Implemented "leaves", removing the requirement for LightGBM to be ins…

…talled on the system

README.md

@@ -57,9 +57,7 @@ Whilst the concept behind Impact Rating can in theory be implemented using any b
 
 ## Download
 
-> **Important:** CS:GO Impact Rating requires [LightGBM](https://github.com/Microsoft/LightGBM) to be installed on the system. Download the latest binary release from [github.com/Microsoft/LightGBM/releases](https://github.com/Microsoft/LightGBM/releases) and either make sure the executable is added to the system path, or placed in the same directory as the csgo-impact-rating executable.
-
-The latest Impact Rating executable and LightGBM model file (`LightGBM_model.txt`) can be downloaded from this project's release page here: [github.com/Phil-Holland/csgo-impact-rating/releases](https://github.com/Phil-Holland/csgo-impact-rating/releases). These can be placed anywhere on the system - add the parent directory to the system path to access the executable from any location.
+The latest Impact Rating executable and LightGBM model file (`LightGBM_model.txt`) can be downloaded from this project's release page here: [github.com/Phil-Holland/csgo-impact-rating/releases](https://github.com/Phil-Holland/csgo-impact-rating/releases). These can be placed together anywhere on the system - add the parent directory to the system path to access the executable from any location.
 
 ## Usage
 
@@ -85,6 +83,7 @@ the console and a '.rating.json' file.
 ## Built With
 
 - [demoinfocs-golang](https://github.com/markus-wa/demoinfocs-golang) - used to parse CS:GO demo files
+- [leaves](https://github.com/dmitryikh/leaves) - used to process LightGBM models internally
 - [pflag](https://github.com/spf13/pflag) - used to build the command line interface
 - [pb (v3)](https://github.com/cheggaaa/pb) - used for progress visualisation
 - [LightGBM](https://github.com/Microsoft/LightGBM) - used for model training/round outcome prediction

go.mod

@@ -4,6 +4,7 @@ go 1.14
 
 require (
 	github.com/cheggaaa/pb/v3 v3.0.4
+	github.com/dmitryikh/leaves v0.0.0-20200503205002-939b6fa631dd
 	github.com/markus-wa/demoinfocs-golang/v2 v2.1.0
 	github.com/spf13/pflag v1.0.5
 )

go.sum

@@ -24,6 +24,8 @@ github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSs
 github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
 github.com/dgrijalva/jwt-go v3.2.0+incompatible/go.mod h1:E3ru+11k8xSBh+hMPgOLZmtrrCbhqsmaPHjLKYnJCaQ=
 github.com/dgryski/go-sip13 v0.0.0-20181026042036-e10d5fee7954/go.mod h1:vAd38F8PWV+bWy6jNmig1y/TA+kYO4g3RSRF0IAv0no=
+github.com/dmitryikh/leaves v0.0.0-20200503205002-939b6fa631dd h1:aMtdMlm9R0KQxeQzCLzBbuwA++WlE7owaSoSzcLTySw=
+github.com/dmitryikh/leaves v0.0.0-20200503205002-939b6fa631dd/go.mod h1:wzMig9tMIJB8HsxXHppa9yRPo8BpO0eBM/Z4xnaohCQ=
 github.com/dustin/go-heatmap v0.0.0-20180603032536-b89dbd73785a/go.mod h1:VBmwC4U3p2SMEKr+/m5j0eby7rmUtSoA5TGLwe6P+3A=
 github.com/fatih/color v1.7.0 h1:DkWD4oS2D8LGGgTQ6IvwJJXSL5Vp2ffcQg58nFV38Ys=
 github.com/fatih/color v1.7.0/go.mod h1:Zm6kSWBoL9eyXnKyktHP6abPY2pDugNf5KwzbycvMj4=

internal/evaluate.go

@@ -6,68 +6,52 @@ import (
 	"io"
 	"io/ioutil"
 	"os"
-	"os/exec"
 	"sort"
 	"strconv"
 	"strings"
 	"text/tabwriter"
+
+	"github.com/dmitryikh/leaves"
 )
 
 // EvaluateDemo processes a .tagged.json file, producing an Impact Rating report which is written to
 // the console and a '.rating.json' file
 func EvaluateDemo(taggedFilePath string, verbosity int, modelPath string) {
-	// prepare a csv file
+	// load in the tagged json
 	fmt.Printf("Reading contents of json file: \"%s\"\n", taggedFilePath)
 	jsonRaw, _ := ioutil.ReadFile(taggedFilePath)
 
 	var demo Demo
 	err := json.Unmarshal(jsonRaw, &demo)
 	if err != nil {
-		panic(err.Error())
-	}
-
-	// create a temp file to write the csv to
-	file, err := ioutil.TempFile(".", "temp.*.csv")
-	if err != nil {
-		panic(err.Error())
+		panic(err)
 	}
-	defer os.Remove(file.Name())
 
-	fmt.Printf("Writing csv to temporary file: \"%s\"\n", file.Name())
-	output := "roundWinner,aliveCt,aliveT,bombDefused,bombPlanted,meanHealthCt,meanHealthT,meanValueCT,meanValueT,roundTime\n"
-	for _, tick := range demo.Ticks {
-		csvLine := makeCSVLine(&tick)
-		output += csvLine + "\n"
+	// build the input float slice
+	cols := 9
+	input := make([]float64, len(demo.Ticks)*cols)
+	for idx, tick := range demo.Ticks {
+		input[idx*cols] = float64(tick.GameState.AliveCT)
+		input[idx*cols+1] = float64(tick.GameState.AliveT)
+		input[idx*cols+2] = bToF64(tick.GameState.BombDefused)
+		input[idx*cols+3] = bToF64(tick.GameState.BombPlanted)
+		input[idx*cols+4] = float64(tick.GameState.MeanHealthCT)
+		input[idx*cols+5] = float64(tick.GameState.MeanHealthT)
+		input[idx*cols+6] = float64(tick.GameState.MeanValueCT)
+		input[idx*cols+7] = float64(tick.GameState.MeanValueT)
+		input[idx*cols+8] = float64(tick.GameState.RoundTime)
 	}
-	file.WriteString(output)
-	file.Close()
 
-	// create a temp file to write prediction results to
-	rfile, err := ioutil.TempFile(".", "temp.*.txt")
-	if err != nil {
-		panic(err.Error())
-	}
-	rfile.Close()
-	defer os.Remove(rfile.Name())
-
-	// invoke lightgbm prediction
-	cmd := exec.Command("lightgbm", "task=predict", "data=\""+file.Name()+"\"",
-		"header=true", "label_column=name:roundWinner", "input_model=\""+modelPath+"\"",
-		"output_result=\""+rfile.Name()+"\"")
-	fmt.Printf("Running command: %s\n", cmd.String())
-	stdout, err := cmd.Output()
+	// load the lightgbm model in using leaves
+	fmt.Printf("Loading LightGBM model from \"%s\"\n", modelPath)
+	model, err := leaves.LGEnsembleFromFile(modelPath, true)
 	if err != nil {
-		panic(err.Error())
+		panic(err)
 	}
-	fmt.Print(string(stdout))
-	fmt.Printf("Output results written to temporary txt file: \"%s\"\n", rfile.Name())
+	fmt.Printf("LightGBM model loaded successfully\n")
 
-	// read prediction results in
-	results, err := ioutil.ReadFile(rfile.Name())
-	if err != nil {
-		panic(err.Error())
-	}
-	lines := strings.Split(string(results), "\n")
+	preds := make([]float64, len(demo.Ticks))
+	model.PredictDense(input, len(demo.Ticks), cols, preds, 0, 1)
 
 	var ratingOutput Rating
 
@@ -109,10 +93,8 @@ func EvaluateDemo(taggedFilePath string, verbosity int, modelPath string) {
 			roundsPlayed = tick.ScoreCT + tick.ScoreT + 1
 		}
 
-		pred, err := strconv.ParseFloat(lines[idx], 64)
-		if err != nil {
-			panic(err.Error())
-		}
+		// get the prediction for this tick
+		pred := preds[idx]
 
 		// amend the prediction if no CTs are alive - certain T win
 		if tick.GameState.AliveCT == 0 {
@@ -480,6 +462,13 @@ func EvaluateDemo(taggedFilePath string, verbosity int, modelPath string) {
 	}
 }
 
+func bToF64(b bool) float64 {
+	if b {
+		return 1.0
+	}
+	return 0.0
+}
+
 func makeCSVLine(tick *Tick) string {
 	roundWinner := strconv.FormatInt(int64(tick.RoundWinner), 10)