Merge pull request #108 from aseemanand/uday_010825

Added additional API endpoints in api_catch.py

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pycatcher"
-version = "0.0.63"
+version = "0.0.64"
 description = "This package identifies outlier(s) for a given time-series dataset in simple steps. It supports day, week, month and quarter level time-series data."
 authors = ["Aseem Anand <[email protected]>"]
 maintainers = ["Jagadish Pamarthi <[email protected]>"]

src/pycatcher/api_catch.py

@@ -2,7 +2,7 @@
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 import pandas as pd
-from src.pycatcher.catch import find_outliers_iqr
+from src.pycatcher.catch import (find_outliers_iqr, detect_outliers_stl, detect_outliers_today_classic)
 
 # Define the FastAPI app
 app = FastAPI(
@@ -48,5 +48,70 @@ async def find_outliers_api(inputs: InputModel):
 
         return OutputModel(outliers=outliers_list)
 
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/detect_outliers_stl", response_model=OutputModel, summary="Detect outliers using STL")
+async def detect_outliers_stl_api(inputs: InputModel):
+    """
+    API endpoint to detect outliers using the STL method.
+    """
+    try:
+        # Convert input data into a pandas DataFrame
+        df = pd.DataFrame(data=inputs.data, columns=inputs.columns)
+
+        # Ensure the first column is in datetime format
+        if not pd.api.types.is_datetime64_any_dtype(df.iloc[:, 0]):
+            try:
+                df.iloc[:, 0] = pd.to_datetime(df.iloc[:, 0])
+            except Exception as e:
+                raise HTTPException(status_code=400, detail=f"Error converting first column to datetime: {e}")
+
+        # Call the `detect_outliers_stl` function
+        if not callable(detect_outliers_stl):
+            raise HTTPException(status_code=500, detail="`detect_outliers_stl` is not defined or not callable")
+
+        # Call the function and get the result
+        outliers = detect_outliers_stl(df)
+
+        # Check if the result is a DataFrame
+        if isinstance(outliers, pd.DataFrame):
+            outliers_list = outliers.reset_index().to_dict(orient="records")
+        else:
+            raise HTTPException(status_code=400, detail="No outliers detected or an error occurred.")
+
+        return OutputModel(outliers=outliers_list)
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/detect_outliers_today_classic", response_model=OutputModel, summary="Detect today's outliers using "
+                                                                                "Classic method")
+async def detect_outliers_today_classic_api(inputs: InputModel):
+    """
+    API endpoint to detect today's outliers using the Classic method.
+    """
+    try:
+        df = pd.DataFrame(data=inputs.data, columns=inputs.columns)
+
+        if not pd.api.types.is_datetime64_any_dtype(df.iloc[:, 0]):
+            try:
+                df.iloc[:, 0] = pd.to_datetime(df.iloc[:, 0])
+            except Exception as e:
+                raise HTTPException(status_code=400, detail=f"Error converting first column to datetime: {e}")
+
+        outliers = detect_outliers_today_classic(df)
+
+        if isinstance(outliers, pd.DataFrame):
+            outliers_list = outliers.reset_index().to_dict(orient="records")
+        elif isinstance(outliers, str):  # Handle the "No Outliers Today!" case
+            outliers_list = [{"message": outliers}]
+        else:
+            raise HTTPException(status_code=400, detail="Unexpected output from the function.")
+
+        return OutputModel(outliers=outliers_list)
+
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))

tests/test_api_catch.py

@@ -2,6 +2,9 @@
 from src.pycatcher.api_catch import app
 import pandas as pd
 
+# Create a TestClient instance for the FastAPI app
+client = TestClient(app)
+
 def test_find_outliers_api():
     # Create a TestClient instance for the FastAPI app
     client = TestClient(app)
@@ -40,4 +43,61 @@ def test_find_outliers_api():
         f"Expected outliers {expected_outliers}, but got {response_data['outliers']}"
     )
 
+def test_detect_outliers_stl_api():
+    # Prepare test data
+    data = {
+        'ID': [1, 2, 3, 4, 5],
+        'Value': [10, 12, 14, 100, 15]
+    }
+    df = pd.DataFrame(data)
+    payload = {
+        "data": df.values.tolist(),
+        "columns": df.columns.tolist()
+    }
+
+    response = client.post("/detect_outliers_stl", json=payload)
+
+    # Expected output
+    expected_outliers = [
+        {'ID': '1970-01-01T00:00:00', 'Value': 100.0, 'index': 3}
+    ]
+
+    assert response.status_code == 200
+    response_data = response.json()
+    assert "outliers" in response_data
+
+    # Validate the outliers detected
+    assert response_data["outliers"] == expected_outliers, (
+        f"Expected outliers {expected_outliers}, but got {response_data['outliers']}"
+    )
+
+def test_detect_outliers_today_classic_api():
+    # Prepare test data
+    data = {
+        'ID': [1, 2, 3, 4, 5],
+        'Value': [10, 12, 14, 100, 15]
+    }
+
+    df = pd.DataFrame(data)
+    payload = {
+        "data": df.values.tolist(),
+        "columns": df.columns.tolist()
+    }
+
+    response = client.post("/detect_outliers_today_classic", json=payload)
+
+    # Expected output
+    expected_outliers = [
+        {'message': 'No Outliers Today!'}
+    ]
+
+    assert response.status_code == 200
+    response_data = response.json()
+    assert "outliers" in response_data
+
+    # Validate the outliers detected
+    assert response_data["outliers"] == expected_outliers, (
+        f"Expected outliers {expected_outliers}, but got {response_data['outliers']}"
+    )
+
     print("Test passed: The API detected outliers correctly.")