Output

STREAMLINE writes outputs under:

<output_path>/<experiment_name>/

For example:

out/UCIHCCPipeline/

Experiment-Level Files

Common experiment-level outputs include:

Path	Description
`run_commands.pickle`	Saved resolved phase arguments for repeat runs.
`DatasetComparisons/`	P9 cross-dataset comparison outputs.
`reporting/<experiment_name>_STREAMLINE_Report.pdf`	Standard P11 report.
`reporting_replication/<experiment_name>_STREAMLINE_Replication_Report.pdf`	Replication P11 report.
`jobsCompleted/`	Completion markers for orchestration.

How To Check A Run Quickly

After a full demo run, check for these files first:

<output_path>/<experiment_name>/<dataset>/model_evaluation/Summary_performance_mean.csv
<output_path>/<experiment_name>/reporting/<experiment_name>_STREAMLINE_Report.pdf

If P10/P11 replication ran, also check:

<output_path>/<experiment_name>/<dataset>/replication/<rep_dataset>/model_evaluation/Summary_performance_mean.csv
<output_path>/<experiment_name>/reporting_replication/<experiment_name>_STREAMLINE_Replication_Report.pdf

Dataset-Level Folders

Each dataset gets a folder under the experiment directory:

Folder	Produced by	Description
`exploratory/`	P1	DataProcessSummary, missingness, feature typing, class counts, and EDA summaries.
`CVDatasets/`	P1-P5	Train/test CV datasets, including selected feature versions.
`impute_scale/`	P2	Imputation/scaling metadata and artifacts.
`feature_learning/`	P3	Learned feature manifests and feature lists.
`feature_importance/`	P4	Feature score files by method and CV.
`feature_selection/`	P5	Informative feature summaries and selected feature artifacts.
`models/`	P6	Fitted models, predictions, metrics, and Optuna accounting.
`model_evaluation/`	P6/P8	Summary metrics and model plots.
`ensemble_evaluation/`	P7/P8	Ensemble metrics and plots for classification runs.
`runtime/`	multiple	Runtime summaries.
`replication/`	P10	Replication predictions, metrics, and plots.

Reports

P11 can generate two report scopes:

python -m streamline.p11_reporting.p11_cli \
  --experiment_path out/UCIHCCPipeline \
  --report_mode standard

python -m streamline.p11_reporting.p11_cli \
  --experiment_path out/UCIHCCPipeline \
  --report_mode replication

The standard report focuses on training/CV experiment outputs. The replication report focuses on external validation outputs under the dataset replication folders.

The first page of each report is intended to answer the practical questions users ask first: what dataset was run, what phases were run, which settings were used, what task type was evaluated, and where the strongest or tied metric results appear.

Report Data

Each report directory also includes report_data.json. This JSON is the structured input used to build the PDF and is useful for debugging report content without parsing the PDF.

Figures

The reporting phase can either reuse existing generated figures or generate missing figures:

python -m streamline.p11_reporting.p11_cli \
  --experiment_path out/UCIHCCPipeline \
  --enable_plots 1 \
  --reuse_existing_figures 1

Set --enable_plots 0 when you want a faster report-only smoke test.