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[ML] Fix weights to maximize minimum recall for multiclass classification when the training data is missing classes #1239

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May 15, 2020
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[ML] Fix weights to maximize minimum recall for multiclass classification when the training data is missing classes #1239

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8176cd7
Trap the case that a class isn't sampled by in the training data
tveasey May 14, 2020
57ba59e
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tveasey May 15, 2020
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58 changes: 34 additions & 24 deletions lib/maths/CDataFrameUtils.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -1072,27 +1072,32 @@ CDataFrameUtils::maximizeMinimumRecallForMulticlass(std::size_t numberThreads,
std::size_t numberSamples{
static_cast<std::size_t>(std::min(1000.0, rowMask.manhattan()))};

TStratifiedSamplerPtr sampler;
std::tie(sampler, std::ignore) = classifierStratifiedCrossValidationRowSampler(
numberThreads, frame, targetColumn, rng, numberSamples, rowMask);

TSizeVec rowIndices;
frame.readRows(1, 0, frame.numberRows(),
[&](TRowItr beginRows, TRowItr endRows) {
for (auto row = beginRows; row != endRows; ++row) {
sampler->sample(*row);
}
},
&rowMask);
sampler->finishSampling(rng, rowIndices);
std::sort(rowIndices.begin(), rowIndices.end());
core::CPackedBitVector sampleMask;
for (auto row : rowIndices) {
sampleMask.extend(false, row - sampleMask.size());
sampleMask.extend(true);
if (numberSamples < static_cast<std::size_t>(rowMask.manhattan())) {
TStratifiedSamplerPtr sampler;
std::tie(sampler, std::ignore) = classifierStratifiedCrossValidationRowSampler(
numberThreads, frame, targetColumn, rng, numberSamples, rowMask);

TSizeVec rowIndices;
frame.readRows(1, 0, frame.numberRows(),
[&](TRowItr beginRows, TRowItr endRows) {
for (auto row = beginRows; row != endRows; ++row) {
sampler->sample(*row);
}
},
&rowMask);
sampler->finishSampling(rng, rowIndices);
std::sort(rowIndices.begin(), rowIndices.end());
LOG_TRACE(<< "# row indices = " << rowIndices.size());

for (auto row : rowIndices) {
sampleMask.extend(false, row - sampleMask.size());
sampleMask.extend(true);
}
sampleMask.extend(false, rowMask.size() - sampleMask.size());
} else {
sampleMask = rowMask;
}
sampleMask.extend(false, rowMask.size() - sampleMask.size());
LOG_TRACE(<< "# row indices = " << rowIndices.size());

// Compute the count of each class in the sample set.
auto readClassCountsAndRecalls = core::bindRetrievableState(
Expand All @@ -1119,6 +1124,11 @@ CDataFrameUtils::maximizeMinimumRecallForMulticlass(std::size_t numberThreads,
copyClassCountsAndRecalls, reduceClassCountsAndRecalls, classCountsAndRecalls);
TDoubleVector classCounts{classCountsAndRecalls.topRows(numberClasses)};
TDoubleVector classRecalls{classCountsAndRecalls.bottomRows(numberClasses)};
// If a class count is zero the derivative of the loss functin w.r.t. that
// class is not well defined. In practice, the objective is independent of
// such a a class so the choice for its count and recall is not important,
// but the count must be non-zero so that we don't run into NaN cascade.
classCounts = classCounts.cwiseMax(1.0);
classRecalls.array() /= classCounts.array();
LOG_TRACE(<< "class counts = " << classCounts.transpose());
LOG_TRACE(<< "class recalls = " << classRecalls.transpose());
Expand Down Expand Up @@ -1219,15 +1229,15 @@ CDataFrameUtils::maximizeMinimumRecallForMulticlass(std::size_t numberThreads,
// and use this to bias initial values.

TMinAccumulator minLoss;
TDoubleVector minWeights;
TDoubleVector minLossWeights;
TDoubleVector w0{TDoubleVector::Ones(numberClasses)};
for (std::size_t i = 0; i < 5; ++i) {
CLbfgs<TDoubleVector> lbfgs{5};
double loss;
std::tie(w0, loss) = lbfgs.minimize(objective, objectiveGradient, std::move(w0));
LOG_TRACE(<< "weights* = " << w0.transpose() << ", loss* = " << loss);
if (minLoss.add(loss)) {
minWeights = std::move(w0);
minLossWeights = std::move(w0);
}
w0 = TDoubleVector::Ones(numberClasses);
for (std::size_t j = 1; j < numberClasses; ++j) {
Expand All @@ -1237,10 +1247,10 @@ CDataFrameUtils::maximizeMinimumRecallForMulticlass(std::size_t numberThreads,

// Since we take argmax_i w_i p_i we can multiply by a constant. We arrange for
// the largest weight to always be one.
minWeights.array() /= minWeights.maxCoeff();
LOG_TRACE(<< "weights = " << minWeights.transpose());
minLossWeights.array() /= minLossWeights.maxCoeff();
LOG_TRACE(<< "weights = " << minLossWeights.transpose());

return minWeights;
return minLossWeights;
}

void CDataFrameUtils::removeMetricColumns(const core::CDataFrame& frame, TSizeVec& columnMask) {
Expand Down