Merge pull request #9 from vaaaaanquish/add_regression

add regression examples

Author: vaaaaanquish

.gitignore

@@ -15,3 +15,4 @@ lightgbm-sys/target
 # example
 examples/binary_classification/target/
 examples/multiclass_classification/target/
+examples/regression/target/

examples/regression/Cargo.toml

@@ -0,0 +1,11 @@
+[package]
+name = "lightgbm-example-regression"
+version = "0.1.0"
+authors = ["vaaaaanquish <[email protected]>"]
+publish = false
+
+[dependencies]
+lightgbm = { path = "../../" }
+csv = "1.1.5"
+itertools = "0.9.0"
+serde_json = "1.0.59"

examples/regression/src/main.rs

@@ -0,0 +1,55 @@
+extern crate lightgbm;
+extern crate csv;
+extern crate serde_json;
+extern crate itertools;
+
+
+use itertools::zip;
+use lightgbm::{Dataset, Booster};
+use serde_json::json;
+
+
+fn load_file(file_path: &str) -> (Vec<Vec<f64>>, Vec<f32>) {
+    let rdr = csv::ReaderBuilder::new().has_headers(false).delimiter(b'\t').from_path(file_path);
+    let mut labels: Vec<f32> = Vec::new();
+    let mut features: Vec<Vec<f64>> = Vec::new();
+    for result in rdr.unwrap().records() {
+        let record = result.unwrap();
+        let label = record[0].parse::<f32>().unwrap();
+        let feature: Vec<f64> = record.iter().map(|x| x.parse::<f64>().unwrap()).collect::<Vec<f64>>()[1..].to_vec();
+        labels.push(label);
+        features.push(feature);
+    }
+    (features, labels)
+}
+
+
+fn main() -> std::io::Result<()> {
+    let (train_features, train_labels) = load_file("../../lightgbm-sys/lightgbm/examples/regression/regression.train");
+    let (test_features, test_labels) = load_file("../../lightgbm-sys/lightgbm/examples/regression/regression.test");
+    let train_dataset = Dataset::from_mat(train_features, train_labels).unwrap();
+
+    let params = json!{
+        {
+            "num_iterations": 100,
+            "objective": "regression",
+            "metric": "l2"
+        }
+    };
+
+    let booster = Booster::train(train_dataset, &params).unwrap();
+    let result = booster.predict(test_features).unwrap();
+
+
+    let mut tp = 0;
+    for (label, pred) in zip(&test_labels, &result[0]){
+        if label == &(1 as f32) && pred > &(0.5 as f64) {
+            tp = tp + 1;
+        } else if label == &(0 as f32) && pred <= &(0.5 as f64) {
+            tp = tp + 1;
+        }
+        println!("{}, {}", label, pred)
+    }
+    println!("{} / {}", &tp, result[0].len());
+    Ok(())
+}