optionally sample from forest to get training instances, rather than k-best it

1 files changed, 73 insertions, 0 deletions

diff --git a/decoder/hg_sampler.cc b/decoder/hg_sampler.cc
new file mode 100644
index 00000000..cdf0ec3c
--- /dev/null
+++ b/decoder/hg_sampler.cc
@@ -0,0 +1,73 @@
+#include "hg_sampler.h"
+
+#include <queue>
+
+#include "viterbi.h"
+#include "inside_outside.h"
+
+using namespace std;
+
+struct SampledDerivationWeightFunction {
+  typedef double Weight;
+  explicit SampledDerivationWeightFunction(const vector<bool>& sampled) : sampled_edges(sampled) {}
+  double operator()(const Hypergraph::Edge& e) const {
+    return static_cast<double>(sampled_edges[e.id_]);
+  }
+  const vector<bool>& sampled_edges;
+};
+
+void HypergraphSampler::sample_hypotheses(const Hypergraph& hg,
+                                          unsigned n,
+                                          MT19937* rng,
+                                          vector<Hypothesis>* hypos) {
+  hypos->clear();
+  hypos->resize(n);
+
+  // compute inside probabilities
+  vector<prob_t> node_probs;
+  Inside<prob_t, EdgeProb>(hg, &node_probs, EdgeProb());
+
+  vector<bool> sampled_edges(hg.edges_.size());
+  queue<unsigned> q;
+  SampleSet<prob_t> ss;
+  for (unsigned i = 0; i < n; ++i) {
+    fill(sampled_edges.begin(), sampled_edges.end(), false);
+    // sample derivation top down
+    assert(q.empty());
+    Hypothesis& hyp = (*hypos)[i];
+    SparseVector<double>& deriv_features = hyp.fmap;
+    q.push(hg.nodes_.size() - 1);
+    prob_t& model_score = hyp.model_score;
+    model_score = prob_t::One();
+    while(!q.empty()) {
+      unsigned cur_node_id = q.front();
+      q.pop();
+      const Hypergraph::Node& node = hg.nodes_[cur_node_id];
+      const unsigned num_in_edges = node.in_edges_.size();
+      unsigned sampled_edge_idx = 0;
+      if (num_in_edges == 1) {
+        sampled_edge_idx = node.in_edges_[0];
+      } else {
+        assert(num_in_edges > 1);
+        ss.clear();
+        for (unsigned j = 0; j < num_in_edges; ++j) {
+          const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
+          prob_t p = edge.edge_prob_;   // edge weight
+          for (unsigned k = 0; k < edge.tail_nodes_.size(); ++k)
+            p *= node_probs[edge.tail_nodes_[k]];  // tail node inside weight
+          ss.add(p);
+        }
+        sampled_edge_idx = node.in_edges_[rng->SelectSample(ss)];
+      }
+      sampled_edges[sampled_edge_idx] = true;
+      const Hypergraph::Edge& sampled_edge = hg.edges_[sampled_edge_idx];
+      deriv_features += sampled_edge.feature_values_;
+      model_score *= sampled_edge.edge_prob_;
+      //sampled_deriv->push_back(sampled_edge_idx);
+      for (unsigned j = 0; j < sampled_edge.tail_nodes_.size(); ++j) {
+        q.push(sampled_edge.tail_nodes_[j]);
+      }
+    }
+    Viterbi(hg, &hyp.words, ESentenceTraversal(), SampledDerivationWeightFunction(sampled_edges));
+  }
+}