From 8e061d1a002486018c95324a4f341dd580b0b47e Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Wed, 8 Aug 2018 16:03:21 -0400
Subject: [PATCH] It runs with mono and mutliview on adaboost
---
.../MonoviewClassifiers/Adaboost.py | 19 +++----
.../Fusion/Methods/EarlyFusion.py | 10 +++-
.../EarlyFusionPackage/WeightedLinear.py | 18 ++++---
.../Fusion/Methods/LateFusion.py | 49 ++++++++++---------
.../LateFusionPackage/BayesianInference.py | 7 ++-
.../LateFusionPackage/MajorityVoting.py | 7 ++-
.../Methods/LateFusionPackage/SCMForLinear.py | 15 ++----
.../Methods/LateFusionPackage/SVMForLinear.py | 25 +++-------
.../LateFusionPackage/WeightedLinear.py | 7 ++-
.../utils/execution.py | 2 +-
10 files changed, 76 insertions(+), 83 deletions(-)
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py
index 05f6545b..6e47cd74 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py
@@ -10,7 +10,7 @@ __status__ = "Prototype" # Production, Development, Prototype
class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
- def __init__(self, random_state=None, n_estimators=10,
+ def __init__(self, random_state=None, n_estimators=50,
base_estimator=None, **kwargs):
super(Adaboost, self).__init__(
random_state=random_state,
@@ -26,14 +26,6 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def paramsToSrt(self, nIter=1):
- """Used for weighted linear early fusion to generate random search sets"""
- paramsSet = []
- for _ in range(nIter):
- paramsSet.append({"n_estimators": self.random_state.randint(1, 150),
- "base_estimator": None})
- return paramsSet
-
def getInterpret(self, directory):
interpretString = ""
interpretString += self.getFeatureImportance(directory)
@@ -45,6 +37,15 @@ def formatCmdArgs(args):
kwargsDict = {'n_estimators': args.Ada_n_est,
'base_estimator': DecisionTreeClassifier()}
return kwargsDict
+
+
+def paramsToSet(nIter, random_state):
+ """Used for weighted linear early fusion to generate random search sets"""
+ paramsSet = []
+ for _ in range(nIter):
+ paramsSet.append({"n_estimators": random_state.randint(1, 500),
+ "base_estimator": None})
+ return paramsSet
# def canProbas():
# return True
#
diff --git a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/EarlyFusion.py b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/EarlyFusion.py
index fa282d9c..0199062d 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/EarlyFusion.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/EarlyFusion.py
@@ -3,7 +3,7 @@
import numpy as np
from ....utils.Dataset import getV
-
+from .... import MonoviewClassifiers
class EarlyFusionClassifier(object):
def __init__(self, randomState, monoviewClassifierName, monoviewClassifierConfig, NB_CORES=1):
@@ -16,8 +16,14 @@ class EarlyFusionClassifier(object):
monoviewClassifierConfig = dict((str(configIndex), config[0]) for configIndex, config in
enumerate(monoviewClassifierConfig
))
+ monoviewClassifierModule = getattr(MonoviewClassifiers, self.monoviewClassifierName)
+ if monoviewClassifierConfig is not None:
+ self.monoviewClassifier = getattr(monoviewClassifierModule, self.monoviewClassifierName)(
+ random_state=randomState, **monoviewClassifierConfig)
+ else:
+ self.monoviewClassifier = getattr(monoviewClassifierModule, self.monoviewClassifierName)(
+ random_state=randomState)
self.monoviewClassifiersConfig = monoviewClassifierConfig
- self.monoviewClassifier = None
self.nbCores = NB_CORES
self.monoviewData = None
self.randomState = randomState
diff --git a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py
index 16fba3f0..c0dd0bfc 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py
@@ -70,6 +70,9 @@ class WeightedLinear(EarlyFusionClassifier):
self.weights = np.ones(len(kwargs["classifiersNames"]), dtype=float)
else:
self.weights = np.array(map(float, kwargs['fusionMethodConfig']))
+ self.weights /= float(max(self.weights))
+
+
def fit_hdf5(self, DATASET, labels, trainIndices=None, viewsIndices=None):
if type(viewsIndices) == type(None):
@@ -78,12 +81,11 @@ class WeightedLinear(EarlyFusionClassifier):
trainIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
self.weights /= float(max(self.weights))
self.makeMonoviewData_hdf5(DATASET, weights=self.weights, usedIndices=trainIndices, viewsIndices=viewsIndices)
- monoviewClassifierModule = getattr(MonoviewClassifiers, self.monoviewClassifierName)
- self.monoviewClassifier = monoviewClassifierModule.fit(self.monoviewData,
- labels[trainIndices],
- self.randomState,
- NB_CORES=self.nbCores,
- **self.monoviewClassifiersConfig)
+ # monoviewClassifierModule = getattr(MonoviewClassifiers, self.monoviewClassifierName)
+ self.monoviewClassifier.fit(self.monoviewData, labels[trainIndices])
+ # self.randomState,
+ # NB_CORES=self.nbCores,
+ # **self.monoviewClassifiersConfig)
def setParams(self, paramsSet):
self.weights = paramsSet[0]
@@ -110,8 +112,8 @@ class WeightedLinear(EarlyFusionClassifier):
def getConfig(self, fusionMethodConfig, monoviewClassifiersNames, monoviewClassifiersConfigs):
configString = "with weighted concatenation, using weights : " + ", ".join(map(str, self.weights)) + \
" with monoview classifier : "
- monoviewClassifierModule = getattr(MonoviewClassifiers, monoviewClassifiersNames)
- configString += monoviewClassifierModule.getConfig(self.monoviewClassifiersConfig)
+ # monoviewClassifierModule = getattr(MonoviewClassifiers, monoviewClassifiersNames)
+ configString += self.monoviewClassifier.getConfig()
return configString
def gridSearch(self, classificationKWARGS):
diff --git a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusion.py b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusion.py
index 15372bf5..f3e5bdf9 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusion.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusion.py
@@ -12,30 +12,28 @@ from .... import Metrics
from ....utils.Dataset import getV
-def canProbasClassifier(classifierConfig):
- try:
- _ = getattr(classifierConfig, "predict_proba")
- return True
- except AttributeError:
- return False
+# def canProbasClassifier(classifierConfig):
+# try:
+# _ = getattr(classifierConfig, "predict_proba")
+# return True
+# except AttributeError:
+# return False
-def fitMonoviewClassifier(classifierName, data, labels, classifierConfig, needProbas, randomState, nbCores=1):
- if type(classifierConfig) == dict:
- monoviewClassifier = getattr(MonoviewClassifiers, classifierName)
+def fitMonoviewClassifier(monoviewClassifier, data, labels, needProbas, randomState, nbCores=1):
if needProbas and not monoviewClassifier.canProbas():
- monoviewClassifier = getattr(MonoviewClassifiers, "DecisionTree")
DTConfig = {"max_depth": 300, "criterion": "entropy", "splitter": "random"}
- classifier = monoviewClassifier.fit(data, labels, randomState, nbCores, **DTConfig)
+ monoviewClassifier = getattr(MonoviewClassifiers, "DecisionTree")(random_state=randomState, **DTConfig)
+ classifier = monoviewClassifier.fit(data, labels)
return classifier
else:
- if type(classifierConfig) is dict:
- pass
- else:
- classifierConfig = dict((str(configIndex), config)
- for configIndex, config in enumerate(classifierConfig))
+ # if type(classifierConfig) is dict:
+ # pass
+ # else:
+ # classifierConfig = dict((str(configIndex), config)
+ # for configIndex, config in enumerate(classifierConfig))
- classifier = monoviewClassifier.fit(data, labels, randomState, nbCores, **classifierConfig)
+ classifier = monoviewClassifier.fit(data, labels)
return classifier
@@ -119,11 +117,18 @@ class LateFusionClassifier(object):
def __init__(self, randomState, monoviewClassifiersNames, monoviewClassifiersConfigs, monoviewSelection,
NB_CORES=1):
self.monoviewClassifiersNames = monoviewClassifiersNames
+ monoviewClassifiersModules = [getattr(MonoviewClassifiers, classifierName)
+ for classifierName in self.monoviewClassifiersNames]
if type(monoviewClassifiersConfigs[0]) == dict:
- self.monoviewClassifiersConfigs = monoviewClassifiersConfigs
- self.monoviewClassifiers = []
+ self.monoviewClassifiers = [
+ getattr(monoviewClassifiersModule, classifierName)(random_state=randomState, **config)
+ for monoviewClassifiersModule, config, classifierName
+ in zip(monoviewClassifiersModules, monoviewClassifiersConfigs, monoviewClassifiersNames)]
else:
- self.monoviewClassifiersConfigs = monoviewClassifiersConfigs
+ self.monoviewClassifiers = [
+ getattr(monoviewClassifiersModule, classifierName)(random_state=randomState,)
+ for monoviewClassifiersModule, config, classifierName
+ in zip(monoviewClassifiersModules, monoviewClassifiersConfigs, monoviewClassifiersNames)]
self.nbCores = NB_CORES
self.accuracies = np.zeros(len(monoviewClassifiersNames))
self.needProbas = False
@@ -136,8 +141,8 @@ class LateFusionClassifier(object):
if trainIndices is None:
trainIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
self.monoviewClassifiers = Parallel(n_jobs=self.nbCores)(
- delayed(fitMonoviewClassifier)(self.monoviewClassifiersNames[index],
+ delayed(fitMonoviewClassifier)(self.monoviewClassifiers[index],
getV(DATASET, viewIndex, trainIndices),
labels[trainIndices],
- self.monoviewClassifiersConfigs[index], self.needProbas, self.randomState)
+ self.needProbas, self.randomState)
for index, viewIndex in enumerate(viewsIndices))
diff --git a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/BayesianInference.py b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/BayesianInference.py
index 1eade801..d825f24a 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/BayesianInference.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/BayesianInference.py
@@ -87,9 +87,8 @@ class BayesianInference(LateFusionClassifier):
def getConfig(self, fusionMethodConfig, monoviewClassifiersNames, monoviewClassifiersConfigs):
configString = "with Bayesian Inference using a weight for each view : " + ", ".join(map(str, self.weights)) + \
"\n\t-With monoview classifiers : "
- for monoviewClassifierConfig, monoviewClassifierName in zip(monoviewClassifiersConfigs,
- monoviewClassifiersNames):
- monoviewClassifierModule = getattr(MonoviewClassifiers, monoviewClassifierName)
- configString += monoviewClassifierModule.getConfig(monoviewClassifierConfig)
+ for monoviewClassifier in self.monoviewClassifiers:
+
+ configString += monoviewClassifier.getConfig()
configString += "\n\t -Method used to select monoview classifiers : " + self.monoviewSelection
return configString
diff --git a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/MajorityVoting.py b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/MajorityVoting.py
index 33b5c2fb..34086cc8 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/MajorityVoting.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/MajorityVoting.py
@@ -100,8 +100,7 @@ class MajorityVoting(LateFusionClassifier):
def getConfig(self, fusionMethodConfig, monoviewClassifiersNames, monoviewClassifiersConfigs):
configString = "with Majority Voting \n\t-With weights : " + str(
self.weights) + "\n\t-With monoview classifiers : "
- for monoviewClassifierConfig, monoviewClassifierName in zip(monoviewClassifiersConfigs,
- monoviewClassifiersNames):
- monoviewClassifierModule = getattr(MonoviewClassifiers, monoviewClassifierName)
- configString += monoviewClassifierModule.getConfig(monoviewClassifierConfig)
+ for monoviewClassifier in self.monoviewClassifiers:
+
+ configString += monoviewClassifier.getConfig()
return configString
diff --git a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/SCMForLinear.py b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/SCMForLinear.py
index 8e70b8f3..705c6a8f 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/SCMForLinear.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/SCMForLinear.py
@@ -119,12 +119,8 @@ class SCMForLinear(LateFusionClassifier):
if trainIndices is None:
trainIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
for index, viewIndex in enumerate(viewsIndices):
- monoviewClassifier = getattr(MonoviewClassifiers, self.monoviewClassifiersNames[index])
- self.monoviewClassifiers.append(
- monoviewClassifier.fit(getV(DATASET, viewIndex, trainIndices),
- labels[trainIndices], self.randomState,
- NB_CORES=self.nbCores,
- **self.monoviewClassifiersConfigs[index]))
+ self.monoviewClassifiers[index].fit(getV(DATASET, viewIndex, trainIndices),
+ labels[trainIndices])
self.SCMForLinearFusionFit(DATASET, labels, usedIndices=trainIndices, viewsIndices=viewsIndices)
def predict_hdf5(self, DATASET, usedIndices=None, viewsIndices=None):
@@ -184,8 +180,7 @@ class SCMForLinear(LateFusionClassifier):
configString = "with SCM for linear with max_attributes : " + str(self.maxAttributes) + ", p : " + str(self.p) + \
" model_type : " + str(self.modelType) + " order : " + str(self.order)+ " has chosen " + \
str(0.1) + " rule(s) \n\t-With monoview classifiers : "
- for monoviewClassifierConfig, monoviewClassifierName in zip(monoviewClassifiersConfigs,
- monoviewClassifiersNames):
- monoviewClassifierModule = getattr(MonoviewClassifiers, monoviewClassifierName)
- configString += monoviewClassifierModule.getConfig(monoviewClassifierConfig)
+ for monoviewClassifier in self.monoviewClassifiers:
+
+ configString += monoviewClassifier.getConfig()
return configString
diff --git a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/SVMForLinear.py b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/SVMForLinear.py
index ce242717..260cef57 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/SVMForLinear.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/SVMForLinear.py
@@ -62,21 +62,9 @@ class SVMForLinear(LateFusionClassifier):
viewsIndices = np.arange(DATASET.get("Metadata").attrs["nbView"])
if trainIndices is None:
trainIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
- if type(self.monoviewClassifiersConfigs[0]) == dict:
- for index, viewIndex in enumerate(viewsIndices):
- monoviewClassifier = getattr(MonoviewClassifiers, self.monoviewClassifiersNames[index])
- if type(self.monoviewClassifiersConfigs[index]) is dict:
- pass
- else:
- self.monoviewClassifiersConfigs[index] = dict((str(configIndex), config)
- for configIndex, config in enumerate(self.monoviewClassifiersConfigs[index]))
- self.monoviewClassifiers.append(
- monoviewClassifier.fit(getV(DATASET, viewIndex, trainIndices),
- labels[trainIndices], self.randomState,
- NB_CORES=self.nbCores,
- **self.monoviewClassifiersConfigs[index]))
- else:
- self.monoviewClassifiers = self.monoviewClassifiersConfigs
+ for index, viewIndex in enumerate(viewsIndices):
+ self.monoviewClassifiers[index].fit(getV(DATASET, viewIndex, trainIndices),
+ labels[trainIndices])
self.SVMForLinearFusionFit(DATASET, labels, usedIndices=trainIndices, viewsIndices=viewsIndices)
def setParams(self, paramsSet):
@@ -109,8 +97,7 @@ class SVMForLinear(LateFusionClassifier):
def getConfig(self, fusionMethodConfig, monoviewClassifiersNames, monoviewClassifiersConfigs):
configString = "with SVM for linear \n\t-With monoview classifiers : "
- for monoviewClassifierConfig, monoviewClassifierName in zip(monoviewClassifiersConfigs,
- monoviewClassifiersNames):
- monoviewClassifierModule = getattr(MonoviewClassifiers, monoviewClassifierName)
- configString += monoviewClassifierModule.getConfig(monoviewClassifierConfig)
+ for monoviewClassifier in self.monoviewClassifiers:
+
+ configString += monoviewClassifier.getConfig()
return configString
diff --git a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/WeightedLinear.py b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/WeightedLinear.py
index db86de2e..ad5b2fad 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/WeightedLinear.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/Methods/LateFusionPackage/WeightedLinear.py
@@ -85,8 +85,7 @@ class WeightedLinear(LateFusionClassifier):
def getConfig(self, fusionMethodConfig, monoviewClassifiersNames, monoviewClassifiersConfigs):
configString = "with Weighted linear using a weight for each view : " + ", ".join(map(str, self.weights)) + \
"\n\t-With monoview classifiers : "
- for monoviewClassifierConfig, monoviewClassifierName in zip(monoviewClassifiersConfigs,
- monoviewClassifiersNames):
- monoviewClassifierModule = getattr(MonoviewClassifiers, monoviewClassifierName)
- configString += monoviewClassifierModule.getConfig(monoviewClassifierConfig)
+ for monoviewClassifier in self.monoviewClassifiers:
+
+ configString += monoviewClassifier.getConfig()
return configString
diff --git a/multiview_platform/MonoMultiViewClassifiers/utils/execution.py b/multiview_platform/MonoMultiViewClassifiers/utils/execution.py
index 961091a2..9493fbee 100644
--- a/multiview_platform/MonoMultiViewClassifiers/utils/execution.py
+++ b/multiview_platform/MonoMultiViewClassifiers/utils/execution.py
@@ -61,7 +61,7 @@ def parseTheArgs(arguments):
'selected', default=["yes", "no"])
groupClass.add_argument('--CL_type', metavar='STRING', action='store', nargs="+",
help='Determine whether to use Multiview and/or Monoview, or Benchmark classification',
- default=['Benchmark'])
+ default=['Monoview', 'Multiview'])
groupClass.add_argument('--CL_algos_monoview', metavar='STRING', action='store', nargs="+",
help='Determine which monoview classifier to use if empty, considering all',
default=[''])
--
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