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

src/metric/elementwise_metric.cc

@@ -163,6 +163,30 @@ struct EvalGammaNLogLik: public EvalEWiseBase<EvalGammaNLogLik> {
   }
 };
 
+struct EvalTweedieNLogLik: public EvalEWiseBase<EvalTweedieNLogLik> {
+  explicit EvalTweedieNLogLik(const char* param) {
+    CHECK(param != nullptr)
+        << "tweedie-nloglik must be in format tweedie-nloglik@rho";
+    rho_ = atof(param);
+    CHECK(rho_ < 2 && rho_ >= 1)
+        << "tweedie variance power must be in interval [1, 2)";
+    std::ostringstream os;
+    os << "tweedie-nloglik@" << rho_;
+    name_ = os.str();
+  }
+  const char *Name() const override {
+    return name_.c_str();
+  }
+  inline float EvalRow(float y, float p) const {
+    float a = y * std::exp((1 - rho_) * std::log(p)) / (1 - rho_);
+    float b = std::exp((2 - rho_) * std::log(p)) / (2 - rho_);
+    return -a + b;
+  }
+ protected:
+  std::string name_;
+  float rho_;
+};
+
 XGBOOST_REGISTER_METRIC(RMSE, "rmse")
 .describe("Rooted mean square error.")
 .set_body([](const char* param) { return new EvalRMSE(); });
@@ -191,5 +215,11 @@ XGBOOST_REGISTER_METRIC(GammaNLogLik, "gamma-nloglik")
 .describe("Negative log-likelihood for gamma regression.")
 .set_body([](const char* param) { return new EvalGammaNLogLik(); });
 
+XGBOOST_REGISTER_METRIC(TweedieNLogLik, "tweedie-nloglik")
+.describe("tweedie-nloglik@rho for tweedie regression.")
+.set_body([](const char* param) {
+  return new EvalTweedieNLogLik(param);
+});
+
 }  // namespace metric
 }  // namespace xgboost

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