Tweedie Regression Post-Rebase (#1737)

* add support for tweedie regression * added back readme line that was accidentally deleted * fixed linting errors * add support for tweedie regression * added back readme line that was accidentally deleted * fixed linting errors * rebased with upstream master and added R example * changed parameter name to tweedie_variance_power * linting error fix * refactored tweedie-nloglik metric to be more like the other parameterized metrics * added upper and lower bound check to tweedie metric * add support for tweedie regression * added back readme line that was accidentally deleted * fixed linting errors * added upper and lower bound check to tweedie metric * added back readme line that was accidentally deleted * rebased with upstream master and added R example * rebased again on top of upstream master * linting error fix * added upper and lower bound check to tweedie metric * rebased with master * lint fix * removed whitespace at end of line 186 - elementwise_metric.cc
2016-11-05 20:02:32 -04:00
parent 52b9867be5
commit 2ad0948444
4 changed files with 156 additions and 0 deletions
--- a/src/objective/regression_obj.cc
+++ b/src/objective/regression_obj.cc
@@ -272,5 +272,75 @@ class GammaRegression : public ObjFunction {
 XGBOOST_REGISTER_OBJECTIVE(GammaRegression, "reg:gamma")
 .describe("Gamma regression for severity data.")
 .set_body([]() { return new GammaRegression(); });
+
+// declare parameter
+struct TweedieRegressionParam : public dmlc::Parameter<TweedieRegressionParam> {
+  float tweedie_variance_power;
+  DMLC_DECLARE_PARAMETER(TweedieRegressionParam) {
+    DMLC_DECLARE_FIELD(tweedie_variance_power).set_range(1.0f, 2.0f).set_default(1.5f)
+      .describe("Tweedie variance power.  Must be between in range [1, 2).");
+  }
+};
+
+// tweedie regression
+class TweedieRegression : public ObjFunction {
+ public:
+  // declare functions
+  void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
+    param_.InitAllowUnknown(args);
+  }
+
+  void GetGradient(const std::vector<float> &preds,
+                   const MetaInfo &info,
+                   int iter,
+                   std::vector<bst_gpair> *out_gpair) override {
+    CHECK_NE(info.labels.size(), 0) << "label set cannot be empty";
+    CHECK_EQ(preds.size(), info.labels.size()) << "labels are not correctly provided";
+    out_gpair->resize(preds.size());
+    // check if label in range
+    bool label_correct = true;
+    // start calculating gradient
+    const omp_ulong ndata = static_cast<omp_ulong>(preds.size()); // NOLINT(*)
+    #pragma omp parallel for schedule(static)
+    for (omp_ulong i = 0; i < ndata; ++i) { // NOLINT(*)
+      float p = preds[i];
+      float w = info.GetWeight(i);
+      float y = info.labels[i];
+      float rho = param_.tweedie_variance_power;
+      if (y >= 0.0f) {
+        float grad = -y * std::exp((1 - rho) * p) + std::exp((2 - rho) * p);
+        float hess = -y * (1 - rho) * std::exp((1 - rho) * p) + (2 - rho) * std::exp((2 - rho) * p);
+        out_gpair->at(i) = bst_gpair(grad * w, hess * w);
+      } else {
+        label_correct = false;
+      }
+    }
+    CHECK(label_correct) << "TweedieRegression: label must be nonnegative";
+  }
+  void PredTransform(std::vector<float> *io_preds) override {
+    std::vector<float> &preds = *io_preds;
+    const long ndata = static_cast<long>(preds.size()); // NOLINT(*)
+    #pragma omp parallel for schedule(static)
+    for (long j = 0; j < ndata; ++j) {  // NOLINT(*)
+      preds[j] = std::exp(preds[j]);
+    }
+  }
+  const char* DefaultEvalMetric(void) const override {
+    std::ostringstream os;
+    os << "tweedie-nloglik@" << param_.tweedie_variance_power;
+    std::string metric = os.str();
+    return metric.c_str();
+  }
+
+ private:
+  TweedieRegressionParam param_;
+};
+
+// register the ojective functions
+DMLC_REGISTER_PARAMETER(TweedieRegressionParam);
+
+XGBOOST_REGISTER_OBJECTIVE(TweedieRegression, "reg:tweedie")
+.describe("Tweedie regression for insurance data.")
+.set_body([]() { return new TweedieRegression(); });
 }  // namespace obj
 }  // namespace xgboost