完成--将数据处理方法整合到一个类中
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api/data_api.py
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api/data_api.py
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api/model_api.py
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api/model_api.py
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api/system_api.py
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api/system_api.py
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from fastapi import APIRouter, HTTPException, Query
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from typing import Optional
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from datetime import datetime
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from function.system_monitor import SystemMonitor
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router = APIRouter()
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system_monitor = SystemMonitor()
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@router.get("/system/resources")
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async def get_system_resources():
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"""获取系统资源使用情况"""
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result = system_monitor.get_system_resources()
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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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@router.get("/system/history")
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async def get_training_history(
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page: int = Query(1, ge=1),
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page_size: int = Query(10, ge=1, le=100),
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start_time: Optional[str] = None,
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end_time: Optional[str] = None,
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status: Optional[str] = None,
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experiment_name: Optional[str] = None
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):
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"""获取训练历史记录"""
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result = system_monitor.get_training_history(
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page=page,
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page_size=page_size,
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start_time=start_time,
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end_time=end_time,
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status=status,
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experiment_name=experiment_name
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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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@router.get("/system/logs")
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async def get_system_logs(
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level: Optional[str] = None,
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start_time: Optional[str] = None,
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end_time: Optional[str] = None,
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module: Optional[str] = None,
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page: int = Query(1, ge=1),
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page_size: int = Query(20, ge=1, le=100)
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):
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"""获取系统日志"""
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result = system_monitor.get_system_logs(
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level=level,
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start_time=start_time,
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end_time=end_time,
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module=module,
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page=page,
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page_size=page_size
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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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{
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"input_file": "dataset/dataset_raw/breast_cancer.csv",
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"timestamp": "2025-02-21T16:33:43.580798",
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"process_methods": [
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{
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"method_name": "SimpleImputer",
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"params": {
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"strategy": "mean",
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"missing_values": NaN
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}
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},
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{
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"method_name": "IsolationForest",
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"params": {
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"contamination": 0.1,
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"random_state": 42
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}
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},
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{
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"method_name": "StandardScaler",
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"params": {
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"with_mean": true,
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"with_std": true
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}
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}
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],
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"feature_methods": [],
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"split_params": {
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"val_size": 0.3
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},
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"steps": [
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{
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"step": "load_data",
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"shape": [
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569,
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31
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]
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},
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{
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"step": "cleaning",
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"method": "SimpleImputer",
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"params": {
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"strategy": "mean",
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"missing_values": NaN
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},
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"shape": [
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569,
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31
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]
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},
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{
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"step": "cleaning",
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"method": "IsolationForest",
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"params": {
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"contamination": 0.1,
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"random_state": 42
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},
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"shape": [
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512,
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31
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]
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},
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{
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"step": "cleaning",
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"method": "StandardScaler",
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"params": {
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"with_mean": true,
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"with_std": true
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},
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"shape": [
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512,
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31
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]
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}
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],
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"output_files": {
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"train": "dataset/dataset_processed/breast_cancer_20250221_163343/train_breast_cancer_20250221_163343.csv",
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"validation": "dataset/dataset_processed/breast_cancer_20250221_163343/val_breast_cancer_20250221_163343.csv",
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"test": ""
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}
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}
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@ -0,0 +1,359 @@
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mean radius,mean texture,mean perimeter,mean area,mean smoothness,mean compactness,mean concavity,mean concave points,mean symmetry,mean fractal dimension,radius error,texture error,perimeter error,area error,smoothness error,compactness error,concavity error,concave points error,symmetry error,fractal dimension error,worst radius,worst texture,worst perimeter,worst area,worst smoothness,worst compactness,worst concavity,worst concave points,worst symmetry,worst fractal dimension,target
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||||
|
117
example/example_data_processor.py
Normal file
117
example/example_data_processor.py
Normal file
@ -0,0 +1,117 @@
|
||||
from function.data_processor_date import DataProcessor
|
||||
import numpy as np
|
||||
|
||||
# 创建处理器实例
|
||||
processor = DataProcessor()
|
||||
|
||||
# 定义数据预处理方法
|
||||
process_methods = [
|
||||
# 缺失值处理
|
||||
{
|
||||
'method_name': 'SimpleImputer',
|
||||
'params': {
|
||||
'strategy': 'mean',
|
||||
'missing_values': np.nan
|
||||
}
|
||||
},
|
||||
# 异常值检测
|
||||
{
|
||||
'method_name': 'IsolationForest',
|
||||
'params': {
|
||||
'contamination': 0.1,
|
||||
'random_state': 42
|
||||
}
|
||||
},
|
||||
# 数据标准化
|
||||
{
|
||||
'method_name': 'StandardScaler',
|
||||
'params': {
|
||||
'with_mean': True,
|
||||
'with_std': True
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
# 定义特征工程方法
|
||||
feature_methods = [
|
||||
# 类别特征编码
|
||||
{
|
||||
'method_name': 'OneHotEncoder',
|
||||
'params': {
|
||||
'sparse': False,
|
||||
'handle_unknown': 'ignore'
|
||||
},
|
||||
|
||||
# columns 要处理的列名
|
||||
'columns': ['categorical_feature1', 'categorical_feature2']
|
||||
},
|
||||
# 数值特征离散化
|
||||
{
|
||||
'method_name': 'KBinsDiscretizer',
|
||||
'params': {
|
||||
'n_bins': 5,
|
||||
'encode': 'onehot',
|
||||
'strategy': 'uniform'
|
||||
},
|
||||
|
||||
# columns 要处理的列名
|
||||
'columns': ['numeric_feature1', 'numeric_feature2']
|
||||
},
|
||||
# 特征选择
|
||||
{
|
||||
'method_name': 'SelectKBest',
|
||||
'params': {
|
||||
'k': 10,
|
||||
'score_func': 'f_classif'
|
||||
},
|
||||
|
||||
# columns 要处理的列名
|
||||
'columns': ['feature1', 'feature2', 'feature3', 'feature4']
|
||||
},
|
||||
# 降维
|
||||
{
|
||||
'method_name': 'PCA',
|
||||
'params': {
|
||||
'n_components': 2,
|
||||
'random_state': 42
|
||||
},
|
||||
|
||||
# columns 要处理的列名, 现并不能通过列的序号来获得列, 注意文件的列名
|
||||
'columns': ['feature5', 'feature6', 'feature7']
|
||||
}
|
||||
]
|
||||
|
||||
# 数据集划分参数
|
||||
split_params = {
|
||||
# 'test_size': 0,
|
||||
'val_size': 0.3
|
||||
}
|
||||
|
||||
# 处理数据集
|
||||
result = processor.process_dataset(
|
||||
input_path='dataset/dataset_raw/breast_cancer.csv',
|
||||
output_dir='dataset/dataset_processed',
|
||||
process_methods=process_methods,
|
||||
# feature_methods=feature_methods,
|
||||
feature_methods=[],
|
||||
split_params=split_params
|
||||
)
|
||||
|
||||
# 打印处理结果
|
||||
print("\n数据处理结果:")
|
||||
print(f"状态: {result['status']}")
|
||||
if result['status'] == 'success':
|
||||
print("\n处理记录:")
|
||||
record = result['process_record']
|
||||
print(f"输入文件: {record['input_file']}")
|
||||
print(f"处理时间: {record['timestamp']}")
|
||||
print("\n输出文件:")
|
||||
for key, path in record['output_files'].items():
|
||||
print(f"{key}: {path}")
|
||||
|
||||
print("\n处理步骤:")
|
||||
for step in record['steps']:
|
||||
if 'shape' in step:
|
||||
print(f"{step['step']}: 数据形状 {step['shape']}")
|
||||
else:
|
||||
print(f"错误信息: {result['message']}")
|
||||
34
example/example_experiment_list.py
Normal file
34
example/example_experiment_list.py
Normal file
@ -0,0 +1,34 @@
|
||||
from function.model_manager import ModelManager
|
||||
|
||||
# 创建模型管理器实例
|
||||
manager = ModelManager()
|
||||
|
||||
# 获取实验列表
|
||||
result = manager.get_experiments(
|
||||
page=2,
|
||||
page_size=10,
|
||||
include_deleted=False
|
||||
)
|
||||
|
||||
# 打印结果
|
||||
print("\nMLFlow实验列表:")
|
||||
print(f"状态: {result['status']}")
|
||||
if result['status'] == 'success':
|
||||
print(f"\n总数: {result['total_count']}")
|
||||
print(f"当前页: {result['page']}")
|
||||
print(f"每页数量: {result['page_size']}")
|
||||
print("\n实验列表:")
|
||||
for exp in result['experiments']:
|
||||
print(f"\n实验ID: {exp['experiment_id']}")
|
||||
print(f"名称: {exp['name']}")
|
||||
print(f"存储位置: {exp['artifact_location']}")
|
||||
print(f"状态: {exp['lifecycle_stage']}")
|
||||
print(f"创建时间: {exp['creation_time']}")
|
||||
print(f"最后更新: {exp['last_update_time']}")
|
||||
print(f"运行次数: {exp['runs_count']}")
|
||||
if exp['tags']:
|
||||
print("标签:")
|
||||
for tag_name, tag_value in exp['tags'].items():
|
||||
print(f" {tag_name}: {tag_value}")
|
||||
else:
|
||||
print(f"错误信息: {result['message']}")
|
||||
5
example/example_get_all_dataset.py
Normal file
5
example/example_get_all_dataset.py
Normal file
@ -0,0 +1,5 @@
|
||||
from function.get_all_dataset import DatasetHistory
|
||||
|
||||
|
||||
t = DatasetHistory()
|
||||
print(t.get_dataset())
|
||||
14
example/example_method_reader_date_feature.py
Normal file
14
example/example_method_reader_date_feature.py
Normal file
@ -0,0 +1,14 @@
|
||||
from function.method_reader_date_feature import MethodReader
|
||||
|
||||
# 创建方法读取器实例
|
||||
reader = MethodReader()
|
||||
|
||||
# 获取所有预处理方法
|
||||
methods = reader.get_preprocessing_methods()
|
||||
print("预处理方法列表:")
|
||||
print(methods)
|
||||
|
||||
# 获取特定方法的详细信息
|
||||
method_details = reader.get_method_details('KBinsDiscretizer')
|
||||
print("\nKBinsDiscretizer方法详情:")
|
||||
print(method_details)
|
||||
14
example/example_method_reader_date_process.py
Normal file
14
example/example_method_reader_date_process.py
Normal file
@ -0,0 +1,14 @@
|
||||
from function.method_reader_date_process import MethodReader
|
||||
|
||||
# 创建方法读取器实例
|
||||
reader = MethodReader()
|
||||
|
||||
# 获取所有预处理方法
|
||||
methods = reader.get_preprocessing_methods()
|
||||
print("预处理方法列表:")
|
||||
print(methods)
|
||||
|
||||
# 获取特定方法的详细信息
|
||||
method_details = reader.get_method_details('StandardScaler')
|
||||
print("\nStandardScaler方法详情:")
|
||||
print(method_details)
|
||||
9
example/example_method_reader_metric.py
Normal file
9
example/example_method_reader_metric.py
Normal file
@ -0,0 +1,9 @@
|
||||
|
||||
from function.method_reader_metric import MethodReader
|
||||
|
||||
|
||||
|
||||
method_reader = MethodReader()
|
||||
|
||||
|
||||
print(method_reader.get_metrics())
|
||||
14
example/example_method_reader_model.py
Normal file
14
example/example_method_reader_model.py
Normal file
@ -0,0 +1,14 @@
|
||||
from function.method_reader_model import MethodReader
|
||||
|
||||
# 创建方法读取器实例
|
||||
reader = MethodReader()
|
||||
|
||||
# 获取所有预处理方法
|
||||
methods = reader.get_models()
|
||||
print("模型列表:")
|
||||
print(methods)
|
||||
|
||||
# 获取特定方法的详细信息
|
||||
method_details = reader.get_model_details('SVC')
|
||||
print("\nSVC方法详情:")
|
||||
print(method_details)
|
||||
6
example/example_model_delete.py
Normal file
6
example/example_model_delete.py
Normal file
@ -0,0 +1,6 @@
|
||||
from function.model_manager import ModelManager
|
||||
|
||||
# 创建模型管理器实例
|
||||
manager = ModelManager()
|
||||
back = manager.delete_model('7970364d490f4e0aa0375c2db26215f3')
|
||||
print(back)
|
||||
41
example/example_model_manager.py
Normal file
41
example/example_model_manager.py
Normal file
@ -0,0 +1,41 @@
|
||||
from function.model_manager import ModelManager
|
||||
|
||||
# 创建模型管理器实例
|
||||
manager = ModelManager()
|
||||
|
||||
# 获取所有已训练模型
|
||||
result = manager.get_finished_models(
|
||||
page=1,
|
||||
page_size=10,
|
||||
experiment_name='breast_cancer_classification_3'
|
||||
)
|
||||
|
||||
# 打印结果
|
||||
print("\n已训练模型列表:")
|
||||
print(f"状态: {result['status']}")
|
||||
if result['status'] == 'success':
|
||||
print(f"\n总数: {result['total_count']}")
|
||||
print(f"当前页: {result['page']}")
|
||||
print(f"每页数量: {result['page_size']}")
|
||||
print("\n模型列表:")
|
||||
for model in result['models']:
|
||||
'''
|
||||
'run_id': run['run_id'],
|
||||
'experiment_id': run['experiment_id'],
|
||||
'''
|
||||
print(f"run_id", model['run_id'])
|
||||
print(f"experiment_id", model['experiment_id'])
|
||||
print(f"算法: {model['algorithm']}")
|
||||
print(f"任务类型: {model['task_type']}")
|
||||
print(f"数据集: {model['dataset']}")
|
||||
|
||||
print(f"训练开始时间: {model['training_start_time']}")
|
||||
print(f"训练结束时间: {model['training_end_time']}")
|
||||
print("模型参数:")
|
||||
for k, v in model['parameters'].items():
|
||||
print(f" {k}: {v}")
|
||||
print("评估指标:")
|
||||
for metric_name, metric_value in model['metrics'].items():
|
||||
print(f" {metric_name}: {metric_value:.4f}")
|
||||
else:
|
||||
print(f"错误信息: {result['message']}")
|
||||
8
example/example_model_predict.py
Normal file
8
example/example_model_predict.py
Normal file
@ -0,0 +1,8 @@
|
||||
from function.model_manager import ModelManager
|
||||
|
||||
model_manager = ModelManager()
|
||||
|
||||
print(model_manager.predict(run_id = "33939ea6d8ce4d43a268f23f7361651e",\
|
||||
data_path="/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_145614/test_breast_cancer_20250219_145614.csv",\
|
||||
output_path="predictions/pred_breast_cancer_20250219_145614.csv" ,\
|
||||
metrics= ["accuracy", "f1", "precision", "recall"] ))
|
||||
56
example/example_model_trainer.py
Normal file
56
example/example_model_trainer.py
Normal file
@ -0,0 +1,56 @@
|
||||
from function.model_trainer import ModelTrainer
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
# 创建训练器实例
|
||||
trainer = ModelTrainer()
|
||||
|
||||
# 加载数据
|
||||
train_data = pd.read_csv('/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629/train_breast_cancer_20250219_144629.csv')
|
||||
val_data = pd.read_csv('/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629/val_breast_cancer_20250219_144629.csv')
|
||||
|
||||
# 准备特征和标签
|
||||
X_train = train_data.drop('target', axis=1)
|
||||
y_train = train_data['target']
|
||||
X_val = val_data.drop('target', axis=1)
|
||||
y_val = val_data['target']
|
||||
|
||||
# 模型配置
|
||||
model_config = {
|
||||
'algorithm': 'XGBClassifier',
|
||||
'task_type': 'classification',
|
||||
'dataset' : '/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629',
|
||||
'params': {
|
||||
'n_estimators': 100,
|
||||
'learning_rate': 0.1,
|
||||
'max_depth': 6,
|
||||
'random_state': 42
|
||||
}
|
||||
}
|
||||
|
||||
# 训练模型, 删除训练实验时要删除 mlruns/.trash/ 回收站里的文件
|
||||
# 模型文件 直接在 mlruns/文件夹下
|
||||
for i in range(3, 4):
|
||||
result = trainer.train_model(
|
||||
{
|
||||
'features': X_train,
|
||||
'labels': y_train
|
||||
},
|
||||
{
|
||||
'features': X_val,
|
||||
'labels': y_val
|
||||
},
|
||||
model_config,
|
||||
f'breast_cancer_classification_{i}'
|
||||
)
|
||||
|
||||
# 打印结果
|
||||
print("\n训练结果:")
|
||||
print(f"状态: {result['status']}")
|
||||
if result['status'] == 'success':
|
||||
print(f"\nMLflow运行ID: {result['run_id']}")
|
||||
print("\n评估指标:")
|
||||
for metric_name, metric_value in result['metrics'].items():
|
||||
print(f"{metric_name}: {metric_value:.4f}")
|
||||
else:
|
||||
print(f"错误信息: {result['message']}")
|
||||
152
example_data_manager.py
Normal file
152
example_data_manager.py
Normal file
@ -0,0 +1,152 @@
|
||||
from function.data_manager import DataManager
|
||||
import numpy as np
|
||||
|
||||
|
||||
data_manager = DataManager()
|
||||
|
||||
print('----------------------------预处理方法----------------------------------------------------------------------------------------------------------')
|
||||
# 获取所有预处理方法
|
||||
methods = data_manager.get_preprocessing_methods()
|
||||
print("预处理方法列表:")
|
||||
print(methods)
|
||||
|
||||
# 获取特定方法的详细信息
|
||||
method_details = data_manager.get_preprocessing_method_details('StandardScaler')
|
||||
print("\nStandardScaler方法详情:")
|
||||
print(method_details)
|
||||
print('----------------------------预处理方法end----------------------------------------------------------------------------------------------------------')
|
||||
|
||||
print('----------------------------特征工程方法----------------------------------------------------------------------------------------------------------')
|
||||
methods = data_manager.get_feature_engineering_methods()
|
||||
print("特诊工程方法")
|
||||
print(methods)
|
||||
|
||||
# 获取特定方法的详细信息
|
||||
method_details = data_manager.get_feature_engineering_method_details('KBinsDiscretizer')
|
||||
print("\nKBinsDiscretizer方法详情:")
|
||||
print(method_details)
|
||||
print('----------------------------特征工程方法end----------------------------------------------------------------------------------------------------------')
|
||||
|
||||
print('----------------------------获取数据集----------------------------------------------------------------------------------------------------------')
|
||||
print('获取数据集')
|
||||
print(data_manager.get_dataset())
|
||||
print('----------------------------获取数据集end----------------------------------------------------------------------------------------------------------')
|
||||
|
||||
print('----------------------------处理数据集----------------------------------------------------------------------------------------------------------')
|
||||
|
||||
# 定义数据预处理方法
|
||||
process_methods = [
|
||||
# 缺失值处理
|
||||
{
|
||||
'method_name': 'SimpleImputer',
|
||||
'params': {
|
||||
'strategy': 'mean',
|
||||
'missing_values': np.nan
|
||||
}
|
||||
},
|
||||
# 异常值检测
|
||||
{
|
||||
'method_name': 'IsolationForest',
|
||||
'params': {
|
||||
'contamination': 0.1,
|
||||
'random_state': 42
|
||||
}
|
||||
},
|
||||
# 数据标准化
|
||||
{
|
||||
'method_name': 'StandardScaler',
|
||||
'params': {
|
||||
'with_mean': True,
|
||||
'with_std': True
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
# 定义特征工程方法
|
||||
feature_methods = [
|
||||
# 类别特征编码
|
||||
{
|
||||
'method_name': 'OneHotEncoder',
|
||||
'params': {
|
||||
'sparse': False,
|
||||
'handle_unknown': 'ignore'
|
||||
},
|
||||
|
||||
# columns 要处理的列名
|
||||
'columns': ['categorical_feature1', 'categorical_feature2']
|
||||
},
|
||||
# 数值特征离散化
|
||||
{
|
||||
'method_name': 'KBinsDiscretizer',
|
||||
'params': {
|
||||
'n_bins': 5,
|
||||
'encode': 'onehot',
|
||||
'strategy': 'uniform'
|
||||
},
|
||||
|
||||
# columns 要处理的列名
|
||||
'columns': ['numeric_feature1', 'numeric_feature2']
|
||||
},
|
||||
# 特征选择
|
||||
{
|
||||
'method_name': 'SelectKBest',
|
||||
'params': {
|
||||
'k': 10,
|
||||
'score_func': 'f_classif'
|
||||
},
|
||||
|
||||
# columns 要处理的列名
|
||||
'columns': ['feature1', 'feature2', 'feature3', 'feature4']
|
||||
},
|
||||
# 降维
|
||||
{
|
||||
'method_name': 'PCA',
|
||||
'params': {
|
||||
'n_components': 2,
|
||||
'random_state': 42
|
||||
},
|
||||
|
||||
# columns 要处理的列名, 现并不能通过列的序号来获得列, 注意文件的列名
|
||||
'columns': ['feature5', 'feature6', 'feature7']
|
||||
}
|
||||
]
|
||||
|
||||
# 数据集划分参数
|
||||
split_params = {
|
||||
# 'test_size': 0,
|
||||
'val_size': 0.3
|
||||
}
|
||||
|
||||
# 处理数据集
|
||||
result = data_manager.process_dataset(
|
||||
input_path='dataset/dataset_raw/breast_cancer.csv',
|
||||
output_dir='dataset/dataset_processed',
|
||||
process_methods=process_methods,
|
||||
# feature_methods=feature_methods,
|
||||
feature_methods=[],
|
||||
split_params=split_params
|
||||
)
|
||||
|
||||
# 打印处理结果
|
||||
print("\n数据处理结果:")
|
||||
print(f"状态: {result['status']}")
|
||||
if result['status'] == 'success':
|
||||
print("\n处理记录:")
|
||||
record = result['process_record']
|
||||
print(f"输入文件: {record['input_file']}")
|
||||
print(f"处理时间: {record['timestamp']}")
|
||||
print("\n输出文件:")
|
||||
for key, path in record['output_files'].items():
|
||||
print(f"{key}: {path}")
|
||||
|
||||
print("\n处理步骤:")
|
||||
for step in record['steps']:
|
||||
if 'shape' in step:
|
||||
print(f"{step['step']}: 数据形状 {step['shape']}")
|
||||
else:
|
||||
print(f"错误信息: {result['message']}")
|
||||
|
||||
print('----------------------------处理数据集end----------------------------------------------------------------------------------------------------------')
|
||||
|
||||
|
||||
|
||||
@ -1,8 +1,35 @@
|
||||
from function.model_manager import ModelManager
|
||||
import pandas as pd
|
||||
|
||||
# 创建模型管理器实例
|
||||
manager = ModelManager()
|
||||
|
||||
|
||||
|
||||
# 获取所有预处理方法
|
||||
print("--------------------------------------------获取预处理方法---------------------------------------------------")
|
||||
methods = manager.get_models()
|
||||
print("模型列表:")
|
||||
print(methods)
|
||||
print("--------------------------------------------获取预处理方法 end ---------------------------------------------------")
|
||||
|
||||
|
||||
print("--------------------------------------------获取方法详细信息---------------------------------------------------")
|
||||
# 获取特定方法的详细信息
|
||||
method_details = manager.get_model_details('SVC')
|
||||
print("\nSVC方法详情:")
|
||||
print(method_details)
|
||||
print("--------------------------------------------获取方法详细信息 end ---------------------------------------------------")
|
||||
|
||||
|
||||
|
||||
print("--------------------------------------------评价指标 ---------------------------------------------------")
|
||||
# 获取评价指标
|
||||
print(manager.get_metrics())
|
||||
print("--------------------------------------------评价指标 end ---------------------------------------------------")
|
||||
|
||||
|
||||
print("--------------------------------------------获取所有已训练模型 ---------------------------------------------------")
|
||||
# 获取所有已训练模型
|
||||
result = manager.get_finished_models(
|
||||
page=1,
|
||||
@ -38,4 +65,68 @@ if result['status'] == 'success':
|
||||
for metric_name, metric_value in model['metrics'].items():
|
||||
print(f" {metric_name}: {metric_value:.4f}")
|
||||
else:
|
||||
print(f"错误信息: {result['message']}")
|
||||
print(f"错误信息: {result['message']}")
|
||||
|
||||
print("--------------------------------------------获取所有已训练模型 end ---------------------------------------------------")
|
||||
|
||||
|
||||
print("--------------------------------------------模型训练---------------------------------------------------")
|
||||
# 加载数据
|
||||
train_data = pd.read_csv('/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629/train_breast_cancer_20250219_144629.csv')
|
||||
val_data = pd.read_csv('/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629/val_breast_cancer_20250219_144629.csv')
|
||||
|
||||
# 准备特征和标签
|
||||
X_train = train_data.drop('target', axis=1)
|
||||
y_train = train_data['target']
|
||||
X_val = val_data.drop('target', axis=1)
|
||||
y_val = val_data['target']
|
||||
|
||||
# 模型配置
|
||||
model_config = {
|
||||
'algorithm': 'XGBClassifier',
|
||||
'task_type': 'classification',
|
||||
'dataset' : '/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629',
|
||||
'params': {
|
||||
'n_estimators': 100,
|
||||
'learning_rate': 0.1,
|
||||
'max_depth': 6,
|
||||
'random_state': 42
|
||||
}
|
||||
}
|
||||
|
||||
# 训练模型, 删除训练实验时要删除 mlruns/.trash/ 回收站里的文件
|
||||
# 模型文件 直接在 mlruns/文件夹下
|
||||
for i in range(3, 4):
|
||||
result = manager.train_model(
|
||||
{
|
||||
'features': X_train,
|
||||
'labels': y_train
|
||||
},
|
||||
{
|
||||
'features': X_val,
|
||||
'labels': y_val
|
||||
},
|
||||
model_config,
|
||||
f'breast_cancer_classification_{i}'
|
||||
)
|
||||
|
||||
# 打印结果
|
||||
print("\n训练结果:")
|
||||
print(f"状态: {result['status']}")
|
||||
if result['status'] == 'success':
|
||||
print(f"\nMLflow运行ID: {result['run_id']}")
|
||||
print("\n评估指标:")
|
||||
for metric_name, metric_value in result['metrics'].items():
|
||||
print(f"{metric_name}: {metric_value:.4f}")
|
||||
else:
|
||||
print(f"错误信息: {result['message']}")
|
||||
|
||||
print("-------------------------------------------模型训练 end ---------------------------------------------------")
|
||||
|
||||
print("--------------------------------------------模型预测 ---------------------------------------------------")
|
||||
print(manager.predict(run_id = "33939ea6d8ce4d43a268f23f7361651e",\
|
||||
data_path="/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_145614/test_breast_cancer_20250219_145614.csv",\
|
||||
output_path="predictions/pred_breast_cancer_20250219_145614.csv" ,\
|
||||
metrics= ["accuracy", "f1", "precision", "recall"] ))
|
||||
|
||||
print("-------------------------------------------模型预测 end ---------------------------------------------------")
|
||||
BIN
function/__pycache__/data_manager.cpython-39.pyc
Normal file
BIN
function/__pycache__/data_manager.cpython-39.pyc
Normal file
Binary file not shown.
BIN
function/__pycache__/model_manager_new.cpython-39.pyc
Normal file
BIN
function/__pycache__/model_manager_new.cpython-39.pyc
Normal file
Binary file not shown.
618
function/data_manager.py
Normal file
618
function/data_manager.py
Normal file
@ -0,0 +1,618 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Dict, List, Tuple, Optional
|
||||
import logging
|
||||
from pathlib import Path
|
||||
import datetime
|
||||
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, Normalizer, Binarizer, LabelEncoder, KBinsDiscretizer, FunctionTransformer, PowerTransformer, QuantileTransformer, PolynomialFeatures, OneHotEncoder
|
||||
from sklearn.feature_extraction import FeatureHasher, DictVectorizer
|
||||
from sklearn.feature_selection import SelectKBest, RFE
|
||||
from sklearn.decomposition import PCA
|
||||
from sklearn.experimental import enable_iterative_imputer # noqa
|
||||
from sklearn.impute import SimpleImputer, IterativeImputer, KNNImputer, MissingIndicator
|
||||
from sklearn.ensemble import IsolationForest
|
||||
from sklearn.svm import OneClassSVM
|
||||
from sklearn.neighbors import LocalOutlierFactor
|
||||
from sklearn.covariance import EllipticEnvelope
|
||||
from sklearn.model_selection import train_test_split
|
||||
import json
|
||||
import yaml
|
||||
import os
|
||||
|
||||
|
||||
class DataManager:
|
||||
"""数据处理类"""
|
||||
|
||||
def __init__(self, config: Dict = None):
|
||||
"""初始化数据处理器"""
|
||||
self.config = config or {}
|
||||
self.logger = logging.getLogger(__name__)
|
||||
self._setup_logging()
|
||||
|
||||
self.method_config = self._load_method_config()
|
||||
self.parameter_config = self._load_parameter_config()
|
||||
|
||||
self.dataset_processed_path = 'dataset/dataset_processed'
|
||||
|
||||
# 数据预处理方法
|
||||
self.preprocessing_methods = {
|
||||
# 缺失值处理
|
||||
'SimpleImputer': SimpleImputer,
|
||||
'IterativeImputer': IterativeImputer,
|
||||
'KNNImputer': KNNImputer,
|
||||
'MissingIndicator': MissingIndicator,
|
||||
# 异常值处理
|
||||
'IsolationForest': IsolationForest,
|
||||
'OneClassSVM': OneClassSVM,
|
||||
'LocalOutlierFactor': LocalOutlierFactor,
|
||||
'EllipticEnvelope': EllipticEnvelope,
|
||||
# 数据缩放
|
||||
'StandardScaler': StandardScaler,
|
||||
'MinMaxScaler': MinMaxScaler,
|
||||
'RobustScaler': RobustScaler,
|
||||
'Normalizer': Normalizer,
|
||||
'Binarizer': Binarizer
|
||||
}
|
||||
|
||||
# 特征工程方法
|
||||
self.feature_engineering_methods = {
|
||||
# 特征编码
|
||||
'LabelEncoder': LabelEncoder,
|
||||
'OneHotEncoder': OneHotEncoder,
|
||||
# 特征离散化
|
||||
'KBinsDiscretizer': KBinsDiscretizer,
|
||||
# 特征变换
|
||||
'FunctionTransformer': FunctionTransformer,
|
||||
'PowerTransformer': PowerTransformer,
|
||||
'QuantileTransformer': QuantileTransformer,
|
||||
'PolynomialFeatures': PolynomialFeatures,
|
||||
# 特征提取
|
||||
'FeatureHasher': FeatureHasher,
|
||||
'DictVectorizer': DictVectorizer,
|
||||
# 特征选择和降维
|
||||
'PCA': PCA,
|
||||
'SelectKBest': SelectKBest,
|
||||
'RFE': RFE
|
||||
}
|
||||
|
||||
def _load_method_config(self) -> Dict:
|
||||
"""加载方法配置文件"""
|
||||
try:
|
||||
config_path = Path('date_preprocessing/method.yaml')
|
||||
if not config_path.exists():
|
||||
raise FileNotFoundError(f"Method config file not found at {config_path}")
|
||||
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
config_process = yaml.safe_load(f)
|
||||
|
||||
self.logger.info("Successfully loaded method config")
|
||||
|
||||
config_path = Path('date_feature/method.yaml')
|
||||
if not config_path.exists():
|
||||
raise FileNotFoundError(f"Method config file not found at {config_path}")
|
||||
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
config_feature = yaml.safe_load(f)
|
||||
|
||||
self.logger.info("Successfully loaded feature config")
|
||||
|
||||
config = {**config_process, **config_feature}
|
||||
|
||||
return config
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error loading method/feature config: {str(e)}")
|
||||
raise
|
||||
|
||||
def _load_parameter_config(self) -> Dict:
|
||||
"""加载参数配置文件"""
|
||||
try:
|
||||
config_path = Path('date_preprocessing/parameter.yaml')
|
||||
if not config_path.exists():
|
||||
raise FileNotFoundError(f"Parameter config file not found at {config_path}")
|
||||
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
config_process = yaml.safe_load(f)
|
||||
|
||||
self.logger.info("Successfully loaded process parameter config")
|
||||
|
||||
config_path = Path('date_feature/parameter.yaml')
|
||||
if not config_path.exists():
|
||||
raise FileNotFoundError(f"Parameter config file not found at {config_path}")
|
||||
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
config_feature = yaml.safe_load(f)
|
||||
|
||||
self.logger.info("Successfully loaded feature parameter config")
|
||||
|
||||
config = {**config_process, **config_feature}
|
||||
|
||||
|
||||
return config
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error loading parameter config: {str(e)}")
|
||||
raise
|
||||
|
||||
|
||||
|
||||
def _setup_logging(self):
|
||||
"""设置日志"""
|
||||
log_dir = Path('.log')
|
||||
log_dir.mkdir(exist_ok=True)
|
||||
|
||||
file_handler = logging.FileHandler(
|
||||
log_dir / f'data_processing_{datetime.datetime.now():%Y%m%d_%H%M%S}.log'
|
||||
)
|
||||
file_handler.setFormatter(
|
||||
logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
||||
)
|
||||
self.logger.addHandler(file_handler)
|
||||
self.logger.setLevel(logging.INFO)
|
||||
|
||||
def process_dataset(
|
||||
self,
|
||||
input_path: str,
|
||||
output_dir: str,
|
||||
process_methods: List[Dict],
|
||||
feature_methods: List[Dict],
|
||||
split_params: Dict
|
||||
) -> Dict:
|
||||
"""
|
||||
处理数据集
|
||||
|
||||
Args:
|
||||
input_path: 输入数据路径
|
||||
output_dir: 输出目录
|
||||
cleaning_methods: 数据清洗方法列表，每个方法是一个字典，包含method_name和params
|
||||
feature_methods: 特征工程方法列表，每个方法是一个字典，包含method_name和params
|
||||
split_params: 数据集划分参数，包含test_size和val_size
|
||||
|
||||
Returns:
|
||||
处理结果字典
|
||||
"""
|
||||
# try:
|
||||
|
||||
# 数据集名
|
||||
file_name = input_path.split("/")[-1].split(".")[0]
|
||||
|
||||
# 生成时间戳
|
||||
timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
|
||||
# 创建输出目录
|
||||
output_path = Path(output_dir+'/'+file_name+'_'+timestamp)
|
||||
output_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# 记录处理过程
|
||||
process_record = {
|
||||
'input_file': input_path,
|
||||
'timestamp': datetime.datetime.now().isoformat(),
|
||||
'process_methods': process_methods,
|
||||
'feature_methods': feature_methods,
|
||||
'split_params': split_params,
|
||||
'steps': []
|
||||
}
|
||||
|
||||
# 读取数据
|
||||
self.logger.info(f"Loading data from {input_path}")
|
||||
df = pd.read_csv(input_path)
|
||||
process_record['steps'].append({
|
||||
'step': 'load_data',
|
||||
'shape': df.shape
|
||||
})
|
||||
|
||||
|
||||
|
||||
# 数据预处理
|
||||
for method in process_methods:
|
||||
df = self._apply_process_methods(df, method)
|
||||
process_record['steps'].append({
|
||||
'step': 'cleaning',
|
||||
'method': method['method_name'],
|
||||
'params': method['params'],
|
||||
'shape': df.shape
|
||||
})
|
||||
|
||||
# 特征工程
|
||||
for method in feature_methods:
|
||||
df = self._apply_feature_method(df, method)
|
||||
process_record['steps'].append({
|
||||
'step': 'feature_engineering',
|
||||
'method': method['method_name'],
|
||||
'params': method['params'],
|
||||
'shape': df.shape
|
||||
})
|
||||
|
||||
# 数据集划分
|
||||
train_data, val_data, test_data = self._split_dataset(
|
||||
df,
|
||||
test_size=split_params.get('test_size', 0),
|
||||
val_size=split_params.get('val_size', 0)
|
||||
)
|
||||
|
||||
|
||||
|
||||
# 保存处理后的数据集
|
||||
train_path = output_path / f'train_{file_name}_{timestamp}.csv'
|
||||
val_path = output_path / f'val_{file_name}_{timestamp}.csv'
|
||||
test_path = output_path / f'test_{file_name}_{timestamp}.csv'
|
||||
|
||||
if train_data is not None:
|
||||
train_data.to_csv(train_path, index=False)
|
||||
|
||||
if val_data is not None:
|
||||
val_data.to_csv(val_path, index=False)
|
||||
if test_data is not None:
|
||||
test_data.to_csv(test_path, index=False)
|
||||
|
||||
# 记录输出文件路径
|
||||
process_record['output_files'] = {
|
||||
'train': str(train_path) if train_data is not None else "",
|
||||
'validation': str(val_path) if val_data is not None else "",
|
||||
'test': str(test_path) if test_data is not None else ""
|
||||
}
|
||||
|
||||
# 保存处理记录
|
||||
record_path = output_path / f'process_record__{file_name}_{timestamp}.json'
|
||||
with open(record_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(process_record, f, indent=2, ensure_ascii=False)
|
||||
|
||||
self.logger.info(f"Data processing completed. Results saved to {output_path}")
|
||||
|
||||
return {
|
||||
'status': 'success',
|
||||
'message': 'Data processing completed successfully',
|
||||
'process_record': process_record
|
||||
}
|
||||
|
||||
# except Exception as e:
|
||||
# error_msg = f"Error processing dataset: {str(e)}"
|
||||
# self.logger.error(error_msg)
|
||||
# return {
|
||||
# 'status': 'error',
|
||||
# 'message': error_msg
|
||||
# }
|
||||
|
||||
def _apply_process_methods(self, df: pd.DataFrame, method: Dict) -> pd.DataFrame:
|
||||
"""应用数据预处理方法"""
|
||||
try:
|
||||
method_name = method['method_name']
|
||||
params = method['params']
|
||||
|
||||
if method_name not in self.preprocessing_methods:
|
||||
raise ValueError(f"Unknown preprocessing method: {method_name}")
|
||||
|
||||
processor = self.preprocessing_methods[method_name](**params)
|
||||
|
||||
# 分离特征和标签
|
||||
features = df.drop('target', axis=1)
|
||||
target = df['target']
|
||||
|
||||
# 根据不同类型的方法进行处理
|
||||
if method_name in ['IsolationForest', 'OneClassSVM', 'LocalOutlierFactor', 'EllipticEnvelope']:
|
||||
# 异常值检测方法
|
||||
mask = processor.fit_predict(features) != -1
|
||||
features = features[mask]
|
||||
target = target[mask]
|
||||
else:
|
||||
# 其他预处理方法
|
||||
features = pd.DataFrame(
|
||||
processor.fit_transform(features),
|
||||
columns=features.columns,
|
||||
index=features.index
|
||||
)
|
||||
|
||||
# 重新组合特征和标签
|
||||
df = pd.concat([features, target], axis=1)
|
||||
|
||||
self.logger.info(f"Applied preprocessing method {method_name}")
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error applying preprocessing method {method_name}: {str(e)}")
|
||||
raise
|
||||
|
||||
def _apply_feature_method(self, df: pd.DataFrame, method: Dict) -> pd.DataFrame:
|
||||
"""应用特征工程方法"""
|
||||
try:
|
||||
method_name = method['method_name']
|
||||
params = method.get('params', {})
|
||||
columns = method.get('columns', df.drop('target', axis=1).columns) # 排除target列
|
||||
|
||||
if method_name not in self.feature_engineering_methods:
|
||||
raise ValueError(f"Unknown feature engineering method: {method_name}")
|
||||
|
||||
processor = self.feature_engineering_methods[method_name](**params)
|
||||
|
||||
# 分离特征和标签
|
||||
features = df.drop('target', axis=1)
|
||||
target = df['target']
|
||||
|
||||
# 根据不同类型的特征工程方法进行处理
|
||||
if method_name in ['LabelEncoder', 'OneHotEncoder']:
|
||||
# 编码方法
|
||||
df_temp = features[columns].copy()
|
||||
if method_name == 'LabelEncoder':
|
||||
for col in columns:
|
||||
df_temp[col] = processor.fit_transform(df_temp[col])
|
||||
else: # OneHotEncoder
|
||||
encoded = processor.fit_transform(df_temp)
|
||||
if isinstance(encoded, np.ndarray):
|
||||
encoded = pd.DataFrame(
|
||||
encoded,
|
||||
columns=[f"{col}_{i}" for col in columns for i in range(encoded.shape[1]//len(columns))],
|
||||
index=features.index
|
||||
)
|
||||
df_temp = encoded
|
||||
|
||||
# 更新特征数据框
|
||||
features = features.drop(columns=columns)
|
||||
features = pd.concat([features, df_temp], axis=1)
|
||||
|
||||
elif method_name in ['KBinsDiscretizer']:
|
||||
# 离散化方法
|
||||
transformed = processor.fit_transform(features[columns])
|
||||
if processor.encode == 'onehot':
|
||||
feature_names = [f"{col}_{i}" for col in columns for i in range(processor.n_bins_)]
|
||||
else:
|
||||
feature_names = [f"{col}_binned" for col in columns]
|
||||
|
||||
df_temp = pd.DataFrame(
|
||||
transformed,
|
||||
columns=feature_names,
|
||||
index=features.index
|
||||
)
|
||||
features = features.drop(columns=columns)
|
||||
features = pd.concat([features, df_temp], axis=1)
|
||||
|
||||
elif method_name in ['PCA', 'SelectKBest', 'RFE']:
|
||||
# 降维和特征选择方法
|
||||
transformed = processor.fit_transform(features[columns])
|
||||
n_features = transformed.shape[1]
|
||||
df_temp = pd.DataFrame(
|
||||
transformed,
|
||||
columns=[f"feature_{i}" for i in range(n_features)],
|
||||
index=features.index
|
||||
)
|
||||
features = features.drop(columns=columns)
|
||||
features = pd.concat([features, df_temp], axis=1)
|
||||
|
||||
else:
|
||||
# 其他特征工程方法
|
||||
transformed = processor.fit_transform(features[columns])
|
||||
df_temp = pd.DataFrame(
|
||||
transformed,
|
||||
columns=[f"{col}_transformed" for col in columns],
|
||||
index=features.index
|
||||
)
|
||||
features = features.drop(columns=columns)
|
||||
features = pd.concat([features, df_temp], axis=1)
|
||||
|
||||
# 重新组合特征和标签
|
||||
df = pd.concat([features, target], axis=1)
|
||||
|
||||
self.logger.info(f"Applied feature engineering method {method_name}")
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error applying feature engineering method {method_name}: {str(e)}")
|
||||
raise
|
||||
|
||||
def _split_dataset(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
test_size: float = 0,
|
||||
val_size: float = 0
|
||||
) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
|
||||
"""划分数据集"""
|
||||
try:
|
||||
# 首先划分训练集和测试集
|
||||
if test_size > 0:
|
||||
train_val_data, test_data = train_test_split(
|
||||
df,
|
||||
test_size=test_size,
|
||||
random_state=42
|
||||
)
|
||||
else:
|
||||
train_val_data = df
|
||||
test_data = None
|
||||
if val_size > 0:
|
||||
# 再划分训练集和验证集
|
||||
val_size_adjusted = val_size / (1 - test_size)
|
||||
train_data, val_data = train_test_split(
|
||||
train_val_data,
|
||||
test_size=val_size_adjusted,
|
||||
random_state=42
|
||||
)
|
||||
else:
|
||||
train_data = train_val_data
|
||||
val_data = None
|
||||
|
||||
self.logger.info(
|
||||
f"Dataset split - Train: {len(train_data) if train_data is not None else 0}, "
|
||||
f"Val: {len(val_data) if val_data is not None else 0} , Test: {len(test_data) if test_data is not None else 0}"
|
||||
)
|
||||
|
||||
return train_data, val_data, test_data
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error splitting dataset: {str(e)}")
|
||||
raise
|
||||
|
||||
def get_preprocessing_methods(self) -> Dict:
|
||||
"""获取预处理方法列表"""
|
||||
try:
|
||||
methods = []
|
||||
|
||||
# 数据缩放方法
|
||||
scaler_methods = list(self.method_config.get('data_scaler_methods', {}).keys())
|
||||
if scaler_methods:
|
||||
methods.append({
|
||||
"name": "data_scaler",
|
||||
"description": "数据缩放处理",
|
||||
"method": scaler_methods
|
||||
})
|
||||
|
||||
# 缺失值处理方法
|
||||
missing_methods = list(self.method_config.get('missing_value_handling_methods', {}).keys())
|
||||
if missing_methods:
|
||||
methods.append({
|
||||
"name": "missing_value_handler",
|
||||
"description": "缺失值处理",
|
||||
"method": missing_methods
|
||||
})
|
||||
|
||||
# 异常值检测方法
|
||||
outlier_methods = list(self.method_config.get('outlier_detection_methods', {}).keys())
|
||||
if outlier_methods:
|
||||
methods.append({
|
||||
"name": "outlier_detector",
|
||||
"description": "异常值检测",
|
||||
"method": outlier_methods
|
||||
})
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"methods": methods
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error getting preprocessing methods: {str(e)}")
|
||||
return {
|
||||
"status": "error",
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
def get_preprocessing_method_details(self, method_name: str) -> Dict:
|
||||
"""获取指定方法的详细信息"""
|
||||
try:
|
||||
# 在各个方法类别中查找方法原理和优缺点
|
||||
method_info = None
|
||||
for category in ['data_scaler_methods', 'missing_value_handling_methods', 'outlier_detection_methods']:
|
||||
if method_name in self.method_config.get(category, {}):
|
||||
method_info = self.method_config[category][method_name]
|
||||
break
|
||||
|
||||
if method_info is None:
|
||||
raise ValueError(f"Method {method_name} not found in method config")
|
||||
|
||||
# 查找方法参数信息
|
||||
parameter_info = None
|
||||
for category in ['data_scaler_methods', 'missing_value_handling_methods', 'outlier_detection_methods']:
|
||||
if method_name in self.parameter_config.get(category, {}):
|
||||
parameter_info = self.parameter_config[category][method_name]
|
||||
break
|
||||
|
||||
if parameter_info is None:
|
||||
raise ValueError(f"Method {method_name} not found in parameter config")
|
||||
|
||||
# 组合返回信息
|
||||
return {
|
||||
"status": "success",
|
||||
"method": {
|
||||
"name": method_name,
|
||||
"description": parameter_info.get('description', ''),
|
||||
"principle": method_info.get('principle', ''),
|
||||
"advantages": method_info.get('advantages', []),
|
||||
"disadvantages": method_info.get('disadvantages', []),
|
||||
"applicable_scenarios": method_info.get('applicable_scenarios', []),
|
||||
"parameters": parameter_info.get('parameters', [])
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error getting method details: {str(e)}")
|
||||
return {
|
||||
"status": "error",
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
def get_feature_engineering_methods(self) -> Dict:
|
||||
"""获取特征工程方法列表"""
|
||||
try:
|
||||
methods = []
|
||||
|
||||
|
||||
# 获取特征工程方法
|
||||
feature_engineering_methods = list(self.method_config.get('feature_engineering_methods', {}).keys())
|
||||
if feature_engineering_methods:
|
||||
methods.append({
|
||||
"name": "feature_engineering_methods",
|
||||
"description": "特征工程方法",
|
||||
"method": feature_engineering_methods
|
||||
})
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"methods": methods
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error getting preprocessing methods: {str(e)}")
|
||||
return {
|
||||
"status": "error",
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
def get_feature_engineering_method_details(self, method_name: str) -> Dict:
|
||||
"""获取指定方法的详细信息"""
|
||||
try:
|
||||
# 在各个方法类别中查找方法原理和优缺点
|
||||
method_info = None
|
||||
for category in ['feature_engineering_methods']:
|
||||
if method_name in self.method_config.get(category, {}):
|
||||
method_info = self.method_config[category][method_name]
|
||||
break
|
||||
|
||||
if method_info is None:
|
||||
raise ValueError(f"Method {method_name} not found in method config")
|
||||
|
||||
# 查找方法参数信息
|
||||
parameter_info = None
|
||||
for category in ['feature_engineering_methods']:
|
||||
if method_name in self.parameter_config.get(category, {}):
|
||||
parameter_info = self.parameter_config[category][method_name]
|
||||
break
|
||||
|
||||
if parameter_info is None:
|
||||
raise ValueError(f"Method {method_name} not found in parameter config")
|
||||
|
||||
# 组合返回信息
|
||||
return {
|
||||
"status": "success",
|
||||
"method": {
|
||||
"name": method_name,
|
||||
"description": parameter_info.get('description', ''),
|
||||
"principle": method_info.get('principle', ''),
|
||||
"advantages": method_info.get('advantages', []),
|
||||
"disadvantages": method_info.get('disadvantages', []),
|
||||
"applicable_scenarios": method_info.get('applicable_scenarios', []),
|
||||
"parameters": parameter_info.get('parameters', [])
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error getting method details: {str(e)}")
|
||||
return {
|
||||
"status": "error",
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
def get_dataset(self):
|
||||
back = list()
|
||||
|
||||
dataset_files_path = os.listdir(self.dataset_processed_path)
|
||||
|
||||
for dataset_file in dataset_files_path:
|
||||
path = os.path.join(self.dataset_processed_path, dataset_file)
|
||||
# 指定要查看的文件夹路径
|
||||
folder_path = Path(path)
|
||||
|
||||
# 获取文件夹下所有以 .json 结尾的文件
|
||||
json_files = list(folder_path.glob('*.json'))
|
||||
|
||||
for json_file in json_files:
|
||||
with open(json_file.as_posix(), 'r', encoding='utf-8') as f:
|
||||
json_data = json.load(f)
|
||||
|
||||
back.append(json_data)
|
||||
return back
|
||||
79
function_old/method_reader_metric.py
Normal file
79
function_old/method_reader_metric.py
Normal file
@ -0,0 +1,79 @@
|
||||
import yaml
|
||||
from typing import Dict, List
|
||||
import os
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
class MethodReader:
|
||||
"""方法配置读取器"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化方法读取器"""
|
||||
self.logger = logging.getLogger(__name__)
|
||||
self.method_config = self._load_metrics()
|
||||
|
||||
|
||||
def _load_metrics(self) -> Dict:
|
||||
"""加载方法配置文件"""
|
||||
try:
|
||||
config_path = Path('model/metrics.yaml')
|
||||
if not config_path.exists():
|
||||
raise FileNotFoundError(f"Method config file not found at {config_path}")
|
||||
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
self.logger.info("Successfully loaded method config")
|
||||
return config
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error loading method config: {str(e)}")
|
||||
raise
|
||||
|
||||
|
||||
|
||||
def get_metrics(self) -> Dict:
|
||||
"""获取预处理方法列表"""
|
||||
try:
|
||||
metrics = []
|
||||
|
||||
# 分类方法
|
||||
classification_metrics = self.method_config.get('classification', {})
|
||||
if classification_metrics:
|
||||
metrics.append({
|
||||
"name": "classification_metrics",
|
||||
"description": "分类方法评价指标",
|
||||
"metric": classification_metrics
|
||||
})
|
||||
|
||||
# 回归方法
|
||||
regression_metrics = self.method_config.get('regression', {})
|
||||
if regression_metrics:
|
||||
metrics.append({
|
||||
"name": "regression_metrics",
|
||||
"description": "回归方法评价指标",
|
||||
"metric": regression_metrics
|
||||
})
|
||||
|
||||
# 聚类方法
|
||||
clustering_metrics = self.method_config.get('clustering', {})
|
||||
if clustering_metrics:
|
||||
metrics.append({
|
||||
"name": "clustering_metrics",
|
||||
"description": "聚类方法评价指标",
|
||||
"metric": clustering_metrics
|
||||
})
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"metric": metrics
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error getting preprocessing methods: {str(e)}")
|
||||
return {
|
||||
"status": "error",
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
|
||||
135
function_old/method_reader_model.py
Normal file
135
function_old/method_reader_model.py
Normal file
@ -0,0 +1,135 @@
|
||||
import yaml
|
||||
from typing import Dict, List
|
||||
import os
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
class MethodReader:
|
||||
"""方法配置读取器"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化方法读取器"""
|
||||
self.logger = logging.getLogger(__name__)
|
||||
self.method_config = self._load_model_config()
|
||||
self.parameter_config = self._load_parameter_config()
|
||||
|
||||
def _load_model_config(self) -> Dict:
|
||||
"""加载方法配置文件"""
|
||||
try:
|
||||
config_path = Path('model/model.yaml')
|
||||
if not config_path.exists():
|
||||
raise FileNotFoundError(f"Method config file not found at {config_path}")
|
||||
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
self.logger.info("Successfully loaded method config")
|
||||
return config
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error loading method config: {str(e)}")
|
||||
raise
|
||||
|
||||
def _load_parameter_config(self) -> Dict:
|
||||
"""加载参数配置文件"""
|
||||
try:
|
||||
config_path = Path('model/parameter.yaml')
|
||||
if not config_path.exists():
|
||||
raise FileNotFoundError(f"Parameter config file not found at {config_path}")
|
||||
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
self.logger.info("Successfully loaded parameter config")
|
||||
return config
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error loading parameter config: {str(e)}")
|
||||
raise
|
||||
|
||||
def get_models(self) -> Dict:
|
||||
"""获取预处理方法列表"""
|
||||
try:
|
||||
models = []
|
||||
|
||||
# 分类方法
|
||||
classification_algorithms = list(self.method_config.get('classification_algorithms', {}).keys())
|
||||
if classification_algorithms:
|
||||
models.append({
|
||||
"name": "classification_algorithms",
|
||||
"description": "分类方法",
|
||||
"method": classification_algorithms
|
||||
})
|
||||
|
||||
# 回归方法
|
||||
regression_algorithms = list(self.method_config.get('regression_algorithms', {}).keys())
|
||||
if regression_algorithms:
|
||||
models.append({
|
||||
"name": "regression_algorithms",
|
||||
"description": "回归方法",
|
||||
"method": regression_algorithms
|
||||
})
|
||||
|
||||
# 聚类方法
|
||||
clustering_algorithms = list(self.method_config.get('clustering_algorithms', {}).keys())
|
||||
if clustering_algorithms:
|
||||
models.append({
|
||||
"name": "clustering_algorithms",
|
||||
"description": "聚类方法",
|
||||
"method": clustering_algorithms
|
||||
})
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"models": models
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error getting preprocessing methods: {str(e)}")
|
||||
return {
|
||||
"status": "error",
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
def get_model_details(self, method_name: str) -> Dict:
|
||||
"""获取指定方法的详细信息"""
|
||||
try:
|
||||
# 在各个方法类别中查找方法原理和优缺点
|
||||
method_info = None
|
||||
for category in ['classification_algorithms', 'regression_algorithms', 'clustering_algorithms']:
|
||||
if method_name in self.method_config.get(category, {}):
|
||||
method_info = self.method_config[category][method_name]
|
||||
break
|
||||
|
||||
if method_info is None:
|
||||
raise ValueError(f"Method {method_name} not found in method config")
|
||||
|
||||
# 查找方法参数信息
|
||||
parameter_info = None
|
||||
for category in ['classification_algorithms', 'regression_algorithms', 'clustering_algorithms']:
|
||||
if method_name in self.parameter_config.get(category, {}):
|
||||
parameter_info = self.parameter_config[category][method_name]
|
||||
break
|
||||
|
||||
if parameter_info is None:
|
||||
raise ValueError(f"Method {method_name} not found in parameter config")
|
||||
|
||||
# 组合返回信息
|
||||
return {
|
||||
"status": "success",
|
||||
"method": {
|
||||
"name": method_name,
|
||||
"description": parameter_info.get('description', ''),
|
||||
"principle": method_info.get('principle', ''),
|
||||
"advantages": method_info.get('advantages', []),
|
||||
"disadvantages": method_info.get('disadvantages', []),
|
||||
"applicable_scenarios": method_info.get('applicable_scenarios', []),
|
||||
"parameters": parameter_info.get('parameters', [])
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error getting method details: {str(e)}")
|
||||
return {
|
||||
"status": "error",
|
||||
"error": str(e)
|
||||
}
|
||||
454
function_old/model_manager.py
Normal file
454
function_old/model_manager.py
Normal file
@ -0,0 +1,454 @@
|
||||
import mlflow
|
||||
from mlflow.tracking import MlflowClient
|
||||
import pandas as pd
|
||||
from typing import Dict, List, Optional
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
import yaml
|
||||
import json
|
||||
import time
|
||||
import os
|
||||
import numpy as np
|
||||
from sklearn.metrics import (
|
||||
accuracy_score, precision_score, recall_score, f1_score, roc_auc_score,
|
||||
mean_absolute_error, mean_squared_error, r2_score, explained_variance_score,
|
||||
adjusted_rand_score, homogeneity_score, completeness_score, silhouette_score
|
||||
)
|
||||
import torch
|
||||
from torch.utils.data import DataLoader, TensorDataset
|
||||
|
||||
class ModelManager:
|
||||
"""模型管理类"""
|
||||
|
||||
def __init__(self, config: Dict = None):
|
||||
"""初始化模型管理器"""
|
||||
self.config = config or {}
|
||||
self.logger = logging.getLogger(__name__)
|
||||
self._setup_logging()
|
||||
self._metrics_map()
|
||||
|
||||
# 初始化MLflow客户端
|
||||
self.mlflow_uri = self.config.get('mlflow_uri', 'http://10.0.0.202:5000')
|
||||
mlflow.set_tracking_uri(self.mlflow_uri)
|
||||
self.client = MlflowClient()
|
||||
|
||||
def _setup_logging(self):
|
||||
"""设置日志"""
|
||||
log_dir = Path('.log')
|
||||
log_dir.mkdir(exist_ok=True)
|
||||
|
||||
file_handler = logging.FileHandler(
|
||||
log_dir / f'model_manager_{datetime.now():%Y%m%d_%H%M%S}.log'
|
||||
)
|
||||
file_handler.setFormatter(
|
||||
logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
||||
)
|
||||
self.logger.addHandler(file_handler)
|
||||
self.logger.setLevel(logging.INFO)
|
||||
|
||||
def _metrics_map(self):
|
||||
self.metrics_map={
|
||||
'accuracy' : accuracy_score,
|
||||
'precision' : precision_score,
|
||||
'recall' : recall_score,
|
||||
'f1' : f1_score,
|
||||
'mae' : mean_absolute_error,
|
||||
'mse' : mean_squared_error,
|
||||
# 'rmse' : np.sqrt(mean_absolute_error), # 这里要特殊处理一下
|
||||
'r2': r2_score,
|
||||
'explained_variance' : explained_variance_score,
|
||||
'adjusted_rand' : adjusted_rand_score,
|
||||
'homogeneity' : homogeneity_score,
|
||||
'completeness': completeness_score,
|
||||
'silhouette' : silhouette_score
|
||||
}
|
||||
|
||||
def get_finished_models(
|
||||
self,
|
||||
page: int = 1,
|
||||
page_size: int = 10,
|
||||
experiment_name: Optional[str] = None
|
||||
) -> Dict:
|
||||
"""
|
||||
获取已训练完成的模型列表
|
||||
|
||||
Args:
|
||||
page: 页码
|
||||
page_size: 每页数量
|
||||
experiment_name: 实验名称过滤
|
||||
|
||||
Returns:
|
||||
模型列表信息
|
||||
"""
|
||||
try:
|
||||
# 获取所有实验
|
||||
if experiment_name:
|
||||
experiment = mlflow.get_experiment_by_name(experiment_name)
|
||||
if experiment is None:
|
||||
return {
|
||||
'status': 'error',
|
||||
'message': f'Experiment {experiment_name} not found'
|
||||
}
|
||||
experiments = [experiment]
|
||||
else:
|
||||
experiments = mlflow.search_experiments()
|
||||
|
||||
# 获取所有运行记录
|
||||
all_runs = []
|
||||
for exp in experiments:
|
||||
runs = mlflow.search_runs(
|
||||
experiment_ids=[exp.experiment_id],
|
||||
filter_string="status = 'FINISHED'"
|
||||
)
|
||||
all_runs.extend(runs.to_dict('records'))
|
||||
|
||||
# 计算分页
|
||||
total_count = len(all_runs)
|
||||
start_idx = (page - 1) * page_size
|
||||
end_idx = start_idx + page_size
|
||||
page_runs = all_runs[start_idx:end_idx]
|
||||
|
||||
# 格式化模型信息
|
||||
models = []
|
||||
for run in page_runs:
|
||||
print(run.keys())
|
||||
# 收集所有参数
|
||||
params = {}
|
||||
metrics = {}
|
||||
for key, value in run.items():
|
||||
if key.startswith('params.'):
|
||||
params[key.replace('params.', '')] = value
|
||||
elif key.startswith('metrics.'):
|
||||
metrics[key.replace('metrics.', '')] = value
|
||||
|
||||
# 转换时间为本地时间
|
||||
start_time = pd.to_datetime(run['start_time']).tz_convert('Asia/Shanghai')
|
||||
end_time = pd.to_datetime(run['end_time']).tz_convert('Asia/Shanghai')
|
||||
|
||||
# 构建模型信息
|
||||
model_info = {
|
||||
'run_id': run['run_id'],
|
||||
'experiment_id': run['experiment_id'],
|
||||
'algorithm': params['algorithm'], # 从配置或其他地方获取
|
||||
'task_type': params['task_type'], # 从配置或其他地方获取
|
||||
'dataset': params['dataset'], # 从配置或其他地方获取
|
||||
'training_start_time': start_time.strftime('%Y-%m-%d %H:%M:%S'), # 格式化为本地时间字符串
|
||||
'training_end_time': end_time.strftime('%Y-%m-%d %H:%M:%S'),
|
||||
'metrics': metrics,
|
||||
'parameters': {
|
||||
k: v for k, v in params.items()
|
||||
if k not in ['principle', 'advantages', 'disadvantages']
|
||||
},
|
||||
'algorithm_info': {
|
||||
'principle': params.get('principle', ''),
|
||||
'advantages': params.get('advantages', ''),
|
||||
'disadvantages': params.get('disadvantages', '')
|
||||
},
|
||||
'mlflow_run_id': run['run_id'],
|
||||
'model_path': f"models/{run['run_id']}"
|
||||
}
|
||||
models.append(model_info)
|
||||
|
||||
return {
|
||||
'status': 'success',
|
||||
'models': models,
|
||||
'total_count': total_count,
|
||||
'page': page,
|
||||
'page_size': page_size
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"Error getting finished models: {str(e)}"
|
||||
self.logger.error(error_msg)
|
||||
return {
|
||||
'status': 'error',
|
||||
'message': error_msg
|
||||
}
|
||||
|
||||
def get_experiments(
|
||||
self,
|
||||
page: int = 1,
|
||||
page_size: int = 10,
|
||||
include_deleted: bool = False
|
||||
) -> Dict:
|
||||
"""
|
||||
获取MLflow中保存的实验列表
|
||||
|
||||
Args:
|
||||
page: 页码
|
||||
page_size: 每页数量
|
||||
include_deleted: 是否包含已删除的实验
|
||||
|
||||
Returns:
|
||||
实验列表信息
|
||||
"""
|
||||
try:
|
||||
# 获取所有实验
|
||||
experiments = mlflow.search_experiments()
|
||||
|
||||
# 过滤已删除的实验
|
||||
if not include_deleted:
|
||||
experiments = [exp for exp in experiments if exp.lifecycle_stage == 'active']
|
||||
|
||||
# 计算分页
|
||||
total_count = len(experiments)
|
||||
start_idx = (page - 1) * page_size
|
||||
end_idx = start_idx + page_size
|
||||
page_experiments = experiments[start_idx:end_idx]
|
||||
|
||||
# 格式化实验信息
|
||||
experiment_list = []
|
||||
for exp in page_experiments:
|
||||
# 获取实验的运行次数
|
||||
runs = mlflow.search_runs(
|
||||
experiment_ids=[exp.experiment_id],
|
||||
filter_string="status = 'FINISHED'"
|
||||
)
|
||||
|
||||
# 获取最后更新时间并转换为本地时间
|
||||
if len(runs) > 0:
|
||||
last_update_time = pd.to_datetime(runs['end_time'].max())
|
||||
else:
|
||||
last_update_time = pd.to_datetime(exp.creation_time)
|
||||
|
||||
# 创建时间转换为本地时间
|
||||
creation_time = pd.to_datetime(exp.creation_time)
|
||||
|
||||
experiment_info = {
|
||||
'experiment_id': exp.experiment_id,
|
||||
'name': exp.name,
|
||||
'artifact_location': exp.artifact_location,
|
||||
'lifecycle_stage': exp.lifecycle_stage,
|
||||
'creation_time': creation_time.strftime('%Y-%m-%d %H:%M:%S'),
|
||||
'last_update_time': last_update_time.strftime('%Y-%m-%d %H:%M:%S'),
|
||||
'tags': exp.tags,
|
||||
'runs_count': len(runs)
|
||||
}
|
||||
experiment_list.append(experiment_info)
|
||||
|
||||
return {
|
||||
'status': 'success',
|
||||
'experiments': experiment_list,
|
||||
'total_count': total_count,
|
||||
'page': page,
|
||||
'page_size': page_size
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"Error getting experiments: {str(e)}"
|
||||
self.logger.error(error_msg)
|
||||
return {
|
||||
'status': 'error',
|
||||
'message': error_msg
|
||||
}
|
||||
|
||||
def delete_model(self, run_id: str) -> Dict:
|
||||
"""
|
||||
删除指定的训练好的模型
|
||||
|
||||
Args:
|
||||
run_id: MLflow运行ID
|
||||
|
||||
Returns:
|
||||
删除操作的结果信息
|
||||
"""
|
||||
try:
|
||||
# 获取运行信息
|
||||
run = self.client.get_run(run_id)
|
||||
if not run:
|
||||
return {
|
||||
'status': 'error',
|
||||
'message': f'未找到运行ID为 {run_id} 的模型'
|
||||
}
|
||||
|
||||
# 获取实验ID
|
||||
experiment_id = run.info.experiment_id
|
||||
|
||||
# 获取模型信息
|
||||
run_data = mlflow.get_run(run_id)
|
||||
model_name = run_data.data.params.get('algorithm', 'Unknown')
|
||||
|
||||
# 获取工件列表
|
||||
artifacts = []
|
||||
for artifact in self.client.list_artifacts(run_id):
|
||||
artifacts.append(artifact.path)
|
||||
|
||||
# 删除运行记录
|
||||
self.client.delete_run(run_id)
|
||||
|
||||
# 记录日志
|
||||
self.logger.info(f"已删除模型 - Run ID: {run_id}, 实验ID: {experiment_id}, 模型: {model_name}")
|
||||
|
||||
return {
|
||||
'status': 'success',
|
||||
'message': '模型删除成功',
|
||||
'details': {
|
||||
'run_id': run_id,
|
||||
'experiment_id': experiment_id,
|
||||
'model_name': model_name,
|
||||
'deleted_artifacts': artifacts
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"删除模型时发生错误: {str(e)}"
|
||||
self.logger.error(error_msg)
|
||||
return {
|
||||
'status': 'error',
|
||||
'message': error_msg
|
||||
}
|
||||
|
||||
def predict(
|
||||
self,
|
||||
run_id: str,
|
||||
data_path: str,
|
||||
output_path: str,
|
||||
batch_size: int = 32,
|
||||
device: str = 'cuda' if torch.cuda.is_available() else 'cpu',
|
||||
return_proba: bool = True,
|
||||
metrics: List[str] = None
|
||||
) -> Dict:
|
||||
"""
|
||||
使用指定的模型进行预测
|
||||
|
||||
Args:
|
||||
run_id: MLflow运行ID
|
||||
data_path: 输入数据路径
|
||||
output_path: 预测结果保存路径
|
||||
batch_size: 批处理大小
|
||||
device: 计算设备 ('cuda' or 'cpu')
|
||||
return_proba: 是否返回概率预测
|
||||
metrics: 评估指标列表
|
||||
|
||||
Returns:
|
||||
预测结果信息
|
||||
"""
|
||||
# try:
|
||||
start_time = time.time()
|
||||
|
||||
# 获取模型信息
|
||||
run = self.client.get_run(run_id)
|
||||
if not run:
|
||||
return {
|
||||
'status': 'error',
|
||||
'message': f'未找到运行ID为 {run_id} 的模型'
|
||||
}
|
||||
|
||||
# 加载模型
|
||||
model = mlflow.pyfunc.load_model(f"runs:/{run_id}/model")
|
||||
model_name = run.data.params.get('algorithm', 'Unknown')
|
||||
|
||||
# 加载数据
|
||||
try:
|
||||
data = pd.read_csv(data_path)
|
||||
if 'label' in data.columns:
|
||||
y_true = data.pop('label').values
|
||||
has_labels = True
|
||||
elif 'target' in data.columns:
|
||||
y_true = data.pop('target').values
|
||||
has_labels = True
|
||||
else:
|
||||
has_labels = False
|
||||
X = data.values
|
||||
except Exception as e:
|
||||
return {
|
||||
'status': 'error',
|
||||
'message': '数据加载失败',
|
||||
'details': {
|
||||
'error_type': type(e).__name__,
|
||||
'error_message': str(e)
|
||||
}
|
||||
}
|
||||
|
||||
# 创建预测ID
|
||||
pred_id = f"pred_{datetime.now():%Y%m%d_%H%M%S}"
|
||||
|
||||
# 进行预测
|
||||
if isinstance(model, torch.nn.Module):
|
||||
# PyTorch模型预测
|
||||
model.to(device)
|
||||
model.eval()
|
||||
|
||||
dataset = TensorDataset(torch.FloatTensor(X))
|
||||
dataloader = DataLoader(dataset, batch_size=batch_size)
|
||||
|
||||
predictions = []
|
||||
probas = []
|
||||
|
||||
with torch.no_grad():
|
||||
for batch in dataloader:
|
||||
batch = batch[0].to(device)
|
||||
outputs = model(batch)
|
||||
|
||||
if return_proba:
|
||||
proba = torch.softmax(outputs, dim=1)
|
||||
probas.append(proba.cpu().numpy())
|
||||
|
||||
preds = outputs.argmax(dim=1)
|
||||
predictions.append(preds.cpu().numpy())
|
||||
|
||||
predictions = np.concatenate(predictions)
|
||||
if return_proba:
|
||||
probas = np.concatenate(probas)
|
||||
else:
|
||||
# 其他模型预测
|
||||
predictions = model.predict(X)
|
||||
if return_proba and hasattr(model, 'predict_proba'):
|
||||
probas = model.predict_proba(X)
|
||||
else:
|
||||
probas = []
|
||||
|
||||
# 计算评估指标
|
||||
metrics_results = {}
|
||||
if has_labels and metrics:
|
||||
for metric in metrics:
|
||||
if metric in self.metrics_map.keys():
|
||||
metrics_results[metric] = float(self.metrics_map[metric](y_true, predictions))
|
||||
|
||||
# 保存预测结果
|
||||
results_df = pd.DataFrame({
|
||||
'prediction': predictions
|
||||
})
|
||||
if return_proba and len(probas) > 0:
|
||||
for i in range(probas.shape[1]):
|
||||
results_df[f'probability_{i}'] = probas[:, i]
|
||||
|
||||
# 确保输出目录存在
|
||||
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
||||
results_df.to_csv(output_path, index=False)
|
||||
|
||||
# 计算执行时间
|
||||
execution_time = time.time() - start_time
|
||||
|
||||
# 记录日志
|
||||
self.logger.info(
|
||||
f"预测完成 - Run ID: {run_id}, 模型: {model_name}, "
|
||||
f"样本数: {len(predictions)}, 耗时: {execution_time:.2f}s"
|
||||
)
|
||||
|
||||
return {
|
||||
'status': 'success',
|
||||
'prediction': {
|
||||
'id': pred_id,
|
||||
'run_id': run_id,
|
||||
'model_name': model_name,
|
||||
'output_file': output_path,
|
||||
'prediction_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||||
'samples_count': len(predictions),
|
||||
'metrics': metrics_results,
|
||||
'execution_time': f"{execution_time:.2f}s"
|
||||
}
|
||||
}
|
||||
|
||||
# except Exception as e:
|
||||
# error_msg = f"预测过程发生错误: {str(e)}"
|
||||
# self.logger.error(error_msg)
|
||||
# return {
|
||||
# 'status': 'error',
|
||||
# 'message': '模型预测失败',
|
||||
# 'details': {
|
||||
# 'error_type': type(e).__name__,
|
||||
# 'error_message': str(e)
|
||||
# }
|
||||
# }
|
||||
298
function_old/model_trainer.py
Normal file
298
function_old/model_trainer.py
Normal file
@ -0,0 +1,298 @@
|
||||
import numpy as pd
|
||||
import numpy as np
|
||||
from typing import Dict, List, Optional
|
||||
import logging
|
||||
from pathlib import Path
|
||||
import datetime
|
||||
import yaml
|
||||
import mlflow
|
||||
from sklearn.metrics import (
|
||||
accuracy_score, precision_score, recall_score, f1_score, roc_auc_score,
|
||||
mean_absolute_error, mean_squared_error, r2_score, explained_variance_score,
|
||||
adjusted_rand_score, homogeneity_score, completeness_score, silhouette_score
|
||||
)
|
||||
|
||||
class ModelTrainer:
|
||||
"""模型训练类"""
|
||||
|
||||
def __init__(self, config: Dict = None):
|
||||
"""初始化模型训练器"""
|
||||
self.config = config or {}
|
||||
self.logger = logging.getLogger(__name__)
|
||||
self._setup_logging()
|
||||
self._load_metrics()
|
||||
self._load_parameters()
|
||||
self._load_model_info()
|
||||
|
||||
# with open("confg/config.yaml", 'r', encoding='utf-8') as f:
|
||||
# config = yaml.safe_load(f)
|
||||
|
||||
# 初始化MLflow
|
||||
mlflow.set_tracking_uri(self.config.get('mlflow_uri', 'http://10.0.0.202:5000'))
|
||||
|
||||
def _setup_logging(self):
|
||||
"""设置日志"""
|
||||
log_dir = Path('.log')
|
||||
log_dir.mkdir(exist_ok=True)
|
||||
|
||||
file_handler = logging.FileHandler(
|
||||
log_dir / f'model_training_{datetime.datetime.now():%Y%m%d_%H%M%S}.log'
|
||||
)
|
||||
file_handler.setFormatter(
|
||||
logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
||||
)
|
||||
self.logger.addHandler(file_handler)
|
||||
self.logger.setLevel(logging.INFO)
|
||||
|
||||
def _load_metrics(self):
|
||||
"""加载评估指标配置"""
|
||||
try:
|
||||
with open('model/metrics.yaml', 'r', encoding='utf-8') as f:
|
||||
self.metrics_config = yaml.safe_load(f)
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error loading metrics config: {str(e)}")
|
||||
raise
|
||||
|
||||
def _load_parameters(self):
|
||||
"""加载模型参数配置"""
|
||||
try:
|
||||
with open('model/parameter.yaml', 'r', encoding='utf-8') as f:
|
||||
self.parameter_config = yaml.safe_load(f)
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error loading parameter config: {str(e)}")
|
||||
raise
|
||||
|
||||
def _load_model_info(self):
|
||||
"""加载模型信息配置"""
|
||||
try:
|
||||
with open('model/model.yaml', 'r', encoding='utf-8') as f:
|
||||
self.model_info = yaml.safe_load(f)
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error loading model info: {str(e)}")
|
||||
raise
|
||||
|
||||
def _get_model_class(self, algorithm_name: str):
|
||||
"""获取模型类"""
|
||||
# 分类算法
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.svm import SVC, OneClassSVM
|
||||
from sklearn.tree import DecisionTreeClassifier
|
||||
from sklearn.ensemble import (
|
||||
RandomForestClassifier, GradientBoostingClassifier,
|
||||
AdaBoostClassifier, IsolationForest
|
||||
)
|
||||
from sklearn.naive_bayes import GaussianNB
|
||||
from sklearn.neighbors import KNeighborsClassifier
|
||||
from sklearn.neural_network import MLPClassifier
|
||||
import xgboost as xgb
|
||||
import lightgbm as lgb
|
||||
from catboost import CatBoostClassifier
|
||||
|
||||
# 回归算法
|
||||
from sklearn.linear_model import (
|
||||
LinearRegression, Ridge, Lasso,
|
||||
ElasticNet
|
||||
)
|
||||
from sklearn.svm import SVR
|
||||
from sklearn.tree import DecisionTreeRegressor
|
||||
from sklearn.ensemble import (
|
||||
RandomForestRegressor, GradientBoostingRegressor,
|
||||
AdaBoostRegressor
|
||||
)
|
||||
from catboost import CatBoostRegressor
|
||||
from sklearn.neural_network import MLPRegressor
|
||||
|
||||
# 聚类算法
|
||||
from sklearn.cluster import (
|
||||
KMeans, AgglomerativeClustering,
|
||||
DBSCAN, SpectralClustering
|
||||
)
|
||||
from sklearn.mixture import GaussianMixture
|
||||
|
||||
algorithm_map = {
|
||||
# 分类算法
|
||||
'LogisticRegression': LogisticRegression,
|
||||
'SVC': SVC,
|
||||
'SVDD': OneClassSVM, # SVDD使用OneClassSVM实现
|
||||
'DecisionTreeClassifier': DecisionTreeClassifier,
|
||||
'RandomForestClassifier': RandomForestClassifier,
|
||||
'XGBClassifier': xgb.XGBClassifier,
|
||||
'AdaBoostClassifier': AdaBoostClassifier,
|
||||
'CatBoostClassifier': CatBoostClassifier,
|
||||
'LGBMClassifier': lgb.LGBMClassifier,
|
||||
'GaussianNB': GaussianNB,
|
||||
'KNeighborsClassifier': KNeighborsClassifier,
|
||||
'MLPClassifier': MLPClassifier,
|
||||
'GradientBoostingClassifier': GradientBoostingClassifier,
|
||||
|
||||
# 回归算法
|
||||
'LinearRegression': LinearRegression,
|
||||
'Ridge': Ridge,
|
||||
'Lasso': Lasso,
|
||||
'ElasticNet': ElasticNet,
|
||||
'SVR': SVR,
|
||||
'DecisionTreeRegressor': DecisionTreeRegressor,
|
||||
'RandomForestRegressor': RandomForestRegressor,
|
||||
'XGBRegressor': xgb.XGBRegressor,
|
||||
'AdaBoostRegressor': AdaBoostRegressor,
|
||||
'CatBoostRegressor': CatBoostRegressor,
|
||||
'LGBMRegressor': lgb.LGBMRegressor,
|
||||
'MLPRegressor': MLPRegressor,
|
||||
|
||||
# 聚类算法
|
||||
'KMeans': KMeans,
|
||||
'KMeansPlusPlus': KMeans, # KMeans++使用KMeans实现，通过init参数控制
|
||||
'AgglomerativeClustering': AgglomerativeClustering,
|
||||
'DBSCAN': DBSCAN,
|
||||
'GaussianMixture': GaussianMixture,
|
||||
'SpectralClustering': SpectralClustering
|
||||
}
|
||||
|
||||
if algorithm_name not in algorithm_map:
|
||||
raise ValueError(f"Unknown algorithm: {algorithm_name}")
|
||||
|
||||
return algorithm_map[algorithm_name]
|
||||
|
||||
def _get_algorithm_info(self, algorithm_name: str) -> Dict:
|
||||
"""获取算法信息"""
|
||||
for category in ['classification_algorithms', 'regression_algorithms', 'clustering_algorithms']:
|
||||
if algorithm_name in self.model_info.get(category, {}):
|
||||
return self.model_info[category][algorithm_name]
|
||||
raise ValueError(f"Algorithm {algorithm_name} not found in model info")
|
||||
|
||||
def train_model(
|
||||
self,
|
||||
train_data: Dict,
|
||||
val_data: Dict,
|
||||
model_config: Dict,
|
||||
experiment_name: str
|
||||
) -> Dict:
|
||||
"""
|
||||
训练模型
|
||||
|
||||
Args:
|
||||
train_data: 训练数据，包含特征和标签
|
||||
val_data: 验证数据，包含特征和标签
|
||||
model_config: 模型配置，包含算法名称和参数
|
||||
experiment_name: MLflow实验名称
|
||||
|
||||
Returns:
|
||||
训练结果字典
|
||||
"""
|
||||
try:
|
||||
# 检查实验是否存在且被删除
|
||||
experiment = mlflow.get_experiment_by_name(experiment_name)
|
||||
if experiment and experiment.lifecycle_stage == 'deleted':
|
||||
# 如果实验被删除，则创建一个新的实验名称
|
||||
timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
new_experiment_name = f"{experiment_name}_{timestamp}"
|
||||
self.logger.info(f"Original experiment was deleted, creating new experiment: {new_experiment_name}")
|
||||
experiment_name = new_experiment_name
|
||||
|
||||
# 设置MLflow实验
|
||||
mlflow.set_experiment(experiment_name)
|
||||
|
||||
with mlflow.start_run() as run:
|
||||
# 记录基本信息
|
||||
mlflow.log_param('algorithm', model_config['algorithm'])
|
||||
mlflow.log_param('task_type', model_config['task_type'])
|
||||
# mlflow.log_param('dataset', experiment_name.split('_')[0]) # 从实验名称提取数据集名称
|
||||
mlflow.log_param('dataset', model_config['dataset']) # 直接写数据集路径
|
||||
|
||||
# timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
# mlflow.log_param('start_time', timestamp)
|
||||
|
||||
# 记录模型参数
|
||||
for param_name, param_value in model_config['params'].items():
|
||||
mlflow.log_param(param_name, param_value)
|
||||
|
||||
# 记录算法信息
|
||||
algorithm_info = self._get_algorithm_info(model_config['algorithm'])
|
||||
mlflow.log_param('principle', algorithm_info['principle'])
|
||||
mlflow.log_param('advantages', str(algorithm_info['advantages']))
|
||||
mlflow.log_param('disadvantages', str(algorithm_info['disadvantages']))
|
||||
|
||||
# 特殊处理KMeans++
|
||||
if model_config['algorithm'] == 'KMeansPlusPlus':
|
||||
model_config['params']['init'] = 'k-means++'
|
||||
|
||||
# 获取模型类和信息
|
||||
model_class = self._get_model_class(model_config['algorithm'])
|
||||
|
||||
# 创建模型实例
|
||||
model = model_class(**model_config['params'])
|
||||
|
||||
# 训练模型
|
||||
self.logger.info(f"Starting training {model_config['algorithm']}")
|
||||
model.fit(train_data['features'], train_data['labels'])
|
||||
|
||||
# timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
# mlflow.log_param('end_time', timestamp)
|
||||
|
||||
# 在验证集上评估
|
||||
val_predictions = model.predict(val_data['features'])
|
||||
metrics = self._calculate_metrics(
|
||||
val_data['labels'],
|
||||
val_predictions,
|
||||
model_config['task_type']
|
||||
)
|
||||
|
||||
# 记录指标
|
||||
for metric_name, metric_value in metrics.items():
|
||||
mlflow.log_metric(metric_name, metric_value)
|
||||
|
||||
# 保存模型
|
||||
mlflow.sklearn.log_model(model, "model")
|
||||
|
||||
self.logger.info(f"Training completed. Run ID: {run.info.run_id}")
|
||||
|
||||
return {
|
||||
'status': 'success',
|
||||
'run_id': run.info.run_id,
|
||||
'metrics': metrics,
|
||||
'algorithm_info': algorithm_info
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"Error training model: {str(e)}"
|
||||
self.logger.error(error_msg)
|
||||
return {
|
||||
'status': 'error',
|
||||
'message': error_msg
|
||||
}
|
||||
|
||||
def _calculate_metrics(
|
||||
self,
|
||||
true_labels: np.ndarray,
|
||||
predictions: np.ndarray,
|
||||
task_type: str
|
||||
) -> Dict:
|
||||
"""计算评估指标"""
|
||||
metrics = {}
|
||||
|
||||
if task_type == 'classification':
|
||||
metrics['accuracy'] = accuracy_score(true_labels, predictions)
|
||||
metrics['precision'] = precision_score(true_labels, predictions, average='weighted')
|
||||
metrics['recall'] = recall_score(true_labels, predictions, average='weighted')
|
||||
metrics['f1'] = f1_score(true_labels, predictions, average='weighted')
|
||||
if len(np.unique(true_labels)) == 2: # 二分类问题
|
||||
metrics['roc_auc'] = roc_auc_score(true_labels, predictions)
|
||||
|
||||
elif task_type == 'regression':
|
||||
metrics['mae'] = mean_absolute_error(true_labels, predictions)
|
||||
metrics['mse'] = mean_squared_error(true_labels, predictions)
|
||||
metrics['rmse'] = np.sqrt(metrics['mse'])
|
||||
metrics['r2'] = r2_score(true_labels, predictions)
|
||||
metrics['explained_variance'] = explained_variance_score(true_labels, predictions)
|
||||
|
||||
elif task_type == 'clustering':
|
||||
metrics['adjusted_rand'] = adjusted_rand_score(true_labels, predictions)
|
||||
metrics['homogeneity'] = homogeneity_score(true_labels, predictions)
|
||||
metrics['completeness'] = completeness_score(true_labels, predictions)
|
||||
if len(np.unique(predictions)) > 1: # 确保有多个簇
|
||||
metrics['silhouette'] = silhouette_score(
|
||||
true_labels.reshape(-1, 1),
|
||||
predictions
|
||||
)
|
||||
|
||||
return metrics
|
||||
72
main.py
Normal file
72
main.py
Normal file
@ -0,0 +1,72 @@
|
||||
from fastapi import FastAPI, HTTPException, Depends, BackgroundTasks
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from fastapi.security import OAuth2PasswordBearer
|
||||
from typing import Optional, Dict, List
|
||||
import uvicorn
|
||||
from pathlib import Path
|
||||
import logging
|
||||
import yaml
|
||||
from datetime import datetime
|
||||
|
||||
# 导入API路由
|
||||
from api.data_api import router as data_router
|
||||
from api.model_api import router as model_router
|
||||
from api.system_api import router as system_router
|
||||
|
||||
# 创建FastAPI应用
|
||||
app = FastAPI(
|
||||
title="机器学习平台API",
|
||||
description="提供数据处理、模型训练和系统监控功能的API服务",
|
||||
version="1.0.0"
|
||||
)
|
||||
|
||||
# 配置CORS
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"],
|
||||
allow_credentials=True,
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
# 设置日志
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
||||
handlers=[
|
||||
logging.FileHandler(f'.log/server_{datetime.now():%Y%m%d_%H%M%S}.log'),
|
||||
logging.StreamHandler()
|
||||
]
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 加载配置
|
||||
def load_config():
|
||||
try:
|
||||
with open('config/config.yaml', 'r', encoding='utf-8') as f:
|
||||
return yaml.safe_load(f)
|
||||
except Exception as e:
|
||||
logger.error(f"Error loading config: {str(e)}")
|
||||
return {}
|
||||
|
||||
config = load_config()
|
||||
|
||||
# 注册路由
|
||||
app.include_router(data_router, prefix="/api", tags=["数据处理"])
|
||||
app.include_router(model_router, prefix="/api", tags=["模型管理"])
|
||||
app.include_router(system_router, prefix="/api", tags=["系统监控"])
|
||||
|
||||
# 健康检查
|
||||
@app.get("/health")
|
||||
async def health_check():
|
||||
return {"status": "healthy", "timestamp": datetime.now().isoformat()}
|
||||
|
||||
if __name__ == "__main__":
|
||||
uvicorn.run(
|
||||
"main:app",
|
||||
host=config.get('host', '0.0.0.0'),
|
||||
port=config.get('port', 8000),
|
||||
reload=True,
|
||||
workers=config.get('workers', 4)
|
||||
)
|
||||
@ -0,0 +1,20 @@
|
||||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
env:
|
||||
conda: conda.yaml
|
||||
virtualenv: python_env.yaml
|
||||
loader_module: mlflow.sklearn
|
||||
model_path: model.pkl
|
||||
predict_fn: predict
|
||||
python_version: 3.9.19
|
||||
sklearn:
|
||||
code: null
|
||||
pickled_model: model.pkl
|
||||
serialization_format: cloudpickle
|
||||
sklearn_version: 1.5.2
|
||||
mlflow_version: 2.20.1
|
||||
model_size_bytes: 96534
|
||||
model_uuid: 4f7ee305cfe34dd3bbad3e03db1b81ee
|
||||
run_id: 307d0d9cebfe46b4848f365a7f8f6b28
|
||||
utc_time_created: '2025-02-21 03:33:23.487185'
|
||||
@ -0,0 +1,15 @@
|
||||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.9.19
|
||||
- pip<=24.0
|
||||
- pip:
|
||||
- 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
|
||||
name: mlflow-env
|
||||
Binary file not shown.
@ -0,0 +1,7 @@
|
||||
python: 3.9.19
|
||||
build_dependencies:
|
||||
- pip==24.0
|
||||
- setuptools==60.2.0
|
||||
- wheel==0.43.0
|
||||
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 +1,20 @@
|
||||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
env:
|
||||
conda: conda.yaml
|
||||
virtualenv: python_env.yaml
|
||||
loader_module: mlflow.sklearn
|
||||
model_path: model.pkl
|
||||
predict_fn: predict
|
||||
python_version: 3.9.19
|
||||
sklearn:
|
||||
code: null
|
||||
pickled_model: model.pkl
|
||||
serialization_format: cloudpickle
|
||||
sklearn_version: 1.5.2
|
||||
mlflow_version: 2.20.1
|
||||
model_size_bytes: 96534
|
||||
model_uuid: ab2ef994a0394e9a954176ab47a35449
|
||||
run_id: 3ed116cdfd054ac8b1cef841e8939943
|
||||
utc_time_created: '2025-02-21 03:27:30.842040'
|
||||
@ -0,0 +1,15 @@
|
||||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.9.19
|
||||
- pip<=24.0
|
||||
- pip:
|
||||
- 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
|
||||
name: mlflow-env
|
||||
Binary file not shown.
@ -0,0 +1,7 @@
|
||||
python: 3.9.19
|
||||
build_dependencies:
|
||||
- pip==24.0
|
||||
- setuptools==60.2.0
|
||||
- wheel==0.43.0
|
||||
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 +1,20 @@
|
||||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
env:
|
||||
conda: conda.yaml
|
||||
virtualenv: python_env.yaml
|
||||
loader_module: mlflow.sklearn
|
||||
model_path: model.pkl
|
||||
predict_fn: predict
|
||||
python_version: 3.9.19
|
||||
sklearn:
|
||||
code: null
|
||||
pickled_model: model.pkl
|
||||
serialization_format: cloudpickle
|
||||
sklearn_version: 1.5.2
|
||||
mlflow_version: 2.20.1
|
||||
model_size_bytes: 96534
|
||||
model_uuid: a5121dde7992439c86606f7fc45a1201
|
||||
run_id: c5457499fc434691890f5050d27dbcb2
|
||||
utc_time_created: '2025-02-21 02:59:22.229841'
|
||||
@ -0,0 +1,15 @@
|
||||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.9.19
|
||||
- pip<=24.0
|
||||
- pip:
|
||||
- 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
|
||||
name: mlflow-env
|
||||
Binary file not shown.
@ -0,0 +1,7 @@
|
||||
python: 3.9.19
|
||||
build_dependencies:
|
||||
- pip==24.0
|
||||
- setuptools==60.2.0
|
||||
- wheel==0.43.0
|
||||
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:/433321862082712659/307d0d9cebfe46b4848f365a7f8f6b28/artifacts
|
||||
end_time: 1740108805766
|
||||
entry_point_name: ''
|
||||
experiment_id: '433321862082712659'
|
||||
lifecycle_stage: active
|
||||
run_id: 307d0d9cebfe46b4848f365a7f8f6b28
|
||||
run_name: fun-vole-49
|
||||
run_uuid: 307d0d9cebfe46b4848f365a7f8f6b28
|
||||
source_name: ''
|
||||
source_type: 4
|
||||
source_version: ''
|
||||
start_time: 1740108803134
|
||||
status: 3
|
||||
tags: []
|
||||
user_id: admin-root
|
||||
@ -0,0 +1 @@
|
||||
1740108803464 0.961038961038961 0
|
||||
@ -0,0 +1 @@
|
||||
1740108803476 0.9612318007520749 0
|
||||
@ -0,0 +1 @@
|
||||
1740108803468 0.9617833147244911 0
|
||||
@ -0,0 +1 @@
|
||||
1740108803472 0.961038961038961 0
|
||||
@ -0,0 +1 @@
|
||||
1740108803480 0.9607692307692308 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
|
||||
@ -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": "307d0d9cebfe46b4848f365a7f8f6b28", "artifact_path": "model", "utc_time_created": "2025-02-21 03:33:23.487185", "model_uuid": "4f7ee305cfe34dd3bbad3e03db1b81ee", "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 @@
|
||||
fun-vole-49
|
||||
@ -0,0 +1 @@
|
||||
d222cfe96807a8e9c61e17497d77f5f40a125bd7
|
||||
@ -0,0 +1 @@
|
||||
/home/admin-root/haotian/MLPlatform/example_model_manager_new.py
|
||||
@ -0,0 +1 @@
|
||||
LOCAL
|
||||
@ -0,0 +1 @@
|
||||
admin-root
|
||||
@ -0,0 +1,15 @@
|
||||
artifact_uri: mlflow-artifacts:/433321862082712659/3ed116cdfd054ac8b1cef841e8939943/artifacts
|
||||
end_time: 1740108453229
|
||||
entry_point_name: ''
|
||||
experiment_id: '433321862082712659'
|
||||
lifecycle_stage: active
|
||||
run_id: 3ed116cdfd054ac8b1cef841e8939943
|
||||
run_name: worried-wasp-551
|
||||
run_uuid: 3ed116cdfd054ac8b1cef841e8939943
|
||||
source_name: ''
|
||||
source_type: 4
|
||||
source_version: ''
|
||||
start_time: 1740108450493
|
||||
status: 3
|
||||
tags: []
|
||||
user_id: admin-root
|
||||
@ -0,0 +1 @@
|
||||
1740108450818 0.961038961038961 0
|
||||
@ -0,0 +1 @@
|
||||
1740108450831 0.9612318007520749 0
|
||||
@ -0,0 +1 @@
|
||||
1740108450822 0.9617833147244911 0
|
||||
@ -0,0 +1 @@
|
||||
1740108450827 0.961038961038961 0
|
||||
@ -0,0 +1 @@
|
||||
1740108450836 0.9607692307692308 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
|
||||
@ -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": "3ed116cdfd054ac8b1cef841e8939943", "artifact_path": "model", "utc_time_created": "2025-02-21 03:27:30.842040", "model_uuid": "ab2ef994a0394e9a954176ab47a35449", "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 @@
|
||||
worried-wasp-551
|
||||
@ -0,0 +1 @@
|
||||
d222cfe96807a8e9c61e17497d77f5f40a125bd7
|
||||
@ -0,0 +1 @@
|
||||
/home/admin-root/haotian/MLPlatform/example_model_manager_new.py
|
||||
@ -0,0 +1 @@
|
||||
LOCAL
|
||||
@ -0,0 +1 @@
|
||||
admin-root
|
||||
@ -0,0 +1,15 @@
|
||||
artifact_uri: mlflow-artifacts:/433321862082712659/c5457499fc434691890f5050d27dbcb2/artifacts
|
||||
end_time: 1740106765431
|
||||
entry_point_name: ''
|
||||
experiment_id: '433321862082712659'
|
||||
lifecycle_stage: active
|
||||
run_id: c5457499fc434691890f5050d27dbcb2
|
||||
run_name: respected-lark-937
|
||||
run_uuid: c5457499fc434691890f5050d27dbcb2
|
||||
source_name: ''
|
||||
source_type: 4
|
||||
source_version: ''
|
||||
start_time: 1740106761872
|
||||
status: 3
|
||||
tags: []
|
||||
user_id: admin-root
|
||||
@ -0,0 +1 @@
|
||||
1740106762207 0.961038961038961 0
|
||||
@ -0,0 +1 @@
|
||||
1740106762219 0.9612318007520749 0
|
||||
@ -0,0 +1 @@
|
||||
1740106762211 0.9617833147244911 0
|
||||
@ -0,0 +1 @@
|
||||
1740106762215 0.961038961038961 0
|
||||
@ -0,0 +1 @@
|
||||
1740106762223 0.9607692307692308 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
|
||||
@ -0,0 +1 @@
|
||||
['参数较多，调优较复杂。']
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user