446 lines
11 KiB
Python
446 lines
11 KiB
Python
import argparse
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import csv
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import json
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import math
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import os
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import random
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import joblib
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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from xgboost import XGBRegressor
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from sklearn.preprocessing import StandardScaler
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from sklearn.metrics import mean_absolute_error
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# =========================================================
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# Hyper Parameters
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# =========================================================
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TRAIN_SIZE = 0
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VALID_SIZE = 0
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RANDOM_STATE = 42
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N_ESTIMATORS = 500
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MAX_DEPTH = 6
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LEARNING_RATE = 0.05
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SUBSAMPLE = 0.8
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COLSAMPLE_BYTREE = 0.8
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CONTINUE_TRAINING = True
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# 예측 성공으로 간주할 허용 오차
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ERROR_TOLERANCE = 0.1
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# =========================================================
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# 파일명
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# =========================================================
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FEATURES_FILENAME = "features.json"
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MEASURE_FILENAME = "measure.csv"
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MODEL_FILENAME = "model.pkl"
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SCALER_FILENAME = "scaler.pkl"
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FEATURE_NAMES_FILENAME = "features.txt"
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# =========================================================
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# 무시할 key
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# =========================================================
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IGNORE_KEYS = {
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"songno",
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"difficulty"
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}
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# =========================================================
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# safe float
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# =========================================================
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def safe_float(value):
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if value is None:
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return 0.0
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x = float(value)
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if not math.isfinite(x):
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return 0.0
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return x
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# =========================================================
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# train
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# =========================================================
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def train_model(
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working_dir: str,
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data_dir: str
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):
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random.seed(RANDOM_STATE)
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# =====================================================
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# path
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# =====================================================
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features_path = os.path.join(
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working_dir,
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FEATURES_FILENAME
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)
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measure_path = os.path.join(
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data_dir,
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MEASURE_FILENAME
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)
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model_path = os.path.join(
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working_dir,
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MODEL_FILENAME
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)
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scaler_path = os.path.join(
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working_dir,
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SCALER_FILENAME
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)
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feature_names_path = os.path.join(
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working_dir,
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FEATURE_NAMES_FILENAME
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)
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# =====================================================
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# features.json
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# =====================================================
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with open(features_path, "r", encoding="utf-8") as f:
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feature_data = json.load(f)
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if len(feature_data) == 0:
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raise ValueError("features.json is empty")
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# =====================================================
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# feature map
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# =====================================================
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feature_map = {}
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for item in feature_data:
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key = (
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str(item["songno"]),
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str(item["difficulty"])
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)
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feature_map[key] = item
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# =====================================================
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# feature names
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# =====================================================
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feature_names = sorted([
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k for k in feature_data[0].keys()
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if k not in IGNORE_KEYS
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])
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# =====================================================
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# dataset build
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# =====================================================
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dataset = []
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with open(measure_path, "r", encoding="utf-8") as f:
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reader = csv.reader(f)
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next(reader, None)
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for row in reader:
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if len(row) < 3:
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continue
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measure = safe_float(row[0])
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songno = str(row[1])
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diff = str(row[2])
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key = (songno, diff)
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if key not in feature_map:
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print(
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f"[WARN] feature not found: "
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f"{songno} {diff}"
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)
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continue
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feature_item = feature_map[key]
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features = [
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safe_float(feature_item.get(k, 0))
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for k in feature_names
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]
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dataset.append((
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features,
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measure,
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songno,
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diff
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))
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# =====================================================
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# shuffle
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# =====================================================
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random.shuffle(dataset)
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required_size = TRAIN_SIZE + VALID_SIZE
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if len(dataset) < required_size:
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raise ValueError(
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f"Not enough dataset "
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f"({len(dataset)} < {required_size})"
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)
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# =====================================================
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# split
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# =====================================================
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train_dataset = dataset[:TRAIN_SIZE]
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valid_dataset = dataset[
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TRAIN_SIZE:
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TRAIN_SIZE + VALID_SIZE
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]
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X_train = np.array(
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[x for x, _, _, _ in train_dataset],
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dtype=np.float32
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)
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y_train = np.array(
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[y for _, y, _, _ in train_dataset],
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dtype=np.float32
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)
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X_valid = np.array(
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[x for x, _, _, _ in valid_dataset],
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dtype=np.float32
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)
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y_valid = np.array(
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[y for _, y, _, _ in valid_dataset],
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dtype=np.float32
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)
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valid_info = [
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(s, d) for _, _, s, d in valid_dataset
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]
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print(f"Train Size: {len(X_train)}")
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print(f"Valid Size: {len(X_valid)}")
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print(f"Feature Count: {len(feature_names)}")
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# =====================================================
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# scaler
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# =====================================================
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if CONTINUE_TRAINING and os.path.exists(scaler_path):
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print("Loading existing scaler...")
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scaler = joblib.load(scaler_path)
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else:
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print("Creating new scaler...")
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scaler = StandardScaler()
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scaler.fit(X_train)
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X_train = scaler.transform(X_train)
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X_valid = scaler.transform(X_valid)
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# =====================================================
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# model
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# =====================================================
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if CONTINUE_TRAINING and os.path.exists(model_path):
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print("Loading existing model...")
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model = joblib.load(model_path)
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previous_booster = model.get_booster()
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model.fit(
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X_train,
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y_train,
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xgb_model=previous_booster
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)
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else:
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print("Creating new model...")
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model = XGBRegressor(
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n_estimators=N_ESTIMATORS,
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max_depth=MAX_DEPTH,
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learning_rate=LEARNING_RATE,
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subsample=SUBSAMPLE,
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colsample_bytree=COLSAMPLE_BYTREE,
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objective="reg:squarederror",
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random_state=RANDOM_STATE
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)
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model.fit(X_train, y_train)
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# =====================================================
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# evaluate
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# =====================================================
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pred = model.predict(X_valid)
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mae = mean_absolute_error(y_valid, pred)
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correct = np.sum(
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np.abs(pred - y_valid) <= ERROR_TOLERANCE
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)
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accuracy = correct / len(y_valid)
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print(f"\nMAE: {mae:.4f}")
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print(
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f"Accuracy "
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f"(±{ERROR_TOLERANCE}): "
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f"{accuracy:.4f}"
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)
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# =====================================================
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# feature importance
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# =====================================================
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print("\nFeature Importance:")
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importance = model.feature_importances_
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pairs = list(zip(feature_names, importance))
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pairs.sort(key=lambda x: x[1], reverse=True)
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for name, score in pairs:
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print(f"{name:25} {score:.6f}")
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# =====================================================
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# save validate.json
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# =====================================================
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validate_details = []
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for i in range(len(y_valid)):
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actual = float(y_valid[i])
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predicted = float(pred[i])
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songno, diff = valid_info[i]
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validate_details.append({
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"songno": songno,
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"diff": diff,
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"actual": actual,
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"predicted": predicted,
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"error": actual - predicted
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})
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# 에러 절댓값 기준 정렬
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validate_details.sort(key=lambda x: abs(x["error"]), reverse=True)
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validate_result = {
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"summary": {
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"total_compared": len(y_valid),
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"average_absolute_error": float(mae),
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"accuracy": float(accuracy),
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"timestamp": "now",
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"script_used": "train/train_xgboost.py"
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},
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"details": validate_details
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}
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validate_path = os.path.join(working_dir, "validate.json")
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with open(validate_path, "w", encoding="utf-8") as f:
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json.dump(validate_result, f, indent=2, ensure_ascii=False)
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print(f"Validation result saved: {validate_path}")
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# =====================================================
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# save validate.png
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# =====================================================
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try:
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plt.switch_backend('Agg') # GUI 없는 환경 대응
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df_plot = pd.DataFrame(validate_details)
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df_plot['abs_error'] = df_plot['error'].abs()
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df_plot = df_plot.sort_values('abs_error', ascending=False).reset_index(drop=True)
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plt.figure(figsize=(12, 6))
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sns.scatterplot(data=df_plot, x=df_plot.index, y='abs_error', alpha=0.6, s=20, color='darkorange')
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plt.axhline(0.2, color='green', linestyle='--', linewidth=0.8, alpha=0.5, label='Target (0.2)')
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plt.axhline(0.5, color='blue', linestyle='--', linewidth=0.8, alpha=0.5)
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plt.axhline(1.0, color='red', linestyle='--', linewidth=0.8, alpha=0.5)
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plt.ylim(0, max(3.5, df_plot['abs_error'].max() + 0.5))
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plt.title(f'Validation Absolute Error - {os.path.basename(working_dir)}', fontsize=14)
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plt.xlabel('Samples (Sorted by Error Magnitude)', fontsize=12)
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plt.ylabel('Absolute Error', fontsize=12)
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plt.grid(True, axis='y', alpha=0.3)
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plot_path = os.path.join(working_dir, "validate.png")
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plt.savefig(plot_path)
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plt.close()
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print(f"Validation plot saved: {plot_path}")
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except Exception as e:
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print(f"[WARN] Failed to create validation plot: {e}")
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# =====================================================
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# save
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# =====================================================
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joblib.dump(model, model_path)
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joblib.dump(scaler, scaler_path)
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with open(feature_names_path, "w", encoding="utf-8") as f:
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for name in feature_names:
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f.write(name + "\n")
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print("\nSaved:")
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print(model_path)
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print(scaler_path)
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print(feature_names_path)
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# =========================================================
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# main
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# =========================================================
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--workingDir",
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required=True
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)
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parser.add_argument(
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"--dataDir",
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required=True
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)
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parser.add_argument(
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"--trainSize",
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required=True
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)
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parser.add_argument(
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"--validSize",
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required=True
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)
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args = parser.parse_args()
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TRAIN_SIZE = int(args.trainSize)
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VALID_SIZE = int(args.validSize)
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train_model(
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args.workingDir,
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args.dataDir
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)
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