diff --git a/README.md b/README.md index 46fbd6e..ab94bec 100644 --- a/README.md +++ b/README.md @@ -77,7 +77,19 @@ The dummy example uses the same defaults as a real run: first batch size 12, lat ## Use Deepdraw On Your Own Project -Deepdraw expects you to start with an unlabeled design pool. You do not need any experimental measurements for the first round. +Deepdraw expects you to start with an unlabeled design pool. You do not need any experimental measurements for the first round. The full workflow is: + +| Step | What you provide or run | What Deepdraw writes | +| --- | --- | --- | +| 1 | Input design pool: `designs.csv` | - | +| 2 | Run `deepdraw embed-alphagenome` | `embeddings_alphagenome_raw.npz` | +| 3 | Run `deepdraw pca` | `embeddings_alphagenome_kneedle.npz` | +| 4 | Run `deepdraw init` | `round_000_to_measure.csv` | +| 5 | Measure `round_000_to_measure.csv` designs and append labels | `measurements.csv` | +| 6 | Run `deepdraw suggest` with `measurements.csv` | `round_001_to_measure.csv` | +| 7 | Repeat measurement and `deepdraw suggest` rounds | `round_002_to_measure.csv`, ... | + +All workflow code needed for this path lives in this repository. Deepdraw provides the AlphaGenome embedding command, PCA command, and active-learning commands. The AlphaGenome runtime is kept in `envs/alphagenome` so its JAX/Hugging Face dependencies do not affect the normal Deepdraw environment. Model weights are still downloaded or read from your Hugging Face cache according to AlphaGenome's access terms. ### 1. Prepare A Design Pool @@ -94,7 +106,7 @@ The stable ID column is recommended because it makes measurement files easier to ### 2. Generate GFM Embeddings -Generate one embedding vector per design using your chosen genomic foundation model. Deepdraw currently expects embeddings to be provided as an NPZ file; embedding generation itself is outside the `deepdraw` CLI. +Generate one embedding vector per design using your chosen genomic foundation model. Deepdraw includes an AlphaGenome embedding command and a PCA reduction command; you can also provide an NPZ generated by another model. Required NPZ structure: @@ -107,6 +119,50 @@ Required NPZ structure: `sample_ids` is also accepted instead of `ids`. If you pass `--id-column variant_id`, the NPZ IDs should match that CSV column. If you do not pass an ID column, use row indices `0, 1, 2, ...`. +AlphaGenome has a much heavier JAX/Hugging Face runtime than the normal Deepdraw loop, so keep it in the separate environment bundled under `envs/alphagenome`: + +```bash +uv sync --project envs/alphagenome --python 3.11 +``` + +#### Recommended Hardware For Embeddings + +Embedding is usually the most computationally expensive Deepdraw setup step. For production-size design pools, use a Linux machine with an NVIDIA CUDA GPU; the isolated AlphaGenome environment installs CUDA-enabled JAX on Linux. CPU inference is supported and useful for smoke tests or small pools, but it will be slower, so keep `--batch-size 1` unless you have already checked memory use on your machine. + +On Apple Silicon, this environment uses JAX CPU for AlphaGenome. The M-series GPU is visible through JAX Metal on some machines, but the Metal plugin is still experimental and is not the recommended Deepdraw path for AlphaGenome embedding. For GPU throughput, run the same Deepdraw command on Linux with a CUDA GPU. + +Before the first embedding run, accept access to the gated AlphaGenome model on Hugging Face and authenticate the AlphaGenome environment: + +```bash +uv run --project envs/alphagenome hf auth login +``` + +You can also provide a token with `HF_TOKEN`. Without model access and authentication, Hugging Face will return `401 Unauthorized` when Deepdraw tries to download `google/alphagenome-all-folds`. + +Generate AlphaGenome embeddings from the same design pool you will pass to `deepdraw init`: + +```bash +uv run --project envs/alphagenome deepdraw embed-alphagenome \ + --pool-csv designs.csv \ + --sequence-column sequence \ + --id-column variant_id \ + --output embeddings_alphagenome_raw.npz \ + --resolution 128 \ + --pooling mean \ + --batch-size 1 +``` + +Then run kneedle PCA in the regular Deepdraw environment: + +```bash +uv run deepdraw pca \ + --input embeddings_alphagenome_raw.npz \ + --output embeddings_alphagenome_kneedle.npz \ + --selection-method kneedle +``` + +The PCA output, `embeddings_alphagenome_kneedle.npz`, is the embeddings file you pass to `deepdraw init` in the next step. + ### 3. Select The First Batch Run `deepdraw init` to choose the first experimental batch from embeddings only. @@ -114,7 +170,7 @@ Run `deepdraw init` to choose the first experimental batch from embeddings only. ```bash uv run deepdraw init \ --pool-csv designs.csv \ - --embeddings embeddings.npz \ + --embeddings embeddings_alphagenome_kneedle.npz \ --sequence-column sequence \ --id-column variant_id \ --output-dir runs/my_deepdraw_run \ @@ -136,6 +192,8 @@ Send `round_000_to_measure.csv` to the wet lab. Recommendation CSVs keep the original design-pool columns; `selection_history.csv` adds only `deepdraw_round` so you can see which batch each design came from. +If you generated embeddings with a different model, replace `embeddings_alphagenome_kneedle.npz` with your own Deepdraw-compatible NPZ. + ### 4. Add Measurements After the first experiment, create one cumulative measurements CSV, such as `measurements.csv`. The easiest approach is to copy `round_000_to_measure.csv` and add a measured label column. @@ -185,32 +243,7 @@ The next output will be: runs/my_deepdraw_run/round_002_to_measure.csv ``` -The loop is: - -```text -design pool + embeddings - | - v -deepdraw init - | - v -measure round_000 - | - v -deepdraw suggest - | - v -measure round_001 - | - v -append round_001 labels to measurements.csv - | - v -deepdraw suggest - | - v -repeat -``` +Continue the same measure, append, and `deepdraw suggest` loop for each later round. ## Recommended Defaults @@ -219,7 +252,7 @@ For a first real campaign, use the defaults unless you have a reason to compare ```bash uv run deepdraw init \ --pool-csv designs.csv \ - --embeddings embeddings.npz \ + --embeddings embeddings_alphagenome_kneedle.npz \ --sequence-column sequence \ --id-column variant_id \ --output-dir runs/my_deepdraw_run \ @@ -246,7 +279,7 @@ Change the number of designs per round: ```bash uv run deepdraw init \ --pool-csv designs.csv \ - --embeddings embeddings.npz \ + --embeddings embeddings_alphagenome_kneedle.npz \ --sequence-column sequence \ --id-column variant_id \ --output-dir runs/my_deepdraw_run \ @@ -259,7 +292,7 @@ Choose where outputs are written: ```bash uv run deepdraw init \ --pool-csv designs.csv \ - --embeddings embeddings.npz \ + --embeddings embeddings_alphagenome_kneedle.npz \ --sequence-column sequence \ --id-column variant_id \ --output-dir runs/my_deepdraw_run @@ -313,6 +346,18 @@ Train on measured labels and select the next batch: uv run deepdraw suggest --help ``` +Generate AlphaGenome embeddings: + +```bash +uv run --project envs/alphagenome deepdraw embed-alphagenome --help +``` + +Reduce embeddings with PCA/kneedle: + +```bash +uv run deepdraw pca --help +``` + Required/common arguments: - `--pool-csv`: CSV containing candidate designs. @@ -327,6 +372,7 @@ Required/common arguments: ```text ├── deepdraw/ # User-facing Deepdraw CLI and workflow +├── envs/alphagenome/ # Optional isolated AlphaGenome embedding runtime ├── examples/deepdraw_dummy/ # Tiny runnable example ├── core/ # Active learning models, strategies, and trainers ├── job_sub/ # Retrospective benchmark and Slurm/Hydra tooling diff --git a/deepdraw/cli.py b/deepdraw/cli.py index 30b0ab3..52de797 100644 --- a/deepdraw/cli.py +++ b/deepdraw/cli.py @@ -6,6 +6,8 @@ import logging from pathlib import Path +from deepdraw.embeddings.alphagenome import embed_design_pool, embed_fasta +from deepdraw.embeddings.pca import reduce_embeddings_pca from deepdraw.workflow import initialize_run, suggest_next_batch _LOG_LEVEL_CHOICES = ("DEBUG", "INFO", "WARNING", "ERROR") @@ -88,6 +90,88 @@ def build_parser() -> argparse.ArgumentParser: suggest_parser.add_argument("--measurements", required=True, type=Path) suggest_parser.add_argument("--label-column") suggest_parser.add_argument("--measurement-id-column") + + embed_parser = subparsers.add_parser( + "embed-alphagenome", + help="Generate AlphaGenome embeddings for a design pool or FASTA file.", + ) + _add_log_level_argument(embed_parser) + input_group = embed_parser.add_mutually_exclusive_group(required=True) + input_group.add_argument("--pool-csv", type=Path) + input_group.add_argument("--fasta", type=Path) + embed_parser.add_argument("--output", required=True, type=Path) + embed_parser.add_argument("--sequence-column") + embed_parser.add_argument("--id-column") + embed_parser.add_argument("--model-version", default="all_folds") + embed_parser.add_argument("--batch-size", type=int, default=1) + embed_parser.add_argument("--pooling", choices=["mean", "none"], default="mean") + embed_parser.add_argument("--resolution", type=int, choices=[1, 128], default=128) + embed_parser.add_argument( + "--species", + choices=["human", "mouse"], + default="human", + ) + embed_parser.add_argument( + "--no-pad-to-multiple", + action="store_true", + help="Disable AlphaGenome input padding to multiples of 2048 bp.", + ) + embed_parser.add_argument( + "--no-validate", + action="store_true", + help="Disable DNA sequence validation before embedding.", + ) + embed_parser.add_argument( + "--device", + choices=["cpu", "gpu", "tpu"], + help="Force an AlphaGenome runtime device. Defaults to GPU/TPU if available.", + ) + + pca_parser = subparsers.add_parser( + "pca", + help="Reduce an embedding NPZ with PCA and kneedle component selection.", + ) + _add_log_level_argument(pca_parser) + pca_parser.add_argument("--input", dest="input_file", required=True, type=Path) + pca_parser.add_argument("--output", required=True, type=Path) + pca_parser.add_argument( + "--n-components", + type=int, + help=( + "Number of PCA components to fit. By default this also keeps exactly " + "that many PCs unless --select-components is passed." + ), + ) + pca_parser.set_defaults(exact_n_components=None) + pca_parser.add_argument( + "--exact-n-components", + dest="exact_n_components", + action="store_true", + help="Keep exactly --n-components.", + ) + pca_parser.add_argument( + "--select-components", + dest="exact_n_components", + action="store_false", + help="Use --selection-method after fitting --n-components as an upper cap.", + ) + pca_parser.add_argument("--target-variance", type=float, default=0.95) + pca_parser.add_argument( + "--selection-method", + choices=["target-variance", "elbow", "kneedle", "l-method"], + default="kneedle", + ) + pca_parser.add_argument( + "--power-of-two", + action=argparse.BooleanOptionalAction, + default=True, + help="Round target-variance selection to a power of two.", + ) + pca_parser.add_argument( + "--use-mean-pooling", + action="store_true", + help="Mean-pool 3D or ragged embeddings over sequence positions before PCA.", + ) return parser @@ -102,7 +186,7 @@ def main(argv: list[str] | None = None) -> None: try: _run_command(args, parser) - except (OSError, ValueError) as exc: + except (OSError, RuntimeError, ValueError) as exc: if log_level == "DEBUG": raise parser.exit(1, f"Error: {exc}\n") @@ -142,6 +226,56 @@ def _run_command(args: argparse.Namespace, parser: argparse.ArgumentParser) -> N print(f"Wrote Deepdraw round {latest_round}: {round_path}") return + if args.command == "embed-alphagenome": + if args.pool_csv is not None: + embed_design_pool( + pool_csv=args.pool_csv, + output_path=args.output, + sequence_column=args.sequence_column, + id_column=args.id_column, + model_version=args.model_version, + batch_size=args.batch_size, + pooling=args.pooling, + resolution=args.resolution, + species=args.species, + pad_to_multiple=not args.no_pad_to_multiple, + validate=not args.no_validate, + device=args.device, + ) + else: + embed_fasta( + fasta_path=args.fasta, + output_path=args.output, + model_version=args.model_version, + batch_size=args.batch_size, + pooling=args.pooling, + resolution=args.resolution, + species=args.species, + pad_to_multiple=not args.no_pad_to_multiple, + validate=not args.no_validate, + device=args.device, + ) + print(f"Wrote AlphaGenome embeddings: {args.output}") + return + + if args.command == "pca": + summary = reduce_embeddings_pca( + input_file=args.input_file, + output_file=args.output, + n_components=args.n_components, + target_variance=args.target_variance, + use_mean_pooling=args.use_mean_pooling, + exact_n_components=args.exact_n_components, + power_of_two=args.power_of_two, + selection_method=args.selection_method.replace("-", "_"), + ) + print( + f"Wrote PCA embeddings: {args.output} " + f"({summary.n_components} PCs, " + f"{summary.cumulative_explained_variance:.2%} variance)" + ) + return + parser.error(f"Unknown command {args.command}") diff --git a/deepdraw/embeddings/__init__.py b/deepdraw/embeddings/__init__.py new file mode 100644 index 0000000..dbb4a7e --- /dev/null +++ b/deepdraw/embeddings/__init__.py @@ -0,0 +1,5 @@ +"""Embedding preparation helpers for Deepdraw.""" + +from deepdraw.embeddings.pca import reduce_embeddings_pca + +__all__ = ["reduce_embeddings_pca"] diff --git a/deepdraw/embeddings/alphagenome.py b/deepdraw/embeddings/alphagenome.py new file mode 100644 index 0000000..1616716 --- /dev/null +++ b/deepdraw/embeddings/alphagenome.py @@ -0,0 +1,726 @@ +"""AlphaGenome embedding extraction for Deepdraw design pools.""" + +from __future__ import annotations + +import logging +import os +from contextlib import nullcontext +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd + +logger = logging.getLogger(__name__) + +VALID_DNA_CHARS = set("ACGTRYSWKMBDHVN") +SEQUENCE_COLUMN_CANDIDATES = ( + "sequence", + "Sequence", + "seq", + "Seq", + "dna_sequence", + "DNA_sequence", +) +ENV_XDG_CACHE_HOME = "XDG_CACHE_HOME" +DEFAULT_CACHE_DIR = "~/.cache" + + +@dataclass(frozen=True) +class SequenceRecord: + """A design ID and DNA sequence to embed.""" + + sample_id: str + sequence: str + + +@dataclass(frozen=True) +class _AlphaGenomeRuntime: + hk: Any + jax: Any + jnp: Any + jmp: Any + huggingface_hub: Any + core_model: Any + research_dna_model: Any + metadata_lib: Any + + +def read_design_pool_sequences( + pool_csv: str | Path, + *, + sequence_column: str | None = None, + id_column: str | None = None, + validate: bool = True, +) -> list[SequenceRecord]: + """Read IDs and DNA sequences from a Deepdraw design-pool CSV.""" + + pool_path = Path(pool_csv).expanduser().resolve() + if not pool_path.exists(): + raise FileNotFoundError(f"Design pool CSV does not exist: {pool_path}") + + pool_df = pd.read_csv(pool_path) + resolved_sequence_column = _resolve_sequence_column(pool_df, sequence_column) + if id_column is not None and id_column not in pool_df.columns: + raise ValueError(f"id_column '{id_column}' was not found in {pool_path}.") + + records: list[SequenceRecord] = [] + ids: list[str] = [] + for idx, row in pool_df.iterrows(): + sample_id = str(row[id_column]) if id_column is not None else str(idx) + sequence = str(row[resolved_sequence_column]) + if validate: + sequence = normalize_dna_sequence(sequence, sample_id=sample_id) + else: + sequence = _strip_sequence_whitespace(sequence).upper() + records.append(SequenceRecord(sample_id=sample_id, sequence=sequence)) + ids.append(sample_id) + + _ensure_unique_ids(ids, label="design pool") + logger.info( + "Read %d sequences from %s (sequence column: %s; id: %s).", + len(records), + pool_path, + resolved_sequence_column, + id_column if id_column is not None else "row index", + ) + return records + + +def read_fasta_sequences( + fasta_path: str | Path, + *, + validate: bool = True, +) -> list[SequenceRecord]: + """Read DNA sequences from a FASTA file without requiring Biopython.""" + + path = Path(fasta_path).expanduser().resolve() + if not path.exists(): + raise FileNotFoundError(f"FASTA file does not exist: {path}") + + records: list[SequenceRecord] = [] + header: str | None = None + chunks: list[str] = [] + + def flush_record() -> None: + if header is None: + return + sample_id = header.split()[0] + sequence = "".join(chunks) + if validate: + sequence = normalize_dna_sequence(sequence, sample_id=sample_id) + else: + sequence = _strip_sequence_whitespace(sequence).upper() + records.append(SequenceRecord(sample_id=sample_id, sequence=sequence)) + + with path.open() as handle: + for line in handle: + line = line.strip() + if not line: + continue + if line.startswith(">"): + flush_record() + header = line[1:].strip() + if not header: + raise ValueError(f"Empty FASTA header found in {path}.") + chunks = [] + else: + if header is None: + raise ValueError( + f"FASTA sequence encountered before header in {path}." + ) + chunks.append(line) + flush_record() + + if not records: + raise ValueError(f"No FASTA records found in {path}.") + _ensure_unique_ids([record.sample_id for record in records], label="FASTA") + logger.info("Read %d sequences from %s", len(records), path) + return records + + +def normalize_dna_sequence(sequence: str, *, sample_id: str = "") -> str: + """Normalize and validate a DNA sequence.""" + + normalized = _strip_sequence_whitespace(sequence).upper() + if not normalized: + raise ValueError(f"Empty sequence found{_format_sample_suffix(sample_id)}.") + + invalid_chars = sorted(set(normalized) - VALID_DNA_CHARS) + if invalid_chars: + raise ValueError( + f"Invalid DNA characters {invalid_chars}" + f"{_format_sample_suffix(sample_id)}." + ) + return normalized + + +def embed_design_pool( + *, + pool_csv: str | Path, + output_path: str | Path, + sequence_column: str | None = None, + id_column: str | None = None, + model_version: str = "all_folds", + batch_size: int = 1, + pooling: str = "mean", + resolution: int = 128, + species: str = "human", + pad_to_multiple: bool = True, + validate: bool = True, + device: str | None = None, +) -> None: + """Embed a Deepdraw design-pool CSV with AlphaGenome.""" + + records = read_design_pool_sequences( + pool_csv, + sequence_column=sequence_column, + id_column=id_column, + validate=validate, + ) + embed_sequences( + records, + output_path=output_path, + model_version=model_version, + batch_size=batch_size, + pooling=pooling, + resolution=resolution, + species=species, + pad_to_multiple=pad_to_multiple, + device=device, + ) + + +def embed_fasta( + *, + fasta_path: str | Path, + output_path: str | Path, + model_version: str = "all_folds", + batch_size: int = 1, + pooling: str = "mean", + resolution: int = 128, + species: str = "human", + pad_to_multiple: bool = True, + validate: bool = True, + device: str | None = None, +) -> None: + """Embed a FASTA file with AlphaGenome.""" + + records = read_fasta_sequences(fasta_path, validate=validate) + embed_sequences( + records, + output_path=output_path, + model_version=model_version, + batch_size=batch_size, + pooling=pooling, + resolution=resolution, + species=species, + pad_to_multiple=pad_to_multiple, + device=device, + ) + + +def embed_sequences( + records: list[SequenceRecord], + *, + output_path: str | Path, + model_version: str = "all_folds", + batch_size: int = 1, + pooling: str = "mean", + resolution: int = 128, + species: str = "human", + pad_to_multiple: bool = True, + device: str | None = None, +) -> None: + """Run AlphaGenome and save embeddings to a Deepdraw-compatible NPZ.""" + + if not records: + raise ValueError("No sequences were provided for AlphaGenome embedding.") + if batch_size <= 0: + raise ValueError(f"batch_size must be positive; got {batch_size}.") + if pooling not in {"mean", "none"}: + raise ValueError("pooling must be one of: 'mean', 'none'.") + if resolution not in {1, 128}: + raise ValueError("resolution must be one of: 1, 128.") + + runtime = _load_alphagenome_runtime() + device_obj = _auto_device(runtime, device) + if _is_cpu_device(device_obj): + _patch_cpu_attention_precision(runtime) + model = _create_model_from_huggingface( + runtime, + model_version=model_version, + device=device_obj, + ) + _adapt_model_precision_for_device(runtime, model, device_obj) + forward = _build_forward( + runtime, + force_float32_compute=getattr(device_obj, "platform", None) == "cpu", + ) + organism_index = _get_organism_index(species) + multiple_of = 2048 if pad_to_multiple else 1 + + logger.info( + "Embedding %d sequences with AlphaGenome %s at %dbp resolution.", + len(records), + model_version, + resolution, + ) + all_embeddings: list[np.ndarray] = [] + embedding_lengths: list[int] = [] + + total_batches = (len(records) + batch_size - 1) // batch_size + for batch_num, start in enumerate(range(0, len(records), batch_size), start=1): + batch_records = records[start : start + batch_size] + logger.info( + "AlphaGenome batch %d/%d (%d sequences).", + batch_num, + total_batches, + len(batch_records), + ) + batch_embeddings, batch_lengths = _embed_batch( + runtime=runtime, + model=model, + forward=forward, + records=batch_records, + organism_index=organism_index, + multiple_of=multiple_of, + resolution=resolution, + pooling=pooling, + device=device_obj, + ) + all_embeddings.extend(batch_embeddings) + embedding_lengths.extend(batch_lengths) + + ids = np.asarray([record.sample_id for record in records], dtype=object) + sequence_lengths_bp = np.asarray([len(record.sequence) for record in records]) + _save_embeddings( + embeddings=all_embeddings, + ids=ids, + sequence_lengths_bp=sequence_lengths_bp, + embedding_lengths=np.asarray(embedding_lengths, dtype=np.int64), + output_path=output_path, + pooling=pooling, + resolution=resolution, + model_version=model_version, + species=species, + ) + + +def _embed_batch( + *, + runtime: _AlphaGenomeRuntime, + model: Any, + forward: Any, + records: list[SequenceRecord], + organism_index: int, + multiple_of: int, + resolution: int, + pooling: str, + device: Any | None, +) -> tuple[list[np.ndarray], list[int]]: + onehot_arrays = [ + np.asarray(model._one_hot_encoder.encode(record.sequence), dtype=np.float32) + for record in records + ] + padded = _pad_onehot_to_multiple(onehot_arrays, multiple_of) + with _default_device(runtime, device): + onehot = runtime.jnp.array(padded, dtype=_model_input_dtype(runtime, model)) + org_idx = runtime.jnp.array( + [organism_index] * len(records), + dtype=runtime.jnp.int32, + ) + + embeds, _ = forward.apply( + model._params, + model._state, + None, + onehot, + org_idx, + model._metadata, + ) + + if resolution == 1: + embeddings = embeds.embeddings_1bp + mask_factor = 1 + else: + embeddings = embeds.embeddings_128bp + mask_factor = 128 + + lengths_bp = np.asarray( + [len(record.sequence) for record in records], dtype=np.int64 + ) + max_len = int(embeddings.shape[1]) * mask_factor + base_mask = np.zeros((len(records), max_len, 1), dtype=np.float32) + for idx, seq_len in enumerate(lengths_bp): + base_mask[idx, : int(seq_len), 0] = 1.0 + with _default_device(runtime, device): + pool_mask = runtime.jnp.array(base_mask) + if mask_factor != 1: + pool_mask = _downsample_padding_mask(runtime, pool_mask, mask_factor) + + if pooling == "mean": + pooled = _mean_pool(runtime, embeddings, pool_mask) + return [np.asarray(row, dtype=np.float32) for row in np.asarray(pooled)], [ + 1 + ] * len(records) + + pool_lengths = np.asarray(pool_mask.sum(axis=1)).reshape(len(records)) + + output_embeddings: list[np.ndarray] = [] + output_lengths: list[int] = [] + for idx in range(len(records)): + embedding_len = int(pool_lengths[idx]) + output_embeddings.append(np.asarray(embeddings[idx, :embedding_len, :])) + output_lengths.append(embedding_len) + return output_embeddings, output_lengths + + +def _load_alphagenome_runtime() -> _AlphaGenomeRuntime: + try: + import haiku as hk + import huggingface_hub + import jax + import jax.numpy as jnp + import jmp + from alphagenome_research.model import dna_model as research_dna_model + from alphagenome_research.model import model as core_model + from alphagenome_research.model.metadata import metadata as metadata_lib + except ImportError as exc: + raise RuntimeError( + "AlphaGenome embedding extraction needs the separate AlphaGenome " + "runtime. Create it with " + "`uv sync --project envs/alphagenome --python 3.11`, then run " + "`uv run --project envs/alphagenome deepdraw embed-alphagenome ...`." + ) from exc + + return _AlphaGenomeRuntime( + hk=hk, + jax=jax, + jnp=jnp, + jmp=jmp, + huggingface_hub=huggingface_hub, + core_model=core_model, + research_dna_model=research_dna_model, + metadata_lib=metadata_lib, + ) + + +def _create_model_from_huggingface( + runtime: _AlphaGenomeRuntime, + *, + model_version: str, + device: Any | None, +) -> Any: + repo_id = f"google/alphagenome-{model_version.replace('_', '-').lower()}" + offline = os.environ.get("HF_HUB_OFFLINE", "").lower() in {"1", "true", "yes"} + try: + checkpoint_path = runtime.huggingface_hub.snapshot_download( + repo_id=repo_id, + local_files_only=offline, + cache_dir=_get_hf_cache_dir(), + ) + except Exception as exc: + raise RuntimeError( + f"Could not download or load {repo_id}. AlphaGenome model weights are " + "gated on Hugging Face; accept access to the model and authenticate " + "with `uv run --project envs/alphagenome hf auth login` or " + "set HF_TOKEN. If you are running offline, make sure the model is " + "already present in the Hugging Face cache." + ) from exc + organism_settings = { + runtime.research_dna_model.Organism.HOMO_SAPIENS: ( + runtime.research_dna_model.OrganismSettings( + metadata=runtime.metadata_lib.load( + runtime.research_dna_model.Organism.HOMO_SAPIENS + ) + ) + ), + runtime.research_dna_model.Organism.MUS_MUSCULUS: ( + runtime.research_dna_model.OrganismSettings( + metadata=runtime.metadata_lib.load( + runtime.research_dna_model.Organism.MUS_MUSCULUS + ) + ) + ), + } + with _default_device(runtime, device): + return runtime.research_dna_model.create( + checkpoint_path, + organism_settings=organism_settings, + device=device, + ) + + +def _get_hf_cache_dir() -> str: + hf_hub_cache = os.environ.get("HF_HUB_CACHE") + if hf_hub_cache: + return os.path.expanduser(hf_hub_cache) + + hf_home = os.environ.get("HF_HOME") + if hf_home: + return os.path.join(os.path.expanduser(hf_home), "hub") + + base = os.environ.get(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR) + return os.path.expanduser(os.path.join(base, "alphagenome")) + + +def _build_forward( + runtime: _AlphaGenomeRuntime, + *, + force_float32_compute: bool = False, +) -> Any: + policy_name = ( + "params=float32,compute=float32,output=float32" + if force_float32_compute + else "params=float32,compute=bfloat16,output=bfloat16" + ) + policy = runtime.jmp.get_policy(policy_name) + + def forward(dna_onehot: Any, organism_index: Any, output_metadata: Any) -> Any: + with runtime.hk.mixed_precision.push_policy( + runtime.core_model.AlphaGenome, + policy, + ): + model = runtime.core_model.AlphaGenome(output_metadata=output_metadata) + _, embeds = model(dna_onehot, organism_index) + return embeds + + return runtime.hk.transform_with_state(forward) + + +def _pad_onehot_to_multiple( + onehot_arrays: list[np.ndarray], + multiple_of: int, +) -> np.ndarray: + max_len = max(arr.shape[0] for arr in onehot_arrays) + if multiple_of > 1: + remainder = max_len % multiple_of + if remainder: + max_len += multiple_of - remainder + + padded = np.zeros((len(onehot_arrays), max_len, 4), dtype=np.float32) + for idx, arr in enumerate(onehot_arrays): + padded[idx, : arr.shape[0], :] = arr + return padded + + +def _mean_pool(runtime: _AlphaGenomeRuntime, embeddings: Any, padding_mask: Any) -> Any: + if hasattr(padding_mask, "astype"): + padding_mask = padding_mask.astype(embeddings.dtype) + summed = (embeddings * padding_mask).sum(axis=1) + denominator = padding_mask.sum(axis=1).clip(min=1.0) + return summed / denominator + + +def _adapt_model_precision_for_device( + runtime: _AlphaGenomeRuntime, + model: Any, + device: Any | None, +) -> None: + if not _is_cpu_device(device): + return + + def cast_cpu_float(value: Any) -> Any: + dtype = getattr(value, "dtype", None) + if dtype is not None and dtype == getattr(runtime.jnp, "bfloat16", None): + return value.astype(runtime.jnp.float32) + return value + + model._params = runtime.jax.tree.map(cast_cpu_float, model._params) + model._state = runtime.jax.tree.map(cast_cpu_float, model._state) + + +def _patch_cpu_attention_precision(runtime: _AlphaGenomeRuntime) -> None: + """Relax AlphaGenome's BF16 attention preset on CPU-only JAX backends.""" + + if getattr(runtime.jnp.einsum, "_deepdraw_cpu_precision_patch", False): + return + + original_einsum = runtime.jnp.einsum + bf16_preset = runtime.jax.lax.DotAlgorithmPreset.BF16_BF16_F32 + + def einsum_without_cpu_bf16_preset(*args: Any, **kwargs: Any) -> Any: + if kwargs.get("precision") == bf16_preset: + kwargs = dict(kwargs) + kwargs["precision"] = None + return original_einsum(*args, **kwargs) + + einsum_without_cpu_bf16_preset._deepdraw_cpu_precision_patch = True + runtime.jnp.einsum = einsum_without_cpu_bf16_preset + + +def _model_input_dtype(runtime: _AlphaGenomeRuntime, model: Any) -> Any: + dtype = _first_floating_dtype(runtime, model._params) + if dtype is not None: + return dtype + return getattr(runtime.jnp, "bfloat16", runtime.jnp.float32) + + +def _first_floating_dtype(runtime: _AlphaGenomeRuntime, tree: Any) -> Any | None: + if not hasattr(runtime, "jax"): + return None + leaves = runtime.jax.tree.leaves(tree) + for leaf in leaves: + dtype = getattr(leaf, "dtype", None) + if dtype is not None and runtime.jnp.issubdtype(dtype, runtime.jnp.floating): + return dtype + return None + + +def _downsample_padding_mask( + runtime: _AlphaGenomeRuntime, + padding_mask: Any, + factor: int, +) -> Any: + if factor <= 1: + return padding_mask + batch_size, seq_len, _ = padding_mask.shape + if seq_len % factor != 0: + raise ValueError( + f"Padding length {seq_len} must be divisible by downsample factor {factor}." + ) + reduced = padding_mask.reshape(batch_size, seq_len // factor, factor, 1) + return reduced.max(axis=2) + + +def _auto_device(runtime: _AlphaGenomeRuntime, requested: str | None) -> Any: + if requested is not None: + return _get_device(runtime, requested) + + devices = runtime.jax.devices() + for preferred in ("gpu", "tpu"): + for device in devices: + if device.platform.lower() == preferred: + return device + logger.warning("No GPU/TPU found; falling back to CPU.") + return _get_device(runtime, "cpu") + + +def _get_device(runtime: _AlphaGenomeRuntime, requested: str) -> Any: + requested = requested.lower() + try: + backend_devices = runtime.jax.devices(requested) + except Exception: + backend_devices = [] + if backend_devices: + return backend_devices[0] + + for device in runtime.jax.devices(): + platform = device.platform.lower() + if platform == requested: + return device + if requested == "gpu" and any( + device.platform.lower() == "metal" for device in runtime.jax.devices() + ): + raise ValueError( + "A JAX Metal device is visible, but Deepdraw's AlphaGenome embedding " + "path is not supported on Apple Metal. Use --device cpu on Apple " + "Silicon, or run on a CUDA GPU." + ) + raise ValueError( + f"Requested device '{requested}' not available: {runtime.jax.devices()}" + ) + + +def _is_cpu_device(device: Any | None) -> bool: + return getattr(device, "platform", None) == "cpu" + + +def _default_device(runtime: _AlphaGenomeRuntime, device: Any | None) -> Any: + if device is None: + return nullcontext() + return runtime.jax.default_device(device) + + +def _get_organism_index(species: str) -> int: + mapping = { + "human": 0, + "mouse": 1, + } + key = species.lower() + if key not in mapping: + raise ValueError("species must be one of: human, mouse.") + return mapping[key] + + +def _save_embeddings( + *, + embeddings: list[np.ndarray], + ids: np.ndarray, + sequence_lengths_bp: np.ndarray, + embedding_lengths: np.ndarray, + output_path: str | Path, + pooling: str, + resolution: int, + model_version: str, + species: str, +) -> None: + output = Path(output_path).expanduser().resolve() + output.parent.mkdir(parents=True, exist_ok=True) + + if pooling == "mean": + embeddings_array = np.stack(embeddings).astype(np.float32) + else: + embeddings_array = _pad_embeddings(embeddings) + + np.savez_compressed( + output, + embeddings=embeddings_array, + ids=ids, + sequence_lengths_bp=sequence_lengths_bp, + embedding_lengths=embedding_lengths, + pooling=np.asarray(pooling), + resolution=np.asarray(resolution, dtype=np.int32), + model=np.asarray(f"alphagenome-{model_version}"), + species=np.asarray(species), + ) + logger.info( + "Saved AlphaGenome embeddings with shape %s to %s", + embeddings_array.shape, + output, + ) + + +def _pad_embeddings(embeddings: list[np.ndarray]) -> np.ndarray: + max_len = max(arr.shape[0] for arr in embeddings) + dim = embeddings[0].shape[1] + padded = np.zeros((len(embeddings), max_len, dim), dtype=np.float32) + for idx, arr in enumerate(embeddings): + if arr.ndim != 2: + raise ValueError(f"Expected 2D unpooled embedding; got shape {arr.shape}.") + if arr.shape[1] != dim: + raise ValueError("All embeddings must have the same feature dimension.") + padded[idx, : arr.shape[0], :] = arr + return padded + + +def _resolve_sequence_column( + pool_df: pd.DataFrame, + requested: str | None, +) -> str: + if requested: + if requested not in pool_df.columns: + raise ValueError(f"sequence_column '{requested}' was not found.") + return requested + for candidate in SEQUENCE_COLUMN_CANDIDATES: + if candidate in pool_df.columns: + return candidate + raise ValueError( + "Could not infer the sequence column. Pass --sequence-column explicitly." + ) + + +def _ensure_unique_ids(ids: list[str], *, label: str) -> None: + unique_count = len(set(ids)) + if unique_count != len(ids): + raise ValueError(f"{label} ids contain {len(ids) - unique_count} duplicates.") + + +def _strip_sequence_whitespace(sequence: str) -> str: + return "".join(str(sequence).split()) + + +def _format_sample_suffix(sample_id: str) -> str: + return f" in sequence '{sample_id}'" if sample_id else "" diff --git a/deepdraw/embeddings/pca.py b/deepdraw/embeddings/pca.py new file mode 100644 index 0000000..889ed67 --- /dev/null +++ b/deepdraw/embeddings/pca.py @@ -0,0 +1,431 @@ +"""PCA reduction for Deepdraw embedding NPZ files.""" + +from __future__ import annotations + +import logging +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import numpy as np +from sklearn.decomposition import PCA + +logger = logging.getLogger(__name__) + +LAPACK_INT32_MAX = np.iinfo(np.int32).max +SELECTION_METHODS = {"target_variance", "elbow", "kneedle", "l_method"} + + +@dataclass(frozen=True) +class EmbeddingData: + """Embeddings loaded from a Deepdraw-compatible NPZ file.""" + + embeddings: np.ndarray + ids: np.ndarray + lengths: np.ndarray | None = None + + +@dataclass(frozen=True) +class PCASummary: + """Summary of a PCA reduction.""" + + n_components: int + cumulative_explained_variance: float + fitted_components: int + selection_method: str + explained_variance_ratio: np.ndarray + + +def load_embeddings(input_file: str | Path) -> EmbeddingData: + """Load embeddings, IDs, and optional sequence lengths from an NPZ file.""" + + input_path = Path(input_file).expanduser().resolve() + if not input_path.exists(): + raise FileNotFoundError(f"Input embeddings NPZ does not exist: {input_path}") + + data = np.load(input_path, allow_pickle=True) + if "embeddings" not in data: + raise ValueError( + f"'embeddings' array not found in {input_path}. " + f"Available keys: {list(data.keys())}" + ) + ids = _extract_ids(data, input_path) + embeddings = np.asarray(data["embeddings"]) + if embeddings.shape[0] != len(ids): + raise ValueError( + f"Embedding rows ({embeddings.shape[0]}) do not match ids ({len(ids)})." + ) + + lengths = _extract_embedding_lengths(data) + if lengths is not None and len(lengths) != len(ids): + raise ValueError( + f"Embedding lengths ({len(lengths)}) do not match ids ({len(ids)})." + ) + + logger.info("Loaded %d embeddings from %s", len(ids), input_path) + return EmbeddingData(embeddings=embeddings, ids=ids, lengths=lengths) + + +def flatten_embeddings( + embeddings: np.ndarray, + *, + lengths: np.ndarray | None = None, + use_mean_pooling: bool = False, +) -> np.ndarray: + """Convert 2D, 3D, or ragged embeddings to a 2D PCA input matrix.""" + + if embeddings.dtype == object and embeddings.ndim == 1: + return _flatten_ragged_embeddings( + embeddings, + use_mean_pooling=use_mean_pooling, + ) + + if embeddings.ndim == 2: + if use_mean_pooling: + logger.warning("Embeddings are already 2D; no mean pooling was applied.") + return np.asarray(embeddings, dtype=np.float32) + + if embeddings.ndim != 3: + raise ValueError( + "Embeddings must be 2D, 3D, or a 1D object array of per-sequence " + f"2D arrays; got shape {embeddings.shape}." + ) + + if use_mean_pooling: + pooled = _mean_pool_padded_embeddings(embeddings, lengths=lengths) + logger.info( + "Mean pooled embeddings from %s to %s", embeddings.shape, pooled.shape + ) + return pooled + + n_samples, seq_len, dim = embeddings.shape + flattened = np.asarray(embeddings, dtype=np.float32).reshape( + n_samples, + seq_len * dim, + ) + logger.info("Flattened embeddings from %s to %s", embeddings.shape, flattened.shape) + return flattened + + +def reduce_dimensionality( + embeddings: np.ndarray, + *, + n_components: int | None = None, + target_variance: float = 0.95, + exact_n_components: bool = False, + power_of_two: bool = True, + selection_method: str = "kneedle", +) -> tuple[np.ndarray, PCASummary]: + """Fit PCA and select the output dimensionality.""" + + if embeddings.ndim != 2: + raise ValueError(f"PCA input must be 2D; got shape {embeddings.shape}.") + n_samples, n_features = embeddings.shape + if n_samples < 2: + raise ValueError("PCA requires at least two samples.") + + max_rank = min(n_samples, n_features) + if n_components is None: + n_components = max_rank + if n_components <= 0: + raise ValueError(f"n_components must be positive; got {n_components}.") + if n_components > max_rank: + raise ValueError( + f"n_components ({n_components}) cannot exceed " + f"min(n_samples, n_features) ({max_rank})." + ) + if not 0.0 < target_variance <= 1.0: + raise ValueError( + f"target_variance must be in the interval (0, 1]; got {target_variance}." + ) + if selection_method not in SELECTION_METHODS: + raise ValueError( + f"selection_method must be one of {sorted(SELECTION_METHODS)}; " + f"got {selection_method!r}." + ) + + matrix_elements = int(n_samples) * int(n_features) + if matrix_elements > LAPACK_INT32_MAX and n_components >= max_rank: + raise ValueError( + "Full-rank CPU PCA would exceed LAPACK integer indexing limits for " + f"matrix shape {embeddings.shape}. Set --n-components below {max_rank}." + ) + + svd_solver = "randomized" if n_components < max_rank else "auto" + logger.info( + "Fitting PCA with %d components on matrix shape %s using %s solver.", + n_components, + embeddings.shape, + svd_solver, + ) + pca = PCA(n_components=n_components, svd_solver=svd_solver, random_state=42) + transformed = pca.fit_transform(np.asarray(embeddings, dtype=np.float32)) + evr = np.asarray(pca.explained_variance_ratio_, dtype=np.float64) + + if exact_n_components: + chosen_k = n_components + elif selection_method == "target_variance": + chosen_k = _select_target_variance_k( + evr, + target_variance=target_variance, + power_of_two=power_of_two, + ) + elif selection_method == "elbow": + chosen_k = _select_elbow_k(np.cumsum(evr)) + elif selection_method == "kneedle": + chosen_k = _select_kneedle_k(evr) + else: + chosen_k = _select_l_method_k(evr) + + chosen_k = max(1, min(int(chosen_k), len(evr))) + cumulative = float(np.cumsum(evr)[chosen_k - 1]) + logger.info( + "Keeping %d PCs selected by %s (%.2f%% cumulative explained variance).", + chosen_k, + "exact_n_components" if exact_n_components else selection_method, + cumulative * 100.0, + ) + return transformed[:, :chosen_k], PCASummary( + n_components=chosen_k, + cumulative_explained_variance=cumulative, + fitted_components=n_components, + selection_method="exact_n_components" + if exact_n_components + else selection_method, + explained_variance_ratio=evr, + ) + + +def reduce_embeddings_pca( + input_file: str | Path, + output_file: str | Path, + *, + n_components: int | None = None, + target_variance: float = 0.95, + use_mean_pooling: bool = False, + exact_n_components: bool | None = None, + power_of_two: bool = True, + selection_method: str = "kneedle", +) -> PCASummary: + """Reduce an embedding NPZ file and save a Deepdraw-compatible 2D NPZ.""" + + data = load_embeddings(input_file) + matrix = flatten_embeddings( + data.embeddings, + lengths=data.lengths, + use_mean_pooling=use_mean_pooling, + ) + if exact_n_components is None: + exact_n_components = n_components is not None + + reduced, summary = reduce_dimensionality( + matrix, + n_components=n_components, + target_variance=target_variance, + exact_n_components=exact_n_components, + power_of_two=power_of_two, + selection_method=selection_method, + ) + save_reduced_embeddings(reduced, data.ids, output_file, summary) + return summary + + +def save_reduced_embeddings( + embeddings: np.ndarray, + ids: np.ndarray, + output_file: str | Path, + summary: PCASummary, +) -> None: + """Save reduced embeddings with IDs and PCA metadata.""" + + output_path = Path(output_file).expanduser().resolve() + output_path.parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed( + output_path, + embeddings=np.asarray(embeddings, dtype=np.float32), + ids=ids, + pca_n_components=np.asarray(summary.n_components, dtype=np.int32), + pca_fitted_components=np.asarray(summary.fitted_components, dtype=np.int32), + pca_cumulative_explained_variance=np.asarray( + summary.cumulative_explained_variance, + dtype=np.float64, + ), + pca_selection_method=np.asarray(summary.selection_method), + pca_explained_variance_ratio=summary.explained_variance_ratio, + ) + logger.info( + "Saved %d reduced embeddings with shape %s to %s", + len(ids), + embeddings.shape, + output_path, + ) + + +def _extract_ids(data: Any, input_path: Path) -> np.ndarray: + if "ids" in data: + return np.asarray(data["ids"]) + if "sample_ids" in data: + return np.asarray(data["sample_ids"]) + raise ValueError( + f"{input_path} must contain an 'ids' or 'sample_ids' array. " + f"Available keys: {list(data.keys())}" + ) + + +def _extract_embedding_lengths(data: Any) -> np.ndarray | None: + if "embedding_lengths" in data: + return np.asarray(data["embedding_lengths"], dtype=np.int64) + if "lengths" in data: + return np.asarray(data["lengths"], dtype=np.int64) + if "sequence_lengths_bp" not in data: + return None + + sequence_lengths = np.asarray(data["sequence_lengths_bp"], dtype=np.int64) + resolution = 1 + if "resolution" in data: + resolution = int(np.asarray(data["resolution"]).item()) + if resolution <= 0: + raise ValueError(f"resolution must be positive; got {resolution}.") + return np.ceil(sequence_lengths / resolution).astype(np.int64) + + +def _flatten_ragged_embeddings( + embeddings: np.ndarray, + *, + use_mean_pooling: bool, +) -> np.ndarray: + arrays = [np.asarray(item, dtype=np.float32) for item in embeddings.tolist()] + if not arrays: + raise ValueError("Embeddings array cannot be empty.") + for idx, arr in enumerate(arrays): + if arr.ndim != 2: + raise ValueError( + "Ragged embeddings must contain per-sequence 2D arrays; " + f"item {idx} has shape {arr.shape}." + ) + + if use_mean_pooling: + return np.stack([arr.mean(axis=0) for arr in arrays]).astype(np.float32) + + first_shape = arrays[0].shape + if any(arr.shape != first_shape for arr in arrays): + raise ValueError( + "Ragged embeddings have variable sequence lengths. Use " + "--use-mean-pooling before PCA, or regenerate padded embeddings." + ) + stacked = np.stack(arrays) + return stacked.reshape(stacked.shape[0], stacked.shape[1] * stacked.shape[2]) + + +def _mean_pool_padded_embeddings( + embeddings: np.ndarray, + *, + lengths: np.ndarray | None, +) -> np.ndarray: + embeddings = np.asarray(embeddings, dtype=np.float32) + if lengths is None: + return embeddings.mean(axis=1) + + n_samples, seq_len, dim = embeddings.shape + pooled = np.empty((n_samples, dim), dtype=np.float32) + for idx, length in enumerate(np.asarray(lengths, dtype=np.int64)): + if length <= 0 or length > seq_len: + raise ValueError( + f"Invalid embedding length {length} for row {idx}; " + f"expected a value in [1, {seq_len}]." + ) + pooled[idx] = embeddings[idx, : int(length), :].mean(axis=0) + return pooled + + +def _select_target_variance_k( + evr: np.ndarray, + *, + target_variance: float, + power_of_two: bool, +) -> int: + cumulative = np.cumsum(evr) + if not power_of_two: + return int(np.searchsorted(cumulative, target_variance, side="left") + 1) + + candidates: list[int] = [] + value = 1 + while value <= len(evr): + candidates.append(value) + value *= 2 + for candidate in candidates: + if cumulative[candidate - 1] >= target_variance: + return candidate + return candidates[-1] + + +def _select_elbow_k(cumulative_variance: np.ndarray) -> int: + n_points = len(cumulative_variance) + if n_points <= 2: + return n_points + + x = np.arange(1, n_points + 1, dtype=np.float64) + y = cumulative_variance.astype(np.float64) + start = np.array([x[0], y[0]]) + end = np.array([x[-1], y[-1]]) + line_vec = end - start + line_norm = float(np.linalg.norm(line_vec)) + if line_norm == 0.0: + return 1 + + points = np.column_stack((x, y)) + vec_from_start = points - start + distances = ( + np.abs(line_vec[0] * vec_from_start[:, 1] - line_vec[1] * vec_from_start[:, 0]) + / line_norm + ) + distances[0] = -np.inf + distances[-1] = -np.inf + return int(np.argmax(distances) + 1) + + +def _select_kneedle_k(evr: np.ndarray) -> int: + n_points = len(evr) + if n_points <= 2: + return n_points + + x = np.linspace(0.0, 1.0, n_points, dtype=np.float64) + y = evr.astype(np.float64) + y_min = float(np.min(y)) + y_max = float(np.max(y)) + if y_max <= y_min: + return 1 + + y_norm = (y - y_min) / (y_max - y_min) + y_increasing = 1.0 - y_norm + distances = y_increasing - x + distances[0] = -np.inf + distances[-1] = -np.inf + return int(np.argmax(distances) + 1) + + +def _select_l_method_k(evr: np.ndarray) -> int: + n_points = len(evr) + if n_points <= 2: + return n_points + + x = np.arange(1, n_points + 1, dtype=np.float64) + y = np.log(np.clip(evr.astype(np.float64), 1e-12, None)) + best_k = 2 + best_score = np.inf + for k in range(2, n_points): + left_score = _linear_sse(x[:k], y[:k]) + right_score = _linear_sse(x[k - 1 :], y[k - 1 :]) + score = (left_score + right_score) / n_points + if score < best_score: + best_score = score + best_k = k + return int(best_k) + + +def _linear_sse(x: np.ndarray, y: np.ndarray) -> float: + if x.size <= 1: + return 0.0 + slope, intercept = np.polyfit(x, y, deg=1) + residuals = y - (slope * x + intercept) + return float(np.sum(residuals * residuals)) diff --git a/envs/alphagenome/pyproject.toml b/envs/alphagenome/pyproject.toml new file mode 100644 index 0000000..d69009c --- /dev/null +++ b/envs/alphagenome/pyproject.toml @@ -0,0 +1,28 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "deepdraw-alphagenome-env" +version = "0.1.0" +description = "Isolated runtime for Deepdraw AlphaGenome embedding extraction" +requires-python = ">=3.11,<3.12" +dependencies = [ + "deepdraw", + "alphagenome>=0.6.1", + "alphagenome_research", + "dm-haiku", + "huggingface_hub", + "jax>=0.6.0; platform_system != 'Linux'", + "jax[cuda12]>=0.6.0; platform_system == 'Linux'", + "numpy", + "pandas", + "socksio", +] + +[tool.uv] +package = false + +[tool.uv.sources] +deepdraw = { path = "../..", 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upload-time = "2025-09-14T22:16:53.878Z" }, +] diff --git a/test/test_alphagenome_embeddings.py b/test/test_alphagenome_embeddings.py new file mode 100644 index 0000000..2b78632 --- /dev/null +++ b/test/test_alphagenome_embeddings.py @@ -0,0 +1,112 @@ +from types import SimpleNamespace + +import numpy as np +import pandas as pd +import pytest + +import deepdraw.embeddings.alphagenome as alphagenome +from deepdraw.embeddings.alphagenome import ( + embed_design_pool, + normalize_dna_sequence, + read_design_pool_sequences, + read_fasta_sequences, +) + + +def test_read_design_pool_sequences_uses_stable_ids(tmp_path): + pool_path = tmp_path / "designs.csv" + pd.DataFrame( + { + "variant_id": ["v1", "v2"], + "sequence": ["atgc", "NNNN"], + } + ).to_csv(pool_path, index=False) + + records = read_design_pool_sequences( + pool_path, + sequence_column="sequence", + id_column="variant_id", + ) + + assert [record.sample_id for record in records] == ["v1", "v2"] + assert [record.sequence for record in records] == ["ATGC", "NNNN"] + + +def test_read_fasta_sequences_parses_multiline_records(tmp_path): + fasta_path = tmp_path / "designs.fasta" + fasta_path.write_text(">v1 first design\nATG\nC\n>v2\nNNNN\n") + + records = read_fasta_sequences(fasta_path) + + assert [record.sample_id for record in records] == ["v1", "v2"] + assert [record.sequence for record in records] == ["ATGC", "NNNN"] + + +def test_normalize_dna_sequence_rejects_invalid_characters(): + with pytest.raises(ValueError, match="Invalid DNA characters"): + normalize_dna_sequence("ATGX", sample_id="bad") + + +def test_embed_design_pool_writes_npz_with_fake_runtime(monkeypatch, tmp_path): + pool_path = tmp_path / "designs.csv" + output_path = tmp_path / "embeddings.npz" + pd.DataFrame( + { + "variant_id": ["v1", "v2"], + "sequence": ["ATGCATGC", "ATGCNNNN"], + } + ).to_csv(pool_path, index=False) + + class FakeEncoder: + def encode(self, sequence): + return np.ones((len(sequence), 4), dtype=np.float32) + + class FakeForward: + def apply(self, params, state, rng, onehot, organism_index, metadata): + batch_size, padded_len, _ = onehot.shape + embeddings = SimpleNamespace( + embeddings_1bp=np.ones((batch_size, padded_len, 3), dtype=np.float32), + embeddings_128bp=np.ones( + (batch_size, padded_len // 128, 3), + dtype=np.float32, + ), + ) + return embeddings, {} + + fake_model = SimpleNamespace( + _one_hot_encoder=FakeEncoder(), + _params={}, + _state={}, + _metadata={}, + ) + monkeypatch.setattr( + alphagenome, + "_load_alphagenome_runtime", + lambda: SimpleNamespace(jnp=np), + ) + monkeypatch.setattr(alphagenome, "_auto_device", lambda runtime, device: None) + monkeypatch.setattr( + alphagenome, + "_create_model_from_huggingface", + lambda runtime, model_version, device: fake_model, + ) + monkeypatch.setattr( + alphagenome, + "_build_forward", + lambda runtime, **kwargs: FakeForward(), + ) + + embed_design_pool( + pool_csv=pool_path, + output_path=output_path, + sequence_column="sequence", + id_column="variant_id", + resolution=128, + pooling="mean", + ) + + data = np.load(output_path, allow_pickle=True) + assert data["ids"].tolist() == ["v1", "v2"] + assert data["embeddings"].shape == (2, 3) + assert data["pooling"].item() == "mean" + assert data["resolution"].item() == 128 diff --git a/test/test_deepdraw_cli.py b/test/test_deepdraw_cli.py index 0309b98..e359fae 100644 --- a/test/test_deepdraw_cli.py +++ b/test/test_deepdraw_cli.py @@ -1,3 +1,4 @@ +import numpy as np import pytest from deepdraw import cli @@ -49,3 +50,68 @@ def fail_suggest(**kwargs): "Expression", ] ) + + +def test_cli_pca_command_writes_output(tmp_path, capsys): + rng = np.random.default_rng(44) + input_path = tmp_path / "embeddings.npz" + output_path = tmp_path / "embeddings_pca.npz" + np.savez_compressed( + input_path, + embeddings=rng.normal(size=(8, 4)).astype(np.float32), + ids=np.asarray([f"v{idx}" for idx in range(8)], dtype=object), + ) + + cli.main( + [ + "pca", + "--input", + str(input_path), + "--output", + str(output_path), + "--selection-method", + "kneedle", + ] + ) + + captured = capsys.readouterr() + assert "Wrote PCA embeddings" in captured.out + assert output_path.exists() + + +def test_cli_embed_alphagenome_delegates_pool_csv(monkeypatch, tmp_path, capsys): + calls = {} + + def fake_embed_design_pool(**kwargs): + calls.update(kwargs) + + monkeypatch.setattr(cli, "embed_design_pool", fake_embed_design_pool) + pool_path = tmp_path / "designs.csv" + output_path = tmp_path / "embeddings.npz" + + cli.main( + [ + "embed-alphagenome", + "--pool-csv", + str(pool_path), + "--sequence-column", + "sequence", + "--id-column", + "variant_id", + "--output", + str(output_path), + "--resolution", + "1", + "--device", + "cpu", + ] + ) + + captured = capsys.readouterr() + assert "Wrote AlphaGenome embeddings" in captured.out + assert calls["pool_csv"] == pool_path + assert calls["output_path"] == output_path + assert calls["sequence_column"] == "sequence" + assert calls["id_column"] == "variant_id" + assert calls["resolution"] == 1 + assert calls["device"] == "cpu" diff --git a/test/test_embedding_pca.py b/test/test_embedding_pca.py new file mode 100644 index 0000000..674764a --- /dev/null +++ b/test/test_embedding_pca.py @@ -0,0 +1,60 @@ +import numpy as np +import pytest + +from deepdraw.embeddings.pca import flatten_embeddings, reduce_embeddings_pca + + +def test_reduce_embeddings_pca_kneedle_writes_deepdraw_npz(tmp_path): + rng = np.random.default_rng(123) + latent = rng.normal(size=(16, 3)) + weights = rng.normal(size=(3, 6)) + embeddings = (latent @ weights + 0.01 * rng.normal(size=(16, 6))).astype(np.float32) + ids = np.asarray([f"variant_{idx}" for idx in range(16)], dtype=object) + input_path = tmp_path / "raw_embeddings.npz" + output_path = tmp_path / "pca_embeddings.npz" + np.savez_compressed(input_path, embeddings=embeddings, ids=ids) + + summary = reduce_embeddings_pca( + input_path, + output_path, + selection_method="kneedle", + ) + + reduced = np.load(output_path, allow_pickle=True) + assert reduced["embeddings"].shape == (16, summary.n_components) + assert reduced["embeddings"].dtype == np.float32 + assert reduced["ids"].tolist() == ids.tolist() + assert reduced["pca_selection_method"].item() == "kneedle" + assert 1 <= summary.n_components <= min(embeddings.shape) + + +def test_flatten_embeddings_mean_pools_with_lengths(): + embeddings = np.asarray( + [ + [[1.0, 3.0], [3.0, 5.0], [100.0, 100.0]], + [[2.0, 4.0], [4.0, 6.0], [6.0, 8.0]], + ], + dtype=np.float32, + ) + lengths = np.asarray([2, 3]) + + pooled = flatten_embeddings( + embeddings, + lengths=lengths, + use_mean_pooling=True, + ) + + np.testing.assert_allclose(pooled, [[2.0, 4.0], [4.0, 6.0]]) + + +def test_ragged_embeddings_require_mean_pooling_when_lengths_vary(): + embeddings = np.asarray( + [ + np.ones((2, 3), dtype=np.float32), + np.ones((4, 3), dtype=np.float32), + ], + dtype=object, + ) + + with pytest.raises(ValueError, match="variable sequence lengths"): + flatten_embeddings(embeddings, use_mean_pooling=False)