def run(lsFile, tsFile, **kwargs):
randomState = None
if random:
randomState = 100
#======INSTANTIATING========#
os.environ["JOBLIB_TEMP_FOLDER"] = "/home/jmbegon/jmbegon/code/work/tmp/"
#--Pixit--
randConvCoord = coordinatorRandConvFactory(
nbFilters=nb_filters,
filterMinVal=filter_min_val,
filterMaxVal=filter_max_val,
filterMinSize=filterMinSize,
filterMaxSize=filterMaxSize,
nbSubwindows=nbSubwindows,
subwindowMinSizeRatio=subwindowMinSizeRatio,
subwindowMaxSizeRatio=subwindowMaxSizeRatio,
subwindowTargetWidth=subwindowTargetWidth,
subwindowTargetHeight=subwindowTargetHeight,
poolings=poolings,
filterNormalisation=filterNormalisation,
subwindowInterpolation=subwindowInterpolation,
includeOriginalImage=includeOriginalImage,
nbJobs=nbJobs,
verbosity=verbosity,
tempFolder=tempFolder,
random=random)
#--Extra-tree--
baseClassif = ExtraTreesClassifier(nbTrees,
max_features=maxFeatures,
max_depth=maxDepth,
min_samples_split=minSamplesSplit,
min_samples_leaf=minSamplesLeaf,
bootstrap=bootstrap,
n_jobs=nbJobsEstimator,
random_state=randomState,
verbose=verbose)
#--Classifier
classifier = Classifier(randConvCoord, baseClassif)
#--Data--
with open(lsFile, "wb") as f:
lsSize, Xls, yls = pickle.load(f, protocol=2)
loader = CifarFromNumpies(learningSetDir, learningIndexFile)
learningSet = FileImageBuffer(loader.getFiles(), NumpyImageLoader())
learningSet = learningSet[0:lsSize]
with open(tsFile, "wb") as f:
tsSize, Xts, yts = pickle.load(f, protocol=2)
loader = CifarFromNumpies(testingSetDir, testingIndexFile)
testingSet = FileImageBuffer(loader.getFiles(), NumpyImageLoader())
testingSet = testingSet[0:tsSize]
#=====COMPUTATION=====#
#--Learning--#
print "Starting learning"
fitStart = time()
baseClassif.fit(Xls, yls)
fitEnd = time()
print "Learning done", (fitEnd-fitStart), "seconds (no extraction)"
sys.stdout.flush()
#--Testing--#
y_truth = testingSet.getLabels()
predStart = time()
y_pred = classifier._predict(Xts, lsSize)
predEnd = time()
#====ANALYSIS=====#
accuracy = classifier.accuracy(y_pred, y_truth)
confMat = classifier.confusionMatrix(y_pred, y_truth)
importance, order = randConvCoord.importancePerFeatureGrp(baseClassif)
print "========================================="
print "--------ExtraTrees----------"
print "nbTrees", nbTrees
print "maxFeatures", maxFeatures
print "maxDepth", maxDepth
print "minSamplesSplit", minSamplesSplit
print "minSamplesLeaf", minSamplesLeaf
print "bootstrap", bootstrap
print "nbJobsEstimator", nbJobsEstimator
print "verbose", verbose
print "randomState", randomState
print "------------Data---------------"
print "LearningSet size", len(learningSet)
print "TestingSet size", len(testingSet)
print "-------------------------------"
print "Fit time (no extraction)", (fitEnd-fitStart), "seconds"
print "Classifcation time (no extraction)", (predEnd-predStart), "seconds"
print "Accuracy", accuracy
return accuracy, confMat, importance, order
