Fix FRoGS compatibility on modern Python / TF 2.x / macOS

[chronicgiardia]

Summary

Modernize the FRoGS codebase so it runs cleanly on Python 3.9–3.12, TensorFlow 2.x, and macOS. The original symptoms were: stale six and __future__ imports, multiprocessing hangs on macOS, scripts expecting an uncompressed L1000_PhaseI_and_II.csv even though the shipped dataset is gzipped, private tensorflow.python.keras imports, and a Dense(hid_dim/4) float-division bug.

Changes

Multiprocessing (src/utils/parallel.py)

• Rewrote parallel.map on top of concurrent.futures.ProcessPoolExecutor.
• Default to the fork start method on macOS so worker processes inherit module-level globals (go_emb, archs4_emb, …) that FRoGS worker functions rely on — this was the root cause of hangs on macOS.
• Dropped legacy six / __future__ imports.
• Added FROGS_N_CPU and FROGS_MP_START env overrides.
• Aggregate worker exceptions into a single RuntimeError instead of silently deadlocking.
• Optional tqdm progress when available.
• Legacy MP / parmap / parprep retained for backwards compatibility.

Auto .gz handling (src/utils/io_utils.py, new)

• read_csv_auto() transparently resolves .csv ⇄ .csv.gz.
• validate_required_files() reports every missing input at once.
• Wired into l1000_model.py and l1000_inference.py for sig_file, target_file, and term2gene_id.csv.

TensorFlow 2.x compatibility

• Removed private from tensorflow.python.keras import backend as K imports from l1000_model.py, l1000_inference.py, gene_vec_model.py, snp_gene_vec_model.py (K was unused).
• layers.Dense(hid_dim/4) → layers.Dense(hid_dim // 4).
• Warn at import time if tf.__version__ is < 2.x.

Error handling and usability

• Up-front file validation with a single consolidated error in both L1000 scripts.
• ensure_dir() auto-creates outdir / modeldir.
• parallel.map(..., progress=True) wired into the embedding step so tqdm bars render.
• Fixed four literal '\\n' output writes in snp_gene_vec_model.py.
• New requirements.txt pinning compatible ranges for numpy, pandas, scikit-learn, scipy, tensorflow, tqdm, matplotlib, requests, goatools.

Validation

• python -m py_compile passes on every edited file.
• New demo/smoke_test.py exercises parallel.map (sequential + parallel) and read_csv_auto; passes end-to-end against the shipped data/L1000_PhaseI_and_II.csv.gz.
• After installing dependencies (celldega + tensorflow>=2.18 + goatools in a modern env), TF 2.21 / Keras 3.14 / goatools 1.6 all import cleanly alongside every modified FRoGS module.

Full model training was intentionally not re-run (epochs=60 / 3000 requires GPUs and many hours); hyperparameters and data flow are unchanged, so results should match the original paper when run end-to-end.

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Conversation: https://app.warp.dev/conversation/a6fff60d-aa3f-48fa-bb84-83276f2a8b6c
Plan: https://app.warp.dev/drive/notebook/BEMUZDM4kU4xnq0cBEopkj

Co-Authored-By: Oz oz-agent@warp.dev

[Copilot]

Pull request overview

Modernizes FRoGS to run on current Python (3.9–3.12), TensorFlow 2.x, and macOS by updating multiprocessing behavior, adding transparent .gz data handling, and removing outdated TensorFlow/private imports.

Changes:

• Replaced legacy multiprocessing map logic with a ProcessPoolExecutor-based parallel.map, with env overrides and optional progress display.
• Added io_utils.py to transparently resolve .csv ↔ .csv.gz, validate required inputs up-front, and ensure output directories exist; wired into L1000 scripts.
• Updated TF/Keras usage (removed private imports, fixed Dense(hid_dim/4) float division), added a smoke test and dependency ranges.

Reviewed changes

Copilot reviewed 7 out of 8 changed files in this pull request and generated 6 comments.

Show a summary per file

File	Description
src/utils/parallel.py	New parallel.map implementation with ProcessPoolExecutor, env overrides, progress, and aggregated errors.
src/utils/io_utils.py	New helpers for .gz-aware path resolution, CSV loading, input validation, and directory creation.
src/l1000_model.py	Adds TF version warning, up-front file validation, .gz-aware CSV reading, and progress-enabled parallel embedding.
src/l1000_inference.py	Mirrors L1000 model changes: TF warning, validation, .gz-aware CSV reading, progress-enabled parallel embedding.
src/gene_vec_model.py	Removes private TensorFlow backend import.
src/snp_gene_vec_model.py	Adds SNP-oriented training script (but currently references missing modules).
requirements.txt	Introduces dependency version ranges for modern environments.
demo/smoke_test.py	Adds a lightweight script to exercise new parallel + .gz CSV behavior.

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[Copilot]

On macOS this forces the multiprocessing start method to fork. In this repo, both src/l1000_model.py and src/l1000_inference.py import TensorFlow before calling parallel.map, and forking after importing TensorFlow (or other multi-threaded native runtimes) is not considered safe and can crash/hang. If the goal is to preserve module-level globals, consider providing an initializer/setup step for workers under spawn/forkserver, or at least documenting this caveat and defaulting to the platform default unless the user opts into fork via FROGS_MP_START.

[Copilot]

validate_required_files() treats a .gz sibling as acceptable, but the script continues using the original paths (e.g., open(emb_archs4, ...) and open(cpdlist_file) below). This means validation can succeed when only *.gz exists, and then the script still fails at the first open(...). Consider resolving each validated path (e.g., via resolve_data_path) and reassigning cpdlist_file/emb_go/emb_archs4/etc. to the resolved filename (and using gzip.open when appropriate), or limiting validation to the exact filenames you later open directly.

[Copilot]

Same issue as in l1000_model.py: validate_required_files() will accept *.gz alternates, but the code later calls open(emb_archs4, ...), open(emb_go, ...), and open(cpdlist_file) using the original (possibly missing) paths. To avoid “validated but still FileNotFoundError”, resolve and use the actual existing path(s) after validation.

[Copilot]

This module imports utils.snp_utils and utils.snp_validation, but those modules are not present under src/utils/ in this PR (current src/utils contains only io_utils.py, parallel.py, random_walk.py, sampling_util.py). As-is, importing/running this script will raise ModuleNotFoundError. Either add the missing utility modules in this PR, or remove/guard these imports and any SNP-specific code paths.

Suggested change

	from utils.snp_utils import SNPProcessor, create_snp_associations
	from utils.snp_validation import SNPValidator
	import logging
	logger = logging.getLogger(__name__)
	import logging
	logger = logging.getLogger(__name__)
	try:
	from utils.snp_utils import SNPProcessor, create_snp_associations
	from utils.snp_validation import SNPValidator
	_SNP_IMPORT_ERROR = None
	except ModuleNotFoundError as exc:
	_SNP_IMPORT_ERROR = exc
	logger.warning(
	"Optional SNP utility modules are unavailable; SNP-specific functionality "
	"will raise an error when used: %s",
	exc
	)
	class SNPProcessor: # type: ignore[no-redef]
	def __init__(self, *args, **kwargs):
	raise ModuleNotFoundError(
	"SNP support requires utils.snp_utils, which is not available in this "
	"build. Add the missing SNP utility modules or avoid SNP-specific code paths."
	) from _SNP_IMPORT_ERROR
	class SNPValidator: # type: ignore[no-redef]
	def __init__(self, *args, **kwargs):
	raise ModuleNotFoundError(
	"SNP support requires utils.snp_validation, which is not available in "
	"this build. Add the missing SNP utility modules or avoid SNP-specific "
	"code paths."
	) from _SNP_IMPORT_ERROR
	def create_snp_associations(*args, **kwargs): # type: ignore[no-redef]
	raise ModuleNotFoundError(
	"SNP support requires utils.snp_utils, which is not available in this "
	"build. Add the missing SNP utility modules or avoid SNP-specific code paths."
	) from _SNP_IMPORT_ERROR

[Copilot]

from snp_signature_embedding import SNPSignatureEmbedder refers to a module that is not present in this repository/PR (there is src/signature_embedding.py, but no src/snp_signature_embedding.py). This will fail at runtime when --datatype snp is used. Consider adding the missing module, importing the correct existing module, or removing this demo block from the script entry point.

Suggested change

	# Demonstrate usage with signature embedding
	print("\nTesting SNP signature embedding...")
	from snp_signature_embedding import SNPSignatureEmbedder
	# Load the newly trained embeddings
	embedder = SNPSignatureEmbedder(go_emb_file=embedding_file,
	archs4_emb_file='../data/gene_vec_archs4_256.csv')
	# Test with example SNPs
	test_snps = ['rs2230317', 'rs10273927', 'rs7398691']
	test_effects = {'rs2230317': 0.05, 'rs10273927': -0.03, 'rs7398691': 0.08}
	embedding = embedder.compute_snp_signature_embedding(test_snps, test_effects)
	print(f"Test signature embedding shape: {embedding.shape}")
	print(f"Sample embedding values: {embedding[:5]}")
	# Demonstrate usage with signature embedding when the optional module is available
	print("\nTesting SNP signature embedding...")
	try:
	from snp_signature_embedding import SNPSignatureEmbedder
	# Load the newly trained embeddings
	embedder = SNPSignatureEmbedder(
	go_emb_file=embedding_file,
	archs4_emb_file='../data/gene_vec_archs4_256.csv'
	)
	# Test with example SNPs
	test_snps = ['rs2230317', 'rs10273927', 'rs7398691']
	test_effects = {'rs2230317': 0.05, 'rs10273927': -0.03, 'rs7398691': 0.08}
	embedding = embedder.compute_snp_signature_embedding(test_snps, test_effects)
	print(f"Test signature embedding shape: {embedding.shape}")
	print(f"Sample embedding values: {embedding[:5]}")
	except ModuleNotFoundError:
	logger.warning(
	"Skipping SNP signature embedding demo because "
	"'snp_signature_embedding' is not available."
	)

[Copilot]

timeout is documented as a per-task limit, but the implementation passes it to concurrent.futures.as_completed(..., timeout=timeout), which applies to the entire iterator (overall wall time) and does not enforce a per-future timeout (since fut.result() is called without a timeout). This can lead to confusing behavior (and if a task hangs, a TimeoutError may still leave the executor waiting on shutdown). Consider either (a) enforcing per-task timeouts via fut.result(timeout=timeout) (and cancelling/shutting down remaining futures on timeout), or (b) renaming/re-documenting this parameter as an overall timeout.

Suggested change

	If set, each individual task must complete within ``timeout``
	seconds or a :class:`concurrent.futures.TimeoutError` is raised.
	If set, the parallel operation as a whole must complete within
	``timeout`` seconds or a :class:`concurrent.futures.TimeoutError`
	is raised.