From 22fe70c49db0941178b0c97f0669828e546fbf4c Mon Sep 17 00:00:00 2001
From: opi <opitz@cl.uni-heidelberg.de>
Date: Tue, 23 Jul 2019 15:50:41 +0200
Subject: [PATCH] added comments
---
src/graph_helpers.py | 23 +++++++++++++++++++----
1 file changed, 19 insertions(+), 4 deletions(-)
diff --git a/src/graph_helpers.py b/src/graph_helpers.py
index 359f92e..581c0d1 100644
--- a/src/graph_helpers.py
+++ b/src/graph_helpers.py
@@ -23,6 +23,11 @@ def contract_gender(G, gender="male"):
return G,vns[0][0]
def simplify_text_description_nodes(G,node_index_dict,mode="None",min_freq=1):
+ """function takes our graph and simplifies text description nodes
+ E.g., felouneously stealing, on the 10th Decembre, two silver watches ----> watches
+
+ """
+
if mode == "None":
return G, node_index_dict
#collect all descriptions and their neighbor category
@@ -32,32 +37,42 @@ def simplify_text_description_nodes(G,node_index_dict,mode="None",min_freq=1):
trialnodes=[n for n in G.nodes(data=True) if isinstance(n[1]["nodeobj"],dh.TrialNode)]
descr_nodes=[]
mask=[]
+
+ # we iterate over all trials
for i,tn in enumerate(trialnodes):
#get corresponding cat node
catn = [n for n in G.neighbors(tn[0]) if isinstance(G.nodes[n]["nodeobj"],dh.OffenceNode)][0]
catn=[catn,G.nodes[catn]]
- #print(catn)
- #catn=[cat]
category=catn[1]["nodeobj"].d["category"]
descr_vectors = None
tid = None
+ # we iterate over all neighbors of the trial
for nb in G[tn[0]]:
for edge_id in G[tn[0]][nb]:
+
+ # and grab nodes which describe a offence
if G[tn[0]][nb][edge_id]["edge_class"] == "with-offence-description":
- #print(G.nodes[nb])
+
+ # we collect the noun chunk vectors
descr_vectors,_ = G.nodes[nb]["nodeobj"].get_noun_chunk_vectors()
descr_nodes.append(G.nodes[nb]["nodeobj"])
- #descr_nodes[-1].simplify_to_direct_object()
tid=(tn[0],nb)
Xid.append(tid)
for dv in descr_vectors:
+ #put noun chunk vector into training data
Xvector.append(dv)
+ #put label into training data
related_cat.append(category)
if mode=="classifier":
+ #fit a classifier to learn a mapping between noun chunks and labels
clf=LogisticRegression()
clf.fit(Xvector,related_cat)
+
+
+ # now we can remove the textdescription nodes and insert their simplified fporms
for i,idx in enumerate(Xid):
+
if idx[1] in G:
G.remove_node(idx[1])
#node_index_dict.pop(descr_nodes[i])
--
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