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Refactor nbc program to allow loading/saving models.

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This commit is contained in:
ryan
2015-12-21 12:32:49 -05:00
parent df229e45a5
commit 09cbc6e13a
2 changed files with 124 additions and 54 deletions
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src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp
+2 -2
View File
@@ -71,8 +71,8 @@ class NaiveBayesClassifier
* Train() before calling Classify(), otherwise the results may be
* meaningless.
*/
NaiveBayesClassifier(const size_t dimensionality,
const size_t classes);
NaiveBayesClassifier(const size_t dimensionality = 0,
const size_t classes = 0);
/**
* Train the Naive Bayes classifier on the given dataset. If the incremental
src/mlpack/methods/naive_bayes/nbc_main.cpp
+122 -52
View File
@@ -24,87 +24,157 @@ PROGRAM_INFO("Parametric Naive Bayes Classifier",
"use an incremental algorithm for calculating variance. This is slower, "
"but can help avoid loss of precision in some cases.");
PARAM_STRING_REQ("train_file", "A file containing the training set.", "t");
PARAM_STRING_REQ("test_file", "A file containing the test set.", "T");
// Model loading/saving.
PARAM_STRING("input_model_file", "File containing input Naive Bayes model.",
"m", "");
PARAM_STRING("output_model_file", "File to save trained Naive Bayes model to.",
"M", "");
// Training parameters.
PARAM_STRING("training_file", "A file containing the training set.", "t", "");
PARAM_STRING("labels_file", "A file containing labels for the training set.",
"l", "");
PARAM_STRING("output_file", "The file in which the predicted labels for the "
"test set will be written.", "o", "output.csv");
PARAM_FLAG("incremental_variance", "The variance of each class will be "
"calculated incrementally.", "I");
// Test parameters.
PARAM_STRING("test_file", "A file containing the test set.", "T", "");
PARAM_STRING("output_file", "The file in which the predicted labels for the "
"test set will be written.", "o", "");
using namespace mlpack;
using namespace mlpack::naive_bayes;
using namespace std;
using namespace arma;
// A struct for saving the model with mappings.
struct NBCModel
{
//! The model itself.
NaiveBayesClassifier<> nbc;
//! The mappings for labels.
Col<size_t> mappings;
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(nbc, "nbc");
ar & data::CreateNVP(mappings, "mappings");
}
};
int main(int argc, char* argv[])
{
CLI::ParseCommandLine(argc, argv);
// Check input parameters.
const string trainingDataFilename = CLI::GetParam<string>("train_file");
mat trainingData;
data::Load(trainingDataFilename, trainingData, true);
if (CLI::HasParam("training_file") && CLI::HasParam("input_model_file"))
Log::Fatal << "Cannot specify both --training_file (-t) and "
<< "--input_model_file (-m)!" << endl;
// Normalize labels.
Row<size_t> labels;
Col<size_t> mappings;
if (!CLI::HasParam("training_file") && !CLI::HasParam("input_model_file"))
Log::Fatal << "Neither --training_file (-t) nor --input_model_file (-m) are"
<< " specified!" << endl;
// Did the user pass in labels?
const string labelsFilename = CLI::GetParam<string>("labels_file");
if (labelsFilename != "")
if (!CLI::HasParam("training_file") && CLI::HasParam("labels_file"))
Log::Warn << "--labels_file (-l) ignored because --training_file (-t) is "
<< "not specified." << endl;
if (!CLI::HasParam("training_file") && CLI::HasParam("incremental_variance"))
Log::Warn << "--incremental_variance (-I) ignored because --training_file "
<< "(-t) is not specified." << endl;
if (!CLI::HasParam("output_file") && !CLI::HasParam("output_model_file"))
Log::Warn << "Neither --output_file (-o) nor --output_model_file (-M) "
<< "specified; no output will be saved!" << endl;
if (CLI::HasParam("output_file") && !CLI::HasParam("test_file"))
Log::Warn << "--output_file (-o) ignored because no test file specified "
<< "with --test_file (-T)." << endl;
if (!CLI::HasParam("output_file") && CLI::HasParam("test_file"))
Log::Warn << "--test_file (-T) specified, but classification results will "
<< "not be saved because --output_file (-o) is not specified." << endl;
// Either we have to train a model, or load a model.
NBCModel model;
if (CLI::HasParam("training_file"))
{
// Load labels.
mat rawLabels;
data::Load(labelsFilename, rawLabels, true, false);
const string trainingFile = CLI::GetParam<string>("training_file");
mat trainingData;
data::Load(trainingFile, trainingData, true);
// Do the labels need to be transposed?
if (rawLabels.n_cols == 1)
rawLabels = rawLabels.t();
Row<size_t> labels;
data::NormalizeLabels(rawLabels.row(0), labels, mappings);
// Did the user pass in labels?
const string labelsFilename = CLI::GetParam<string>("labels_file");
if (labelsFilename != "")
{
// Load labels.
mat rawLabels;
data::Load(labelsFilename, rawLabels, true, false);
// Do the labels need to be transposed?
if (rawLabels.n_cols == 1)
rawLabels = rawLabels.t();
data::NormalizeLabels(rawLabels.row(0), labels, model.mappings);
}
else
{
// Use the last row of the training data as the labels.
Log::Info << "Using last dimension of training data as training labels."
<< endl;
data::NormalizeLabels(trainingData.row(trainingData.n_rows - 1), labels,
model.mappings);
// Remove the label row.
trainingData.shed_row(trainingData.n_rows - 1);
}
const bool incrementalVariance = CLI::HasParam("incremental_variance");
Timer::Start("nbc_training");
model.nbc = NaiveBayesClassifier<>(trainingData, labels,
model.mappings.n_elem, incrementalVariance);
Timer::Stop("nbc_training");
}
else
{
// Use the last row of the training data as the labels.
Log::Info << "Using last dimension of training data as training labels."
<< endl;
data::NormalizeLabels(trainingData.row(trainingData.n_rows - 1), labels,
mappings);
// Remove the label row.
trainingData.shed_row(trainingData.n_rows - 1);
// Load the model from file.
data::Load(CLI::GetParam<string>("input_model_file"), "nbc_model", model);
}
const string testingDataFilename = CLI::GetParam<std::string>("test_file");
mat testingData;
data::Load(testingDataFilename, testingData, true);
// Do we need to do testing?
if (CLI::HasParam("test_file"))
{
const string testingDataFilename = CLI::GetParam<std::string>("test_file");
mat testingData;
data::Load(testingDataFilename, testingData, true);
if (testingData.n_rows != trainingData.n_rows)
Log::Fatal << "Test data dimensionality (" << testingData.n_rows << ") "
<< "must be the same as training data (" << trainingData.n_rows
<< ")!" << std::endl;
if (testingData.n_rows != model.nbc.Means().n_rows)
Log::Fatal << "Test data dimensionality (" << testingData.n_rows << ") "
<< "must be the same as training data (" << model.nbc.Means().n_rows
<< ")!" << std::endl;
const bool incrementalVariance = CLI::HasParam("incremental_variance");
// Time the running of the Naive Bayes Classifier.
Row<size_t> results;
Timer::Start("nbc_testing");
model.nbc.Classify(testingData, results);
Timer::Stop("nbc_testing");
// Create and train the classifier.
Timer::Start("training");
NaiveBayesClassifier<> nbc(trainingData, labels, mappings.n_elem,
incrementalVariance);
Timer::Stop("training");
if (CLI::HasParam("output_file"))
{
// Un-normalize labels to prepare output.
Row<size_t> rawResults;
data::RevertLabels(results, model.mappings, rawResults);
// Time the running of the Naive Bayes Classifier.
Row<size_t> results;
Timer::Start("testing");
nbc.Classify(testingData, results);
Timer::Stop("testing");
// Output results.
const string outputFilename = CLI::GetParam<string>("output_file");
data::Save(outputFilename, rawResults, true);
}
}
// Un-normalize labels to prepare output.
Row<size_t> rawResults;
data::RevertLabels(results, mappings, rawResults);
// Output results.
const string outputFilename = CLI::GetParam<string>("output_file");
data::Save(outputFilename, rawResults, true);
if (CLI::HasParam("output_model_file"))
data::Save(CLI::GetParam<string>("output_model_file"), "nbc_model", model,
false);
}