[docs] aliases (#1209)

* added new aliases for regression_l2 to docs

* fixed new line

* fixed tweedie_variance_power description

* moved gamma and tweedie metrics to other regression metrics

* added new aliases for l2 to docs

* added new alias for l1 to docs

* added new alias for log loss to docs

* added new aliases for multiclass to docs

* added description for default value of metric param

* fixed link to poisson regression and capitalized regressions' names

* removed unused earlier alias type for gamma deviance

* added aliases for multiclass and multiclass_ova

* updated Quick-Start.rst according to Parameters.rst

docs/Parameters.rst

@@ -54,13 +54,13 @@ Core Parameters
    - **Note**: Only can be used in CLI version.
 
 -  ``application``, default=\ ``regression``, type=enum,
-   options=\ ``regression``, ``regression_l1``, ``huber``, ``fair``, ``poisson``, ``quantile``, ``mape``,
-   ``binary``, ``multiclass``, ``multiclassova``, ``xentropy``, ``xentlambda``, ``lambdarank``, ``gammma``, ``tweedie``,
+   options=\ ``regression``, ``regression_l1``, ``huber``, ``fair``, ``poisson``, ``quantile``, ``mape``, ``gammma``, ``tweedie``,
+   ``binary``, ``multiclass``, ``multiclassova``, ``xentropy``, ``xentlambda``, ``lambdarank``,
    alias=\ ``objective``, ``app``
 
    -  regression application
 
-      -  ``regression_l2``, L2 loss, alias=\ ``regression``, ``mean_squared_error``, ``mse``
+      -  ``regression_l2``, L2 loss, alias=\ ``regression``, ``mean_squared_error``, ``mse``, ``l2_root``, ``root_mean_squared_error``, ``rmse``
 
       -  ``regression_l1``, L1 loss, alias=\ ``mean_absolute_error``, ``mae``
 
@@ -74,17 +74,19 @@ Core Parameters
 
       -  ``mape``, `MAPE loss`_, alias=\ ``mean_absolute_percentage_error``
 
-      -  ``gamma``, gamma regression with log-link. It might be useful, e.g., for modeling insurance claims severity, or for any target that might be `gamma-distributed`_
+      -  ``gamma``, Gamma regression with log-link. It might be useful, e.g., for modeling insurance claims severity, or for any target that might be `gamma-distributed`_
 
-      -  ``tweedie``, tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any target that might be `tweedie-distributed`_.
+      -  ``tweedie``, Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any target that might be `tweedie-distributed`_
 
    -  ``binary``, binary `log loss`_ classification application
 
    -  multi-class classification application
 
-      -  ``multiclass``, `softmax`_ objective function, ``num_class`` should be set as well
+      -  ``multiclass``, `softmax`_ objective function, alias=\ ``softmax``
 
-      -  ``multiclassova``, `One-vs-All`_ binary objective function, ``num_class`` should be set as well
+      -  ``multiclassova``, `One-vs-All`_ binary objective function, alias=\ ``multiclass_ova``, ``ova``, ``ovr``
+
+      -  ``num_class`` should be set as well
 
    -  cross-entropy application
 
@@ -562,7 +564,10 @@ Objective Parameters
    -  will fit ``sqrt(label)`` instead and prediction result will be also automatically converted to ``pow2(prediction)``
 
 -  ``tweedie_variance_power``, default=\ ``1.5``, type=\ ``double``, range=\ ``[1,2)``
-   - parameter that controls the variance of the tweedie distribution
+
+   - only used in ``tweedie`` regression
+
+   - controls the variance of the tweedie distribution
    
    - set closer to 2 to shift towards a gamma distribution
    
@@ -573,9 +578,11 @@ Metric Parameters
 
 -  ``metric``, default=``None``, type=multi-enum
 
-   -  ``l1``, absolute loss, alias=\ ``mean_absolute_error``, ``mae``
+   -  if ``None``, metric corresponding to specified application will be used
 
-   -  ``l2``, square loss, alias=\ ``mean_squared_error``, ``mse``
+   -  ``l1``, absolute loss, alias=\ ``mean_absolute_error``, ``mae``, ``regression_l1``
+
+   -  ``l2``, square loss, alias=\ ``mean_squared_error``, ``mse``, ``regression_l2``, ``regression``
 
    -  ``l2_root``, root square loss, alias=\ ``root_mean_squared_error``, ``rmse``
 
@@ -587,7 +594,13 @@ Metric Parameters
 
    -  ``fair``, `Fair loss`_
 
-   -  ``poisson``, negative log-likelihood for Poisson regression
+   -  ``poisson``, negative log-likelihood for `Poisson regression`_
+
+   -  ``gamma``, negative log-likelihood for Gamma regression
+
+   -  ``gamma_deviance``, residual deviance for Gamma regression
+
+   -  ``tweedie``, negative log-likelihood for Tweedie regression
 
    -  ``ndcg``, `NDCG`_
 
@@ -595,11 +608,11 @@ Metric Parameters
 
    -  ``auc``, `AUC`_
 
-   -  ``binary_logloss``, `log loss`_
+   -  ``binary_logloss``, `log loss`_, alias=\ ``binary``
 
    -  ``binary_error``, for one sample: ``0`` for correct classification, ``1`` for error classification
 
-   -  ``multi_logloss``, log loss for mulit-class classification
+   -  ``multi_logloss``, log loss for mulit-class classification, alias=\ ``multiclass``, ``softmax``, ``multiclassova``, ``multiclass_ova``, ``ova``, ``ovr``
 
    -  ``multi_error``, error rate for mulit-class classification
 
@@ -609,12 +622,6 @@ Metric Parameters
 
    -  ``kldiv``, `Kullback-Leibler divergence`_, alias=\ ``kullback_leibler``
 
-   -  ``gamma``, negative log-likelihood for gamma regression
-
-   -  ``gamma_deviance``, residual deviance for gamma regression, alias=\ ``gamma-deviance``
-
-   -  ``tweedie``, negative log-likelihood for tweedie regression
-
    -  support multi metrics, separated by ``,``
 
 -  ``metric_freq``, default=\ ``1``, type=int, alias=\ ``output_freq``

docs/Quick-Start.rst

@@ -74,7 +74,7 @@ Some important parameters:
 
    -  regression application
 
-      -  ``regression_l2``, L2 loss, alias=\ ``regression``, ``mean_squared_error``, ``mse``
+      -  ``regression_l2``, L2 loss, alias=\ ``regression``, ``mean_squared_error``, ``mse``, ``l2_root``, ``root_mean_squared_error``, ``rmse``
 
       -  ``regression_l1``, L1 loss, alias=\ ``mean_absolute_error``, ``mae``
 
@@ -96,9 +96,11 @@ Some important parameters:
 
    -  multi-class classification application
 
-      -  ``multiclass``, `softmax`_ objective function, ``num_class`` should be set as well
+      -  ``multiclass``, `softmax`_ objective function, alias=\ ``softmax``
 
-      -  ``multiclassova``, `One-vs-All`_ binary objective function, ``num_class`` should be set as well
+      -  ``multiclassova``, `One-vs-All`_ binary objective function, alias=\ ``multiclass_ova``, ``ova``, ``ovr``
+
+      -  ``num_class`` should be set as well
 
    -  cross-entropy application
 
@@ -114,6 +116,8 @@ Some important parameters:
 
       -  ``label_gain`` can be used to set the gain(weight) of ``int`` label
 
+      -  all values in ``label`` must be smaller than number of elements in ``label_gain``
+
 - ``boosting``, default=\ ``gbdt``, type=enum,
   options=\ ``gbdt``, ``rf``, ``dart``, ``goss``,
   alias=\ ``boost``, ``boosting_type``
@@ -254,4 +258,4 @@ Examples
 
 .. _gamma-distributed: https://en.wikipedia.org/wiki/Gamma_distribution#Applications
 
-.. _tweedie-distributed: https://en.wikipedia.org/wiki/Tweedie_distribution#Applications
\ No newline at end of file
+.. _tweedie-distributed: https://en.wikipedia.org/wiki/Tweedie_distribution#Applications

src/metric/metric.cpp

@@ -33,7 +33,7 @@ Metric* Metric::CreateMetric(const std::string& type, const MetricConfig& config
     return new NDCGMetric(config);
   } else if (type == std::string("map") || type == std::string("mean_average_precision")) {
     return new MapMetric(config);
-  } else if (type == std::string("multi_logloss") || type == std::string("multiclass") || type == std::string("multiclass_ova")) {
+  } 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 new MultiSoftmaxLoglossMetric(config);
   } else if (type == std::string("multi_error")) {
     return new MultiErrorMetric(config);
@@ -47,7 +47,7 @@ Metric* Metric::CreateMetric(const std::string& type, const MetricConfig& config
     return new MAPEMetric(config);
   } else if (type == std::string("gamma")) {
     return new GammaMetric(config);
-  } else if (type == std::string("gamma-deviance") || type == std::string("gamma_deviance")) {
+  } else if (type == std::string("gamma_deviance")) {
     return new GammaDevianceMetric(config);
   } else if (type == std::string("tweedie")) {
     return new TweedieMetric(config);

src/objective/objective_function.cpp

@@ -26,9 +26,9 @@ ObjectiveFunction* ObjectiveFunction::CreateObjectiveFunction(const std::string&
     return new BinaryLogloss(config);
   } else if (type == std::string("lambdarank")) {
     return new LambdarankNDCG(config);
-  } else if (type == std::string("multiclass")) {
+  } else if (type == std::string("multiclass") || type == std::string("softmax")) {
     return new MulticlassSoftmax(config);
-  } else if (type == std::string("multiclassova")) {
+  } else if (type == std::string("multiclassova") || type == std::string("multiclass_ova") || type == std::string("ova") || type == std::string("ovr")) {
     return new MulticlassOVA(config);
   } else if (type == std::string("xentropy") || type == std::string("cross_entropy")) {
     return new CrossEntropy(config);