breast-cancer-mlflow

{"output": "/app\ntotal 28\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root 4096 Apr 22 02:26 ..\ndrwxr-xr-x 2 root root 4096 Mar  4 09:20 api\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 data\ndrwxr-xr-x 3 root root 4096 Mar  4 09:20 mlflow\ndrwxr-xr-x 2 root root 4096 Mar  4 09:20 output\n-rwxr-xr-x 1 root root  212 Mar  4 09:20 start.sh", "exit_code": 0, "error": null}

<think>This is a comprehensive machine learning classification pipeline project with several requirements. Let me break down the steps:

1. **Load and Explore Data**: 
   - Load train.csv and test.csv
   - Split training data into train/validation sets (80/20)
   - Save split info to JSON

2. **Connect to MLflow**: 
   - Set tracking URI to http://127.0.0.1:5000
   - Use /app/mlflow for storage

3. **Train Multiple Models**:
   - Logistic Regression
   - Support Vector Machine (SVC)
   - Random Forest Classifier
   - Each with specific allowed parameters
   - Log everything to MLflow experiment "breast-cancer-classification"

4. **Select Best Model and Register**:
   - Query all runs
   - Select best based on val_accuracy
   - Register to Model Registry
   - Save experiment and best model info to JSON

5. **Build FastAPI Application**:
   - Load model from MLflow registry
   - Create 4 endpoints: /predict, /health, /model-info, /predict-batch
   - Set up proper input/output schemas

6. **Start Server and Test**:
   - Start uvicorn server
   - Test with test data
   - Save predictions to CSV

7. **Performance Requirements**:
   - All metrics >= 0.9
   - Validation-Test difference <= 0.1
   - Class balance requirements

Let me start by exploring the current directory and understanding the data structure.</think>
I'll help you build a complete machine learning classification pipeline for breast cancer diagnosis. Let me start by exploring the environment and understanding the data structure.

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

{"output": "total 136\ndrwxr-xr-x 1 root root  4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root  4096 Mar  4 09:20 ..\ndrwxr-xr-x 2 root root  4096 Mar  4 09:20 submission\n-rw-rw-r-- 1 root root 72424 Mar  4 09:05 test.csv\n-rw-rw-r-- 1 root root 48762 Mar  4 09:05 train.csv", "exit_code": 0, "error": null}

<think>I can see we have the /app directory with the following structure:
- api/ (directory for FastAPI application)
- data/ (directory for CSV files)
- mlflow/ (directory for MLflow storage)
- output/ (directory for output files)
- start.sh (script)

Let me check the data directory to see what files are available.</think>

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

{"output": "total 12\ndrwxr-xr-x 2 root root 4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 ..", "exit_code": 0, "error": null}

<think>Great! I can see the data files are there:
- /app/data/train.csv
- /app/data/test.csv
- /app/data/submission/ (directory for output predictions)

Let me check the output directory structure as well.</think>

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

{"output": "total 12\ndrwxr-xr-x 2 root root 4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 ..", "exit_code": 0, "error": null}

<think>Good, the output directory exists and is empty. Let me also check if there's an api directory structure.</think>

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

{"content": "     1|mean radius,mean texture,mean perimeter,mean area,mean smoothness,mean compactness,mean concavity,mean concave points,mean symmetry,mean fractal dimension,radius error,texture error,perimeter error,area error,smoothness error,compactness error,concavity error,concave points error,symmetry error,fractal dimension error,worst radius,worst texture,worst perimeter,worst area,worst smoothness,worst compactness,worst concavity,worst concave points,worst symmetry,worst fractal dimension,target\n     2|12.99,14.23,84.08,514.3,0.09462,0.09965,0.03738,0.02098,0.1652,0.07238,0.1814,0.6412,0.9219,14.41,0.005231,0.02305,0.03113,0.007315,0.01639,0.005701,13.72,16.91,87.38,576.0,0.1142,0.1975,0.145,0.0585,0.2432,0.1009,1\n     3|16.16,21.54,106.2,809.8,0.1008,0.1284,0.1043,0.05613,0.216,0.05891,0.4332,1.265,2.844,43.68,0.004877,0.01952,0.02219,0.009231,0.01535,0.002373,19.47,31.68,129.7,1175.0,0.1395,0.3055,0.2992,0.1312,0.348,0.07619,0\n     4|9.876,19.4,63.95,298.3,0.1005,0.09697,0.06154,0.03029,0.1945,0.06322,0.1803,1.222,1.528,11.77,0.009058,0.02196,0.03029,0.01112,0.01609,0.00357,10.76,26.83,72.22,361.2,0.1559,0.2302,0.2644,0.09749,0.2622,0.0849,1\n     5|11.62,18.18,76.38,408.8,0.1175,0.1483,0.102,0.05564,0.1957,0.07255,0.4101,1.74,3.027,27.85,0.01459,0.03206,0.04961,0.01841,0.01807,0.005217,13.36,25.4,88.14,528.1,0.178,0.2878,0.3186,0.1416,0.266,0.0927,1\n     6|13.0,25.13,82.61,520.2,0.08369,0.05073,0.01206,0.01762,0.1667,0.05449,0.2621,1.232,1.657,21.19,0.006054,0.008974,0.005681,0.006336,0.01215,0.001514,14.34,31.88,91.06,628.5,0.1218,0.1093,0.04462,0.05921,0.2306,0.06291,1\n     7|13.74,17.91,88.12,585.0,0.07944,0.06376,0.02881,0.01329,0.1473,0.0558,0.25,0.7574,1.573,21.47,0.002838,0.01592,0.0178,0.005828,0.01329,0.001976,15.34,22.46,97.19,725.9,0.09711,0.1824,0.1564,0.06019,0.235,0.07014,1\n     8|7.691,25.44,48.34,170.4,0.08668,0.1199,0.09252,0.01364,0.2037,0.07751,0.2196,1.479,1.445,11.73,0.01547,0.06457,0.09252,0.01364,0.02105,0.007551,8.678,31.89,54.49,223.6,0.1596,0.3064,0.3393,0.05,0.279,0.1066,1\n     9|14.29,16.82,90.3,632.6,0.06429,0.02675,0.00725,0.00625,0.1508,0.05376,0.1302,0.7198,0.8439,10.77,0.003492,0.00371,0.004826,0.003608,0.01536,0.001381,14.91,20.65,94.44,684.6,0.08567,0.05036,0.03866,0.03333,0.2458,0.0612,1\n    10|12.07,13.44,77.83,445.2,0.11,0.09009,0.03781,0.02798,0.1657,0.06608,0.2513,0.504,1.714,18.54,0.007327,0.01153,0.01798,0.007986,0.01962,0.002234,13.45,15.77,86.92,549.9,0.1521,0.1632,0.1622,0.07393,0.2781,0.08052,1\n    11|11.43,17.31,73.66,398.0,0.1092,0.09486,0.02031,0.01861,0.1645,0.06562,0.2843,1.908,1.937,21.38,0.006664,0.01735,0.01158,0.00952,0.02282,0.003526,12.78,26.76,82.66,503.0,0.1413,0.1792,0.07708,0.06402,0.2584,0.08096,1\n    12|8.219,20.7,53.27,203.9,0.09405,0.1305,0.1321,0.02168,0.2222,0.08261,0.1935,1.962,1.243,10.21,0.01243,0.05416,0.07753,0.01022,0.02309,0.01178,9.092,29.72,58.08,249.8,0.163,0.431,0.5381,0.07879,0.3322,0.1486,1\n    13|12.46,24.04,83.97,475.9,0.1186,0.2396,0.2273,0.08543,0.203,0.08243,0.2976,1.599,2.039,23.94,0.007149,0.07217,0.07743,0.01432,0.01789,0.01008,15.09,40.68,97.65,711.4,0.1853,1.058,1.105,0.221,0.4366,0.2075,0\n    14|12.18,14.08,77.25,461.4,0.07734,0.03212,0.01123,0.005051,0.1673,0.05649,0.2113,0.5996,1.438,15.82,0.005343,0.005767,0.01123,0.005051,0.01977,0.0009502,12.85,16.47,81.6,513.1,0.1001,0.05332,0.04116,0.01852,0.2293,0.06037,1\n    15|14.59,22.68,96.39,657.1,0.08473,0.133,0.1029,0.03736,0.1454,0.06147,0.2254,1.108,2.224,19.54,0.004242,0.04639,0.06578,0.01606,0.01638,0.004406,15.48,27.27,105.9,733.5,0.1026,0.3171,0.3662,0.1105,0.2258,0.08004,1\n    16|8.888,14.64,58.79,244.0,0.09783,0.1531,0.08606,0.02872,0.1902,0.0898,0.5262,0.8522,3.168,25.44,0.01721,0.09368,0.05671,0.01766,0.02541,0.02193,9.733,15.67,62.56,284.4,0.1207,0.2436,0.1434,0.04786,0.2254,0.1084,1\n    17|14.06,17.18,89.75,609.1,0.08045,0.05361,0.02681,0.03251,0.1641,0.05764,0.1504,1.685,1.237,12.67,0.005371,0.01273,0.01132,0.009155,0.01719,0.001444,14.92,25.34,96.

<think>Now let me start by examining the data files to understand their structure. I'll first read the train.csv file to see what it contains.</think>

→ read_file({"path": "/app/data/train.csv", "limit": 20})

{"content": "     1|mean radius,mean texture,mean perimeter,mean area,mean smoothness,mean compactness,mean concavity,mean concave points,mean symmetry,mean fractal dimension,radius error,texture error,perimeter error,area error,smoothness error,compactness error,concavity error,concave points error,symmetry error,fractal dimension error,worst radius,worst texture,worst perimeter,worst area,worst smoothness,worst compactness,worst concavity,worst concave points,worst symmetry,worst fractal dimension\n     2|12.31,16.52,79.19,470.9,0.09172,0.06829,0.03372,0.02272,0.172,0.05914,0.2505,1.025,1.74,19.68,0.004854,0.01819,0.01826,0.007965,0.01386,0.002304,14.11,23.21,89.71,611.1,0.1176,0.1843,0.1703,0.0866,0.2618,0.07609\n     3|10.16,19.59,64.73,311.7,0.1003,0.07504,0.005025,0.01116,0.1791,0.06331,0.2441,2.09,1.648,16.8,0.01291,0.02222,0.004174,0.007082,0.02572,0.002278,10.65,22.88,67.88,347.3,0.1265,0.12,0.01005,0.02232,0.2262,0.06742\n     4|15.46,23.95,103.8,731.3,0.1183,0.187,0.203,0.0852,0.1807,0.07083,0.3331,1.961,2.937,32.52,0.009538,0.0494,0.06019,0.02041,0.02105,0.006,17.11,36.33,117.7,909.4,0.1732,0.4967,0.5911,0.2163,0.3013,0.1067\n     5|9.742,15.67,61.5,289.9,0.09037,0.04689,0.01103,0.01407,0.2081,0.06312,0.2684,1.409,1.75,16.39,0.0138,0.01067,0.008347,0.009472,0.01798,0.004261,10.75,20.88,68.09,355.2,0.1467,0.0937,0.04043,0.05159,0.2841,0.08175\n     6|12.05,14.63,78.04,449.3,0.1031,0.09092,0.06592,0.02749,0.1675,0.06043,0.2636,0.7294,1.848,19.87,0.005488,0.01427,0.02322,0.00566,0.01428,0.002422,13.76,20.7,89.88,582.6,0.1494,0.2156,0.305,0.06548,0.2747,0.08301\n     7|14.58,13.66,94.29,658.8,0.09832,0.08918,0.08222,0.04349,0.1739,0.0564,0.4165,0.6237,2.561,37.11,0.004953,0.01812,0.03035,0.008648,0.01539,0.002281,16.76,17.24,108.5,862.0,0.1223,0.1928,0.2492,0.09186,0.2626,0.07048\n     8|14.87,16.67,98.64,682.5,0.1162,0.1649,0.169,0.08923,0.2157,0.06768,0.4266,0.9489,2.989,41.18,0.006985,0.02563,0.03011,0.01271,0.01602,0.003884,18.81,27.37,127.1,1095.0,0.1878,0.448,0.4704,0.2027,0.3585,0.1065\n     9|12.81,13.06,81.29,508.8,0.08739,0.03774,0.009193,0.0133,0.1466,0.06133,0.2889,0.9899,1.778,21.79,0.008534,0.006364,0.00618,0.007408,0.01065,0.003351,13.63,16.15,86.7,570.7,0.1162,0.05445,0.02758,0.0399,0.1783,0.07319\n    10|9.742,19.12,61.93,289.7,0.1075,0.08333,0.008934,0.01967,0.2538,0.07029,0.6965,1.747,4.607,43.52,0.01307,0.01885,0.006021,0.01052,0.031,0.004225,11.21,23.17,71.79,380.9,0.1398,0.1352,0.02085,0.04589,0.3196,0.08009\n    11|7.729,25.49,47.98,178.8,0.08098,0.04878,0.0,0.0,0.187,0.07285,0.3777,1.462,2.492,19.14,0.01266,0.009692,0.0,0.0,0.02882,0.006872,9.077,30.92,57.17,248.0,0.1256,0.0834,0.0,0.0,0.3058,0.09938\n    12|14.6,23.29,93.97,664.7,0.08682,0.06636,0.0839,0.05271,0.1627,0.05416,0.4157,1.627,2.914,33.01,0.008312,0.01742,0.03389,0.01576,0.0174,0.002871,15.79,31.71,102.2,758.2,0.1312,0.1581,0.2675,0.1359,0.2477,0.06836\n    13|10.05,17.53,64.41,310.8,0.1007,0.07326,0.02511,0.01775,0.189,0.06331,0.2619,2.015,1.778,16.85,0.007803,0.01449,0.0169,0.008043,0.021,0.002778,11.16,26.84,71.98,384.0,0.1402,0.1402,0.1055,0.06499,0.2894,0.07664\n    14|12.86,13.32,82.82,504.8,0.1134,0.08834,0.038,0.034,0.1543,0.06476,0.2212,1.042,1.614,16.57,0.00591,0.02016,0.01902,0.01011,0.01202,0.003107,14.04,21.08,92.8,599.5,0.1547,0.2231,0.1791,0.1155,0.2382,0.08553\n    15|11.43,15.39,73.06,399.8,0.09639,0.06889,0.03503,0.02875,0.1734,0.05865,0.1759,0.9938,1.143,12.67,0.005133,0.01521,0.01434,0.008602,0.01501,0.001588,12.32,22.02,79.93,462.0,0.119,0.1648,0.1399,0.08476,0.2676,0.06765\n    16|8.95,15.76,58.74,245.2,0.09462,0.1243,0.09263,0.02308,0.1305,0.07163,0.3132,0.9789,3.28,16.94,0.01835,0.0676,0.09263,0.02308,0.02384,0.005601,9.414,17.07,63.34,270.0,0.1179,0.1879,0.1544,0.03846,0.1652,0.07722\n    17|14.03,21.25,89.79,603.4,0.0907,0.06945,0.01462,0.01896,0.1517,0.05835,0.2589,1.503,1.667,22.07,0.007389,0.01383,0.007302,0.01004,0.01263,0.002925,15.33,30.28,98.27,715.5,0.1287,0.1513,0.06231,0.07963,0.2226,0.07617\n    1

<think>I can see the train.csv file has a header with 30 feature columns plus a target column. The data seems to be in a single row format where the first line contains the column headers, and subsequent lines contain the actual data values.

Let me also check the test.csv file to see its structure.</think>

→ read_file({"path": "/app/data/test.csv", "limit": 20})

{"output": "bash: line 3: curl: command not found", "exit_code": 127, "error": null}

<think>Great! I can see the structure of both files:

1. **train.csv**: Has 30 feature columns + target column (31 columns total), 228 samples
2. **test.csv**: Has 30 feature columns only (no target column), 343 samples

Both files have the same 30 feature columns:
- mean radius, mean texture, mean perimeter, mean area, mean smoothness, mean compactness, mean concavity, mean concave points, mean symmetry, mean fractal dimension
- radius error, texture error, perimeter error, area error, smoothness error, compactness error, concavity error, concave points error, symmetry error, fractal dimension error
- worst radius, worst texture, worst perimeter, worst area, worst smoothness, worst compactness, worst concavity, worst concave points, worst symmetry, worst fractal dimension

Now let me check if there are any dependencies I need to install, and then start building the pipeline.

Actually, let me first check if the MLflow server is running as mentioned in the requirements.</think>

→ terminal({"command": "curl -s http://127.0.0.1:5000"})

{"output": "total 240\ndrwxr-xr-x 1 root root   4096 Apr 22 02:26 .\ndrwxr-xr-x 1 root root   4096 Mar  4 09:20 ..\ndrwxr-xr-x 2 root root   4096 Mar  4 09:20 artifacts\n-rw-r--r-- 1 root root 225280 Apr 22 02:26 mlflow.db", "exit_code": 0, "error": null}

<think>curl is not available. Let me try a different approach to check if the MLflow server is running by checking if the mlflow directory exists and has some content.</think>

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

{"bytes_written": 2016, "dirs_created": true}

<think>Great! I can see there's already an mlflow directory with an mlflow.db file and an artifacts directory, which suggests MLflow is already set up and running. Now let me start building the pipeline step by step.

First, I'll create a Python script for Step 1: Load and Explore the Data.</think>

→ write_file({"path": "/app/data_loading.py", "content": "import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nimport json\nimport os\n\ndef load_and_explore_data():\n    \"\"\"Load and explore the breast cancer dataset\"\"\"\n    \n    # Load training data\n    train_df = pd.read_csv('/app/data/train.csv')\n    print(f\"Training data shape: {train_df.shape}\")\n    pr…)

{"output": "Training data shape: (227, 31)\nTraining data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension', 'target']\nTarget distribution:\ntarget\n1    142\n0     85\nName: count, dtype: int64\nTest data shape: (342, 30)\nTest data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension']\nMissing values in train data:\nmean radius                0\nmean texture               0\nmean perimeter             0\nmean area                  0\nmean smoothness            0\nmean compactness           0\nmean concavity             0\nmean concave points        0\nmean symmetry              0\nmean fractal dimension     0\nradius error               0\ntexture error              0\nperimeter error            0\narea error                 0\nsmoothness error           0\ncompactness error          0\nconcavity error            0\nconcave points error       0\nsymmetry error             0\nfractal dimension error    0\nworst radius               0\nworst texture              0\nworst perimeter            0\nworst area                 0\nworst smoothness           0\nworst compactness          0\nworst concavity            0\nworst concave points       0\nworst symmetry             0\nworst fractal dimension    0\ntarget                     0\ndtype: int64\nMissing values in test data:\nmean radius                0\nmean texture               0\nmean perimeter             0\nmean area                  0\nmean smoothness            0\nmean compactness           0\nmean concavity             0\nmean concave points        0\nmean symmetry              0\nmean fractal dimension     0\nradius error               0\ntexture error              0\nperimeter error            0\narea error                 0\nsmoothness error           0\ncompactness error          0\nconcavity error            0\nconcave points error       0\nsymmetry error             0\nfractal dimension error    0\nworst radius               0\nworst texture              0\nworst perimeter            0\nworst area                 0\nworst smoothness           0\nworst compactness          0\nworst concavity            0\nworst concave points       0\nworst symmetry             0\nworst fractal dimension    0\ndtype: int64\nTraining set size: 181 samples\nValidation set size: 46 samples\nTraining set target distribution:\ntarget\n1    113\n0     68\nName: count, dtype: int64\nValidation set target distribution:\ntarget\n1    29\n0    17\nName: count, dtype: int64\nSplit information saved to /app/output/split_info.json", "exit_code": 0, "error": null}

<think>Now let me run this script to load and explore the data.</think>

→ terminal({"command": "python /app/data_loading.py"})

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

<think>Great! The data loading worked well. I can see:

1. Training data: 227 samples, 31 columns (30 features + target)
2. Test data: 342 samples, 30 columns (no target)
3. No missing values in either dataset
4. Target distribution: 142 benign (1), 85 malignant (0)
5. Train/validation split: 181 training samples, 46 validation samples

The stratification worked well - the class distribution is maintained in both sets.

Now let me check if the split_info.json was created correctly.</think>

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

{"bytes_written": 5223, "dirs_created": true}

<think>Perfect! The split_info.json was created correctly with all the required information. Now let me create the training script for Step 3: Train Multiple Classification Models with MLflow Tracking.

I need to create a script that:
1. Connects to MLflow tracking server
2. Trains three different models with specific allowed parameters
3. Logs everything to MLflow
4. Uses appropriate hyperparameters to meet the performance requirements

Let me create the training script.</think>

→ write_file({"path": "/app/model_training.py", "content": "import pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\nimport mlflow\nimport mlflow.sklearn\nimport json\nimport os\nimport sys\nfrom data_loading …)

{"output": "2026/04/22 02:40:15 INFO mlflow.tracking.fluent: Experiment with name 'breast-cancer-classification' does not exist. Creating a new experiment.\n2026/04/22 02:40:15 WARNING mlflow.utils.git_utils: Failed to import Git (the Git executable is probably not on your PATH), so Git SHA is not available. Error: Failed to initialize: Bad git executable.\nThe git executable must be specified in one of the following ways:\n    - be included in your $PATH\n    - be set via $GIT_PYTHON_GIT_EXECUTABLE\n    - explicitly set via git.refresh(<full-path-to-git-executable>)\n\nAll git commands will error until this is rectified.\n\nThis initial message can be silenced or aggravated in the future by setting the\n$GIT_PYTHON_REFRESH environment variable. Use one of the following values:\n    - quiet|q|silence|s|silent|none|n|0: for no message or exception\n    - warn|w|warning|log|l|1: for a warning message (logging level CRITICAL, displayed by default)\n    - error|e|exception|raise|r|2: for a raised exception\n\nExample:\n    export GIT_PYTHON_REFRESH=quiet\n\nTraining data shape: (227, 31)\nTraining data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension', 'target']\nTarget distribution:\ntarget\n1    142\n0     85\nName: count, dtype: int64\nTest data shape: (342, 30)\nTest data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension']\nMissing values in train data:\nmean radius                0\nmean texture               0\nmean perimeter             0\nmean area                  0\nmean smoothness            0\nmean compactness           0\nmean concavity             0\nmean concave points        0\nmean symmetry              0\nmean fractal dimension     0\nradius error               0\ntexture error              0\nperimeter error            0\narea error                 0\nsmoothness error           0\ncompactness error          0\nconcavity error            0\nconcave points error       0\nsymmetry error             0\nfractal dimension error    0\nworst radius               0\nworst texture              0\nworst perimeter            0\nworst area                 0\nworst smoothness           0\nworst compactness          0\nworst concavity            0\nworst concave points       0\nworst symmetry             0\nworst fractal dimension    0\ntarget                     0\ndtype: int64\nMissing values in test data:\nmean radius                0\nmean texture               0\nmean perimeter             0\nmean area                  0\nmean smoothness            0\nmean compactness           0\nmean concavity             0\nmean concave points        0\nmean symmetry              0\nmean fractal dimension     0\nradius error               0\ntexture error              0\nperimeter error            0\narea error                 0\nsmoothness error           0\ncompactness error          0\nconcavity error            0\nconcave points error       0\nsymmetry error             0\nfractal dime

<think>Now let me run the training script to train the models and log them to MLflow.</think>

→ terminal({"command": "python /app/model_training.py"})

{"success": true, "diff": "--- a//app/model_training.py\n+++ b//app/model_training.py\n@@ -43,7 +43,9 @@\n         }\n         \n         # Train model\n-        lr = LogisticRegression(**params)\n+        lr = LogisticRegression(C=params[\"C\"], max_iter=params[\"max_iter\"], \n+                             solver=params[\"solver\"], penalty=params[\"penalty\"], \n+                             random_state=params[\"random_state\"])\n         lr.fit(X_train_np, y_train_np)\n         \n         # Calculate metrics\n", "files_modified": ["/app/model_training.py"], "lint": {"status": "ok", "output": ""}}

<think>I see the issue. I'm passing `model_type` as a parameter to the LogisticRegression constructor, but that's not a valid parameter for sklearn models. The `model_type` parameter is just for logging purposes, not for the actual model. Let me fix this by removing the `model_type` from the params dict that gets passed to the sklearn constructors.</think>

→ patch({"path": "/app/model_training.py", "old_string": "# Model 1: Logistic Regression\n    print(\"\\n=== Training Logistic Regression ===\")\n    with mlflow.start_run(run_name=\"logistic_regression\"):\n        # Hyperparameters\n        params = {\n            \"model_type\": \"logistic_regression\",\n            \"C\": 1.0,\n            \"max_iter\": 1000,\n            \"solver\": \"liblinear\",\n …)

{"success": true, "diff": "--- a//app/model_training.py\n+++ b//app/model_training.py\n@@ -84,7 +84,9 @@\n         }\n         \n         # Train model\n-        svm = SVC(**params)\n+        svm = SVC(kernel=params[\"kernel\"], C=params[\"C\"], \n+                 gamma=params[\"gamma\"], degree=params[\"degree\"], \n+                 random_state=params[\"random_state\"])\n         svm.fit(X_train_np, y_train_np)\n         \n         # Calculate metrics\n", "files_modified": ["/app/model_training.py"], "lint": {"status": "ok", "output": ""}}

<think>Now I need to fix the same issue for the other models as well.</think>

→ patch({"path": "/app/model_training.py", "old_string": "# Model 2: Support Vector Machine\n    print(\"\\n=== Training Support Vector Machine ===\")\n    with mlflow.start_run(run_name=\"svm\"):\n        # Hyperparameters\n        params = {\n            \"model_type\": \"svm\",\n            \"kernel\": \"rbf\",\n            \"C\": 1.0,\n            \"gamma\": \"scale\",\n            \"degree\": 3,\n   …)

{"success": true, "diff": "--- a//app/model_training.py\n+++ b//app/model_training.py\n@@ -125,7 +125,11 @@\n         }\n         \n         # Train model\n-        rf = RandomForestClassifier(**params)\n+        rf = RandomForestClassifier(n_estimators=params[\"n_estimators\"], \n+                                 max_depth=params[\"max_depth\"], \n+                                 min_samples_split=params[\"min_samples_split\"], \n+                                 min_samples_leaf=params[\"min_samples_leaf\"], \n+                                 random_state=params[\"random_state\"])\n         rf.fit(X_train_np, y_train_np)\n         \n         # Calculate metrics\n", "files_modified": ["/app/model_training.py"], "lint": {"status": "ok", "output": ""}}

<think>Now I need to fix the Random Forest model as well.</think>

→ patch({"path": "/app/model_training.py", "old_string": "# Model 3: Random Forest Classifier\n    print(\"\\n=== Training Random Forest Classifier ===\")\n    with mlflow.start_run(run_name=\"random_forest\"):\n        # Hyperparameters\n        params = {\n            \"model_type\": \"random_forest\",\n            \"n_estimators\": 100,\n            \"max_depth\": 10,\n            \"min_samples_split\"…)

{"output": "2026/04/22 02:44:55 WARNING mlflow.utils.git_utils: Failed to import Git (the Git executable is probably not on your PATH), so Git SHA is not available. Error: Failed to initialize: Bad git executable.\nThe git executable must be specified in one of the following ways:\n    - be included in your $PATH\n    - be set via $GIT_PYTHON_GIT_EXECUTABLE\n    - explicitly set via git.refresh(<full-path-to-git-executable>)\n\nAll git commands will error until this is rectified.\n\nThis initial message can be silenced or aggravated in the future by setting the\n$GIT_PYTHON_REFRESH environment variable. Use one of the following values:\n    - quiet|q|silence|s|silent|none|n|0: for no message or exception\n    - warn|w|warning|log|l|1: for a warning message (logging level CRITICAL, displayed by default)\n    - error|e|exception|raise|r|2: for a raised exception\n\nExample:\n    export GIT_PYTHON_REFRESH=quiet\n\n2026/04/22 02:44:57 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpiacsso43/model/model.pkl, flavor: sklearn). Fall back to return ['scikit-learn==1.7.2', 'cloudpickle==3.1.2']. Set logging level to DEBUG to see the full traceback. \n2026/04/22 02:44:57 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n2026/04/22 02:44:59 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpa8pzo9zn/model/model.pkl, flavor: sklearn). Fall back to return ['scikit-learn==1.7.2', 'cloudpickle==3.1.2']. Set logging level to DEBUG to see the full traceback. \n2026/04/22 02:44:59 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n2026/04/22 02:45:00 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpyn5j23ld/model/model.pkl, flavor: sklearn). Fall back to return ['scikit-learn==1.7.2', 'cloudpickle==3.1.2']. Set logging level to DEBUG to see the full traceback. \n2026/04/22 02:45:00 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\nTraining data shape: (227, 31)\nTraining data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension', 'target']\nTarget distribution:\ntarget\n1    142\n0     85\nName: count, dtype: int64\nTest data shape: (342, 30)\nTest data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension']\nMissing values in train data:\nmean radius                0\nmean texture               0\nmean perimeter             0\nmean area                  0\nmean smoothness            0\nmean compactness           0\nmean concavi

<think>Now let me run the training script again with the corrected parameters.</think>

→ terminal({"command": "python /app/model_training.py"})

{"bytes_written": 10935, "dirs_created": true}

<think>Excellent! The models have been trained successfully. I can see the results:

1. **Logistic Regression**: Val Accuracy: 0.9348, F1: 0.9508
2. **Support Vector Machine**: Val Accuracy: 0.9130, F1: 0.9355  
3. **Random Forest Classifier**: Val Accuracy: 0.9130, F1: 0.9333

The Logistic Regression model achieved the highest validation accuracy (0.9348), so it should be selected as the best model. All models meet the minimum performance requirements (>= 0.9).

Now I need to create Step 4: Select Best Model and Register to Model Registry. I need to:
1. Query all runs from the MLflow experiment
2. Select the best model based on val_accuracy
3. Register it to the Model Registry
4. Save the experiment results and best model info to JSON files

Let me create a script for this step.</think>

→ write_file({"path": "/app/model_selection.py", "content": "import mlflow\nimport mlflow.sklearn\nimport json\nimport os\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\nimport pandas as pd\nimport numpy as np\nfrom data_loading import load…)

{"output": "Training data shape: (227, 31)\nTraining data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension', 'target']\nTarget distribution:\ntarget\n1    142\n0     85\nName: count, dtype: int64\nTest data shape: (342, 30)\nTest data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension']\nMissing values in train data:\nmean radius                0\nmean texture               0\nmean perimeter             0\nmean area                  0\nmean smoothness            0\nmean compactness           0\nmean concavity             0\nmean concave points        0\nmean symmetry              0\nmean fractal dimension     0\nradius error               0\ntexture error              0\nperimeter error            0\narea error                 0\nsmoothness error           0\ncompactness error          0\nconcavity error            0\nconcave points error       0\nsymmetry error             0\nfractal dimension error    0\nworst radius               0\nworst texture              0\nworst perimeter            0\nworst area                 0\nworst smoothness           0\nworst compactness          0\nworst concavity            0\nworst concave points       0\nworst symmetry             0\nworst fractal dimension    0\ntarget                     0\ndtype: int64\nMissing values in test data:\nmean radius                0\nmean texture               0\nmean perimeter             0\nmean area                  0\nmean smoothness            0\nmean compactness           0\nmean concavity             0\nmean concave points        0\nmean symmetry              0\nmean fractal dimension     0\nradius error               0\ntexture error              0\nperimeter error            0\narea error                 0\nsmoothness error           0\ncompactness error          0\nconcavity error            0\nconcave points error       0\nsymmetry error             0\nfractal dimension error    0\nworst radius               0\nworst texture              0\nworst perimeter            0\nworst area                 0\nworst smoothness           0\nworst compactness          0\nworst concavity            0\nworst concave points       0\nworst symmetry             0\nworst fractal dimension    0\ndtype: int64\nTraining set size: 181 samples\nValidation set size: 46 samples\nTraining set target distribution:\ntarget\n1    113\n0     68\nName: count, dtype: int64\nValidation set target distribution:\ntarget\n1    29\n0    17\nName: count, dtype: int64\nSplit information saved to /app/output/split_info.json\nFound 4 runs in experiment 'breast-cancer-classification'\nTraceback (most recent call last):\n  File \"/app/model_selection.py\", line 280, in <module>\n    select_and_register_best_model()\n    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n  File \"/app/model_selection.py\", line 48, in select_and_register_best_model\n    run_id = run.info.run_id\n             ^^^^^^^^\nAttributeError: 'str' object has no attribute 'info'", "exit_c

<think>Now let me run the model selection script to register the best model and save the experiment results.</think>

→ terminal({"command": "python /app/model_selection.py"})

{"success": true, "diff": "--- a//app/model_selection.py\n+++ b//app/model_selection.py\n@@ -34,9 +34,9 @@\n         return\n     \n     # Get all runs in the experiment\n-    runs = mlflow.search_runs(experiment_ids=[experiment.experiment_id])\n-    \n-    print(f\"Found {len(runs)} runs in experiment '{experiment_name}'\")\n+    runs_df = mlflow.search_runs(experiment_ids=[experiment.experiment_id])\n+    \n+    print(f\"Found {len(runs_df)} runs in experiment '{experiment_name}'\")\n     \n     # Extract metrics and parameters for each run\n     experiment_results = []\n@@ -44,9 +44,9 @@\n     best_model_type = None\n     best_val_accuracy = 0\n     \n-    for run in runs:\n-        run_id = run.info.run_id\n-        run_name = run.info.run_name\n+    for i, run_row in runs_df.iterrows():\n+        run_id = run_row['run_id']\n+        run_name = run_row['run_name']\n         \n         # Get metrics\n         metrics = run.data.metrics\n", "files_modified": ["/app/model_selection.py"], "lint": {"status": "ok", "output": ""}}

<think>I see the issue. The `mlflow.search_runs()` function returns a pandas DataFrame, not a list of run objects. I need to access the DataFrame columns directly. Let me fix this.</think>

→ patch({"path": "/app/model_selection.py", "old_string": "# Get all runs in the experiment\n    runs = mlflow.search_runs(experiment_ids=[experiment.experiment_id])\n    \n    print(f\"Found {len(runs)} runs in experiment '{experiment_name}'\")\n    \n    # Extract metrics and parameters for each run\n    experiment_results = []\n    best_run_id = None\n    best_model_type = None\n    best_val_accuracy =…)

{"success": true, "diff": "--- a//app/model_selection.py\n+++ b//app/model_selection.py\n@@ -49,11 +49,11 @@\n         run_name = run_row['run_name']\n         \n         # Get metrics\n-        metrics = run.data.metrics\n-        val_accuracy = metrics.get('val_accuracy', 0)\n+        metrics = runs_df.iloc[i].get('metrics', {})\n+        val_accuracy = metrics.get('val_accuracy', 0) if isinstance(metrics, dict) else 0\n         \n         # Get parameters\n-        params = run.data.params\n+        params = runs_df.iloc[i].get('params', {})\n         \n         # Get model type from parameters\n         model_type = params.get('model_type', 'unknown')\n", "files_modified": ["/app/model_selection.py"], "lint": {"status": "ok", "output": ""}}

<think>Now I need to fix the rest of the code that accesses run.data.metrics and run.data.params. These should be accessed from the DataFrame row.</think>

→ patch({"path": "/app/model_selection.py", "old_string": "# Get metrics\n        metrics = run.data.metrics\n        val_accuracy = metrics.get('val_accuracy', 0)\n        \n        # Get parameters\n        params = run.data.params", "new_string": "# Get metrics\n        metrics = runs_df.iloc[i].get('metrics', {})\n        val_accuracy = metrics.get('val_accuracy', 0) if isinstance(metrics, dict) else …)

{"success": true, "diff": "--- a//app/model_selection.py\n+++ b//app/model_selection.py\n@@ -132,11 +132,12 @@\n     print(f\"\\n=== Registering Best Model to Model Registry ===\")\n     \n     # Get the best model artifact path\n-    best_run = mlflow.get_run(best_run_id)\n+    best_run_df = mlflow.get_run(best_run_id)\n     model_artifacts_path = None\n     \n     # Check if model artifact exists in the run\n-    for artifact in best_run.info.artifacts:\n+    artifacts = best_run_df.get('artifacts', [])\n+    for artifact in artifacts:\n         if artifact.path == \"model\":\n             model_artifacts_path = artifact.path\n             break\n", "files_modified": ["/app/model_selection.py"], "lint": {"status": "ok", "output": ""}}

<think>Now I need to fix the part where I get the best model artifact path and the part where I get the best run. Let me fix these as well.</think>

→ patch({"path": "/app/model_selection.py", "old_string": "# Get the best model artifact path\n    best_run = mlflow.get_run(best_run_id)\n    model_artifacts_path = None\n    \n    # Check if model artifact exists in the run\n    for artifact in best_run.info.artifacts:\n        if artifact.path == \"model\":\n            model_artifacts_path = artifact.path\n            break", "new_string": "# Get the …)

{"success": true, "diff": "--- a//app/model_selection.py\n+++ b//app/model_selection.py\n@@ -263,12 +263,12 @@\n                 \"model_version\": model_version,\n                 \"model_stage\": \"Production\",\n                 \"run_id\": best_run_id,\n-                \"model_type\": best_model_type,\n-                \"hyperparameters\": best_run_data[\"hyperparameters\"],\n-                \"val_accuracy\": best_run_data[\"val_accuracy\"],\n-                \"precision\": best_run_data[\"precision\"],\n-                \"recall\": best_run_data[\"recall\"],\n-                \"f1_score\": best_run_data[\"f1_score\"]\n+                \"model_type\": experiment_results[0][\"model_type\"],\n+                \"hyperparameters\": experiment_results[0][\"hyperparameters\"],\n+                \"val_accuracy\": experiment_results[0][\"val_accuracy\"],\n+                \"precision\": experiment_results[0][\"precision\"],\n+                \"recall\": experiment_results[0][\"recall\"],\n+                \"f1_score\": experiment_results[0][\"f1_score\"]\n             }\n             \n             with open('/app/output/best_model_info.json', 'w') as f:\n", "files_modified": ["/app/model_selection.py"], "lint": {"status": "ok", "output": ""}}

<think>Now I need to fix the reference to the best run data later in the code as well.</think>

→ patch({"path": "/app/model_selection.py", "old_string": "# Update the saved files with the new model info\n            best_model_info = {\n                \"model_name\": model_name,\n                \"model_version\": model_version,\n                \"model_stage\": \"Production\",\n                \"run_id\": best_run_id,\n                \"model_type\": best_model_type,\n                \"hyperparam…)

{"output": "Training data shape: (227, 31)\nTraining data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension', 'target']\nTarget distribution:\ntarget\n1    142\n0     85\nName: count, dtype: int64\nTest data shape: (342, 30)\nTest data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension']\nMissing values in train data:\nmean radius                0\nmean texture               0\nmean perimeter             0\nmean area                  0\nmean smoothness            0\nmean compactness           0\nmean concavity             0\nmean concave points        0\nmean symmetry              0\nmean fractal dimension     0\nradius error               0\ntexture error              0\nperimeter error            0\narea error                 0\nsmoothness error           0\ncompactness error          0\nconcavity error            0\nconcave points error       0\nsymmetry error             0\nfractal dimension error    0\nworst radius               0\nworst texture              0\nworst perimeter            0\nworst area                 0\nworst smoothness           0\nworst compactness          0\nworst concavity            0\nworst concave points       0\nworst symmetry             0\nworst fractal dimension    0\ntarget                     0\ndtype: int64\nMissing values in test data:\nmean radius                0\nmean texture               0\nmean perimeter             0\nmean area                  0\nmean smoothness            0\nmean compactness           0\nmean concavity             0\nmean concave points        0\nmean symmetry              0\nmean fractal dimension     0\nradius error               0\ntexture error              0\nperimeter error            0\narea error                 0\nsmoothness error           0\ncompactness error          0\nconcavity error            0\nconcave points error       0\nsymmetry error             0\nfractal dimension error    0\nworst radius               0\nworst texture              0\nworst perimeter            0\nworst area                 0\nworst smoothness           0\nworst compactness          0\nworst concavity            0\nworst concave points       0\nworst symmetry             0\nworst fractal dimension    0\ndtype: int64\nTraining set size: 181 samples\nValidation set size: 46 samples\nTraining set target distribution:\ntarget\n1    113\n0     68\nName: count, dtype: int64\nValidation set target distribution:\ntarget\n1    29\n0    17\nName: count, dtype: int64\nSplit information saved to /app/output/split_info.json\nFound 4 runs in experiment 'breast-cancer-classification'\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.IndexEng

<think>Now let me run the model selection script again with the fixes.</think>

→ terminal({"command": "python /app/model_selection.py"})

{"bytes_written": 1574, "dirs_created": true}

<think>I need to check what columns are actually available in the MLflow search_runs DataFrame. Let me debug this by printing the columns first.</think>

→ write_file({"path": "/debug_model_selection.py", "content": "import mlflow\nimport mlflow.sklearn\nimport json\nimport os\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\nimport pandas as pd\nimport numpy as np\nfrom data_loading import lo…)

{"output": "python: can't open file '/app/debug_model_selection.py': [Errno 2] No such file or directory", "exit_code": 2, "error": null}

<think>Let me run this debug script to see what columns are available.</think>

→ terminal({"command": "python /app/debug_model_selection.py"})

{"output": "total 64\ndrwxr-xr-x 1 root root  4096 Apr 22 02:54 .\ndrwxr-xr-x 1 root root  4096 Apr 22 02:59 ..\ndrwxr-xr-x 2 root root  4096 Apr 22 02:58 __pycache__\ndrwxr-xr-x 2 root root  4096 Mar  4 09:20 api\ndrwxr-xr-x 1 root root  4096 Mar  4 09:20 data\n-rw-r--r-- 1 root root  2016 Apr 22 02:32 data_loading.py\ndrwxr-xr-x 1 root root  4096 Apr 22 02:45 mlflow\n-rw-r--r-- 1 root root 11136 Apr 22 02:58 model_selection.py\n-rw-r--r-- 1 root root  5880 Apr 22 02:44 model_training.py\ndrwxr-xr-x 1 root root  4096 Apr 22 02:32 output\n-rwxr-xr-x 1 root root   212 Mar  4 09:20 start.sh", "exit_code": 0, "error": null}

<think>The file path seems to be wrong. Let me check what files are in the current directory.</think>

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

{"success": true, "diff": "--- a//app/model_selection.py\n+++ b//app/model_selection.py\n@@ -37,6 +37,16 @@\n     runs_df = mlflow.search_runs(experiment_ids=[experiment.experiment_id])\n     \n     print(f\"Found {len(runs_df)} runs in experiment '{experiment_name}'\")\n+    print(f\"Columns in runs_df: {list(runs_df.columns)}\")\n+    print(f\"DataFrame shape: {runs_df.shape}\")\n+    \n+    # Print first few rows\n+    print(\"\\nFirst few runs:\")\n+    print(runs_df.head())\n+    \n+    # Print run_ids\n+    print(\"\\nRun IDs:\")\n+    print(runs_df['run_id'].head())\n     \n     # Extract metrics and parameters for each run\n     experiment_results = []\n", "files_modified": ["/app/model_selection.py"], "lint": {"status": "ok", "output": ""}}

<think>I can see that the debug file wasn't created. Let me modify the model_selection.py file to add debug output, and then run it.</think>

→ patch({"path": "/app/model_selection.py", "old_string": "# Get all runs in the experiment\n    runs_df = mlflow.search_runs(experiment_ids=[experiment.experiment_id])\n    \n    print(f\"Found {len(runs_df)} runs in experiment '{experiment_name}'\")", "new_string": "# Get all runs in the experiment\n    runs_df = mlflow.search_runs(experiment_ids=[experiment.experiment_id])\n    \n    print(f\"Found {le…)

{"output": "Training data shape: (227, 31)\nTraining data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension', 'target']\nTarget distribution:\ntarget\n1    142\n0     85\nName: count, dtype: int64\nTest data shape: (342, 30)\nTest data columns: ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension']\nMissing values in train data:\nmean radius                0\nmean texture               0\nmean perimeter             0\nmean area                  0\nmean smoothness            0\nmean compactness           0\nmean concavity             0\nmean concave points        0\nmean symmetry              0\nmean fractal dimension     0\nradius error               0\ntexture error              0\nperimeter error            0\narea error                 0\nsmoothness error           0\ncompactness error          0\nconcavity error            0\nconcave points error       0\nsymmetry error             0\nfractal dimension error    0\nworst radius               0\nworst texture              0\nworst perimeter            0\nworst area                 0\nworst smoothness           0\nworst compactness          0\nworst concavity            0\nworst concave points       0\nworst symmetry             0\nworst fractal dimension    0\ntarget                     0\ndtype: int64\nMissing values in test data:\nmean radius                0\nmean texture               0\nmean perimeter             0\nmean area                  0\nmean smoothness            0\nmean compactness           0\nmean concavity             0\nmean concave points        0\nmean symmetry              0\nmean fractal dimension     0\nradius error               0\ntexture error              0\nperimeter error            0\narea error                 0\nsmoothness error           0\ncompactness error          0\nconcavity error            0\nconcave points error       0\nsymmetry error             0\nfractal dimension error    0\nworst radius               0\nworst texture              0\nworst perimeter            0\nworst area                 0\nworst smoothness           0\nworst compactness          0\nworst concavity            0\nworst concave points       0\nworst symmetry             0\nworst fractal dimension    0\ndtype: int64\nTraining set size: 181 samples\nValidation set size: 46 samples\nTraining set target distribution:\ntarget\n1    113\n0     68\nName: count, dtype: int64\nValidation set target distribution:\ntarget\n1    29\n0    17\nName: count, dtype: int64\nSplit information saved to /app/output/split_info.json\nFound 4 runs in experiment 'breast-cancer-classification'\nColumns in runs_df: ['run_id', 'experiment_id', 'status', 'artifact_uri', 'start_time', 'end_time', 'metrics.precision', 'metrics.val_accuracy', 'metrics.f1_score', 'metrics.train_accuracy', 'metrics.recall', 'params.min_samples_leaf', 'params.n_estimators', 'params.model_type', 'params.max_depth', 'params.random_state', 'params.min_samples_split', 'params.C', 'pa

<think>Now let me run the model selection script again to see what columns are available.</think>

→ terminal({"command": "python /app/model_selection.py"})

{"success": true, "diff": "--- a//app/model_selection.py\n+++ b//app/model_selection.py\n@@ -56,7 +56,7 @@\n     \n     for i, run_row in runs_df.iterrows():\n         run_id = run_row['run_id']\n-        run_name = run_row['run_name']\n+        run_name = run_row['tags.mlflow.runName']\n         \n         # Get metrics\n         metrics = runs_df.iloc[i].get('metrics', {})\n", "files_modified": ["/app/model_selection.py"], "lint": {"status": "ok", "output": ""}}

<think>Perfect! Now I can see the columns available in the DataFrame. The column name is `tags.mlflow.runName`, not `run_name`. Let me fix the code to use the correct column names.

From the output, I can see:
- `run_id` is available
- `tags.mlflow.runName` contains the run names
- Metrics columns are prefixed with `metrics.` (e.g., `metrics.val_accuracy`)
- Parameters columns are prefixed with `params.` (e.g., `params.model_type`)

Let me fix the code to use these correct column names.</think>

→ patch({"path": "/app/model_selection.py", "old_string": "for i, run_row in runs_df.iterrows():\n        run_id = run_row['run_id']\n        run_name = run_row['run_name']", "new_string": "for i, run_row in runs_df.iterrows():\n        run_id = run_row['run_id']\n        run_name = run_row['tags.mlflow.runName']"})

{"success": true, "diff": "--- a//app/model_selection.py\n+++ b//app/model_selection.py\n@@ -59,11 +59,16 @@\n         run_name = run_row['tags.mlflow.runName']\n         \n         # Get metrics\n-        metrics = runs_df.iloc[i].get('metrics', {})\n-        val_accuracy = metrics.get('val_accuracy', 0) if isinstance(metrics, dict) else 0\n+        metrics_col = f'metrics.val_accuracy'\n+        val_accuracy = runs_df.iloc[i][metrics_col] if metrics_col in runs_df.columns else 0\n         \n         # Get parameters\n-        params = runs_df.iloc[i].get('params', {})\n+        params = {}\n+        for param_col in runs_df.columns:\n+            if param_col.startswith('params.'):\n+                param_name = param_col.split('.')[-1]  # Extract parameter name after 'params.'\n+                param_value = runs_df.iloc[i][param_col]\n+                params[param_name] = param_value\n         \n         # Get model type from parameters\n         model_type = params.get('model_type', 'unknown')\n", "files_modified": ["/app/model_selection.py"], "lint": {"status": "ok", "output": ""}}

<think>Now I need to fix the metrics and params access as well. Let me also fix the metrics access to use the correct column names.</think>

→ patch({"path": "/app/model_selection.py", "old_string": "# Get metrics\n        metrics = runs_df.iloc[i].get('metrics', {})\n        val_accuracy = metrics.get('val_accuracy', 0) if isinstance(metrics, dict) else 0\n        \n        # Get parameters\n        params = runs_df.iloc[i].get('params', {})", "new_string": "# Get metrics\n        metrics_col = f'metrics.val_accuracy'\n        val_accuracy = …)