Support of Python 3.12

.github/workflows/azureml-cpu-nightly.yml

@@ -69,7 +69,7 @@ jobs:
     strategy:
       max-parallel: 50 # Usage limits: https://docs.github.com/en/actions/learn-github-actions/usage-limits-billing-and-administration
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         test-group:  ${{ fromJSON(needs.get-test-groups.outputs.test_groups) }}
     steps:
       - name: Check out repository code

.github/workflows/azureml-gpu-nightly.yml

@@ -69,7 +69,7 @@ jobs:
     strategy:
       max-parallel: 50 # Usage limits: https://docs.github.com/en/actions/learn-github-actions/usage-limits-billing-and-administration
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         test-group:  ${{ fromJSON(needs.get-test-groups.outputs.test_groups) }}
     steps:
       - name: Check out repository code

.github/workflows/azureml-release-pipeline.yml

@@ -37,7 +37,7 @@ jobs:
       - name: Setup python
         uses: actions/setup-python@v5
         with: 
-          python-version: "3.10"
+          python-version: "3.12"
       - name: Install wheel package
         run: pip install wheel
       - name: Create wheel from setup.py

.github/workflows/azureml-spark-nightly.yml

@@ -68,7 +68,7 @@ jobs:
     strategy:
       max-parallel: 50 # Usage limits: https://docs.github.com/en/actions/learn-github-actions/usage-limits-billing-and-administration
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         test-group:  ${{ fromJSON(needs.get-test-groups.outputs.test_groups) }}
     steps:
       - name: Check out repository code

.github/workflows/azureml-unit-tests.yml

@@ -58,7 +58,7 @@ jobs:
     strategy:
       max-parallel: 50 # Usage limits: https://docs.github.com/en/actions/learn-github-actions/usage-limits-billing-and-administration
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         test-group:  ${{ fromJSON(needs.get-test-groups.outputs.test_groups) }}
     steps:
       - name: Check out repository code

pyproject.toml

@@ -1,11 +1,11 @@
 [build-system]
 requires = [
-    "setuptools>=52",
+    "setuptools>=67",
     "wheel>=0.36",
     "numpy>=1.15,<2",
 ]
 dependencies = [
-    "setuptools>=52",
+    "setuptools>=67",
     "wheel>=0.36",
     "numpy>=1.15,<2",
 ]
@@ -17,4 +17,4 @@ markers = [
     "gpu: tests running on GPU",
     "notebooks: tests for notebooks",
     "spark: tests that requires Spark",
-]
+]

recommenders/README.md

@@ -74,9 +74,11 @@ We are currently evaluating inclusion of the following dependencies:
 
 ## Other dependencies
 
-Some dependencies are not available via the recommenders PyPI package, but can be installed in the following ways: 
- - pymanopt: this dependency is required for the RLRMC and GeoIMC algorithms; a version of this code compatible with TensorFlow 2 can be
- installed with `pip install "pymanopt@https://github.com/pymanopt/pymanopt/archive/fb36a272cdeecb21992cfd9271eb82baafeb316d.zip"`. 
+Some dependencies are not available via the recommenders PyPI package, but can be installed with the [requirements-external.txt](./requirements-external.txt) file:
+
+```
+pip install -r requirements-external.txt
+```
 
 ## NNI dependencies
 

recommenders/models/deeprec/models/dkn.py

@@ -1,5 +1,7 @@
 # Copyright (c) Recommenders contributors.
 # Licensed under the MIT License.
+import os
+os.environ['TF_USE_LEGACY_KERAS'] = "1"
 
 import numpy as np
 import tensorflow as tf

recommenders/models/geoimc/geoimc_algorithm.py

@@ -11,8 +11,9 @@
 from numba import njit, prange
 from pymanopt import Problem
 from pymanopt.manifolds import Stiefel, Product, SymmetricPositiveDefinite
-from pymanopt.solvers import ConjugateGradient
-from pymanopt.solvers.linesearch import LineSearchBackTracking
+from pymanopt.autodiff.backends import numpy as backend_decorator
+from pymanopt.optimizers import ConjugateGradient
+from pymanopt.optimizers.line_search import BackTrackingLineSearcher
 
 
 class IMCProblem(object):
@@ -68,23 +69,19 @@ def _computeLoss_csrmatrix(a, b, cd, indices, indptr, residual_global):
                 residual_global[j] = num - cd[j]
         return residual_global
 
-    def _cost(self, params, residual_global):
+    def _cost(self, U, S, VT, residual_global):
         """Compute the cost of GeoIMC optimization problem
 
         Args:
             params (Iterator): An iterator containing the manifold point at which
             the cost needs to be evaluated.
             residual_global (csr_matrix): Residual matrix.
         """
-        U = params[0]
-        B = params[1]
-        V = params[2]
-
-        regularizer = 0.5 * self.lambda1 * np.sum(B**2)
+        regularizer = 0.5 * self.lambda1 * np.sum(S**2)
 
         IMCProblem._computeLoss_csrmatrix(
-            self.X.dot(U.dot(B)),
-            V.T.dot(self.Z.T),
+            self.X.dot(U.dot(S)),
+            VT.T.dot(self.Z.T),
             self.Y.data,
             self.Y.indices,
             self.Y.indptr,
@@ -94,37 +91,33 @@ def _cost(self, params, residual_global):
 
         return cost
 
-    def _egrad(self, params, residual_global):
+    def _egrad(self, U, S, VT, residual_global):
         """Computes the euclidean gradient
 
         Args:
             params (Iterator): An iterator containing the manifold point at which
             the cost needs to be evaluated.
             residual_global (csr_matrix): Residual matrix.
         """
-        U = params[0]
-        B = params[1]
-        V = params[2]
-
         residual_global_csr = csr_matrix(
             (residual_global, self.Y.indices, self.Y.indptr),
             shape=self.shape,
         )
 
         gradU = (
-            np.dot(self.X.T, residual_global_csr.dot(self.Z.dot(V.dot(B.T))))
+            np.dot(self.X.T, residual_global_csr.dot(self.Z.dot(VT.dot(S.T))))
             / self.nSamples
         )
 
         gradB = (
-            np.dot((self.X.dot(U)).T, residual_global_csr.dot(self.Z.dot(V)))
+            np.dot((self.X.dot(U)).T, residual_global_csr.dot(self.Z.dot(VT)))
             / self.nSamples
-            + self.lambda1 * B
+            + self.lambda1 * S
         )
         gradB_sym = (gradB + gradB.T) / 2
 
         gradV = (
-            np.dot((self.X.dot(U.dot(B))).T, residual_global_csr.dot(self.Z)).T
+            np.dot((self.X.dot(U.dot(S))).T, residual_global_csr.dot(self.Z)).T
             / self.nSamples
         )
 
@@ -154,20 +147,29 @@ def _optimize(self, max_opt_time, max_opt_iter, verbosity):
         residual_global = np.zeros(self.Y.data.shape)
 
         solver = ConjugateGradient(
-            maxtime=max_opt_time,
-            maxiter=max_opt_iter,
-            linesearch=LineSearchBackTracking(),
+            max_time=max_opt_time,
+            max_iterations=max_opt_iter,
+            line_searcher=BackTrackingLineSearcher(),
+            verbosity=verbosity,
         )
+
+        @backend_decorator(self.manifold)
+        def _cost(u, s, vt):
+            return self._cost(u, s, vt, residual_global)
+
+        @backend_decorator(self.manifold)
+        def _egrad(u, s, vt):
+            return self._egrad(u, s, vt, residual_global)
+
         prb = Problem(
             manifold=self.manifold,
-            cost=lambda x: self._cost(x, residual_global),
-            egrad=lambda z: self._egrad(z, residual_global),
-            verbosity=verbosity,
+            cost=_cost,
+            euclidean_gradient=_egrad,
         )
-        solution = solver.solve(prb, x=self.W)
-        self.W = [solution[0], solution[1], solution[2]]
+        solution = solver.run(prb, initial_point=self.W)
+        self.W = [solution.point[0], solution.point[1], solution.point[2]]
 
-        return self._cost(self.W, residual_global)
+        return solution.cost
 
     def reset(self):
         """Reset the model."""

requirements-external.txt

@@ -0,0 +1,5 @@
+# 2024/02/29: pymanopt bumped to python 3.8
+pymanopt @git+https://github.com/pymanopt/pymanopt@e13cecaec3089c790cc93174840b2f557d179b3f ; python_version<'3.12'
+
+# Jun 2024: Fixes py312
+pymanopt @git+https://github.com/pymanopt/pymanopt@1de3b6f47258820fdc072fceaeaa763b9fd263b0 ; python_version>='3.12'

setup.py

@@ -34,6 +34,7 @@
     "locust>=2.12.2,<3",  # requires jinja2
     "memory-profiler>=0.61.0,<1",
     "nltk>=3.8.1,<4",  # requires tqdm
+    "numpy>=1.26.4;python_version>='3.12'", # https://stackoverflow.com/a/77364602/4505998
     "notebook>=6.5.5,<8",  # requires ipykernel, jinja2, jupyter, nbconvert, nbformat, packaging, requests
     "numba>=0.57.0,<1",
     "pandas>2.0.0,<3.0.0",  # requires numpy
@@ -55,8 +56,12 @@
         "numpy<1.25.0;python_version<='3.8'",
         "nvidia-ml-py>=11.525.84",
         "spacy<=3.7.5;python_version<='3.8'",
-        "tensorflow>=2.8.4,!=2.9.0.*,!=2.9.1,!=2.9.2,!=2.10.0.*,<2.16",  # Fixed TF due to constant security problems and breaking changes #2073
+        "tensorflow>=2.8.4,!=2.9.0.*,!=2.9.1,!=2.9.2,!=2.10.0.*,<2.16; python_version<='3.8'",  # Fixed TF due to constant security problems and breaking changes #2073
+        "tensorflow~=2.16; python_version>'3.8'",  # Version needed for python 3.12
         "tf-slim>=1.1.0",  # No python_requires in its setup.py
+        # Use keras 2 instead of keras 3
+        "tf-keras~=2.15; python_version<='3.8'", # Not compatible with py3.12
+        "tf-keras~=2.16; python_version>'3.8'",
         "torch>=2.0.1,<3",
     ],
     "spark": [
@@ -86,8 +91,8 @@
 ]
 
 # The following dependency can be installed as below, however PyPI does not allow direct URLs.
-# Temporary fix for pymanopt, only this commit works with TF2
-# "pymanopt@https://github.com/pymanopt/pymanopt/archive/fb36a272cdeecb21992cfd9271eb82baafeb316d.zip",
+# Temporary fix for pymanopt, pypi version does not work with TF2
+# pip install -r requirements-external.txt
 
 setup(
     name="recommenders",
@@ -114,6 +119,7 @@
         "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
         "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
         "Operating System :: POSIX :: Linux",
     ],
     extras_require=extras_require,
@@ -126,5 +132,7 @@
         where=".",
         exclude=["contrib", "docs", "examples", "scenarios", "tests", "tools"],
     ),
-    setup_requires=["numpy>=1.19"],
+    setup_requires=[
+        "numpy>=1.15",
+    ],
 )

tests/unit/recommenders/models/test_geoimc.py

@@ -17,7 +17,7 @@
         reduce_dims,
     )
     from pymanopt.manifolds import Stiefel, SymmetricPositiveDefinite
-except:
+except ImportError:
     pass  # skip if pymanopt not installed
 
 
@@ -141,9 +141,9 @@ def test_inferer_infer(dataPtr):
     colFeatureDim = test_data.get_entity("col").shape[1]
     rank = 2
     W = [
-        Stiefel(rowFeatureDim, rank).rand(),
-        SymmetricPositiveDefinite(rank).rand(),
-        Stiefel(colFeatureDim, rank).rand(),
+        Stiefel(rowFeatureDim, rank).random_point(),
+        SymmetricPositiveDefinite(rank).random_point(),
+        Stiefel(colFeatureDim, rank).random_point(),
     ]
 
     Inferer(method="dot").infer(test_data, W)