image
This commit is contained in:
216
train/feature/train_lightgbm.py
Normal file
216
train/feature/train_lightgbm.py
Normal file
@@ -0,0 +1,216 @@
|
||||
import argparse
|
||||
import csv
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import joblib
|
||||
import numpy as np
|
||||
import lightgbm as lgb
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.metrics import mean_absolute_error
|
||||
|
||||
# =========================================================
|
||||
# Hyper Parameters
|
||||
# =========================================================
|
||||
|
||||
TRAIN_SIZE = 0
|
||||
VALID_SIZE = 0
|
||||
|
||||
RANDOM_STATE = 42
|
||||
|
||||
# LightGBM 특정 하이퍼파라미터
|
||||
PARAMS = {
|
||||
'objective': 'regression',
|
||||
'metric': 'mae',
|
||||
'verbosity': -1,
|
||||
'boosting_type': 'gbdt',
|
||||
'random_state': RANDOM_STATE,
|
||||
'learning_rate': 0.02, # 더 정밀한 학습을 위해 하향
|
||||
'num_leaves': 63, # 더 복잡한 패턴 학습을 위해 상향
|
||||
'feature_fraction': 0.9,
|
||||
'bagging_fraction': 0.8,
|
||||
'bagging_freq': 5,
|
||||
'n_estimators': 3000 # 학습량 대폭 상향
|
||||
}
|
||||
|
||||
CONTINUE_TRAINING = True
|
||||
ERROR_TOLERANCE = 0.1
|
||||
|
||||
# =========================================================
|
||||
# 파일명
|
||||
# =========================================================
|
||||
|
||||
FEATURES_FILENAME = "features.json"
|
||||
MEASURE_FILENAME = "measure.csv"
|
||||
MODEL_FILENAME = "model.pkl"
|
||||
SCALER_FILENAME = "scaler.pkl"
|
||||
FEATURE_NAMES_FILENAME = "features.txt"
|
||||
|
||||
IGNORE_KEYS = {"songno", "difficulty"}
|
||||
|
||||
def safe_float(value):
|
||||
if value is None: return 0.0
|
||||
x = float(value)
|
||||
return x if math.isfinite(x) else 0.0
|
||||
|
||||
def train_model(working_dir: str, data_dir: str):
|
||||
random.seed(RANDOM_STATE)
|
||||
|
||||
features_path = os.path.join(working_dir, FEATURES_FILENAME)
|
||||
measure_path = os.path.join(data_dir, MEASURE_FILENAME)
|
||||
model_path = os.path.join(working_dir, MODEL_FILENAME)
|
||||
scaler_path = os.path.join(working_dir, SCALER_FILENAME)
|
||||
feature_names_path = os.path.join(working_dir, FEATURE_NAMES_FILENAME)
|
||||
|
||||
with open(features_path, "r", encoding="utf-8") as f:
|
||||
feature_data = json.load(f)
|
||||
|
||||
if len(feature_data) == 0:
|
||||
raise ValueError("features.json is empty")
|
||||
|
||||
feature_map = {(str(item["songno"]), str(item["difficulty"])): item for item in feature_data}
|
||||
feature_names = sorted([k for k in feature_data[0].keys() if k not in IGNORE_KEYS])
|
||||
|
||||
dataset = []
|
||||
with open(measure_path, "r", encoding="utf-8") as f:
|
||||
reader = csv.reader(f)
|
||||
next(reader, None)
|
||||
for row in reader:
|
||||
if len(row) < 3: continue
|
||||
measure, songno, diff = safe_float(row[0]), str(row[1]), str(row[2])
|
||||
key = (songno, diff)
|
||||
if key in feature_map:
|
||||
features = [safe_float(feature_map[key].get(k, 0)) for k in feature_names]
|
||||
dataset.append((features, measure, songno, diff))
|
||||
|
||||
random.shuffle(dataset)
|
||||
if len(dataset) < (TRAIN_SIZE + VALID_SIZE):
|
||||
raise ValueError(f"Not enough dataset ({len(dataset)} < {TRAIN_SIZE + VALID_SIZE})")
|
||||
|
||||
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)
|
||||
valid_info = [(s, d) for _, _, s, d in valid_dataset]
|
||||
|
||||
print(f"Train Size: {len(X_train)} | Valid Size: {len(X_valid)} | Features: {len(feature_names)}")
|
||||
|
||||
if CONTINUE_TRAINING and os.path.exists(scaler_path):
|
||||
print("Loading existing scaler...")
|
||||
scaler = joblib.load(scaler_path)
|
||||
else:
|
||||
print("Creating new scaler...")
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
|
||||
X_train = scaler.transform(X_train)
|
||||
X_valid = scaler.transform(X_valid)
|
||||
|
||||
if CONTINUE_TRAINING and os.path.exists(model_path):
|
||||
print("Loading existing model for incremental training...")
|
||||
model = joblib.load(model_path)
|
||||
model.fit(
|
||||
X_train, y_train,
|
||||
eval_set=[(X_valid, y_valid)],
|
||||
init_model=model,
|
||||
callbacks=[lgb.early_stopping(stopping_rounds=100)]
|
||||
)
|
||||
else:
|
||||
print("Creating new LightGBM model...")
|
||||
model = lgb.LGBMRegressor(**PARAMS)
|
||||
model.fit(
|
||||
X_train, y_train,
|
||||
eval_set=[(X_valid, y_valid)],
|
||||
callbacks=[lgb.early_stopping(stopping_rounds=100)]
|
||||
)
|
||||
|
||||
pred = model.predict(X_valid)
|
||||
mae = mean_absolute_error(y_valid, pred)
|
||||
accuracy = np.sum(np.abs(pred - y_valid) <= ERROR_TOLERANCE) / len(y_valid)
|
||||
|
||||
print(f"\nMAE: {mae:.4f} | Accuracy (±{ERROR_TOLERANCE}): {accuracy:.4f}")
|
||||
|
||||
# =====================================================
|
||||
# save validate.json
|
||||
# =====================================================
|
||||
validate_details = []
|
||||
for i in range(len(y_valid)):
|
||||
actual = float(y_valid[i])
|
||||
predicted = float(pred[i])
|
||||
songno, diff = valid_info[i]
|
||||
validate_details.append({
|
||||
"songno": songno,
|
||||
"diff": diff,
|
||||
"actual": actual,
|
||||
"predicted": predicted,
|
||||
"error": actual - predicted
|
||||
})
|
||||
|
||||
validate_details.sort(key=lambda x: abs(x["error"]), reverse=True)
|
||||
validate_result = {
|
||||
"summary": {
|
||||
"total_compared": len(y_valid),
|
||||
"average_absolute_error": float(mae),
|
||||
"accuracy": float(accuracy),
|
||||
"timestamp": "now",
|
||||
"script_used": "train/train_lightgbm.py"
|
||||
},
|
||||
"details": validate_details
|
||||
}
|
||||
|
||||
validate_path = os.path.join(working_dir, "validate.json")
|
||||
with open(validate_path, "w", encoding="utf-8") as f:
|
||||
json.dump(validate_result, f, indent=2, ensure_ascii=False)
|
||||
print(f"Validation result saved: {validate_path}")
|
||||
|
||||
# =====================================================
|
||||
# save validate.png
|
||||
# =====================================================
|
||||
try:
|
||||
plt.switch_backend('Agg')
|
||||
df_plot = pd.DataFrame(validate_details)
|
||||
df_plot['abs_error'] = df_plot['error'].abs()
|
||||
df_plot = df_plot.sort_values('abs_error', ascending=False).reset_index(drop=True)
|
||||
|
||||
plt.figure(figsize=(12, 6))
|
||||
sns.scatterplot(data=df_plot, x=df_plot.index, y='abs_error', alpha=0.6, s=20, color='darkorange')
|
||||
plt.axhline(0.2, color='green', linestyle='--', linewidth=0.8, alpha=0.5, label='Target (0.2)')
|
||||
plt.axhline(0.5, color='blue', linestyle='--', linewidth=0.8, alpha=0.5)
|
||||
plt.axhline(1.0, color='red', linestyle='--', linewidth=0.8, alpha=0.5)
|
||||
plt.ylim(0, max(3.5, df_plot['abs_error'].max() + 0.5))
|
||||
plt.title(f'Validation Absolute Error - {os.path.basename(working_dir)}', fontsize=14)
|
||||
plt.xlabel('Samples (Sorted by Error Magnitude)', fontsize=12)
|
||||
plt.ylabel('Absolute Error', fontsize=12)
|
||||
plt.grid(True, axis='y', alpha=0.3)
|
||||
|
||||
plot_path = os.path.join(working_dir, "validate.png")
|
||||
plt.savefig(plot_path)
|
||||
plt.close()
|
||||
print(f"Validation plot saved: {plot_path}")
|
||||
except Exception as e:
|
||||
print(f"[WARN] Failed to create validation plot: {e}")
|
||||
|
||||
joblib.dump(model, model_path)
|
||||
joblib.dump(scaler, scaler_path)
|
||||
with open(feature_names_path, "w", encoding="utf-8") as f:
|
||||
for name in feature_names: f.write(name + "\n")
|
||||
|
||||
print(f"\nSaved to {working_dir}: {MODEL_FILENAME}, {SCALER_FILENAME}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--workingDir", required=True)
|
||||
parser.add_argument("--dataDir", required=True)
|
||||
parser.add_argument("--trainSize", required=True, type=int)
|
||||
parser.add_argument("--validSize", required=True, type=int)
|
||||
args = parser.parse_args()
|
||||
|
||||
TRAIN_SIZE, VALID_SIZE = args.trainSize, args.validSize
|
||||
train_model(args.workingDir, args.dataDir)
|
||||
Reference in New Issue
Block a user