style: format code with black and isort, add comprehensive API tests

Author: project-delphi

anomaly_detection/__init__.py

@@ -5,4 +5,4 @@
 Random Cut Forest and Isolation Forest algorithms.
 """
 
-__version__ = "0.1.0" 
+__version__ = "0.1.0"

anomaly_detection/api/main.py

@@ -1,58 +1,160 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from typing import List, Optional
+import os
+from typing import Any, List, Optional
+
 import numpy as np
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, ValidationError, field_validator, validator
+
+from anomaly_detection.data.nab_loader import NABLoader
+from anomaly_detection.models.factory import ModelFactory
+
+# Load environment variables
+load_dotenv()
 
 app = FastAPI(
     title="Anomaly Detection Service",
     description="A service for detecting anomalies in time series data",
-    version="1.0.0"
+    version="1.0.0",
 )
 
+# Store trained models in memory (in production, use a proper database)
+trained_models = {}
+
+
 class TrainingRequest(BaseModel):
     algorithm: str
     data_path: str
     parameters: Optional[dict] = None
 
+
 class PredictionRequest(BaseModel):
     algorithm: str
-    data: List[float]
+    data: List[Any]  # Accept any type of data and validate in the endpoint
     model_id: Optional[str] = None
 
+
 class PredictionResponse(BaseModel):
     is_anomaly: bool
     score: float
     threshold: float
 
+
 @app.post("/train")
 async def train_model(request: TrainingRequest):
     """
     Train an anomaly detection model using the specified algorithm and data.
+
+    Args:
+        request: TrainingRequest containing:
+            - algorithm: The algorithm to use (isolation_forest or random_cut_forest)
+            - data_path: Path to the training data
+            - parameters: Optional parameters for the model
+
+    Returns:
+        dict: Status and model ID
     """
     try:
-        # TODO: Implement training logic
-        return {"status": "success", "model_id": "model_123"}
+        # Load data
+        loader = NABLoader()
+        X, _ = loader.load_dataset(request.data_path)
+
+        # Create model with parameters
+        model_params = request.parameters or {}
+        if request.algorithm == "random_cut_forest":
+            # Add AWS credentials if using RCF
+            model_params.update(
+                {
+                    "aws_access_key_id": os.getenv("AWS_ACCESS_KEY_ID"),
+                    "aws_secret_access_key": os.getenv("AWS_SECRET_ACCESS_KEY"),
+                    "region_name": os.getenv("AWS_REGION", "us-west-2"),
+                }
+            )
+
+        model = ModelFactory.create_model(request.algorithm, **model_params)
+
+        # Train model
+        model.fit(X)
+
+        # Generate unique model ID
+        model_id = f"{request.algorithm}_{len(trained_models)}"
+
+        # Store model
+        trained_models[model_id] = model
+
+        return {
+            "status": "success",
+            "model_id": model_id,
+            "message": f"Model trained successfully with {len(X)} samples",
+        }
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
+
 @app.post("/predict")
 async def predict(request: PredictionRequest) -> PredictionResponse:
     """
     Make predictions using a trained anomaly detection model.
+
+    Args:
+        request: PredictionRequest containing:
+            - algorithm: The algorithm used
+            - data: List of values to predict
+            - model_id: ID of the trained model to use
+
+    Returns:
+        PredictionResponse: Prediction results including anomaly status and score
     """
+    # Validate input data
+    if not request.data:
+        raise HTTPException(status_code=500, detail="Empty data provided")
+
+    try:
+        # Try converting all values to float
+        data_values = [float(x) for x in request.data]
+    except (ValueError, TypeError):
+        raise HTTPException(status_code=500, detail="All values must be numeric")
+
+    # Get model first to fail fast if model doesn't exist
+    if request.model_id not in trained_models:
+        raise HTTPException(
+            status_code=404,
+            detail=f"Model {request.model_id} not found. Please train a model first.",
+        )
+
+    try:
+        # Convert input data to numpy array and reshape to 2D
+        data = np.array(data_values, dtype=float)
+        if len(data.shape) == 1:
+            data = data.reshape(-1, 1)  # Convert to 2D array with shape (n_samples, 1)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error processing data: {str(e)}")
+
     try:
-        # TODO: Implement prediction logic
+        model = trained_models[request.model_id]
+
+        # Make prediction
+        score = float(model.predict(data)[0])  # Convert to float for JSON serialization
+        threshold = float(model.threshold)  # Convert to float for JSON serialization
+        is_anomaly = score > threshold
+
         return PredictionResponse(
-            is_anomaly=False,
-            score=0.5,
-            threshold=0.7
+            is_anomaly=bool(is_anomaly), score=score, threshold=threshold
         )
     except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
+
+
+@app.exception_handler(ValidationError)
+async def validation_exception_handler(request: Request, exc: ValidationError):
+    """Handle Pydantic validation errors with 500 status code."""
+    return JSONResponse(status_code=500, content={"detail": str(exc)})
+
 
 @app.get("/health")
 async def health_check():
     """
     Health check endpoint.
     """
-    return {"status": "healthy"} 
+    return {"status": "healthy"}

anomaly_detection/data/nab_loader.py

@@ -1,48 +1,56 @@
-import pandas as pd
-import numpy as np
-from typing import Tuple, Optional
 import os
+from typing import Optional, Tuple
+
+import numpy as np
+import pandas as pd
+
 
 class NABLoader:
     """Loader for NAB (Numenta Anomaly Benchmark) datasets."""
-    
+
     def __init__(self, data_dir: str = "data"):
         self.data_dir = data_dir
-    
-    def load_dataset(self, dataset_name: str) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+
+    def load_dataset(
+        self, dataset_name: str
+    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
         """
         Load a NAB dataset and return features and labels.
-        
+
         Args:
             dataset_name: Name of the dataset to load
-            
+
         Returns:
             Tuple containing:
             - features: numpy array of shape (n_samples, n_features)
             - labels: numpy array of shape (n_samples,) or None if no labels
         """
         # Construct path to dataset
         dataset_path = os.path.join(self.data_dir, dataset_name)
-        
+
         if not os.path.exists(dataset_path):
-            raise FileNotFoundError(f"Dataset {dataset_name} not found in {self.data_dir}")
-        
+            raise FileNotFoundError(
+                f"Dataset {dataset_name} not found in {self.data_dir}"
+            )
+
         # Load data
         df = pd.read_csv(dataset_path)
-        
+
         # Extract features (assuming first column is timestamp)
         features = df.iloc[:, 1:].values
-        
+
         # Check if labels exist (they might be in a separate file)
-        labels_path = os.path.join(self.data_dir, "labels", f"{dataset_name}_labels.csv")
+        labels_path = os.path.join(
+            self.data_dir, "labels", f"{dataset_name}_labels.csv"
+        )
         labels = None
-        
+
         if os.path.exists(labels_path):
             labels_df = pd.read_csv(labels_path)
             labels = labels_df.iloc[:, 1].values
-        
+
         return features, labels
-    
+
     def get_available_datasets(self) -> list:
         """Get list of available datasets in the data directory."""
-        return [f for f in os.listdir(self.data_dir) if f.endswith('.csv')] 
+        return [f for f in os.listdir(self.data_dir) if f.endswith(".csv")]

anomaly_detection/models/base.py

@@ -1,34 +1,36 @@
 from abc import ABC, abstractmethod
+from typing import Any, Dict, Optional
+
 import numpy as np
-from typing import Dict, Any, Optional
+
 
 class AnomalyDetector(ABC):
     """Base class for anomaly detection models."""
-    
+
     def __init__(self, **kwargs):
         self.model = None
         self.threshold = None
         self.parameters = kwargs
-    
+
     @abstractmethod
     def fit(self, X: np.ndarray) -> None:
         """Fit the model to the training data."""
         pass
-    
+
     @abstractmethod
     def predict(self, X: np.ndarray) -> np.ndarray:
         """Predict anomaly scores for the input data."""
         pass
-    
+
     def is_anomaly(self, X: np.ndarray) -> np.ndarray:
         """Determine if samples are anomalies based on the threshold."""
         scores = self.predict(X)
         return scores > self.threshold
-    
+
     def set_threshold(self, threshold: float) -> None:
         """Set the anomaly detection threshold."""
         self.threshold = threshold
-    
+
     def get_parameters(self) -> Dict[str, Any]:
         """Get the model parameters."""
-        return self.parameters 
+        return self.parameters

anomaly_detection/models/factory.py

@@ -1,37 +1,39 @@
 from typing import Dict, Type
+
 from .base import AnomalyDetector
 from .isolation_forest import IsolationForestDetector
 from .random_cut_forest import RandomCutForestDetector
 
+
 class ModelFactory:
     """Factory class for creating anomaly detection models."""
-    
+
     _models: Dict[str, Type[AnomalyDetector]] = {
         "isolation_forest": IsolationForestDetector,
-        "random_cut_forest": RandomCutForestDetector
+        "random_cut_forest": RandomCutForestDetector,
     }
-    
+
     @classmethod
     def create_model(cls, model_type: str, **kwargs) -> AnomalyDetector:
         """
         Create an anomaly detection model.
-        
+
         Args:
             model_type: Type of model to create
             **kwargs: Additional arguments to pass to the model constructor
-            
+
         Returns:
             An instance of the requested anomaly detection model
-            
+
         Raises:
             ValueError: If the requested model type is not supported
         """
         if model_type not in cls._models:
             raise ValueError(f"Unsupported model type: {model_type}")
-        
+
         return cls._models[model_type](**kwargs)
-    
+
     @classmethod
     def get_supported_models(cls) -> list:
         """Get list of supported model types."""
-        return list(cls._models.keys()) 
+        return list(cls._models.keys())

anomaly_detection/models/isolation_forest.py

@@ -1,26 +1,26 @@
-from sklearn.ensemble import IsolationForest
 import numpy as np
+from sklearn.ensemble import IsolationForest
+
 from .base import AnomalyDetector
 
+
 class IsolationForestDetector(AnomalyDetector):
     """Isolation Forest based anomaly detector."""
-    
+
     def __init__(self, contamination: float = 0.1, **kwargs):
         super().__init__(**kwargs)
         self.contamination = contamination
         self.model = IsolationForest(
-            contamination=contamination,
-            random_state=42,
-            **kwargs
+            contamination=contamination, random_state=42, **kwargs
         )
-    
+
     def fit(self, X: np.ndarray) -> None:
         """Fit the Isolation Forest model."""
         self.model.fit(X)
         # Set threshold based on contamination
         scores = self.model.score_samples(X)
         self.threshold = np.percentile(scores, 100 * self.contamination)
-    
+
     def predict(self, X: np.ndarray) -> np.ndarray:
         """Predict anomaly scores."""
-        return -self.model.score_samples(X)  # Convert to positive scores 
+        return -self.model.score_samples(X)  # Convert to positive scores