Fixed argument parsing in eval scripts.

eval_scripts/citation_eval.py

@@ -30,9 +30,9 @@ def run_regression(train_embeds, train_labels, test_embeds, test_labels):
 
 if __name__ == '__main__':
     parser = ArgumentParser("Run evaluation on citation data.")
-    parser.add_argument("dataset_dir", "Path to directory containing the dataset.")
+    parser.add_argument("dataset_dir", help="Path to directory containing the dataset.")
-    parser.add_argument("data_dir", "Path to directory containing the learned node embeddings.")
+    parser.add_argument("data_dir", help="Path to directory containing the learned node embeddings.")
-    parser.add_argument("setting", "Either val or test.")
+    parser.add_argument("setting", help="Either val or test.")
     args = parser.parse_args()
     dataset_dir = args.dataset_dir
     data_dir = args.data_dir

eval_scripts/ppi_eval.py

@@ -11,7 +11,7 @@ def run_regression(train_embeds, train_labels, test_embeds, test_labels):
     from sklearn.dummy import DummyClassifier
     from sklearn.metrics import f1_score
     from sklearn.multioutput import MultiOutputClassifier
-    dummy = MultiOutputClassifier(DummyClassifier(strategy='uniform'))
+    dummy = MultiOutputClassifier(DummyClassifier())
     dummy.fit(train_embeds, train_labels)
     log = MultiOutputClassifier(SGDClassifier(loss="log"), n_jobs=10)
     log.fit(train_embeds, train_labels)
@@ -20,9 +20,9 @@ def run_regression(train_embeds, train_labels, test_embeds, test_labels):
 
 if __name__ == '__main__':
     parser = ArgumentParser("Run evaluation on PPI data.")
-    parser.add_argument("dataset_dir", "Path to directory containing the dataset.")
+    parser.add_argument("dataset_dir", help="Path to directory containing the dataset.")
-    parser.add_argument("data_dir", "Path to directory containing the learned node embeddings. Set to 'feat' for raw features.")
+    parser.add_argument("data_dir", help="Path to directory containing the learned node embeddings. Set to 'feat' for raw features.")
-    parser.add_argument("setting", "Either val or test.")
+    parser.add_argument("setting", help="Either val or test.")
     args = parser.parse_args()
     dataset_dir = args.dataset_dir
     data_dir = args.data_dir
@@ -41,8 +41,8 @@ if __name__ == '__main__':
 
     if data_dir == "feat":
         print("Using only features..")
-        feats = np.load(data_dir + "/ppi-feats.npy")
+        feats = np.load(dataset_dir + "/ppi-feats.npy")
-        ## Logistic gets through off by big counts, so log transform num comments and score
+        ## Logistic gets thrown off by big counts, so log transform num comments and score
         feats[:,0] = np.log(feats[:,0]+1.0)
         feats[:,1] = np.log(feats[:,1]-min(np.min(feats[:,1]), -1))
         feat_id_map = json.load(open("/dfs/scratch0/graphnet/ppi/ppi-id_map.json"))

eval_scripts/reddit_eval.py

@@ -12,23 +12,20 @@ def run_regression(train_embeds, train_labels, test_embeds, test_labels):
     from sklearn.metrics import f1_score
     dummy = DummyClassifier()
     dummy.fit(train_embeds, train_labels)
-    log = SGDClassifier(loss="log", n_jobs=55, n_iter=50)
+    log = SGDClassifier(loss="log", n_jobs=55)
     log.fit(train_embeds, train_labels)
     print("Test scores")
     print(f1_score(test_labels, log.predict(test_embeds), average="micro"))
-    print(f1_score(test_labels, log.predict(test_embeds), average="macro"))
     print("Train scores")
     print(f1_score(train_labels, log.predict(train_embeds), average="micro"))
-    print(f1_score(train_labels, log.predict(train_embeds), average="macro"))
     print("Random baseline")
     print(f1_score(test_labels, dummy.predict(test_embeds), average="micro"))
-    print(f1_score(test_labels, dummy.predict(test_embeds), average="macro"))
 
 if __name__ == '__main__':
     parser = ArgumentParser("Run evaluation on Reddit data.")
-    parser.add_argument("dataset_dir", "Path to directory containing the dataset.")
+    parser.add_argument("dataset_dir", help="Path to directory containing the dataset.")
-    parser.add_argument("data_dir", "Path to directory containing the learned node embeddings. Set to 'feat' for raw features.")
+    parser.add_argument("data_dir", help="Path to directory containing the learned node embeddings. Set to 'feat' for raw features.")
-    parser.add_argument("setting", "Either val or test.")
+    parser.add_argument("setting", help="Either val or test.")
     args = parser.parse_args()
     dataset_dir = args.dataset_dir
     data_dir = args.data_dir
@@ -37,8 +34,6 @@ if __name__ == '__main__':
     print("Loading data...")
     G = json_graph.node_link_graph(json.load(open(dataset_dir + "/reddit-G.json")))
     labels = json.load(open(dataset_dir + "/reddit-class_map.json"))
-    data_dir = sys.argv[1]
-    setting = sys.argv[2]
     
     train_ids = [n for n in G.nodes() if not G.node[n]['val'] and not G.node[n]['test']]
     test_ids = [n for n in G.nodes() if G.node[n][setting]]
@@ -48,7 +43,7 @@ if __name__ == '__main__':
     if data_dir == "feat":
         print("Using only features..")
         feats = np.load(dataset_dir + "/reddit-feats.npy")
-        ## Logistic gets through off by big counts, so log transform num comments and score
+        ## Logistic gets thrown off by big counts, so log transform num comments and score
         feats[:,0] = np.log(feats[:,0]+1.0)
         feats[:,1] = np.log(feats[:,1]-min(np.min(feats[:,1]), -1))
         feat_id_map = json.load(open(dataset_dir + "reddit-id_map.json"))

graphsage/utils.py

@@ -1,6 +1,9 @@
+from __future__ import print_function
+
 import numpy as np
 import random
 import json
+import sys
 
 from networkx.readwrite import json_graph
 
@@ -52,7 +55,6 @@ def load_data(prefix, normalize=True):
     return G, feats, id_map, walks, class_map
 
 def run_random_walks(G, nodes, num_walks=N_WALKS):
-    print("Subgraph for walks is of size", len(G))
     pairs = []
     for count, node in enumerate(nodes):
         if G.degree(node) == 0:
@@ -66,5 +68,17 @@ def run_random_walks(G, nodes, num_walks=N_WALKS):
                     pairs.append((node,curr_node))
                 curr_node = next_node
         if count % 1000 == 0:
-            print(count)
+            print("Done walks for", count, "nodes")
     return pairs
+
+if __name__ == "__main__":
+    """ Run random walks """
+    graph_file = sys.argv[1]
+    out_file = sys.argv[2]
+    G_data = json.load(open(graph_file))
+    G = json_graph.node_link_graph(G_data)
+    nodes = [n for n in G.nodes() if not G.node[n]["val"] and not G.node[n]["test"]]
+    G = G.subgraph(nodes)
+    pairs = run_random_walks(G, nodes)
+    with open(out_file, "w") as fp:
+        fp.write("\n".join([p[0] + "\t" + p[1] for p in pairs]))