Drop Python 3.8 and fix one-hot error (#107)

* Update pyproject.toml

* Update README.md

* Update pyproject.toml

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Update pyproject.toml

* Update test.yaml

* Update pyproject.toml

* Update pyproject.toml

* Update pyproject.toml

* Update pyproject.toml

* Update pyproject.toml

* Update _utils.py

* Update CHANGELOG.md

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.github/workflows/test.yaml

@@ -20,7 +20,7 @@ jobs:
         strategy:
             fail-fast: false
             matrix:
-                python: ["3.8", "3.10"]
+                python: ["3.9", "3.10"]
                 os: [ubuntu-latest]
 
         env:

CHANGELOG.md

@@ -8,6 +8,13 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## 0.4.0 (2023-09-19)
+
+-   Drop Python 3.8 ([#107][])
+-   Fix jax one-hot error ([#107][])
+
+[#107]: https://github.com/YosefLab/scib-metrics/pull/107
+
 ## 0.3.3 (2023-03-29)
 
 ### Fixed

README.md

@@ -19,7 +19,7 @@ Please refer to the [documentation][link-docs].
 
 ## Installation
 
-You need to have Python 3.8 or newer installed on your system. If you don't have
+You need to have Python 3.9 or newer installed on your system. If you don't have
 Python installed, we recommend installing [Miniconda](https://docs.conda.io/en/latest/miniconda.html).
 
 There are several alternative options to install scib-metrics:

pyproject.toml

@@ -8,7 +8,7 @@ name = "scib-metrics"
 version = "0.3.3"
 description = "Accelerated and Python-only scIB metrics"
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.9"
 license = {file = "LICENSE"}
 authors = [
     {name = "Adam Gayoso"},
@@ -28,7 +28,7 @@ dependencies = [
     "pandas",
     "scipy",
     "scikit-learn",
-    "scanpy",
+    "scanpy>=1.9",
     "rich",
     "pynndescent",
     "igraph>0.9.0",
@@ -53,6 +53,7 @@ doc = [
     "ipython",
     "ipykernel",
     "sphinx-copybutton",
+    "numba>=0.57.1",
 ]
 test = [
     "pytest",
@@ -63,6 +64,7 @@ test = [
     # For vscode Python extension testing
     "flake8",
     "black",
+    "numba>=0.57.1",
 ]
 parallel = [
     "joblib"
@@ -95,8 +97,7 @@ xfail_strict = true
 
 [tool.ruff]
 src = ["src"]
-line-length = 119
-target-version = "py38"
+line-length = 120
 select = [
     "F",  # Errors detected by Pyflakes
     "E",  # Error detected by Pycodestyle
@@ -152,7 +153,6 @@ convention = "numpy"
 
 [tool.black]
 line-length = 120
-target-version = ['py38']
 include = '\.pyi?$'
 exclude = '''
 (

src/scib_metrics/_kbet.py

@@ -1,6 +1,6 @@
 import logging
 from functools import partial
-from typing import Tuple, Union
+from typing import Union
 
 import chex
 import jax
@@ -102,7 +102,7 @@ def kbet_per_label(
     alpha: float = 0.05,
     diffusion_n_comps: int = 100,
     return_df: bool = False,
-) -> Union[float, Tuple[float, pd.DataFrame]]:
+) -> Union[float, tuple[float, pd.DataFrame]]:
     """Compute kBET score per cell type label as in :cite:p:`luecken2022benchmarking`.
 
     This approximates the method used in the original scib package. Notably, the underlying

src/scib_metrics/_nmi_ari.py

@@ -1,6 +1,5 @@
 import logging
 import warnings
-from typing import Dict, Tuple
 
 import numpy as np
 import scanpy as sc
@@ -30,14 +29,14 @@ def _compute_nmi_ari_cluster_labels(
     X: np.ndarray,
     labels: np.ndarray,
     resolution: float = 1.0,
-) -> Tuple[float, float]:
+) -> tuple[float, float]:
     labels_pred = _compute_clustering_leiden(X, resolution)
     nmi = normalized_mutual_info_score(labels, labels_pred, average_method="arithmetic")
     ari = adjusted_rand_score(labels, labels_pred)
     return nmi, ari
 
 
-def nmi_ari_cluster_labels_kmeans(X: np.ndarray, labels: np.ndarray) -> Dict[str, float]:
+def nmi_ari_cluster_labels_kmeans(X: np.ndarray, labels: np.ndarray) -> dict[str, float]:
     """Compute nmi and ari between k-means clusters and labels.
 
     This deviates from the original implementation in scib by using k-means
@@ -69,7 +68,7 @@ def nmi_ari_cluster_labels_kmeans(X: np.ndarray, labels: np.ndarray) -> Dict[str
 
 def nmi_ari_cluster_labels_leiden(
     X: spmatrix, labels: np.ndarray, optimize_resolution: bool = True, resolution: float = 1.0, n_jobs: int = 1
-) -> Dict[str, float]:
+) -> dict[str, float]:
     """Compute nmi and ari between leiden clusters and labels.
 
     This deviates from the original implementation in scib by using leiden instead of

src/scib_metrics/benchmark/_core.py

@@ -3,7 +3,7 @@
 from dataclasses import asdict, dataclass
 from enum import Enum
 from functools import partial
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 import matplotlib
 import matplotlib.pyplot as plt
@@ -20,7 +20,7 @@
 import scib_metrics
 from scib_metrics.nearest_neighbors import NeighborsOutput, pynndescent
 
-Kwargs = Dict[str, Any]
+Kwargs = dict[str, Any]
 MetricType = Union[bool, Kwargs]
 
 _LABELS = "labels"
@@ -131,7 +131,7 @@ def __init__(
         adata: AnnData,
         batch_key: str,
         label_key: str,
-        embedding_obsm_keys: List[str],
+        embedding_obsm_keys: list[str],
         bio_conservation_metrics: Optional[BioConservation] = None,
         batch_correction_metrics: Optional[BatchCorrection] = None,
         pre_integrated_embedding_obsm_key: Optional[str] = None,

src/scib_metrics/utils/_lisi.py

@@ -1,5 +1,5 @@
 from functools import partial
-from typing import Tuple, Union
+from typing import Union
 
 import chex
 import jax
@@ -23,7 +23,7 @@ class _NeighborProbabilityState:
 
 
 @jax.jit
-def _Hbeta(knn_dists_row: jnp.ndarray, beta: float) -> Tuple[jnp.ndarray, jnp.ndarray]:
+def _Hbeta(knn_dists_row: jnp.ndarray, beta: float) -> tuple[jnp.ndarray, jnp.ndarray]:
     P = jnp.exp(-knn_dists_row * beta)
     sumP = jnp.nansum(P)
     H = jnp.where(sumP == 0, 0, jnp.log(sumP) + beta * jnp.nansum(knn_dists_row * P) / sumP)
@@ -34,7 +34,7 @@ def _Hbeta(knn_dists_row: jnp.ndarray, beta: float) -> Tuple[jnp.ndarray, jnp.nd
 @jax.jit
 def _get_neighbor_probability(
     knn_dists_row: jnp.ndarray, perplexity: float, tol: float
-) -> Tuple[jnp.ndarray, jnp.ndarray]:
+) -> tuple[jnp.ndarray, jnp.ndarray]:
     beta = 1
     betamin = -jnp.inf
     betamax = jnp.inf

src/scib_metrics/utils/_pca.py

@@ -1,4 +1,4 @@
-from typing import Optional, Tuple
+from typing import Optional
 
 import jax.numpy as jnp
 from chex import dataclass
@@ -128,7 +128,7 @@ def pca(
 @jit
 def _pca(
     X: NdArray,
-) -> Tuple[NdArray, NdArray, NdArray, NdArray, NdArray]:
+) -> tuple[NdArray, NdArray, NdArray, NdArray, NdArray]:
     """Principal component analysis.
 
     Parameters

src/scib_metrics/utils/_silhouette.py

@@ -1,5 +1,4 @@
 from functools import partial
-from typing import Tuple
 
 import jax
 import jax.numpy as jnp
@@ -13,7 +12,7 @@
 @jax.jit
 def _silhouette_reduce(
     D_chunk: jnp.ndarray, start: int, labels: jnp.ndarray, label_freqs: jnp.ndarray
-) -> Tuple[jnp.ndarray, jnp.ndarray]:
+) -> tuple[jnp.ndarray, jnp.ndarray]:
     """Accumulate silhouette statistics for vertical chunk of X.
 
     Follows scikit-learn implementation.

src/scib_metrics/utils/_utils.py

@@ -1,5 +1,5 @@
 import warnings
-from typing import Optional, Tuple
+from typing import Optional
 
 import jax
 import jax.numpy as jnp
@@ -33,7 +33,7 @@ def one_hot(y: NdArray, n_classes: Optional[int] = None) -> jnp.ndarray:
     one_hot: jnp.ndarray
         Array of shape (n_cells, n_classes).
     """
-    n_classes = n_classes or jnp.max(y) + 1
+    n_classes = n_classes or int(jax.device_get(jnp.max(y))) + 1
     return nn.one_hot(jnp.ravel(y), n_classes)
 
 
@@ -48,7 +48,7 @@ def check_square(X: ArrayLike):
         raise ValueError("X must be a square matrix")
 
 
-def convert_knn_graph_to_idx(X: csr_matrix) -> Tuple[np.ndarray, np.ndarray]:
+def convert_knn_graph_to_idx(X: csr_matrix) -> tuple[np.ndarray, np.ndarray]:
     """Convert a kNN graph to indices and distances."""
     check_array(X, accept_sparse="csr")
     check_square(X)