model_1

1name: model_1
2version: 0.1.0
3runtime: python-base
4description: Mock model for testing V2 interface
5parameters:
6  sensitivity:
7    type: float
8    default: 0.85
9    description: Base risk score sensitivity
10

1from typing import Any, Dict, Optional
2import logging
3
4logger = logging.getLogger(__name__)
5
6class ModelContext:
7    '''
8    Mock context for local execution/testing if not provided by runner
9    '''
10    def __init__(self, df=None, params: Dict[str, Any] = None):
11        self.df = df
12        self.params = params or {}
13        self.logger = logger
14
15class Model:
16    def __init__(self):
17        self.model_state = {}
18
19    def train(self, ctx: Any) -> Dict[str, Any]:
20        '''
21        Train the model
22        '''
23        ctx.logger.info("model_1 v2 training...")
24        # Simulate training logic
25        self.model_state["status"] = "trained"
26        self.model_state["accuracy"] = 0.95
27        
28        return {
29            "status": "success",
30            "metrics": {
31                "accuracy": 0.95,
32                "loss": 0.05
33            },
34            "artifacts": ["model.pt"] # Mock artifact list
35        }
36
37    def infer(self, ctx: Any, loaded_model: Any = None) -> Any:
38        '''
39        Run inference
40        '''
41        import pandas as pd
42        ctx.logger.info("model_1 v2 inference...")
43        # Simulate inference logic
44        results = []
45        for i in range(5):
46            results.append({
47                "user_id": f"user_{i}",
48                "risk_score": 0.85 + (i * 0.01),
49                "reason": "simulated_anomaly"
50            })
51        
52        return pd.DataFrame(results)
53
54