完成--完成模型训练方法

dataset/dataset_processed/breast_cancer_20250218_094848/test_breast_cancer_20250218_094848.csv

@@ -1,104 +0,0 @@
-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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dataset/dataset_processed/breast_cancer_20250218_094848/train_breast_cancer_20250218_094848.csv

@@ -1,358 +0,0 @@
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dataset/dataset_processed/breast_cancer_20250218_094848/val_breast_cancer_20250218_094848.csv

@@ -1,53 +0,0 @@
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dataset/dataset_processed/breast_cancer_20250218_094909/process_record__breast_cancer_20250218_094909.json

@@ -1,82 +0,0 @@
-{
-  "input_file": "dataset/dataset_raw/breast_cancer.csv",
-  "timestamp": "2025-02-18T09:49:09.665087",
-  "process_methods": [
-    {
-      "method_name": "SimpleImputer",
-      "params": {
-        "strategy": "mean",
-        "missing_values": NaN
-      }
-    },
-    {
-      "method_name": "IsolationForest",
-      "params": {
-        "contamination": 0.1,
-        "random_state": 42
-      }
-    },
-    {
-      "method_name": "StandardScaler",
-      "params": {
-        "with_mean": true,
-        "with_std": true
-      }
-    }
-  ],
-  "feature_methods": [],
-  "split_params": {
-    "test_size": 0.2,
-    "val_size": 0.1
-  },
-  "steps": [
-    {
-      "step": "load_data",
-      "shape": [
-        569,
-        31
-      ]
-    },
-    {
-      "step": "cleaning",
-      "method": "SimpleImputer",
-      "params": {
-        "strategy": "mean",
-        "missing_values": NaN
-      },
-      "shape": [
-        569,
-        31
-      ]
-    },
-    {
-      "step": "cleaning",
-      "method": "IsolationForest",
-      "params": {
-        "contamination": 0.1,
-        "random_state": 42
-      },
-      "shape": [
-        512,
-        31
-      ]
-    },
-    {
-      "step": "cleaning",
-      "method": "StandardScaler",
-      "params": {
-        "with_mean": true,
-        "with_std": true
-      },
-      "shape": [
-        512,
-        31
-      ]
-    }
-  ],
-  "output_files": {
-    "train": "dataset/dataset_processed/breast_cancer_20250218_094909/train_breast_cancer_20250218_094909.csv",
-    "validation": "dataset/dataset_processed/breast_cancer_20250218_094909/val_breast_cancer_20250218_094909.csv",
-    "test": "dataset/dataset_processed/breast_cancer_20250218_094909/test_breast_cancer_20250218_094909.csv"
-  }
-}

dataset/dataset_processed/breast_cancer_20250218_094909/test_breast_cancer_20250218_094909.csv

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dataset/dataset_processed/breast_cancer_20250218_094909/train_breast_cancer_20250218_094909.csv

@@ -1,358 +0,0 @@
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dataset/dataset_processed/breast_cancer_20250218_094909/val_breast_cancer_20250218_094909.csv

@@ -1,53 +0,0 @@
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dataset/dataset_processed/breast_cancer_20250219_144629/process_record__breast_cancer_20250219_144629.json

@@ -1,6 +1,6 @@
 {
   "input_file": "dataset/dataset_raw/breast_cancer.csv",
-  "timestamp": "2025-02-18T09:48:48.983863",
+  "timestamp": "2025-02-19T14:46:29.309875",
   "process_methods": [
     {
       "method_name": "SimpleImputer",
@@ -26,8 +26,7 @@
   ],
   "feature_methods": [],
   "split_params": {
-    "test_size": 0.2,
-    "val_size": 0.1
+    "val_size": 0.3
   },
   "steps": [
     {
@@ -75,8 +74,8 @@
     }
   ],
   "output_files": {
-    "train": "dataset/dataset_processed/breast_cancer_20250218_094848/train_breast_cancer_20250218_094848.csv",
-    "validation": "dataset/dataset_processed/breast_cancer_20250218_094848/val_breast_cancer_20250218_094848.csv",
-    "test": "dataset/dataset_processed/breast_cancer_20250218_094848/test_breast_cancer_20250218_094848.csv"
+    "train": "dataset/dataset_processed/breast_cancer_20250219_144629/train_breast_cancer_20250219_144629.csv",
+    "validation": "dataset/dataset_processed/breast_cancer_20250219_144629/val_breast_cancer_20250219_144629.csv",
+    "test": "dataset/dataset_processed/breast_cancer_20250219_144629/test_breast_cancer_20250219_144629.csv"
   }
 }

dataset/dataset_processed/breast_cancer_20250219_144629/test_breast_cancer_20250219_144629.csv

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example_data_processor.py

@@ -83,8 +83,8 @@ feature_methods = [
 
 # 数据集划分参数
 split_params = {
-    'test_size': 0.2,
-    'val_size': 0.1
+    # 'test_size': 0,
+    'val_size': 0.3
 }
 
 # 处理数据集

example_model_trainer.py

@@ -6,8 +6,8 @@ import numpy as np
 trainer = ModelTrainer()
 
 # 加载数据
-train_data = pd.read_csv('/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250218_094909/train_breast_cancer_20250218_094909.csv')
-val_data = pd.read_csv('/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250218_094909/val_breast_cancer_20250218_094909.csv')
+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)

function/model_trainer.py

@@ -6,9 +6,11 @@ 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
-from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, explained_variance_score
-from sklearn.metrics import adjusted_rand_score, homogeneity_score, completeness_score, silhouette_score
+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"""
@@ -20,6 +22,7 @@ class ModelTrainer:
         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)
@@ -59,6 +62,104 @@ class ModelTrainer:
             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,
@@ -86,8 +187,18 @@ class ModelTrainer:
                 # 记录参数
                 mlflow.log_params(model_config['params'])
                 
-                # 获取模型类
+                # 获取模型类和信息
                 model_class = self._get_model_class(model_config['algorithm'])
+                algorithm_info = self._get_algorithm_info(model_config['algorithm'])
+                
+                # 记录算法信息
+                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++'
                 
                 # 创建模型实例
                 model = model_class(**model_config['params'])
@@ -116,7 +227,8 @@ class ModelTrainer:
                 return {
                     'status': 'success',
                     'run_id': run.info.run_id,
-                    'metrics': metrics
+                    'metrics': metrics,
+                    'algorithm_info': algorithm_info
                 }
                 
         except Exception as e:
@@ -127,31 +239,6 @@ class ModelTrainer:
                 'message': error_msg
             }
             
-    def _get_model_class(self, algorithm_name: str):
-        """获取模型类"""
-        from sklearn.linear_model import LogisticRegression
-        from sklearn.svm import SVC
-        from sklearn.tree import DecisionTreeClassifier
-        from sklearn.ensemble import RandomForestClassifier
-        from xgboost import XGBClassifier
-        from lightgbm import LGBMClassifier
-        from catboost import CatBoostClassifier
-        
-        algorithm_map = {
-            'LogisticRegression': LogisticRegression,
-            'SVC': SVC,
-            'DecisionTreeClassifier': DecisionTreeClassifier,
-            'RandomForestClassifier': RandomForestClassifier,
-            'XGBClassifier': XGBClassifier,
-            'LGBMClassifier': LGBMClassifier,
-            'CatBoostClassifier': CatBoostClassifier
-        }
-        
-        if algorithm_name not in algorithm_map:
-            raise ValueError(f"Unknown algorithm: {algorithm_name}")
-            
-        return algorithm_map[algorithm_name]
-        
     def _calculate_metrics(
         self,
         true_labels: np.ndarray,
@@ -166,10 +253,13 @@ class ModelTrainer:
             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)
             
@@ -177,6 +267,10 @@ class ModelTrainer:
             metrics['adjusted_rand'] = adjusted_rand_score(true_labels, predictions)
             metrics['homogeneity'] = homogeneity_score(true_labels, predictions)
             metrics['completeness'] = completeness_score(true_labels, predictions)
-            metrics['silhouette'] = silhouette_score(true_labels.reshape(-1, 1), 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 

mlartifacts/824808021957073935/1aa3721352e943389fd01fb333f1716b/artifacts/model/MLmodel

@@ -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: 19864c6f1b92444f950d5293de0511bb
+run_id: 1aa3721352e943389fd01fb333f1716b
+utc_time_created: '2025-02-19 06:49:38.195255'

mlartifacts/824808021957073935/1aa3721352e943389fd01fb333f1716b/artifacts/model/conda.yaml

@@ -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

mlartifacts/824808021957073935/1aa3721352e943389fd01fb333f1716b/artifacts/model/python_env.yaml

@@ -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

mlartifacts/824808021957073935/1aa3721352e943389fd01fb333f1716b/artifacts/model/requirements.txt

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+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

mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/meta.yaml

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mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/metrics/accuracy

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mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/metrics/f1

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mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/metrics/precision

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mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/metrics/recall

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mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/metrics/roc_auc

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mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/params/advantages

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+['计算效率高，支持并行计算。', '具有内置的缺失值处理能力。']

mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/params/disadvantages

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+['参数较多，调优较复杂。']

mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/params/learning_rate

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+0.1

mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/params/max_depth

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+6

mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/params/n_estimators

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+100

mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/params/principle

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+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。

mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/params/random_state

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+42

mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/tags/mlflow.log-model.history

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+[{"run_id": "1aa3721352e943389fd01fb333f1716b", "artifact_path": "model", "utc_time_created": "2025-02-19 06:49:38.195255", "model_uuid": "19864c6f1b92444f950d5293de0511bb", "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}}}]

mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/tags/mlflow.runName

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mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/tags/mlflow.source.git.commit

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mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/tags/mlflow.source.name

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mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/tags/mlflow.source.type

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+LOCAL

mlruns/824808021957073935/1aa3721352e943389fd01fb333f1716b/tags/mlflow.user

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+admin-root

mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/meta.yaml

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+artifact_uri: mlflow-artifacts:/824808021957073935/95d6adc474f5434f84b10947067bffdb/artifacts
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mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/params/advantages

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+['计算效率高，支持并行计算。', '具有内置的缺失值处理能力。']

mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/params/disadvantages

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+['参数较多，调优较复杂。']

mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/params/learning_rate

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+0.1

mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/params/max_depth

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mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/params/n_estimators

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+100

mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/params/principle

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+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。

mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/params/random_state

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+42

mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/tags/mlflow.runName

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mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/tags/mlflow.source.git.commit

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mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/tags/mlflow.source.name

@@ -0,0 +1 @@
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mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/tags/mlflow.source.type

@@ -0,0 +1 @@
+LOCAL

mlruns/824808021957073935/95d6adc474f5434f84b10947067bffdb/tags/mlflow.user

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+admin-root

mlruns/824808021957073935/meta.yaml

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+artifact_location: mlflow-artifacts:/824808021957073935
+creation_time: 1739946957244
+experiment_id: '824808021957073935'
+last_update_time: 1739946957244
+lifecycle_stage: active
+name: breast_cancer_classification