proto select

README.md

@@ -62,6 +62,28 @@ Just pass the alignment object to the `graph_align` function.
 
 You can obtain a vector representation of a binding pocket by building your own prototype set, or by using our pre-built prototype sets.
 
+### Building your prototype set
+
+The output of the GED computation on a set of graphs is a pickled list object containing all pairwise comparisions and some additional information.
+
+You can convert this output to a distance matrix and a list indicating the graphs each entry in the distance matrix correspond to.
+
+```python
+>>> from RNAmigos.post_ged import data_prepare
+>>> geds = '../data/geds_delta.pickle'
+>>> fps = '../data/all_rna_ligands_fingerprints.pickle'
+>>> DM, L, graphlist = data_prepare(geds, fps)
+```
+
+The distance matrix can be passed to a prototype selector.
+
+```python
+>>> from RNAmigos.dissimilarity_embed import prototype_select
+>>> prototypes = prototype_select(DM, 20)
+```
+
+
+
 ## Fingerprint Prediction
 
 

RNAmigos/dissimilarity_embed.py

@@ -173,7 +173,7 @@ def k_centers(DM, k, return_assignments=False):
     # return protos
 
 
-def prototypes(D, m, DM, heuristic='sphere'):
+def prototypes(DM,m, heuristic='spanning'):
     """
     Compute set of m prototype graphs.
 
@@ -188,51 +188,6 @@ def prototypes(D, m, DM, heuristic='sphere'):
     """
     logging.info(f"Using {heuristic} heuristic")
 
-    if heuristic == 'sphere':
-        """
-            Select prototypes from a sphere induced on the dataset.
-        """
-
-        prototypes = []
-        
-        logging.info("Computing distance matrix...")
-
-        logging.info("Finding center of graph set")
-        #get center graph
-        distances = np.sum(DM, axis=1)
-        center_index = np.argmin(distances)
-        center = D[center_index]
-
-        #get graph furthest from center
-        border_index = np.argmax(DM[center_index])
-        border = D[border_index]
-        radius = DM[center_index][border_index]
-
-        #define interval along radius
-        interval = radius / m
-
-        proto_indices = []
-        prototypes += [center, border]
-        proto_indices += [center_index, border_index]
-
-        mask = np.zeros(DM.shape[0])
-        mask[border_index] = 1
-        mask[center_index] = 1
-
-        center_ref = np.ma.MaskedArray(DM[center_index], mask)
-
-        logging.info("Obtaining prototype graphs...")
-
-        for i in range(m-2):
-            border_dist = abs(center_ref - (i*interval))
-            dist_mask = np.ma.MaskedArray(border_dist, mask)
-            proto_index = dist_mask.argmin()
-            proto_indices.append(proto_index)
-            prototypes.append(D[proto_index])
-            #mask the prototype we selected
-            mask[proto_index] = 1
-        return proto_indices
-
     if heuristic == 'spanning':
         return spanning_selection(DM, m)
     if heuristic == "k-centers":