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"""
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文件名: detect_num.py
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推理部分代码
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作者: 王春林
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创建日期: 2023年7月14日
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最后修改日期: 2023年7月18日
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版本号: 1.0.0
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"""
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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import torch
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from torch import nn
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from torch.utils.data import DataLoader, TensorDataset
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# 检查GPU是否可用
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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#device = torch.device("cpu")
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# 读取特征和标签
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data = pd.read_excel('feature_label.xlsx')
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# 获取编号列
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sample_ids = data['编号'].values
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# 以下是你的特征名
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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"]
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# 将特征和标签分开，并做归一化处理
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X = data[feature_names].values
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y = data['label'].values - 1 # 将标签从1-4转换为0-3
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scaler = MinMaxScaler()
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X = scaler.fit_transform(X)
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# 定义 MLP 网络
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class MLP(nn.Module):
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def __init__(self):
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super(MLP, self).__init__()
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self.model = nn.Sequential(
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nn.Linear(17, 32), # 输入层
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nn.ReLU(), # 激活函数
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nn.Linear(32, 128), # 隐藏层
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nn.ReLU(), # 激活函数
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nn.Linear(128, 32), # 隐藏层
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nn.ReLU(), # 激活函数
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nn.Linear(32, 4), # 输出层，4个类别
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)
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def forward(self, x):
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return self.model(x)
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# 加载模型
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model = MLP().to(device)
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model.load_state_dict(torch.load('Psychological_Classification_4Classes.pth'))
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model.eval()
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# 创建数据加载器
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dataset = TensorDataset(torch.from_numpy(X).float().to(device), torch.from_numpy(y).long().to(device))
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loader = DataLoader(dataset, batch_size=32)
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# 推理
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corrects = 0
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sample_index = 0
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for inputs, targets in loader:
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outputs = model(inputs)
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_, preds = torch.max(outputs, 1)
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corrects += torch.sum(preds == targets.data)
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# 打印每个样本的推理结果
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for i in range(len(inputs)):
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print(f'Sample ID: {sample_ids[sample_index]} | Target: {targets[i]} | Prediction: {preds[i]} (-1 in excel)')
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sample_index += 1
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# 计算整体推理的正确率
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accuracy = corrects.double().cpu() / len(loader.dataset)
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print(f'Overall Accuracy: {accuracy:.4f}')
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# ...（之前的代码）
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# 创建文件来存储预测结果为0和1的学号
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file_1st_warning = open("一级预警.txt", "w", encoding="utf-8")
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file_2nd_warning = open("二级预警.txt", "w", encoding="utf-8")
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# 初始化每个类别的计数器
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class_counts = {0: 0, 1: 0, 2: 0, 3: 0}
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class_corrects = {0: 0, 1: 0, 2: 0, 3: 0}
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# 进行推理
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corrects = 0
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sample_index = 0
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for inputs, targets in loader:
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outputs = model(inputs)
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_, preds = torch.max(outputs, 1)
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corrects += torch.sum(preds == targets.data)
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# 记录预测结果为0和1的学号
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for i in range(len(inputs)):
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if preds[i] == 0:
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file_1st_warning.write(f"{sample_ids[sample_index]}\n")
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elif preds[i] == 1:
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file_2nd_warning.write(f"{sample_ids[sample_index]}\n")
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sample_index += 1
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# 更新每个类别的计数器和正确计数器
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for i in range(len(targets)):
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class_counts[targets[i].item()] += 1
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if preds[i] == targets[i]:
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class_corrects[targets[i].item()] += 1
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# 关闭文件
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file_1st_warning.close()
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file_2nd_warning.close()
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# 计算整体准确率
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accuracy = corrects.double().cpu() / len(loader.dataset)
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print(f'整体准确率: {accuracy:.4f}')
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# 打印每个类别的信息
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for class_idx in range(4):
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class_accuracy = class_corrects[class_idx] / class_counts[class_idx]
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print(f'类别 {class_idx + 1} | 预测数量: {class_counts[class_idx]} | 准确率: {class_accuracy:.4f}')
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