Merge pull request #8 from vaaaaanquish/add_multi_classification

Add multi classification examples

.gitignore

@@ -13,4 +13,6 @@ Cargo.lock
 lightgbm-sys/target
 
 # example
-examples/target
+examples/binary_classification/target/
+examples/multiclass_classification/target/
+examples/regression/target/

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lightgbm"
-version = "0.1.1"
+version = "0.1.2"
 authors = ["vaaaaanquish <[email protected]>"]
 license = "MIT"
 repository = "https://github.com/vaaaaanquish/LightGBM"
@@ -11,3 +11,5 @@ exclude = [".gitignore", ".gitmodules", "examples", "lightgbm-sys"]
 [dependencies]
 lightgbm-sys = "0.1.0"
 libc = "0.2.81"
+derive_builder = "0.5.1"
+serde_json = "1.0.59"

examples/binary_classification/Cargo.toml

@@ -0,0 +1,11 @@
+[package]
+name = "lightgbm-example-binary-classification"
+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/binary_classification/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/binary_classification/binary.train");
+    let (test_features, test_labels) = load_file("../../lightgbm-sys/lightgbm/examples/binary_classification/binary.test");
+    let train_dataset = Dataset::from_mat(train_features, train_labels).unwrap();
+
+    let params = json!{
+        {
+            "num_iterations": 100,
+            "objective": "binary",
+            "metric": "auc"
+        }
+    };
+
+    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(())
+}

examples/multiclass_classification/Cargo.toml

@@ -0,0 +1,11 @@
+[package]
+name = "lightgbm-example-multiclass-classification"
+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/multiclass_classification/src/main.rs

@@ -0,0 +1,72 @@
+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 argmax<T: PartialOrd>(xs: &[T]) -> usize {
+    if xs.len() == 1 {
+        0
+    } else {
+        let mut maxval = &xs[0];
+        let mut max_ixs: Vec<usize> = vec![0];
+        for (i, x) in xs.iter().enumerate().skip(1) {
+            if x > maxval {
+                maxval = x;
+                max_ixs = vec![i];
+            } else if x == maxval {
+                max_ixs.push(i);
+            }
+        }
+        max_ixs[0]
+    }
+}
+
+fn main() -> std::io::Result<()> {
+    let (train_features, train_labels) = load_file("../../lightgbm-sys/lightgbm/examples/multiclass_classification/multiclass.train");
+    let (test_features, test_labels) = load_file("../../lightgbm-sys/lightgbm/examples/multiclass_classification/multiclass.test");
+    let train_dataset = Dataset::from_mat(train_features, train_labels).unwrap();
+
+    let params = json!{
+        {
+            "num_iterations": 100,
+            "objective": "multiclass",
+            "metric": "multi_logloss",
+            "num_class": 5,
+        }
+    };
+
+    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){
+        let argmax_pred = argmax(&pred);
+        if *label == argmax_pred as f32 {
+            tp = tp + 1;
+        }
+        println!("{}, {}, {:?}", label, argmax_pred, &pred);
+    }
+    println!("{} / {}", &tp, result.len());
+    Ok(())
+}

examples/Cargo.toml → examples/regression/Cargo.toml

@@ -1,10 +1,11 @@
 [package]
-name = "lightgbm-example"
+name = "lightgbm-example-regression"
 version = "0.1.0"
 authors = ["vaaaaanquish <[email protected]>"]
 publish = false
 
 [dependencies]
-lightgbm = "0.1.1"
+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(())
+}