Documentation Index Fetch the complete documentation index at: https://mintlify.com/RaviTejaMedarametla/Data-Science-AI-Portfolio/llms.txt
Use this file to discover all available pages before exploring further.
Reproducibility is enforced through three mechanisms: global seed control, configuration-driven training, and cryptographic lineage tracking. Every training run produces verifiable artifacts that can be reproduced bit-for-bit.
Seed Control All random operations are controlled by a global seed defined in config.yaml.
Configuration Implementation The seed is set globally before any random operations:
def set_global_seed ( seed : int ) -> None : random.seed(seed) np.random.seed(seed)
All training operations initialize from this seed:
def main () -> None : config = load_config() set_global_seed( int (config[ "seed" ])) # All subsequent operations use seeded randomness df = load_dataset(config) X_train, X_test, y_train, y_test = split_data(df, config)
Seeded Components The seed propagates to all stochastic operations:
Train/Test Split
Cross-Validation
Model Initialization
return train_test_split( X, y, test_size = float (config[ "data" ][ "test_size" ]), random_state = int (config[ "seed" ]), stratify = y, )
Configuration-Driven Training All training behavior is controlled by declarative configuration, eliminating hard-coded parameters.
Complete Configuration seed : 42 data : path : ml_datasource.csv target : purchased test_size : 0.2 features : epsilon : 1.0e-06 engagement : minutes_watched_weight : 0.6 days_on_platform_weight : 0.3 courses_started_weight : 10.0 preprocessing : outlier_factor : 1.5 numeric_imputer : median categorical_imputer : most_frequent models : logistic_regression : max_iter : 2000 knn : n_neighbors : 7 svm : C : 1.0 kernel : rbf gamma : scale decision_tree : max_depth : 8 min_samples_leaf : 10 random_forest : n_estimators : 400 min_samples_leaf : 2 cv : n_splits : 5 business : target_precision : 0.9 artifacts : model_dir : artifacts model_file : best_model.joblib threshold_file : threshold.txt metrics_file : metrics.json drift_baseline_file : drift_baseline.json lineage_file : lineage.json
Configuration Loading Configuration is loaded once and passed through the pipeline:
def load_config ( path : str | Path = "config.yaml" ) -> dict : with open (path, "r" , encoding = "utf-8" ) as f: return yaml.safe_load(f)
Configuration Validation The configuration structure is validated at runtime through type checking and bounds validation in model initialization.
Lineage Tracking Every training run generates a lineage manifest with SHA256 hashes of all inputs and outputs.
Lineage Generation The training script computes hashes of all artifacts:
def _sha256_bytes ( data : bytes ) -> str : return hashlib.sha256(data).hexdigest() def _sha256_file ( path : Path) -> str : return _sha256_bytes(path.read_bytes()) # After training completes run_id = str (uuid.uuid4()) config_hash = _sha256_file(Path( "config.yaml" )) dataset_hash = _sha256_file(Path(config[ "data" ][ "path" ])) model_hash = _sha256_file(model_path) lineage = { "run_id" : run_id, "dataset" : { "path" : config[ "data" ][ "path" ], "sha256" : dataset_hash, }, "config" : { "path" : "config.yaml" , "sha256" : config_hash, }, "model" : { "path" : str (model_path), "sha256" : model_hash, }, "threshold" : { "path" : str (threshold_path), "sha256" : _sha256_file(threshold_path), }, } lineage_path.write_text(json.dumps(lineage, indent = 2 ), encoding = "utf-8" )
Lineage Structure The lineage file (artifacts/lineage.json) records complete provenance:
{ "run_id" : "a3f2b891-4c5d-4e2f-9a1b-8c3d5e6f7a8b" , "dataset" : { "path" : "ml_datasource.csv" , "sha256" : "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855" }, "config" : { "path" : "config.yaml" , "sha256" : "d4f5e6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3d4e5" }, "model" : { "path" : "artifacts/best_model.joblib" , "sha256" : "a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2" }, "threshold" : { "path" : "artifacts/threshold.txt" , "sha256" : "b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3" } }
Reproducibility Verification The reproducibility check script validates that artifacts match their lineage hashes.
Running the Check python scripts/reproducibility_check.py
Implementation scripts/reproducibility_check.py
def sha256 ( path : Path) -> str : return hashlib.sha256(path.read_bytes()).hexdigest() def main () -> None : lineage_path = Path( "artifacts/lineage.json" ) if not lineage_path.exists(): raise FileNotFoundError ( "Missing artifacts/lineage.json. Run training first." ) lineage = json.loads(lineage_path.read_text( encoding = "utf-8" )) checks = { "dataset" : (Path(lineage[ "dataset" ][ "path" ]), lineage[ "dataset" ][ "sha256" ]), "config" : (Path(lineage[ "config" ][ "path" ]), lineage[ "config" ][ "sha256" ]), "model" : (Path(lineage[ "model" ][ "path" ]), lineage[ "model" ][ "sha256" ]), "threshold" : (Path(lineage[ "threshold" ][ "path" ]), lineage[ "threshold" ][ "sha256" ]), } report = { "run_id" : lineage.get( "run_id" ), "checks" : {}} all_passed = True for name, (path, expected) in checks.items(): actual = sha256(path) passed = actual == expected all_passed &= passed report[ "checks" ][name] = { "path" : str (path), "expected" : expected, "actual" : actual, "passed" : passed, } report[ "passed" ] = all_passed Path( "artifacts/reproducibility_report.json" ).write_text(json.dumps(report, indent = 2 ), encoding = "utf-8" ) print (json.dumps(report, indent = 2 )) if not all_passed: raise SystemExit ( 1 )
Verification Report The script generates artifacts/reproducibility_report.json:
artifacts/reproducibility_report.json
{ "run_id" : "a3f2b891-4c5d-4e2f-9a1b-8c3d5e6f7a8b" , "checks" : { "dataset" : { "path" : "ml_datasource.csv" , "expected" : "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855" , "actual" : "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855" , "passed" : true }, "config" : { "path" : "config.yaml" , "expected" : "d4f5e6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3d4e5" , "actual" : "d4f5e6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3d4e5" , "passed" : true }, "model" : { "path" : "artifacts/best_model.joblib" , "expected" : "a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2" , "actual" : "a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2" , "passed" : true }, "threshold" : { "path" : "artifacts/threshold.txt" , "expected" : "b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3" , "actual" : "b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3" , "passed" : true } }, "passed" : true }
Environment Tracking The script also captures environment metadata:
scripts/reproducibility_check.py
environment = { "python_version" : sys.version, "platform" : platform.platform(), "cwd" : str (Path.cwd()), } Path( "artifacts/reproducibility_environment.json" ).write_text(json.dumps(environment, indent = 2 ), encoding = "utf-8" )
Reproducibility Guarantees Given identical inputs (dataset + config), training produces:
Identical model parameters (verified by SHA256)
Identical threshold values
Identical cross-validation splits
Identical metric values
Best Practices
Version Configuration
Commit config.yaml to version control alongside code
Track Dataset Versions
Store dataset hashes in config/datasets.yaml and validate before training
Verify Lineage
Run python scripts/reproducibility_check.py in CI to detect accidental changes
Archive Artifacts
Store lineage manifests with model artifacts for audit trails
Document Seed Changes
Changing the seed produces different models - document why in commit messages
CI Integration Add reproducibility checks to CI pipelines:
- name : Train model run : python -m src.train - name : Verify reproducibility run : python scripts/reproducibility_check.py - name : Archive lineage uses : actions/upload-artifact@v3 with : name : lineage-${{ github.sha }} path : artifacts/lineage.json
Limitations Reproducibility is not guaranteed when:
Using non-deterministic hardware (GPU with non-deterministic ops)
Parallel execution order varies (set n_jobs=1 for strict reproducibility)
Python/library versions differ
System-level randomness is not controlled