Python Classifier.confusionMatrix方法代码示例

# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import confusionMatrix [as 别名]
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

# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import confusionMatrix [as 别名]

#.........这里部分代码省略.........
        subwindowTargetWidth=subwindowTargetWidth,
        subwindowTargetHeight=subwindowTargetHeight,
        subwindowInterpolation=subwindowInterpolation,
        includeOriginalImage=includeOriginalImage,
        nbJobs=nbJobs,
        verbosity=verbosity,
        tempFolder=tempFolder,
        random=random,
    )

    randConvCoord = LoadCoordinator(randConvCoord, learnFile, testFile)

    # --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--
    loader = CifarFromNumpies(learningSetDir, learningIndexFile)
    learningSet = FileImageBuffer(loader.getFiles(), NumpyImageLoader())
    learningSet = learningSet[0:lsSize]

    loader = CifarFromNumpies(testingSetDir, testingIndexFile)
    testingSet = FileImageBuffer(loader.getFiles(), NumpyImageLoader())
    testingSet = testingSet[0:tsSize]

    # =====COMPUTATION=====#
    # --Learning--#
    print "Starting learning"
    fitStart = time()
    classifier.fit(learningSet)
    fitEnd = time()
    print "Learning done", formatDuration(fitEnd - fitStart)
    sys.stdout.flush()

    # --Testing--#
    y_truth = testingSet.getLabels()
    predStart = time()
    y_prob, y_pred = classifier.predict_predict_proba(testingSet)
    predEnd = time()
    accuracy = classifier.accuracy(y_pred, y_truth)
    confMat = classifier.confusionMatrix(y_pred, y_truth)

    # ====ANALYSIS=====#
    importance, order = randConvCoord.importancePerFeatureGrp(baseClassif)

    print "==================RandConv================"
    print "-----------Filtering--------------"
    print "nb_filters", nb_filters
    print "filterPolicy", filterPolicy
    print "----------Pooling--------------"
    print "poolings", poolings
    print "--------SW extractor----------"
    print "#Subwindows", nbSubwindows
    print "subwindowMinSizeRatio", subwindowMinSizeRatio
    print "subwindowMaxSizeRatio", subwindowMaxSizeRatio
    print "subwindowTargetWidth", subwindowTargetWidth
    print "subwindowTargetHeight", subwindowTargetHeight
    print "fixedSize", fixedSize
    print "------------Misc-----------------"
    print "includeOriginalImage", includeOriginalImage
    print "random", random
    print "tempFolder", tempFolder
    print "verbosity", verbosity
    print "nbJobs", nbJobs
    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 "-------------------------------"
    if shouldSave:
        print "saveFile", saveFile
    print "Fit time", formatDuration(fitEnd - fitStart)
    print "Classifcation time", formatDuration(predEnd - predStart)
    print "Accuracy", accuracy

    if shouldSave:
        np.save(saveFile, y_prob)

    return accuracy, confMat, importance, order

# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import confusionMatrix [as 别名]
    testingSet = FileImageBuffer(loader.getFiles(), NumpyImageLoader())
    testingSet = testingSet[0:testingUse]

    # =====COMPUTATION=====#
    # --Learning--#
    fitStart = time()
    classifier.fit(learningSet)
    fitEnd = time()

    # --Testing--#
    y_truth = testingSet.getLabels()
    predStart = time()
    y_pred = classifier.predict(testingSet)
    predEnd = time()
    accuracy = classifier.accuracy(y_pred, y_truth)
    confMat = classifier.confusionMatrix(y_pred, y_truth)

    print "========================================="
    print "--------SW extractor----------"
    print "#Subwindows", nbSubwindows
    print "subwindowMinSizeRatio", subwindowMinSizeRatio
    print "subwindowMaxSizeRatio", subwindowMaxSizeRatio
    print "subwindowTargetWidth", subwindowTargetWidth
    print "subwindowTargetHeight", subwindowTargetHeight
    print "fixedSize", fixedSize
    print "nbJobs", nbJobs
    print "--------ExtraTrees----------"
    print "nbTrees", nbTrees
    print "maxFeatures", maxFeatures
    print "maxDepth", maxDepth
    print "minSamplesSplit", minSamplesSplit

# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import confusionMatrix [as 别名]
def run(poolings=poolings,
        nbSubwindows=nbSubwindows,
        subwindowMinSizeRatio=subwindowMinSizeRatio,
        subwindowMaxSizeRatio=subwindowMaxSizeRatio,
        subwindowTargetWidth=subwindowTargetWidth,
        subwindowTargetHeight=subwindowTargetHeight,
        fixedSize=fixedSize,
        subwindowInterpolation=subwindowInterpolation,
        includeOriginalImage=includeOriginalImage,
        random=random,
        nbJobs=nbJobs,
        verbosity=verbosity,
        tempFolder=tempFolder,
        nbTrees=nbTrees,
        maxFeatures=maxFeatures,
        maxDepth=maxDepth,
        minSamplesSplit=minSamplesSplit,
        minSamplesLeaf=minSamplesLeaf,
        bootstrap=bootstrap,
        nbJobsEstimator=nbJobsEstimator,
        verbose=verbose,
        learningUse=learningUse,
        testingUse=testingUse):

    randomState = None
    if random:
        randomState = 100

    lsSize = learningUse
    if learningUse > maxLearningSize:
        lsSize = maxLearningSize

    tsSize = testingUse
    if testingUse > maxTestingSize:
        tsSize = maxTestingSize

    #======INSTANTIATING========#
    os.environ["JOBLIB_TEMP_FOLDER"] = "/home/jmbegon/jmbegon/code/work/tmp/"
    #--customRandConv--
    randConvCoord = customRandConvFactory(
        nbSubwindows=nbSubwindows,
        subwindowMinSizeRatio=subwindowMinSizeRatio,
        subwindowMaxSizeRatio=subwindowMaxSizeRatio,
        subwindowTargetWidth=subwindowTargetWidth,
        subwindowTargetHeight=subwindowTargetHeight,
        poolings=poolings,
        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--
    loader = CifarFromNumpies(learningSetDir, learningIndexFile)
    learningSet = FileImageBuffer(loader.getFiles(), NumpyImageLoader())
    learningSet = learningSet[0:lsSize]

    loader = CifarFromNumpies(testingSetDir, testingIndexFile)
    testingSet = FileImageBuffer(loader.getFiles(), NumpyImageLoader())
    testingSet = testingSet[0:tsSize]

    #=====COMPUTATION=====#
    #--Learning--#
    print "Starting learning"
    fitStart = time()
    classifier.fit(learningSet)
    fitEnd = time()
    print "Learning done", (fitEnd-fitStart), "seconds"
    sys.stdout.flush()

    #--Testing--#
    y_truth = testingSet.getLabels()
    predStart = time()
    y_pred = classifier.predict(testingSet)
    predEnd = time()
    accuracy = classifier.accuracy(y_pred, y_truth)
    confMat = classifier.confusionMatrix(y_pred, y_truth)

    #====ANALYSIS=====#
    importance, order = randConvCoord.importancePerFeatureGrp(baseClassif)

    print "==================CUSTOM======================="
    print "----------Pooling--------------"
    print "poolings", poolings
    print "--------SW extractor----------"
#.........这里部分代码省略.........

# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import confusionMatrix [as 别名]

#.........这里部分代码省略.........
        subwindowMinSizeRatio=subwindowMinSizeRatio,
        subwindowMaxSizeRatio=subwindowMaxSizeRatio,
        subwindowTargetWidth=subwindowTargetWidth,
        subwindowTargetHeight=subwindowTargetHeight,
        poolings=poolings,
        filterNormalisation=filterNormalisation,
        subwindowInterpolation=subwindowInterpolation,
        includeOriginalImage=includeOriginalImage,
        compressorType=compressorType,
        nbCompressedFeatures=nbCompressedFeatures,
        compressOriginalImage=compressOriginalImage,
        nbJobs=nbJobs,
        verbosity=verbosity,
        tempFolder=tempFolder)

    #--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--
    loader = CifarFromNumpies(learningSetDir, learningIndexFile)
    learningSet = FileImageBuffer(loader.getFiles(), NumpyImageLoader())
    learningSet = learningSet[0:learningUse]

    loader = CifarFromNumpies(testingSetDir, testingIndexFile)
    testingSet = FileImageBuffer(loader.getFiles(), NumpyImageLoader())
    testingSet = testingSet[0:testingUse]

    #=====COMPUTATION=====#
    #--Learning--#
    print "Starting learning"
    fitStart = time()
    classifier.fit(learningSet)
    fitEnd = time()
    print "Learning done", (fitEnd-fitStart), "seconds"
    sys.stdout.flush()

    #--Testing--#
    y_truth = testingSet.getLabels()
    predStart = time()
    y_pred = classifier.predict(testingSet)
    predEnd = time()
    accuracy = classifier.accuracy(y_pred, y_truth)
    confMat = classifier.confusionMatrix(y_pred, y_truth)

    #====ANALYSIS=====#
    importance, order = randConvCoord.importancePerFeatureGrp(baseClassif)

    print "========================================="
    print "-----------Filtering--------------"
    print "nb_filters", nb_filters
    print "filter_min_val", filter_min_val
    print "filter_max_val", filter_max_val
    print "filterMinSize", filterMinSize
    print "filterMaxSize", filterMaxSize
    print "filterNormalisation", filterNormalisation
    print "----------Pooling--------------"
    print "poolings", poolings
    print "--------SW extractor----------"
    print "#Subwindows", nbSubwindows
    print "subwindowMinSizeRatio", subwindowMinSizeRatio
    print "subwindowMaxSizeRatio", subwindowMaxSizeRatio
    print "subwindowTargetWidth", subwindowTargetWidth
    print "subwindowTargetHeight", subwindowTargetHeight
    print "fixedSize", fixedSize
    print "------------Misc-----------------"
    print "includeOriginalImage", includeOriginalImage
    print "compressorType", compressorType
    print "nbCompressedFeatures", nbCompressedFeatures
    print "compressOriginalImage",  compressOriginalImage
    print "nbJobs", nbJobs
    print "--------ExtraTrees----------"
    print "nbTrees", nbTrees
    print "maxFeatures", maxFeatures
    print "maxDepth", maxDepth
    print "minSamplesSplit", minSamplesSplit
    print "minSamplesLeaf", minSamplesLeaf
    print "bootstrap", bootstrap
    print "nbJobsEstimator", nbJobsEstimator
    print "randomState", randomState
    print "------------Data---------------"
    print "LearningSet size", len(learningSet)
    print "TestingSet size", len(testingSet)
    print "-------------------------------"
    print "Fit time", (fitEnd-fitStart), "seconds"
    print "Classifcation time", (predEnd-predStart), "seconds"
    print "Accuracy", accuracy
    print "Confusion matrix :\n", confMat

    return accuracy, confMat, importance, order

# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import confusionMatrix [as 别名]

#.........这里部分代码省略.........
                                            min_samples_leaf=minSamplesLeaf,
                                            bootstrap=bootstrap,
                                            n_jobs=nbJobsEstimator,
                                            random_state=randomState,
                                            verbose=verbose)

        optiClassif = Classifier(randConvCoord, baseClassif)
        print "Starting optimization"
        optiStart = time()
        optiClassif.fit(learningSet)
        optiEnd = time()
        print "optimization done", formatDuration(optiEnd-optiStart)
        _, order = randConvOptimizer.importancePerFeatureGrp(totallyTrees)

        filtersTmp = randConvCoord._convolExtractor._finiteFilter._filters
        filters = [x for x, _, _ in filtersTmp]
        if not includeOriginalImage:
            bestIndices = order[:nb_filters]
        else:
            count = 0
            bestIndices = []
            for index in order:
                if count == nb_filters-1:
                    break
                if index != 0:
                    bestIndices.append(index-1)
                    count += 1
        bestFlters = []
        for i in bestIndices:
            bestFlters.append(filters[i])

        best3Filters = Finite3SameFilter(bestFlters)
        randConvCoord._convolExtractor._finiteFilter = best3Filters


    #--Learning--#
    print "Starting learning"
    fitStart = time()
    classifier.fit(learningSet)
    fitEnd = time()
    print "Learning done", formatDuration(fitEnd-fitStart)
    sys.stdout.flush()

    #--Testing--#
    y_truth = testingSet.getLabels()
    predStart = time()
    y_prob, y_pred = classifier.predict_predict_proba(testingSet)
    predEnd = time()
    accuracy = classifier.accuracy(y_pred, y_truth)
    confMat = classifier.confusionMatrix(y_pred, y_truth)

    #====ANALYSIS=====#
    importance, order = randConvCoord.importancePerFeatureGrp(baseClassif)

    print "==================RandConv================"
    print "-----------Filtering--------------"
    print "nb_filters", nb_filters
    print "filterPolicy", filterPolicy
    print "----------Pooling--------------"
    print "poolings", poolings
    print "--------SW extractor----------"
    print "#Subwindows", nbSubwindows
    print "subwindowMinSizeRatio", subwindowMinSizeRatio
    print "subwindowMaxSizeRatio", subwindowMaxSizeRatio
    print "subwindowTargetWidth", subwindowTargetWidth
    print "subwindowTargetHeight", subwindowTargetHeight
    print "fixedSize", fixedSize
    print "------------Misc-----------------"
    print "includeOriginalImage", includeOriginalImage
    print "random", random
    print "tempFolder", tempFolder
    print "verbosity", verbosity
    print "nbJobs", nbJobs
    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 "-------------------------------"
    if shouldSave:
        print "saveFile", saveFile
    print "Fit time", formatDuration(fitEnd-fitStart)
    print "Classifcation time", formatDuration(predEnd-predStart)
    print "Accuracy", accuracy

    if shouldSave:
        np.save(saveFile, y_prob)

    filtersTmp = randConvCoord._convolExtractor._finiteFilter._filters
    filters = [x for x, _, _ in filtersTmp]

    return accuracy, confMat, importance, order, filters