Modernize for Python 3 and update dependencies

.github/workflows/test.yml

@@ -0,0 +1,34 @@
+name: Tests
+
+on:
+  push:
+    branches: [master, main]
+  pull_request:
+    branches: [master, main]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
+
+      - name: Set up Python 3.12
+        run: uv python install 3.12
+
+      - name: Install dependencies
+        run: uv sync --extra dev
+
+      - name: Run ruff linting
+        run: uv run ruff check .
+
+      - name: Run ruff format check
+        run: uv run ruff format --check .
+
+      - name: Run tests
+        run: uv run pytest test/ -v

.pre-commit-config.yaml

@@ -0,0 +1,7 @@
+default_stages: [pre-push]
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.4.0
+    hooks:
+      - id: ruff-format
+      - id: ruff

README.md

@@ -2,8 +2,7 @@ climin
 ------
 
 climin is a Python package for optimization, heavily biased to machine learning
-scenarios distributed under the BSD 3-clause license. It works on top of numpy
-and (partially) gnumpy.
+scenarios distributed under the BSD 3-clause license. It works on top of numpy.
 
 The project was started in winter 2011 by Christian Osendorfer and Justin Bayer.
 Since then, Sarah Diot-Girard, Thomas Rueckstiess and Sebastian Urban have 
@@ -24,8 +23,7 @@ Important links
 Dependencies
 ------------
 
-The software is tested under Python 2.7 with numpy 1.10.4, scipy 0.17. The tests
-are run with nosetests.
+Requires Python 3.12+ with numpy 1.26+ and scipy 1.11+.
 
 
 Installation
@@ -38,4 +36,4 @@ in the clone to intall in your local user space.
 Testing
 -------
 
-From the download directory run ``nosetests tests/``.
+From the downloaded directory run ``pytest``.

climin/__init__.py

@@ -1,32 +1,31 @@
-from __future__ import absolute_import
-
 # Control breaking does not work on Windows after e.g. scipy.stats is
 # imported because certain Fortran libraries register their own signal handler
 # See:
 # http://stackoverflow.com/questions/15457786/ctrl-c-crashes-python-after-importing-scipy-stats
 # and https://github.com/numpy/numpy/issues/6923
 import sys
 import os
-import imp
 import ctypes
 
-if sys.platform == 'win32':
+if sys.platform == "win32":
     # For setups where Intel Fortran compiler version >= 16.0 (This is the case
     # for Anaconda version 4.1.5 which comes with numpy version 1.10.4) is used,
     # the following flag allows to disable the additionally introduced signal
     # handler, older versios make no use of this environment variable
-    env = 'FOR_DISABLE_CONSOLE_CTRL_HANDLER'
+    env = "FOR_DISABLE_CONSOLE_CTRL_HANDLER"
     if env not in os.environ:
-        os.environ[env] = '1'
+        os.environ[env] = "1"
     # In setups with an older version, ensuring that the respective dlls are
     # loaded from the numpy core and not somewhere else (e.g. the Windows System
     # folder) helps
-    basepath = imp.find_module('numpy')[1]
+    import numpy
+
+    basepath = os.path.dirname(numpy.__file__)
     # dll loading fails when Intel Fortran compiler version >= 16.0, therefore
     # use try/catch
     try:
-        ctypes.CDLL(os.path.join(basepath, 'core', 'libmmd.dll'))
-        ctypes.CDLL(os.path.join(basepath, 'core', 'libifcoremd.dll'))
+        ctypes.CDLL(os.path.join(basepath, "core", "libmmd.dll"))
+        ctypes.CDLL(os.path.join(basepath, "core", "libifcoremd.dll"))
     except Exception:
         pass
 

climin/adadelta.py

@@ -2,14 +2,12 @@
 
 """This module provides an implementation of adadelta."""
 
-from __future__ import absolute_import
-
 from .base import Minimizer
 from .mathadapt import sqrt, ones_like, clip
 
 
 class Adadelta(Minimizer):
-    """Adadelta optimizer.
+    r"""Adadelta optimizer.
 
     Adadelta [zeiler2013adadelta]_ is a method that uses the magnitude of recent
     gradients and steps to obtain an adaptive step rate. An exponential moving
@@ -53,10 +51,9 @@ class Adadelta(Minimizer):
        arXiv preprint arXiv:1212.5701 (2012).
     """
 
-    state_fields = 'n_iter gms sms step step_rate decay offset momentum'.split()
+    state_fields = "n_iter gms sms step step_rate decay offset momentum".split()
 
-    def __init__(self, wrt, fprime, step_rate=1, decay=0.9, momentum=0,
-                 offset=1e-4, args=None):
+    def __init__(self, wrt, fprime, step_rate=1, decay=0.9, momentum=0, offset=1e-4, args=None):
         """Create an Adadelta object.
 
         Parameters
@@ -113,18 +110,18 @@ def _iterate(self):
 
             gradient = self.fprime(self.wrt, *args, **kwargs)
 
-            self.gms = (d * self.gms) + (1 - d) * gradient ** 2
+            self.gms = (d * self.gms) + (1 - d) * gradient**2
             step2 = sqrt(self.sms + o) / sqrt(self.gms + o) * gradient * self.step_rate
             self.wrt -= step2
 
             self.step = step1 + step2
-            self.sms = (d * self.sms) + (1 - d) * self.step ** 2
+            self.sms = (d * self.sms) + (1 - d) * self.step**2
 
             self.n_iter += 1
 
             yield {
-                'n_iter': self.n_iter,
-                'gradient': gradient,
-                'args': args,
-                'kwargs': kwargs,
+                "n_iter": self.n_iter,
+                "gradient": gradient,
+                "args": args,
+                "kwargs": kwargs,
             }

climin/adam.py

@@ -2,15 +2,13 @@
 
 """This module provides an implementation of Adam."""
 
-from __future__ import absolute_import
-
 import warnings
 
 from .base import Minimizer
 
 
 class Adam(Minimizer):
-    """Adaptive moment estimation optimizer. (Adam).
+    r"""Adaptive moment estimation optimizer. (Adam).
 
     Adam is a method for the optimization of stochastic objective functions.
 
@@ -90,14 +88,22 @@ class Adam(Minimizer):
        Stanford University, Tech. Rep (2015).
     """
 
-    state_fields = 'n_iter step_rate decay_mom1 decay_mom2 step offset est_mom1_b est_mom2_b'.split()
-
-    def __init__(self, wrt, fprime, step_rate=.0002,
-                 decay=None,
-                 decay_mom1=0.1,
-                 decay_mom2=0.001,
-                 momentum=0,
-                 offset=1e-8, args=None):
+    state_fields = (
+        "n_iter step_rate decay_mom1 decay_mom2 step offset est_mom1_b est_mom2_b".split()
+    )
+
+    def __init__(
+        self,
+        wrt,
+        fprime,
+        step_rate=0.0002,
+        decay=None,
+        decay_mom1=0.1,
+        decay_mom2=0.001,
+        momentum=0,
+        offset=1e-8,
+        args=None,
+    ):
         """Create an Adam object.
 
         Parameters
@@ -136,16 +142,20 @@ def __init__(self, wrt, fprime, step_rate=.0002,
             Iterator over arguments which ``fprime`` will be called with.
         """
         if not 0 < decay_mom1 <= 1:
-            raise ValueError('decay_mom1 has to lie in (0, 1]')
+            raise ValueError("decay_mom1 has to lie in (0, 1]")
         if not 0 < decay_mom2 <= 1:
-            raise ValueError('decay_mom2 has to lie in (0, 1]')
+            raise ValueError("decay_mom2 has to lie in (0, 1]")
         if not (1 - decay_mom1 * 2) / (1 - decay_mom2) ** 0.5 < 1:
-            warnings.warn("constraint from convergence analysis for adam not "
-                          "satisfied; check original paper to see if you "
-                          "really want to do this.")
+            warnings.warn(
+                "constraint from convergence analysis for adam not "
+                "satisfied; check original paper to see if you "
+                "really want to do this."
+            )
         if decay is not None:
-            warnings.warn('decay parameter was used in a previous verion of '
-                          'Adam and no longer has any effect.')
+            warnings.warn(
+                "decay parameter was used in a previous verion of "
+                "Adam and no longer has any effect."
+            )
 
         super(Adam, self).__init__(wrt, args=args)
 
@@ -176,20 +186,19 @@ def _iterate(self):
 
             gradient = self.fprime(self.wrt, *args, **kwargs)
             self.est_mom1_b = dm1 * gradient + (1 - dm1) * est_mom1_b_m1
-            self.est_mom2_b = dm2 * gradient ** 2 + (1 - dm2) * est_mom2_b_m1
+            self.est_mom2_b = dm2 * gradient**2 + (1 - dm2) * est_mom2_b_m1
 
-            step_t = self.step_rate * (1 - (1 - dm2) ** t) ** 0.5 / \
-                     (1 - (1 - dm1) ** t)
-            step2 = step_t * self.est_mom1_b / (self.est_mom2_b ** 0.5 + o)
+            step_t = self.step_rate * (1 - (1 - dm2) ** t) ** 0.5 / (1 - (1 - dm1) ** t)
+            step2 = step_t * self.est_mom1_b / (self.est_mom2_b**0.5 + o)
 
             self.wrt -= step2
             self.step = step1 + step2
 
             self.n_iter += 1
 
             yield {
-                'n_iter': self.n_iter,
-                'gradient': gradient,
-                'args': args,
-                'kwargs': kwargs,
+                "n_iter": self.n_iter,
+                "gradient": gradient,
+                "args": args,
+                "kwargs": kwargs,
             }

climin/asgd.py

@@ -1,18 +1,14 @@
 # -*- coding: utf-8 -*-
 
 # TODO: document module
-# TODO: check if gnumpy compatible
-
-from __future__ import absolute_import
 
 from .base import Minimizer
 
 
 class Asgd(Minimizer):
     # TODO: document class
 
-    def __init__(self, wrt, fprime, eta0=1e-5, lmbd=1e-4, alpha=0.75, t0=1e8,
-                 args=None):
+    def __init__(self, wrt, fprime, eta0=1e-5, lmbd=1e-4, alpha=0.75, t0=1e8, args=None):
         # TODO document method
         # TODO rename parameters to sth sensible, not greek letters
         # TODO give reference
@@ -27,10 +23,10 @@ def __init__(self, wrt, fprime, eta0=1e-5, lmbd=1e-4, alpha=0.75, t0=1e8,
         self.eta_t = eta0
 
     def set_from_info(self, info):
-        raise NotImplemented('nobody has found the time to implement this yet')
+        raise NotImplementedError("nobody has found the time to implement this yet")
 
     def extended_info(self, **kwargs):
-        raise NotImplemented('nobody has found the time to implement this yet')
+        raise NotImplementedError("nobody has found the time to implement this yet")
 
     def __iter__(self):
         # do 'determineEta0' (see Bottou) here?
@@ -43,7 +39,7 @@ def __iter__(self):
             gradient = self.fprime(self.w, *args, **kwargs)
 
             # decay w
-            self.w *= (1 - self.lmbd * self.eta_t)
+            self.w *= 1 - self.lmbd * self.eta_t
             # and update (descent direction)
             self.w -= self.eta_t * gradient
 
@@ -55,15 +51,15 @@ def __iter__(self):
                 self.wrt *= 0
                 self.wrt += self.w
 
-            self.mu_t = 1. / max(1, (i + 1) - self.t0)
+            self.mu_t = 1.0 / max(1, (i + 1) - self.t0)
             self.eta_t = self.eta0 / ((1 + self.lmbd * self.eta0 * (i + 1)) ** self.alpha)
 
             yield {
-                'n_iter': i,
-                'gradient': gradient,
-                'mu_t': self.mu_t,
-                'eta_t': self.eta_t,
-                'step': step,
-                'args': args,
-                'kwargs': kwargs
+                "n_iter": i,
+                "gradient": gradient,
+                "mu_t": self.mu_t,
+                "eta_t": self.eta_t,
+                "step": step,
+                "args": args,
+                "kwargs": kwargs,
             }

climin/base.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 
 import itertools
-import collections
+import collections.abc
 
 import numpy as np
 
@@ -14,7 +14,6 @@ def repeat_or_iter(obj):
 
 
 class Minimizer(object):
-
     def __init__(self, wrt, args=None):
         self.wrt = wrt
         if args is None:
@@ -67,10 +66,10 @@ def minimize_until(self, criterions):
         an optimizer, returns a boolean indicating whether to stop. False means
         to continue, True means to stop."""
         if not criterions:
-            raise ValueError('need to supply at least one criterion')
+            raise ValueError("need to supply at least one criterion")
 
         # if criterions is a single criterion, wrap it in iterable list
-        if not isinstance(criterions, collections.Iterable):
+        if not isinstance(criterions, collections.abc.Iterable):
             criterions = [criterions]
 
         info = {}