Fix: Allow optional installation of matplotlib

pymoo/util/running_metric.py

@@ -1,4 +1,3 @@
-import matplotlib.pyplot as plt
 import numpy as np
 
 from pymoo.core.callback import Callback
@@ -7,7 +6,7 @@
 from pymoo.termination.ftol import calc_delta_norm
 from pymoo.util.normalization import normalize
 from pymoo.util.sliding_window import SlidingWindow
-from pymoo.visualization.video.callback_video import AnimationCallback
+from pymoo.visualization.matplotlib import is_matplotlib_available
 
 
 class RunningMetric(Callback):
@@ -64,65 +63,85 @@ def update(self, algorithm):
 
         self.delta_ideal, self.delta_nadir, self.delta_f = delta_ideal, delta_nadir, delta_f
 
-
-class RunningMetricAnimation(AnimationCallback):
-
-    def __init__(self,
-                 delta_gen,
-                 n_plots=4,
-                 key_press=True,
-                 **kwargs) -> None:
-
-        super().__init__(**kwargs)
-        self.running = RunningMetric()
-        self.delta_gen = delta_gen
-        self.key_press = key_press
-        self.data = SlidingWindow(n_plots)
-
-    def draw(self, data, ax):
-
-        for tau, x, f, v in data[:-1]:
-            ax.plot(x, f, label="t=%s" % tau, alpha=0.6, linewidth=3)
-
-        tau, x, f, v = data[-1]
-        ax.plot(x, f, label="t=%s (*)" % tau, alpha=0.9, linewidth=3)
-
-        for k in range(len(v)):
-            if v[k]:
-                ax.plot([k + 1, k + 1], [0, f[k]], color="black", linewidth=0.5, alpha=0.5)
-                ax.plot([k + 1], [f[k]], "o", color="black", alpha=0.5, markersize=2)
-
-        ax.set_yscale("symlog")
-        ax.legend()
-
-        ax.set_xlabel("Generation")
-        ax.set_ylabel("$\Delta \, f$", rotation=0)
-
-    def do(self, _, algorithm, force_plot=False, **kwargs):
-        running = self.running
-
-        # update the running metric to have the most recent information
-        running.update(algorithm)
-
-        tau = algorithm.n_gen
-
-        if (tau > 0 and tau % self.delta_gen == 0) or force_plot:
-
-            f = running.delta_f
-            x = np.arange(len(f)) + 1
-            v = [max(ideal, nadir) > 0.005 for ideal, nadir in zip(running.delta_ideal, running.delta_nadir)]
-            self.data.append((tau, x, f, v))
-
-            fig, ax = plt.subplots()
-            self.draw(self.data, ax)
-
-            if self.key_press:
-                def press(event):
-                    if event.key == 'q':
-                        algorithm.termination.force_termination = True
-
-                fig.canvas.mpl_connect('key_press_event', press)
-
-                plt.draw()
-                plt.waitforbuttonpress()
-                plt.close('all')
+if is_matplotlib_available():
+    # only conditionally define `RunningMetricAnimation` if matplotlib is available
+    from pymoo.visualization.matplotlib import plt
+    from pymoo.visualization.video.callback_video import AnimationCallback
+
+    class RunningMetricAnimation(AnimationCallback):
+
+        def __init__(self,
+                    delta_gen,
+                    n_plots=4,
+                    key_press=True,
+                    **kwargs) -> None:
+
+            super().__init__(**kwargs)
+            self.running = RunningMetric()
+            self.delta_gen = delta_gen
+            self.key_press = key_press
+            self.data = SlidingWindow(n_plots)
+
+        def draw(self, data, ax):
+
+            for tau, x, f, v in data[:-1]:
+                ax.plot(x, f, label="t=%s" % tau, alpha=0.6, linewidth=3)
+
+            tau, x, f, v = data[-1]
+            ax.plot(x, f, label="t=%s (*)" % tau, alpha=0.9, linewidth=3)
+
+            for k in range(len(v)):
+                if v[k]:
+                    ax.plot([k + 1, k + 1], [0, f[k]], color="black", linewidth=0.5, alpha=0.5)
+                    ax.plot([k + 1], [f[k]], "o", color="black", alpha=0.5, markersize=2)
+
+            ax.set_yscale("symlog")
+            ax.legend()
+
+            ax.set_xlabel("Generation")
+            ax.set_ylabel("$\Delta \, f$", rotation=0)
+
+        def do(self, _, algorithm, force_plot=False, **kwargs):
+            running = self.running
+
+            # update the running metric to have the most recent information
+            running.update(algorithm)
+
+            tau = algorithm.n_gen
+
+            if (tau > 0 and tau % self.delta_gen == 0) or force_plot:
+
+                f = running.delta_f
+                x = np.arange(len(f)) + 1
+                v = [max(ideal, nadir) > 0.005 for ideal, nadir in zip(running.delta_ideal, running.delta_nadir)]
+                self.data.append((tau, x, f, v))
+
+                fig, ax = plt.subplots()
+                self.draw(self.data, ax)
+
+                if self.key_press:
+                    def press(event):
+                        if event.key == 'q':
+                            algorithm.termination.force_termination = True
+
+                    fig.canvas.mpl_connect('key_press_event', press)
+
+                    plt.draw()
+                    plt.waitforbuttonpress()
+                    plt.close('all')
+else:
+    class RunningMetricAnimation:
+        """Helper class that raises informative errors when matplotlib is not available."""
+
+        def __getattr__(self, name):
+            raise ImportError(
+                "Visualization features require matplotlib.\n"
+                "Install with: pip install pymoo[visualization]"
+            )
+
+        def __call__(self, *args, **kwargs):
+            raise ImportError(
+                "Visualization features require matplotlib.\n"
+                "Install with: pip install pymoo[visualization]"
+            )
+

pymoo/util/value_functions.py

@@ -5,7 +5,6 @@
 from scipy.optimize import minimize as scimin
 from scipy.optimize import OptimizeResult
 from pymoo.core.problem import Problem
-import matplotlib.pyplot as plt
 from scipy.optimize import NonlinearConstraint
 from scipy.optimize import Bounds
 from pymoo.algorithms.soo.nonconvex.es import ES
@@ -278,7 +277,8 @@ def poly_vf(P, x):
 
 
 def plot_vf(P, vf, show=True): 
-
+    # import plt function locally as matplotlib is an optional dependency
+    from pymoo.visualization.matplotlib import plt
     plt.scatter(P[:,0], P[:,1], marker=".", color="red", s=200 )
 
 

pymoo/visualization/matplotlib.py

@@ -24,7 +24,7 @@
     # Export all commonly used matplotlib objects
     __all__ = [
         'matplotlib', 'plt', 'patches', 'colors', 'cm', 'animation', 
-        'LineCollection', 'PatchCollection', 'ListedColormap', 'is_available'
+        'LineCollection', 'PatchCollection', 'ListedColormap', 'is_matplotlib_available'
     ]
 
 except ImportError:
@@ -57,7 +57,7 @@ def __call__(self, *args, **kwargs):
     ListedColormap = _MatplotlibNotAvailable()
 
 
-def is_available():
+def is_matplotlib_available():
     """Check if matplotlib is available for visualization."""
     return _MATPLOTLIB_AVAILABLE
 

pyproject.toml

@@ -26,7 +26,6 @@ requires-python = ">= 3.9"
 dependencies = [
     "numpy>=1.19.3",
     "scipy>=1.1",
-    "matplotlib>=3",
     "autograd>=1.4",
     "cma>=3.2.2",
     "moocore>=0.1.7",