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Diffstat (limited to 'training/creg.cc')
| -rw-r--r-- | training/creg.cc | 334 |
1 files changed, 334 insertions, 0 deletions
diff --git a/training/creg.cc b/training/creg.cc new file mode 100644 index 00000000..58adea00 --- /dev/null +++ b/training/creg.cc @@ -0,0 +1,334 @@ +#include <cstdlib> +#include <iostream> +#include <vector> +#include <tr1/unordered_map> + +#include <boost/program_options.hpp> +#include <boost/program_options/variables_map.hpp> + +#include "json_feature_map_lexer.h" +#include "prob.h" +#include "filelib.h" +#include "weights.h" +#include "sparse_vector.h" +#include "liblbfgs/lbfgs++.h" + +using namespace std; +using namespace std::tr1; +namespace po = boost::program_options; + +void InitCommandLine(int argc, char** argv, po::variables_map* conf) { + po::options_description opts("Configuration options"); + opts.add_options() + ("training_features,x", po::value<string>(), "File containing training instance features (ARKRegression format)") + ("training_responses,y", po::value<string>(), "File containing training response features (ARKRegression format)") + ("linear,n", "Linear (rather than logistic) regression") + ("l1",po::value<double>()->default_value(0.0), "l_1 regularization strength") + ("l2",po::value<double>()->default_value(0.0), "l_2 regularization strength") + ("weights,w", po::value<string>(), "Initial weights") + ("epsilon,e", po::value<double>()->default_value(1e-4), "Epsilon for convergence test. Terminates when ||g|| < epsilon * max(1, ||x||)") + ("memory_buffers,m",po::value<unsigned>()->default_value(40), "Number of memory buffers for LBFGS") + ("help,h", "Help"); + po::options_description dcmdline_options; + dcmdline_options.add(opts); + po::store(parse_command_line(argc, argv, dcmdline_options), *conf); + if (conf->count("help") || !conf->count("training_features") || !conf->count("training_responses")) { + cerr << dcmdline_options << endl; + exit(1); + } +} + +struct TrainingInstance { + SparseVector<float> x; + union { + unsigned label; // for categorical predictions + float value; // for continuous predictions + } y; +}; + +struct ReaderHelper { + explicit ReaderHelper(vector<TrainingInstance>* xyp) : xy_pairs(xyp), lc(), flag() {} + unordered_map<string, unsigned> id2ind; + vector<TrainingInstance>* xy_pairs; + int lc; + bool flag; +}; + +void ReaderCB(const string& id, const SparseVector<float>& fmap, void* extra) { + ReaderHelper& rh = *reinterpret_cast<ReaderHelper*>(extra); + ++rh.lc; + if (rh.lc % 1000 == 0) { cerr << '.'; rh.flag = true; } + if (rh.lc % 40000 == 0) { cerr << " [" << rh.lc << "]\n"; rh.flag = false; } + const unordered_map<string, unsigned>::iterator it = rh.id2ind.find(id); + if (it == rh.id2ind.end()) { + cerr << "Unlabeled example in line " << rh.lc << endl; + abort(); + } + (*rh.xy_pairs)[it->second - 1].x = fmap; +} + +void ReadLabeledInstances(const string& ffeats, + const string& fresp, + const bool is_continuous, + vector<TrainingInstance>* xy_pairs, + vector<string>* labels) { + bool flag = false; + xy_pairs->clear(); + int lc = 0; + ReaderHelper rh(xy_pairs); + unordered_map<string, unsigned> label2id; + cerr << "Reading training responses from " << fresp << " ..." << endl; + ReadFile fr(fresp); + for (unsigned i = 0; i < labels->size(); ++i) + label2id[(*labels)[i]] = i; + istream& in = *fr.stream(); + string line; + while(getline(in, line)) { + ++lc; + if (lc % 1000 == 0) { cerr << '.'; flag = true; } + if (lc % 40000 == 0) { cerr << " [" << lc << "]\n"; flag = false; } + if (line.size() == 0) continue; + if (line[0] == '#') continue; + unsigned p = 0; + while (p < line.size() && line[p] != ' ' && line[p] != '\t') { ++p; } + unsigned& ind = rh.id2ind[line.substr(0, p)]; + if (ind != 0) { cerr << "ID " << line.substr(0, p) << " duplicated in line " << lc << endl; abort(); } + while (p < line.size() && (line[p] == ' ' || line[p] == '\t')) { ++p; } + assert(p < line.size()); + xy_pairs->push_back(TrainingInstance()); + ind = xy_pairs->size(); + if (is_continuous) { + xy_pairs->back().y.value = strtof(&line[p], 0); + } else { // categorical predictions + unordered_map<string, unsigned>::iterator it = label2id.find(line.substr(p)); + if (it == label2id.end()) { + const string label = line.substr(p); + it = label2id.insert(make_pair(label, labels->size())).first; + labels->push_back(label); + } + xy_pairs->back().y.label = it->second; // label id + } + } + if (flag) cerr << endl; + if (!is_continuous) { + cerr << "LABELS:"; + for (unsigned j = 0; j < labels->size(); ++j) + cerr << " " << (*labels)[j]; + cerr << endl; + } + cerr << "Reading training features from " << ffeats << " ..." << endl; + ReadFile ff(ffeats); + JSONFeatureMapLexer::ReadRules(ff.stream(), ReaderCB, &rh); + if (rh.flag) cerr << endl; +} + +// helper base class (not polymorphic- just a container and some helper functions) for loss functions +// real loss functions should implement double operator()(const vector<double>& x, double* g), +// which should evaluate f(x) and g = f'(x) +struct BaseLoss { + // dimp1 = number of categorial outputs possible for logistic regression + // for linear regression, it should be 1 more than the dimension of the response variable + BaseLoss( + const vector<TrainingInstance>& tr, + unsigned dimp1, + unsigned numfeats, + unsigned ll2) : training(tr), K(dimp1), p(numfeats), l2(ll2) {} + + // weight vector layout for K classes, with p features + // w[0 : K-1] = bias weights + // w[y*p + K : y*p + K + p - 1] = feature weights for y^th class + // this representation is used in ComputeDotProducts and GradAdd + void ComputeDotProducts(const SparseVector<float>& fx, // feature vector of x + const vector<double>& w, // full weight vector + vector<double>* pdotprods) const { + vector<double>& dotprods = *pdotprods; + const unsigned km1 = K - 1; + dotprods.resize(km1); + for (unsigned y = 0; y < km1; ++y) + dotprods[y] = w[y]; // bias terms + for (SparseVector<float>::const_iterator it = fx.begin(); it != fx.end(); ++it) { + const float fval = it->second; + const unsigned fid = it->first; + for (unsigned y = 0; y < km1; ++y) + dotprods[y] += w[fid + y * p + km1] * fval; + } + } + + double ApplyRegularizationTerms(const vector<double>& weights, + double* g) const { + double reg = 0; + for (size_t i = K - 1; i < weights.size(); ++i) { + const double& w_i = weights[i]; + reg += l2 * w_i * w_i; + g[i] += 2 * l2 * w_i; + } + return reg; + } + + void GradAdd(const SparseVector<float>& fx, + const unsigned y, + const double scale, + double* acc) const { + acc[y] += scale; // class bias + for (SparseVector<float>::const_iterator it = fx.begin(); + it != fx.end(); ++it) + acc[it->first + y * p + K - 1] += it->second * scale; + } + + const vector<TrainingInstance>& training; + const unsigned K, p; + const double l2; +}; + +struct UnivariateSquaredLoss : public BaseLoss { + UnivariateSquaredLoss( + const vector<TrainingInstance>& tr, + unsigned numfeats, + const double l2) : BaseLoss(tr, 2, numfeats, l2) {} + + // evaluate squared loss and gradient + double operator()(const vector<double>& x, double* g) const { + fill(g, g + x.size(), 0.0); + double cll = 0; + vector<double> dotprods(1); // univariate prediction + for (int i = 0; i < training.size(); ++i) { + const SparseVector<float>& fmapx = training[i].x; + const double refy = training[i].y.value; + ComputeDotProducts(fmapx, x, &dotprods); + double diff = dotprods[0] - refy; + cll += diff * diff; + + double scale = 2 * diff; + GradAdd(fmapx, 0, scale, g); + } + double reg = ApplyRegularizationTerms(x, g); + return cll + reg; + } +}; + +struct MulticlassLogLoss : public BaseLoss { + MulticlassLogLoss( + const vector<TrainingInstance>& tr, + unsigned k, + unsigned numfeats, + const double l2) : BaseLoss(tr, k, numfeats, l2) {} + + // evaluate log loss and gradient + double operator()(const vector<double>& x, double* g) const { + fill(g, g + x.size(), 0.0); + vector<double> dotprods(K - 1); // K-1 degrees of freedom + vector<prob_t> probs(K); + double cll = 0; + for (int i = 0; i < training.size(); ++i) { + const SparseVector<float>& fmapx = training[i].x; + const unsigned refy = training[i].y.label; + //cerr << "FMAP: " << fmapx << endl; + ComputeDotProducts(fmapx, x, &dotprods); + prob_t z; + for (unsigned j = 0; j < dotprods.size(); ++j) + z += (probs[j] = prob_t(dotprods[j], init_lnx())); + z += (probs.back() = prob_t::One()); + for (unsigned y = 0; y < probs.size(); ++y) { + probs[y] /= z; + //cerr << " p(y=" << y << ")=" << probs[y].as_float() << "\tz=" << z << endl; + } + cll -= log(probs[refy]); // log p(y | x) + + for (unsigned y = 0; y < dotprods.size(); ++y) { + double scale = probs[y].as_float(); + if (y == refy) { scale -= 1.0; } + GradAdd(fmapx, y, scale, g); + } + } + double reg = ApplyRegularizationTerms(x, g); + return cll + reg; + } +}; + +template <class LossFunction> +double LearnParameters(LossFunction& loss, + const double l1, + const unsigned l1_start, + const unsigned memory_buffers, + const double eps, + vector<double>* px) { + LBFGS<LossFunction> lbfgs(px, loss, memory_buffers, l1, l1_start, eps); + lbfgs.MinimizeFunction(); + return 0; +} + +int main(int argc, char** argv) { + po::variables_map conf; + InitCommandLine(argc, argv, &conf); + string line; + vector<TrainingInstance> training; + const string xfile = conf["training_features"].as<string>(); + const string yfile = conf["training_responses"].as<string>(); + double l1 = conf["l1"].as<double>(); + double l2 = conf["l2"].as<double>(); + const unsigned memory_buffers = conf["memory_buffers"].as<unsigned>(); + const double epsilon = conf["epsilon"].as<double>(); + if (l1 < 0.0) { + cerr << "L1 strength must be >= 0\n"; + return 1; + } + if (l2 < 0.0) { + cerr << "L2 strength must be >= 0\n"; + return 2; + } + + const bool is_continuous = conf.count("linear"); + vector<string> labels; // only populated for non-continuous models + ReadLabeledInstances(xfile, yfile, is_continuous, &training, &labels); + + if (conf.count("weights")) { + cerr << "Initial weights are not implemented, please implement." << endl; + // TODO read weights for categorical and continuous predictions + // can't use normal cdec weight framework + abort(); + } + + cerr << " Number of features: " << FD::NumFeats() << endl; + cerr << "Number of training examples: " << training.size() << endl; + const unsigned p = FD::NumFeats(); + cout.precision(15); + + if (conf.count("linear")) { // linear regression + vector<double> weights(1 + FD::NumFeats(), 0.0); + cerr << " Number of parameters: " << weights.size() << endl; + UnivariateSquaredLoss loss(training, p, l2); + LearnParameters(loss, l1, 1, memory_buffers, epsilon, &weights); + cout << p << "\t***CONTINUOUS***" << endl; + cout << "***BIAS***\t" << weights[0] << endl; + for (unsigned f = 0; f < p; ++f) { + const double w = weights[1 + f]; + if (w) + cout << FD::Convert(f) << "\t" << w << endl; + } + } else { // logistic regression + vector<double> weights((1 + FD::NumFeats()) * (labels.size() - 1), 0.0); + cerr << " Number of parameters: " << weights.size() << endl; + cerr << " Number of labels: " << labels.size() << endl; + const unsigned K = labels.size(); + const unsigned km1 = K - 1; + MulticlassLogLoss loss(training, K, p, l2); + LearnParameters(loss, l1, km1, memory_buffers, epsilon, &weights); + + cout << p << "\t***CATEGORICAL***"; + for (unsigned y = 0; y < K; ++y) + cout << '\t' << labels[y]; + cout << endl; + for (unsigned y = 0; y < km1; ++y) + cout << labels[y] << "\t***BIAS***\t" << weights[y] << endl; + for (unsigned y = 0; y < km1; ++y) { + for (unsigned f = 0; f < p; ++f) { + const double w = weights[km1 + y * p + f]; + if (w) + cout << labels[y] << "\t" << FD::Convert(f) << "\t" << w << endl; + } + } + } + + return 0; +} + |