Clean up packages and switch to Trust-Region optimization

README.md

@@ -1 +1,3 @@
 # cellcommunication-pf2
+
+Fully single-cell analysis of cell-to-cell communication.

cellcommunicationpf2/cc_pf2.py

@@ -3,7 +3,7 @@
 import pymanopt
 from pymanopt import Problem
 from pymanopt.manifolds import Stiefel
-from pymanopt.optimizers import ConjugateGradient
+from pymanopt.optimizers import TrustRegions
 
 
 def project_data(tensor: np.ndarray, proj_matrix: np.ndarray) -> np.ndarray:
@@ -42,13 +42,12 @@ def objective_function(proj):
         problem = Problem(manifold=manifold, cost=objective_function)
 
         # Solve the problem
-        solver = ConjugateGradient(
-            verbosity=1, min_gradient_norm=1e-9, min_step_size=1e-12
+        solver = TrustRegions(
+            verbosity=0, min_gradient_norm=1e-9, min_step_size=1e-12
         )
         proj = solver.run(problem).point
 
         U, _, Vt = np.linalg.svd(proj, full_matrices=False)
-
         proj = U @ Vt
 
         projections.append(proj)

pyproject.toml

@@ -7,14 +7,8 @@ license = "MIT"
 requires-python = ">= 3.11"
 
 dependencies = [
-    "numpy>=1.21,<2.1",
+    "numpy>=2.0.2",
     "scipy>=1.12",
-    "tensorly~=0.9.0",
-    "tqdm>=4.66",
-    "cupy-cuda12x>=13.0",
-    "scikit-learn>=1.4.2",
-    "pacmap>=0.7.2",
-    "tlviz>=0.1.1",
     "pymanopt>=2.2.1",
     "autograd>=1.7.0",
 ]
@@ -33,7 +27,7 @@ build-backend = "hatchling.build"
 managed = true
 dev-dependencies = [
     "pytest>=8.0",
-    "pytest-cov>=5.0",
+    "pytest-cov>=6.0",
     "pyright>=1.1",
 ]
 

requirements-dev.lock

@@ -10,119 +10,31 @@
 #   universal: false
 
 -e file:.
-annoy==1.17.3
-    # via pacmap
 autograd==1.7.0
     # via cellcommunicationpf2
-certifi==2024.8.30
-    # via requests
-charset-normalizer==3.3.2
-    # via requests
-contourpy==1.3.0
-    # via matplotlib
 coverage==7.6.1
     # via pytest-cov
-cupy-cuda12x==13.3.0
-    # via cellcommunicationpf2
-cycler==0.12.1
-    # via matplotlib
-fastrlock==0.8.2
-    # via cupy-cuda12x
-fonttools==4.54.1
-    # via matplotlib
-idna==3.10
-    # via requests
 iniconfig==2.0.0
     # via pytest
-joblib==1.4.2
-    # via scikit-learn
-kiwisolver==1.4.7
-    # via matplotlib
-llvmlite==0.43.0
-    # via numba
-matplotlib==3.9.2
-    # via tlviz
 nodeenv==1.9.1
     # via pyright
-numba==0.60.0
-    # via pacmap
 numpy==2.0.2
     # via autograd
     # via cellcommunicationpf2
-    # via contourpy
-    # via cupy-cuda12x
-    # via matplotlib
-    # via numba
-    # via pacmap
-    # via pandas
-    # via patsy
     # via pymanopt
-    # via scikit-learn
     # via scipy
-    # via statsmodels
-    # via tensorly
-    # via tlviz
-    # via xarray
 packaging==24.1
-    # via matplotlib
     # via pytest
-    # via statsmodels
-    # via xarray
-pacmap==0.7.3
-    # via cellcommunicationpf2
-pandas==2.2.3
-    # via statsmodels
-    # via tlviz
-    # via xarray
-patsy==0.5.6
-    # via statsmodels
-pillow==10.4.0
-    # via matplotlib
 pluggy==1.5.0
     # via pytest
 pymanopt==2.2.1
     # via cellcommunicationpf2
-pyparsing==3.1.4
-    # via matplotlib
 pyright==1.1.383
 pytest==8.3.3
     # via pytest-cov
-pytest-cov==5.0.0
-python-dateutil==2.9.0.post0
-    # via matplotlib
-    # via pandas
-pytz==2024.2
-    # via pandas
-requests==2.32.3
-    # via tlviz
-scikit-learn==1.5.2
-    # via cellcommunicationpf2
-    # via pacmap
+pytest-cov==6.0.0
 scipy==1.14.1
     # via cellcommunicationpf2
     # via pymanopt
-    # via scikit-learn
-    # via statsmodels
-    # via tensorly
-    # via tlviz
-six==1.16.0
-    # via patsy
-    # via python-dateutil
-statsmodels==0.14.4
-    # via tlviz
-tensorly==0.9.0
-    # via cellcommunicationpf2
-threadpoolctl==3.5.0
-    # via scikit-learn
-tlviz==0.1.1
-    # via cellcommunicationpf2
-tqdm==4.66.5
-    # via cellcommunicationpf2
 typing-extensions==4.12.2
     # via pyright
-tzdata==2024.2
-    # via pandas
-urllib3==2.2.3
-    # via requests
-xarray==2024.9.0
-    # via tlviz

requirements.lock

@@ -10,104 +10,15 @@
 #   universal: false
 
 -e file:.
-annoy==1.17.3
-    # via pacmap
 autograd==1.7.0
     # via cellcommunicationpf2
-certifi==2024.8.30
-    # via requests
-charset-normalizer==3.3.2
-    # via requests
-contourpy==1.3.0
-    # via matplotlib
-cupy-cuda12x==13.3.0
-    # via cellcommunicationpf2
-cycler==0.12.1
-    # via matplotlib
-fastrlock==0.8.2
-    # via cupy-cuda12x
-fonttools==4.54.1
-    # via matplotlib
-idna==3.10
-    # via requests
-joblib==1.4.2
-    # via scikit-learn
-kiwisolver==1.4.7
-    # via matplotlib
-llvmlite==0.43.0
-    # via numba
-matplotlib==3.9.2
-    # via tlviz
-numba==0.60.0
-    # via pacmap
 numpy==2.0.2
     # via autograd
     # via cellcommunicationpf2
-    # via contourpy
-    # via cupy-cuda12x
-    # via matplotlib
-    # via numba
-    # via pacmap
-    # via pandas
-    # via patsy
     # via pymanopt
-    # via scikit-learn
     # via scipy
-    # via statsmodels
-    # via tensorly
-    # via tlviz
-    # via xarray
-packaging==24.1
-    # via matplotlib
-    # via statsmodels
-    # via xarray
-pacmap==0.7.3
-    # via cellcommunicationpf2
-pandas==2.2.3
-    # via statsmodels
-    # via tlviz
-    # via xarray
-patsy==0.5.6
-    # via statsmodels
-pillow==10.4.0
-    # via matplotlib
 pymanopt==2.2.1
     # via cellcommunicationpf2
-pyparsing==3.1.4
-    # via matplotlib
-python-dateutil==2.9.0.post0
-    # via matplotlib
-    # via pandas
-pytz==2024.2
-    # via pandas
-requests==2.32.3
-    # via tlviz
-scikit-learn==1.5.2
-    # via cellcommunicationpf2
-    # via pacmap
 scipy==1.14.1
     # via cellcommunicationpf2
     # via pymanopt
-    # via scikit-learn
-    # via statsmodels
-    # via tensorly
-    # via tlviz
-six==1.16.0
-    # via patsy
-    # via python-dateutil
-statsmodels==0.14.4
-    # via tlviz
-tensorly==0.9.0
-    # via cellcommunicationpf2
-threadpoolctl==3.5.0
-    # via scikit-learn
-tlviz==0.1.1
-    # via cellcommunicationpf2
-tqdm==4.66.5
-    # via cellcommunicationpf2
-tzdata==2024.2
-    # via pandas
-urllib3==2.2.3
-    # via requests
-xarray==2024.9.0
-    # via tlviz