Doc modernization (#3474)

* Change doc build to reST exclusively

* Rewrite Intro doc in reST; create toctree

* Update parameter and contribute

* Convert tutorials to reST

* Convert Python tutorials to reST

* Convert CLI and Julia docs to reST

* Enable markdown for R vignettes

* Done migrating to reST

* Add guzzle_sphinx_theme to requirements

* Add breathe to requirements

* Fix search bar

* Add link to user forum

doc/get_started.rst

@@ -0,0 +1,94 @@
+########################
+Get Started with XGBoost
+########################
+
+This is a quick start tutorial showing snippets for you to quickly try out XGBoost
+on the demo dataset on a binary classification task.
+
+********************************
+Links to Other Helpful Resources
+********************************
+- See :doc:`Installation Guide </build>` on how to install XGBoost.
+- See :doc:`Text Input Format </tutorials/input_format>` on using text format for specifying training/testing data.
+- See :doc:`Tutorials </tutorials/index>` for tips and tutorials.
+- See `Learning to use XGBoost by Examples <https://github.com/dmlc/xgboost/tree/master/demo>`_ for more code examples.
+
+******
+Python
+******
+
+.. code-block:: python
+
+  import xgboost as xgb
+  # read in data
+  dtrain = xgb.DMatrix('demo/data/agaricus.txt.train')
+  dtest = xgb.DMatrix('demo/data/agaricus.txt.test')
+  # specify parameters via map
+  param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
+  num_round = 2
+  bst = xgb.train(param, dtrain, num_round)
+  # make prediction
+  preds = bst.predict(dtest)
+
+***
+R
+***
+
+.. code-block:: R
+
+  # load data
+  data(agaricus.train, package='xgboost')
+  data(agaricus.test, package='xgboost')
+  train <- agaricus.train
+  test <- agaricus.test
+  # fit model
+  bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2,
+                 nthread = 2, objective = "binary:logistic")
+  # predict
+  pred <- predict(bst, test$data)
+
+*****
+Julia
+*****
+
+.. code-block:: julia
+
+  using XGBoost
+  # read data
+  train_X, train_Y = readlibsvm("demo/data/agaricus.txt.train", (6513, 126))
+  test_X, test_Y = readlibsvm("demo/data/agaricus.txt.test", (1611, 126))
+  # fit model
+  num_round = 2
+  bst = xgboost(train_X, num_round, label=train_Y, eta=1, max_depth=2)
+  # predict
+  pred = predict(bst, test_X)
+
+*****
+Scala
+*****
+
+.. code-block:: scala
+
+  import ml.dmlc.xgboost4j.scala.DMatrix
+  import ml.dmlc.xgboost4j.scala.XGBoost
+  
+  object XGBoostScalaExample {
+    def main(args: Array[String]) {
+      // read trainining data, available at xgboost/demo/data
+      val trainData =
+        new DMatrix("/path/to/agaricus.txt.train")
+      // define parameters
+      val paramMap = List(
+        "eta" -> 0.1,
+        "max_depth" -> 2,
+        "objective" -> "binary:logistic").toMap
+      // number of iterations
+      val round = 2
+      // train the model
+      val model = XGBoost.train(trainData, paramMap, round)
+      // run prediction
+      val predTrain = model.predict(trainData)
+      // save model to the file.
+      model.saveModel("/local/path/to/model")
+    }
+  }