fix warning

src/metric/regression_metric.hpp

@@ -74,9 +74,9 @@ public:
 
 protected:
   /*! \brief delta for Huber loss */
-  double huber_delta_;
+  score_t huber_delta_;
   /*! \brief c for Fair loss */
-  double fair_c_;
+  score_t fair_c_;
 
 private:
   /*! \brief Number of data */
@@ -126,40 +126,40 @@ public:
 /*! \brief Huber loss for regression task */
 class HuberLossMetric: public RegressionMetric<HuberLossMetric> {
 public:
-    explicit HuberLossMetric(const MetricConfig& config) :RegressionMetric<HuberLossMetric>(config) {
-        huber_delta_ = config.huber_delta;
-    }
+  explicit HuberLossMetric(const MetricConfig& config) :RegressionMetric<HuberLossMetric>(config) {
+    huber_delta_ = static_cast<score_t>(config.huber_delta);
+  }
 
-    inline static score_t LossOnPoint(float label, score_t score, float delta, float) {
-        const double diff = score - label;
-        if (std::abs(diff) <= delta) {
-            return 0.5 * diff * diff;
-        } else {
-            return delta * (std::abs(diff) - 0.5 * delta);
-        }
+  inline static score_t LossOnPoint(float label, score_t score, float delta, float) {
+    const score_t diff = score - label;
+    if (std::abs(diff) <= delta) {
+      return 0.5f * diff * diff;
+    } else {
+      return delta * (std::abs(diff) - 0.5f * delta);
     }
+  }
 
-    inline static const char* Name() {
-        return "huber";
-    }
+  inline static const char* Name() {
+    return "huber";
+  }
 };
 
 /*! \brief Fair loss for regression task */
 // http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html
 class FairLossMetric: public RegressionMetric<FairLossMetric> {
 public:
-    explicit FairLossMetric(const MetricConfig& config) :RegressionMetric<FairLossMetric>(config) {
-        fair_c_ = config.fair_c;
-    }
+  explicit FairLossMetric(const MetricConfig& config) :RegressionMetric<FairLossMetric>(config) {
+    fair_c_ = static_cast<score_t>(config.fair_c);
+  }
 
-    inline static score_t LossOnPoint(float label, score_t score, float, float c) {
-        const double x = std::abs(score - label);
-        return c * x - c * c * std::log(1.0 + x / c);
-    }
+  inline static score_t LossOnPoint(float label, score_t score, float, float c) {
+    const score_t x = std::fabs(score - label);
+    return c * x - c * c * std::log(1.0f + x / c);
+  }
 
-    inline static const char* Name() {
-        return "fair";
-    }
+  inline static const char* Name() {
+    return "fair";
+  }
 };
 
 }  // namespace LightGBM

src/objective/regression_objective.hpp

@@ -23,15 +23,15 @@ public:
   }
 
   void GetGradients(const score_t* score, score_t* gradients,
-                    score_t* hessians) const override {
+    score_t* hessians) const override {
     if (weights_ == nullptr) {
-      #pragma omp parallel for schedule(static)
+#pragma omp parallel for schedule(static)
       for (data_size_t i = 0; i < num_data_; ++i) {
         gradients[i] = (score[i] - label_[i]);
         hessians[i] = 1.0;
       }
     } else {
-      #pragma omp parallel for schedule(static)
+#pragma omp parallel for schedule(static)
       for (data_size_t i = 0; i < num_data_; ++i) {
         gradients[i] = (score[i] - label_[i]) * weights_[i];
         hessians[i] = weights_[i];
@@ -52,10 +52,12 @@ private:
   const float* weights_;
 };
 
-
+/*!
+* \brief L1 regression loss
+*/
 class RegressionL1loss: public ObjectiveFunction {
 public:
-  explicit RegressionL1loss(const ObjectiveConfig& config) {}
+  explicit RegressionL1loss(const ObjectiveConfig&) {}
 
   ~RegressionL1loss() {}
 
@@ -66,28 +68,28 @@ public:
   }
 
   void GetGradients(const score_t* score, score_t* gradients,
-                    score_t* hessians) const override {
+    score_t* hessians) const override {
     if (weights_ == nullptr) {
-      #pragma omp parallel for schedule(static)
+#pragma omp parallel for schedule(static)
       for (data_size_t i = 0; i < num_data_; ++i) {
-        const double diff = score[i] - label_[i];
-        if (diff >= 0.0) {
-          gradients[i] = 1.0;
+        const score_t diff = score[i] - label_[i];
+        if (diff >= 0.0f) {
+          gradients[i] = 1.0f;
         } else {
-          gradients[i] = -1.0;
+          gradients[i] = -1.0f;
         }
-        hessians[i] = Common::ApproximateHessianWithGaussian(score[i], label_[i], gradients[i]);
+        hessians[i] = static_cast<score_t>(Common::ApproximateHessianWithGaussian(score[i], label_[i], gradients[i]));
       }
     } else {
-      #pragma omp parallel for schedule(static)
+#pragma omp parallel for schedule(static)
       for (data_size_t i = 0; i < num_data_; ++i) {
-        const double diff = score[i] - label_[i];
-        if (diff >= 0.0) {
+        const score_t diff = score[i] - label_[i];
+        if (diff >= 0.0f) {
           gradients[i] = weights_[i];
         } else {
           gradients[i] = -weights_[i];
         }
-        hessians[i] = Common::ApproximateHessianWithGaussian(score[i], label_[i], gradients[i], weights_[i]);
+        hessians[i] = static_cast<score_t>(Common::ApproximateHessianWithGaussian(score[i], label_[i], gradients[i], weights_[i]));
       }
     }
   }
@@ -105,11 +107,13 @@ private:
   const float* weights_;
 };
 
-
+/*!
+* \brief Huber regression loss
+*/
 class RegressionHuberLoss: public ObjectiveFunction {
 public:
   explicit RegressionHuberLoss(const ObjectiveConfig& config) {
-    delta_ = config.huber_delta;
+    delta_ = static_cast<score_t>(config.huber_delta);
   }
 
   ~RegressionHuberLoss() {
@@ -122,39 +126,39 @@ public:
   }
 
   void GetGradients(const score_t* score, score_t* gradients,
-                    score_t* hessians) const override {
+    score_t* hessians) const override {
     if (weights_ == nullptr) {
-      #pragma omp parallel for schedule(static)
+#pragma omp parallel for schedule(static)
       for (data_size_t i = 0; i < num_data_; ++i) {
-        const double diff = score[i] - label_[i];
+        const score_t diff = score[i] - label_[i];
 
         if (std::abs(diff) <= delta_) {
           gradients[i] = diff;
-          hessians[i] = 1.0;
+          hessians[i] = 1.0f;
         } else {
-            if (diff >= 0.0) {
-              gradients[i] = delta_;
-            } else {
-              gradients[i] = -delta_;
-            }
-            hessians[i] = Common::ApproximateHessianWithGaussian(score[i], label_[i], gradients[i]);
+          if (diff >= 0.0f) {
+            gradients[i] = delta_;
+          } else {
+            gradients[i] = -delta_;
+          }
+          hessians[i] = static_cast<score_t>(Common::ApproximateHessianWithGaussian(score[i], label_[i], gradients[i]));
         }
       }
     } else {
-      #pragma omp parallel for schedule(static)
+#pragma omp parallel for schedule(static)
       for (data_size_t i = 0; i < num_data_; ++i) {
-        const double diff = score[i] - label_[i];
+        const score_t diff = score[i] - label_[i];
 
         if (std::abs(diff) <= delta_) {
           gradients[i] = diff * weights_[i];
           hessians[i] = weights_[i];
         } else {
-            if (diff >= 0.0) {
-              gradients[i] = delta_ * weights_[i];
-            } else {
-              gradients[i] = -delta_ * weights_[i];
-            }
-            hessians[i] = Common::ApproximateHessianWithGaussian(score[i], label_[i], gradients[i], weights_[i]);
+          if (diff >= 0.0f) {
+            gradients[i] = delta_ * weights_[i];
+          } else {
+            gradients[i] = -delta_ * weights_[i];
+          }
+          hessians[i] = static_cast<score_t>(Common::ApproximateHessianWithGaussian(score[i], label_[i], gradients[i], weights_[i]));
         }
       }
     }
@@ -172,7 +176,7 @@ private:
   /*! \brief Pointer of weights */
   const float* weights_;
   /*! \brief delta for Huber loss */
-  double delta_;
+  score_t delta_;
 };
 
 
@@ -180,7 +184,7 @@ private:
 class RegressionFairLoss: public ObjectiveFunction {
 public:
   explicit RegressionFairLoss(const ObjectiveConfig& config) {
-    c_ = config.fair_c;
+    c_ = static_cast<score_t>(config.fair_c);
   }
 
   ~RegressionFairLoss() {}
@@ -192,18 +196,18 @@ public:
   }
 
   void GetGradients(const score_t* score, score_t* gradients,
-                    score_t* hessians) const override {
+    score_t* hessians) const override {
     if (weights_ == nullptr) {
-      #pragma omp parallel for schedule(static)
+#pragma omp parallel for schedule(static)
       for (data_size_t i = 0; i < num_data_; ++i) {
-        const double x = score[i] - label_[i];
+        const score_t x = score[i] - label_[i];
         gradients[i] = c_ * x / (std::fabs(x) + c_);
         hessians[i] = c_ * c_ / ((std::fabs(x) + c_) * (std::fabs(x) + c_));
       }
     } else {
-      #pragma omp parallel for schedule(static)
+#pragma omp parallel for schedule(static)
       for (data_size_t i = 0; i < num_data_; ++i) {
-        const double x = score[i] - label_[i];
+        const score_t x = score[i] - label_[i];
         gradients[i] = c_ * x / (std::fabs(x) + c_);
         gradients[i] *= weights_[i];
         hessians[i] = c_ * c_ / ((std::fabs(x) + c_) * (std::fabs(x) + c_));
@@ -224,7 +228,7 @@ private:
   /*! \brief Pointer of weights */
   const float* weights_;
   /*! \brief c for Fair loss */
-  double c_;
+  score_t c_;
 };
 
 }  // namespace LightGBM