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
================== 15 failed, 15 passed, 2 warnings in 6.46s ===================

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)

I'll help you build a complete machine learning regression pipeline for California housing price prediction with FastAPI. Let's break this down step by step.

First, let's explore the environment and unzip the data files:

terminal({"command": "ls -la /app/data/"})

terminal({"command": "unzip /app/data/train.zip -d /app/data/"})

terminal({"command": "unzip /app/data/test.zip -d /app/data/"})

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

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

Great! The data files have been unzipped successfully. Now let's load the training data and examine its structure:

read_file({"path": "/app/data/train.csv", "limit": 10})

{"content": "     1|MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude,target\n     2|3.2596,33.0,5.017656500802568,1.0064205457463884,2300.0,3.691813804173355,32.71,-117.03,1.03\n     3|3.8125,49.0,4.473544973544974,1.041005291005291,1314.0,1.7380952380952381,33.77,-118.16,3.821\n     4|4.1563,4.0,5.645833333333333,0.9851190476190477,915.0,2.7232142857142856,34.66,-120.48,1.726\n     5|1.9425,36.0,4.002816901408451,1.0338028169014084,1418.0,3.9943661971830986,32.69,-117.11,0.934\n     6|3.5542,43.0,6.268421052631579,1.1342105263157896,874.0,2.3,36.78,-119.8,0.965\n     7|6.6227,20.0,6.282147315855181,1.0087390761548065,2695.0,3.3645443196004994,37.42,-121.86,2.648\n     8|2.5192,28.0,4.345360824742268,1.074742268041237,1355.0,3.4922680412371134,34.04,-117.97,1.573\n     9|7.9892,37.0,6.052757793764988,0.9544364508393285,999.0,2.3956834532374103,37.91,-122.53,5.00001\n    10|1.5,5.0,3.620578778135048,1.0160771704180065,819.0,2.6334405144694535,34.13,-117.9,1.398\n    11|", "total_lines": 16513, "file_size": 1516656, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 16513 lines)", "is_binary": false, "is_image": false}

Now let's load the data and split it into training and validation sets. I'll create the split information file as required.

terminal({"command": "python3 -c \"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nimport json\n\n# Load training data\ndata = pd.read_csv('/app/data/train.csv')\n\n# Reset index to ensure consistent indexing\ndata = data.reset_index(drop=True)\n\n# Separate features and target\nX = data.drop('target', axis=1)\ny = data['target']\n\n# Split data (80/20)\nX_tra…)

{"output": "Traceback (most recent call last):\n  File \"<string>\", line 27, in <module>\n    'train_indices': sorted(train_indices.tolist()),\n                            ^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'list' object has no attribute 'tolist'", "exit_code": 1, "error": null}

I need to fix the code - the issue is with how I'm handling the indices. Let me correct it:

terminal({"command": "python3 -c \"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nimport json\n\n# Load training data\ndata = pd.read_csv('/app/data/train.csv')\n\n# Reset index to ensure consistent indexing\ndata = data.reset_index(drop=True)\n\n# Separate features and target\nX = data.drop('target', axis=1)\ny = data['target']\n\n# Split data (80/20)\nX_tra…)

{"output": "Train size: 13209\nValidation size: 3303\nSplit info saved to /app/output/split_info.json", "exit_code": 0, "error": null}

Great! The data has been split successfully. Now let's train the three regression models and evaluate them:

terminal({"command": "python3 -c \"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression, Ridge\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\nimport json\nimport pickle\nimport os\n\n# Load training data\ndata = pd.read_csv('/app/da…)

{"output": "File \"<string>\", line 95\n    print(f{model_info[model_name]}:\n          ^^^^^^^^^^^^^^^^^^^^^^^^^\nSyntaxError: invalid syntax. Perhaps you forgot a comma?", "exit_code": 1, "error": null}

I need to fix the syntax error in the print statement. Let me correct it:

terminal({"command": "python3 -c \"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression, Ridge\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\nimport json\nimport pickle\nimport os\n\n# Load training data\ndata = pd.read_csv('/app/da…)

{"output": "File \"<string>\", line 95\n    print(f{model_info[model_name]}:\n          ^^^^^^^^^^^^^^^^^^^^^^^^^\nSyntaxError: invalid syntax. Perhaps you forgot a comma?", "exit_code": 1, "error": null}

I apologize for the syntax error. Let me fix the print statements with proper f-string syntax:

terminal({"command": "python3 -c \"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression, Ridge\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\nimport json\nimport pickle\nimport os\n\n# Load training data\ndata = pd.read_csv('/app/da…)

{"output": "File \"<string>\", line 95\n    print(f{model_info[model_name]}:\n          ^^^^^^^^^^^^^^^^^^^^^^^^^\nSyntaxError: invalid syntax. Perhaps you forgot a comma?", "exit_code": 1, "error": null}

I apologize for the persistent syntax error. Let me fix the print statements with the correct f-string syntax:

terminal({"command": "python3 -c \"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression, Ridge\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\nimport json\nimport pickle\nimport os\n\n# Load training data\ndata = pd.read_csv('/app/da…)

{"output": "Model Comparison:\nlinear_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", "exit_code": 0, "error": null}

Great! The models have been trained successfully. The Random Forest Regressor performed the best with a validation R² of 0.7985 and MAE of 0.3459, which meets our quality requirements (R² > 0.6, MAE < 0.8).

Now I'll create the FastAPI application at `/app/api/app.py` with all the required endpoints:




<tool_call>
{"name": "write_file", "arguments": {"path": "/app/api/app.py", "content": "\"\"\"\nFastAPI application for California Housing Price Prediction\n\"\"\"\n\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom typing import List\nimport pickle\nimport os\n\n# Load the best model at startup\nmodel_path = \"/app/models/best_model.pkl\"\nmodel = None\n\n# Define input schemas\nclass PredictionInput(BaseModel):\n    features: List[float]\n\nclass BatchPredictionInput(BaseModel):\n    features_list: List[List[float]]\n\n# Initialize FastAPI app\napp = FastAPI(title=\"California Housing Price Prediction API\")\n\n@app.on_event(\"startup\")\ndef load_model():\n    global model\n    try:\n        with open(model_path, 'rb') as f:\n            model = pickle.load(f)\n        print(f\"Model loaded successfully from {model_path}\")\n    except Exception as e:\n        print(f\"Error loading model: {e}\")\n        model = None\n\n@app.get(\"/health\")\ndef health():\n    return {\n        \"status\": \"healthy\",\n        \"model_loaded\": model is not None\n    }\n\n@app.get(\"/model-info\")\ndef model_info():\n    if model is None:\n        return {\n            \"model_name\": \"\",\n            \"model_type\": \"\",\n            \"features_count\": 0\n        }\n    \n    # Get model type\n    model_type = type(model).__name__\n    model_name = \"\"\n    \n    # Try to get model name from the model object\n    if hasattr(model, 'estimator'):\n        model_name = type(model.estimator).__name__\n    else:\n        model_name = model_type\n    \n    return {\n        \"model_name\": model_name,\n        \"model_type\": model_type,\n        \"features_count\": 8\n    }\n\n@app.post(\"/predict\")\ndef predict(input_data: PredictionInput):\n    if model is None:\n        return {\"error\": \"Model not loaded\"}\n    \n    # Validate input\n    if len(input_data.features) != 8:\n        return {\"error\": \"Exactly 8 features required\"}\n    \n    # Make prediction\n    prediction = model.predict([input_data.features])[0]\n    \n    # Get model name\n    model_name = type(model).__name__\n    if hasattr(model, 'estimator'):\n        model_name = type(model.estimator).__name__\n    \n    return {\n        \"prediction\": float(prediction),\n        \"model_name\": model_name\n    }\n\n@app.post(\"/predict-batch\")\ndef predict_batch(input_data: BatchPredictionInput):\n    if model is None:\n        return {\"error\": \"Model not loaded\"}\n    \n    # Validate input\n    for features in input_data.features_list:\n        if len(features) != 8:\n            return {\"error\": \"Each sample must have exactly 8 features\"}\n    \n    # Make predictions\n    predictions = model.predict(input_data.features_list)\n    \n    # Get model name\n    model_name = type(model).__name__\n    if hasattr(model, 'estimator'):\n        model_name = type(model.estimator).__name__\n    \n    return {\n        \"predictions\": [float(p) for p in predictions],\n        \"model_name\": model_name,\n        \"count\": len(predictions)\n    }\"\"\"}}
</tool_call>