release: v0.2.1

fix: missing seaborn dependency

.readthedocs.yaml

@@ -25,3 +25,4 @@ python:
       path: .
       extra_requirements:
         - doc
+

docs/api.md

@@ -1,45 +1,52 @@
 # API
 
-## scanvi_explainer
+```
+import scanvi_explainer
+```
 
-### Utilities: `utils`
+```{eval-rst}
+.. currentmodule:: scanvi_explainer
+
+```
+
+## SCANVIDeep
 
 ```{eval-rst}
 .. currentmodule:: scanvi_explainer
 ```
 
 ```{eval-rst}
 .. autosummary::
-    :toctree: generated
+    :toctree: generated/
     :nosignatures:
 
-    utils.train_test_group_split
+    scanvi_deep.SCANVIDeep
 ```
 
-### Plots: `plots`
+## Utilities
 
 ```{eval-rst}
 .. currentmodule:: scanvi_explainer
 ```
 
 ```{eval-rst}
 .. autosummary::
-    :toctree: generated
     :nosignatures:
+    :toctree: generated/
 
-    plots.feature_plot
+    utils.train_test_group_split
 ```
 
-## SCANVIDeep
+## Plots
 
 ```{eval-rst}
 .. currentmodule:: scanvi_explainer
 ```
 
 ```{eval-rst}
 .. autosummary::
-    :toctree: generated
+    :toctree: generated/
     :nosignatures:
 
-    scanvi_deep
+    plots.feature_plot
 ```

docs/conf.py

@@ -123,5 +123,7 @@
 nitpick_ignore = [
     # If building the documentation fails because of a missing link that is outside your control,
     # you can add an exception to this list.
-    ("optional"),
+    ("py:class", "type"),
+    ("py:class", "scvi.model.SCANVI"),
+    ("py:class", "scvi.model._scanvi.SCANVI"),
 ]

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "scanvi-explainer"
-version = "0.2.0"
+version = "0.2.1"
 description = "Exapliner of scANVI using SHAP"
 authors = [
     {name = "Martin Proks", email = "[email protected]"},
@@ -17,6 +17,8 @@ keywords = ["shap", "scanvi", "explainer", "interpretability"]
 dependencies = [
     "anndata",
     "rich",
+    "seaborn",
+    "scvi-tools",
     "shap>=0.41.0",
 ]
 classifiers = [
@@ -36,10 +38,10 @@ Issues = "https://github.com/brickmanlab/scanvi-explainer/issues"
 Changelog = "https://github.com/brickmanlab/scanvi-explainer/blob/master/CHANGELOG.md"
 
 [project.optional-dependencies]
-dev = ["ruff", "scvi-tools", "huggingface_hub"]
+dev = ["ruff", "huggingface_hub"]
 doc = [
   "setuptools",
-  "sphinx",
+  "sphinx>=4.0",
   "myst-nb>=1.1",
   "sphinx-book-theme>=1",
   "sphinxcontrib-bibtex>=1"

src/scanvi_explainer/__init__.py

@@ -6,6 +6,6 @@
     raise ImportError("Missing torch package! Run pip install torch")
 
 if not importlib.util.find_spec("scvi"):
-    raise ImportError("Missing torch package! Run pip install torch")
+    raise ImportError("Missing torch package! Run pip install scvi-tools")
 
 __all__ = ["SCANVIDeep", "utils", "plots"]

src/scanvi_explainer/plots.py

@@ -1,5 +1,5 @@
 import matplotlib.pyplot as plt
-import numpy.typing as npt
+import numpy as np
 import pandas as pd
 import seaborn as sns
 from scvi import REGISTRY_KEYS
@@ -9,19 +9,19 @@
 
 def feature_plot(
     explainer: SCANVIDeep,
-    shap_values: npt.NDArray,
+    shap_values: np.ndarray,
     subset: bool = False,
     top_n: int = 10,
 ) -> None:
     """Prints feature contribution (absolute mean SHAP value) for each cell type (top 10).
 
     Parameters
     ----------
-    explainer : SCANVIDeep
-        SCANVIDeep explainer
-    shap_values : npt.NDArray
-        Expected SHAP values
-    subset : bool, optional
+    explainer
+        :class:`~scanvi_explainer.scanvi_deep.SCANVIDeep` explainer
+    shap_values
+        :class:`~numpy.ndarray` Expected SHAP values
+    subset
         When set to true, calculate contribution by subsetting for test cells which belong to that
         particual classifier.
         When set to false, be generic and return contributing features even when testing set has

src/scanvi_explainer/scanvi_deep.py

@@ -5,7 +5,7 @@
 import rich
 from packaging import version
 from scvi import REGISTRY_KEYS
-from scvi.model._scanvi import SCANVI
+from scvi.model import SCANVI
 from shap import Explainer
 from torch import Tensor
 from tqdm.auto import tqdm
@@ -20,7 +20,7 @@ class SCANVIDeep(Explainer):
 
     Parameters
     ----------
-    Explainer : type[Explainer]
+    Explainer
         Main Explainer class from shap package
     """
 
@@ -36,12 +36,12 @@ def __init__(
 
         Parameters
         ----------
-        model : SCANVI
-            Trained scANVI model
-        train_size : float, optional
-            Training size (background), by default 0.8
-        batch_size : int, optional
-            Number of cells used from each group, by default 128
+        model
+            Trained :class:`~scvi.model.SCANVI` model
+        train_size
+            :obj:`float` Training size (background), by default 0.8
+        batch_size
+            :obj:`int` Number of cells used from each group, by default 128
             To ignore the batch_size subsetting, set batch_size=-1
         """
         import torch

src/scanvi_explainer/utils.py

@@ -4,7 +4,7 @@
 import numpy as np
 import torch
 from scvi import REGISTRY_KEYS
-from scvi.model._scanvi import SCANVI
+from scvi.model import SCANVI
 from sklearn.model_selection import train_test_split
 
 
@@ -14,7 +14,7 @@ def get_labels_key(model: SCANVI) -> str:
     Parameters
     ----------
     model : SCANVI
-        SCANVI model
+        :class:`~scvi.model.SCANVI` model
 
     Returns
     -------
@@ -29,8 +29,8 @@ def get_layer_key(model: SCANVI) -> str:
 
     Parameters
     ----------
-    model : SCANVI
-        SCANVI model
+    model
+        :class:`~scvi.model.SCANVI` model
 
     Returns
     -------
@@ -56,14 +56,14 @@ def train_test_group_split(
 
     Parameters
     ----------
-    adata : anndata.AnnData
-        Annotated dataset
+    adata
+        :class:`~anndata.AnnData` Annotated dataset
     groupby : str
         Column in metadata by which the dataset should be split by
-    train_size : float, optional
-        Training size (background), by default 0.8
-    batch_size : int, optional
-        Number of cells used from each group, by default 128
+    train_size
+        :obj:`float` Training size (background), by default 0.8
+    batch_size
+        :obj:`int` Number of cells used from each group, by default 128
 
     Returns
     -------