Plotting parameters vs. node output (#286)

* Add support for giving seed to generate

* Implement elfi.plot_some for plotting parameters vs. node output

* Fix giving seed to generate

* Support giving a function to apply to node output

* Remove empty subplot axes

* Fix linting

* Rename function to 'plot_params_vs_node'

CHANGELOG.rst

@@ -8,6 +8,8 @@ dev
 - Fix crashing summary and plots for samples with multivariate priors
 - Add progress bar for inference methods
 - Add method save to Sample objects
+- Add support for giving seed to `generate`
+- Implement elfi.plot_params_vs_node for plotting parameters vs. node output
 
 0.7.2 (2018-06-20)
 ------------------

elfi/__init__.py

@@ -19,6 +19,7 @@
 from elfi.model.extensions import ScipyLikeDistribution as Distribution
 from elfi.store import OutputPool, ArrayPool
 from elfi.visualization.visualization import nx_draw as draw
+from elfi.visualization.visualization import plot_params_vs_node
 from elfi.methods.bo.gpy_regression import GPyRegression
 
 __author__ = 'ELFI authors'

elfi/model/elfi_model.py

@@ -260,8 +260,8 @@ def observed(self, observed):
                              "name as the key")
         self.source_net.graph['observed'] = observed
 
-    def generate(self, batch_size=1, outputs=None, with_values=None):
-        """Generate a batch of outputs using the global numpy seed.
+    def generate(self, batch_size=1, outputs=None, with_values=None, seed=None):
+        """Generate a batch of outputs.
 
         This method is useful for testing that the ELFI graph works.
 
@@ -271,6 +271,8 @@ def generate(self, batch_size=1, outputs=None, with_values=None):
         outputs : list, optional
         with_values : dict, optional
             You can specify values for nodes to use when generating data
+        seed : int, optional
+            Defaults to global numpy seed.
 
         """
         if outputs is None:
@@ -280,11 +282,14 @@ def generate(self, batch_size=1, outputs=None, with_values=None):
         if not isinstance(outputs, list):
             raise ValueError('Outputs must be a list of node names')
 
+        if seed is None:
+            seed = 'global'
+
         pool = None
         if with_values is not None:
             pool = OutputPool(with_values.keys())
             pool.add_batch(with_values, 0)
-        context = ComputationContext(batch_size, seed='global', pool=pool)
+        context = ComputationContext(batch_size, seed=seed, pool=pool)
 
         client = elfi.client.get_client()
         compiled_net = client.compile(self.source_net, outputs)

elfi/visualization/visualization.py

@@ -278,3 +278,84 @@ def progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=100,
         print('\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end='\r')
         if iteration == total:
             print()
+
+
+def plot_params_vs_node(node, n_samples=100, func=None, seed=None, axes=None, **kwargs):
+    """Plot some realizations of parameters vs. `node`.
+
+    Useful e.g. for exploring how a summary statistic varies with parameters.
+    Currently only nodes with scalar output are supported, though a function `func` can
+    be given to reduce node output. This allows giving the simulator as the `node` and
+    applying a summarizing function without incorporating it into the ELFI graph.
+
+    If `node` is one of the model parameters, its histogram is plotted.
+
+    Parameters
+    ----------
+    node : elfi.NodeReference
+        The node which to evaluate. Its output must be scalar (shape=(batch_size,1)).
+    n_samples : int, optional
+        How many samples to plot.
+    func : callable, optional
+        A function to apply to node output.
+    seed : int, optional
+    axes : one or an iterable of plt.Axes, optional
+
+    Returns
+    -------
+    axes : np.array of plt.Axes
+
+    """
+    model = node.model
+    parameters = model.parameter_names
+    node_name = node.name
+
+    if node_name in parameters:
+        outputs = [node_name]
+        shape = (1, 1)
+        bins = kwargs.pop('bins', 20)
+
+    else:
+        outputs = parameters + [node_name]
+        n_params = len(parameters)
+        ncols = n_params if n_params < 5 else 5
+        ncols = kwargs.pop('ncols', ncols)
+        edgecolor = kwargs.pop('edgecolor', 'none')
+        dot_size = kwargs.pop('s', 20)
+        shape = (1 + n_params // (ncols+1), ncols)
+
+    data = model.generate(batch_size=n_samples, outputs=outputs, seed=seed)
+
+    if func is not None:
+        if hasattr(func, '__name__'):
+            node_name = func.__name__
+        else:
+            node_name = 'func'
+        data[node_name] = func(data[node.name])  # leaves rest of the code unmodified
+
+    if data[node_name].shape != (n_samples,):
+        raise NotImplementedError("The plotted quantity must have shape ({},), was {}."
+                                  .format(n_samples, data[node_name].shape))
+
+    axes, kwargs = _create_axes(axes, shape, sharey=True, **kwargs)
+    axes = axes.ravel()
+
+    if len(outputs) == 1:
+        axes[0].hist(data[node_name], bins=bins, normed=True)
+        axes[0].set_xlabel(node_name)
+
+    else:
+        for idx, key in enumerate(parameters):
+            axes[idx].scatter(data[key],
+                              data[node_name],
+                              s=dot_size,
+                              edgecolor=edgecolor,
+                              **kwargs)
+
+            axes[idx].set_xlabel(key)
+        axes[0].set_ylabel(node_name)
+
+        for idx in range(len(parameters), len(axes)):
+            axes[idx].set_axis_off()
+
+    return axes