1

xlsx2csv/csv_output/1.py

@@ -0,0 +1,77 @@
+import pandas as pd
+import numpy as np
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.gaussian_process.kernels import RBF, WhiteKernel, ConstantKernel as C
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.pipeline import make_pipeline
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import PolynomialFeatures
+
+try:
+    # 1. 加载数据
+    df0 = pd.read_csv('input_file_0.csv')
+    df1 = pd.read_csv('input_file_1.csv')
+    df = pd.concat([df0, df1], axis=0, ignore_index=True)
+    
+    # 清洗列名
+    df.columns = [c.strip() for c in df.columns]
+
+    # 筛选男胎
+    male_df = df[df['Y染色体浓度'].notna() & (df['Y染色体浓度'] > 0)].copy()
+
+    # 解析孕周
+    def parse_ga(s):
+        try:
+            if pd.isna(s): return np.nan
+            s = str(s).lower().replace('w', ' ').replace('+', ' ').split()
+            return float(s[0]) + (float(s[1])/7.0 if len(s) > 1 else 0)
+        except:
+            return np.nan
+
+    male_df['GA_numeric'] = male_df['检测孕周'].apply(parse_ga)
+    # 剔除缺失值
+    male_df = male_df.dropna(subset=['Y染色体浓度', 'GA_numeric', '孕妇BMI'])
+
+    # 2. 准备数据
+    X = male_df[['GA_numeric', '孕妇BMI']].values
+    y = male_df['Y染色体浓度'].values
+
+    # 数据集划分
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # 3. 建立 GPR 模型
+    # 关键：数据标准化对 GPR 至关重要
+    scaler_X = StandardScaler()
+    X_train_scaled = scaler_X.fit_transform(X_train)
+    X_test_scaled = scaler_X.transform(X_test)
+
+    # Kernel设计: RBF(趋势) + WhiteKernel(噪声)
+    # 初始 length_scale 设为 1.0 (因为数据已标准化)
+    kernel = C(1.0) * RBF(length_scale=1.0) + WhiteKernel(noise_level=0.1)
+
+    # 训练 (n_restarts_optimizer=0 以节省时间，实际建模建议设为 5-10)
+    gpr = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=0, random_state=42)
+    gpr.fit(X_train_scaled, y_train)
+
+    # 4. 建立对比模型 (交互项回归)
+    poly = make_pipeline(
+        PolynomialFeatures(degree=2, interaction_only=True, include_bias=False), 
+        LinearRegression()
+    )
+    poly.fit(X_train, y_train)
+
+    # 5. 评估
+    y_pred_gpr, y_std = gpr.predict(X_test_scaled, return_std=True)
+    y_pred_poly = poly.predict(X_test)
+
+    print("=== 模型性能大比拼 (Test Set) ===")
+    print(f"GPR RMSE: {np.sqrt(mean_squared_error(y_test, y_pred_gpr)):.6f}")
+    print(f"Poly RMSE: {np.sqrt(mean_squared_error(y_test, y_pred_poly)):.6f}")
+    print(f"GPR R2: {r2_score(y_test, y_pred_gpr):.4f}")
+    print(f"Poly R2: {r2_score(y_test, y_pred_poly):.4f}")
+    print(f"\nGPR Learned Kernel Params: {gpr.kernel_}")
+
+except Exception as e:
+    print(f"Error: {e}")