"""
文件名: detect_num.py

推理部分代码

作者: 王春林
创建日期: 2023年7月14日
最后修改日期: 2023年7月18日
版本号: 1.0.0

"""
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

# 检查GPU是否可用
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#device = torch.device("cpu")

# 读取特征和标签
data = pd.read_excel('feature_label.xlsx')

# 获取编号列
sample_ids = data['编号'].values

# 以下是你的特征名
feature_names = ["躯体化", "强迫症状", "人际关系敏感", "抑郁", "焦虑", "敌对", "恐怖", "偏执", "精神病性", "其他", "父亲教养方式数字化", "母亲教养方式数字化", "自评家庭经济条件数字化", "有无心理治疗（咨询）史数字化", "出勤情况数字化", "学业情况数字化", "权重数字化值"]

# 将特征和标签分开，并做归一化处理
X = data[feature_names].values
y = data['label'].values - 1  # 将标签从1-4转换为0-3

scaler = MinMaxScaler()
X = scaler.fit_transform(X)

# 定义 MLP 网络
class MLP(nn.Module):
    def __init__(self):
        super(MLP, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(17, 32),  # 输入层
            nn.ReLU(),  # 激活函数
            nn.Linear(32, 128),  # 隐藏层
            nn.ReLU(),  # 激活函数
            nn.Linear(128, 32),  # 隐藏层
            nn.ReLU(),  # 激活函数
            nn.Linear(32, 4),  # 输出层，4个类别
        )

    def forward(self, x):
        return self.model(x)

# 加载模型
model = MLP().to(device)
model.load_state_dict(torch.load('Psychological_Classification_4Classes.pth'))
model.eval()

# 创建数据加载器
dataset = TensorDataset(torch.from_numpy(X).float().to(device), torch.from_numpy(y).long().to(device))
loader = DataLoader(dataset, batch_size=32)

# 推理
corrects = 0
sample_index = 0
for inputs, targets in loader:
    outputs = model(inputs)
    _, preds = torch.max(outputs, 1)
    corrects += torch.sum(preds == targets.data)

    # 打印每个样本的推理结果
    for i in range(len(inputs)):
        print(f'Sample ID: {sample_ids[sample_index]} | Target: {targets[i]} | Prediction: {preds[i]} (-1 in excel)')
        sample_index += 1

# 计算整体推理的正确率
accuracy = corrects.double().cpu() / len(loader.dataset)
print(f'Overall Accuracy: {accuracy:.4f}')

# ...（之前的代码）

# 创建文件来存储预测结果为0和1的学号
file_1st_warning = open("一级预警.txt", "w", encoding="utf-8")
file_2nd_warning = open("二级预警.txt", "w", encoding="utf-8")

# 初始化每个类别的计数器
class_counts = {0: 0, 1: 0, 2: 0, 3: 0}
class_corrects = {0: 0, 1: 0, 2: 0, 3: 0}

# 进行推理
corrects = 0
sample_index = 0
for inputs, targets in loader:
    outputs = model(inputs)
    _, preds = torch.max(outputs, 1)
    corrects += torch.sum(preds == targets.data)

    # 记录预测结果为0和1的学号
    for i in range(len(inputs)):
        if preds[i] == 0:
            file_1st_warning.write(f"{sample_ids[sample_index]}\n")
        elif preds[i] == 1:
            file_2nd_warning.write(f"{sample_ids[sample_index]}\n")
        sample_index += 1

    # 更新每个类别的计数器和正确计数器
    for i in range(len(targets)):
        class_counts[targets[i].item()] += 1
        if preds[i] == targets[i]:
            class_corrects[targets[i].item()] += 1

# 关闭文件
file_1st_warning.close()
file_2nd_warning.close()

# 计算整体准确率
accuracy = corrects.double().cpu() / len(loader.dataset)
print(f'整体准确率: {accuracy:.4f}')

# 打印每个类别的信息
for class_idx in range(4):
    class_accuracy = class_corrects[class_idx] / class_counts[class_idx]
    print(f'类别 {class_idx + 1} | 预测数量: {class_counts[class_idx]} | 准确率: {class_accuracy:.4f}')