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analyzeResults.py 13.30 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(scoresOnTainByIter, scoresOnTestByIter, features, classifierAnalysis):
x = range(len(scoresOnTainByIter))
figure = plt.figure()
ax1 = figure.add_subplot(111)
axes = figure.gca()
axes.set_ylim([0.40, 1.00])
titleString = ""
for view, classifierConfig in zip(features, classifierAnalysis):
titleString += "\n" + view + " : " + classifierConfig
ax1.set_title("Score 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, scoresOnTainByIter, c='red', label='Train')
ax1.plot(x, scoresOnTestByIter, c='black', label='Test')
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(classifier, classificationKWARGS, nbCores, viewNames, hyperParamSearch, nIter, times):
maxIter = classificationKWARGS["maxIter"]
minIter = classificationKWARGS["minIter"]
threshold = classificationKWARGS["threshold"]
extractionTime, classificationTime = times
weakClassifierConfigs = [getattr(globals()[classifierName], 'getConfig')(classifiersConfig) for classifiersConfig,
classifierName
in zip(classifier.classifiersConfigs, classifier.classifiersNames)]
classifierAnalysis = [classifierName + " " + weakClassifierConfig + "on " + feature for classifierName,
weakClassifierConfig,
feature
in zip(classifier.classifiersNames, weakClassifierConfigs, viewNames)]
gridSearchString = ""
if hyperParamSearch:
gridSearchString += "Configurations found by randomized search with " + str(nIter) + " iterations"
algoString = "\n\nMumbo configuration : \n\t-Used " + str(nbCores) + " 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(nbCores) + " cores : \n\tDatabase extraction time : " + str(
hms(seconds=int(extractionTime))) + "\n\t"
row_format = "{:>15}" * 3
algoString += row_format.format("", *['Learn', 'Prediction'])
algoString += '\n\t'
algoString += "\n\tSo a total classification time of " + str(hms(seconds=int(classificationTime))) + ".\n\n"
algoString += "\n\n"
return algoString, classifierAnalysis
def getReport(classifier, CLASS_LABELS, classificationIndices, DATASET, trainLabels,
testLabels, viewIndices, metric):
learningIndices, validationIndices = classificationIndices
nbView = len(viewIndices)
NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
metricModule = getattr(Metrics, metric[0])
fakeViewsIndicesDict = dict(
(viewIndex, fakeViewIndex) for viewIndex, fakeViewIndex in zip(viewIndices, range(nbView)))
trainScore = metricModule.score(CLASS_LABELS[learningIndices], trainLabels)
testScore = metricModule.score(CLASS_LABELS[validationIndices], testLabels)
mumboClassifier = classifier
maxIter = mumboClassifier.iterIndex
meanAverageAccuracies = np.mean(mumboClassifier.averageAccuracies, axis=0)
viewsStats = np.array([float(list(mumboClassifier.bestViews).count(viewIndex)) /
len(mumboClassifier.bestViews) for viewIndex in range(nbView)])
PredictedTrainLabelsByIter = mumboClassifier.classifyMumbobyIter_hdf5(DATASET, fakeViewsIndicesDict,
usedIndices=learningIndices,
NB_CLASS=NB_CLASS)
PredictedTestLabelsByIter = mumboClassifier.classifyMumbobyIter_hdf5(DATASET, fakeViewsIndicesDict,
usedIndices=validationIndices,
NB_CLASS=NB_CLASS)
scoresByIter = np.zeros((len(PredictedTestLabelsByIter), 2))
for iterIndex, (iterPredictedTrainLabels, iterPredictedTestLabels) in enumerate(
zip(PredictedTrainLabelsByIter, PredictedTestLabelsByIter)):
scoresByIter[iterIndex, 0] = metricModule.score(CLASS_LABELS[learningIndices], iterPredictedTrainLabels)
scoresByIter[iterIndex, 1] = metricModule.score(CLASS_LABELS[validationIndices], iterPredictedTestLabels)
scoresOnTainByIter = [scoresByIter[iterIndex, 0] for iterIndex in range(maxIter)]
scoresOnTestByIter = [scoresByIter[iterIndex, 1] for iterIndex in range(maxIter)]
return (trainScore, testScore, meanAverageAccuracies, viewsStats, scoresOnTainByIter,
scoresOnTestByIter)
def iterRelevant(iterIndex, kFoldClassifierStats):
relevants = np.zeros(len(kFoldClassifierStats[0]), dtype=bool)
for statsIterIndex, kFoldClassifier in enumerate(kFoldClassifierStats):
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 printMetricScore(metricScores, metrics):
metricScoreString = "\n\n"
for metric in metrics:
metricModule = getattr(Metrics, metric[0])
if metric[1] is not 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(metricScores[metric[0]][0])
metricScoreString += "\n\t\t- Score on test : " + str(metricScores[metric[0]][1])
metricScoreString += "\n\n"
return metricScoreString
def getTotalMetricScores(metric, trainLabels, testLabels,
DATASET, validationIndices, learningIndices):
labels = DATASET.get("Labels").value
metricModule = getattr(Metrics, metric[0])
if metric[1] is not None:
metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
else:
metricKWARGS = {}
validationIndices = validationIndices
trainScore = metricModule.score(labels[learningIndices], trainLabels, **metricKWARGS)
testScore = metricModule.score(labels[validationIndices], testLabels, **metricKWARGS)
return [trainScore, testScore]
def getMetricsScores(metrics, trainLabels, testLabels,
DATASET, validationIndices, learningIndices):
metricsScores = {}
for metric in metrics:
metricsScores[metric[0]] = getTotalMetricScores(metric, trainLabels, testLabels,
DATASET, validationIndices, learningIndices)
return metricsScores
def getMeanIterations(kFoldClassifierStats, foldIndex):
iterations = np.array([kFoldClassifier[foldIndex].iterIndex + 1 for kFoldClassifier in kFoldClassifierStats])
return np.mean(iterations)
def execute(classifier, trainLabels,
testLabels, DATASET,
classificationKWARGS, classificationIndices,
LABELS_DICTIONARY, views, nbCores, times,
databaseName, KFolds,
hyperParamSearch, nIter, metrics,
viewsIndices, randomState):
learningIndices, validationIndices = classificationIndices
LEARNING_RATE = len(learningIndices) / (len(learningIndices) + len(validationIndices))
nbFolds = KFolds.n_splits
CLASS_LABELS = DATASET.get("Labels")[...]
dbConfigurationString, viewNames = getDBConfig(DATASET, LEARNING_RATE, nbFolds, databaseName, validationIndices,
LABELS_DICTIONARY)
algoConfigurationString, classifierAnalysis = getAlgoConfig(classifier, classificationKWARGS, nbCores, viewNames,
hyperParamSearch, nIter, times)
(totalScoreOnTrain, totalScoreOnTest, meanAverageAccuracies, viewsStats, scoresOnTainByIter,
scoresOnTestByIter) = getReport(classifier, CLASS_LABELS, classificationIndices, DATASET,
trainLabels, testLabels, viewsIndices, metrics[0])
stringAnalysis = "\t\tResult for Multiview classification with Mumbo with random state : " + str(randomState) + \
"\n\nAverage " + metrics[0][0] + " :\n\t-On Train : " + str(
totalScoreOnTrain) + "\n\t-On Test : " + \
str(totalScoreOnTest)
stringAnalysis += dbConfigurationString
stringAnalysis += algoConfigurationString
metricsScores = getMetricsScores(metrics, trainLabels, testLabels,
DATASET, validationIndices, learningIndices)
stringAnalysis += printMetricScore(metricsScores, metrics)
stringAnalysis += "Mean average scores and stats :"
for viewIndex, (meanAverageAccuracy, bestViewStat) in enumerate(zip(meanAverageAccuracies, viewsStats)):
stringAnalysis += "\n\t- On " + viewNames[viewIndex] + \
" : \n\t\t- Mean average Accuracy : " + str(meanAverageAccuracy) + \
"\n\t\t- Percentage of time chosen : " + str(bestViewStat)
stringAnalysis += "\n\n For each iteration : "
for iterIndex in range(len(scoresOnTainByIter)):
stringAnalysis += "\n\t- Iteration " + str(iterIndex + 1)
stringAnalysis += "\n\t\tScore on train : " + \
str(scoresOnTainByIter[iterIndex]) + '\n\t\tScore on test : ' + \
str(scoresOnTestByIter[iterIndex])
name, image = plotAccuracyByIter(scoresOnTainByIter, scoresOnTestByIter, views, classifierAnalysis)
imagesAnalysis = {name: image}
return stringAnalysis, imagesAnalysis, metricsScores