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Fixed argument parsing in eval scripts.

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This commit is contained in:
williamleif 2017-06-07 08:35:13 -07:00
parent ecc71c6f9e
commit fc88dc47cc
4 changed files with 30 additions and 21 deletions
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6
eval_scripts/citation_eval.py
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@ -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("data_dir", "Path to directory containing the learned node embeddings.")
parser.add_argument("setting", "Either val or test.")
parser.add_argument("dataset_dir", help="Path to directory containing the dataset.")
parser.add_argument("data_dir", help="Path to directory containing the learned node embeddings.")
parser.add_argument("setting", help="Either val or test.")
args = parser.parse_args()
dataset_dir = args.dataset_dir
data_dir = args.data_dir

12
eval_scripts/ppi_eval.py
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@ -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("data_dir", "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("dataset_dir", help="Path to directory containing the dataset.")
parser.add_argument("data_dir", help="Path to directory containing the learned node embeddings. Set to 'feat' for raw features.")
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")
## Logistic gets through off by big counts, so log transform num comments and score
feats = np.load(dataset_dir + "/ppi-feats.npy")
## 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"))

15
eval_scripts/reddit_eval.py
View File

@ -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("data_dir", "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("dataset_dir", help="Path to directory containing the dataset.")
parser.add_argument("data_dir", help="Path to directory containing the learned node embeddings. Set to 'feat' for raw features.")
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"))

18
graphsage/utils.py
View File

@ -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]))