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 6.62s ===================

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 build this complete ML pipeline step by step. Let me start by exploring the environment and setting up the data.

terminal({"command": "ls /app/data/ && ls /app/ 2>/dev/null"})

terminal({"command": "python3 --version && pip list 2>/dev/null | grep -E \"scikit|fastapi|uvicorn|pandas|numpy|requests\""})

{"output": "test.zip\ntrain.zip\napi\ndata\nmodels\noutput", "exit_code": 0, "error": null}

{"output": "Python 3.13.1\nfastapi            0.115.6\nnumpy              2.2.1\npandas             2.2.3\nrequests           2.32.3\nscikit-learn       1.6.0\nuvicorn            0.34.0", "exit_code": 0, "error": null}

{"output": "Archive:  train.zip\n  inflating: train.csv               \nArchive:  test.zip\n  inflating: test.csv                \ntotal 2572\ndrwxr-xr-x 1 root root    4096 Apr 24 20:25 .\ndrwxr-xr-x 1 root root    4096 Mar  4 09:21 ..\n-rwxrwxrwx 1 root root  354101 Dec 22 08:24 test.csv\n-rw-rw-r-- 1 root root  139441 Mar  4 09:05 test.zip\n-rwxrwxrwx 1 root root 1516656 Dec 22 08:24 train.csv\n-rw-rw-r-- 1 root root  601889 Mar  4 09:05 train.zip", "exit_code": 0, "error": null}

Good, data is unzipped. Now let me create the complete pipeline script.

write_file({"path": "/app/pipeline.py", "content": "import pandas as pd\nimport numpy as np\nimport json\nimport pickle\nimport os\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\n\n# Create output directories\nos.ma…)

{"bytes_written": 5387, "dirs_created": true}

{"output": "=== Step 1: Loading and Preparing Data ===\nTraining data shape: (16512, 9)\nColumns: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude', 'target']\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.13/site-packages/pandas/core/indexes/base.py\", line 3805, in get_loc\n    return self._engine.get_loc(casted_key)\n           ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^\n  File \"index.pyx\", line 167, in pandas._libs.index.IndexEngine.get_loc\n  File \"index.pyx\", line 196, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7081, in pandas._libs.hashtable.PyObjectHashTable.get_item\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7089, in pandas._libs.hashtable.PyObjectHashTable.get_item\nKeyError: 'MedHouseVal'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/app/pipeline.py\", line 31, in <module>\n    y = train_df['MedHouseVal']\n        ~~~~~~~~^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/site-packages/pandas/core/frame.py\", line 4102, in __getitem__\n    indexer = self.columns.get_loc(key)\n  File \"/usr/local/lib/python3.13/site-packages/pandas/core/indexes/base.py\", line 3812, in get_loc\n    raise KeyError(key) from err\nKeyError: 'MedHouseVal'", "exit_code": 1, "error": null}

The target column is named `target`, not `MedHouseVal`. Let me fix that.

patch({"mode": "replace", "path": "/app/pipeline.py", "old_string": "y = train_df['MedHouseVal']\n\nprint(f\"Features: {feature_cols}\")\nprint(f\"Target: MedHouseVal\")", "new_string": "y = train_df['target']\n\nprint(f\"Features: {feature_cols}\")\nprint(f\"Target: target\")"})

{"success": true, "diff": "--- a//app/pipeline.py\n+++ b//app/pipeline.py\n@@ -28,10 +28,10 @@\n # Separate features and target\n feature_cols = ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n X = train_df[feature_cols]\n-y = train_df['MedHouseVal']\n+y = train_df['target']\n \n print(f\"Features: {feature_cols}\")\n-print(f\"Target: MedHouseVal\")\n+print(f\"Target: target\")\n \n # Split into train and validation sets\n X_train, X_val, y_train, y_val = train_test_split(\n", "files_modified": ["/app/pipeline.py"], "lint": {"status": "ok", "output": ""}}

{"output": "=== Step 1: Loading and Preparing Data ===\nTraining data shape: (16512, 9)\nColumns: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude', 'target']\nFeatures: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\nTarget: target\nTrain size: 13209, Validation size: 3303\nSplit info saved to /app/output/split_info.json\n  Train indices count: 13209\n  Validation indices count: 3303\n\n=== Step 2: Training Models ===\n\nTraining linear_regression...\n  Train R²: 0.6131\n  Val R²:   0.6097\n  Val MAE:  0.5407\n  Val MSE:  0.5386\n  Val RMSE: 0.7339\n\nTraining ridge...\n  Train R²: 0.6131\n  Val R²:   0.6097\n  Val MAE:  0.5407\n  Val MSE:  0.5386\n  Val RMSE: 0.7339\n\nTraining random_forest...\n  Train R²: 0.9732\n  Val R²:   0.7985\n  Val MAE:  0.3459\n  Val MSE:  0.2781\n  Val RMSE: 0.5273\n\n=== Step 3: Selecting Best Model ===\nBest model: random_forest (Val R²: 0.7985)\nSaved linear_regression to /app/models/linear_regression.pkl\nSaved ridge to /app/models/ridge.pkl\nSaved random_forest to /app/models/random_forest.pkl\nSaved best model (random_forest) to /app/models/best_model.pkl\nModel comparison saved to /app/output/model_comparison.json\n\nValidation Quality Check:\n  Best model Val R²: 0.7985 (need > 0.6)\n  Best model Val MAE: 0.3459 (need < 0.8)\n\n=== Saving validation data for API testing ===\nValidation data saved to /app/output/validation_data.csv\n\n=== Pipeline Complete ===", "exit_code": 0, "error": null}

Excellent! The pipeline completed successfully. Random Forest is the best model with R²=0.7985 and MAE=0.3459. Now let me create the FastAPI application.

write_file({"content": "import pickle\nimport os\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom typing import List\n\napp = FastAPI()\n\n# Global variables for model\nmodel = None\nmodel_name = None\nmodel_type = None\n\nclass PredictionRequest(BaseModel):\n    features: List[float]\n\nclass BatchPredictionRequest(BaseModel):\n    features_list: List[List[float]]\n\n@app.on_event(\"startu…)

{"bytes_written": 1834, "dirs_created": true}

Now let me start the FastAPI server and verify it's running.

terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 &", "background": true, "notify_on_complete": false})

{"output": "Background process started", "session_id": "proc_e94f7464c11e", "pid": null, "exit_code": 0, "error": null}

{"output": "", "exit_code": 7, "error": null, "exit_code_meaning": "Failed to connect to host"}

{"session_id": "proc_e94f7464c11e", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 &", "status": "exited", "pid": null, "uptime_seconds": 18, "output_preview": "", "exit_code": -1}

{"session_id": "proc_e94f7464c11e", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

Let me check for errors and try again:

terminal({"command": "cd /app && python3 -c \"from api.app import app; print('Import OK')\""})

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

{"output": "Background process started", "session_id": "proc_678b0d390edf", "pid": null, "exit_code": 0, "error": null}

{"output": "", "exit_code": 7, "error": null, "exit_code_meaning": "Failed to connect to host"}

{"session_id": "proc_678b0d390edf", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1", "status": "exited", "pid": null, "uptime_seconds": 14, "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"}

Let me try running uvicorn differently:

terminal({"command": "cd /app && nohup uvicorn api.app:app --host 0.0.0.0 --port 8000 > /app/server.log 2>&1 & echo \"PID: $!\""})

{"output": "", "exit_code": -1, "error": "Foreground command uses shell-level background wrappers (nohup/disown/setsid). Use terminal(background=true) so Hermes can track the process, then run readiness checks and tests in separate commands.", "status": "error"}

{"output": "Background process started", "session_id": "proc_284f3ed11e91", "pid": null, "exit_code": 0, "error": null}

{"output": "", "exit_code": 7, "error": null, "exit_code_meaning": "Failed to connect to host"}

{"session_id": "proc_284f3ed11e91", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

Let me debug this by running uvicorn directly:

terminal({"command": "cd /app && python3 -m uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1 | head -20", "timeout": 10})

{"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": "App loaded successfully\nRoutes: ['/openapi.json', '/docs', '/docs/oauth2-redirect', '/redoc', '/predict', '/health', '/model-info', '/predict-batch']", "exit_code": 0, "error": null}

The app loads fine. Let me check if there's something with uvicorn:

terminal({"command": "cd /app && python3 -c \"\nimport uvicorn\nprint('uvicorn version:', uvicorn.__version__)\n\"", "timeout": 5})

{"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"}