california-housing-api

{"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": "total 24\ndrwxr-xr-x 1 root root 4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root 4096 Apr 22 05:07 ..\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}

<think>Let me break down this task step by step:

1. Unzip data files from `/app/data/train.zip` and `/app/data/test.zip`
2. Load training data from CSV
3. Split into train/validation sets (80/20)
4. Train 3 regression models (LinearRegression, Ridge, RandomForest)
5. Select best model based on validation R²
6. Save all models and comparison to JSON
7. Build FastAPI application with 4 endpoints
8. Start uvicorn server
9. Test API with validation set
10. Calculate and save API metrics

Let me start by exploring the environment and unzipping the data files.
</think>

I'll work through this task step by step. Let me start by exploring the environment and unzipping the data files.

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

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

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

<think>Good, I can see the zip files in /app/data/. Let me unzip them to get the CSV files.
</think>

Let me unzip the data files first.

→ terminal({"command": "cd /app/data && unzip train.zip && unzip test.zip"})

{"output": "-rwxrwxrwx 1 root root  354101 Dec 22 08:24 /app/data/test.csv\n-rwxrwxrwx 1 root root 1516656 Dec 22 08:24 /app/data/train.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": "16513 /app/data/train.csv\n4129 /app/data/test.csv", "exit_code": 0, "error": null}

<think>Good, the files are unzipped. Now let me verify and check the data structure.
</think>

→ terminal({"command": "ls -la /app/data/*.csv"})

→ terminal({"command": "head -5 /app/data/train.csv"})

→ terminal({"command": "wc -l /app/data/train.csv && wc -l /app/data/test.csv"})

{"bytes_written": 5080, "dirs_created": true}

<think>Good, I have:
- train.csv: 16512 samples (16513 lines including header)
- test.csv: 4128 samples (4129 lines including header)
- 8 features + 1 target

Now let me:
1. Load the data
2. Split into train/validation
3. Train models
4. Save all required files

Let me create a Python script to do all this work.
</think>

Now let me create the Python script to process the data, train models, and save all required outputs.

→ write_file({"path": "/app/scripts/train_models.py", "content": "#!/usr/bin/env python3\n\"\"\"Train regression models for California Housing Price Prediction.\"\"\"\n\nimport json\nimport pickle\nimport os\nimport numpy as np\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\nfrom sklearn.linear_model import LinearRegression…)

{"output": "Loading training data...\nTraining data shape: (16512, 9)\nColumns: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude', 'target']\nX shape: (16512, 8), y shape: (16512,)\n\nSplitting data into train/validation sets (80/20)...\nTrain set size: 13209\nValidation set size: 3303\nTrain indices count: 13209\nValidation indices count: 3303\n\nSaving split_info.json...\nsplit_info.json saved!\n\n==================================================\nTraining regression models...\n==================================================\n\nTraining linear_regression...\n  Train R²: 0.613149\n  Validation R²: 0.609702\n  MAE: 0.540714\n  MSE: 0.538606\n  RMSE: 0.733898\nSaving linear_regression to /app/models/linear_regression.pkl...\nSaved linear_regression successfully!\n\nTraining ridge...\n  Train R²: 0.613149\n  Validation R²: 0.609700\n  MAE: 0.540731\n  MSE: 0.538610\n  RMSE: 0.733900\nSaving ridge to /app/models/ridge.pkl...\nSaved ridge successfully!\n\nTraining random_forest...\n  Train R²: 0.973080\n  Validation R²: 0.797842\n  MAE: 0.346504\n  MSE: 0.278976\n  RMSE: 0.528182\nSaving random_forest to /app/models/random_forest.pkl...\nSaved random_forest successfully!\n\n==================================================\nSelecting best model based on validation R²...\n==================================================\nModel: linear_regression, Val R²: 0.609702\nModel: ridge, Val R²: 0.6097\nModel: random_forest, Val R²: 0.797842\n\nBest model: random_forest with validation R² = 0.797842\n\nSaving best model to /app/models/best_model.pkl...\nBest model saved!\n\nSaving model comparison...\nmodel_comparison.json saved!\n\n==================================================\nTraining complete!\n==================================================", "exit_code": 0, "error": null}

<think>Let me run this script to train the models.
</think>

→ terminal({"command": "cd /app && python3 /app/scripts/train_models.py"})

{"output": "total 226364\ndrwxr-xr-x 1 root root      4096 Apr 22 05:13 .\ndrwxr-xr-x 1 root root      4096 Apr 22 05:13 ..\n-rw-r--r-- 1 root root 115880532 Apr 22 05:13 best_model.pkl\n-rw-r--r-- 1 root root       709 Apr 22 05:13 linear_regression.pkl\n-rw-r--r-- 1 root root 115880354 Apr 22 05:13 random_forest.pkl\n-rw-r--r-- 1 root root       681 Apr 22 05:13 ridge.pkl", "exit_code": 0, "error": null}

{"output": "total 180\ndrwxr-xr-x 1 root root   4096 Apr 22 05:13 .\ndrwxr-xr-x 1 root root   4096 Apr 22 05:13 ..\n-rw-r--r-- 1 root root    567 Apr 22 05:13 model_comparison.json\n-rw-r--r-- 1 root root 166258 Apr 22 05:13 split_info.json", "exit_code": 0, "error": null}

{"content": "     1|{\n     2|  \"train_size\": 13209,\n     3|  \"validation_size\": 3303,\n     4|  \"random_state\": 42,\n     5|  \"test_size\": 0.2,\n     6|  \"train_indices\": [\n     7|    0,\n     8|    1,\n     9|    2,\n    10|    3,\n    11|    4,\n    12|    5,\n    13|    6,\n    14|    7,\n    15|    8,\n    16|    9,\n    17|    10,\n    18|    11,\n    19|    12,\n    20|    13,\n    21|    14,\n    22|    15,\n    23|    16,\n    24|    17,\n    25|    18,\n    26|    19,\n    27|    20,\n    28|    21,\n    29|    22,\n    30|    23,\n    31|    24,\n    32|    25,\n    33|    26,\n    34|    27,\n    35|    28,\n    36|    29,\n    37|    30,\n    38|    31,\n    39|    32,\n    40|    33,\n    41|    34,\n    42|    35,\n    43|    36,\n    44|    37,\n    45|    38,\n    46|    39,\n    47|    40,\n    48|    41,\n    49|    42,\n    50|    43,\n    51|    44,\n    52|    45,\n    53|    46,\n    54|    47,\n    55|    48,\n    56|    49,\n    57|    50,\n    58|    51,\n    59|    52,\n    60|    53,\n    61|    54,\n    62|    55,\n    63|    56,\n    64|    57,\n    65|    58,\n    66|    59,\n    67|    60,\n    68|    61,\n    69|    62,\n    70|    63,\n    71|    64,\n    72|    65,\n    73|    66,\n    74|    67,\n    75|    68,\n    76|    69,\n    77|    70,\n    78|    71,\n    79|    72,\n    80|    73,\n    81|    74,\n    82|    75,\n    83|    76,\n    84|    77,\n    85|    78,\n    86|    79,\n    87|    80,\n    88|    81,\n    89|    82,\n    90|    83,\n    91|    84,\n    92|    85,\n    93|    86,\n    94|    87,\n    95|    88,\n    96|    89,\n    97|    90,\n    98|    91,\n    99|    92,\n   100|    93,\n   101|    94,\n   102|    95,\n   103|    96,\n   104|    97,\n   105|    98,\n   106|    99,\n   107|    100,\n   108|    101,\n   109|    102,\n   110|    103,\n   111|    104,\n   112|    105,\n   113|    106,\n   114|    107,\n   115|    108,\n   116|    109,\n   117|    110,\n   118|    111,\n   119|    112,\n   120|    113,\n   121|    114,\n   122|    115,\n   123|    116,\n   124|    117,\n   125|    118,\n   126|    119,\n   127|    120,\n   128|    121,\n   129|    122,\n   130|    123,\n   131|    124,\n   132|    125,\n   133|    126,\n   134|    127,\n   135|    128,\n   136|    129,\n   137|    130,\n   138|    131,\n   139|    132,\n   140|    133,\n   141|    134,\n   142|    135,\n   143|    136,\n   144|    137,\n   145|    138,\n   146|    139,\n   147|    140,\n   148|    141,\n   149|    142,\n   150|    143,\n   151|    144,\n   152|    145,\n   153|    146,\n   154|    147,\n   155|    148,\n   156|    149,\n   157|    150,\n   158|    151,\n   159|    152,\n   160|    153,\n   161|    154,\n   162|    155,\n   163|    156,\n   164|    157,\n   165|    158,\n   166|    159,\n   167|    160,\n   168|    161,\n   169|    162,\n   170|    163,\n   171|    164,\n   172|    165,\n   173|    166,\n   174|    167,\n   175|    168,\n   176|    169,\n   177|    170,\n   178|    171,\n   179|    172,\n   180|    173,\n   181|    174,\n   182|    175,\n   183|    176,\n   184|    177,\n   185|    178,\n   186|    179,\n   187|    180,\n   188|    181,\n   189|    182,\n   190|    183,\n   191|    184,\n   192|    185,\n   193|    186,\n   194|    187,\n   195|    188,\n   196|    189,\n   197|    190,\n   198|    191,\n   199|    192,\n   200|    193,\n   201|    194,\n   202|    195,\n   203|    196,\n   204|    197,\n   205|    198,\n   206|    199,\n   207|    200,\n   208|    201,\n   209|    202,\n   210|    203,\n   211|    204,\n   212|    205,\n   213|    206,\n   214|    207,\n   215|    208,\n   216|    209,\n   217|    210,\n   218|    211,\n   219|    212,\n   220|    213,\n   221|    214,\n   222|    215,\n   223|    216,\n   224|    217,\n   225|    218,\n   226|    219,\n   227|    220,\n   228|    221,\n   229|    222,\n   230|    223,\n   231|    224,\n   232|    225,\n   233|    226,\n   234|    227,\n   235|    228,\n   236|    229,\n   237|    230

<think>Excellent! The models are trained and saved. Random Forest has the best validation R² of 0.797842. Let me verify the files were created correctly.
</think>

Models trained successfully! Random Forest has the best validation R² (0.797842). Let me verify the output files.

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

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

→ read_file({"path": "/app/output/split_info.json"})

{"content": "     1|{\n     2|  \"models\": [\n     3|    {\n     4|      \"model_name\": \"linear_regression\",\n     5|      \"train_r2\": 0.613149,\n     6|      \"val_r2\": 0.609702,\n     7|      \"mae\": 0.540714,\n     8|      \"mse\": 0.538606,\n     9|      \"rmse\": 0.733898\n    10|    },\n    11|    {\n    12|      \"model_name\": \"ridge\",\n    13|      \"train_r2\": 0.613149,\n    14|      \"val_r2\": 0.6097,\n    15|      \"mae\": 0.540731,\n    16|      \"mse\": 0.53861,\n    17|      \"rmse\": 0.7339\n    18|    },\n    19|    {\n    20|      \"model_name\": \"random_forest\",\n    21|      \"train_r2\": 0.97308,\n    22|      \"val_r2\": 0.797842,\n    23|      \"mae\": 0.346504,\n    24|      \"mse\": 0.278976,\n    25|      \"rmse\": 0.528182\n    26|    }\n    27|  ],\n    28|  \"best_model_name\": \"random_forest\"\n    29|}", "total_lines": 28, "file_size": 567, "truncated": false, "is_binary": false, "is_image": false}

{"output": "<class 'sklearn.ensemble._forest.RandomForestRegressor'>\nTrue", "exit_code": 0, "error": null}

<think>The split_info.json file is very large with all indices. Let me check the model comparison JSON and verify the files are correct. Then I need to create the FastAPI application.
</think>

Let me check the model comparison JSON and verify the model files.

→ read_file({"path": "/app/output/model_comparison.json"})

→ terminal({"command": "python3 -c \"import pickle; m = open('/app/models/best_model.pkl', 'rb'); model = pickle.load(m); print(type(model)); print(hasattr(model, 'predict'))\""})

{"bytes_written": 2628, "dirs_created": true}

<think>Good, the model files are correct. Random Forest is the best model with validation R² = 0.797842 and MAE = 0.346504, which is well below the required 0.8 threshold.

Now I need to create the FastAPI application. Let me create the app.py file with all the required endpoints.
</think>

Now let me create the FastAPI application with all required endpoints.

→ write_file({"path": "/app/api/app.py", "content": "#!/usr/bin/env python3\n\"\"\"FastAPI application for California Housing Price Prediction.\"\"\"\n\nfrom fastapi import FastAPI\nfrom fastapi import HTTPException\nimport pickle\nimport json\nimport uvicorn\n\napp = FastAPI()\n\n# Load model at startup\nprint(\"Loading best model from /app/models/best_model.pkl...\")\ntry:\n    with open('/app/models/best_mo…)

{"bytes_written": 2769, "dirs_created": true}

<think>I need to create a proper FastAPI application. Let me rewrite this with correct FastAPI syntax.
</think>

Let me create a proper FastAPI application with correct syntax.

→ write_file({"path": "/app/api/app.py", "content": "#!/usr/bin/env python3\n\"\"\"FastAPI application for California Housing Price Prediction.\"\"\"\n\nfrom fastapi import FastAPI\nfrom fastapi import HTTPException\nimport pickle\nimport json\nimport numpy as np\n\n# Initialize FastAPI app\napp = FastAPI()\n\n# Load model at startup using lifespan context manager\nprint(\"Loading best model from /app/models/b…)

{"bytes_written": 2586, "dirs_created": true}

<think>The FastAPI syntax is still not quite right. Let me create a proper FastAPI application with the correct structure.
</think>

Let me create a proper FastAPI application with the correct structure.

→ write_file({"path": "/app/api/app.py", "content": "#!/usr/bin/env python3\n\"\"\"FastAPI application for California Housing Price Prediction.\"\"\"\n\nimport pickle\nimport json\nimport numpy as np\nimport uvicorn\n\n# Initialize model at module level\nMODEL_LOADED = False\nMODEL = None\nMODEL_NAME = \"unknown\"\n\n# Try to load model at startup\nprint(\"Loading best model from /app/models/best_model.pkl...\…)

{"bytes_written": 2998, "dirs_created": true}

<think>I need to create a proper FastAPI application with Pydantic models for request validation. Let me rewrite this properly.
</think>

Let me create a proper FastAPI application with correct Pydantic models and request/response schemas.

→ write_file({"path": "/app/api/app.py", "content": "#!/usr/bin/env python3\n\"\"\"FastAPI application for California Housing Price Prediction.\"\"\"\n\nimport pickle\nimport json\nimport numpy as np\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\n\n# Initialize model at module level\nMODEL_LOADED = False\nMODEL = None\nMODEL_NAME = \"unknown\"\n\n# Try to load model at startup\nprint(\"Loading b…)