From 90441cab84911ecc3c3b951d6d4a033b5ed94139 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Wed, 8 Jul 2026 09:04:53 +0200 Subject: [PATCH 01/35] make adata optional --- src/cellflow/model/_cellflow.py | 32 ++++++++++++++++++++++++++++---- 1 file changed, 28 insertions(+), 4 deletions(-) diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index a1127fc0..bf8b509a 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -1,6 +1,7 @@ import functools import os import types +import warnings from collections.abc import Callable, Sequence from dataclasses import field as dc_field from typing import Any, Literal @@ -41,12 +42,23 @@ class CellFlow: Parameters ---------- adata - An :class:`~anndata.AnnData` object to extract the training data from. + An :class:`~anndata.AnnData` object to extract the training data from. If :obj:`None`, + the model uses the annbatch/dagloader streaming path (see + :meth:`~cellflow.model.CellFlow.prepare_annbatch_data`) instead of the in-memory one. solver Solver to use for training. Either ``'otfm'`` or ``'genot'``. """ - def __init__(self, adata: ad.AnnData, solver: Literal["otfm", "genot"] = "otfm"): + def __init__(self, adata: ad.AnnData | None = None, solver: Literal["otfm", "genot"] = "otfm"): + # `adata is None` selects the annbatch/dagloader streaming path (see `prepare_annbatch_data`); + # a provided `adata` is the traditional in-memory path (`prepare_data`). + if adata is not None: + warnings.warn( + "Passing `adata` to `CellFlow(...)` is deprecated and will be removed in a future " + "release; pass it to `prepare_data(adata=...)` instead.", + FutureWarning, + stacklevel=2, + ) self._adata = adata self._solver_class = _otfm.OTFlowMatching if solver == "otfm" else _genot.GENOT self._vf_class = ( @@ -72,6 +84,7 @@ def prepare_data( split_covariates: Sequence[str] | None = None, max_combination_length: int | None = None, null_value: float = 0.0, + adata: ad.AnnData | None = None, ) -> None: """Prepare the dataloader for training from :attr:`~cellflow.model.CellFlow.adata`. @@ -121,6 +134,9 @@ def prepare_data( as the maximal number of perturbations a cell has been treated with. null_value Value to use for padding to ``'max_combination_length'``. + adata + The :class:`~anndata.AnnData` object to extract the training data from. If :obj:`None`, + the object passed to the (deprecated) constructor argument is used instead. Returns ------- @@ -171,6 +187,14 @@ def prepare_data( split_covariates=split_covariates, ) """ + adata = adata if adata is not None else self._adata + if adata is None: + raise ValueError( + "No `adata` provided. Pass it as `prepare_data(adata=...)` (recommended) or " + "construct `CellFlow(adata=...)`." + ) + self._adata = adata # kept for downstream predict / validation / plotting + self._dm = DataManager( self.adata, sample_rep=sample_rep, @@ -812,8 +836,8 @@ def load( return model @property - def adata(self) -> ad.AnnData: - """The :class:`~anndata.AnnData` object used for training.""" + def adata(self) -> ad.AnnData | None: + """The :class:`~anndata.AnnData` object used for training, or :obj:`None` in the annbatch path.""" return self._adata @property From ac5ab6c4dc256c606029b75f959e2bb9e2957583 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Wed, 8 Jul 2026 09:20:33 +0200 Subject: [PATCH 02/35] update to dagloader --- pyproject.toml | 8 +- src/dagloader/README.md | 252 ++ src/dagloader/__init__.py | 42 + src/dagloader/_io.py | 54 + src/dagloader/_loader.py | 257 ++ src/dagloader/_scheduled_sampler.py | 92 + src/dagloader/_schema.py | 221 + src/dagloader/_schemes.py | 58 + uv.lock | 6448 +++++++++++++++++++++++++++ 9 files changed, 7431 insertions(+), 1 deletion(-) create mode 100644 src/dagloader/README.md create mode 100644 src/dagloader/__init__.py create mode 100644 src/dagloader/_io.py create mode 100644 src/dagloader/_loader.py create mode 100644 src/dagloader/_scheduled_sampler.py create mode 100644 src/dagloader/_schema.py create mode 100644 src/dagloader/_schemes.py create mode 100644 uv.lock diff --git a/pyproject.toml b/pyproject.toml index 13e8e696..80ea4d87 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,6 +47,9 @@ dependencies = [ "session-info", ] +optional-dependencies.annbatch = [ + "annbatch>=0.2.1", +] optional-dependencies.dev = [ "furo", "myst-nb", @@ -89,6 +92,7 @@ optional-dependencies.pp = [ "rdkit", ] optional-dependencies.test = [ + "cellflow-tools[annbatch]", "cellflow-tools[embedding]", "cellflow-tools[external]", "cellflow-tools[pp]", @@ -105,7 +109,9 @@ urls.Home-page = "https://github.com/theislab/cellflow" urls.Source = "https://github.com/theislab/cellflow" [tool.hatch.build.targets.wheel] -packages = [ 'src/cellflow' ] +# `dagloader` is vendored for now but kept as a standalone top-level package (import path +# `dagloader`) so it can later be extracted into an external dependency with no code changes. +packages = [ 'src/cellflow', 'src/dagloader' ] [tool.hatch.version] source = "vcs" diff --git a/src/dagloader/README.md b/src/dagloader/README.md new file mode 100644 index 00000000..5ccf0b4a --- /dev/null +++ b/src/dagloader/README.md @@ -0,0 +1,252 @@ +# `dagloader` — declarative, index-free sampling over annbatch + +A small, declarative layer that turns a **scheme of columns + weights** into a stream of matched +`{source, target, condition}` training batches, read (in- or out-of-core) through +[`annbatch`](https://github.com/laminlabs/annbatch). It is the streaming sampler that both +**cellflow** (this repo's model) and **sc-flow-tools** (`theislab/sc-flow-tools`) can share: one +condition per batch, source↔target matching, categorical + continuous condition payloads, and +reproducible weighted sampling — expressed as data, not as materialized row-index tables. + +``` +Scheme (sources + nodes + binds + weights) + SamplerConfig (batch/chunk/preload) + │ │ + └───────────────────► DAGLoader ◄────────────┘ + │ + {"target": X[perturbed], "source": X[matched control], "condition": emb} + │ + ├─ every node streams through its own ScheduledClassSampler + annbatch.Loader + └─ root category ~ weights; each bound child's category derived from the parent's +``` + +Structure (`Scheme`) and read parameters (`SamplerConfig`) are separate objects, both handed to the +loader — so the same scheme can run with different batch/chunk settings, and neither lives on the +`Node`. + +--- + +## 1. Mental model + +- **Source** — a named cell store: an in-memory `AnnData` **or** an out-of-core + `annbatch.DatasetCollection`. The loader never branches on which; obs is read for grouping, cells + are read only when a batch is materialized. +- **Node** — a partition of *one source's* cells into **leaves** = the unique combinations of its + `cols`, plus a `{combo: weight}` mapping. `cols` are the grouping / condition columns; `key` is the + single representation to stream (`"X"`, `"obsm/X_pca"`, `"layers/…"`) — add another node for another + rep. The `Node` carries no read parameters; those live in `SamplerConfig` (below). +- **Weight = the selection.** A combination with weight `0` (or absent from the mapping) is simply + not sampled. There is **no separate `select` mask** — inclusion *is* a positive weight. This is + native to annbatch's `ClassSampler`. +- **Bind(parent, child, common)** — condition the child on the parent: each batch, the child's leaf + is derived from the parent's leaf by matching the `common` column values. With `common` = the + context (cell line, …), the child (control/source) is drawn from the **same context** as the parent + (perturbed/target). This is the source↔target matching. +- **Scheme** — sources + nodes + a rooted tree of binds + the reproducibility `seed`. Pure structure. + The **root** is the streamed target; bound children are the sources. Batches-per-pass is *not* set + here — the loader derives it from the root (a natural epoch, `root_n_obs // batch_size`). +- **SamplerConfig** — the annbatch read parameters (`batch_size`, `chunk_size`, `preload_nchunks`), + kept off the `Node` and the `Scheme`. Passed to the loader as one config (all nodes) or a + `{node_name: SamplerConfig}` mapping (per-node). Nodes may use **different** `batch_size`\s (source + and target row counts need not match); the root's drives the shared batch count. +- **DAGLoader** — iterates batches. Each node streams through its own `ScheduledClassSampler`. + +### Batch contract + +```python +{ + "target": ndarray (B_t, d), # root node's streamed rep (Node.key); B_t = root batch_size + "source": ndarray (B_s, d), # bound child's streamed rep — matched to target's context + "condition": ndarray (B_t, e), # condition_fn(leaf) broadcast over the target batch (optional) +} +``` +Each node's row count is its own `SamplerConfig.batch_size` (`B_t` for the target, `B_s` for the +source) — they need not be equal. Sparse `X` stays sparse. + +--- + +## 2. Sampling schemes + +The "sampling scheme" is entirely the **weights** plus the per-node sampler cadence — nothing is +hardcoded. + +**Weight builders** (plain functions returning `{combo: weight}`; use them or write the dict yourself): + +| helper | meaning | +|---|---| +| `uniform(combos)` | every combination equally likely | +| `frequency(counts)` | sample ∝ cell count (favor abundant conditions) | +| `inverse_frequency(counts)` | sample ∝ 1 / cell count (balance rare vs abundant) | + +**`ScheduledClassSampler`** — the one genuinely new piece. It is an annbatch `ClassSampler` whose +per-batch category can be *supplied* (`set_schedule`) instead of drawn internally. It does **not copy** +annbatch's chunk math: `ClassSampler._iter_requests` makes exactly one *weighted* draw +(`rng.choice(n_classes, size=n_groups, p=…)`) to choose each group's class, so `ScheduledClassSampler` +runs that method unchanged but temporarily wraps `self._rng` so that one call returns the scheduled +positions. Every other draw (run selection, slice starts, shuffling) uses the real generator, so a +scheduled pass is bit-identical to what annbatch would produce for those group classes. That is what +makes the bind work: + +- the **root** schedule is drawn from the root's weights (one category per batch, `_n_batches` total); +- each **bound child**'s schedule is *derived* from the root's (parent leaf → `common` value → + matching child leaf; the child's RNG breaks ties / picks a fallback); +- the loaders then zip batch-for-batch with **no per-step reconfiguration**. + +With `schedule=None`, `ScheduledClassSampler` is exactly `ClassSampler` (draws ∝ weights) — a strict +superset. (If annbatch exposed a `_group_positions(n_groups)` hook, even the rng-wrapping would go +away and this would be a one-method override — an upstream candidate.) + +**Read parameters — `SamplerConfig` (batch / chunk / preload):** + +- `batch_size` (`B`): rows per emitted batch for that node. Nodes may differ (target rows need not + equal source rows) — every node still draws the same *number* of batches (the root's), so the + schedules zip; only the per-batch row count differs. +- `chunk_size` (default `1`): annbatch read-slice size. `1` = per-row reads (any layout). `>1` = + contiguous chunked reads for higher on-disk throughput; it assumes each sampled leaf is a contiguous + run ≥ `chunk_size` (which a condition-sorted collection provides) and must divide `batch_size`. +- `preload_nchunks` (default `batch_size // chunk_size`): chunks per annbatch read window; a larger + multiple packs more classes into one read. + +Pass one `SamplerConfig` (applied to every node) or a `{node_name: SamplerConfig}` mapping (per-node — +e.g. a chunked root and a per-row control). + +**Cadence / reproducibility (on the `Scheme`):** + +- batches per pass: **derived**, not configured — the loader uses a natural epoch over the root + (`root_n_obs // root batch_size`) and restarts each pass → effectively infinite with a fixed, + reproducible restart cadence. All nodes restart together (the root drives the count). +- `seed`: per-node RNG streams are spawned from one `SeedSequence(seed)` (one independent stream per + node, by sorted name) so nodes never correlate and the full `(source, target)` sequence is + reproducible from the seed. + +> **On layout / sortedness:** the default (`chunk_size=1`) reads per-row and is indifferent to on-disk +> order. `chunk_size>1` is purely a throughput opt-in for a condition-sorted collection; the loader +> does not police layout — it forwards annbatch's own behavior. Ordering never affects *correctness* +> (matching is recovered from the schedule / columns, not from row order). + +--- + +## 3. The cellflow case + +cellflow trains a conditional flow from a **control** population to a **perturbed** population, +matched within a context, one perturbation condition per batch. That maps directly onto a two-node +scheme built by `perturbation_scheme` (`_schemes.py`): + +| cellflow concept | dagloader | +|---|---| +| `sample_rep` (`X` / an obsm key) | `Node.key` | +| `split_covariates` (context / grouping) | `context` → the `Bind.common` columns | +| `perturbation_covariates` columns | `perturbation` → the extra `cols` on both nodes | +| `control_key` / which cells are control | `control_values` → which combos land in the `ctrl` node | +| control = same group as target | `Bind("pert", "ctrl", common=context)` | +| one perturbation per batch, weighted | root `pert` node weights + `ScheduledClassSampler` | +| `batch_size` | `SamplerConfig.batch_size` | +| `perturbation_covariate_reps` (embeddings) | `condition_fn(leaf) -> embedding` | + +```python +from dagloader import DAGLoader, SamplerConfig, perturbation_scheme + +scheme = perturbation_scheme( + adata_or_collection, + context=["cell_line"], # split_covariates + perturbation=["drug"], # perturbation_covariates + control_values={"drug": "control"}, + key="X", # sample_rep + seed=0, +) +loader = DAGLoader( + scheme, + SamplerConfig(batch_size=256), # or chunk_size=… for chunked on-disk reads + condition_fn=lambda leaf: DRUG_EMB[leaf[-1]], +) +batch = next(loader) # {"target", "source", "condition"} — source is a matched-cell-line control +``` + +**How cellflow uses it.** The collection/streaming path is **additive** to cellflow's in-memory +`adata` + `perturbation_covariates` API — add `perturbation_scheme` + `DAGLoader` as a new +dataloader alongside the existing ones (see `docs/perturbation_combinations.md` → "minimal churn"). +The condition encoding (categorical embeddings, combinations, pooling) stays in the model; the loader +only needs a `condition_fn` that turns a leaf tuple into the per-condition embedding. + +--- + +## 4. The sc-flow-tools cases + +sc-flow-tools already owns grouping (`HierarchicalIndexer`), matching (`control_values_dict`, +`matched_keys`), representations, and an in-memory multi-node `TrainSampler`. The one thing it lacks +is **out-of-core streaming**. `DAGLoader` is exactly that streaming sampler: its scheme +reproduces what a `DataManager` config already describes, reading cells from a `DatasetCollection` +instead of holding them in memory. + +| sc-flow-tools concept | dagloader | +|---|---| +| `HierarchicalIndexer` grouping (obs Categorical codes) | `Node.cols` → leaves (obs-only, computed at build) | +| `control_values_dict` (which values are control) | the `ctrl` node's weighted combos (zero-weight = excluded) | +| default same-context coupling (one-to-many) | `Bind(..., common=context)` | +| `matched_keys` (explicit fixed source→target pairing) | a `Bind` whose `common` covers the pairing columns, so parent leaf → child leaf is forced | +| `sample_rep` in PCA space (an obsm key) | `Node.key = "obsm/X_pca"` | +| `*_reps` (uns embedding tables) | `condition_fn(leaf)` (concatenate per-covariate embeddings) | +| weighted / multi-node `TrainSampler` | root weights + a rooted tree of binds | + +**Direct scheme construction** (the sc-flow-tools style — assemble nodes from a `DataManager` config): + +```python +from dagloader import Bind, DAGLoader, Node, SamplerConfig, Scheme, uniform + +# grouping columns: context (cell_line) + perturbation (drug); state read from a PCA obsm key +cols = ("cell_line", "drug") +scheme = Scheme( + sources={"data": collection}, + nodes={ + "target": Node("data", cols, key="obsm/X_pca", weights=uniform(perturbed_combos)), + "control": Node("data", cols, key="obsm/X_pca", weights=uniform(control_combos)), + }, + root="target", + binds=(Bind("target", "control", common=("cell_line",)),), # control_values_dict + same-context + seed=0, +) +loader = DAGLoader( + scheme, + SamplerConfig(batch_size=256), # or {"target": SamplerConfig(..., chunk_size=32), "control": SamplerConfig(...)} + condition_fn=lambda leaf: concat(cell_emb[leaf[0]], drug_emb[leaf[1]]), # *_reps combination +) +batch = next(loader) # {"target", "source", "condition"} +``` + +**How sc-flow-tools uses it.** Per the shared-data-layer plan (`docs/shared-data-layer-status.md`), +sc-flow-tools hosts the shared data layer and CellFlow2 depends on it. In that plan the tree nodes +carry **row indices** (obs-only), and the annbatch loader (this package's `DAGLoader`) streams +the sampled indices from a `DatasetCollection`. The `TrainSampler`'s node dispatch becomes a scheme: +each node → a `Node`, the matched-subpopulation tree → the binds, `control_values_dict` → weights, +`matched_keys` → binds with pairing columns in `common`. + +### What is and isn't in this prototype + +- **In:** single-level bind (root + one bound child) matched on shared columns; a single streamed rep + per node (`X` / obsm / layer); categorical condition via `condition_fn`; in-memory and out-of-core + sources; weighted per-condition sampling; per-node `SamplerConfig`; reproducible per-node RNG. +- **Documented mapping, not yet wired:** multi-child trees emit a single `source` today (the batch + contract would grow a `sources[child_name]` map); **per-cell continuous covariates / Gromov + `state_lin`+`state_quad`** (multiple aligned reps for the same streamed rows) would need annbatch to + return several reps per batch, or a second aligned read — deliberately left out rather than + reintroduce a bespoke row-gather sampler. + +--- + +## 5. Files + +| file | contents | +|---|---| +| `_schema.py` | `Node`, `Bind`, `Scheme`, `SamplerConfig`, `Weights`, `uniform` / `frequency` / `inverse_frequency` — structural, data-free | +| `_scheduled_sampler.py` | `ScheduledClassSampler` (reuses annbatch `_iter_requests` via an rng wrapper; the upstream candidate) | +| `_io.py` | container-agnostic obs / leaf-code / rep-backing helpers (AnnData or DatasetCollection; X / obsm / layers) | +| `_loader.py` | `DAGLoader` (config resolution, per-pass scheduling, bind derivation, batch assembly) | +| `_schemes.py` | `perturbation_scheme` factory (the cellflow-shaped case) | + +Tests live in `tests/dagloader/`, organized by the cases above: + +| test file | case | +|---|---| +| `test_scheduled_sampler.py` | `ScheduledClassSampler`: schedule adherence, chunk coherence, `schedule=None` ≡ `ClassSampler`, validation | +| `test_sampling_schemes.py` | weights as the selection; `uniform` / `frequency` / `inverse_frequency` empirical distributions; per-node independent RNG; reproducibility; `Node` / `Scheme` / `SamplerConfig` validation | +| `test_cellflow_case.py` | `perturbation_scheme`: matched control↔perturbed batches, condition, obsm rep, on-disk collection, `chunk_size>1` | +| `test_scflow_cases.py` | direct scheme construction: multi-covariate condition, explicit matched pairing via `common`, obsm rep as state, out-of-core | +| `test_train.py` | end-to-end: scheme → loader → a real flow-matching training step (loss decreases), in-memory + DatasetCollection + obsm + `chunk_size>1` | diff --git a/src/dagloader/__init__.py b/src/dagloader/__init__.py new file mode 100644 index 00000000..57fa44cf --- /dev/null +++ b/src/dagloader/__init__.py @@ -0,0 +1,42 @@ +"""Declarative, index-free class-mapping sampler over annbatch. + +A :class:`Scheme` is a rooted tree of :class:`Node`\\s over named cell sources; each node partitions +its source's cells into leaves (unique column-combinations) with a per-combination weight mapping. +:class:`DAGLoader` streams matched ``{source, target, condition}`` batches — one condition per +batch — where every node streams through its own :class:`ScheduledClassSampler`, the root's per-batch +category is drawn from its weights, and each bound child's category is *derived* from the parent's so +the loaders zip batch-for-batch. No row indices are exposed: the scheme is columns / keys / weights. + +See ``README.md`` (next to this file) for the model, the sampling schemes, and the mapping to +cellflow and sc-flow-tools use-cases. +""" + +from dagloader._loader import DAGLoader +from dagloader._schema import ( + Bind, + Container, + Node, + SamplerConfig, + Scheme, + Weights, + frequency, + inverse_frequency, + uniform, +) +from dagloader._scheduled_sampler import ScheduledClassSampler +from dagloader._schemes import perturbation_scheme + +__all__ = [ + "Bind", + "Container", + "DAGLoader", + "Node", + "SamplerConfig", + "Scheme", + "ScheduledClassSampler", + "Weights", + "frequency", + "inverse_frequency", + "perturbation_scheme", + "uniform", +] diff --git a/src/dagloader/_io.py b/src/dagloader/_io.py new file mode 100644 index 00000000..fa7b4bbb --- /dev/null +++ b/src/dagloader/_io.py @@ -0,0 +1,54 @@ +"""Container-agnostic data access for the loader: obs columns, leaf codes, and rep backings. + +Every helper works uniformly over an in-memory ``AnnData`` and an out-of-core annbatch +``DatasetCollection``, and over ``X`` / ``obsm`` / ``layers`` representations — so the loader never +branches on the source kind. No cell matrices are touched for grouping (obs only); cells are read +only when a batch is materialized (by annbatch's own loader over the returned backings). +""" + +from __future__ import annotations + +from collections.abc import Sequence + +import anndata as ad +import numpy as np +import pandas as pd + +from dagloader._schema import Container + +__all__ = ["densify", "key_backings", "leaf_codes", "obs_columns"] + + +def densify(x) -> np.ndarray: + """A streamed batch → a dense float32 array (annbatch may yield sparse for a sparse source).""" + return np.asarray(x.todense() if hasattr(x, "todense") else x, dtype=np.float32) + + +def key_backings(source: Container, loc: str) -> list: + """The array(s) backing rep ``loc`` for a source, ready to feed one annbatch ``add_datasets``. + + annbatch's ``add_datasets`` concatenates on the obs axis and needs equal feature dims, so each rep + gets its own loader over its own array(s). For a ``DatasetCollection`` the per-dataset arrays are + gathered in order (matching the global row layout). + """ + if loc == "X": + return [source.X] if isinstance(source, ad.AnnData) else [g["X"] for g in source] + field, sub = loc.split("/", 1) # "obsm/X_pca" | "layers/log1p" + if isinstance(source, ad.AnnData): + return [getattr(source, field)[sub]] + return [g[field][sub] for g in source] # DatasetCollection: one zarr array per dataset + + +def leaf_codes(obs: pd.DataFrame, cols: Sequence[str]) -> tuple[np.ndarray, list[tuple]]: + """Per-cell leaf code + the ordered leaf combinations (the grouping over ``cols``).""" + tuples = [tuple(row) for row in obs[list(cols)].to_numpy()] + leaves = sorted(set(tuples), key=lambda t: tuple(map(str, t))) + code_of = {lf: i for i, lf in enumerate(leaves)} + return np.array([code_of[t] for t in tuples], dtype=np.int64), leaves + + +def obs_columns(source: Container, cols: Sequence[str]) -> pd.DataFrame: + """obs columns from either container (AnnData attr vs DatasetCollection reader) — no cell matrices.""" + if isinstance(source, ad.AnnData): + return source.obs[list(cols)] + return source.obs(columns=list(cols)) # DatasetCollection diff --git a/src/dagloader/_loader.py b/src/dagloader/_loader.py new file mode 100644 index 00000000..c47d5f61 --- /dev/null +++ b/src/dagloader/_loader.py @@ -0,0 +1,257 @@ +"""``DAGLoader`` — streams matched ``{source, target, condition}`` batches from a :class:`Scheme`. + +Every node streams through its own :class:`~dagloader.ScheduledClassSampler` + annbatch +``Loader``, configured by a :class:`SamplerConfig` (one shared, or one per node). Each pass, the loader +draws the root's per-batch category schedule from the root's weights and *derives* each bound child's +schedule from the parent's (via the bind's shared columns), pushes the schedules onto the samplers, and +zips the loaders batch-for-batch. See ``README.md``. +""" + +from __future__ import annotations + +from collections.abc import Callable, Iterator, Mapping + +import numpy as np +import pandas as pd +from annbatch import Loader + +from dagloader._io import densify, key_backings, leaf_codes, obs_columns +from dagloader._schema import Bind, SamplerConfig, Scheme, _weight_vector +from dagloader._scheduled_sampler import ScheduledClassSampler + +__all__ = ["DAGLoader"] + + +class DAGLoader: + """Yields ``{"source", "target", "condition"}`` batches; every node streams through its own loader.""" + + def __init__( + self, + scheme: Scheme, + sampler_config: SamplerConfig | Mapping[str, SamplerConfig], + condition_fn: Callable[[tuple], np.ndarray | Mapping[str, np.ndarray]] | None = None, + ) -> None: + self.s = scheme + self._cond_fn = condition_fn + self._cfg = self._resolve_configs(sampler_config) + self._B = self._cfg[self.s.root].batch_size # root/target batch size — drives the pass length + + self._children: dict[str, list[Bind]] = {} + for b in scheme.binds: + self._children.setdefault(b.parent, []).append(b) + + # per-node independent RNG streams from one seed (by sorted node name). Each node splits into a + # *decision* rng (root schedule draw / child tie-break) and a *row* seed shared identically by + # that node's per-key samplers — identical samplers → the SAME rows, so a node's reps are aligned. + self._rngs: dict[str, np.random.Generator] = {} + self._row_seqs: dict[str, np.random.SeedSequence] = {} + for name, seq in zip(sorted(scheme.nodes), np.random.SeedSequence(scheme.seed).spawn(len(scheme.nodes))): + decision_seq, row_seq = seq.spawn(2) + self._rngs[name] = np.random.default_rng(decision_seq) + self._row_seqs[name] = row_seq + + # per-node leaf partition + weights (obs only — no cell matrices) + self._st: dict[str, dict] = {} + for name, node in scheme.nodes.items(): + obs = obs_columns(scheme.sources[node.source], node.cols) + codes, leaves = leaf_codes(obs, node.cols) + self._st[name] = {"node": node, "codes": codes, "leaves": leaves, "w": _weight_vector(node.weights, leaves)} + + # Batches per pass is derived, not configured: a natural epoch over the root (target) node — + # its cell count // the root's batch_size. The root drives the zip, so every node draws the same + # number of batches; each node's num_samples == _n_batches * that node's batch_size (see + # `_build_loaders`), which lets nodes use different batch sizes (source rows need not equal target). + n_root_obs = len(self._st[self.s.root]["codes"]) + self._n_batches = max(1, n_root_obs // self._B) + + self._build_loaders() + self._build_bind_maps() + self._iters: dict[str, Iterator[dict]] | None = None + self._schedules: dict[str, np.ndarray] = {} + self._pos = 0 + + def _resolve_configs(self, cfg: SamplerConfig | Mapping[str, SamplerConfig]) -> dict[str, SamplerConfig]: + """Normalize to one config per node. Nodes may use different batch sizes (each draws + ``_n_batches`` batches of its own size; source and target row counts need not match).""" + if isinstance(cfg, SamplerConfig): + resolved = {name: cfg for name in self.s.nodes} + else: + missing = set(self.s.nodes) - set(cfg) + if missing: + raise ValueError(f"sampler_config mapping is missing node(s): {sorted(missing)}.") + resolved = {name: cfg[name] for name in self.s.nodes} + return resolved + + # ── build ──────────────────────────────────────────────────────────── + def _build_loaders(self) -> None: + """Per ``(node, key)``: a ScheduledClassSampler + Loader. All of a node's keys are seeded with + the SAME row-draw seed, so their (otherwise identical) samplers select the same rows every batch + — every rep of a node is the same cells. Reps need separate Loaders regardless (annbatch can't + mix feature dims in one loader), and each rep gets native chunked reads. + """ + self._samplers: dict[str, dict[str, ScheduledClassSampler]] = {} + self._loaders: dict[str, dict[str, Loader]] = {} + for name, st in self._st.items(): + node = st["node"] + src = self.s.sources[node.source] + cfg = self._cfg[name] + # Per node: _n_batches batches of this node's own batch_size. Same batch count across nodes + # (the root's _n_batches) → schedules align; sizes may differ (source rows ≠ target rows OK). + num_samples = self._n_batches * cfg.batch_size + K = len(st["leaves"]) + classes = pd.Categorical([str(c) for c in st["codes"]], categories=[str(i) for i in range(K)]) + self._samplers[name], self._loaders[name] = {}, {} + for ki, key in enumerate(node.keys): + try: # annbatch enforces its own run-length rule for chunk>1; forward with node context + sampler = ScheduledClassSampler( + chunk_size=cfg.chunk_size, preload_nchunks=cfg.preload_nchunks, batch_size=cfg.batch_size, + classes=classes, num_samples=num_samples, class_weights=st["w"], + drop_last=True, rng=np.random.default_rng(self._row_seqs[name]), # identical across keys + ) + except ValueError as e: + raise ValueError(f"node {name!r}: {e}") from e + return_index = name == self.s.root and ki == 0 # only for the schedule↔row alignment check + loader = Loader(batch_sampler=sampler, return_index=return_index, to_torch=False, + preload_to_gpu=False).add_datasets(key_backings(src, key)) + self._samplers[name][key] = sampler + self._loaders[name][key] = loader + + def _node_reps(self, name: str) -> dict[str, np.ndarray]: + """One batch per key of a node — identical samplers pick the same rows, so the reps are aligned.""" + node = self._st[name]["node"] + return {key: densify(next(self._iters[name][key])["X"]) for key in node.keys} + + def _build_bind_maps(self) -> None: + """Precompute, per bound child, the maps to turn the parent's schedule into the child's.""" + self._bindmap: dict[str, dict] = {} + for b in self._children.get(self.s.root, []): + rst, cst = self._st[b.parent], self._st[b.child] + rleaves, cleaves = rst["leaves"], cst["leaves"] + if b.matched is not None: # explicit parent-leaf → child-leaf pairing (sc-flow's matched_keys) + root_idx = {lf: i for i, lf in enumerate(rleaves)} + child_idx = {lf: i for i, lf in enumerate(cleaves)} + code_to_child: dict[int, int] = {} + for pk, ck in b.matched.items(): + pk, ck = tuple(pk), tuple(ck) + if pk not in root_idx: + raise ValueError(f"bind {b.parent!r}→{b.child!r}: matched key {pk!r} is not a {b.parent!r} leaf.") + if ck not in child_idx or cst["w"][child_idx[ck]] <= 0: + raise ValueError( + f"bind {b.parent!r}→{b.child!r}: matched value {ck!r} is not a positive-weight {b.child!r} leaf." + ) + code_to_child[root_idx[pk]] = child_idx[ck] + self._bindmap[b.child] = {"bind": b, "mode": "matched", "root_leaves": rleaves, + "code_to_child": code_to_child} + continue + rcols, ccols = rst["node"].cols, cst["node"].cols + # parent leaf code → shared-column value (empty tuple when common=() ⇒ unconditional bind) + root_code_to_cv = {i: tuple(lf[rcols.index(c)] for c in b.common) for i, lf in enumerate(rleaves)} + # shared-column value → positive-weight child leaf codes carrying it (key () holds all when common=()) + grouped: dict[tuple, list[int]] = {} + for code, lf in enumerate(cleaves): + if cst["w"][code] > 0: + grouped.setdefault(tuple(lf[ccols.index(c)] for c in b.common), []).append(code) + self._bindmap[b.child] = { + "bind": b, "mode": "common", "root_leaves": rleaves, "child_w": cst["w"], + "root_code_to_cv": root_code_to_cv, + "common_to_child": {cv: np.asarray(codes, dtype=np.int64) for cv, codes in grouped.items()}, + } + + # ── per-pass scheduling ──────────────────────────────────────────────── + def _draw_root_schedule(self) -> np.ndarray: + """Draw one root leaf code per batch (``_n_batches``) ∝ the root's weights (its own RNG stream).""" + w = self._st[self.s.root]["w"] + pos = np.flatnonzero(w > 0) + return self._rngs[self.s.root].choice(pos, size=self._n_batches, p=w[pos] / w[pos].sum()).astype(np.int64) + + def _derive_child_schedule(self, child: str, root_sched: np.ndarray) -> np.ndarray: + """Map each root batch's category to a matching child leaf. + + ``matched`` binds map each parent leaf to exactly one child leaf. Otherwise the child is matched + on ``common`` columns; when several child leaves share the bound value (the child partitions on + columns beyond ``common``, e.g. child cols ``(a, x)`` bound on ``a``) one is drawn ∝ the child's + leaf weights, so ``P(child extra cols | common)`` is weight-controlled. Conditioning is required: + an unmatched value raises (no silent fallback); ``Bind(..., common=())`` opts into unconditional. + """ + m = self._bindmap[child] + b = m["bind"] + out = np.empty(len(root_sched), dtype=np.int64) + if m["mode"] == "matched": + cmap = m["code_to_child"] + for j, rc in enumerate(root_sched): + rc = int(rc) + if rc not in cmap: + raise ValueError( + f"bind {b.parent!r}→{b.child!r}: no matched child leaf for root leaf " + f"{m['root_leaves'][rc]!r}. Add it to `matched`." + ) + out[j] = cmap[rc] + return out + rng = self._rngs[child] + cw = m["child_w"] + for j, rc in enumerate(root_sched): + cv = m["root_code_to_cv"][int(rc)] + codes = m["common_to_child"].get(cv) + if codes is None: # no positive-weight child leaf carries this value — do NOT silently fall back + raise ValueError( + f"bind {b.parent!r}→{b.child!r}: no positive-weight child leaf matches common " + f"{b.common}={cv!r} (root leaf {m['root_leaves'][int(rc)]!r}). Add a child leaf for " + f"it, or declare Bind(..., common=()) to sample {b.child!r} unconditionally." + ) + out[j] = int(codes[0]) if len(codes) == 1 else int(rng.choice(codes, p=cw[codes] / cw[codes].sum())) + return out + + def _start_pass(self) -> None: + """Draw schedules (root + derived children), push them onto the samplers, rebuild iterators. + + Ordering matters: ``Loader.__iter__`` re-reads ``sampler.sample()`` and the scheduled draw is + read up front, so ``set_schedule`` must land before ``iter(loader)``. + """ + root_sched = self._draw_root_schedule() + self._schedules = {self.s.root: root_sched} + for smp in self._samplers[self.s.root].values(): # every key of the node gets the same schedule + smp.set_schedule(root_sched) + for b in self._children.get(self.s.root, []): + child_sched = self._derive_child_schedule(b.child, root_sched) + self._schedules[b.child] = child_sched + for smp in self._samplers[b.child].values(): + smp.set_schedule(child_sched) + self._iters = {name: {key: iter(ld) for key, ld in loaders.items()} + for name, loaders in self._loaders.items()} + self._pos = 0 + + # ── iteration ────────────────────────────────────────────────────────── + def __iter__(self) -> DAGLoader: + return self + + def __next__(self) -> dict[str, np.ndarray]: + if self._iters is None or self._pos >= self._n_batches: + self._start_pass() + j = self._pos + self._pos += 1 + + st = self._st[self.s.root] + node = st["node"] + leaf = st["leaves"][int(self._schedules[self.s.root][j])] # per-batch category — from the schedule + reps = self._node_reps(self.s.root) + target = reps[node.keys[0]] # primary streamed rep + B = target.shape[0] + + out: dict = {"target": target} + if len(node.keys) > 1: # aligned reps of the target cells (state, per-cell condition, …) + out["target_reps"] = reps + if self._cond_fn is not None: + cond = self._cond_fn(leaf) + if isinstance(cond, Mapping): # per-leaf structured condition (e.g. set-encoded) — emit as-is + out["condition"] = {k: np.asarray(v, dtype=np.float32) for k, v in cond.items()} + else: # a per-leaf vector broadcast across the (class-coherent) batch + cond = np.asarray(cond, dtype=np.float32) + out["condition"] = np.broadcast_to(cond, (B, cond.shape[-1])).copy() + + for b in self._children.get(self.s.root, []): # bound child source, conditioned via its schedule + cnode = self._st[b.child]["node"] + creps = self._node_reps(b.child) + out["source"] = creps[cnode.keys[0]] + if len(cnode.keys) > 1: + out["source_reps"] = creps + return out diff --git a/src/dagloader/_scheduled_sampler.py b/src/dagloader/_scheduled_sampler.py new file mode 100644 index 00000000..34347b9e --- /dev/null +++ b/src/dagloader/_scheduled_sampler.py @@ -0,0 +1,92 @@ +"""``ScheduledClassSampler`` — an annbatch ``ClassSampler`` whose per-batch category can be *supplied*. + +``set_schedule(codes)`` fixes the category (leaf code) of each batch for the next pass; with +``schedule=None`` the sampler is exactly ``ClassSampler`` (draws ∝ ``class_weights``). That ownership +is what lets :class:`~dagloader.DAGLoader` zip a parent and its bound children +batch-for-batch: the root schedule is drawn from the root's weights, each child's schedule is +*derived* from the parent's. + +Rather than copy annbatch's ``_iter_requests`` (all the chunk/RLE/slice math), this reuses it wholesale +and only redirects the one draw that picks each group's class. ``ClassSampler._iter_requests`` makes a +single *weighted* ``self._rng.choice(n_classes, size=n_groups, p=...)`` to choose group classes; we run +that method unchanged but with ``self._rng`` temporarily wrapped so that one call returns the scheduled +positions instead. Everything else (run selection, slice starts, shuffling) uses the real generator, so +a scheduled pass is bit-identical to what annbatch would produce for those same group classes. +""" + +from __future__ import annotations + +from collections.abc import Iterator + +import numpy as np +from annbatch.samplers import ClassSampler + +__all__ = ["ScheduledClassSampler"] + + +class _ScheduleRng: + """Wraps a ``numpy.random.Generator``; the first *weighted* ``choice`` returns supplied positions. + + ``ClassSampler._iter_requests`` draws group classes with the only ``choice(..., p=)`` call + in that method; we intercept it (identified by ``p is not None``) and return the pre-mapped + schedule positions, validating the group count. Every other attribute/method — ``integers``, + ``shuffle``, any unweighted ``choice`` — delegates to the real generator, so RNG state and + reproducibility are unchanged. + """ + + def __init__(self, rng: np.random.Generator, positions: np.ndarray) -> None: + self._rng = rng + self._positions = positions + self._fired = False + + def choice(self, a, size=None, replace=True, p=None): + if p is not None and not self._fired: # the per-group class draw in _iter_requests + self._fired = True + if size != len(self._positions): + raise ValueError( + f"schedule length {len(self._positions)} != n_groups {size}; one category per batch " + "requires chunk_size to divide batch_size and len(schedule) == num_samples // batch_size." + ) + return self._positions + return self._rng.choice(a, size=size, replace=replace, p=p) + + def __getattr__(self, name): # integers / shuffle / permutation / ... → real generator + return getattr(self._rng, name) + + +class ScheduledClassSampler(ClassSampler): + """``ClassSampler`` whose per-batch category sequence can be supplied via :meth:`set_schedule`. + + The schedule is one leaf code per batch (``len == num_samples // batch_size``); it is valid only + when ``chunk_size`` divides ``batch_size`` (exactly one category per batch). + """ + + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self._schedule: np.ndarray | None = None + + def set_schedule(self, category_codes: np.ndarray | None) -> None: + """Set (or clear) the per-batch category codes used for the next ``iter(loader)`` pass.""" + self._schedule = None if category_codes is None else np.asarray(category_codes, dtype=np.int64) + + def _schedule_positions(self) -> np.ndarray: + """Map the scheduled leaf codes to positions into the sampleable-class table (as annbatch expects).""" + info = self._per_class_sampling_info + code_to_pos = {int(c): i for i, c in enumerate(info.index.to_numpy())} + try: + return np.array([code_to_pos[int(c)] for c in self._schedule], dtype=np.int64) + except KeyError as e: + raise ValueError(f"schedule contains a non-sampleable (zero-weight) category code {e}.") from e + + def _sample(self, n_obs: int) -> Iterator[dict]: + if self._schedule is None: # standalone → identical to ClassSampler + yield from super()._sample(n_obs) + return + self._ensure_runs(n_obs) # build _per_class_sampling_info before mapping codes → positions + positions = self._schedule_positions() + real_rng = self._rng + self._rng = _ScheduleRng(real_rng, positions) # redirect only the group-class draw + try: + yield from super()._sample(n_obs) + finally: + self._rng = real_rng diff --git a/src/dagloader/_schema.py b/src/dagloader/_schema.py new file mode 100644 index 00000000..522a035d --- /dev/null +++ b/src/dagloader/_schema.py @@ -0,0 +1,221 @@ +"""Declarative schema for :class:`~dagloader.DAGLoader`. + +A :class:`Scheme` is a rooted tree of :class:`Node`\\s over named cell *sources* — pure structure +(sources, grouping columns, weights, binds). How each node is *read* (chunk / preload / batch sizes) +lives in a separate :class:`SamplerConfig` passed to the loader, deliberately kept off the ``Node`` so +the same structure can be run with different sampler settings. + +Each node partitions its source's cells into **leaves** (unique combinations of ``cols``) with a +per-combination :data:`Weights` mapping. A weight of 0 (or a combination absent from the mapping) is +*excluded* — that IS the selection, native to annbatch's ``ClassSampler``. :class:`Bind` links a +parent to a child on shared columns, so the child is sampled *conditioned* on the parent's values. +See ``README.md`` for the model and the cellflow / sc-flow-tools mapping. +""" + +from __future__ import annotations + +from collections.abc import Mapping, Sequence +from dataclasses import dataclass, field + +import anndata as ad +import numpy as np +from annbatch import DatasetCollection + +# A cell source: an in-memory AnnData or an out-of-core annbatch DatasetCollection. +Container = ad.AnnData | DatasetCollection + +# A sampling scheme is just a mapping {combination -> weight}. A combination absent from the mapping +# (or with weight 0) is excluded — that IS the selection. ``uniform`` / ``frequency`` / +# ``inverse_frequency`` are plain helpers that build such a dict; nothing about them is privileged. +Weights = Mapping[tuple, float] + +__all__ = [ + "Bind", + "Container", + "Node", + "SamplerConfig", + "Scheme", + "Weights", + "frequency", + "inverse_frequency", + "uniform", +] + + +def uniform(combos: Sequence[tuple]) -> dict[tuple, float]: + """Every combination equally likely.""" + return {tuple(c): 1.0 for c in combos} + + +def frequency(counts: Mapping[tuple, int]) -> dict[tuple, float]: + """Sample each combination ∝ its cell count (favor abundant conditions).""" + return {tuple(k): float(c) for k, c in counts.items()} + + +def inverse_frequency(counts: Mapping[tuple, int]) -> dict[tuple, float]: + """Sample each combination ∝ 1 / cell count (balance rare vs abundant conditions).""" + return {tuple(k): 1.0 / c for k, c in counts.items()} + + +def _weight_vector(weights: Weights, leaves: Sequence[tuple]) -> np.ndarray: + """Resolve ``{combo: weight}`` to normalized per-leaf weights (→ ``ClassSampler.class_weights``).""" + v = np.array([float(weights.get(tuple(lf), 0.0)) for lf in leaves], dtype=float) + s = v.sum() + if s <= 0: + raise ValueError("weights resolve to all-zero over these leaves — nothing to sample.") + return v / s + + +@dataclass(frozen=True) +class Node: + """A partition of one source's cells into leaves, with a per-leaf sampling weight. + + Parameters + ---------- + source + Key into :attr:`Scheme.sources`. + cols + Tree levels → leaves are the unique combinations of these columns (over ALL the source's + cells). These are the grouping/condition columns (cellflow's ``split_covariates`` + + ``perturbation_covariates`` columns; sc-flow-tools' grouping keys). + keys + Representation location(s) to stream: ``"X"`` | ``"obsm/"`` | ``"layers/"`` + (cellflow's ``sample_rep``). A single string streams one rep; a tuple streams SEVERAL + **aligned** reps of the *same* sampled cells — e.g. the state plus a per-cell continuous + condition. The first key drives sampling (via annbatch's ``ClassSampler``); the rest are + read back for the exact same rows, so every rep of a batch is the same cells. + weights + ``{combo: weight}``; a combination absent or with weight 0 is excluded (= the selection). + """ + + source: str + cols: tuple[str, ...] + keys: str | tuple[str, ...] = "X" # one rep, or several aligned reps of the same cells + weights: Weights = field(default_factory=dict) + + def __post_init__(self) -> None: # structural checks (data-free) + object.__setattr__(self, "keys", (self.keys,) if isinstance(self.keys, str) else tuple(self.keys)) + if not self.cols: + raise ValueError("Node.cols must be non-empty.") + if not self.keys or any(not k for k in self.keys): + raise ValueError("Node.keys must be one or more non-empty representation locations.") + for k in self.weights: + if len(k) != len(self.cols): + raise ValueError(f"weight key {k!r} arity != cols {self.cols}.") + if any(w < 0 for w in self.weights.values()): + raise ValueError("weights must be non-negative.") + + +@dataclass(frozen=True) +class Bind: + """Condition ``child`` on ``parent``: match on the ``common`` columns (⊆ their shared cols). + + Each batch, the child's sampled leaf is derived from the parent's leaf via the ``common`` values + (parent leaf → shared-column values → matching child leaf). This is the source↔target matching: + with ``common`` = the context (e.g. cell line), the child (control) is drawn from the *same* + context as the parent (perturbed) — cellflow's "control = same group", sc-flow-tools' + ``control_values_dict`` + default same-context coupling. + + Conditioning is **required**: if a parent value has no matching positive-weight child leaf the + loader raises (no silent fallback). When several child leaves share the bound value — the child + partitions on columns beyond ``common`` (e.g. child cols ``(a, x)`` bound on ``a``) — one is drawn + ∝ the child's leaf weights, so ``P(child extra cols | common)`` is weight-controlled. Pass + ``common=()`` to opt into unconditional child sampling explicitly. + + For pairings that are NOT a shared-column match — sc-flow-tools' ``matched_keys`` (explicit + source↔target pairs) — pass ``matched={parent_leaf: child_leaf, ...}`` instead of ``common``. Each + parent leaf then maps to exactly one child leaf; a root leaf absent from the map raises. + """ + + parent: str + child: str + common: tuple[str, ...] = () # ⊆ parent.cols ∩ child.cols; () ⇒ unconditional (unless `matched`) + matched: Mapping[tuple, tuple] | None = None # explicit parent-leaf → child-leaf pairing; overrides `common` + + def __post_init__(self) -> None: + if self.matched is not None and self.common: + raise ValueError("Bind: give either `common` (column matching) or `matched` (explicit pairing), not both.") + + +@dataclass(frozen=True) +class SamplerConfig: + """annbatch read parameters for a node's sampler — kept separate from the structural :class:`Node`. + + Passed to :class:`~dagloader.DAGLoader` as either one config (applied to every node) + or a ``{node_name: SamplerConfig}`` mapping (per-node). Nodes may use **different** ``batch_size``\\s: + every node draws the same number of batches (the root's, derived from its cell count), but a node's + batch carries its own row count — so source and target row counts need not match. + + Parameters + ---------- + batch_size + Rows per emitted batch for this node (target rows for the root; source rows for a bound child). + chunk_size + annbatch read-slice size. ``1`` (default) ⇒ per-row reads (any on-disk layout). ``>1`` ⇒ + contiguous chunked reads (higher throughput on disk), assuming each sampled leaf sits in a + contiguous run ≥ ``chunk_size``. Must divide ``batch_size`` (one category per batch). + preload_nchunks + Chunks per annbatch read window. ``None`` (default) ⇒ ``batch_size // chunk_size`` (one batch + per window). If given, must be a positive multiple of ``batch_size // chunk_size``. + """ + + batch_size: int + chunk_size: int = 1 + preload_nchunks: int + +@dataclass(frozen=True) +class Scheme: + """The structural sampling spec: sources, a rooted tree of nodes, and the reproducibility cadence. + + Read parameters (chunk / preload / batch sizes) are NOT here — they are a separate + :class:`SamplerConfig` given to the loader. + + Parameters + ---------- + sources + ``{name: AnnData | DatasetCollection}`` — the cell sources the nodes reference. + nodes + ``{name: Node}``. Exactly one is the ``root`` (the streamed target); the rest are bound + children (sources/controls) via ``binds``. + root + Name of the root node (must have no parent). + seed + Reproducibility seed. Per-node RNG streams are spawned from one ``SeedSequence(seed)`` so nodes + do not correlate and the whole stream is reproducible. + binds + Parent→child links (see :class:`Bind`). Must form a rooted tree over ``nodes``. + + Notes + ----- + Batches per with-replacement pass is *not* configured here: the loader derives it from the root + (target) node — a natural epoch of ``root_n_obs // batch_size`` — and restarts each pass. The root + drives the zip, so every node's sampler draws the same number of batches. + """ + + sources: Mapping[str, Container] + nodes: Mapping[str, Node] + root: str + seed: int + binds: tuple[Bind, ...] = () + + def __post_init__(self) -> None: # structural: rooted tree + references + if self.root not in self.nodes: + raise ValueError(f"root {self.root!r} not in nodes.") + for name, n in self.nodes.items(): + if n.source not in self.sources: + raise ValueError(f"node {name!r} references unknown source {n.source!r}.") + parents: dict[str, str] = {} + for b in self.binds: + if b.parent not in self.nodes or b.child not in self.nodes: + raise ValueError("bind references unknown node.") + if b.child in parents: + raise ValueError(f"node {b.child!r} has multiple parents — must be a rooted tree.") + parents[b.child] = b.parent + shared = set(self.nodes[b.parent].cols) & set(self.nodes[b.child].cols) + if not set(b.common) <= shared: + raise ValueError(f"bind.common {b.common} must be ⊆ shared cols of {b.parent}&{b.child} ({shared}).") + if self.root in parents: + raise ValueError("root must have no parent.") + for name in self.nodes: + if name != self.root and name not in parents: + raise ValueError(f"non-root node {name!r} is not bound to the tree.") diff --git a/src/dagloader/_schemes.py b/src/dagloader/_schemes.py new file mode 100644 index 00000000..74898c96 --- /dev/null +++ b/src/dagloader/_schemes.py @@ -0,0 +1,58 @@ +"""Factory helpers ("the above layer") that fill a :class:`Scheme` from an obs table. + +These are conveniences, not privileged: they build the same ``Node`` / ``Bind`` / ``Scheme`` a caller +could assemble by hand. :func:`perturbation_scheme` is the cellflow-shaped case (control → perturbed, +matched on context). Read parameters (batch / chunk / preload) are a separate :class:`SamplerConfig` +given to the loader, so the factory only builds structure. sc-flow-tools-shaped schemes are usually +assembled directly from a ``DataManager`` config; see ``README.md``. +""" + +from __future__ import annotations + +from collections.abc import Mapping, Sequence + +from dagloader._io import obs_columns +from dagloader._schema import Bind, Container, Node, Scheme, uniform + +__all__ = ["perturbation_scheme"] + + +def perturbation_scheme( + source: Container, + *, + context: Sequence[str], + perturbation: Sequence[str], + control_values: Mapping[str, object], + key: str = "X", + seed: int = 0, +) -> Scheme: + """Fill a perturbation Scheme from the obs table: root = perturbed combos, child = control combos. + + ``source`` is an in-memory AnnData or an on-disk DatasetCollection. There is no ``select`` step — + control vs perturbed is encoded purely by which combinations carry weight. The control node is + bound to the perturbed root on ``context``, so each batch's control cells come from the same + context (cell line, …) as the perturbed cells — the source↔target matching. + + Parameters mirror cellflow: ``context`` = ``split_covariates`` (grouping/context), ``perturbation`` + = the perturbation columns, ``control_values`` = which value marks control per column, ``key`` = + ``sample_rep``. Read parameters (batch/chunk/preload) go to the loader's ``SamplerConfig``. + """ + cols = (*context, *perturbation) + combos = [tuple(r) for r in obs_columns(source, cols).drop_duplicates().to_numpy()] + + def is_control(combo: tuple) -> bool: + return all(combo[cols.index(c)] == v for c, v in control_values.items()) + + pert = [c for c in combos if not is_control(c)] + ctrl = [c for c in combos if is_control(c)] + return Scheme( + sources={"data": source}, + nodes={ + # non-control combos weighted (rest excluded = the selection); control combos weighted. + "pert": Node("data", cols, key, uniform(pert)), + "ctrl": Node("data", cols, key, uniform(ctrl)), + }, + root="pert", + binds=(Bind("pert", "ctrl", common=tuple(context)),), + seed=seed, + ) diff --git a/uv.lock b/uv.lock new file mode 100644 index 00000000..1d32fb04 --- /dev/null +++ b/uv.lock @@ -0,0 +1,6448 @@ +version = 1 +revision = 3 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.14' and 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857557043f0d21c30f6fa45f9489ece51c9ac095 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Wed, 8 Jul 2026 09:22:21 +0200 Subject: [PATCH 03/35] update dagloader again --- src/dagloader/_loader.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/dagloader/_loader.py b/src/dagloader/_loader.py index c47d5f61..ad616831 100644 --- a/src/dagloader/_loader.py +++ b/src/dagloader/_loader.py @@ -116,10 +116,10 @@ def _build_loaders(self) -> None: self._samplers[name][key] = sampler self._loaders[name][key] = loader - def _node_reps(self, name: str) -> dict[str, np.ndarray]: + def _nodes_next(self, name: str) -> dict[str, np.ndarray]: """One batch per key of a node — identical samplers pick the same rows, so the reps are aligned.""" node = self._st[name]["node"] - return {key: densify(next(self._iters[name][key])["X"]) for key in node.keys} + return {key: next(self._iters[name][key])["X"] for key in node.keys} def _build_bind_maps(self) -> None: """Precompute, per bound child, the maps to turn the parent's schedule into the child's.""" @@ -233,7 +233,7 @@ def __next__(self) -> dict[str, np.ndarray]: st = self._st[self.s.root] node = st["node"] leaf = st["leaves"][int(self._schedules[self.s.root][j])] # per-batch category — from the schedule - reps = self._node_reps(self.s.root) + reps = self._nodes_next(self.s.root) target = reps[node.keys[0]] # primary streamed rep B = target.shape[0] From d4f63c7dec9bc6e21762494841c01ebc92c4c4b9 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Wed, 8 Jul 2026 23:25:13 +0200 Subject: [PATCH 04/35] Add annbatch-path scaffolding + extract condition helpers CellFlow: - Make `adata` optional; passing it to the constructor is deprecated (FutureWarning) in favour of `prepare_data(adata=...)`. `adata is None` selects the annbatch path. - Add `prepare_annbatch_data` stub (signature + docstring + mode guard, no body yet). Shared condition logic (single source of truth in cellflow.data._condition): - `get_max_combination_length` extracted; DataManager delegates to it. - `enumerate_perturbations`: pandas target-combo enumeration (no dask/masks), parity-tested to match DataManager._get_condition_data exactly. dagloader: - SamplerConfig uses `kw_only=True` so the required `preload_nchunks` (no hidden default) imports cleanly; README examples pass it explicitly. Tests: tests/data/test_condition.py, tests/model/test_annbatch_path.py. Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/_condition.py | 73 +++++++++++++++++++++++++++++ src/cellflow/data/_datamanager.py | 15 ++---- src/cellflow/model/_cellflow.py | 57 ++++++++++++++++++++++- src/dagloader/README.md | 4 +- src/dagloader/_schema.py | 8 ++-- tests/data/test_condition.py | 77 +++++++++++++++++++++++++++++++ tests/model/test_annbatch_path.py | 59 +++++++++++++++++++++++ 7 files changed, 275 insertions(+), 18 deletions(-) create mode 100644 src/cellflow/data/_condition.py create mode 100644 tests/data/test_condition.py create mode 100644 tests/model/test_annbatch_path.py diff --git a/src/cellflow/data/_condition.py b/src/cellflow/data/_condition.py new file mode 100644 index 00000000..0096104e --- /dev/null +++ b/src/cellflow/data/_condition.py @@ -0,0 +1,73 @@ +"""Pure helpers for building condition data from ``obs`` + covariate representations. + +Extracted from :class:`~cellflow.data._datamanager.DataManager` so the in-memory path and the +annbatch/dagloader streaming path share **one** implementation — they must produce identical +condition embeddings. Nothing here touches the cell matrix (``X``); only the covariate spec, the +``obs`` table, and the representation dict (``uns``) are used. ``DataManager`` delegates to these. +""" + +from __future__ import annotations + +from collections.abc import Sequence + +import pandas as pd + +from cellflow._logging import logger + +__all__ = ["enumerate_perturbations", "get_max_combination_length"] + + +def get_max_combination_length( + perturbation_covariates: dict[str, Sequence[str]], + max_combination_length: int | None = None, +) -> int: + """Maximum number of perturbations in a combination (a pure function of the spec). + + This is the largest covariate-group size in ``perturbation_covariates`` (e.g. ``{"drug": + ("drug_1", "drug_2")}`` ⇒ ``2``). ``max_combination_length`` acts only as a floor: a value below + the observed maximum is raised to it (with a warning); a larger value is kept as-is. + """ + obs_max_combination_length = max(len(comb) for comb in perturbation_covariates.values()) + if max_combination_length is None: + return obs_max_combination_length + elif max_combination_length < obs_max_combination_length: + logger.warning( + f"Provided `max_combination_length` is smaller than the observed maximum combination length of the perturbation covariates. Setting maximum combination length to {obs_max_combination_length}.", + stacklevel=2, + ) + return obs_max_combination_length + else: + return max_combination_length + + +def enumerate_perturbations( + obs: pd.DataFrame, + *, + control_key: str, + perturb_covar_keys: Sequence[str], + split_covariates: Sequence[str], + sample_covariates: Sequence[str], +) -> dict[int, tuple]: + """Map each perturbation index to its covariate tuple, matching ``DataManager`` (no dask/masks). + + Reproduces the target-combination enumeration inside + :meth:`~cellflow.data._datamanager.DataManager._get_condition_data`: the unique non-control + combinations, indexed by ``arange`` after sorting by ``all_combs_keys``, each tuple laid out in + ``perturbation_covariates_keys + uniq_sample_keys`` order. Uses plain pandas (no per-cell masks, + no dask), so it is cheap enough to run off a streamed ``obs`` table. + """ + uniq_sample_keys = list(split_covariates) if len(split_covariates) else list(sample_covariates) + perturbation_covariates_keys = [k for k in perturb_covar_keys if k not in uniq_sample_keys] + if len(split_covariates): + all_combs_keys = uniq_sample_keys + perturbation_covariates_keys + else: + all_combs_keys = perturbation_covariates_keys + uniq_sample_keys + tuple_keys = perturbation_covariates_keys + uniq_sample_keys + + df = obs[[*all_combs_keys, control_key]].copy() + for col in all_combs_keys: # mirror DataManager: categorical sort order (only cast if needed) + if df[col].dtype != "category": + df[col] = df[col].astype("category") + df = df[~df[control_key].astype(bool)] + combos = df[all_combs_keys].drop_duplicates().sort_values(by=all_combs_keys).reset_index(drop=True) + return {i: tuple(combos.loc[i, tuple_keys]) for i in range(len(combos))} diff --git a/src/cellflow/data/_datamanager.py b/src/cellflow/data/_datamanager.py index 1b085d39..ce4ff87f 100644 --- a/src/cellflow/data/_datamanager.py +++ b/src/cellflow/data/_datamanager.py @@ -13,8 +13,8 @@ from dask.diagnostics import ProgressBar from pandas.api.types import is_numeric_dtype -from cellflow._logging import logger from cellflow._types import ArrayLike +from cellflow.data._condition import get_max_combination_length from cellflow.data._data import ConditionData, PredictionData, ReturnData, TrainingData, ValidationData from ._utils import _flatten_list, _to_list @@ -998,17 +998,8 @@ def _get_max_combination_length( perturbation_covariates: dict[str, list[str]], max_combination_length: int | None, ) -> int: - obs_max_combination_length = max(len(comb) for comb in perturbation_covariates.values()) - if max_combination_length is None: - return obs_max_combination_length - elif max_combination_length < obs_max_combination_length: - logger.warning( - f"Provided `max_combination_length` is smaller than the observed maximum combination length of the perturbation covariates. Setting maximum combination length to {obs_max_combination_length}.", - stacklevel=2, - ) - return obs_max_combination_length - else: - return max_combination_length + # Delegates to the shared implementation so the in-memory and streaming paths stay identical. + return get_max_combination_length(perturbation_covariates, max_combination_length) def _get_primary_covar_encoder( self, diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index bf8b509a..8e907d76 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -4,7 +4,7 @@ import warnings from collections.abc import Callable, Sequence from dataclasses import field as dc_field -from typing import Any, Literal +from typing import TYPE_CHECKING, Any, Literal import anndata as ad import cloudpickle @@ -29,6 +29,9 @@ from cellflow.training._trainer import CellFlowTrainer from cellflow.utils import match_linear +if TYPE_CHECKING: + from annbatch import DatasetCollection # optional dep — only imported for typing + __all__ = ["CellFlow"] @@ -211,6 +214,58 @@ def prepare_data( self.train_data = self._dm.get_train_data(self.adata) self._data_dim = self.train_data.cell_data.shape[-1] # type: ignore[union-attr] + def prepare_annbatch_data( + self, + source: "DatasetCollection", + sample_rep: str, + control_key: str, + perturbation_covariates: dict[str, Sequence[str]], + perturbation_covariate_reps: dict[str, str] | None = None, + sample_covariates: Sequence[str] | None = None, + sample_covariate_reps: dict[str, str] | None = None, + split_covariates: Sequence[str] | None = None, + max_combination_length: int | None = None, + null_value: float = 0.0, + batch_size: int = 1024, + chunk_size: int = 1, + seed: int = 0, + ) -> None: + """Prepare an out-of-core, annbatch-streamed training path (no in-memory ``adata``). + + The condition-covariate arguments — ``sample_rep``, ``control_key``, + ``perturbation_covariates``, ``perturbation_covariate_reps``, ``sample_covariates``, + ``sample_covariate_reps``, ``split_covariates``, ``max_combination_length`` and ``null_value`` + — mean exactly what they do in :meth:`prepare_data`. The only difference is *where the cells + come from*: instead of materializing ``cell_data`` in memory, cells are streamed on demand from + an out-of-core :class:`annbatch.DatasetCollection` via the ``dagloader`` sampler. + + Requires the ``annbatch`` optional dependency (``pip install cellflow-tools[annbatch]``) and a + model constructed **without** ``adata`` (``CellFlow()``) — that is what selects this path. + + Parameters + ---------- + source + An out-of-core :class:`annbatch.DatasetCollection` to stream cells from. Its ``obs`` supplies + the grouping / condition columns; ``sample_rep`` selects the streamed representation. + batch_size + Rows per streamed batch (the target/root batch size). + chunk_size + annbatch read-slice size: ``1`` (default) ⇒ per-row reads (any on-disk layout); ``>1`` ⇒ + contiguous chunked reads for higher on-disk throughput. + seed + Reproducibility seed for the ``dagloader`` per-node RNG streams. + + Returns + ------- + :obj:`None`, and sets up the streaming training data used by :meth:`train`. + """ + if self._adata is not None: + raise ValueError( + "`prepare_annbatch_data` is the out-of-core streaming path; construct the model " + "without `adata` (`CellFlow()`). Use `prepare_data(adata=...)` for the in-memory path." + ) + raise NotImplementedError("prepare_annbatch_data: body not implemented yet.") + def prepare_validation_data( self, adata: ad.AnnData, diff --git a/src/dagloader/README.md b/src/dagloader/README.md index 5ccf0b4a..5e5fe741 100644 --- a/src/dagloader/README.md +++ b/src/dagloader/README.md @@ -154,7 +154,7 @@ scheme = perturbation_scheme( ) loader = DAGLoader( scheme, - SamplerConfig(batch_size=256), # or chunk_size=… for chunked on-disk reads + SamplerConfig(batch_size=256, preload_nchunks=256), # preload_nchunks is explicit (no hidden default) condition_fn=lambda leaf: DRUG_EMB[leaf[-1]], ) batch = next(loader) # {"target", "source", "condition"} — source is a matched-cell-line control @@ -205,7 +205,7 @@ scheme = Scheme( ) loader = DAGLoader( scheme, - SamplerConfig(batch_size=256), # or {"target": SamplerConfig(..., chunk_size=32), "control": SamplerConfig(...)} + SamplerConfig(batch_size=256, preload_nchunks=256), # per-node: {"target": SamplerConfig(batch_size=256, chunk_size=32, preload_nchunks=8), "control": SamplerConfig(batch_size=256, preload_nchunks=256)} condition_fn=lambda leaf: concat(cell_emb[leaf[0]], drug_emb[leaf[1]]), # *_reps combination ) batch = next(loader) # {"target", "source", "condition"} diff --git a/src/dagloader/_schema.py b/src/dagloader/_schema.py index 522a035d..895a447b 100644 --- a/src/dagloader/_schema.py +++ b/src/dagloader/_schema.py @@ -137,7 +137,7 @@ def __post_init__(self) -> None: raise ValueError("Bind: give either `common` (column matching) or `matched` (explicit pairing), not both.") -@dataclass(frozen=True) +@dataclass(frozen=True, kw_only=True) class SamplerConfig: """annbatch read parameters for a node's sampler — kept separate from the structural :class:`Node`. @@ -155,14 +155,16 @@ class SamplerConfig: contiguous chunked reads (higher throughput on disk), assuming each sampled leaf sits in a contiguous run ≥ ``chunk_size``. Must divide ``batch_size`` (one category per batch). preload_nchunks - Chunks per annbatch read window. ``None`` (default) ⇒ ``batch_size // chunk_size`` (one batch - per window). If given, must be a positive multiple of ``batch_size // chunk_size``. + Chunks per annbatch read window. **Required** and explicit — there is no hidden default; set it + deliberately (e.g. ``batch_size // chunk_size`` for one batch per window). Must be a positive + multiple of ``batch_size // chunk_size``. """ batch_size: int chunk_size: int = 1 preload_nchunks: int + @dataclass(frozen=True) class Scheme: """The structural sampling spec: sources, a rooted tree of nodes, and the reproducibility cadence. diff --git a/tests/data/test_condition.py b/tests/data/test_condition.py new file mode 100644 index 00000000..e58354f8 --- /dev/null +++ b/tests/data/test_condition.py @@ -0,0 +1,77 @@ +"""Tests for the extracted condition helpers in :mod:`cellflow.data._condition`. + +These guard the "act the same" contract: the standalone functions must agree with the +:class:`~cellflow.data._datamanager.DataManager` methods they were extracted from (DataManager +delegates to them, so any divergence is a regression). +""" + +import anndata as ad +import pytest + +from cellflow.data._condition import enumerate_perturbations, get_max_combination_length +from cellflow.data._datamanager import DataManager + +SPECS = [ + {"drug": ["drug1"]}, + {"drug": ["drug1", "drug2"]}, + {"drug": ["drug1", "drug2"], "dosage": ["dosage_a", "dosage_b"]}, + {"drug": ["drug_a", "drug_b", "drug_c"], "dosage": ["dosage_a", "dosage_b", "dosage_c"]}, +] + + +class TestGetMaxCombinationLength: + @pytest.mark.parametrize("spec", SPECS) + @pytest.mark.parametrize("override", [None, 1, 2, 5, 100]) + def test_matches_datamanager(self, spec, override): + # single source of truth: standalone == DataManager (which delegates to it) + assert get_max_combination_length(spec, override) == DataManager._get_max_combination_length(spec, override) + + def test_no_override_is_observed_max(self): + assert get_max_combination_length({"drug": ["d1", "d2"], "dose": ["x1"]}) == 2 + + def test_larger_override_kept(self): + assert get_max_combination_length({"drug": ["d1", "d2"]}, 5) == 5 + + def test_smaller_override_raised_to_observed_with_warning(self, caplog): + assert get_max_combination_length({"drug": ["d1", "d2"]}, 1) == 2 + + +ENUM_CASES = [ + ({"drug": ["drug1"]}, ["cell_type"], []), + ({"drug": ["drug1", "drug2"]}, ["cell_type"], []), + ({"drug": ["drug1"]}, [], []), + ({"drug": ["drug1"]}, [], ["dosage_c"]), + ({"drug": ["drug1"], "dosage": ["dosage_a"]}, ["cell_type"], []), +] + + +class TestEnumeratePerturbations: + @pytest.mark.parametrize(("perturbation_covariates", "split_covariates", "sample_covariates"), ENUM_CASES) + def test_matches_datamanager_idx_to_covariates( + self, + adata_perturbation: ad.AnnData, + perturbation_covariates, + split_covariates, + sample_covariates, + ): + dm = DataManager( + adata_perturbation, + sample_rep="X", + control_key="control", + perturbation_covariates=perturbation_covariates, + perturbation_covariate_reps={"drug": "drug"}, + split_covariates=split_covariates, + sample_covariates=sample_covariates, + ) + expected = { + int(k): tuple(v) + for k, v in dm._get_condition_data(adata=adata_perturbation).perturbation_idx_to_covariates.items() + } + got = enumerate_perturbations( + adata_perturbation.obs, + control_key="control", + perturb_covar_keys=dm._perturb_covar_keys, + split_covariates=dm._split_covariates, + sample_covariates=dm._sample_covariates, + ) + assert got == expected diff --git a/tests/model/test_annbatch_path.py b/tests/model/test_annbatch_path.py new file mode 100644 index 00000000..2fc68f8f --- /dev/null +++ b/tests/model/test_annbatch_path.py @@ -0,0 +1,59 @@ +"""Tests for the annbatch-path scaffolding on :class:`~cellflow.model.CellFlow`. + +Covers the ``adata``-optional constructor (deprecation of the constructor argument), passing +``adata`` to :meth:`prepare_data`, and the mode guard on :meth:`prepare_annbatch_data`. +""" + +import anndata as ad +import pytest + +import cellflow + +PERT_COVARS = {"drug": ["drug1"]} +PERT_COVAR_REPS = {"drug": "drug"} + + +class TestAnnbatchPathScaffolding: + def test_constructor_adata_emits_futurewarning(self, adata_perturbation: ad.AnnData): + with pytest.warns(FutureWarning, match="prepare_data"): + cellflow.model.CellFlow(adata_perturbation) + + def test_constructor_without_adata_is_silent(self): + import warnings + + with warnings.catch_warnings(): + warnings.simplefilter("error", FutureWarning) + cf = cellflow.model.CellFlow() + assert cf.adata is None + + def test_prepare_data_with_adata_kwarg(self, adata_perturbation: ad.AnnData): + cf = cellflow.model.CellFlow() + cf.prepare_data( + sample_rep="X", + control_key="control", + perturbation_covariates=PERT_COVARS, + perturbation_covariate_reps=PERT_COVAR_REPS, + adata=adata_perturbation, + ) + assert cf.train_data is not None + assert cf.adata is adata_perturbation + + def test_prepare_data_without_any_adata_raises(self): + cf = cellflow.model.CellFlow() + with pytest.raises(ValueError, match="No `adata` provided"): + cf.prepare_data(sample_rep="X", control_key="control", perturbation_covariates=PERT_COVARS) + + def test_prepare_annbatch_rejects_adata_model(self, adata_perturbation: ad.AnnData): + with pytest.warns(FutureWarning): + cf = cellflow.model.CellFlow(adata_perturbation) + with pytest.raises(ValueError, match="without `adata`"): + cf.prepare_annbatch_data( + source=None, sample_rep="X", control_key="control", perturbation_covariates=PERT_COVARS + ) + + def test_prepare_annbatch_guard_passes_then_not_implemented(self): + cf = cellflow.model.CellFlow() + with pytest.raises(NotImplementedError): + cf.prepare_annbatch_data( + source=None, sample_rep="X", control_key="control", perturbation_covariates=PERT_COVARS + ) From aca61120e160146c50cde4c15a40981b18aa0df8 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Wed, 8 Jul 2026 23:35:45 +0200 Subject: [PATCH 05/35] Extract build_condition_data (shared embedding assembly) - cellflow.data._condition: add `build_condition_data`, which enumerates perturbations and reuses DataManager._get_embeddings over a plain serial loop to assemble condition_data (idx-aligned embeddings) with no dask/masks. Add shared `_key_layout` helper; refactor `enumerate_perturbations` onto it. - Parity-tested: build_condition_data output equals DataManager._get_condition_data(...).condition_data exactly (incl. the one-hot-encoder path, sample covariates, and combinations). Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/_condition.py | 97 ++++++++++++++++++++++++++++++--- tests/data/test_condition.py | 57 ++++++++++++++++++- 2 files changed, 144 insertions(+), 10 deletions(-) diff --git a/src/cellflow/data/_condition.py b/src/cellflow/data/_condition.py index 0096104e..bd43a599 100644 --- a/src/cellflow/data/_condition.py +++ b/src/cellflow/data/_condition.py @@ -8,13 +8,38 @@ from __future__ import annotations -from collections.abc import Sequence +from collections import OrderedDict +from collections.abc import Mapping, Sequence +from typing import Any +import numpy as np import pandas as pd from cellflow._logging import logger +from cellflow.data._utils import _to_list -__all__ = ["enumerate_perturbations", "get_max_combination_length"] +__all__ = ["build_condition_data", "enumerate_perturbations", "get_max_combination_length"] + + +def _key_layout( + perturb_covar_keys: Sequence[str], + split_covariates: Sequence[str], + sample_covariates: Sequence[str], +) -> tuple[list[str], list[str]]: + """Column layouts used by DataManager's condition enumeration. + + Returns ``(all_combs_keys, tuple_keys)``: the sort order for assigning perturbation indices, and + the order each ``perturbation_idx_to_covariates`` tuple is laid out in. Shared by + :func:`enumerate_perturbations` and :func:`build_condition_data` so both agree with DataManager. + """ + uniq_sample_keys = list(split_covariates) if len(split_covariates) else list(sample_covariates) + perturbation_covariates_keys = [k for k in perturb_covar_keys if k not in uniq_sample_keys] + if len(split_covariates): + all_combs_keys = uniq_sample_keys + perturbation_covariates_keys + else: + all_combs_keys = perturbation_covariates_keys + uniq_sample_keys + tuple_keys = perturbation_covariates_keys + uniq_sample_keys + return all_combs_keys, tuple_keys def get_max_combination_length( @@ -56,13 +81,7 @@ def enumerate_perturbations( ``perturbation_covariates_keys + uniq_sample_keys`` order. Uses plain pandas (no per-cell masks, no dask), so it is cheap enough to run off a streamed ``obs`` table. """ - uniq_sample_keys = list(split_covariates) if len(split_covariates) else list(sample_covariates) - perturbation_covariates_keys = [k for k in perturb_covar_keys if k not in uniq_sample_keys] - if len(split_covariates): - all_combs_keys = uniq_sample_keys + perturbation_covariates_keys - else: - all_combs_keys = perturbation_covariates_keys + uniq_sample_keys - tuple_keys = perturbation_covariates_keys + uniq_sample_keys + all_combs_keys, tuple_keys = _key_layout(perturb_covar_keys, split_covariates, sample_covariates) df = obs[[*all_combs_keys, control_key]].copy() for col in all_combs_keys: # mirror DataManager: categorical sort order (only cast if needed) @@ -71,3 +90,63 @@ def enumerate_perturbations( df = df[~df[control_key].astype(bool)] combos = df[all_combs_keys].drop_duplicates().sort_values(by=all_combs_keys).reset_index(drop=True) return {i: tuple(combos.loc[i, tuple_keys]) for i in range(len(combos))} + + +def build_condition_data( + obs: pd.DataFrame, + rep_dict: Mapping[str, Any], + *, + control_key: str, + perturb_covar_keys: Sequence[str], + split_covariates: Sequence[str], + sample_covariates: Sequence[str], + perturbation_covariates: Mapping[str, Sequence[str]], + covariate_reps: Mapping[str, str], + covar_to_idx: Mapping[str, int], + is_categorical: bool, + primary_one_hot_encoder: Any, + primary_group: str, + linked_perturb_covars: Mapping[str, Any], + max_combination_length: int, + null_value: float, +) -> dict[str, np.ndarray]: + """Assemble the per-condition embeddings (``condition_data``), matching ``DataManager`` (no dask). + + Enumerates the perturbations (:func:`enumerate_perturbations`), then reuses the same per-condition + embedding function as the in-memory path — :meth:`DataManager._get_embeddings` — over a plain + serial loop instead of DataManager's ``dask.delayed`` fan-out. The returned dict maps each covariate + group to an ``(n_perturbations, max_combination_length, dim)`` array, aligned to the perturbation + index from :func:`enumerate_perturbations`. Values are identical to + ``DataManager._get_condition_data(...).condition_data`` (parity-tested); only the orchestration + differs, so it is cheap enough to run off a streamed ``obs`` table. + """ + from cellflow.data._datamanager import DataManager # lazy: reuse the shared per-condition embedding + + _, tuple_keys = _key_layout(perturb_covar_keys, split_covariates, sample_covariates) + idx_to_covariates = enumerate_perturbations( + obs, + control_key=control_key, + perturb_covar_keys=perturb_covar_keys, + split_covariates=split_covariates, + sample_covariates=sample_covariates, + ) + perturb_covariates = OrderedDict({k: sorted(_to_list(v)) for k, v in perturbation_covariates.items()}) + condition_data: dict[str, list[np.ndarray]] = {k: [] for k in covar_to_idx} + for idx in sorted(idx_to_covariates): + tgt_cond = dict(zip(tuple_keys, idx_to_covariates[idx], strict=True)) + embeddings = DataManager._get_embeddings( + condition_data=tgt_cond, + rep_dict=rep_dict, + perturb_covariates=perturb_covariates, + covariate_reps=covariate_reps, + is_categorical=is_categorical, + primary_one_hot_encoder=primary_one_hot_encoder, + null_value=null_value, + max_combination_length=max_combination_length, + linked_perturb_covars=linked_perturb_covars, + sample_covariates=sample_covariates, + primary_group=primary_group, + ) + for pert_cov, emb in embeddings.items(): + condition_data[pert_cov].append(emb) + return {pert_cov: np.array(emb) for pert_cov, emb in condition_data.items()} diff --git a/tests/data/test_condition.py b/tests/data/test_condition.py index e58354f8..f01aed36 100644 --- a/tests/data/test_condition.py +++ b/tests/data/test_condition.py @@ -6,9 +6,10 @@ """ import anndata as ad +import numpy as np import pytest -from cellflow.data._condition import enumerate_perturbations, get_max_combination_length +from cellflow.data._condition import build_condition_data, enumerate_perturbations, get_max_combination_length from cellflow.data._datamanager import DataManager SPECS = [ @@ -75,3 +76,57 @@ def test_matches_datamanager_idx_to_covariates( sample_covariates=dm._sample_covariates, ) assert got == expected + + +BUILD_CASES = [ + ({"drug": ["drug1"]}, {"drug": "drug"}, ["cell_type"], []), + ({"drug": ["drug1", "drug2"]}, {"drug": "drug"}, ["cell_type"], []), + ({"drug": ["drug1"]}, {"drug": "drug"}, [], ["dosage_c"]), + ({"drug": ["drug1"], "dosage": ["dosage_a"]}, {"drug": "drug"}, ["cell_type"], []), + ({"drug": ["drug1"]}, {}, ["cell_type"], []), # no rep -> primary one-hot-encoder path +] + + +class TestBuildConditionData: + @pytest.mark.parametrize( + ("perturbation_covariates", "perturbation_covariate_reps", "split_covariates", "sample_covariates"), + BUILD_CASES, + ) + def test_matches_datamanager_condition_data( + self, + adata_perturbation: ad.AnnData, + perturbation_covariates, + perturbation_covariate_reps, + split_covariates, + sample_covariates, + ): + dm = DataManager( + adata_perturbation, + sample_rep="X", + control_key="control", + perturbation_covariates=perturbation_covariates, + perturbation_covariate_reps=perturbation_covariate_reps, + split_covariates=split_covariates, + sample_covariates=sample_covariates, + ) + ref = dm._get_condition_data(adata=adata_perturbation).condition_data + got = build_condition_data( + adata_perturbation.obs, + adata_perturbation.uns, + control_key="control", + perturb_covar_keys=dm.perturb_covar_keys, + split_covariates=dm.split_covariates, + sample_covariates=dm.sample_covariates, + perturbation_covariates=dm.perturbation_covariates, + covariate_reps=dm.covariate_reps, + covar_to_idx=dm._covar_to_idx, + is_categorical=dm.is_categorical, + primary_one_hot_encoder=dm.primary_one_hot_encoder, + primary_group=dm.primary_group, + linked_perturb_covars=dm.linked_perturb_covars, + max_combination_length=dm.max_combination_length, + null_value=dm.null_value, + ) + assert set(got) == set(ref) + for key in ref: + np.testing.assert_array_equal(got[key], ref[key]) From 7a2a299e8cd56a7aab3a3aba72c7473955f9e225 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Jul 2026 13:07:38 +0200 Subject: [PATCH 06/35] Add Scheme splitting (dagloader.split_scheme) + CellFlow split step MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Split a Scheme's target combinations into named train/val/test splits — a weights-only transform (weight = the selection), holding out whole condition combinations (OOD split), mirroring CellFlow2's combination-level splitter. Controls / bound children are carried through unchanged. - dagloader._split: `split_scheme` (Scheme -> {split: Scheme}) and `split_assignment` (inspection table); exported from the package. - CellFlow.split_annbatch_data: class-level split step; also wired into `prepare_annbatch_data` via split_* params. Building the Scheme from an annbatch source remains a TODO (NotImplementedError on the normal path). - Tests: pure split_scheme/split_assignment (no DatasetCollection, no loader) and the CellFlow split step (via an injected Scheme). Co-Authored-By: Claude Opus 4.8 --- src/cellflow/model/_cellflow.py | 109 ++++++++++++++++++- src/dagloader/__init__.py | 5 +- src/dagloader/_split.py | 167 +++++++++++++++++++++++++++++ tests/dagloader/test_split.py | 155 ++++++++++++++++++++++++++ tests/model/test_annbatch_split.py | 117 ++++++++++++++++++++ 5 files changed, 550 insertions(+), 3 deletions(-) create mode 100644 src/dagloader/_split.py create mode 100644 tests/dagloader/test_split.py create mode 100644 tests/model/test_annbatch_split.py diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index 8e907d76..10a79150 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -2,7 +2,7 @@ import os import types import warnings -from collections.abc import Callable, Sequence +from collections.abc import Callable, Mapping, Sequence from dataclasses import field as dc_field from typing import TYPE_CHECKING, Any, Literal @@ -75,6 +75,12 @@ def __init__(self, adata: ad.AnnData | None = None, solver: Literal["otfm", "gen self._solver: _otfm.OTFlowMatching | _genot.GENOT | None = None self._condition_dim: int | None = None self._vf: _velocity_field.ConditionalVelocityField | _velocity_field.GENOTConditionalVelocityField | None = None + # annbatch/dagloader streaming path (set by `prepare_annbatch_data`, partitioned by + # `split_annbatch_data`). Kept untyped here to avoid importing the optional `dagloader`/`annbatch` + # dependency at module import time. + self._scheme = None + self._split_schemes: dict[str, Any] | None = None + self._split_assignment: pd.DataFrame | None = None def prepare_data( self, @@ -229,6 +235,10 @@ def prepare_annbatch_data( batch_size: int = 1024, chunk_size: int = 1, seed: int = 0, + split_by: Sequence[str] | None = None, + split_ratios: Mapping[str, float] | None = None, + split_force_training_values: Mapping[str, Any] | None = None, + split_random_state: int = 42, ) -> None: """Prepare an out-of-core, annbatch-streamed training path (no in-memory ``adata``). @@ -254,17 +264,112 @@ def prepare_annbatch_data( contiguous chunked reads for higher on-disk throughput. seed Reproducibility seed for the ``dagloader`` per-node RNG streams. + split_by + If given, split the prepared ``Scheme``'s target combinations into train/val/test in the + same call (delegates to :meth:`split_annbatch_data`). Columns whose unique combinations are + held out (⊆ the target columns). If :obj:`None`, no split is made (the model would train on + all combinations). + split_ratios + ``{split_name: fraction}`` summing to 1.0 for the split. Defaults to + ``{"train": 0.6, "val": 0.2, "test": 0.2}``. Only used when ``split_by`` is given. + split_force_training_values + ``{column: value}`` (keys ⊆ ``split_by``) forced into the training split. Only used when + ``split_by`` is given. + split_random_state + Seed for the split's combination shuffle. Only used when ``split_by`` is given. Returns ------- :obj:`None`, and sets up the streaming training data used by :meth:`train`. + + Notes + ----- + Building the ``Scheme`` (and the loader) from the annbatch ``source`` is **not implemented + yet** — that step is a TODO. The splitting step is already wired here and delegated to + :meth:`split_annbatch_data`; it runs once the ``Scheme`` exists. """ if self._adata is not None: raise ValueError( "`prepare_annbatch_data` is the out-of-core streaming path; construct the model " "without `adata` (`CellFlow()`). Use `prepare_data(adata=...)` for the in-memory path." ) - raise NotImplementedError("prepare_annbatch_data: body not implemented yet.") + + # TODO(annbatch): build the streaming `Scheme` (+ `SamplerConfig`, `condition_fn`, `DAGLoader`) + # from `source` and the covariate spec, and set `self._scheme` / the train loader here. Until + # that lands, the normal path (no pre-built scheme) raises below; the splitting step further + # down is already wired and is exercised in tests by injecting `self._scheme`. + if self._scheme is None: + raise NotImplementedError( + "prepare_annbatch_data: building the `Scheme` from an annbatch source is not implemented yet." + ) + + # Splitting step — kept in `prepare_annbatch_data` so preparing and splitting are one call. + if split_by is not None: + self._split_assignment = self.split_annbatch_data( + split_by=split_by, + ratios=split_ratios, + force_training_values=split_force_training_values, + random_state=split_random_state, + ) + + def split_annbatch_data( + self, + *, + split_by: Sequence[str], + ratios: Mapping[str, float] | None = None, + force_training_values: Mapping[str, Any] | None = None, + random_state: int = 42, + ) -> pd.DataFrame: + """Partition the prepared annbatch ``Scheme``'s target combinations into named splits. + + Must be called after :meth:`prepare_annbatch_data` (which builds the ``Scheme``). Splits are + over whole *combinations* of ``split_by`` — a subset of the perturbation / split-covariate + columns — not over cells, so this holds out entire conditions (an out-of-distribution split), + mirroring CellFlow2's combination-level splitter. Because a leaf's weight *is* the selection in + the ``dagloader`` model, each split is the same ``Scheme`` with the target (root) node's weights + restricted to that split's combinations; the control / source populations are carried through + unchanged (a matched control must stay available in every split). + + Parameters + ---------- + split_by + Columns whose unique combinations are partitioned across splits (⊆ the scheme's target + columns). + ratios + ``{split_name: fraction}`` summing to 1.0. Defaults to + ``{"train": 0.6, "val": 0.2, "test": 0.2}``. The first split is the training split. + force_training_values + ``{column: value}`` (keys ⊆ ``split_by``): any combination matching a value is forced into + the training (first) split. + random_state + Seed for the combination shuffle. + + Returns + ------- + A :class:`~pandas.DataFrame` of the target combinations and their assigned split. Also stores + the per-split schemes on the model. + """ + from dagloader import split_assignment, split_scheme + + if self._scheme is None: + raise ValueError( + "No annbatch `Scheme` to split. Call `prepare_annbatch_data(...)` first " + "(the out-of-core streaming path)." + ) + self._split_schemes = split_scheme( + self._scheme, + split_by=split_by, + ratios=ratios, + force_training_values=force_training_values, + random_state=random_state, + ) + # TODO(annbatch): wire the split Schemes into loaders once `prepare_annbatch_data` builds the + # Scheme + SamplerConfig + condition_fn. That step (which needs the DatasetCollection and the + # DAGLoader) will set, from `self._split_schemes`: + # self._dataloader = DAGLoader(self._split_schemes["train"], sampler_config, condition_fn) + # self._validation_data["val"] = + # and keep self._split_schemes["test"] for prediction / final evaluation. + return split_assignment(self._split_schemes) def prepare_validation_data( self, diff --git a/src/dagloader/__init__.py b/src/dagloader/__init__.py index 57fa44cf..0bd4d2af 100644 --- a/src/dagloader/__init__.py +++ b/src/dagloader/__init__.py @@ -12,6 +12,7 @@ """ from dagloader._loader import DAGLoader +from dagloader._scheduled_sampler import ScheduledClassSampler from dagloader._schema import ( Bind, Container, @@ -23,8 +24,8 @@ inverse_frequency, uniform, ) -from dagloader._scheduled_sampler import ScheduledClassSampler from dagloader._schemes import perturbation_scheme +from dagloader._split import split_assignment, split_scheme __all__ = [ "Bind", @@ -38,5 +39,7 @@ "frequency", "inverse_frequency", "perturbation_scheme", + "split_assignment", + "split_scheme", "uniform", ] diff --git a/src/dagloader/_split.py b/src/dagloader/_split.py new file mode 100644 index 00000000..5a05cf54 --- /dev/null +++ b/src/dagloader/_split.py @@ -0,0 +1,167 @@ +"""Split a :class:`~dagloader.Scheme`'s target combinations into named splits — weights-only. + +A "split" needs no new data structure here. Because a leaf's **weight is the selection** (weight 0 / +absent ⇒ not sampled), holding out conditions is just restricting the root node's weights to a subset +of its combinations. :func:`split_scheme` partitions the root's positive-weight combinations — grouped +by ``split_by`` (a subset of the root's ``cols``) — into named splits and returns one :class:`Scheme` +per split, identical to the input except the root's ``weights`` are restricted. Bound children +(controls / sources) are **carried through unchanged**: a matched control must stay available in every +split, since each batch's control is drawn from whatever context the (split) target carries. + +This mirrors CellFlow2's ``GroupedDistributionSplitter`` — split whole *combinations*, not cells (an +out-of-distribution generalization split) — but expressed over the ``Scheme``, so it touches no cells, +no ``DatasetCollection`` and no loader: it only rewrites a weights dict. +""" + +from __future__ import annotations + +from collections.abc import Mapping, Sequence +from dataclasses import replace + +import numpy as np +import pandas as pd + +from dagloader._schema import Scheme + +__all__ = ["split_assignment", "split_scheme"] + +_DEFAULT_RATIOS = {"train": 0.6, "val": 0.2, "test": 0.2} + + +def _allocate(n: int, ratios: Mapping[str, float], names: Sequence[str]) -> dict[str, int]: + """Largest-remainder apportionment of ``n`` groups to splits by ratio (the sizes sum to ``n``).""" + exact = {name: ratios[name] * n for name in names} + sizes = {name: int(exact[name]) for name in names} # floor + remainder = n - sum(sizes.values()) + order = sorted(names, key=lambda nm: exact[nm] - sizes[nm], reverse=True) # largest fractional part first + for nm in order[:remainder]: + sizes[nm] += 1 + return sizes + + +def split_scheme( + scheme: Scheme, + *, + split_by: Sequence[str], + ratios: Mapping[str, float] | None = None, + force_training_values: Mapping[str, object] | None = None, + random_state: int = 42, +) -> dict[str, Scheme]: + """Partition the root (target) combinations of ``scheme`` into named splits. + + Splits are over whole *combinations* of ``split_by`` (all combos sharing the same ``split_by`` + values land in the same split — CellFlow2's ``drop_duplicates(subset=split_by)`` + merge-back), so + this is an out-of-distribution split that holds out entire conditions rather than random cells. + + Parameters + ---------- + scheme + The prepared scheme; its ``root`` node's positive-weight combinations are the split universe. + split_by + Columns whose unique combinations are partitioned across splits — a subset of the root's + ``cols``. + ratios + ``{split_name: fraction}`` summing to 1.0 (all > 0). Defaults to + ``{"train": 0.6, "val": 0.2, "test": 0.2}``. Insertion order defines the split order; the first + split is the "training" split that :paramref:`force_training_values` forces into. + force_training_values + ``{column: value}`` (keys ⊆ ``split_by``): any group matching *any* of these (column, value) + pairs is forced into the first split, regardless of the shuffle. + random_state + Seed for the group shuffle (reproducible). + + Returns + ------- + ``{split_name: Scheme}`` — each a copy of ``scheme`` with the root node's ``weights`` restricted to + that split's combinations; every other node (bound children / controls) is carried through + unchanged. + """ + ratios = dict(_DEFAULT_RATIOS if ratios is None else ratios) + split_by = tuple(split_by) + force = dict(force_training_values or {}) + + root = scheme.nodes[scheme.root] + cols = root.cols + + # --- validation ----------------------------------------------------------------------------- + if not split_by: + raise ValueError("split_by must be non-empty.") + missing = [c for c in split_by if c not in cols] + if missing: + raise ValueError(f"split_by columns {missing} are not in the root node's cols {cols}.") + if not ratios: + raise ValueError("ratios must be a non-empty {split_name: fraction} mapping.") + if any(r <= 0 for r in ratios.values()): + raise ValueError(f"ratios must all be > 0; got {ratios}.") + if not np.isclose(sum(ratios.values()), 1.0): + raise ValueError(f"ratios must sum to 1.0; got {ratios} (sum={sum(ratios.values())}).") + bad_force = [k for k in force if k not in split_by] + if bad_force: + raise ValueError(f"force_training_values keys {bad_force} must be a subset of split_by {split_by}.") + + names = list(ratios) # deterministic split order (insertion order) + train_name = names[0] # forced groups land here + + # --- the split universe: the root's positive-weight combinations (weight = the selection) ----- + combos = [combo for combo, w in root.weights.items() if w > 0] + if not combos: + raise ValueError("root node has no positive-weight combinations to split.") + + proj_idx = [cols.index(c) for c in split_by] + + def project(combo: tuple) -> tuple: # a combination → its split_by group + return tuple(combo[i] for i in proj_idx) + + groups = sorted({project(c) for c in combos}, key=lambda g: tuple(map(str, g))) # deterministic + + def is_forced(group: tuple) -> bool: # OR across keys, like CellFlow2._contains_value + gd = dict(zip(split_by, group, strict=True)) + return any(gd.get(k) == v for k, v in force.items()) + + forced = [g for g in groups if is_forced(g)] + free = [g for g in groups if not is_forced(g)] + + rng = np.random.default_rng(random_state) + free = [free[i] for i in rng.permutation(len(free))] + + sizes = _allocate(len(free), ratios, names) + # a split that must hold groups but got 0 is an error (mirrors CellFlow2). The train split is + # exempt when it will still receive forced groups. + empty = [n for n in names if sizes[n] == 0 and not (n == train_name and forced)] + if empty: + raise ValueError( + f"splits {empty} received 0 of {len(groups)} `split_by` group(s); increase the number of " + f"groups or adjust ratios {ratios}." + ) + + split_of: dict[tuple, str] = {} + pos = 0 + for name in names: + for g in free[pos : pos + sizes[name]]: + split_of[g] = name + pos += sizes[name] + for g in forced: # forced groups always go to the first (training) split + split_of[g] = train_name + + # --- build one Scheme per split: restrict the root weights, carry everything else ------------ + out: dict[str, Scheme] = {} + for name in names: + kept = {combo: root.weights[combo] for combo in combos if split_of[project(combo)] == name} + new_root = replace(root, weights=kept) + out[name] = replace(scheme, nodes={**scheme.nodes, scheme.root: new_root}) + return out + + +def split_assignment(splits: Mapping[str, Scheme]) -> pd.DataFrame: + """A tidy ``{*root cols, "split"}`` table of which target combination went to which split. + + Reconstructed from the split schemes' root weights (positive-weight combinations); useful for + inspecting a :func:`split_scheme` result without touching any cells. + """ + if not splits: + raise ValueError("splits mapping is empty.") + any_scheme = next(iter(splits.values())) + cols = any_scheme.nodes[any_scheme.root].cols + rows = [(*combo, name) for name, sch in splits.items() for combo, w in sch.nodes[sch.root].weights.items() if w > 0] + df = pd.DataFrame(rows, columns=[*cols, "split"]) + return df.sort_values(["split", *cols]).reset_index(drop=True) diff --git a/tests/dagloader/test_split.py b/tests/dagloader/test_split.py new file mode 100644 index 00000000..cb3c9d9a --- /dev/null +++ b/tests/dagloader/test_split.py @@ -0,0 +1,155 @@ +"""Tests for :func:`dagloader.split_scheme` / :func:`dagloader.split_assignment`. + +The split is a weights-only transform on a :class:`~dagloader.Scheme`, so these exercise it on a scheme +built from a tiny in-memory ``AnnData`` via :func:`~dagloader.perturbation_scheme` — no +``DatasetCollection`` and no ``DAGLoader`` are constructed. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +pytest.importorskip("annbatch") # dagloader imports annbatch; skip the module if it is not installed + +import anndata as ad + +from dagloader import Scheme, perturbation_scheme, split_assignment, split_scheme + + +def _toy_scheme(n_drugs: int = 5, cell_lines: tuple[str, ...] = ("A", "B"), seed: int = 0) -> Scheme: + """A perturbation scheme with ``n_drugs`` perturbations across ``cell_lines`` (+ a control each).""" + drugs = ["control", *[f"d{i}" for i in range(1, n_drugs + 1)]] + rows = [(cl, dr) for cl in cell_lines for dr in drugs for _ in range(2)] # 2 cells per combo + obs = pd.DataFrame(rows, columns=["cell_line", "drug"]) + obs["control"] = obs["drug"] == "control" + obs.index = obs.index.astype(str) + adata = ad.AnnData(X=np.zeros((len(obs), 3), dtype="float32"), obs=obs) + return perturbation_scheme( + adata, + context=["cell_line"], + perturbation=["drug"], + control_values={"drug": "control"}, + key="X", + seed=seed, + ) + + +def _combos(scheme: Scheme, node: str) -> set[tuple]: + n = scheme.nodes[node] + return {combo for combo, w in n.weights.items() if w > 0} + + +def _drugs(scheme: Scheme) -> set[str]: + root = scheme.nodes[scheme.root] + i = root.cols.index("drug") + return {combo[i] for combo, w in root.weights.items() if w > 0} + + +class TestSplitScheme: + def test_returns_named_schemes(self): + splits = split_scheme(_toy_scheme(), split_by=["drug"]) + assert set(splits) == {"train", "val", "test"} + for sch in splits.values(): + assert isinstance(sch, Scheme) + assert sch.root == "pert" # root node name unchanged, only its weights are restricted + + def test_partition_covers_and_is_disjoint(self): + scheme = _toy_scheme(n_drugs=5) + splits = split_scheme(scheme, split_by=["drug"], random_state=0) + drug_sets = [_drugs(s) for s in splits.values()] + # disjoint + for a in range(len(drug_sets)): + for b in range(a + 1, len(drug_sets)): + assert drug_sets[a].isdisjoint(drug_sets[b]) + # cover exactly the original perturbation drugs + assert set().union(*drug_sets) == _drugs(scheme) + # and the combinations partition the original root combinations + combo_union = set().union(*(_combos(s, "pert") for s in splits.values())) + assert combo_union == _combos(scheme, "pert") + + def test_default_ratio_sizes(self): + # 5 drugs, 60/20/20 -> 3/1/1 drug-groups; each drug spans 2 cell lines -> 6/2/2 combos + splits = split_scheme(_toy_scheme(n_drugs=5), split_by=["drug"], random_state=1) + assert {k: len(_drugs(v)) for k, v in splits.items()} == {"train": 3, "val": 1, "test": 1} + assert {k: len(_combos(v, "pert")) for k, v in splits.items()} == {"train": 6, "val": 2, "test": 2} + + def test_groups_by_projection_when_split_by_is_subset(self): + # split_by=["drug"] but cols=(cell_line, drug): both cell lines of a drug share a split + splits = split_scheme(_toy_scheme(cell_lines=("A", "B")), split_by=["drug"], random_state=2) + for sch in splits.values(): + root = sch.nodes[sch.root] + by_drug: dict[str, set[str]] = {} + di, ci = root.cols.index("drug"), root.cols.index("cell_line") + for combo, w in root.weights.items(): + if w > 0: + by_drug.setdefault(combo[di], set()).add(combo[ci]) + for drug, lines in by_drug.items(): + assert lines == {"A", "B"}, f"{drug} split across cell lines: {lines}" + + def test_controls_carried_through_unchanged(self): + scheme = _toy_scheme() + splits = split_scheme(scheme, split_by=["drug"]) + for sch in splits.values(): + assert sch.nodes["ctrl"].weights == scheme.nodes["ctrl"].weights + assert sch.sources is scheme.sources # same source objects, no copy + + def test_reproducible_for_same_seed(self): + a = split_scheme(_toy_scheme(), split_by=["drug"], random_state=7) + b = split_scheme(_toy_scheme(), split_by=["drug"], random_state=7) + assert {k: _drugs(v) for k, v in a.items()} == {k: _drugs(v) for k, v in b.items()} + + def test_force_training_values(self): + # d1 must land in train regardless of the shuffle seed + for seed in range(5): + splits = split_scheme( + _toy_scheme(), split_by=["drug"], force_training_values={"drug": "d1"}, random_state=seed + ) + assert "d1" in _drugs(splits["train"]) + assert "d1" not in _drugs(splits["val"]) + assert "d1" not in _drugs(splits["test"]) + + def test_split_by_context_column_with_custom_ratios(self): + # "or whatever": hold out a whole cell line, two-way split + scheme = _toy_scheme(cell_lines=("A", "B")) + splits = split_scheme(scheme, split_by=["cell_line"], ratios={"train": 0.5, "test": 0.5}) + assert set(splits) == {"train", "test"} + lines = [{c[scheme.nodes["pert"].cols.index("cell_line")] for c in _combos(s, "pert")} for s in splits.values()] + assert lines[0].isdisjoint(lines[1]) + assert set().union(*lines) == {"A", "B"} + for sch in splits.values(): # controls for BOTH lines stay available in each split + assert sch.nodes["ctrl"].weights == scheme.nodes["ctrl"].weights + + +class TestSplitAssignment: + def test_assignment_table(self): + scheme = _toy_scheme(n_drugs=5) + splits = split_scheme(scheme, split_by=["drug"], random_state=0) + df = split_assignment(splits) + assert list(df.columns) == ["cell_line", "drug", "split"] + assert len(df) == len(_combos(scheme, "pert")) # one row per target combination + assert set(df["split"]) == {"train", "val", "test"} + # every (cell_line, drug) appears exactly once + assert not df.duplicated(subset=["cell_line", "drug"]).any() + + +class TestSplitValidation: + @pytest.mark.parametrize( + ("kwargs", "match"), + [ + ({"split_by": []}, "split_by must be non-empty"), + ({"split_by": ["nope"]}, "not in the root node's cols"), + ({"split_by": ["drug"], "ratios": {"train": 0.5, "val": 0.2, "test": 0.2}}, "sum to 1.0"), + ({"split_by": ["drug"], "ratios": {"train": 1.2, "val": -0.2}}, "must all be > 0"), + ({"split_by": ["drug"], "force_training_values": {"cell_line": "A"}}, "subset of split_by"), + ], + ) + def test_invalid_arguments_raise(self, kwargs, match): + with pytest.raises(ValueError, match=match): + split_scheme(_toy_scheme(), **kwargs) + + def test_too_few_groups_for_ratios_raises(self): + # 2 cell lines cannot fill 3 non-empty splits + with pytest.raises(ValueError, match="received 0 of"): + split_scheme(_toy_scheme(cell_lines=("A", "B")), split_by=["cell_line"]) diff --git a/tests/model/test_annbatch_split.py b/tests/model/test_annbatch_split.py new file mode 100644 index 00000000..5f2a14da --- /dev/null +++ b/tests/model/test_annbatch_split.py @@ -0,0 +1,117 @@ +"""Tests for :meth:`cellflow.model.CellFlow.split_annbatch_data`. + +``prepare_annbatch_data`` is not implemented yet (it will build the ``Scheme`` from a +``DatasetCollection``), so these inject a scheme built from a tiny in-memory ``AnnData`` directly onto +the model — exercising the split wiring without a collection or a loader. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +pytest.importorskip("annbatch") # dagloader (and thus split_annbatch_data) needs annbatch + +import anndata as ad + +import cellflow +from dagloader import perturbation_scheme + + +def _toy_scheme(): + drugs = ["control", "d1", "d2", "d3", "d4", "d5"] + rows = [(cl, dr) for cl in ("A", "B") for dr in drugs for _ in range(2)] + obs = pd.DataFrame(rows, columns=["cell_line", "drug"]) + obs["control"] = obs["drug"] == "control" + obs.index = obs.index.astype(str) + adata = ad.AnnData(X=np.zeros((len(obs), 3), dtype="float32"), obs=obs) + return perturbation_scheme( + adata, + context=["cell_line"], + perturbation=["drug"], + control_values={"drug": "control"}, + key="X", + ) + + +class TestSplitAnnbatchData: + def test_without_scheme_raises(self): + cf = cellflow.model.CellFlow() + with pytest.raises(ValueError, match="No annbatch `Scheme`"): + cf.split_annbatch_data(split_by=["drug"]) + + def test_split_with_injected_scheme(self): + cf = cellflow.model.CellFlow() + cf._scheme = _toy_scheme() # stand in for `prepare_annbatch_data` (still a TODO) + + df = cf.split_annbatch_data(split_by=["drug"], random_state=0) + + assert isinstance(df, pd.DataFrame) + assert list(df.columns) == ["cell_line", "drug", "split"] + assert set(df["split"]) == {"train", "val", "test"} + assert cf._split_schemes is not None + assert set(cf._split_schemes) == {"train", "val", "test"} + # controls carried through into every split (matched-control availability) + for sch in cf._split_schemes.values(): + assert sch.nodes["ctrl"].weights == cf._scheme.nodes["ctrl"].weights + + def test_custom_ratios_and_force(self): + cf = cellflow.model.CellFlow() + cf._scheme = _toy_scheme() + df = cf.split_annbatch_data( + split_by=["drug"], + ratios={"train": 0.8, "test": 0.2}, + force_training_values={"drug": "d1"}, + random_state=3, + ) + assert set(df["split"]) == {"train", "test"} + assert df.loc[df["drug"] == "d1", "split"].eq("train").all() + + +class TestPrepareAnnbatchSplitStep: + """The split step wired into `prepare_annbatch_data` (the Scheme-building part is still a TODO).""" + + def test_without_scheme_raises_not_implemented(self): + # normal path: no pre-built scheme → the Scheme-building TODO raises + cf = cellflow.model.CellFlow() + with pytest.raises(NotImplementedError, match="not implemented yet"): + cf.prepare_annbatch_data( + source=None, + sample_rep="X", + control_key="control", + perturbation_covariates={"drug": ["drug"]}, + split_by=["drug"], + ) + + def test_split_step_runs_when_scheme_present(self): + # inject a Scheme (stands in for the TODO Scheme-building) → the split step runs in-call + cf = cellflow.model.CellFlow() + cf._scheme = _toy_scheme() + result = cf.prepare_annbatch_data( + source=None, + sample_rep="X", + control_key="control", + perturbation_covariates={"drug": ["drug"]}, + split_covariates=["cell_line"], + split_by=["drug"], + split_ratios={"train": 0.6, "val": 0.2, "test": 0.2}, + split_random_state=0, + ) + assert result is None # prepare_* returns None, like prepare_data + assert cf._split_schemes is not None + assert set(cf._split_schemes) == {"train", "val", "test"} + assert isinstance(cf._split_assignment, pd.DataFrame) + assert set(cf._split_assignment["split"]) == {"train", "val", "test"} + + def test_no_split_when_split_by_omitted(self): + cf = cellflow.model.CellFlow() + cf._scheme = _toy_scheme() + cf.prepare_annbatch_data( + source=None, + sample_rep="X", + control_key="control", + perturbation_covariates={"drug": ["drug"]}, + ) + assert cf._split_schemes is None + assert cf._split_assignment is None From cff875869c02326ddfab9960e1bc17521d64b43e Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Jul 2026 13:56:07 +0200 Subject: [PATCH 07/35] Per-split SamplerConfig for the annbatch path; require chunk_size MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - prepare_annbatch_data takes `sampler_config`: a single SamplerConfig applied to every split, or a {split_name: SamplerConfig} mapping that must cover all splits. Resolved via new `dagloader.resolve_split_configs` into {split: SamplerConfig} (no-split case → a single "train"). Replaces the old scalar batch_size/chunk_size params. - SamplerConfig.chunk_size is now required (no hidden default of 1), matching preload_nchunks; README examples and tests updated to pass it explicitly. Building the Scheme/loader from an annbatch source remains a TODO; the split + config-resolution steps are exercised via an injected Scheme (no collection, no loader). Co-Authored-By: Claude Opus 4.8 --- src/cellflow/model/_cellflow.py | 28 ++++++++++---- src/dagloader/README.md | 4 +- src/dagloader/__init__.py | 3 +- src/dagloader/_schema.py | 9 +++-- src/dagloader/_split.py | 46 +++++++++++++++++++++- tests/dagloader/test_split.py | 42 +++++++++++++++++++- tests/model/test_annbatch_split.py | 62 +++++++++++++++++++++++++++++- 7 files changed, 176 insertions(+), 18 deletions(-) diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index 10a79150..2435cd24 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -32,6 +32,8 @@ if TYPE_CHECKING: from annbatch import DatasetCollection # optional dep — only imported for typing + from dagloader import SamplerConfig # optional dep — only imported for typing + __all__ = ["CellFlow"] @@ -81,6 +83,7 @@ def __init__(self, adata: ad.AnnData | None = None, solver: Literal["otfm", "gen self._scheme = None self._split_schemes: dict[str, Any] | None = None self._split_assignment: pd.DataFrame | None = None + self._annbatch_sampler_configs: dict[str, Any] | None = None def prepare_data( self, @@ -232,8 +235,7 @@ def prepare_annbatch_data( split_covariates: Sequence[str] | None = None, max_combination_length: int | None = None, null_value: float = 0.0, - batch_size: int = 1024, - chunk_size: int = 1, + sampler_config: "SamplerConfig | Mapping[str, SamplerConfig] | None" = None, seed: int = 0, split_by: Sequence[str] | None = None, split_ratios: Mapping[str, float] | None = None, @@ -257,11 +259,11 @@ def prepare_annbatch_data( source An out-of-core :class:`annbatch.DatasetCollection` to stream cells from. Its ``obs`` supplies the grouping / condition columns; ``sample_rep`` selects the streamed representation. - batch_size - Rows per streamed batch (the target/root batch size). - chunk_size - annbatch read-slice size: ``1`` (default) ⇒ per-row reads (any on-disk layout); ``>1`` ⇒ - contiguous chunked reads for higher on-disk throughput. + sampler_config + The read parameters for the streamed loader(s), **one per split**. Either a single + :class:`dagloader.SamplerConfig` applied to every split, or a per-split mapping + ``{split_name: SamplerConfig}`` that must cover **all** splits (see + :func:`dagloader.resolve_split_configs`). When no split is made the only split is ``"train"``. seed Reproducibility seed for the ``dagloader`` per-node RNG streams. split_by @@ -312,6 +314,18 @@ def prepare_annbatch_data( random_state=split_random_state, ) + # One `SamplerConfig` per split (a flat spec ⇒ all splits; a per-split dict ⇒ all specified). + # The splits are the split schemes when a split was made, else the single ``"train"`` scheme. + if sampler_config is not None: + from dagloader import resolve_split_configs + + split_names = list(self._split_schemes) if self._split_schemes is not None else ["train"] + self._annbatch_sampler_configs = resolve_split_configs(sampler_config, split_names) + + # TODO(annbatch): build a `DAGLoader` per split from `self._split_schemes` (or the full + # `self._scheme`) using the matching `self._annbatch_sampler_configs[name]`, wrap it in the + # trainer's sampler interface, and set `self._dataloader` / `self._validation_data`. + def split_annbatch_data( self, *, diff --git a/src/dagloader/README.md b/src/dagloader/README.md index 5e5fe741..32b3daed 100644 --- a/src/dagloader/README.md +++ b/src/dagloader/README.md @@ -154,7 +154,7 @@ scheme = perturbation_scheme( ) loader = DAGLoader( scheme, - SamplerConfig(batch_size=256, preload_nchunks=256), # preload_nchunks is explicit (no hidden default) + SamplerConfig(batch_size=256, chunk_size=1, preload_nchunks=256), # all read params explicit (no hidden defaults) condition_fn=lambda leaf: DRUG_EMB[leaf[-1]], ) batch = next(loader) # {"target", "source", "condition"} — source is a matched-cell-line control @@ -205,7 +205,7 @@ scheme = Scheme( ) loader = DAGLoader( scheme, - SamplerConfig(batch_size=256, preload_nchunks=256), # per-node: {"target": SamplerConfig(batch_size=256, chunk_size=32, preload_nchunks=8), "control": SamplerConfig(batch_size=256, preload_nchunks=256)} + SamplerConfig(batch_size=256, chunk_size=1, preload_nchunks=256), # per-node: {"target": SamplerConfig(batch_size=256, chunk_size=32, preload_nchunks=8), "control": SamplerConfig(batch_size=256, chunk_size=1, preload_nchunks=256)} condition_fn=lambda leaf: concat(cell_emb[leaf[0]], drug_emb[leaf[1]]), # *_reps combination ) batch = next(loader) # {"target", "source", "condition"} diff --git a/src/dagloader/__init__.py b/src/dagloader/__init__.py index 0bd4d2af..4041a2fc 100644 --- a/src/dagloader/__init__.py +++ b/src/dagloader/__init__.py @@ -25,7 +25,7 @@ uniform, ) from dagloader._schemes import perturbation_scheme -from dagloader._split import split_assignment, split_scheme +from dagloader._split import resolve_split_configs, split_assignment, split_scheme __all__ = [ "Bind", @@ -39,6 +39,7 @@ "frequency", "inverse_frequency", "perturbation_scheme", + "resolve_split_configs", "split_assignment", "split_scheme", "uniform", diff --git a/src/dagloader/_schema.py b/src/dagloader/_schema.py index 895a447b..f2297a44 100644 --- a/src/dagloader/_schema.py +++ b/src/dagloader/_schema.py @@ -151,9 +151,10 @@ class SamplerConfig: batch_size Rows per emitted batch for this node (target rows for the root; source rows for a bound child). chunk_size - annbatch read-slice size. ``1`` (default) ⇒ per-row reads (any on-disk layout). ``>1`` ⇒ - contiguous chunked reads (higher throughput on disk), assuming each sampled leaf sits in a - contiguous run ≥ ``chunk_size``. Must divide ``batch_size`` (one category per batch). + annbatch read-slice size. **Required** and explicit — there is no hidden default; set it + deliberately. ``1`` ⇒ per-row reads (any on-disk layout); ``>1`` ⇒ contiguous chunked reads + (higher throughput on disk), assuming each sampled leaf sits in a contiguous run ≥ + ``chunk_size``. Must divide ``batch_size`` (one category per batch). preload_nchunks Chunks per annbatch read window. **Required** and explicit — there is no hidden default; set it deliberately (e.g. ``batch_size // chunk_size`` for one batch per window). Must be a positive @@ -161,7 +162,7 @@ class SamplerConfig: """ batch_size: int - chunk_size: int = 1 + chunk_size: int preload_nchunks: int diff --git a/src/dagloader/_split.py b/src/dagloader/_split.py index 5a05cf54..63ad2936 100644 --- a/src/dagloader/_split.py +++ b/src/dagloader/_split.py @@ -21,9 +21,9 @@ import numpy as np import pandas as pd -from dagloader._schema import Scheme +from dagloader._schema import SamplerConfig, Scheme -__all__ = ["split_assignment", "split_scheme"] +__all__ = ["resolve_split_configs", "split_assignment", "split_scheme"] _DEFAULT_RATIOS = {"train": 0.6, "val": 0.2, "test": 0.2} @@ -165,3 +165,45 @@ def split_assignment(splits: Mapping[str, Scheme]) -> pd.DataFrame: rows = [(*combo, name) for name, sch in splits.items() for combo, w in sch.nodes[sch.root].weights.items() if w > 0] df = pd.DataFrame(rows, columns=[*cols, "split"]) return df.sort_values(["split", *cols]).reset_index(drop=True) + + +def resolve_split_configs( + config: SamplerConfig | Mapping[str, SamplerConfig], + split_names: Sequence[str], +) -> dict[str, SamplerConfig]: + """Resolve a read-parameter spec into exactly one :class:`SamplerConfig` per split. + + ``config`` is either + + * **a single** :class:`SamplerConfig` — applied to every split; or + * **a per-split mapping** ``{split_name: SamplerConfig}`` — in which case **every** split in + ``split_names`` must be present (no partial specs, no unknown split names). + + Returns ``{split_name: SamplerConfig}`` for exactly ``split_names``. + """ + names = list(split_names) + if not names: + raise ValueError("split_names must be non-empty.") + + if isinstance(config, SamplerConfig): # one config → all splits + return dict.fromkeys(names, config) + + if isinstance(config, Mapping): # per-split mapping: every split specified, no extras + missing = [n for n in names if n not in config] + if missing: + raise ValueError( + f"sampler_config is per-split but is missing config(s) for split(s) {missing}; specify all of {names}." + ) + extra = [k for k in config if k not in names] + if extra: + raise ValueError(f"sampler_config has config(s) for unknown split(s) {extra}; splits are {names}.") + bad = [k for k, v in config.items() if not isinstance(v, SamplerConfig)] + if bad: + raise ValueError( + f"sampler_config values must be SamplerConfig instances; got a non-SamplerConfig for {bad}." + ) + return {name: config[name] for name in names} + + raise ValueError( + f"sampler_config must be a SamplerConfig or a {{split: SamplerConfig}} mapping; got {type(config).__name__}." + ) diff --git a/tests/dagloader/test_split.py b/tests/dagloader/test_split.py index cb3c9d9a..e8203cd7 100644 --- a/tests/dagloader/test_split.py +++ b/tests/dagloader/test_split.py @@ -15,7 +15,14 @@ import anndata as ad -from dagloader import Scheme, perturbation_scheme, split_assignment, split_scheme +from dagloader import ( + SamplerConfig, + Scheme, + perturbation_scheme, + resolve_split_configs, + split_assignment, + split_scheme, +) def _toy_scheme(n_drugs: int = 5, cell_lines: tuple[str, ...] = ("A", "B"), seed: int = 0) -> Scheme: @@ -153,3 +160,36 @@ def test_too_few_groups_for_ratios_raises(self): # 2 cell lines cannot fill 3 non-empty splits with pytest.raises(ValueError, match="received 0 of"): split_scheme(_toy_scheme(cell_lines=("A", "B")), split_by=["cell_line"]) + + +class TestResolveSplitConfigs: + NAMES = ["train", "val", "test"] + + def test_single_config_object_applies_to_all(self): + cfg = SamplerConfig(batch_size=256, chunk_size=1, preload_nchunks=256) + out = resolve_split_configs(cfg, self.NAMES) + assert set(out) == set(self.NAMES) + assert all(out[n] is cfg for n in self.NAMES) # same object shared + + def test_per_split_mapping_ok(self): + train, val, test = ( + SamplerConfig(batch_size=256, chunk_size=1, preload_nchunks=256), + SamplerConfig(batch_size=64, chunk_size=1, preload_nchunks=64), + SamplerConfig(batch_size=64, chunk_size=1, preload_nchunks=64), + ) + out = resolve_split_configs({"train": train, "val": val, "test": test}, self.NAMES) + assert out == {"train": train, "val": val, "test": test} + + def test_per_split_missing_split_raises(self): + with pytest.raises(ValueError, match="missing config"): + resolve_split_configs({"train": SamplerConfig(batch_size=1, chunk_size=1, preload_nchunks=1)}, self.NAMES) + + def test_per_split_unknown_split_raises(self): + cfg = SamplerConfig(batch_size=1, chunk_size=1, preload_nchunks=1) + with pytest.raises(ValueError, match="unknown split"): + resolve_split_configs({"train": cfg, "val": cfg, "test": cfg, "bogus": cfg}, self.NAMES) + + def test_per_split_non_sampler_config_value_raises(self): + cfg = SamplerConfig(batch_size=1, chunk_size=1, preload_nchunks=1) + with pytest.raises(ValueError, match="must be SamplerConfig"): + resolve_split_configs({"train": cfg, "val": cfg, "test": {"batch_size": 1}}, self.NAMES) diff --git a/tests/model/test_annbatch_split.py b/tests/model/test_annbatch_split.py index 5f2a14da..15a08554 100644 --- a/tests/model/test_annbatch_split.py +++ b/tests/model/test_annbatch_split.py @@ -16,7 +16,7 @@ import anndata as ad import cellflow -from dagloader import perturbation_scheme +from dagloader import SamplerConfig, perturbation_scheme def _toy_scheme(): @@ -115,3 +115,63 @@ def test_no_split_when_split_by_omitted(self): ) assert cf._split_schemes is None assert cf._split_assignment is None + + +class TestPrepareAnnbatchSamplerConfig: + """`sampler_config` resolves to one SamplerConfig per split (Scheme-building still a TODO).""" + + def test_single_config_applies_to_all_splits(self): + cf = cellflow.model.CellFlow() + cf._scheme = _toy_scheme() + cfg = SamplerConfig(batch_size=128, chunk_size=1, preload_nchunks=128) + cf.prepare_annbatch_data( + source=None, + sample_rep="X", + control_key="control", + perturbation_covariates={"drug": ["drug"]}, + split_by=["drug"], + sampler_config=cfg, + ) + assert set(cf._annbatch_sampler_configs) == {"train", "val", "test"} + assert all(c is cfg for c in cf._annbatch_sampler_configs.values()) + + def test_per_split_config(self): + cf = cellflow.model.CellFlow() + cf._scheme = _toy_scheme() + train = SamplerConfig(batch_size=256, chunk_size=1, preload_nchunks=256) + small = SamplerConfig(batch_size=64, chunk_size=1, preload_nchunks=64) + cf.prepare_annbatch_data( + source=None, + sample_rep="X", + control_key="control", + perturbation_covariates={"drug": ["drug"]}, + split_by=["drug"], + sampler_config={"train": train, "val": small, "test": small}, + ) + assert cf._annbatch_sampler_configs["train"].batch_size == 256 + assert cf._annbatch_sampler_configs["val"].batch_size == 64 + + def test_per_split_missing_split_raises(self): + cf = cellflow.model.CellFlow() + cf._scheme = _toy_scheme() + with pytest.raises(ValueError, match="missing config"): + cf.prepare_annbatch_data( + source=None, + sample_rep="X", + control_key="control", + perturbation_covariates={"drug": ["drug"]}, + split_by=["drug"], + sampler_config={"train": SamplerConfig(batch_size=1, chunk_size=1, preload_nchunks=1)}, + ) + + def test_no_split_uses_single_train_config(self): + cf = cellflow.model.CellFlow() + cf._scheme = _toy_scheme() + cf.prepare_annbatch_data( + source=None, + sample_rep="X", + control_key="control", + perturbation_covariates={"drug": ["drug"]}, + sampler_config=SamplerConfig(batch_size=32, chunk_size=1, preload_nchunks=32), + ) + assert set(cf._annbatch_sampler_configs) == {"train"} # no split → single "train" scheme From a172943f8a4abcd23f27f0a739a71387689f6bd8 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Jul 2026 14:26:09 +0200 Subject: [PATCH 08/35] Implement the annbatch streaming path end-to-end (build + condition + train) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit prepare_annbatch_data now builds the dagloader Scheme (perturbed root, matched- control child) + a condition_fn from the covariate spec, one DAGLoader per split, and wires the "train" split to train(); val/test loaders are kept on _annbatch_loaders. Training runs end-to-end over the streamed batches. - cellflow/data/_annbatch.py: build_annbatch_training(...) — reuses DataManager (as a cell-free encoder factory over obs+uns, never X) and build_condition_data, so condition embeddings are byte-identical to the in-memory path (parity-checked). New `rep_dict` arg supplies the uns-equivalent embeddings (no adata in this path). - cellflow/data/_dataloader.py: DAGLoaderTrainSampler — key-rename adapter mapping the DAGLoader {target,source,condition} stream to the {tgt,src,condition}_cell_data .sample(rng) contract, so both paths reach the solver identically. - _cellflow: prepare_model / train branch to source condition data + dataloader from the annbatch path; in-memory behavior unchanged. - dagloader/_loader.py: fix DAGLoader.__next__ calling a non-existent _node_reps (should be _nodes_next) — it crashed on every bound child, so no source batch could be produced. One-line bug fix; no design change. - Tests: build/split/per-split-config + end-to-end training over an in-memory AnnData source (no DatasetCollection needed). Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/_annbatch.py | 159 +++++++++++++++++++++++++++ src/cellflow/data/_dataloader.py | 33 +++++- src/cellflow/model/_cellflow.py | 121 ++++++++++++++------- src/dagloader/_loader.py | 2 +- tests/model/test_annbatch_path.py | 7 -- tests/model/test_annbatch_split.py | 169 ++++++++++++----------------- tests/model/test_annbatch_train.py | 72 ++++++++++++ 7 files changed, 413 insertions(+), 150 deletions(-) create mode 100644 src/cellflow/data/_annbatch.py create mode 100644 tests/model/test_annbatch_train.py diff --git a/src/cellflow/data/_annbatch.py b/src/cellflow/data/_annbatch.py new file mode 100644 index 00000000..d56ae928 --- /dev/null +++ b/src/cellflow/data/_annbatch.py @@ -0,0 +1,159 @@ +"""Build the annbatch/dagloader streaming training path from a CellFlow covariate spec. + +Turns the same ``prepare_data`` covariate arguments into a :class:`dagloader.Scheme` (perturbed root, +matched-control child) plus a ``condition_fn`` that maps a sampled leaf to its per-condition embedding. +The condition embeddings reuse the **in-memory** machinery unchanged: a cell-free "shell" ``AnnData`` +(``obs`` + ``uns`` only, no ``X``) drives a :class:`~cellflow.data._datamanager.DataManager` purely as a +covariate-encoder factory, and :func:`~cellflow.data._condition.build_condition_data` assembles the +embeddings — byte-for-byte identical to the in-memory path (parity-tested), so nothing is duplicated. + +Only ``obs`` (and the embedding tables) are read here; cells are streamed later by ``dagloader``. +""" + +from __future__ import annotations + +from collections.abc import Callable, Mapping, Sequence +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any + +import anndata as ad +import numpy as np + +from cellflow.data._condition import _key_layout, build_condition_data, enumerate_perturbations +from cellflow.data._datamanager import DataManager + +if TYPE_CHECKING: + from dagloader import Scheme + +__all__ = ["AnnbatchTraining", "build_annbatch_training", "sample_rep_to_key"] + + +def sample_rep_to_key(sample_rep: str) -> str: + """CellFlow ``sample_rep`` → dagloader representation key (``"X"`` or ``"obsm/"``).""" + return "X" if sample_rep == "X" else f"obsm/{sample_rep}" + + +@dataclass(frozen=True) +class AnnbatchTraining: + """Everything the model needs to stream-train, assembled from a covariate spec (cells untouched).""" + + scheme: Scheme + condition_fn: Callable[[tuple], dict[str, np.ndarray]] + condition_data: dict[str, np.ndarray] + data_manager: DataManager + data_dim: int + max_combination_length: int + + +def build_annbatch_training( + source: Any, + *, + sample_rep: str, + control_key: str, + perturbation_covariates: Mapping[str, Sequence[str]], + perturbation_covariate_reps: Mapping[str, str] | None = None, + sample_covariates: Sequence[str] | None = None, + sample_covariate_reps: Mapping[str, str] | None = None, + split_covariates: Sequence[str] | None = None, + max_combination_length: int | None = None, + null_value: float = 0.0, + rep_dict: Mapping[str, Any] | None = None, + seed: int = 0, +) -> AnnbatchTraining: + """Assemble the :class:`dagloader.Scheme` + ``condition_fn`` for the streaming path (obs only). + + ``source`` is an out-of-core :class:`annbatch.DatasetCollection` or an in-memory ``AnnData`` (the + dagloader is container-agnostic). ``rep_dict`` supplies the covariate embedding tables that + ``adata.uns`` would hold in the in-memory path (keys match ``*_covariate_reps`` values); it may be + :obj:`None` when the primary covariate is categorical (one-hot encoded, no external embeddings). + """ + from dagloader import Bind, Node, Scheme, uniform + from dagloader._io import key_backings, obs_columns + + context = tuple(split_covariates or ()) + pert_cols = tuple(c for grp in perturbation_covariates.values() for c in grp) + samp_cols = tuple(sample_covariates or ()) + cols = tuple(dict.fromkeys((*context, *pert_cols, *samp_cols))) # grouping cols (deduped, ordered) + key = sample_rep_to_key(sample_rep) + + obs = obs_columns(source, [*cols, control_key]) + + # DataManager as a pure covariate-encoder factory. It reads only obs + uns (never X), so a cell-free + # shell suffices; `sample_rep="X"` sidesteps obsm verification (the real rep is streamed by the Scheme). + shell = ad.AnnData(obs=obs.copy()) + shell.uns = dict(rep_dict or {}) + dm = DataManager( + shell, + sample_rep="X", + control_key=control_key, + perturbation_covariates=dict(perturbation_covariates), + perturbation_covariate_reps=dict(perturbation_covariate_reps) if perturbation_covariate_reps else None, + sample_covariates=list(samp_cols), + sample_covariate_reps=dict(sample_covariate_reps) if sample_covariate_reps else None, + split_covariates=list(context), + max_combination_length=max_combination_length, + null_value=null_value, + ) + + # Per-condition embeddings — the shared helper → identical to the in-memory path (parity-tested). + condition_data = build_condition_data( + obs, + shell.uns, + control_key=control_key, + perturb_covar_keys=dm._perturb_covar_keys, + split_covariates=list(context), + sample_covariates=list(samp_cols), + perturbation_covariates=dict(perturbation_covariates), + covariate_reps=dm._covariate_reps, + covar_to_idx=dm.covar_to_idx, + is_categorical=dm.is_categorical, + primary_one_hot_encoder=dm.primary_one_hot_encoder, + primary_group=dm.primary_group, + linked_perturb_covars=dm.linked_perturb_covars, + max_combination_length=dm.max_combination_length, + null_value=null_value, + ) + + # leaf (in `cols` order) → perturbation index → per-condition embedding. `enumerate_perturbations` + # lays its covariate tuples out in `tuple_keys` order (pert + sample/split), which differs from + # `cols`, so re-project the leaf onto that layout before the lookup. String-normalize both sides so + # dtype quirks (categorical / numpy scalars) never break the match. + idx_to_cov = enumerate_perturbations( + obs, + control_key=control_key, + perturb_covar_keys=dm._perturb_covar_keys, + split_covariates=list(context), + sample_covariates=list(samp_cols), + ) + _, tuple_keys = _key_layout(dm._perturb_covar_keys, list(context), list(samp_cols)) + cov_to_idx = {tuple(map(str, cov)): i for i, cov in idx_to_cov.items()} + reorder = [cols.index(c) for c in tuple_keys] + + def condition_fn(leaf: tuple) -> dict[str, np.ndarray]: + idx = cov_to_idx[tuple(str(leaf[i]) for i in reorder)] + return {group: condition_data[group][[idx]] for group in condition_data} + + # The Scheme: root = perturbed combos, child = matched-control combos (bound on the context columns). + ctrl_flag = obs[control_key].to_numpy().astype(bool) + pert = [tuple(r) for r in obs.loc[~ctrl_flag, list(cols)].drop_duplicates().to_numpy()] + ctrl = [tuple(r) for r in obs.loc[ctrl_flag, list(cols)].drop_duplicates().to_numpy()] + scheme = Scheme( + sources={"data": source}, + nodes={ + "pert": Node("data", cols, key, uniform(pert)), + "ctrl": Node("data", cols, key, uniform(ctrl)), + }, + root="pert", + binds=(Bind("pert", "ctrl", common=context),), + seed=seed, + ) + + data_dim = int(key_backings(source, key)[0].shape[1]) + return AnnbatchTraining( + scheme=scheme, + condition_fn=condition_fn, + condition_data=condition_data, + data_manager=dm, + data_dim=data_dim, + max_combination_length=dm.max_combination_length, + ) diff --git a/src/cellflow/data/_dataloader.py b/src/cellflow/data/_dataloader.py index 70bd91ef..75827603 100644 --- a/src/cellflow/data/_dataloader.py +++ b/src/cellflow/data/_dataloader.py @@ -9,7 +9,38 @@ from cellflow.data._data import PredictionData, TrainingData, ValidationData -__all__ = ["TrainSampler", "ValidationSampler", "PredictionSampler", "OOCTrainSampler"] +__all__ = [ + "TrainSampler", + "ValidationSampler", + "PredictionSampler", + "OOCTrainSampler", + "DAGLoaderTrainSampler", +] + + +class DAGLoaderTrainSampler: + """Adapt a ``dagloader.DAGLoader`` stream to the :meth:`TrainSampler.sample` batch contract. + + ``DAGLoader`` yields ``{"target", "source", "condition"}`` (one perturbation per batch, the + condition already carrying the leading ``(1, ...)`` axis); the trainer/solver consumes + ``{"src_cell_data", "tgt_cell_data", "condition"}``. This is a key-rename over the stream — no cells + are copied — so the in-memory and streaming paths hit the solver through the same interface. + """ + + def __init__(self, loader: Any): + self._loader = loader + self._iter = iter(loader) + + def sample(self, rng: Any = None) -> dict[str, Any]: + """Return the next streamed batch as a trainer batch dict. + + ``rng`` is unused — the ``DAGLoader`` owns its own reproducible RNG. + """ + batch = next(self._iter) + out = {"src_cell_data": batch["source"], "tgt_cell_data": batch["target"]} + if "condition" in batch: + out["condition"] = batch["condition"] + return out class TrainSampler: diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index 2435cd24..2356c793 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -19,7 +19,13 @@ from cellflow._compat import BrownianBridge, ConstantNoiseFlow from cellflow._types import ArrayLike, Layers_separate_input_t, Layers_t from cellflow.data._data import ConditionData, TrainingData, ValidationData -from cellflow.data._dataloader import OOCTrainSampler, PredictionSampler, TrainSampler, ValidationSampler +from cellflow.data._dataloader import ( + DAGLoaderTrainSampler, + OOCTrainSampler, + PredictionSampler, + TrainSampler, + ValidationSampler, +) from cellflow.data._datamanager import DataManager from cellflow.model._utils import _write_predictions from cellflow.networks import _velocity_field @@ -71,6 +77,7 @@ def __init__(self, adata: ad.AnnData | None = None, solver: Literal["otfm", "gen if solver == "otfm" else _velocity_field.GENOTConditionalVelocityField ) + self._train_data: TrainingData | None = None # set by `prepare_data` (in-memory path only) self._dataloader: TrainSampler | OOCTrainSampler | None = None self._trainer: CellFlowTrainer | None = None self._validation_data: dict[str, ValidationData] = {"predict_kwargs": {}} @@ -84,6 +91,9 @@ def __init__(self, adata: ad.AnnData | None = None, solver: Literal["otfm", "gen self._split_schemes: dict[str, Any] | None = None self._split_assignment: pd.DataFrame | None = None self._annbatch_sampler_configs: dict[str, Any] | None = None + self._annbatch_loaders: dict[str, Any] | None = None # {split: DAGLoader} + self._condition_data: dict[str, np.ndarray] | None = None # annbatch-path condition embeddings + self._max_combination_length: int | None = None # annbatch-path (in-memory keeps it on train_data) def prepare_data( self, @@ -235,6 +245,7 @@ def prepare_annbatch_data( split_covariates: Sequence[str] | None = None, max_combination_length: int | None = None, null_value: float = 0.0, + rep_dict: Mapping[str, Any] | None = None, sampler_config: "SamplerConfig | Mapping[str, SamplerConfig] | None" = None, seed: int = 0, split_by: Sequence[str] | None = None, @@ -257,8 +268,14 @@ def prepare_annbatch_data( Parameters ---------- source - An out-of-core :class:`annbatch.DatasetCollection` to stream cells from. Its ``obs`` supplies - the grouping / condition columns; ``sample_rep`` selects the streamed representation. + An out-of-core :class:`annbatch.DatasetCollection` to stream cells from (an in-memory + ``AnnData`` also works — the ``dagloader`` is container-agnostic). Its ``obs`` supplies the + grouping / condition columns; ``sample_rep`` selects the streamed representation. + rep_dict + The covariate embedding tables that ``adata.uns`` would hold in the in-memory path (keys + match the values of ``perturbation_covariate_reps`` / ``sample_covariate_reps``). Required + when a covariate group is embedded; may be :obj:`None` when the primary covariate is + categorical (one-hot encoded). sampler_config The read parameters for the streamed loader(s), **one per split**. Either a single :class:`dagloader.SamplerConfig` applied to every split, or a per-split mapping @@ -286,9 +303,8 @@ def prepare_annbatch_data( Notes ----- - Building the ``Scheme`` (and the loader) from the annbatch ``source`` is **not implemented - yet** — that step is a TODO. The splitting step is already wired here and delegated to - :meth:`split_annbatch_data`; it runs once the ``Scheme`` exists. + The ``"train"`` split's loader is wired to :meth:`train`; when a split is made, the ``val`` / + ``test`` split loaders are kept on :attr:`_annbatch_loaders` for later use. """ if self._adata is not None: raise ValueError( @@ -296,14 +312,30 @@ def prepare_annbatch_data( "without `adata` (`CellFlow()`). Use `prepare_data(adata=...)` for the in-memory path." ) - # TODO(annbatch): build the streaming `Scheme` (+ `SamplerConfig`, `condition_fn`, `DAGLoader`) - # from `source` and the covariate spec, and set `self._scheme` / the train loader here. Until - # that lands, the normal path (no pre-built scheme) raises below; the splitting step further - # down is already wired and is exercised in tests by injecting `self._scheme`. - if self._scheme is None: - raise NotImplementedError( - "prepare_annbatch_data: building the `Scheme` from an annbatch source is not implemented yet." - ) + from cellflow.data._annbatch import build_annbatch_training + from dagloader import DAGLoader, resolve_split_configs + + # Build the Scheme + condition_fn + condition embeddings from the covariate spec (obs only). + built = build_annbatch_training( + source, + sample_rep=sample_rep, + control_key=control_key, + perturbation_covariates=perturbation_covariates, + perturbation_covariate_reps=perturbation_covariate_reps, + sample_covariates=sample_covariates, + sample_covariate_reps=sample_covariate_reps, + split_covariates=split_covariates, + max_combination_length=max_combination_length, + null_value=null_value, + rep_dict=rep_dict, + seed=seed, + ) + self._scheme = built.scheme + self._dm = built.data_manager + self._condition_data = built.condition_data + self._data_dim = built.data_dim + self._max_combination_length = built.max_combination_length + condition_fn = built.condition_fn # Splitting step — kept in `prepare_annbatch_data` so preparing and splitting are one call. if split_by is not None: @@ -314,17 +346,19 @@ def prepare_annbatch_data( random_state=split_random_state, ) - # One `SamplerConfig` per split (a flat spec ⇒ all splits; a per-split dict ⇒ all specified). + # One `SamplerConfig` per split (a single spec ⇒ all splits; a per-split dict ⇒ all specified). # The splits are the split schemes when a split was made, else the single ``"train"`` scheme. - if sampler_config is not None: - from dagloader import resolve_split_configs - - split_names = list(self._split_schemes) if self._split_schemes is not None else ["train"] - self._annbatch_sampler_configs = resolve_split_configs(sampler_config, split_names) - - # TODO(annbatch): build a `DAGLoader` per split from `self._split_schemes` (or the full - # `self._scheme`) using the matching `self._annbatch_sampler_configs[name]`, wrap it in the - # trainer's sampler interface, and set `self._dataloader` / `self._validation_data`. + schemes = self._split_schemes if self._split_schemes is not None else {"train": self._scheme} + if sampler_config is None: + raise ValueError("`sampler_config` is required: give a SamplerConfig or a {split: SamplerConfig} mapping.") + self._annbatch_sampler_configs = resolve_split_configs(sampler_config, list(schemes)) + + # One streaming DAGLoader per split; the "train" split feeds `train()`, others are kept for reuse. + self._annbatch_loaders = { + name: DAGLoader(schemes[name], self._annbatch_sampler_configs[name], condition_fn=condition_fn) + for name in schemes + } + self._dataloader = DAGLoaderTrainSampler(self._annbatch_loaders["train"]) def split_annbatch_data( self, @@ -377,12 +411,8 @@ def split_annbatch_data( force_training_values=force_training_values, random_state=random_state, ) - # TODO(annbatch): wire the split Schemes into loaders once `prepare_annbatch_data` builds the - # Scheme + SamplerConfig + condition_fn. That step (which needs the DatasetCollection and the - # DAGLoader) will set, from `self._split_schemes`: - # self._dataloader = DAGLoader(self._split_schemes["train"], sampler_config, condition_fn) - # self._validation_data["val"] = - # and keep self._split_schemes["test"] for prediction / final evaluation. + # `prepare_annbatch_data` turns these split Schemes into per-split DAGLoaders (train feeds + # `train()`; val/test are kept on `_annbatch_loaders`); here we only produce the schemes + table. return split_assignment(self._split_schemes) def prepare_validation_data( @@ -619,8 +649,14 @@ def prepare_model( - :attr:`cellflow.model.CellFlow.trainer` - an instance of the :class:`cellflow.training.CellFlowTrainer`. """ - if self.train_data is None: - raise ValueError("Dataloader not initialized. Please call `prepare_data` first.") + if self.train_data is None and self._scheme is None: + raise ValueError("Dataloader not initialized. Please call `prepare_data` or `prepare_annbatch_data` first.") + # Condition embeddings + max combination length come from `train_data` (in-memory) or, on the + # annbatch path, from what `prepare_annbatch_data` assembled. + condition_data = self.train_data.condition_data if self.train_data is not None else self._condition_data + max_combination_length = ( + self.train_data.max_combination_length if self.train_data is not None else self._max_combination_length + ) if condition_mode == "stochastic": if regularization == 0.0: @@ -644,7 +680,7 @@ def prepare_model( self.vf = self._vf_class( output_dim=self._data_dim, - max_combination_length=self.train_data.max_combination_length, + max_combination_length=max_combination_length, condition_mode=condition_mode, regularization=regularization, condition_embedding_dim=condition_embedding_dim, @@ -687,7 +723,7 @@ def prepare_model( match_fn=match_fn, probability_path=probability_path, optimizer=optimizer, - conditions=self.train_data.condition_data, + conditions=condition_data, rng=jax.random.PRNGKey(seed), **solver_kwargs, ) @@ -699,7 +735,7 @@ def prepare_model( source_dim=self._data_dim, target_dim=self._data_dim, optimizer=optimizer, - conditions=self.train_data.condition_data, + conditions=condition_data, rng=jax.random.PRNGKey(seed), **solver_kwargs, ) @@ -751,16 +787,19 @@ def train( - :attr:`cellflow.model.CellFlow.dataloader` - the training dataloader. - :attr:`cellflow.model.CellFlow.solver` - the trained solver. """ - if self.train_data is None: - raise ValueError("Data not initialized. Please call `prepare_data` first.") + if self.train_data is None and self._dataloader is None: + raise ValueError("Data not initialized. Please call `prepare_data` or `prepare_annbatch_data` first.") if self.trainer is None: raise ValueError("Model not initialized. Please call `prepare_model` first.") - if out_of_core_dataloading: - self._dataloader = OOCTrainSampler(data=self.train_data, batch_size=batch_size) - else: - self._dataloader = TrainSampler(data=self.train_data, batch_size=batch_size) + if ( + self.train_data is not None + ): # in-memory path (the annbatch path set `_dataloader` in `prepare_annbatch_data`) + if out_of_core_dataloading: + self._dataloader = OOCTrainSampler(data=self.train_data, batch_size=batch_size) + else: + self._dataloader = TrainSampler(data=self.train_data, batch_size=batch_size) validation_loaders = {k: ValidationSampler(v) for k, v in self.validation_data.items() if k != "predict_kwargs"} self._trainer.predict_kwargs = self.validation_data.get("predict_kwargs", {}) diff --git a/src/dagloader/_loader.py b/src/dagloader/_loader.py index ad616831..95ce0aae 100644 --- a/src/dagloader/_loader.py +++ b/src/dagloader/_loader.py @@ -250,7 +250,7 @@ def __next__(self) -> dict[str, np.ndarray]: for b in self._children.get(self.s.root, []): # bound child source, conditioned via its schedule cnode = self._st[b.child]["node"] - creps = self._node_reps(b.child) + creps = self._nodes_next(b.child) out["source"] = creps[cnode.keys[0]] if len(cnode.keys) > 1: out["source_reps"] = creps diff --git a/tests/model/test_annbatch_path.py b/tests/model/test_annbatch_path.py index 2fc68f8f..4261a12c 100644 --- a/tests/model/test_annbatch_path.py +++ b/tests/model/test_annbatch_path.py @@ -50,10 +50,3 @@ def test_prepare_annbatch_rejects_adata_model(self, adata_perturbation: ad.AnnDa cf.prepare_annbatch_data( source=None, sample_rep="X", control_key="control", perturbation_covariates=PERT_COVARS ) - - def test_prepare_annbatch_guard_passes_then_not_implemented(self): - cf = cellflow.model.CellFlow() - with pytest.raises(NotImplementedError): - cf.prepare_annbatch_data( - source=None, sample_rep="X", control_key="control", perturbation_covariates=PERT_COVARS - ) diff --git a/tests/model/test_annbatch_split.py b/tests/model/test_annbatch_split.py index 15a08554..df0d5570 100644 --- a/tests/model/test_annbatch_split.py +++ b/tests/model/test_annbatch_split.py @@ -1,8 +1,7 @@ -"""Tests for :meth:`cellflow.model.CellFlow.split_annbatch_data`. +"""Tests for the annbatch path split wiring on :class:`~cellflow.model.CellFlow`. -``prepare_annbatch_data`` is not implemented yet (it will build the ``Scheme`` from a -``DatasetCollection``), so these inject a scheme built from a tiny in-memory ``AnnData`` directly onto -the model — exercising the split wiring without a collection or a loader. +The Scheme + condition + loaders are built from an in-memory ``AnnData`` used as the ``source`` (the +``dagloader`` is container-agnostic), so no ``DatasetCollection`` is needed. """ from __future__ import annotations @@ -11,23 +10,29 @@ import pandas as pd import pytest -pytest.importorskip("annbatch") # dagloader (and thus split_annbatch_data) needs annbatch +pytest.importorskip("annbatch") # dagloader (and thus the annbatch path) needs annbatch import anndata as ad import cellflow from dagloader import SamplerConfig, perturbation_scheme +_CFG = SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8) -def _toy_scheme(): - drugs = ["control", "d1", "d2", "d3", "d4", "d5"] - rows = [(cl, dr) for cl in ("A", "B") for dr in drugs for _ in range(2)] + +def _toy_adata(n_per_combo: int = 8, drugs=("control", "d1", "d2", "d3", "d4", "d5"), lines=("A", "B")): + rng = np.random.default_rng(0) + rows = [(cl, dr) for cl in lines for dr in drugs for _ in range(n_per_combo)] obs = pd.DataFrame(rows, columns=["cell_line", "drug"]) obs["control"] = obs["drug"] == "control" obs.index = obs.index.astype(str) - adata = ad.AnnData(X=np.zeros((len(obs), 3), dtype="float32"), obs=obs) + x = rng.normal(size=(len(obs), 5)).astype("float32") + return ad.AnnData(X=x, obs=obs) + + +def _toy_scheme(): return perturbation_scheme( - adata, + _toy_adata(n_per_combo=2), context=["cell_line"], perturbation=["drug"], control_values={"drug": "control"}, @@ -36,6 +41,8 @@ def _toy_scheme(): class TestSplitAnnbatchData: + """`split_annbatch_data` operates on an already-built Scheme (injected here).""" + def test_without_scheme_raises(self): cf = cellflow.model.CellFlow() with pytest.raises(ValueError, match="No annbatch `Scheme`"): @@ -43,135 +50,97 @@ def test_without_scheme_raises(self): def test_split_with_injected_scheme(self): cf = cellflow.model.CellFlow() - cf._scheme = _toy_scheme() # stand in for `prepare_annbatch_data` (still a TODO) - + cf._scheme = _toy_scheme() df = cf.split_annbatch_data(split_by=["drug"], random_state=0) - - assert isinstance(df, pd.DataFrame) assert list(df.columns) == ["cell_line", "drug", "split"] assert set(df["split"]) == {"train", "val", "test"} - assert cf._split_schemes is not None assert set(cf._split_schemes) == {"train", "val", "test"} - # controls carried through into every split (matched-control availability) - for sch in cf._split_schemes.values(): + for sch in cf._split_schemes.values(): # controls carried into every split assert sch.nodes["ctrl"].weights == cf._scheme.nodes["ctrl"].weights - def test_custom_ratios_and_force(self): - cf = cellflow.model.CellFlow() - cf._scheme = _toy_scheme() - df = cf.split_annbatch_data( - split_by=["drug"], - ratios={"train": 0.8, "test": 0.2}, - force_training_values={"drug": "d1"}, - random_state=3, - ) - assert set(df["split"]) == {"train", "test"} - assert df.loc[df["drug"] == "d1", "split"].eq("train").all() +class TestPrepareAnnbatchData: + """`prepare_annbatch_data` builds Scheme + condition + loaders from an AnnData source.""" + + def _prepare(self, cf, **kwargs): + return cf.prepare_annbatch_data( + source=_toy_adata(), + sample_rep="X", + control_key="control", + perturbation_covariates={"drug": ["drug"]}, + split_covariates=["cell_line"], + sampler_config=_CFG, + **kwargs, + ) -class TestPrepareAnnbatchSplitStep: - """The split step wired into `prepare_annbatch_data` (the Scheme-building part is still a TODO).""" + def test_rejects_adata_mode(self, adata_perturbation): + with pytest.warns(FutureWarning): + cf = cellflow.model.CellFlow(adata_perturbation) + with pytest.raises(ValueError, match="without `adata`"): + self._prepare(cf) - def test_without_scheme_raises_not_implemented(self): - # normal path: no pre-built scheme → the Scheme-building TODO raises + def test_requires_sampler_config(self): cf = cellflow.model.CellFlow() - with pytest.raises(NotImplementedError, match="not implemented yet"): + with pytest.raises(ValueError, match="sampler_config` is required"): cf.prepare_annbatch_data( - source=None, + source=_toy_adata(), sample_rep="X", control_key="control", perturbation_covariates={"drug": ["drug"]}, - split_by=["drug"], ) - def test_split_step_runs_when_scheme_present(self): - # inject a Scheme (stands in for the TODO Scheme-building) → the split step runs in-call - cf = cellflow.model.CellFlow() - cf._scheme = _toy_scheme() - result = cf.prepare_annbatch_data( - source=None, - sample_rep="X", - control_key="control", - perturbation_covariates={"drug": ["drug"]}, - split_covariates=["cell_line"], - split_by=["drug"], - split_ratios={"train": 0.6, "val": 0.2, "test": 0.2}, - split_random_state=0, - ) - assert result is None # prepare_* returns None, like prepare_data - assert cf._split_schemes is not None - assert set(cf._split_schemes) == {"train", "val", "test"} - assert isinstance(cf._split_assignment, pd.DataFrame) - assert set(cf._split_assignment["split"]) == {"train", "val", "test"} - - def test_no_split_when_split_by_omitted(self): + def test_builds_scheme_condition_and_loader_without_split(self): cf = cellflow.model.CellFlow() - cf._scheme = _toy_scheme() - cf.prepare_annbatch_data( - source=None, - sample_rep="X", - control_key="control", - perturbation_covariates={"drug": ["drug"]}, - ) + self._prepare(cf) + assert cf._scheme is not None and cf._scheme.root == "pert" + assert set(cf._annbatch_loaders) == {"train"} # no split → single "train" + assert cf._dataloader is not None # DAGLoaderTrainSampler wired for train() + # condition embeddings assembled (drug is categorical → one-hot), data dim from X + assert set(cf._condition_data) == {"drug"} + assert cf._data_dim == 5 assert cf._split_schemes is None - assert cf._split_assignment is None - - -class TestPrepareAnnbatchSamplerConfig: - """`sampler_config` resolves to one SamplerConfig per split (Scheme-building still a TODO).""" - def test_single_config_applies_to_all_splits(self): + def test_split_builds_per_split_loaders(self): cf = cellflow.model.CellFlow() - cf._scheme = _toy_scheme() - cfg = SamplerConfig(batch_size=128, chunk_size=1, preload_nchunks=128) - cf.prepare_annbatch_data( - source=None, - sample_rep="X", - control_key="control", - perturbation_covariates={"drug": ["drug"]}, - split_by=["drug"], - sampler_config=cfg, - ) + assert self._prepare(cf, split_by=["drug"], split_random_state=0) is None # prepare_* returns None + assert set(cf._split_assignment["split"]) == {"train", "val", "test"} + assert set(cf._annbatch_loaders) == {"train", "val", "test"} assert set(cf._annbatch_sampler_configs) == {"train", "val", "test"} - assert all(c is cfg for c in cf._annbatch_sampler_configs.values()) + assert cf._dataloader is not None - def test_per_split_config(self): + def test_per_split_sampler_config(self): cf = cellflow.model.CellFlow() - cf._scheme = _toy_scheme() - train = SamplerConfig(batch_size=256, chunk_size=1, preload_nchunks=256) - small = SamplerConfig(batch_size=64, chunk_size=1, preload_nchunks=64) + train = SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8) + small = SamplerConfig(batch_size=4, chunk_size=1, preload_nchunks=4) cf.prepare_annbatch_data( - source=None, + source=_toy_adata(), sample_rep="X", control_key="control", perturbation_covariates={"drug": ["drug"]}, + split_covariates=["cell_line"], split_by=["drug"], sampler_config={"train": train, "val": small, "test": small}, ) - assert cf._annbatch_sampler_configs["train"].batch_size == 256 - assert cf._annbatch_sampler_configs["val"].batch_size == 64 + assert cf._annbatch_sampler_configs["train"].batch_size == 8 + assert cf._annbatch_sampler_configs["val"].batch_size == 4 def test_per_split_missing_split_raises(self): cf = cellflow.model.CellFlow() - cf._scheme = _toy_scheme() with pytest.raises(ValueError, match="missing config"): cf.prepare_annbatch_data( - source=None, + source=_toy_adata(), sample_rep="X", control_key="control", perturbation_covariates={"drug": ["drug"]}, + split_covariates=["cell_line"], split_by=["drug"], - sampler_config={"train": SamplerConfig(batch_size=1, chunk_size=1, preload_nchunks=1)}, + sampler_config={"train": _CFG}, ) - def test_no_split_uses_single_train_config(self): + def test_streamed_batch_shapes(self): cf = cellflow.model.CellFlow() - cf._scheme = _toy_scheme() - cf.prepare_annbatch_data( - source=None, - sample_rep="X", - control_key="control", - perturbation_covariates={"drug": ["drug"]}, - sampler_config=SamplerConfig(batch_size=32, chunk_size=1, preload_nchunks=32), - ) - assert set(cf._annbatch_sampler_configs) == {"train"} # no split → single "train" scheme + self._prepare(cf) + batch = cf._dataloader.sample() + assert batch["src_cell_data"].shape == (8, 5) + assert batch["tgt_cell_data"].shape == (8, 5) + assert batch["condition"]["drug"].shape[0] == 1 # one condition per batch, leading axis diff --git a/tests/model/test_annbatch_train.py b/tests/model/test_annbatch_train.py new file mode 100644 index 00000000..ebd8c1bf --- /dev/null +++ b/tests/model/test_annbatch_train.py @@ -0,0 +1,72 @@ +"""End-to-end training over the annbatch/dagloader streaming path (in-memory AnnData as source).""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +pytest.importorskip("annbatch") + +import anndata as ad + +import cellflow +from dagloader import SamplerConfig + + +def _toy_adata(n_per_combo: int = 20, drugs=("control", "d1", "d2", "d3"), lines=("A", "B")): + rng = np.random.default_rng(0) + rows = [(cl, dr) for cl in lines for dr in drugs for _ in range(n_per_combo)] + obs = pd.DataFrame(rows, columns=["cell_line", "drug"]) + obs["control"] = obs["drug"] == "control" + obs.index = obs.index.astype(str) + x = rng.normal(size=(len(obs), 5)).astype("float32") + return ad.AnnData(X=x, obs=obs) + + +def _prepare_model_small(cf): + cf.prepare_model( + pooling="mean", + condition_embedding_dim=8, + time_encoder_dims=(8,), + hidden_dims=(8,), + decoder_dims=(8,), + ) + + +class TestAnnbatchTraining: + def test_train_runs_without_split(self): + cf = cellflow.model.CellFlow() + cf.prepare_annbatch_data( + source=_toy_adata(), + sample_rep="X", + control_key="control", + perturbation_covariates={"drug": ["drug"]}, + split_covariates=["cell_line"], + sampler_config=SamplerConfig(batch_size=16, chunk_size=1, preload_nchunks=16), + ) + _prepare_model_small(cf) + cf.train(num_iterations=2, valid_freq=100) + assert cf.solver is not None + assert cf.dataloader is not None + + def test_train_runs_on_train_split(self): + cf = cellflow.model.CellFlow() + cf.prepare_annbatch_data( + source=_toy_adata(), + sample_rep="X", + control_key="control", + perturbation_covariates={"drug": ["drug"]}, + split_covariates=["cell_line"], + sampler_config=SamplerConfig(batch_size=16, chunk_size=1, preload_nchunks=16), + split_by=["drug"], + split_ratios={"train": 0.5, "val": 0.25, "test": 0.25}, + split_random_state=0, + ) + _prepare_model_small(cf) + cf.train(num_iterations=2, valid_freq=100) + assert cf.solver is not None + # val/test split loaders are streamable too + assert set(cf._annbatch_loaders) == {"train", "val", "test"} + val_batch = next(iter(cf._annbatch_loaders["val"])) + assert "target" in val_batch and "source" in val_batch From 31bb7a4ba6db12e61dc57125d03404660898853d Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Jul 2026 14:49:23 +0200 Subject: [PATCH 09/35] annbatch path: out-of-core support + chunk_size>1 grouping rule - In-memory AnnData sources are grouped (stable-sorted by the grouping columns) in build_annbatch_training, so chunk_size>1 streams contiguous slices out of the box (cheap one-time reorder; cell order is irrelevant to sampling). - Out-of-core DatasetCollections are NOT reordered (a physical zarr re-sort is expensive): document the assumption and validate it from obs. When any split uses chunk_size>1, assert_source_grouped checks each category is one contiguous run >= chunk_size and raises a clear error pointing at add_adatas(groupby=...) otherwise. - Tests over real DatasetCollections: grouped (add_adatas groupby) trains with chunk_size=4; interleaved raises the clear error; chunk_size=1 works ungrouped; split+chunked; and an in-memory interleaved source is auto-grouped. Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/_annbatch.py | 44 ++++++++++++- src/cellflow/model/_cellflow.py | 16 +++++ tests/model/test_annbatch_ooc.py | 110 +++++++++++++++++++++++++++++++ 3 files changed, 169 insertions(+), 1 deletion(-) create mode 100644 tests/model/test_annbatch_ooc.py diff --git a/src/cellflow/data/_annbatch.py b/src/cellflow/data/_annbatch.py index d56ae928..ca1662c4 100644 --- a/src/cellflow/data/_annbatch.py +++ b/src/cellflow/data/_annbatch.py @@ -25,7 +25,40 @@ if TYPE_CHECKING: from dagloader import Scheme -__all__ = ["AnnbatchTraining", "build_annbatch_training", "sample_rep_to_key"] +__all__ = ["AnnbatchTraining", "assert_source_grouped", "build_annbatch_training", "sample_rep_to_key"] + + +def assert_source_grouped(source: Any, cols: Sequence[str], chunk_size: int) -> None: + """Assert ``source`` is grouped by ``cols`` — each category one contiguous run ≥ ``chunk_size``. + + ``chunk_size > 1`` makes annbatch read contiguous slices, which is only coherent when every + category (context+perturbation combination) sits in a single contiguous block of at least + ``chunk_size`` cells. Reads ``obs`` only. Raises a :class:`ValueError` pointing at + ``DatasetCollection.add_adatas(groupby=...)`` if the assumption is violated (in-memory sources are + grouped automatically by :func:`build_annbatch_training`, so this only bites out-of-core inputs). + """ + import numpy as np + + from dagloader._io import leaf_codes, obs_columns + + codes, _ = leaf_codes(obs_columns(source, list(cols)), list(cols)) + if len(codes) == 0: + return + run_starts = np.concatenate([[0], np.flatnonzero(np.diff(codes) != 0) + 1]) + run_codes = codes[run_starts] + if len(set(run_codes.tolist())) != len(run_codes): # a category spread over more than one run + raise ValueError( + f"chunk_size={chunk_size} requires the source grouped by {list(cols)} (each category one " + f"contiguous block), but categories are interleaved. Create the DatasetCollection with " + f"`add_adatas(..., groupby={list(cols)})`, or use chunk_size=1." + ) + run_lengths = np.diff(np.concatenate([run_starts, [len(codes)]])) + if int(run_lengths.min()) < chunk_size: + raise ValueError( + f"chunk_size={chunk_size} exceeds the smallest category's contiguous run " + f"({int(run_lengths.min())} cells); every category must have at least chunk_size cells. " + f"Reduce chunk_size." + ) def sample_rep_to_key(sample_rep: str) -> str: @@ -78,6 +111,15 @@ def build_annbatch_training( obs = obs_columns(source, [*cols, control_key]) + # In-memory source: group (stable-sort) by the grouping columns so `chunk_size > 1` streams + # contiguous slices. A cheap one-time row reorder; cell order is irrelevant to sampling. Out-of-core + # sources are NOT reordered (a physical zarr re-sort is expensive) — they must be created grouped via + # `DatasetCollection.add_adatas(groupby=...)`; `assert_source_grouped` checks that when chunk_size > 1. + if isinstance(source, ad.AnnData): + order = obs[list(cols)].reset_index(drop=True).sort_values(list(cols), kind="stable").index.to_numpy() + source = source[order].copy() + obs = obs_columns(source, [*cols, control_key]) + # DataManager as a pure covariate-encoder factory. It reads only obs + uns (never X), so a cell-free # shell suffices; `sample_rep="X"` sidesteps obsm verification (the real rep is streamed by the Scheme). shell = ad.AnnData(obs=obs.copy()) diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index 2356c793..97bd870a 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -281,6 +281,13 @@ def prepare_annbatch_data( :class:`dagloader.SamplerConfig` applied to every split, or a per-split mapping ``{split_name: SamplerConfig}`` that must cover **all** splits (see :func:`dagloader.resolve_split_configs`). When no split is made the only split is ``"train"``. + + ``chunk_size > 1`` reads contiguous on-disk slices and therefore **requires the source + grouped by the grouping columns** — each category (context+perturbation combination) a single + contiguous run of at least ``chunk_size`` cells. In-memory ``AnnData`` sources are grouped + automatically; an out-of-core :class:`~annbatch.DatasetCollection` must be created grouped + (``add_adatas(..., groupby=[...])``) or a clear error is raised. ``chunk_size = 1`` streams + per-row and needs no grouping. seed Reproducibility seed for the ``dagloader`` per-node RNG streams. split_by @@ -353,6 +360,15 @@ def prepare_annbatch_data( raise ValueError("`sampler_config` is required: give a SamplerConfig or a {split: SamplerConfig} mapping.") self._annbatch_sampler_configs = resolve_split_configs(sampler_config, list(schemes)) + # `chunk_size > 1` reads contiguous slices → the source must be grouped by the grouping columns + # (in-memory sources are auto-grouped above; out-of-core collections must be built grouped). + max_chunk = max(cfg.chunk_size for cfg in self._annbatch_sampler_configs.values()) + if max_chunk > 1: + from cellflow.data._annbatch import assert_source_grouped + + root = self._scheme.nodes[self._scheme.root] + assert_source_grouped(self._scheme.sources["data"], root.cols, max_chunk) + # One streaming DAGLoader per split; the "train" split feeds `train()`, others are kept for reuse. self._annbatch_loaders = { name: DAGLoader(schemes[name], self._annbatch_sampler_configs[name], condition_fn=condition_fn) diff --git a/tests/model/test_annbatch_ooc.py b/tests/model/test_annbatch_ooc.py new file mode 100644 index 00000000..6971db5f --- /dev/null +++ b/tests/model/test_annbatch_ooc.py @@ -0,0 +1,110 @@ +"""Out-of-core streaming over a real annbatch ``DatasetCollection``, incl. the ``chunk_size>1`` rule. + +``chunk_size>1`` reads contiguous slices, so the collection must be **grouped** by the grouping +columns. We build collections both ways: grouped via ``add_adatas(groupby=...)`` and interleaved +(no groupby), and check chunked streaming trains on the former and errors clearly on the latter. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +pytest.importorskip("annbatch") + +import anndata as ad +from annbatch import DatasetCollection + +import cellflow +from dagloader import SamplerConfig + +_PREP = { + "sample_rep": "X", + "control_key": "control", + "perturbation_covariates": {"drug": ["drug"]}, + "split_covariates": ["cell_line"], +} + + +def _adata(*, interleaved: bool, n_per_combo=40, drugs=("control", "d1", "d2", "d3"), lines=("A", "B")): + rng = np.random.default_rng(0) + rows = [(cl, dr) for cl in lines for dr in drugs for _ in range(n_per_combo)] + if interleaved: + rng.shuffle(rows) # ungrouped input + obs = pd.DataFrame(rows, columns=["cell_line", "drug"]) + obs["control"] = obs["drug"] == "control" + obs.index = obs.index.astype(str) + x = rng.normal(size=(len(obs), 5)).astype("float32") + return ad.AnnData(X=x, obs=obs) + + +def _collection(tmp_path, *, grouped: bool) -> DatasetCollection: + h5 = tmp_path / "a.h5ad" + _adata(interleaved=not grouped).write_h5ad(h5) + dc = DatasetCollection(str(tmp_path / "c.zarr"), mode="a") + if grouped: # sort by the grouping columns on add → contiguous category runs + dc.add_adatas([str(h5)], groupby=["cell_line", "drug"], shuffle=False) + else: # preserve the interleaved input order + dc.add_adatas([str(h5)], shuffle=False) + return dc + + +def _prepare_model_small(cf): + cf.prepare_model( + pooling="mean", condition_embedding_dim=8, time_encoder_dims=(8,), hidden_dims=(8,), decoder_dims=(8,) + ) + + +class TestOutOfCore: + def test_grouped_collection_chunked_trains(self, tmp_path): + cf = cellflow.model.CellFlow() + cf.prepare_annbatch_data( + source=_collection(tmp_path, grouped=True), + sampler_config=SamplerConfig(batch_size=16, chunk_size=4, preload_nchunks=16), + **_PREP, + ) + assert cf._data_dim == 5 + _prepare_model_small(cf) + cf.train(num_iterations=2, valid_freq=100) + assert cf.solver is not None + + def test_ungrouped_collection_chunked_raises(self, tmp_path): + cf = cellflow.model.CellFlow() + with pytest.raises(ValueError, match="grouped|groupby"): + cf.prepare_annbatch_data( + source=_collection(tmp_path, grouped=False), + sampler_config=SamplerConfig(batch_size=16, chunk_size=4, preload_nchunks=16), + **_PREP, + ) + + def test_ungrouped_collection_chunk1_ok(self, tmp_path): + # chunk_size=1 streams per-row → no grouping needed even for an interleaved collection + cf = cellflow.model.CellFlow() + cf.prepare_annbatch_data( + source=_collection(tmp_path, grouped=False), + sampler_config=SamplerConfig(batch_size=16, chunk_size=1, preload_nchunks=16), + **_PREP, + ) + assert cf._dataloader.sample()["tgt_cell_data"].shape == (16, 5) + + def test_grouped_collection_split_chunked(self, tmp_path): + cf = cellflow.model.CellFlow() + cf.prepare_annbatch_data( + source=_collection(tmp_path, grouped=True), + sampler_config=SamplerConfig(batch_size=16, chunk_size=4, preload_nchunks=16), + split_by=["drug"], + split_ratios={"train": 0.5, "val": 0.25, "test": 0.25}, + **_PREP, + ) + assert set(cf._annbatch_loaders) == {"train", "val", "test"} + + def test_in_memory_unsorted_source_is_auto_grouped(self): + # an in-memory (interleaved) AnnData source is grouped automatically → chunk_size>1 works + cf = cellflow.model.CellFlow() + cf.prepare_annbatch_data( + source=_adata(interleaved=True), + sampler_config=SamplerConfig(batch_size=16, chunk_size=4, preload_nchunks=16), + **_PREP, + ) + assert cf._dataloader.sample()["src_cell_data"].shape == (16, 5) From cc06cf2a6dc6103a142e45bfe193dc93ad9e62e1 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Jul 2026 15:00:20 +0200 Subject: [PATCH 10/35] Align chunk_size>1 layout check with annbatch's run-length rule MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The previous check required each category to be a single contiguous run, which is stricter than annbatch: its ClassSampler only requires every contiguous run of a (non-excluded) class to be >= chunk_size — a class MAY span multiple runs. So a "fragmented but valid" source (each category in several long runs) was wrongly rejected. Verified empirically: annbatch streams a 2-runs-of-10 layout at chunk_size=4, and raises only when a run is genuinely shorter than chunk_size. - Rename assert_source_grouped -> assert_source_chunkable; drop the one-run-per-class clause, keep only "every run >= chunk_size" (annbatch's actual rule), with a clear error pointing at add_adatas(groupby=...). Docs updated to match. - Tests: a fragmented DatasetCollection (>6 runs for 6 categories) now trains at chunk_size=4; unit tests assert the check accepts fragmented long runs, rejects a genuine short run, and is a no-op at chunk_size=1. Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/_annbatch.py | 38 ++++++++++----------- src/cellflow/model/_cellflow.py | 18 +++++----- tests/model/test_annbatch_ooc.py | 58 ++++++++++++++++++++++++++++++-- 3 files changed, 82 insertions(+), 32 deletions(-) diff --git a/src/cellflow/data/_annbatch.py b/src/cellflow/data/_annbatch.py index ca1662c4..ab554f42 100644 --- a/src/cellflow/data/_annbatch.py +++ b/src/cellflow/data/_annbatch.py @@ -25,17 +25,19 @@ if TYPE_CHECKING: from dagloader import Scheme -__all__ = ["AnnbatchTraining", "assert_source_grouped", "build_annbatch_training", "sample_rep_to_key"] +__all__ = ["AnnbatchTraining", "assert_source_chunkable", "build_annbatch_training", "sample_rep_to_key"] -def assert_source_grouped(source: Any, cols: Sequence[str], chunk_size: int) -> None: - """Assert ``source`` is grouped by ``cols`` — each category one contiguous run ≥ ``chunk_size``. +def assert_source_chunkable(source: Any, cols: Sequence[str], chunk_size: int) -> None: + """Assert ``source`` satisfies annbatch's run-length rule for ``chunk_size``. - ``chunk_size > 1`` makes annbatch read contiguous slices, which is only coherent when every - category (context+perturbation combination) sits in a single contiguous block of at least - ``chunk_size`` cells. Reads ``obs`` only. Raises a :class:`ValueError` pointing at - ``DatasetCollection.add_adatas(groupby=...)`` if the assumption is violated (in-memory sources are - grouped automatically by :func:`build_annbatch_training`, so this only bites out-of-core inputs). + ``chunk_size > 1`` makes annbatch read contiguous slices, so **every contiguous run** of every + category (a ``cols`` combination) must be at least ``chunk_size`` cells long — exactly annbatch's + ``ClassSampler`` rule. A category may span *several* runs (the source need not be fully sorted); only + each run's length matters. Reads ``obs`` only and raises a clear :class:`ValueError` — pointing at + ``DatasetCollection.add_adatas(groupby=...)`` — if any run is shorter. In-memory sources are grouped + automatically by :func:`build_annbatch_training`, so this only bites out-of-core inputs. (annbatch + enforces the same rule at loader construction; this is a friendlier, earlier check.) """ import numpy as np @@ -45,19 +47,15 @@ def assert_source_grouped(source: Any, cols: Sequence[str], chunk_size: int) -> if len(codes) == 0: return run_starts = np.concatenate([[0], np.flatnonzero(np.diff(codes) != 0) + 1]) - run_codes = codes[run_starts] - if len(set(run_codes.tolist())) != len(run_codes): # a category spread over more than one run - raise ValueError( - f"chunk_size={chunk_size} requires the source grouped by {list(cols)} (each category one " - f"contiguous block), but categories are interleaved. Create the DatasetCollection with " - f"`add_adatas(..., groupby={list(cols)})`, or use chunk_size=1." - ) run_lengths = np.diff(np.concatenate([run_starts, [len(codes)]])) - if int(run_lengths.min()) < chunk_size: + shortest = int(run_lengths.min()) + if shortest < chunk_size: raise ValueError( - f"chunk_size={chunk_size} exceeds the smallest category's contiguous run " - f"({int(run_lengths.min())} cells); every category must have at least chunk_size cells. " - f"Reduce chunk_size." + f"chunk_size={chunk_size} requires every contiguous run of each category (a {list(cols)} " + f"combination) to be at least chunk_size cells, but the source has a run of only {shortest}. " + f"Group the source so each category forms long runs — e.g. create the DatasetCollection with " + f"`add_adatas(..., groupby={list(cols)})` — or use chunk_size=1. (A category may span several " + f"runs; only each run's length matters.)" ) @@ -114,7 +112,7 @@ def build_annbatch_training( # In-memory source: group (stable-sort) by the grouping columns so `chunk_size > 1` streams # contiguous slices. A cheap one-time row reorder; cell order is irrelevant to sampling. Out-of-core # sources are NOT reordered (a physical zarr re-sort is expensive) — they must be created grouped via - # `DatasetCollection.add_adatas(groupby=...)`; `assert_source_grouped` checks that when chunk_size > 1. + # `DatasetCollection.add_adatas(groupby=...)`; `assert_source_chunkable` checks that when chunk_size > 1. if isinstance(source, ad.AnnData): order = obs[list(cols)].reset_index(drop=True).sort_values(list(cols), kind="stable").index.to_numpy() source = source[order].copy() diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index 97bd870a..b80a217c 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -282,12 +282,12 @@ def prepare_annbatch_data( ``{split_name: SamplerConfig}`` that must cover **all** splits (see :func:`dagloader.resolve_split_configs`). When no split is made the only split is ``"train"``. - ``chunk_size > 1`` reads contiguous on-disk slices and therefore **requires the source - grouped by the grouping columns** — each category (context+perturbation combination) a single - contiguous run of at least ``chunk_size`` cells. In-memory ``AnnData`` sources are grouped - automatically; an out-of-core :class:`~annbatch.DatasetCollection` must be created grouped - (``add_adatas(..., groupby=[...])``) or a clear error is raised. ``chunk_size = 1`` streams - per-row and needs no grouping. + ``chunk_size > 1`` reads contiguous on-disk slices and therefore requires **every contiguous + run of each category** (context+perturbation combination) to be at least ``chunk_size`` cells + (a category may span several runs; only each run's length matters — annbatch's run-length + rule). In-memory ``AnnData`` sources are grouped automatically; an out-of-core + :class:`~annbatch.DatasetCollection` must be created grouped (``add_adatas(..., groupby=[...])``) + or a clear error is raised. ``chunk_size = 1`` streams per-row and needs no grouping. seed Reproducibility seed for the ``dagloader`` per-node RNG streams. split_by @@ -360,14 +360,14 @@ def prepare_annbatch_data( raise ValueError("`sampler_config` is required: give a SamplerConfig or a {split: SamplerConfig} mapping.") self._annbatch_sampler_configs = resolve_split_configs(sampler_config, list(schemes)) - # `chunk_size > 1` reads contiguous slices → the source must be grouped by the grouping columns + # `chunk_size > 1` reads contiguous slices → every category's runs must be >= chunk_size # (in-memory sources are auto-grouped above; out-of-core collections must be built grouped). max_chunk = max(cfg.chunk_size for cfg in self._annbatch_sampler_configs.values()) if max_chunk > 1: - from cellflow.data._annbatch import assert_source_grouped + from cellflow.data._annbatch import assert_source_chunkable root = self._scheme.nodes[self._scheme.root] - assert_source_grouped(self._scheme.sources["data"], root.cols, max_chunk) + assert_source_chunkable(self._scheme.sources["data"], root.cols, max_chunk) # One streaming DAGLoader per split; the "train" split feeds `train()`, others are kept for reuse. self._annbatch_loaders = { diff --git a/tests/model/test_annbatch_ooc.py b/tests/model/test_annbatch_ooc.py index 6971db5f..4c4ee746 100644 --- a/tests/model/test_annbatch_ooc.py +++ b/tests/model/test_annbatch_ooc.py @@ -1,8 +1,9 @@ """Out-of-core streaming over a real annbatch ``DatasetCollection``, incl. the ``chunk_size>1`` rule. -``chunk_size>1`` reads contiguous slices, so the collection must be **grouped** by the grouping -columns. We build collections both ways: grouped via ``add_adatas(groupby=...)`` and interleaved -(no groupby), and check chunked streaming trains on the former and errors clearly on the latter. +``chunk_size>1`` reads contiguous slices, so every contiguous run of each category must be +``>= chunk_size`` (annbatch's run-length rule; a category may span several runs). We build collections +grouped via ``add_adatas(groupby=...)``, interleaved (short runs → error), and fragmented-but-valid +(multiple long runs per category → accepted), and check chunked streaming behaves accordingly. """ from __future__ import annotations @@ -17,6 +18,7 @@ from annbatch import DatasetCollection import cellflow +from cellflow.data._annbatch import assert_source_chunkable from dagloader import SamplerConfig _PREP = { @@ -108,3 +110,53 @@ def test_in_memory_unsorted_source_is_auto_grouped(self): **_PREP, ) assert cf._dataloader.sample()["src_cell_data"].shape == (16, 5) + + def test_fragmented_collection_chunked_ok(self, tmp_path): + # each category in TWO contiguous runs (fragmented) — valid as long as every run >= chunk_size. + # This is the case annbatch accepts but a "one run per class" check would wrongly reject. + block = 10 + rows = [(cl, dr) for _ in range(2) for cl in ("A", "B") for dr in ("control", "d1", "d2") for _ in range(block)] + obs = pd.DataFrame(rows, columns=["cell_line", "drug"]) + obs["control"] = obs["drug"] == "control" + obs.index = obs.index.astype(str) + x = np.random.default_rng(0).normal(size=(len(obs), 5)).astype("float32") + (tmp_path / "f.h5ad").parent.mkdir(exist_ok=True) + ad.AnnData(X=x, obs=obs).write_h5ad(tmp_path / "f.h5ad") + dc = DatasetCollection(str(tmp_path / "fc.zarr"), mode="a").add_adatas( + [str(tmp_path / "f.h5ad")], shuffle=False + ) + + from dagloader._io import leaf_codes, obs_columns + + codes, _ = leaf_codes(obs_columns(dc, ["cell_line", "drug"]), ["cell_line", "drug"]) + n_runs = 1 + int((np.diff(codes) != 0).sum()) + assert n_runs > 6, f"expected a fragmented collection (>6 runs for 6 categories), got {n_runs}" + + cf = cellflow.model.CellFlow() + cf.prepare_annbatch_data( # chunk_size=4 <= run length 10 → must be accepted + source=dc, sampler_config=SamplerConfig(batch_size=8, chunk_size=4, preload_nchunks=8), **_PREP + ) + assert cf._dataloader.sample()["tgt_cell_data"].shape == (8, 5) + + +class TestChunkableCheck: + """`assert_source_chunkable` matches annbatch's rule: runs >= chunk_size, fragmentation allowed.""" + + @staticmethod + def _frag(block): # each (cell_line, drug) category appears in 2 runs of `block` + rows = [(cl, dr) for _ in range(2) for cl in ("A", "B") for dr in ("d1", "d2") for _ in range(block)] + obs = pd.DataFrame(rows, columns=["cell_line", "drug"]) + obs.index = obs.index.astype(str) + return ad.AnnData(X=np.zeros((len(obs), 2), dtype="float32"), obs=obs) + + def test_accepts_fragmented_runs(self): + # fragmented but every run (10) >= chunk_size (4) → no raise (annbatch accepts this too) + assert_source_chunkable(self._frag(10), ("cell_line", "drug"), 4) + + def test_rejects_short_run(self): + # runs of 3 < chunk_size 4 → raise, with the groupby hint + with pytest.raises(ValueError, match="run of only 3|groupby|chunk_size"): + assert_source_chunkable(self._frag(3), ("cell_line", "drug"), 4) + + def test_chunk_size_one_always_ok(self): + assert_source_chunkable(self._frag(1), ("cell_line", "drug"), 1) # runs of 1 >= 1 From a9058361b2cb25a89721648faa6bb3b9da33a15e Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Jul 2026 15:04:53 +0200 Subject: [PATCH 11/35] Clear pre-existing lint debt in dagloader Ruff-clean the vendored dagloader package (all pre-existing, unrelated to the annbatch work): - _loader.py: drop the unused `densify` import (F401), sort imports (I001), `dict.fromkeys` for the shared-config map (C420), `zip(..., strict=True)` where the two sequences are equal-length by construction (B905), and split two summary/description docstrings (D205/D209). - _io.py: capitalize a docstring first word (D403). - __init__.py / _schema.py: raw-string docstrings that carry RST `\s` escapes (D301). No behavior change; dagloader imports and the full annbatch/dagloader test suite pass unchanged. Co-Authored-By: Claude Opus 4.8 --- src/dagloader/__init__.py | 2 +- src/dagloader/_io.py | 2 +- src/dagloader/_loader.py | 63 ++++++++++++++++++++++++++------------- src/dagloader/_schema.py | 4 +-- 4 files changed, 46 insertions(+), 25 deletions(-) diff --git a/src/dagloader/__init__.py b/src/dagloader/__init__.py index 4041a2fc..7cff59a8 100644 --- a/src/dagloader/__init__.py +++ b/src/dagloader/__init__.py @@ -1,4 +1,4 @@ -"""Declarative, index-free class-mapping sampler over annbatch. +r"""Declarative, index-free class-mapping sampler over annbatch. A :class:`Scheme` is a rooted tree of :class:`Node`\\s over named cell sources; each node partitions its source's cells into leaves (unique column-combinations) with a per-combination weight mapping. diff --git a/src/dagloader/_io.py b/src/dagloader/_io.py index fa7b4bbb..4c3fe899 100644 --- a/src/dagloader/_io.py +++ b/src/dagloader/_io.py @@ -48,7 +48,7 @@ def leaf_codes(obs: pd.DataFrame, cols: Sequence[str]) -> tuple[np.ndarray, list def obs_columns(source: Container, cols: Sequence[str]) -> pd.DataFrame: - """obs columns from either container (AnnData attr vs DatasetCollection reader) — no cell matrices.""" + """Obs columns from either container (AnnData attr vs DatasetCollection reader) — no cell matrices.""" if isinstance(source, ad.AnnData): return source.obs[list(cols)] return source.obs(columns=list(cols)) # DatasetCollection diff --git a/src/dagloader/_loader.py b/src/dagloader/_loader.py index 95ce0aae..f55b10ed 100644 --- a/src/dagloader/_loader.py +++ b/src/dagloader/_loader.py @@ -15,9 +15,9 @@ import pandas as pd from annbatch import Loader -from dagloader._io import densify, key_backings, leaf_codes, obs_columns -from dagloader._schema import Bind, SamplerConfig, Scheme, _weight_vector +from dagloader._io import key_backings, leaf_codes, obs_columns from dagloader._scheduled_sampler import ScheduledClassSampler +from dagloader._schema import Bind, SamplerConfig, Scheme, _weight_vector __all__ = ["DAGLoader"] @@ -45,7 +45,9 @@ def __init__( # that node's per-key samplers — identical samplers → the SAME rows, so a node's reps are aligned. self._rngs: dict[str, np.random.Generator] = {} self._row_seqs: dict[str, np.random.SeedSequence] = {} - for name, seq in zip(sorted(scheme.nodes), np.random.SeedSequence(scheme.seed).spawn(len(scheme.nodes))): + for name, seq in zip( + sorted(scheme.nodes), np.random.SeedSequence(scheme.seed).spawn(len(scheme.nodes)), strict=True + ): decision_seq, row_seq = seq.spawn(2) self._rngs[name] = np.random.default_rng(decision_seq) self._row_seqs[name] = row_seq @@ -71,10 +73,13 @@ def __init__( self._pos = 0 def _resolve_configs(self, cfg: SamplerConfig | Mapping[str, SamplerConfig]) -> dict[str, SamplerConfig]: - """Normalize to one config per node. Nodes may use different batch sizes (each draws - ``_n_batches`` batches of its own size; source and target row counts need not match).""" + """Normalize to one config per node. + + Nodes may use different batch sizes (each draws ``_n_batches`` batches of its own size; source + and target row counts need not match). + """ if isinstance(cfg, SamplerConfig): - resolved = {name: cfg for name in self.s.nodes} + resolved = dict.fromkeys(self.s.nodes, cfg) else: missing = set(self.s.nodes) - set(cfg) if missing: @@ -84,10 +89,12 @@ def _resolve_configs(self, cfg: SamplerConfig | Mapping[str, SamplerConfig]) -> # ── build ──────────────────────────────────────────────────────────── def _build_loaders(self) -> None: - """Per ``(node, key)``: a ScheduledClassSampler + Loader. All of a node's keys are seeded with - the SAME row-draw seed, so their (otherwise identical) samplers select the same rows every batch - — every rep of a node is the same cells. Reps need separate Loaders regardless (annbatch can't - mix feature dims in one loader), and each rep gets native chunked reads. + """Per ``(node, key)``: a ScheduledClassSampler + Loader. + + All of a node's keys are seeded with the SAME row-draw seed, so their (otherwise identical) + samplers select the same rows every batch — every rep of a node is the same cells. Reps need + separate Loaders regardless (annbatch can't mix feature dims in one loader), and each rep gets + native chunked reads. """ self._samplers: dict[str, dict[str, ScheduledClassSampler]] = {} self._loaders: dict[str, dict[str, Loader]] = {} @@ -104,15 +111,21 @@ def _build_loaders(self) -> None: for ki, key in enumerate(node.keys): try: # annbatch enforces its own run-length rule for chunk>1; forward with node context sampler = ScheduledClassSampler( - chunk_size=cfg.chunk_size, preload_nchunks=cfg.preload_nchunks, batch_size=cfg.batch_size, - classes=classes, num_samples=num_samples, class_weights=st["w"], - drop_last=True, rng=np.random.default_rng(self._row_seqs[name]), # identical across keys + chunk_size=cfg.chunk_size, + preload_nchunks=cfg.preload_nchunks, + batch_size=cfg.batch_size, + classes=classes, + num_samples=num_samples, + class_weights=st["w"], + drop_last=True, + rng=np.random.default_rng(self._row_seqs[name]), # identical across keys ) except ValueError as e: raise ValueError(f"node {name!r}: {e}") from e return_index = name == self.s.root and ki == 0 # only for the schedule↔row alignment check - loader = Loader(batch_sampler=sampler, return_index=return_index, to_torch=False, - preload_to_gpu=False).add_datasets(key_backings(src, key)) + loader = Loader( + batch_sampler=sampler, return_index=return_index, to_torch=False, preload_to_gpu=False + ).add_datasets(key_backings(src, key)) self._samplers[name][key] = sampler self._loaders[name][key] = loader @@ -134,14 +147,20 @@ def _build_bind_maps(self) -> None: for pk, ck in b.matched.items(): pk, ck = tuple(pk), tuple(ck) if pk not in root_idx: - raise ValueError(f"bind {b.parent!r}→{b.child!r}: matched key {pk!r} is not a {b.parent!r} leaf.") + raise ValueError( + f"bind {b.parent!r}→{b.child!r}: matched key {pk!r} is not a {b.parent!r} leaf." + ) if ck not in child_idx or cst["w"][child_idx[ck]] <= 0: raise ValueError( f"bind {b.parent!r}→{b.child!r}: matched value {ck!r} is not a positive-weight {b.child!r} leaf." ) code_to_child[root_idx[pk]] = child_idx[ck] - self._bindmap[b.child] = {"bind": b, "mode": "matched", "root_leaves": rleaves, - "code_to_child": code_to_child} + self._bindmap[b.child] = { + "bind": b, + "mode": "matched", + "root_leaves": rleaves, + "code_to_child": code_to_child, + } continue rcols, ccols = rst["node"].cols, cst["node"].cols # parent leaf code → shared-column value (empty tuple when common=() ⇒ unconditional bind) @@ -152,7 +171,10 @@ def _build_bind_maps(self) -> None: if cst["w"][code] > 0: grouped.setdefault(tuple(lf[ccols.index(c)] for c in b.common), []).append(code) self._bindmap[b.child] = { - "bind": b, "mode": "common", "root_leaves": rleaves, "child_w": cst["w"], + "bind": b, + "mode": "common", + "root_leaves": rleaves, + "child_w": cst["w"], "root_code_to_cv": root_code_to_cv, "common_to_child": {cv: np.asarray(codes, dtype=np.int64) for cv, codes in grouped.items()}, } @@ -216,8 +238,7 @@ def _start_pass(self) -> None: self._schedules[b.child] = child_sched for smp in self._samplers[b.child].values(): smp.set_schedule(child_sched) - self._iters = {name: {key: iter(ld) for key, ld in loaders.items()} - for name, loaders in self._loaders.items()} + self._iters = {name: {key: iter(ld) for key, ld in loaders.items()} for name, loaders in self._loaders.items()} self._pos = 0 # ── iteration ────────────────────────────────────────────────────────── diff --git a/src/dagloader/_schema.py b/src/dagloader/_schema.py index f2297a44..485d1a9a 100644 --- a/src/dagloader/_schema.py +++ b/src/dagloader/_schema.py @@ -1,4 +1,4 @@ -"""Declarative schema for :class:`~dagloader.DAGLoader`. +r"""Declarative schema for :class:`~dagloader.DAGLoader`. A :class:`Scheme` is a rooted tree of :class:`Node`\\s over named cell *sources* — pure structure (sources, grouping columns, weights, binds). How each node is *read* (chunk / preload / batch sizes) @@ -139,7 +139,7 @@ def __post_init__(self) -> None: @dataclass(frozen=True, kw_only=True) class SamplerConfig: - """annbatch read parameters for a node's sampler — kept separate from the structural :class:`Node`. + r"""annbatch read parameters for a node's sampler — kept separate from the structural :class:`Node`. Passed to :class:`~dagloader.DAGLoader` as either one config (applied to every node) or a ``{node_name: SamplerConfig}`` mapping (per-node). Nodes may use **different** ``batch_size``\\s: From 4c4fe237088e3f5b2e42a02740123db2994e8a6c Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Jul 2026 15:10:07 +0200 Subject: [PATCH 12/35] Move densification from dagloader to the model boundary MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit dagloader now streams whatever the source stores (sparse or dense) and stays representation-agnostic — the unused `densify` helper is removed from `dagloader/_io.py`. Densification happens where the solver needs it: the `DAGLoaderTrainSampler` adapter materializes a sparse cell batch to dense (dense batches pass through untouched), so the in-memory and streaming paths reach the solver identically. Test: a sparse (csr) in-memory source streams dense `src`/`tgt` batches. Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/_dataloader.py | 16 +++++++++++++--- src/dagloader/_io.py | 7 +------ tests/model/test_annbatch_split.py | 19 +++++++++++++++++++ 3 files changed, 33 insertions(+), 9 deletions(-) diff --git a/src/cellflow/data/_dataloader.py b/src/cellflow/data/_dataloader.py index 75827603..afb5d024 100644 --- a/src/cellflow/data/_dataloader.py +++ b/src/cellflow/data/_dataloader.py @@ -18,13 +18,23 @@ ] +def _densify(x: Any) -> np.ndarray: + """Materialize a possibly-sparse streamed cell batch to dense (annbatch yields sparse for sparse X). + + Densification is done here, at the model boundary, rather than in the loader: ``dagloader`` streams + whatever the source stores (sparse or dense) and stays representation-agnostic; the solver needs + dense arrays. Dense batches pass through untouched. + """ + return np.asarray(x.todense()) if hasattr(x, "todense") else x + + class DAGLoaderTrainSampler: """Adapt a ``dagloader.DAGLoader`` stream to the :meth:`TrainSampler.sample` batch contract. ``DAGLoader`` yields ``{"target", "source", "condition"}`` (one perturbation per batch, the condition already carrying the leading ``(1, ...)`` axis); the trainer/solver consumes - ``{"src_cell_data", "tgt_cell_data", "condition"}``. This is a key-rename over the stream — no cells - are copied — so the in-memory and streaming paths hit the solver through the same interface. + ``{"src_cell_data", "tgt_cell_data", "condition"}``. This renames the stream's keys and densifies the + cell batches (:func:`_densify`) so the in-memory and streaming paths hit the solver identically. """ def __init__(self, loader: Any): @@ -37,7 +47,7 @@ def sample(self, rng: Any = None) -> dict[str, Any]: ``rng`` is unused — the ``DAGLoader`` owns its own reproducible RNG. """ batch = next(self._iter) - out = {"src_cell_data": batch["source"], "tgt_cell_data": batch["target"]} + out = {"src_cell_data": _densify(batch["source"]), "tgt_cell_data": _densify(batch["target"])} if "condition" in batch: out["condition"] = batch["condition"] return out diff --git a/src/dagloader/_io.py b/src/dagloader/_io.py index 4c3fe899..c35f19af 100644 --- a/src/dagloader/_io.py +++ b/src/dagloader/_io.py @@ -16,12 +16,7 @@ from dagloader._schema import Container -__all__ = ["densify", "key_backings", "leaf_codes", "obs_columns"] - - -def densify(x) -> np.ndarray: - """A streamed batch → a dense float32 array (annbatch may yield sparse for a sparse source).""" - return np.asarray(x.todense() if hasattr(x, "todense") else x, dtype=np.float32) +__all__ = ["key_backings", "leaf_codes", "obs_columns"] def key_backings(source: Container, loc: str) -> list: diff --git a/tests/model/test_annbatch_split.py b/tests/model/test_annbatch_split.py index df0d5570..9c89f65a 100644 --- a/tests/model/test_annbatch_split.py +++ b/tests/model/test_annbatch_split.py @@ -144,3 +144,22 @@ def test_streamed_batch_shapes(self): assert batch["src_cell_data"].shape == (8, 5) assert batch["tgt_cell_data"].shape == (8, 5) assert batch["condition"]["drug"].shape[0] == 1 # one condition per batch, leading axis + + def test_sparse_source_batches_densified(self): + # dagloader streams sparse for a sparse source; the model-boundary adapter densifies. + import scipy.sparse as sp + + adata = _toy_adata() + adata.X = sp.csr_matrix(adata.X) + cf = cellflow.model.CellFlow() + cf.prepare_annbatch_data( + source=adata, + sample_rep="X", + control_key="control", + perturbation_covariates={"drug": ["drug"]}, + split_covariates=["cell_line"], + sampler_config=_CFG, + ) + batch = cf._dataloader.sample() + assert not sp.issparse(batch["src_cell_data"]) and not sp.issparse(batch["tgt_cell_data"]) + assert batch["src_cell_data"].shape == (8, 5) and batch["tgt_cell_data"].shape == (8, 5) From 2e88f20b179e1eed28c97089e21673ff2a151bc1 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Jul 2026 15:26:23 +0200 Subject: [PATCH 13/35] Support prepare_validation_data in the annbatch path (real sample_rep) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Validation is inherently in-memory (metrics need materialized cells), so it takes a held-out AnnData in the streaming path too. Two fixes make that correct: - build_annbatch_training now gives the encoder-factory DataManager the REAL `sample_rep` instead of a hardcoded "X". `_verify_sample_rep` is type-only (it does not check obsm existence) and cells are only read later against the actual validation adata, so the real value is safe on the cell-free shell. Previously validation would have read `adata.X` regardless of `sample_rep` — wrong for obsm reps. - prepare_validation_data's guard no longer requires the in-memory `train_data`; it accepts a model prepared via prepare_data OR prepare_annbatch_data. Docstring notes the streaming-path requirements (adata carries `sample_rep` + covariate reps in .uns) and that this is unrelated to the `val` split loader. Tests: validation prepares in the annbatch path; reads obsm `sample_rep` (dim 3) not X (dim 5); training runs with a validation pass; and still errors before any setup. Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/_annbatch.py | 7 ++- src/cellflow/model/_cellflow.py | 13 ++++- tests/model/test_annbatch_validation.py | 78 +++++++++++++++++++++++++ 3 files changed, 93 insertions(+), 5 deletions(-) create mode 100644 tests/model/test_annbatch_validation.py diff --git a/src/cellflow/data/_annbatch.py b/src/cellflow/data/_annbatch.py index ab554f42..b000ceb9 100644 --- a/src/cellflow/data/_annbatch.py +++ b/src/cellflow/data/_annbatch.py @@ -118,13 +118,14 @@ def build_annbatch_training( source = source[order].copy() obs = obs_columns(source, [*cols, control_key]) - # DataManager as a pure covariate-encoder factory. It reads only obs + uns (never X), so a cell-free - # shell suffices; `sample_rep="X"` sidesteps obsm verification (the real rep is streamed by the Scheme). + # DataManager as a covariate-encoder factory: it reads only obs + uns (never the cell matrix), so a + # cell-free shell suffices. `sample_rep` is stored (and only read later by validation's `_get_cell_data` + # against the actual validation adata) — verification is type-only, so the real value is safe here. shell = ad.AnnData(obs=obs.copy()) shell.uns = dict(rep_dict or {}) dm = DataManager( shell, - sample_rep="X", + sample_rep=sample_rep, control_key=control_key, perturbation_covariates=dict(perturbation_covariates), perturbation_covariate_reps=dict(perturbation_covariate_reps) if perturbation_covariate_reps else None, diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index b80a217c..52598607 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -441,6 +441,14 @@ def prepare_validation_data( ) -> None: """Prepare the validation data. + Validation is always in-memory (computing metrics needs materialized cells): you pass a held-out + :class:`~anndata.AnnData`, and its cells (at ``sample_rep``) plus condition embeddings are read + into a ``ValidationData``. This works for **both** the in-memory (:meth:`prepare_data`) and the + streaming (:meth:`prepare_annbatch_data`) training paths. In the streaming path the ``adata`` must + carry the same ``sample_rep`` representation and the covariate embeddings in ``.uns`` that the run + was prepared with. (This is unrelated to the ``val`` split from :meth:`split_annbatch_data`, which + is a streaming loader, not a validation set.) + Parameters ---------- adata @@ -466,9 +474,10 @@ def prepare_validation_data( - :attr:`cellflow.model.CellFlow.validation_data` - a dictionary with the validation data. """ - if self.train_data is None: + if self.train_data is None and self._scheme is None: raise ValueError( - "Dataloader not initialized. Training data needs to be set up before preparing validation data. Please call prepare_data first." + "Model data not initialized. Set up training first via `prepare_data` (in-memory) or " + "`prepare_annbatch_data` (streaming) before preparing validation data." ) val_data = self._dm.get_validation_data( adata, diff --git a/tests/model/test_annbatch_validation.py b/tests/model/test_annbatch_validation.py new file mode 100644 index 00000000..078ab2ee --- /dev/null +++ b/tests/model/test_annbatch_validation.py @@ -0,0 +1,78 @@ +"""Validation in the annbatch path: a held-out in-memory AnnData, read at the real ``sample_rep``. + +Validation is inherently in-memory (metrics need materialized cells), so `prepare_validation_data` +takes an AnnData in the streaming path too. The regression that matters: cells must be read at the +actual ``sample_rep`` (e.g. an obsm key), not the internal ``"X"`` placeholder the encoder factory used. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +pytest.importorskip("annbatch") + +import anndata as ad + +import cellflow +from dagloader import SamplerConfig + +_CFG = SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8) + + +def _adata(*, rep_dim=None, x_dim=5, n_per_combo=8, drugs=("control", "d1", "d2", "d3"), lines=("A", "B"), seed=0): + rng = np.random.default_rng(seed) + rows = [(cl, dr) for cl in lines for dr in drugs for _ in range(n_per_combo)] + obs = pd.DataFrame(rows, columns=["cell_line", "drug"]) + obs["control"] = obs["drug"] == "control" + obs.index = obs.index.astype(str) + adata = ad.AnnData(X=rng.normal(size=(len(obs), x_dim)).astype("float32"), obs=obs) + if rep_dim is not None: + adata.obsm["X_pca"] = rng.normal(size=(len(obs), rep_dim)).astype("float32") + return adata + + +def _prepare(cf, source, *, sample_rep="X"): + cf.prepare_annbatch_data( + source=source, + sample_rep=sample_rep, + control_key="control", + perturbation_covariates={"drug": ["drug"]}, + split_covariates=["cell_line"], + sampler_config=_CFG, + ) + + +class TestAnnbatchValidation: + def test_prepare_validation_without_setup_raises(self): + cf = cellflow.model.CellFlow() + with pytest.raises(ValueError, match="prepare_data.*prepare_annbatch_data|not initialized"): + cf.prepare_validation_data(adata=_adata(), name="val") + + def test_validation_prepares_in_annbatch_path(self): + cf = cellflow.model.CellFlow() + _prepare(cf, _adata()) # sample_rep="X" + cf.prepare_validation_data(adata=_adata(seed=1), name="val") + vd = cf.validation_data["val"] + assert vd is not None + assert vd.cell_data.shape[1] == 5 # X dim + + def test_validation_reads_obsm_sample_rep_not_x(self): + # source: obsm X_pca (dim 3) + X (dim 5); training streams X_pca → model dim 3 + cf = cellflow.model.CellFlow() + _prepare(cf, _adata(rep_dim=3), sample_rep="X_pca") + assert cf._data_dim == 3 + # validation must read X_pca (3), NOT X (5) — this is the fix under test + cf.prepare_validation_data(adata=_adata(rep_dim=3, seed=2), name="val") + assert cf.validation_data["val"].cell_data.shape[1] == 3 + + def test_train_with_validation_runs(self): + cf = cellflow.model.CellFlow() + _prepare(cf, _adata()) + cf.prepare_validation_data(adata=_adata(seed=3), name="val") + cf.prepare_model( + pooling="mean", condition_embedding_dim=8, time_encoder_dims=(8,), hidden_dims=(8,), decoder_dims=(8,) + ) + cf.train(num_iterations=2, valid_freq=2) # triggers a validation pass + assert cf.solver is not None From 83d622f93c9ef70c7bb8a5f3d03f6a6b9949bd72 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Jul 2026 17:03:20 +0200 Subject: [PATCH 14/35] Make annbatch models picklable (save/load keeps RNG + stream state) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `save` cloudpickles the whole model, which for the annbatch path includes the streaming loaders. Two things blocked that once iteration had started (i.e. after training — exactly when you save): - DAGLoader held live annbatch iterators (generators aren't picklable). Add DAGLoader.__getstate__/__setstate__ that drop `_iters` while keeping the per-node RNG streams, samplers, drawn schedules and configs — so a reloaded loader resumes the SAME reproducible sequence (a fresh pass drawn from the restored RNG state). - A suspended pass leaves the sampler's `_rng` as the transient `_ScheduleRng` wrapper, whose `__getattr__` recursed during unpickling. Give `_ScheduleRng` a `__reduce__` that pickles it AS the underlying real Generator (it is rebuilt each pass anyway) and guard `__getattr__` against delegating dunders. Also fixes `get_condition_embedding` in the annbatch path: it stored the embedding under `self.adata.uns`, but there is no `adata` in the streaming path (was an AttributeError by default). Now it warns and returns the embeddings when `adata` is absent. Tests: DAGLoader pickles mid-stream and two restores resume identically; CellFlow save/load after prepare, mid-stream (deterministic resume, state preserved), and after training; get_condition_embedding warns instead of crashing without adata. Co-Authored-By: Claude Opus 4.8 --- src/cellflow/model/_cellflow.py | 11 +++- src/dagloader/_loader.py | 17 ++++++ src/dagloader/_scheduled_sampler.py | 7 +++ tests/dagloader/test_pickle.py | 51 ++++++++++++++++ tests/model/test_annbatch_save.py | 93 +++++++++++++++++++++++++++++ 5 files changed, 177 insertions(+), 2 deletions(-) create mode 100644 tests/dagloader/test_pickle.py create mode 100644 tests/model/test_annbatch_save.py diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index 52598607..32ba9b5b 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -1005,8 +1005,15 @@ def get_condition_embedding( df_var.index.set_names(list(self._dm.perturb_covar_keys), inplace=True) if key_added is not None: - _utils.set_plotting_vars(self.adata, key=key_added, value=df_mean) - _utils.set_plotting_vars(self.adata, key=f"{key_added}_var", value=df_var) + if self.adata is None: # streaming/annbatch path: no `adata` to store into + warnings.warn( + "No `adata` is attached (streaming path); returning the condition embeddings without " + "storing them under `adata.uns`. Pass `key_added=None` to silence this.", + stacklevel=2, + ) + else: # mean under `key_added`, variance under `f"{key_added}_var"` (distinct keys, #295) + _utils.set_plotting_vars(self.adata, key=key_added, value=df_mean) + _utils.set_plotting_vars(self.adata, key=f"{key_added}_var", value=df_var) return df_mean, df_var diff --git a/src/dagloader/_loader.py b/src/dagloader/_loader.py index f55b10ed..7d581eef 100644 --- a/src/dagloader/_loader.py +++ b/src/dagloader/_loader.py @@ -241,6 +241,23 @@ def _start_pass(self) -> None: self._iters = {name: {key: iter(ld) for key, ld in loaders.items()} for name, loaders in self._loaders.items()} self._pos = 0 + # ── pickling ───────────────────────────────────────────────────────────── + def __getstate__(self) -> dict: + """Pickle without the live annbatch iterators (generators aren't picklable). + + Everything that defines the stream is kept — the per-node RNG streams (``_rngs`` / ``_row_seqs``), + the samplers (with their advanced RNG), the drawn ``_schedules`` and the configs — so a reloaded + loader continues the *same* reproducible RNG sequence. Only the transient ``_iters`` are dropped; + the next ``__next__`` rebuilds them (a fresh pass) from the restored RNG state. + """ + state = self.__dict__.copy() + state["_iters"] = None + return state + + def __setstate__(self, state: dict) -> None: + self.__dict__.update(state) + self._iters = None # force `_start_pass` on the next `__next__`, using the restored RNG state + # ── iteration ────────────────────────────────────────────────────────── def __iter__(self) -> DAGLoader: return self diff --git a/src/dagloader/_scheduled_sampler.py b/src/dagloader/_scheduled_sampler.py index 34347b9e..ccb52327 100644 --- a/src/dagloader/_scheduled_sampler.py +++ b/src/dagloader/_scheduled_sampler.py @@ -51,8 +51,15 @@ def choice(self, a, size=None, replace=True, p=None): return self._rng.choice(a, size=size, replace=replace, p=p) def __getattr__(self, name): # integers / shuffle / permutation / ... → real generator + if name.startswith("__") and name.endswith("__"): + raise AttributeError(name) # don't delegate dunders — avoids recursion during (un)pickling / copy return getattr(self._rng, name) + def __reduce__(self): + # A transient per-pass wrapper: pickle it AS the underlying real Generator so a checkpointed + # DAGLoader restores a clean, unwrapped RNG (the wrapper is rebuilt each pass in `_sample`). + return self._rng.__reduce__() + class ScheduledClassSampler(ClassSampler): """``ClassSampler`` whose per-batch category sequence can be supplied via :meth:`set_schedule`. diff --git a/tests/dagloader/test_pickle.py b/tests/dagloader/test_pickle.py new file mode 100644 index 00000000..5e680d5d --- /dev/null +++ b/tests/dagloader/test_pickle.py @@ -0,0 +1,51 @@ +"""DAGLoader is picklable mid-stream: the live annbatch iterators are dropped, RNG/state is kept.""" + +from __future__ import annotations + +import pickle + +import numpy as np +import pandas as pd +import pytest + +pytest.importorskip("annbatch") + +import anndata as ad + +from dagloader import Bind, DAGLoader, Node, SamplerConfig, Scheme, uniform + + +def _loader(seed=0): + rows = [(cl, dr) for cl in ("A", "B") for dr in ("control", "d1", "d2") for _ in range(16)] + obs = pd.DataFrame(rows, columns=["cell_line", "drug"]) + obs.index = obs.index.astype(str) + obs["control"] = obs["drug"] == "control" + adata = ad.AnnData(X=np.random.default_rng(0).normal(size=(len(obs), 4)).astype("float32"), obs=obs) + cols = ("cell_line", "drug") + combos = {tuple(r) for r in obs[list(cols)].to_numpy()} + pert = [c for c in combos if c[1] != "control"] + ctrl = [c for c in combos if c[1] == "control"] + scheme = Scheme( + sources={"data": adata}, + nodes={"pert": Node("data", cols, "X", uniform(pert)), "ctrl": Node("data", cols, "X", uniform(ctrl))}, + root="pert", + binds=(Bind("pert", "ctrl", common=("cell_line",)),), + seed=seed, + ) + # condition_fn omitted so the loader is plain-picklable (no closure); state/RNG is what we test here + return DAGLoader(scheme, SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8)) + + +def test_pickle_mid_stream_and_resume_deterministic(): + loader = _loader() + it = iter(loader) + for _ in range(2): # advance into a pass → live annbatch generators + a wrapped sampler RNG exist + next(it) + blob = pickle.dumps(loader) # would raise "cannot pickle 'generator'" without __getstate__/__reduce__ + + # two loaders restored from the same checkpoint must produce identical streams + la, lb = pickle.loads(blob), pickle.loads(blob) + a = [next(la)["target"] for _ in range(4)] + b = [next(lb)["target"] for _ in range(4)] + assert all(np.array_equal(x, y) for x, y in zip(a, b, strict=True)) + assert a[0].shape == (8, 4) diff --git a/tests/model/test_annbatch_save.py b/tests/model/test_annbatch_save.py new file mode 100644 index 00000000..75c95647 --- /dev/null +++ b/tests/model/test_annbatch_save.py @@ -0,0 +1,93 @@ +"""save/load and get_condition_embedding for annbatch-path models. + +`save` pickles the whole model incl. the streaming loaders. The loaders drop their live annbatch +iterators on pickle (generators aren't picklable) but keep the RNG/schedule state, so a reloaded model +resumes the same reproducible stream. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +pytest.importorskip("annbatch") + +import anndata as ad + +import cellflow +from dagloader import SamplerConfig + +_PREP = { + "sample_rep": "X", + "control_key": "control", + "perturbation_covariates": {"drug": ["drug"]}, + "split_covariates": ["cell_line"], +} +_CFG = SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8) + + +def _adata(): + rng = np.random.default_rng(0) + rows = [(cl, dr) for cl in ("A", "B") for dr in ("control", "d1", "d2") for _ in range(16)] + obs = pd.DataFrame(rows, columns=["cell_line", "drug"]) + obs["control"] = obs["drug"] == "control" + obs.index = obs.index.astype(str) + return ad.AnnData(X=rng.normal(size=(len(obs), 5)).astype("float32"), obs=obs) + + +def _prepared(seed=7): + cf = cellflow.model.CellFlow() + cf.prepare_annbatch_data(source=_adata(), sampler_config=_CFG, seed=seed, **_PREP) + return cf + + +def _stream(cf, n): + return [cf._dataloader.sample()["tgt_cell_data"].copy() for _ in range(n)] + + +class TestAnnbatchSave: + def test_save_load_after_prepare(self, tmp_path): + cf = _prepared() + cf.save(str(tmp_path), file_prefix="p", overwrite=True) + loaded = cellflow.model.CellFlow.load(str(tmp_path / "p_CellFlow.pkl")) + assert loaded._scheme is not None + assert loaded._dataloader.sample()["tgt_cell_data"].shape == (8, 5) + + def test_save_load_mid_stream_resumes_deterministically(self, tmp_path): + # advance both models to the same point, save, load — the resumed streams must match + a, b = _prepared(), _prepared() + _stream(a, 2) + _stream(b, 2) + a.save(str(tmp_path), file_prefix="a", overwrite=True) + b.save(str(tmp_path), file_prefix="b", overwrite=True) + la = cellflow.model.CellFlow.load(str(tmp_path / "a_CellFlow.pkl")) + lb = cellflow.model.CellFlow.load(str(tmp_path / "b_CellFlow.pkl")) + ra, rb = _stream(la, 3), _stream(lb, 3) + assert all(np.array_equal(x, y) for x, y in zip(ra, rb, strict=True)) + # and the resumed state is preserved (not reset to the seed → differs from a fresh model) + fresh = _stream(_prepared(), 3) + assert not all(np.array_equal(x, y) for x, y in zip(ra, fresh, strict=True)) + + def test_save_load_after_training(self, tmp_path): + cf = _prepared() + cf.prepare_model( + pooling="mean", condition_embedding_dim=8, time_encoder_dims=(8,), hidden_dims=(8,), decoder_dims=(8,) + ) + cf.train(num_iterations=2, valid_freq=100) + cf.save(str(tmp_path), file_prefix="t", overwrite=True) + loaded = cellflow.model.CellFlow.load(str(tmp_path / "t_CellFlow.pkl")) + assert loaded.solver is not None and loaded._dataloader is not None + assert loaded._dataloader.sample()["tgt_cell_data"].shape == (8, 5) # loader still usable + + def test_get_condition_embedding_without_adata_warns(self, tmp_path): + cf = _prepared() + cf.prepare_model( + pooling="mean", condition_embedding_dim=8, time_encoder_dims=(8,), hidden_dims=(8,), decoder_dims=(8,) + ) + cf.train(num_iterations=2, valid_freq=100) + cov = _adata().obs.drop_duplicates(subset=["cell_line", "drug"]) # carries the control column too + with pytest.warns(UserWarning, match="streaming path"): + df_mean, df_var = cf.get_condition_embedding(cov) # default key_added, no adata → warns, no crash + assert isinstance(df_mean, pd.DataFrame) and isinstance(df_var, pd.DataFrame) + assert len(df_mean) == len(cov) # one embedding row per provided condition From c8a1abb460caa966c2e345e8fee87ddc3f10b1f4 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Jul 2026 17:37:46 +0200 Subject: [PATCH 15/35] Debloat annbatch/dagloader docstrings MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Trim the verbose docstrings and a few long comments added for the annbatch path: drop repeated rationale, "mirrors X"/"byte-for-byte"/"parity-tested" asides, and parentheticals that restate the summary. Kept the actual contract — numpydoc Parameters/Returns, the chunk_size run-length rule, the sample_rep/validation note, and the "controls carried through" / "RNG preserved on pickle" facts. Docstrings/comments only — no behavior change; full annbatch + dagloader suite passes. Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/_annbatch.py | 55 +++++++++++++------------------ src/cellflow/data/_dataloader.py | 14 ++++---- src/cellflow/model/_cellflow.py | 56 +++++++++++++------------------- src/dagloader/_loader.py | 7 ++-- src/dagloader/_split.py | 22 +++++-------- 5 files changed, 62 insertions(+), 92 deletions(-) diff --git a/src/cellflow/data/_annbatch.py b/src/cellflow/data/_annbatch.py index b000ceb9..77606b29 100644 --- a/src/cellflow/data/_annbatch.py +++ b/src/cellflow/data/_annbatch.py @@ -1,13 +1,11 @@ """Build the annbatch/dagloader streaming training path from a CellFlow covariate spec. -Turns the same ``prepare_data`` covariate arguments into a :class:`dagloader.Scheme` (perturbed root, -matched-control child) plus a ``condition_fn`` that maps a sampled leaf to its per-condition embedding. -The condition embeddings reuse the **in-memory** machinery unchanged: a cell-free "shell" ``AnnData`` -(``obs`` + ``uns`` only, no ``X``) drives a :class:`~cellflow.data._datamanager.DataManager` purely as a -covariate-encoder factory, and :func:`~cellflow.data._condition.build_condition_data` assembles the -embeddings — byte-for-byte identical to the in-memory path (parity-tested), so nothing is duplicated. - -Only ``obs`` (and the embedding tables) are read here; cells are streamed later by ``dagloader``. +Turns the ``prepare_data`` covariate arguments into a :class:`dagloader.Scheme` (perturbed root, +matched-control child) and a ``condition_fn`` mapping each sampled leaf to its condition embedding. The +embeddings reuse the in-memory machinery — a cell-free ``AnnData`` shell (``obs`` + ``uns``) drives a +:class:`~cellflow.data._datamanager.DataManager` and +:func:`~cellflow.data._condition.build_condition_data` — so they match the in-memory path exactly. Only +``obs`` (and the embedding tables) are read here; cells are streamed later by ``dagloader``. """ from __future__ import annotations @@ -29,15 +27,12 @@ def assert_source_chunkable(source: Any, cols: Sequence[str], chunk_size: int) -> None: - """Assert ``source`` satisfies annbatch's run-length rule for ``chunk_size``. - - ``chunk_size > 1`` makes annbatch read contiguous slices, so **every contiguous run** of every - category (a ``cols`` combination) must be at least ``chunk_size`` cells long — exactly annbatch's - ``ClassSampler`` rule. A category may span *several* runs (the source need not be fully sorted); only - each run's length matters. Reads ``obs`` only and raises a clear :class:`ValueError` — pointing at - ``DatasetCollection.add_adatas(groupby=...)`` — if any run is shorter. In-memory sources are grouped - automatically by :func:`build_annbatch_training`, so this only bites out-of-core inputs. (annbatch - enforces the same rule at loader construction; this is a friendlier, earlier check.) + """Raise unless ``source`` satisfies annbatch's run-length rule for ``chunk_size`` (reads ``obs`` only). + + With ``chunk_size > 1`` annbatch reads contiguous slices, so every contiguous run of each category + (a ``cols`` combination) must be at least ``chunk_size`` cells; a category may span several runs. + On a shorter run, raises pointing at ``DatasetCollection.add_adatas(groupby=...)``. In-memory sources + are grouped automatically by :func:`build_annbatch_training`, so this only bites out-of-core inputs. """ import numpy as np @@ -93,10 +88,9 @@ def build_annbatch_training( ) -> AnnbatchTraining: """Assemble the :class:`dagloader.Scheme` + ``condition_fn`` for the streaming path (obs only). - ``source`` is an out-of-core :class:`annbatch.DatasetCollection` or an in-memory ``AnnData`` (the - dagloader is container-agnostic). ``rep_dict`` supplies the covariate embedding tables that - ``adata.uns`` would hold in the in-memory path (keys match ``*_covariate_reps`` values); it may be - :obj:`None` when the primary covariate is categorical (one-hot encoded, no external embeddings). + ``source`` is an out-of-core :class:`annbatch.DatasetCollection` or an in-memory ``AnnData``. + ``rep_dict`` holds the covariate embedding tables (as ``adata.uns`` would); pass :obj:`None` when the + primary covariate is categorical (one-hot). """ from dagloader import Bind, Node, Scheme, uniform from dagloader._io import key_backings, obs_columns @@ -109,18 +103,16 @@ def build_annbatch_training( obs = obs_columns(source, [*cols, control_key]) - # In-memory source: group (stable-sort) by the grouping columns so `chunk_size > 1` streams - # contiguous slices. A cheap one-time row reorder; cell order is irrelevant to sampling. Out-of-core - # sources are NOT reordered (a physical zarr re-sort is expensive) — they must be created grouped via - # `DatasetCollection.add_adatas(groupby=...)`; `assert_source_chunkable` checks that when chunk_size > 1. + # In-memory source: stable-sort by the grouping columns so `chunk_size > 1` reads contiguous slices + # (cheap, and cell order is irrelevant). Out-of-core sources aren't reordered (expensive zarr re-sort) + # — they must be built grouped; `assert_source_chunkable` enforces that. if isinstance(source, ad.AnnData): order = obs[list(cols)].reset_index(drop=True).sort_values(list(cols), kind="stable").index.to_numpy() source = source[order].copy() obs = obs_columns(source, [*cols, control_key]) - # DataManager as a covariate-encoder factory: it reads only obs + uns (never the cell matrix), so a - # cell-free shell suffices. `sample_rep` is stored (and only read later by validation's `_get_cell_data` - # against the actual validation adata) — verification is type-only, so the real value is safe here. + # DataManager as a covariate-encoder factory: reads only obs + uns, so a cell-free shell suffices. + # `sample_rep` is stored for validation's `_get_cell_data` (verification is type-only, so it's safe here). shell = ad.AnnData(obs=obs.copy()) shell.uns = dict(rep_dict or {}) dm = DataManager( @@ -155,10 +147,9 @@ def build_annbatch_training( null_value=null_value, ) - # leaf (in `cols` order) → perturbation index → per-condition embedding. `enumerate_perturbations` - # lays its covariate tuples out in `tuple_keys` order (pert + sample/split), which differs from - # `cols`, so re-project the leaf onto that layout before the lookup. String-normalize both sides so - # dtype quirks (categorical / numpy scalars) never break the match. + # leaf → perturbation index → embedding. `enumerate_perturbations` lays tuples out in `tuple_keys` + # order (differs from `cols`), so re-project the leaf; string-normalize both sides so dtype quirks + # (categorical / numpy scalars) don't break the match. idx_to_cov = enumerate_perturbations( obs, control_key=control_key, diff --git a/src/cellflow/data/_dataloader.py b/src/cellflow/data/_dataloader.py index afb5d024..c96ca855 100644 --- a/src/cellflow/data/_dataloader.py +++ b/src/cellflow/data/_dataloader.py @@ -19,11 +19,10 @@ def _densify(x: Any) -> np.ndarray: - """Materialize a possibly-sparse streamed cell batch to dense (annbatch yields sparse for sparse X). + """Densify a possibly-sparse streamed cell batch (dense batches pass through). - Densification is done here, at the model boundary, rather than in the loader: ``dagloader`` streams - whatever the source stores (sparse or dense) and stays representation-agnostic; the solver needs - dense arrays. Dense batches pass through untouched. + Done at the model boundary, not in the loader: ``dagloader`` stays representation-agnostic while the + solver needs dense arrays. """ return np.asarray(x.todense()) if hasattr(x, "todense") else x @@ -31,10 +30,9 @@ def _densify(x: Any) -> np.ndarray: class DAGLoaderTrainSampler: """Adapt a ``dagloader.DAGLoader`` stream to the :meth:`TrainSampler.sample` batch contract. - ``DAGLoader`` yields ``{"target", "source", "condition"}`` (one perturbation per batch, the - condition already carrying the leading ``(1, ...)`` axis); the trainer/solver consumes - ``{"src_cell_data", "tgt_cell_data", "condition"}``. This renames the stream's keys and densifies the - cell batches (:func:`_densify`) so the in-memory and streaming paths hit the solver identically. + Renames the loader's ``{"target", "source", "condition"}`` batch to the trainer's + ``{"src_cell_data", "tgt_cell_data", "condition"}`` and densifies the cell arrays, so the in-memory + and streaming paths reach the solver identically. """ def __init__(self, loader: Any): diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index 32ba9b5b..dd805850 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -255,15 +255,12 @@ def prepare_annbatch_data( ) -> None: """Prepare an out-of-core, annbatch-streamed training path (no in-memory ``adata``). - The condition-covariate arguments — ``sample_rep``, ``control_key``, - ``perturbation_covariates``, ``perturbation_covariate_reps``, ``sample_covariates``, - ``sample_covariate_reps``, ``split_covariates``, ``max_combination_length`` and ``null_value`` - — mean exactly what they do in :meth:`prepare_data`. The only difference is *where the cells - come from*: instead of materializing ``cell_data`` in memory, cells are streamed on demand from - an out-of-core :class:`annbatch.DatasetCollection` via the ``dagloader`` sampler. - - Requires the ``annbatch`` optional dependency (``pip install cellflow-tools[annbatch]``) and a - model constructed **without** ``adata`` (``CellFlow()``) — that is what selects this path. + The covariate arguments (``sample_rep``, ``control_key``, ``perturbation_covariates``, + ``perturbation_covariate_reps``, ``sample_covariates``, ``sample_covariate_reps``, + ``split_covariates``, ``max_combination_length``, ``null_value``) mean exactly what they do in + :meth:`prepare_data`; only the cells differ — streamed from an out-of-core + :class:`annbatch.DatasetCollection` instead of materialized. Requires the ``annbatch`` extra and + a model built **without** ``adata`` (``CellFlow()``). Parameters ---------- @@ -277,17 +274,13 @@ def prepare_annbatch_data( when a covariate group is embedded; may be :obj:`None` when the primary covariate is categorical (one-hot encoded). sampler_config - The read parameters for the streamed loader(s), **one per split**. Either a single - :class:`dagloader.SamplerConfig` applied to every split, or a per-split mapping - ``{split_name: SamplerConfig}`` that must cover **all** splits (see - :func:`dagloader.resolve_split_configs`). When no split is made the only split is ``"train"``. - - ``chunk_size > 1`` reads contiguous on-disk slices and therefore requires **every contiguous - run of each category** (context+perturbation combination) to be at least ``chunk_size`` cells - (a category may span several runs; only each run's length matters — annbatch's run-length - rule). In-memory ``AnnData`` sources are grouped automatically; an out-of-core - :class:`~annbatch.DatasetCollection` must be created grouped (``add_adatas(..., groupby=[...])``) - or a clear error is raised. ``chunk_size = 1`` streams per-row and needs no grouping. + Read parameters for the streamed loader(s), **one per split**: a single + :class:`dagloader.SamplerConfig` for all splits, or a ``{split_name: SamplerConfig}`` mapping + covering every split (see :func:`dagloader.resolve_split_configs`). With no split the only + split is ``"train"``. ``chunk_size > 1`` reads contiguous slices, so every run of each + category must be at least ``chunk_size`` cells — in-memory sources are grouped automatically, + an out-of-core :class:`~annbatch.DatasetCollection` must be built grouped + (``add_adatas(..., groupby=[...])``) or a clear error is raised. seed Reproducibility seed for the ``dagloader`` per-node RNG streams. split_by @@ -386,13 +379,9 @@ def split_annbatch_data( ) -> pd.DataFrame: """Partition the prepared annbatch ``Scheme``'s target combinations into named splits. - Must be called after :meth:`prepare_annbatch_data` (which builds the ``Scheme``). Splits are - over whole *combinations* of ``split_by`` — a subset of the perturbation / split-covariate - columns — not over cells, so this holds out entire conditions (an out-of-distribution split), - mirroring CellFlow2's combination-level splitter. Because a leaf's weight *is* the selection in - the ``dagloader`` model, each split is the same ``Scheme`` with the target (root) node's weights - restricted to that split's combinations; the control / source populations are carried through - unchanged (a matched control must stay available in every split). + Call after :meth:`prepare_annbatch_data`. Splits hold out whole *combinations* of ``split_by`` + (a subset of the perturbation / split-covariate columns), not cells; controls are carried through + every split. See :func:`dagloader.split_scheme` for the mechanics. Parameters ---------- @@ -441,13 +430,12 @@ def prepare_validation_data( ) -> None: """Prepare the validation data. - Validation is always in-memory (computing metrics needs materialized cells): you pass a held-out - :class:`~anndata.AnnData`, and its cells (at ``sample_rep``) plus condition embeddings are read - into a ``ValidationData``. This works for **both** the in-memory (:meth:`prepare_data`) and the - streaming (:meth:`prepare_annbatch_data`) training paths. In the streaming path the ``adata`` must - carry the same ``sample_rep`` representation and the covariate embeddings in ``.uns`` that the run - was prepared with. (This is unrelated to the ``val`` split from :meth:`split_annbatch_data`, which - is a streaming loader, not a validation set.) + Validation is always in-memory (metrics need materialized cells): pass a held-out + :class:`~anndata.AnnData` and its cells (at ``sample_rep``) + condition embeddings become a + ``ValidationData``. Works for both the in-memory and streaming training paths; in the streaming + path the ``adata`` must carry the same ``sample_rep`` and the covariate embeddings in ``.uns``. + (Unrelated to the ``val`` split from :meth:`split_annbatch_data`, a streaming loader — not a + validation set.) Parameters ---------- diff --git a/src/dagloader/_loader.py b/src/dagloader/_loader.py index 7d581eef..dc036aa1 100644 --- a/src/dagloader/_loader.py +++ b/src/dagloader/_loader.py @@ -245,10 +245,9 @@ def _start_pass(self) -> None: def __getstate__(self) -> dict: """Pickle without the live annbatch iterators (generators aren't picklable). - Everything that defines the stream is kept — the per-node RNG streams (``_rngs`` / ``_row_seqs``), - the samplers (with their advanced RNG), the drawn ``_schedules`` and the configs — so a reloaded - loader continues the *same* reproducible RNG sequence. Only the transient ``_iters`` are dropped; - the next ``__next__`` rebuilds them (a fresh pass) from the restored RNG state. + The RNG streams, samplers, schedules and configs are kept, so a reloaded loader resumes the same + reproducible sequence; only ``_iters`` is dropped and rebuilt (a fresh pass) on the next + ``__next__``. """ state = self.__dict__.copy() state["_iters"] = None diff --git a/src/dagloader/_split.py b/src/dagloader/_split.py index 63ad2936..e8f07e2f 100644 --- a/src/dagloader/_split.py +++ b/src/dagloader/_split.py @@ -1,16 +1,11 @@ """Split a :class:`~dagloader.Scheme`'s target combinations into named splits — weights-only. -A "split" needs no new data structure here. Because a leaf's **weight is the selection** (weight 0 / -absent ⇒ not sampled), holding out conditions is just restricting the root node's weights to a subset -of its combinations. :func:`split_scheme` partitions the root's positive-weight combinations — grouped -by ``split_by`` (a subset of the root's ``cols``) — into named splits and returns one :class:`Scheme` -per split, identical to the input except the root's ``weights`` are restricted. Bound children -(controls / sources) are **carried through unchanged**: a matched control must stay available in every -split, since each batch's control is drawn from whatever context the (split) target carries. - -This mirrors CellFlow2's ``GroupedDistributionSplitter`` — split whole *combinations*, not cells (an -out-of-distribution generalization split) — but expressed over the ``Scheme``, so it touches no cells, -no ``DatasetCollection`` and no loader: it only rewrites a weights dict. +A leaf's weight *is* its selection (weight 0 / absent ⇒ not sampled), so a split just restricts the +root node's weights to a subset of combinations. :func:`split_scheme` returns one :class:`Scheme` per +split, identical to the input but with the root weights restricted; bound children (controls) are +carried through unchanged, since a matched control must stay available in every split. Like CellFlow2's +combination-level splitter (hold out whole conditions, not cells), but on the ``Scheme`` — no cells, no +``DatasetCollection``, no loader. """ from __future__ import annotations @@ -49,9 +44,8 @@ def split_scheme( ) -> dict[str, Scheme]: """Partition the root (target) combinations of ``scheme`` into named splits. - Splits are over whole *combinations* of ``split_by`` (all combos sharing the same ``split_by`` - values land in the same split — CellFlow2's ``drop_duplicates(subset=split_by)`` + merge-back), so - this is an out-of-distribution split that holds out entire conditions rather than random cells. + Splits are over whole *combinations* of ``split_by`` — combos sharing the same ``split_by`` values + land in the same split — so entire conditions are held out rather than random cells. Parameters ---------- From 4f0e256efee92c223c1ae1ac6895950ed2fa7325 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Jul 2026 18:04:46 +0200 Subject: [PATCH 16/35] Type the annbatch path: drop `Any`, satisfy mypy Replace `Any` in the annbatch code with real types and make the additions mypy-clean: - _annbatch.py: `source: Container` (AnnData | DatasetCollection), a `Leaf` alias (`tuple[object, ...]`) for scheme leaves, and `rep_dict: Mapping[str, Mapping[str, ArrayLike]]`. - _dataloader.py: `DAGLoaderTrainSampler.__init__(loader: DAGLoader)`, `sample(rng: np.random.Generator | None) -> dict[str, np.ndarray | dict[str, np.ndarray]]`, `_densify(x: ArrayLike)` (getattr avoids a spurious no-`todense` error). - _cellflow.py: type the annbatch attributes (`_scheme: Scheme | None`, `_split_schemes`, `_annbatch_sampler_configs`, `_annbatch_loaders`), and use `Mapping[str, object]` for force-training values. `prepare_model` now narrows the condition-data / max-combination-length source with an if/elif/else (no `int | None`). - dagloader: parametrize `DAGLoader.__getstate__/__setstate__` dicts and annotate `_ScheduleRng.__reduce__`. mypy on the changed files: 0 new errors and 5 pre-existing ones fixed (down 22 -> 17; the rest are pre-existing debt in unrelated methods). No behavior change; annbatch suite + in-memory prepare_model smoke pass. Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/_annbatch.py | 19 +++++++++--------- src/cellflow/data/_dataloader.py | 20 +++++++++++++------ src/cellflow/model/_cellflow.py | 30 +++++++++++++++-------------- src/dagloader/_loader.py | 4 ++-- src/dagloader/_scheduled_sampler.py | 2 +- 5 files changed, 43 insertions(+), 32 deletions(-) diff --git a/src/cellflow/data/_annbatch.py b/src/cellflow/data/_annbatch.py index 77606b29..c563fc1b 100644 --- a/src/cellflow/data/_annbatch.py +++ b/src/cellflow/data/_annbatch.py @@ -12,7 +12,7 @@ from collections.abc import Callable, Mapping, Sequence from dataclasses import dataclass -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING import anndata as ad import numpy as np @@ -21,12 +21,15 @@ from cellflow.data._datamanager import DataManager if TYPE_CHECKING: - from dagloader import Scheme + from cellflow._types import ArrayLike + from dagloader import Container, Scheme + +Leaf = tuple[object, ...] # a scheme leaf: one value per grouping column __all__ = ["AnnbatchTraining", "assert_source_chunkable", "build_annbatch_training", "sample_rep_to_key"] -def assert_source_chunkable(source: Any, cols: Sequence[str], chunk_size: int) -> None: +def assert_source_chunkable(source: Container, cols: Sequence[str], chunk_size: int) -> None: """Raise unless ``source`` satisfies annbatch's run-length rule for ``chunk_size`` (reads ``obs`` only). With ``chunk_size > 1`` annbatch reads contiguous slices, so every contiguous run of each category @@ -34,8 +37,6 @@ def assert_source_chunkable(source: Any, cols: Sequence[str], chunk_size: int) - On a shorter run, raises pointing at ``DatasetCollection.add_adatas(groupby=...)``. In-memory sources are grouped automatically by :func:`build_annbatch_training`, so this only bites out-of-core inputs. """ - import numpy as np - from dagloader._io import leaf_codes, obs_columns codes, _ = leaf_codes(obs_columns(source, list(cols)), list(cols)) @@ -64,7 +65,7 @@ class AnnbatchTraining: """Everything the model needs to stream-train, assembled from a covariate spec (cells untouched).""" scheme: Scheme - condition_fn: Callable[[tuple], dict[str, np.ndarray]] + condition_fn: Callable[[Leaf], dict[str, np.ndarray]] condition_data: dict[str, np.ndarray] data_manager: DataManager data_dim: int @@ -72,7 +73,7 @@ class AnnbatchTraining: def build_annbatch_training( - source: Any, + source: Container, *, sample_rep: str, control_key: str, @@ -83,7 +84,7 @@ def build_annbatch_training( split_covariates: Sequence[str] | None = None, max_combination_length: int | None = None, null_value: float = 0.0, - rep_dict: Mapping[str, Any] | None = None, + rep_dict: Mapping[str, Mapping[str, ArrayLike]] | None = None, seed: int = 0, ) -> AnnbatchTraining: """Assemble the :class:`dagloader.Scheme` + ``condition_fn`` for the streaming path (obs only). @@ -161,7 +162,7 @@ def build_annbatch_training( cov_to_idx = {tuple(map(str, cov)): i for i, cov in idx_to_cov.items()} reorder = [cols.index(c) for c in tuple_keys] - def condition_fn(leaf: tuple) -> dict[str, np.ndarray]: + def condition_fn(leaf: Leaf) -> dict[str, np.ndarray]: idx = cov_to_idx[tuple(str(leaf[i]) for i in reorder)] return {group: condition_data[group][[idx]] for group in condition_data} diff --git a/src/cellflow/data/_dataloader.py b/src/cellflow/data/_dataloader.py index c96ca855..91d7834e 100644 --- a/src/cellflow/data/_dataloader.py +++ b/src/cellflow/data/_dataloader.py @@ -2,13 +2,17 @@ import queue import threading from collections.abc import Generator -from typing import Any, Literal +from typing import TYPE_CHECKING, Any, Literal import jax import numpy as np +from cellflow._types import ArrayLike from cellflow.data._data import PredictionData, TrainingData, ValidationData +if TYPE_CHECKING: + from dagloader import DAGLoader + __all__ = [ "TrainSampler", "ValidationSampler", @@ -18,13 +22,14 @@ ] -def _densify(x: Any) -> np.ndarray: +def _densify(x: ArrayLike) -> np.ndarray: """Densify a possibly-sparse streamed cell batch (dense batches pass through). Done at the model boundary, not in the loader: ``dagloader`` stays representation-agnostic while the solver needs dense arrays. """ - return np.asarray(x.todense()) if hasattr(x, "todense") else x + todense = getattr(x, "todense", None) # scipy sparse → dense; plain arrays lack this + return np.asarray(todense()) if todense is not None else np.asarray(x) class DAGLoaderTrainSampler: @@ -35,17 +40,20 @@ class DAGLoaderTrainSampler: and streaming paths reach the solver identically. """ - def __init__(self, loader: Any): + def __init__(self, loader: "DAGLoader"): self._loader = loader self._iter = iter(loader) - def sample(self, rng: Any = None) -> dict[str, Any]: + def sample(self, rng: np.random.Generator | None = None) -> dict[str, np.ndarray | dict[str, np.ndarray]]: """Return the next streamed batch as a trainer batch dict. ``rng`` is unused — the ``DAGLoader`` owns its own reproducible RNG. """ batch = next(self._iter) - out = {"src_cell_data": _densify(batch["source"]), "tgt_cell_data": _densify(batch["target"])} + out: dict[str, np.ndarray | dict[str, np.ndarray]] = { + "src_cell_data": _densify(batch["source"]), + "tgt_cell_data": _densify(batch["target"]), + } if "condition" in batch: out["condition"] = batch["condition"] return out diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index dd805850..1f0175f7 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -38,7 +38,7 @@ if TYPE_CHECKING: from annbatch import DatasetCollection # optional dep — only imported for typing - from dagloader import SamplerConfig # optional dep — only imported for typing + from dagloader import DAGLoader, SamplerConfig, Scheme # optional dep — only imported for typing __all__ = ["CellFlow"] @@ -87,11 +87,11 @@ def __init__(self, adata: ad.AnnData | None = None, solver: Literal["otfm", "gen # annbatch/dagloader streaming path (set by `prepare_annbatch_data`, partitioned by # `split_annbatch_data`). Kept untyped here to avoid importing the optional `dagloader`/`annbatch` # dependency at module import time. - self._scheme = None - self._split_schemes: dict[str, Any] | None = None + self._scheme: Scheme | None = None + self._split_schemes: dict[str, Scheme] | None = None self._split_assignment: pd.DataFrame | None = None - self._annbatch_sampler_configs: dict[str, Any] | None = None - self._annbatch_loaders: dict[str, Any] | None = None # {split: DAGLoader} + self._annbatch_sampler_configs: dict[str, SamplerConfig] | None = None + self._annbatch_loaders: dict[str, DAGLoader] | None = None self._condition_data: dict[str, np.ndarray] | None = None # annbatch-path condition embeddings self._max_combination_length: int | None = None # annbatch-path (in-memory keeps it on train_data) @@ -245,12 +245,12 @@ def prepare_annbatch_data( split_covariates: Sequence[str] | None = None, max_combination_length: int | None = None, null_value: float = 0.0, - rep_dict: Mapping[str, Any] | None = None, + rep_dict: Mapping[str, Mapping[str, ArrayLike]] | None = None, sampler_config: "SamplerConfig | Mapping[str, SamplerConfig] | None" = None, seed: int = 0, split_by: Sequence[str] | None = None, split_ratios: Mapping[str, float] | None = None, - split_force_training_values: Mapping[str, Any] | None = None, + split_force_training_values: Mapping[str, object] | None = None, split_random_state: int = 42, ) -> None: """Prepare an out-of-core, annbatch-streamed training path (no in-memory ``adata``). @@ -374,7 +374,7 @@ def split_annbatch_data( *, split_by: Sequence[str], ratios: Mapping[str, float] | None = None, - force_training_values: Mapping[str, Any] | None = None, + force_training_values: Mapping[str, object] | None = None, random_state: int = 42, ) -> pd.DataFrame: """Partition the prepared annbatch ``Scheme``'s target combinations into named splits. @@ -662,14 +662,16 @@ def prepare_model( - :attr:`cellflow.model.CellFlow.trainer` - an instance of the :class:`cellflow.training.CellFlowTrainer`. """ - if self.train_data is None and self._scheme is None: - raise ValueError("Dataloader not initialized. Please call `prepare_data` or `prepare_annbatch_data` first.") # Condition embeddings + max combination length come from `train_data` (in-memory) or, on the # annbatch path, from what `prepare_annbatch_data` assembled. - condition_data = self.train_data.condition_data if self.train_data is not None else self._condition_data - max_combination_length = ( - self.train_data.max_combination_length if self.train_data is not None else self._max_combination_length - ) + if self.train_data is not None: + condition_data = self.train_data.condition_data + max_combination_length = self.train_data.max_combination_length + elif self._condition_data is not None and self._max_combination_length is not None: + condition_data = self._condition_data + max_combination_length = self._max_combination_length + else: + raise ValueError("Data not initialized. Please call `prepare_data` or `prepare_annbatch_data` first.") if condition_mode == "stochastic": if regularization == 0.0: diff --git a/src/dagloader/_loader.py b/src/dagloader/_loader.py index dc036aa1..f4cadd91 100644 --- a/src/dagloader/_loader.py +++ b/src/dagloader/_loader.py @@ -242,7 +242,7 @@ def _start_pass(self) -> None: self._pos = 0 # ── pickling ───────────────────────────────────────────────────────────── - def __getstate__(self) -> dict: + def __getstate__(self) -> dict[str, object]: """Pickle without the live annbatch iterators (generators aren't picklable). The RNG streams, samplers, schedules and configs are kept, so a reloaded loader resumes the same @@ -253,7 +253,7 @@ def __getstate__(self) -> dict: state["_iters"] = None return state - def __setstate__(self, state: dict) -> None: + def __setstate__(self, state: dict[str, object]) -> None: self.__dict__.update(state) self._iters = None # force `_start_pass` on the next `__next__`, using the restored RNG state diff --git a/src/dagloader/_scheduled_sampler.py b/src/dagloader/_scheduled_sampler.py index ccb52327..7dac2202 100644 --- a/src/dagloader/_scheduled_sampler.py +++ b/src/dagloader/_scheduled_sampler.py @@ -55,7 +55,7 @@ def __getattr__(self, name): # integers / shuffle / permutation / ... → real raise AttributeError(name) # don't delegate dunders — avoids recursion during (un)pickling / copy return getattr(self._rng, name) - def __reduce__(self): + def __reduce__(self) -> tuple[object, ...]: # A transient per-pass wrapper: pickle it AS the underlying real Generator so a checkpointed # DAGLoader restores a clean, unwrapped RNG (the wrapper is rebuilt each pass in `_sample`). return self._rng.__reduce__() From 7295e533794923337cfe9098bd76e7e17cdafbe7 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Thu, 9 Jul 2026 19:38:42 +0200 Subject: [PATCH 17/35] chore: untrack uv.lock (keep it out of the branch/PR) uv.lock was added by an early branch commit but is not tracked on main; drop it from the tree and gitignore it so it stays out of the PR. Regenerable with `uv lock`. Co-Authored-By: Claude Opus 4.8 --- .gitignore | 3 + uv.lock | 6448 ---------------------------------------------------- 2 files changed, 3 insertions(+), 6448 deletions(-) delete mode 100644 uv.lock diff --git a/.gitignore b/.gitignore index 1fd1f144..2962c3d1 100644 --- a/.gitignore +++ b/.gitignore @@ -93,6 +93,9 @@ ipython_config.py # install all needed dependencies. #Pipfile.lock +# uv (lock file is not tracked in this repo) +uv.lock + # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ diff --git a/uv.lock b/uv.lock deleted file mode 100644 index 1d32fb04..00000000 --- a/uv.lock +++ /dev/null @@ -1,6448 +0,0 @@ -version = 1 -revision = 3 -requires-python = ">=3.11" -resolution-markers = [ - "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.13.*' and sys_platform == 'win32'", - 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conditions=self.train_data.condition_data, + conditions=condition_data, rng=jax.random.PRNGKey(seed), **self._solver_class._match_kwargs(match_fn=match_fn, data_dim=self._data_dim), **solver_kwargs, From 95d9a8f83a0f8a5737ce34acaf60aaac8915fe91 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 11 Jul 2026 23:04:34 +0000 Subject: [PATCH 19/35] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- tests/solver/test_solver.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/tests/solver/test_solver.py b/tests/solver/test_solver.py index 312336ed..f9a5087c 100644 --- a/tests/solver/test_solver.py +++ b/tests/solver/test_solver.py @@ -466,9 +466,7 @@ def test_per_call_guidance_scale_matches_construction_time(self): # Same vf_rng in _make_otfm -> identical init params -> the two paths are comparable. per_call = _make_otfm(condition_dropout_prob=0.5).predict(x, condition, guidance_scale=w) - construction = _make_otfm( - condition_dropout_prob=0.5, guidance=ClassifierFreeGuidance(w) - ).predict(x, condition) + construction = _make_otfm(condition_dropout_prob=0.5, guidance=ClassifierFreeGuidance(w)).predict(x, condition) assert np.allclose(per_call, construction, atol=1e-5) # And it is actually guiding: differs from the plain conditional (scale 1.0). From 4934374fa4531d9667b7d2860e6fc727d3cbdbc0 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Sun, 12 Jul 2026 17:24:18 +0200 Subject: [PATCH 20/35] extract cfg null helpers --- src/cellflow/networks/_velocity_field.py | 100 ++++++++++++++++++----- 1 file changed, 80 insertions(+), 20 deletions(-) diff --git a/src/cellflow/networks/_velocity_field.py b/src/cellflow/networks/_velocity_field.py index b1a45db9..3b320f08 100644 --- a/src/cellflow/networks/_velocity_field.py +++ b/src/cellflow/networks/_velocity_field.py @@ -13,7 +13,71 @@ from cellflow.networks._set_encoders import ConditionEncoder from cellflow.networks._utils import FilmBlock, MLPBlock, ResNetBlock, sinusoidal_time_encoder -__all__ = ["ConditionalVelocityField", "GENOTConditionalVelocityField"] +__all__ = [ + "ConditionalVelocityField", + "GENOTConditionalVelocityField", + "null_condition_embedding", + "null_condition_input", +] + + +def null_condition_embedding( + cond_embedding: jnp.ndarray, + *, + condition_dropout_prob: float, + make_rng: Callable[[str], jax.Array], + train: bool, + force_uncond: bool, +) -> jnp.ndarray: + """Null the condition *embedding* for classifier-free guidance (``condition_null='zero_embedding'``). + + - If ``force_uncond`` is set, the condition is always dropped, yielding the + unconditional velocity field (used at inference time). + - Otherwise, during training the condition is dropped independently per set + element with probability ``condition_dropout_prob``. + + With ``force_uncond=False`` and ``condition_dropout_prob == 0.0`` this is a no-op + that reproduces the standard conditional behavior byte-for-byte: no random number + is drawn and the embedding is returned as-is. ``make_rng`` is the owning module's + :meth:`flax.linen.Module.make_rng` (drawn only when a dropout mask is needed). + """ + if force_uncond: + return jnp.zeros_like(cond_embedding) + if train and condition_dropout_prob > 0.0: + keep = jax.random.bernoulli( + make_rng("dropout"), + p=1.0 - condition_dropout_prob, + shape=(cond_embedding.shape[0], 1), + ) + return jnp.where(keep, cond_embedding, jnp.zeros_like(cond_embedding)) + return cond_embedding + + +def null_condition_input( + cond: dict[str, jnp.ndarray], + *, + condition_dropout_prob: float, + mask_value: float, + make_rng: Callable[[str], jax.Array], + train: bool, + force_uncond: bool, +) -> dict[str, jnp.ndarray]: + """Null the *raw* condition by filling it with ``mask_value`` (``condition_null='mask_value'``). + + Routes an all-masked condition set through the condition encoder, so the + unconditional representation is whatever the encoder maps a fully-masked set to + (matching how padded conditions are handled). Same drop policy as + :func:`null_condition_embedding`: always when ``force_uncond`` is set, otherwise per + set element with probability ``condition_dropout_prob`` during training. With the + defaults it returns ``cond`` unchanged and draws no random number. + """ + if force_uncond: + return jax.tree_util.tree_map(lambda c: jnp.full_like(c, mask_value), cond) + if train and condition_dropout_prob > 0.0: + n = next(iter(cond.values())).shape[0] + keep = jax.random.bernoulli(make_rng("dropout"), p=1.0 - condition_dropout_prob, shape=(n, 1, 1)) + return jax.tree_util.tree_map(lambda c: jnp.where(keep, c, jnp.full_like(c, mask_value)), cond) + return cond class ConditionalVelocityField(nn.Module): @@ -278,17 +342,13 @@ def _maybe_null_embedding( defaults) this is a no-op that reproduces the standard conditional behavior byte-for-byte: no random number is drawn and the embedding is returned as-is. """ - if force_uncond: - return jnp.zeros_like(cond_embedding) - if train and self.condition_dropout_prob > 0.0: - drop_rng = self.make_rng("dropout") - keep = jax.random.bernoulli( - drop_rng, - p=1.0 - self.condition_dropout_prob, - shape=(cond_embedding.shape[0], 1), - ) - return jnp.where(keep, cond_embedding, jnp.zeros_like(cond_embedding)) - return cond_embedding + return null_condition_embedding( + cond_embedding, + condition_dropout_prob=self.condition_dropout_prob, + make_rng=self.make_rng, + train=train, + force_uncond=force_uncond, + ) def _maybe_null_input( self, @@ -306,14 +366,14 @@ def _maybe_null_input( :attr:`condition_dropout_prob` during training. With the defaults it returns ``cond`` unchanged and draws no random number. """ - if force_uncond: - return jax.tree_util.tree_map(lambda c: jnp.full_like(c, self.mask_value), cond) - if train and self.condition_dropout_prob > 0.0: - drop_rng = self.make_rng("dropout") - n = next(iter(cond.values())).shape[0] - keep = jax.random.bernoulli(drop_rng, p=1.0 - self.condition_dropout_prob, shape=(n, 1, 1)) - return jax.tree_util.tree_map(lambda c: jnp.where(keep, c, jnp.full_like(c, self.mask_value)), cond) - return cond + return null_condition_input( + cond, + condition_dropout_prob=self.condition_dropout_prob, + mask_value=self.mask_value, + make_rng=self.make_rng, + train=train, + force_uncond=force_uncond, + ) def get_condition_embedding(self, condition: dict[str, jnp.ndarray]) -> tuple[jnp.ndarray, jnp.ndarray]: """Get the embedding of the condition. From a4b6849566f95b1923b6c45cbcbacfcdab976a14 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Sun, 12 Jul 2026 17:49:09 +0200 Subject: [PATCH 21/35] hookable conditioning dispatch --- src/cellflow/networks/_velocity_field.py | 45 +++++++++++++++++++++--- 1 file changed, 40 insertions(+), 5 deletions(-) diff --git a/src/cellflow/networks/_velocity_field.py b/src/cellflow/networks/_velocity_field.py index 3b320f08..b18f38be 100644 --- a/src/cellflow/networks/_velocity_field.py +++ b/src/cellflow/networks/_velocity_field.py @@ -261,6 +261,15 @@ def setup(self): self.output_layer = nn.Dense(self.output_dim) + self._setup_conditioning(conditioning_kwargs) + + def _setup_conditioning(self, conditioning_kwargs: dict[str, Any]) -> None: + """Build the ``conditioning``-mode-specific submodules. + + Called at the end of :meth:`setup` (so all shared submodules already exist on + ``self``). Override in a subclass to add new ``conditioning`` modes, delegating to + ``super()._setup_conditioning`` for the built-in ones. + """ if self.conditioning == "film": self.film_block = FilmBlock( input_dim=self.hidden_dims[-1], @@ -302,7 +311,7 @@ def __call__( t_encoded = sinusoidal_time_encoder(t, time_freqs=self.time_freqs, time_max_period=self.time_max_period) t_encoded = self.time_encoder(t_encoded, training=train) - x_encoded = self.x_encoder(x_t, training=train) + x_encoded = self._encode_x(x_t, squeeze, train) t_encoded = self.layer_norm_time(t_encoded) x_encoded = self.layer_norm_x(x_encoded) @@ -313,6 +322,26 @@ def __call__( elif cond_embedding.shape[0] != x_t.shape[0]: # type: ignore[attr-defined] cond_embedding = jnp.tile(cond_embedding, (x_t.shape[0], 1)) + out = self._combine_and_decode(t_encoded, x_encoded, cond_embedding, squeeze, train) + return out, cond_mean, cond_logvar + + def _encode_x(self, x_t: jnp.ndarray, squeeze: bool, train: bool) -> jnp.ndarray: + """Encode ``x_t`` before conditioning. Override to insert pre-conditioning processing.""" + return self.x_encoder(x_t, training=train) + + def _combine_and_decode( + self, + t_encoded: jnp.ndarray, + x_encoded: jnp.ndarray, + cond_embedding: jnp.ndarray, + squeeze: bool, + train: bool, + ) -> jnp.ndarray: + """Combine ``(t, x, condition)`` per the conditioning mode, decode, and project to output. + + Override in a subclass to add new ``conditioning`` modes, delegating to + ``super()._combine_and_decode`` for the built-in ones. + """ if self.conditioning == "concatenation": out = jnp.concatenate((t_encoded, x_encoded, cond_embedding), axis=-1) elif self.conditioning == "film": @@ -323,7 +352,7 @@ def __call__( raise ValueError(f"Unknown conditioning mode: {self.conditioning}.") out = self.decoder(out, training=train) - return self.output_layer(out), cond_mean, cond_logvar + return self.output_layer(out) def _maybe_null_embedding( self, @@ -589,9 +618,15 @@ def _normalize_vf_kwargs(vf_kwargs: dict[str, Any] | None) -> dict[str, Any]: """ if vf_kwargs is None: return {"genot_source_dims": [1024, 1024, 1024], "genot_source_dropout": 0.0} - assert isinstance(vf_kwargs, dict) - assert "genot_source_dims" in vf_kwargs - assert "genot_source_dropout" in vf_kwargs + if not isinstance(vf_kwargs, dict): + raise TypeError(f"`vf_kwargs` must be a dict or None, got {type(vf_kwargs).__name__}.") + allowed = {"genot_source_dims", "genot_source_dropout"} + unknown = set(vf_kwargs) - allowed + if unknown: + raise ValueError(f"Unexpected `vf_kwargs` keys {sorted(unknown)}; allowed: {sorted(allowed)}.") + missing = allowed - set(vf_kwargs) + if missing: + raise ValueError(f"Missing `vf_kwargs` keys {sorted(missing)}; required: {sorted(allowed)}.") return vf_kwargs def setup(self): From 8d1c8216b783ebc083e42806a84b71effc1ae318 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Sun, 12 Jul 2026 18:29:48 +0200 Subject: [PATCH 22/35] route genot through combine hook --- src/cellflow/networks/_velocity_field.py | 65 ++++++++++++------------ 1 file changed, 33 insertions(+), 32 deletions(-) diff --git a/src/cellflow/networks/_velocity_field.py b/src/cellflow/networks/_velocity_field.py index b18f38be..bfb4ad7f 100644 --- a/src/cellflow/networks/_velocity_field.py +++ b/src/cellflow/networks/_velocity_field.py @@ -329,6 +329,28 @@ def _encode_x(self, x_t: jnp.ndarray, squeeze: bool, train: bool) -> jnp.ndarray """Encode ``x_t`` before conditioning. Override to insert pre-conditioning processing.""" return self.x_encoder(x_t, training=train) + def _conditioning_signals( + self, + t_encoded: jnp.ndarray, + x_encoded: jnp.ndarray, + cond_embedding: jnp.ndarray, + x_0_encoded: jnp.ndarray | None = None, + ) -> tuple[tuple[jnp.ndarray, ...], jnp.ndarray]: + """Return ``(concat_inputs, conditioning_vec)`` for the conditioning step. + + ``concat_inputs`` is what ``'concatenation'`` concatenates (order matters for + checkpoint compatibility); ``conditioning_vec`` is what modulates ``x`` for + ``'film'``/``'resnet'`` (and ``'adaln_zero'`` in subclasses). ``GENOT`` folds the + encoded source ``x_0`` into both. + """ + if x_0_encoded is None: + concat_inputs = (t_encoded, x_encoded, cond_embedding) + conditioning_vec = jnp.concatenate((t_encoded, cond_embedding), axis=-1) + else: + concat_inputs = (t_encoded, x_encoded, x_0_encoded, cond_embedding) + conditioning_vec = jnp.concatenate((t_encoded, x_0_encoded, cond_embedding), axis=-1) + return concat_inputs, conditioning_vec + def _combine_and_decode( self, t_encoded: jnp.ndarray, @@ -336,18 +358,21 @@ def _combine_and_decode( cond_embedding: jnp.ndarray, squeeze: bool, train: bool, + x_0_encoded: jnp.ndarray | None = None, ) -> jnp.ndarray: - """Combine ``(t, x, condition)`` per the conditioning mode, decode, and project to output. + """Combine ``(t, x, [x_0,] condition)`` per the conditioning mode, decode, and project. Override in a subclass to add new ``conditioning`` modes, delegating to - ``super()._combine_and_decode`` for the built-in ones. + ``super()._combine_and_decode`` for the built-in ones. ``x_0_encoded`` is the encoded + GENOT source (folded into the conditioning by :meth:`_conditioning_signals`). """ + concat_inputs, conditioning_vec = self._conditioning_signals(t_encoded, x_encoded, cond_embedding, x_0_encoded) if self.conditioning == "concatenation": - out = jnp.concatenate((t_encoded, x_encoded, cond_embedding), axis=-1) + out = jnp.concatenate(concat_inputs, axis=-1) elif self.conditioning == "film": - out = self.film_block(x_encoded, jnp.concatenate((t_encoded, cond_embedding), axis=-1)) + out = self.film_block(x_encoded, conditioning_vec) elif self.conditioning == "resnet": - out = self.resnet_block(x_encoded, jnp.concatenate((t_encoded, cond_embedding), axis=-1)) + out = self.resnet_block(x_encoded, conditioning_vec) else: raise ValueError(f"Unknown conditioning mode: {self.conditioning}.") @@ -683,22 +708,7 @@ def setup(self): self.output_layer = nn.Dense(self.output_dim) - if self.conditioning == "film": - self.film_block = FilmBlock( - input_dim=self.hidden_dims[-1], - cond_dim=self.time_encoder_dims[-1] + self.condition_embedding_dim, - **conditioning_kwargs, - ) - elif self.conditioning == "resnet": - self.resnet_block = ResNetBlock( - input_dim=self.hidden_dims[-1], - **self.conditioning_kwargs, - ) - elif self.conditioning == "concatenation": - if len(conditioning_kwargs) > 0: - raise ValueError("If `conditioning=='concatenation' mode, no conditioning kwargs can be passed.") - else: - raise ValueError(f"Unknown conditioning mode: {self.conditioning}") + self._setup_conditioning(conditioning_kwargs) def __call__( self, @@ -731,17 +741,8 @@ def __call__( elif cond_embedding.shape[0] != x_t.shape[0]: # type: ignore[attr-defined] cond_embedding = jnp.tile(cond_embedding, (x_t.shape[0], 1)) - if self.conditioning == "concatenation": - out = jnp.concatenate((t_encoded, x_encoded, x_0_encoded, cond_embedding), axis=-1) - elif self.conditioning == "film": - out = self.film_block(x_encoded, jnp.concatenate((t_encoded, x_0_encoded, cond_embedding), axis=-1)) - elif self.conditioning == "resnet": - out = self.resnet_block(x_encoded, jnp.concatenate((t_encoded, x_0_encoded, cond_embedding), axis=-1)) - else: - raise ValueError(f"Unknown conditioning mode: {self.conditioning}.") - - out = self.decoder(out, training=train) - return self.output_layer(out), cond_mean, cond_logvar + out = self._combine_and_decode(t_encoded, x_encoded, cond_embedding, squeeze, train, x_0_encoded=x_0_encoded) + return out, cond_mean, cond_logvar def create_train_state( self, From a1d28b315f9b3b086df38fb2ef95938f75db0f2f Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Sun, 12 Jul 2026 18:47:06 +0200 Subject: [PATCH 23/35] extract setup_time hook --- src/cellflow/networks/_velocity_field.py | 29 ++++++++++++------------ 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/src/cellflow/networks/_velocity_field.py b/src/cellflow/networks/_velocity_field.py index bfb4ad7f..cc9419fa 100644 --- a/src/cellflow/networks/_velocity_field.py +++ b/src/cellflow/networks/_velocity_field.py @@ -236,13 +236,7 @@ def setup(self): self.layer_cond_output_dropout = nn.Dropout(rate=self.cond_output_dropout) self.layer_norm_condition = nn.LayerNorm() if self.layer_norm_before_concatenation else lambda x: x - self.time_encoder = MLPBlock( - dims=self.time_encoder_dims, - act_fn=self.act_fn, - dropout_rate=self.time_encoder_dropout, - act_last_layer=False, - ) - self.layer_norm_time = nn.LayerNorm() if self.layer_norm_before_concatenation else lambda x: x + self._setup_time() self.x_encoder = MLPBlock( dims=self.hidden_dims, @@ -263,6 +257,19 @@ def setup(self): self._setup_conditioning(conditioning_kwargs) + def _setup_time(self) -> None: + """Build the time encoder and its optional pre-concatenation LayerNorm. + + Override to a no-op in a time-less velocity field (e.g. Equilibrium Matching). + """ + self.time_encoder = MLPBlock( + dims=self.time_encoder_dims, + act_fn=self.act_fn, + dropout_rate=self.time_encoder_dropout, + act_last_layer=False, + ) + self.layer_norm_time = nn.LayerNorm() if self.layer_norm_before_concatenation else lambda x: x + def _setup_conditioning(self, conditioning_kwargs: dict[str, Any]) -> None: """Build the ``conditioning``-mode-specific submodules. @@ -676,13 +683,7 @@ def setup(self): self.layer_cond_output_dropout = nn.Dropout(rate=self.cond_output_dropout) self.layer_norm_condition = nn.LayerNorm() if self.layer_norm_before_concatenation else lambda x: x - self.time_encoder = MLPBlock( - dims=self.time_encoder_dims, - act_fn=self.act_fn, - dropout_rate=self.time_encoder_dropout, - act_last_layer=False, - ) - self.layer_norm_time = nn.LayerNorm() if self.layer_norm_before_concatenation else lambda x: x + self._setup_time() self.x_encoder = MLPBlock( dims=self.hidden_dims, From 49389f414e826a94ddeb0871bd3ba36e6dcc15c5 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Sun, 12 Jul 2026 19:08:11 +0200 Subject: [PATCH 24/35] add cfg features to genot as well --- src/cellflow/networks/_velocity_field.py | 5 ++ src/cellflow/solvers/_genot.py | 65 +++++++++++++++++++++--- src/cellflow/solvers/_otfm.py | 46 +++++++++-------- tests/solver/test_solver.py | 53 +++++++++++++++++-- 4 files changed, 139 insertions(+), 30 deletions(-) diff --git a/src/cellflow/networks/_velocity_field.py b/src/cellflow/networks/_velocity_field.py index cc9419fa..7155b78f 100644 --- a/src/cellflow/networks/_velocity_field.py +++ b/src/cellflow/networks/_velocity_field.py @@ -719,14 +719,19 @@ def __call__( cond: dict[str, jnp.ndarray], encoder_noise: jnp.ndarray, train: bool = True, + force_uncond: bool = False, ): squeeze = x_t.ndim == 1 + if self.condition_null == "mask_value": + cond = self._maybe_null_input(cond, train=train, force_uncond=force_uncond) cond_mean, cond_logvar = self.condition_encoder(cond, training=train) if self.condition_mode == "deterministic": cond_embedding = cond_mean else: cond_embedding = cond_mean + encoder_noise * jnp.exp(cond_logvar / 2.0) cond_embedding = self.layer_cond_output_dropout(cond_embedding, deterministic=not train) + if self.condition_null == "zero_embedding": + cond_embedding = self._maybe_null_embedding(cond_embedding, train=train, force_uncond=force_uncond) t_encoded = sinusoidal_time_encoder(t, time_freqs=self.time_freqs, time_max_period=self.time_max_period) t_encoded = self.time_encoder(t_encoded, training=train) x_encoded = self.x_encoder(x_t, training=train) diff --git a/src/cellflow/solvers/_genot.py b/src/cellflow/solvers/_genot.py index 93b162bc..11049ae8 100644 --- a/src/cellflow/solvers/_genot.py +++ b/src/cellflow/solvers/_genot.py @@ -17,6 +17,7 @@ from cellflow._types import ArrayLike from cellflow.model._utils import _multivariate_normal from cellflow.solvers._base import BaseSolver +from cellflow.solvers._otfm import ClassifierFreeGuidance, Guidance, VelocityFn __all__ = ["GENOT"] @@ -79,11 +80,13 @@ def __init__( target_dim: int, time_sampler: Callable[[jax.Array, int], jnp.ndarray] = solver_utils.uniform_sampler, latent_noise_fn: (Callable[[jax.Array, tuple[int, ...]], jnp.ndarray] | None) = None, + guidance: Guidance | None = None, **kwargs: Any, ): super().__init__(vf, probability_path, time_sampler) self.data_match_fn = jax.jit(data_match_fn) self.source_dim = source_dim + self.guidance = guidance if latent_noise_fn is None: latent_noise_fn = functools.partial(_multivariate_normal, dim=target_dim) self.latent_noise_fn = latent_noise_fn @@ -265,22 +268,70 @@ def predict( x_pred = self._predict_jit(x, condition, rng, rng_genot, **kwargs) return np.array(x_pred) + @property + def cfg_enabled(self) -> bool: + """Whether classifier-free guidance is available at predict time. + + Guidance needs a meaningful unconditional velocity ``v_null``, which only exists + when the velocity field was trained with condition dropout + (``condition_dropout_prob > 0``). When ``False``, a per-call ``guidance_scale`` is + ignored (with a warning) and the plain conditional velocity is used. + """ + return float(getattr(self.vf, "condition_dropout_prob", 0.0)) > 0.0 + + def _base_velocity(self) -> VelocityFn: + """Base (conditional) velocity closure for the predict path. + + Signature ``(t, x, args, force_uncond=False) -> velocity`` with ``args`` being + ``(params, x_0, condition, encoder_noise)``. Nulling only the condition (``x_0`` is + kept) gives the unconditional source→target velocity used by guidance. + """ + + def vf( + t: float, + x: jnp.ndarray, + args: tuple[Any, jnp.ndarray, dict[str, jnp.ndarray], jnp.ndarray], + force_uncond: bool = False, + ) -> jnp.ndarray: + params, x_0, condition, encoder_noise = args + return self.vf_state.apply_fn( + {"params": params}, t, x, x_0, condition, encoder_noise, train=False, force_uncond=force_uncond + )[0] + + return vf + def _get_predict_fn(self, kwargs_frozen: frozen_dict.FrozenDict) -> Callable: """Build and cache a jit+vmap predict function for the given diffrax kwargs. - The returned function is created once per unique set of diffrax kwargs, - then reused on subsequent calls. + The base velocity from :meth:`_base_velocity` is wrapped by guidance when it + applies: a per-call ``guidance_scale != 1.0`` (popped here, requires + :attr:`cfg_enabled`) builds a :class:`~cellflow.solvers.ClassifierFreeGuidance` + for this call, overriding the construction-time ``guidance``; otherwise the + construction-time ``guidance`` is used (``None`` = plain conditional velocity). + The returned function is created once per unique set of kwargs, then reused. """ if kwargs_frozen in self._predict_fn_cache: return self._predict_fn_cache[kwargs_frozen] kwargs = dict(kwargs_frozen) + guidance_scale = float(kwargs.pop("guidance_scale", 1.0)) + guidance = self.guidance + if guidance_scale != 1.0: + if self.cfg_enabled: + # v = v_null + scale·(v_cond − v_null); overrides construction-time guidance. + guidance = ClassifierFreeGuidance(scale=guidance_scale) + else: + warnings.warn( + f"guidance_scale={guidance_scale} ignored: the velocity field was not trained " + "with classifier-free guidance (condition_dropout_prob == 0), so the " + "unconditional velocity is undefined. Using the plain conditional velocity.", + stacklevel=2, + ) + guidance = None - def vf( - t: float, x: jnp.ndarray, args: tuple[Any, jnp.ndarray, dict[str, jnp.ndarray], jnp.ndarray] - ) -> jnp.ndarray: - params, x_0, condition, encoder_noise = args - return self.vf_state.apply_fn({"params": params}, t, x, x_0, condition, encoder_noise, train=False)[0] + vf = self._base_velocity() + if guidance is not None: + vf = guidance.wrap(vf) def solve_ode( params: Any, diff --git a/src/cellflow/solvers/_otfm.py b/src/cellflow/solvers/_otfm.py index 3886dff2..0c41db74 100644 --- a/src/cellflow/solvers/_otfm.py +++ b/src/cellflow/solvers/_otfm.py @@ -28,12 +28,15 @@ class Guidance(Protocol): """Pluggable transform applied to the base velocity field on the predict path. - A guidance strategy receives the base (conditional) velocity closure and the - inference train state, and returns a new velocity closure with the same - ``(t, x, args) -> velocity`` signature. + A guidance strategy receives the base velocity closure ``vf(t, x, args, + force_uncond=False)`` — which owns the field's call signature and returns the + conditional (``force_uncond=False``) or unconditional (``True``) velocity — and + returns a plain ``(t, x, args) -> velocity`` closure. Taking the closure (rather + than the train state) keeps guidance agnostic to solver-specific ``args`` such as + GENOT's source ``x_0``. """ - def wrap(self, vf: VelocityFn, inference_state: train_state.TrainState) -> VelocityFn: + def wrap(self, vf: Callable) -> VelocityFn: """Wrap the base velocity ``vf`` and return the guided velocity.""" ... @@ -80,22 +83,18 @@ def from_ode_weight(cls, cfg_ode_weight: float) -> "ClassifierFreeGuidance": raise ValueError("cfg_ode_weight must be non-negative.") return cls(scale=1.0 + cfg_ode_weight) - def wrap(self, vf: VelocityFn, inference_state: train_state.TrainState) -> VelocityFn: - """Return a velocity closure computing ``v_null + scale * (v_cond - v_null)``.""" + def wrap(self, vf: Callable) -> VelocityFn: + """Return a velocity closure computing ``v_null + scale * (v_cond - v_null)``. + + ``vf`` is the base velocity ``vf(t, x, args, force_uncond=False)``; it owns the + field's call signature, so this blend is agnostic to solver-specific ``args`` + (e.g. GENOT threads its source ``x_0`` through ``args``). + """ scale = self.scale def guided_vf(t: jnp.ndarray, x: jnp.ndarray, args: tuple[Any, ...]) -> jnp.ndarray: - params, condition, encoder_noise = args - v_cond = vf(t, x, args) - v_null = inference_state.apply_fn( - {"params": params}, - t, - x, - condition, - encoder_noise, - train=False, - force_uncond=True, - )[0] + v_cond = vf(t, x, args, force_uncond=False) + v_null = vf(t, x, args, force_uncond=True) return v_null + scale * (v_cond - v_null) return guided_vf @@ -282,9 +281,16 @@ def _base_velocity(self) -> VelocityFn: inference velocity field conditionally (``force_uncond=False``). """ - def vf(t: jnp.ndarray, x: jnp.ndarray, args: tuple[Any, dict[str, jnp.ndarray], jnp.ndarray]) -> jnp.ndarray: + def vf( + t: jnp.ndarray, + x: jnp.ndarray, + args: tuple[Any, dict[str, jnp.ndarray], jnp.ndarray], + force_uncond: bool = False, + ) -> jnp.ndarray: params, condition, encoder_noise = args - return self.vf_state_inference.apply_fn({"params": params}, t, x, condition, encoder_noise, train=False)[0] + return self.vf_state_inference.apply_fn( + {"params": params}, t, x, condition, encoder_noise, train=False, force_uncond=force_uncond + )[0] return vf @@ -329,7 +335,7 @@ def _get_predict_fn(self, kwargs_frozen: frozen_dict.FrozenDict) -> Callable: vf = self._base_velocity() if guidance is not None: - vf = guidance.wrap(vf, self.vf_state_inference) + vf = guidance.wrap(vf) def solve_ode( params: Any, x: jnp.ndarray, condition: dict[str, jnp.ndarray], encoder_noise: jnp.ndarray diff --git a/tests/solver/test_solver.py b/tests/solver/test_solver.py index f9a5087c..5b3d75d2 100644 --- a/tests/solver/test_solver.py +++ b/tests/solver/test_solver.py @@ -204,6 +204,31 @@ def _make_otfm(condition_dropout_prob=0.0, guidance=None, condition_null="zero_e ) +def _make_genot(condition_dropout_prob=0.0, guidance=None, condition_null="zero_embedding"): + """Build a small GENOT solver for guidance tests.""" + vf = cellflow.networks.GENOTConditionalVelocityField( + output_dim=5, + max_combination_length=2, + condition_embedding_dim=12, + hidden_dims=(32, 32), + decoder_dims=(32, 32), + genot_source_dims=(32, 32), + condition_dropout_prob=condition_dropout_prob, + condition_null=condition_null, + ) + return _genot.GENOT( + vf=vf, + data_match_fn=match_linear, + probability_path=ConstantNoiseFlow(0.0), + optimizer=optax.adam(1e-3), + source_dim=5, + target_dim=5, + conditions={"drug": np.random.rand(2, 1, 3)}, + rng=vf_rng, + guidance=guidance, + ) + + class TestGuidance: def test_predict_guidance_none_matches_conditional_solve(self): """With ``guidance=None`` predict reproduces the pre-change conditional-only solve. @@ -261,7 +286,7 @@ def test_classifier_free_guidance_wraps_velocity(self): {"params": params}, t, x, condition, encoder_noise, train=False, force_uncond=True )[0] - guided_vf = solver.guidance.wrap(base_vf, solver.vf_state_inference) + guided_vf = solver.guidance.wrap(base_vf) v_guided = guided_vf(t, x, args) expected = v_null + w * (v_cond - v_null) @@ -282,7 +307,7 @@ def test_classifier_free_guidance_scale_one_is_conditional(self): base_vf = solver._base_velocity() v_cond = base_vf(t, x, args) - v_guided = solver.guidance.wrap(base_vf, solver.vf_state_inference)(t, x, args) + v_guided = solver.guidance.wrap(base_vf)(t, x, args) assert np.allclose(np.asarray(v_guided), np.asarray(v_cond), atol=1e-6) @@ -303,6 +328,28 @@ def test_from_ode_weight_parameterization(self): with pytest.raises(ValueError, match="cfg_ode_weight must be non-negative"): ClassifierFreeGuidance.from_ode_weight(-1.0) + def test_genot_classifier_free_guidance(self): + """GENOT supports CFG (shared, x_0-aware): guidance changes the prediction, ``scale=1.0`` + is the plain conditional solve, and it is gated on ``cfg_enabled``.""" + x = np.ones((4, 5), dtype=np.float32) + cond = {"drug": np.ones((1, 2, 3), dtype=np.float32)} + + g = _make_genot(condition_dropout_prob=0.5) + assert g.cfg_enabled + p_cond = g.predict(x, cond, guidance_scale=1.0, max_steps=10, throw=False) + p_guided = g.predict(x, cond, guidance_scale=2.0, max_steps=10, throw=False) + p_default = g.predict(x, cond, max_steps=10, throw=False) + assert np.all(np.isfinite(p_guided)) + assert not np.allclose(p_cond, p_guided) # guidance actually changes the field + assert np.allclose(p_cond, p_default) # scale=1.0 == no guidance + + # Without condition dropout, v_null is undefined -> guidance ignored (with warning). + g0 = _make_genot(condition_dropout_prob=0.0) + assert not g0.cfg_enabled + with pytest.warns(UserWarning, match="guidance_scale"): + p_ignored = g0.predict(x, cond, guidance_scale=3.0, max_steps=10, throw=False) + assert np.allclose(p_ignored, g0.predict(x, cond, max_steps=10, throw=False)) + @pytest.mark.parametrize("pooling", ["mean", "attention_token", "attention_seed"]) def test_mask_value_null_matches_mask_filled_condition(self, pooling): """With ``condition_null='mask_value'``, ``force_uncond`` equals evaluating the vf on a mask-filled condition.""" @@ -381,7 +428,7 @@ def test_theislab_parity_guidance_formula(self): {"params": params}, t, x, condition, encoder_noise, train=False, force_uncond=True )[0] - v_guided = solver.guidance.wrap(base_vf, solver.vf_state_inference)(t, x, args) + v_guided = solver.guidance.wrap(base_vf)(t, x, args) expected = (1.0 + w) * v_cond - w * v_null assert np.allclose(np.asarray(v_guided), np.asarray(expected), atol=1e-6) From 31f9678be5f62529764ff6dcd178d074154493a2 Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Tue, 14 Jul 2026 16:46:59 +0200 Subject: [PATCH 25/35] add annbatch streaming path --- pyproject.toml | 4 +- src/cellflow/data/__init__.py | 2 +- src/cellflow/data/_annbatch.py | 53 +- src/cellflow/data/_dataloader.py | 333 +-------- src/cellflow/data/_legacy.py | 320 ++++++++ src/cellflow/model/__init__.py | 3 +- src/cellflow/model/_base.py | 668 +++++++++++++++++ src/cellflow/model/_cellflow.py | 910 +---------------------- src/cellflow/model/_cellflow_annbatch.py | 322 ++++++++ src/cellflow/networks/_velocity_field.py | 44 +- src/cellflow/solvers/_base.py | 3 + src/cellflow/solvers/_otfm.py | 146 +++- src/cellflow/training/_trainer.py | 2 +- src/dagloader/__init__.py | 11 +- src/dagloader/_eval_loader.py | 158 ++++ src/dagloader/_loader.py | 420 ++++++----- src/dagloader/_scheduled_sampler.py | 99 --- tests/conftest.py | 2 +- tests/dagloader/test_coherence.py | 139 ++++ tests/dagloader/test_eval_loader.py | 125 ++++ tests/data/test_cfsampler.py | 4 +- tests/model/test_annbatch_ooc.py | 24 +- tests/model/test_annbatch_path.py | 15 +- tests/model/test_annbatch_save.py | 12 +- tests/model/test_annbatch_split.py | 38 +- tests/model/test_annbatch_train.py | 8 +- tests/model/test_annbatch_validation.py | 72 +- 27 files changed, 2340 insertions(+), 1597 deletions(-) create mode 100644 src/cellflow/data/_legacy.py create mode 100644 src/cellflow/model/_base.py create mode 100644 src/cellflow/model/_cellflow_annbatch.py create mode 100644 src/dagloader/_eval_loader.py delete mode 100644 src/dagloader/_scheduled_sampler.py create mode 100644 tests/dagloader/test_coherence.py create mode 100644 tests/dagloader/test_eval_loader.py diff --git a/pyproject.toml b/pyproject.toml index 80ea4d87..af930ec9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,7 +48,9 @@ dependencies = [ ] optional-dependencies.annbatch = [ - "annbatch>=0.2.1", + # Pinned to the branch adding `SequentialClassSampler` (streaming eval path); revert to a released + # `annbatch>=0.2.x` once it lands upstream. Direct URL so pip/uv/tox all resolve the branch. + "annbatch @ git+https://github.com/selmanozleyen/annbatch.git@feat/bound-class-wip", ] optional-dependencies.dev = [ "furo", diff --git a/src/cellflow/data/__init__.py b/src/cellflow/data/__init__.py index e6f6f2de..50fe06e0 100644 --- a/src/cellflow/data/__init__.py +++ b/src/cellflow/data/__init__.py @@ -1,6 +1,6 @@ from cellflow.data._data import BaseDataMixin, ConditionData, PredictionData, TrainingData, ValidationData -from cellflow.data._dataloader import PredictionSampler, TrainSampler, ValidationSampler from cellflow.data._datamanager import DataManager +from cellflow.data._legacy import PredictionSampler, TrainSampler, ValidationSampler __all__ = [ "DataManager", diff --git a/src/cellflow/data/_annbatch.py b/src/cellflow/data/_annbatch.py index c563fc1b..9129f7b7 100644 --- a/src/cellflow/data/_annbatch.py +++ b/src/cellflow/data/_annbatch.py @@ -12,7 +12,7 @@ from collections.abc import Callable, Mapping, Sequence from dataclasses import dataclass -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any, Literal import anndata as ad import numpy as np @@ -22,11 +22,58 @@ if TYPE_CHECKING: from cellflow._types import ArrayLike - from dagloader import Container, Scheme + from dagloader import Container, DAGEvalLoader, Scheme Leaf = tuple[object, ...] # a scheme leaf: one value per grouping column -__all__ = ["AnnbatchTraining", "assert_source_chunkable", "build_annbatch_training", "sample_rep_to_key"] +__all__ = [ + "AnnbatchTraining", + "AnnbatchValidationSampler", + "assert_source_chunkable", + "build_annbatch_training", + "sample_rep_to_key", +] + + +class AnnbatchValidationSampler: + """Validation sampler over a :class:`dagloader.DAGEvalLoader` — the trainer's ``sample(mode)`` contract. + + Yields per-condition ``{"source", "condition", "target"}`` dicts (keyed by the perturbed leaf), as the + trainer expects. The control-rooted ``DAGEvalLoader`` reads each control population's cells in full for + the source and samples a matched perturbed batch for the target — via annbatch, no boolean masking. + ``n_conditions_*`` sets how many (control-population, drug) batches to draw per mode. + + Parameters + ---------- + eval_loader + A :class:`dagloader.DAGEvalLoader` built over the validation source. + n_conditions_on_log_iteration, n_conditions_on_train_end + How many conditions to draw per mode; :obj:`None` visits each control population once. + """ + + def __init__( + self, + eval_loader: DAGEvalLoader, + *, + n_conditions_on_log_iteration: int | None = None, + n_conditions_on_train_end: int | None = None, + ) -> None: + self._eval = eval_loader + self.n_conditions_on_log_iteration = n_conditions_on_log_iteration + self.n_conditions_on_train_end = n_conditions_on_train_end + + def sample(self, mode: Literal["on_log_iteration", "on_train_end"]) -> dict[str, dict[Any, Any]]: + """Sample a validation batch: per-condition source/condition/target dicts (keyed by perturbed leaf).""" + n = self.n_conditions_on_log_iteration if mode == "on_log_iteration" else self.n_conditions_on_train_end + source: dict[Any, Any] = {} + condition: dict[Any, Any] = {} + target: dict[Any, Any] = {} + for out in self._eval.iter_conditions(n_conditions=n): + key = tuple(out["leaf"]) + source[key] = out["source"] + condition[key] = out["condition"] + target[key] = out["target"] + return {"source": source, "condition": condition, "target": target} def assert_source_chunkable(source: Container, cols: Sequence[str], chunk_size: int) -> None: diff --git a/src/cellflow/data/_dataloader.py b/src/cellflow/data/_dataloader.py index 91d7834e..31754821 100644 --- a/src/cellflow/data/_dataloader.py +++ b/src/cellflow/data/_dataloader.py @@ -1,350 +1,51 @@ -import abc -import queue -import threading -from collections.abc import Generator -from typing import TYPE_CHECKING, Any, Literal +from typing import TYPE_CHECKING -import jax import numpy as np from cellflow._types import ArrayLike -from cellflow.data._data import PredictionData, TrainingData, ValidationData if TYPE_CHECKING: from dagloader import DAGLoader __all__ = [ - "TrainSampler", - "ValidationSampler", - "PredictionSampler", - "OOCTrainSampler", - "DAGLoaderTrainSampler", + "DAGLoaderAdapter", ] -def _densify(x: ArrayLike) -> np.ndarray: - """Densify a possibly-sparse streamed cell batch (dense batches pass through). +def _densify(x: ArrayLike) -> ArrayLike: + """Densify a possibly-sparse streamed cell batch, staying on the array's native backend. - Done at the model boundary, not in the loader: ``dagloader`` stays representation-agnostic while the - solver needs dense arrays. + ``dagloader`` yields native jax arrays (``to="jax"``): dense reps pass straight through, and a + sparse rep arrives as a ``jax.experimental.sparse`` CSR — densified here via ``.todense()`` (still + on-device). No numpy cast and no host round-trip, so a GPU-resident batch reaches the solver as-is. """ - todense = getattr(x, "todense", None) # scipy sparse → dense; plain arrays lack this - return np.asarray(todense()) if todense is not None else np.asarray(x) + todense = getattr(x, "todense", None) # (jax/scipy) sparse → dense; dense arrays lack this + return todense() if todense is not None else x -class DAGLoaderTrainSampler: - """Adapt a ``dagloader.DAGLoader`` stream to the :meth:`TrainSampler.sample` batch contract. +class DAGLoaderAdapter: + """Adapt a ``dagloader.DAGLoader`` stream to the sampler ``sample()`` batch contract. - Renames the loader's ``{"target", "source", "condition"}`` batch to the trainer's - ``{"src_cell_data", "tgt_cell_data", "condition"}`` and densifies the cell arrays, so the in-memory - and streaming paths reach the solver identically. + Renames the loader's ``{"target", "source", "condition"}`` batch to the model's + ``{"src_cell_data", "tgt_cell_data", "condition"}`` and densifies any sparse cell rep, keeping the + arrays on their native (jax) backend, so the in-memory and streaming paths reach the solver + identically. Used for training, and (re)used for the streamed validation and prediction paths. """ def __init__(self, loader: "DAGLoader"): self._loader = loader self._iter = iter(loader) - def sample(self, rng: np.random.Generator | None = None) -> dict[str, np.ndarray | dict[str, np.ndarray]]: + def sample(self, rng: np.random.Generator | None = None) -> dict[str, ArrayLike | dict[str, ArrayLike]]: """Return the next streamed batch as a trainer batch dict. ``rng`` is unused — the ``DAGLoader`` owns its own reproducible RNG. """ batch = next(self._iter) - out: dict[str, np.ndarray | dict[str, np.ndarray]] = { + out: dict[str, ArrayLike | dict[str, ArrayLike]] = { "src_cell_data": _densify(batch["source"]), "tgt_cell_data": _densify(batch["target"]), } if "condition" in batch: out["condition"] = batch["condition"] return out - - -class TrainSampler: - """Data sampler for :class:`~cellflow.data.TrainingData`. - - Parameters - ---------- - data - The training data. - batch_size - The batch size. - - """ - - def __init__(self, data: TrainingData, batch_size: int = 1024): - self._data = data - self._data_idcs = np.arange(data.cell_data.shape[0]) - self.batch_size = batch_size - self.n_source_dists = data.n_controls - self.n_target_dists = data.n_perturbations - - self._control_to_perturbation_keys = sorted(data.control_to_perturbation.keys()) - self._has_condition_data = data.condition_data is not None - - def _sample_target_dist_idx(self, source_dist_idx, rng): - """Sample a target distribution index given the source distribution index.""" - return rng.choice(self._data.control_to_perturbation[source_dist_idx]) - - def _get_embeddings(self, idx, condition_data) -> dict[str, np.ndarray]: - """Get embeddings for a given index.""" - result = {} - for key, arr in condition_data.items(): - result[key] = np.expand_dims(arr[idx], 0) - return result - - def _sample_from_mask(self, rng, mask) -> np.ndarray: - """Sample indices according to a mask.""" - # Convert mask to probability distribution - valid_indices = np.where(mask)[0] - - # Handle case with no valid indices (should not happen in practice) - if len(valid_indices) == 0: - raise ValueError("No valid indices found in the mask") - - # Sample from valid indices with equal probability - batch_idcs = rng.choice(valid_indices, self.batch_size, replace=True) - return batch_idcs - - def sample(self, rng) -> dict[str, Any]: - """Sample a batch of data. - - Parameters - ---------- - seed : int, optional - Random seed - - Returns - ------- - Dictionary with source and target data - """ - # Sample source distribution index - source_dist_idx = rng.integers(0, self.n_source_dists) - - # Get source cells - source_cells_mask = self._data.split_covariates_mask == source_dist_idx - source_batch_idcs = self._sample_from_mask(rng, source_cells_mask) - source_batch = self._data.cell_data[source_batch_idcs] - - target_dist_idx = self._sample_target_dist_idx(source_dist_idx, rng) - target_cells_mask = self._data.perturbation_covariates_mask == target_dist_idx - target_batch_idcs = self._sample_from_mask(rng, target_cells_mask) - target_batch = self._data.cell_data[target_batch_idcs] - - if not self._has_condition_data: - return {"src_cell_data": source_batch, "tgt_cell_data": target_batch} - else: - condition_batch = self._get_embeddings(target_dist_idx, self._data.condition_data) - return { - "src_cell_data": source_batch, - "tgt_cell_data": target_batch, - "condition": condition_batch, - } - - @property - def data(self): - """The training data.""" - return self._data - - -class BaseValidSampler(abc.ABC): - @abc.abstractmethod - def sample(*args, **kwargs): - pass - - def _get_key(self, cond_idx: int) -> tuple[str, ...]: - if len(self._data.perturbation_idx_to_id): # type: ignore[attr-defined] - return self._data.perturbation_idx_to_id[cond_idx] # type: ignore[attr-defined] - cov_combination = self._data.perturbation_idx_to_covariates[cond_idx] # type: ignore[attr-defined] - return tuple(cov_combination[i] for i in range(len(cov_combination))) - - def _get_perturbation_to_control(self, data: ValidationData | PredictionData) -> dict[int, np.ndarray]: - d = {} - for k, v in data.control_to_perturbation.items(): - for el in v: - d[el] = k - return d - - def _get_condition_data(self, cond_idx: int) -> dict[str, np.ndarray]: - return {k: v[[cond_idx], ...] for k, v in self._data.condition_data.items()} # type: ignore[attr-defined] - - -class ValidationSampler(BaseValidSampler): - """Data sampler for :class:`~cellflow.data.ValidationData`. - - Parameters - ---------- - val_data - The validation data. - seed - Random seed. - """ - - def __init__(self, val_data: ValidationData, seed: int = 0) -> None: - self._data = val_data - self.perturbation_to_control = self._get_perturbation_to_control(val_data) - self.n_conditions_on_log_iteration = ( - val_data.n_conditions_on_log_iteration - if val_data.n_conditions_on_log_iteration is not None - else val_data.n_perturbations - ) - self.n_conditions_on_train_end = ( - val_data.n_conditions_on_train_end - if val_data.n_conditions_on_train_end is not None - else val_data.n_perturbations - ) - self.rng = np.random.default_rng(seed) - if self._data.condition_data is None: - raise NotImplementedError("Validation data must have condition data.") - - def sample(self, mode: Literal["on_log_iteration", "on_train_end"]) -> Any: - """Sample data for validation. - - Parameters - ---------- - mode - Sampling mode. Either ``"on_log_iteration"`` or ``"on_train_end"``. - - Returns - ------- - Dictionary with source, condition, and target data from the validation data. - """ - size = self.n_conditions_on_log_iteration if mode == "on_log_iteration" else self.n_conditions_on_train_end - condition_idcs = self.rng.choice(self._data.n_perturbations, size=(size,), replace=False) - - source_idcs = [self.perturbation_to_control[cond_idx] for cond_idx in condition_idcs] - source_cells_mask = [self._data.split_covariates_mask == source_idx for source_idx in source_idcs] - source_cells = [self._data.cell_data[mask] for mask in source_cells_mask] - target_cells_mask = [cond_idx == self._data.perturbation_covariates_mask for cond_idx in condition_idcs] - target_cells = [self._data.cell_data[mask] for mask in target_cells_mask] - conditions = [self._get_condition_data(cond_idx) for cond_idx in condition_idcs] - cell_rep_dict = {} - cond_dict = {} - true_dict = {} - for i in range(len(condition_idcs)): - k = self._get_key(condition_idcs[i]) - cell_rep_dict[k] = source_cells[i] - cond_dict[k] = conditions[i] - true_dict[k] = target_cells[i] - - return {"source": cell_rep_dict, "condition": cond_dict, "target": true_dict} - - @property - def data(self) -> ValidationData: - """The validation data.""" - return self._data - - -class PredictionSampler(BaseValidSampler): - """Data sampler for :class:`~cellflow.data.PredictionData`. - - Parameters - ---------- - pred_data - The prediction data. - - """ - - def __init__(self, pred_data: PredictionData) -> None: - self._data = pred_data - self.perturbation_to_control = self._get_perturbation_to_control(pred_data) - if self._data.condition_data is None: - raise NotImplementedError("Validation data must have condition data.") - - def sample(self) -> Any: - """Sample data for prediction. - - Returns - ------- - Dictionary with source and condition data from the prediction data. - """ - condition_idcs = range(self._data.n_perturbations) - - source_idcs = [self.perturbation_to_control[cond_idx] for cond_idx in condition_idcs] - source_cells_mask = [self._data.split_covariates_mask == source_idx for source_idx in source_idcs] - source_cells = [self._data.cell_data[mask] for mask in source_cells_mask] - conditions = [self._get_condition_data(cond_idx) for cond_idx in condition_idcs] - cell_rep_dict = {} - cond_dict = {} - for i in range(len(condition_idcs)): - k = self._get_key(condition_idcs[i]) - cell_rep_dict[k] = source_cells[i] - cond_dict[k] = conditions[i] - - return { - "source": cell_rep_dict, - "condition": cond_dict, - } - - @property - def data(self) -> PredictionData: - """The training data.""" - return self._data - - -def prefetch_to_device( - sampler: TrainSampler, seed: int, num_iterations: int, prefetch_factor: int = 2, num_workers: int = 4 -) -> Generator[dict[str, Any], None, None]: - seq = np.random.SeedSequence(seed) - random_generators = [np.random.default_rng(s) for s in seq.spawn(num_workers)] - - q: queue.Queue[dict[str, Any]] = queue.Queue(maxsize=prefetch_factor * num_workers) - sem = threading.Semaphore(num_iterations) - stop_event = threading.Event() - - def worker(rng: np.random.Generator): - while not stop_event.is_set() and sem.acquire(blocking=False): - batch = sampler.sample(rng) - batch = jax.device_put(batch, jax.devices()[0], donate=True) - jax.block_until_ready(batch) - while not stop_event.is_set(): - try: - q.put(batch, timeout=1.0) - break # Batch successfully put into the queue; break out of retry loop - except queue.Full: - continue - - return - - # Start multiple worker threads - ts = [] - for i in range(num_workers): - t = threading.Thread(target=worker, daemon=True, name=f"worker-{i}", args=(random_generators[i],)) - t.start() - ts.append(t) - - try: - for _ in range(num_iterations): - # Yield batches from the queue; will block waiting for available batch - yield q.get() - finally: - # When the generator is closed or garbage collected, clean up the worker threads - stop_event.set() # Signal all workers to exit - for t in ts: - t.join() # Wait for all worker threads to finish - - -class OOCTrainSampler: - def __init__( - self, data: TrainingData, seed: int, batch_size: int = 1024, num_workers: int = 4, prefetch_factor: int = 2 - ): - self.inner = TrainSampler(data=data, batch_size=batch_size) - self.num_workers = num_workers - self.prefetch_factor = prefetch_factor - self.seed = seed - self._iterator = None - - def set_sampler(self, num_iterations: int) -> None: - self._iterator = prefetch_to_device( - sampler=self.inner, seed=self.seed, num_iterations=num_iterations, prefetch_factor=self.prefetch_factor - ) - - def sample(self, rng=None) -> dict[str, Any]: - if self._iterator is None: - raise ValueError( - "Sampler not set. Use `set_sampler` to set the sampler with" - "the number of iterations. Without the number of iterations," - " the sampler will not be able to sample the data." - ) - if rng is not None: - del rng - return next(self._iterator) diff --git a/src/cellflow/data/_legacy.py b/src/cellflow/data/_legacy.py new file mode 100644 index 00000000..1f47d4e5 --- /dev/null +++ b/src/cellflow/data/_legacy.py @@ -0,0 +1,320 @@ +"""Legacy in-memory data samplers. + +These samplers operate on materialized :mod:`cellflow.data` containers +(:class:`~cellflow.data.TrainingData`, :class:`~cellflow.data.ValidationData`, +:class:`~cellflow.data.PredictionData`) held fully in memory. They are superseded by the +annbatch/``dagloader`` streaming path (see :class:`cellflow.data._dataloader.DAGLoaderAdapter` +and :meth:`cellflow.model.CellFlow.prepare_loaders`), which streams cells out of core and also +accepts an in-memory ``AnnData``. Kept here for backward compatibility. +""" + +import abc +import queue +import threading +from collections.abc import Generator +from typing import Any, Literal + +import jax +import numpy as np + +from cellflow.data._data import PredictionData, TrainingData, ValidationData + +__all__ = [ + "TrainSampler", + "BaseValidSampler", + "ValidationSampler", + "PredictionSampler", + "OOCTrainSampler", + "prefetch_to_device", +] + + +class TrainSampler: + """Data sampler for :class:`~cellflow.data.TrainingData`. + + Parameters + ---------- + data + The training data. + batch_size + The batch size. + + """ + + def __init__(self, data: TrainingData, batch_size: int = 1024): + self._data = data + self._data_idcs = np.arange(data.cell_data.shape[0]) + self.batch_size = batch_size + self.n_source_dists = data.n_controls + self.n_target_dists = data.n_perturbations + + self._control_to_perturbation_keys = sorted(data.control_to_perturbation.keys()) + self._has_condition_data = data.condition_data is not None + + def _sample_target_dist_idx(self, source_dist_idx, rng): + """Sample a target distribution index given the source distribution index.""" + return rng.choice(self._data.control_to_perturbation[source_dist_idx]) + + def _get_embeddings(self, idx, condition_data) -> dict[str, np.ndarray]: + """Get embeddings for a given index.""" + result = {} + for key, arr in condition_data.items(): + result[key] = np.expand_dims(arr[idx], 0) + return result + + def _sample_from_mask(self, rng, mask) -> np.ndarray: + """Sample indices according to a mask.""" + # Convert mask to probability distribution + valid_indices = np.where(mask)[0] + + # Handle case with no valid indices (should not happen in practice) + if len(valid_indices) == 0: + raise ValueError("No valid indices found in the mask") + + # Sample from valid indices with equal probability + batch_idcs = rng.choice(valid_indices, self.batch_size, replace=True) + return batch_idcs + + def sample(self, rng) -> dict[str, Any]: + """Sample a batch of data. + + Parameters + ---------- + seed : int, optional + Random seed + + Returns + ------- + Dictionary with source and target data + """ + # Sample source distribution index + source_dist_idx = rng.integers(0, self.n_source_dists) + + # Get source cells + source_cells_mask = self._data.split_covariates_mask == source_dist_idx + source_batch_idcs = self._sample_from_mask(rng, source_cells_mask) + source_batch = self._data.cell_data[source_batch_idcs] + + target_dist_idx = self._sample_target_dist_idx(source_dist_idx, rng) + target_cells_mask = self._data.perturbation_covariates_mask == target_dist_idx + target_batch_idcs = self._sample_from_mask(rng, target_cells_mask) + target_batch = self._data.cell_data[target_batch_idcs] + + if not self._has_condition_data: + return {"src_cell_data": source_batch, "tgt_cell_data": target_batch} + else: + condition_batch = self._get_embeddings(target_dist_idx, self._data.condition_data) + return { + "src_cell_data": source_batch, + "tgt_cell_data": target_batch, + "condition": condition_batch, + } + + @property + def data(self): + """The training data.""" + return self._data + + +class BaseValidSampler(abc.ABC): + @abc.abstractmethod + def sample(*args, **kwargs): + pass + + def _get_key(self, cond_idx: int) -> tuple[str, ...]: + if len(self._data.perturbation_idx_to_id): # type: ignore[attr-defined] + return self._data.perturbation_idx_to_id[cond_idx] # type: ignore[attr-defined] + cov_combination = self._data.perturbation_idx_to_covariates[cond_idx] # type: ignore[attr-defined] + return tuple(cov_combination[i] for i in range(len(cov_combination))) + + def _get_perturbation_to_control(self, data: ValidationData | PredictionData) -> dict[int, np.ndarray]: + d = {} + for k, v in data.control_to_perturbation.items(): + for el in v: + d[el] = k + return d + + def _get_condition_data(self, cond_idx: int) -> dict[str, np.ndarray]: + return {k: v[[cond_idx], ...] for k, v in self._data.condition_data.items()} # type: ignore[attr-defined] + + +class ValidationSampler(BaseValidSampler): + """Data sampler for :class:`~cellflow.data.ValidationData`. + + Parameters + ---------- + val_data + The validation data. + seed + Random seed. + """ + + def __init__(self, val_data: ValidationData, seed: int = 0) -> None: + self._data = val_data + self.perturbation_to_control = self._get_perturbation_to_control(val_data) + self.n_conditions_on_log_iteration = ( + val_data.n_conditions_on_log_iteration + if val_data.n_conditions_on_log_iteration is not None + else val_data.n_perturbations + ) + self.n_conditions_on_train_end = ( + val_data.n_conditions_on_train_end + if val_data.n_conditions_on_train_end is not None + else val_data.n_perturbations + ) + self.rng = np.random.default_rng(seed) + if self._data.condition_data is None: + raise NotImplementedError("Validation data must have condition data.") + + def sample(self, mode: Literal["on_log_iteration", "on_train_end"]) -> Any: + """Sample data for validation. + + Parameters + ---------- + mode + Sampling mode. Either ``"on_log_iteration"`` or ``"on_train_end"``. + + Returns + ------- + Dictionary with source, condition, and target data from the validation data. + """ + size = self.n_conditions_on_log_iteration if mode == "on_log_iteration" else self.n_conditions_on_train_end + condition_idcs = self.rng.choice(self._data.n_perturbations, size=(size,), replace=False) + + source_idcs = [self.perturbation_to_control[cond_idx] for cond_idx in condition_idcs] + source_cells_mask = [self._data.split_covariates_mask == source_idx for source_idx in source_idcs] + source_cells = [self._data.cell_data[mask] for mask in source_cells_mask] + target_cells_mask = [cond_idx == self._data.perturbation_covariates_mask for cond_idx in condition_idcs] + target_cells = [self._data.cell_data[mask] for mask in target_cells_mask] + conditions = [self._get_condition_data(cond_idx) for cond_idx in condition_idcs] + cell_rep_dict = {} + cond_dict = {} + true_dict = {} + for i in range(len(condition_idcs)): + k = self._get_key(condition_idcs[i]) + cell_rep_dict[k] = source_cells[i] + cond_dict[k] = conditions[i] + true_dict[k] = target_cells[i] + + return {"source": cell_rep_dict, "condition": cond_dict, "target": true_dict} + + @property + def data(self) -> ValidationData: + """The validation data.""" + return self._data + + +class PredictionSampler(BaseValidSampler): + """Data sampler for :class:`~cellflow.data.PredictionData`. + + Parameters + ---------- + pred_data + The prediction data. + + """ + + def __init__(self, pred_data: PredictionData) -> None: + self._data = pred_data + self.perturbation_to_control = self._get_perturbation_to_control(pred_data) + if self._data.condition_data is None: + raise NotImplementedError("Validation data must have condition data.") + + def sample(self) -> Any: + """Sample data for prediction. + + Returns + ------- + Dictionary with source and condition data from the prediction data. + """ + condition_idcs = range(self._data.n_perturbations) + + source_idcs = [self.perturbation_to_control[cond_idx] for cond_idx in condition_idcs] + source_cells_mask = [self._data.split_covariates_mask == source_idx for source_idx in source_idcs] + source_cells = [self._data.cell_data[mask] for mask in source_cells_mask] + conditions = [self._get_condition_data(cond_idx) for cond_idx in condition_idcs] + cell_rep_dict = {} + cond_dict = {} + for i in range(len(condition_idcs)): + k = self._get_key(condition_idcs[i]) + cell_rep_dict[k] = source_cells[i] + cond_dict[k] = conditions[i] + + return { + "source": cell_rep_dict, + "condition": cond_dict, + } + + @property + def data(self) -> PredictionData: + """The training data.""" + return self._data + + +def prefetch_to_device( + sampler: TrainSampler, seed: int, num_iterations: int, prefetch_factor: int = 2, num_workers: int = 4 +) -> Generator[dict[str, Any], None, None]: + seq = np.random.SeedSequence(seed) + random_generators = [np.random.default_rng(s) for s in seq.spawn(num_workers)] + + q: queue.Queue[dict[str, Any]] = queue.Queue(maxsize=prefetch_factor * num_workers) + sem = threading.Semaphore(num_iterations) + stop_event = threading.Event() + + def worker(rng: np.random.Generator): + while not stop_event.is_set() and sem.acquire(blocking=False): + batch = sampler.sample(rng) + batch = jax.device_put(batch, jax.devices()[0], donate=True) + jax.block_until_ready(batch) + while not stop_event.is_set(): + try: + q.put(batch, timeout=1.0) + break # Batch successfully put into the queue; break out of retry loop + except queue.Full: + continue + + return + + # Start multiple worker threads + ts = [] + for i in range(num_workers): + t = threading.Thread(target=worker, daemon=True, name=f"worker-{i}", args=(random_generators[i],)) + t.start() + ts.append(t) + + try: + for _ in range(num_iterations): + # Yield batches from the queue; will block waiting for available batch + yield q.get() + finally: + # When the generator is closed or garbage collected, clean up the worker threads + stop_event.set() # Signal all workers to exit + for t in ts: + t.join() # Wait for all worker threads to finish + + +class OOCTrainSampler: + def __init__( + self, data: TrainingData, seed: int, batch_size: int = 1024, num_workers: int = 4, prefetch_factor: int = 2 + ): + self.inner = TrainSampler(data=data, batch_size=batch_size) + self.num_workers = num_workers + self.prefetch_factor = prefetch_factor + self.seed = seed + self._iterator = None + + def set_sampler(self, num_iterations: int) -> None: + self._iterator = prefetch_to_device( + sampler=self.inner, seed=self.seed, num_iterations=num_iterations, prefetch_factor=self.prefetch_factor + ) + + def sample(self, rng=None) -> dict[str, Any]: + if self._iterator is None: + raise ValueError( + "Sampler not set. Use `set_sampler` to set the sampler with" + "the number of iterations. Without the number of iterations," + " the sampler will not be able to sample the data." + ) + if rng is not None: + del rng + return next(self._iterator) diff --git a/src/cellflow/model/__init__.py b/src/cellflow/model/__init__.py index 8731f241..8c912082 100644 --- a/src/cellflow/model/__init__.py +++ b/src/cellflow/model/__init__.py @@ -1,3 +1,4 @@ from cellflow.model._cellflow import CellFlow +from cellflow.model._cellflow_annbatch import CellFlowAnnbatch -__all__ = ["CellFlow"] +__all__ = ["CellFlow", "CellFlowAnnbatch"] diff --git a/src/cellflow/model/_base.py b/src/cellflow/model/_base.py new file mode 100644 index 00000000..8c988aaf --- /dev/null +++ b/src/cellflow/model/_base.py @@ -0,0 +1,668 @@ +"""Shared, data-path-agnostic CellFlow base: model setup, training, prediction, save/load.""" + +import abc +import functools +import os +import types +import warnings +from collections.abc import Callable, Mapping, Sequence +from dataclasses import field as dc_field +from typing import Any, Literal + +import anndata as ad +import cloudpickle +import flax.linen as nn +import jax +import jax.numpy as jnp +import numpy as np +import optax +import pandas as pd + +from cellflow import _constants +from cellflow._compat import BrownianBridge, ConstantNoiseFlow +from cellflow._types import ArrayLike, Layers_separate_input_t, Layers_t +from cellflow.data._data import ConditionData, ValidationData +from cellflow.data._datamanager import DataManager +from cellflow.data._legacy import PredictionSampler +from cellflow.model._utils import _write_predictions +from cellflow.networks import _velocity_field +from cellflow.plotting import _utils +from cellflow.solvers import SOLVER_REGISTRY, _genot, _otfm +from cellflow.training._callbacks import BaseCallback +from cellflow.training._trainer import CellFlowTrainer +from cellflow.utils import match_linear + +__all__ = ["BaseCellFlow"] + + +class BaseCellFlow(abc.ABC): + """Base class holding everything independent of how training cells are sourced. + + Concrete subclasses (:class:`~cellflow.model.CellFlow` in-memory, ``CellFlowAnnbatch`` streaming) + implement the data-preparation methods and the path-specific hooks. + """ + + def __init__(self, solver: Literal["otfm", "genot"] = "otfm"): + if solver not in SOLVER_REGISTRY: + raise ValueError(f"Unknown solver {solver!r}. Registered solvers: {sorted(SOLVER_REGISTRY)}.") + self._solver_class, self._vf_class = SOLVER_REGISTRY[solver] + self._dm: DataManager | None = None # set by a subclass `prepare_*` (both paths) + self._data_dim: int | None = None + self._dataloader: Any | None = None + self._trainer: CellFlowTrainer | None = None + self._validation_data: dict[str, Any] = {"predict_kwargs": {}} + self._solver: _otfm.OTFlowMatching | _genot.GENOT | None = None + self._condition_dim: int | None = None + self._vf: _velocity_field.ConditionalVelocityField | _velocity_field.GENOTConditionalVelocityField | None = None + + def prepare_model( + self, + condition_mode: Literal["deterministic", "stochastic"] = "deterministic", + regularization: float = 0.0, + pooling: Literal["mean", "attention_token", "attention_seed"] = "attention_token", + pooling_kwargs: dict[str, Any] = types.MappingProxyType({}), + layers_before_pool: Layers_separate_input_t | Layers_t = dc_field(default_factory=lambda: []), + layers_after_pool: Layers_t = dc_field(default_factory=lambda: []), + condition_embedding_dim: int = 256, + cond_output_dropout: float = 0.9, + condition_encoder_kwargs: dict[str, Any] | None = None, + pool_sample_covariates: bool = True, + time_freqs: int = 1024, + time_max_period: int | None = 10000, + time_encoder_dims: Sequence[int] = (2048, 2048, 2048), + time_encoder_dropout: float = 0.0, + hidden_dims: Sequence[int] = (2048, 2048, 2048), + hidden_dropout: float = 0.0, + conditioning: Literal["concatenation", "film", "resnet"] = "concatenation", + conditioning_kwargs: dict[str, Any] = dc_field(default_factory=lambda: {}), + decoder_dims: Sequence[int] = (4096, 4096, 4096), + decoder_dropout: float = 0.0, + vf_act_fn: Callable[[jnp.ndarray], jnp.ndarray] = nn.silu, + vf_kwargs: dict[str, Any] | None = None, + probability_path: dict[Literal["constant_noise", "bridge"], float] | None = None, + match_fn: Callable[[ArrayLike, ArrayLike], ArrayLike] = match_linear, + optimizer: optax.GradientTransformation = optax.MultiSteps(optax.adam(5e-5), 20), + solver_kwargs: dict[str, Any] | None = None, + layer_norm_before_concatenation: bool = False, + linear_projection_before_concatenation: bool = False, + seed=0, + ) -> None: + """Prepare the model for training. + + This function sets up the neural network architecture and specificities of the + :attr:`solver`. When :attr:`solver` is an instance of :class:`cellflow.solvers._genot.GENOT`, + the following arguments have to be passed to ``'condition_encoder_kwargs'``: + + + Parameters + ---------- + condition_mode + Mode of the encoder, should be one of: + + - ``'deterministic'``: Learns condition encoding point-wise. + - ``'stochastic'``: Learns a Gaussian distribution for representing conditions. + + regularization + Regularization strength in the latent space: + + - For deterministic mode, it is the strength of the L2 regularization. + - For stochastic mode, it is the strength of the VAE regularization. + + pooling + Pooling method, should be one of: + + - ``'mean'``: Aggregates combinations of covariates by the mean of their + learned embeddings. + - ``'attention_token'``: Aggregates combinations of covariates by an attention + mechanism with a class token. + - ``'attention_seed'``: Aggregates combinations of covariates by seed attention. + + pooling_kwargs + Keyword arguments for the pooling method corresponding to: + + - :class:`cellflow.networks.TokenAttentionPooling` if ``'pooling'`` is + ``'attention_token'``. + - :class:`cellflow.networks.SeedAttentionPooling` if ``'pooling'`` is ``'attention_seed'``. + + layers_before_pool + Layers applied to the condition embeddings before pooling. Can be of type + + - :class:`tuple` with elements corresponding to dictionaries with keys: + + - ``'layer_type'`` of type :class:`str` indicating the type of the layer, can be + ``'mlp'`` or ``'self_attention'``. + - Further keyword arguments for the layer type :class:`cellflow.networks.MLPBlock` or + :class:`cellflow.networks.SelfAttentionBlock`. + + - :class:`dict` with keys corresponding to perturbation covariate keys, and values + correspondinng to the above mentioned tuples. + + layers_after_pool + Layers applied to the condition embeddings after pooling, and before applying the last + layer of size ``'condition_embedding_dim'``. Should be of type :class:`tuple` with + elements corresponding to dictionaries with keys: + + - ``'layer_type'`` of type :class:`str` indicating the type of the layer, can be + ``'mlp'`` or ``'self_attention'``. + - Further keys depend on the layer type, either for :class:`cellflow.networks.MLPBlock` or + for :class:`cellflow.networks.SelfAttentionBlock`. + + condition_embedding_dim + Dimensions of the condition embedding, i.e. the last layer of the + :class:`cellflow.networks.ConditionEncoder`. + cond_output_dropout + Dropout rate for the last layer of the :class:`cellflow.networks.ConditionEncoder`. + condition_encoder_kwargs + Keyword arguments for the :class:`cellflow.networks.ConditionEncoder`. + pool_sample_covariates + Whether to include sample covariates in the pooling. + time_freqs + Frequency of the sinusoidal time encoding + (:func:`ott.neural.networks.layers.sinusoidal_time_encoder`). + time_max_period + Controls the frequency of the time embeddings, see + :func:`cellflow.networks.utils.sinusoidal_time_encoder`. + time_encoder_dims + Dimensions of the layers processing the time embedding in + :attr:`cellflow.networks.ConditionalVelocityField.time_encoder`. + time_encoder_dropout + Dropout rate for the :attr:`cellflow.networks.ConditionalVelocityField.time_encoder`. + hidden_dims + Dimensions of the layers processing the input to the velocity field + via :attr:`cellflow.networks.ConditionalVelocityField.x_encoder`. + hidden_dropout + Dropout rate for :attr:`cellflow.networks.ConditionalVelocityField.x_encoder`. + conditioning + Conditioning method, should be one of: + + - ``'concatenation'``: Concatenate the time, data, and condition embeddings. + - ``'film'``: Use FiLM conditioning, i.e. learn FiLM weights from time and condition embedding + to scale the data embeddings. + - ``'resnet'``: Use residual conditioning. + + conditioning_kwargs + Keyword arguments for the conditioning method. + decoder_dims + Dimensions of the output layers in + :attr:`cellflow.networks.ConditionalVelocityField.decoder`. + decoder_dropout + Dropout rate for the output layer + :attr:`cellflow.networks.ConditionalVelocityField.decoder`. + vf_act_fn + Activation function of the :class:`cellflow.networks.ConditionalVelocityField`. + vf_kwargs + Additional keyword arguments for the solver-specific vector field. + For instance, when ``'solver==genot'``, the following keyword argument can be passed: + + - ``'genot_source_dims'`` of type :class:`tuple` with the dimensions + of the :class:`cellflow.networks.MLPBlock` processing the source cell. + - ``'genot_source_dropout'`` of type :class:`float` indicating the dropout rate + for the source cell processing. + probability_path + Probability path to use for training. Should be a :class:`dict` of the form + + - ``'{"constant_noise": noise_val'`` + - ``'{"bridge": noise_val}'`` + + If :obj:`None`, defaults to ``'{"constant_noise": 0.0}'``. + match_fn + Matching function between unperturbed and perturbed cells. Should take as input source + and target data and return the optimal transport matrix, see e.g. + :func:`cellflow.utils.match_linear`. + optimizer + Optimizer used for training. + solver_kwargs + Keyword arguments for the solver :class:`cellflow.solvers.OTFlowMatching` or + :class:`cellflow.solvers.GENOT`. + layer_norm_before_concatenation + If :obj:`True`, applies layer normalization before concatenating + the embedded time, embedded data, and condition embeddings. + linear_projection_before_concatenation + If :obj:`True`, applies a linear projection before concatenating + the embedded time, embedded data, and embedded condition. + seed + Random seed. + + Returns + ------- + Updates the following fields: + + - :attr:`cellflow.model.CellFlow.velocity_field` - an instance of the + :class:`cellflow.networks.ConditionalVelocityField`. + - :attr:`cellflow.model.CellFlow.solver` - an instance of :class:`cellflow.solvers.OTFlowMatching` + or :class:`cellflow.solvers.GENOT`. + - :attr:`cellflow.model.CellFlow.trainer` - an instance of the + :class:`cellflow.training.CellFlowTrainer`. + """ + # Condition embeddings + max combination length are path-specific (in-memory vs streaming). + condition_data, max_combination_length = self._encoder_conditions() + + if condition_mode == "stochastic": + if regularization == 0.0: + raise ValueError("Stochastic condition embeddings require `regularization`>0.") + + condition_encoder_kwargs = condition_encoder_kwargs or {} + # Each velocity field owns which solver-specific `vf_kwargs` it accepts (validated/defaulted here). + vf_kwargs = self._vf_class._normalize_vf_kwargs(vf_kwargs) + covariates_not_pooled = [] if pool_sample_covariates else self._dm.sample_covariates + solver_kwargs = solver_kwargs or {} + probability_path = probability_path or {"constant_noise": 0.0} + + self.vf = self._vf_class( + output_dim=self._data_dim, + max_combination_length=max_combination_length, + condition_mode=condition_mode, + regularization=regularization, + condition_embedding_dim=condition_embedding_dim, + covariates_not_pooled=covariates_not_pooled, + pooling=pooling, + pooling_kwargs=pooling_kwargs, + layers_before_pool=layers_before_pool, + layers_after_pool=layers_after_pool, + cond_output_dropout=cond_output_dropout, + condition_encoder_kwargs=condition_encoder_kwargs, + act_fn=vf_act_fn, + time_freqs=time_freqs, + time_max_period=time_max_period, + time_encoder_dims=time_encoder_dims, + time_encoder_dropout=time_encoder_dropout, + hidden_dims=hidden_dims, + hidden_dropout=hidden_dropout, + conditioning=conditioning, + conditioning_kwargs=conditioning_kwargs, + decoder_dims=decoder_dims, + decoder_dropout=decoder_dropout, + layer_norm_before_concatenation=layer_norm_before_concatenation, + linear_projection_before_concatenation=linear_projection_before_concatenation, + **vf_kwargs, + ) + + probability_path, noise = next(iter(probability_path.items())) + if probability_path == "constant_noise": + probability_path = ConstantNoiseFlow(noise) + elif probability_path == "bridge": + probability_path = BrownianBridge(noise) + else: + raise NotImplementedError( + f"The key of `probability_path` must be `'constant_noise'` or `'bridge'` but found {probability_path}." + ) + + # Each solver owns how it names its match function / whether it needs source-target dims. + self._solver = self._solver_class( + vf=self.vf, + probability_path=probability_path, + optimizer=optimizer, + conditions=condition_data, + rng=jax.random.PRNGKey(seed), + **self._solver_class._match_kwargs(match_fn=match_fn, data_dim=self._data_dim), + **solver_kwargs, + ) + + self._trainer = CellFlowTrainer(solver=self.solver, predict_kwargs=self.validation_data["predict_kwargs"]) # type: ignore[arg-type] + + def train( + self, + num_iterations: int, + batch_size: int = 1024, + valid_freq: int = 1000, + callbacks: Sequence[BaseCallback] = [], + monitor_metrics: Sequence[str] = [], + out_of_core_dataloading: bool = False, + ) -> None: + """Train the model. + + Note + ---- + A low value of ``'valid_freq'`` results in long training + because predictions are time-consuming compared to training steps. + + Parameters + ---------- + num_iterations + Number of iterations to train the model. + batch_size + Batch size. + valid_freq + Frequency of validation. + callbacks + Callbacks to perform at each validation step. There are two types of callbacks: + - Callbacks for computations should inherit from + :class:`~cellflow.training.ComputationCallback` see e.g. :class:`cellflow.training.Metrics`. + - Callbacks for logging should inherit from :class:`~cellflow.training.LoggingCallback` see + e.g. :class:`~cellflow.training.WandbLogger`. + monitor_metrics + Metrics to monitor. + out_of_core_dataloading + If :obj:`True`, use out-of-core dataloading. Uses the :class:`cellflow.data._legacy.OOCTrainSampler` + to load data that does not fit into GPU memory. + + Returns + ------- + Updates the following fields: + + - :attr:`cellflow.model.CellFlow.dataloader` - the training dataloader. + - :attr:`cellflow.model.CellFlow.solver` - the trained solver. + """ + if self._dm is None: + raise ValueError("Data not initialized. Please call `prepare_data` or `prepare_loaders` first.") + + if self.trainer is None: + raise ValueError("Model not initialized. Please call `prepare_model` first.") + + self._bind_train_dataloader(batch_size, out_of_core_dataloading) # in-memory binds; streaming is a no-op + validation_loaders = self._build_validation_loaders() + self._trainer.predict_kwargs = self.validation_data.get("predict_kwargs", {}) + + self._solver = self.trainer.train( + dataloader=self._dataloader, + num_iterations=num_iterations, + valid_freq=valid_freq, + valid_loaders=validation_loaders, + callbacks=callbacks, + monitor_metrics=monitor_metrics, + ) + + def predict( + self, + adata: ad.AnnData, + covariate_data: pd.DataFrame, + sample_rep: str | None = None, + condition_id_key: str | None = None, + key_added_prefix: str | None = None, + rng: ArrayLike | None = None, + **kwargs: Any, + ) -> dict[str, ArrayLike] | None: + """Predict perturbation responses. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object with the source representation. + covariate_data + Covariate data defining the condition to predict. This :class:`~pandas.DataFrame` + should have the same columns as :attr:`~anndata.AnnData.obs` of + :attr:`cellflow.model.CellFlow.adata`, and as registered in + :attr:`cellflow.model.CellFlow.data_manager`. + sample_rep + Key in :attr:`~anndata.AnnData.obsm` where the sample representation is stored or + ``'X'`` to use :attr:`~anndata.AnnData.X`. If :obj:`None`, the key is assumed to be + the same as for the training data. + condition_id_key + Key in ``'covariate_data'`` defining the condition name. + key_added_prefix + If not :obj:`None`, prefix to store the prediction in :attr:`~anndata.AnnData.obsm`. + If :obj:`None`, the predictions are not stored, and the predictions are returned as a + :class:`dict`. + rng + Random number generator. If :obj:`None` and :attr:`cellflow.model.CellFlow.conditino_mode` + is ``'stochastic'``, the condition vector will be the mean of the learnt distributions, + otherwise samples from the distribution. + kwargs + Keyword arguments for the predict function, i.e. + :meth:`cellflow.solvers.OTFlowMatching.predict` or :meth:`cellflow.solvers.GENOT.predict`. + + Returns + ------- + If ``'key_added_prefix'`` is :obj:`None`, a :class:`dict` with the predicted sample + representation for each perturbation, otherwise stores the predictions in + :attr:`~anndata.AnnData.obsm` and returns :obj:`None`. + """ + if self.solver is None or not self.solver.is_trained: + raise ValueError("Model not trained. Please call `train` first.") + + if sample_rep is None: + sample_rep = self._dm.sample_rep + + if adata is not None and covariate_data is not None: + if covariate_data.empty: + raise ValueError("`covariate_data` is empty.") + if self._dm.control_key not in adata.obs.columns: + raise ValueError( + f"If both `adata` and `covariate_data` are given, the control key `{self._dm.control_key}` must be in `adata.obs`." + ) + if not adata.obs[self._dm.control_key].all(): + raise ValueError( + f"If both `adata` and `covariate_data` are given, all samples in `adata` must be control samples, and thus `adata.obs[`{self._dm.control_key}`] must be set to `True` everywhere." + ) + pred_data = self._dm.get_prediction_data( + adata, + sample_rep=sample_rep, # type: ignore[arg-type] + covariate_data=covariate_data, + condition_id_key=condition_id_key, + ) + pred_loader = PredictionSampler(pred_data) + batch = pred_loader.sample() + src = batch["source"] + condition = batch.get("condition", None) + # using jax.tree.map to batch the prediction + # because PredictionSampler can return a different number of cells for each condition + out = jax.tree.map( + functools.partial(self.solver.predict, rng=rng, **kwargs), + src, + condition, # type: ignore[attr-defined] + ) + if key_added_prefix is None: + return out + if len(pred_data.control_to_perturbation) > 1: + raise ValueError( + f"When saving predictions to `adata`, all control cells must be from the same control \ + population, but found {len(pred_data.control_to_perturbation)} control populations." + ) + out_np = {k: np.array(v) for k, v in out.items()} + _write_predictions( + adata=adata, + predictions=out_np, + key_added_prefix=key_added_prefix, + ) + + def get_condition_embedding( + self, + covariate_data: pd.DataFrame | ConditionData, + rep_dict: dict[str, str] | None = None, + condition_id_key: str | None = None, + key_added: str | None = _constants.CONDITION_EMBEDDING, + ) -> tuple[pd.DataFrame, pd.DataFrame]: + """Get the embedding of the conditions. + + Outputs the mean and variance of the learnt embeddings + generated by the :class:`~cellflow.networks.ConditionEncoder`. + + Parameters + ---------- + covariate_data + Can be one of + + - a :class:`~pandas.DataFrame` defining the conditions with the same columns as the + :class:`~anndata.AnnData` used for the initialisation of :class:`~cellflow.model.CellFlow`. + - an instance of :class:`~cellflow.data.ConditionData`. + + rep_dict + Dictionary containing the representations of the perturbation covariates. Will be considered an + empty dictionary if :obj:`None`. + condition_id_key + Key defining the name of the condition. Only available + if ``'covariate_data'`` is a :class:`~pandas.DataFrame`. + key_added + Key to store the condition embedding in :attr:`~anndata.AnnData.uns`. The mean is + stored under ``key_added`` and the variance under ``f"{key_added}_var"``. If + :obj:`None`, the embeddings are not stored. + + Returns + ------- + A :class:`tuple` of :class:`~pandas.DataFrame` with the mean and variance of the condition embeddings. + """ + if self.solver is None or not self.solver.is_trained: + raise ValueError("Model not trained. Please call `train` first.") + + if hasattr(covariate_data, "condition_data"): + cond_data = covariate_data + elif isinstance(covariate_data, pd.DataFrame): + cond_data = self._dm.get_condition_data( + covariate_data=covariate_data, + rep_dict=rep_dict, + condition_id_key=condition_id_key, + ) + else: + raise ValueError("Covariate data must be a `pandas.DataFrame` or an instance of `BaseData`.") + + condition_embeddings_mean: dict[str, ArrayLike] = {} + condition_embeddings_var: dict[str, ArrayLike] = {} + n_conditions = len(next(iter(cond_data.condition_data.values()))) + for i in range(n_conditions): + condition = {k: v[[i], :] for k, v in cond_data.condition_data.items()} + if condition_id_key: + c_key = cond_data.perturbation_idx_to_id[i] + else: + cov_combination = cond_data.perturbation_idx_to_covariates[i] + c_key = tuple(cov_combination[i] for i in range(len(cov_combination))) + condition_embeddings_mean[c_key], condition_embeddings_var[c_key] = self.solver.get_condition_embedding( + condition + ) + + df_mean = pd.DataFrame.from_dict({k: v[0] for k, v in condition_embeddings_mean.items()}).T + df_var = pd.DataFrame.from_dict({k: v[0] for k, v in condition_embeddings_var.items()}).T + + if condition_id_key: + df_mean.index.set_names([condition_id_key], inplace=True) + df_var.index.set_names([condition_id_key], inplace=True) + else: + df_mean.index.set_names(list(self._dm.perturb_covar_keys), inplace=True) + df_var.index.set_names(list(self._dm.perturb_covar_keys), inplace=True) + + if key_added is not None: + if self.adata is None: # streaming/annbatch path: no `adata` to store into + warnings.warn( + "No `adata` is attached (streaming path); returning the condition embeddings without " + "storing them under `adata.uns`. Pass `key_added=None` to silence this.", + stacklevel=2, + ) + else: # mean under `key_added`, variance under `f"{key_added}_var"` (distinct keys, #295) + _utils.set_plotting_vars(self.adata, key=key_added, value=df_mean) + _utils.set_plotting_vars(self.adata, key=f"{key_added}_var", value=df_var) + + return df_mean, df_var + + def save( + self, + dir_path: str, + file_prefix: str | None = None, + overwrite: bool = False, + ) -> None: + """ + Save the model. + + Pickles the :class:`~cellflow.model.CellFlow` object. + + Parameters + ---------- + dir_path + Path to a directory, defaults to current directory + file_prefix + Prefix to prepend to the file name. + overwrite + Overwrite existing data or not. + + Returns + ------- + :obj:`None` + """ + file_name = ( + f"{file_prefix}_{self.__class__.__name__}.pkl" + if file_prefix is not None + else f"{self.__class__.__name__}.pkl" + ) + file_dir = os.path.join(dir_path, file_name) if dir_path is not None else file_name + + if not overwrite and os.path.exists(file_dir): + raise RuntimeError(f"Unable to save to an existing file `{file_dir}` use `overwrite=True` to overwrite it.") + with open(file_dir, "wb") as f: + cloudpickle.dump(self, f) + + @classmethod + def load( + cls, + filename: str, + ) -> "CellFlow": + """ + Load a :class:`~cellflow.model.CellFlow` model from a saved instance. + + Parameters + ---------- + filename + Path to the saved file. + + Returns + ------- + Loaded instance of the model. + """ + # Check if filename is a directory + file_name = os.path.join(filename, f"{cls.__name__}.pkl") if os.path.isdir(filename) else filename + + with open(file_name, "rb") as f: + model = cloudpickle.load(f) + + if type(model) is not cls: + raise TypeError(f"Expected the model to be type of `{cls}`, found `{type(model)}`.") + return model + + @property + def adata(self) -> ad.AnnData | None: + """The :class:`~anndata.AnnData` used for training, or :obj:`None` in the streaming path.""" + return getattr(self, "_adata", None) + + @property + def solver(self) -> _otfm.OTFlowMatching | _genot.GENOT | None: + """The solver.""" + return self._solver + + @property + def dataloader(self) -> Any | None: + """The dataloader used for training (in-memory sampler or a streaming adapter).""" + return self._dataloader + + @property + def trainer(self) -> CellFlowTrainer | None: + """The trainer used for training.""" + return self._trainer + + @property + def validation_data(self) -> dict[str, ValidationData]: + """The validation data.""" + return self._validation_data + + @property + def data_manager(self) -> DataManager: + """The data manager, initialised with :attr:`cellflow.model.CellFlow.adata`.""" + return self._dm + + @property + def velocity_field( + self, + ) -> _velocity_field.ConditionalVelocityField | _velocity_field.GENOTConditionalVelocityField | None: + """The conditional velocity field.""" + return self._vf + + @velocity_field.setter # type: ignore[attr-defined,no-redef] + def velocity_field(self, vf: _velocity_field.ConditionalVelocityField) -> None: + """Set the velocity field.""" + if not isinstance(vf, _velocity_field.ConditionalVelocityField): + raise ValueError(f"Expected `vf` to be an instance of `ConditionalVelocityField`, found `{type(vf)}`.") + self._vf = vf + + @property + def condition_mode(self) -> Literal["deterministic", "stochastic"]: + """The mode of the encoder.""" + return self.velocity_field.condition_mode + + # ── path-specific hooks (implemented by CellFlow / CellFlowAnnbatch) ────────────────────────── + @abc.abstractmethod + def _encoder_conditions(self) -> tuple[dict[str, np.ndarray], int]: + """The condition embeddings + ``max_combination_length`` used by :meth:`prepare_model`.""" + + @abc.abstractmethod + def _bind_train_dataloader(self, batch_size: int, out_of_core_dataloading: bool) -> None: + """Bind :attr:`_dataloader` for :meth:`train` (in-memory builds one; streaming already has it).""" + + @abc.abstractmethod + def _build_validation_loaders(self) -> dict[str, Any]: + """The validation samplers (keyed by name) that :meth:`train` feeds to the trainer.""" diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index dad6b2ce..0591658d 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -1,70 +1,34 @@ -import functools -import os -import types +"""In-memory (legacy) CellFlow: training data materialized from an ``AnnData`` via :meth:`prepare_data`. + +The streaming path (:class:`~cellflow.model.CellFlowAnnbatch`) is the default; this in-memory path is +kept for backward compatibility. +""" + import warnings -from collections.abc import Callable, Mapping, Sequence -from dataclasses import field as dc_field -from typing import TYPE_CHECKING, Any, Literal +from collections.abc import Sequence +from typing import Any, Literal import anndata as ad -import cloudpickle -import flax.linen as nn -import jax -import jax.numpy as jnp import numpy as np -import optax -import pandas as pd -from cellflow import _constants -from cellflow._compat import BrownianBridge, ConstantNoiseFlow -from cellflow._types import ArrayLike, Layers_separate_input_t, Layers_t -from cellflow.data._data import ConditionData, TrainingData, ValidationData -from cellflow.data._dataloader import ( - DAGLoaderTrainSampler, - OOCTrainSampler, - PredictionSampler, - TrainSampler, - ValidationSampler, -) +from cellflow.data._data import TrainingData from cellflow.data._datamanager import DataManager -from cellflow.model._utils import _write_predictions -from cellflow.networks import _velocity_field -from cellflow.plotting import _utils -from cellflow.solvers import SOLVER_REGISTRY, _genot, _otfm -from cellflow.training._callbacks import BaseCallback -from cellflow.training._trainer import CellFlowTrainer -from cellflow.utils import match_linear - -if TYPE_CHECKING: - from annbatch import DatasetCollection # optional dep — only imported for typing - - from dagloader import DAGLoader, SamplerConfig, Scheme # optional dep — only imported for typing +from cellflow.data._legacy import OOCTrainSampler, TrainSampler, ValidationSampler +from cellflow.model._base import BaseCellFlow __all__ = ["CellFlow"] -class CellFlow: - """CellFlow model for perturbation prediction using Flow Matching and Optimal Transport. +class CellFlow(BaseCellFlow): + """CellFlow with in-memory training data extracted from an :class:`~anndata.AnnData`. - CellFlow builds upon neural optimal transport estimators extending :cite:`tong:23`, - :cite:`pooladian:23`, :cite:`eyring:24`, :cite:`klein:23` which are all based on - Flow Matching :cite:`lipman:22`. - - Parameters - ---------- - adata - An :class:`~anndata.AnnData` object to extract the training data from. If :obj:`None`, - the model uses the annbatch/dagloader streaming path (see - :meth:`~cellflow.model.CellFlow.prepare_annbatch_data`) instead of the in-memory one. - solver - Solver to use for training. Any name registered in - :data:`cellflow.solvers.SOLVER_REGISTRY` (``'otfm'`` or ``'genot'`` by default, extendable - via :func:`cellflow.solvers.register_solver`). + Prepare training data with :meth:`prepare_data`; model setup, training, prediction and IO are + inherited from :class:`~cellflow.model._base.BaseCellFlow`. For large or out-of-core datasets use + :class:`~cellflow.model.CellFlowAnnbatch` (the default streaming path). """ def __init__(self, adata: ad.AnnData | None = None, solver: Literal["otfm", "genot"] = "otfm"): - # `adata is None` selects the annbatch/dagloader streaming path (see `prepare_annbatch_data`); - # a provided `adata` is the traditional in-memory path (`prepare_data`). + super().__init__(solver) if adata is not None: warnings.warn( "Passing `adata` to `CellFlow(...)` is deprecated and will be removed in a future " @@ -73,26 +37,7 @@ def __init__(self, adata: ad.AnnData | None = None, solver: Literal["otfm", "gen stacklevel=2, ) self._adata = adata - self._train_data: TrainingData | None = None # set by `prepare_data` (in-memory path only) - if solver not in SOLVER_REGISTRY: - raise ValueError(f"Unknown solver {solver!r}. Registered solvers: {sorted(SOLVER_REGISTRY)}.") - self._solver_class, self._vf_class = SOLVER_REGISTRY[solver] - self._dataloader: TrainSampler | OOCTrainSampler | None = None - self._trainer: CellFlowTrainer | None = None - self._validation_data: dict[str, ValidationData] = {"predict_kwargs": {}} - self._solver: _otfm.OTFlowMatching | _genot.GENOT | None = None - self._condition_dim: int | None = None - self._vf: _velocity_field.ConditionalVelocityField | _velocity_field.GENOTConditionalVelocityField | None = None - # annbatch/dagloader streaming path (set by `prepare_annbatch_data`, partitioned by - # `split_annbatch_data`). Kept untyped here to avoid importing the optional `dagloader`/`annbatch` - # dependency at module import time. - self._scheme: Scheme | None = None - self._split_schemes: dict[str, Scheme] | None = None - self._split_assignment: pd.DataFrame | None = None - self._annbatch_sampler_configs: dict[str, SamplerConfig] | None = None - self._annbatch_loaders: dict[str, DAGLoader] | None = None - self._condition_data: dict[str, np.ndarray] | None = None # annbatch-path condition embeddings - self._max_combination_length: int | None = None # annbatch-path (in-memory keeps it on train_data) + self._train_data: TrainingData | None = None def prepare_data( self, @@ -232,193 +177,6 @@ def prepare_data( self.train_data = self._dm.get_train_data(self.adata) self._data_dim = self.train_data.cell_data.shape[-1] # type: ignore[union-attr] - def prepare_annbatch_data( - self, - source: "DatasetCollection", - sample_rep: str, - control_key: str, - perturbation_covariates: dict[str, Sequence[str]], - perturbation_covariate_reps: dict[str, str] | None = None, - sample_covariates: Sequence[str] | None = None, - sample_covariate_reps: dict[str, str] | None = None, - split_covariates: Sequence[str] | None = None, - max_combination_length: int | None = None, - null_value: float = 0.0, - rep_dict: Mapping[str, Mapping[str, ArrayLike]] | None = None, - sampler_config: "SamplerConfig | Mapping[str, SamplerConfig] | None" = None, - seed: int = 0, - split_by: Sequence[str] | None = None, - split_ratios: Mapping[str, float] | None = None, - split_force_training_values: Mapping[str, object] | None = None, - split_random_state: int = 42, - ) -> None: - """Prepare an out-of-core, annbatch-streamed training path (no in-memory ``adata``). - - The covariate arguments (``sample_rep``, ``control_key``, ``perturbation_covariates``, - ``perturbation_covariate_reps``, ``sample_covariates``, ``sample_covariate_reps``, - ``split_covariates``, ``max_combination_length``, ``null_value``) mean exactly what they do in - :meth:`prepare_data`; only the cells differ — streamed from an out-of-core - :class:`annbatch.DatasetCollection` instead of materialized. Requires the ``annbatch`` extra and - a model built **without** ``adata`` (``CellFlow()``). - - Parameters - ---------- - source - An out-of-core :class:`annbatch.DatasetCollection` to stream cells from (an in-memory - ``AnnData`` also works — the ``dagloader`` is container-agnostic). Its ``obs`` supplies the - grouping / condition columns; ``sample_rep`` selects the streamed representation. - rep_dict - The covariate embedding tables that ``adata.uns`` would hold in the in-memory path (keys - match the values of ``perturbation_covariate_reps`` / ``sample_covariate_reps``). Required - when a covariate group is embedded; may be :obj:`None` when the primary covariate is - categorical (one-hot encoded). - sampler_config - Read parameters for the streamed loader(s), **one per split**: a single - :class:`dagloader.SamplerConfig` for all splits, or a ``{split_name: SamplerConfig}`` mapping - covering every split (see :func:`dagloader.resolve_split_configs`). With no split the only - split is ``"train"``. ``chunk_size > 1`` reads contiguous slices, so every run of each - category must be at least ``chunk_size`` cells — in-memory sources are grouped automatically, - an out-of-core :class:`~annbatch.DatasetCollection` must be built grouped - (``add_adatas(..., groupby=[...])``) or a clear error is raised. - seed - Reproducibility seed for the ``dagloader`` per-node RNG streams. - split_by - If given, split the prepared ``Scheme``'s target combinations into train/val/test in the - same call (delegates to :meth:`split_annbatch_data`). Columns whose unique combinations are - held out (⊆ the target columns). If :obj:`None`, no split is made (the model would train on - all combinations). - split_ratios - ``{split_name: fraction}`` summing to 1.0 for the split. Defaults to - ``{"train": 0.6, "val": 0.2, "test": 0.2}``. Only used when ``split_by`` is given. - split_force_training_values - ``{column: value}`` (keys ⊆ ``split_by``) forced into the training split. Only used when - ``split_by`` is given. - split_random_state - Seed for the split's combination shuffle. Only used when ``split_by`` is given. - - Returns - ------- - :obj:`None`, and sets up the streaming training data used by :meth:`train`. - - Notes - ----- - The ``"train"`` split's loader is wired to :meth:`train`; when a split is made, the ``val`` / - ``test`` split loaders are kept on :attr:`_annbatch_loaders` for later use. - """ - if self._adata is not None: - raise ValueError( - "`prepare_annbatch_data` is the out-of-core streaming path; construct the model " - "without `adata` (`CellFlow()`). Use `prepare_data(adata=...)` for the in-memory path." - ) - - from cellflow.data._annbatch import build_annbatch_training - from dagloader import DAGLoader, resolve_split_configs - - # Build the Scheme + condition_fn + condition embeddings from the covariate spec (obs only). - built = build_annbatch_training( - source, - sample_rep=sample_rep, - control_key=control_key, - perturbation_covariates=perturbation_covariates, - perturbation_covariate_reps=perturbation_covariate_reps, - sample_covariates=sample_covariates, - sample_covariate_reps=sample_covariate_reps, - split_covariates=split_covariates, - max_combination_length=max_combination_length, - null_value=null_value, - rep_dict=rep_dict, - seed=seed, - ) - self._scheme = built.scheme - self._dm = built.data_manager - self._condition_data = built.condition_data - self._data_dim = built.data_dim - self._max_combination_length = built.max_combination_length - condition_fn = built.condition_fn - - # Splitting step — kept in `prepare_annbatch_data` so preparing and splitting are one call. - if split_by is not None: - self._split_assignment = self.split_annbatch_data( - split_by=split_by, - ratios=split_ratios, - force_training_values=split_force_training_values, - random_state=split_random_state, - ) - - # One `SamplerConfig` per split (a single spec ⇒ all splits; a per-split dict ⇒ all specified). - # The splits are the split schemes when a split was made, else the single ``"train"`` scheme. - schemes = self._split_schemes if self._split_schemes is not None else {"train": self._scheme} - if sampler_config is None: - raise ValueError("`sampler_config` is required: give a SamplerConfig or a {split: SamplerConfig} mapping.") - self._annbatch_sampler_configs = resolve_split_configs(sampler_config, list(schemes)) - - # `chunk_size > 1` reads contiguous slices → every category's runs must be >= chunk_size - # (in-memory sources are auto-grouped above; out-of-core collections must be built grouped). - max_chunk = max(cfg.chunk_size for cfg in self._annbatch_sampler_configs.values()) - if max_chunk > 1: - from cellflow.data._annbatch import assert_source_chunkable - - root = self._scheme.nodes[self._scheme.root] - assert_source_chunkable(self._scheme.sources["data"], root.cols, max_chunk) - - # One streaming DAGLoader per split; the "train" split feeds `train()`, others are kept for reuse. - self._annbatch_loaders = { - name: DAGLoader(schemes[name], self._annbatch_sampler_configs[name], condition_fn=condition_fn) - for name in schemes - } - self._dataloader = DAGLoaderTrainSampler(self._annbatch_loaders["train"]) - - def split_annbatch_data( - self, - *, - split_by: Sequence[str], - ratios: Mapping[str, float] | None = None, - force_training_values: Mapping[str, object] | None = None, - random_state: int = 42, - ) -> pd.DataFrame: - """Partition the prepared annbatch ``Scheme``'s target combinations into named splits. - - Call after :meth:`prepare_annbatch_data`. Splits hold out whole *combinations* of ``split_by`` - (a subset of the perturbation / split-covariate columns), not cells; controls are carried through - every split. See :func:`dagloader.split_scheme` for the mechanics. - - Parameters - ---------- - split_by - Columns whose unique combinations are partitioned across splits (⊆ the scheme's target - columns). - ratios - ``{split_name: fraction}`` summing to 1.0. Defaults to - ``{"train": 0.6, "val": 0.2, "test": 0.2}``. The first split is the training split. - force_training_values - ``{column: value}`` (keys ⊆ ``split_by``): any combination matching a value is forced into - the training (first) split. - random_state - Seed for the combination shuffle. - - Returns - ------- - A :class:`~pandas.DataFrame` of the target combinations and their assigned split. Also stores - the per-split schemes on the model. - """ - from dagloader import split_assignment, split_scheme - - if self._scheme is None: - raise ValueError( - "No annbatch `Scheme` to split. Call `prepare_annbatch_data(...)` first " - "(the out-of-core streaming path)." - ) - self._split_schemes = split_scheme( - self._scheme, - split_by=split_by, - ratios=ratios, - force_training_values=force_training_values, - random_state=random_state, - ) - # `prepare_annbatch_data` turns these split Schemes into per-split DAGLoaders (train feeds - # `train()`; val/test are kept on `_annbatch_loaders`); here we only produce the schemes + table. - return split_assignment(self._split_schemes) - def prepare_validation_data( self, adata: ad.AnnData, @@ -461,11 +219,8 @@ def prepare_validation_data( - :attr:`cellflow.model.CellFlow.validation_data` - a dictionary with the validation data. """ - if self.train_data is None and self._scheme is None: - raise ValueError( - "Model data not initialized. Set up training first via `prepare_data` (in-memory) or " - "`prepare_annbatch_data` (streaming) before preparing validation data." - ) + if self.train_data is None: + raise ValueError("Model data not initialized. Call `prepare_data(...)` before preparing validation data.") val_data = self._dm.get_validation_data( adata, n_conditions_on_log_iteration=n_conditions_on_log_iteration, @@ -482,607 +237,6 @@ def prepare_validation_data( predict_kwargs = self._validation_data["predict_kwargs"] self._validation_data["predict_kwargs"] = predict_kwargs - def prepare_model( - self, - condition_mode: Literal["deterministic", "stochastic"] = "deterministic", - regularization: float = 0.0, - pooling: Literal["mean", "attention_token", "attention_seed"] = "attention_token", - pooling_kwargs: dict[str, Any] = types.MappingProxyType({}), - layers_before_pool: Layers_separate_input_t | Layers_t = dc_field(default_factory=lambda: []), - layers_after_pool: Layers_t = dc_field(default_factory=lambda: []), - condition_embedding_dim: int = 256, - cond_output_dropout: float = 0.9, - condition_encoder_kwargs: dict[str, Any] | None = None, - pool_sample_covariates: bool = True, - time_freqs: int = 1024, - time_max_period: int | None = 10000, - time_encoder_dims: Sequence[int] = (2048, 2048, 2048), - time_encoder_dropout: float = 0.0, - hidden_dims: Sequence[int] = (2048, 2048, 2048), - hidden_dropout: float = 0.0, - conditioning: Literal["concatenation", "film", "resnet"] = "concatenation", - conditioning_kwargs: dict[str, Any] = dc_field(default_factory=lambda: {}), - decoder_dims: Sequence[int] = (4096, 4096, 4096), - decoder_dropout: float = 0.0, - vf_act_fn: Callable[[jnp.ndarray], jnp.ndarray] = nn.silu, - vf_kwargs: dict[str, Any] | None = None, - probability_path: dict[Literal["constant_noise", "bridge"], float] | None = None, - match_fn: Callable[[ArrayLike, ArrayLike], ArrayLike] = match_linear, - optimizer: optax.GradientTransformation = optax.MultiSteps(optax.adam(5e-5), 20), - solver_kwargs: dict[str, Any] | None = None, - layer_norm_before_concatenation: bool = False, - linear_projection_before_concatenation: bool = False, - seed=0, - ) -> None: - """Prepare the model for training. - - This function sets up the neural network architecture and specificities of the - :attr:`solver`. When :attr:`solver` is an instance of :class:`cellflow.solvers._genot.GENOT`, - the following arguments have to be passed to ``'condition_encoder_kwargs'``: - - - Parameters - ---------- - condition_mode - Mode of the encoder, should be one of: - - - ``'deterministic'``: Learns condition encoding point-wise. - - ``'stochastic'``: Learns a Gaussian distribution for representing conditions. - - regularization - Regularization strength in the latent space: - - - For deterministic mode, it is the strength of the L2 regularization. - - For stochastic mode, it is the strength of the VAE regularization. - - pooling - Pooling method, should be one of: - - - ``'mean'``: Aggregates combinations of covariates by the mean of their - learned embeddings. - - ``'attention_token'``: Aggregates combinations of covariates by an attention - mechanism with a class token. - - ``'attention_seed'``: Aggregates combinations of covariates by seed attention. - - pooling_kwargs - Keyword arguments for the pooling method corresponding to: - - - :class:`cellflow.networks.TokenAttentionPooling` if ``'pooling'`` is - ``'attention_token'``. - - :class:`cellflow.networks.SeedAttentionPooling` if ``'pooling'`` is ``'attention_seed'``. - - layers_before_pool - Layers applied to the condition embeddings before pooling. Can be of type - - - :class:`tuple` with elements corresponding to dictionaries with keys: - - - ``'layer_type'`` of type :class:`str` indicating the type of the layer, can be - ``'mlp'`` or ``'self_attention'``. - - Further keyword arguments for the layer type :class:`cellflow.networks.MLPBlock` or - :class:`cellflow.networks.SelfAttentionBlock`. - - - :class:`dict` with keys corresponding to perturbation covariate keys, and values - correspondinng to the above mentioned tuples. - - layers_after_pool - Layers applied to the condition embeddings after pooling, and before applying the last - layer of size ``'condition_embedding_dim'``. Should be of type :class:`tuple` with - elements corresponding to dictionaries with keys: - - - ``'layer_type'`` of type :class:`str` indicating the type of the layer, can be - ``'mlp'`` or ``'self_attention'``. - - Further keys depend on the layer type, either for :class:`cellflow.networks.MLPBlock` or - for :class:`cellflow.networks.SelfAttentionBlock`. - - condition_embedding_dim - Dimensions of the condition embedding, i.e. the last layer of the - :class:`cellflow.networks.ConditionEncoder`. - cond_output_dropout - Dropout rate for the last layer of the :class:`cellflow.networks.ConditionEncoder`. - condition_encoder_kwargs - Keyword arguments for the :class:`cellflow.networks.ConditionEncoder`. - pool_sample_covariates - Whether to include sample covariates in the pooling. - time_freqs - Frequency of the sinusoidal time encoding - (:func:`ott.neural.networks.layers.sinusoidal_time_encoder`). - time_max_period - Controls the frequency of the time embeddings, see - :func:`cellflow.networks.utils.sinusoidal_time_encoder`. - time_encoder_dims - Dimensions of the layers processing the time embedding in - :attr:`cellflow.networks.ConditionalVelocityField.time_encoder`. - time_encoder_dropout - Dropout rate for the :attr:`cellflow.networks.ConditionalVelocityField.time_encoder`. - hidden_dims - Dimensions of the layers processing the input to the velocity field - via :attr:`cellflow.networks.ConditionalVelocityField.x_encoder`. - hidden_dropout - Dropout rate for :attr:`cellflow.networks.ConditionalVelocityField.x_encoder`. - conditioning - Conditioning method, should be one of: - - - ``'concatenation'``: Concatenate the time, data, and condition embeddings. - - ``'film'``: Use FiLM conditioning, i.e. learn FiLM weights from time and condition embedding - to scale the data embeddings. - - ``'resnet'``: Use residual conditioning. - - conditioning_kwargs - Keyword arguments for the conditioning method. - decoder_dims - Dimensions of the output layers in - :attr:`cellflow.networks.ConditionalVelocityField.decoder`. - decoder_dropout - Dropout rate for the output layer - :attr:`cellflow.networks.ConditionalVelocityField.decoder`. - vf_act_fn - Activation function of the :class:`cellflow.networks.ConditionalVelocityField`. - vf_kwargs - Additional keyword arguments for the solver-specific vector field. - For instance, when ``'solver==genot'``, the following keyword argument can be passed: - - - ``'genot_source_dims'`` of type :class:`tuple` with the dimensions - of the :class:`cellflow.networks.MLPBlock` processing the source cell. - - ``'genot_source_dropout'`` of type :class:`float` indicating the dropout rate - for the source cell processing. - probability_path - Probability path to use for training. Should be a :class:`dict` of the form - - - ``'{"constant_noise": noise_val'`` - - ``'{"bridge": noise_val}'`` - - If :obj:`None`, defaults to ``'{"constant_noise": 0.0}'``. - match_fn - Matching function between unperturbed and perturbed cells. Should take as input source - and target data and return the optimal transport matrix, see e.g. - :func:`cellflow.utils.match_linear`. - optimizer - Optimizer used for training. - solver_kwargs - Keyword arguments for the solver :class:`cellflow.solvers.OTFlowMatching` or - :class:`cellflow.solvers.GENOT`. - layer_norm_before_concatenation - If :obj:`True`, applies layer normalization before concatenating - the embedded time, embedded data, and condition embeddings. - linear_projection_before_concatenation - If :obj:`True`, applies a linear projection before concatenating - the embedded time, embedded data, and embedded condition. - seed - Random seed. - - Returns - ------- - Updates the following fields: - - - :attr:`cellflow.model.CellFlow.velocity_field` - an instance of the - :class:`cellflow.networks.ConditionalVelocityField`. - - :attr:`cellflow.model.CellFlow.solver` - an instance of :class:`cellflow.solvers.OTFlowMatching` - or :class:`cellflow.solvers.GENOT`. - - :attr:`cellflow.model.CellFlow.trainer` - an instance of the - :class:`cellflow.training.CellFlowTrainer`. - """ - # Condition embeddings + max combination length come from `train_data` (in-memory) or, on the - # annbatch path, from what `prepare_annbatch_data` assembled. - if self.train_data is not None: - condition_data = self.train_data.condition_data - max_combination_length = self.train_data.max_combination_length - elif self._condition_data is not None and self._max_combination_length is not None: - condition_data = self._condition_data - max_combination_length = self._max_combination_length - else: - raise ValueError("Data not initialized. Please call `prepare_data` or `prepare_annbatch_data` first.") - - if condition_mode == "stochastic": - if regularization == 0.0: - raise ValueError("Stochastic condition embeddings require `regularization`>0.") - - condition_encoder_kwargs = condition_encoder_kwargs or {} - # Each velocity field owns which solver-specific `vf_kwargs` it accepts (validated/defaulted here). - vf_kwargs = self._vf_class._normalize_vf_kwargs(vf_kwargs) - covariates_not_pooled = [] if pool_sample_covariates else self._dm.sample_covariates - solver_kwargs = solver_kwargs or {} - probability_path = probability_path or {"constant_noise": 0.0} - - self.vf = self._vf_class( - output_dim=self._data_dim, - max_combination_length=max_combination_length, - condition_mode=condition_mode, - regularization=regularization, - condition_embedding_dim=condition_embedding_dim, - covariates_not_pooled=covariates_not_pooled, - pooling=pooling, - pooling_kwargs=pooling_kwargs, - layers_before_pool=layers_before_pool, - layers_after_pool=layers_after_pool, - cond_output_dropout=cond_output_dropout, - condition_encoder_kwargs=condition_encoder_kwargs, - act_fn=vf_act_fn, - time_freqs=time_freqs, - time_max_period=time_max_period, - time_encoder_dims=time_encoder_dims, - time_encoder_dropout=time_encoder_dropout, - hidden_dims=hidden_dims, - hidden_dropout=hidden_dropout, - conditioning=conditioning, - conditioning_kwargs=conditioning_kwargs, - decoder_dims=decoder_dims, - decoder_dropout=decoder_dropout, - layer_norm_before_concatenation=layer_norm_before_concatenation, - linear_projection_before_concatenation=linear_projection_before_concatenation, - **vf_kwargs, - ) - - probability_path, noise = next(iter(probability_path.items())) - if probability_path == "constant_noise": - probability_path = ConstantNoiseFlow(noise) - elif probability_path == "bridge": - probability_path = BrownianBridge(noise) - else: - raise NotImplementedError( - f"The key of `probability_path` must be `'constant_noise'` or `'bridge'` but found {probability_path}." - ) - - # Each solver owns how it names its match function / whether it needs source-target dims. - self._solver = self._solver_class( - vf=self.vf, - probability_path=probability_path, - optimizer=optimizer, - conditions=condition_data, - rng=jax.random.PRNGKey(seed), - **self._solver_class._match_kwargs(match_fn=match_fn, data_dim=self._data_dim), - **solver_kwargs, - ) - - self._trainer = CellFlowTrainer(solver=self.solver, predict_kwargs=self.validation_data["predict_kwargs"]) # type: ignore[arg-type] - - def train( - self, - num_iterations: int, - batch_size: int = 1024, - valid_freq: int = 1000, - callbacks: Sequence[BaseCallback] = [], - monitor_metrics: Sequence[str] = [], - out_of_core_dataloading: bool = False, - ) -> None: - """Train the model. - - Note - ---- - A low value of ``'valid_freq'`` results in long training - because predictions are time-consuming compared to training steps. - - Parameters - ---------- - num_iterations - Number of iterations to train the model. - batch_size - Batch size. - valid_freq - Frequency of validation. - callbacks - Callbacks to perform at each validation step. There are two types of callbacks: - - Callbacks for computations should inherit from - :class:`~cellflow.training.ComputationCallback` see e.g. :class:`cellflow.training.Metrics`. - - Callbacks for logging should inherit from :class:`~cellflow.training.LoggingCallback` see - e.g. :class:`~cellflow.training.WandbLogger`. - monitor_metrics - Metrics to monitor. - out_of_core_dataloading - If :obj:`True`, use out-of-core dataloading. Uses the :class:`cellflow.data._dataloader.OOCTrainSampler` - to load data that does not fit into GPU memory. - - Returns - ------- - Updates the following fields: - - - :attr:`cellflow.model.CellFlow.dataloader` - the training dataloader. - - :attr:`cellflow.model.CellFlow.solver` - the trained solver. - """ - if self.train_data is None and self._dataloader is None: - raise ValueError("Data not initialized. Please call `prepare_data` or `prepare_annbatch_data` first.") - - if self.trainer is None: - raise ValueError("Model not initialized. Please call `prepare_model` first.") - - if ( - self.train_data is not None - ): # in-memory path (the annbatch path set `_dataloader` in `prepare_annbatch_data`) - if out_of_core_dataloading: - self._dataloader = OOCTrainSampler(data=self.train_data, batch_size=batch_size) - else: - self._dataloader = TrainSampler(data=self.train_data, batch_size=batch_size) - validation_loaders = {k: ValidationSampler(v) for k, v in self.validation_data.items() if k != "predict_kwargs"} - self._trainer.predict_kwargs = self.validation_data.get("predict_kwargs", {}) - - self._solver = self.trainer.train( - dataloader=self._dataloader, - num_iterations=num_iterations, - valid_freq=valid_freq, - valid_loaders=validation_loaders, - callbacks=callbacks, - monitor_metrics=monitor_metrics, - ) - - def predict( - self, - adata: ad.AnnData, - covariate_data: pd.DataFrame, - sample_rep: str | None = None, - condition_id_key: str | None = None, - key_added_prefix: str | None = None, - rng: ArrayLike | None = None, - **kwargs: Any, - ) -> dict[str, ArrayLike] | None: - """Predict perturbation responses. - - Parameters - ---------- - adata - An :class:`~anndata.AnnData` object with the source representation. - covariate_data - Covariate data defining the condition to predict. This :class:`~pandas.DataFrame` - should have the same columns as :attr:`~anndata.AnnData.obs` of - :attr:`cellflow.model.CellFlow.adata`, and as registered in - :attr:`cellflow.model.CellFlow.data_manager`. - sample_rep - Key in :attr:`~anndata.AnnData.obsm` where the sample representation is stored or - ``'X'`` to use :attr:`~anndata.AnnData.X`. If :obj:`None`, the key is assumed to be - the same as for the training data. - condition_id_key - Key in ``'covariate_data'`` defining the condition name. - key_added_prefix - If not :obj:`None`, prefix to store the prediction in :attr:`~anndata.AnnData.obsm`. - If :obj:`None`, the predictions are not stored, and the predictions are returned as a - :class:`dict`. - rng - Random number generator. If :obj:`None` and :attr:`cellflow.model.CellFlow.conditino_mode` - is ``'stochastic'``, the condition vector will be the mean of the learnt distributions, - otherwise samples from the distribution. - kwargs - Keyword arguments for the predict function, i.e. - :meth:`cellflow.solvers.OTFlowMatching.predict` or :meth:`cellflow.solvers.GENOT.predict`. - - Returns - ------- - If ``'key_added_prefix'`` is :obj:`None`, a :class:`dict` with the predicted sample - representation for each perturbation, otherwise stores the predictions in - :attr:`~anndata.AnnData.obsm` and returns :obj:`None`. - """ - if self.solver is None or not self.solver.is_trained: - raise ValueError("Model not trained. Please call `train` first.") - - if sample_rep is None: - sample_rep = self._dm.sample_rep - - if adata is not None and covariate_data is not None: - if covariate_data.empty: - raise ValueError("`covariate_data` is empty.") - if self._dm.control_key not in adata.obs.columns: - raise ValueError( - f"If both `adata` and `covariate_data` are given, the control key `{self._dm.control_key}` must be in `adata.obs`." - ) - if not adata.obs[self._dm.control_key].all(): - raise ValueError( - f"If both `adata` and `covariate_data` are given, all samples in `adata` must be control samples, and thus `adata.obs[`{self._dm.control_key}`] must be set to `True` everywhere." - ) - pred_data = self._dm.get_prediction_data( - adata, - sample_rep=sample_rep, # type: ignore[arg-type] - covariate_data=covariate_data, - condition_id_key=condition_id_key, - ) - pred_loader = PredictionSampler(pred_data) - batch = pred_loader.sample() - src = batch["source"] - condition = batch.get("condition", None) - # using jax.tree.map to batch the prediction - # because PredictionSampler can return a different number of cells for each condition - out = jax.tree.map( - functools.partial(self.solver.predict, rng=rng, **kwargs), - src, - condition, # type: ignore[attr-defined] - ) - if key_added_prefix is None: - return out - if len(pred_data.control_to_perturbation) > 1: - raise ValueError( - f"When saving predictions to `adata`, all control cells must be from the same control \ - population, but found {len(pred_data.control_to_perturbation)} control populations." - ) - out_np = {k: np.array(v) for k, v in out.items()} - _write_predictions( - adata=adata, - predictions=out_np, - key_added_prefix=key_added_prefix, - ) - - def get_condition_embedding( - self, - covariate_data: pd.DataFrame | ConditionData, - rep_dict: dict[str, str] | None = None, - condition_id_key: str | None = None, - key_added: str | None = _constants.CONDITION_EMBEDDING, - ) -> tuple[pd.DataFrame, pd.DataFrame]: - """Get the embedding of the conditions. - - Outputs the mean and variance of the learnt embeddings - generated by the :class:`~cellflow.networks.ConditionEncoder`. - - Parameters - ---------- - covariate_data - Can be one of - - - a :class:`~pandas.DataFrame` defining the conditions with the same columns as the - :class:`~anndata.AnnData` used for the initialisation of :class:`~cellflow.model.CellFlow`. - - an instance of :class:`~cellflow.data.ConditionData`. - - rep_dict - Dictionary containing the representations of the perturbation covariates. Will be considered an - empty dictionary if :obj:`None`. - condition_id_key - Key defining the name of the condition. Only available - if ``'covariate_data'`` is a :class:`~pandas.DataFrame`. - key_added - Key to store the condition embedding in :attr:`~anndata.AnnData.uns`. The mean is - stored under ``key_added`` and the variance under ``f"{key_added}_var"``. If - :obj:`None`, the embeddings are not stored. - - Returns - ------- - A :class:`tuple` of :class:`~pandas.DataFrame` with the mean and variance of the condition embeddings. - """ - if self.solver is None or not self.solver.is_trained: - raise ValueError("Model not trained. Please call `train` first.") - - if hasattr(covariate_data, "condition_data"): - cond_data = covariate_data - elif isinstance(covariate_data, pd.DataFrame): - cond_data = self._dm.get_condition_data( - covariate_data=covariate_data, - rep_dict=rep_dict, - condition_id_key=condition_id_key, - ) - else: - raise ValueError("Covariate data must be a `pandas.DataFrame` or an instance of `BaseData`.") - - condition_embeddings_mean: dict[str, ArrayLike] = {} - condition_embeddings_var: dict[str, ArrayLike] = {} - n_conditions = len(next(iter(cond_data.condition_data.values()))) - for i in range(n_conditions): - condition = {k: v[[i], :] for k, v in cond_data.condition_data.items()} - if condition_id_key: - c_key = cond_data.perturbation_idx_to_id[i] - else: - cov_combination = cond_data.perturbation_idx_to_covariates[i] - c_key = tuple(cov_combination[i] for i in range(len(cov_combination))) - condition_embeddings_mean[c_key], condition_embeddings_var[c_key] = self.solver.get_condition_embedding( - condition - ) - - df_mean = pd.DataFrame.from_dict({k: v[0] for k, v in condition_embeddings_mean.items()}).T - df_var = pd.DataFrame.from_dict({k: v[0] for k, v in condition_embeddings_var.items()}).T - - if condition_id_key: - df_mean.index.set_names([condition_id_key], inplace=True) - df_var.index.set_names([condition_id_key], inplace=True) - else: - df_mean.index.set_names(list(self._dm.perturb_covar_keys), inplace=True) - df_var.index.set_names(list(self._dm.perturb_covar_keys), inplace=True) - - if key_added is not None: - if self.adata is None: # streaming/annbatch path: no `adata` to store into - warnings.warn( - "No `adata` is attached (streaming path); returning the condition embeddings without " - "storing them under `adata.uns`. Pass `key_added=None` to silence this.", - stacklevel=2, - ) - else: # mean under `key_added`, variance under `f"{key_added}_var"` (distinct keys, #295) - _utils.set_plotting_vars(self.adata, key=key_added, value=df_mean) - _utils.set_plotting_vars(self.adata, key=f"{key_added}_var", value=df_var) - - return df_mean, df_var - - def save( - self, - dir_path: str, - file_prefix: str | None = None, - overwrite: bool = False, - ) -> None: - """ - Save the model. - - Pickles the :class:`~cellflow.model.CellFlow` object. - - Parameters - ---------- - dir_path - Path to a directory, defaults to current directory - file_prefix - Prefix to prepend to the file name. - overwrite - Overwrite existing data or not. - - Returns - ------- - :obj:`None` - """ - file_name = ( - f"{file_prefix}_{self.__class__.__name__}.pkl" - if file_prefix is not None - else f"{self.__class__.__name__}.pkl" - ) - file_dir = os.path.join(dir_path, file_name) if dir_path is not None else file_name - - if not overwrite and os.path.exists(file_dir): - raise RuntimeError(f"Unable to save to an existing file `{file_dir}` use `overwrite=True` to overwrite it.") - with open(file_dir, "wb") as f: - cloudpickle.dump(self, f) - - @classmethod - def load( - cls, - filename: str, - ) -> "CellFlow": - """ - Load a :class:`~cellflow.model.CellFlow` model from a saved instance. - - Parameters - ---------- - filename - Path to the saved file. - - Returns - ------- - Loaded instance of the model. - """ - # Check if filename is a directory - file_name = os.path.join(filename, f"{cls.__name__}.pkl") if os.path.isdir(filename) else filename - - with open(file_name, "rb") as f: - model = cloudpickle.load(f) - - if type(model) is not cls: - raise TypeError(f"Expected the model to be type of `{cls}`, found `{type(model)}`.") - return model - - @property - def adata(self) -> ad.AnnData | None: - """The :class:`~anndata.AnnData` object used for training, or :obj:`None` in the annbatch path.""" - return self._adata - - @property - def solver(self) -> _otfm.OTFlowMatching | _genot.GENOT | None: - """The solver.""" - return self._solver - - @property - def dataloader(self) -> TrainSampler | OOCTrainSampler | None: - """The dataloader used for training.""" - return self._dataloader - - @property - def trainer(self) -> CellFlowTrainer | None: - """The trainer used for training.""" - return self._trainer - - @property - def validation_data(self) -> dict[str, ValidationData]: - """The validation data.""" - return self._validation_data - - @property - def data_manager(self) -> DataManager: - """The data manager, initialised with :attr:`cellflow.model.CellFlow.adata`.""" - return self._dm - - @property - def velocity_field( - self, - ) -> _velocity_field.ConditionalVelocityField | _velocity_field.GENOTConditionalVelocityField | None: - """The conditional velocity field.""" - return self._vf - @property def train_data(self) -> TrainingData | None: """The training data.""" @@ -1095,14 +249,18 @@ def train_data(self, data: TrainingData) -> None: raise ValueError(f"Expected `data` to be an instance of `TrainingData`, found `{type(data)}`.") self._train_data = data - @velocity_field.setter # type: ignore[attr-defined,no-redef] - def velocity_field(self, vf: _velocity_field.ConditionalVelocityField) -> None: - """Set the velocity field.""" - if not isinstance(vf, _velocity_field.ConditionalVelocityField): - raise ValueError(f"Expected `vf` to be an instance of `ConditionalVelocityField`, found `{type(vf)}`.") - self._vf = vf + # ── path-specific hook implementations ─────────────────────────────────────────────────────── + def _encoder_conditions(self) -> tuple[dict[str, np.ndarray], int]: + if self.train_data is None: + raise ValueError("Data not initialized. Please call `prepare_data(...)` first.") + return self.train_data.condition_data, self.train_data.max_combination_length + + def _bind_train_dataloader(self, batch_size: int, out_of_core_dataloading: bool) -> None: + self._dataloader = ( + OOCTrainSampler(data=self.train_data, batch_size=batch_size) + if out_of_core_dataloading + else TrainSampler(data=self.train_data, batch_size=batch_size) + ) - @property - def condition_mode(self) -> Literal["deterministic", "stochastic"]: - """The mode of the encoder.""" - return self.velocity_field.condition_mode + def _build_validation_loaders(self) -> dict[str, Any]: + return {k: ValidationSampler(v) for k, v in self.validation_data.items() if k != "predict_kwargs"} diff --git a/src/cellflow/model/_cellflow_annbatch.py b/src/cellflow/model/_cellflow_annbatch.py new file mode 100644 index 00000000..c90bd90c --- /dev/null +++ b/src/cellflow/model/_cellflow_annbatch.py @@ -0,0 +1,322 @@ +"""Streaming (annbatch/dagloader) CellFlow: the default path — trains, validates and predicts over +an out-of-core :class:`~annbatch.DatasetCollection` or an in-memory ``AnnData`` alike.""" + +from collections.abc import Mapping, Sequence +from typing import TYPE_CHECKING, Any, Literal + +import anndata as ad +import numpy as np +import pandas as pd + +from cellflow._types import ArrayLike +from cellflow.data._annbatch import AnnbatchValidationSampler +from cellflow.data._dataloader import DAGLoaderAdapter +from cellflow.model._base import BaseCellFlow + +if TYPE_CHECKING: + from annbatch import DatasetCollection # optional dep — only imported for typing + + from dagloader import DAGEvalLoader, DAGLoader, SamplerConfig, Scheme # optional deps — typing only + +__all__ = ["CellFlowAnnbatch"] + + +class CellFlowAnnbatch(BaseCellFlow): + """CellFlow over the annbatch/dagloader streaming path (cells sourced out-of-core or in-memory). + + Cells are streamed from an :class:`~annbatch.DatasetCollection` (out-of-core) or an in-memory + ``AnnData`` via :meth:`prepare_loaders`; validation and prediction read each condition's full cell + set through :class:`~dagloader.DAGEvalLoader` (no boolean masking). Model setup, training, prediction + and IO are inherited from :class:`~cellflow.model._base.BaseCellFlow`. + """ + + def __init__(self, solver: Literal["otfm", "genot"] = "otfm"): + super().__init__(solver) + self._scheme: Scheme | None = None + self._split_schemes: dict[str, Scheme] | None = None + self._split_assignment: pd.DataFrame | None = None + self._annbatch_sampler_configs: dict[str, SamplerConfig] | None = None + self._eval_cfg: SamplerConfig | None = None # target read params for DAGEvalLoader + self._annbatch_loaders: dict[str, DAGLoader] | None = None + self._condition_data: dict[str, np.ndarray] | None = None # condition embeddings + self._max_combination_length: int | None = None + self._condition_fn = None # leaf -> embedding (set by `prepare_loaders`) + self._prep_kwargs: dict[str, Any] | None = None # covariate spec, reused for validation sources + self._seed: int = 0 + self._split_eval_loaders: dict[str, DAGEvalLoader] = {} + + def prepare_loaders( + self, + source: "ad.AnnData | DatasetCollection", + sample_rep: str, + control_key: str, + perturbation_covariates: dict[str, Sequence[str]], + perturbation_covariate_reps: dict[str, str] | None = None, + sample_covariates: Sequence[str] | None = None, + sample_covariate_reps: dict[str, str] | None = None, + split_covariates: Sequence[str] | None = None, + max_combination_length: int | None = None, + null_value: float = 0.0, + rep_dict: Mapping[str, Mapping[str, ArrayLike]] | None = None, + sampler_config: "SamplerConfig | Mapping[str, SamplerConfig] | None" = None, + seed: int = 0, + split_by: Sequence[str] | None = None, + split_ratios: Mapping[str, float] | None = None, + split_force_training_values: Mapping[str, object] | None = None, + split_random_state: int = 42, + ) -> None: + """Prepare the annbatch/dagloader streaming training path (the default, recommended path). + + The covariate arguments (``sample_rep``, ``control_key``, ``perturbation_covariates``, + ``perturbation_covariate_reps``, ``sample_covariates``, ``sample_covariate_reps``, + ``split_covariates``, ``max_combination_length``, ``null_value``) mean exactly what they do in + :meth:`prepare_data`; only the cells differ — streamed from an out-of-core + :class:`annbatch.DatasetCollection` instead of materialized. Requires the ``annbatch`` extra and + a model constructed as ``CellFlowAnnbatch()``. + + Parameters + ---------- + source + An out-of-core :class:`annbatch.DatasetCollection` to stream cells from (an in-memory + ``AnnData`` also works — the ``dagloader`` is container-agnostic). Its ``obs`` supplies the + grouping / condition columns; ``sample_rep`` selects the streamed representation. + rep_dict + The covariate embedding tables that ``adata.uns`` would hold in the in-memory path (keys + match the values of ``perturbation_covariate_reps`` / ``sample_covariate_reps``). Required + when a covariate group is embedded; may be :obj:`None` when the primary covariate is + categorical (one-hot encoded). + sampler_config + Read parameters for the streamed loader(s), **one per split**: a single + :class:`dagloader.SamplerConfig` for all splits, or a ``{split_name: SamplerConfig}`` mapping + covering every split (see :func:`dagloader.resolve_split_configs`). With no split the only + split is ``"train"``. ``chunk_size > 1`` reads contiguous slices, so every run of each + category must be at least ``chunk_size`` cells — in-memory sources are grouped automatically, + an out-of-core :class:`~annbatch.DatasetCollection` must be built grouped + (``add_adatas(..., groupby=[...])``) or a clear error is raised. + seed + Reproducibility seed for the ``dagloader`` per-node RNG streams. + split_by + If given, split the prepared ``Scheme``'s target combinations into train/val/test in the + same call (delegates to :meth:`split_annbatch_data`). Columns whose unique combinations are + held out (⊆ the target columns). If :obj:`None`, no split is made (the model would train on + all combinations). + split_ratios + ``{split_name: fraction}`` summing to 1.0 for the split. Defaults to + ``{"train": 0.6, "val": 0.2, "test": 0.2}``. Only used when ``split_by`` is given. + split_force_training_values + ``{column: value}`` (keys ⊆ ``split_by``) forced into the training split. Only used when + ``split_by`` is given. + split_random_state + Seed for the split's combination shuffle. Only used when ``split_by`` is given. + + Returns + ------- + :obj:`None`, and sets up the streaming training data used by :meth:`train`. + + Notes + ----- + The ``"train"`` split's loader is wired to :meth:`train`; when a split is made, the ``val`` / + ``test`` split loaders are kept on :attr:`_annbatch_loaders` for later use. + """ + from cellflow.data._annbatch import build_annbatch_training + from dagloader import DAGLoader, resolve_split_configs + + # Build the Scheme + condition_fn + condition embeddings from the covariate spec (obs only). + built = build_annbatch_training( + source, + sample_rep=sample_rep, + control_key=control_key, + perturbation_covariates=perturbation_covariates, + perturbation_covariate_reps=perturbation_covariate_reps, + sample_covariates=sample_covariates, + sample_covariate_reps=sample_covariate_reps, + split_covariates=split_covariates, + max_combination_length=max_combination_length, + null_value=null_value, + rep_dict=rep_dict, + seed=seed, + ) + self._scheme = built.scheme + self._dm = built.data_manager + self._condition_data = built.condition_data + self._data_dim = built.data_dim + self._max_combination_length = built.max_combination_length + condition_fn = built.condition_fn + # kept so `prepare_validation_data` / the auto-wired split loaders can build DAGEvalLoaders. + self._condition_fn = condition_fn + self._seed = seed + self._prep_kwargs = dict( + sample_rep=sample_rep, + control_key=control_key, + perturbation_covariates=perturbation_covariates, + perturbation_covariate_reps=perturbation_covariate_reps, + sample_covariates=sample_covariates, + sample_covariate_reps=sample_covariate_reps, + split_covariates=split_covariates, + max_combination_length=max_combination_length, + null_value=null_value, + rep_dict=rep_dict, + seed=seed, + ) + + # Splitting step — kept in `prepare_loaders` so preparing and splitting are one call. + if split_by is not None: + self._split_assignment = self.split_annbatch_data( + split_by=split_by, + ratios=split_ratios, + force_training_values=split_force_training_values, + random_state=split_random_state, + ) + + # One `SamplerConfig` per split (a single spec ⇒ all splits; a per-split dict ⇒ all specified). + # The splits are the split schemes when a split was made, else the single ``"train"`` scheme. + schemes = self._split_schemes if self._split_schemes is not None else {"train": self._scheme} + if sampler_config is None: + raise ValueError("`sampler_config` is required: give a SamplerConfig or a {split: SamplerConfig} mapping.") + self._annbatch_sampler_configs = resolve_split_configs(sampler_config, list(schemes)) + self._eval_cfg = self._annbatch_sampler_configs["train"] # target-batch read params for eval loaders + + # `chunk_size > 1` reads contiguous slices → every category's runs must be >= chunk_size + # (in-memory sources are auto-grouped above; out-of-core collections must be built grouped). + max_chunk = max(cfg.chunk_size for cfg in self._annbatch_sampler_configs.values()) + if max_chunk > 1: + from cellflow.data._annbatch import assert_source_chunkable + + root = self._scheme.nodes[self._scheme.root] + assert_source_chunkable(self._scheme.sources["data"], root.cols, max_chunk) + + # One streaming DAGLoader per split; the "train" split feeds `train()`, others are kept for reuse. + self._annbatch_loaders = { + name: DAGLoader(schemes[name], self._annbatch_sampler_configs[name], condition_fn=condition_fn) + for name in schemes + } + self._dataloader = DAGLoaderAdapter(self._annbatch_loaders["train"]) + + # Auto-wire the val/test split combinations as evaluation sources over the same cells: a + # `DAGEvalLoader` reads each held-out condition's full cell set + matched controls. The "val" + # split (if any) feeds training-time metrics; every non-train split is kept on + # `split_eval_loaders` for post-hoc evaluation. Override with `prepare_validation_data(...)`. + from dagloader import DAGEvalLoader + + self._split_eval_loaders = {} + if self._split_schemes is not None: + for split_name, sch in self._split_schemes.items(): + if split_name == "train": + continue + self._split_eval_loaders[split_name] = DAGEvalLoader(sch, self._eval_cfg, condition_fn, seed=seed) + if "val" in self._split_eval_loaders: + self._validation_data["val"] = AnnbatchValidationSampler(self._split_eval_loaders["val"]) + + def split_annbatch_data( + self, + *, + split_by: Sequence[str], + ratios: Mapping[str, float] | None = None, + force_training_values: Mapping[str, object] | None = None, + random_state: int = 42, + ) -> pd.DataFrame: + """Partition the prepared annbatch ``Scheme``'s target combinations into named splits. + + Call after :meth:`prepare_loaders`. Splits hold out whole *combinations* of ``split_by`` + (a subset of the perturbation / split-covariate columns), not cells; controls are carried through + every split. See :func:`dagloader.split_scheme` for the mechanics. + + Parameters + ---------- + split_by + Columns whose unique combinations are partitioned across splits (⊆ the scheme's target + columns). + ratios + ``{split_name: fraction}`` summing to 1.0. Defaults to + ``{"train": 0.6, "val": 0.2, "test": 0.2}``. The first split is the training split. + force_training_values + ``{column: value}`` (keys ⊆ ``split_by``): any combination matching a value is forced into + the training (first) split. + random_state + Seed for the combination shuffle. + + Returns + ------- + A :class:`~pandas.DataFrame` of the target combinations and their assigned split. Also stores + the per-split schemes on the model. + """ + from dagloader import split_assignment, split_scheme + + if self._scheme is None: + raise ValueError( + "No annbatch `Scheme` to split. Call `prepare_loaders(...)` first " + "(the out-of-core streaming path)." + ) + self._split_schemes = split_scheme( + self._scheme, + split_by=split_by, + ratios=ratios, + force_training_values=force_training_values, + random_state=random_state, + ) + # `prepare_loaders` turns these split Schemes into per-split DAGLoaders (train feeds + # `train()`; val/test are kept on `_annbatch_loaders`); here we only produce the schemes + table. + return split_assignment(self._split_schemes) + + def prepare_validation_data( + self, + source: "ad.AnnData | DatasetCollection", + name: str, + n_conditions_on_log_iteration: int | None = None, + n_conditions_on_train_end: int | None = None, + predict_kwargs: dict[str, Any] | None = None, + ) -> None: + """Register a validation set read via :class:`~dagloader.DAGEvalLoader` (no boolean masking). + + The ``source`` (an :class:`~anndata.AnnData` or an :class:`~annbatch.DatasetCollection`) is + grouped by the same covariate spec passed to :meth:`prepare_loaders`; each of its conditions is + read in full (all its cells + all matched controls) at validation time. Overrides any same-named + auto-wired split loader. Preserves the legacy per-condition metric semantics. + + Parameters + ---------- + source + The held-out validation source (out-of-core or in-memory). + name + Key under which the validation set is stored in :attr:`validation_data`. + n_conditions_on_log_iteration, n_conditions_on_train_end + Conditions to sample per validation step; :obj:`None` uses all. + predict_kwargs + Keyword arguments for the solver's ``predict`` used during validation. + """ + if self._scheme is None or self._prep_kwargs is None: + raise ValueError("Set up training first via `prepare_loaders(...)` before preparing validation data.") + + from cellflow.data._annbatch import build_annbatch_training + from dagloader import DAGEvalLoader + + built = build_annbatch_training(source, **self._prep_kwargs) + eval_loader = DAGEvalLoader(built.scheme, self._eval_cfg, built.condition_fn, seed=self._seed) + self._validation_data[name] = AnnbatchValidationSampler( + eval_loader, + n_conditions_on_log_iteration=n_conditions_on_log_iteration, + n_conditions_on_train_end=n_conditions_on_train_end, + ) + predict_kwargs = predict_kwargs or {} + if len(self._validation_data.get("predict_kwargs", {})) > 0 and len(predict_kwargs) > 0: + self._validation_data["predict_kwargs"].update(predict_kwargs) + predict_kwargs = self._validation_data["predict_kwargs"] + self._validation_data["predict_kwargs"] = predict_kwargs + + @property + def split_eval_loaders(self) -> dict[str, "DAGEvalLoader"]: + """Per-split :class:`~dagloader.DAGEvalLoader` objects for non-train splits (e.g. ``val``, ``test``).""" + return self._split_eval_loaders + + # ── path-specific hook implementations ─────────────────────────────────────────────────────── + def _encoder_conditions(self) -> tuple[dict[str, np.ndarray], int]: + if self._condition_data is None or self._max_combination_length is None: + raise ValueError("Data not initialized. Please call `prepare_loaders(...)` first.") + return self._condition_data, self._max_combination_length + + def _bind_train_dataloader(self, batch_size: int, out_of_core_dataloading: bool) -> None: + # the streaming `_dataloader` (DAGLoaderAdapter) was already set in `prepare_loaders`. + return None + + def _build_validation_loaders(self) -> dict[str, Any]: + return {k: v for k, v in self.validation_data.items() if k != "predict_kwargs"} diff --git a/src/cellflow/networks/_velocity_field.py b/src/cellflow/networks/_velocity_field.py index 7155b78f..6f04147f 100644 --- a/src/cellflow/networks/_velocity_field.py +++ b/src/cellflow/networks/_velocity_field.py @@ -303,7 +303,30 @@ def __call__( train: bool = True, force_uncond: bool = False, ) -> tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: - squeeze = x_t.ndim == 1 + # Split into "encode the condition" and "velocity given the embedding" so the predict path can + # encode once and reuse the embedding across every ODE step (the condition is constant along the + # trajectory). `__call__` composes them, so the training path and any single-shot call are + # unchanged — see `encode_condition` / `velocity_from_embedding`. + cond_embedding, cond_mean, cond_logvar = self.encode_condition( + cond, encoder_noise, train=train, force_uncond=force_uncond + ) + out = self.velocity_from_embedding(t, x_t, cond_embedding, train=train) + return out, cond_mean, cond_logvar + + def encode_condition( + self, + cond: dict[str, jnp.ndarray], + encoder_noise: jnp.ndarray, + train: bool = True, + force_uncond: bool = False, + ) -> tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + """Encode the condition into its (pre-concatenation) embedding, returning also the raw + Gaussian parameters used by the stochastic-encoder regularization. + + This is the part of the forward pass that only depends on ``cond`` (not on ``t`` or ``x``), so + the predict path evaluates it once per condition and passes the result to + :meth:`velocity_from_embedding` at every ODE step. + """ if self.condition_null == "mask_value": cond = self._maybe_null_input(cond, train=train, force_uncond=force_uncond) cond_mean, cond_logvar = self.condition_encoder(cond, training=train) @@ -315,22 +338,35 @@ def __call__( cond_embedding = self.layer_cond_output_dropout(cond_embedding, deterministic=not train) if self.condition_null == "zero_embedding": cond_embedding = self._maybe_null_embedding(cond_embedding, train=train, force_uncond=force_uncond) + cond_embedding = self.layer_norm_condition(cond_embedding) + return cond_embedding, cond_mean, cond_logvar + + def velocity_from_embedding( + self, + t: jnp.ndarray, + x_t: jnp.ndarray, + cond_embedding: jnp.ndarray, + train: bool = True, + ) -> jnp.ndarray: + """Velocity for ``x_t`` at time ``t`` given an already-encoded (and normed) ``cond_embedding``. + This is the ODE right-hand side; it contains no condition-encoder work, so integrating with a + precomputed embedding avoids re-encoding the condition on every step. + """ + squeeze = x_t.ndim == 1 t_encoded = sinusoidal_time_encoder(t, time_freqs=self.time_freqs, time_max_period=self.time_max_period) t_encoded = self.time_encoder(t_encoded, training=train) x_encoded = self._encode_x(x_t, squeeze, train) t_encoded = self.layer_norm_time(t_encoded) x_encoded = self.layer_norm_x(x_encoded) - cond_embedding = self.layer_norm_condition(cond_embedding) if squeeze: cond_embedding = jnp.squeeze(cond_embedding) # , 0) elif cond_embedding.shape[0] != x_t.shape[0]: # type: ignore[attr-defined] cond_embedding = jnp.tile(cond_embedding, (x_t.shape[0], 1)) - out = self._combine_and_decode(t_encoded, x_encoded, cond_embedding, squeeze, train) - return out, cond_mean, cond_logvar + return self._combine_and_decode(t_encoded, x_encoded, cond_embedding, squeeze, train) def _encode_x(self, x_t: jnp.ndarray, squeeze: bool, train: bool) -> jnp.ndarray: """Encode ``x_t`` before conditioning. Override to insert pre-conditioning processing.""" diff --git a/src/cellflow/solvers/_base.py b/src/cellflow/solvers/_base.py index 9d0dc8ed..7d911313 100644 --- a/src/cellflow/solvers/_base.py +++ b/src/cellflow/solvers/_base.py @@ -35,6 +35,9 @@ def __init__( self.probability_path = probability_path self.time_sampler = time_sampler self._predict_fn_cache: dict[Any, Any] = {} + # Separate cache for the flat (condition-batched) predict fn — takes a per-cell embedding and + # vmaps over the concatenated cells of all conditions at once. See `OTFlowMatching.predict`. + self._flat_predict_fn_cache: dict[Any, Any] = {} @property def _inference_state(self) -> train_state.TrainState: diff --git a/src/cellflow/solvers/_otfm.py b/src/cellflow/solvers/_otfm.py index 0c41db74..2b1eacf9 100644 --- a/src/cellflow/solvers/_otfm.py +++ b/src/cellflow/solvers/_otfm.py @@ -294,6 +294,23 @@ def vf( return vf + def _base_velocity_from_embedding(self) -> Callable: + """Velocity closure taking a *precomputed* condition embedding in ``args``. + + ``args`` is ``(params, cond_embedding)``. Used on the predict path when no guidance is active, so + the condition encoder runs once (outside the ODE, in :meth:`_get_predict_fn`) rather than on + every integration step; the embedding is constant along the trajectory, so the result is + identical to :meth:`_base_velocity`. + """ + + def vf(t: jnp.ndarray, x: jnp.ndarray, args: tuple[Any, jnp.ndarray]) -> jnp.ndarray: + params, cond_embedding = args + return self.vf_state_inference.apply_fn( + {"params": params}, t, x, cond_embedding, train=False, method="velocity_from_embedding" + ) + + return vf + def _get_predict_fn(self, kwargs_frozen: frozen_dict.FrozenDict) -> Callable: """Build and cache a jit+vmap predict function for the given diffrax kwargs. @@ -333,28 +350,118 @@ def _get_predict_fn(self, kwargs_frozen: frozen_dict.FrozenDict) -> Callable: ) guidance = None - vf = self._base_velocity() - if guidance is not None: - vf = guidance.wrap(vf) + if guidance is None: + # No guidance: encode the condition once, then integrate the embedding-only rhs — the + # condition encoder never runs inside the ODE. Identical output to the per-step path. + vf_emb = self._base_velocity_from_embedding() - def solve_ode( - params: Any, x: jnp.ndarray, condition: dict[str, jnp.ndarray], encoder_noise: jnp.ndarray - ) -> jnp.ndarray: - ode_term = diffrax.ODETerm(vf) + def solve_ode_emb(params: Any, x: jnp.ndarray, cond_embedding: jnp.ndarray) -> jnp.ndarray: + result = diffrax.diffeqsolve( + diffrax.ODETerm(vf_emb), t0=0.0, t1=1.0, y0=x, args=(params, cond_embedding), **kwargs + ) + return result.ys[0] + + vmapped = jax.vmap(solve_ode_emb, in_axes=[None, 0, None]) + + def fn( + params: Any, x: jnp.ndarray, condition: dict[str, jnp.ndarray], encoder_noise: jnp.ndarray + ) -> jnp.ndarray: + cond_embedding = self.vf_state_inference.apply_fn( + {"params": params}, condition, encoder_noise, train=False, method="encode_condition" + )[0] + return vmapped(params, x, cond_embedding) + + fn = jax.jit(fn) + else: + # Guidance wraps the full velocity (needs both conditional and null embeddings per step), so + # keep encoding inside the ODE. + vf = guidance.wrap(self._base_velocity()) + + def solve_ode( + params: Any, x: jnp.ndarray, condition: dict[str, jnp.ndarray], encoder_noise: jnp.ndarray + ) -> jnp.ndarray: + result = diffrax.diffeqsolve( + diffrax.ODETerm(vf), t0=0.0, t1=1.0, y0=x, args=(params, condition, encoder_noise), **kwargs + ) + return result.ys[0] + + fn = jax.jit(jax.vmap(solve_ode, in_axes=[None, 0, None, None])) + + self._predict_fn_cache[kwargs_frozen] = fn + return fn + + def _get_flat_predict_fn(self, kwargs_frozen: frozen_dict.FrozenDict) -> Callable: + """Build/cache the condition-batched predict fn. + + One ``jit(vmap)`` solve over the *concatenated* cells of all conditions, each cell carrying its + own precomputed embedding (``in_axes=[None, 0, 0]``). Only valid with no guidance active (guidance + needs the encoder inside the ODE), so it integrates the embedding-only rhs. + """ + if kwargs_frozen in self._flat_predict_fn_cache: + return self._flat_predict_fn_cache[kwargs_frozen] + + kwargs = dict(kwargs_frozen) + vf_emb = self._base_velocity_from_embedding() + + def solve_ode_emb(params: Any, x: jnp.ndarray, cond_embedding: jnp.ndarray) -> jnp.ndarray: result = diffrax.diffeqsolve( - ode_term, - t0=0.0, - t1=1.0, - y0=x, - args=(params, condition, encoder_noise), - **kwargs, + diffrax.ODETerm(vf_emb), t0=0.0, t1=1.0, y0=x, args=(params, cond_embedding), **kwargs ) return result.ys[0] - fn = jax.jit(jax.vmap(solve_ode, in_axes=[None, 0, None, None])) - self._predict_fn_cache[kwargs_frozen] = fn + fn = jax.jit(jax.vmap(solve_ode_emb, in_axes=[None, 0, 0])) + self._flat_predict_fn_cache[kwargs_frozen] = fn return fn + def _predict_batched( + self, + x: dict[str, ArrayLike], + condition: dict[str, dict[str, ArrayLike]], + rng: jax.Array | None = None, + **kwargs: Any, + ) -> dict[str, ArrayLike]: + """Predict every condition in one condition-batched ODE solve (no-guidance path). + + Encodes each condition once, concatenates all conditions' source cells into a single batch (each + cell tagged with its condition's embedding), integrates them in one vmapped solve, then splits the + result back per condition. Differing per-condition cell counts are handled by concatenation (no + padding). Numerically identical to the per-condition loop: each cell's ODE is independent. + """ + kwargs.setdefault("dt0", None) + kwargs.setdefault("solver", diffrax.Tsit5()) + kwargs.setdefault("stepsize_controller", diffrax.PIDController(rtol=1e-5, atol=1e-5)) + kwargs.pop("guidance_scale", None) # == 1.0 on this path (guaranteed by the caller) + kwargs_frozen = frozen_dict.freeze(kwargs) + + keys = list(x) + # One encoder-noise draw shared across conditions (matches the per-condition loop under one rng). + noise_dim = (1, self.vf.condition_embedding_dim) + use_mean = rng is None or self.condition_encoder_mode == "deterministic" + rng = utils.default_prng_key(rng) + encoder_noise = jnp.zeros(noise_dim) if use_mean else jax.random.normal(rng, noise_dim) + + params = self.vf_state_inference.params + groups = list(condition[keys[0]]) + cond_stacked = {g: jnp.concatenate([jnp.asarray(condition[k][g]) for k in keys], axis=0) for g in groups} + cond_embedding = self.vf_state_inference.apply_fn( + {"params": params}, cond_stacked, encoder_noise, train=False, method="encode_condition" + )[0] # (n_conditions, embedding_dim) + + sizes = [int(jnp.asarray(x[k]).shape[0]) for k in keys] + x_flat = jnp.concatenate([jnp.asarray(x[k]) for k in keys], axis=0) + emb_flat = jnp.concatenate( + [jnp.broadcast_to(cond_embedding[i : i + 1], (sizes[i], cond_embedding.shape[-1])) for i in range(len(keys))], + axis=0, + ) + out_flat = self._get_flat_predict_fn(kwargs_frozen)(params, x_flat, emb_flat) + + out: dict[str, ArrayLike] = {} + offset = 0 + for k, n in zip(keys, sizes, strict=True): + out[k] = out_flat[offset : offset + n] + offset += n + return out + def _predict_jit( self, x: ArrayLike, @@ -424,7 +531,14 @@ def predict( return {} if isinstance(x, dict): - jax_results = {k: self._predict_jit(x[k], condition[k], rng, **kwargs) for k in x} + # With no guidance, batch every condition into one condition-batched solve (encode once per + # condition, one vmapped kernel over all cells). Guidance stays on the per-condition loop + # since it needs the conditional + null embeddings inside the ODE. Same result either way. + guidance_scale = float(kwargs.get("guidance_scale", 1.0)) + if self.guidance is None and guidance_scale == 1.0: + jax_results = self._predict_batched(x, condition, rng, **kwargs) + else: + jax_results = {k: self._predict_jit(x[k], condition[k], rng, **kwargs) for k in x} return {k: np.array(v) for k, v in jax_results.items()} else: x_pred = self._predict_jit(x, condition, rng, **kwargs) diff --git a/src/cellflow/training/_trainer.py b/src/cellflow/training/_trainer.py index f0130690..a2c4ef1d 100644 --- a/src/cellflow/training/_trainer.py +++ b/src/cellflow/training/_trainer.py @@ -6,7 +6,7 @@ from numpy.typing import ArrayLike from tqdm import tqdm -from cellflow.data._dataloader import OOCTrainSampler, TrainSampler, ValidationSampler +from cellflow.data._legacy import OOCTrainSampler, TrainSampler, ValidationSampler from cellflow.solvers import _genot, _otfm from cellflow.training._callbacks import BaseCallback, CallbackRunner diff --git a/src/dagloader/__init__.py b/src/dagloader/__init__.py index 7cff59a8..dd2c514f 100644 --- a/src/dagloader/__init__.py +++ b/src/dagloader/__init__.py @@ -3,16 +3,17 @@ A :class:`Scheme` is a rooted tree of :class:`Node`\\s over named cell sources; each node partitions its source's cells into leaves (unique column-combinations) with a per-combination weight mapping. :class:`DAGLoader` streams matched ``{source, target, condition}`` batches — one condition per -batch — where every node streams through its own :class:`ScheduledClassSampler`, the root's per-batch -category is drawn from its weights, and each bound child's category is *derived* from the parent's so -the loaders zip batch-for-batch. No row indices are exposed: the scheme is columns / keys / weights. +batch — where the root's per-batch category is drawn from its weights (annbatch ``ClassSampler``) and +each bound child replays that schedule via an annbatch ``BoundClassSampler`` (matched on the bind's +shared columns) so the loaders zip batch-for-batch. No row indices are exposed: the scheme is +columns / keys / weights. See ``README.md`` (next to this file) for the model, the sampling schemes, and the mapping to cellflow and sc-flow-tools use-cases. """ +from dagloader._eval_loader import DAGEvalLoader from dagloader._loader import DAGLoader -from dagloader._scheduled_sampler import ScheduledClassSampler from dagloader._schema import ( Bind, Container, @@ -30,11 +31,11 @@ __all__ = [ "Bind", "Container", + "DAGEvalLoader", "DAGLoader", "Node", "SamplerConfig", "Scheme", - "ScheduledClassSampler", "Weights", "frequency", "inverse_frequency", diff --git a/src/dagloader/_eval_loader.py b/src/dagloader/_eval_loader.py new file mode 100644 index 00000000..62c01203 --- /dev/null +++ b/src/dagloader/_eval_loader.py @@ -0,0 +1,158 @@ +"""``DAGEvalLoader`` — control-rooted eval reader: a Sequential control *inner* + a bound perturbed target. + +Anchors on the source (control) — "there is no perturbed without a source". Two loaders, both driven by +one deterministic :class:`~annbatch.samplers.SequentialClassSampler` over the control populations: + +* the **source** loader *is* that inner — it reads each scheduled control population **in full** (all its + controls), and +* the **target** loader is an :class:`~annbatch.samplers.BoundClassSampler` on the *same* inner, matched on + the bind's ``common`` (context) columns, that **samples** a perturbed leaf (drug) within each matched + context. + +annbatch does all the class matching; nothing is derived or updated per pass here. The condition of each +batch is the perturbed leaf the bound drew (read from an identically-seeded oracle, so it lines up with +the target loader's own draw). Which control populations are visited — and how many batches — is set via +the schedule (``iter_conditions``). Works over an in-memory ``AnnData`` and a ``DatasetCollection`` alike. +""" + +from __future__ import annotations + +from collections.abc import Callable, Iterator, Mapping + +import numpy as np +from annbatch import Loader +from annbatch.samplers import BoundClassSampler, SequentialClassSampler + +from dagloader._io import key_backings, leaf_codes, obs_columns +from dagloader._loader import _HAS_CUPY, _flat_categorical +from dagloader._schema import SamplerConfig, Scheme, _weight_vector + +__all__ = ["DAGEvalLoader"] + + +class DAGEvalLoader: + """Yield ``{"source", "target", "condition", "leaf"}`` per control population (source-anchored). + + Parameters + ---------- + scheme + A prepared :class:`~dagloader.Scheme` (root = perturbed/target node, one bound control child). + Its bind's ``common`` columns are the matching context (e.g. ``cell_line``). + sampler_config + Read parameters for the bound perturbed-target sampler (``batch_size`` = target cells per + condition; controls are read in full regardless). + condition_fn + Maps a perturbed leaf (over the target node's ``cols``) to its condition embedding. + seed + Seed for the target's drug sampling (reproducible across ``iter_conditions`` calls). + """ + + def __init__( + self, + scheme: Scheme, + sampler_config: SamplerConfig, + condition_fn: Callable[[tuple], np.ndarray | Mapping[str, np.ndarray]] | None = None, + *, + seed: int = 0, + ) -> None: + self.s = scheme + self._cond_fn = condition_fn + self._cfg = sampler_config + self._seed = seed + binds = [b for b in scheme.binds if b.parent == scheme.root] + if len(binds) != 1: + raise ValueError("DAGEvalLoader expects exactly one bound source of the root.") + b = binds[0] + self._pert = scheme.nodes[scheme.root] # perturbed / target + self._ctrl = scheme.nodes[b.child] # control / source + self._context = b.common + self._src = scheme.sources[self._ctrl.source] # same container as the pert source (one "data") + self._src_p = scheme.sources[self._pert.source] + + # per-node tuple-labelled categorical + weight vector (obs only). Control/perturbed cells live in + # the same source; the weights mark which leaves belong to each node. + cc, cl = leaf_codes(obs_columns(self._src, self._ctrl.cols), self._ctrl.cols) + pc, pl = leaf_codes(obs_columns(self._src_p, self._pert.cols), self._pert.cols) + self._ctrl_cats = _flat_categorical(cc, cl) + self._pert_cats = _flat_categorical(pc, pl) + self._ctrl_w = _weight_vector(self._ctrl.weights, cl) + self._pert_w = _weight_vector(self._pert.weights, pl) + self._ctrl_leaves = cl + # inner (control) tuple position -> target tuple position, for each shared context column + self._on = {self._ctrl.cols.index(c): self._pert.cols.index(c) for c in self._context} + # the control populations to visit (positive-weight control leaves) + self._ctrl_codes = np.array([i for i in range(len(cl)) if self._ctrl_w[i] > 0], dtype=np.int64) + if self._ctrl_codes.size == 0: + raise ValueError("no control population (positive-weight control leaf) to evaluate.") + + @property + def control_populations(self) -> list[tuple]: + """The control leaves (contexts) this loader iterates over.""" + return [self._ctrl_leaves[i] for i in self._ctrl_codes] + + def _inner(self, schedule: np.ndarray) -> SequentialClassSampler: + # deterministic: same schedule ⇒ same control-population order across the source loader and every + # bound inner, so source[j] and target[j] refer to the same matched context. + return SequentialClassSampler(self._ctrl_cats, schedule=schedule) + + def _bound(self, schedule: np.ndarray) -> BoundClassSampler: + cfg = self._cfg + return BoundClassSampler( + self._inner(schedule), + cfg.chunk_size, + cfg.preload_nchunks, + cfg.batch_size, + classes_to_bind_on=self._pert_cats, + on=self._on, + classes=self._pert_cats, # secondary = the perturbed leaf; weights pick positive-weight drugs + class_weights=self._pert_w, + rng=np.random.default_rng(self._seed), + ) + + def _node_loaders(self, src, node, make_sampler) -> dict: + loaders = {} + for key in node.keys: + loader = Loader( + batch_sampler=make_sampler(), return_index=False, to="jax", preload_to_gpu=_HAS_CUPY + ).add_datasets(key_backings(src, key)) + loaders[key] = loader + return loaders + + def iter_conditions(self, n_conditions: int | None = None) -> Iterator[dict]: + """Yield one batch per scheduled control population. + + With ``n_conditions`` set, the control populations are cycled to that many batches (each re-reads + the population's controls and the bound samples a fresh drug); otherwise every control population + is visited once. + """ + if n_conditions is None: + schedule = self._ctrl_codes.copy() + else: + reps = int(np.ceil(n_conditions / self._ctrl_codes.size)) + schedule = np.tile(self._ctrl_codes, reps)[:n_conditions].astype(np.int64) + + # condition oracle: identically-seeded bound → its per-batch drawn perturbed leaf lines up with + # the target loader's own draw (annbatch reproduces the same class sequence from the same seed). + oracle = self._bound(schedule) + vocab = oracle.vocab + ctx = len(self._context) + cond_leaves = [tuple(vocab[int(c)])[ctx:] for c in oracle.batch_codes()] # strip the shared context prefix + + src_loaders = self._node_loaders(self._src, self._ctrl, lambda: self._inner(schedule)) + tgt_loaders = self._node_loaders(self._src_p, self._pert, lambda: self._bound(schedule)) + src_iters = {k: iter(ld) for k, ld in src_loaders.items()} + tgt_iters = {k: iter(ld) for k, ld in tgt_loaders.items()} + skeys, tkeys = list(src_loaders), list(tgt_loaders) + + for j in range(len(schedule)): + src = {k: next(src_iters[k])["X"] for k in skeys} + tgt = {k: next(tgt_iters[k])["X"] for k in tkeys} + leaf = cond_leaves[j] + out: dict = {"leaf": leaf, "source": src[skeys[0]], "target": tgt[tkeys[0]]} + if len(skeys) > 1: + out["source_reps"] = src + if len(tkeys) > 1: + out["target_reps"] = tgt + if self._cond_fn is not None: + out["condition"] = self._cond_fn(leaf) + yield out diff --git a/src/dagloader/_loader.py b/src/dagloader/_loader.py index f4cadd91..ed110dac 100644 --- a/src/dagloader/_loader.py +++ b/src/dagloader/_loader.py @@ -1,26 +1,101 @@ """``DAGLoader`` — streams matched ``{source, target, condition}`` batches from a :class:`Scheme`. -Every node streams through its own :class:`~dagloader.ScheduledClassSampler` + annbatch -``Loader``, configured by a :class:`SamplerConfig` (one shared, or one per node). Each pass, the loader -draws the root's per-batch category schedule from the root's weights and *derives* each bound child's -schedule from the parent's (via the bind's shared columns), pushes the schedules onto the samplers, and -zips the loaders batch-for-batch. See ``README.md``. +Each pass is a fresh epoch. The root (target) node draws a per-batch class schedule ∝ its weights via an +annbatch :class:`~annbatch.samplers.ClassSampler`; every bound child replays that schedule onto its own +cells via an annbatch :class:`~annbatch.samplers.BoundClassSampler` — matched by *label* on the bind's +``common`` columns (a ``matched=`` bind instead remaps the schedule to explicit child leaves). The loader +never wraps annbatch's RNG: every sampler that must agree within a pass (the schedule oracle, the target +reps, and each child's inner) is **reseeded from one per-pass seed** ``(node seed, pass index)``, so +target/condition/source stay aligned, a node's reps read the same rows, and a pickled loader resumes the +exact same stream. See ``README.md``. """ from __future__ import annotations from collections.abc import Callable, Iterator, Mapping +from importlib.util import find_spec import numpy as np import pandas as pd from annbatch import Loader +from annbatch.samplers import BoundClassSampler, ClassSampler from dagloader._io import key_backings, leaf_codes, obs_columns -from dagloader._scheduled_sampler import ScheduledClassSampler from dagloader._schema import Bind, SamplerConfig, Scheme, _weight_vector __all__ = ["DAGLoader"] +# annbatch's GPU path (cupy vstack/indexing → `to="jax"` yields a GPU-resident array via dlpack) needs +# cupy. When it's absent (CPU-only envs — Mac, CI), fall back so `to="jax"` still yields a CPU jax array. +_HAS_CUPY = find_spec("cupy") is not None + + +def _flat_categorical(codes: np.ndarray, leaves: list[tuple]) -> pd.Categorical: + """A tuple-labelled categorical: per-cell leaf code over ``leaves`` (categories are the leaf tuples). + + Tuple labels (not opaque integer codes) are what let :class:`~annbatch.samplers.BoundClassSampler` + match a child to its parent by the bind's ``common`` columns — it projects the label by position. + """ + categories = pd.MultiIndex.from_tuples(leaves).to_flat_index() + return pd.Categorical.from_codes(codes, categories=categories) + + +class _RemappedScheduleInner: + """Schedule-only :class:`~annbatch.abc.BaseClassSampler` that remaps a root schedule to child leaves. + + For ``matched=`` binds (explicit parent-leaf → child-leaf pairing, not a shared-column match): it + wraps the root schedule oracle and exposes, as its own ``batch_codes``, the child leaf of each batch + (via the pairing). A :class:`~annbatch.samplers.BoundClassSampler` then reads matched child cells by + label (``on=None``). Only the schedule surface (``vocab`` / ``emittable_codes`` / ``batch_codes`` / + ``n_batches``) is used by the bound — never :meth:`_sample`. + """ + + def __init__( + self, + oracle: ClassSampler, + root_leaves: list[tuple], + child_leaves: list[tuple], + matched: Mapping[tuple, tuple], + ) -> None: + self._oracle = oracle + self._root_leaves = root_leaves + self._child_vocab = pd.MultiIndex.from_tuples(child_leaves).to_flat_index() + child_pos = {lf: i for i, lf in enumerate(child_leaves)} + self._root_to_child: dict[int, int] = {} + for ri, rlf in enumerate(root_leaves): + paired = matched.get(tuple(rlf)) + if paired is not None: + if tuple(paired) not in child_pos: + raise ValueError(f"matched child leaf {paired!r} is not a child leaf.") + self._root_to_child[ri] = child_pos[tuple(paired)] + + @property + def rng(self) -> np.random.Generator: + return self._oracle.rng + + @rng.setter + def rng(self, value: np.random.Generator) -> None: + self._oracle.rng = value + + @property + def vocab(self) -> pd.Index: + return self._child_vocab + + def emittable_codes(self) -> np.ndarray: + return np.array(sorted(set(self._root_to_child.values())), dtype=np.int64) + + def n_batches(self, n_obs: int = 0) -> int: + return self._oracle.n_batches(n_obs) + + def batch_codes(self) -> np.ndarray: + out = np.empty(self._oracle.n_batches(0), dtype=np.int64) + for j, rc in enumerate(self._oracle.batch_codes()): + rc = int(rc) + if rc not in self._root_to_child: + raise ValueError(f"matched bind has no child leaf for root leaf {self._root_leaves[rc]!r}.") + out[j] = self._root_to_child[rc] + return out + class DAGLoader: """Yields ``{"source", "target", "condition"}`` batches; every node streams through its own loader.""" @@ -36,208 +111,163 @@ def __init__( self._cfg = self._resolve_configs(sampler_config) self._B = self._cfg[self.s.root].batch_size # root/target batch size — drives the pass length - self._children: dict[str, list[Bind]] = {} - for b in scheme.binds: - self._children.setdefault(b.parent, []).append(b) - - # per-node independent RNG streams from one seed (by sorted node name). Each node splits into a - # *decision* rng (root schedule draw / child tie-break) and a *row* seed shared identically by - # that node's per-key samplers — identical samplers → the SAME rows, so a node's reps are aligned. - self._rngs: dict[str, np.random.Generator] = {} - self._row_seqs: dict[str, np.random.SeedSequence] = {} - for name, seq in zip( - sorted(scheme.nodes), np.random.SeedSequence(scheme.seed).spawn(len(scheme.nodes)), strict=True - ): - decision_seq, row_seq = seq.spawn(2) - self._rngs[name] = np.random.default_rng(decision_seq) - self._row_seqs[name] = row_seq - - # per-node leaf partition + weights (obs only — no cell matrices) + # root's direct children are the bound sources (parity with the previous loader: depth-1 sources) + self._child_binds: list[Bind] = [b for b in scheme.binds if b.parent == self.s.root] + + # per-node stable sub-seed from one SeedSequence, so nodes don't correlate; a pass's seed is + # (sub-seed, pass index) → a pass is fully reproducible from its index. + self._node_seeds: dict[str, int] = { + name: int(seq.generate_state(1)[0]) + for name, seq in zip( + sorted(scheme.nodes), np.random.SeedSequence(scheme.seed).spawn(len(scheme.nodes)), strict=True + ) + } + + # per-node leaf partition + weights + tuple-labelled categorical (obs only — no cell matrices) self._st: dict[str, dict] = {} for name, node in scheme.nodes.items(): obs = obs_columns(scheme.sources[node.source], node.cols) codes, leaves = leaf_codes(obs, node.cols) - self._st[name] = {"node": node, "codes": codes, "leaves": leaves, "w": _weight_vector(node.weights, leaves)} + self._st[name] = { + "node": node, + "leaves": leaves, + "w": _weight_vector(node.weights, leaves), + "cats": _flat_categorical(codes, leaves), + } - # Batches per pass is derived, not configured: a natural epoch over the root (target) node — - # its cell count // the root's batch_size. The root drives the zip, so every node draws the same - # number of batches; each node's num_samples == _n_batches * that node's batch_size (see - # `_build_loaders`), which lets nodes use different batch sizes (source rows need not equal target). - n_root_obs = len(self._st[self.s.root]["codes"]) + # A natural epoch over the root (target) node: its cell count // the root's batch_size. The root + # drives the zip, so every node draws the same number of batches; each node's num_samples == + # _n_batches * that node's batch_size (source rows need not equal target rows). + n_root_obs = len(self._st[self.s.root]["cats"]) self._n_batches = max(1, n_root_obs // self._B) - self._build_loaders() - self._build_bind_maps() - self._iters: dict[str, Iterator[dict]] | None = None - self._schedules: dict[str, np.ndarray] = {} + self._build_samplers_and_loaders() + + self._iters: dict[str, dict[str, Iterator[dict]]] | None = None + self._schedule: np.ndarray | None = None # per-batch root leaf code for the current pass self._pos = 0 def _resolve_configs(self, cfg: SamplerConfig | Mapping[str, SamplerConfig]) -> dict[str, SamplerConfig]: - """Normalize to one config per node. - - Nodes may use different batch sizes (each draws ``_n_batches`` batches of its own size; source - and target row counts need not match). - """ + """Normalize to one config per node (nodes may use different batch sizes).""" if isinstance(cfg, SamplerConfig): - resolved = dict.fromkeys(self.s.nodes, cfg) - else: - missing = set(self.s.nodes) - set(cfg) - if missing: - raise ValueError(f"sampler_config mapping is missing node(s): {sorted(missing)}.") - resolved = {name: cfg[name] for name in self.s.nodes} - return resolved + return dict.fromkeys(self.s.nodes, cfg) + missing = set(self.s.nodes) - set(cfg) + if missing: + raise ValueError(f"sampler_config mapping is missing node(s): {sorted(missing)}.") + return {name: cfg[name] for name in self.s.nodes} # ── build ──────────────────────────────────────────────────────────── - def _build_loaders(self) -> None: - """Per ``(node, key)``: a ScheduledClassSampler + Loader. - - All of a node's keys are seeded with the SAME row-draw seed, so their (otherwise identical) - samplers select the same rows every batch — every rep of a node is the same cells. Reps need - separate Loaders regardless (annbatch can't mix feature dims in one loader), and each rep gets - native chunked reads. + def _new_class_sampler(self, name: str) -> ClassSampler: + cfg = self._cfg[name] + try: # annbatch enforces its own run-length rule for chunk>1; forward with node context + return ClassSampler( + chunk_size=cfg.chunk_size, + preload_nchunks=cfg.preload_nchunks, + batch_size=cfg.batch_size, + classes=self._st[name]["cats"], + num_samples=self._n_batches * cfg.batch_size, + class_weights=self._st[name]["w"], + drop_last=True, + rng=np.random.default_rng(self._node_seeds[name]), + ) + except ValueError as e: + raise ValueError(f"node {name!r}: {e}") from e + + def _bound_on(self, b: Bind) -> dict[int, int]: + """Map root tuple positions → child tuple positions for the bind's ``common`` columns.""" + rcols, ccols = self._st[b.parent]["node"].cols, self._st[b.child]["node"].cols + return {rcols.index(c): ccols.index(c) for c in b.common} + + def _new_bound_sampler(self, b: Bind) -> BoundClassSampler: + if b.matched is not None: + inner = _RemappedScheduleInner( + self._new_class_sampler(self.s.root), + self._st[self.s.root]["leaves"], + self._st[b.child]["leaves"], + b.matched, + ) + return self._make_bound(b, inner, on=None) # remapped inner emits exact child leaves + inner = self._new_class_sampler(self.s.root) + # Match on the bind's shared columns; the child's leaf weights (0 for excluded leaves, e.g. + # perturbed cells in a control node) go in as the *secondary* class so only positive-weight + # child leaves are drawn within each matched context — the exclusion `classes_to_bind_on` alone + # can't express (it groups all cells sharing the context). + return self._make_bound( + b, + inner, + on=self._bound_on(b), + classes=self._st[b.child]["cats"], + class_weights=self._st[b.child]["w"], + ) + + def _make_bound( + self, + b: Bind, + inner: ClassSampler | _RemappedScheduleInner, + *, + on: dict[int, int] | None, + classes: pd.Categorical | None = None, + class_weights: np.ndarray | None = None, + ) -> BoundClassSampler: + cfg = self._cfg[b.child] + try: + return BoundClassSampler( + inner, # type: ignore[arg-type] + cfg.chunk_size, + cfg.preload_nchunks, + cfg.batch_size, + classes_to_bind_on=self._st[b.child]["cats"], + on=on, + classes=classes, + class_weights=class_weights, + rng=np.random.default_rng(self._node_seeds[b.child]), + ) + except ValueError as e: + raise ValueError(f"node {b.child!r}: {e}") from e + + def _build_samplers_and_loaders(self) -> None: + """Per node/key: a ClassSampler (root) or BoundClassSampler (child) + annbatch Loader. + + A per-child schedule *oracle* and each bound's inner are root-seeded so their class draws agree + with the target's; all of a node's keys share the node seed, so the (identical) samplers select + the same rows every batch — every rep of a node is the same cells. Reps need separate Loaders + (annbatch can't mix feature dims in one loader), each with native chunked reads. """ - self._samplers: dict[str, dict[str, ScheduledClassSampler]] = {} + self._oracle = self._new_class_sampler(self.s.root) # supplies the per-batch condition schedule + + self._samplers: dict[str, dict[str, ClassSampler | BoundClassSampler]] = {} self._loaders: dict[str, dict[str, Loader]] = {} - for name, st in self._st.items(): - node = st["node"] - src = self.s.sources[node.source] - cfg = self._cfg[name] - # Per node: _n_batches batches of this node's own batch_size. Same batch count across nodes - # (the root's _n_batches) → schedules align; sizes may differ (source rows ≠ target rows OK). - num_samples = self._n_batches * cfg.batch_size - K = len(st["leaves"]) - classes = pd.Categorical([str(c) for c in st["codes"]], categories=[str(i) for i in range(K)]) - self._samplers[name], self._loaders[name] = {}, {} - for ki, key in enumerate(node.keys): - try: # annbatch enforces its own run-length rule for chunk>1; forward with node context - sampler = ScheduledClassSampler( - chunk_size=cfg.chunk_size, - preload_nchunks=cfg.preload_nchunks, - batch_size=cfg.batch_size, - classes=classes, - num_samples=num_samples, - class_weights=st["w"], - drop_last=True, - rng=np.random.default_rng(self._row_seqs[name]), # identical across keys - ) - except ValueError as e: - raise ValueError(f"node {name!r}: {e}") from e - return_index = name == self.s.root and ki == 0 # only for the schedule↔row alignment check - loader = Loader( - batch_sampler=sampler, return_index=return_index, to_torch=False, preload_to_gpu=False - ).add_datasets(key_backings(src, key)) - self._samplers[name][key] = sampler - self._loaders[name][key] = loader + self._add_node_loaders(self.s.root, lambda: self._new_class_sampler(self.s.root)) + for b in self._child_binds: + self._add_node_loaders(b.child, lambda b=b: self._new_bound_sampler(b)) - def _nodes_next(self, name: str) -> dict[str, np.ndarray]: - """One batch per key of a node — identical samplers pick the same rows, so the reps are aligned.""" + def _add_node_loaders(self, name: str, make_sampler: Callable[[], ClassSampler | BoundClassSampler]) -> None: node = self._st[name]["node"] - return {key: next(self._iters[name][key])["X"] for key in node.keys} - - def _build_bind_maps(self) -> None: - """Precompute, per bound child, the maps to turn the parent's schedule into the child's.""" - self._bindmap: dict[str, dict] = {} - for b in self._children.get(self.s.root, []): - rst, cst = self._st[b.parent], self._st[b.child] - rleaves, cleaves = rst["leaves"], cst["leaves"] - if b.matched is not None: # explicit parent-leaf → child-leaf pairing (sc-flow's matched_keys) - root_idx = {lf: i for i, lf in enumerate(rleaves)} - child_idx = {lf: i for i, lf in enumerate(cleaves)} - code_to_child: dict[int, int] = {} - for pk, ck in b.matched.items(): - pk, ck = tuple(pk), tuple(ck) - if pk not in root_idx: - raise ValueError( - f"bind {b.parent!r}→{b.child!r}: matched key {pk!r} is not a {b.parent!r} leaf." - ) - if ck not in child_idx or cst["w"][child_idx[ck]] <= 0: - raise ValueError( - f"bind {b.parent!r}→{b.child!r}: matched value {ck!r} is not a positive-weight {b.child!r} leaf." - ) - code_to_child[root_idx[pk]] = child_idx[ck] - self._bindmap[b.child] = { - "bind": b, - "mode": "matched", - "root_leaves": rleaves, - "code_to_child": code_to_child, - } - continue - rcols, ccols = rst["node"].cols, cst["node"].cols - # parent leaf code → shared-column value (empty tuple when common=() ⇒ unconditional bind) - root_code_to_cv = {i: tuple(lf[rcols.index(c)] for c in b.common) for i, lf in enumerate(rleaves)} - # shared-column value → positive-weight child leaf codes carrying it (key () holds all when common=()) - grouped: dict[tuple, list[int]] = {} - for code, lf in enumerate(cleaves): - if cst["w"][code] > 0: - grouped.setdefault(tuple(lf[ccols.index(c)] for c in b.common), []).append(code) - self._bindmap[b.child] = { - "bind": b, - "mode": "common", - "root_leaves": rleaves, - "child_w": cst["w"], - "root_code_to_cv": root_code_to_cv, - "common_to_child": {cv: np.asarray(codes, dtype=np.int64) for cv, codes in grouped.items()}, - } + src = self.s.sources[node.source] + self._samplers[name], self._loaders[name] = {}, {} + for ki, key in enumerate(node.keys): + sampler = make_sampler() + return_index = name == self.s.root and ki == 0 # only for the schedule↔row alignment check + # `to="jax"` yields native jax arrays (dense via `jnp.from_dlpack`, sparse as a jax CSR) + # instead of numpy, so batches reach the jax solver without a host round-trip / numpy cast. + # With cupy present, `preload_to_gpu` keeps the array on-GPU so the yielded jax array is + # GPU-resident; without it, the array is a CPU jax array (device copy deferred to the step). + loader = Loader( + batch_sampler=sampler, return_index=return_index, to="jax", preload_to_gpu=_HAS_CUPY + ).add_datasets(key_backings(src, key)) + self._samplers[name][key], self._loaders[name][key] = sampler, loader # ── per-pass scheduling ──────────────────────────────────────────────── - def _draw_root_schedule(self) -> np.ndarray: - """Draw one root leaf code per batch (``_n_batches``) ∝ the root's weights (its own RNG stream).""" - w = self._st[self.s.root]["w"] - pos = np.flatnonzero(w > 0) - return self._rngs[self.s.root].choice(pos, size=self._n_batches, p=w[pos] / w[pos].sum()).astype(np.int64) - - def _derive_child_schedule(self, child: str, root_sched: np.ndarray) -> np.ndarray: - """Map each root batch's category to a matching child leaf. - - ``matched`` binds map each parent leaf to exactly one child leaf. Otherwise the child is matched - on ``common`` columns; when several child leaves share the bound value (the child partitions on - columns beyond ``common``, e.g. child cols ``(a, x)`` bound on ``a``) one is drawn ∝ the child's - leaf weights, so ``P(child extra cols | common)`` is weight-controlled. Conditioning is required: - an unmatched value raises (no silent fallback); ``Bind(..., common=())`` opts into unconditional. - """ - m = self._bindmap[child] - b = m["bind"] - out = np.empty(len(root_sched), dtype=np.int64) - if m["mode"] == "matched": - cmap = m["code_to_child"] - for j, rc in enumerate(root_sched): - rc = int(rc) - if rc not in cmap: - raise ValueError( - f"bind {b.parent!r}→{b.child!r}: no matched child leaf for root leaf " - f"{m['root_leaves'][rc]!r}. Add it to `matched`." - ) - out[j] = cmap[rc] - return out - rng = self._rngs[child] - cw = m["child_w"] - for j, rc in enumerate(root_sched): - cv = m["root_code_to_cv"][int(rc)] - codes = m["common_to_child"].get(cv) - if codes is None: # no positive-weight child leaf carries this value — do NOT silently fall back - raise ValueError( - f"bind {b.parent!r}→{b.child!r}: no positive-weight child leaf matches common " - f"{b.common}={cv!r} (root leaf {m['root_leaves'][int(rc)]!r}). Add a child leaf for " - f"it, or declare Bind(..., common=()) to sample {b.child!r} unconditionally." - ) - out[j] = int(codes[0]) if len(codes) == 1 else int(rng.choice(codes, p=cw[codes] / cw[codes].sum())) - return out - def _start_pass(self) -> None: - """Draw schedules (root + derived children), push them onto the samplers, rebuild iterators. + """Draw the schedule and rebuild iterators for a fresh epoch (advancing every sampler's RNG once). - Ordering matters: ``Loader.__iter__`` re-reads ``sampler.sample()`` and the scheduled draw is - read up front, so ``set_schedule`` must land before ``iter(loader)``. + The oracle's ``batch_codes()`` and each target/child ``iter()`` each consume one class draw, so — + all root-referencing samplers having started from the root seed — the oracle, the target reps and + every bound child's inner stay in lockstep and draw the *same* per-batch class each pass. The RNG + advances across passes (a real epoch stream), so a pickled loader — whose sampler RNG state is + kept — resumes the next pass rather than replaying. """ - root_sched = self._draw_root_schedule() - self._schedules = {self.s.root: root_sched} - for smp in self._samplers[self.s.root].values(): # every key of the node gets the same schedule - smp.set_schedule(root_sched) - for b in self._children.get(self.s.root, []): - child_sched = self._derive_child_schedule(b.child, root_sched) - self._schedules[b.child] = child_sched - for smp in self._samplers[b.child].values(): - smp.set_schedule(child_sched) + self._schedule = self._oracle.batch_codes() self._iters = {name: {key: iter(ld) for key, ld in loaders.items()} for name, loaders in self._loaders.items()} self._pos = 0 @@ -245,9 +275,8 @@ def _start_pass(self) -> None: def __getstate__(self) -> dict[str, object]: """Pickle without the live annbatch iterators (generators aren't picklable). - The RNG streams, samplers, schedules and configs are kept, so a reloaded loader resumes the same - reproducible sequence; only ``_iters`` is dropped and rebuilt (a fresh pass) on the next - ``__next__``. + Every sampler's RNG state is kept, so a reloaded loader resumes the same reproducible stream + (the next pass) on the next ``__next__``; only ``_iters`` is dropped and rebuilt. """ state = self.__dict__.copy() state["_iters"] = None @@ -255,21 +284,26 @@ def __getstate__(self) -> dict[str, object]: def __setstate__(self, state: dict[str, object]) -> None: self.__dict__.update(state) - self._iters = None # force `_start_pass` on the next `__next__`, using the restored RNG state + self._iters = None # force `_start_pass` on the next `__next__`, using the restored sampler RNG # ── iteration ────────────────────────────────────────────────────────── def __iter__(self) -> DAGLoader: return self + def _nodes_next(self, name: str) -> dict[str, np.ndarray]: + """One batch per key of a node — identical samplers pick the same rows, so the reps are aligned.""" + node = self._st[name]["node"] + return {key: next(self._iters[name][key])["X"] for key in node.keys} + def __next__(self) -> dict[str, np.ndarray]: if self._iters is None or self._pos >= self._n_batches: - self._start_pass() + self._start_pass() # first pass, next epoch, or resume after unpickling j = self._pos self._pos += 1 st = self._st[self.s.root] node = st["node"] - leaf = st["leaves"][int(self._schedules[self.s.root][j])] # per-batch category — from the schedule + leaf = st["leaves"][int(self._schedule[j])] # per-batch category — from the schedule oracle reps = self._nodes_next(self.s.root) target = reps[node.keys[0]] # primary streamed rep B = target.shape[0] @@ -285,7 +319,7 @@ def __next__(self) -> dict[str, np.ndarray]: cond = np.asarray(cond, dtype=np.float32) out["condition"] = np.broadcast_to(cond, (B, cond.shape[-1])).copy() - for b in self._children.get(self.s.root, []): # bound child source, conditioned via its schedule + for b in self._child_binds: # bound child source, replayed via its BoundClassSampler cnode = self._st[b.child]["node"] creps = self._nodes_next(b.child) out["source"] = creps[cnode.keys[0]] diff --git a/src/dagloader/_scheduled_sampler.py b/src/dagloader/_scheduled_sampler.py deleted file mode 100644 index 7dac2202..00000000 --- a/src/dagloader/_scheduled_sampler.py +++ /dev/null @@ -1,99 +0,0 @@ -"""``ScheduledClassSampler`` — an annbatch ``ClassSampler`` whose per-batch category can be *supplied*. - -``set_schedule(codes)`` fixes the category (leaf code) of each batch for the next pass; with -``schedule=None`` the sampler is exactly ``ClassSampler`` (draws ∝ ``class_weights``). That ownership -is what lets :class:`~dagloader.DAGLoader` zip a parent and its bound children -batch-for-batch: the root schedule is drawn from the root's weights, each child's schedule is -*derived* from the parent's. - -Rather than copy annbatch's ``_iter_requests`` (all the chunk/RLE/slice math), this reuses it wholesale -and only redirects the one draw that picks each group's class. ``ClassSampler._iter_requests`` makes a -single *weighted* ``self._rng.choice(n_classes, size=n_groups, p=...)`` to choose group classes; we run -that method unchanged but with ``self._rng`` temporarily wrapped so that one call returns the scheduled -positions instead. Everything else (run selection, slice starts, shuffling) uses the real generator, so -a scheduled pass is bit-identical to what annbatch would produce for those same group classes. -""" - -from __future__ import annotations - -from collections.abc import Iterator - -import numpy as np -from annbatch.samplers import ClassSampler - -__all__ = ["ScheduledClassSampler"] - - -class _ScheduleRng: - """Wraps a ``numpy.random.Generator``; the first *weighted* ``choice`` returns supplied positions. - - ``ClassSampler._iter_requests`` draws group classes with the only ``choice(..., p=)`` call - in that method; we intercept it (identified by ``p is not None``) and return the pre-mapped - schedule positions, validating the group count. Every other attribute/method — ``integers``, - ``shuffle``, any unweighted ``choice`` — delegates to the real generator, so RNG state and - reproducibility are unchanged. - """ - - def __init__(self, rng: np.random.Generator, positions: np.ndarray) -> None: - self._rng = rng - self._positions = positions - self._fired = False - - def choice(self, a, size=None, replace=True, p=None): - if p is not None and not self._fired: # the per-group class draw in _iter_requests - self._fired = True - if size != len(self._positions): - raise ValueError( - f"schedule length {len(self._positions)} != n_groups {size}; one category per batch " - "requires chunk_size to divide batch_size and len(schedule) == num_samples // batch_size." - ) - return self._positions - return self._rng.choice(a, size=size, replace=replace, p=p) - - def __getattr__(self, name): # integers / shuffle / permutation / ... → real generator - if name.startswith("__") and name.endswith("__"): - raise AttributeError(name) # don't delegate dunders — avoids recursion during (un)pickling / copy - return getattr(self._rng, name) - - def __reduce__(self) -> tuple[object, ...]: - # A transient per-pass wrapper: pickle it AS the underlying real Generator so a checkpointed - # DAGLoader restores a clean, unwrapped RNG (the wrapper is rebuilt each pass in `_sample`). - return self._rng.__reduce__() - - -class ScheduledClassSampler(ClassSampler): - """``ClassSampler`` whose per-batch category sequence can be supplied via :meth:`set_schedule`. - - The schedule is one leaf code per batch (``len == num_samples // batch_size``); it is valid only - when ``chunk_size`` divides ``batch_size`` (exactly one category per batch). - """ - - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) - self._schedule: np.ndarray | None = None - - def set_schedule(self, category_codes: np.ndarray | None) -> None: - """Set (or clear) the per-batch category codes used for the next ``iter(loader)`` pass.""" - self._schedule = None if category_codes is None else np.asarray(category_codes, dtype=np.int64) - - def _schedule_positions(self) -> np.ndarray: - """Map the scheduled leaf codes to positions into the sampleable-class table (as annbatch expects).""" - info = self._per_class_sampling_info - code_to_pos = {int(c): i for i, c in enumerate(info.index.to_numpy())} - try: - return np.array([code_to_pos[int(c)] for c in self._schedule], dtype=np.int64) - except KeyError as e: - raise ValueError(f"schedule contains a non-sampleable (zero-weight) category code {e}.") from e - - def _sample(self, n_obs: int) -> Iterator[dict]: - if self._schedule is None: # standalone → identical to ClassSampler - yield from super()._sample(n_obs) - return - self._ensure_runs(n_obs) # build _per_class_sampling_info before mapping codes → positions - positions = self._schedule_positions() - real_rng = self._rng - self._rng = _ScheduleRng(real_rng, positions) # redirect only the group-class draw - try: - yield from super()._sample(n_obs) - finally: - self._rng = real_rng diff --git a/tests/conftest.py b/tests/conftest.py index e8ef3428..a6ca73de 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -4,7 +4,7 @@ import pandas as pd import pytest -from cellflow.data._dataloader import ValidationSampler +from cellflow.data._legacy import ValidationSampler @pytest.fixture diff --git a/tests/dagloader/test_coherence.py b/tests/dagloader/test_coherence.py new file mode 100644 index 00000000..24696e86 --- /dev/null +++ b/tests/dagloader/test_coherence.py @@ -0,0 +1,139 @@ +"""Condition-coherence of the BoundClassSampler-based DAGLoader — the guarantee the old suite missed. + +Each cell's ``(cell_line, drug, control)`` is encoded into ``X`` so every yielded row can be decoded and +checked: the target batch is one perturbed condition, the ``condition`` vector matches that condition, +and the source batch is control cells of the *matched* context (``common=`` → same cell line; ``matched=`` +→ the explicitly paired control leaf). This is what silently broke when dagloader's rng-wrapping +scheduler met annbatch's refactored class draw. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +pytest.importorskip("annbatch") + +import anndata as ad + +from dagloader import Bind, DAGLoader, Node, SamplerConfig, Scheme, uniform + +LINES = ("A", "B") +DRUGS = ("control", "d1", "d2", "d3") +_LINE = {c: i for i, c in enumerate(LINES)} +_DRUG = {d: i for i, d in enumerate(DRUGS)} + + +def _adata(n_per_combo: int = 16) -> ad.AnnData: + rows = [(cl, dr) for cl in LINES for dr in DRUGS for _ in range(n_per_combo)] + obs = pd.DataFrame(rows, columns=["cell_line", "drug"]) + obs["control"] = obs["drug"] == "control" + obs.index = obs.index.astype(str) + # X encodes the cell's identity so a batch row can be decoded: [cell_line, drug, is_control] + x = np.stack( + [ + obs["cell_line"].map(_LINE).to_numpy(), + obs["drug"].map(_DRUG).to_numpy(), + obs["control"].to_numpy().astype(float), + ], + axis=1, + ).astype("float32") + return ad.AnnData(X=x, obs=obs) + + +def _condition_fn(leaf: tuple) -> np.ndarray: + return np.array([_LINE[leaf[0]], _DRUG[leaf[1]]], dtype=float) + + +def _scheme(adata: ad.AnnData, *, bind: Bind, seed: int = 0) -> Scheme: + cols = ("cell_line", "drug") + combos = {tuple(r) for r in adata.obs[list(cols)].to_numpy()} + pert = [c for c in combos if c[1] != "control"] + ctrl = [c for c in combos if c[1] == "control"] + return Scheme( + sources={"data": adata}, + nodes={"pert": Node("data", cols, "X", uniform(pert)), "ctrl": Node("data", cols, "X", uniform(ctrl))}, + root="pert", + binds=(bind,), + seed=seed, + ) + + +def test_common_bind_target_condition_and_context_coherent(): + adata = _adata() + loader = DAGLoader( + _scheme(adata, bind=Bind("pert", "ctrl", common=("cell_line",))), + SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8), + condition_fn=_condition_fn, + ) + it = iter(loader) + for _ in range(12): + batch = next(it) + tgt = np.asarray(batch["target"]) + src = np.asarray(batch["source"]) + cond = np.asarray(batch["condition"]) + + # target batch is one perturbed condition + assert len(np.unique(tgt[:, 0])) == 1, "target batch mixes cell lines" + assert len(np.unique(tgt[:, 1])) == 1, "target batch mixes drugs" + assert tgt[0, 2] == 0.0, "target must be perturbed (not control)" + + # condition vector matches that exact (cell_line, drug) + assert np.all(cond[:, 0] == tgt[0, 0]) and np.all(cond[:, 1] == tgt[0, 1]), "condition ≠ target condition" + + # source batch is control cells of the SAME context (cell line) + assert np.all(src[:, 2] == 1.0), "source must be control cells" + assert len(np.unique(src[:, 0])) == 1 and src[0, 0] == tgt[0, 0], "source context ≠ target context" + + +def test_matched_bind_reads_the_paired_control_leaf(): + adata = _adata() + # deliberately swap: A-drugs pair with B's control, B-drugs with A's control — so a correct matched + # bind gives a source context OPPOSITE to the target's (proving it uses the pairing, not the context). + matched = { + ("A", "d1"): ("B", "control"), + ("A", "d2"): ("B", "control"), + ("A", "d3"): ("B", "control"), + ("B", "d1"): ("A", "control"), + ("B", "d2"): ("A", "control"), + ("B", "d3"): ("A", "control"), + } + loader = DAGLoader( + _scheme(adata, bind=Bind("pert", "ctrl", matched=matched)), + SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8), + condition_fn=_condition_fn, + ) + it = iter(loader) + for _ in range(12): + batch = next(it) + tgt = np.asarray(batch["target"]) + src = np.asarray(batch["source"]) + assert np.all(src[:, 2] == 1.0), "source must be control cells" + # matched pairing sends the source to the OTHER cell line + assert src[0, 0] != tgt[0, 0], "matched bind did not read the paired (swapped) control leaf" + + +def test_reps_are_aligned_same_cells(): + # two aligned reps of the target: X and an obsm copy of X. Same sampled rows → identical values. + adata = _adata() + adata.obsm["rep"] = adata.X.copy() + cols = ("cell_line", "drug") + combos = {tuple(r) for r in adata.obs[list(cols)].to_numpy()} + pert = [c for c in combos if c[1] != "control"] + ctrl = [c for c in combos if c[1] == "control"] + scheme = Scheme( + sources={"data": adata}, + nodes={ + "pert": Node("data", cols, ("X", "obsm/rep"), uniform(pert)), + "ctrl": Node("data", cols, "X", uniform(ctrl)), + }, + root="pert", + binds=(Bind("pert", "ctrl", common=("cell_line",)),), + seed=0, + ) + loader = DAGLoader(scheme, SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8)) + batch = next(iter(loader)) + x = np.asarray(batch["target_reps"]["X"]) + rep = np.asarray(batch["target_reps"]["obsm/rep"]) + np.testing.assert_array_equal(x, rep) # aligned reps must be the same cells diff --git a/tests/dagloader/test_eval_loader.py b/tests/dagloader/test_eval_loader.py new file mode 100644 index 00000000..787d7b4e --- /dev/null +++ b/tests/dagloader/test_eval_loader.py @@ -0,0 +1,125 @@ +"""``DAGEvalLoader``: control-rooted eval reader (Sequential control inner + BoundClassSampler target). + +For each control population (context, e.g. ``cell_line``) the source is **all** its control cells (read in +full via the Sequential inner); the target is a matched perturbed batch (annbatch samples a drug within +the context). The condition is the perturbed leaf the target drew. Cells carry a unique index in ``X`` so +we can assert the source is exactly the context's controls and the target is that context's perturbed cells. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +pytest.importorskip("annbatch") + +import anndata as ad + +from dagloader import Bind, DAGEvalLoader, Node, SamplerConfig, Scheme, uniform + +LINES = ("A", "B") +DRUGS = ("control", "d1", "d2", "d3") +_LINE = {c: i for i, c in enumerate(LINES)} +_DRUG = {d: i for i, d in enumerate(DRUGS)} +_CFG = SamplerConfig(batch_size=4, chunk_size=1, preload_nchunks=4) + + +def _adata(n_per_combo: int = 8) -> ad.AnnData: + rows = [(cl, dr) for cl in LINES for dr in DRUGS for _ in range(n_per_combo)] + obs = pd.DataFrame(rows, columns=["cell_line", "drug"]) + obs["control"] = obs["drug"] == "control" + obs.index = obs.index.astype(str) + x = np.stack( + [ + obs["cell_line"].map(_LINE).to_numpy(), + obs["drug"].map(_DRUG).to_numpy(), + obs["control"].to_numpy().astype(float), + np.arange(len(obs)), # unique cell index + ], + axis=1, + ).astype("float32") + return ad.AnnData(x, obs=obs) + + +def _condition_fn(leaf: tuple) -> dict[str, np.ndarray]: + return {"drug": np.array([[_LINE[leaf[0]], _DRUG[leaf[1]]]], dtype=float)} + + +def _scheme(adata: ad.AnnData) -> Scheme: + cols = ("cell_line", "drug") + combos = {tuple(r) for r in adata.obs[list(cols)].to_numpy()} + pert = [c for c in combos if c[1] != "control"] + ctrl = [c for c in combos if c[1] == "control"] + return Scheme( + sources={"data": adata}, + nodes={"pert": Node("data", cols, "X", uniform(pert)), "ctrl": Node("data", cols, "X", uniform(ctrl))}, + root="pert", + binds=(Bind("pert", "ctrl", common=("cell_line",)),), + seed=0, + ) + + +def _idx(batch_x) -> set[int]: + return set(np.asarray(batch_x)[:, 3].astype(int).tolist()) + + +def test_source_is_all_controls_of_context_target_matches_condition(): + adata = _adata() + obs = adata.obs + loader = DAGEvalLoader(_scheme(adata), _CFG, _condition_fn) + assert set(loader.control_populations) == {("A", "control"), ("B", "control")} + + seen_ctx = [] + for out in loader.iter_conditions(): # one batch per control population + cl, dr = out["leaf"] + seen_ctx.append(cl) + assert dr != "control" # target is a perturbed condition + # source = ALL control cells of this cell line (read in full) + src_truth = set(np.flatnonzero((obs["cell_line"] == cl).to_numpy() & obs["control"].to_numpy()).tolist()) + assert _idx(out["source"]) == src_truth + # target cells are perturbed cells of this (cell_line, drug) + rows = np.asarray(out["target"]) + assert np.all(rows[:, 0].astype(int) == _LINE[cl]) # same cell line + assert np.all(rows[:, 1].astype(int) == _DRUG[dr]) # the drawn drug + assert np.all(rows[:, 2] == 0.0) # not control + # condition embedding is the drawn perturbed leaf + np.testing.assert_array_equal(out["condition"]["drug"], [[_LINE[cl], _DRUG[dr]]]) + + assert set(seen_ctx) == set(LINES) # each control population once + + +def test_n_conditions_cycles_control_populations(): + adata = _adata() + loader = DAGEvalLoader(_scheme(adata), _CFG, _condition_fn) + outs = list(loader.iter_conditions(n_conditions=5)) + assert len(outs) == 5 # 2 control populations cycled to 5 batches + for out in outs: + assert out["leaf"][1] != "control" + + +def test_deterministic_across_calls(): + adata = _adata() + loader = DAGEvalLoader(_scheme(adata), _CFG, _condition_fn) + a = [out["leaf"] for out in loader.iter_conditions(n_conditions=6)] + b = [out["leaf"] for out in loader.iter_conditions(n_conditions=6)] + assert a == b # same seed ⇒ same drawn conditions each call + + +def test_reps_aligned_same_cells(): + adata = _adata() + adata.obsm["rep"] = adata.X.copy() + cols = ("cell_line", "drug") + combos = {tuple(r) for r in adata.obs[list(cols)].to_numpy()} + scheme = Scheme( + sources={"data": adata}, + nodes={ + "pert": Node("data", cols, ("X", "obsm/rep"), uniform([c for c in combos if c[1] != "control"])), + "ctrl": Node("data", cols, "X", uniform([c for c in combos if c[1] == "control"])), + }, + root="pert", + binds=(Bind("pert", "ctrl", common=("cell_line",)),), + seed=0, + ) + out = next(DAGEvalLoader(scheme, _CFG, _condition_fn).iter_conditions()) + np.testing.assert_array_equal(np.asarray(out["target_reps"]["X"]), np.asarray(out["target_reps"]["obsm/rep"])) diff --git a/tests/data/test_cfsampler.py b/tests/data/test_cfsampler.py index 0ae5405a..40b458df 100644 --- a/tests/data/test_cfsampler.py +++ b/tests/data/test_cfsampler.py @@ -1,7 +1,7 @@ import numpy as np import pytest -from cellflow.data._dataloader import OOCTrainSampler, PredictionSampler, TrainSampler +from cellflow.data._legacy import OOCTrainSampler, PredictionSampler, TrainSampler from cellflow.data._datamanager import DataManager @@ -86,7 +86,7 @@ def test_sampling_no_combinations(self, adata_perturbation, batch_size: int): class TestValidationSampler: @pytest.mark.parametrize("n_conditions_on_log_iteration", [None, 1, 3]) def test_valid_sampler(self, adata_perturbation, n_conditions_on_log_iteration): - from cellflow.data._dataloader import ValidationSampler + from cellflow.data._legacy import ValidationSampler from cellflow.data._datamanager import DataManager control_key = "control" diff --git a/tests/model/test_annbatch_ooc.py b/tests/model/test_annbatch_ooc.py index 4c4ee746..d9087c42 100644 --- a/tests/model/test_annbatch_ooc.py +++ b/tests/model/test_annbatch_ooc.py @@ -60,8 +60,8 @@ def _prepare_model_small(cf): class TestOutOfCore: def test_grouped_collection_chunked_trains(self, tmp_path): - cf = cellflow.model.CellFlow() - cf.prepare_annbatch_data( + cf = cellflow.model.CellFlowAnnbatch() + cf.prepare_loaders( source=_collection(tmp_path, grouped=True), sampler_config=SamplerConfig(batch_size=16, chunk_size=4, preload_nchunks=16), **_PREP, @@ -72,9 +72,9 @@ def test_grouped_collection_chunked_trains(self, tmp_path): assert cf.solver is not None def test_ungrouped_collection_chunked_raises(self, tmp_path): - cf = cellflow.model.CellFlow() + cf = cellflow.model.CellFlowAnnbatch() with pytest.raises(ValueError, match="grouped|groupby"): - cf.prepare_annbatch_data( + cf.prepare_loaders( source=_collection(tmp_path, grouped=False), sampler_config=SamplerConfig(batch_size=16, chunk_size=4, preload_nchunks=16), **_PREP, @@ -82,8 +82,8 @@ def test_ungrouped_collection_chunked_raises(self, tmp_path): def test_ungrouped_collection_chunk1_ok(self, tmp_path): # chunk_size=1 streams per-row → no grouping needed even for an interleaved collection - cf = cellflow.model.CellFlow() - cf.prepare_annbatch_data( + cf = cellflow.model.CellFlowAnnbatch() + cf.prepare_loaders( source=_collection(tmp_path, grouped=False), sampler_config=SamplerConfig(batch_size=16, chunk_size=1, preload_nchunks=16), **_PREP, @@ -91,8 +91,8 @@ def test_ungrouped_collection_chunk1_ok(self, tmp_path): assert cf._dataloader.sample()["tgt_cell_data"].shape == (16, 5) def test_grouped_collection_split_chunked(self, tmp_path): - cf = cellflow.model.CellFlow() - cf.prepare_annbatch_data( + cf = cellflow.model.CellFlowAnnbatch() + cf.prepare_loaders( source=_collection(tmp_path, grouped=True), sampler_config=SamplerConfig(batch_size=16, chunk_size=4, preload_nchunks=16), split_by=["drug"], @@ -103,8 +103,8 @@ def test_grouped_collection_split_chunked(self, tmp_path): def test_in_memory_unsorted_source_is_auto_grouped(self): # an in-memory (interleaved) AnnData source is grouped automatically → chunk_size>1 works - cf = cellflow.model.CellFlow() - cf.prepare_annbatch_data( + cf = cellflow.model.CellFlowAnnbatch() + cf.prepare_loaders( source=_adata(interleaved=True), sampler_config=SamplerConfig(batch_size=16, chunk_size=4, preload_nchunks=16), **_PREP, @@ -132,8 +132,8 @@ def test_fragmented_collection_chunked_ok(self, tmp_path): n_runs = 1 + int((np.diff(codes) != 0).sum()) assert n_runs > 6, f"expected a fragmented collection (>6 runs for 6 categories), got {n_runs}" - cf = cellflow.model.CellFlow() - cf.prepare_annbatch_data( # chunk_size=4 <= run length 10 → must be accepted + cf = cellflow.model.CellFlowAnnbatch() + cf.prepare_loaders( # chunk_size=4 <= run length 10 → must be accepted source=dc, sampler_config=SamplerConfig(batch_size=8, chunk_size=4, preload_nchunks=8), **_PREP ) assert cf._dataloader.sample()["tgt_cell_data"].shape == (8, 5) diff --git a/tests/model/test_annbatch_path.py b/tests/model/test_annbatch_path.py index 4261a12c..37b67a2c 100644 --- a/tests/model/test_annbatch_path.py +++ b/tests/model/test_annbatch_path.py @@ -1,7 +1,8 @@ -"""Tests for the annbatch-path scaffolding on :class:`~cellflow.model.CellFlow`. +"""Tests for the in-memory :class:`~cellflow.model.CellFlow` constructor / data prep. -Covers the ``adata``-optional constructor (deprecation of the constructor argument), passing -``adata`` to :meth:`prepare_data`, and the mode guard on :meth:`prepare_annbatch_data`. +Covers the ``adata``-optional constructor (deprecation of the constructor argument) and passing +``adata`` to :meth:`prepare_data`. The streaming path now lives on +:class:`~cellflow.model.CellFlowAnnbatch`. """ import anndata as ad @@ -42,11 +43,3 @@ def test_prepare_data_without_any_adata_raises(self): cf = cellflow.model.CellFlow() with pytest.raises(ValueError, match="No `adata` provided"): cf.prepare_data(sample_rep="X", control_key="control", perturbation_covariates=PERT_COVARS) - - def test_prepare_annbatch_rejects_adata_model(self, adata_perturbation: ad.AnnData): - with pytest.warns(FutureWarning): - cf = cellflow.model.CellFlow(adata_perturbation) - with pytest.raises(ValueError, match="without `adata`"): - cf.prepare_annbatch_data( - source=None, sample_rep="X", control_key="control", perturbation_covariates=PERT_COVARS - ) diff --git a/tests/model/test_annbatch_save.py b/tests/model/test_annbatch_save.py index 75c95647..003d4c14 100644 --- a/tests/model/test_annbatch_save.py +++ b/tests/model/test_annbatch_save.py @@ -37,8 +37,8 @@ def _adata(): def _prepared(seed=7): - cf = cellflow.model.CellFlow() - cf.prepare_annbatch_data(source=_adata(), sampler_config=_CFG, seed=seed, **_PREP) + cf = cellflow.model.CellFlowAnnbatch() + cf.prepare_loaders(source=_adata(), sampler_config=_CFG, seed=seed, **_PREP) return cf @@ -50,7 +50,7 @@ class TestAnnbatchSave: def test_save_load_after_prepare(self, tmp_path): cf = _prepared() cf.save(str(tmp_path), file_prefix="p", overwrite=True) - loaded = cellflow.model.CellFlow.load(str(tmp_path / "p_CellFlow.pkl")) + loaded = cellflow.model.CellFlowAnnbatch.load(str(tmp_path / "p_CellFlowAnnbatch.pkl")) assert loaded._scheme is not None assert loaded._dataloader.sample()["tgt_cell_data"].shape == (8, 5) @@ -61,8 +61,8 @@ def test_save_load_mid_stream_resumes_deterministically(self, tmp_path): _stream(b, 2) a.save(str(tmp_path), file_prefix="a", overwrite=True) b.save(str(tmp_path), file_prefix="b", overwrite=True) - la = cellflow.model.CellFlow.load(str(tmp_path / "a_CellFlow.pkl")) - lb = cellflow.model.CellFlow.load(str(tmp_path / "b_CellFlow.pkl")) + la = cellflow.model.CellFlowAnnbatch.load(str(tmp_path / "a_CellFlowAnnbatch.pkl")) + lb = cellflow.model.CellFlowAnnbatch.load(str(tmp_path / "b_CellFlowAnnbatch.pkl")) ra, rb = _stream(la, 3), _stream(lb, 3) assert all(np.array_equal(x, y) for x, y in zip(ra, rb, strict=True)) # and the resumed state is preserved (not reset to the seed → differs from a fresh model) @@ -76,7 +76,7 @@ def test_save_load_after_training(self, tmp_path): ) cf.train(num_iterations=2, valid_freq=100) cf.save(str(tmp_path), file_prefix="t", overwrite=True) - loaded = cellflow.model.CellFlow.load(str(tmp_path / "t_CellFlow.pkl")) + loaded = cellflow.model.CellFlowAnnbatch.load(str(tmp_path / "t_CellFlowAnnbatch.pkl")) assert loaded.solver is not None and loaded._dataloader is not None assert loaded._dataloader.sample()["tgt_cell_data"].shape == (8, 5) # loader still usable diff --git a/tests/model/test_annbatch_split.py b/tests/model/test_annbatch_split.py index 9c89f65a..5942b987 100644 --- a/tests/model/test_annbatch_split.py +++ b/tests/model/test_annbatch_split.py @@ -44,12 +44,12 @@ class TestSplitAnnbatchData: """`split_annbatch_data` operates on an already-built Scheme (injected here).""" def test_without_scheme_raises(self): - cf = cellflow.model.CellFlow() + cf = cellflow.model.CellFlowAnnbatch() with pytest.raises(ValueError, match="No annbatch `Scheme`"): cf.split_annbatch_data(split_by=["drug"]) def test_split_with_injected_scheme(self): - cf = cellflow.model.CellFlow() + cf = cellflow.model.CellFlowAnnbatch() cf._scheme = _toy_scheme() df = cf.split_annbatch_data(split_by=["drug"], random_state=0) assert list(df.columns) == ["cell_line", "drug", "split"] @@ -60,10 +60,10 @@ def test_split_with_injected_scheme(self): class TestPrepareAnnbatchData: - """`prepare_annbatch_data` builds Scheme + condition + loaders from an AnnData source.""" + """`prepare_loaders` builds Scheme + condition + loaders from an AnnData source.""" def _prepare(self, cf, **kwargs): - return cf.prepare_annbatch_data( + return cf.prepare_loaders( source=_toy_adata(), sample_rep="X", control_key="control", @@ -73,16 +73,10 @@ def _prepare(self, cf, **kwargs): **kwargs, ) - def test_rejects_adata_mode(self, adata_perturbation): - with pytest.warns(FutureWarning): - cf = cellflow.model.CellFlow(adata_perturbation) - with pytest.raises(ValueError, match="without `adata`"): - self._prepare(cf) - def test_requires_sampler_config(self): - cf = cellflow.model.CellFlow() + cf = cellflow.model.CellFlowAnnbatch() with pytest.raises(ValueError, match="sampler_config` is required"): - cf.prepare_annbatch_data( + cf.prepare_loaders( source=_toy_adata(), sample_rep="X", control_key="control", @@ -90,18 +84,18 @@ def test_requires_sampler_config(self): ) def test_builds_scheme_condition_and_loader_without_split(self): - cf = cellflow.model.CellFlow() + cf = cellflow.model.CellFlowAnnbatch() self._prepare(cf) assert cf._scheme is not None and cf._scheme.root == "pert" assert set(cf._annbatch_loaders) == {"train"} # no split → single "train" - assert cf._dataloader is not None # DAGLoaderTrainSampler wired for train() + assert cf._dataloader is not None # DAGLoaderAdapter wired for train() # condition embeddings assembled (drug is categorical → one-hot), data dim from X assert set(cf._condition_data) == {"drug"} assert cf._data_dim == 5 assert cf._split_schemes is None def test_split_builds_per_split_loaders(self): - cf = cellflow.model.CellFlow() + cf = cellflow.model.CellFlowAnnbatch() assert self._prepare(cf, split_by=["drug"], split_random_state=0) is None # prepare_* returns None assert set(cf._split_assignment["split"]) == {"train", "val", "test"} assert set(cf._annbatch_loaders) == {"train", "val", "test"} @@ -109,10 +103,10 @@ def test_split_builds_per_split_loaders(self): assert cf._dataloader is not None def test_per_split_sampler_config(self): - cf = cellflow.model.CellFlow() + cf = cellflow.model.CellFlowAnnbatch() train = SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8) small = SamplerConfig(batch_size=4, chunk_size=1, preload_nchunks=4) - cf.prepare_annbatch_data( + cf.prepare_loaders( source=_toy_adata(), sample_rep="X", control_key="control", @@ -125,9 +119,9 @@ def test_per_split_sampler_config(self): assert cf._annbatch_sampler_configs["val"].batch_size == 4 def test_per_split_missing_split_raises(self): - cf = cellflow.model.CellFlow() + cf = cellflow.model.CellFlowAnnbatch() with pytest.raises(ValueError, match="missing config"): - cf.prepare_annbatch_data( + cf.prepare_loaders( source=_toy_adata(), sample_rep="X", control_key="control", @@ -138,7 +132,7 @@ def test_per_split_missing_split_raises(self): ) def test_streamed_batch_shapes(self): - cf = cellflow.model.CellFlow() + cf = cellflow.model.CellFlowAnnbatch() self._prepare(cf) batch = cf._dataloader.sample() assert batch["src_cell_data"].shape == (8, 5) @@ -151,8 +145,8 @@ def test_sparse_source_batches_densified(self): adata = _toy_adata() adata.X = sp.csr_matrix(adata.X) - cf = cellflow.model.CellFlow() - cf.prepare_annbatch_data( + cf = cellflow.model.CellFlowAnnbatch() + cf.prepare_loaders( source=adata, sample_rep="X", control_key="control", diff --git a/tests/model/test_annbatch_train.py b/tests/model/test_annbatch_train.py index ebd8c1bf..e63d6b58 100644 --- a/tests/model/test_annbatch_train.py +++ b/tests/model/test_annbatch_train.py @@ -36,8 +36,8 @@ def _prepare_model_small(cf): class TestAnnbatchTraining: def test_train_runs_without_split(self): - cf = cellflow.model.CellFlow() - cf.prepare_annbatch_data( + cf = cellflow.model.CellFlowAnnbatch() + cf.prepare_loaders( source=_toy_adata(), sample_rep="X", control_key="control", @@ -51,8 +51,8 @@ def test_train_runs_without_split(self): assert cf.dataloader is not None def test_train_runs_on_train_split(self): - cf = cellflow.model.CellFlow() - cf.prepare_annbatch_data( + cf = cellflow.model.CellFlowAnnbatch() + cf.prepare_loaders( source=_toy_adata(), sample_rep="X", control_key="control", diff --git a/tests/model/test_annbatch_validation.py b/tests/model/test_annbatch_validation.py index 078ab2ee..a0d7f5e8 100644 --- a/tests/model/test_annbatch_validation.py +++ b/tests/model/test_annbatch_validation.py @@ -1,8 +1,10 @@ -"""Validation in the annbatch path: a held-out in-memory AnnData, read at the real ``sample_rep``. +"""Validation in the streaming path: each condition's full cell set is read via ``DAGEvalLoader``. -Validation is inherently in-memory (metrics need materialized cells), so `prepare_validation_data` -takes an AnnData in the streaming path too. The regression that matters: cells must be read at the -actual ``sample_rep`` (e.g. an obsm key), not the internal ``"X"`` placeholder the encoder factory used. +Validation preserves the legacy per-condition contract (``{source, condition, target}`` dicts, one entry +per condition) through :class:`~cellflow.data._annbatch.AnnbatchValidationSampler`, but reads cells via +annbatch slice reads instead of boolean-masking a materialized matrix. The regression that matters: cells +are read at the real ``sample_rep`` (e.g. an obsm key), not the internal ``"X"`` encoder-factory +placeholder. The ``val``/``test`` splits from ``split_by`` are auto-wired as evaluation sources. """ from __future__ import annotations @@ -16,6 +18,7 @@ import anndata as ad import cellflow +from cellflow.data._annbatch import AnnbatchValidationSampler from dagloader import SamplerConfig _CFG = SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8) @@ -33,46 +36,69 @@ def _adata(*, rep_dim=None, x_dim=5, n_per_combo=8, drugs=("control", "d1", "d2" return adata -def _prepare(cf, source, *, sample_rep="X"): - cf.prepare_annbatch_data( +def _prepare(cf, source, *, sample_rep="X", **kwargs): + cf.prepare_loaders( source=source, sample_rep=sample_rep, control_key="control", perturbation_covariates={"drug": ["drug"]}, split_covariates=["cell_line"], sampler_config=_CFG, + **kwargs, ) class TestAnnbatchValidation: def test_prepare_validation_without_setup_raises(self): - cf = cellflow.model.CellFlow() - with pytest.raises(ValueError, match="prepare_data.*prepare_annbatch_data|not initialized"): - cf.prepare_validation_data(adata=_adata(), name="val") + cf = cellflow.model.CellFlowAnnbatch() + with pytest.raises(ValueError, match="prepare_loaders"): + cf.prepare_validation_data(source=_adata(), name="val") - def test_validation_prepares_in_annbatch_path(self): - cf = cellflow.model.CellFlow() + def test_validation_sampler_reads_matched_source_and_target(self): + cf = cellflow.model.CellFlowAnnbatch() _prepare(cf, _adata()) # sample_rep="X" - cf.prepare_validation_data(adata=_adata(seed=1), name="val") - vd = cf.validation_data["val"] - assert vd is not None - assert vd.cell_data.shape[1] == 5 # X dim + cf.prepare_validation_data(source=_adata(seed=1), name="val") + vs = cf.validation_data["val"] + assert isinstance(vs, AnnbatchValidationSampler) + batch = vs.sample("on_train_end") + assert set(batch) == {"source", "condition", "target"} + # control-rooted: one batch per control population (2 cell lines), keyed by the drawn (line, drug) + assert len(batch["target"]) == 2 + for k in batch["target"]: + assert k[1] != "control" # target is a perturbed condition + assert np.asarray(batch["target"][k]).shape[1] == 5 # X dim + assert np.asarray(batch["source"][k]).shape[1] == 5 # matched controls, same rep def test_validation_reads_obsm_sample_rep_not_x(self): - # source: obsm X_pca (dim 3) + X (dim 5); training streams X_pca → model dim 3 - cf = cellflow.model.CellFlow() + # training streams X_pca (dim 3); validation must read X_pca, NOT X (dim 5) + cf = cellflow.model.CellFlowAnnbatch() _prepare(cf, _adata(rep_dim=3), sample_rep="X_pca") assert cf._data_dim == 3 - # validation must read X_pca (3), NOT X (5) — this is the fix under test - cf.prepare_validation_data(adata=_adata(rep_dim=3, seed=2), name="val") - assert cf.validation_data["val"].cell_data.shape[1] == 3 + cf.prepare_validation_data(source=_adata(rep_dim=3, seed=2), name="val") + batch = cf.validation_data["val"].sample("on_train_end") + for k in batch["target"]: + assert np.asarray(batch["target"][k]).shape[1] == 3 + assert np.asarray(batch["source"][k]).shape[1] == 3 + + def test_val_test_splits_auto_wired_as_eval_sources(self): + cf = cellflow.model.CellFlowAnnbatch() + _prepare( + cf, + _adata(), + split_by=["drug"], + split_ratios={"train": 0.5, "val": 0.25, "test": 0.25}, + split_random_state=0, + ) + # non-train splits become DAGEvalLoaders; "val" also feeds training-time validation + assert set(cf.split_eval_loaders) == {"val", "test"} + assert isinstance(cf.validation_data.get("val"), AnnbatchValidationSampler) def test_train_with_validation_runs(self): - cf = cellflow.model.CellFlow() + cf = cellflow.model.CellFlowAnnbatch() _prepare(cf, _adata()) - cf.prepare_validation_data(adata=_adata(seed=3), name="val") + cf.prepare_validation_data(source=_adata(seed=3), name="val") cf.prepare_model( pooling="mean", condition_embedding_dim=8, time_encoder_dims=(8,), hidden_dims=(8,), decoder_dims=(8,) ) - cf.train(num_iterations=2, valid_freq=2) # triggers a validation pass + cf.train(num_iterations=2, valid_freq=2) # triggers a DAGEvalLoader validation pass assert cf.solver is not None From dc75ed6223ec148c874744337c342786233123ac Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Tue, 14 Jul 2026 22:15:59 +0200 Subject: [PATCH 26/35] dagloader: drop matched= bind; matching is select + projection MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit An explicit parent→child leaf pairing (Bind.matched, served by the _RemappedScheduleInner schedule shim) is redundant: being a function it reduces to a derived key column + common=. Remove Bind.matched, _RemappedScheduleInner, and its only test; keep the select(weights) + project(on) path via BoundClassSampler. Co-Authored-By: Claude Opus 4.8 --- src/dagloader/_loader.py | 71 ++----------------------------- src/dagloader/_schema.py | 14 +++--- tests/dagloader/test_coherence.py | 32 +------------- 3 files changed, 10 insertions(+), 107 deletions(-) diff --git a/src/dagloader/_loader.py b/src/dagloader/_loader.py index ed110dac..8627c3b5 100644 --- a/src/dagloader/_loader.py +++ b/src/dagloader/_loader.py @@ -3,7 +3,7 @@ Each pass is a fresh epoch. The root (target) node draws a per-batch class schedule ∝ its weights via an annbatch :class:`~annbatch.samplers.ClassSampler`; every bound child replays that schedule onto its own cells via an annbatch :class:`~annbatch.samplers.BoundClassSampler` — matched by *label* on the bind's -``common`` columns (a ``matched=`` bind instead remaps the schedule to explicit child leaves). The loader +``common`` columns (select via child weights + project via ``common``). The loader never wraps annbatch's RNG: every sampler that must agree within a pass (the schedule oracle, the target reps, and each child's inner) is **reseeded from one per-pass seed** ``(node seed, pass index)``, so target/condition/source stay aligned, a node's reps read the same rows, and a pickled loader resumes the @@ -40,63 +40,6 @@ def _flat_categorical(codes: np.ndarray, leaves: list[tuple]) -> pd.Categorical: return pd.Categorical.from_codes(codes, categories=categories) -class _RemappedScheduleInner: - """Schedule-only :class:`~annbatch.abc.BaseClassSampler` that remaps a root schedule to child leaves. - - For ``matched=`` binds (explicit parent-leaf → child-leaf pairing, not a shared-column match): it - wraps the root schedule oracle and exposes, as its own ``batch_codes``, the child leaf of each batch - (via the pairing). A :class:`~annbatch.samplers.BoundClassSampler` then reads matched child cells by - label (``on=None``). Only the schedule surface (``vocab`` / ``emittable_codes`` / ``batch_codes`` / - ``n_batches``) is used by the bound — never :meth:`_sample`. - """ - - def __init__( - self, - oracle: ClassSampler, - root_leaves: list[tuple], - child_leaves: list[tuple], - matched: Mapping[tuple, tuple], - ) -> None: - self._oracle = oracle - self._root_leaves = root_leaves - self._child_vocab = pd.MultiIndex.from_tuples(child_leaves).to_flat_index() - child_pos = {lf: i for i, lf in enumerate(child_leaves)} - self._root_to_child: dict[int, int] = {} - for ri, rlf in enumerate(root_leaves): - paired = matched.get(tuple(rlf)) - if paired is not None: - if tuple(paired) not in child_pos: - raise ValueError(f"matched child leaf {paired!r} is not a child leaf.") - self._root_to_child[ri] = child_pos[tuple(paired)] - - @property - def rng(self) -> np.random.Generator: - return self._oracle.rng - - @rng.setter - def rng(self, value: np.random.Generator) -> None: - self._oracle.rng = value - - @property - def vocab(self) -> pd.Index: - return self._child_vocab - - def emittable_codes(self) -> np.ndarray: - return np.array(sorted(set(self._root_to_child.values())), dtype=np.int64) - - def n_batches(self, n_obs: int = 0) -> int: - return self._oracle.n_batches(n_obs) - - def batch_codes(self) -> np.ndarray: - out = np.empty(self._oracle.n_batches(0), dtype=np.int64) - for j, rc in enumerate(self._oracle.batch_codes()): - rc = int(rc) - if rc not in self._root_to_child: - raise ValueError(f"matched bind has no child leaf for root leaf {self._root_leaves[rc]!r}.") - out[j] = self._root_to_child[rc] - return out - - class DAGLoader: """Yields ``{"source", "target", "condition"}`` batches; every node streams through its own loader.""" @@ -179,14 +122,6 @@ def _bound_on(self, b: Bind) -> dict[int, int]: return {rcols.index(c): ccols.index(c) for c in b.common} def _new_bound_sampler(self, b: Bind) -> BoundClassSampler: - if b.matched is not None: - inner = _RemappedScheduleInner( - self._new_class_sampler(self.s.root), - self._st[self.s.root]["leaves"], - self._st[b.child]["leaves"], - b.matched, - ) - return self._make_bound(b, inner, on=None) # remapped inner emits exact child leaves inner = self._new_class_sampler(self.s.root) # Match on the bind's shared columns; the child's leaf weights (0 for excluded leaves, e.g. # perturbed cells in a control node) go in as the *secondary* class so only positive-weight @@ -203,7 +138,7 @@ def _new_bound_sampler(self, b: Bind) -> BoundClassSampler: def _make_bound( self, b: Bind, - inner: ClassSampler | _RemappedScheduleInner, + inner: ClassSampler, *, on: dict[int, int] | None, classes: pd.Categorical | None = None, @@ -212,7 +147,7 @@ def _make_bound( cfg = self._cfg[b.child] try: return BoundClassSampler( - inner, # type: ignore[arg-type] + inner, cfg.chunk_size, cfg.preload_nchunks, cfg.batch_size, diff --git a/src/dagloader/_schema.py b/src/dagloader/_schema.py index 485d1a9a..4235e259 100644 --- a/src/dagloader/_schema.py +++ b/src/dagloader/_schema.py @@ -122,19 +122,15 @@ class Bind: ∝ the child's leaf weights, so ``P(child extra cols | common)`` is weight-controlled. Pass ``common=()`` to opt into unconditional child sampling explicitly. - For pairings that are NOT a shared-column match — sc-flow-tools' ``matched_keys`` (explicit - source↔target pairs) — pass ``matched={parent_leaf: child_leaf, ...}`` instead of ``common``. Each - parent leaf then maps to exactly one child leaf; a root leaf absent from the map raises. + Matching is thus *select* (the child's per-leaf weights) + *project* (``common``) — nothing more is + needed. An arbitrary parent-leaf → child-leaf pairing (sc-flow-tools' ``matched_keys``) is not a + separate mechanism: because it is a function, tag each side with a shared key column (parent cells + with the child leaf they map to, child cells with their own leaf) and bind on that column. """ parent: str child: str - common: tuple[str, ...] = () # ⊆ parent.cols ∩ child.cols; () ⇒ unconditional (unless `matched`) - matched: Mapping[tuple, tuple] | None = None # explicit parent-leaf → child-leaf pairing; overrides `common` - - def __post_init__(self) -> None: - if self.matched is not None and self.common: - raise ValueError("Bind: give either `common` (column matching) or `matched` (explicit pairing), not both.") + common: tuple[str, ...] = () # ⊆ parent.cols ∩ child.cols; () ⇒ unconditional @dataclass(frozen=True, kw_only=True) diff --git a/tests/dagloader/test_coherence.py b/tests/dagloader/test_coherence.py index 24696e86..5d50a3da 100644 --- a/tests/dagloader/test_coherence.py +++ b/tests/dagloader/test_coherence.py @@ -2,9 +2,8 @@ Each cell's ``(cell_line, drug, control)`` is encoded into ``X`` so every yielded row can be decoded and checked: the target batch is one perturbed condition, the ``condition`` vector matches that condition, -and the source batch is control cells of the *matched* context (``common=`` → same cell line; ``matched=`` -→ the explicitly paired control leaf). This is what silently broke when dagloader's rng-wrapping -scheduler met annbatch's refactored class draw. +and the source batch is control cells of the *matched* context (``common=`` → same cell line). This is +what silently broke when dagloader's rng-wrapping scheduler met annbatch's refactored class draw. """ from __future__ import annotations @@ -87,33 +86,6 @@ def test_common_bind_target_condition_and_context_coherent(): assert len(np.unique(src[:, 0])) == 1 and src[0, 0] == tgt[0, 0], "source context ≠ target context" -def test_matched_bind_reads_the_paired_control_leaf(): - adata = _adata() - # deliberately swap: A-drugs pair with B's control, B-drugs with A's control — so a correct matched - # bind gives a source context OPPOSITE to the target's (proving it uses the pairing, not the context). - matched = { - ("A", "d1"): ("B", "control"), - ("A", "d2"): ("B", "control"), - ("A", "d3"): ("B", "control"), - ("B", "d1"): ("A", "control"), - ("B", "d2"): ("A", "control"), - ("B", "d3"): ("A", "control"), - } - loader = DAGLoader( - _scheme(adata, bind=Bind("pert", "ctrl", matched=matched)), - SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8), - condition_fn=_condition_fn, - ) - it = iter(loader) - for _ in range(12): - batch = next(it) - tgt = np.asarray(batch["target"]) - src = np.asarray(batch["source"]) - assert np.all(src[:, 2] == 1.0), "source must be control cells" - # matched pairing sends the source to the OTHER cell line - assert src[0, 0] != tgt[0, 0], "matched bind did not read the paired (swapped) control leaf" - - def test_reps_are_aligned_same_cells(): # two aligned reps of the target: X and an obsm copy of X. Same sampled rows → identical values. adata = _adata() From 3afd218c6756d9212a998b6088554f90252ba824 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Tue, 14 Jul 2026 22:52:34 +0200 Subject: [PATCH 27/35] annbatch: rename prepare_loaders -> prepare_data; fix install + lint - pyproject: set `tool.hatch.metadata.allow-direct-references = true` so the git-URL `annbatch` extra builds (otherwise hatchling rejects the direct reference and pip/uv install of the package fails); add the `zarrs` codec dep. - CellFlowAnnbatch.prepare_loaders -> prepare_data, symmetric with the in-memory CellFlow.prepare_data (method, docstrings, error messages, and all annbatch tests). - clear lint debt introduced by 31f9678: drop unused `Mapping` import and fix a stale `-> "CellFlow"` return annotation (-> "BaseCellFlow") in _base.py; fix module docstring format and a dict()->literal in _cellflow_annbatch.py. Co-Authored-By: Claude Opus 4.8 --- pyproject.toml | 9 ++++ src/cellflow/data/_legacy.py | 2 +- src/cellflow/model/_base.py | 6 +-- src/cellflow/model/_cellflow_annbatch.py | 55 +++++++++++++----------- tests/model/test_annbatch_ooc.py | 12 +++--- tests/model/test_annbatch_save.py | 2 +- tests/model/test_annbatch_split.py | 12 +++--- tests/model/test_annbatch_train.py | 4 +- tests/model/test_annbatch_validation.py | 4 +- 9 files changed, 59 insertions(+), 47 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index af930ec9..aeeac53e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,10 @@ optional-dependencies.annbatch = [ # Pinned to the branch adding `SequentialClassSampler` (streaming eval path); revert to a released # `annbatch>=0.2.x` once it lands upstream. Direct URL so pip/uv/tox all resolve the branch. "annbatch @ git+https://github.com/selmanozleyen/annbatch.git@feat/bound-class-wip", + # Rust-backed zarr v3 codec pipeline. Always installed with the streaming path: without it, zarr + # decode runs on a single GIL-bound Python thread (profiled bottleneck when building/reading large + # sparse Tahoe zarrs); `zarrs` decodes multithreaded in Rust and actually uses the allocated cores. + "zarrs", ] optional-dependencies.dev = [ "furo", @@ -119,6 +123,11 @@ packages = [ 'src/cellflow', 'src/dagloader' ] source = "vcs" fallback-version = "0.1.0" +[tool.hatch.metadata] +# the `annbatch` extra pins the fork by git URL (a direct reference); hatchling rejects +# direct references in metadata unless this is set. Drop once annbatch lands upstream. +allow-direct-references = true + [tool.ruff] line-length = 120 src = [ "src" ] diff --git a/src/cellflow/data/_legacy.py b/src/cellflow/data/_legacy.py index 1f47d4e5..975b82eb 100644 --- a/src/cellflow/data/_legacy.py +++ b/src/cellflow/data/_legacy.py @@ -4,7 +4,7 @@ (:class:`~cellflow.data.TrainingData`, :class:`~cellflow.data.ValidationData`, :class:`~cellflow.data.PredictionData`) held fully in memory. They are superseded by the annbatch/``dagloader`` streaming path (see :class:`cellflow.data._dataloader.DAGLoaderAdapter` -and :meth:`cellflow.model.CellFlow.prepare_loaders`), which streams cells out of core and also +and :meth:`cellflow.model.CellFlowAnnbatch.prepare_data`), which streams cells out of core and also accepts an in-memory ``AnnData``. Kept here for backward compatibility. """ diff --git a/src/cellflow/model/_base.py b/src/cellflow/model/_base.py index 8c988aaf..a8740091 100644 --- a/src/cellflow/model/_base.py +++ b/src/cellflow/model/_base.py @@ -5,7 +5,7 @@ import os import types import warnings -from collections.abc import Callable, Mapping, Sequence +from collections.abc import Callable, Sequence from dataclasses import field as dc_field from typing import Any, Literal @@ -344,7 +344,7 @@ def train( - :attr:`cellflow.model.CellFlow.solver` - the trained solver. """ if self._dm is None: - raise ValueError("Data not initialized. Please call `prepare_data` or `prepare_loaders` first.") + raise ValueError("Data not initialized. Please call `prepare_data` first.") if self.trainer is None: raise ValueError("Model not initialized. Please call `prepare_model` first.") @@ -582,7 +582,7 @@ def save( def load( cls, filename: str, - ) -> "CellFlow": + ) -> "BaseCellFlow": """ Load a :class:`~cellflow.model.CellFlow` model from a saved instance. diff --git a/src/cellflow/model/_cellflow_annbatch.py b/src/cellflow/model/_cellflow_annbatch.py index c90bd90c..4c5fbe9c 100644 --- a/src/cellflow/model/_cellflow_annbatch.py +++ b/src/cellflow/model/_cellflow_annbatch.py @@ -1,5 +1,8 @@ -"""Streaming (annbatch/dagloader) CellFlow: the default path — trains, validates and predicts over -an out-of-core :class:`~annbatch.DatasetCollection` or an in-memory ``AnnData`` alike.""" +"""Streaming (annbatch/dagloader) CellFlow: the default path. + +Trains, validates and predicts over an out-of-core :class:`~annbatch.DatasetCollection` or an +in-memory ``AnnData`` alike. +""" from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Literal @@ -25,7 +28,7 @@ class CellFlowAnnbatch(BaseCellFlow): """CellFlow over the annbatch/dagloader streaming path (cells sourced out-of-core or in-memory). Cells are streamed from an :class:`~annbatch.DatasetCollection` (out-of-core) or an in-memory - ``AnnData`` via :meth:`prepare_loaders`; validation and prediction read each condition's full cell + ``AnnData`` via :meth:`prepare_data`; validation and prediction read each condition's full cell set through :class:`~dagloader.DAGEvalLoader` (no boolean masking). Model setup, training, prediction and IO are inherited from :class:`~cellflow.model._base.BaseCellFlow`. """ @@ -40,12 +43,12 @@ def __init__(self, solver: Literal["otfm", "genot"] = "otfm"): self._annbatch_loaders: dict[str, DAGLoader] | None = None self._condition_data: dict[str, np.ndarray] | None = None # condition embeddings self._max_combination_length: int | None = None - self._condition_fn = None # leaf -> embedding (set by `prepare_loaders`) + self._condition_fn = None # leaf -> embedding (set by `prepare_data`) self._prep_kwargs: dict[str, Any] | None = None # covariate spec, reused for validation sources self._seed: int = 0 self._split_eval_loaders: dict[str, DAGEvalLoader] = {} - def prepare_loaders( + def prepare_data( self, source: "ad.AnnData | DatasetCollection", sample_rep: str, @@ -145,21 +148,21 @@ def prepare_loaders( # kept so `prepare_validation_data` / the auto-wired split loaders can build DAGEvalLoaders. self._condition_fn = condition_fn self._seed = seed - self._prep_kwargs = dict( - sample_rep=sample_rep, - control_key=control_key, - perturbation_covariates=perturbation_covariates, - perturbation_covariate_reps=perturbation_covariate_reps, - sample_covariates=sample_covariates, - sample_covariate_reps=sample_covariate_reps, - split_covariates=split_covariates, - max_combination_length=max_combination_length, - null_value=null_value, - rep_dict=rep_dict, - seed=seed, - ) + self._prep_kwargs = { + "sample_rep": sample_rep, + "control_key": control_key, + "perturbation_covariates": perturbation_covariates, + "perturbation_covariate_reps": perturbation_covariate_reps, + "sample_covariates": sample_covariates, + "sample_covariate_reps": sample_covariate_reps, + "split_covariates": split_covariates, + "max_combination_length": max_combination_length, + "null_value": null_value, + "rep_dict": rep_dict, + "seed": seed, + } - # Splitting step — kept in `prepare_loaders` so preparing and splitting are one call. + # Splitting step — kept in `prepare_data` so preparing and splitting are one call. if split_by is not None: self._split_assignment = self.split_annbatch_data( split_by=split_by, @@ -217,7 +220,7 @@ def split_annbatch_data( ) -> pd.DataFrame: """Partition the prepared annbatch ``Scheme``'s target combinations into named splits. - Call after :meth:`prepare_loaders`. Splits hold out whole *combinations* of ``split_by`` + Call after :meth:`prepare_data`. Splits hold out whole *combinations* of ``split_by`` (a subset of the perturbation / split-covariate columns), not cells; controls are carried through every split. See :func:`dagloader.split_scheme` for the mechanics. @@ -244,7 +247,7 @@ def split_annbatch_data( if self._scheme is None: raise ValueError( - "No annbatch `Scheme` to split. Call `prepare_loaders(...)` first " + "No annbatch `Scheme` to split. Call `prepare_data(...)` first " "(the out-of-core streaming path)." ) self._split_schemes = split_scheme( @@ -254,7 +257,7 @@ def split_annbatch_data( force_training_values=force_training_values, random_state=random_state, ) - # `prepare_loaders` turns these split Schemes into per-split DAGLoaders (train feeds + # `prepare_data` turns these split Schemes into per-split DAGLoaders (train feeds # `train()`; val/test are kept on `_annbatch_loaders`); here we only produce the schemes + table. return split_assignment(self._split_schemes) @@ -269,7 +272,7 @@ def prepare_validation_data( """Register a validation set read via :class:`~dagloader.DAGEvalLoader` (no boolean masking). The ``source`` (an :class:`~anndata.AnnData` or an :class:`~annbatch.DatasetCollection`) is - grouped by the same covariate spec passed to :meth:`prepare_loaders`; each of its conditions is + grouped by the same covariate spec passed to :meth:`prepare_data`; each of its conditions is read in full (all its cells + all matched controls) at validation time. Overrides any same-named auto-wired split loader. Preserves the legacy per-condition metric semantics. @@ -285,7 +288,7 @@ def prepare_validation_data( Keyword arguments for the solver's ``predict`` used during validation. """ if self._scheme is None or self._prep_kwargs is None: - raise ValueError("Set up training first via `prepare_loaders(...)` before preparing validation data.") + raise ValueError("Set up training first via `prepare_data(...)` before preparing validation data.") from cellflow.data._annbatch import build_annbatch_training from dagloader import DAGEvalLoader @@ -311,11 +314,11 @@ def split_eval_loaders(self) -> dict[str, "DAGEvalLoader"]: # ── path-specific hook implementations ─────────────────────────────────────────────────────── def _encoder_conditions(self) -> tuple[dict[str, np.ndarray], int]: if self._condition_data is None or self._max_combination_length is None: - raise ValueError("Data not initialized. Please call `prepare_loaders(...)` first.") + raise ValueError("Data not initialized. Please call `prepare_data(...)` first.") return self._condition_data, self._max_combination_length def _bind_train_dataloader(self, batch_size: int, out_of_core_dataloading: bool) -> None: - # the streaming `_dataloader` (DAGLoaderAdapter) was already set in `prepare_loaders`. + # the streaming `_dataloader` (DAGLoaderAdapter) was already set in `prepare_data`. return None def _build_validation_loaders(self) -> dict[str, Any]: diff --git a/tests/model/test_annbatch_ooc.py b/tests/model/test_annbatch_ooc.py index d9087c42..8c120ce6 100644 --- a/tests/model/test_annbatch_ooc.py +++ b/tests/model/test_annbatch_ooc.py @@ -61,7 +61,7 @@ def _prepare_model_small(cf): class TestOutOfCore: def test_grouped_collection_chunked_trains(self, tmp_path): cf = cellflow.model.CellFlowAnnbatch() - cf.prepare_loaders( + cf.prepare_data( source=_collection(tmp_path, grouped=True), sampler_config=SamplerConfig(batch_size=16, chunk_size=4, preload_nchunks=16), **_PREP, @@ -74,7 +74,7 @@ def test_grouped_collection_chunked_trains(self, tmp_path): def test_ungrouped_collection_chunked_raises(self, tmp_path): cf = cellflow.model.CellFlowAnnbatch() with pytest.raises(ValueError, match="grouped|groupby"): - cf.prepare_loaders( + cf.prepare_data( source=_collection(tmp_path, grouped=False), sampler_config=SamplerConfig(batch_size=16, chunk_size=4, preload_nchunks=16), **_PREP, @@ -83,7 +83,7 @@ def test_ungrouped_collection_chunked_raises(self, tmp_path): def test_ungrouped_collection_chunk1_ok(self, tmp_path): # chunk_size=1 streams per-row → no grouping needed even for an interleaved collection cf = cellflow.model.CellFlowAnnbatch() - cf.prepare_loaders( + cf.prepare_data( source=_collection(tmp_path, grouped=False), sampler_config=SamplerConfig(batch_size=16, chunk_size=1, preload_nchunks=16), **_PREP, @@ -92,7 +92,7 @@ def test_ungrouped_collection_chunk1_ok(self, tmp_path): def test_grouped_collection_split_chunked(self, tmp_path): cf = cellflow.model.CellFlowAnnbatch() - cf.prepare_loaders( + cf.prepare_data( source=_collection(tmp_path, grouped=True), sampler_config=SamplerConfig(batch_size=16, chunk_size=4, preload_nchunks=16), split_by=["drug"], @@ -104,7 +104,7 @@ def test_grouped_collection_split_chunked(self, tmp_path): def test_in_memory_unsorted_source_is_auto_grouped(self): # an in-memory (interleaved) AnnData source is grouped automatically → chunk_size>1 works cf = cellflow.model.CellFlowAnnbatch() - cf.prepare_loaders( + cf.prepare_data( source=_adata(interleaved=True), sampler_config=SamplerConfig(batch_size=16, chunk_size=4, preload_nchunks=16), **_PREP, @@ -133,7 +133,7 @@ def test_fragmented_collection_chunked_ok(self, tmp_path): assert n_runs > 6, f"expected a fragmented collection (>6 runs for 6 categories), got {n_runs}" cf = cellflow.model.CellFlowAnnbatch() - cf.prepare_loaders( # chunk_size=4 <= run length 10 → must be accepted + cf.prepare_data( # chunk_size=4 <= run length 10 → must be accepted source=dc, sampler_config=SamplerConfig(batch_size=8, chunk_size=4, preload_nchunks=8), **_PREP ) assert cf._dataloader.sample()["tgt_cell_data"].shape == (8, 5) diff --git a/tests/model/test_annbatch_save.py b/tests/model/test_annbatch_save.py index 003d4c14..6bab940e 100644 --- a/tests/model/test_annbatch_save.py +++ b/tests/model/test_annbatch_save.py @@ -38,7 +38,7 @@ def _adata(): def _prepared(seed=7): cf = cellflow.model.CellFlowAnnbatch() - cf.prepare_loaders(source=_adata(), sampler_config=_CFG, seed=seed, **_PREP) + cf.prepare_data(source=_adata(), sampler_config=_CFG, seed=seed, **_PREP) return cf diff --git a/tests/model/test_annbatch_split.py b/tests/model/test_annbatch_split.py index 5942b987..c958a7dd 100644 --- a/tests/model/test_annbatch_split.py +++ b/tests/model/test_annbatch_split.py @@ -60,10 +60,10 @@ def test_split_with_injected_scheme(self): class TestPrepareAnnbatchData: - """`prepare_loaders` builds Scheme + condition + loaders from an AnnData source.""" + """`prepare_data` builds Scheme + condition + loaders from an AnnData source.""" def _prepare(self, cf, **kwargs): - return cf.prepare_loaders( + return cf.prepare_data( source=_toy_adata(), sample_rep="X", control_key="control", @@ -76,7 +76,7 @@ def _prepare(self, cf, **kwargs): def test_requires_sampler_config(self): cf = cellflow.model.CellFlowAnnbatch() with pytest.raises(ValueError, match="sampler_config` is required"): - cf.prepare_loaders( + cf.prepare_data( source=_toy_adata(), sample_rep="X", control_key="control", @@ -106,7 +106,7 @@ def test_per_split_sampler_config(self): cf = cellflow.model.CellFlowAnnbatch() train = SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8) small = SamplerConfig(batch_size=4, chunk_size=1, preload_nchunks=4) - cf.prepare_loaders( + cf.prepare_data( source=_toy_adata(), sample_rep="X", control_key="control", @@ -121,7 +121,7 @@ def test_per_split_sampler_config(self): def test_per_split_missing_split_raises(self): cf = cellflow.model.CellFlowAnnbatch() with pytest.raises(ValueError, match="missing config"): - cf.prepare_loaders( + cf.prepare_data( source=_toy_adata(), sample_rep="X", control_key="control", @@ -146,7 +146,7 @@ def test_sparse_source_batches_densified(self): adata = _toy_adata() adata.X = sp.csr_matrix(adata.X) cf = cellflow.model.CellFlowAnnbatch() - cf.prepare_loaders( + cf.prepare_data( source=adata, sample_rep="X", control_key="control", diff --git a/tests/model/test_annbatch_train.py b/tests/model/test_annbatch_train.py index e63d6b58..dfd6bf39 100644 --- a/tests/model/test_annbatch_train.py +++ b/tests/model/test_annbatch_train.py @@ -37,7 +37,7 @@ def _prepare_model_small(cf): class TestAnnbatchTraining: def test_train_runs_without_split(self): cf = cellflow.model.CellFlowAnnbatch() - cf.prepare_loaders( + cf.prepare_data( source=_toy_adata(), sample_rep="X", control_key="control", @@ -52,7 +52,7 @@ def test_train_runs_without_split(self): def test_train_runs_on_train_split(self): cf = cellflow.model.CellFlowAnnbatch() - cf.prepare_loaders( + cf.prepare_data( source=_toy_adata(), sample_rep="X", control_key="control", diff --git a/tests/model/test_annbatch_validation.py b/tests/model/test_annbatch_validation.py index a0d7f5e8..cc120ec9 100644 --- a/tests/model/test_annbatch_validation.py +++ b/tests/model/test_annbatch_validation.py @@ -37,7 +37,7 @@ def _adata(*, rep_dim=None, x_dim=5, n_per_combo=8, drugs=("control", "d1", "d2" def _prepare(cf, source, *, sample_rep="X", **kwargs): - cf.prepare_loaders( + cf.prepare_data( source=source, sample_rep=sample_rep, control_key="control", @@ -51,7 +51,7 @@ def _prepare(cf, source, *, sample_rep="X", **kwargs): class TestAnnbatchValidation: def test_prepare_validation_without_setup_raises(self): cf = cellflow.model.CellFlowAnnbatch() - with pytest.raises(ValueError, match="prepare_loaders"): + with pytest.raises(ValueError, match="prepare_data"): cf.prepare_validation_data(source=_adata(), name="val") def test_validation_sampler_reads_matched_source_and_target(self): From 3c1c802d5a60433e0265d05c2d86f3eaf6fd7243 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Wed, 15 Jul 2026 03:58:23 +0200 Subject: [PATCH 28/35] annbatch: control_in_memory + streaming-validation densify fix + unify loaders MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - prepare_data(control_in_memory=False): materialize the matched control cells into an in-memory AnnData (a second Scheme source) so the source is served from RAM while the perturbed target keeps streaming out of core — cuts the dataloader cost (controls are re-drawn every batch). Reads dense OR anndata sparse-zarr-group backings; sorts controls by the grouping cols so chunk_size>1 still reads contiguous runs. - fix streaming validation: AnnbatchValidationSampler now densifies the eval source/target (with cupy, annbatch yields a jax sparse CSR that solver.predict can't index). - unify loaders: drop the unused per-split `_annbatch_loaders` dict; build only the train streaming loader (`_dataloader`); val/test are read via `split_eval_loaders` (DAGEvalLoader). - factor `_backing_nrows`/`_backing_ncols` so data_dim + control materialization both handle sparse zarr groups. Tests updated for the unified loader state. Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/_annbatch.py | 86 +++++++++++++++++++++--- src/cellflow/model/_cellflow_annbatch.py | 25 +++---- tests/model/test_annbatch_ooc.py | 2 +- tests/model/test_annbatch_split.py | 4 +- tests/model/test_annbatch_train.py | 8 +-- 5 files changed, 98 insertions(+), 27 deletions(-) diff --git a/src/cellflow/data/_annbatch.py b/src/cellflow/data/_annbatch.py index 9129f7b7..9894d64b 100644 --- a/src/cellflow/data/_annbatch.py +++ b/src/cellflow/data/_annbatch.py @@ -64,15 +64,20 @@ def __init__( def sample(self, mode: Literal["on_log_iteration", "on_train_end"]) -> dict[str, dict[Any, Any]]: """Sample a validation batch: per-condition source/condition/target dicts (keyed by perturbed leaf).""" + # Densify the streamed cell reps exactly as the training adapter does: with the GPU read path + # (cupy) annbatch yields a jax *sparse* CSR, which the solver's `predict` can't index — so the + # eval source/target must be densified here too, or validation crashes on `jnp.asarray(sparse)`. + from cellflow.data._dataloader import _densify + n = self.n_conditions_on_log_iteration if mode == "on_log_iteration" else self.n_conditions_on_train_end source: dict[Any, Any] = {} condition: dict[Any, Any] = {} target: dict[Any, Any] = {} for out in self._eval.iter_conditions(n_conditions=n): key = tuple(out["leaf"]) - source[key] = out["source"] + source[key] = _densify(out["source"]) condition[key] = out["condition"] - target[key] = out["target"] + target[key] = _densify(out["target"]) return {"source": source, "condition": condition, "target": target} @@ -119,6 +124,60 @@ class AnnbatchTraining: max_combination_length: int +def _backing_nrows(b) -> int: + """Obs count of a rep backing — a dense array or an anndata sparse zarr group (shape lives in attrs).""" + return int(b.shape[0]) if hasattr(b, "shape") else int(tuple(b.attrs["shape"])[0]) + + +def _backing_ncols(b) -> int: + """Feature dim of a rep backing (dense array or anndata sparse zarr group).""" + return int(b.shape[1]) if hasattr(b, "shape") else int(tuple(b.attrs["shape"])[1]) + + +def _materialize_controls(source: Container, obs, cols: Sequence[str], control_key: str, key: str) -> ad.AnnData: + """Read the control cells' rep into an in-memory (dense) ``AnnData`` — a second Scheme source in RAM. + + Controls are the small population re-drawn for *every* target batch, so re-streaming them from disk + each step is the dataloader bottleneck; pinning them in memory removes it. Reads only control rows + (per-dataset gather over the rep backings), densifies, and sorts by ``cols`` so ``chunk_size > 1`` + still reads contiguous runs. Requires the controls to fit in host RAM (they are small by design). + """ + from dagloader._io import key_backings + + # a rep backing is a dense array (ndarray / zarr array / scipy sparse) OR an anndata sparse zarr + # *group* (CSR: data/indices/indptr sub-arrays) — the latter has no `.shape` and needs a sparse reader. + def _rows(b, loc: np.ndarray) -> np.ndarray: + if hasattr(b, "shape"): + sub = b.oindex[loc] if hasattr(b, "oindex") else b[loc] + else: # sparse zarr group → anndata sparse dataset supports efficient row fancy-indexing + try: + from anndata.io import sparse_dataset + except ImportError: # older anndata + from anndata.experimental import sparse_dataset + sub = sparse_dataset(b)[loc] + dense = getattr(sub, "todense", None) + return np.asarray(dense()) if dense is not None else np.asarray(sub) + + ctrl_mask = obs[control_key].to_numpy().astype(bool) + ctrl_idx = np.flatnonzero(ctrl_mask) + backings = key_backings(source, key) # global-obs-order backings: 1 (AnnData) or per-dataset (collection) + offs = np.concatenate([[0], np.cumsum([_backing_nrows(b) for b in backings])]).astype(np.int64) + parts = [] + for d, b in enumerate(backings): + loc = np.sort(ctrl_idx[(ctrl_idx >= offs[d]) & (ctrl_idx < offs[d + 1])] - offs[d]) + if loc.size: + parts.append(_rows(b, loc)) + rep = np.concatenate(parts, axis=0) if parts else np.empty((0, _backing_ncols(backings[0])), dtype=np.float32) + ctrl_obs = obs.loc[ctrl_mask, [*cols, control_key]].reset_index(drop=True) + order = ctrl_obs.sort_values(list(cols), kind="stable").index.to_numpy() # contiguous runs for chunk_size>1 + ctrl_obs, rep = ctrl_obs.iloc[order].reset_index(drop=True), rep[order] + if key == "X": + return ad.AnnData(X=rep, obs=ctrl_obs) # construct WITH X so n_var is inferred (not 0) + ctrl_ad = ad.AnnData(obs=ctrl_obs) # obsm-only rep; X unused (n_var=0 is fine, the node reads obsm/) + ctrl_ad.obsm[key.split("/", 1)[1]] = rep + return ctrl_ad + + def build_annbatch_training( source: Container, *, @@ -133,12 +192,18 @@ def build_annbatch_training( null_value: float = 0.0, rep_dict: Mapping[str, Mapping[str, ArrayLike]] | None = None, seed: int = 0, + control_in_memory: bool = False, ) -> AnnbatchTraining: """Assemble the :class:`dagloader.Scheme` + ``condition_fn`` for the streaming path (obs only). ``source`` is an out-of-core :class:`annbatch.DatasetCollection` or an in-memory ``AnnData``. ``rep_dict`` holds the covariate embedding tables (as ``adata.uns`` would); pass :obj:`None` when the primary covariate is categorical (one-hot). + + ``control_in_memory`` materializes the control cells into an in-memory ``AnnData`` (a second Scheme + source) so the matched source/control is served from RAM while the perturbed target keeps streaming + out of core — a large dataloader speedup, since controls are re-drawn every batch. Only enable it when + the controls fit in host RAM (they are the small population by design). """ from dagloader import Bind, Node, Scheme, uniform from dagloader._io import key_backings, obs_columns @@ -217,18 +282,23 @@ def condition_fn(leaf: Leaf) -> dict[str, np.ndarray]: ctrl_flag = obs[control_key].to_numpy().astype(bool) pert = [tuple(r) for r in obs.loc[~ctrl_flag, list(cols)].drop_duplicates().to_numpy()] ctrl = [tuple(r) for r in obs.loc[ctrl_flag, list(cols)].drop_duplicates().to_numpy()] + # `control_in_memory`: serve the matched control from a RAM-resident AnnData (a second source) while + # the perturbed target keeps streaming out of core. The bind matches by label across sources. + if control_in_memory: + sources = {"data": source, "ctrl_mem": _materialize_controls(source, obs, cols, control_key, key)} + ctrl_node = Node("ctrl_mem", cols, key, uniform(ctrl)) + else: + sources = {"data": source} + ctrl_node = Node("data", cols, key, uniform(ctrl)) scheme = Scheme( - sources={"data": source}, - nodes={ - "pert": Node("data", cols, key, uniform(pert)), - "ctrl": Node("data", cols, key, uniform(ctrl)), - }, + sources=sources, + nodes={"pert": Node("data", cols, key, uniform(pert)), "ctrl": ctrl_node}, root="pert", binds=(Bind("pert", "ctrl", common=context),), seed=seed, ) - data_dim = int(key_backings(source, key)[0].shape[1]) + data_dim = _backing_ncols(key_backings(source, key)[0]) return AnnbatchTraining( scheme=scheme, condition_fn=condition_fn, diff --git a/src/cellflow/model/_cellflow_annbatch.py b/src/cellflow/model/_cellflow_annbatch.py index 4c5fbe9c..c869fa83 100644 --- a/src/cellflow/model/_cellflow_annbatch.py +++ b/src/cellflow/model/_cellflow_annbatch.py @@ -19,7 +19,7 @@ if TYPE_CHECKING: from annbatch import DatasetCollection # optional dep — only imported for typing - from dagloader import DAGEvalLoader, DAGLoader, SamplerConfig, Scheme # optional deps — typing only + from dagloader import DAGEvalLoader, SamplerConfig, Scheme # optional deps — typing only __all__ = ["CellFlowAnnbatch"] @@ -40,7 +40,6 @@ def __init__(self, solver: Literal["otfm", "genot"] = "otfm"): self._split_assignment: pd.DataFrame | None = None self._annbatch_sampler_configs: dict[str, SamplerConfig] | None = None self._eval_cfg: SamplerConfig | None = None # target read params for DAGEvalLoader - self._annbatch_loaders: dict[str, DAGLoader] | None = None self._condition_data: dict[str, np.ndarray] | None = None # condition embeddings self._max_combination_length: int | None = None self._condition_fn = None # leaf -> embedding (set by `prepare_data`) @@ -63,6 +62,7 @@ def prepare_data( rep_dict: Mapping[str, Mapping[str, ArrayLike]] | None = None, sampler_config: "SamplerConfig | Mapping[str, SamplerConfig] | None" = None, seed: int = 0, + control_in_memory: bool = False, split_by: Sequence[str] | None = None, split_ratios: Mapping[str, float] | None = None, split_force_training_values: Mapping[str, object] | None = None, @@ -118,8 +118,8 @@ def prepare_data( Notes ----- - The ``"train"`` split's loader is wired to :meth:`train`; when a split is made, the ``val`` / - ``test`` split loaders are kept on :attr:`_annbatch_loaders` for later use. + The ``"train"`` split feeds :meth:`train`; when a split is made, the ``val`` / ``test`` splits are + read via :class:`~dagloader.DAGEvalLoader` (see :attr:`split_eval_loaders`), not streamed. """ from cellflow.data._annbatch import build_annbatch_training from dagloader import DAGLoader, resolve_split_configs @@ -138,6 +138,7 @@ def prepare_data( null_value=null_value, rep_dict=rep_dict, seed=seed, + control_in_memory=control_in_memory, ) self._scheme = built.scheme self._dm = built.data_manager @@ -160,6 +161,7 @@ def prepare_data( "null_value": null_value, "rep_dict": rep_dict, "seed": seed, + "control_in_memory": control_in_memory, } # Splitting step — kept in `prepare_data` so preparing and splitting are one call. @@ -188,12 +190,11 @@ def prepare_data( root = self._scheme.nodes[self._scheme.root] assert_source_chunkable(self._scheme.sources["data"], root.cols, max_chunk) - # One streaming DAGLoader per split; the "train" split feeds `train()`, others are kept for reuse. - self._annbatch_loaders = { - name: DAGLoader(schemes[name], self._annbatch_sampler_configs[name], condition_fn=condition_fn) - for name in schemes - } - self._dataloader = DAGLoaderAdapter(self._annbatch_loaders["train"]) + # Only the "train" split is streamed (feeds `train()`); val/test are read via DAGEvalLoader + # below, so we don't build unused per-split streaming loaders. + self._dataloader = DAGLoaderAdapter( + DAGLoader(schemes["train"], self._annbatch_sampler_configs["train"], condition_fn=condition_fn) + ) # Auto-wire the val/test split combinations as evaluation sources over the same cells: a # `DAGEvalLoader` reads each held-out condition's full cell set + matched controls. The "val" @@ -257,8 +258,8 @@ def split_annbatch_data( force_training_values=force_training_values, random_state=random_state, ) - # `prepare_data` turns these split Schemes into per-split DAGLoaders (train feeds - # `train()`; val/test are kept on `_annbatch_loaders`); here we only produce the schemes + table. + # `prepare_data` wires the "train" split into the streaming loader and the val/test splits into + # `DAGEvalLoader`s (`split_eval_loaders`); here we only produce the schemes + table. return split_assignment(self._split_schemes) def prepare_validation_data( diff --git a/tests/model/test_annbatch_ooc.py b/tests/model/test_annbatch_ooc.py index 8c120ce6..aa8c4feb 100644 --- a/tests/model/test_annbatch_ooc.py +++ b/tests/model/test_annbatch_ooc.py @@ -99,7 +99,7 @@ def test_grouped_collection_split_chunked(self, tmp_path): split_ratios={"train": 0.5, "val": 0.25, "test": 0.25}, **_PREP, ) - assert set(cf._annbatch_loaders) == {"train", "val", "test"} + assert set(cf.split_eval_loaders) == {"val", "test"} # non-train splits read via DAGEvalLoader def test_in_memory_unsorted_source_is_auto_grouped(self): # an in-memory (interleaved) AnnData source is grouped automatically → chunk_size>1 works diff --git a/tests/model/test_annbatch_split.py b/tests/model/test_annbatch_split.py index c958a7dd..c5d5437f 100644 --- a/tests/model/test_annbatch_split.py +++ b/tests/model/test_annbatch_split.py @@ -87,8 +87,8 @@ def test_builds_scheme_condition_and_loader_without_split(self): cf = cellflow.model.CellFlowAnnbatch() self._prepare(cf) assert cf._scheme is not None and cf._scheme.root == "pert" - assert set(cf._annbatch_loaders) == {"train"} # no split → single "train" assert cf._dataloader is not None # DAGLoaderAdapter wired for train() + assert cf.split_eval_loaders == {} # no split → no eval loaders # condition embeddings assembled (drug is categorical → one-hot), data dim from X assert set(cf._condition_data) == {"drug"} assert cf._data_dim == 5 @@ -98,7 +98,7 @@ def test_split_builds_per_split_loaders(self): cf = cellflow.model.CellFlowAnnbatch() assert self._prepare(cf, split_by=["drug"], split_random_state=0) is None # prepare_* returns None assert set(cf._split_assignment["split"]) == {"train", "val", "test"} - assert set(cf._annbatch_loaders) == {"train", "val", "test"} + assert set(cf.split_eval_loaders) == {"val", "test"} # eval loaders for the non-train splits assert set(cf._annbatch_sampler_configs) == {"train", "val", "test"} assert cf._dataloader is not None diff --git a/tests/model/test_annbatch_train.py b/tests/model/test_annbatch_train.py index dfd6bf39..3b67e3a7 100644 --- a/tests/model/test_annbatch_train.py +++ b/tests/model/test_annbatch_train.py @@ -66,7 +66,7 @@ def test_train_runs_on_train_split(self): _prepare_model_small(cf) cf.train(num_iterations=2, valid_freq=100) assert cf.solver is not None - # val/test split loaders are streamable too - assert set(cf._annbatch_loaders) == {"train", "val", "test"} - val_batch = next(iter(cf._annbatch_loaders["val"])) - assert "target" in val_batch and "source" in val_batch + # val/test are read via DAGEvalLoader (control-rooted eval), not streamed as train-style loaders + assert set(cf.split_eval_loaders) == {"val", "test"} + val_out = next(cf.split_eval_loaders["val"].iter_conditions()) + assert "target" in val_out and "source" in val_out From 50326766a85ff51b91eed90ac1fb4aeb0a635f7d Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Wed, 15 Jul 2026 04:08:11 +0200 Subject: [PATCH 29/35] annbatch: validation densify fix, control_in_memory, loader unify + adapter rename MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - fix streaming validation: DAGEvalAdapter densifies eval source/target (with the cupy GPU read path annbatch yields a jax sparse CSR that solver.predict can't index). - add CellFlowAnnbatch.prepare_data(control_in_memory=...): materialize the matched controls into a RAM-resident AnnData (a second Scheme source) while the perturbed target streams out of core; handles dense arrays and anndata sparse-zarr-group backings. Cuts dataloader ~1/3. - unify loaders: drop the unused per-split `_annbatch_loaders` dict — train streams via `_dataloader`, val/test read via `split_eval_loaders` (DAGEvalLoader). - rename adapters for symmetry, both now in `_dataloader.py`: DAGLoaderAdapter -> DAGTrainAdapter, AnnbatchValidationSampler -> DAGEvalAdapter. - move TrainingData to `_legacy.py` (in-memory-path container). Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/__init__.py | 4 +- src/cellflow/data/_annbatch.py | 51 +-------------------- src/cellflow/data/_data.py | 42 ----------------- src/cellflow/data/_dataloader.py | 58 +++++++++++++++++++++--- src/cellflow/data/_datamanager.py | 3 +- src/cellflow/data/_legacy.py | 47 ++++++++++++++++++- src/cellflow/model/_cellflow.py | 3 +- src/cellflow/model/_cellflow_annbatch.py | 11 ++--- tests/model/test_annbatch_split.py | 2 +- tests/model/test_annbatch_validation.py | 8 ++-- 10 files changed, 114 insertions(+), 115 deletions(-) diff --git a/src/cellflow/data/__init__.py b/src/cellflow/data/__init__.py index 50fe06e0..dd0292ec 100644 --- a/src/cellflow/data/__init__.py +++ b/src/cellflow/data/__init__.py @@ -1,6 +1,6 @@ -from cellflow.data._data import BaseDataMixin, ConditionData, PredictionData, TrainingData, ValidationData +from cellflow.data._data import BaseDataMixin, ConditionData, PredictionData, ValidationData from cellflow.data._datamanager import DataManager -from cellflow.data._legacy import PredictionSampler, TrainSampler, ValidationSampler +from cellflow.data._legacy import PredictionSampler, TrainingData, TrainSampler, ValidationSampler __all__ = [ "DataManager", diff --git a/src/cellflow/data/_annbatch.py b/src/cellflow/data/_annbatch.py index 9894d64b..13fc9095 100644 --- a/src/cellflow/data/_annbatch.py +++ b/src/cellflow/data/_annbatch.py @@ -12,7 +12,7 @@ from collections.abc import Callable, Mapping, Sequence from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Literal +from typing import TYPE_CHECKING import anndata as ad import numpy as np @@ -22,65 +22,18 @@ if TYPE_CHECKING: from cellflow._types import ArrayLike - from dagloader import Container, DAGEvalLoader, Scheme + from dagloader import Container, Scheme Leaf = tuple[object, ...] # a scheme leaf: one value per grouping column __all__ = [ "AnnbatchTraining", - "AnnbatchValidationSampler", "assert_source_chunkable", "build_annbatch_training", "sample_rep_to_key", ] -class AnnbatchValidationSampler: - """Validation sampler over a :class:`dagloader.DAGEvalLoader` — the trainer's ``sample(mode)`` contract. - - Yields per-condition ``{"source", "condition", "target"}`` dicts (keyed by the perturbed leaf), as the - trainer expects. The control-rooted ``DAGEvalLoader`` reads each control population's cells in full for - the source and samples a matched perturbed batch for the target — via annbatch, no boolean masking. - ``n_conditions_*`` sets how many (control-population, drug) batches to draw per mode. - - Parameters - ---------- - eval_loader - A :class:`dagloader.DAGEvalLoader` built over the validation source. - n_conditions_on_log_iteration, n_conditions_on_train_end - How many conditions to draw per mode; :obj:`None` visits each control population once. - """ - - def __init__( - self, - eval_loader: DAGEvalLoader, - *, - n_conditions_on_log_iteration: int | None = None, - n_conditions_on_train_end: int | None = None, - ) -> None: - self._eval = eval_loader - self.n_conditions_on_log_iteration = n_conditions_on_log_iteration - self.n_conditions_on_train_end = n_conditions_on_train_end - - def sample(self, mode: Literal["on_log_iteration", "on_train_end"]) -> dict[str, dict[Any, Any]]: - """Sample a validation batch: per-condition source/condition/target dicts (keyed by perturbed leaf).""" - # Densify the streamed cell reps exactly as the training adapter does: with the GPU read path - # (cupy) annbatch yields a jax *sparse* CSR, which the solver's `predict` can't index — so the - # eval source/target must be densified here too, or validation crashes on `jnp.asarray(sparse)`. - from cellflow.data._dataloader import _densify - - n = self.n_conditions_on_log_iteration if mode == "on_log_iteration" else self.n_conditions_on_train_end - source: dict[Any, Any] = {} - condition: dict[Any, Any] = {} - target: dict[Any, Any] = {} - for out in self._eval.iter_conditions(n_conditions=n): - key = tuple(out["leaf"]) - source[key] = _densify(out["source"]) - condition[key] = out["condition"] - target[key] = _densify(out["target"]) - return {"source": source, "condition": condition, "target": target} - - def assert_source_chunkable(source: Container, cols: Sequence[str], chunk_size: int) -> None: """Raise unless ``source`` satisfies annbatch's run-length rule for ``chunk_size`` (reads ``obs`` only). diff --git a/src/cellflow/data/_data.py b/src/cellflow/data/_data.py index 0f51d304..b56c8815 100644 --- a/src/cellflow/data/_data.py +++ b/src/cellflow/data/_data.py @@ -11,7 +11,6 @@ "BaseDataMixin", "ConditionData", "PredictionData", - "TrainingData", "ValidationData", ] @@ -81,47 +80,6 @@ class ConditionData(BaseDataMixin): data_manager: Any -@dataclass -class TrainingData(BaseDataMixin): - """Training data. - - Parameters - ---------- - cell_data - The representation of cell data, e.g. PCA of gene expression data. - split_covariates_mask - Mask of the split covariates. - split_idx_to_covariates - Dictionary explaining values in ``split_covariates_mask``. - perturbation_covariates_mask - Mask of the perturbation covariates. - perturbation_idx_to_covariates - Dictionary explaining values in ``perturbation_covariates_mask``. - condition_data - Dictionary with embeddings for conditions. - control_to_perturbation - Mapping from control index to target distribution indices. - max_combination_length - Maximum number of covariates in a combination. - data_manager - The data manager - """ - - cell_data: np.ndarray # (n_cells, n_features) - split_covariates_mask: np.ndarray # (n_cells,), which cell assigned to which source distribution - split_idx_to_covariates: dict[int, tuple[Any, ...]] # (n_sources,) dictionary explaining split_covariates_mask - perturbation_covariates_mask: np.ndarray # (n_cells,), which cell assigned to which target distribution - perturbation_idx_to_covariates: dict[ - int, tuple[str, ...] - ] # (n_targets,), dictionary explaining perturbation_covariates_mask - perturbation_idx_to_id: dict[int, Any] - condition_data: dict[str, np.ndarray] # (n_targets,) all embeddings for conditions - control_to_perturbation: dict[int, np.ndarray] # mapping from control idx to target distribution idcs - max_combination_length: int - null_value: Any - data_manager: Any - - @dataclass class ValidationData(BaseDataMixin): """Data container for the validation data. diff --git a/src/cellflow/data/_dataloader.py b/src/cellflow/data/_dataloader.py index 31754821..3c4d1fbc 100644 --- a/src/cellflow/data/_dataloader.py +++ b/src/cellflow/data/_dataloader.py @@ -1,14 +1,15 @@ -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any, Literal import numpy as np from cellflow._types import ArrayLike if TYPE_CHECKING: - from dagloader import DAGLoader + from dagloader import DAGEvalLoader, DAGLoader __all__ = [ - "DAGLoaderAdapter", + "DAGEvalAdapter", + "DAGTrainAdapter", ] @@ -23,13 +24,13 @@ def _densify(x: ArrayLike) -> ArrayLike: return todense() if todense is not None else x -class DAGLoaderAdapter: - """Adapt a ``dagloader.DAGLoader`` stream to the sampler ``sample()`` batch contract. +class DAGTrainAdapter: + """Adapt a ``dagloader.DAGLoader`` stream to the trainer's ``sample(rng)`` batch contract. Renames the loader's ``{"target", "source", "condition"}`` batch to the model's ``{"src_cell_data", "tgt_cell_data", "condition"}`` and densifies any sparse cell rep, keeping the arrays on their native (jax) backend, so the in-memory and streaming paths reach the solver - identically. Used for training, and (re)used for the streamed validation and prediction paths. + identically. This is the *training* adapter; validation uses :class:`DAGEvalAdapter`. """ def __init__(self, loader: "DAGLoader"): @@ -49,3 +50,48 @@ def sample(self, rng: np.random.Generator | None = None) -> dict[str, ArrayLike if "condition" in batch: out["condition"] = batch["condition"] return out + + +class DAGEvalAdapter: + """Adapt a ``dagloader.DAGEvalLoader`` to the trainer's validation ``sample(mode)`` contract. + + Yields per-condition ``{"source", "condition", "target"}`` dicts (keyed by the perturbed leaf), as the + trainer's validation step expects. The control-rooted ``DAGEvalLoader`` reads each control population's + cells in full for the source and samples a matched perturbed batch for the target — via annbatch, no + boolean masking. ``n_conditions_*`` sets how many (control-population, drug) batches to draw per mode. + This mirrors the in-memory :class:`~cellflow.data._legacy.ValidationSampler` contract for the streaming + path, so the trainer's validation step is identical either way. + + Parameters + ---------- + eval_loader + A :class:`dagloader.DAGEvalLoader` built over the validation source. + n_conditions_on_log_iteration, n_conditions_on_train_end + How many conditions to draw per mode; :obj:`None` visits each control population once. + """ + + def __init__( + self, + eval_loader: "DAGEvalLoader", + *, + n_conditions_on_log_iteration: int | None = None, + n_conditions_on_train_end: int | None = None, + ) -> None: + self._eval = eval_loader + self.n_conditions_on_log_iteration = n_conditions_on_log_iteration + self.n_conditions_on_train_end = n_conditions_on_train_end + + def sample(self, mode: Literal["on_log_iteration", "on_train_end"]) -> dict[str, dict[Any, Any]]: + """Sample a validation batch: per-condition source/condition/target dicts (keyed by perturbed leaf).""" + # Densify exactly as the training adapter does: with the GPU read path (cupy) annbatch yields a + # jax *sparse* CSR, which the solver's `predict` can't index — so eval source/target must be dense. + n = self.n_conditions_on_log_iteration if mode == "on_log_iteration" else self.n_conditions_on_train_end + source: dict[Any, Any] = {} + condition: dict[Any, Any] = {} + target: dict[Any, Any] = {} + for out in self._eval.iter_conditions(n_conditions=n): + key = tuple(out["leaf"]) + source[key] = _densify(out["source"]) + condition[key] = out["condition"] + target[key] = _densify(out["target"]) + return {"source": source, "condition": condition, "target": target} diff --git a/src/cellflow/data/_datamanager.py b/src/cellflow/data/_datamanager.py index ce4ff87f..5dad7ce4 100644 --- a/src/cellflow/data/_datamanager.py +++ b/src/cellflow/data/_datamanager.py @@ -15,7 +15,8 @@ from cellflow._types import ArrayLike from cellflow.data._condition import get_max_combination_length -from cellflow.data._data import ConditionData, PredictionData, ReturnData, TrainingData, ValidationData +from cellflow.data._data import ConditionData, PredictionData, ReturnData, ValidationData +from cellflow.data._legacy import TrainingData from ._utils import _flatten_list, _to_list diff --git a/src/cellflow/data/_legacy.py b/src/cellflow/data/_legacy.py index 975b82eb..0bee1b35 100644 --- a/src/cellflow/data/_legacy.py +++ b/src/cellflow/data/_legacy.py @@ -3,7 +3,7 @@ These samplers operate on materialized :mod:`cellflow.data` containers (:class:`~cellflow.data.TrainingData`, :class:`~cellflow.data.ValidationData`, :class:`~cellflow.data.PredictionData`) held fully in memory. They are superseded by the -annbatch/``dagloader`` streaming path (see :class:`cellflow.data._dataloader.DAGLoaderAdapter` +annbatch/``dagloader`` streaming path (see :class:`cellflow.data._dataloader.DAGTrainAdapter` and :meth:`cellflow.model.CellFlowAnnbatch.prepare_data`), which streams cells out of core and also accepts an in-memory ``AnnData``. Kept here for backward compatibility. """ @@ -12,14 +12,16 @@ import queue import threading from collections.abc import Generator +from dataclasses import dataclass from typing import Any, Literal import jax import numpy as np -from cellflow.data._data import PredictionData, TrainingData, ValidationData +from cellflow.data._data import BaseDataMixin, PredictionData, ValidationData __all__ = [ + "TrainingData", "TrainSampler", "BaseValidSampler", "ValidationSampler", @@ -29,6 +31,47 @@ ] +@dataclass +class TrainingData(BaseDataMixin): + """Training data (in-memory path). + + Parameters + ---------- + cell_data + The representation of cell data, e.g. PCA of gene expression data. + split_covariates_mask + Mask of the split covariates. + split_idx_to_covariates + Dictionary explaining values in ``split_covariates_mask``. + perturbation_covariates_mask + Mask of the perturbation covariates. + perturbation_idx_to_covariates + Dictionary explaining values in ``perturbation_covariates_mask``. + condition_data + Dictionary with embeddings for conditions. + control_to_perturbation + Mapping from control index to target distribution indices. + max_combination_length + Maximum number of covariates in a combination. + data_manager + The data manager + """ + + cell_data: np.ndarray # (n_cells, n_features) + split_covariates_mask: np.ndarray # (n_cells,), which cell assigned to which source distribution + split_idx_to_covariates: dict[int, tuple[Any, ...]] # (n_sources,) dictionary explaining split_covariates_mask + perturbation_covariates_mask: np.ndarray # (n_cells,), which cell assigned to which target distribution + perturbation_idx_to_covariates: dict[ + int, tuple[str, ...] + ] # (n_targets,), dictionary explaining perturbation_covariates_mask + perturbation_idx_to_id: dict[int, Any] + condition_data: dict[str, np.ndarray] # (n_targets,) all embeddings for conditions + control_to_perturbation: dict[int, np.ndarray] # mapping from control idx to target distribution idcs + max_combination_length: int + null_value: Any + data_manager: Any + + class TrainSampler: """Data sampler for :class:`~cellflow.data.TrainingData`. diff --git a/src/cellflow/model/_cellflow.py b/src/cellflow/model/_cellflow.py index 0591658d..9bf047c3 100644 --- a/src/cellflow/model/_cellflow.py +++ b/src/cellflow/model/_cellflow.py @@ -11,9 +11,8 @@ import anndata as ad import numpy as np -from cellflow.data._data import TrainingData from cellflow.data._datamanager import DataManager -from cellflow.data._legacy import OOCTrainSampler, TrainSampler, ValidationSampler +from cellflow.data._legacy import OOCTrainSampler, TrainingData, TrainSampler, ValidationSampler from cellflow.model._base import BaseCellFlow __all__ = ["CellFlow"] diff --git a/src/cellflow/model/_cellflow_annbatch.py b/src/cellflow/model/_cellflow_annbatch.py index c869fa83..9a8271ea 100644 --- a/src/cellflow/model/_cellflow_annbatch.py +++ b/src/cellflow/model/_cellflow_annbatch.py @@ -12,8 +12,7 @@ import pandas as pd from cellflow._types import ArrayLike -from cellflow.data._annbatch import AnnbatchValidationSampler -from cellflow.data._dataloader import DAGLoaderAdapter +from cellflow.data._dataloader import DAGEvalAdapter, DAGTrainAdapter from cellflow.model._base import BaseCellFlow if TYPE_CHECKING: @@ -192,7 +191,7 @@ def prepare_data( # Only the "train" split is streamed (feeds `train()`); val/test are read via DAGEvalLoader # below, so we don't build unused per-split streaming loaders. - self._dataloader = DAGLoaderAdapter( + self._dataloader = DAGTrainAdapter( DAGLoader(schemes["train"], self._annbatch_sampler_configs["train"], condition_fn=condition_fn) ) @@ -209,7 +208,7 @@ def prepare_data( continue self._split_eval_loaders[split_name] = DAGEvalLoader(sch, self._eval_cfg, condition_fn, seed=seed) if "val" in self._split_eval_loaders: - self._validation_data["val"] = AnnbatchValidationSampler(self._split_eval_loaders["val"]) + self._validation_data["val"] = DAGEvalAdapter(self._split_eval_loaders["val"]) def split_annbatch_data( self, @@ -296,7 +295,7 @@ def prepare_validation_data( built = build_annbatch_training(source, **self._prep_kwargs) eval_loader = DAGEvalLoader(built.scheme, self._eval_cfg, built.condition_fn, seed=self._seed) - self._validation_data[name] = AnnbatchValidationSampler( + self._validation_data[name] = DAGEvalAdapter( eval_loader, n_conditions_on_log_iteration=n_conditions_on_log_iteration, n_conditions_on_train_end=n_conditions_on_train_end, @@ -319,7 +318,7 @@ def _encoder_conditions(self) -> tuple[dict[str, np.ndarray], int]: return self._condition_data, self._max_combination_length def _bind_train_dataloader(self, batch_size: int, out_of_core_dataloading: bool) -> None: - # the streaming `_dataloader` (DAGLoaderAdapter) was already set in `prepare_data`. + # the streaming `_dataloader` (DAGTrainAdapter) was already set in `prepare_data`. return None def _build_validation_loaders(self) -> dict[str, Any]: diff --git a/tests/model/test_annbatch_split.py b/tests/model/test_annbatch_split.py index c5d5437f..79000ecb 100644 --- a/tests/model/test_annbatch_split.py +++ b/tests/model/test_annbatch_split.py @@ -87,7 +87,7 @@ def test_builds_scheme_condition_and_loader_without_split(self): cf = cellflow.model.CellFlowAnnbatch() self._prepare(cf) assert cf._scheme is not None and cf._scheme.root == "pert" - assert cf._dataloader is not None # DAGLoaderAdapter wired for train() + assert cf._dataloader is not None # DAGTrainAdapter wired for train() assert cf.split_eval_loaders == {} # no split → no eval loaders # condition embeddings assembled (drug is categorical → one-hot), data dim from X assert set(cf._condition_data) == {"drug"} diff --git a/tests/model/test_annbatch_validation.py b/tests/model/test_annbatch_validation.py index cc120ec9..945e3836 100644 --- a/tests/model/test_annbatch_validation.py +++ b/tests/model/test_annbatch_validation.py @@ -1,7 +1,7 @@ """Validation in the streaming path: each condition's full cell set is read via ``DAGEvalLoader``. Validation preserves the legacy per-condition contract (``{source, condition, target}`` dicts, one entry -per condition) through :class:`~cellflow.data._annbatch.AnnbatchValidationSampler`, but reads cells via +per condition) through :class:`~cellflow.data._dataloader.DAGEvalAdapter`, but reads cells via annbatch slice reads instead of boolean-masking a materialized matrix. The regression that matters: cells are read at the real ``sample_rep`` (e.g. an obsm key), not the internal ``"X"`` encoder-factory placeholder. The ``val``/``test`` splits from ``split_by`` are auto-wired as evaluation sources. @@ -18,7 +18,7 @@ import anndata as ad import cellflow -from cellflow.data._annbatch import AnnbatchValidationSampler +from cellflow.data._dataloader import DAGEvalAdapter from dagloader import SamplerConfig _CFG = SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8) @@ -59,7 +59,7 @@ def test_validation_sampler_reads_matched_source_and_target(self): _prepare(cf, _adata()) # sample_rep="X" cf.prepare_validation_data(source=_adata(seed=1), name="val") vs = cf.validation_data["val"] - assert isinstance(vs, AnnbatchValidationSampler) + assert isinstance(vs, DAGEvalAdapter) batch = vs.sample("on_train_end") assert set(batch) == {"source", "condition", "target"} # control-rooted: one batch per control population (2 cell lines), keyed by the drawn (line, drug) @@ -91,7 +91,7 @@ def test_val_test_splits_auto_wired_as_eval_sources(self): ) # non-train splits become DAGEvalLoaders; "val" also feeds training-time validation assert set(cf.split_eval_loaders) == {"val", "test"} - assert isinstance(cf.validation_data.get("val"), AnnbatchValidationSampler) + assert isinstance(cf.validation_data.get("val"), DAGEvalAdapter) def test_train_with_validation_runs(self): cf = cellflow.model.CellFlowAnnbatch() From 7ca1fd49fafc1180871865a8750664a30f874b88 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Wed, 15 Jul 2026 04:12:10 +0200 Subject: [PATCH 30/35] data: move ValidationData to _legacy (in-memory-path container) ValidationData is only ever constructed by DataManager.get_validation_data, which only the in-memory CellFlow path calls (streaming validation uses DAGEvalAdapter). Group it with the other in-memory-only container (TrainingData) in _legacy; _data keeps the shared containers (ConditionData/PredictionData/ ReturnData/BaseDataMixin, used by the streaming predict / condition-embedding / DataManager paths). Re-exported from cellflow.data, so public imports are unchanged. Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/__init__.py | 4 +-- src/cellflow/data/_data.py | 50 ----------------------------- src/cellflow/data/_datamanager.py | 4 +-- src/cellflow/data/_legacy.py | 52 ++++++++++++++++++++++++++++++- src/cellflow/model/_base.py | 4 +-- 5 files changed, 57 insertions(+), 57 deletions(-) diff --git a/src/cellflow/data/__init__.py b/src/cellflow/data/__init__.py index dd0292ec..71205c14 100644 --- a/src/cellflow/data/__init__.py +++ b/src/cellflow/data/__init__.py @@ -1,6 +1,6 @@ -from cellflow.data._data import BaseDataMixin, ConditionData, PredictionData, ValidationData +from cellflow.data._data import BaseDataMixin, ConditionData, PredictionData from cellflow.data._datamanager import DataManager -from cellflow.data._legacy import PredictionSampler, TrainingData, TrainSampler, ValidationSampler +from cellflow.data._legacy import PredictionSampler, TrainingData, TrainSampler, ValidationData, ValidationSampler __all__ = [ "DataManager", diff --git a/src/cellflow/data/_data.py b/src/cellflow/data/_data.py index b56c8815..ee82897f 100644 --- a/src/cellflow/data/_data.py +++ b/src/cellflow/data/_data.py @@ -11,7 +11,6 @@ "BaseDataMixin", "ConditionData", "PredictionData", - "ValidationData", ] @@ -80,55 +79,6 @@ class ConditionData(BaseDataMixin): data_manager: Any -@dataclass -class ValidationData(BaseDataMixin): - """Data container for the validation data. - - Parameters - ---------- - cell_data - The representation of cell data, e.g. PCA of gene expression data. - split_covariates_mask - Mask of the split covariates. - split_idx_to_covariates - Dictionary explaining values in ``split_covariates_mask``. - perturbation_covariates_mask - Mask of the perturbation covariates. - perturbation_idx_to_covariates - Dictionary explaining values in ``perturbation_covariates_mask``. - condition_data - Dictionary with embeddings for conditions. - control_to_perturbation - Mapping from control index to target distribution indices. - max_combination_length - Maximum number of covariates in a combination. - data_manager - The data manager - n_conditions_on_log_iteration - Number of conditions to use for computation callbacks at each logged iteration. - If :obj:`None`, use all conditions. - n_conditions_on_train_end - Number of conditions to use for computation callbacks at the end of training. - If :obj:`None`, use all conditions. - """ - - cell_data: np.ndarray # (n_cells, n_features) - split_covariates_mask: np.ndarray # (n_cells,), which cell assigned to which source distribution - split_idx_to_covariates: dict[int, tuple[Any, ...]] # (n_sources,) dictionary explaining split_covariates_mask - perturbation_covariates_mask: np.ndarray # (n_cells,), which cell assigned to which target distribution - perturbation_idx_to_covariates: dict[ - int, tuple[str, ...] - ] # (n_targets,), dictionary explaining perturbation_covariates_mask - perturbation_idx_to_id: dict[int, Any] - condition_data: dict[str, np.ndarray] # (n_targets,) all embeddings for conditions - control_to_perturbation: dict[int, np.ndarray] # mapping from control idx to target distribution idcs - max_combination_length: int - null_value: Any - data_manager: Any - n_conditions_on_log_iteration: int | None = None - n_conditions_on_train_end: int | None = None - - @dataclass class PredictionData(BaseDataMixin): """Data container to perform prediction. diff --git a/src/cellflow/data/_datamanager.py b/src/cellflow/data/_datamanager.py index 5dad7ce4..63845348 100644 --- a/src/cellflow/data/_datamanager.py +++ b/src/cellflow/data/_datamanager.py @@ -15,8 +15,8 @@ from cellflow._types import ArrayLike from cellflow.data._condition import get_max_combination_length -from cellflow.data._data import ConditionData, PredictionData, ReturnData, ValidationData -from cellflow.data._legacy import TrainingData +from cellflow.data._data import ConditionData, PredictionData, ReturnData +from cellflow.data._legacy import TrainingData, ValidationData from ._utils import _flatten_list, _to_list diff --git a/src/cellflow/data/_legacy.py b/src/cellflow/data/_legacy.py index 0bee1b35..3abbafbd 100644 --- a/src/cellflow/data/_legacy.py +++ b/src/cellflow/data/_legacy.py @@ -18,10 +18,11 @@ import jax import numpy as np -from cellflow.data._data import BaseDataMixin, PredictionData, ValidationData +from cellflow.data._data import BaseDataMixin, PredictionData __all__ = [ "TrainingData", + "ValidationData", "TrainSampler", "BaseValidSampler", "ValidationSampler", @@ -72,6 +73,55 @@ class TrainingData(BaseDataMixin): data_manager: Any +@dataclass +class ValidationData(BaseDataMixin): + """Validation data (in-memory path). + + Parameters + ---------- + cell_data + The representation of cell data, e.g. PCA of gene expression data. + split_covariates_mask + Mask of the split covariates. + split_idx_to_covariates + Dictionary explaining values in ``split_covariates_mask``. + perturbation_covariates_mask + Mask of the perturbation covariates. + perturbation_idx_to_covariates + Dictionary explaining values in ``perturbation_covariates_mask``. + condition_data + Dictionary with embeddings for conditions. + control_to_perturbation + Mapping from control index to target distribution indices. + max_combination_length + Maximum number of covariates in a combination. + data_manager + The data manager + n_conditions_on_log_iteration + Number of conditions to use for computation callbacks at each logged iteration. + If :obj:`None`, use all conditions. + n_conditions_on_train_end + Number of conditions to use for computation callbacks at the end of training. + If :obj:`None`, use all conditions. + """ + + cell_data: np.ndarray # (n_cells, n_features) + split_covariates_mask: np.ndarray # (n_cells,), which cell assigned to which source distribution + split_idx_to_covariates: dict[int, tuple[Any, ...]] # (n_sources,) dictionary explaining split_covariates_mask + perturbation_covariates_mask: np.ndarray # (n_cells,), which cell assigned to which target distribution + perturbation_idx_to_covariates: dict[ + int, tuple[str, ...] + ] # (n_targets,), dictionary explaining perturbation_covariates_mask + perturbation_idx_to_id: dict[int, Any] + condition_data: dict[str, np.ndarray] # (n_targets,) all embeddings for conditions + control_to_perturbation: dict[int, np.ndarray] # mapping from control idx to target distribution idcs + max_combination_length: int + null_value: Any + data_manager: Any + n_conditions_on_log_iteration: int | None = None + n_conditions_on_train_end: int | None = None + + class TrainSampler: """Data sampler for :class:`~cellflow.data.TrainingData`. diff --git a/src/cellflow/model/_base.py b/src/cellflow/model/_base.py index a8740091..463f7051 100644 --- a/src/cellflow/model/_base.py +++ b/src/cellflow/model/_base.py @@ -21,9 +21,9 @@ from cellflow import _constants from cellflow._compat import BrownianBridge, ConstantNoiseFlow from cellflow._types import ArrayLike, Layers_separate_input_t, Layers_t -from cellflow.data._data import ConditionData, ValidationData +from cellflow.data._data import ConditionData from cellflow.data._datamanager import DataManager -from cellflow.data._legacy import PredictionSampler +from cellflow.data._legacy import PredictionSampler, ValidationData from cellflow.model._utils import _write_predictions from cellflow.networks import _velocity_field from cellflow.plotting import _utils From 30444e1711e3cd4bacea2593070d80ed0a9149a9 Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Wed, 15 Jul 2026 04:23:04 +0200 Subject: [PATCH 31/35] dagloader: read a collection's sparse rep via sparse_dataset `key_backings` returned a `DatasetCollection`'s sparse X as a raw zarr group (no `.shape`), which annbatch's `add_datasets` rejects (`item.shape[0]` -> AttributeError). Wrap a sparse group as an anndata `CSRDataset` (exposes `.shape` + row indexing) so out-of-core sparse collections load; dense arrays and in-memory scipy/numpy reps pass through unchanged. Co-Authored-By: Claude Opus 4.8 --- src/dagloader/_io.py | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/src/dagloader/_io.py b/src/dagloader/_io.py index c35f19af..0d921dfa 100644 --- a/src/dagloader/_io.py +++ b/src/dagloader/_io.py @@ -19,19 +19,34 @@ __all__ = ["key_backings", "leaf_codes", "obs_columns"] +def _readable(x): + """A rep backing annbatch can read (dense passes through; a sparse zarr group gets wrapped). + + A **sparse** rep in a ``DatasetCollection`` is a zarr *group* (CSR: data/indices/indptr) with no + ``.shape``, so wrap it as an anndata ``CSRDataset`` (which exposes ``.shape`` + row indexing). + In-memory ``AnnData`` reps (scipy/numpy) and dense zarr arrays already qualify and pass through. + """ + if hasattr(x, "shape"): + return x + from anndata.io import sparse_dataset + + return sparse_dataset(x) + + def key_backings(source: Container, loc: str) -> list: """The array(s) backing rep ``loc`` for a source, ready to feed one annbatch ``add_datasets``. annbatch's ``add_datasets`` concatenates on the obs axis and needs equal feature dims, so each rep gets its own loader over its own array(s). For a ``DatasetCollection`` the per-dataset arrays are - gathered in order (matching the global row layout). + gathered in order (matching the global row layout); a sparse rep's zarr group is wrapped so it is + readable (see :func:`_readable`). """ if loc == "X": - return [source.X] if isinstance(source, ad.AnnData) else [g["X"] for g in source] + return [source.X] if isinstance(source, ad.AnnData) else [_readable(g["X"]) for g in source] field, sub = loc.split("/", 1) # "obsm/X_pca" | "layers/log1p" if isinstance(source, ad.AnnData): return [getattr(source, field)[sub]] - return [g[field][sub] for g in source] # DatasetCollection: one zarr array per dataset + return [_readable(g[field][sub]) for g in source] # DatasetCollection: one backing per dataset def leaf_codes(obs: pd.DataFrame, cols: Sequence[str]) -> tuple[np.ndarray, list[tuple]]: From 3628a4e6acdbe8ed5c0ec35809450bb3b2a1551e Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Wed, 15 Jul 2026 04:24:48 +0200 Subject: [PATCH 32/35] put plans --- src/dagloader/_eval_loader.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/dagloader/_eval_loader.py b/src/dagloader/_eval_loader.py index 62c01203..f09dcebf 100644 --- a/src/dagloader/_eval_loader.py +++ b/src/dagloader/_eval_loader.py @@ -109,6 +109,8 @@ def _bound(self, schedule: np.ndarray) -> BoundClassSampler: rng=np.random.default_rng(self._seed), ) + + # TODO(selmanozleyen): rename to _loaders_from_node def _node_loaders(self, src, node, make_sampler) -> dict: loaders = {} for key in node.keys: @@ -138,6 +140,7 @@ def iter_conditions(self, n_conditions: int | None = None) -> Iterator[dict]: ctx = len(self._context) cond_leaves = [tuple(vocab[int(c)])[ctx:] for c in oracle.batch_codes()] # strip the shared context prefix + # TODO(selmanozleyen): inline x_loaders since there is no need src_loaders = self._node_loaders(self._src, self._ctrl, lambda: self._inner(schedule)) tgt_loaders = self._node_loaders(self._src_p, self._pert, lambda: self._bound(schedule)) src_iters = {k: iter(ld) for k, ld in src_loaders.items()} From 0345f20f706d2078b1f187913292cae145d1fb9a Mon Sep 17 00:00:00 2001 From: selmanozleyen Date: Wed, 15 Jul 2026 11:32:46 +0200 Subject: [PATCH 33/35] dagloader: configurable to/preload_to_gpu + general in-memory materialization MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - SamplerConfig gains `to` ("jax"/"torch"/None) and `preload_to_gpu` (None ⇒ auto-cupy); DAGLoader and DAGEvalLoader read them instead of hardcoding to="jax" / _HAS_CUPY. - Node.in_memory + dagloader._io.materialize_node: a general "read a node's positive-weight cells into an in-memory AnnData" op (handles dense + sparse-group backings). The loader materializes any in_memory node once and streams it from RAM. - cellflow's control_in_memory now just sets Node.in_memory on the ctrl node (single source) — the bespoke _materialize_controls / second "ctrl_mem" source is removed. cellflow tells dagloader; dagloader owns the read. - tests: sparse-collection construction (regression for the add_datasets 'Group' fix), control_in_memory materialization, materialize_node row selection, and Node.in_memory + to/preload_to_gpu config. Co-Authored-By: Claude Opus 4.8 --- src/cellflow/data/_annbatch.py | 83 ++++++------------------------- src/dagloader/_eval_loader.py | 10 ++-- src/dagloader/_io.py | 42 +++++++++++++++- src/dagloader/_loader.py | 25 ++++++---- src/dagloader/_schema.py | 14 ++++++ tests/dagloader/test_coherence.py | 39 +++++++++++++++ tests/model/test_annbatch_ooc.py | 34 +++++++++++-- 7 files changed, 161 insertions(+), 86 deletions(-) diff --git a/src/cellflow/data/_annbatch.py b/src/cellflow/data/_annbatch.py index 13fc9095..5503b408 100644 --- a/src/cellflow/data/_annbatch.py +++ b/src/cellflow/data/_annbatch.py @@ -77,60 +77,6 @@ class AnnbatchTraining: max_combination_length: int -def _backing_nrows(b) -> int: - """Obs count of a rep backing — a dense array or an anndata sparse zarr group (shape lives in attrs).""" - return int(b.shape[0]) if hasattr(b, "shape") else int(tuple(b.attrs["shape"])[0]) - - -def _backing_ncols(b) -> int: - """Feature dim of a rep backing (dense array or anndata sparse zarr group).""" - return int(b.shape[1]) if hasattr(b, "shape") else int(tuple(b.attrs["shape"])[1]) - - -def _materialize_controls(source: Container, obs, cols: Sequence[str], control_key: str, key: str) -> ad.AnnData: - """Read the control cells' rep into an in-memory (dense) ``AnnData`` — a second Scheme source in RAM. - - Controls are the small population re-drawn for *every* target batch, so re-streaming them from disk - each step is the dataloader bottleneck; pinning them in memory removes it. Reads only control rows - (per-dataset gather over the rep backings), densifies, and sorts by ``cols`` so ``chunk_size > 1`` - still reads contiguous runs. Requires the controls to fit in host RAM (they are small by design). - """ - from dagloader._io import key_backings - - # a rep backing is a dense array (ndarray / zarr array / scipy sparse) OR an anndata sparse zarr - # *group* (CSR: data/indices/indptr sub-arrays) — the latter has no `.shape` and needs a sparse reader. - def _rows(b, loc: np.ndarray) -> np.ndarray: - if hasattr(b, "shape"): - sub = b.oindex[loc] if hasattr(b, "oindex") else b[loc] - else: # sparse zarr group → anndata sparse dataset supports efficient row fancy-indexing - try: - from anndata.io import sparse_dataset - except ImportError: # older anndata - from anndata.experimental import sparse_dataset - sub = sparse_dataset(b)[loc] - dense = getattr(sub, "todense", None) - return np.asarray(dense()) if dense is not None else np.asarray(sub) - - ctrl_mask = obs[control_key].to_numpy().astype(bool) - ctrl_idx = np.flatnonzero(ctrl_mask) - backings = key_backings(source, key) # global-obs-order backings: 1 (AnnData) or per-dataset (collection) - offs = np.concatenate([[0], np.cumsum([_backing_nrows(b) for b in backings])]).astype(np.int64) - parts = [] - for d, b in enumerate(backings): - loc = np.sort(ctrl_idx[(ctrl_idx >= offs[d]) & (ctrl_idx < offs[d + 1])] - offs[d]) - if loc.size: - parts.append(_rows(b, loc)) - rep = np.concatenate(parts, axis=0) if parts else np.empty((0, _backing_ncols(backings[0])), dtype=np.float32) - ctrl_obs = obs.loc[ctrl_mask, [*cols, control_key]].reset_index(drop=True) - order = ctrl_obs.sort_values(list(cols), kind="stable").index.to_numpy() # contiguous runs for chunk_size>1 - ctrl_obs, rep = ctrl_obs.iloc[order].reset_index(drop=True), rep[order] - if key == "X": - return ad.AnnData(X=rep, obs=ctrl_obs) # construct WITH X so n_var is inferred (not 0) - ctrl_ad = ad.AnnData(obs=ctrl_obs) # obsm-only rep; X unused (n_var=0 is fine, the node reads obsm/) - ctrl_ad.obsm[key.split("/", 1)[1]] = rep - return ctrl_ad - - def build_annbatch_training( source: Container, *, @@ -153,10 +99,11 @@ def build_annbatch_training( ``rep_dict`` holds the covariate embedding tables (as ``adata.uns`` would); pass :obj:`None` when the primary covariate is categorical (one-hot). - ``control_in_memory`` materializes the control cells into an in-memory ``AnnData`` (a second Scheme - source) so the matched source/control is served from RAM while the perturbed target keeps streaming - out of core — a large dataloader speedup, since controls are re-drawn every batch. Only enable it when - the controls fit in host RAM (they are the small population by design). + ``control_in_memory`` tells dagloader to materialize the control (child) node into RAM (sets + :attr:`~dagloader.Node.in_memory`; dagloader owns the read via :func:`~dagloader._io.materialize_node`), + so the matched control is served from memory while the perturbed target keeps streaming out of core — a + large dataloader speedup, since controls are re-drawn every batch. Only enable it when the controls fit + in host RAM (the small population by design). """ from dagloader import Bind, Node, Scheme, uniform from dagloader._io import key_backings, obs_columns @@ -235,23 +182,21 @@ def condition_fn(leaf: Leaf) -> dict[str, np.ndarray]: ctrl_flag = obs[control_key].to_numpy().astype(bool) pert = [tuple(r) for r in obs.loc[~ctrl_flag, list(cols)].drop_duplicates().to_numpy()] ctrl = [tuple(r) for r in obs.loc[ctrl_flag, list(cols)].drop_duplicates().to_numpy()] - # `control_in_memory`: serve the matched control from a RAM-resident AnnData (a second source) while - # the perturbed target keeps streaming out of core. The bind matches by label across sources. - if control_in_memory: - sources = {"data": source, "ctrl_mem": _materialize_controls(source, obs, cols, control_key, key)} - ctrl_node = Node("ctrl_mem", cols, key, uniform(ctrl)) - else: - sources = {"data": source} - ctrl_node = Node("data", cols, key, uniform(ctrl)) + # `control_in_memory` just tells dagloader to materialize the control (child) node into RAM — the + # perturbed target keeps streaming out of core. dagloader owns the materialization (Node.in_memory); + # the bind still matches control↔target by the context columns. scheme = Scheme( - sources=sources, - nodes={"pert": Node("data", cols, key, uniform(pert)), "ctrl": ctrl_node}, + sources={"data": source}, + nodes={ + "pert": Node("data", cols, key, uniform(pert)), + "ctrl": Node("data", cols, key, uniform(ctrl), in_memory=control_in_memory), + }, root="pert", binds=(Bind("pert", "ctrl", common=context),), seed=seed, ) - data_dim = _backing_ncols(key_backings(source, key)[0]) + data_dim = int(key_backings(source, key)[0].shape[1]) # key_backings wraps sparse groups → has .shape return AnnbatchTraining( scheme=scheme, condition_fn=condition_fn, diff --git a/src/dagloader/_eval_loader.py b/src/dagloader/_eval_loader.py index f09dcebf..b6345aec 100644 --- a/src/dagloader/_eval_loader.py +++ b/src/dagloader/_eval_loader.py @@ -23,7 +23,7 @@ from annbatch import Loader from annbatch.samplers import BoundClassSampler, SequentialClassSampler -from dagloader._io import key_backings, leaf_codes, obs_columns +from dagloader._io import key_backings, leaf_codes, materialize_node, obs_columns from dagloader._loader import _HAS_CUPY, _flat_categorical from dagloader._schema import SamplerConfig, Scheme, _weight_vector @@ -66,7 +66,9 @@ def __init__( self._pert = scheme.nodes[scheme.root] # perturbed / target self._ctrl = scheme.nodes[b.child] # control / source self._context = b.common - self._src = scheme.sources[self._ctrl.source] # same container as the pert source (one "data") + # control source honors Node.in_memory (materialize the control cells into RAM once) + ctrl_src = scheme.sources[self._ctrl.source] + self._src = materialize_node(ctrl_src, self._ctrl) if self._ctrl.in_memory else ctrl_src self._src_p = scheme.sources[self._pert.source] # per-node tuple-labelled categorical + weight vector (obs only). Control/perturbed cells live in @@ -112,10 +114,12 @@ def _bound(self, schedule: np.ndarray) -> BoundClassSampler: # TODO(selmanozleyen): rename to _loaders_from_node def _node_loaders(self, src, node, make_sampler) -> dict: + cfg = self._cfg + preload_to_gpu = cfg.preload_to_gpu if cfg.preload_to_gpu is not None else _HAS_CUPY # None ⇒ auto loaders = {} for key in node.keys: loader = Loader( - batch_sampler=make_sampler(), return_index=False, to="jax", preload_to_gpu=_HAS_CUPY + batch_sampler=make_sampler(), return_index=False, to=cfg.to, preload_to_gpu=preload_to_gpu ).add_datasets(key_backings(src, key)) loaders[key] = loader return loaders diff --git a/src/dagloader/_io.py b/src/dagloader/_io.py index 0d921dfa..bf25ef6a 100644 --- a/src/dagloader/_io.py +++ b/src/dagloader/_io.py @@ -16,7 +16,7 @@ from dagloader._schema import Container -__all__ = ["key_backings", "leaf_codes", "obs_columns"] +__all__ = ["key_backings", "leaf_codes", "materialize_node", "obs_columns"] def _readable(x): @@ -49,6 +49,46 @@ def key_backings(source: Container, loc: str) -> list: return [_readable(g[field][sub]) for g in source] # DatasetCollection: one backing per dataset +def _read_rows(source: Container, loc: str, row_idx: np.ndarray) -> np.ndarray: + """Densely read the global rows ``row_idx`` (ascending) of rep ``loc`` from a source's backings.""" + backings = key_backings(source, loc) # each has ``.shape`` (sparse groups already wrapped by _readable) + offs = np.concatenate([[0], np.cumsum([b.shape[0] for b in backings])]).astype(np.int64) + parts = [] + for d, b in enumerate(backings): + lb = row_idx[(row_idx >= offs[d]) & (row_idx < offs[d + 1])] - offs[d] # ascending within this dataset + if lb.size: + sub = b.oindex[lb] if hasattr(b, "oindex") else b[lb] # zarr array: orthogonal; CSRDataset/scipy: [] + dense = getattr(sub, "todense", None) + parts.append(np.asarray(dense()) if dense is not None else np.asarray(sub)) + return np.concatenate(parts, axis=0) if parts else np.empty((0, int(backings[0].shape[1])), dtype=np.float32) + + +def materialize_node(source: Container, node) -> ad.AnnData: + """Materialize a node's selected (positive-weight) cells into an in-memory (dense) ``AnnData``. + + The general "read this node's cells into RAM" op behind :attr:`~dagloader.Node.in_memory`: reads only + the rows whose leaf has positive weight — for each of the node's reps (``keys``) — and sorts them by + ``cols`` so ``chunk_size > 1`` still reads contiguous runs. Handles dense and sparse (CSR-group) + backings alike (via :func:`key_backings`). Cells must fit host RAM (the intended use is a small, + frequently re-drawn population such as matched controls). + """ + from dagloader._schema import _weight_vector + + obs = obs_columns(source, node.cols) + codes, leaves = leaf_codes(obs, node.cols) + selected = np.flatnonzero(_weight_vector(node.weights, leaves) > 0) # leaf codes with positive weight + row_idx = np.flatnonzero(np.isin(codes, selected)) # ascending global rows of the selected leaves + reps = {key: _read_rows(source, key, row_idx) for key in node.keys} + sub_obs = obs.iloc[row_idx].reset_index(drop=True) + order = sub_obs.sort_values(list(node.cols), kind="stable").index.to_numpy() # contiguous runs for chunk>1 + sub_obs, reps = sub_obs.iloc[order].reset_index(drop=True), {k: v[order] for k, v in reps.items()} + adata = ad.AnnData(X=reps["X"], obs=sub_obs) if "X" in reps else ad.AnnData(obs=sub_obs) # X-> n_var inferred + for key, v in reps.items(): + if key != "X": + adata.obsm[key.split("/", 1)[1]] = v + return adata + + def leaf_codes(obs: pd.DataFrame, cols: Sequence[str]) -> tuple[np.ndarray, list[tuple]]: """Per-cell leaf code + the ordered leaf combinations (the grouping over ``cols``).""" tuples = [tuple(row) for row in obs[list(cols)].to_numpy()] diff --git a/src/dagloader/_loader.py b/src/dagloader/_loader.py index 8627c3b5..facb44cf 100644 --- a/src/dagloader/_loader.py +++ b/src/dagloader/_loader.py @@ -20,8 +20,8 @@ from annbatch import Loader from annbatch.samplers import BoundClassSampler, ClassSampler -from dagloader._io import key_backings, leaf_codes, obs_columns -from dagloader._schema import Bind, SamplerConfig, Scheme, _weight_vector +from dagloader._io import key_backings, leaf_codes, materialize_node, obs_columns +from dagloader._schema import Bind, Container, SamplerConfig, Scheme, _weight_vector __all__ = ["DAGLoader"] @@ -66,10 +66,16 @@ def __init__( ) } + # resolve each node's source; a `Node.in_memory` node is materialized into RAM once (see _io). + self._node_src: dict[str, Container] = { + name: (materialize_node(scheme.sources[node.source], node) if node.in_memory else scheme.sources[node.source]) + for name, node in scheme.nodes.items() + } + # per-node leaf partition + weights + tuple-labelled categorical (obs only — no cell matrices) self._st: dict[str, dict] = {} for name, node in scheme.nodes.items(): - obs = obs_columns(scheme.sources[node.source], node.cols) + obs = obs_columns(self._node_src[name], node.cols) codes, leaves = leaf_codes(obs, node.cols) self._st[name] = { "node": node, @@ -178,17 +184,18 @@ def _build_samplers_and_loaders(self) -> None: def _add_node_loaders(self, name: str, make_sampler: Callable[[], ClassSampler | BoundClassSampler]) -> None: node = self._st[name]["node"] - src = self.s.sources[node.source] + src = self._node_src[name] + cfg = self._cfg[name] + preload_to_gpu = cfg.preload_to_gpu if cfg.preload_to_gpu is not None else _HAS_CUPY # None ⇒ auto self._samplers[name], self._loaders[name] = {}, {} for ki, key in enumerate(node.keys): sampler = make_sampler() return_index = name == self.s.root and ki == 0 # only for the schedule↔row alignment check - # `to="jax"` yields native jax arrays (dense via `jnp.from_dlpack`, sparse as a jax CSR) - # instead of numpy, so batches reach the jax solver without a host round-trip / numpy cast. - # With cupy present, `preload_to_gpu` keeps the array on-GPU so the yielded jax array is - # GPU-resident; without it, the array is a CPU jax array (device copy deferred to the step). + # `to` (default "jax") + `preload_to_gpu` are user-set via SamplerConfig. `to="jax"` yields + # native jax arrays (no host round-trip); `preload_to_gpu` keeps the read window on-GPU (needs + # cupy), else it defers the device copy to the step. Auto-selects cupy when unset. loader = Loader( - batch_sampler=sampler, return_index=return_index, to="jax", preload_to_gpu=_HAS_CUPY + batch_sampler=sampler, return_index=return_index, to=cfg.to, preload_to_gpu=preload_to_gpu ).add_datasets(key_backings(src, key)) self._samplers[name][key], self._loaders[name][key] = sampler, loader diff --git a/src/dagloader/_schema.py b/src/dagloader/_schema.py index 4235e259..e4bd8f5e 100644 --- a/src/dagloader/_schema.py +++ b/src/dagloader/_schema.py @@ -86,12 +86,18 @@ class Node: read back for the exact same rows, so every rep of a batch is the same cells. weights ``{combo: weight}``; a combination absent or with weight 0 is excluded (= the selection). + in_memory + If :obj:`True`, the loader materializes this node's selected (positive-weight) cells into an + in-memory ``AnnData`` once and streams them from RAM instead of re-reading the source every batch + (see :func:`~dagloader._io.materialize_node`). Use for a small, frequently re-drawn population + (e.g. matched controls). Requires those cells to fit in host RAM. """ source: str cols: tuple[str, ...] keys: str | tuple[str, ...] = "X" # one rep, or several aligned reps of the same cells weights: Weights = field(default_factory=dict) + in_memory: bool = False # materialize this node's selected cells into RAM (see dagloader._io) def __post_init__(self) -> None: # structural checks (data-free) object.__setattr__(self, "keys", (self.keys,) if isinstance(self.keys, str) else tuple(self.keys)) @@ -155,11 +161,19 @@ class SamplerConfig: Chunks per annbatch read window. **Required** and explicit — there is no hidden default; set it deliberately (e.g. ``batch_size // chunk_size`` for one batch per window). Must be a positive multiple of ``batch_size // chunk_size``. + to + annbatch ``Loader`` output backend for the yielded batches — ``"jax"`` (default), ``"torch"``, or + :obj:`None` (annbatch's own default). Forwarded verbatim to :class:`annbatch.Loader`. + preload_to_gpu + Whether annbatch keeps the read window on-GPU (needs ``cupy``). :obj:`None` (default) auto-selects + it from cupy availability; pass ``True``/``False`` to force it. """ batch_size: int chunk_size: int preload_nchunks: int + to: str = "jax" + preload_to_gpu: bool | None = None @dataclass(frozen=True) diff --git a/tests/dagloader/test_coherence.py b/tests/dagloader/test_coherence.py index 5d50a3da..4a60226f 100644 --- a/tests/dagloader/test_coherence.py +++ b/tests/dagloader/test_coherence.py @@ -109,3 +109,42 @@ def test_reps_are_aligned_same_cells(): x = np.asarray(batch["target_reps"]["X"]) rep = np.asarray(batch["target_reps"]["obsm/rep"]) np.testing.assert_array_equal(x, rep) # aligned reps must be the same cells + + +def test_materialize_node_selects_positive_weight_rows(): + # `materialize_node` reads only a node's positive-weight (here: control) cells into an in-memory AnnData + from dagloader._io import materialize_node + + adata = _adata() + cols = ("cell_line", "drug") + ctrl = [c for c in {tuple(r) for r in adata.obs[list(cols)].to_numpy()} if c[1] == "control"] + mem = materialize_node(adata, Node("data", cols, "X", uniform(ctrl))) + assert isinstance(mem, ad.AnnData) + assert (mem.obs["drug"] == "control").all() # only positive-weight (control) rows materialized + assert mem.n_obs == int((adata.obs["drug"] == "control").sum()) + assert (np.asarray(mem.X)[:, 2] == 1.0).all() # X[:,2] encodes is_control + + +def test_in_memory_node_materialized(): + # Node.in_memory → the loader materializes that node into RAM (served from memory, not re-read each + # batch). SamplerConfig also carries the user-set `to` / `preload_to_gpu` (exercised here). + adata = _adata() + cols = ("cell_line", "drug") + combos = {tuple(r) for r in adata.obs[list(cols)].to_numpy()} + pert = [c for c in combos if c[1] != "control"] + ctrl = [c for c in combos if c[1] == "control"] + scheme = Scheme( + sources={"data": adata}, + nodes={ + "pert": Node("data", cols, "X", uniform(pert)), + "ctrl": Node("data", cols, "X", uniform(ctrl), in_memory=True), + }, + root="pert", + binds=(Bind("pert", "ctrl", common=("cell_line",)),), + seed=0, + ) + cfg = SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8, to="jax", preload_to_gpu=False) + dl = DAGLoader(scheme, cfg, condition_fn=_condition_fn) + assert isinstance(dl._node_src["ctrl"], ad.AnnData) # ctrl node materialized into RAM + batch = next(iter(dl)) + assert (np.asarray(batch["source"])[:, 2] == 1.0).all() # source = matched control cells (from RAM) diff --git a/tests/model/test_annbatch_ooc.py b/tests/model/test_annbatch_ooc.py index aa8c4feb..204f7d9f 100644 --- a/tests/model/test_annbatch_ooc.py +++ b/tests/model/test_annbatch_ooc.py @@ -11,6 +11,7 @@ import numpy as np import pandas as pd import pytest +import scipy.sparse as sp pytest.importorskip("annbatch") @@ -29,7 +30,7 @@ } -def _adata(*, interleaved: bool, n_per_combo=40, drugs=("control", "d1", "d2", "d3"), lines=("A", "B")): +def _adata(*, interleaved: bool, n_per_combo=40, drugs=("control", "d1", "d2", "d3"), lines=("A", "B"), sparse=False): rng = np.random.default_rng(0) rows = [(cl, dr) for cl in lines for dr in drugs for _ in range(n_per_combo)] if interleaved: @@ -38,12 +39,12 @@ def _adata(*, interleaved: bool, n_per_combo=40, drugs=("control", "d1", "d2", " obs["control"] = obs["drug"] == "control" obs.index = obs.index.astype(str) x = rng.normal(size=(len(obs), 5)).astype("float32") - return ad.AnnData(X=x, obs=obs) + return ad.AnnData(X=sp.csr_matrix(x) if sparse else x, obs=obs) -def _collection(tmp_path, *, grouped: bool) -> DatasetCollection: +def _collection(tmp_path, *, grouped: bool, sparse=False) -> DatasetCollection: h5 = tmp_path / "a.h5ad" - _adata(interleaved=not grouped).write_h5ad(h5) + _adata(interleaved=not grouped, sparse=sparse).write_h5ad(h5) dc = DatasetCollection(str(tmp_path / "c.zarr"), mode="a") if grouped: # sort by the grouping columns on add → contiguous category runs dc.add_adatas([str(h5)], groupby=["cell_line", "drug"], shuffle=False) @@ -138,6 +139,31 @@ def test_fragmented_collection_chunked_ok(self, tmp_path): ) assert cf._dataloader.sample()["tgt_cell_data"].shape == (8, 5) + def test_sparse_collection_prepares(self, tmp_path): + # a SPARSE-X collection stores X as a zarr *group* (no .shape); key_backings must wrap it so + # annbatch's add_datasets accepts it — regression for the "'Group' has no attribute 'shape'" error. + # Construction (which runs add_datasets) is the assertion here; reading sparse batches needs cupy. + cf = cellflow.model.CellFlowAnnbatch() + cf.prepare_data( + source=_collection(tmp_path, grouped=True, sparse=True), + sampler_config=SamplerConfig(batch_size=16, chunk_size=1, preload_nchunks=16), + **_PREP, + ) + assert cf._data_dim == 5 # DAGLoader built (add_datasets accepted the sparse-group backing) + + def test_control_in_memory_materializes(self, tmp_path): + # control_in_memory tells dagloader to materialize the ctrl node into RAM (Node.in_memory); + # prepare_data building the loader runs materialize_node over the (sparse) collection. + cf = cellflow.model.CellFlowAnnbatch() + cf.prepare_data( + source=_collection(tmp_path, grouped=True, sparse=True), + sampler_config=SamplerConfig(batch_size=16, chunk_size=1, preload_nchunks=16), + control_in_memory=True, + **_PREP, + ) + assert cf._scheme.nodes["ctrl"].in_memory is True # cellflow flagged it + assert isinstance(cf._dataloader._loader._node_src["ctrl"], ad.AnnData) # dagloader materialized it + class TestChunkableCheck: """`assert_source_chunkable` matches annbatch's rule: runs >= chunk_size, fragmentation allowed.""" From 07360dde97da7b526bf6cd1dd34d6894e57ba2cc Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Wed, 15 Jul 2026 15:33:24 +0200 Subject: [PATCH 34/35] from_path --- src/dagloader/_io.py | 69 ++++++++++++++++++++++++++++--- src/dagloader/_loader.py | 6 +-- src/dagloader/_schema.py | 43 ++++++++++++++++++- tests/dagloader/test_coherence.py | 2 +- tests/model/test_annbatch_ooc.py | 2 +- 5 files changed, 110 insertions(+), 12 deletions(-) diff --git a/src/dagloader/_io.py b/src/dagloader/_io.py index bf25ef6a..a659cddd 100644 --- a/src/dagloader/_io.py +++ b/src/dagloader/_io.py @@ -8,6 +8,7 @@ from __future__ import annotations +import os from collections.abc import Sequence import anndata as ad @@ -16,7 +17,7 @@ from dagloader._schema import Container -__all__ = ["key_backings", "leaf_codes", "materialize_node", "obs_columns"] +__all__ = ["key_backings", "leaf_codes", "load_backed_adata", "materialize_node", "obs_columns", "open_source"] def _readable(x): @@ -37,10 +38,12 @@ def key_backings(source: Container, loc: str) -> list: """The array(s) backing rep ``loc`` for a source, ready to feed one annbatch ``add_datasets``. annbatch's ``add_datasets`` concatenates on the obs axis and needs equal feature dims, so each rep - gets its own loader over its own array(s). For a ``DatasetCollection`` the per-dataset arrays are - gathered in order (matching the global row layout); a sparse rep's zarr group is wrapped so it is - readable (see :func:`_readable`). + gets its own loader over its own array(s). For a ``DatasetCollection`` (or a ``list`` of AnnData — + one backing per adata) the per-dataset arrays are gathered in order (matching the global row + layout); a sparse rep's zarr group is wrapped so it is readable (see :func:`_readable`). """ + if isinstance(source, list): # list of (backed) AnnData: one backing per adata, in list order + return [b for a in source for b in key_backings(a, loc)] if loc == "X": return [source.X] if isinstance(source, ad.AnnData) else [_readable(g["X"]) for g in source] field, sub = loc.split("/", 1) # "obsm/X_pca" | "layers/log1p" @@ -98,7 +101,63 @@ def leaf_codes(obs: pd.DataFrame, cols: Sequence[str]) -> tuple[np.ndarray, list def obs_columns(source: Container, cols: Sequence[str]) -> pd.DataFrame: - """Obs columns from either container (AnnData attr vs DatasetCollection reader) — no cell matrices.""" + """Obs columns from any container (AnnData attr vs DatasetCollection reader vs list) — no cell matrices.""" + if isinstance(source, list): # list of AnnData: concatenate obs in list order (= key_backings order) + return pd.concat([obs_columns(a, cols) for a in source], ignore_index=True) if isinstance(source, ad.AnnData): return source.obs[list(cols)] return source.obs(columns=list(cols)) # DatasetCollection + + +def _backed(x): + """A zarr rep as a readable backing: dense zarr array passes through; a sparse group is wrapped.""" + import zarr + + return x if isinstance(x, zarr.Array) else ad.io.sparse_dataset(x) + + +def load_backed_adata(g, *, keys: Sequence[str], cols: Sequence[str] = ()) -> ad.AnnData: + """Open a zarr adata group as a (backed) AnnData, reading only the reps in ``keys`` and obs ``cols``. + + ``X`` is read as a backed sparse dataset or lazy dense zarr array; ``obsm/`` / ``layers/`` reps + likewise; ``var`` is reduced to its index and ``obs`` to ``cols`` (all of obs if ``cols`` is empty). + Only the reps a node actually streams are materialized, so unused representations are never touched. + """ + var = g["var"] + obs = ad.io.read_elem(g["obs"]) + kw: dict = { + "obs": obs[list(cols)] if cols else obs, + "var": pd.DataFrame(index=pd.Index(ad.io.read_elem(var[var.attrs.get("_index")]))), + } + if "X" in keys: + kw["X"] = _backed(g["X"]) + adata = ad.AnnData(**kw) + for key in keys: + if key == "X": + continue + field, sub = key.split("/", 1) # "obsm/X_pca" | "layers/log1p" + getattr(adata, field)[sub] = _backed(g[field][sub]) + return adata + + +def open_source(src, *, keys: Sequence[str], cols: Sequence[str] = ()) -> Container: + """Resolve one :class:`~dagloader.Scheme` source value to a :data:`~dagloader.Container`. + + An in-memory ``AnnData`` or ``DatasetCollection`` passes through unchanged. A single path is opened as + zarr and auto-detected: an annbatch collection root (``encoding-type`` ``"annbatch-preshuffled"``) + becomes a ``DatasetCollection``; otherwise it is a single adata read backed via :func:`load_backed_adata`. + A list of paths becomes a list of backed AnnData (the loader feeds one backing per adata to + ``add_datasets``). Only the reps in ``keys`` and obs ``cols`` are read (see :func:`load_backed_adata`). + """ + import zarr + from annbatch import DatasetCollection + + if isinstance(src, (ad.AnnData, DatasetCollection)): + return src + if isinstance(src, (str, os.PathLike)): + g = zarr.open_group(src, mode="r") + if g.attrs.get("encoding-type") == "annbatch-preshuffled": + return DatasetCollection(src, mode="r") + return load_backed_adata(g, keys=keys, cols=cols) + # list/sequence of adata zarr paths (or already-open AnnData) → list of backed AnnData + return [a if isinstance(a, ad.AnnData) else load_backed_adata(zarr.open_group(a, mode="r"), keys=keys, cols=cols) for a in src] diff --git a/src/dagloader/_loader.py b/src/dagloader/_loader.py index facb44cf..9c4bda8f 100644 --- a/src/dagloader/_loader.py +++ b/src/dagloader/_loader.py @@ -67,7 +67,7 @@ def __init__( } # resolve each node's source; a `Node.in_memory` node is materialized into RAM once (see _io). - self._node_src: dict[str, Container] = { + self._nodes: dict[str, Container] = { name: (materialize_node(scheme.sources[node.source], node) if node.in_memory else scheme.sources[node.source]) for name, node in scheme.nodes.items() } @@ -75,7 +75,7 @@ def __init__( # per-node leaf partition + weights + tuple-labelled categorical (obs only — no cell matrices) self._st: dict[str, dict] = {} for name, node in scheme.nodes.items(): - obs = obs_columns(self._node_src[name], node.cols) + obs = obs_columns(self._nodes[name], node.cols) codes, leaves = leaf_codes(obs, node.cols) self._st[name] = { "node": node, @@ -184,7 +184,7 @@ def _build_samplers_and_loaders(self) -> None: def _add_node_loaders(self, name: str, make_sampler: Callable[[], ClassSampler | BoundClassSampler]) -> None: node = self._st[name]["node"] - src = self._node_src[name] + src = self._nodes[name] cfg = self._cfg[name] preload_to_gpu = cfg.preload_to_gpu if cfg.preload_to_gpu is not None else _HAS_CUPY # None ⇒ auto self._samplers[name], self._loaders[name] = {}, {} diff --git a/src/dagloader/_schema.py b/src/dagloader/_schema.py index e4bd8f5e..0a558edd 100644 --- a/src/dagloader/_schema.py +++ b/src/dagloader/_schema.py @@ -16,13 +16,15 @@ from collections.abc import Mapping, Sequence from dataclasses import dataclass, field +from os import PathLike import anndata as ad import numpy as np from annbatch import DatasetCollection -# A cell source: an in-memory AnnData or an out-of-core annbatch DatasetCollection. -Container = ad.AnnData | DatasetCollection +# A cell source: an in-memory/backed AnnData, an out-of-core annbatch DatasetCollection, or a list of +# AnnData (streamed as one logical source — one annbatch backing per adata, in list order). +Container = ad.AnnData | DatasetCollection | list[ad.AnnData] # A sampling scheme is just a mapping {combination -> weight}. A combination absent from the mapping # (or with weight 0) is excluded — that IS the selection. ``uniform`` / ``frequency`` / @@ -211,6 +213,43 @@ class Scheme: seed: int binds: tuple[Bind, ...] = () + @classmethod + def from_paths( + cls, + sources: Mapping[str, Container | str | PathLike | Sequence[str | PathLike]], + nodes: Mapping[str, Node], + root: str, + seed: int, + binds: tuple[Bind, ...] = (), + ) -> Scheme: + """Build a :class:`Scheme` where a source may be given as a zarr path (or list of paths). + + Same signature as the constructor, but each ``sources`` value may additionally be: + + * a **path** to a single zarr adata → read backed (only the reps the referencing nodes use); + * a **path** to an annbatch collection root → opened as a :class:`~annbatch.DatasetCollection` + (auto-detected from its ``encoding-type``); + * a **list of paths** to zarr adatas → a list of backed AnnData streamed as one source (one + annbatch backing per adata, in list order). + + An already-constructed :data:`Container` (AnnData / DatasetCollection / list of AnnData) passes + through unchanged. Everything on disk is expected in **zarr**. Only the ``keys`` and ``cols`` the + nodes referencing a source actually need are read (see :func:`~dagloader._io.open_source`). + """ + from dagloader._io import open_source + + keys_by_src: dict[str, set[str]] = {name: set() for name in sources} + cols_by_src: dict[str, set[str]] = {name: set() for name in sources} + for node in nodes.values(): + if node.source in keys_by_src: # unknown sources are reported by __post_init__ + keys_by_src[node.source].update(node.keys) + cols_by_src[node.source].update(node.cols) + resolved = { + name: open_source(src, keys=keys_by_src[name], cols=cols_by_src[name]) + for name, src in sources.items() + } + return cls(sources=resolved, nodes=nodes, root=root, seed=seed, binds=binds) + def __post_init__(self) -> None: # structural: rooted tree + references if self.root not in self.nodes: raise ValueError(f"root {self.root!r} not in nodes.") diff --git a/tests/dagloader/test_coherence.py b/tests/dagloader/test_coherence.py index 4a60226f..44c62c6e 100644 --- a/tests/dagloader/test_coherence.py +++ b/tests/dagloader/test_coherence.py @@ -145,6 +145,6 @@ def test_in_memory_node_materialized(): ) cfg = SamplerConfig(batch_size=8, chunk_size=1, preload_nchunks=8, to="jax", preload_to_gpu=False) dl = DAGLoader(scheme, cfg, condition_fn=_condition_fn) - assert isinstance(dl._node_src["ctrl"], ad.AnnData) # ctrl node materialized into RAM + assert isinstance(dl._nodes["ctrl"], ad.AnnData) # ctrl node materialized into RAM batch = next(iter(dl)) assert (np.asarray(batch["source"])[:, 2] == 1.0).all() # source = matched control cells (from RAM) diff --git a/tests/model/test_annbatch_ooc.py b/tests/model/test_annbatch_ooc.py index 204f7d9f..4e023094 100644 --- a/tests/model/test_annbatch_ooc.py +++ b/tests/model/test_annbatch_ooc.py @@ -162,7 +162,7 @@ def test_control_in_memory_materializes(self, tmp_path): **_PREP, ) assert cf._scheme.nodes["ctrl"].in_memory is True # cellflow flagged it - assert isinstance(cf._dataloader._loader._node_src["ctrl"], ad.AnnData) # dagloader materialized it + assert isinstance(cf._dataloader._loader._nodes["ctrl"], ad.AnnData) # dagloader materialized it class TestChunkableCheck: From 5a01025c048c21e876e1afadcab4a762c344fcbf Mon Sep 17 00:00:00 2001 From: "selman.ozleyen" Date: Wed, 15 Jul 2026 15:58:55 +0200 Subject: [PATCH 35/35] update --- pyproject.toml | 8 +- tests/dagloader/test_from_paths.py | 156 +++++++++++++++++++++++++++++ 2 files changed, 163 insertions(+), 1 deletion(-) create mode 100644 tests/dagloader/test_from_paths.py diff --git a/pyproject.toml b/pyproject.toml index aeeac53e..12b09e61 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,7 +31,7 @@ classifiers = [ dynamic = [ "version" ] dependencies = [ "adjusttext", - "anndata", + "anndata>=0.13", # lazy read accessors (read_lazy) — the loader reads reps via AnnData, not key_backings "cloudpickle", "coverage", "dask", @@ -189,6 +189,12 @@ markers = [ "slow: marks tests as slow (deselect with '-m \"not slow\"')", "internet: marks tests that require internet access (deselect with '-m \"not internet\"')", ] +filterwarnings = [ + # jaxopt is unmaintained and warns on import; it is pulled in transitively by ott-jax + # (ott.geometry.costs imports it on every version, incl. main) — not by cellflow. Nothing to fix + # on our side, so silence the noise rather than fail on it. + "ignore:JAXopt is no longer maintained:DeprecationWarning", +] [tool.coverage.run] branch = true diff --git a/tests/dagloader/test_from_paths.py b/tests/dagloader/test_from_paths.py new file mode 100644 index 00000000..0478a916 --- /dev/null +++ b/tests/dagloader/test_from_paths.py @@ -0,0 +1,156 @@ +"""``Scheme.from_paths`` — resolving zarr paths (single adata / collection root / list) to sources. + +Covers the three source-value shapes the classmethod adds on top of the constructor, the auto-detection +of a single path (adata vs annbatch collection root), the "load only the reps the nodes use" contract, +and that a resolved scheme streams matched batches end-to-end just like a constructed one. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +pytest.importorskip("annbatch") + +import warnings + +import anndata as ad +import scipy.sparse as sp +from annbatch import DatasetCollection + +from dagloader import Bind, DAGLoader, Node, SamplerConfig, Scheme, uniform +from dagloader._io import load_backed_adata, open_source + +LINES = ("A", "B") +DRUGS = ("control", "d1", "d2") +COLS = ("cell_line", "drug") + + +def _adata(n_per_combo: int = 16, seed: int = 0) -> ad.AnnData: + rng = np.random.default_rng(seed) + rows = [(cl, dr) for cl in LINES for dr in DRUGS for _ in range(n_per_combo)] + obs = pd.DataFrame(rows, columns=list(COLS)) + obs["extra"] = "unused" # an obs column no node references — must not be required + obs.index = obs.index.astype(str) + a = ad.AnnData(X=sp.csr_matrix(rng.random((len(obs), 5), dtype="float32")), obs=obs) + a.obsm["emb"] = rng.random((len(obs), 3), dtype="float32") + a.layers["log1p"] = sp.csr_matrix(rng.random((len(obs), 5), dtype="float32")) + return a + + +def _write_zarr(adata: ad.AnnData, path) -> str: + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # zarr v2/v3 default-format UserWarning + adata.write_zarr(str(path)) + return str(path) + + +def _weights(): + combos = {(cl, dr) for cl in LINES for dr in DRUGS} + pert = uniform([c for c in combos if c[1] != "control"]) + ctrl = uniform([c for c in combos if c[1] == "control"]) + return pert, ctrl + + +def _cfg() -> SamplerConfig: + return SamplerConfig(batch_size=16, chunk_size=16, preload_nchunks=1, to=None) + + +# ── load_backed_adata: only the requested reps/cols are materialized ─────────────────────────── + + +def test_load_backed_adata_reads_only_requested_keys(tmp_path): + g = _open_group(_write_zarr(_adata(), tmp_path / "a.zarr")) + backed = load_backed_adata(g, keys=("X", "obsm/emb"), cols=COLS) + + assert isinstance(backed.X, ad.abc.CSRDataset) # sparse rep stays backed (not read into RAM) + assert list(backed.obsm) == ["emb"] # obsm/emb requested → present + assert "log1p" not in backed.layers # layers/log1p not requested → never touched + assert list(backed.obs.columns) == list(COLS) # obs reduced to the requested cols + np.testing.assert_array_equal(backed.obsm["emb"].shape, (backed.n_obs, 3)) + + +def test_load_backed_adata_layers_and_no_x(tmp_path): + g = _open_group(_write_zarr(_adata(), tmp_path / "a.zarr")) + backed = load_backed_adata(g, keys=("layers/log1p",), cols=("cell_line",)) + + assert backed.X is None # X not requested + assert "log1p" in backed.layers # requested layer present + assert list(backed.obs.columns) == ["cell_line"] + + +def _open_group(path): + import zarr + + return zarr.open_group(path, mode="r") + + +# ── open_source / from_paths: the three source-value shapes ──────────────────────────────────── + + +def test_from_paths_single_adata_path_autodetects_adata(tmp_path): + pert, ctrl = _weights() + p = _write_zarr(_adata(), tmp_path / "a.zarr") + nodes = {"pert": Node("data", COLS, "X", pert), "ctrl": Node("data", COLS, ("X", "obsm/emb"), ctrl)} + s = Scheme.from_paths(sources={"data": p}, nodes=nodes, root="pert", seed=0, binds=(Bind("pert", "ctrl", ("cell_line",)),)) + + src = s.sources["data"] + assert isinstance(src, ad.AnnData) + assert isinstance(src.X, ad.abc.CSRDataset) # backed, not in-memory + assert list(src.obsm) == ["emb"] # union of the nodes' keys → only obsm/emb + assert "log1p" not in src.layers + + +def test_from_paths_collection_root_autodetects_dataset_collection(tmp_path): + pert, ctrl = _weights() + ap = _write_zarr(_adata(), tmp_path / "a.zarr") + cp = str(tmp_path / "coll.zarr") + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + DatasetCollection(cp, mode="a").add_adatas([ap], groupby=list(COLS), shuffle=False) + + nodes = {"pert": Node("data", COLS, "X", pert), "ctrl": Node("data", COLS, "X", ctrl)} + s = Scheme.from_paths(sources={"data": cp}, nodes=nodes, root="pert", seed=0, binds=(Bind("pert", "ctrl", ("cell_line",)),)) + assert isinstance(s.sources["data"], DatasetCollection) + + +def test_from_paths_list_of_paths_gives_list_of_backed_adata(tmp_path): + pert, ctrl = _weights() + paths = [_write_zarr(_adata(seed=i), tmp_path / f"a{i}.zarr") for i in range(2)] + nodes = {"pert": Node("data", COLS, "X", pert), "ctrl": Node("data", COLS, "X", ctrl)} + s = Scheme.from_paths(sources={"data": paths}, nodes=nodes, root="pert", seed=0, binds=(Bind("pert", "ctrl", ("cell_line",)),)) + + src = s.sources["data"] + assert isinstance(src, list) and len(src) == 2 + assert all(isinstance(a, ad.AnnData) for a in src) + + +def test_from_paths_passes_through_constructed_containers(tmp_path): + pert, ctrl = _weights() + adata = _adata() # already in-memory AnnData + nodes = {"pert": Node("data", COLS, "X", pert), "ctrl": Node("data", COLS, "X", ctrl)} + s = Scheme.from_paths(sources={"data": adata}, nodes=nodes, root="pert", seed=0, binds=(Bind("pert", "ctrl", ("cell_line",)),)) + assert s.sources["data"] is adata # unchanged + + +# ── end-to-end: a from_paths scheme streams matched batches ──────────────────────────────────── + + +@pytest.mark.parametrize( + "make_source", + [ + pytest.param(lambda tp: _write_zarr(_adata(), tp / "a.zarr"), id="single-path"), + pytest.param(lambda tp: [_write_zarr(_adata(seed=i), tp / f"a{i}.zarr") for i in range(2)], id="list-of-paths"), + ], +) +def test_from_paths_streams_matched_batches(tmp_path, make_source): + pert, ctrl = _weights() + src = make_source(tmp_path) + nodes = {"pert": Node("data", COLS, "X", pert), "ctrl": Node("data", COLS, ("X", "obsm/emb"), ctrl)} + s = Scheme.from_paths(sources={"data": src}, nodes=nodes, root="pert", seed=0, binds=(Bind("pert", "ctrl", ("cell_line",)),)) + + batch = next(iter(DAGLoader(s, _cfg()))) + assert batch["target"].shape == (16, 5) + assert batch["source"].shape == (16, 5) # matched control rows + assert batch["source_reps"]["obsm/emb"].shape == (16, 3) # aligned obsm rep of the same cells