Merge pull request #6 from infinite-Joy/fix.readme_example

changing the README example as the main api has changed.

Author: davechallis

README.md

@@ -12,7 +12,7 @@ Basic usage example:
 ```rust
 extern crate xgboost;
 
-use xgboost::{parameters, dmatrix::DMatrix, booster::Booster};
+use xgboost::{parameters, DMatrix, Booster};
 
 fn main() {
     // training matrix with 5 training examples and 3 features
@@ -37,14 +37,37 @@ fn main() {
     let mut dtest = DMatrix::from_dense(x_test, num_rows).unwrap();
     dtest.set_labels(y_test).unwrap();
 
-    // build overall training parameters
-    let params = parameters::ParametersBuilder::default().build().unwrap();
+    // configure objectives, metrics, etc.
+    let learning_params = parameters::learning::LearningTaskParametersBuilder::default()
+        .objective(parameters::learning::Objective::BinaryLogistic)
+        .build().unwrap();
+
+    // configure the tree-based learning model's parameters
+    let tree_params = parameters::tree::TreeBoosterParametersBuilder::default()
+            .max_depth(2)
+            .eta(1.0)
+            .build().unwrap();
+
+    // overall configuration for Booster
+    let booster_params = parameters::BoosterParametersBuilder::default()
+        .booster_type(parameters::BoosterType::Tree(tree_params))
+        .learning_params(learning_params)
+        .verbose(true)
+        .build().unwrap();
 
     // specify datasets to evaluate against during training
     let evaluation_sets = &[(&dtrain, "train"), (&dtest, "test")];
 
+    // overall configuration for training/evaluation
+    let params = parameters::TrainingParametersBuilder::default()
+        .dtrain(&dtrain)                         // dataset to train with
+        .boost_rounds(2)                         // number of training iterations
+        .booster_params(booster_params)          // model parameters
+        .evaluation_sets(Some(evaluation_sets)) // optional datasets to evaluate against in each iteration
+        .build().unwrap();
+
     // train model, and print evaluation data
-    let bst = Booster::train(&params, &dtrain, 3, evaluation_sets).unwrap();
+    let bst = Booster::train(&params).unwrap();
 
     println!("{:?}", bst.predict(&dtest).unwrap());
 }