breast-cancer-mlflow

grader verdict: FAIL (exit 0)
eNotFoundEr...
FAILED ../../tests/test_outputs.py::test_test_predictions_precision_recall - ...
FAILED ../../tests/test_outputs.py::test_val_metrics_threshold - FileNotFound...
FAILED ../../tests/test_outputs.py::test_val_test_performance_consistency - F...
FAILED ../../tests/test_outputs.py::test_class_prediction_distribution - File...
FAILED ../../tests/test_outputs.py::test_model_reproducible_from_hyperparameters
======================== 31 failed, 3 passed in 10.25s =========================

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.

## Goal

Build a complete machine learning classification pipeline for breast cancer diagnosis with MLflow experiment tracking, model registry, and FastAPI deployment. Train multiple classification models, track experiments with MLflow, register the best model, and serve predictions through a REST API.

## Background

You will work with pre-split Breast Cancer Wisconsin data containing diagnostic information for classifying tumors as malignant (0) or benign (1). The dataset includes 30 numerical features computed from digitized images of fine needle aspirate (FNA) of breast masses. The training data includes the target column, but the test data does NOT include the target column - you must predict it.

Data files are located at:
- /app/data/train.csv - Training data with 30 features + target column (227 samples, 31 columns)
- /app/data/test.csv - Test data with 30 features only, NO target column (342 samples, 30 columns)

You have access to scikit-learn (version 1.7.2), MLflow (version 2.19.0), FastAPI (version 0.115.6), uvicorn (version 0.34.0), pandas (version 2.2.3), and requests (version 2.32.3).

## Steps

### Step 1. Load and Explore the Data

- Load training data from /app/data/train.csv
  - Contains 30 feature columns + "target" column (31 columns total)
  - Target: 0=malignant, 1=benign
- Load test data from /app/data/test.csv
  - Contains 30 feature columns only (NO target column, 30 columns total)
  - You must predict the target values for these samples
- Verify the data structure and check for any missing values
- For training, split train.csv into train/validation sets:
  - test_size=0.2
  - random_state=42
  - stratify=y (maintain class distribution)
- Save the split information to /app/output/split_info.json:
  ```json
  {
    "random_state": 42,
    "test_size": 0.2,
    "train_size": <int>,
    "val_size": <int>,
    "train_indices": [<list of original indices used for training>],
    "val_indices": [<list of original indices used for validation>]
  }
  ```

### Step 2. Connect to MLflow Tracking Server

An MLflow tracking server is already running at http://127.0.0.1:5000.
- Set MLflow tracking URI to http://127.0.0.1:5000 in your training script
- The server uses /app/mlflow directory for storage

### Step 3. Train Multiple Classification Models with MLflow Tracking

Train at least THREE different classification models with MLflow experiment tracking:

**Model 1 - Logistic Regression** (LogisticRegression from sklearn.linear_model)
- Allowed parameters (use only these): C, max_iter, solver, penalty, random_state
- Create an MLflow experiment named "breast-cancer-classification"
- Log parameters: model_type="logistic_regression" and all hyperparameters used
- Log metrics: train_accuracy, val_accuracy, precision, recall, f1_score
- Log the trained model using mlflow.sklearn.log_model()

**Model 2 - Support Vector Machine** (SVC from sklearn.svm)
- Allowed parameters (use only these): kernel, C, gamma, degree, random_state
- Use the same experiment "breast-cancer-classification"
- Log parameters: model_type="svm" and all hyperparameters used
- Log metrics: train_accuracy, val_accuracy, precision, recall, f1_score
- Log the trained model using mlflow.sklearn.log_model()

**Model 3 - Random Forest Classifier** (RandomForestClassifier from sklearn.ensemble)
- Allowed parameters (use only these): n_estimators, max_depth, min_samples_split, min_samples_leaf, random_state
- Use the same experiment "breast-cancer-classification"
- Log parameters: model_type="random_forest" and all hyperparameters used
- Log metrics: train_accuracy, val_accuracy, precision, recall, f1_score
- Log the trained model using mlflow.sklearn.log_model()

Choose appropriate hyperparameters for each model to achieve good classification performance.

For EACH model:
- Start an MLflow run using mlflow.start_run()
- Train on the training data (X_train, y_train from train.csv split)
- Calculate training accuracy
- Calculate validation set metrics: 
… (8213 more chars)

<think>Let me break down this complex task into steps:

1. Load and explore data from /app/data/train.csv and /app/data/test.csv
2. Split training data into train/validation sets
3. Train 3 classification models with MLflow tracking
4. Select best model and register to MLflow Model Registry
5. Build FastAPI application with 4 endpoints
6. Start FastAPI server
7. Test API with test data predictions

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

I'll build a complete ML pipeline for breast cancer diagnosis. Let me start by exploring the environment and loading the data.

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

{"output": "total 28\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root 4096 Apr 22 05:03 ..\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>Good, I can see the directory structure. Let me explore the data files and check the mlflow server.
</think>

terminal({"command": "cd /app/data && ls -la && head -5 train.csv && head -5 test.csv"})

{"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\nmean 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\nmean 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\n12.31,16.52,79.19,470.9,0.09172,0.06829,0.03372,0.02272,0.172,0.05914,0.2505,1.025,1.74,19.68,0.004854,0.01819,0.01826,0.007965,0.01386,0.002304,14.11,23.21,89.71,611.1,0.1176,0.1843,0.1703,0.0866,0.2618,0.07609\n10.16,19.59,64.73,311.7,0.1003,0.07504,0.005025,0.01116,0.1791,0.06331,0.2441,2.09,1.648,16.8,0.01291,0.02222,0.004174,0.007082,0.02572,0.002278,10.65,22.88,67.88,347.3,0.1265,0.12,0.01005,0.02232,0.2262,0.06742\n15.46,23.95,103.8,731.3,0.1183,0.187,0.203,0.0852,0.1807,0.07083,0.3331,1.961,2.937,32.52,0.009538,0.0494,0.06019,0.02041,0.02105,0.006,17.11,36.33,117.7,909.4,0.1732,0.4967,0.5911,0.2163,0.3013,0.1067\n9.742,15.67,61.5,289.9,0.09037,0.04689,0.01103,0.01407,0.2081,0.06312,0.2684,1.409,1.75,16.39,0.0138,0.01067,0.008347,0.009472,0.01798,0.004261,10.75,20.88,68.09,355.2,0.1467,0.0937,0.04043,0.05159,0.2841,0.08175", "exit_code": 0, "error": null}

<think>I can see the data structure. Let me now create a Python script to:
1. Load and explore the data
2. Split into train/validation
3. Train 3 models with MLflow tracking
4. Register the best model
5. Create FastAPI application

Let me first check the MLflow server status and then create the training script.
</think>

terminal({"command": "wc -l train.csv test.csv"})

{"output": "228 train.csv\n   343 test.csv\n   571 total", "exit_code": 0, "error": null}

<think>227 training samples + 1 header = 228 lines
342 test samples + 1 header = 343 lines

Now let me check if the MLflow server is running and create the training script.
</think>

terminal({"command": "curl -s http://127.0.0.1:5000/health 2>&1 || echo \"Server check failed\""})

{"output": "bash: line 3: curl: command not found\nServer check failed", "exit_code": 0, "error": null}

<think>Let me try with Python to check the server and run the training. I'll create a comprehensive Python script that handles all the steps.
</think>

terminal({"command": "python3 -c \"import requests; print(requests.get('http://127.0.0.1:5000/health').status_code)\""})

{"output": "200", "exit_code": 0, "error": null}

<think>The MLflow server is running at http://127.0.0.1:5000. Now let me create the complete training script.
</think>

The MLflow server is running. Now let me create the comprehensive training script:

write_file({"path": "/app/training.py", "content": "\"\"\"\nComplete ML Pipeline for breast cancer diagnosis with MLflow tracking and FastAPI deployment.\n\"\"\"\n\nimport pandas as pd\nimport sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import RandomForestClassifier\nimport mlflow\nimport numpy as np\nfrom sklearn.metrics import precision_score, recall_s…)

{"bytes_written": 6100, "dirs_created": true}

<think>The file was created but I need to fix the imports - they were split incorrectly. Let me fix the script and run it properly.
</think>

read_file({"path": "/app/training.py"})

{"content": "     1|\"\"\"\n     2|Complete ML Pipeline for breast cancer diagnosis with MLflow tracking and FastAPI deployment.\n     3|\"\"\"\n     4|\n     5|import pandas as pd\n     6|import sklearn.linear_model import LogisticRegression\n     7|from sklearn.svm import SVC\n     8|from sklearn.ensemble import RandomForestClassifier\n     9|import mlflow\n    10|import numpy as np\n    11|from sklearn.metrics import precision_score, recall_score, f1_score\n    12|\n    13|# Set MLflow tracking URI\n    14|MLFLOW_TRACKING_URI = \"http://127.0.0.1:5000\"\n    15|\n    16|# Load data\n    17|train_df = pd.read_csv(\"/app/data/train.csv\")\n    18|test_df = pd.read_csv(\"/app/data/test.csv\")\n    19|\n    20|# Features and target\n    21|feature_cols = [col for col.startswith(\"mean\") or col.startswith(\"radius\") or col.startswith(\"texture\") or col.startswith(\"perimeter\") or col.startswith(\"area\") or col.startswith(\"smoothness\") or col.startswith(\"compactness\") or col.startswith(\"concavity\") or col.startswith(\"concave\") or col.startswith(\"symmetry\") or col.startswith(\"fractal\") or col.startswith(\"worst\")]\n    22|feature_cols = [col for col in train_df.columns if col != \"target\"]\n    23|\n    24|# Split into train/validation\n    25|X = train_df[feature_cols]\n    26|y = train_df[\"target\"]\n    27|\n    28|X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n    29|\n    30|# Save split info\n    31|split_info = {\n    32|    \"random_state\": 42,\n    33|    \"test_size\": 0.2,\n    34|    \"train_size\": len(X_train),\n    35|    \"val_size\": len(X_val),\n    36|    \"train_indices\": list(X_train.index),\n    37|    \"val_indices\": list(X_val.index)\n    38|}\n    39|\n    40|# Initialize MLflow\n    41|mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)\n    42|mlflow.start_beat_server()\n    43|\n    44|# Create experiment\n    45|experiment_name = \"breast-cancer-classification\"\n    46|mlflow.set_env(mlflow_beat_server=\"http://127.0.0.1:5000\")\n    47|\n    48|# Train models\n    49|models_results = []\n    50|\n    51|# Model 1: Logistic Regression\n    52|with mlflow.start_run(run_name=\"logistic_regression\"):\n    53|    model = LogisticRegression(C=1.0, max_iter=1000, solver=\"default\", penalty=\"l2\", random_state=42)\n    54|    model.fit(X_train, y_train)\n    55|    \n    56|    # Calculate metrics\n    57|    train_acc = accuracy_score(y_train, model.predict(X_train))\n    58|    val_acc = accuracy_score(y_val, model.predict(X_val))\n    59|    val_precision = precision_score(y_val, model.predict(X_val), average=\"binary\")\n    60|    val_recall = recall_score(y_val, model.predict(X_val), average=\"binary\")\n    61|    val_f1 = f1_score(y_val, model.predict(X_val), average=\"binary\")\n    62|    \n    63|    # Log parameters\n    64|    mlflow.log_params(model_type=\"logistic_regression\", C=1.0, max_iter=1000, solver=\"default\", penalty=\"l2\", random_state=42)\n    65|    mlflow.evalMetrics(val_precision, val_recall, val_f1)\n    66|    mlflow.log_model(model)\n    67|    \n    68|    models_results.append({\n    69|        \"model_type\": \"logistic_regression\",\n    70|        \"hyperparameters\": {\"C\": 1.0, \"max_iter\": 1000, \"solver\": \"default\", \"penalty\": \"l2\", \"random_state\": 42},\n    71|        \"train_accuracy\": train_acc,\n    72|        \"val_accuracy\": val_acc,\n    73|        \"precision\": val_precision,\n    74|        \"recall\": val_recall,\n    75|        \"f1_score\": val_f1\n    76|    })\n    77|\n    78|# Model 2: SVM\n    79|with mlflow.start_run(run_name=\"svm\"):\n    80|    model = SVC(kernel=\"default\", C=1.0, gamma=\"default\", degree=3, random_state=42)\n    81|    model.fit(X_train, y_train)\n    82|    \n    83|    train_acc = accuracy_score(y_train, model.predict(X_train))\n    84|    val_acc = accuracy_score(y_val, model.predict(X_val))\n    85|    val_precision = precision_score(y
… (3716 more chars)

<think>I need to fix the imports and write a proper training script. Let me rewrite it completely with correct imports and syntax.
</think>

Let me fix the training script with proper imports and syntax:

write_file({"path": "/app/training.py", "content": "\"\"\"\nComplete ML Pipeline for breast cancer diagnosis with MLflow tracking and FastAPI deployment.\n\"\"\"\n\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 RandomForestClassifier\nfrom sklearn.metrics import accuracy…)

{"bytes_written": 6060, "dirs_created": true}

<think>The script has some syntax issues. Let me check the MLflow API and write a proper working script. I need to use the correct MLflow API.
</think>

Let me check the MLflow API and write a proper working script:

terminal({"command": "python3 -c \"import mlflow; print(dir(mlflow))\" 2>&1 | head -50"})

{"output": "['ActiveRun', 'Image', 'LazyLoader', 'MLFLOW_CONFIGURE_LOGGING', 'MlflowClient', 'MlflowException', 'RunOperations', 'VERSION', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', '_configure_mlflow_loggers', 'active_run', 'add_trace', 'anthropic', 'artifacts', 'autogen', 'autolog', 'azure', 'catboost', 'client', 'config', 'contextlib', 'create_experiment', 'crewai', 'data', 'delete_experiment', 'delete_run', 'delete_tag', 'disable_system_metrics_logging', 'diviner', 'doctor', 'dspy', 'enable_system_metrics_logging', 'end_run', 'entities', 'environment_variables', 'evaluate', 'exceptions', 'fastai', 'flush_artifact_async_logging', 'flush_async_logging', 'flush_trace_async_logging', 'gateway', 'gemini', 'get_artifact_uri', 'get_current_active_span', 'get_experiment', 'get_experiment_by_name', 'get_last_active_trace', 'get_parent_run', 'get_registry_uri', 'get_run', 'get_trace', 'get_tracking_uri', 'gluon', 'h2o', 'is_tracking_uri_set', 'johnsnowlabs', 'keras', 'langchain', 'last_active_run', 'legacy_databricks_cli', 'lightgbm', 'litellm', 'llama_index', 'llm', 'load_table', 'log_artifact', 'log_artifacts', 'log_dict', 'log_figure', 'log_image', 'log_input', 'log_metric', 'log_metrics', 'log_param', 'log_params', 'log_table', 'log_text', 'login', 'metrics', 'ml_package_versions', 'mleap', 'models', 'onnx', 'openai', 'paddle', 'pmdarima', 'projects', 'promptflow', 'promptlab', 'prophet', 'protos', 'pyfunc', 'pyspark', 'pytorch', 'recipes', 'register_model', 'rfunc', 'run', 'search_experiments', 'search_model_versions', 'search_registered_models', 'search_runs', 'search_traces', 'sentence_transformers', 'set_experiment', 'set_experiment_tag', 'set_experiment_tags', 'set_registry_uri', 'set_system_metrics_node_id', 'set_system_metrics_samples_before_logging', 'set_system_metrics_sampling_interval', 'set_tag', 'set_tags', 'set_tracking_uri', 'shap', 'sklearn', 'spacy', 'spark', 'start_run', 'start_span', 'statsmodels', 'store', 'system_metrics', 'tensorflow', 'trace', 'tracing', 'tracking', 'transformers', 'types', 'update_current_trace', 'utils', 'validate_evaluation_results', 'version', 'xgboost']", "exit_code": 0, "error": null}

<think>Good, now I can see the MLflow API. Let me write a proper script using the correct API. I need to use `mlflow.start_run()`, `mlflow.log_metric()`, `mlflow.log_model()`, `mlflow.register_model()`, etc.
</think>

write_file({"path": "/app/training.py", "content": "\"\"\"\nComplete ML Pipeline for breast cancer diagnosis with MLflow tracking and FastAPI deployment.\n\"\"\"\n\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 RandomForestClassifier\nfrom sklearn.metrics import accuracy…)

{"bytes_written": 6691, "dirs_created": true}

<think>The script has some issues - I need to check the actual MLflow API and write a proper script. Let me check the MLflow sklearn API.
</think>

terminal({"command": "python3 -c \"import mlflow.sklearn; print(dir(mlflow.sklearn))\" 2>&1 | head -30"})

{"output": "['Any', 'CodeDatasetSource', 'DEFAULT_AWAIT_MAX_SLEEP_SECONDS', 'DatasetInput', 'FLAVOR_NAME', 'INPUT_EXAMPLE_SAMPLE_ROWS', 'INTERNAL_ERROR', 'INVALID_PARAMETER_VALUE', 'InputTag', 'LOG_MODEL_PARAM_DOCS', 'MLFLOW_AUTOLOGGING', 'MLFLOW_DATASET_CONTEXT', 'MLMODEL_FILE_NAME', 'MlflowAutologgingQueueingClient', 'MlflowClient', 'MlflowException', 'Model', 'ModelInputExample', 'ModelSignature', 'Optional', 'OrderedDict', 'SERIALIZATION_FORMAT_CLOUDPICKLE', 'SERIALIZATION_FORMAT_PICKLE', 'SUPPORTED_SERIALIZATION_FORMATS', 'Version', '_AUTOLOGGING_METRICS_MANAGER', '_AutologgingMetricsManager', '_CONDA_ENV_FILE_NAME', '_CONSTRAINTS_FILE_NAME', '_MODEL_DATA_SUBPATH', '_PYTHON_ENV_FILE_NAME', '_PythonEnv', '_REQUIREMENTS_FILE_NAME', '_SklearnCustomModelPicklingError', '_SklearnModelWrapper', '_SklearnTrainingSession', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_add_code_from_conf_to_system_path', '_apis_autologging_disabled', '_autolog', '_download_artifact_from_uri', '_dump_model', '_gen_estimators_to_patch', '_get_flavor_configuration', '_get_metric_name_list', '_get_new_training_session_class', '_get_pinned_requirement', '_infer_signature_from_input_example', '_inspect_original_var_name', '_load_model_from_local_file', '_load_pyfunc', '_logger', '_metric_api_excluding_list', '_mlflow_conda_env', '_patch_estimator_method_if_available', '_process_conda_env', '_process_pip_requirements', '_save_example', '_save_model', '_validate_and_copy_code_paths', '_validate_and_prepare_target_save_path', '_validate_env_arguments', 'autolog', 'autologging_integration', 'deepcopy', 'defaultdict', 'disable_autologging', 'format_docstring', 'from_numpy', 'from_pandas', 'functools', 'get_autologging_config', 'get_default_conda_env', 'get_default_pip_requirements', 'get_instance_method_first_arg_value', 'get_total_file_size', 'gorilla', 'inspect', 'load_model', 'log_model', 'logging', 'mlflow', 'np', 'os', 'pickle', 'pyfunc', 'resolve_input_example_and_signature', 'safe_patch', 'save_model', 'update_wrapper_extended', 'weakref', 'write_to', 'yaml']", "exit_code": 0, "error": null}