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Commit e71216c0 authored by bbauvin's avatar bbauvin
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OK for late fusion, working on early & result analysis

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Code/MultiView/ExecMultiview.py
+ 4
− 3
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...@@ -61,14 +61,15 @@ groupFusion = parser.add_argument_group('Fusion arguments') ...@@ -61,14 +61,15 @@ groupFusion = parser.add_argument_group('Fusion arguments')
groupFusion.add_argument('--FU_type', metavar='STRING', action='store', groupFusion.add_argument('--FU_type', metavar='STRING', action='store',
help='Determine which type of fusion to use', default='LateFusion') help='Determine which type of fusion to use', default='LateFusion')
groupFusion.add_argument('--FU_method', metavar='STRING', action='store', groupFusion.add_argument('--FU_method', metavar='STRING', action='store',
help='Determine which method of fusion to use', default='SVMForLinear') help='Determine which method of fusion to use', default='WeightedLinear')
groupFusion.add_argument('--FU_method_config', metavar='STRING', action='store', nargs='+', groupFusion.add_argument('--FU_method_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the fusion method', default=['1:1:1:1']) help='Configuration for the fusion method', default=['1:1:1:1'])
groupFusion.add_argument('--FU_cl_names', metavar='STRING', action='store', groupFusion.add_argument('--FU_cl_names', metavar='STRING', action='store',
help='Names of the monoview classifiers used', help='Names of the monoview classifiers used',
default='RandomForest:RandomForest:RandomForest:RandomForest') default='RandomForest:SGD:SVC:DecisionTree')
groupFusion.add_argument('--FU_cl_config', metavar='STRING', action='store', nargs='+', groupFusion.add_argument('--FU_cl_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the monoview classifiers used', default=['3:4', '3:4', '3:4', '3:4']) help='Configuration for the monoview classifiers used', default=['3:4', 'log:l2', '10:linear',
'4'])
args = parser.parse_args() args = parser.parse_args()
views = args.views.split(":") views = args.views.split(":")
......
Code/MultiView/Fusion/Methods/LateFusion.py
+ 117
− 73
View file @ e71216c0
...@@ -6,6 +6,8 @@ import sys ...@@ -6,6 +6,8 @@ import sys
from sklearn.svm import SVC from sklearn.svm import SVC
from sklearn.multiclass import OneVsOneClassifier from sklearn.multiclass import OneVsOneClassifier
import MonoviewClassifiers import MonoviewClassifiers
from joblib import Parallel, delayed
# Our method in multiclass classification will be One-vs-One or One-vs-All # Our method in multiclass classification will be One-vs-One or One-vs-All
# classifiers, so if we can get the output of these classifiers, we are # classifiers, so if we can get the output of these classifiers, we are
...@@ -15,28 +17,37 @@ import MonoviewClassifiers ...@@ -15,28 +17,37 @@ import MonoviewClassifiers
# monoViewDecisions : (nbExample * nbFeature * NB_CLASS) array with the OVO/OVA scores for each # monoViewDecisions : (nbExample * nbFeature * NB_CLASS) array with the OVO/OVA scores for each
# example, feature and each class # example, feature and each class
# weights : (nbFeature) array with the weights for each feature # weights : (nbFeature) array with the weights for each feature
def fifMonoviewClassifier(classifierName, data, labels, classifierConfig):
class linearWeighted: monoviewClassifier = getattr(MonoviewClassifiers, classifierName)
classifier = monoviewClassifier.fit(data,labels,**dict((str(configIndex), config) for configIndex, config in
def __init__(self, NB_CORES=1, **kwargs): enumerate(classifierConfig
self.monoviewClassifiersNames = kwargs['monoviewClassifiersNames'] )))
self.monoviewClassifiersConfigs = kwargs['monoviewClassifiersConfigs'] return classifier
class LateFusionClassifier:
def __init__(self, monoviewClassifiersNames, monoviewClassifiersConfigs, NB_CORES=1):
self.monoviewClassifiersNames = monoviewClassifiersNames
self.monoviewClassifiersConfigs = monoviewClassifiersConfigs
self.monoviewClassifiers = [] self.monoviewClassifiers = []
self.nbCores = NB_CORES self.nbCores = NB_CORES
self.weights = map(float,kwargs['fusionMethodConfig'][0])
def fit_hdf5(self, DATASET, trainIndices=None): def fit_hdf5(self, DATASET, trainIndices=None):
if trainIndices == None: if trainIndices == None:
trainIndices = range(DATASET.get("datasetLength").value) trainIndices = range(DATASET.get("datasetLength").value)
nbViews = DATASET.get("nbView").value nbView = DATASET.get("nbView").value
for viewIndex in range(nbViews): self.monoviewClassifiers = Parallel(n_jobs=self.nbCores)(
monoviewClassifier = getattr(MonoviewClassifiers, self.monoviewClassifiersNames[viewIndex]) delayed(fifMonoviewClassifier)(self.monoviewClassifiersNames[viewIndex],
self.monoviewClassifiers.append(monoviewClassifier.fit(DATASET["/View"+str(viewIndex)+"/matrix"][trainIndices], DATASET["/View"+str(viewIndex)+"/matrix"][trainIndices, :],
DATASET["/Labels/labelsArray"][trainIndices], DATASET["/Labels/labelsArray"][trainIndices],
NB_CORES=self.nbCores, self.monoviewClassifiersConfigs[viewIndex])
**dict((str(configIndex), config) for configIndex, config in for viewIndex in range(nbView))
enumerate(self.monoviewClassifiersConfigs[viewIndex]
))))
class WeightedLinear(LateFusionClassifier):
def __init__(self, NB_CORES=1, **kwargs):
LateFusionClassifier.__init__(self, kwargs['monoviewClassifiersNames'], kwargs['monoviewClassifiersConfigs'],
NB_CORES=NB_CORES)
self.weights = map(float, kwargs['fusionMethodConfig'][0])
def predict_hdf5(self, DATASET, usedIndices=None): def predict_hdf5(self, DATASET, usedIndices=None):
# Normalize weights ? # Normalize weights ?
...@@ -47,11 +58,12 @@ class linearWeighted: ...@@ -47,11 +58,12 @@ class linearWeighted:
predictedLabels = [] predictedLabels = []
viewScores = np.zeros((DATASET.get("nbView").value, len(usedIndices), DATASET.get("nbClass").value)) viewScores = np.zeros((DATASET.get("nbView").value, len(usedIndices), DATASET.get("nbClass").value))
for viewIndex in range(DATASET.get("nbView").value): for viewIndex in range(DATASET.get("nbView").value):
monoviewClassifier = getattr(MonoviewClassifiers, self.monoviewClassifiersNames[viewIndex]) viewScores[viewIndex] = self.monoviewClassifiers[viewIndex].predict_proba(
viewScores[viewIndex] = self.monoviewClassifiers[viewIndex].predict_proba(DATASET["/View"+str(viewIndex)+"/matrix"][usedIndices], DATASET["/View" + str(viewIndex) + "/matrix"][usedIndices])
self.monoviewClassifiers[viewIndex])
for currentIndex, usedIndex in enumerate(usedIndices): for currentIndex, usedIndex in enumerate(usedIndices):
predictedLabel = np.argmax(np.array([max(viewScore) * weight for viewScore, weight in zip(viewScores[:,currentIndex], self.weights)], dtype=float)) predictedLabel = np.argmax(np.array(
[max(viewScore) * weight for viewScore, weight in zip(viewScores[:, currentIndex], self.weights)],
dtype=float))
predictedLabels.append(predictedLabel) predictedLabels.append(predictedLabel)
# fusedExamples = np.array([sum(np.array([featureScores * weight for weight, featureScores in zip(weights, exampleDecisions)])) for exampleDecisions in monoViewDecisions]) # fusedExamples = np.array([sum(np.array([featureScores * weight for weight, featureScores in zip(weights, exampleDecisions)])) for exampleDecisions in monoViewDecisions])
else: else:
...@@ -63,12 +75,10 @@ class linearWeighted: ...@@ -63,12 +75,10 @@ class linearWeighted:
# The SVMClassifier is here used to find the right weights for linear fusion # The SVMClassifier is here used to find the right weights for linear fusion
# Here we have a function to train it, one to fuse. # Here we have a function to train it, one to fuse.
# And one to do both. # And one to do both.
class SVMForLinear: class SVMForLinear(LateFusionClassifier):
def __init__(self, NB_CORES=1, **kwargs): def __init__(self, NB_CORES=1, **kwargs):
self.monoviewClassifiersNames = kwargs['monoviewClassifiersNames'] LateFusionClassifier.__init__(self, kwargs['monoviewClassifiersNames'], kwargs['monoviewClassifiersConfigs'],
self.monoviewClassifiersConfigs = kwargs['monoviewClassifiersConfigs'] NB_CORES=NB_CORES)
self.monoviewClassifiers = []
self.nbCores = NB_CORES
self.SVMClassifier = None self.SVMClassifier = None
def fit_hdf5(self, DATASET, trainIndices=None): def fit_hdf5(self, DATASET, trainIndices=None):
...@@ -77,7 +87,8 @@ class SVMForLinear: ...@@ -77,7 +87,8 @@ class SVMForLinear:
nbViews = DATASET.get("nbView").value nbViews = DATASET.get("nbView").value
for viewIndex in range(nbViews): for viewIndex in range(nbViews):
monoviewClassifier = getattr(MonoviewClassifiers, self.monoviewClassifiersNames[viewIndex]) monoviewClassifier = getattr(MonoviewClassifiers, self.monoviewClassifiersNames[viewIndex])
self.monoviewClassifiers.append(monoviewClassifier.fit(DATASET["/View"+str(viewIndex)+"/matrix"][trainIndices], self.monoviewClassifiers.append(
monoviewClassifier.fit(DATASET["/View" + str(viewIndex) + "/matrix"][trainIndices],
DATASET["/Labels/labelsArray"][trainIndices], DATASET["/Labels/labelsArray"][trainIndices],
NB_CORES=self.nbCores, NB_CORES=self.nbCores,
**dict((str(configIndex), config) for configIndex, config in **dict((str(configIndex), config) for configIndex, config in
...@@ -94,7 +105,8 @@ class SVMForLinear: ...@@ -94,7 +105,8 @@ class SVMForLinear:
monoviewDecisions = np.zeros((len(usedIndices), DATASET.get("nbView").value), dtype=int) monoviewDecisions = np.zeros((len(usedIndices), DATASET.get("nbView").value), dtype=int)
for viewIndex in range(DATASET.get("nbView").value): for viewIndex in range(DATASET.get("nbView").value):
monoviewClassifier = getattr(MonoviewClassifiers, self.monoviewClassifiersNames[viewIndex]) monoviewClassifier = getattr(MonoviewClassifiers, self.monoviewClassifiersNames[viewIndex])
monoviewDecisions[:, viewIndex] = self.monoviewClassifiers[viewIndex].predict(DATASET["/View"+str(viewIndex)+"/matrix"][usedIndices]) monoviewDecisions[:, viewIndex] = self.monoviewClassifiers[viewIndex].predict(
DATASET["/View" + str(viewIndex) + "/matrix"][usedIndices])
predictedLabels = self.SVMClassifier.predict(monoviewDecisions) predictedLabels = self.SVMClassifier.predict(monoviewDecisions)
else: else:
predictedLabels = [] predictedLabels = []
...@@ -104,36 +116,49 @@ class SVMForLinear: ...@@ -104,36 +116,49 @@ class SVMForLinear:
self.SVMClassifier = OneVsOneClassifier(SVC()) self.SVMClassifier = OneVsOneClassifier(SVC())
monoViewDecisions = np.zeros((len(usedIndices), DATASET.get("nbView").value), dtype=int) monoViewDecisions = np.zeros((len(usedIndices), DATASET.get("nbView").value), dtype=int)
for viewIndex in range(DATASET.get("nbView").value): for viewIndex in range(DATASET.get("nbView").value):
monoViewDecisions[:, viewIndex]=self.monoviewClassifiers[viewIndex].predict(DATASET["/View"+str(viewIndex)+"/matrix"][usedIndices]) monoViewDecisions[:, viewIndex] = self.monoviewClassifiers[viewIndex].predict(
DATASET["/View" + str(viewIndex) + "/matrix"][usedIndices])
self.SVMClassifier.fit(monoViewDecisions, DATASET["/Labels/labelsArray"][usedIndices]) self.SVMClassifier.fit(monoViewDecisions, DATASET["/Labels/labelsArray"][usedIndices])
# For majority voting, we have a problem : we have 5 fetures and 101 classes # For majority voting, we have a problem : we have 5 fetures and 101 classes
# on Calthech, so if each feature votes for one class, we can't find a good # on Calthech, so if each feature votes for one class, we can't find a good
# result # result
def majorityVoting(monoViewDecisions, NB_CLASS): class MajorityVoting(LateFusionClassifier):
nbExample = len(monoViewDecisions) def __init__(self, NB_CORES=1, **kwargs):
votes = np.array([np.zeros(NB_CLASS) for example in monoViewDecisions]) LateFusionClassifier.__init__(self, kwargs['monoviewClassifiersNames'], kwargs['monoviewClassifiersConfigs'],
for exampleIndice in range(nbExample): NB_CORES=NB_CORES)
for featureClassification in monoViewDecisions[exampleIndice]:
votes[exampleIndice, featureClassification] += 1 def predict_hdf5(self, DATASET, usedIndices=None):
nbMaximum = len(np.where(votes[exampleIndice] == max(votes[exampleIndice]))[0]) if usedIndices == None:
usedIndices = range(DATASET.get("datasetLength").value)
if usedIndices:
datasetLength = len(usedIndices)
votes = np.zeros((datasetLength, DATASET.get("nbClass").value), dtype=int)
monoViewDecisions = np.zeros((len(usedIndices), DATASET.get("nbView").value), dtype=int)
for viewIndex in range(DATASET.get("nbView").value):
monoViewDecisions[:, viewIndex] = self.monoviewClassifiers[viewIndex].predict(
DATASET["/View" + str(viewIndex) + "/matrix"][usedIndices])
for exampleIndex in range(datasetLength):
for featureClassification in monoViewDecisions[exampleIndex, :]:
votes[exampleIndex, featureClassification] += 1
nbMaximum = len(np.where(votes[exampleIndex] == max(votes[exampleIndex]))[0])
try: try:
assert nbMaximum != nbFeature assert nbMaximum != DATASET.get("nbView").value
except: except:
print "Majority voting can't decide, each classifier has voted for a different class" print "Majority voting can't decide, each classifier has voted for a different class"
raise raise
predictedLabels = np.argmax(votes, axis=1)
# Can be upgraded by restarting a new classification process if # Can be upgraded by restarting a new classification process if
# there are multiple maximums ?: # there are multiple maximums ?:
# while nbMaximum>1: # while nbMaximum>1:
# relearn with only the classes that have a maximum number of vote # relearn with only the classes that have a maximum number of vote
# votes = revote # votes = revote
# nbMaximum = len(np.where(votes==max(votes))[0]) # nbMaximum = len(np.where(votes==max(votes))[0])
return np.array([np.argmax(exampleVotes) for exampleVotes in votes]) else:
predictedLabels = []
return predictedLabels
# For probabilistic classifiers, we need to add more late fusion methods # For probabilistic classifiers, we need to add more late fusion methods
...@@ -141,24 +166,43 @@ def majorityVoting(monoViewDecisions, NB_CLASS): ...@@ -141,24 +166,43 @@ def majorityVoting(monoViewDecisions, NB_CLASS):
# probabilisticClassifiers is a nbExample array of sklearn probabilistic classifiers # probabilisticClassifiers is a nbExample array of sklearn probabilistic classifiers
# (such as Naive Bayesian Gaussian # (such as Naive Bayesian Gaussian
# http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB) # http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB)
def bayesianInference(probabilisticClassifiers): class BayesianInference(LateFusionClassifier):
nbFeatures = len(probabilisticClassifiers) def __init__(self, NB_CORES=1, **kwargs):
classifiersProbasByFeature = np.array([probabilisticClassifier.class_prior_ \ LateFusionClassifier.__init__(self, kwargs['monoviewClassifiersNames'], kwargs['monoviewClassifiersConfigs'],
for probabilisticClassifier in probabilisticClassifiers]) NB_CORES=NB_CORES)
classifiersProbasByExample = np.transpose(classifiersProbasByFeature) self.weights = np.array(map(float, kwargs['fusionMethodConfig'][0]))
probabilities = np.array([weightedProduct(featureProbas, weights) / nbFeatures for featureProbas in classifiersProbasByExample])
return np.argmax(probabilities / sum(probabilities))
def predict_hdf5(self, DATASET, usedIndices=None):
nbView = DATASET.get("nbView").value
if usedIndices == None:
usedIndices = range(DATASET.get("datasetLength").value)
if sum(self.weights)!=1.0:
self.weights = self.weights/sum(self.weights)
if usedIndices:
viewScores = np.zeros((nbView, len(usedIndices), DATASET.get("nbClass").value))
for viewIndex in range(nbView):
viewScores[viewIndex] = np.power(self.monoviewClassifiers[viewIndex].predict_proba(DATASET["/View" +
str(viewIndex) +
"/matrix"]
[usedIndices]),
self.weights[viewIndex])
predictedLabels = np.argmax(np.prod(viewScores, axis=1), axis=1)
else:
predictedLabels = []
return predictedLabels
#
# def weightedProduct(featureProbas, weights):
# try:
# assert np.sum(weights) == 1.0
# except:
# print "Need to give a weight array that sums to one"
# raise
# weightedProbas = np.power(featureProbas, weights)
# product = np.prod(weightedProbas)
# return product
def weightedProduct(featureProbas, weights):
try:
assert np.sum(weights) == 1.0
except:
print "Need to give a weight array that sums to one"
raise
weightedProbas = np.power(featureProbas, weights)
product = np.prod(weightedProbas)
return product
# #
# #
# # Main for testing # # Main for testing
......
Code/MultiView/Fusion/Methods/MonoviewClassifiers/DecisionTree.py 0 → 100644
+ 8
− 0
View file @ e71216c0
from sklearn.tree import DecisionTreeClassifier
def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
maxDepth = int(kwargs['0'])
classifier = DecisionTreeClassifier(max_depth=maxDepth)
classifier.fit(DATASET, CLASS_LABELS)
return classifier
Code/MultiView/Fusion/Methods/MonoviewClassifiers/SGD.py 0 → 100644
+ 13
− 0
View file @ e71216c0
from sklearn.linear_model import SGDClassifier
def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
loss = kwargs['0']
penalty = kwargs['1']
try:
alpha = int(kwargs['2'])
except:
alpha = 0.15
classifier = SGDClassifier(loss=loss, penalty=penalty, alpha=alpha)
classifier.fit(DATASET, CLASS_LABELS)
return classifier
\ No newline at end of file
Code/MultiView/Fusion/Methods/MonoviewClassifiers/SVC.py 0 → 100644
+ 9
− 0
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from sklearn.svm import SVC
def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
C = int(kwargs['0'])
kernel = kwargs['1']
classifier = SVC(C=C, kernel=kernel, probability=True)
classifier.fit(DATASET, CLASS_LABELS)
return classifier
Code/MultiView/Results/2016_08_18-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-MultiOmic-LOG 0 → 100644
+ 22
− 0
View file @ e71216c0
2016-08-18 10:31:50,543 INFO: ### Main Programm for Multiview Classification
2016-08-18 10:31:50,543 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
2016-08-18 10:31:50,543 INFO: Start: Read HDF5 Database Files for MultiOmic
2016-08-18 10:31:50,545 INFO: Info: Labels used: No, Yes
2016-08-18 10:31:50,545 INFO: Info: Length of dataset:347
2016-08-18 10:31:50,546 INFO: Info: Shape of Methyl :[ 347 25978]
2016-08-18 10:31:50,547 INFO: Info: Shape of MiRNA_ :[ 347 1046]
2016-08-18 10:31:50,548 INFO: Info: Shape of RNASeq :[ 347 73599]
2016-08-18 10:31:50,549 INFO: Info: Shape of Clinic :[347 127]
2016-08-18 10:31:50,549 INFO: Done: Read Database Files
2016-08-18 10:31:50,549 INFO: Start: Determine validation split for ratio 0.3
2016-08-18 10:31:50,552 INFO: Done: Determine validation split
2016-08-18 10:31:50,552 INFO: Start: Determine 4 folds
2016-08-18 10:31:50,560 INFO: Info: Length of Learning Sets: 184
2016-08-18 10:31:50,560 INFO: Info: Length of Testing Sets: 60
2016-08-18 10:31:50,560 INFO: Info: Length of Validation Set: 103
2016-08-18 10:31:50,560 INFO: Done: Determine folds
2016-08-18 10:31:50,560 INFO: Start: Learning with Fusion and 4 folds
2016-08-18 10:31:50,560 INFO: Start: Fold number 1
2016-08-18 10:32:08,183 INFO: Start: Classification
2016-08-18 10:32:13,217 INFO: Done: Fold number 1
2016-08-18 10:32:13,218 INFO: Start: Fold number 2
Code/MultiView/Results/2016_08_18-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-MultiOmic-LOG.log 0 → 100644
+ 19
− 0
View file @ e71216c0
2016-08-18 09:25:42,824 INFO: ### Main Programm for Multiview Classification
2016-08-18 09:25:42,825 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
2016-08-18 09:25:42,825 INFO: Start: Read HDF5 Database Files for MultiOmic
2016-08-18 09:25:43,042 INFO: Info: Labels used: No, Yes
2016-08-18 09:25:43,042 INFO: Info: Length of dataset:347
2016-08-18 09:25:43,043 INFO: Info: Shape of Methyl :[ 347 25978]
2016-08-18 09:25:43,044 INFO: Info: Shape of MiRNA_ :[ 347 1046]
2016-08-18 09:25:43,045 INFO: Info: Shape of RNASeq :[ 347 73599]
2016-08-18 09:25:43,046 INFO: Info: Shape of Clinic :[347 127]
2016-08-18 09:25:43,046 INFO: Done: Read Database Files
2016-08-18 09:25:43,046 INFO: Start: Determine validation split for ratio 0.3
2016-08-18 09:25:43,049 INFO: Done: Determine validation split
2016-08-18 09:25:43,049 INFO: Start: Determine 4 folds
2016-08-18 09:25:43,056 INFO: Info: Length of Learning Sets: 184
2016-08-18 09:25:43,056 INFO: Info: Length of Testing Sets: 60
2016-08-18 09:25:43,056 INFO: Info: Length of Validation Set: 103
2016-08-18 09:25:43,056 INFO: Done: Determine folds
2016-08-18 09:25:43,057 INFO: Start: Learning with Fusion and 4 folds
2016-08-18 09:25:43,057 INFO: Start: Fold number 1
Code/MultiView/run.py
+ 1
− 1
View file @ e71216c0
import os import os
os.system('python ExecMultiview.py -log --name MultiOmic --type .hdf5 --views Methyl:MiRNA:RNASEQ:Clinical --pathF /home/bbauvin/Documents/Data/Data_multi_omics/ --CL_split 0.3 --CL_nbFolds 4 --CL_nb_class 2 --CL_classes Positive:Negative --CL_type Fusion --CL_cores 4 ') os.system('python ExecMultiview.py -log --name MultiOmic --type .hdf5 --views Methyl:MiRNA:RNASEQ:Clinical --pathF /home/bbauvin/Documents/Data/Data_multi_omics/ --CL_split 0.3 --CL_nbFolds 4 --CL_nb_class 2 --CL_classes Positive:Negative --CL_type Fusion --CL_cores 4 --FU_method SVMForLinear')
# /donnees/pj_bdd_bbauvin/Data_multi_omics/ # /donnees/pj_bdd_bbauvin/Data_multi_omics/
# --MU_type DecisionTree:DecisionTree:DecisionTree:DecisionTree --MU_config 1:0.015 1:0.015 1:0.1 2:0.3 --MU_iter 100 # --MU_type DecisionTree:DecisionTree:DecisionTree:DecisionTree --MU_config 1:0.015 1:0.015 1:0.1 2:0.3 --MU_iter 100
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