编写了数据处理已经预训练的基础代码

dataset/load_dataset.py

@@ -0,0 +1,40 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
+import torch
+
+# 加载数据
+df = pd.read_csv("dataset/processed_new_data.csv")
+# 准备数据集
+def prepare_dataset(df, tokenizer, max_length=512):
+    input_ids = []
+    attention_masks = []
+    labels = []
+
+    for _, row in df.iterrows():
+        encoded = tokenizer.encode_plus(
+            row['Description'],
+            add_special_tokens=True,
+            max_length=max_length,
+            padding='max_length',
+            truncation=True,
+            return_attention_mask=True,
+            return_tensors='pt',
+        )
+        input_ids.append(encoded['input_ids'])
+        attention_masks.append(encoded['attention_mask'])
+        labels.append([row['SAS_Class'], row['SDS_Class']])
+
+    input_ids = torch.cat(input_ids, dim=0)
+    attention_masks = torch.cat(attention_masks, dim=0)
+    labels = torch.tensor(labels, dtype=torch.float)
+
+    return TensorDataset(input_ids, attention_masks, labels)
+
+# 分割数据集
+train_df, val_df = train_test_split(df, test_size=0.1)  # 以90%训练，10%验证的比例分割数据集
+
+
+# 创建DataLoader
+
+

dataset/new_data.csv

@@ -0,0 +1,878 @@
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dataset/process_scripts_transfer.py

@@ -0,0 +1,59 @@
+# 重新加载数据集，现在我们有了列名的准确英文对照和如何处理SAS和SDS列的具体信息
+import pandas as pd
+
+columns_english = ["Gender", "Education", "Hemoglobin", "Total_Protein", "Albumin", "HDL", "AST", "Direct_Bilirubin",
+                   "Sleep", "SF-36", "SAS", "SDS"]
+file_path = "new_data.csv"
+# 由于文件未变，直接使用新列名重新加载数据
+data = pd.read_csv(file_path, encoding='ISO-8859-1', names=columns_english, header=0)
+# 定义学历对应的英文描述
+education_levels = {
+    0: "elementary school",
+    1: "junior high school",
+    2: "high school/diploma",
+    3: "bachelor/master/phd"
+}
+# 定义性别对应的英文描述
+gender_dict = {
+    0: "male",
+    1: "female"
+}
+
+
+# 定义SAS和SDS得分的分类
+def classify_sas_sds(score, threshold, mild, moderate, severe):
+    if score < threshold:
+        return 0  # Normal
+    elif threshold <= score < mild:
+        return 1  # Mild
+    elif mild <= score < moderate:
+        return 2  # Moderate
+    elif score >= severe:
+        return 3  # Severe
+
+
+# 转换每行数据为自然语言描述
+descriptions = ""
+# 根据要求，将创建一个新的DataFrame，其中包括更新后的描述和单独的sas_class和sds_class列
+new_data = pd.DataFrame({
+    "Description": descriptions,
+    "SAS_Class": [classify_sas_sds(row["SAS"], 50, 59, 69, 70) for _, row in data.iterrows()],
+    "SDS_Class": [classify_sas_sds(row["SDS"], 53, 62, 72, 73) for _, row in data.iterrows()]
+})
+
+# 生成不包含sas_class和sds_class标签的描述
+new_descriptions = []
+for _, row in data.iterrows():
+    gender = gender_dict[row["Gender"]]
+    education = education_levels[row["Education"]]
+    description = f"A patient of gender {gender} and educational background {education}, has hemoglobin {row['Hemoglobin']}g/L, total protein {row['Total_Protein']}g/L, albumin {row['Albumin']}g/L, HDL {row['HDL']}mmol/L, AST {row['AST']}U/L, direct bilirubin {row['Direct_Bilirubin']}μmol/L, sleeps {row['Sleep']} hours, and has an SF-36 score of {row['SF-36']}."
+    new_descriptions.append(description)
+
+# 更新DataFrame中的描述列
+new_data["Description"] = new_descriptions
+
+# 将新的DataFrame保存为CSV文件
+new_csv_path = 'processed_new_data.csv'
+new_data.to_csv(new_csv_path, index=False)
+
+print(new_csv_path)

new.py

@@ -0,0 +1,54 @@
+from transformers import AdamW, get_linear_schedule_with_warmup, AutoTokenizer, AutoModelForSequenceClassification
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
+from torch.nn import CrossEntropyLoss
+import torch
+from sklearn.model_selection import train_test_split
+
+from dataset.load_dataset import df, prepare_dataset
+
+epochs = 10
+tokenizer = AutoTokenizer.from_pretrained(
+    "pretrained_models/Bio_ClinicalBERT-finetuned-medicalcondition")  # 用于将文本转换为模型所需输入格式的tokenizer
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 创建一个device对象，如果有可用的GPU就使用它，否则使用CPU
+
+# 分割数据集
+train_df, val_df = train_test_split(df, test_size=0.1)  # 以90%训练，10%验证的比例分割数据集
+
+# 准备训练和验证数据集
+train_dataset = prepare_dataset(train_df, tokenizer)
+val_dataset = prepare_dataset(val_df, tokenizer)
+# 现在train_dataloader和validation_dataloader已准备好，可用于模型训练和验证
+train_dataloader = DataLoader(train_dataset, sampler=RandomSampler(train_dataset), batch_size=64)
+validation_dataloader = DataLoader(val_dataset, sampler=SequentialSampler(val_dataset), batch_size=64)
+
+model = AutoModelForSequenceClassification.from_pretrained(
+    "pretrained_models/Bio_ClinicalBERT-finetuned-medicalcondition").to(device)
+input = tokenizer("I love using transformers for natural language processing.", return_tensors="pt")
+# 使用模型进行预测
+# with torch.no_grad():
+#     logits = model(**input).logits
+# 解析预测结果
+# predicted_class_id = logits.argmax().item()
+# print(f"Predicted class id: {predicted_class_id}")
+# 准备优化器和学习率调度器
+optimizer = AdamW(model.parameters(), lr=2e-5, eps=1e-8)
+total_steps = len(train_dataloader) * epochs  # epochs是您想要训练的轮数
+scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps)
+
+# 微调模型
+model.train()
+for epoch in range(epochs):  # 迭代多个epoch
+    for step, batch in enumerate(train_dataloader):
+        # 将数据加载到GPU
+        batch = tuple(t.to(device) for t in batch)
+        b_input_ids, b_input_mask, b_labels = batch
+        model.zero_grad()
+        # 前向传播
+        outputs = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask, labels=b_labels)
+        loss = outputs.loss
+        logits = outputs.logits
+        # 反向传播
+        loss.backward()
+        optimizer.step()
+        scheduler.step()
+# 评估阶段省略，但在实际应用中非常重要

test.py

@@ -0,0 +1,18 @@
+# Load model directly
+import ast
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+# assets_path = cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="download")
+# something_path = assets_path / "config.json" # Do anything you like in your assets folder !
+
+tokenizer = AutoTokenizer.from_pretrained("pretrained_models/Bio_ClinicalBERT-finetuned-medicalcondition")
+model = AutoModelForSequenceClassification.from_pretrained("pretrained_models/Bio_ClinicalBERT-finetuned-medicalcondition")
+input=tokenizer("I love using transformers for natural language processing.", return_tensors="pt")
+
+# 使用模型进行预测
+with torch.no_grad():
+    logits = model(**input).logits
+# 解析预测结果
+predicted_class_id = logits.argmax().item()
+print(f"Predicted class id: {predicted_class_id}")

test2.py

@@ -0,0 +1,23 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# 指定预训练模型
+model_name = "bert-base-uncased"
+
+# 加载分词器和模型
+tokenizer = AutoTokenizer.from_pretrained(model_name,force_download=True, resume_download=False)
+model = AutoModelForSequenceClassification.from_pretrained(model_name,force_download=True, resume_download=False)
+
+# 要进行分类的文本
+text = "I love using transformers for natural language processing."
+
+# 使用分词器处理文本
+inputs = tokenizer(text, return_tensors="pt")
+
+# 使用模型进行预测
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+# 解析预测结果
+predicted_class_id = logits.argmax().item()
+print(f"Predicted class id: {predicted_class_id}")

train.py

@@ -0,0 +1,36 @@
+import sagemaker
+import boto3
+from sagemaker.huggingface import HuggingFace
+
+try:
+    role = sagemaker.get_execution_role()
+except ValueError:
+    iam = boto3.client('iam')
+    role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']
+
+hyperparameters = {
+    'model_name_or_path': 'emilyalsentzer/Bio_ClinicalBERT',
+    'output_dir': '/opt/ml/model'
+    # add your remaining hyperparameters
+    # more info here https://github.com/huggingface/transformers/tree/v4.37.0/examples/pytorch/text-classification
+}
+
+# git configuration to download our fine-tuning script
+git_config = {'repo': 'https://github.com/huggingface/transformers.git', 'branch': 'v4.37.0'}
+
+# creates Hugging Face estimator
+huggingface_estimator = HuggingFace(
+    entry_point='run_glue.py',
+    source_dir='./examples/pytorch/text-classification',
+    instance_type='ml.p3.2xlarge',
+    instance_count=1,
+    role=role,
+    git_config=git_config,
+    transformers_version='4.37.0',
+    pytorch_version='2.1.0',
+    py_version='py310',
+    hyperparameters=hyperparameters
+)
+
+# starting the train job
+huggingface_estimator.fit()