Switch from progressbar to tqdm

modopt/opt/algorithms/base.py

@@ -4,7 +4,7 @@
 from inspect import getmro
 
 import numpy as np
-from progressbar import ProgressBar
+from tqdm.auto import tqdm
 
 from modopt.base import backend
 from modopt.base.observable import MetricObserver, Observable
@@ -15,7 +15,7 @@ class SetUp(Observable):
     r"""Algorithm Set-Up.
 
     This class contains methods for checking the set-up of an optimisation
-    algotithm and produces warnings if they do not comply.
+    algorithm and produces warnings if they do not comply.
 
     Parameters
     ----------
@@ -38,7 +38,6 @@ class SetUp(Observable):
     --------
     modopt.base.observable.Observable : parent class
     modopt.base.observable.MetricObserver : definition of metrics
-
     """
 
     def __init__(
@@ -240,9 +239,8 @@ def _iterations(self, max_iter, progbar=None):
         ----------
         max_iter : int
             Maximum number of iterations
-        progbar : progressbar.bar.ProgressBar
-            Progress bar (default is ``None``)
-
+        progbar: tqdm.tqdm
+            Progress bar handle (default is ``None``)
         """
         for idx in range(max_iter):
             self.idx = idx
@@ -268,10 +266,10 @@ def _iterations(self, max_iter, progbar=None):
                     print(' - Converged!')
                 break
 
-            if not isinstance(progbar, type(None)):
-                progbar.update(idx)
+            if progbar:
+                progbar.update()
 
-    def _run_alg(self, max_iter):
+    def _run_alg(self, max_iter, progbar=None):
         """Run algorithm.
 
         Run the update step of a given algorithm up to the maximum number of
@@ -281,17 +279,34 @@ def _run_alg(self, max_iter):
         ----------
         max_iter : int
             Maximum number of iterations
+        progbar: tqdm.tqdm
+            Progress bar handle (default is ``None``)
 
         See Also
         --------
-        progressbar.bar.ProgressBar
+        tqdm.tqdm
 
         """
-        if self.progress:
-            with ProgressBar(
-                redirect_stdout=True,
-                max_value=max_iter,
-            ) as progbar:
-                self._iterations(max_iter, progbar=progbar)
+        if self.progress and progbar is None:
+            with tqdm(total=max_iter) as pb:
+                self._iterations(max_iter, progbar=pb)
+        elif progbar:
+            self._iterations(max_iter, progbar=progbar)
         else:
             self._iterations(max_iter)
+
+    def _update(self):
+        raise NotImplementedError
+
+    def get_notify_observers_kwargs(self):
+        """Notify Observers.
+
+        Return the mapping between the metrics call and the iterated
+        variables.
+
+        Raises
+        ------
+        NotImplementedError
+            This method should be overriden by subclasses.
+        """
+        raise NotImplementedError

modopt/opt/algorithms/forward_backward.py

@@ -467,7 +467,7 @@ def _update(self):
                 or self._cost_func.get_cost(self._x_new)
             )
 
-    def iterate(self, max_iter=150):
+    def iterate(self, max_iter=150, progbar=None):
         """Iterate.
 
         This method calls update until either the convergence criteria is met
@@ -477,9 +477,10 @@ def iterate(self, max_iter=150):
         ----------
         max_iter : int, optional
             Maximum number of iterations (default is ``150``)
-
+        progbar: tqdm.tqdm
+            Progress bar handle (default is ``None``)
         """
-        self._run_alg(max_iter)
+        self._run_alg(max_iter, progbar)
 
         # retrieve metrics results
         self.retrieve_outputs()
@@ -750,7 +751,7 @@ def _update(self):
         if self._cost_func:
             self.converge = self._cost_func.get_cost(self._x_new)
 
-    def iterate(self, max_iter=150):
+    def iterate(self, max_iter=150, progbar=None):
         """Iterate.
 
         This method calls update until either convergence criteria is met or
@@ -760,9 +761,10 @@ def iterate(self, max_iter=150):
         ----------
         max_iter : int, optional
             Maximum number of iterations (default is ``150``)
-
+        progbar: tqdm.tqdm
+            Progress bar handle (default is ``None``)
         """
-        self._run_alg(max_iter)
+        self._run_alg(max_iter, progbar)
 
         # retrieve metrics results
         self.retrieve_outputs()
@@ -995,7 +997,7 @@ def _update(self):
                 or self._cost_func.get_cost(self._x_new)
             )
 
-    def iterate(self, max_iter=150):
+    def iterate(self, max_iter=150, progbar=None):
         """Iterate.
 
         This method calls update until either convergence criteria is met or
@@ -1005,9 +1007,10 @@ def iterate(self, max_iter=150):
         ----------
         max_iter : int, optional
             Maximum number of iterations (default is ``150``)
-
+        progbar: tqdm.tqdm
+            Progress bar handle (default is ``None``)
         """
-        self._run_alg(max_iter)
+        self._run_alg(max_iter, progbar)
 
         # retrieve metrics results
         self.retrieve_outputs()

modopt/opt/algorithms/primal_dual.py

@@ -225,7 +225,7 @@ def _update(self):
                 or self._cost_func.get_cost(self._x_new, self._y_new)
             )
 
-    def iterate(self, max_iter=150, n_rewightings=1):
+    def iterate(self, max_iter=150, n_rewightings=1, progbar=None):
         """Iterate.
 
         This method calls update until either convergence criteria is met or
@@ -237,14 +237,17 @@ def iterate(self, max_iter=150, n_rewightings=1):
             Maximum number of iterations (default is ``150``)
         n_rewightings : int, optional
             Number of reweightings to perform (default is ``1``)
-
+        progbar: tqdm.tqdm
+            Progress bar handle (default is ``None``)
         """
-        self._run_alg(max_iter)
+        self._run_alg(max_iter, progbar)
 
         if not isinstance(self._reweight, type(None)):
             for _ in range(n_rewightings):
                 self._reweight.reweight(self._linear.op(self._x_new))
-                self._run_alg(max_iter)
+                if progbar:
+                    progbar.reset(total=max_iter)
+                self._run_alg(max_iter, progbar)
 
         # retrieve metrics results
         self.retrieve_outputs()

requirements.txt

@@ -1,4 +1,4 @@
 importlib_metadata>=3.7.0
 numpy>=1.19.5
 scipy>=1.5.4
-progressbar2>=3.53.1
+tqdm>=4.64.0