california-housing-api

grader verdict: FAIL (exit 0)
t - Failed: Fa...
FAILED ../tests/test_outputs.py::test_api_predict_batch_matches_single_predict
FAILED ../tests/test_outputs.py::test_api_predict_batch_order_preserved - Fai...
FAILED ../tests/test_outputs.py::test_api_prediction_matches_model - Failed: ...
FAILED ../tests/test_outputs.py::test_validation_vs_test_metrics - FileNotFou...
FAILED ../tests/test_outputs.py::test_predictions_reproducible_from_saved_model
================== 14 failed, 16 passed, 2 warnings in 6.80s ===================

You are an expert terminal agent. You MUST use the provided tools to complete tasks. Use the terminal tool to run shell commands, read_file to read files, write_file to write files, search_files to search, and patch to edit files. Do NOT write out solutions as text - execute them using the tools. Always start by exploring the environment with terminal commands.

# California Housing Price Prediction with FastAPI

## Goal

Build a complete machine learning regression pipeline that predicts California housing prices. Train multiple regression models, select the best performer, deploy it as a FastAPI web service, and validate predictions through API calls.

## Background

You will work with the California Housing dataset. The dataset is pre-split and provided as CSV files:
- `/app/data/train.csv`: Training data with 8 features and target column (16512 samples)
- `/app/data/test.csv`: Test data with 8 features only, no target column (4128 samples)

The 8 features are: MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude
The target is median house value in $100,000s.

Available libraries: scikit-learn (1.6.0), FastAPI (0.115.6), uvicorn (0.34.0), pandas (2.2.3), numpy (2.2.1), requests (2.32.3).

## Steps

### Step 1. Load and Prepare Data

1.1. Unzip the data files:
- Unzip `/app/data/train.zip` to get `/app/data/train.csv`
- Unzip `/app/data/test.zip` to get `/app/data/test.csv`

1.2. Load training data from `/app/data/train.csv`:
- This file contains 8 feature columns and 1 target column (9 columns total)

1.3. Split the training data into train and validation sets:
- Use sklearn's train_test_split
- test_size=0.2
- random_state=42
- Reset DataFrame index before splitting to ensure consistent indexing

1.4. Save split information to `/app/output/split_info.json`:
```json
{
  "train_size": <int>,
  "validation_size": <int>,
  "random_state": 42,
  "test_size": 0.2,
  "train_indices": [<int>, ...],
  "validation_indices": [<int>, ...]
}
```
- train_indices and validation_indices are 0-based row indices from the reset DataFrame
- Indices should be sorted in ascending order

### Step 2. Train Multiple Regression Models

Train at least THREE different regression models:

**Model 1 - Linear Regression:**
- Use LinearRegression from sklearn.linear_model

**Model 2 - Ridge Regression:**
- Use Ridge from sklearn.linear_model

**Model 3 - Random Forest Regressor:**
- Use RandomForestRegressor from sklearn.ensemble

For EACH model:
- Train on the train set (X_train, y_train)
- Calculate training R² score
- Calculate validation R² score
- Calculate validation set metrics: MAE, MSE, RMSE

### Step 3. Select Best Model and Save All Models

3.1. Compare all models based on validation R² score (higher is better)

3.2. Select the model with the highest validation R² score

3.3. Save ALL trained models to `/app/models/` directory:
- `/app/models/linear_regression.pkl`
- `/app/models/ridge.pkl`
- `/app/models/random_forest.pkl`
- `/app/models/best_model.pkl` (copy of the best performing model)
- Save the trained scikit-learn model object directly (not wrapped in a dictionary or metadata object) so that pickle.load() returns an object with a callable predict() method

3.4. Save model comparison to `/app/output/model_comparison.json`:
```json
{
  "models": [
    {
      "model_name": "linear_regression",
      "train_r2": <float>,
      "val_r2": <float>,
      "mae": <float>,
      "mse": <float>,
      "rmse": <float>
    },
    ...
  ],
  "best_model_name": "<string>"
}
```

3.5. Validation quality requirements:
- A properly trained model should achieve on validation set:
  - R² > 0.6
  - MAE < 0.8

### Step 4. Build FastAPI Application

Create a FastAPI application at `/app/api/app.py` with the following requirements:

**Implementation Notes (Recommended):**
- Load the best model from `/app/models/best_model.pkl` at application startup
- Store the model in a module-level or global variable for reuse across requests
- Consider using a startup event handler (e.g., @app.on_event("startup") or lifespan context manager)

**Endpoints:**

**Endpoint 1: POST /predict**
- Input schema:
  ```json
  {
    "features": [float, float, float, float, float, float, float, float]
  }
  ```
- Use the pre-loaded model from application startup
- Output schema:
  ```json
  {
    "prediction": <flo
… (4272 more chars)

{"output": "/app\ntotal 24\ndrwxr-xr-x 1 root root 4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root 4096 Apr 23 00:12 ..\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 api\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 data\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 models\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 output", "exit_code": 0, "error": null}

{"output": "total 736\ndrwxr-xr-x 2 root root   4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root   4096 Mar  4 09:21 ..\n-rw-rw-r-- 1 root root 139441 Mar  4 09:05 test.zip\n-rw-rw-r-- 1 root root 601889 Mar  4 09:05 train.zip", "exit_code": 0, "error": null}

{"output": "Archive:  data/train.zip\n  inflating: data/train.csv", "exit_code": 0, "error": null}

{"output": "Archive:  data/test.zip\n  inflating: data/test.csv", "exit_code": 0, "error": null}

{"output": "MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude,target\n3.2596,33.0,5.017656500802568,1.0064205457463884,2300.0,3.691813804173355,32.71,-117.03,1.03\n3.8125,49.0,4.473544973544974,1.041005291005291,1314.0,1.7380952380952381,33.77,-118.16,3.821\n4.1563,4.0,5.645833333333333,0.9851190476190477,915.0,2.7232142857142856,34.66,-120.48,1.726\n1.9425,36.0,4.002816901408451,1.0338028169014084,1418.0,3.9943661971830986,32.69,-117.11,0.934", "exit_code": 0, "error": null}

{"output": "", "exit_code": 0, "error": null}

{"bytes_written": 1821, "dirs_created": true}

{"output": "Split information saved to /app/output/split_info.json\nTrain set size: 13209, Validation set size: 3303", "exit_code": 0, "error": null}

{"bytes_written": 2697, "dirs_created": true}

{"output": "linear_regression: Val R2=0.6097, MAE=0.5407\nridge: Val R2=0.6097, MAE=0.5407\nrandom_forest: Val R2=0.7985, MAE=0.3459\nBest model: random_forest with validation R2: 0.7985", "exit_code": 0, "error": null}

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{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

{"output": "Background process started", "session_id": "proc_ac709cc81d02", "pid": null, "exit_code": 0, "error": null}

{"session_id": "proc_ac709cc81d02", "command": "cd /app && python -m uvicorn api.app:app --host 0.0.0.0 --port 8000 --background", "status": "exited", "pid": null, "uptime_seconds": 35, "output_preview": "", "exit_code": -1}

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

{"content": "     1|import json\n     2|import pickle\n     3|from fastapi import FastAPI, HTTPException\n     4|from pydantic import BaseModel\n     5|from typing import List\n     6|import uvicorn\n     7|import numpy as np\n     8|\n     9|app = FastAPI(title=\"California Housing Price Prediction API\")\n    10|\n    11|# Global variable for the model\n    12|model = None\n    13|model_name = None\n    14|\n    15|# Load the model at startup\n    16|@app.on_event(\"startup\")\n    17|async def load_model():\n    18|    global model, model_name\n    19|    model_path = \"/app/models/best_model.pkl\"\n    20|    try:\n    21|        with open(model_path, 'rb') as f:\n    22|            model = pickle.load(f)\n    23|        # Extract model name from the file path or we can set it based on the file\n    24|        # Since we saved the best model as a copy, we can get the name from the file\n    25|        # Alternatively, we can store the model name in a separate file, but for simplicity, we'll set it.\n    26|        # We know from training that the best model is random_forest, but let's make it generic.\n    27|        # We'll try to infer from the file name, but since it's best_model.pkl, we can't.\n    28|        # Instead, we can store the model name in a JSON file or we can set it here.\n    29|        # Let's set it by reading the model_comparison.json to get the best model name.\n    30|        comparison_path = \"/app/output/model_comparison.json\"\n    31|        with open(comparison_path, 'r') as f:\n    32|            comparison = json.load(f)\n    33|        model_name = comparison['best_model_name']\n    34|    except Exception as e:\n    35|        raise RuntimeError(f\"Failed to load model: {e}\")\n    36|\n    37|# Define the input schema for single prediction\n    38|class Features(BaseModel):\n    39|    features: List[float]\n    40|\n    41|# Define the input schema for batch prediction\n    42|class FeaturesList(BaseModel):\n    43|    features_list: List[List[float]]\n    44|\n    45|# Define the output schema for single prediction\n    46|class PredictionResponse(BaseModel):\n    47|    prediction: float\n    48|    model_name: str\n    49|\n    50|# Define the output schema for batch prediction\n    51|class BatchPredictionResponse(BaseModel):\n    52|    predictions: List[float]\n    53|    model_name: str\n    54|    count: int\n    55|\n    56|# Health endpoint\n    57|@app.get(\"/health\")\n    58|async def health():\n    59|    return {\n    60|        \"status\": \"healthy\",\n    61|        \"model_loaded\": model is not None\n    62|    }\n    63|\n    64|# Model info endpoint\n    65|@app.get(\"/model-info\")\n    66|async def model_info():\n    67|    if model is None:\n    68|        raise HTTPException(status_code=503, detail=\"Model not loaded\")\n    69|    # We can get more details about the model if needed, but for now:\n    70|    return {\n    71|        \"model_name\": model_name,\n    72|        \"model_type\": type(model).__name__,\n    73|        \"features_count\": 8\n    74|    }\n    75|\n    76|# Predict endpoint\n    77|@app.post(\"/predict\", response_model=PredictionResponse)\n    78|async def predict(features: Features):\n    79|    if model is None:\n    80|        raise HTTPException(status_code=503, detail=\"Model not loaded\")\n    81|    if len(features.features) != 8:\n    82|        raise HTTPException(status_code=400, detail=\"Exactly 8 features required\")\n    83|    # Convert to 2D array for prediction\n    84|    X = np.array(features.features).reshape(1, -1)\n    85|    try:\n    86|        pred = model.predict(X)[0]\n    87|    except Exception as e:\n    88|        raise HTTPException(status_code=500, detail=f\"Prediction error: {e}\")\n    89|    return {\n    90|        \"prediction\": float(pred),\n    91|        \"model_name\": model_name\n    92|    }\n    93|\n    94|# Predict batch endpoint\n    95|@app.post(\"/predict-batch\", response_model=BatchPre
… (1213 more chars)

{"output": "total 226596\ndrwxr-xr-x 1 root root      4096 Apr 23 00:22 .\ndrwxr-xr-x 1 root root      4096 Apr 23 00:22 ..\n-rw-r--r-- 1 root root 116002611 Apr 23 00:22 best_model.pkl\n-rw-r--r-- 1 root root       709 Apr 23 00:22 linear_regression.pkl\n-rw-r--r-- 1 root root 116002611 Apr 23 00:22 random_forest.pkl\n-rw-r--r-- 1 root root       682 Apr 23 00:22 ridge.pkl", "exit_code": 0, "error": null}

{"output": "Background process started", "session_id": "proc_1245698a31d7", "pid": null, "exit_code": 0, "error": null}

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{"output": "File \"<string>\", line 1\n    import pickle; import json; model_path='/app/models/best_model.pkl'; with open(model_path, 'rb') as f: m=pickle.load(f); print('Model loaded:', type(m)); comparison_path='/app/output/model_comparison.json'; with open(comparison_path, 'r') as f: c=json.load(f); print('Best model:', c['best_model_name'])\n                                                                         ^^^^\nSyntaxError: invalid syntax", "exit_code": 1, "error": null}

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

{"output": "total 1664\ndrwxr-xr-x 1 root root    4096 Apr 23 00:22 .\ndrwxr-xr-x 1 root root    4096 Apr 23 00:22 ..\n-rw-r--r-- 1 root root     720 Apr 23 00:22 model_comparison.json\n-rw-r--r-- 1 root root  170671 Apr 23 00:17 split_info.json\n-rw-r--r-- 1 root root 1209698 Apr 23 00:17 train_set.csv\n-rw-r--r-- 1 root root  302525 Apr 23 00:17 validation_set.csv", "exit_code": 0, "error": null}

{"content": "     1|{\n     2|  \"models\": [\n     3|    {\n     4|      \"model_name\": \"linear_regression\",\n     5|      \"train_r2\": 0.6131488911003316,\n     6|      \"val_r2\": 0.6097023914123513,\n     7|      \"mae\": 0.5407140746076833,\n     8|      \"mse\": 0.5386059660291211,\n     9|      \"rmse\": 0.733897789906143\n    10|    },\n    11|    {\n    12|      \"model_name\": \"ridge\",\n    13|      \"train_r2\": 0.6131487849169934,\n    14|      \"val_r2\": 0.6096997448135448,\n    15|      \"mae\": 0.5407313629383259,\n    16|      \"mse\": 0.538609618303374,\n    17|      \"rmse\": 0.7339002781736589\n    18|    },\n    19|    {\n    20|      \"model_name\": \"random_forest\",\n    21|      \"train_r2\": 0.9732129585612862,\n    22|      \"val_r2\": 0.7984855340683008,\n    23|      \"mae\": 0.3458606364819863,\n    24|      \"mse\": 0.278087518867314,\n    25|      \"rmse\": 0.5273400410241138\n    26|    }\n    27|  ],\n    28|  \"best_model_name\": \"random_forest\"\n    29|}", "total_lines": 28, "file_size": 720, "truncated": false, "is_binary": false, "is_image": false}

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{"output": "/usr/local/lib/python3.13/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n  warnings.warn(\nModel type: <class 'sklearn.ensemble._forest.RandomForestRegressor'>\nPrediction: 1.3311700000000002", "exit_code": 0, "error": null}

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{"output": "Server is ready\nPrediction successful: {'prediction': 1.3311700000000002, 'model_name': 'random_forest'}\nHealth check: {'status': 'healthy', 'model_loaded': True}\nModel info: {'model_name': 'random_forest', 'model_type': 'RandomForestRegressor', 'features_count': 8}\nServer terminated", "exit_code": 0, "error": null}

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{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

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{"session_id": "proc_99164125bf43", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

{"session_id": "proc_99164125bf43", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}