Run Parameters

STREAMLINE parameters can be supplied through notebooks, .cfg files, or phase CLI flags. The .cfg names intentionally match the command-line names where possible.

Shared Run Parameters

Parameter	Typical value	Used by	Description
output_path	out	all phases	Parent folder for experiment outputs.
experiment_name	UCIHCCPipeline	all phases	Experiment folder name.
outcome_label	Class, MPG	P1, P6, P8, P9, P11	Outcome column.
outcome_type	Binary, Multiclass, Continuous	P1, P6, P8, P9, P11	Learning task type.
instance_label	InstanceID	P1, P6-P11	Optional row identifier column.
n_splits	3, 5, 10	CV-aware phases	Number of CV folds.
run_cluster	Serial, Local, Parallel, BashSLURM, BashLSF	all phases	Execution mode. Local uses a local Dask cluster; Parallel uses local joblib parallelism.
random_state	42	stochastic phases	Seed for reproducibility.

Phase Toggles

The [phases] section controls which phases run:

[phases]
phase_order = p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11
do_p1 = True
do_p2 = True
do_p3 = True
do_p4 = True
do_p5 = True
do_p6 = True
do_p7 = True
do_p8 = True
do_p9 = True
do_p10 = True
do_p11 = True

The runner also accepts old-style broad flags such as do_till_report.

P1 Data Process

Parameter	Default or example	Description
data_path	data/UCIBinaryClassification	Folder containing one or more input datasets.
categorical_features	data/UCIFeatureTypes/hcc_survival_categorical_features.csv	Optional feature-name file.
quantitative_features	data/UCIFeatureTypes/hcc_survival_quantitative_features.csv	Optional feature-name file.
ignore_features	empty	Optional feature-name file/list to drop.
partition_method	Stratified or Random	CV partitioning strategy.
categorical_cutoff	10	Inference threshold when feature type files are absent.
one_hot_encoding	True	Expand categorical features in P1.
force	False	Overwrite existing phase outputs.

P2 Impute And Scale

Parameter	Default or example	Description
imputer_id	phase default	Registry imputer.
scaler_id	phase default	Registry scaler.
smote	False	Apply training-fold oversampling after imputation/scaling.
smote_method	auto	Use SMOTENC when categorical features are present, otherwise SMOTE.

P3 Feature Learning

Parameter	Default or example	Description
learner_id	pca	Feature learner registry ID.
learner_params	{}	JSON/Python-literal dictionary of learner parameters.
keep_original_features	True	Keep input features alongside learned features.

P4 Feature Importance

Parameter	Default or example	Description
models	all registered methods	Feature-importance methods to run.
models_params	method dictionary	Per-method parameter dictionary. STREAMLINE injects ReBATE categorical_features from saved feature-type artifacts.
instance_subset	not used unless provided	Optional sampling limit for expensive methods.

P5 Feature Selection

Parameter	Default or example	Description
selector_id	default	Feature selector registry ID.
algorithms	auto	Feature-importance methods considered by selector logic.
top_features	20	Number of features to keep when applicable.

P6 Modeling

Parameter	Default or example	Description
outcome_type	Binary, Multiclass, Continuous	Modeling task. model_type is still accepted as a backward-compatible alias.
models	NB,LR,DT	Model registry IDs.
scoring_metric	balanced_accuracy, explained_variance	Optuna/evaluation metric.
metric_direction	maximize or minimize	Optimization direction.
n_trials	200	Optuna trial budget.
timeout	900	Optuna time budget in seconds.
training_subsample	0	Optional training subset size.
calibrate	0 or 1	Classification calibration toggle.
bypass_one_hot_for_native_models	True	Allow native categorical model path.
native_categorical_models	CGB,ExSTraCS	Models allowed when P1 did not one-hot encode.

P6 records Optuna trial accounting in model outputs so reports can show how many trials actually ran within the requested budget.

P7 Ensembles

P7 is classification-only in the current codebase.

Parameter	Default or example	Description
ensembles	hard_voting,soft_voting,stack_lr	Ensemble registry IDs.
base_models	NB,LR,DT	Base model predictions to combine.
meta_train_source	train	Source for stacking meta-training.

P8 To P11

Phase	Key parameters	Notes
P8 Summary	scoring_metric, metric_weight, top_features, include_ensembles	Aggregates model, ensemble, and feature outputs.
P9 Compare	sig_cutoff, show_plots	Compares datasets in an experiment.
P10 Replication	rep_data_path, dataset_for_rep, show_plots	Applies trained workflows to external data.
P11 Reporting	report_modes, report_mode, make_pdf, enable_plots, reuse_existing_figures	Builds standard and replication reports.

Saved Run Command Controls

All phase CLIs support:

Flag	Behavior
--ignore_saved_run_command	Ignore run_commands.pickle for this run.
--no_update_saved_run_command	Do not update run_commands.pickle after the run.