Merge branch 'scatter-plot' into 'master'

Make the model optional in the scatter plot

See merge request !12

bob/fusion/base/script/boundary.py

@@ -51,11 +51,14 @@ def plot_boundary_decision(algorithm, scores, score_labels, threshold,
   xx, yy = np.meshgrid(
       np.linspace(x_min, x_max, resolution),
       np.linspace(y_min, y_max, resolution))
-  temp = np.c_[xx.ravel(), yy.ravel()]
-  temp = algorithm.preprocess(temp)
-  Z = (algorithm.fuse(temp) > threshold).reshape(xx.shape)
 
-  contourf = plt.contour(xx, yy, Z, 1, alpha=1, cmap=plt.cm.gray)
+  contourf = None
+  if algorithm is not None:
+    temp = np.c_[xx.ravel(), yy.ravel()]
+    temp = algorithm.preprocess(temp)
+    Z = (algorithm.fuse(temp) > threshold).reshape(xx.shape)
+
+    contourf = plt.contour(xx, yy, Z, 1, alpha=1, cmap=plt.cm.gray)
 
   if do_grouping:
     gen = grouping(X[Y == 0, :], **kwargs)
@@ -94,9 +97,9 @@ $ bob fusion boundary -vvv {sys1,sys2}/scores-eval -m /path/to/Model.pkl
 """)
 @click.argument('scores', nargs=-1, required=True,
                 type=click.Path(exists=True))
-@click.option('-m', '--model-file', required=True,
+@click.option('-m', '--model-file', required=False,
               help='The path to where the algorithm will be loaded from.')
-@click.option('-t', '--threshold', type=click.FLOAT, required=True,
+@click.option('-t', '--threshold', type=click.FLOAT, required=False,
               help='The threshold to classify scores after fusion. Usually '
               'calculated from fused development set.')
 @click.option('-g', '--group', type=click.INT, default=0, show_default=True,
@@ -127,8 +130,10 @@ def boundary(scores, model_file, threshold, group, grouping, output, x_label,
   plotted on the x-axis.
   """
   # load the algorithm
-  algorithm = Algorithm()
-  algorithm = algorithm.load(model_file)
+  algorithm = None
+  if model_file:
+    algorithm = Algorithm().load(model_file)
+    assert threshold is not None, "threshold must be provided with the model"
 
   # load the scores
   score_lines_list_eval = [load_score(path) for path in scores]