Fix detecting alias of objective in loading from model strings (#2565)

Fix #2564

include/LightGBM/config.h

@@ -964,6 +964,59 @@ struct ParameterAlias {
   }
 };
 
+inline std::string ParseObjectiveAlias(const std::string& type) {
+  if (type == std::string("regression") || type == std::string("regression_l2")
+    || type == std::string("mean_squared_error") || type == std::string("mse") || type == std::string("l2")
+    || type == std::string("l2_root") || type == std::string("root_mean_squared_error") || type == std::string("rmse")) {
+    return "regression";
+  } else if (type == std::string("regression_l1") || type == std::string("mean_absolute_error")
+    || type == std::string("l1") || type == std::string("mae")) {
+    return "regression_l1";
+  } else if (type == std::string("multiclass") || type == std::string("softmax")) {
+    return "multiclass";
+  } else if (type == std::string("multiclassova") || type == std::string("multiclass_ova") || type == std::string("ova") || type == std::string("ovr")) {
+    return "multiclassova";
+  } else if (type == std::string("xentropy") || type == std::string("cross_entropy")) {
+    return "cross_entropy";
+  } else if (type == std::string("xentlambda") || type == std::string("cross_entropy_lambda")) {
+    return "cross_entropy_lambda";
+  } else if (type == std::string("mean_absolute_percentage_error") || type == std::string("mape")) {
+    return "mape";
+  } else if (type == std::string("none") || type == std::string("null") || type == std::string("custom") || type == std::string("na")) {
+    return "custom";
+  }
+  return type;
+}
+
+inline std::string ParseMetricAlias(const std::string& type) {
+  if (type == std::string("regression") || type == std::string("regression_l2") || type == std::string("l2") || type == std::string("mean_squared_error") || type == std::string("mse")) {
+    return "l2";
+  } else if (type == std::string("l2_root") || type == std::string("root_mean_squared_error") || type == std::string("rmse")) {
+    return "rmse";
+  } else if (type == std::string("regression_l1") || type == std::string("l1") || type == std::string("mean_absolute_error") || type == std::string("mae")) {
+    return "l1";
+  } else if (type == std::string("binary_logloss") || type == std::string("binary")) {
+    return "binary_logloss";
+  } else if (type == std::string("ndcg") || type == std::string("lambdarank")) {
+    return "ndcg";
+  } else if (type == std::string("map") || type == std::string("mean_average_precision")) {
+    return "map";
+  } else if (type == std::string("multi_logloss") || type == std::string("multiclass") || type == std::string("softmax") || type == std::string("multiclassova") || type == std::string("multiclass_ova") || type == std::string("ova") || type == std::string("ovr")) {
+    return "multi_logloss";
+  } else if (type == std::string("xentropy") || type == std::string("cross_entropy")) {
+    return "cross_entropy";
+  } else if (type == std::string("xentlambda") || type == std::string("cross_entropy_lambda")) {
+    return "cross_entropy_lambda";
+  } else if (type == std::string("kldiv") || type == std::string("kullback_leibler")) {
+    return "kullback_leibler";
+  } else if (type == std::string("mean_absolute_percentage_error") || type == std::string("mape")) {
+    return "mape";
+  } else if (type == std::string("none") || type == std::string("null") || type == std::string("custom") || type == std::string("na")) {
+    return "custom";
+  }
+  return type;
+}
+
 }   // namespace LightGBM
 
 #endif   // LightGBM_CONFIG_H_
\ No newline at end of file

src/boosting/gbdt_model_text.cpp

@@ -2,6 +2,7 @@
  * Copyright (c) 2017 Microsoft Corporation. All rights reserved.
  * Licensed under the MIT License. See LICENSE file in the project root for license information.
  */
+#include <LightGBM/config.h>
 #include <LightGBM/metric.h>
 #include <LightGBM/objective_function.h>
 #include <LightGBM/utils/common.h>
@@ -455,9 +456,10 @@ bool GBDT::LoadModelFromString(const char* buffer, size_t len) {
 
   if (key_vals.count("objective")) {
     auto str = key_vals["objective"];
-    loaded_objective_.reset(ObjectiveFunction::CreateObjectiveFunction(str));
+    loaded_objective_.reset(ObjectiveFunction::CreateObjectiveFunction(ParseObjectiveAlias(str)));
     objective_function_ = loaded_objective_.get();
   }
+
   if (!key_vals.count("tree_sizes")) {
     while (p < end) {
       auto line_len = Common::GetLine(p);

src/io/config.cpp

@@ -66,59 +66,6 @@ void GetBoostingType(const std::unordered_map<std::string, std::string>& params,
   }
 }
 
-std::string ParseObjectiveAlias(const std::string& type) {
-  if (type == std::string("regression") || type == std::string("regression_l2")
-    || type == std::string("mean_squared_error") || type == std::string("mse") || type == std::string("l2")
-    || type == std::string("l2_root") || type == std::string("root_mean_squared_error") || type == std::string("rmse")) {
-    return "regression";
-  } else if (type == std::string("regression_l1") || type == std::string("mean_absolute_error")
-    || type == std::string("l1") || type == std::string("mae")) {
-    return "regression_l1";
-  } else if (type == std::string("multiclass") || type == std::string("softmax")) {
-    return "multiclass";
-  } else if (type == std::string("multiclassova") || type == std::string("multiclass_ova") || type == std::string("ova") || type == std::string("ovr")) {
-    return "multiclassova";
-  } else if (type == std::string("xentropy") || type == std::string("cross_entropy")) {
-    return "cross_entropy";
-  } else if (type == std::string("xentlambda") || type == std::string("cross_entropy_lambda")) {
-    return "cross_entropy_lambda";
-  } else if (type == std::string("mean_absolute_percentage_error") || type == std::string("mape")) {
-    return "mape";
-  } else if (type == std::string("none") || type == std::string("null") || type == std::string("custom") || type == std::string("na")) {
-    return "custom";
-  }
-  return type;
-}
-
-std::string ParseMetricAlias(const std::string& type) {
-  if (type == std::string("regression") || type == std::string("regression_l2") || type == std::string("l2") || type == std::string("mean_squared_error") || type == std::string("mse")) {
-    return "l2";
-  } else if (type == std::string("l2_root") || type == std::string("root_mean_squared_error") || type == std::string("rmse")) {
-    return "rmse";
-  } else if (type == std::string("regression_l1") || type == std::string("l1") || type == std::string("mean_absolute_error") || type == std::string("mae")) {
-    return "l1";
-  } else if (type == std::string("binary_logloss") || type == std::string("binary")) {
-    return "binary_logloss";
-  } else if (type == std::string("ndcg") || type == std::string("lambdarank")) {
-    return "ndcg";
-  } else if (type == std::string("map") || type == std::string("mean_average_precision")) {
-    return "map";
-  } else if (type == std::string("multi_logloss") || type == std::string("multiclass") || type == std::string("softmax") || type == std::string("multiclassova") || type == std::string("multiclass_ova") || type == std::string("ova") || type == std::string("ovr")) {
-    return "multi_logloss";
-  } else if (type == std::string("xentropy") || type == std::string("cross_entropy")) {
-    return "cross_entropy";
-  } else if (type == std::string("xentlambda") || type == std::string("cross_entropy_lambda")) {
-    return "cross_entropy_lambda";
-  } else if (type == std::string("kldiv") || type == std::string("kullback_leibler")) {
-    return "kullback_leibler";
-  } else if (type == std::string("mean_absolute_percentage_error") || type == std::string("mape")) {
-    return "mape";
-  } else if (type == std::string("none") || type == std::string("null") || type == std::string("custom") || type == std::string("na")) {
-    return "custom";
-  }
-  return type;
-}
-
 void ParseMetrics(const std::string& value, std::vector<std::string>* out_metric) {
   std::unordered_set<std::string> metric_sets;
   out_metric->clear();