Add alias for l1 and l2 metric like objective functions (#181)

docs/Parameters.md

@@ -188,8 +188,8 @@ The parameter format is ```key1=value1 key2=value2 ... ``` . And parameters can
 ## Metric parameters
 
 * ```metric```, default={```l2``` for regression}, {```binary_logloss``` for binary classification},{```ndcg``` for lambdarank}, type=multi-enum, options=```l1```,```l2```,```ndcg```,```auc```,```binary_logloss```,```binary_error```
-  * ```l1```, absolute loss
-  * ```l2```, square loss
+  * ```l1```, absolute loss, alias=```mean_absolute_error```, ```mae```
+  * ```l2```, square loss, alias=```mean_squared_error```, ```mse```
   * ```huber```, [Huber loss](https://en.wikipedia.org/wiki/Huber_loss "Huber loss - Wikipedia")
   * ```fair```, [Fair loss](http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html)
   * ```ndcg```, [NDCG](https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG)

src/metric/metric.cpp

@@ -7,9 +7,9 @@
 namespace LightGBM {
 
 Metric* Metric::CreateMetric(const std::string& type, const MetricConfig& config) {
-  if (type == std::string("l2")) {
+  if (type == std::string("l2") || type == std::string("mean_squared_error") || type == std::string("mse")) {
     return new L2Metric(config);
-  } else if (type == std::string("l1")) {
+  } else if (type == std::string("l1") || type == std::string("mean_absolute_error")  || type == std::string("mae")) {
     return new L1Metric(config);
   } else if (type == std::string("huber")) {
     return new HuberLossMetric(config);