Fix FRoGS compatibility on modern Python / TF 2.x / macOS by chronicgiardia · Pull Request #2 · chenhcs/FRoGS

chronicgiardia · 2026-04-23T03:24:40Z

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.

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

Modernize the codebase so FRoGS runs cleanly on Python 3.9-3.12, TensorFlow 2.x, and macOS. Multiprocessing (src/utils/parallel.py) - Rewrite 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 the FRoGS worker functions rely on; this was the root cause of hangs on macOS. - Drop legacy 'six' / __future__ imports. - Add FROGS_N_CPU and FROGS_MP_START env overrides. - Aggregate worker exceptions into a single RuntimeError instead of silently deadlocking. - Optional tqdm progress bar when available. - Retain legacy MP/parmap/parprep helpers for backwards compatibility. Auto .gz data 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 - Remove private 'from tensorflow.python.keras import backend as K' imports from l1000_model.py, l1000_inference.py, gene_vec_model.py, and snp_gene_vec_model.py (K was unused). - Fix 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 in both L1000 scripts with a single consolidated error message (including .gz alternates). - ensure_dir() auto-creates outdir/modeldir. - parallel.map(..., progress=True) wired into the embedding step so tqdm bars render during long runs. - Fix four literal '\\n' output writes in snp_gene_vec_model.py that would have emitted a backslash-n instead of a newline. - Add requirements.txt pinning compatible ranges for numpy, pandas, scikit-learn, scipy, tensorflow, tqdm, matplotlib, requests, goatools. Validation - py_compile passes on every edited file. - New demo/smoke_test.py exercises parallel.map (seq + par) and read_csv_auto; passes end-to-end against the shipped data file. 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 · 2026-04-23T03:38:00Z

+    if sys.platform == "darwin":
+        try:
+            return multiprocessing.get_context("fork")
+        except ValueError:
+            pass
+    return multiprocessing.get_context()


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 · 2026-04-23T03:38:01Z

+# Validate all required inputs up-front so the user learns about every
+# missing file at once rather than one failure at a time.
+ok, missing = validate_required_files([cpdlist_file, target_file, sig_file, emb_go, emb_archs4])
+if not ok:
+    sys.stderr.write(
+        "ERROR: the following required input files could not be found\n"
+        "(a .gz counterpart was also tried for each):\n  - "
+        + "\n  - ".join(missing)
+        + "\n"
+    )
+    sys.exit(2)
+ensure_dir(outdir)
+ensure_dir(modeldir)


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 · 2026-04-23T03:38:01Z

+ok, missing = validate_required_files([cpdlist_file, sig_file, emb_go, emb_archs4])
+if not ok:
+    sys.stderr.write(
+        "ERROR: the following required input files could not be found\n"
+        "(a .gz counterpart was also tried for each):\n  - "
+        + "\n  - ".join(missing)
+        + "\n"
+    )
+    sys.exit(2)
+ensure_dir(outdir)
+


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 · 2026-04-23T03:38:01Z

+from utils.snp_utils import SNPProcessor, create_snp_associations
+from utils.snp_validation import SNPValidator
+import logging
+
+logger = logging.getLogger(__name__)
+


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 · 2026-04-23T03:38:01Z

+        # 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]}")
+


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 · 2026-04-23T03:38:02Z

+        If set, each individual task must complete within ``timeout``
+        seconds or a :class:`concurrent.futures.TimeoutError` is raised.


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.

Copilot AI review requested due to automatic review settings April 23, 2026 03:24

Copilot started reviewing on behalf of chronicgiardia April 23, 2026 03:25 View session

Copilot AI reviewed Apr 23, 2026

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

Fix FRoGS compatibility on modern Python / TF 2.x / macOS#2
chronicgiardia wants to merge 1 commit intochenhcs:mainfrom
chronicgiardia:fix/modernize-compat-macos

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-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

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

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chronicgiardia commented Apr 23, 2026

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