注释掉下线的任务

ad_threshold_update_task.sh

@@ -3,14 +3,14 @@ echo $ROV_OFFLINE_ENV
 if [[ $ROV_OFFLINE_ENV == 'test' ]]; then
     cd /data2/rov-offline &&
     /root/anaconda3/bin/python /data2/rov-offline/ad_users_data_update.py &&
-    /root/anaconda3/bin/python /data2/rov-offline/ad_users_data_update_new.py &&
+#    /root/anaconda3/bin/python /data2/rov-offline/ad_users_data_update_new.py &&
     /root/anaconda3/bin/python /data2/rov-offline/ad_user_data_with_out_update.py &&
     /root/anaconda3/bin/python /data2/rov-offline/ad_video_data_update.py &&
     /root/anaconda3/bin/python /data2/rov-offline/ad_user_video_predict.py
 elif [[ $ROV_OFFLINE_ENV == 'pro' ]]; then
     cd /data/rov-offline &&
     /root/anaconda3/bin/python /data/rov-offline/ad_users_data_update.py &&
-    /root/anaconda3/bin/python /data/rov-offline/ad_users_data_update_new.py &&
+#    /root/anaconda3/bin/python /data/rov-offline/ad_users_data_update_new.py &&
     /root/anaconda3/bin/python /data/rov-offline/ad_user_data_with_out_update.py &&
     /root/anaconda3/bin/python /data/rov-offline/ad_video_data_update.py &&
     /root/anaconda3/bin/python /data/rov-offline/ad_user_video_predict.py

celibration/a.txt

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celibration/test.py

@@ -0,0 +1,77 @@
+import numpy as np
+import pandas as pd
+from scipy.optimize import minimize
+from sklearn.metrics import mean_squared_error
+
+# 1. 读取数据
+# 假设文件名为 "data.csv"，有两列：预测值 (pred) 和真实值 (label)
+file_path = "a.txt"
+y_pred = []
+y_true = []
+with open(file_path, 'r') as f:
+    for line in f.readlines():
+        lines = line.strip().split("\t")
+        y_pred.append(float(lines[0]))
+        y_true.append(float(lines[1]))
+y_pred = np.array(y_pred)
+y_true = np.array(y_true)
+# 2. 定义对数-线性缩放函数
+def logistic_scaling(pred, a, b):
+    """
+    对数-线性缩放函数
+    :param pred: 原始预测值 (范围 0 到 1)
+    :param a: 缩放参数
+    :param b: 偏移参数
+    :return: 校准后的预测值
+    """
+    # logit_pred = np.log(pred / (1 - pred))  # 计算 logit
+    # calibrated_logit = a * logit_pred + b  # 线性变换
+    # return 1 / (1 + np.exp(-calibrated_logit))  # 通过 Sigmoid 归一化
+    return np.power(pred + a, b)
+
+# 3. 定义优化目标函数
+def objective(params, y_pred, y_true):
+    """
+    目标函数，用于优化 a 和 b
+    :param params: 待优化的参数 (a, b)
+    :param y_pred: 原始预测值
+    :param y_true: 真实值 (连续值)
+    :return: MSE 损失
+    """
+    a, b = params
+    # 使用 Logistic Scaling 校准预测值
+    calibrated_pred = logistic_scaling(y_pred, a, b)
+    # 计算均方误差 (MSE)
+    return mean_squared_error(y_true, calibrated_pred)
+
+# 4. 优化参数
+# 初始化参数 a=1, b=0
+initial_params = [0.0, 1.0]
+
+# 使用 scipy.optimize.minimize 进行优化
+result = minimize(objective, initial_params, args=(y_pred, y_true), method='L-BFGS-B')
+
+# 获取优化后的参数 a 和 b
+optimized_a, optimized_b = result.x
+print(f"Optimized a: {optimized_a}, Optimized b: {optimized_b}")
+
+# 5. 应用校准模型
+calibrated_preds = logistic_scaling(y_pred, optimized_a, optimized_b)
+
+
+# 7. 验证效果
+# 计算校准前后的 MSE
+original_mse = mean_squared_error(y_true, y_pred)
+calibrated_mse = mean_squared_error(y_true, calibrated_preds)
+
+print(f"Original MSE: {original_mse:.6f}")
+print(f"Calibrated MSE: {calibrated_mse:.6f}")
+
+def calibration_function(p, x0=0.07, k1=15, k2=15, p_max=0.13):
+    # Sigmoid 平滑校正
+    sigmoid_part = 1 / (1 + np.exp(-k1 * (p - x0)))
+    # 线性与非线性的平滑过渡
+    calibrated = p + sigmoid_part / (1 + np.exp(-k2 * (p - x0))) * (p_max - p)
+    return calibrated
+
+print(calibration_function(0.00001))