Merge branch 'main' of github.com:mackelab/labproject into main

.github/workflows/push_to_overleaf.yml

@@ -5,7 +5,7 @@ on:
       - main
 jobs:
   build:
-    runs-on: ubuntu-latest
+    runs-on: self-hosted
     steps:
       - name: Checkout Overleaf project
         run: |
@@ -45,4 +45,10 @@ jobs:
             else
               git commit -m "Update figures automatically"
               git push
-            fi
+            fi
+      - name: Clean up - delete repositories
+        run: |
+          cd ${{ github.workspace }}
+          cd ..
+          rm -rf overleaf/
+          rm -rf github/

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/psf/black
-    rev: stable
+    rev: 24.1.1
     hooks:
       - id: black
         args: ['--line-length=100']

README.md

@@ -9,9 +9,7 @@ conda create -n labproject python=3.9
 conda activate labproject
 
 # install labproject package with dependencies
-pip install -e .
-# if you want to edit the tutorials, install the docs dependencies
-pip install -e ".[docs]"
+pip install -e ".[dev,docs]"
 
 # install pre-commit hooks for black auto-formatting
 pre-commit install

labproject/data.py

@@ -1,5 +1,7 @@
 import torch
+
 torch.manual_seed(0)
 
+
 def random_dataset(n=1000, d=10):
     return torch.randn(n, d)

labproject/metrics/sliced_wasserstein.py

@@ -1,8 +1,10 @@
 # STOLEN from Julius: https://github.com/mackelab/wasserstein_source/blob/main/wasser/sliced_wasserstein.py
 
 import numpy as np
+
 np.random.seed(0)
 import torch
+
 torch.manual_seed(0)
 
 
@@ -19,8 +21,7 @@ def rand_projections(embedding_dim, num_samples: int):
     """
 
     projection = [
-        w / np.sqrt((w**2).sum())
-        for w in np.random.normal(size=(num_samples, embedding_dim))
+        w / np.sqrt((w**2).sum()) for w in np.random.normal(size=(num_samples, embedding_dim))
     ]
     projection = np.array(projection)
     return torch.from_numpy(projection).type(torch.FloatTensor)
@@ -49,9 +50,7 @@ def sliced_wasserstein_distance(
 
     encoded_projections = encoded_samples.matmul(projections.transpose(-2, -1))
 
-    distribution_projections = distribution_samples.matmul(
-        projections.transpose(-2, -1)
-    )
+    distribution_projections = distribution_samples.matmul(projections.transpose(-2, -1))
 
     wasserstein_distance = (
         torch.sort(encoded_projections.transpose(-2, -1), dim=-1)[0]
@@ -64,4 +63,4 @@ def sliced_wasserstein_distance(
     # No p-th root is applied
 
     # return torch.pow(torch.mean(wasserstein_distance, dim=(-2, -1)), 1 / p)
-    return torch.mean(wasserstein_distance, dim=(-2, -1))
+    return torch.mean(wasserstein_distance, dim=(-2, -1))

labproject/plotting.py

@@ -9,6 +9,10 @@ def plot_scaling_metric_dimensionality(dimensionality, distances, metric_name, d
     plt.xlabel("Dimensionality")
     plt.ylabel(metric_name)
     plt.title(f"{metric_name} with increasing dimensionality for {dataset_name}")
-    plt.savefig(os.path.join(plots_path,
-                        f"{metric_name.lower().replace(' ', '_')}_dimensionality_{dataset_name.lower().replace(' ', '_')}.png"))
+    plt.savefig(
+        os.path.join(
+            plots_path,
+            f"{metric_name.lower().replace(' ', '_')}_dimensionality_{dataset_name.lower().replace(' ', '_')}.png",
+        )
+    )
     plt.close()

labproject/run.py

@@ -3,5 +3,7 @@
 
 print("Running experiments...")
 dimensionality, distances = scaling_sliced_wasserstein_samples()
-plot_scaling_metric_dimensionality(dimensionality, distances, "Sliced Wasserstein", "Random Dataset")
-print("Finished running experiments.")
+plot_scaling_metric_dimensionality(
+    dimensionality, distances, "Sliced Wasserstein", "Random Dataset"
+)
+print("Finished running experiments.")

pyproject.toml

@@ -7,9 +7,6 @@ name = "labproject"
 version = "0.1"
 
 dependencies = [
-    "black",
-    "pre-commit",
-    "jupyter",
     "numpy",
     "scipy",
     "matplotlib",
@@ -22,6 +19,11 @@ docs = [
     # Newer versions currently produce a "'mermaid.js' is undefined" error, see https://github.com/danielfrg/mkdocs-jupyter/issues/176
     "mkdocs-jupyter==0.24.2"
 ]
+dev = [
+    "black",
+    "pre-commit",
+    "jupyter"
+]
 
 [tool.setuptools.packages.find]
 include=["labproject"]
@@ -33,7 +35,7 @@ line-length = 100  # Example: set the line length to 100
 # Pre-commit Configuration
 [[tool.pre-commit.repos]]
 repo = "https://github.com/psf/black"
-rev = "stable"
+rev = "24.1.1"
 hooks = [
     { id = "black", language_version = "python3" }
 ]