extern crate gbdt; use gbdt::decision_tree::{PredVec, ValueType}; use gbdt::gradient_boost::GBDT; use gbdt::input; use std::fs::File; use std::io::{BufRead, BufReader}; fn main() { // Use xg.py in xgb-data/xgb_binary_logistic to generate a model and get prediction results from xgboost. // Call this command to convert xgboost model: // python examples/convert_xgboost.py xgb-data/xgb_binary_logistic/xgb.model "binary:logistic" xgb-data/xgb_binary_logistic/gbdt.model // load model let gbdt = GBDT::from_xgboost_dump("xgb-data/xgb_binary_logistic/gbdt.model", "binary:logistic") .expect("failed to load model"); // load test data let test_file = "xgb-data/xgb_binary_logistic/agaricus.txt.test"; let mut input_format = input::InputFormat::txt_format(); input_format.set_feature_size(126); input_format.set_delimeter(' '); let test_data = input::load(test_file, input_format).expect("failed to load test data"); // inference println!("start prediction"); let predicted: PredVec = gbdt.predict(&test_data); assert_eq!(predicted.len(), test_data.len()); // compare to xgboost prediction results let predict_result = "xgb-data/xgb_binary_logistic/pred.csv"; let mut xgb_results = Vec::new(); let file = File::open(predict_result).expect("failed to load pred.csv"); let reader = BufReader::new(file); for line in reader.lines() { let text = line.expect("failed to read data from pred.csv"); let value: ValueType = text.parse().expect("failed to parse data from pred.csv"); xgb_results.push(value); } let mut max_diff: ValueType = -1.0; for (value1, value2) in predicted.iter().zip(xgb_results.iter()) { println!("{} {}", value1, value2); let diff = (value1 - value2).abs(); if diff > max_diff { max_diff = diff; } } println!( "Compared to results from xgboost, max error is: {:.10}", max_diff ); assert!(max_diff < 0.01); }