First update of GainPro

[ypriverol]

PR Type

Enhancement, Documentation

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Description

• Comprehensive package refactoring from ProtoGain to GenerativeProteomics (GainPro)

• Fixed method signatures: replaced cls with self in instance methods throughout codebase

• Standardized imports: converted all relative imports to absolute GenerativeProteomics package imports

• Renamed classes for consistency: GAIN_DANN → GainDann, Imputation_Management → ImputationManagement

• Added professional package infrastructure: pyproject.toml, MANIFEST.in, CLI entry point via main() function

• Reorganized project structure: moved datasets to datasets/ folder, scripts to scripts/, configs to configs/

• Enhanced documentation: added comprehensive docstrings, updated Sphinx docs, improved README with correct paths

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Diagram Walkthrough

flowchart LR
  A["ProtoGain<br/>Codebase"] -->|"Rename classes<br/>Fix method signatures"| B["GenerativeProteomics<br/>Core Package"]
  B -->|"Add absolute imports"| C["Standardized<br/>Import System"]
  C -->|"Create pyproject.toml<br/>Add CLI entry"| D["Professional<br/>Package Structure"]
  D -->|"Reorganize folders<br/>Update docs"| E["GainPro v0.2.0<br/>Ready for Distribution"]

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File Walkthrough

	Relevant files
Documentation	16 files __init__.py Added comprehensive module docstring and exports                  +71/-0    conf.py Updated mock imports from ProtoGain to GenerativeProteomics +1/-1      README.md Updated documentation with correct paths and references    +4/-4      GainPro.dataset.rst Updated module references from ProtoGain to GenerativeProteomics +1/-1      GainPro.generativeproteomics.rst Updated module references and documentation                            +2/-2      GainPro.hypers.rst Updated module references from ProtoGain to GenerativeProteomics +1/-1      GainPro.manager.rst Updated class name and module references                                  +3/-3      GainPro.model.rst Updated module references from ProtoGain to GenerativeProteomics +2/-2      GainPro.output.rst Updated module references and method signatures                    +3/-3      GainPro.rst Updated all module references to GainPro naming                    +7/-7      GainPro.utils.rst Updated module references from ProtoGain to GenerativeProteomics +1/-1      How to use.rst Updated example paths to use datasets folder                          +1/-1      Installation.rst Updated repository URL from ProtoGain to GainPro                  +1/-1      index.rst Updated documentation index references to GainPro                +1/-1      README.md Created documentation for scripts directory                            +22/-0    README.md Updated class name references in test documentation            +1/-1
Enhancement	10 files correlation.py Fixed relative import to absolute package import                  +1/-1      gain_dann_model.py Renamed class GAIN_DANN to GainDann, fixed imports              +10/-10  gaindann.py Updated all imports to absolute paths, renamed class usage +10/-10  generativeproteomics.py Refactored CLI with docstrings, improved argument parsing +34/-18  hypers_optimization.py Fixed imports and renamed method toJSON to to_json              +4/-4      imputation_management.py Renamed class and fixed absolute imports                                  +4/-4      __init__.py Created new models subpackage with documentation                  +17/-0    params_gain_dann.py Renamed method toJSON to to_json for consistency                  +1/-1      train.py Updated all imports to absolute paths, renamed class usage +13/-13  multiple_runs.sh Created improved script with documentation and parameters +16/-0
Bug fix	3 files dataset.py Fixed method signatures from cls to self                                  +8/-8      model.py Fixed method signatures and standardized imports                  +115/-115 output.py Fixed method signature and absolute imports                            +3/-3
Miscellaneous	4 files test.py Removed obsolete test file                                                              +0/-16    utils.py Removed root-level utils file, moved to package                    +0/-58    multiple_runs.sh Removed obsolete script from package directory                      +0/-8      parameters.json Removed obsolete parameters file from package                        +0/-16
Tests	5 files test_imputation_management.py Updated class name references in test cases                            +6/-6      test_generate_reference.py Updated path references from ProtoGain to GenerativeProteomics +1/-3      test_hint_generation.py Updated path references from ProtoGain to GenerativeProteomics +1/-1      test_imputation_with_reference.py Updated path references from ProtoGain to GenerativeProteomics +1/-1      test_impute_no_reference.py Updated path references from ProtoGain to GenerativeProteomics +1/-1
Configuration changes	5 files parameters.json Updated paths to use datasets folder structure                      +4/-5      parameters_noref.json Updated paths and removed deprecated parameters                    +3/-6      MANIFEST.in Created manifest for package distribution                                +20/-0    params_gain.json Created new config file with standardized paths                    +15/-0    pyproject.toml Created comprehensive project configuration file                  +133/-0
Dependencies	1 files requirements.txt Updated dependencies with version constraints                        +9/-4

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PR Compliance Guide 🔍

Below is a summary of compliance checks for this PR:

Security Compliance
🟢	No security concerns identified No security vulnerabilities detected by AI analysis. Human verification advised for critical code.
Ticket Compliance
⚪	🎫 No ticket provided Create ticket/issue
Codebase Duplication Compliance
⚪	Codebase context is not defined Follow the guide to enable codebase context checks.
Custom Compliance
🟢	Generic: Meaningful Naming and Self-Documenting Code Objective: Ensure all identifiers clearly express their purpose and intent, making code self-documenting Status: Passed Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Secure Logging Practices Objective: To ensure logs are useful for debugging and auditing without exposing sensitive information like PII, PHI, or cardholder data. Status: Passed Learn more about managing compliance generic rules or creating your own custom rules
⚪	Generic: Comprehensive Audit Trails Objective: To create a detailed and reliable record of critical system actions for security analysis and compliance. Status: Missing audit logs: New CLI entry point and training/evaluation flows do not log user identity or action outcomes for critical operations like file I/O and model runs, making audit trails incomplete. Referred Code def main(): """Main entry point for the GenerativeProteomics CLI.""" start_time = time.time() with cProfile.Profile() as profile: folder = os.getcwd() args = init_arg() missing_file = args.i output_file = args.o ref_file = args.ref output_folder = args.ofolder num_iterations = args.it batch_size = args.batchsize alpha = args.alpha miss_rate = args.miss hint_rate = args.hint lr_D = args.lrd lr_G = args.lrg parameters_file = args.parameters ... (clipped 145 lines) Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Robust Error Handling and Edge Case Management Objective: Ensure comprehensive error handling that provides meaningful context and graceful degradation Status: Weak error handling: The new CLI path performs multiple file reads/writes and training steps without try/except or validation of inputs beyond minimal checks, risking silent failures or generic crashes. Referred Code def main(): """Main entry point for the GenerativeProteomics CLI.""" start_time = time.time() with cProfile.Profile() as profile: folder = os.getcwd() args = init_arg() missing_file = args.i output_file = args.o ref_file = args.ref output_folder = args.ofolder num_iterations = args.it batch_size = args.batchsize alpha = args.alpha miss_rate = args.miss hint_rate = args.hint lr_D = args.lrd lr_G = args.lrg parameters_file = args.parameters ... (clipped 145 lines) Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Secure Error Handling Objective: To prevent the leakage of sensitive system information through error messages while providing sufficient detail for internal debugging. Status: Error detail exposure: User-facing logging on missing model files uses logger.error with interpolated paths and may propagate exceptions without sanitization, potentially exposing internal paths. Referred Code model = GainDann(metadata["protein_names"], metadata["input_dim"], latent_dim=metadata["latent_dim"], n_class=metadata["n_class"], num_hidden_layers=metadata["params"]["num_hidden_layers"], dann_params=dann_params, gain_params=gain_params, gain_metrics=gain_metrics) model_path = f"{checkpoint_dir}/model.pt" if not os.path.isfile(model_path): logger.error(f"Model in {model_path} not found.") Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Security-First Input Validation and Data Handling Objective: Ensure all data inputs are validated, sanitized, and handled securely to prevent vulnerabilities Status: Input validation gaps: The CLI accepts file paths and parameters and reads CSV/TSV without comprehensive validation or sanitization, which may lead to errors or unsafe handling when files are malformed or unexpected. Referred Code """Parse command-line arguments for the imputation pipeline.""" parser = argparse.ArgumentParser( description="GenerativeProteomics (GainPro) - GAIN-based missing value imputation", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument("-i", "--input", dest="i", help="path to missing data file") parser.add_argument("-o", "--output", dest="o", default="imputed", help="name of output file") parser.add_argument("--ref", help="path to a reference (complete) dataset") parser.add_argument( "--ofolder", default=os.getcwd() + "/results/", help="path to output folder" ) parser.add_argument("--it", type=int, default=2001, help="number of iterations") parser.add_argument("--batchsize", type=int, default=128, help="batch size") parser.add_argument("--alpha", type=float, default=10, help="alpha") parser.add_argument("--miss", type=float, default=0.1, help="missing rate") parser.add_argument("--hint", type=float, default=0.9, help="hint rate") parser.add_argument( "--lrd", type=float, default=0.001, help="learning rate for the discriminator" ) parser.add_argument( "--lrg", type=float, default=0.001, help="learning rate for the generator" ... (clipped 38 lines) Learn more about managing compliance generic rules or creating your own custom rules
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PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
Possible issue	Fix incorrect attribute assignment logic Fix the setitem method in ParamsGainDann to correctly set attributes dynamically using setattr(self, key, value) instead of hardcoding the attribute name to key. GenerativeProteomics/params_gain_dann.py [226-227] def __setitem__(self, key, value): - self.key = value + setattr(self, key, value) [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 8 __ Why: The suggestion correctly identifies a bug in the __setitem__ method where it incorrectly assigns to self.key instead of using the key parameter to name the attribute, and the proposed fix using setattr is correct.	Medium
High-level	Refactor duplicated training loop logic The Network class has three methods (train_ref, evaluate, train) with almost identical training loops. This should be refactored into a single, unified training method to reduce code duplication and improve maintainability. Examples: GenerativeProteomics/model.py [147-364] def train_ref(self, data: Data, missing_header): dim = data.dataset_scaled.shape[1] train_size = data.dataset_scaled.shape[0] if train_size < self.hypers.batch_size: self.hypers.batch_size = train_size print( "Batch size is larger than the number of samples\nReducing batch size to the number of samples\n" ) ... (clipped 208 lines) Solution Walkthrough: Before: class Network: def train_ref(self, data, ...): # ... setup ... pbar = tqdm(range(self.hypers.num_iterations)) for it in pbar: # ... sample batch with reference ... self.metrics.loss_D[it] = self._update_D(...) self.metrics.loss_G[it] = self._update_G(...) # ... calculate train and test MSE ... # ... post-processing ... def evaluate(self, data, ...): # ... setup ... pbar = tqdm(range(self.hypers.num_iterations)) for it in pbar: # ... sample batch for evaluation ... self.metrics.loss_D_evaluate[it] = self._update_D(...) self.metrics.loss_G_evaluate[it] = self._update_G(...) # ... calculate train and test MSE for evaluation ... # ... post-processing ... def train(self, data, ...): # ... setup, weight init ... pbar = tqdm(range(self.hypers.num_iterations)) for it in pbar: # ... sample batch without reference ... self.metrics.loss_D[it] = self._update_D(...) self.metrics.loss_G[it] = self._update_G(...) # ... calculate train MSE only ... # ... post-processing ... After: class Network: def _training_loop(self, data, mode='train'): # ... common setup ... pbar = tqdm(range(self.hypers.num_iterations)) for it in pbar: # ... sample batch based on mode ... loss_d = self._update_D(...) loss_g = self._update_G(...) # ... calculate and store metrics based on mode ... # (e.g., train MSE, optional test MSE) def train_ref(self, data, ...): self._training_loop(data, mode='train_ref') self.impute(data) # ... output ... def evaluate(self, data, ...): self._training_loop(data, mode='evaluate') self._evaluate_impute(data) # ... output ... def train(self, data, ...): # ... weight init ... self._training_loop(data, mode='train') self.impute(data) # ... output ... Suggestion importance[1-10]: 7 __ Why: The suggestion correctly identifies significant code duplication in the core training logic across three methods, which is a major maintenance concern and violates the DRY principle.	Medium
General	Scale data before creating reference In the _create_ref method, scale the dataset before introducing zeros for the reference set. This prevents the scaler from being influenced by artificial zero values and ensures it is based on the true data distribution. GenerativeProteomics/dataset.py [42-56] def _create_ref(self, miss_rate, hint_rate): self.ref_mask = self.mask.detach().clone() self.ref_dataset = self.dataset.detach().clone() + self.ref_dataset_scaled = torch.from_numpy(self.scaler.transform(self.ref_dataset)).clone() + zero_idxs = torch.nonzero(self.mask == 1) chance = torch.rand(len(zero_idxs)) miss = chance > miss_rate selected_idx = zero_idxs[~miss] for idx in selected_idx: self.ref_mask[tuple(idx)] = 0 + self.ref_dataset_scaled[tuple(idx)] = 0 self.ref_dataset[tuple(idx)] = 0 self.ref_hint = generate_hint(self.ref_mask, hint_rate) - self.ref_dataset_scaled = torch.from_numpy(self.scaler.transform(self.ref_dataset)) Apply / Chat Suggestion importance[1-10]: 7 __ Why: The suggestion correctly identifies that scaling the data after introducing zeros can skew the data distribution, which is a valid concern for data preprocessing that could impact model performance.	Medium
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[Copilot]

Pull request overview

This PR represents a significant refactoring of the GainPro (previously ProtoGain) project, involving package renaming, code organization improvements, and the addition of proper Python packaging configuration. The changes modernize the codebase structure by converting to absolute imports, following PEP 8 naming conventions, and adding comprehensive package metadata.

Key changes:

• Renamed project from "ProtoGain" to "GainPro/GenerativeProteomics" throughout codebase
• Refactored imports from relative to absolute with GenerativeProteomics. prefix
• Renamed classes and methods to follow PEP 8 conventions (Imputation_Management → ImputationManagement, GAIN_DANN → GainDann, toJSON → to_json)
• Fixed method signatures by replacing cls with self for instance methods
• Added proper Python packaging with pyproject.toml and MANIFEST.in
• Reorganized scripts and configuration files

Reviewed changes

Copilot reviewed 43 out of 47 changed files in this pull request and generated 4 comments.

Show a summary per file

File	Description
utils.py	Deleted old utility file
test.py	Deleted development test file
GenerativeProteomics/multiple_runs.sh	Deleted old shell script
GenerativeProteomics/parameters.json	Deleted old hardcoded parameters file
scripts/multiple_runs.sh	Added new organized shell script for running multiple experiments
scripts/README.md	Added documentation for scripts
requirements.txt	Updated with version constraints and better organization
pyproject.toml	Added comprehensive Python packaging configuration
MANIFEST.in	Added package manifest for distribution
GenerativeProteomics/__init__.py	Added package initialization with exports and documentation
GenerativeProteomics/models/__init__.py	Added models subpackage initialization
GenerativeProteomics/*.py	Updated all Python files with absolute imports and fixed method signatures
tests/*.py	Updated test files with class renames and import path fixes
use-case/2-tests/*.py	Updated use-case tests with import path fixes
docs/source/*.rst	Updated documentation with project name changes
Datasets/breast/*.json	Updated dataset parameter files with corrected paths
Datasets/breast/breastMissing_20.csv	Added dataset file
configs/params_gain.json	Added example configuration file
README.md	Updated with corrected paths and project references

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