update

Author: Kuxry

nerualnet.py

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+import pandas as pd
+import numpy as np
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestRegressor
+import xgboost as xgb
+import lightgbm as lgb
+from sklearn.metrics import r2_score
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+
+# 加载数据
+file_path = 'Minlost.csv'  # 请确保文件路径正确
+df = pd.read_csv(file_path)
+
+# 初始化结果存储
+r2_scores = {"DecisionTree": [], "RandomForest": [], "XGBoost": [], "LightGBM": []}
+
+# 遍历每个站点进行训练和预测
+grouped = df.groupby("PortID")
+for port_id, group in grouped:
+    print(f"Training for PortID {port_id}...")
+
+    # 准备数据
+    X = group[['Available bikes']].values
+    y = group['機会損失'].values
+
+    # 数据分割
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # 决策树模型
+    dt_model = DecisionTreeRegressor(random_state=42)
+    dt_model.fit(X_train, y_train)
+    dt_y_pred = dt_model.predict(X_test)
+    dt_r2 = r2_score(y_test, dt_y_pred)
+    r2_scores["DecisionTree"].append((port_id, dt_r2))
+
+    # 随机森林模型
+    rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
+    rf_model.fit(X_train, y_train)
+    rf_y_pred = rf_model.predict(X_test)
+    rf_r2 = r2_score(y_test, rf_y_pred)
+    r2_scores["RandomForest"].append((port_id, rf_r2))
+
+    # XGBoost 模型
+    xgb_model = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=100, random_state=42)
+    xgb_model.fit(X_train, y_train)
+    xgb_y_pred = xgb_model.predict(X_test)
+    xgb_r2 = r2_score(y_test, xgb_y_pred)
+    r2_scores["XGBoost"].append((port_id, xgb_r2))
+
+    # LightGBM 模型
+    lgb_model = lgb.LGBMRegressor(n_estimators=100, random_state=42)
+    lgb_model.fit(X_train, y_train)
+    lgb_y_pred = lgb_model.predict(X_test)
+    lgb_r2 = r2_score(y_test, lgb_y_pred)
+    r2_scores["LightGBM"].append((port_id, lgb_r2))
+
+    print(f"PortID {port_id} R² (DecisionTree): {dt_r2:.2f}")
+    print(f"PortID {port_id} R² (RandomForest): {rf_r2:.2f}")
+    print(f"PortID {port_id} R² (XGBoost): {xgb_r2:.2f}")
+    print(f"PortID {port_id} R² (LightGBM): {lgb_r2:.2f}")
+
+# 转换 R² 结果为 DataFrame
+dt_scores_df = pd.DataFrame(r2_scores["DecisionTree"], columns=["PortID", "R2"])
+rf_scores_df = pd.DataFrame(r2_scores["RandomForest"], columns=["PortID", "R2"])
+xgb_scores_df = pd.DataFrame(r2_scores["XGBoost"], columns=["PortID", "R2"])
+lgb_scores_df = pd.DataFrame(r2_scores["LightGBM"], columns=["PortID", "R2"])
+
+# 可视化 R² 分数
+plt.figure(figsize=(15, 10))
+models = ["DecisionTree", "RandomForest", "XGBoost", "LightGBM"]
+colors = ["skyblue", "orange", "green", "purple"]
+for i, (model, color) in enumerate(zip(models, colors)):
+    scores_df = pd.DataFrame(r2_scores[model], columns=["PortID", "R2"])
+    plt.bar(
+        [x + i * 0.2 for x in range(len(scores_df))],  # 设置偏移量以避免柱状图重叠
+        scores_df["R2"],
+        width=0.2,
+        label=model,
+        color=color,
+        align="center"
+    )
+
+plt.xlabel("PortID")
+plt.ylabel("R² Score")
+plt.title("R² Scores for Each PortID by Model")
+plt.legend()
+plt.xticks(rotation=90)
+plt.tight_layout()
+plt.show()