init

.gitignore

@@ -0,0 +1,3 @@
+*.pyc
+*.m~
+*-swp

COPYING

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    {one line to give the program's name and a brief idea of what it does.}
+    Copyright (C) {year}  {name of author}
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    {project}  Copyright (C) {year}  {fullname}
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

COPYING.LESSER

@@ -0,0 +1,165 @@
+                   GNU LESSER GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+
+  This version of the GNU Lesser General Public License incorporates
+the terms and conditions of version 3 of the GNU General Public
+License, supplemented by the additional permissions listed below.
+
+  0. Additional Definitions.
+
+  As used herein, "this License" refers to version 3 of the GNU Lesser
+General Public License, and the "GNU GPL" refers to version 3 of the GNU
+General Public License.
+
+  "The Library" refers to a covered work governed by this License,
+other than an Application or a Combined Work as defined below.
+
+  An "Application" is any work that makes use of an interface provided
+by the Library, but which is not otherwise based on the Library.
+Defining a subclass of a class defined by the Library is deemed a mode
+of using an interface provided by the Library.
+
+  A "Combined Work" is a work produced by combining or linking an
+Application with the Library.  The particular version of the Library
+with which the Combined Work was made is also called the "Linked
+Version".
+
+  The "Minimal Corresponding Source" for a Combined Work means the
+Corresponding Source for the Combined Work, excluding any source code
+for portions of the Combined Work that, considered in isolation, are
+based on the Application, and not on the Linked Version.
+
+  The "Corresponding Application Code" for a Combined Work means the
+object code and/or source code for the Application, including any data
+and utility programs needed for reproducing the Combined Work from the
+Application, but excluding the System Libraries of the Combined Work.
+
+  1. Exception to Section 3 of the GNU GPL.
+
+  You may convey a covered work under sections 3 and 4 of this License
+without being bound by section 3 of the GNU GPL.
+
+  2. Conveying Modified Versions.
+
+  If you modify a copy of the Library, and, in your modifications, a
+facility refers to a function or data to be supplied by an Application
+that uses the facility (other than as an argument passed when the
+facility is invoked), then you may convey a copy of the modified
+version:
+
+   a) under this License, provided that you make a good faith effort to
+   ensure that, in the event an Application does not supply the
+   function or data, the facility still operates, and performs
+   whatever part of its purpose remains meaningful, or
+
+   b) under the GNU GPL, with none of the additional permissions of
+   this License applicable to that copy.
+
+  3. Object Code Incorporating Material from Library Header Files.
+
+  The object code form of an Application may incorporate material from
+a header file that is part of the Library.  You may convey such object
+code under terms of your choice, provided that, if the incorporated
+material is not limited to numerical parameters, data structure
+layouts and accessors, or small macros, inline functions and templates
+(ten or fewer lines in length), you do both of the following:
+
+   a) Give prominent notice with each copy of the object code that the
+   Library is used in it and that the Library and its use are
+   covered by this License.
+
+   b) Accompany the object code with a copy of the GNU GPL and this license
+   document.
+
+  4. Combined Works.
+
+  You may convey a Combined Work under terms of your choice that,
+taken together, effectively do not restrict modification of the
+portions of the Library contained in the Combined Work and reverse
+engineering for debugging such modifications, if you also do each of
+the following:
+
+   a) Give prominent notice with each copy of the Combined Work that
+   the Library is used in it and that the Library and its use are
+   covered by this License.
+
+   b) Accompany the Combined Work with a copy of the GNU GPL and this license
+   document.
+
+   c) For a Combined Work that displays copyright notices during
+   execution, include the copyright notice for the Library among
+   these notices, as well as a reference directing the user to the
+   copies of the GNU GPL and this license document.
+
+   d) Do one of the following:
+
+       0) Convey the Minimal Corresponding Source under the terms of this
+       License, and the Corresponding Application Code in a form
+       suitable for, and under terms that permit, the user to
+       recombine or relink the Application with a modified version of
+       the Linked Version to produce a modified Combined Work, in the
+       manner specified by section 6 of the GNU GPL for conveying
+       Corresponding Source.
+
+       1) Use a suitable shared library mechanism for linking with the
+       Library.  A suitable mechanism is one that (a) uses at run time
+       a copy of the Library already present on the user's computer
+       system, and (b) will operate properly with a modified version
+       of the Library that is interface-compatible with the Linked
+       Version.
+
+   e) Provide Installation Information, but only if you would otherwise
+   be required to provide such information under section 6 of the
+   GNU GPL, and only to the extent that such information is
+   necessary to install and execute a modified version of the
+   Combined Work produced by recombining or relinking the
+   Application with a modified version of the Linked Version. (If
+   you use option 4d0, the Installation Information must accompany
+   the Minimal Corresponding Source and Corresponding Application
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+   Information in the manner specified by section 6 of the GNU GPL
+   for conveying Corresponding Source.)
+
+  5. Combined Libraries.
+
+  You may place library facilities that are a work based on the
+Library side by side in a single library together with other library
+facilities that are not Applications and are not covered by this
+License, and convey such a combined library under terms of your
+choice, if you do both of the following:
+
+   a) Accompany the combined library with a copy of the same work based
+   on the Library, uncombined with any other library facilities,
+   conveyed under the terms of this License.
+
+   b) Give prominent notice with the combined library that part of it
+   is a work based on the Library, and explaining where to find the
+   accompanying uncombined form of the same work.
+
+  6. Revised Versions of the GNU Lesser General Public License.
+
+  The Free Software Foundation may publish revised and/or new versions
+of the GNU Lesser General Public License from time to time. Such new
+versions will be similar in spirit to the present version, but may
+differ in detail to address new problems or concerns.
+
+  Each version is given a distinguishing version number. If the
+Library as you received it specifies that a certain numbered version
+of the GNU Lesser General Public License "or any later version"
+applies to it, you have the option of following the terms and
+conditions either of that published version or of any later version
+published by the Free Software Foundation. If the Library as you
+received it does not specify a version number of the GNU Lesser
+General Public License, you may choose any version of the GNU Lesser
+General Public License ever published by the Free Software Foundation.
+
+  If the Library as you received it specifies that a proxy can decide
+whether future versions of the GNU Lesser General Public License shall
+apply, that proxy's public statement of acceptance of any version is
+permanent authorization for you to choose that version for the
+Library.

README.md

@@ -0,0 +1,25 @@
+## Expected Model Output Change (EMOC) for regression
+
+Source code for method described in the following paper:
+
+- Active Learning for Regression Tasks with Expected Model Output Changes.
+  Christoph Käding and Erik Rodner and Alexander Freytag and Oliver Mothes and Björn Barz and Joachim Denzler.
+  British Machine Vision Conference (BMVC). 2018.
+
+If you use parts of the code, please cite this paper.
+
+
+##### Dependencies
+
+- Python 2.7
+- numpy
+- scipy
+- scikit-learn
+
+
+##### Usage
+
+1. define setup (see example_setup.cfg)
+2. precompute setup (run evaluation/PrecomputeExperimentalSetup.py setup.cfg)
+3. start experiment (run evaluation/RunExperiment.py setup.cfg)
+4. see results (stored in results.mat)

activeLearning/activeLearningGPemoc.py

@@ -0,0 +1,58 @@
+import math
+import scipy
+import numpy
+import sklearn.kernel_ridge
+
+import sys
+import os
+sys.path.append(os.path.join(os.path.abspath(os.path.dirname(__file__)),os.pardir))
+import helperFunctions
+
+import activeLearningGPprototype
+
+class Regressor(activeLearningGPprototype.RegressorPrototype):
+
+    def __init__(self, sigmaN = 0.01, gamma = None, kernel = 'rbf', norm = 1, configFile=None, verbose=True):
+
+        activeLearningGPprototype.RegressorPrototype.__init__(self, sigmaN=sigmaN, gamma=gamma, kernel=kernel, configFile=configFile, verbose=verbose)
+        self.norm = helperFunctions.getConfig(configFile, 'activeLearning', 'norm', norm, 'int', verbose)
+
+
+    def gaussianAbsoluteMoment(self, muTilde, predVar):
+
+        f11 = scipy.special.hyp1f1(-0.5*self.norm, 0.5, -0.5*numpy.divide(muTilde**2,predVar))
+        prefactors = ((2 * predVar**2)**(self.norm/2.0) * math.gamma((1 + self.norm)/2.0)) / numpy.sqrt(numpy.pi)
+
+        return numpy.multiply(prefactors,f11)
+
+
+    def calcEMOC(self, x):
+
+        emocScores = numpy.asmatrix(numpy.empty([x.shape[0],1], dtype=numpy.float))
+        muTilde =numpy.asmatrix(numpy.zeros([x.shape[0],1], dtype=numpy.float))
+
+        kAll = self.kernelFunc(numpy.vstack([self.X, x]))
+        k = kAll[0:self.X.shape[0],self.X.shape[0]:]
+        selfKdiag = numpy.asmatrix(numpy.diag(kAll[self.X.shape[0]:,self.X.shape[0]:])).T
+
+        sigmaF = self.calcSigmaF(x, k, selfKdiag)
+        moments = numpy.asmatrix(self.gaussianAbsoluteMoment(numpy.asarray(muTilde), numpy.asarray(sigmaF)))
+
+        term1 = 1.0 / (sigmaF + self.sigmaN)
+
+        term2 = numpy.asmatrix(numpy.ones((self.X.shape[0] + 1,x.shape[0])), dtype=numpy.float)*(-1.0)
+        term2[0:self.X.shape[0],:] = numpy.linalg.solve(self.K + numpy.identity(self.X.shape[0], dtype=numpy.float)*self.sigmaN, k)
+
+        preCalcMult = numpy.dot(term2[:-1,:].T, kAll[0:self.X.shape[0],:])
+
+        for idx in range(x.shape[0]):
+
+            vAll = term1[idx,:]*(preCalcMult[idx,:] + numpy.dot(term2[-1,idx].T, kAll[self.X.shape[0] + idx,:]))
+            emocScores[idx,:] = numpy.mean(numpy.power(numpy.abs(vAll),self.norm))
+
+        return numpy.multiply(emocScores,moments)
+
+
+    def calcAlScores(self, x):
+
+        return self.calcEMOC(x)

activeLearning/activeLearningGPprototype.py

@@ -0,0 +1,149 @@
+import math
+import scipy
+import numpy
+import sklearn.kernel_ridge
+
+import sys
+import os
+sys.path.append(os.path.join(os.path.abspath(os.path.dirname(__file__)),os.pardir))
+import helperFunctions
+
+class RegressorPrototype:
+
+    def __init__(self, sigmaN=0.01, gamma=None, kernel='rbf', configFile=None, verbose=True):
+
+        self.sigmaN = helperFunctions.getConfig(configFile, 'activeLearning', 'sigmaN', sigmaN, 'float', verbose)
+        self.kernel = helperFunctions.getConfig(configFile, 'activeLearning', 'kernel', kernel, 'str', verbose)
+        self.gamma = helperFunctions.getConfig(configFile, 'activeLearning', 'gamma', gamma, 'float', verbose)
+        self.numKernelCores = helperFunctions.getConfig(configFile, 'activeLearning', 'numKernelCores', 1, 'int', verbose)
+        self.verbose = helperFunctions.getConfig(configFile, 'activeLearning', 'verbose', verbose, 'bool', verbose)
+
+        self.K = []
+        self.alpha = []
+        self.X = []
+        self.y = []
+
+        self.allowedKernels = ['rbf', 'sigmoid', 'polynomial', 'poly', 'linear', 'cosine']
+
+        if self.kernel not in self.allowedKernels:
+            raise Exception('Unknown kernel %s!'%self.kernel)
+
+
+    def kernelFunc(self, x1, x2=None, gamma=None):
+
+        if gamma is not None:
+            self.gamma = gamma
+
+        if self.kernel in ['rbf']:
+            return numpy.asmatrix(sklearn.kernel_ridge.pairwise_kernels(x1, x2, metric=self.kernel, gamma=self.gamma, n_jobs=self.numKernelCores), dtype=numpy.float)
+        else:
+            return numpy.asmatrix(sklearn.kernel_ridge.pairwise_kernels(x1, x2, metric=self.kernel, n_jobs=self.numKernelCores), dtype=numpy.float)
+
+
+    def train(self, X, y, sigmaN=None, gamma=None, kernel=None, numKernelCores=None):
+
+        if sigmaN is not None:
+            if self.verbose:
+                print 'regressor - switched sigmaN from {} to {}'.format(self.sigmaN, sigmaN)
+            self.sigmaN = sigmaN
+
+        if gamma is not None:
+            if self.verbose:
+                print 'regressor - switched gamma from {} to {}'.format(self.gamma, gamma)
+            self.gamma = gamma
+
+        if kernel is not None and kernel in self.allowedKernels:
+            if self.verbose:
+                print 'regressor - switched kernel from {} to {}'.format(self.kernel, kernel)
+            self.kernel = kernel
+
+        if numKernelCores is not None:
+            if self.verbose:
+                print 'regressor - switched numKernelCores from {} to {}'.format(self.numKernelCores, numKernelCores)
+            self.numKernelCores = numKernelCores
+
+        self.X = X
+        self.y = y
+        self.K = self.kernelFunc(X, gamma=self.gamma)
+        self.alpha = numpy.linalg.solve(self.K + numpy.identity(self.X.shape[0], dtype=numpy.float)*self.sigmaN, self.y)
+
+        self.checkVars()
+
+
+    def update(self, x, y):
+
+        k = self.kernelFunc(self.X, x)
+        selfKdiag = self.getSelfKdiag(x)
+
+        term1 = 1.0 / (self.calcSigmaF(x, k, selfKdiag) + self.sigmaN)
+
+        term2 = numpy.asmatrix(numpy.ones((self.X.shape[0] + 1,x.shape[0])), dtype=numpy.float) * -1.0
+        term2[0:self.X.shape[0],:] = numpy.linalg.solve(self.K + numpy.identity(self.X.shape[0], dtype=numpy.float)*self.sigmaN, k)
+
+        term3 = self.predict(x) - y
+
+        self.alpha = numpy.append(self.alpha, numpy.zeros((1,self.alpha.shape[1])), axis=0) + numpy.dot(numpy.dot(term1,term2.T).T,term3)
+        self.K = numpy.append(numpy.append(self.K, k, axis=1), numpy.append(k.T, selfKdiag, axis=1), axis=0)
+        self.X = numpy.append(self.X, x, axis=0)
+        self.y = numpy.append(self.y, y, axis=0)
+
+        self.checkVars()
+
+
+    def checkVars(self):
+
+        if not numpy.all(numpy.isfinite(self.X)):
+            raise Exception('not numpy.all(numpy.isfinite(self.X))')
+
+        if not numpy.all(numpy.isfinite(self.y)):
+            raise Exception('not numpy.all(numpy.isfinite(self.y))')
+
+        if not numpy.all(numpy.isfinite(self.K)):
+            raise Exception('not numpy.all(numpy.isfinite(self.K))')
+
+        if not numpy.all(numpy.isfinite(self.alpha)):
+            raise Exception('not numpy.all(numpy.isfinite(self.alpha))')
+
+
+    def predict(self, x):
+
+        k = self.kernelFunc(self.X, x)
+        return numpy.dot(k.T, self.alpha)
+
+
+    def calcSigmaF(self, x, k=None, selfKdiag=None):
+
+        if k is None:
+            k = self.kernelFunc(self.X, x)
+
+        if selfKdiag is None:
+            selfKdiag = self.getSelfKdiag(x)
+
+        return selfKdiag - numpy.sum(numpy.multiply(k, numpy.linalg.solve(self.K + numpy.identity(self.K.shape[0], dtype=numpy.float)*self.sigmaN, k)), axis=0).T
+
+
+    def getSelfKdiag(self, x):
+
+        selfKdiag = numpy.asmatrix(numpy.empty([x.shape[0],1], dtype=numpy.float))
+        for idx in range(x.shape[0]):
+            selfKdiag[idx,:] = self.kernelFunc(x[idx,:])
+
+        return selfKdiag
+
+
+    def calcAlScores(self, x):
+
+        return None
+
+
+    def chooseSample(self, x):
+
+        alScores = self.calcAlScores(x)
+
+        if alScores.shape[0] != x.shape[0] or alScores.shape[1] != 1:
+            raise Exception('alScores.shape[0] != x.shape[0] or alScores.shape[1] != 1')
+
+        if not numpy.all(numpy.isfinite(alScores)):
+            raise Exception('not numpy.all(numpy.isfinite(alScores))')
+
+        return numpy.argmax(alScores, axis=0).item(0)

datasetAcquisition.py

@@ -0,0 +1,94 @@
+import numpy
+import os
+import sys
+import csv
+import scipy.io
+import pickle
+import time
+import datetime
+import glob
+
+import helperFunctions
+
+###
+
+def readData(configFile):
+
+    dataset = helperFunctions.getConfig(configFile, 'data', 'dataset', None, 'str', True)
+
+    if dataset == 'uci':
+        x,y = readUCIcsv(configFile)
+
+    else:
+        raise Exception('Unknown dataset %s!'%dataset)
+
+    if x.shape[0] != y.shape[0]:
+        raise Exception('#data = {} != #labels = {}'.format(x.shape[0],y.shape[0]))
+
+    if not numpy.all(numpy.isfinite(x)):
+        raise Exception('not numpy.all(numpy.isfinite(x))')
+
+    if not numpy.all(numpy.isfinite(y)):
+        raise Exception('not numpy.all(numpy.isfinite(y))')
+
+    return x,y
+
+###
+
+def readUCIcsv(configFile):
+
+    dataFileName = helperFunctions.getConfig(configFile, 'data', 'dataFileName', None, 'str', True)
+    labelCol = helperFunctions.getConfig(configFile, 'data', 'labelCol', None, 'int', True)
+    forbiddenCols = helperFunctions.getConfig(configFile, 'data', 'forbiddenCols', None, 'intList', True)
+    delimiter = helperFunctions.getConfig(configFile, 'data', 'delimiter', ',', 'str', True)
+    quoteChar = helperFunctions.getConfig(configFile, 'data', 'quoteChar', '|', 'str', True)
+    firstDataRowNumber = helperFunctions.getConfig(configFile, 'data', 'firstDataRowNumber', 1, 'int', True)
+    normalizeFeatures = helperFunctions.getConfig(configFile, 'data', 'normalizeFeatures', -1, 'str', True)
+
+    if delimiter == '':
+        delimiter = ' '
+
+    ###
+
+    csvFile = open(dataFileName, 'rb')
+    csvReader = csv.reader(csvFile, delimiter=delimiter, quotechar=quoteChar)
+    idx = 0;
+    x = None
+    y = None
+
+    for row in csvReader:
+
+        xRow = []
+
+        if idx < firstDataRowNumber:
+            idx = idx + 1
+            continue
+
+        for colIdx in range(len(row)):
+
+            if forbiddenCols is not None and colIdx in forbiddenCols:
+                continue
+            elif colIdx == labelCol:
+                yRow = float(row[colIdx])
+            else:
+                xRow.append(float(row[colIdx]))
+
+        if x is None:
+            x = numpy.asmatrix(xRow, dtype=numpy.float)
+        else:
+            x = numpy.append(x, numpy.asmatrix(xRow), axis=0)
+
+        if y is None:
+            y = numpy.asmatrix(yRow, dtype=numpy.float)
+        else:
+            y = numpy.append(y, numpy.asmatrix(yRow), axis=0)
+
+    csvFile.close()
+
+    if normalizeFeatures == 'uci':
+        x = helperFunctions.normalizeUCI(x)
+
+    elif int(normalizeFeatures) > 0:
+        x = helperFunctions.normalizeLP(x, int(normalizeFeatures))
+
+    return x,y

evaluation/PrecomputeExperimentalSetup.py

@@ -0,0 +1,70 @@
+
+import pickle
+import socket
+import datetime
+import numpy
+
+###
+
+import sys
+import os
+sys.path.append(os.path.join(os.path.abspath(os.path.dirname(__file__)),os.pardir))
+
+###
+
+import helperFunctions
+import datasetAcquisition
+
+###
+
+if len(sys.argv) != 2:
+    raise Exception('No config file given!')
+
+print ''
+print ' -- config -- '
+print ''
+
+defaultFname =  os.path.join(os.path.dirname(sys.argv[1]),os.pardir,'setup.cfg')
+if not os.path.isfile(defaultFname):
+    defaultFname = sys.argv[1]
+
+expSetup = helperFunctions.getConfig(sys.argv[1], 'experiment', 'extExpSetup', defaultFname, 'str', True)
+numRndInits = helperFunctions.getConfig(expSetup, 'experiment', 'numRndInits', None, 'int', True)
+numInitSamples = helperFunctions.getConfig(expSetup, 'experiment', 'numInitSamples', None, 'int', True)
+numTestSamples = helperFunctions.getConfig(expSetup, 'experiment', 'numTestSamples', None, 'int', True)
+indicesFileName = helperFunctions.getConfig(expSetup, 'experiment', 'indicesFileName', None, 'str', True)
+
+print ''
+print 'host:', socket.gethostname()
+print 'pid:', os.getpid()
+print 'now:', datetime.datetime.strftime(datetime.datetime.now(), '%d.%m.%Y %H:%M:%S')
+print 'git:', helperFunctions.getGitHash()
+print ''
+sys.stdout.flush()
+
+###
+
+#trainIdxs = numpy.empty((numTasks,numRndInits,numInitCls*numInitSamplesPerCls), dtype=numpy.int)
+#testIdxs = numpy.empty((numTasks,numRndInits,numCls*numTestSamplesPerCls), dtype=numpy.int)
+
+trainIdxs = list()
+testIdxs = list()
+
+###
+
+x,y = datasetAcquisition.readData(expSetup)
+
+for rndIdx in range(numRndInits):
+
+    rndIdxs = numpy.random.permutation(y.shape[0])
+    trainIdxs.append(rndIdxs[:numInitSamples])
+    testIdxs.append(rndIdxs[numInitSamples+1:numInitSamples+numTestSamples+1])
+
+###
+
+out = open(indicesFileName, 'w')
+pickle.dump({'trainIdxs': trainIdxs, 'testIdxs': testIdxs}, out)
+out.close()
+print ''
+print 'done'
+

evaluation/RunExperiment.py

@@ -0,0 +1,220 @@
+#! /usr/bin/python
+
+import scipy.io
+import numpy
+import time
+import socket
+import datetime
+import pickle
+
+###
+
+import sys
+import os
+sys.path.append(os.path.join(os.path.abspath(os.path.dirname(__file__)),os.pardir))
+
+###
+
+import helperFunctions
+import methodSelection
+import datasetAcquisition
+
+###
+
+if len(sys.argv) != 2:
+    raise Exception('No config file given!')
+
+print ''
+print ' -- config -- '
+print ''
+
+defaultFname =  os.path.join(os.path.dirname(sys.argv[1]),os.pardir,'setup.cfg')
+if not os.path.isfile(defaultFname):
+    defaultFname = sys.argv[1]
+
+expSetup = helperFunctions.getConfig(sys.argv[1], 'experiment', 'extExpSetup', defaultFname, 'str', True)
+alMethod = helperFunctions.getConfig(sys.argv[1], 'activeLearning', 'method', None, 'str', True)
+identifier = helperFunctions.getConfig(sys.argv[1], 'experiment', 'identifier', alMethod, 'str', True)
+
+sigmaN = helperFunctions.getConfig(expSetup, 'activeLearning', 'sigmaN', None, 'float', True)
+kernel = helperFunctions.getConfig(expSetup, 'activeLearning', 'kernel', None, 'str', True)
+gamma = helperFunctions.getConfig(expSetup, 'activeLearning', 'gamma', None, 'float', True)
+numKernelCores = helperFunctions.getConfig(expSetup, 'activeLearning', 'numKernelCores', None, 'int', True)
+
+numRndInits = helperFunctions.getConfig(expSetup, 'experiment', 'numRndInits', None, 'int', True)
+indicesFileName = helperFunctions.getConfig(expSetup, 'experiment', 'indicesFileName', None, 'str', True)
+numSteps = helperFunctions.getConfig(expSetup, 'experiment', 'numSteps', 100, 'int', True)
+startInitIdx = helperFunctions.getConfig(expSetup, 'experiment', 'startInitIdx', 0, 'int', True)
+endInitIdx = helperFunctions.getConfig(expSetup, 'experiment', 'endInitIdx', numRndInits - 1, 'int', True)
+numTestSamples = helperFunctions.getConfig(expSetup, 'experiment', 'numTestSamples', None, 'int', True)
+
+continueExperiment = helperFunctions.getConfig(sys.argv[1], 'experiment', 'continueExperiment', False, 'bool', True)
+continueExperiment = helperFunctions.getConfig(expSetup, 'experiment', 'continueExperiment', continueExperiment, 'bool', True)
+
+writeAttemptsNmb = helperFunctions.getConfig(expSetup, 'experiment', 'writeAttemptsNmb', 5, 'int', True)
+writeAttemptsDelay = helperFunctions.getConfig(expSetup, 'experiment', 'writeAttemptsDelay', 30, 'int', True)
+
+if startInitIdx != endInitIdx:
+    resultsFileName = helperFunctions.getConfig(sys.argv[1], 'experiment', 'resultsFileName', os.path.dirname(os.path.abspath(sys.argv[1])) + '/results.mat', 'str', True)
+else:
+    resultsFileName = helperFunctions.getConfig(sys.argv[1], 'experiment', 'resultsFileName', os.path.dirname(os.path.abspath(sys.argv[1])) + '/results' + str(startInitIdx) + '.mat', 'str', True)
+
+print ''
+print 'host:', socket.gethostname()
+print 'pid:', os.getpid()
+print 'now:', datetime.datetime.strftime(datetime.datetime.now(), '%d.%m.%Y %H:%M:%S')
+print 'git:', helperFunctions.getGitHash()
+print ''
+sys.stdout.flush()
+
+if not os.path.isdir(os.path.dirname(os.path.abspath(resultsFileName))) or not os.path.exists(os.path.dirname(os.path.abspath(resultsFileName))):
+    raise Exception('Results path deas not exist!')
+
+if os.getcwd() != os.path.dirname(os.path.abspath(resultsFileName)):
+    print ''
+    print '>>> current path != results path <<<'
+    print 'current:', os.getcwd()
+    print 'rerults:', os.path.dirname(os.path.abspath(resultsFileName))
+    print ''
+
+###
+
+print 'loading data ...'
+sys.stdout.flush()
+
+x,y = datasetAcquisition.readData(expSetup)
+
+###
+
+if continueExperiment:
+
+    print ''
+    print 'loading previous results ...'
+    sys.stdout.flush()
+
+    loaded = scipy.io.loadmat(resultsFileName)
+
+    queriedIdxs = loaded['queriedIdxs']
+    timeNeeded = loaded['timeNeeded']
+    errors_singleRMSEs = loaded['errors_singleRMSEs']
+    errors_meanRMSEs = loaded['errors_meanRMSEs']
+
+    startInitIdx = int(loaded['lastRndInitIdx'])
+    print 'resuming with init {} / {}'.format(startInitIdx + 1,endInitIdx + 1)
+
+else:
+    queriedIdxs = numpy.zeros((numRndInits,numSteps))
+    timeNeeded = numpy.zeros((numRndInits,numSteps))
+    errors_singleRMSEs = numpy.zeros((numRndInits,numSteps + 1,y.shape[1]))
+    errors_meanRMSEs = numpy.zeros((numRndInits,numSteps + 1))
+
+###
+
+pickleIn = open(indicesFileName)
+indices = pickle.load(pickleIn)
+pickleIn.close()
+
+###
+
+timePast = 0
+totalRuns = (endInitIdx + 1)*numSteps - startInitIdx*numSteps
+queriesPast = 0
+t0 = time.time()
+
+###
+
+for rndInitIdx in range(startInitIdx, endInitIdx + 1):
+
+    print ''
+    trainIdxs = indices['trainIdxs'][rndInitIdx]
+    testIdxs = indices['testIdxs'][rndInitIdx]
+    poolIdxs = numpy.delete(numpy.asarray(range(y.shape[0])), numpy.concatenate((trainIdxs,testIdxs)))
+
+    xTest = x[testIdxs,:]
+    yTest = y[testIdxs,:]
+    xTrain = x[trainIdxs,:]
+    yTrain = y[trainIdxs,:]
+    xPool = x[poolIdxs,:]
+    yPool = y[poolIdxs,:]
+
+    orgIdxs = numpy.asmatrix(range(1,yPool.shape[0] + 1))
+
+    regressor = methodSelection.selectActiveLearning(alMethod, sys.argv[1])
+    regressor.train(xTrain, yTrain, sigmaN=sigmaN, gamma=gamma, kernel=kernel, numKernelCores=numKernelCores)
+    print ''
+    sys.stdout.flush()
+
+    preds = regressor.predict(xTest)
+
+    errors_singleRMSEs[rndInitIdx,0,:] = helperFunctions.getSingleRMSEs(yTest, preds)
+    errors_meanRMSEs[rndInitIdx,0] = helperFunctions.getMeanRMSE(yTest, preds)
+
+    print 'xTrain:', xTrain.shape, ', yTrain:', yTrain.shape, ', xTest:', xTest.shape, ', yTest:', yTest.shape, ', xPool:', xPool.shape, ', yPool:', yPool.shape
+    print 'next rndInit:', rndInitIdx, ', initial meanRMSE:', errors_meanRMSEs[rndInitIdx,0]
+    sys.stdout.flush()
+
+    for step in range(numSteps):
+
+        t1 = time.time()
+
+        alScores = regressor.calcAlScores(xPool)
+
+        if alScores.shape[0] != xPool.shape[0]:
+            raise Exception('alScores.shape[0] != xPool.shape[0]')
+
+        if alScores.shape[1] != 1:
+            raise Exception('alScores.shape[1] != 1')
+
+        if not numpy.all(numpy.isfinite(alScores)):
+            raise Exception('not numpy.all(numpy.isfinite(alScores))')
+
+        chosenIdx = numpy.argmax(alScores, axis=0).item(0)
+
+        newX = xPool[chosenIdx,:]
+        newY = yPool[chosenIdx,:]
+
+        regressor.update(newX, newY)
+
+        queriedIdxs[rndInitIdx,step] = orgIdxs[0,chosenIdx]
+
+        preds = regressor.predict(xTest)
+
+        errors_singleRMSEs[rndInitIdx,step+1,:] = helperFunctions.getSingleRMSEs(yTest, preds)
+        errors_meanRMSEs[rndInitIdx,step+1] = helperFunctions.getMeanRMSE(yTest, preds)
+
+        xPool = numpy.delete(xPool, (chosenIdx), axis=0)
+        yPool = numpy.delete(yPool, (chosenIdx), axis=0)
+        orgIdxs = numpy.delete(orgIdxs, (chosenIdx), axis=1)
+
+        t2 = time.time()
+        timeNeeded[rndInitIdx,step] = (t1 - t2)
+
+        queriesPast = queriesPast + 1
+        timeStats = helperFunctions.estTimeLeft(t0, t2, queriesPast, totalRuns)
+
+        print queriesPast, '/', totalRuns, '- t past:', '%.3f'%(timeStats[0]/3600.0), 'h, ~t per pass:', '%.3f'%timeStats[1], 's, ~t left:', '%.3f'%(timeStats[2]/3600.0), 'h, ~t total:', '%.3f'%(timeStats[3]/3600.0), 'h'
+        print 'chosenIdx:', chosenIdx,  ', rndInit:', rndInitIdx, ', step:', step, ', meanRMSE:', '%.5f'%errors_meanRMSEs[rndInitIdx,step+1]
+        sys.stdout.flush()
+
+    writeAttempt = 0
+    while True:
+        try:
+            scipy.io.savemat(resultsFileName, dict(queriedIdxs=queriedIdxs,
+                                                name=identifier,
+                                                identifier=identifier,
+                                                timeNeeded=timeNeeded,
+                                                lastRndInitIdx=rndInitIdx,
+                                                errors_singleRMSEs=errors_singleRMSEs,
+                                                errors_meanRMSEs=errors_meanRMSEs)
+            break
+        except:
+            writeAttempt = writeAttempt + 1
+            if writeAttempt >= writeAttemptsNmb:
+                raise Exception('ERROR: Writing file {} failed {} times!'.format(resultsFileName, writeAttempt))
+            print ''
+            print 'WARNING: Writing file {} failed ({} / {}, retry after {} seconds)!'.format(resultsFileName, writeAttempt, writeAttemptsNmb, writeAttemptsDelay)
+            sys.stdout.flush()
+            time.sleep(writeAttemptsDelay)
+
+print 'done'
+print 'now', datetime.datetime.strftime(datetime.datetime.now(), '%d.%m.%Y %H:%M:%S')

example_setup.cfg

@@ -0,0 +1,25 @@
+[experiment]
+
+# selection of experimental setup
+numRndInits: 3
+numSteps: 1000
+numInitSamples: 5
+numTestSamples: 20000
+
+indicesFileName: ./indices.pickle
+
+
+[data]
+
+# dataset, see datasetAcquisition.ps for neccessary parameters
+datatype: none
+
+
+[activeLearning]
+
+# config for al method, see methodSelection.py for possible choices
+method: GPemoc
+kernel: linear
+sigmaN: 0.3
+verbose: False
+norm: 1

helperFunctions.py

@@ -0,0 +1,123 @@
+import numpy
+import ConfigParser
+import os
+import sys
+import subprocess
+import time
+import sklearn.covariance
+
+
+def getSingleRMSEs(yTest, pred):
+
+    rmses = numpy.asarray(numpy.empty([1,yTest.shape[1]]), dtype=numpy.float)
+    rmses[:,:] = numpy.sqrt(numpy.mean(numpy.square(yTest - pred), axis=0))
+
+    return rmses
+
+
+def getMeanRMSE(yTest, pred):
+
+    return numpy.mean(getSingleRMSEs(yTest, pred))
+
+
+def normLP(x, norm, axis=1):
+
+    if norm == 1:
+        return numpy.sum(numpy.abs(x), axis=axis)
+    elif norm == 2:
+        return numpy.sqrt(numpy.sum(numpy.square(x), axis=axis))
+    else:
+        raise Exception('Yet unsupported norm %d!'%norm)
+
+
+def normalizeLP(x, norm):
+
+    return numpy.divide(x, normLP(x, norm))
+
+
+def normalizeUCI(x):
+
+    normX = x
+
+    # TODO: vectorize
+    for i in range(x.shape[0]):
+        for j in range(x.shape[1]):
+
+            normX[i,j] = (x[i,j] - numpy.min(x[:,j])) / (numpy.max(x[:,j]) - numpy.min(x[:,j]))
+
+    return normX
+
+
+def estTimeLeft(t0, t1, curRun, totalRuns):
+
+    timePast = float(t1 - t0)
+    avgTimePerPass = timePast / float(curRun)
+    estTimeOva = avgTimePerPass * totalRuns
+    estTimeLeft = estTimeOva - timePast
+
+    return [timePast , avgTimePerPass , estTimeLeft , estTimeOva]
+
+
+def getConfig(pathtoConfig, section, option, default=None, dtype='str', verbose=False):
+
+    # set default
+    value = default
+    defaultUsed = True
+
+    # check if file is available
+    if pathtoConfig is not None and os.path.isfile(pathtoConfig):
+
+        # init
+        config = ConfigParser.ConfigParser()
+        configFile = open(pathtoConfig)
+        config.readfp(configFile)
+        configFile.close()
+
+        # check if section and option is available
+        if config.has_section(section) and config.has_option(section, option):
+
+            # get requested type
+            if dtype == 'str':
+                value = config.get(section, option)
+            elif dtype == 'int':
+                value = config.getint(section, option)
+            elif dtype == 'float':
+                value = config.getfloat(section, option)
+            elif dtype == 'bool':
+                value = config.getboolean(section, option)
+            elif dtype == 'strList':
+                value = config.get(section, option).split(',')
+            elif dtype == 'intList':
+                value = [int(entry) for entry in config.get(section, option).split(',')]
+            elif dtype == 'floatList':
+                value = [float(entry) for entry in config.get(section, option).split(',')]
+            elif dtype == 'boolList':
+                value = [bool(entry) for entry in config.get(section, option).split(',')]
+            else:
+                raise Exception('Unknown dtype!')
+
+            defaultUsed = False
+
+    # print config
+    if verbose:
+        aux = ''
+        if 'List' in dtype and value is not None and len(value) > 0:
+            aux = '| entryDtype:' + str(type(value[0]))
+        print 'default:', defaultUsed, '| section:', section, '| option:', option, '| value:', value, '| dtype:', type(value), aux
+
+    # return
+    return value
+
+
+def getGitHash(gitPath=os.path.dirname(os.path.abspath(__file__))):
+    curDir = os.getcwd()
+    os.chdir(gitPath)
+    gitHash = subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'], stderr=subprocess.STDOUT).strip()
+    os.chdir(curDir)
+    return gitHash
+
+
+def showProgressBarTerminal(current, total, pre):
+
+    sys.stdout.write('\r%s %0.2f %%'%(pre,(float(current)/float(total))*100.0))
+    sys.stdout.flush()

methodSelection.py

@@ -0,0 +1,19 @@
+#! /usr/bin/python
+
+import sys
+import os
+
+import helperFunctions
+
+###
+
+def selectActiveLearning(method, configFile=None, verbose=True):
+
+  sys.path.append(os.path.abspath(os.path.dirname(__file__)) + '/activeLearning')
+
+  if method == 'GPemoc':
+    import activeLearningGPemoc
+    return activeLearningGPemoc.Regressor(configFile=configFile, verbose=verbose)
+
+  else:
+    raise Exception('Unknown method %s!'%method)