Merge remote-tracking branch 'origin/master'

Author: lmcinnes

README.rst

@@ -120,7 +120,7 @@ the dependencies manually using anaconda followed by pulling hdbscan from pip:
 .. code:: bash
 
     conda install cython
-    conda install sklearn
+    conda install scikit-learn
     pip install hdbscan
 
 For a manual install get this package:
@@ -142,14 +142,16 @@ or
 
 .. code:: bash
 
-    conda install sklearn cython
+    conda install scikit-learn cython
 
 Install the package
 
 .. code:: bash
 
     python setup.py install
 
+Coming soon: installing via conda.
+
 ---------
 Licensing
 ---------

hdbscan/plots.py

@@ -111,15 +111,15 @@ def get_plot_data(self, leaf_separation=1, log_size=False):
         cluster_y_coords = {root: 0.0}
 
         for cluster in range(last_leaf, root - 1, -1):
-            split = self._raw_tree[['child', 'lambda']]
+            split = self._raw_tree[['child', 'lambda_val']]
             split = split[(self._raw_tree['parent'] == cluster) &
                           (self._raw_tree['child_size'] > 1)]
             if len(split['child']) > 1:
                 left_child, right_child = split['child']
                 cluster_x_coords[cluster] = np.mean([cluster_x_coords[left_child],
                                                      cluster_x_coords[right_child]])
-                cluster_y_coords[left_child] = split['lambda'][0]
-                cluster_y_coords[right_child] = split['lambda'][1]
+                cluster_y_coords[left_child] = split['lambda_val'][0]
+                cluster_y_coords[right_child] = split['lambda_val'][1]
 
         # We use bars to plot the 'icicles', so we need to generate centers, tops, 
         # bottoms and widths for each rectangle. We can go through each cluster 
@@ -150,20 +150,20 @@ def get_plot_data(self, leaf_separation=1, log_size=False):
             cluster_bounds[c][CB_LEFT] = cluster_x_coords[c] * scaling - (current_size / 2.0)
             cluster_bounds[c][CB_RIGHT] = cluster_x_coords[c] * scaling + (current_size / 2.0)
             cluster_bounds[c][CB_BOTTOM] = cluster_y_coords[c]
-            cluster_bounds[c][CB_TOP] = np.max(c_children['lambda'])
+            cluster_bounds[c][CB_TOP] = np.max(c_children['lambda_val'])
 
-            for i in np.argsort(c_children['lambda']):
+            for i in np.argsort(c_children['lambda_val']):
                 row = c_children[i]
-                if row['lambda'] != current_lambda:
+                if row['lambda_val'] != current_lambda:
                     bar_centers.append(cluster_x_coords[c] * scaling)
-                    bar_tops.append(row['lambda'] - current_lambda)
+                    bar_tops.append(row['lambda_val'] - current_lambda)
                     bar_bottoms.append(current_lambda)
                     bar_widths.append(current_size)
                 if log_size:
                     current_size = np.log(np.exp(current_size) - row['child_size'])
                 else:
                     current_size -= row['child_size']
-                current_lambda = row['lambda']
+                current_lambda = row['lambda_val']
 
         # Finally we need the horizontal lines that occur at cluster splits.
         line_xs = []
@@ -349,15 +349,15 @@ def to_pandas(self):
         """Return a pandas dataframe representation of the condensed tree.
 
         Each row of the dataframe corresponds to an edge in the tree.
-        The columns of the dataframe are `parent`, `child`, `lambda`
+        The columns of the dataframe are `parent`, `child`, `lambda_val`
         and `child_size`. 
 
         The `parent` and `child` are the ids of the
         parent and child nodes in the tree. Node ids less than the number 
         of points in the original dataset represent individual points, while
         ids greater than the number of points are clusters.
 
-        The `lambda` value is the value (1/distance) at which the `child`
+        The `lambda_val` value is the value (1/distance) at which the `child`
         node leaves the cluster.
 
         The `child_size` is the number of points in the `child` node.
@@ -389,7 +389,7 @@ def to_networkx(self):
 
         result = DiGraph()
         for row in self._raw_tree:
-            result.add_edge(row['parent'], row['child'], weight=row['lambda'])
+            result.add_edge(row['parent'], row['child'], weight=row['lambda_val'])
 
         set_node_attributes(result, 'size', dict(self._raw_tree[['child', 'child_size']]))