[mlpack-git] master: unify style of softmax exec with others (746522e)
gitdub at mlpack.org
gitdub at mlpack.org
Sun Jun 5 06:03:19 EDT 2016
Repository : https://github.com/mlpack/mlpack
On branch : master
Link : https://github.com/mlpack/mlpack/compare/4fa39b6ab0baa1428116d0406264b5452e716d06...97402b9098d9d72889aa795923cf8fd67a4d87bf
>---------------------------------------------------------------
commit 746522e725af1584d475281011379ca7c1adb43a
Author: Keon Kim <kwk236 at gmail.com>
Date: Sun Jun 5 19:03:19 2016 +0900
unify style of softmax exec with others
>---------------------------------------------------------------
746522e725af1584d475281011379ca7c1adb43a
.../softmax_regression/softmax_regression_main.cpp | 87 +++++++++++-----------
1 file changed, 44 insertions(+), 43 deletions(-)
diff --git a/src/mlpack/methods/softmax_regression/softmax_regression_main.cpp b/src/mlpack/methods/softmax_regression/softmax_regression_main.cpp
index af78bd2..d3865b3 100644
--- a/src/mlpack/methods/softmax_regression/softmax_regression_main.cpp
+++ b/src/mlpack/methods/softmax_regression/softmax_regression_main.cpp
@@ -21,16 +21,16 @@ PROGRAM_INFO("Softmax Regression", "This program performs softmax regression, "
"(-r), and if an intercept term is not desired in the model, the "
"--no_intercept (-N) can be specified."
"\n\n"
- "The trained model can be saved to a file with the --output_model (-m) "
+ "The trained model can be saved to a file with the --output_model_file (-m) "
"option. If training is not desired, but only testing is, a model can be "
- "loaded with the --input_model (-i) option. At the current time, a loaded "
+ "loaded with the --input_model_file (-i) option. At the current time, a loaded "
"model cannot be trained further, so specifying both -i and -t is not "
"allowed."
"\n\n"
"The program is also able to evaluate a model on test data. A test dataset"
" can be specified with the --test_data (-T) option. Class predictions "
- "will be saved in the file specified with the --predictions_file (-p) "
- "option. If labels are specified for the test data, with the --test_labels"
+ "will be saved in the file specified with the --output_predictions_file (-p) "
+ "option. If labels are specified for the test data, with the --test_labels_file"
" (-L) option, then the program will print the accuracy of the predictions "
"on the given test set and its corresponding labels.");
@@ -41,16 +41,16 @@ PARAM_STRING("labels_file", "A file containing labels (0 or 1) for the points "
"in the training set (y). The labels must order as a row", "l", "");
// Model loading/saving.
-PARAM_STRING("input_model_file", "File containing existing model (parameters).",
+PARAM_STRING("input_model_file_file", "File containing existing model (parameters).",
"m", "");
-PARAM_STRING("output_model_file", "File to save trained softmax regression "
+PARAM_STRING("output_model_file_file", "File to save trained softmax regression "
"model to.", "M", "");
// Testing.
PARAM_STRING("test_data", "File containing test dataset.", "T", "");
-PARAM_STRING("predictions_file", "File to save predictions for test dataset "
+PARAM_STRING("output_predictions_file", "File to save predictions for test dataset "
"into.", "p", "");
-PARAM_STRING("test_labels", "File containing test labels.", "L", "");
+PARAM_STRING("test_labels_file", "File containing test labels.", "L", "");
// Softmax configuration options.
PARAM_INT("max_iterations", "Maximum number of iterations before termination.",
@@ -73,7 +73,7 @@ size_t CalculateNumberOfClasses(const size_t numClasses,
// Test the accuracy of the model.
template<typename Model>
void TestPredictAcc(const string& testFile,
- const string& predictionsFile,
+ const string& outputPredictionsFile,
const string& testLabels,
const size_t numClasses,
const Model& model);
@@ -81,7 +81,7 @@ void TestPredictAcc(const string& testFile,
// Build the softmax model given the parameters.
template<typename Model>
std::unique_ptr<Model> TrainSoftmax(const string& trainingFile,
- const string& labelFile,
+ const string& labelsFile,
const string& inputModelFile,
const size_t maxIterations);
@@ -92,51 +92,52 @@ int main(int argc, char** argv)
CLI::ParseCommandLine(argc, argv);
const std::string trainingFile = CLI::GetParam<std::string>("training_file");
- const std::string inputModelFile = CLI::GetParam<std::string>("input_model");
+ const std::string labelsFile = CLI::GetParam<std::string>("labels_file");
+
+ const std::string inputModelFile =
+ CLI::GetParam<std::string>("input_model_file");
+ const string outputModelFile = CLI::GetParam<string>("output_model_file");
+ const string testLabelsFile = CLI::GetParam<string>("test_labels_file");
+ const int maxIterations = CLI::GetParam<int>("max_iterations");
+ const string outputPredictionsFile =
+ CLI::GetParam<string>("output_predictions_file");
// One of inputFile and modelFile must be specified.
- if (inputModelFile.empty() && trainingFile.empty())
- Log::Fatal << "One of --input_model or --training_file must be specified."
+ if (!CLI::HasParam("input_model_file") && !CLI::HasParam("training_file"))
+ Log::Fatal << "One of --input_model_file or --training_file must be specified."
<< endl;
- const std::string labelFile = CLI::GetParam<std::string>("labels_file");
- if (!trainingFile.empty() && labelFile.empty())
+ if (CLI::HasParam("training_file") && CLI::HasParam("labels_file"))
Log::Fatal << "--labels_file must be specified with --training_file!"
<< endl;
- const int maxIterations = CLI::GetParam<int>("max_iterations");
-
if (maxIterations < 0)
Log::Fatal << "Invalid value for maximum iterations (" << maxIterations
<< ")! Must be greater than or equal to 0." << endl;
- const string outputModelFile = CLI::GetParam<string>("output_model");
- const string testLabelsFile = CLI::GetParam<string>("test_labels");
- const string predictionsFile = CLI::GetParam<string>("predictions_file");
-
// Make sure we have an output file of some sort.
- if (outputModelFile.empty() && testLabelsFile.empty() &&
- predictionsFile.empty())
- Log::Warn << "None of --output_model, --test_labels, or --predictions_file "
- << "are set; no results from this program will be saved." << endl;
+ if (!CLI::HasParam("output_model_file") &&
+ !CLI::HasParam("test_labels_file") &&
+ !CLI::HasParam("output_predictions_file"))
+ Log::Warn << "None of --output_model_file, --test_labels_file, or "
+ << "--output_predictions_file are set; no results from this program "
+ << " will be saved." << endl;
using SM = regression::SoftmaxRegression<>;
std::unique_ptr<SM> sm = TrainSoftmax<SM>(trainingFile,
- labelFile,
+ labelsFile,
inputModelFile,
maxIterations);
TestPredictAcc(CLI::GetParam<string>("test_data"),
- CLI::GetParam<string>("predictions_file"),
- CLI::GetParam<string>("test_labels"),
+ CLI::GetParam<string>("output_predictions_file"),
+ CLI::GetParam<string>("test_labels_file"),
sm->NumClasses(), *sm);
- if (!outputModelFile.empty())
- {
- data::Save(CLI::GetParam<std::string>("output_model"),
+ if (CLI::HasParam("output_model_file"))
+ data::Save(CLI::GetParam<std::string>("output_model_file"),
"softmax_regression_model", *sm, true);
- }
}
size_t CalculateNumberOfClasses(const size_t numClasses,
@@ -156,7 +157,7 @@ size_t CalculateNumberOfClasses(const size_t numClasses,
template<typename Model>
void TestPredictAcc(const string& testFile,
- const string& predictionsFile,
+ const string& outputPredictionsFile,
const string& testLabelsFile,
size_t numClasses,
const Model& model)
@@ -164,19 +165,19 @@ void TestPredictAcc(const string& testFile,
using namespace mlpack;
// If there is no test set, there is nothing to test on.
- if (testFile.empty() && predictionsFile.empty() && testLabelsFile.empty())
+ if (testFile.empty() && outputPredictionsFile.empty() && testLabelsFile.empty())
return;
if (!testLabelsFile.empty() && testFile.empty())
{
- Log::Warn << "--test_labels specified, but --test_file is not specified."
+ Log::Warn << "--test_labels_file specified, but --test_file is not specified."
<< " The parameter will be ignored." << endl;
return;
}
- if (!predictionsFile.empty() && testFile.empty())
+ if (!outputPredictionsFile.empty() && testFile.empty())
{
- Log::Warn << "--predictions_file specified, but --test_file is not "
+ Log::Warn << "--output_predictions_file specified, but --test_file is not "
<< "specified. The parameter will be ignored." << endl;
return;
}
@@ -189,8 +190,8 @@ void TestPredictAcc(const string& testFile,
model.Predict(testData, predictLabels);
// Save predictions, if desired.
- if (!predictionsFile.empty())
- data::Save(predictionsFile, predictLabels);
+ if (!outputPredictionsFile.empty())
+ data::Save(outputPredictionsFile, predictLabels);
// Calculate accuracy, if desired.
if (!testLabelsFile.empty())
@@ -203,8 +204,8 @@ void TestPredictAcc(const string& testFile,
if (testData.n_cols != testLabels.n_elem)
{
Log::Fatal << "Test data in --test_data has " << testData.n_cols
- << " points, but labels in --test_labels have " << testLabels.n_elem
- << " labels!" << endl;
+ << " points, but labels in --test_labels_file have "
+ << testLabels.n_elem << " labels!" << endl;
}
std::vector<size_t> bingoLabels(numClasses, 0);
@@ -235,7 +236,7 @@ void TestPredictAcc(const string& testFile,
template<typename Model>
std::unique_ptr<Model> TrainSoftmax(const string& trainingFile,
- const string& labelFile,
+ const string& labelsFile,
const string& inputModelFile,
const size_t maxIterations)
{
@@ -258,7 +259,7 @@ std::unique_ptr<Model> TrainSoftmax(const string& trainingFile,
//load functions of mlpack do not works on windows, it will complain
//"[FATAL] Unable to detect type of 'softmax_data.txt'; incorrect extension?"
data::Load(trainingFile, trainData, true);
- data::Load(labelFile, tmpTrainLabels, true);
+ data::Load(labelsFile, tmpTrainLabels, true);
trainLabels = tmpTrainLabels.row(0);
if (trainData.n_cols != trainLabels.n_elem)
More information about the mlpack-git
mailing list