完成--完成读取csv文件功能
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@ -14,6 +14,13 @@ class ProcessRequest(BaseModel):
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feature_methods: List[Dict]
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split_params: Dict[str, float]
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class CSVRequest(BaseModel):
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data_path: str
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head: int = 5
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tail: int = 5
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info: bool = True
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describe: bool = True
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@router.get("/preprocessing/methods")
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async def get_preprocessing_methods():
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"""获取数据预处理方法列表"""
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@ -77,3 +84,17 @@ async def get_datasets():
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status_code=500,
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detail=f"获取数据集列表失败: {str(e)}"
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)
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@router.post("/csv")
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async def read_csv(request: CSVRequest):
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"""读取CSV文件并展示"""
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result = data_manager.read_csv(
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data_path=request.data_path,
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head=request.head,
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tail=request.tail,
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info=request.info,
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describe=request.describe
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)
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if result['status'] == 'error':
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raise HTTPException(status_code=500, detail=result['message'])
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return result
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@ -271,6 +271,83 @@ Response:
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]
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}
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```
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### 1.7 读取csv文件并展示
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```http
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POST /data/csv
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Content-Type: application/json
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Request:
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{
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"data_path": "dataset/dataset_raw/data.csv",
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"head": 5, # 可选，默认显示前5行
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"tail": 5, # 可选，默认显示后5行
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"info": true, # 可选，是否显示数据集信息
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"describe": true # 可选，是否显示数据统计信息
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}
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Response:
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{
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"status": "success",
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"data": {
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"head": [ # 数据集前几行
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{
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"column1": "value1",
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"column2": "value2",
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...
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},
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...
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],
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"tail": [ # 数据集后几行
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{
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"column1": "value1",
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"column2": "value2",
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...
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},
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...
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],
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"info": { # 数据集基本信息
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"rows": 1000,
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"columns": 10,
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"column_types": {
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"column1": "int64",
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"column2": "float64",
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"column3": "object",
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...
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},
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"memory_usage": "80.5 KB",
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"missing_values": {
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"column1": 0,
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"column2": 5,
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...
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}
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},
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"describe": { # 数据统计信息
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"column1": {
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"count": 1000,
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"mean": 45.3,
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"std": 12.5,
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"min": 0,
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"25%": 35.0,
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"50%": 45.0,
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"75%": 55.0,
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"max": 100.0
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},
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...
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}
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}
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}
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Error Response:
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{
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"status": "error",
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"message": "读取CSV文件失败",
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"details": {
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"error_type": "FileNotFoundError",
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"error_message": "File not found: dataset/dataset_raw/data.csv"
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}
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}
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```
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## 2. 模型接口
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### 2.1 获取可用模型列表
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@ -646,4 +646,105 @@ class DataManager:
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self.logger.info("获取处理好的数据集")
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# print("可用数据集", back)
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return back
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return back
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def read_csv(
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self,
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data_path: str,
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head: int = 5,
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tail: int = 5,
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info: bool = True,
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describe: bool = True
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) -> Dict:
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"""
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读取并展示CSV文件内容
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Args:
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data_path: CSV文件路径
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head: 显示前几行
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tail: 显示后几行
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info: 是否显示数据集信息
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describe: 是否显示数据统计信息
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Returns:
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数据集信息字典
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"""
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try:
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self.logger.info(f"Reading CSV file: {data_path}")
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# 读取CSV文件
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df = pd.read_csv(data_path)
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result = {
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"status": "success",
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"data": {}
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}
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# 获取前几行数据
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if head > 0:
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result["data"]["head"] = df.head(head).to_dict('records')
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# 获取后几行数据
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if tail > 0:
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result["data"]["tail"] = df.tail(tail).to_dict('records')
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# 获取数据集信息
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if info:
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# 获取每列的缺失值数量
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missing_values = df.isnull().sum().to_dict()
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# 获取每列的数据类型
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column_types = df.dtypes.astype(str).to_dict()
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# 计算内存使用
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memory_usage = df.memory_usage(deep=True).sum()
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if memory_usage < 1024:
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memory_str = f"{memory_usage} B"
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elif memory_usage < 1024 * 1024:
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memory_str = f"{memory_usage/1024:.1f} KB"
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else:
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memory_str = f"{memory_usage/(1024*1024):.1f} MB"
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result["data"]["info"] = {
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"rows": len(df),
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"columns": len(df.columns),
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"column_types": column_types,
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"memory_usage": memory_str,
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"missing_values": missing_values
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}
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# 获取数据统计信息
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if describe:
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# 对数值列进行统计描述
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numeric_describe = df.describe().to_dict()
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# 对分类列进行统计描述
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categorical_columns = df.select_dtypes(include=['object']).columns
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categorical_describe = {}
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for col in categorical_columns:
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categorical_describe[col] = {
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"count": df[col].count(),
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"unique": df[col].nunique(),
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"top": df[col].mode()[0] if not df[col].mode().empty else None,
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"freq": df[col].value_counts().iloc[0] if not df[col].value_counts().empty else 0
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}
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result["data"]["describe"] = {
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**numeric_describe,
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**categorical_describe
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}
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self.logger.info(f"Successfully read CSV file: {data_path}")
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return result
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except Exception as e:
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error_msg = f"Error reading CSV file: {str(e)}"
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self.logger.error(error_msg)
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return {
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"status": "error",
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"message": "读取CSV文件失败",
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"details": {
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"error_type": type(e).__name__,
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"error_message": str(e)
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}
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}
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@ -0,0 +1,20 @@
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artifact_path: model
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flavors:
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python_function:
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env:
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conda: conda.yaml
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virtualenv: python_env.yaml
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loader_module: mlflow.sklearn
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model_path: model.pkl
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predict_fn: predict
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python_version: 3.9.19
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sklearn:
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code: null
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pickled_model: model.pkl
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serialization_format: cloudpickle
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sklearn_version: 1.5.2
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mlflow_version: 2.20.1
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model_size_bytes: 106353
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model_uuid: 978934ba28b44b89aa72d5ad0a472e5e
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run_id: 7a199919f0dc4e929257dd628d0ea068
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utc_time_created: '2025-02-25 01:35:56.104998'
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@ -0,0 +1,15 @@
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channels:
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- conda-forge
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dependencies:
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- python=3.9.19
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- pip<=24.0
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- pip:
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- mlflow==2.20.1
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- cloudpickle==3.1.0
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- numpy==1.26.4
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- pandas==2.2.2
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- psutil==6.0.0
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- scikit-learn==1.5.2
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- scipy==1.13.1
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- xgboost==2.1.4
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name: mlflow-env
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Binary file not shown.
@ -0,0 +1,7 @@
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python: 3.9.19
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build_dependencies:
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dependencies:
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- -r requirements.txt
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@ -0,0 +1,8 @@
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mlflow==2.20.1
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cloudpickle==3.1.0
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numpy==1.26.4
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pandas==2.2.2
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psutil==6.0.0
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scikit-learn==1.5.2
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@ -0,0 +1,15 @@
|
||||
channels:
|
||||
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||||
dependencies:
|
||||
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|
||||
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||||
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- cloudpickle==3.1.0
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||||
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||||
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||||
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||||
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name: mlflow-env
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python: 3.9.19
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||||
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dependencies:
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- -r requirements.txt
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@ -0,0 +1,8 @@
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mlflow==2.20.1
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cloudpickle==3.1.0
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numpy==1.26.4
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||||
pandas==2.2.2
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||||
psutil==6.0.0
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||||
scikit-learn==1.5.2
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flavors:
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env:
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conda: conda.yaml
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loader_module: mlflow.sklearn
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model_path: model.pkl
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predict_fn: predict
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python_version: 3.9.19
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||||
sklearn:
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||||
@ -0,0 +1,15 @@
|
||||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.9.19
|
||||
- pip<=24.0
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||||
- pip:
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||||
- numpy==1.26.4
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||||
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|
||||
- psutil==6.0.0
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||||
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||||
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||||
name: mlflow-env
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||||
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@ -0,0 +1,7 @@
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||||
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||||
dependencies:
|
||||
- -r requirements.txt
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@ -0,0 +1,8 @@
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mlflow==2.20.1
|
||||
cloudpickle==3.1.0
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||||
numpy==1.26.4
|
||||
pandas==2.2.2
|
||||
psutil==6.0.0
|
||||
scikit-learn==1.5.2
|
||||
scipy==1.13.1
|
||||
xgboost==2.1.4
|
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@ -0,0 +1,20 @@
|
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artifact_path: model
|
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flavors:
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python_function:
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env:
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conda: conda.yaml
|
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virtualenv: python_env.yaml
|
||||
loader_module: mlflow.sklearn
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||||
model_path: model.pkl
|
||||
predict_fn: predict
|
||||
python_version: 3.9.19
|
||||
sklearn:
|
||||
code: null
|
||||
pickled_model: model.pkl
|
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serialization_format: cloudpickle
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|
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@ -0,0 +1,15 @@
|
||||
channels:
|
||||
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|
||||
dependencies:
|
||||
- python=3.9.19
|
||||
- pip<=24.0
|
||||
- pip:
|
||||
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|
||||
- cloudpickle==3.1.0
|
||||
- numpy==1.26.4
|
||||
- pandas==2.2.2
|
||||
- psutil==6.0.0
|
||||
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|
||||
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|
||||
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|
||||
name: mlflow-env
|
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@ -0,0 +1,7 @@
|
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python: 3.9.19
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dependencies:
|
||||
- -r requirements.txt
|
||||
@ -0,0 +1,8 @@
|
||||
mlflow==2.20.1
|
||||
cloudpickle==3.1.0
|
||||
numpy==1.26.4
|
||||
pandas==2.2.2
|
||||
psutil==6.0.0
|
||||
scikit-learn==1.5.2
|
||||
scipy==1.13.1
|
||||
xgboost==2.1.4
|
||||
@ -0,0 +1,15 @@
|
||||
artifact_uri: mlflow-artifacts:/433321862082712659/7a199919f0dc4e929257dd628d0ea068/artifacts
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||||
end_time: 1740447358528
|
||||
entry_point_name: ''
|
||||
experiment_id: '433321862082712659'
|
||||
lifecycle_stage: active
|
||||
run_id: 7a199919f0dc4e929257dd628d0ea068
|
||||
run_name: grandiose-seal-133
|
||||
run_uuid: 7a199919f0dc4e929257dd628d0ea068
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||||
source_name: ''
|
||||
source_type: 4
|
||||
source_version: ''
|
||||
start_time: 1740447355512
|
||||
status: 3
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||||
tags: []
|
||||
user_id: admin-root
|
||||
@ -0,0 +1 @@
|
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1740447356081 0.9902912621359223 0
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@ -0,0 +1 @@
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1740447356094 0.990328791886068 0
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1740447356086 0.9905768132495717 0
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1740447356090 0.9902912621359223 0
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@ -0,0 +1 @@
|
||||
1740447356098 0.9928571428571429 0
|
||||
@ -0,0 +1 @@
|
||||
['计算效率高，支持并行计算。', '具有内置的缺失值处理能力。']
|
||||
@ -0,0 +1 @@
|
||||
XGBClassifier
|
||||
@ -0,0 +1 @@
|
||||
/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629
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@ -0,0 +1 @@
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||||
['参数较多，调优较复杂。']
|
||||
@ -0,0 +1 @@
|
||||
0.1
|
||||
@ -0,0 +1 @@
|
||||
6
|
||||
@ -0,0 +1 @@
|
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100
|
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@ -0,0 +1 @@
|
||||
XGBoost（Extreme Gradient Boosting）是一种基于梯度提升树（GBDT）的改进算法，具有更强的正则化和并行处理能力。
|
||||
@ -0,0 +1 @@
|
||||
42
|
||||
@ -0,0 +1 @@
|
||||
classification
|
||||
@ -0,0 +1 @@
|
||||
[{"run_id": "7a199919f0dc4e929257dd628d0ea068", "artifact_path": "model", "utc_time_created": "2025-02-25 01:35:56.104998", "model_uuid": "978934ba28b44b89aa72d5ad0a472e5e", "flavors": {"python_function": {"model_path": "model.pkl", "predict_fn": "predict", "loader_module": "mlflow.sklearn", "python_version": "3.9.19", "env": {"conda": "conda.yaml", "virtualenv": "python_env.yaml"}}, "sklearn": {"pickled_model": "model.pkl", "sklearn_version": "1.5.2", "serialization_format": "cloudpickle", "code": null}}}]
|
||||
@ -0,0 +1 @@
|
||||
grandiose-seal-133
|
||||
@ -0,0 +1 @@
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||||
aede371f38cbfd1418bd074b929dd9bd6ea64a22
|
||||
@ -0,0 +1 @@
|
||||
/home/admin-root/haotian/MLPlatform/example_model_manager.py
|
||||
@ -0,0 +1 @@
|
||||
LOCAL
|
||||
@ -0,0 +1 @@
|
||||
admin-root
|
||||
@ -0,0 +1,15 @@
|
||||
artifact_uri: mlflow-artifacts:/433321862082712659/9f9c80e8e9634eb9b09978a685695619/artifacts
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||||
end_time: 1740448182660
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||||
entry_point_name: ''
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||||
experiment_id: '433321862082712659'
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||||
lifecycle_stage: active
|
||||
run_id: 9f9c80e8e9634eb9b09978a685695619
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||||
run_name: capable-colt-135
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||||
run_uuid: 9f9c80e8e9634eb9b09978a685695619
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||||
source_name: ''
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||||
source_type: 4
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||||
source_version: ''
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||||
start_time: 1740448178742
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||||
status: 3
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||||
tags: []
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||||
user_id: admin-root
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||||
@ -0,0 +1 @@
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||||
1740448179296 0.9902912621359223 0
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||||
@ -0,0 +1 @@
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||||
1740448179309 0.990328791886068 0
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||||
@ -0,0 +1 @@
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||||
1740448179301 0.9905768132495717 0
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||||
@ -0,0 +1 @@
|
||||
1740448179305 0.9902912621359223 0
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||||
@ -0,0 +1 @@
|
||||
1740448179314 0.9928571428571429 0
|
||||
@ -0,0 +1 @@
|
||||
['计算效率高，支持并行计算。', '具有内置的缺失值处理能力。']
|
||||
@ -0,0 +1 @@
|
||||
XGBClassifier
|
||||
@ -0,0 +1 @@
|
||||
/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250224_170615/train_breast_cancer_20250224_170615.csv
|
||||
@ -0,0 +1 @@
|
||||
/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250224_170615/val_breast_cancer_20250224_170615.csv
|
||||
@ -0,0 +1 @@
|
||||
['参数较多，调优较复杂。']
|
||||
@ -0,0 +1 @@
|
||||
0.1
|
||||
@ -0,0 +1 @@
|
||||
6
|
||||
@ -0,0 +1 @@
|
||||
100
|
||||
@ -0,0 +1 @@
|
||||
XGBoost（Extreme Gradient Boosting）是一种基于梯度提升树（GBDT）的改进算法，具有更强的正则化和并行处理能力。
|
||||
@ -0,0 +1 @@
|
||||
42
|
||||
@ -0,0 +1 @@
|
||||
classification
|
||||
@ -0,0 +1 @@
|
||||
[{"run_id": "9f9c80e8e9634eb9b09978a685695619", "artifact_path": "model", "utc_time_created": "2025-02-25 01:49:39.319999", "model_uuid": "190fcacb3df44232bc122cfd5c0768ef", "flavors": {"python_function": {"model_path": "model.pkl", "predict_fn": "predict", "loader_module": "mlflow.sklearn", "python_version": "3.9.19", "env": {"conda": "conda.yaml", "virtualenv": "python_env.yaml"}}, "sklearn": {"pickled_model": "model.pkl", "sklearn_version": "1.5.2", "serialization_format": "cloudpickle", "code": null}}}]
|
||||
@ -0,0 +1 @@
|
||||
capable-colt-135
|
||||
@ -0,0 +1 @@
|
||||
382271e424c6a74f41aa5c92743ec7c8eb3882af
|
||||
@ -0,0 +1 @@
|
||||
/home/admin-root/haotian/MLPlatform/example_model_manager.py
|
||||
@ -0,0 +1 @@
|
||||
LOCAL
|
||||
@ -0,0 +1 @@
|
||||
admin-root
|
||||
@ -0,0 +1,15 @@
|
||||
artifact_uri: mlflow-artifacts:/433321862082712659/b0f8602b2bda4f349cef30e446d08a88/artifacts
|
||||
end_time: 1740447986878
|
||||
entry_point_name: ''
|
||||
experiment_id: '433321862082712659'
|
||||
lifecycle_stage: active
|
||||
run_id: b0f8602b2bda4f349cef30e446d08a88
|
||||
run_name: sneaky-ray-592
|
||||
run_uuid: b0f8602b2bda4f349cef30e446d08a88
|
||||
source_name: ''
|
||||
source_type: 4
|
||||
source_version: ''
|
||||
start_time: 1740447983879
|
||||
status: 3
|
||||
tags: []
|
||||
user_id: admin-root
|
||||
@ -0,0 +1 @@
|
||||
1740447984454 0.9902912621359223 0
|
||||
@ -0,0 +1 @@
|
||||
1740447984467 0.990328791886068 0
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||||
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||||
1740447984458 0.9905768132495717 0
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||||
@ -0,0 +1 @@
|
||||
1740447984463 0.9902912621359223 0
|
||||
@ -0,0 +1 @@
|
||||
1740447984471 0.9928571428571429 0
|
||||
@ -0,0 +1 @@
|
||||
['计算效率高，支持并行计算。', '具有内置的缺失值处理能力。']
|
||||
@ -0,0 +1 @@
|
||||
XGBClassifier
|
||||
@ -0,0 +1 @@
|
||||
/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250224_170615/train_breast_cancer_20250224_170615.csv
|
||||
@ -0,0 +1 @@
|
||||
/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250224_170615/val_breast_cancer_20250224_170615.csv
|
||||
@ -0,0 +1 @@
|
||||
['参数较多，调优较复杂。']
|
||||
@ -0,0 +1 @@
|
||||
0.1
|
||||
@ -0,0 +1 @@
|
||||
6
|
||||
@ -0,0 +1 @@
|
||||
100
|
||||
@ -0,0 +1 @@
|
||||
XGBoost（Extreme Gradient Boosting）是一种基于梯度提升树（GBDT）的改进算法，具有更强的正则化和并行处理能力。
|
||||
@ -0,0 +1 @@
|
||||
42
|
||||
@ -0,0 +1 @@
|
||||
classification
|
||||
@ -0,0 +1 @@
|
||||
[{"run_id": "b0f8602b2bda4f349cef30e446d08a88", "artifact_path": "model", "utc_time_created": "2025-02-25 01:46:24.477384", "model_uuid": "db9eff91af5c46ddb07b49424304108f", "flavors": {"python_function": {"model_path": "model.pkl", "predict_fn": "predict", "loader_module": "mlflow.sklearn", "python_version": "3.9.19", "env": {"conda": "conda.yaml", "virtualenv": "python_env.yaml"}}, "sklearn": {"pickled_model": "model.pkl", "sklearn_version": "1.5.2", "serialization_format": "cloudpickle", "code": null}}}]
|
||||
@ -0,0 +1 @@
|
||||
sneaky-ray-592
|
||||
@ -0,0 +1 @@
|
||||
d21060c67020bc49df3e06a1f0addd03b64f4975
|
||||
@ -0,0 +1 @@
|
||||
/home/admin-root/haotian/MLPlatform/example_model_manager.py
|
||||
@ -0,0 +1 @@
|
||||
LOCAL
|
||||
@ -0,0 +1 @@
|
||||
admin-root
|
||||
@ -0,0 +1,15 @@
|
||||
artifact_uri: mlflow-artifacts:/452770488800904984/41d222c59e3e46c8ba8c101247d5fd02/artifacts
|
||||
end_time: 1740449197273
|
||||
entry_point_name: ''
|
||||
experiment_id: '452770488800904984'
|
||||
lifecycle_stage: active
|
||||
run_id: 41d222c59e3e46c8ba8c101247d5fd02
|
||||
run_name: bright-lamb-489
|
||||
run_uuid: 41d222c59e3e46c8ba8c101247d5fd02
|
||||
source_name: ''
|
||||
source_type: 4
|
||||
source_version: ''
|
||||
start_time: 1740449194009
|
||||
status: 3
|
||||
tags: []
|
||||
user_id: admin-root
|
||||
@ -0,0 +1 @@
|
||||
1740449194317 0.9902912621359223 0
|
||||
@ -0,0 +1 @@
|
||||
1740449194326 0.990328791886068 0
|
||||
@ -0,0 +1 @@
|
||||
1740449194320 0.9905768132495717 0
|
||||
@ -0,0 +1 @@
|
||||
1740449194323 0.9902912621359223 0
|
||||
@ -0,0 +1 @@
|
||||
1740449194328 0.9928571428571429 0
|
||||
@ -0,0 +1 @@
|
||||
['计算效率高，支持并行计算。', '具有内置的缺失值处理能力。']
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user