0.0.1:10113 ec2 --- .. confparam:: ec2.config EC2 config file. This is kept seperate from pebl config because it contains authentication keys, etc. default= .. confparam:: ec2.max_count Maximum number of EC2 instances to create (default=0 means the same number as ec2.min_count). default=0 .. confparam:: ec2.min_count Minimum number of EC2 instances to create (default=1). default=1 pebl-1.0.2/docs/src/posterior.rst000644 000766 000024 00000000430 11010413004 017304 0ustar00shahadstaff000000 000000 :mod:`posterior` -- Posterior distribution ========================================== .. module:: pebl.posterior :synopsis: Posterior distribution This module provides a class for representing and working with posterior distribution. .. autoclass:: Posterior :members: pebl-1.0.2/docs/src/prior.rst000644 000766 000024 00000000470 11030531502 016423 0ustar00shahadstaff000000 000000 :mod:`prior` -- Prior models ============================ .. module:: pebl.prior :synopsis: Posterior models This module provides classes for representing and working with prior models. .. autoclass:: Prior :members: .. autoclass:: UniformPrior :members: .. autoclass:: NullPrior :members: pebl-1.0.2/docs/src/result.rst000644 000766 000024 00000001610 11030116747 016615 0ustar00shahadstaff000000 000000 :mod:`result` -- Learner results ================================ .. module:: pebl.result :synopsis: Learner results This module includes a few classes and functions for working with results of any Pebl learner. Configuration Parameters ------------------------ .. Autogenerated by pebl.config.paramdocs at Tue Jun 24 01:54:59 2008 .. confparam:: result.filename The name of the result output file default=result.pebl .. confparam:: result.format The format for the pebl result file (pickle or html) default=pickle .. confparam:: result.outdir Directory for html report. default=result .. confparam:: result.size Number of top-scoring networks to save. Specify 0 to indicate that all scored networks should be saved. default=1000 Learner Result -------------- .. autoclass:: LearnerResult :members: Functions --------- .. autofunction:: merge .. autofunction:: fromfile pebl-1.0.2/docs/src/static/000755 000766 000024 00000000000 11377315471 016047 5ustar00shahadstaff000000 000000 pebl-1.0.2/docs/src/taskcontroller/000755 000766 000024 00000000000 11377315471 017626 5ustar00shahadstaff000000 000000 pebl-1.0.2/docs/src/tutorial.rst000644 000766 000024 00000035215 11325162357 017157 0ustar00shahadstaff000000 000000 .. highlight:: python :linenothreshold: 3 .. _tutorial: Tutorial ======== This tutorial is organized as a series of examples that highlight some of Pebl's various features. It is assumed that the reader is familiar with Bayesian networks and the python language and has read the :ref:`intro`. Pebl includes a python library and a command line application that can be used with a configuration file. The pebl application is limited compared to the library but requires no programming. Each example in this tutorial will include a python script and a pebl configuration file that runs the same analysis (when possible). .. note:: This tutorial uses Pebl 1.0 Introducing the Problem ----------------------- Bayesian networks have been used to model complex phenomena with non-linear, multimodal relationships between high-dimensional variables. When used to model gene regulatory networks, nodes usually represent the expression profile of genes while edges represent dependencies between them. The learned networks can be interpreted as causal models explaining the data and can hint at the underlying biological mechanisms and functions. For this tutorial, we use the Cell Cycle data from Spellman, et. al [1] as an example dataset. This dataset contains 76 gene expression measurements of 6177 S. cerevisiae ORFs. The experiments include six time series using different cell cycle synchronization methods. In this example, we ignore the temporal aspect of the dataset and treat each measurement as an independent sample from the underlying biological phenomena. Spellman et al. identified 800 genes as cell cycle dependent and we use a small 12 gene subset of that (as show in Fig 8.11 of [2]). `pebl-tutorial-data1.txt <_static/tutorial/pebl-tutorial-data1.txt>`_ contains the gene expression measurements for our 12 genes. Each row contains one sample (the gene expression data from one microarray assay) and each column contains the gene expression profile for a given gene over the 76 measurements. The first row containing the variable names (the gene associated with the measured ORF) is required for all pebl data files. First Example ------------- This is the simplest analysis in Pebl:: >>> from pebl import data >>> from pebl.learner import greedy >>> dataset = data.fromfile("pebl-tutorial-data1.txt") >>> dataset.discretize() >>> learner = greedy.GreedyLearner(dataset) >>> ex1result = learner.run() >>> ex1result.tohtml("example1-result") The code above does the following: * Imports the required modules (lines 1-2) * Loads the dataset (line 3) * Discretizes the continuous values in the dataset (line 4) * Creates and runs a greedy learner with default parameters (lines 5-6) * Creates a html report of the results (line 7) The same analysis can be run with the following configuration file:: [data] filename = pebl-tutorial-data1.txt discretize = 3 [learner] type = greedy.GreedyLearner [result] format = html outdir = example1-result To use the configuration file above, save it as a text file (config1.txt) and run the pebl application:: $/usr/local/bin/pebl run config1.txt .. note:: The location of the pebl application may be different based on your installation method The result of this example is available `here <_static/tutorial/example1-result/index.html>`_. Keep in mind that structure learning of Bayesian networks uses stochastic methods and the results from different runs will not be identical. Also, the results from this short run are not realistic or spectacular; they do, however, serve as a good demostration of the features of the html report. The result is organized intro three tabs. The first tab shows the log scores for the top networks and some overall statistics. The second tab shows the top 10 networks found and the last tab shows consensus networks at different confidence thresholds. The consensus networks are built using estimated marginal posterior probabililty of each edge. Pebl's Data File Format ----------------------- Pebl uses tab-delimited text files for its data. Each column represents a variable and each row represents a sample or observation. The data file can contain any number of comment rows that begin with a "#". The first non-comment row is expected to be a header row that specifies the name and type of each variable. Pebl supports continuous, discrete and class variables. The three variable types have different header formats as shown below: +------------+--------------------------------------------+-------------------------------+ | Type | Header Format | Examples | +============+============================================+===============================+ | continuous + [,continuous] | CLN2 | | | | CLN1,continuous | +------------+--------------------------------------------+-------------------------------+ | discrete | ,discrete() | CLN2,discrete(2) | | | | CLN1,discrete(3) | +------------+--------------------------------------------+-------------------------------+ | class | ,class() | CLN2,class(low,high) | | | | phenotype,class(cancer,normal)| +------------+--------------------------------------------+-------------------------------+ .. note:: Although Pebl accepts continuous values in the data file, they must be discretized before use. Each measured value can be annotated with two indicators. Append or prepend a "!" to the value to indicate that the variable was intervened upon for that sample or observation. The intervention can be a gene knockdown, RNA silencing or any perturbation that directly affect the value for that variable. Missing values are indicated by using "X". This can be the result of a scratch on a micrarray slide or, if all the rows for a variable include "X", a variable that wasn't measured. Each sample (row) can have a name which should be in the first column. This is not used in learning a Bayesian network, but can be used to create subsets of the data based on the sample names. `pebl-tutorial-data2.txt <_static/tutorial/pebl-tutorial-data2.txt>`_ is the discretized version of our data file with sample names and was created with the following script:: >>> from pebl import data >>> dataset = data.fromfile("pebl-tutorial-data1.txt") >>> dataset.discretize(numbins=3) >>> for i,s in enumerate(dataset.samples): >>> s.name = "sample-%d" % i >>> dataset.tofile("pebl-tutorial-data2.txt") Second Example -------------- In the first example, we used the default parameters for the greedy learner (1000 iterations) which is inadequate for a dataset of this size. In this example, we use custom stopping criteria:: >>> from pebl import data, result >>> from pebl.learner import greedy >>> dataset = data.fromfile("pebl-tutorial-data2.txt") >>> learner1 = greedy.GreedyLearner(dataset, max_iterations=1000000) >>> learner2 = greedy.GreedyLearner(dataset, max_time=120) # in seconds >>> result1 = learner1.run() >>> result2 = learner2.run() >>> merged_result = result.merge(result1, result2) >>> merged_result.tohtml("example2-result") The code above does the following: * Imports the required modules (lines 1-2) * Loads the discretized dataset (line 3) * Creates and runs two greedy learners with specified stopping criteria (lines 4-7) * Merges the two learner results and creates html report (lines 8-9) A Pebl configuration file can be used to create multiple learners but they must be of the same type and use the same parameters (stopping criteria in this case). Thus, it is not possible to replicate the above code with a configuration file but it can be approximated:: [data] filename = pebl-tutorial-data2.txt [learner] type = greedy.GreedyLearner numtasks = 2 [greedy] max_iterations = 1000000 [result] format = html outdir = example2-result Third Example ------------- For large datasets, we might wish to do multiple learner runs and use different learners. The following example creates and runs 5 greedy and 5 simulated annealing learners:: >>> from pebl import data, result >>> from pebl.learner import greedy, simanneal >>> dataset = data.fromfile("pebl-tutorial-data2.txt") >>> learners = [ greedy.GreedyLearner(dataset, max_iterations=1000000) for i in range(5) ] + \ >>> [ simanneal.SimulatedAnnealingLearner(dataset) for i in range(5) ] >>> merged_result = result.merge([learner.run() for learner in learners]) >>> merged_result.tohtml("example3-result") The code above is similar to the last example except that we create a list of 10 learners of two different types. The corresponding configuration file has the same caveat as in the previous example:: [data] filename = pebl-tutorial-data2.txt [learner] type = greedy.GreedyLearner numtasks = 10 [greedy] max_iterations = 1000000 [result] format = html outdir = example3-result Fourth Example -------------- In the previous example, we run 10 learners serially. We can use Pebl's taskcontroller package to run these learners in parallel:: >>> from pebl import data, result >>> from pebl.learner import greedy, simanneal >>> from pebl.taskcontroller import multiprocess >>> dataset = data.fromfile("pebl-tutorial-data2.txt") >>> learners = [ greedy.GreedyLearner(dataset, max_iterations=1000000) for i in range(5) ] + \ >>> [ simanneal.SimulatedAnnealingLearner(dataset) for i in range(5) ] >>> tc = multiprocess.MultiProcessController(poolsize=2) >>> results = tc.run(learners) >>> merged_result = result.merge(results) >>> merged_result.tohtml("example4-result") In this example, we import the multiprocess module (line 3), create a multiprocess task controller with a pool size of two processes (line 7), run the learners using the task controller (line 8) and merge the results and create html report as before. The corresponding configuration file (with the caveats mention in the previous example) would be:: [data] filename = pebl-tutorial-data1.txt [learner] type = greedy.GreedyLearner numtasks = 10 [greedy] max_iterations = 1000000 [taskcontroller] type = multiprocess.MultiProcessController [multiprocess] poolsize = 2 [result] format = html outdir = example2-result Pebl provides three other task controllers: * :mod:`pebl.taskcontroller.xgrid` for using Apple's XGrid * :mod:`pebl.taskcontroller.ipy1` for using an Ipython1 cluster * :mod:`pebl.taskcontroller.ec2` for using Amazon EC2 All task controllers can be used with the pebl application and configuration file and the only difference between their usage are the parameters they require. Thus, Pebl allows one to do preliminary analysis on their desktop with perhaps the multiprocess task controller and then do the full analysis using an XGrid or Amazon's EC2 by simply changing one line of code or a few lines in a configuration file. The EC2 task controller is an especially attractive option for large analysis tasks because it allows one to rent the computing resources on an as-needed basis and without any cluster installation or configuration. A Note on Interpreting the Results ---------------------------------- There is no principled way to determine the optimal stopping criteria or simulated annealing parameters for analyzing a given dataset. One common strategy is to construct consensus networks that show network features found with high confidence. Pebl's html reports show such "model-averaged" networks in the third tab and the :mod:`pebl.posterior` module has methods for creating these programatically. Another common strategy is to check for stability of results. You begin with some learning, save the results, do futher learning, merge the two results and see if the top networks and consensus networks have changed much. If they remain relatively stable, you can assume that you've reached a good solution. Keep in mind, however, that you can never guarantee that you've found the optimal network (or that there is a singular optimal network to be found) since structure learning of Bayesian network is a known NP-Hard problem. In the examples above, we've been creating html reports of the results but these cannot be later merged. A better option is to save the result using the :meth:`pebl.result.LearnerResult.tofile` method and then later read it with :func:`pebl.result.fromfile`:: >>> from pebl import data, result >>> result1 = learner.run() >>> result1.tofile("result1.pebl") >>> result1.tohtml("result1") >>> result2 = otherlearner.run() >>> result1 = result.fromfile("result1.pebl") >>> merged_result = result.merge(result1, result2) >>> merged_result.tofile("results_sofar.pebl") A third strategy is to calculate a p-value for each scored network. This will be added to the tutorial shortly. A Note on Scale of Problems Pebl Can Solve ------------------------------------------- Pebl imposes few limits on the scale of problems it can handle other than the limits imposed by the hardware you use. The :confparam:`localscore_cache.maxsize` configuration parameter can be used to control the size of the main cache used by pebl. With a value appropriate to your memory availability, pebl can be used for quite large datasets. We have successfully tested pebl with datasets of size (number variable, number samples) = (10000,10000) and (1000,100000) on a machine with 2GB memory. While pebl can handle such large datasets without crashing, because structure learning is a known NP-Hard problem, using pebl with datasets containing more than a few hundred variables will likely give poor results due to poor coverage of the search space. More Coming Soon ---------------- I will be adding examples for using prior knowledge and for calculating p-values using a bootstrapping approach. Learning More ------------- This tutorial should have given you an overview of using Pebl. For further information about specific components, consult the :ref:`apiref`, which contains detailed information about all parts of pebl. If you would like to add code to pebl, consult the :ref:`devguide`. Feel free to contact me (Abhik Shah ) with any questions or comments. Bibliography ------------ [1] Spellman et al., (1998). Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell 9, 3273-3297. [2] Husmeier et al., Probabilistic Modeling in Bioinformatics and Medical Informatics. Springer, 2004. http://books.google.com/books?id=ND8rjHNkJ-QC pebl-1.0.2/docs/src/userguide.rst000644 000766 000024 00000000026 11010413005 017254 0ustar00shahadstaff000000 000000 User Guide ========== pebl-1.0.2/docs/src/util.rst000644 000766 000024 00000000050 11010413005 016232 0ustar00shahadstaff000000 000000 .. automodule:: pebl.util :members: pebl-1.0.2/docs/src/taskcontroller/ec2.rst000644 000766 000024 00000002326 11030117316 021014 0ustar00shahadstaff000000 000000 :mod:`taskcontroller.ec2` -- Amazon EC2 task controller ======================================================== .. module:: pebl.taskcontroller.ec2 :synopsis: Amazon EC2 task controller Amazon EC2 is a cloud-computing platform that lets users rent out computing resources by the hour. the EC2 task controller reserves and creates EC2 instances, creates a IPython1 cluster on them, updates their installed version of pebl from svn, runs pebl tasks on them and finally terminates the instances when finished. Users need to provide authentication credentials when using EC2. For that reason, EC2-authentication related configuration parameters are defined in a different file and the name of that file is used as the value for ec2.config parameter. Configuration Parameters ------------------------ .. Autogenerated by pebl.config.paramdocs at Tue Jun 17 16:08:56 2008 .. confparam:: ec2.config EC2 config file default= .. confparam:: ec2.max_count Maximum number of EC2 instances to create (default=0 means the same number as ec2.min_count). default=0 .. confparam:: ec2.min_count Minimum number of EC2 instances to create (default=1). default=1 EC2 Classes ----------- .. autoclass:: EC2Controller :members: pebl-1.0.2/docs/src/taskcontroller/ipy1.rst000644 000766 000024 00000001237 11026015234 021226 0ustar00shahadstaff000000 000000 :mod:`taskcontroller.ipy1` -- IPython1 task controller ====================================================== .. module:: pebl.taskcontroller.ipy1 :synopsis: IPython1 task controller The IPython1 task controller runs pebl tasks over a IPython1 cluster. More information about IPython1 can be found at http://ipython.scipy.org/moin/IPython1. Configuration Parameters ------------------------ .. Autogenerated by pebl.config.paramdocs at Tue Jun 17 16:11:42 2008 .. confparam:: ipython1.controller IPython1 TaskController (default is 127.0.0.1:10113) default=127.0.0.1:10113 IPython1 Classes ---------------- .. autoclass:: IPython1Controller :members: pebl-1.0.2/docs/src/taskcontroller/multiprocess.rst000644 000766 000024 00000001221 11010413005 023055 0ustar00shahadstaff000000 000000 :mod:`taskcontroller.multiprocess` -- Multiprocess task controller ================================================================== .. module:: pebl.taskcontroller.multiprocess :synopsis: Multiprocess task controller The multiprocess task controller creates a pool of processes and runs tasks using them. Configuration Parameters ------------------------ .. Autogenerated by pebl.config.paramdocs at Wed Apr 30 14:56:47 2008 .. confparam:: multiprocess.poolsize Number of processes to run concurrently (0 means no limit) default=0 MultiProcessController Class ---------------------------- .. autoclass:: MultiProcessController :members: pebl-1.0.2/docs/src/taskcontroller/serial.rst000644 000766 000024 00000000335 11010413005 021610 0ustar00shahadstaff000000 000000 :mod:`taskcontroller.serial` -- Serial task controller ====================================================== .. module:: pebl.taskcontroller.serial :synopsis: Serial task controller .. autoclass:: SerialController pebl-1.0.2/docs/src/taskcontroller/xgrid.rst000644 000766 000024 00000002075 11010413005 021451 0ustar00shahadstaff000000 000000 :mod:`taskcontroller.xgrid` -- Xgrid task controller ==================================================== .. module:: pebl.taskcontroller.xgrid :synopsis: Xgrid task controller The Xgrid task controller runs tasks over Apple's Xgrid. For more information about XGrid, see: http://www.apple.com/server/macosx/technology/xgrid.html Configuration Parameters ------------------------ .. Autogenerated by pebl.config.paramdocs at Wed Apr 30 15:04:10 2008 .. confparam:: xgrid.controller Hostname or IP of the Xgrid controller. default= .. confparam:: xgrid.grid Id of the grid to use at the Xgrid controller. default=0 .. confparam:: xgrid.password Password for the Xgrid controller. default= .. confparam:: xgrid.peblpath Full path to the pebl script on Xgrid agents. default=pebl .. confparam:: xgrid.pollinterval Time (in secs) to wait between polling the Xgrid controller. default=60.0 XgridTaskController Related Classes ----------------------------------- .. autoclass:: XgridController :members: .. autoclass:: XgridDeferredResult :members: pebl-1.0.2/docs/src/static/tutorial/000755 000766 000024 00000000000 11377315471 017712 5ustar00shahadstaff000000 000000 pebl-1.0.2/docs/src/static/tutorial/example1-result/000755 000766 000024 00000000000 11377315471 022742 5ustar00shahadstaff000000 000000 pebl-1.0.2/docs/src/static/tutorial/pebl-tutorial-data1.txt000644 000766 000024 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0.3 0.01 0.16 -0.13 0.68 0.15 -0.19 0.37 0.61 0.56 0.17 0.42 pebl-1.0.2/docs/src/static/tutorial/pebl-tutorial-data2.txt000644 000766 000024 00000005374 11026252727 024233 0ustar00shahadstaff000000 000000 CLN1,discrete(3) SRO4,discrete(3) RAD51,discrete(3) RNR3,discrete(3) CLN2,discrete(3) SVS1,discrete(3) MNN1,discrete(3) CDC5,discrete(3) CLB2,discrete(3) MYO1,discrete(3) ALK1,discrete(3) ACE2,discrete(3) sample-0 2 2 2 2 2 1 0 1 0 2 2 2 sample-1 2 2 1 2 2 2 2 0 0 1 1 0 sample-2 0 0 0 0 0 0 0 2 2 1 2 1 sample-3 0 0 0 0 0 0 1 2 2 2 2 2 sample-4 0 0 0 0 0 0 0 0 0 0 0 0 sample-5 0 0 2 1 0 0 1 0 1 0 0 1 sample-6 2 1 2 2 2 2 2 0 0 0 0 0 sample-7 1 2 2 2 2 2 2 0 0 0 0 0 sample-8 2 2 2 2 2 2 2 0 0 0 0 0 sample-9 2 2 2 1 1 2 2 1 1 1 1 1 sample-10 1 1 0 0 0 1 1 2 2 2 2 2 sample-11 0 0 0 0 0 0 0 2 2 2 2 2 sample-12 0 0 0 0 0 0 0 2 2 2 2 2 sample-13 0 0 0 0 0 0 0 2 2 2 1 2 sample-14 2 1 1 2 1 1 1 1 1 1 1 1 sample-15 2 2 2 2 2 2 2 1 1 1 0 1 sample-16 2 2 2 2 2 2 2 0 0 0 0 2 sample-17 1 1 1 1 2 2 2 1 1 0 1 1 sample-18 2 1 1 2 1 2 1 1 1 2 1 1 sample-19 1 1 1 1 1 1 1 2 2 2 2 1 sample-20 0 1 0 0 0 0 0 2 2 1 2 2 sample-21 0 0 0 0 0 0 0 2 2 2 2 2 sample-22 0 0 0 0 0 0 0 1 1 0 0 1 sample-23 2 2 2 2 2 2 1 0 0 0 1 0 sample-24 2 2 2 2 2 2 2 0 0 0 0 0 sample-25 1 2 1 0 1 1 2 2 1 1 2 2 sample-26 1 1 1 0 1 1 2 1 2 1 2 1 sample-27 0 0 0 0 0 0 0 2 1 2 2 2 sample-28 0 0 0 0 0 2 0 1 2 2 2 2 sample-29 0 0 0 0 0 0 0 2 2 2 2 2 sample-30 0 0 0 0 0 1 0 1 2 1 1 1 sample-31 1 1 2 2 2 1 1 1 1 1 0 1 sample-32 2 2 2 2 2 1 2 0 0 1 0 0 sample-33 2 2 2 2 2 2 2 0 0 1 0 0 sample-34 2 2 2 2 2 1 2 0 0 1 0 0 sample-35 2 2 2 2 2 2 2 1 0 0 1 0 sample-36 1 2 2 2 2 1 2 0 1 0 0 1 sample-37 0 1 1 1 1 0 2 2 2 1 2 2 sample-38 2 1 1 1 1 1 1 1 1 1 1 1 sample-39 0 0 0 0 0 0 0 2 2 0 2 2 sample-40 0 0 0 1 0 1 0 2 2 2 1 0 sample-41 0 0 0 1 0 1 0 2 2 2 1 1 sample-42 1 2 2 1 1 1 0 0 1 2 1 1 sample-43 1 1 1 2 1 1 1 1 1 1 0 1 sample-44 1 1 1 1 1 0 1 0 0 1 0 0 sample-45 1 0 1 1 1 0 1 1 0 0 1 1 sample-46 1 1 0 2 1 0 0 0 0 0 0 0 sample-47 1 0 1 1 2 0 1 0 0 0 0 0 sample-48 2 2 2 1 2 2 2 0 0 0 0 0 sample-49 2 2 2 2 2 2 2 0 0 0 0 0 sample-50 1 2 1 1 1 2 2 1 1 1 1 1 sample-51 1 1 1 2 1 1 1 1 1 2 2 1 sample-52 1 0 0 1 1 1 1 2 2 2 2 2 sample-53 0 0 0 1 0 0 0 2 2 2 2 2 sample-54 0 0 0 1 0 0 0 2 2 2 2 2 sample-55 2 1 2 1 1 1 0 1 1 1 1 2 sample-56 2 2 2 1 2 2 1 1 1 1 0 1 sample-57 2 2 2 1 2 1 2 0 0 1 1 0 sample-58 1 2 2 0 1 2 2 1 1 0 1 1 sample-59 1 1 1 0 1 2 1 1 1 1 2 1 sample-60 1 1 1 1 0 1 1 1 2 2 1 1 sample-61 0 0 1 0 1 1 1 2 1 2 2 2 sample-62 0 0 0 0 0 0 0 2 1 2 1 1 sample-63 0 0 0 0 0 0 0 0 0 0 0 0 sample-64 0 0 0 0 0 2 1 0 0 0 0 0 sample-65 0 2 2 0 1 0 1 0 1 0 1 0 sample-66 1 1 0 1 1 0 1 0 0 0 0 0 sample-67 1 1 1 2 1 1 2 0 0 0 0 0 sample-68 2 2 2 2 2 2 2 0 0 0 1 0 sample-69 2 2 2 2 2 2 2 1 2 1 1 0 sample-70 2 2 2 2 2 2 1 2 1 1 2 2 sample-71 2 1 1 2 1 2 1 2 1 2 2 2 sample-72 2 2 1 2 2 2 2 2 2 2 2 2 sample-73 1 1 1 1 1 2 2 2 2 2 2 2 sample-74 1 1 1 0 1 1 0 2 2 2 2 2 sample-75 1 1 1 0 0 0 0 2 2 1 1 2 sample-76 1 1 1 1 2 1 1 1 2 2 1 2pebl-1.0.2/docs/src/static/tutorial/example1-result/data/000755 000766 000024 00000000000 11377315471 023653 5ustar00shahadstaff000000 000000 pebl-1.0.2/docs/src/static/tutorial/example1-result/index.html000644 000766 000024 00000010255 11010410473 024720 0ustar00shahadstaff000000 000000 Pebl Results

    Run Statistics
    RunHostRuntime (in mins)
    		Rank:  
    Score:  
    Use common layout for all networks
    Threshold for Consensus Network:
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KJJdBrr2BBB`XpetMߏƤ8ۣE… ޽{1h 2޽CVVV31RDD@H)!""E DDa" ""RBDD0@H)!""E DDa" ""RBDD0@H)!""E DDa" ""RBDD0@H)!""E DD_|y,FCIENDB`pebl-1.0.2/docs/src/learner/exhaustive.rst000644 000766 000024 00000001305 11010413005 021076 0ustar00shahadstaff000000 000000 :mod:`learner.exhaustive` -- Exhaustive learners ================================================ .. module:: pebl.learner.exhaustive :synopsis: Exhaustive learners This modules provides exhaustive learners. Currently, it include the ListLearner class which scores a list of networks. In the future, we might implent exhaustive learners that score all possible networks meeting some constraints (as defined by prior models). Configuration Parameters ------------------------ .. Autogenerated by pebl.config.paramdocs at Wed Apr 30 10:28:31 2008 .. confparam:: listlearner.networks List of networks to score. default= ListLearner Class ----------------- .. autoclass:: ListLearner :members: pebl-1.0.2/docs/src/learner/greedy.rst000644 000766 000024 00000001663 11024275143 020215 0ustar00shahadstaff000000 000000 :mod:`learner.greedy` -- Greedy learner ======================================= .. module:: pebl.learner.greedy :synopsis: Greedy learner This module provides a learner implementing the Greedy Learner algorithm. More information about greedy algorithms: http://en.wikipedia.org/wiki/Greedy_algorithm Configuration Parameters ------------------------ .. Autogenerated by pebl.config.paramdocs at Thu Jun 12 15:28:19 2008 .. confparam:: greedy.max_iterations Maximum number of iterations to run. default=1000 .. confparam:: greedy.max_time Maximum learner runtime in seconds. default=0 .. confparam:: greedy.max_unimproved_iterations Maximum number of iterations without score improvement before a restart. default=restart_after_max_unimproved_iterations(500) .. confparam:: greedy.seed Starting network for a greedy search. default='' GreedyLearner Class ------------------- .. autoclass:: GreedyLearner :members: pebl-1.0.2/docs/src/learner/simanneal.rst000644 000766 000024 00000001670 11010413005 020665 0ustar00shahadstaff000000 000000 :mod:`learner.simanneal` -- Simulated annealing learner ======================================================= .. module:: pebl.learner.simanneal :synopsis: Simulated annealing learner This module provides a learner implementing the Simulated Annealing algorithm. More information about simulated annealing: http://en.wikipedia.org/wiki/Simulated_annealing Configuration Parameters ------------------------ .. Autogenerated by pebl.config.paramdocs at Wed Apr 30 10:09:41 2008 .. confparam:: simanneal.delta_temp Change in temp between steps. default=0.5 .. confparam:: simanneal.max_iters_at_temp Max iterations at any temperature. default=100 .. confparam:: simanneal.seed Starting network for a greedy search. default= .. confparam:: simanneal.start_temp Starting temperature for a run. default=100.0 SimulatedAnnealingLearner Class ------------------------------- .. autoclass:: SimulatedAnnealingLearner :members: pebl-1.0.2/docs/build/dummy000644 000766 000024 00000000174 11331653547 016147 0ustar00shahadstaff000000 000000 File is here so 'python setup.py sdist' will include this directory; this is required for building RPM packages for Fedora.

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