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ExecClassifMonoView.py
analyzeResults.py 19.00 KiB
from sklearn.metrics import precision_recall_fscore_support, accuracy_score, classification_report
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import operator
from datetime import timedelta as hms
import Mumbo
from Classifiers import *
import logging
import Metrics
from utils.Dataset import getV, getShape
# Author-Info
__author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
def findMainView(bestViews):
views = list(set(bestViews))
viewCount = np.array([list(bestViews).count(view) for view in views])
mainView = views[np.argmax(viewCount)]
return mainView
def plotAccuracyByIter(trainAccuracy, testAccuracy, validationAccuracy, NB_ITER, bestViews, features, classifierAnalysis):
print NB_ITER
x = range(NB_ITER)
mainView = findMainView(bestViews)
figure = plt.figure()
ax1 = figure.add_subplot(111)
axes = figure.gca()
axes.set_ylim([40,100])
titleString = ""
for view, classifierConfig in zip(features, classifierAnalysis):
titleString += "\n" + view + " : " + classifierConfig
titleString += "\nBest view = " + features[int(mainView)]
ax1.set_title("Accuracy depending on iteration", fontsize=20)
plt.text(0.5, 1.08, titleString,
horizontalalignment='center',
fontsize=8,
transform = ax1.transAxes)
figure.subplots_adjust(top=0.8)
ax1.set_xlabel("Iteration Index")
ax1.set_ylabel("Accuracy")
ax1.plot(x, trainAccuracy, c='red', label='Train')
ax1.plot(x, testAccuracy, c='black', label='Test')
ax1.plot(x, validationAccuracy, c='blue', label='Validation')
ax1.legend(loc='lower center',
ncol=3, fancybox=True, shadow=True)
return '-accuracyByIteration', figure
def classifyMumbobyIter_hdf5(usedIndices, DATASET, classifiers, alphas, views, NB_CLASS):
DATASET_LENGTH = len(usedIndices)
NB_ITER = len(classifiers)
predictedLabels = np.zeros((DATASET_LENGTH, NB_ITER))
votes = np.zeros((DATASET_LENGTH, NB_CLASS))
for classifier, alpha, view, iterIndex in zip(classifiers, alphas, views, range(NB_ITER)):
votesByIter = np.zeros((DATASET_LENGTH, NB_CLASS))
for usedExampleIndex, exampleIndex in enumerate(usedIndices):
data = np.array([np.array(getV(DATASET,int(view), exampleIndex))])
votesByIter[usedExampleIndex, int(classifier.predict(data))] += alpha
votes[usedExampleIndex] = votes[usedExampleIndex] + np.array(votesByIter[usedExampleIndex])
predictedLabels[usedExampleIndex, iterIndex] = np.argmax(votes[usedExampleIndex])
return np.transpose(predictedLabels)
def error(testLabels, computedLabels):
error = sum(map(operator.ne, computedLabels, testLabels))
return float(error) * 100 / len(computedLabels)
def getDBConfig(DATASET, LEARNING_RATE, nbFolds, databaseName, validationIndices, LABELS_DICTIONARY):
nbView = DATASET.get("Metadata").attrs["nbView"]
viewNames = [DATASET.get("View"+str(viewIndex)).attrs["name"] for viewIndex in range(nbView)]
viewShapes = [getShape(DATASET,viewIndex) for viewIndex in range(nbView)]
DBString = "Dataset info :\n\t-Dataset name : " + databaseName
DBString += "\n\t-Labels : " + ', '.join(LABELS_DICTIONARY.values())
DBString += "\n\t-Views : " + ', '.join([viewName+" of shape "+str(viewShape)
for viewName, viewShape in zip(viewNames, viewShapes)])
DBString += "\n\t-" + str(nbFolds) + " folds"
DBString += "\n\t- Validation set length : "+str(len(validationIndices))+" for learning rate : "+str(LEARNING_RATE)+" on a total number of examples of "+str(DATASET.get("Metadata").attrs["datasetLength"])
DBString += "\n\n"
return DBString, viewNames
def getAlgoConfig(initKWARGS, NB_CORES, viewNames, gridSearch, nIter, times):
classifierNames = initKWARGS["classifiersNames"]
maxIter = initKWARGS["maxIter"]
minIter = initKWARGS["minIter"]
threshold = initKWARGS["threshold"]
classifiersConfig = initKWARGS["classifiersConfigs"]
extractionTime, kFoldLearningTime, kFoldPredictionTime, classificationTime = times
weakClassifierConfigs = [getattr(globals()[classifierName], 'getConfig')(classifiersConfig) for classifiersConfig,
classifierName
in zip(classifiersConfig, classifierNames)]
classifierAnalysis = [classifierName + " " + weakClassifierConfig + "on " + feature for classifierName,
weakClassifierConfig,
feature
in zip(classifierNames, weakClassifierConfigs, viewNames)]
gridSearchString = ""
if gridSearch:
gridSearchString += "Configurations found by randomized search with "+str(nIter)+" iterations"
algoString = "\n\nMumbo configuration : \n\t-Used "+str(NB_CORES)+" core(s)"
algoString += "\n\t-Iterations : min " + str(minIter)+ ", max "+str(maxIter)+", threshold "+str(threshold)
algoString += "\n\t-Weak Classifiers : " + "\n\t\t-".join(classifierAnalysis)
algoString += "\n\n"
algoString += "\n\nComputation time on " + str(NB_CORES) + " cores : \n\tDatabase extraction time : " + str(
hms(seconds=int(extractionTime))) + "\n\t"
row_format = "{:>15}" * 3
algoString += row_format.format("", *['Learn', 'Prediction'])
for index, (learningTime, predictionTime) in enumerate(zip(kFoldLearningTime, kFoldPredictionTime)):
algoString += '\n\t'
algoString += row_format.format("Fold " + str(index + 1), *[str(hms(seconds=int(learningTime))),
str(hms(seconds=int(predictionTime)))])
algoString += '\n\t'
algoString += row_format.format("Total", *[str(hms(seconds=int(sum(kFoldLearningTime)))),
str(hms(seconds=int(sum(kFoldPredictionTime))))])
algoString += "\n\tSo a total classification time of " + str(hms(seconds=int(classificationTime))) + ".\n\n"
algoString += "\n\n"
return algoString, classifierAnalysis
def getClassificationReport(kFolds, kFoldClassifier, CLASS_LABELS, validationIndices, DATASET,
kFoldPredictedTrainLabels, kFoldPredictedTestLabels, kFoldPredictedValidationLabels):
DATASET_LENGTH = DATASET.get("Metadata").attrs["datasetLength"]
nbView = DATASET.get("Metadata").attrs["nbView"]
NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
kFoldPredictedTrainLabelsByIter = []
kFoldPredictedTestLabelsByIter = []
kFoldPredictedValidationLabelsByIter = []
kFoldBestViews = []
kFoldAccuracyOnTrain = []
kFoldAccuracyOnTest = []
kFoldAccuracyOnValidation = []
kFoldAccuracyOnTrainByIter = []
kFoldAccuracyOnTestByIter = []
kFoldAccuracyOnValidationByIter = []
kFoldMeanAverageAccuracies = []
kFoldBestViewsStats = []
for foldIdx, fold in enumerate(kFolds):
if fold != range(DATASET_LENGTH):
mumboClassifier = kFoldClassifier[foldIdx]
meanAverageAccuracies = np.mean(mumboClassifier.averageAccuracies, axis=0)
kFoldMeanAverageAccuracies.append(meanAverageAccuracies)
trainIndices = [index for index in range(DATASET_LENGTH) if (index not in fold) and (index not in validationIndices)]
testLabels = CLASS_LABELS[fold]
trainLabels = CLASS_LABELS[trainIndices]
validationLabels = CLASS_LABELS[validationIndices]
PredictedTrainLabelsByIter = mumboClassifier.classifyMumbobyIter_hdf5(DATASET, usedIndices=trainIndices,
NB_CLASS=NB_CLASS)
kFoldPredictedTrainLabelsByIter.append(PredictedTrainLabelsByIter)
PredictedTestLabelsByIter = mumboClassifier.classifyMumbobyIter_hdf5(DATASET, usedIndices=fold,
NB_CLASS=NB_CLASS)
kFoldPredictedTestLabelsByIter.append(PredictedTestLabelsByIter)
PredictedValidationLabelsByIter = mumboClassifier.classifyMumbobyIter_hdf5(DATASET,
usedIndices=validationIndices,
NB_CLASS=NB_CLASS)
kFoldPredictedValidationLabelsByIter.append(PredictedValidationLabelsByIter)
kFoldAccuracyOnTrainByIter.append([])
kFoldAccuracyOnTestByIter.append([])
kFoldAccuracyOnValidationByIter.append([])
for iterIndex in range(mumboClassifier.iterIndex+1):
if len(PredictedTestLabelsByIter)==mumboClassifier.iterIndex+1:
kFoldAccuracyOnTestByIter[foldIdx].append(100 * accuracy_score(testLabels,
PredictedTestLabelsByIter[iterIndex]))
else:
kFoldAccuracyOnTestByIter[foldIdx].append(0.0)
kFoldAccuracyOnTrainByIter[foldIdx].append(100 * accuracy_score(trainLabels,
PredictedTrainLabelsByIter[iterIndex]))
kFoldAccuracyOnValidationByIter[foldIdx].append(100 * accuracy_score(validationLabels,
PredictedValidationLabelsByIter[iterIndex]))
kFoldBestViews.append(mumboClassifier.bestViews)
kFoldBestViewsStats.append([float(list(mumboClassifier.bestViews).count(viewIndex))/
len(mumboClassifier.bestViews)
for viewIndex in range(nbView)])
kFoldAccuracyOnTrain.append(100 * accuracy_score(trainLabels, kFoldPredictedTrainLabels[foldIdx]))
kFoldAccuracyOnTest.append(100 * accuracy_score(testLabels, kFoldPredictedTestLabels[foldIdx]))
kFoldAccuracyOnValidation.append(100 * accuracy_score(validationLabels,
kFoldPredictedValidationLabels[foldIdx]))
totalAccuracyOnTrain = np.mean(kFoldAccuracyOnTrain)
totalAccuracyOnTest = np.mean(kFoldAccuracyOnTest)
totalAccuracyOnValidation = np.mean(kFoldAccuracyOnValidation)
return (totalAccuracyOnTrain, totalAccuracyOnTest, totalAccuracyOnValidation, kFoldMeanAverageAccuracies,
kFoldBestViewsStats, kFoldAccuracyOnTrainByIter, kFoldAccuracyOnTestByIter, kFoldAccuracyOnValidationByIter,
kFoldBestViews)
def iterRelevant(iterIndex, kFoldClassifier):
relevants = np.zeros(len(kFoldClassifier), dtype=bool)
for classifierIndex, classifier in enumerate(kFoldClassifier):
if classifier.iterIndex >= iterIndex:
relevants[classifierIndex] = True
return relevants
def modifiedMean(surplusAccuracies):
maxLen = 0
for foldAccuracies in surplusAccuracies.values():
if len(foldAccuracies)>maxLen:
maxLen = len(foldAccuracies)
meanAccuracies = []
for accuracyIndex in range(maxLen):
accuraciesToMean = []
for foldIndex in surplusAccuracies.keys():
try:
accuraciesToMean.append(surplusAccuracies[foldIndex][accuracyIndex])
except:
pass
meanAccuracies.append(np.mean(np.array(accuraciesToMean)))
return meanAccuracies
def getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred):
metricModule = getattr(Metrics, metric[0])
if metric[1]!=None:
metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
else:
metricKWARGS = {}
metricScoreString = "\tFor "+metricModule.getConfig(**metricKWARGS)+" : "
metricScoreString += "\n\t\t- Score on train : "+str(metricModule.score(y_train, y_train_pred))
metricScoreString += "\n\t\t- Score on test : "+str(metricModule.score(y_test, y_test_pred))
metricScoreString += "\n"
return metricScoreString
def getTotalMetricScores(metric, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds):
labels = DATASET.get("Labels").value
metricModule = getattr(Metrics, metric[0])
if metric[1]!=None:
metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
else:
metricKWARGS = {}
trainScore = np.mean(np.array([metricModule.score([label for index, label in enumerate(labels) if (index not in fold) and (index not in validationIndices)], predictedLabels, **metricKWARGS) for fold, predictedLabels in zip(kFolds, kFoldPredictedTrainLabels)]))
testScore = np.mean(np.array([metricModule.score(labels[fold], predictedLabels, **metricKWARGS) for fold, predictedLabels in zip(kFolds, kFoldPredictedTestLabels)]))
validationScore = np.mean(np.array([metricModule.score(labels[validationIndices], predictedLabels, **metricKWARGS) for predictedLabels in kFoldPredictedValidationLabels]))
return [trainScore, testScore, validationScore]
def getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds,):
metricsScores = {}
for metric in metrics:
metricsScores[metric[0]] = getTotalMetricScores(metric, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
return metricsScores
def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels, kFoldPredictedValidationLabels,
DATASET, initKWARGS, LEARNING_RATE, LABELS_DICTIONARY, views, NB_CORES, times, kFolds, databaseName,
nbFolds, validationIndices, gridSearch, nIter, metrics):
CLASS_LABELS = DATASET.get("Labels")[...]
maxIter = initKWARGS["maxIter"]
minIter = initKWARGS["minIter"]
dbConfigurationString, viewNames = getDBConfig(DATASET, LEARNING_RATE, nbFolds, databaseName, validationIndices, LABELS_DICTIONARY)
algoConfigurationString, classifierAnalysis = getAlgoConfig(initKWARGS, NB_CORES, viewNames, gridSearch, nIter, times)
(totalAccuracyOnTrain, totalAccuracyOnTest, totalAccuracyOnValidation, kFoldMeanAverageAccuracies,
kFoldBestViewsStats, kFoldAccuracyOnTrainByIter, kFoldAccuracyOnTestByIter, kFoldAccuracyOnValidationByIter,
kFoldBestViews) = getClassificationReport(kFolds, kFoldClassifier, CLASS_LABELS, validationIndices, DATASET,
kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
kFoldPredictedValidationLabels)
nbMinIter = maxIter
nbMaxIter = minIter
for classifier in kFoldClassifier:
if classifier.iterIndex+1<nbMinIter:
nbMinIter = classifier.iterIndex+1
if classifier.iterIndex+1>nbMaxIter:
nbMaxIter = classifier.iterIndex+1
formatedAccuracies = {"Train":np.zeros((nbFolds, nbMinIter)), "Test":np.zeros((nbFolds, nbMinIter)),
"Validation":np.zeros((nbFolds, nbMinIter))}
surplusAccuracies = {"Train":{}, "Test":{},"Validation":{}}
for classifierIndex, classifier in enumerate(kFoldClassifier):
formatedAccuracies["Test"][classifierIndex] = np.array(kFoldAccuracyOnTestByIter[classifierIndex][:nbMinIter])
formatedAccuracies["Train"][classifierIndex] = np.array(kFoldAccuracyOnTrainByIter[classifierIndex][:nbMinIter])
formatedAccuracies["Validation"][classifierIndex] = np.array(kFoldAccuracyOnValidationByIter[classifierIndex][:nbMinIter])
if classifier.iterIndex+1>nbMinIter:
surplusAccuracies["Train"][classifierIndex] = kFoldAccuracyOnTrainByIter[classifierIndex][nbMinIter:]
surplusAccuracies["Test"][classifierIndex] = kFoldAccuracyOnTestByIter[classifierIndex][nbMinIter:]
surplusAccuracies["Validation"][classifierIndex] = kFoldAccuracyOnValidationByIter[classifierIndex][nbMinIter:]
bestViews = [findMainView(np.array(kFoldBestViews)[:, iterIdx]) for iterIdx in range(nbMinIter)]
stringAnalysis = "\t\tResult for Multiview classification with Mumbo" \
"\n\nAverage accuracy :\n\t-On Train : " + str(totalAccuracyOnTrain) + "\n\t-On Test : " + \
str(totalAccuracyOnTest) + "\n\t-On Validation : " + str(totalAccuracyOnValidation)
stringAnalysis += dbConfigurationString
stringAnalysis += algoConfigurationString
stringAnalysis += "Mean average accuracies and stats for each fold :"
for foldIdx in range(nbFolds):
stringAnalysis += "\n\t- Fold "+str(foldIdx)+", used "+str(kFoldClassifier[foldIdx].iterIndex + 1)
for viewIndex, (meanAverageAccuracy, bestViewStat) in enumerate(zip(kFoldMeanAverageAccuracies[foldIdx], kFoldBestViewsStats[foldIdx])):
stringAnalysis+="\n\t\t- On "+viewNames[viewIndex]+\
" : \n\t\t\t- Mean average Accuracy : "+str(meanAverageAccuracy)+\
"\n\t\t\t- Percentage of time chosen : "+str(bestViewStat)
stringAnalysis += "\n\n For each iteration : "
for iterIndex in range(maxIter):
if iterRelevant(iterIndex, kFoldClassifier).any():
stringAnalysis += "\n\t- Iteration " + str(iterIndex + 1)
for foldIdx in [index for index, value in enumerate(iterRelevant(iterIndex, kFoldClassifier)) if value]:
stringAnalysis += "\n\t\t Fold " + str(foldIdx + 1) + "\n\t\t\tAccuracy on train : " + \
str(kFoldAccuracyOnTrainByIter[foldIdx][iterIndex]) + '\n\t\t\tAccuracy on test : ' + \
str(kFoldAccuracyOnTestByIter[foldIdx][iterIndex]) + '\n\t\t\tAccuracy on validation : '+\
str(kFoldAccuracyOnValidationByIter[foldIdx][iterIndex]) + '\n\t\t\tSelected View : ' + \
str(DATASET["View"+str(int(kFoldBestViews[foldIdx][iterIndex]))].attrs["name"])
trainAccuracyByIter = list(formatedAccuracies["Train"].mean(axis=0))+modifiedMean(surplusAccuracies["Train"])
testAccuracyByIter = list(formatedAccuracies["Test"].mean(axis=0))+modifiedMean(surplusAccuracies["Test"])
validationAccuracyByIter = list(formatedAccuracies["Validation"].mean(axis=0))+modifiedMean(surplusAccuracies["Validation"])
name, image = plotAccuracyByIter(trainAccuracyByIter, testAccuracyByIter, validationAccuracyByIter, nbMaxIter,
bestViews, views, classifierAnalysis)
imagesAnalysis = {name: image}
metricsScores = getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
return stringAnalysis, imagesAnalysis, metricsScores