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c687548 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | import sys
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import KFold
from sklearn.preprocessing import RobustScaler
from scipy.stats import pearsonr
import warnings
warnings.filterwarnings('ignore')
# ===== Feature Engineering =====
def feature_engineering(df):
# 保持接口一致，实际特征工程已在feature.py完成
return df
# ===== Configuration =====
class Config:
TRAIN_PATH = "/AI4M/users/mjzhang/workspace/DRW/ZMJ/max_IC_mixed/train_aggregated.parquet"
TEST_PATH = "/AI4M/users/mjzhang/workspace/DRW/ZMJ/max_IC_mixed/test_aggregated.parquet"
SUBMISSION_PATH = "/AI4M/users/mjzhang/workspace/DRW/ZMJ/max_IC_mixed/sample_submission.csv"
LABEL_COLUMN = "label"
N_FOLDS = 3
RANDOM_STATE = 42
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
BATCH_SIZE = 128
EPOCHS = 20
LEARNING_RATE = 1e-3
# ===== MLP Model Definition =====
class MLP(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.net = nn.Sequential(
nn.Linear(input_dim, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
# nn.Linear(1024, 512),
# nn.ReLU(),
# nn.Linear(512, 256),
# nn.ReLU(),
# nn.Linear(256, 128),
# nn.ReLU(),
# nn.Linear(256, 128),
# nn.ReLU(),
nn.Linear(128, 1)
)
def forward(self, x):
return self.net(x)
# ===== Data Loading =====
def load_data():
train_df = pd.read_parquet(Config.TRAIN_PATH)
test_df = pd.read_parquet(Config.TEST_PATH)
submission_df = pd.read_csv(Config.SUBMISSION_PATH)
Config.FEATURES = [col for col in train_df.columns.tolist() if col != Config.LABEL_COLUMN]
print(f"Loaded data - Train: {train_df.shape}, Test: {test_df.shape}, Submission: {submission_df.shape}")
print(f"Total features: {len(Config.FEATURES)}")
return train_df.reset_index(drop=True), test_df.reset_index(drop=True), submission_df
# ===== Model Training =====
def train_mlp(X_train, y_train, X_valid, y_valid, X_test, scaler):
X_train = scaler.transform(X_train)
X_valid = scaler.transform(X_valid)
X_test = scaler.transform(X_test)
X_train = torch.tensor(X_train, dtype=torch.float32, device=Config.DEVICE)
y_train = torch.tensor(y_train.values, dtype=torch.float32, device=Config.DEVICE).view(-1, 1)
X_valid = torch.tensor(X_valid, dtype=torch.float32, device=Config.DEVICE)
y_valid = torch.tensor(y_valid.values, dtype=torch.float32, device=Config.DEVICE).view(-1, 1)
X_test = torch.tensor(X_test, dtype=torch.float32, device=Config.DEVICE)
model = MLP(X_train.shape[1]).to(Config.DEVICE)
optimizer = optim.AdamW(model.parameters(), lr=Config.LEARNING_RATE, weight_decay=1e-4) # L2正则
criterion = nn.MSELoss()
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=Config.EPOCHS)
best_model = None
best_score = -np.inf
patience = 7 # 早停容忍轮数
patience_counter = 0
for epoch in range(Config.EPOCHS):
model.train()
idx = np.random.permutation(len(X_train))
for i in range(0, len(X_train), Config.BATCH_SIZE):
batch_idx = idx[i:i+Config.BATCH_SIZE]
xb = X_train[batch_idx]
yb = y_train[batch_idx]
optimizer.zero_grad()
pred = model(xb)
loss = criterion(pred, yb)
loss.backward()
optimizer.step()
scheduler.step()
# 验证
model.eval()
with torch.no_grad():
val_pred = model(X_valid).cpu().numpy().flatten()
val_score = np.corrcoef(val_pred, y_valid.cpu().numpy().flatten())[0, 1]
if val_score > best_score:
best_score = val_score
best_model = MLP(X_train.shape[1]).to(Config.DEVICE)
best_model.load_state_dict(model.state_dict())
patience_counter = 0
else:
patience_counter += 1
if patience_counter >= patience:
print(f"Early stopping at epoch {epoch+1}, best valid corr: {best_score:.4f}")
break
# 用最佳模型预测
best_model.eval()
with torch.no_grad():
valid_pred = best_model(X_valid).cpu().numpy().flatten()
test_pred = best_model(X_test).cpu().numpy().flatten()
return valid_pred, test_pred
def train_and_evaluate(train_df, test_df):
X_train = train_df[Config.FEATURES]
y_train = train_df[Config.LABEL_COLUMN]
X_test = test_df[Config.FEATURES]
scaler = RobustScaler().fit(X_train)
# 这里直接用全集训练
valid_pred, test_pred = train_mlp(X_train, y_train, X_train, y_train, X_test, scaler)
oof_preds = valid_pred # 全集预测
test_preds = test_pred
score = pearsonr(y_train, valid_pred)[0]
print(f"Train PearsonR (no CV): {score:.4f}")
return oof_preds, test_preds
# ===== Submission =====
def create_submission(train_df, oof_preds, test_preds, submission_df):
score = pearsonr(train_df[Config.LABEL_COLUMN], oof_preds)[0]
print(f"\nMLP OOF PearsonR: {score:.4f}")
submission = submission_df.copy()
submission["prediction"] = test_preds
submission.to_csv("/AI4M/users/mjzhang/workspace/DRW/ZMJ/max_IC_mixed/submission_mlp_new.csv", index=False)
print("Saved: submission_mlp.csv")
return score
# ===== Main Execution =====
if __name__ == "__main__":
print("Loading data...")
train_df, test_df, submission_df = load_data()
print("\nTraining MLP model...")
oof_preds, test_preds = train_and_evaluate(train_df, test_df)
print("\nCreating submission...")
score = create_submission(train_df, oof_preds, test_preds, submission_df)
print(f"\nAll done! MLP OOF PearsonR: {score:.4f}") |