[PLUGIN] Add plugin system

plugin/example/README.md

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+XGBoost Plugin Example
+======================
+This folder provides an example of xgboost plugin.
+
+There are three steps you need to to do to add plugin to xgboost
+- Create your source .cc file, implement a new extension
+  - In this example [custom_obj.cc](custom_obj.cc)
+- Register this extension to xgboost via registration macr
+  - In this example ```XGBOOST_REGISTER_OBJECTIVE``` in [this line](custom_obj.cc#L75)
+- Create a [plugin.mk](plugin.mk) on this folder
+
+To add this plugin, add the following line to ```config.mk```(template in make/config.mk).
+```makefile
+# Add plugin by include the plugin in config
+include plugin/example/plugin.mk
+```
+
+Then you can test this plugin by using ```objective=mylogistic``` parameter.
+
+
+

plugin/example/custom_obj.cc

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+/*!
+ * Copyright 2015 by Contributors
+ * \file custom_metric.cc
+ * \brief This is an example to define plugin of xgboost.
+ *  This plugin defines the additional metric function.
+ */
+#include <xgboost/base.h>
+#include <dmlc/parameter.h>
+#include <xgboost/objective.h>
+
+namespace xgboost {
+namespace obj {
+
+// This is a helpful data structure to define parameters
+// You do not have to use it.
+// see http://dmlc-core.readthedocs.org/en/latest/parameter.html
+// for introduction of this module.
+struct MyLogisticParam : public dmlc::Parameter<MyLogisticParam> {
+  float scale_neg_weight;
+  // declare parameters
+  DMLC_DECLARE_PARAMETER(MyLogisticParam) {
+    DMLC_DECLARE_FIELD(scale_neg_weight).set_default(1.0f).set_lower_bound(0.0f)
+        .describe("Scale the weight of negative examples by this factor");
+  }
+};
+
+DMLC_REGISTER_PARAMETER(MyLogisticParam);
+
+// Define a customized logistic regression objective in C++.
+// Implement the interface.
+class MyLogistic : public ObjFunction {
+ public:
+  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 {
+    out_gpair->resize(preds.size());
+    for (size_t i = 0; i < preds.size(); ++i) {
+      float w = info.GetWeight(i);
+      // scale the negative examples!
+      if (info.labels[i] == 0.0f) w *= param_.scale_neg_weight;
+      // logistic transoformation
+      float p = 1.0f / (1.0f + expf(-preds[i]));
+      // this is the gradient
+      float grad = (p - info.labels[i]) * w;
+      // this is the second order gradient
+      float hess = p * (1.0f - p) * w;
+      out_gpair->at(i) = bst_gpair(grad, hess);
+    }
+  }
+  const char* DefaultEvalMetric() const override {
+    return "error";
+  }
+  void PredTransform(std::vector<float> *io_preds) override {
+    // transform margin value to probability.
+    std::vector<float> &preds = *io_preds;
+    for (size_t i = 0; i < preds.size(); ++i) {
+      preds[i] = 1.0f / (1.0f + expf(-preds[i]));
+    }
+  }
+  float ProbToMargin(float base_score) const override {
+    // transform probability to margin value
+    return -std::log(1.0f / base_score - 1.0f);
+  }
+
+ private:
+  MyLogisticParam param_;
+};
+
+// Finally register the objective function.
+// After it succeeds you can try use xgboost with objective=mylogistic
+XGBOOST_REGISTER_OBJECTIVE(MyLogistic, "mylogistic")
+.describe("User defined logistic regression plugin")
+.set_body([]() { return new MyLogistic(); });
+
+}  // namespace obj
+}  // namespace xgboost

plugin/example/plugin.mk

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+# Add the object files you like to include in this plugin.
+PLUGIN_OBJS += build_plugin/example/custom_obj.o
+# Add additional dependent libraries this plugin might have
+PLUGIN_LDFLAGS +=