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updated test for logistic regression after changes to LogisticRegression class

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This commit is contained in:
Rahul Kavi 2013-10-04 08:34:55 -04:00 committed by Maksim Shabunin
parent d5ad4f3255
commit 64aaa6e1ae
1 changed files with 23 additions and 33 deletions
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56
modules/ml/test/test_lr.cpp
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@ -73,9 +73,8 @@ static bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& e
CV_Assert(_p_labels_temp.total() == _o_labels_temp.total());
CV_Assert(_p_labels_temp.rows == _o_labels_temp.rows);
Mat result = (_p_labels_temp == _o_labels_temp)/255;
accuracy = (float)cv::sum(result)[0]/result.rows;
accuracy = (float)cv::countNonZero(_p_labels_temp == _o_labels_temp)/_p_labels_temp.rows;
error = 1 - accuracy;
return true;
}
@ -133,25 +132,23 @@ void CV_LRTest::run( int /*start_from*/ )
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3);
CvLR_TrainParams params = CvLR_TrainParams();
Mat responses1, responses2;
float error = 0.0f;
CvLR_TrainParams params1 = CvLR_TrainParams();
CvLR_TrainParams params2 = CvLR_TrainParams();
LogisticRegressionParams params1 = LogisticRegressionParams();
LogisticRegressionParams params2 = LogisticRegressionParams();
params1.alpha = 1.0;
params1.num_iters = 10001;
params1.norm = CvLR::REG_L2;
// params1.debug = 1;
params1.norm = LogisticRegression::REG_L2;
params1.regularized = 1;
params1.train_method = CvLR::BATCH;
params1.minibatchsize = 10;
params1.train_method = LogisticRegression::BATCH;
params1.mini_batch_size = 10;
// run LR classifier train classifier
data.convertTo(data, CV_32FC1);
labels.convertTo(labels, CV_32FC1);
CvLR lr1(data, labels, params1);
LogisticRegression lr1(data, labels, params1);
// predict using the same data
lr1.predict(data, responses1);
@ -164,7 +161,6 @@ void CV_LRTest::run( int /*start_from*/ )
ts->printf(cvtest::TS::LOG, "Bad prediction labels\n" );
test_code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if(error > 0.05f)
{
ts->printf(cvtest::TS::LOG, "Bad accuracy of (%f)\n", error);
@ -173,14 +169,13 @@ void CV_LRTest::run( int /*start_from*/ )
params2.alpha = 1.0;
params2.num_iters = 9000;
params2.norm = CvLR::REG_L2;
// params2.debug = 1;
params2.norm = LogisticRegression::REG_L2;
params2.regularized = 1;
params2.train_method = CvLR::MINI_BATCH;
params2.minibatchsize = 10;
params2.train_method = LogisticRegression::MINI_BATCH;
params2.mini_batch_size = 10;
// now train using mini batch gradient descent
CvLR lr2(data, labels, params2);
LogisticRegression lr2(data, labels, params2);
lr2.predict(data, responses2);
responses2.convertTo(responses2, CV_32S);
@ -191,7 +186,6 @@ void CV_LRTest::run( int /*start_from*/ )
ts->printf(cvtest::TS::LOG, "Bad prediction labels\n" );
test_code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if(error > 0.06f)
{
ts->printf(cvtest::TS::LOG, "Bad accuracy of (%f)\n", error);
@ -257,7 +251,7 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3);
CvLR_TrainParams params = CvLR_TrainParams();
// LogisticRegressionParams params = LogisticRegressionParams();
Mat responses1, responses2;
Mat learnt_mat1, learnt_mat2;
@ -265,28 +259,26 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
float errorCount = 0.0;
CvLR_TrainParams params1 = CvLR_TrainParams();
CvLR_TrainParams params2 = CvLR_TrainParams();
LogisticRegressionParams params1 = LogisticRegressionParams();
params1.alpha = 1.0;
params1.num_iters = 10001;
params1.norm = CvLR::REG_L2;
// params1.debug = 1;
params1.norm = LogisticRegression::REG_L2;
params1.regularized = 1;
params1.train_method = CvLR::BATCH;
params1.minibatchsize = 10;
params1.train_method = LogisticRegression::BATCH;
params1.mini_batch_size = 10;
data.convertTo(data, CV_32FC1);
labels.convertTo(labels, CV_32FC1);
// run LR classifier train classifier
CvLR lr1(data, labels, params1);
CvLR lr2;
learnt_mat1 = lr1.get_learnt_mat();
LogisticRegression lr1(data, labels, params1);
LogisticRegression lr2;
learnt_mat1 = lr1.get_learnt_thetas();
lr1.predict(data, responses1);
// now save the classifier
// Write out
string filename = cv::tempfile(".xml");
try
{
@ -312,10 +304,9 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
lr2.predict(data, responses2);
learnt_mat2 = lr2.get_learnt_mat();
learnt_mat2 = lr2.get_learnt_thetas();
// compare difference in prediction outputs before and after loading from disk
pred_result1 = (responses1 == responses2)/255;
CV_Assert(responses1.rows == responses2.rows);
// compare difference in learnt matrices before and after loading from disk
comp_learnt_mats = (learnt_mat1 == learnt_mat2);
@ -326,10 +317,9 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
// compare difference in prediction outputs and stored inputs
// check if there is any difference between computed learnt mat and retreived mat
errorCount += 1 - (float)cv::sum(pred_result1)[0]/pred_result1.rows;
errorCount += 1 - (float)cv::countNonZero(responses1 == responses2)/responses1.rows;
errorCount += 1 - (float)cv::sum(comp_learnt_mats)[0]/comp_learnt_mats.rows;
if(errorCount>0)
{
ts->printf( cvtest::TS::LOG, "Different prediction results before writing and after reading (errorCount=%d).\n", errorCount );