From 535cfe8da7c6107d8415afd1381d59f2a6b9844f Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cab.ark.cs.cmu.edu>
Date: Sun, 24 Jun 2012 15:52:30 -0400
Subject: compute risk / gradient of risk

---
 training/Makefile.am |  3 ++-
 training/risk.cc     | 43 +++++++++++++++++++++++++++++++++++++++++++
 training/risk.h      | 25 +++++++++++++++++++++++++
 3 files changed, 70 insertions(+), 1 deletion(-)
 create mode 100644 training/risk.cc
 create mode 100644 training/risk.h

diff --git a/training/Makefile.am b/training/Makefile.am
index 19ee8f0d..68ebfab4 100644
--- a/training/Makefile.am
+++ b/training/Makefile.am
@@ -27,7 +27,8 @@ noinst_LIBRARIES = libtraining.a
 libtraining_a_SOURCES = \
   candidate_set.cc \
   optimize.cc \
-  online_optimizer.cc
+  online_optimizer.cc \
+  risk.cc
 
 mpi_online_optimize_SOURCES = mpi_online_optimize.cc
 mpi_online_optimize_LDADD = libtraining.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
diff --git a/training/risk.cc b/training/risk.cc
new file mode 100644
index 00000000..347ed3cb
--- /dev/null
+++ b/training/risk.cc
@@ -0,0 +1,43 @@
+#include "risk.h"
+
+#include "prob.h"
+#include "candidate_set.h"
+#include "ns.h"
+
+using namespace std;
+
+namespace training {
+
+// g = \sum_e p(e|f) * loss(e) * (phi(e,f) - E[phi(e,f)])
+double CandidateSetRisk::operator()(const vector<double>& params,
+                                    SparseVector<double>* g) const {
+  prob_t z;
+  for (unsigned i = 0; i < cands_.size(); ++i) {
+    const prob_t u(cands_[i].fmap.dot(params), init_lnx());
+    z += u;
+  }
+  const double log_z = log(z);
+
+  SparseVector<double> exp_feats;
+  if (g) {
+    for (unsigned i = 0; i < cands_.size(); ++i) {
+      const double log_prob = cands_[i].fmap.dot(params) - log_z;
+      const double prob = exp(log_prob);
+      exp_feats += cands_[i].fmap * prob;
+    }
+  }
+
+  double risk = 0;
+  for (unsigned i = 0; i < cands_.size(); ++i) {
+    const double log_prob = cands_[i].fmap.dot(params) - log_z;
+    const double prob = exp(log_prob);
+    const double r = prob * metric_.ComputeScore(cands_[i].eval_feats);
+    risk += r;
+    if (g) (*g) += (cands_[i].fmap - exp_feats) * r;
+  }
+  return risk;
+}
+
+}
+
+
diff --git a/training/risk.h b/training/risk.h
new file mode 100644
index 00000000..00ff60ec
--- /dev/null
+++ b/training/risk.h
@@ -0,0 +1,25 @@
+#ifndef _RISK_H_
+#define _RISK_H_
+
+#include <vector>
+#include "sparse_vector.h"
+class EvaluationMetric;
+
+namespace training {
+  class CandidateSet;
+
+  class CandidateSetRisk {
+    explicit CandidateSetRisk(const CandidateSet& cs, const EvaluationMetric& metric) :
+       cands_(cs),
+       metric_(metric) {}
+    // compute the risk (expected loss) of a CandidateSet
+    // (optional) the gradient of the risk with respect to params
+    double operator()(const std::vector<double>& params,
+                      SparseVector<double>* g = NULL) const;
+   private:
+    const CandidateSet& cands_;
+    const EvaluationMetric& metric_;
+  };
+};
+
+#endif
-- 
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