readme

README.md

@@ -14,7 +14,6 @@ Data used for training, models and binding pocket visualizations available [here
 NOTE: The user friendly API is currently being built and tested. Sample usage described below is still being improved.
 
 ## Requirements
-
 * Python 3.6+
 * Networkx 2.1+
 * BioPython
@@ -91,7 +90,7 @@ You can convert this output to a distance matrix and a list indicating the graph
 >>> 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)
+>>> DM, L, graphlist = prepare_data(geds, fps)
 ```
 
 The distance matrix can be passed to a prototype selector to get the indices in the DM selected as prototypes.
@@ -99,11 +98,34 @@ The distance matrix can be passed to a prototype selector to get the indices in
 ```python
 >>> from RNAmigos.dissimilarity_embed import prototype_select
 >>> prototypes = prototype_select(DM, 20, heuristic='spanning')
+>>> prototypes
+[288,
+ 469,
+ 593,
+ 6,
+ 428,
+ 121,
+ 503,
+ 533,
+ 548,
+ 16,
+ 368,
+ 13,
+ 240,
+ 378,
+ 526,
+ 28,
+ 86,
+ 118,
+ 145,
+ 180]
 ```
 
 ### Embedding a full dataset
 
-To embed the graphs used in the GED comparisons and select prototypes in one call to get a matrix of size N x r where N is the nubmer of graphs in DM and r is the number of prototypes used.
+We can embed the graphs used in the GED comparisons and select prototypes in one call to get a matrix of size N x r where N is the nubmer of graphs in DM and r is the number of prototypes used.
+
+Here, we ebmed each graph in `graphlist` with 20 prorotypes using our previously computed distance matrix and the k-centers prototype selector.
 
 ```python
 >>> from RNAmigos.dissimilarity_embed import full_embed
@@ -119,10 +141,22 @@ array([ 8., 10., 10.,  8., 22., 10.,  6.,  8.,  8., 18., 20.,  8., 10.,
 ```
 
 ### Embedding a single graph
+```python
+>>> G = nx.read_gpickle('/data/1jau.nxpickle')
+>>> prototoypes = pickle.load('/data/sample_prototoypes.pickle')
+>>> graph_embed(G, prototypes)
+[1, 0, 2, 4, 1, 4, 5]
 
-User-friendly API coming soon.
+```
 
+User-friendly API coming soon.
 
 ## Fingerprint Prediction
 
-User-friendly API coming soon.
+Once all graphs are embedded we have the standard machine learning input matrix $X$ with $n$ examples as rows and $r$ features as distances to each prototype. 
+Any type of classification can now be performed using a label (output) vector for single-class classification or matrix for multi-output classification.
+
+
+
+
+Alternatively, we can classify graphs using k-nearest neighbours and skip the embedding procedure.

RNAmigos/dissimilarity_embed.py

@@ -169,7 +169,7 @@ def k_centers(DM, k, return_assignments=False):
     # return protos
 
 
-def prototypes(DM,m, heuristic='spanning'):
+def prototype_select(DM,m, heuristic='spanning'):
     """
     Compute set of m prototype graphs.
 
@@ -202,9 +202,7 @@ def graph_embed(G, prototypes):
     """
     embedding = np.zeros(len(prototypes))
     for p in prototypes:
-        g = pickle.load(open(G, 'rb'))
-        p  = pickle.load(open(p, 'rb'))
-        ops,_,_ = ged((g1,p), source_only=True)
+        ops,_,_ = ged((G,p), source_only=True)
         embedding.append(ops.cost)
     return embedding
 
@@ -217,7 +215,7 @@ def full_embed(D, m, DM=None, dist_mat=None, heuristic='spanning'):
     """
     if dist_mat == None:
         DM = distance_matrix_para(D)
-    P_idx = prototypes(DM,m, heuristic=heuristic)
+    P_idx = prototype_select(DM,m, heuristic=heuristic)
     logging.info("Embedding graphs.")
     embeddings = np.zeros((len(D), m))