diff --git a/.DS_Store b/.DS_Store
index a2eb533..5008ddf 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/predict/predict_xgboost.py b/predict/predict_xgboost.py
index 9d1a21a..f6d044b 100644
--- a/predict/predict_xgboost.py
+++ b/predict/predict_xgboost.py
@@ -39,15 +39,17 @@ def safe_float(value):
 
 def predict(
     working_dir: str,
-    songno: str
+    songno: str,
+    feature: str = None
 ):
     # =====================================================
     # 경로
     # =====================================================
 
-    features_path = os.path.join(
-        working_dir,
-        FEATURES_FILENAME
+    features_path = (
+        os.path.join(working_dir, FEATURES_FILENAME)
+        if feature is None
+        else feature
     )
 
     model_path = os.path.join(
@@ -141,6 +143,11 @@ if __name__ == "__main__":
         "--workingDir",
         required=True
     )
+    
+    parser.add_argument(
+        "--feature",
+        required=False
+    )
 
     parser.add_argument(
         "--songno",
diff --git a/script/compare.ts b/script/compare.ts
new file mode 100644
index 0000000..a34ef85
--- /dev/null
+++ b/script/compare.ts
@@ -0,0 +1,25 @@
+import Bun from 'bun';
+import { parseArgs } from 'node:util';
+
+const { values } = parseArgs({
+    args: Bun.argv,
+    options: {
+        workingDir: {
+            type: "string"
+        },
+        tja: {
+            type: "string"
+        },
+        predictScript: {
+            type: "string"
+        },
+    },
+    allowPositionals: true,
+});
+
+if (!values.tja || !values.workingDir || !values.predictScript) {
+    console.error("--workingDir --dataDir --trainDir");
+    process.exit(1);
+}
+
+const songno = "temp";
diff --git a/script/preprocess.ts b/script/preprocess.ts
index a682191..2603661 100644
--- a/script/preprocess.ts
+++ b/script/preprocess.ts
@@ -2,14 +2,13 @@ import Bun from 'bun';
 import path from 'node:path';
 import { parseArgs } from 'node:util';
 import fs, { mkdirSync } from 'node:fs';
-import { Song } from 'tja-parser';
 import { featurize } from '../preprocess/featurize';
 import { parseTja } from '../preprocess/parse'
 
 const { values } = parseArgs({
     args: Bun.argv,
     options: {
-        outputDir: {
+        workingDir: {
             type: "string"
         },
         dataDir: {
@@ -20,13 +19,13 @@ const { values } = parseArgs({
     allowPositionals: true,
 })
 
-if (!values.dataDir || !values.outputDir) {
-    console.error("--outputDir --dataDir");
+if (!values.dataDir || !values.workingDir) {
+    console.error("--workingDir --dataDir");
     process.exit(1);
 }
 
-const outputDir = values.outputDir ?? '';
-if (!fs.existsSync(outputDir)) mkdirSync(outputDir)
+const workingDir = values.workingDir ?? '';
+if (!fs.existsSync(workingDir)) mkdirSync(workingDir)
 const dataDir = values.dataDir ?? '';
 
 const tjaDir = path.join(dataDir, 'tja');
@@ -61,5 +60,5 @@ for (const file of files) {
     }
 }
 
-const featurePath = path.join(outputDir, 'features.json');
+const featurePath = path.join(workingDir, 'features.json');
 fs.writeFileSync(featurePath, JSON.stringify(features, null, 2), 'utf-8');
\ No newline at end of file
diff --git a/script/train.ts b/script/train.ts
new file mode 100644
index 0000000..9d01125
--- /dev/null
+++ b/script/train.ts
@@ -0,0 +1,91 @@
+import Bun from 'bun';
+import { execSync, spawn, spawnSync } from 'node:child_process';
+import { parseArgs } from 'node:util';
+import fs from 'fs';
+import path from 'path';
+import { featurize } from '../preprocess/featurize';
+import { parseTja } from '../preprocess/parse'
+
+const { values } = parseArgs({
+    args: Bun.argv,
+    options: {
+        workingDir: {
+            type: "string"
+        },
+        dataDir: {
+            type: "string"
+        },
+        trainScript: {
+            type: "string"
+        },
+        trainSize: {
+            type: 'string'
+        },
+        validSize: {
+            type: 'string'
+        }
+    },
+    allowPositionals: true,
+});
+
+if (!values.dataDir || !values.workingDir || !values.trainScript) {
+    console.error("--workingDir --dataDir --trainDir");
+    process.exit(1);
+}
+
+generateFeatures();
+const child = spawn("python3", [values.trainScript,
+    "--workingDir", values.workingDir,
+    "--dataDir", values.dataDir,
+    "--trainSize", (Number(values.trainSize) || 500).toString(),
+    "--validSize", (Number(values.validSize) || 100).toString(),
+]);
+child.stdout.pipe(process.stdout);
+child.stderr.pipe(process.stderr);
+child.on("close", () => {
+    process.exit()
+})
+
+// funcs
+function generateFeatures() {
+    const workingDir = values.workingDir ?? '';
+    if (!fs.existsSync(workingDir)) fs.mkdirSync(workingDir);
+    const featurePath = path.join(workingDir, 'features.json');
+    if (fs.existsSync(featurePath)) return;
+    const dataDir = values.dataDir ?? '';
+
+    const tjaDir = path.join(dataDir, 'tja');
+    const files = fs.readdirSync(tjaDir);
+
+    const features: ({ songno: string, difficulty: 'oni' | 'ura' } & {})[] = [];
+    for (const file of files) {
+        const tja = fs.readFileSync(path.join(tjaDir, file), 'utf-8');
+        const songno = path.basename(file, '.tja');
+        try {
+            const parsed = parseTja(tja);
+            const oni = parsed?.oni;
+            const edit = parsed?.edit;
+            if (oni) {
+                features.push({
+                    songno,
+                    difficulty: 'oni',
+                    ...featurize(oni)
+                })
+            }
+            if (edit) {
+                features.push({
+                    songno,
+                    difficulty: 'ura',
+                    ...featurize(edit)
+                })
+            }
+        }
+        catch (err) {
+            console.error(err);
+            console.error(file);
+        }
+    }
+
+    fs.writeFileSync(featurePath, JSON.stringify(features, null, 2), 'utf-8');
+    console.log('features.json generated')
+}
\ No newline at end of file
diff --git a/test/features.json b/test/features.json
new file mode 100644
index 0000000..f62e775
--- /dev/null
+++ b/test/features.json
@@ -0,0 +1,17594 @@
+[
+  {
+    "songno": "449",
+    "difficulty": "oni",
+    "note_count": 784,
+    "density_avg": 5.587527839643652,
+    "density_peak": 12,
+    "bpm_avg": 280.46938775510205,
+    "bpm_change": 2,
+    "scroll_change": 22,
+    "rhythm_complexity": 670,
+    "color_complexity": 0.006692674014408041
+  },
+  {
+    "songno": "307",
+    "difficulty": "oni",
+    "note_count": 604,
+    "density_avg": 4.531287297527706,
+    "density_peak": 9,
+    "bpm_avg": 176,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 575,
+    "color_complexity": 0.004356983446119197
+  },
+  {
+    "songno": "461",
+    "difficulty": "oni",
+    "note_count": 657,
+    "density_avg": 5.787089080466525,
+    "density_peak": 13,
+    "bpm_avg": 185.9635159817354,
+    "bpm_change": 4,
+    "scroll_change": 4,
+    "rhythm_complexity": 604,
+    "color_complexity": 0.004993864138842205
+  },
+  {
+    "songno": "313",
+    "difficulty": "oni",
+    "note_count": 427,
+    "density_avg": 3.6810344827586206,
+    "density_peak": 9,
+    "bpm_avg": 150,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 392,
+    "color_complexity": 0.001489031508400551
+  },
+  {
+    "songno": "1136",
+    "difficulty": "oni",
+    "note_count": 360,
+    "density_avg": 3.814569536423841,
+    "density_peak": 7,
+    "bpm_avg": 96,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 333,
+    "color_complexity": 0.0009429447111111097
+  },
+  {
+    "songno": "1122",
+    "difficulty": "oni",
+    "note_count": 655,
+    "density_avg": 4.956597239601169,
+    "density_peak": 12,
+    "bpm_avg": 284.18774045801524,
+    "bpm_change": 7,
+    "scroll_change": 16,
+    "rhythm_complexity": 574,
+    "color_complexity": 0.004677826484192639
+  },
+  {
+    "songno": "1122",
+    "difficulty": "ura",
+    "note_count": 1108,
+    "density_avg": 8.070413061908537,
+    "density_peak": 19,
+    "bpm_avg": 283.00328519855594,
+    "bpm_change": 15,
+    "scroll_change": 20,
+    "rhythm_complexity": 994,
+    "color_complexity": 0.025356178647564762
+  },
+  {
+    "songno": "887",
+    "difficulty": "oni",
+    "note_count": 780,
+    "density_avg": 6.6222865412445735,
+    "density_peak": 12,
+    "bpm_avg": 176,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 736,
+    "color_complexity": 0.0068026821675988775
+  },
+  {
+    "songno": "139",
+    "difficulty": "oni",
+    "note_count": 644,
+    "density_avg": 5.310763888888889,
+    "density_peak": 10,
+    "bpm_avg": 190,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 621,
+    "color_complexity": 0.004459180190854104
+  },
+  {
+    "songno": "139",
+    "difficulty": "ura",
+    "note_count": 765,
+    "density_avg": 6.30859375,
+    "density_peak": 11,
+    "bpm_avg": 190,
+    "bpm_change": 0,
+    "scroll_change": 1,
+    "rhythm_complexity": 742,
+    "color_complexity": 0.008277929116327007
+  },
+  {
+    "songno": "663",
+    "difficulty": "oni",
+    "note_count": 476,
+    "density_avg": 4.109953034614047,
+    "density_peak": 9,
+    "bpm_avg": 130.0222563025208,
+    "bpm_change": 3,
+    "scroll_change": 0,
+    "rhythm_complexity": 362,
+    "color_complexity": 0.0018006687913481438
+  },
+  {
+    "songno": "105",
+    "difficulty": "oni",
+    "note_count": 354,
+    "density_avg": 3.0236184143527267,
+    "density_peak": 7,
+    "bpm_avg": 139.7528813559323,
+    "bpm_change": 37,
+    "scroll_change": 38,
+    "rhythm_complexity": 251,
+    "color_complexity": 0.000919907220485541
+  },
+  {
+    "songno": "105",
+    "difficulty": "ura",
+    "note_count": 676,
+    "density_avg": 5.773915390119896,
+    "density_peak": 10,
+    "bpm_avg": 140.52822485207096,
+    "bpm_change": 37,
+    "scroll_change": 38,
+    "rhythm_complexity": 589,
+    "color_complexity": 0.00501915309149266
+  },
+  {
+    "songno": "111",
+    "difficulty": "oni",
+    "note_count": 777,
+    "density_avg": 4.7050971079637005,
+    "density_peak": 10,
+    "bpm_avg": 135.94851994851996,
+    "bpm_change": 1,
+    "scroll_change": 0,
+    "rhythm_complexity": 704,
+    "color_complexity": 0.0034587090618026814
+  },
+  {
+    "songno": "677",
+    "difficulty": "oni",
+    "note_count": 792,
+    "density_avg": 7.97583081570997,
+    "density_peak": 15,
+    "bpm_avg": 200,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 767,
+    "color_complexity": 0.011505421296296236
+  },
+  {
+    "songno": "1308",
+    "difficulty": "oni",
+    "note_count": 773,
+    "density_avg": 5.771693790767178,
+    "density_peak": 13,
+    "bpm_avg": 239.01681759379042,
+    "bpm_change": 8,
+    "scroll_change": 9,
+    "rhythm_complexity": 727,
+    "color_complexity": 0.004041496952479993
+  },
+  {
+    "songno": "1308",
+    "difficulty": "ura",
+    "note_count": 1090,
+    "density_avg": 8.134814687221887,
+    "density_peak": 17,
+    "bpm_avg": 238.0091743119266,
+    "bpm_change": 8,
+    "scroll_change": 9,
+    "rhythm_complexity": 1038,
+    "color_complexity": 0.012863064027688232
+  },
+  {
+    "songno": "844",
+    "difficulty": "oni",
+    "note_count": 853,
+    "density_avg": 7.192956349206349,
+    "density_peak": 12,
+    "bpm_avg": 170,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 811,
+    "color_complexity": 0.009678652688977774
+  },
+  {
+    "songno": "10",
+    "difficulty": "oni",
+    "note_count": 552,
+    "density_avg": 6.224101479915433,
+    "density_peak": 12,
+    "bpm_avg": 160,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 479,
+    "color_complexity": 0.0053731713053946
+  },
+  {
+    "songno": "688",
+    "difficulty": "oni",
+    "note_count": 707,
+    "density_avg": 5.590339370071598,
+    "density_peak": 13,
+    "bpm_avg": 175.99777934936353,
+    "bpm_change": 4,
+    "scroll_change": 2,
+    "rhythm_complexity": 654,
+    "color_complexity": 0.0068829446479436295
+  },
+  {
+    "songno": "850",
+    "difficulty": "oni",
+    "note_count": 872,
+    "density_avg": 6.794553847245661,
+    "density_peak": 17,
+    "bpm_avg": 274.81462155963305,
+    "bpm_change": 6,
+    "scroll_change": 3,
+    "rhythm_complexity": 703,
+    "color_complexity": 0.011439028910402393
+  },
+  {
+    "songno": "38",
+    "difficulty": "oni",
+    "note_count": 765,
+    "density_avg": 5.823086574654956,
+    "density_peak": 13,
+    "bpm_avg": 182,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 751,
+    "color_complexity": 0.006105417539154131
+  },
+  {
+    "songno": "1334",
+    "difficulty": "oni",
+    "note_count": 429,
+    "density_avg": 5.035211267605634,
+    "density_peak": 10,
+    "bpm_avg": 200,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 390,
+    "color_complexity": 0.0029164289624079654
+  },
+  {
+    "songno": "878",
+    "difficulty": "oni",
+    "note_count": 806,
+    "density_avg": 6.302813376391915,
+    "density_peak": 16,
+    "bpm_avg": 255.37493796526053,
+    "bpm_change": 18,
+    "scroll_change": 29,
+    "rhythm_complexity": 676,
+    "color_complexity": 0.00676603514709561
+  },
+  {
+    "songno": "1452",
+    "difficulty": "oni",
+    "note_count": 316,
+    "density_avg": 9.328413284132841,
+    "density_peak": 13,
+    "bpm_avg": 240,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 314,
+    "color_complexity": 0.00370360888888889
+  },
+  {
+    "songno": "1452",
+    "difficulty": "ura",
+    "note_count": 364,
+    "density_avg": 10.7255985267035,
+    "density_peak": 16,
+    "bpm_avg": 240,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 362,
+    "color_complexity": 0.005922417777777777
+  },
+  {
+    "songno": "1446",
+    "difficulty": "oni",
+    "note_count": 474,
+    "density_avg": 4.135464535464536,
+    "density_peak": 12,
+    "bpm_avg": 131,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 287,
+    "color_complexity": 0.0022338303464472424
+  },
+  {
+    "songno": "1320",
+    "difficulty": "oni",
+    "note_count": 808,
+    "density_avg": 5.597655209166001,
+    "density_peak": 10,
+    "bpm_avg": 260,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 660,
+    "color_complexity": 0.004510001949763438
+  },
+  {
+    "songno": "1320",
+    "difficulty": "ura",
+    "note_count": 1111,
+    "density_avg": 8.00387919091161,
+    "density_peak": 17,
+    "bpm_avg": 260,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 1020,
+    "color_complexity": 0.017274985877227887
+  },
+  {
+    "songno": "717",
+    "difficulty": "oni",
+    "note_count": 765,
+    "density_avg": 5.051711580480699,
+    "density_peak": 10,
+    "bpm_avg": 136,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 669,
+    "color_complexity": 0.003257797200705463
+  },
+  {
+    "songno": "717",
+    "difficulty": "ura",
+    "note_count": 888,
+    "density_avg": 5.859679767103348,
+    "density_peak": 10,
+    "bpm_avg": 136,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 780,
+    "color_complexity": 0.0064979530110355664
+  },
+  {
+    "songno": "1283",
+    "difficulty": "oni",
+    "note_count": 292,
+    "density_avg": 2.5710691823899374,
+    "density_peak": 6,
+    "bpm_avg": 112,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 254,
+    "color_complexity": 0.0007190323891200115
+  },
+  {
+    "songno": "1297",
+    "difficulty": "oni",
+    "note_count": 858,
+    "density_avg": 6.889774696707105,
+    "density_peak": 14,
+    "bpm_avg": 139,
+    "bpm_change": 0,
+    "scroll_change": 71,
+    "rhythm_complexity": 690,
+    "color_complexity": 0.00893526861494559
+  },
+  {
+    "songno": "703",
+    "difficulty": "oni",
+    "note_count": 576,
+    "density_avg": 5.337966985230235,
+    "density_peak": 11,
+    "bpm_avg": 160,
+    "bpm_change": 0,
+    "scroll_change": 1,
+    "rhythm_complexity": 532,
+    "color_complexity": 0.004553366071901534
+  },
+  {
+    "songno": "930",
+    "difficulty": "oni",
+    "note_count": 830,
+    "density_avg": 6.856171039844509,
+    "density_peak": 14,
+    "bpm_avg": 170,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 797,
+    "color_complexity": 0.009572243853901754
+  },
+  {
+    "songno": "924",
+    "difficulty": "oni",
+    "note_count": 350,
+    "density_avg": 4.697134566557852,
+    "density_peak": 10,
+    "bpm_avg": 185,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 338,
+    "color_complexity": 0.001804688302505356
+  },
+  {
+    "songno": "1268",
+    "difficulty": "oni",
+    "note_count": 605,
+    "density_avg": 5.332533672783506,
+    "density_peak": 10,
+    "bpm_avg": 219.96846280991733,
+    "bpm_change": 3,
+    "scroll_change": 1,
+    "rhythm_complexity": 552,
+    "color_complexity": 0.0032868798750415457
+  },
+  {
+    "songno": "1268",
+    "difficulty": "ura",
+    "note_count": 862,
+    "density_avg": 7.597758720560962,
+    "density_peak": 16,
+    "bpm_avg": 219.97786542923433,
+    "bpm_change": 3,
+    "scroll_change": 1,
+    "rhythm_complexity": 788,
+    "color_complexity": 0.010894191901963987
+  },
+  {
+    "songno": "1240",
+    "difficulty": "oni",
+    "note_count": 362,
+    "density_avg": 4.114535519125683,
+    "density_peak": 8,
+    "bpm_avg": 208,
+    "bpm_change": 0,
+    "scroll_change": 2,
+    "rhythm_complexity": 341,
+    "color_complexity": 0.0012868878780337735
+  },
+  {
+    "songno": "1240",
+    "difficulty": "ura",
+    "note_count": 657,
+    "density_avg": 7.467540983606558,
+    "density_peak": 14,
+    "bpm_avg": 208,
+    "bpm_change": 0,
+    "scroll_change": 2,
+    "rhythm_complexity": 623,
+    "color_complexity": 0.006901208079133739
+  },
+  {
+    "songno": "918",
+    "difficulty": "oni",
+    "note_count": 687,
+    "density_avg": 5.564132231404959,
+    "density_peak": 10,
+    "bpm_avg": 147,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 655,
+    "color_complexity": 0.0037093791356749206
+  },
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+    "note_count": 806,
+    "density_avg": 6.569155685700342,
+    "density_peak": 12,
+    "bpm_avg": 167,
+    "bpm_change": 0,
+    "scroll_change": 12,
+    "rhythm_complexity": 784,
+    "color_complexity": 0.007836578406899344
+  },
+  {
+    "songno": "478",
+    "difficulty": "oni",
+    "note_count": 555,
+    "density_avg": 5.020559210526316,
+    "density_peak": 12,
+    "bpm_avg": 165,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 532,
+    "color_complexity": 0.0026770483889597546
+  },
+  {
+    "songno": "336",
+    "difficulty": "oni",
+    "note_count": 1304,
+    "density_avg": 11.808920081503283,
+    "density_peak": 21,
+    "bpm_avg": 291.5184049079755,
+    "bpm_change": 1,
+    "scroll_change": 1,
+    "rhythm_complexity": 1255,
+    "color_complexity": 0.03172641728093539
+  },
+  {
+    "songno": "450",
+    "difficulty": "oni",
+    "note_count": 950,
+    "density_avg": 6.918238993710691,
+    "density_peak": 15,
+    "bpm_avg": 220,
+    "bpm_change": 0,
+    "scroll_change": 0,
+    "rhythm_complexity": 910,
+    "color_complexity": 0.01523599996929097
+  },
+  {
+    "songno": "322",
+    "difficulty": "oni",
+    "note_count": 607,
+    "density_avg": 4.979395030714651,
+    "density_peak": 13,
+    "bpm_avg": 149.54920922570017,
+    "bpm_change": 15,
+    "scroll_change": 15,
+    "rhythm_complexity": 560,
+    "color_complexity": 0.005248271768336951
+  }
+]
\ No newline at end of file
diff --git a/test/features.txt b/test/features.txt
new file mode 100644
index 0000000..c3200c2
--- /dev/null
+++ b/test/features.txt
@@ -0,0 +1,8 @@
+bpm_avg
+bpm_change
+color_complexity
+density_avg
+density_peak
+note_count
+rhythm_complexity
+scroll_change
diff --git a/test/model.pkl b/test/model.pkl
new file mode 100644
index 0000000..c24f08c
Binary files /dev/null and b/test/model.pkl differ
diff --git a/test/scaler.pkl b/test/scaler.pkl
new file mode 100644
index 0000000..04d9232
Binary files /dev/null and b/test/scaler.pkl differ
diff --git a/train/train_xgboost.py b/train/train_xgboost.py
index c30edb3..66d0e8f 100644
--- a/train/train_xgboost.py
+++ b/train/train_xgboost.py
@@ -3,11 +3,11 @@ import csv
 import json
 import math
 import os
+import random
 import joblib
 import numpy as np
 
 from xgboost import XGBRegressor
-from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import mean_absolute_error
 
@@ -15,7 +15,9 @@ from sklearn.metrics import mean_absolute_error
 # Hyper Parameters
 # =========================================================
 
-TEST_SIZE = 0.2
+TRAIN_SIZE = 0
+VALID_SIZE = 0
+
 RANDOM_STATE = 42
 
 N_ESTIMATORS = 500
@@ -27,7 +29,7 @@ COLSAMPLE_BYTREE = 0.8
 CONTINUE_TRAINING = True
 
 # 예측 성공으로 간주할 허용 오차
-ERROR_TOLERANCE = 0.2
+ERROR_TOLERANCE = 0.1
 
 # =========================================================
 # 파일명
@@ -50,6 +52,10 @@ IGNORE_KEYS = {
 }
 
 
+# =========================================================
+# safe float
+# =========================================================
+
 def safe_float(value):
     if value is None:
         return 0.0
@@ -62,10 +68,16 @@ def safe_float(value):
     return x
 
 
+# =========================================================
+# train
+# =========================================================
+
 def train_model(
     working_dir: str,
     data_dir: str
 ):
+    random.seed(RANDOM_STATE)
+
     # =====================================================
     # path
     # =====================================================
@@ -129,14 +141,14 @@ def train_model(
     ])
 
     # =====================================================
-    # measure.csv
+    # dataset build
     # =====================================================
 
-    X = []
-    y = []
+    dataset = []
 
     with open(measure_path, "r", encoding="utf-8") as f:
         reader = csv.reader(f)
+
         next(reader, None)
 
         for row in reader:
@@ -163,29 +175,59 @@ def train_model(
                 for k in feature_names
             ]
 
-            X.append(features)
-            y.append(measure)
+            dataset.append((
+                features,
+                measure
+            ))
 
-    if len(X) == 0:
-        raise ValueError("No training data")
+    # =====================================================
+    # shuffle
+    # =====================================================
 
-    X = np.array(X, dtype=np.float32)
-    y = np.array(y, dtype=np.float32)
+    random.shuffle(dataset)
 
-    print(f"Dataset Size: {len(X)}")
-    print(f"Feature Count: {len(feature_names)}")
+    required_size = TRAIN_SIZE + VALID_SIZE
+
+    if len(dataset) < required_size:
+        raise ValueError(
+            f"Not enough dataset "
+            f"({len(dataset)} < {required_size})"
+        )
 
     # =====================================================
     # split
     # =====================================================
 
-    X_train, X_valid, y_train, y_valid = train_test_split(
-        X,
-        y,
-        test_size=TEST_SIZE,
-        random_state=RANDOM_STATE
+    train_dataset = dataset[:TRAIN_SIZE]
+    valid_dataset = dataset[
+        TRAIN_SIZE:
+        TRAIN_SIZE + VALID_SIZE
+    ]
+
+    X_train = np.array(
+        [x for x, _ in train_dataset],
+        dtype=np.float32
     )
 
+    y_train = np.array(
+        [y for _, y in train_dataset],
+        dtype=np.float32
+    )
+
+    X_valid = np.array(
+        [x for x, _ in valid_dataset],
+        dtype=np.float32
+    )
+
+    y_valid = np.array(
+        [y for _, y in valid_dataset],
+        dtype=np.float32
+    )
+
+    print(f"Train Size: {len(X_train)}")
+    print(f"Valid Size: {len(X_valid)}")
+    print(f"Feature Count: {len(feature_names)}")
+
     # =====================================================
     # scaler
     # =====================================================
@@ -251,6 +293,7 @@ def train_model(
     accuracy = correct / len(y_valid)
 
     print(f"\nMAE: {mae:.4f}")
+
     print(
         f"Accuracy "
         f"(±{ERROR_TOLERANCE}): "
@@ -304,8 +347,21 @@ if __name__ == "__main__":
         "--dataDir",
         required=True
     )
+    
+    parser.add_argument(
+        "--trainSize",
+        required=True
+    )
+    
+    parser.add_argument(
+        "--validSize",
+        required=True
+    )
 
     args = parser.parse_args()
+    
+    TRAIN_SIZE = int(args.trainSize)
+    VALID_SIZE = int(args.validSize)
 
     train_model(
         args.workingDir,
diff --git a/train/train_xgboost_pick.py b/train/train_xgboost_full.py
similarity index 80%
rename from train/train_xgboost_pick.py
rename to train/train_xgboost_full.py
index 028cfe3..57242e4 100644
--- a/train/train_xgboost_pick.py
+++ b/train/train_xgboost_full.py
@@ -3,11 +3,11 @@ import csv
 import json
 import math
 import os
-import random
 import joblib
 import numpy as np
 
 from xgboost import XGBRegressor
+from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import mean_absolute_error
 
@@ -15,9 +15,7 @@ from sklearn.metrics import mean_absolute_error
 # Hyper Parameters
 # =========================================================
 
-TRAIN_SIZE = 0
-VALID_SIZE = 0
-
+TEST_SIZE = 0.2
 RANDOM_STATE = 42
 
 N_ESTIMATORS = 500
@@ -29,7 +27,7 @@ COLSAMPLE_BYTREE = 0.8
 CONTINUE_TRAINING = True
 
 # 예측 성공으로 간주할 허용 오차
-ERROR_TOLERANCE = 0.5
+ERROR_TOLERANCE = 0.1
 
 # =========================================================
 # 파일명
@@ -52,10 +50,6 @@ IGNORE_KEYS = {
 }
 
 
-# =========================================================
-# safe float
-# =========================================================
-
 def safe_float(value):
     if value is None:
         return 0.0
@@ -68,16 +62,10 @@ def safe_float(value):
     return x
 
 
-# =========================================================
-# train
-# =========================================================
-
 def train_model(
     working_dir: str,
     data_dir: str
 ):
-    random.seed(RANDOM_STATE)
-
     # =====================================================
     # path
     # =====================================================
@@ -141,14 +129,14 @@ def train_model(
     ])
 
     # =====================================================
-    # dataset build
+    # measure.csv
     # =====================================================
 
-    dataset = []
+    X = []
+    y = []
 
     with open(measure_path, "r", encoding="utf-8") as f:
         reader = csv.reader(f)
-
         next(reader, None)
 
         for row in reader:
@@ -175,59 +163,29 @@ def train_model(
                 for k in feature_names
             ]
 
-            dataset.append((
-                features,
-                measure
-            ))
+            X.append(features)
+            y.append(measure)
 
-    # =====================================================
-    # shuffle
-    # =====================================================
+    if len(X) == 0:
+        raise ValueError("No training data")
 
-    random.shuffle(dataset)
+    X = np.array(X, dtype=np.float32)
+    y = np.array(y, dtype=np.float32)
 
-    required_size = TRAIN_SIZE + VALID_SIZE
-
-    if len(dataset) < required_size:
-        raise ValueError(
-            f"Not enough dataset "
-            f"({len(dataset)} < {required_size})"
-        )
+    print(f"Dataset Size: {len(X)}")
+    print(f"Feature Count: {len(feature_names)}")
 
     # =====================================================
     # split
     # =====================================================
 
-    train_dataset = dataset[:TRAIN_SIZE]
-    valid_dataset = dataset[
-        TRAIN_SIZE:
-        TRAIN_SIZE + VALID_SIZE
-    ]
-
-    X_train = np.array(
-        [x for x, _ in train_dataset],
-        dtype=np.float32
+    X_train, X_valid, y_train, y_valid = train_test_split(
+        X,
+        y,
+        test_size=TEST_SIZE,
+        random_state=RANDOM_STATE
     )
 
-    y_train = np.array(
-        [y for _, y in train_dataset],
-        dtype=np.float32
-    )
-
-    X_valid = np.array(
-        [x for x, _ in valid_dataset],
-        dtype=np.float32
-    )
-
-    y_valid = np.array(
-        [y for _, y in valid_dataset],
-        dtype=np.float32
-    )
-
-    print(f"Train Size: {len(X_train)}")
-    print(f"Valid Size: {len(X_valid)}")
-    print(f"Feature Count: {len(feature_names)}")
-
     # =====================================================
     # scaler
     # =====================================================
@@ -293,7 +251,6 @@ def train_model(
     accuracy = correct / len(y_valid)
 
     print(f"\nMAE: {mae:.4f}")
-
     print(
         f"Accuracy "
         f"(±{ERROR_TOLERANCE}): "
@@ -347,21 +304,8 @@ if __name__ == "__main__":
         "--dataDir",
         required=True
     )
-    
-    parser.add_argument(
-        "--trainSize",
-        required=True
-    )
-    
-    parser.add_argument(
-        "--validSize",
-        required=True
-    )
 
     args = parser.parse_args()
-    
-    TRAIN_SIZE = int(args.trainSize)
-    VALID_SIZE = int(args.validSize)
 
     train_model(
         args.workingDir,