Added helper method to plot param scheduler values with matplotlib

.travis.yml

@@ -29,7 +29,7 @@ before_install: &before_install
   - source activate test-environment
   - if [[ $TRAVIS_PYTHON_VERSION == 2.7 ]]; then pip install enum34; fi
   # Test contrib dependencies
-  - pip install tqdm scikit-learn tensorboardX visdom polyaxon-client mlflow tensorboard
+  - pip install tqdm scikit-learn matplotlib tensorboardX visdom polyaxon-client mlflow tensorboard
   - if [[ $TRAVIS_PYTHON_VERSION == 3.6 ]]; then pip install pynvml; fi
   # Futures should be already installed via visdom -> tornado -> futures
   # Let's reinstall it anyway to be sure
@@ -39,7 +39,7 @@ install:
   - python setup.py install
   - pip install numpy mock pytest codecov pytest-cov pytest-xdist
   # Examples dependencies
-  - pip install matplotlib pandas
+  - pip install pandas
   - pip install gym==0.10.11
 
 script:

ignite/contrib/handlers/param_scheduler.py

@@ -73,7 +73,7 @@ def get_param(self):
 
     @classmethod
     def simulate_values(cls, num_events, **scheduler_kwargs):
-        """Method to simulate scheduled values during num_events events.
+        """Method to simulate scheduled values during `num_events` events.
 
         Args:
             num_events (int): number of events during the simulation.
@@ -107,6 +107,46 @@ def simulate_values(cls, num_events, **scheduler_kwargs):
             values.append([i, scheduler.optimizer_param_groups[0][scheduler.param_name]])
         return values
 
+    @classmethod
+    def plot_values(cls, num_events, **scheduler_kwargs):
+        """Method to plot simulated scheduled values during `num_events` events.
+
+        This class requires `matplotlib package <https://matplotlib.org/>`_ to be installed:
+
+        .. code-block:: bash
+
+            pip install matplotlib
+
+        Args:
+            num_events (int): number of events during the simulation.
+            **scheduler_kwargs : parameter scheduler configuration kwargs.
+
+        Returns:
+            matplotlib.lines.Line2D
+
+        Examples:
+
+            .. code-block:: python
+
+                import matplotlib.pylab as plt
+
+                plt.figure(figsize=(10, 7))
+                LinearCyclicalScheduler.plot_values(num_events=50, param_name='lr',
+                                                    start_value=1e-1, end_value=1e-3, cycle_size=10))
+        """
+        try:
+            import matplotlib.pylab as plt
+        except ImportError:
+            raise RuntimeError("This method requires matplotlib to be installed. "
+                               "Please install it with command: \n pip install matplotlib")
+
+        values = cls.simulate_values(num_events=num_events, **scheduler_kwargs)
+        label = scheduler_kwargs.get("param_name", "learning rate")
+        ax = plt.plot([e for e, _ in values], [v for _, v in values], label=label)
+        plt.legend()
+        plt.grid(which='both')
+        return ax
+
 
 class CyclicalScheduler(ParamScheduler):
     """An abstract class for updating an optimizer's parameter value over a

tests/ignite/contrib/handlers/test_param_scheduler.py

@@ -466,7 +466,11 @@ def save_lr(engine):
     _test(milestones_as_np_int=False)
 
 
-def test_simulate_values():
+def test_simulate_and_plot_values():
+
+    import matplotlib
+    matplotlib.use('Agg')
+
     def _test(scheduler_cls, **scheduler_kwargs):
 
         optimizer = None
@@ -511,6 +515,9 @@ def save_lr(engine):
                                                          **scheduler_kwargs)
         assert lrs == pytest.approx([v for i, v in simulated_values])
 
+        # launch plot values
+        scheduler_cls.plot_values(num_events=len(data) * max_epochs, **scheduler_kwargs)
+
     # LinearCyclicalScheduler
     _test(LinearCyclicalScheduler, param_name="lr", start_value=1.0, end_value=0.0, cycle_size=10)