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Cleaner code for a few test files

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This commit is contained in:
adithya-tp
2020-03-14 19:01:09 +04:00
parent b18dc8bcda
commit dd9b0bca24
3 changed files with 26 additions and 120 deletions
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src/mlpack/tests/adaboost_test.cpp
+22 -92
View File
@@ -58,10 +58,7 @@ BOOST_AUTO_TEST_CASE(HammingLossBoundIris)
arma::Row<size_t> predictedLabels;
a.Classify(inputData, predictedLabels);
size_t countError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != predictedLabels(i))
countError++;
size_t countError = arma::accu(labels != predictedLabels);
double hammingLoss = (double) countError / labels.n_cols;
// Check that ztProduct is finite.
@@ -96,10 +93,7 @@ BOOST_AUTO_TEST_CASE(WeakLearnerErrorIris)
Perceptron<> p(inputData, labels.row(0), numClasses, perceptronIter);
p.Classify(inputData, perceptronPrediction);
size_t countWeakLearnerError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != perceptronPrediction(i))
countWeakLearnerError++;
size_t countWeakLearnerError = arma::accu(labels != perceptronPrediction);
double weakLearnerErrorRate = (double) countWeakLearnerError / labels.n_cols;
// Define parameters for AdaBoost.
@@ -110,10 +104,7 @@ BOOST_AUTO_TEST_CASE(WeakLearnerErrorIris)
arma::Row<size_t> predictedLabels;
a.Classify(inputData, predictedLabels);
size_t countError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != predictedLabels(i))
countError++;
size_t countError = arma::accu(labels != predictedLabels);;
double error = (double) countError / labels.n_cols;
BOOST_REQUIRE_LE(error, weakLearnerErrorRate);
@@ -151,10 +142,7 @@ BOOST_AUTO_TEST_CASE(HammingLossBoundVertebralColumn)
arma::Row<size_t> predictedLabels;
a.Classify(inputData, predictedLabels);
size_t countError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != predictedLabels(i))
countError++;
size_t countError = arma::accu(labels != predictedLabels);
double hammingLoss = (double) countError / labels.n_cols;
// Check that ztProduct is finite.
@@ -187,10 +175,7 @@ BOOST_AUTO_TEST_CASE(WeakLearnerErrorVertebralColumn)
Perceptron<> p(inputData, labels.row(0), numClasses, perceptronIter);
p.Classify(inputData, perceptronPrediction);
size_t countWeakLearnerError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != perceptronPrediction(i))
countWeakLearnerError++;
size_t countWeakLearnerError = arma::accu(labels != perceptronPrediction);
double weakLearnerErrorRate = (double) countWeakLearnerError / labels.n_cols;
// Define parameters for AdaBoost.
@@ -201,10 +186,7 @@ BOOST_AUTO_TEST_CASE(WeakLearnerErrorVertebralColumn)
arma::Row<size_t> predictedLabels;
a.Classify(inputData, predictedLabels);
size_t countError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != predictedLabels(i))
countError++;
size_t countError = arma::accu(labels != predictedLabels);
double error = (double) countError / labels.n_cols;
BOOST_REQUIRE_LE(error, weakLearnerErrorRate);
@@ -242,10 +224,7 @@ BOOST_AUTO_TEST_CASE(HammingLossBoundNonLinearSepData)
arma::Row<size_t> predictedLabels;
a.Classify(inputData, predictedLabels);
size_t countError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != predictedLabels(i))
countError++;
size_t countError = arma::accu(labels == predictedLabels);
double hammingLoss = (double) countError / labels.n_cols;
// Check that ztProduct is finite.
@@ -278,10 +257,7 @@ BOOST_AUTO_TEST_CASE(WeakLearnerErrorNonLinearSepData)
Perceptron<> p(inputData, labels.row(0), numClasses, perceptronIter);
p.Classify(inputData, perceptronPrediction);
size_t countWeakLearnerError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != perceptronPrediction(i))
countWeakLearnerError++;
size_t countWeakLearnerError = arma::accu(labels != perceptronPrediction);
double weakLearnerErrorRate = (double) countWeakLearnerError / labels.n_cols;
// Define parameters for AdaBoost.
@@ -292,10 +268,7 @@ BOOST_AUTO_TEST_CASE(WeakLearnerErrorNonLinearSepData)
arma::Row<size_t> predictedLabels;
a.Classify(inputData, predictedLabels);
size_t countError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != predictedLabels(i))
countError++;
size_t countError = arma::accu(labels != predictedLabels);
double error = (double) countError / labels.n_cols;
BOOST_REQUIRE_LE(error, weakLearnerErrorRate);
@@ -332,10 +305,7 @@ BOOST_AUTO_TEST_CASE(HammingLossIris_DS)
arma::Row<size_t> predictedLabels;
a.Classify(inputData, predictedLabels);
size_t countError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != predictedLabels(i))
countError++;
size_t countError = arma::accu(labels != predictedLabels);
double hammingLoss = (double) countError / labels.n_cols;
// Check that ztProduct is finite.
@@ -371,10 +341,7 @@ BOOST_AUTO_TEST_CASE(WeakLearnerErrorIris_DS)
ID3DecisionStump ds(inputData, labelsvec, numClasses, inpBucketSize);
ds.Classify(inputData, dsPrediction);
size_t countWeakLearnerError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != dsPrediction(i))
countWeakLearnerError++;
size_t countWeakLearnerError = arma::accu(labels != dsPrediction);
double weakLearnerErrorRate = (double) countWeakLearnerError / labels.n_cols;
// Define parameters for AdaBoost.
@@ -387,10 +354,7 @@ BOOST_AUTO_TEST_CASE(WeakLearnerErrorIris_DS)
arma::Row<size_t> predictedLabels;
a.Classify(inputData, predictedLabels);
size_t countError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != predictedLabels(i))
countError++;
size_t countError = arma::accu(labels != predictedLabels);
double error = (double) countError / labels.n_cols;
BOOST_REQUIRE_LE(error, weakLearnerErrorRate);
@@ -430,10 +394,7 @@ BOOST_AUTO_TEST_CASE(HammingLossBoundVertebralColumn_DS)
arma::Row<size_t> predictedLabels;
a.Classify(inputData, predictedLabels);
size_t countError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != predictedLabels(i))
countError++;
size_t countError = arma::accu(labels != predictedLabels);
double hammingLoss = (double) countError / labels.n_cols;
// Check that ztProduct is finite.
@@ -466,11 +427,7 @@ BOOST_AUTO_TEST_CASE(WeakLearnerErrorVertebralColumn_DS)
ID3DecisionStump ds(inputData, labelsvec, numClasses, inpBucketSize);
ds.Classify(inputData, dsPrediction);
size_t countWeakLearnerError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != dsPrediction(i))
countWeakLearnerError++;
size_t countWeakLearnerError = arma::accu(labels != dsPrediction);
double weakLearnerErrorRate = (double) countWeakLearnerError / labels.n_cols;
// Define parameters for AdaBoost.
@@ -482,10 +439,7 @@ BOOST_AUTO_TEST_CASE(WeakLearnerErrorVertebralColumn_DS)
arma::Row<size_t> predictedLabels;
a.Classify(inputData, predictedLabels);
size_t countError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != predictedLabels(i))
countError++;
size_t countError = arma::accu(labels != predictedLabels);
double error = (double) countError / labels.n_cols;
BOOST_REQUIRE_LE(error, weakLearnerErrorRate);
@@ -524,10 +478,7 @@ BOOST_AUTO_TEST_CASE(HammingLossBoundNonLinearSepData_DS)
arma::Row<size_t> predictedLabels;
a.Classify(inputData, predictedLabels);
size_t countError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != predictedLabels(i))
countError++;
size_t countError = arma::accu(labels != predictedLabels);
double hammingLoss = (double) countError / labels.n_cols;
// Check that ztProduct is finite.
@@ -561,10 +512,7 @@ BOOST_AUTO_TEST_CASE(WeakLearnerErrorNonLinearSepData_DS)
ID3DecisionStump ds(inputData, labelsvec, numClasses, inpBucketSize);
ds.Classify(inputData, dsPrediction);
size_t countWeakLearnerError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != dsPrediction(i))
countWeakLearnerError++;
size_t countWeakLearnerError = arma::accu(labels != dsPrediction);
double weakLearnerErrorRate = (double) countWeakLearnerError / labels.n_cols;
// Define parameters for AdaBoost.
@@ -577,10 +525,7 @@ BOOST_AUTO_TEST_CASE(WeakLearnerErrorNonLinearSepData_DS)
arma::Row<size_t> predictedLabels;
a.Classify(inputData, predictedLabels);
size_t countError = 0;
for (size_t i = 0; i < labels.n_cols; i++)
if (labels(i) != predictedLabels(i))
countError++;
size_t countError = arma::accu(labels != predictedLabels);
double error = (double) countError / labels.n_cols;
BOOST_REQUIRE_LE(error, weakLearnerErrorRate);
@@ -650,11 +595,7 @@ BOOST_AUTO_TEST_CASE(ClassifyTest_VERTEBRALCOL)
BOOST_REQUIRE_CLOSE(arma::accu(probabilities.col(i)), 1, 1e-5);
}
size_t localError = 0;
for (size_t i = 0; i < trueTestLabels.n_cols; i++)
if (trueTestLabels(i) != predictedLabels1(i))
localError++;
size_t localError = arma::accu(trueTestLabels != predictedLabels1);
double lError = (double) localError / trueTestLabels.n_cols;
BOOST_REQUIRE_LE(lError, 0.30);
}
@@ -722,11 +663,7 @@ BOOST_AUTO_TEST_CASE(ClassifyTest_NONLINSEP)
BOOST_REQUIRE_CLOSE(arma::accu(probabilities.col(i)), 1, 1e-5);
}
size_t localError = 0;
for (size_t i = 0; i < trueTestLabels.n_cols; i++)
if (trueTestLabels(i) != predictedLabels1(i))
localError++;
size_t localError = arma::accu(trueTestLabels != predictedLabels1);
double lError = (double) localError / trueTestLabels.n_cols;
BOOST_REQUIRE_LE(lError, 0.30);
}
@@ -793,10 +730,7 @@ BOOST_AUTO_TEST_CASE(ClassifyTest_IRIS)
BOOST_REQUIRE_CLOSE(arma::accu(probabilities.col(i)), 1, 1e-5);
}
size_t localError = 0;
for (size_t i = 0; i < trueTestLabels.n_cols; i++)
if (trueTestLabels(i) != predictedLabels1(i))
localError++;
size_t localError = arma::accu(trueTestLabels != predictedLabels1);
double lError = (double) localError / labels.n_cols;
BOOST_REQUIRE_LE(lError, 0.30);
}
@@ -851,11 +785,7 @@ BOOST_AUTO_TEST_CASE(TrainTest)
arma::Row<size_t> predictedLabels(testData.n_cols);
a.Classify(testData, predictedLabels);
int localError = 0;
for (size_t i = 0; i < trueTestLabels.n_cols; i++)
if (trueTestLabels(i) != predictedLabels(i))
localError++;
int localError = arma::accu(trueTestLabels != predictedLabels);
double lError = (double) localError / trueTestLabels.n_cols;
BOOST_REQUIRE_LE(lError, 0.30);
src/mlpack/tests/convolutional_network_test.cpp
+1 -7
View File
@@ -114,13 +114,7 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkTest)
arma::max(predictionTemp.col(i)) == predictionTemp.col(i), 1)) + 1;
}
size_t correct = 0;
for (size_t i = 0; i < X.n_cols; i++)
{
if (prediction(i) == Y(i))
correct++;
}
size_t correct = arma::accu(prediction == Y);
double classificationError = 1 - double(correct) / X.n_cols;
if (classificationError <= 0.25)
{
src/mlpack/tests/feedforward_network_test.cpp
+3 -21
View File
@@ -53,17 +53,8 @@ void TestNetwork(ModelType& model,
arma::max(predictionTemp.col(i)) == predictionTemp.col(i), 1)) + 1;
}
size_t error = 0;
for (size_t i = 0; i < testData.n_cols; i++)
{
if (int(arma::as_scalar(prediction.col(i))) ==
int(arma::as_scalar(testLabels.col(i))))
{
error++;
}
}
double classificationError = 1 - double(error) / testData.n_cols;
size_t correct = arma::accu(prediction == testLabels);
double classificationError = 1 - double(correct) / testData.n_cols;
BOOST_REQUIRE_LE(classificationError, classificationErrorThreshold);
}
@@ -199,16 +190,7 @@ BOOST_AUTO_TEST_CASE(ForwardBackwardTest)
arma::max(currentResuls.col(i)) == currentResuls.col(i), 1)) + 1;
}
size_t correct = 0;
for (size_t i = 0; i < currentLabels.n_cols; i++)
{
if (int(arma::as_scalar(prediction.col(i))) ==
int(arma::as_scalar(currentLabels.col(i))))
{
correct++;
}
}
size_t correct = arma::accu(prediction == currentLabels);
error(1 - (double) correct / batchSize);
}
Log::Debug << "Current training error: " << error.mean() << std::endl;