breast-cancer-mlflow

{"output": "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\n12.99,14.23,84.08,514.3,0.09462,0.09965,0.03738,0.02098,0.1652,0.07238,0.1814,0.6412,0.9219,14.41,0.005231,0.02305,0.03113,0.007315,0.01639,0.005701,13.72,16.91,87.38,576.0,0.1142,0.1975,0.145,0.0585,0.2432,0.1009,1\n16.16,21.54,106.2,809.8,0.1008,0.1284,0.1043,0.05613,0.216,0.05891,0.4332,1.265,2.844,43.68,0.004877,0.01952,0.02219,0.009231,0.01535,0.002373,19.47,31.68,129.7,1175.0,0.1395,0.3055,0.2992,0.1312,0.348,0.07619,0\n9.876,19.4,63.95,298.3,0.1005,0.09697,0.06154,0.03029,0.1945,0.06322,0.1803,1.222,1.528,11.77,0.009058,0.02196,0.03029,0.01112,0.01609,0.00357,10.76,26.83,72.22,361.2,0.1559,0.2302,0.2644,0.09749,0.2622,0.0849,1\n11.62,18.18,76.38,408.8,0.1175,0.1483,0.102,0.05564,0.1957,0.07255,0.4101,1.74,3.027,27.85,0.01459,0.03206,0.04961,0.01841,0.01807,0.005217,13.36,25.4,88.14,528.1,0.178,0.2878,0.3186,0.1416,0.266,0.0927,1\n13.0,25.13,82.61,520.2,0.08369,0.05073,0.01206,0.01762,0.1667,0.05449,0.2621,1.232,1.657,21.19,0.006054,0.008974,0.005681,0.006336,0.01215,0.001514,14.34,31.88,91.06,628.5,0.1218,0.1093,0.04462,0.05921,0.2306,0.06291,1\n13.74,17.91,88.12,585.0,0.07944,0.06376,0.02881,0.01329,0.1473,0.0558,0.25,0.7574,1.573,21.47,0.002838,0.01592,0.0178,0.005828,0.01329,0.001976,15.34,22.46,97.19,725.9,0.09711,0.1824,0.1564,0.06019,0.235,0.07014,1\n7.691,25.44,48.34,170.4,0.08668,0.1199,0.09252,0.01364,0.2037,0.07751,0.2196,1.479,1.445,11.73,0.01547,0.06457,0.09252,0.01364,0.02105,0.007551,8.678,31.89,54.49,223.6,0.1596,0.3064,0.3393,0.05,0.279,0.1066,1\n14.29,16.82,90.3,632.6,0.06429,0.02675,0.00725,0.00625,0.1508,0.05376,0.1302,0.7198,0.8439,10.77,0.003492,0.00371,0.004826,0.003608,0.01536,0.001381,14.91,20.65,94.44,684.6,0.08567,0.05036,0.03866,0.03333,0.2458,0.0612,1\n12.07,13.44,77.83,445.2,0.11,0.09009,0.03781,0.02798,0.1657,0.06608,0.2513,0.504,1.714,18.54,0.007327,0.01153,0.01798,0.007986,0.01962,0.002234,13.45,15.77,86.92,549.9,0.1521,0.1632,0.1622,0.07393,0.2781,0.08052,1\n11.43,17.31,73.66,398.0,0.1092,0.09486,0.02031,0.01861,0.1645,0.06562,0.2843,1.908,1.937,21.38,0.006664,0.01735,0.01158,0.00952,0.02282,0.003526,12.78,26.76,82.66,503.0,0.1413,0.1792,0.07708,0.06402,0.2584,0.08096,1\n8.219,20.7,53.27,203.9,0.09405,0.1305,0.1321,0.02168,0.2222,0.08261,0.1935,1.962,1.243,10.21,0.01243,0.05416,0.07753,0.01022,0.02309,0.01178,9.092,29.72,58.08,249.8,0.163,0.431,0.5381,0.07879,0.3322,0.1486,1\n12.46,24.04,83.97,475.9,0.1186,0.2396,0.2273,0.08543,0.203,0.08243,0.2976,1.599,2.039,23.94,0.007149,0.07217,0.07743,0.01432,0.01789,0.01008,15.09,40.68,97.65,711.4,0.1853,1.058,1.105,0.221,0.4366,0.2075,0\n12.18,14.08,77.25,461.4,0.07734,0.03212,0.01123,0.005051,0.1673,0.05649,0.2113,0.5996,1.438,15.82,0.005343,0.005767,0.01123,0.005051,0.01977,0.0009502,12.85,16.47,81.6,513.1,0.1001,0.05332,0.04116,0.01852,0.2293,0.06037,1\n14.59,22.68,96.39,657.1,0.08473,0.133,0.1029,0.03736,0.1454,0.06147,0.2254,1.108,2.224,19.54,0.004242,0.04639,0.06578,0.01606,0.01638,0.004406,15.48,27.27,105.9,733.5,0.1026,0.3171,0.3662,0.1105,0.2258,0.08004,1\n8.888,14.64,58.79,244.0,0.09783,0.1531,0.08606,0.02872,0.1902,0.0898,0.5262,0.8522,3.168,25.44,0.01721,0.09368,0.05671,0.01766,0.02541,0.02193,9.733,15.67,62.56,284.4,0.1207,0.2436,0.1434,0.04786,0.2254,0.1084,1\n14.06,17.18,89.75,609.1,0.08045,0.05361,0.02681,0.03251,0.1641,0.05764,0.1504,1.685,1.237,12.67,0.005371,0.01273,0.01132,0.009155,0.01719,0.001444,14.92,25.34,96.42,684.5,0.1066,0.1231,0.0846,0.07911,0.2523,0.06609,1\n19.07,24.81,128.3,1104.0,0.09081,0.219,0.2107,0.09961,0.231,0.06

I'll build a complete machine learning classification pipeline for breast cancer diagnosis. Let me start by exploring the data and understanding the structure.

→ terminal({"command": "head -20 /app/data/train.csv && echo \"---\" && wc -l /app/data/train.csv && echo \"---\" && head -20 /app/data/test.csv && echo \"---\" && wc -l /app/data/test.csv"})

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

Now let me create the necessary directories and build the complete pipeline:

→ terminal({"command": "mkdir -p /app/output /app/data/submission /app/api"})

{"bytes_written": 10843, "dirs_created": true}

{"bytes_written": 6209, "dirs_created": true}

{"output": "2026/04/23 06:01:42 INFO mlflow.tracking.fluent: Experiment with name 'breast-cancer-classification' does not exist. Creating a new experiment.\n2026/04/23 06:01:42 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\n/usr/local/lib/python3.13/site-packages/sklearn/linear_model/_logistic.py:473: ConvergenceWarning: lbfgs failed to converge after 1000 iteration(s) (status=1):\nSTOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n\nIncrease the number of iterations to improve the convergence (max_iter=1000).\nYou might also want to scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n2026/04/23 06:01:46 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmppbphxb_v/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. \n2026/04/23 06:01:46 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.\n2026/04/23 06:01:48 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpnblbwayt/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. \n2026/04/23 06:01:48 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.\n2026/04/23 06:01:50 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpy6_aj7ie/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. \n2026/04/23 06:01:50 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.\nSuccessfully registered model 'breast-cancer-classifier'.\n2026/04/23 06:01:50 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: breast-cancer-classifier, version 1\nCreated version '1' of model 'breast-cancer-classifier'.\n/app/train_model.py:176: FutureWarning: ``mlflow.tracking.client.MlflowClient.transition_model_version_stage`` is deprecated since 2.9.0. Model registry stages will be removed in a future major release. To learn more about the deprecation of model registry stages, see our migration guide here: https://mlflow.org/docs/latest/model-registry.html#migrating-from-stages\n  client.transition_model_version_stage(\nLoading data...\nTraining data shape: (227, 30)\nTest data shape: (342, 30)\n\nSplitting data...\nTraining set size

Now let me run the training script:

→ terminal({"command": "cd /app && python train_model.py", "timeout": "180.0"})

{"bytes_written": 11425, "dirs_created": true}

The model training completed but the accuracy is below the required 0.9 threshold. Let me improve the model by using better hyperparameters and potentially feature scaling:

→ write_file({"content": "#!/usr/bin/env python3\n\"\"\"\nBreast Cancer Classification Pipeline\nTrains multiple models with MLflow tracking and registers the best model\n\"\"\"\n\nimport json\nimport os\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import Ra…)

{"output": "2026/04/23 06:12:06 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\nLoading data...\nTraining data shape: (227, 30)\nTest data shape: (342, 30)\n\nSplitting data...\nTraining set size: 181\nValidation set size: 46\nSplit info saved to /app/output/split_info.json\n\nTraining Model 1: Logistic Regression with scaling...\n🏃 View run LogisticRegression at: http://127.0.0.1:5000/#/experiments/1/runs/4ad094e230044619891427dea352a4b6\n🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\nTraceback (most recent call last):\n  File \"/app/train_model.py\", line 315, in <module>\n    main()\n    ~~~~^^\n  File \"/app/train_model.py\", line 236, in main\n    lr_result = train_and_log_model(lr_model, \"logistic_regression\", X_train, y_train, X_val, y_val, \"LogisticRegression\")\n  File \"/app/train_model.py\", line 85, in train_and_log_model\n    mlflow.log_param(\"C\", model.C)\n                          ^^^^^^^\nAttributeError: 'Pipeline' object has no attribute 'C'", "exit_code": 1, "error": null}

{"bytes_written": 12632, "dirs_created": true}

I need to fix the model logging to handle Pipeline objects. Let me update the training script:

→ write_file({"content": "#!/usr/bin/env python3\n\"\"\"\nBreast Cancer Classification Pipeline\nTrains multiple models with MLflow tracking and registers the best model\n\"\"\"\n\nimport json\nimport os\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import Ra…)

{"output": "2026/04/23 06:25:15 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\n2026/04/23 06:25:18 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmph7_ird3x/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. \n2026/04/23 06:25:18 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.\n2026/04/23 06:25:19 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmp1iouct_n/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. \n2026/04/23 06:25:19 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.\nLoading data...\nTraining data shape: (227, 30)\nTest data shape: (342, 30)\n\nSplitting data...\nTraining set size: 181\nValidation set size: 46\nSplit info saved to /app/output/split_info.json\n\nTraining Model 1: Logistic Regression with scaling...\n🏃 View run LogisticRegression at: http://127.0.0.1:5000/#/experiments/1/runs/9817141cf60f470f894abc79d5223660\n🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\nLR - Train Acc: 0.9834, Val Acc: 0.9565\n\nTraining Model 2: Support Vector Machine with scaling...\n🏃 View run SVM at: http://127.0.0.1:5000/#/experiments/1/runs/0f1f5087a9a14c2ea7b2e71008cc7b4a\n🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\nSVM - Train Acc: 1.0000, Val Acc: 0.9565\n\nTraining Model 3: Random Forest...\n🏃 View run RandomForest at: http://127.0.0.1:5000/#/experiments/1/runs/aa2a7ad862204b33b26e7c1746ba77e9\n🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\nTraceback (most recent call last):\n  File \"/app/train_model.py\", line 345, in <module>\n    main()\n    ~~~~^^\n  File \"/app/train_model.py\", line 292, in main\n    rf_result = train_and_log_model(rf_model, \"random_forest\", X_train, y_train, X_val, y_val, \"RandomForest\")\n  File \"/app/train_model.py\", line 109, in train_and_log_model\n    params = get_pipeline_params(model, model_type)\n  File \"/app/train_model.py\", line 84, in get_pipeline_params\n    clf = pipeline.named_steps['clf']\n          ^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'RandomForestClassifier' object has no attribute 'named_steps'", "exit_code": 1, "error": null}