Added eval scripts.
This commit is contained in:
williamleif
parent
c304fe0357
commit
095f63304a
103
eval_scripts/citation_eval.py
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103
eval_scripts/citation_eval.py
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from __future__ import print_function
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import json
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import numpy as np
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from networkx.readwrite import json_graph
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from argparse import ArgumentParser
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def get_class_labels(ids):
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subjs = ["CU", "DA", "DR", "NI", "GU", "IA"]
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class_map = {}
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for i, code in enumerate(subjs):
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with open("/dfs/scratch0/scisurv/clean/{}.tsv".format(code)) as fp:
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fp.readline()
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for line in fp:
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class_map[int(line.split()[0])] = i
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classes = [class_map[i] for i in ids]
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return classes
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def run_regression(train_embeds, train_labels, test_embeds, test_labels):
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np.random.seed(1)
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from sklearn.linear_model import SGDClassifier
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from sklearn.dummy import DummyClassifier
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from sklearn.metrics import f1_score
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dummy = DummyClassifier()
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dummy.fit(train_embeds, train_labels)
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log = SGDClassifier(loss="log", n_jobs=10)
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log.fit(train_embeds, train_labels)
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print("F1 score:", f1_score(test_labels, log.predict(test_embeds), average="micro"))
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print("Random baseline f1 score:", f1_score(test_labels, dummy.predict(test_embeds), average="micro"))
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if __name__ == '__main__':
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parser = ArgumentParser("Run evaluation on citation data.")
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parser.add_argument("dataset_dir", "Path to directory containing the dataset.")
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parser.add_argument("data_dir", "Path to directory containing the learned node embeddings.")
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parser.add_argument("setting", "Either val or test.")
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args = parser.parse_args()
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dataset_dir = args.dataset_dir
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data_dir = args.data_dir
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setting = args.setting
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print("Loading data...")
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G = json_graph.node_link_graph(json.load(open(dataset_dir + "/isi-G.json")))
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train_ids = [n for n in G.nodes() if not G.node[n]['val'] and not G.node[n]['test']]
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test_ids = [n for n in G.nodes() if G.node[n][setting]]
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test_labels = get_class_labels(test_ids)
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train_labels = get_class_labels(train_ids)
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if data_dir == "feat":
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print("Using only features..")
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feats = np.load(dataset_dir + "/isi-feats.npy")
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feat_id_map = json.load(open(dataset_dir + "/isi-id_map.json"))
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feat_id_map = {int(id):val for id,val in feat_id_map.iteritems()}
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train_feats = feats[[feat_id_map[id] for id in train_ids]]
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test_feats = feats[[feat_id_map[id] for id in test_ids]]
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print("Running regression..")
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run_regression(train_feats, train_labels, test_feats, test_labels)
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elif "n2v" in data_dir:
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print("Using n2v vectors.")
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base_embeds = np.load(data_dir + "/val.npy")
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base_id_map = {}
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with open(data_dir + "/val.txt") as fp:
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for i, line in enumerate(fp):
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base_id_map[int(line.strip())] = i
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tuned_embeds = np.load(data_dir + "/val-test.npy")
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tuned_id_map = {}
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with open(data_dir + "/val-test.txt") as fp:
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for i, line in enumerate(fp):
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tuned_id_map[int(line.strip())] = i
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train_embeds = base_embeds[[base_id_map[id] for id in train_ids]]
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test_embeds = tuned_embeds[[tuned_id_map[id] for id in test_ids]]
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print("Running regression..")
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run_regression(train_embeds, train_labels, test_embeds, test_labels)
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# loading feats
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feats = np.load(dataset_dir + "/isi-feats.npy")
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feat_id_map = json.load(open(dataset_dir + "/isi-id_map.json"))
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feat_id_map = {int(id):val for id,val in feat_id_map.iteritems()}
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train_feats = feats[[feat_id_map[id] for id in train_ids]]
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test_feats = feats[[feat_id_map[id] for id in test_ids]]
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train_embeds = np.hstack([train_feats, train_embeds])
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test_embeds = np.hstack([test_feats, test_embeds])
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from sklearn.preprocessing import StandardScaler
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scaler = StandardScaler()
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scaler.fit(train_embeds)
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train_embeds = scaler.transform(train_embeds)
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test_embeds = scaler.transform(test_embeds)
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print("Running regression with feats..")
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run_regression(train_embeds, train_labels, test_embeds, test_labels)
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else:
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embeds = np.load(data_dir + "/val.npy")
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id_map = {}
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with open(data_dir + "/val.txt") as fp:
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for i, line in enumerate(fp):
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id_map[int(line.strip())] = i
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train_embeds = embeds[[id_map[id] for id in train_ids]]
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test_embeds = embeds[[id_map[id] for id in test_ids]]
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print("Running regression..")
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run_regression(train_embeds, train_labels, test_embeds, test_labels)
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69
eval_scripts/ppi_eval.py
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69
eval_scripts/ppi_eval.py
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from __future__ import print_function
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import json
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import numpy as np
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from networkx.readwrite import json_graph
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from argparse import ArgumentParser
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def run_regression(train_embeds, train_labels, test_embeds, test_labels):
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np.random.seed(1)
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from sklearn.linear_model import SGDClassifier
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from sklearn.dummy import DummyClassifier
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from sklearn.metrics import f1_score
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from sklearn.multioutput import MultiOutputClassifier
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dummy = MultiOutputClassifier(DummyClassifier(strategy='uniform'))
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dummy.fit(train_embeds, train_labels)
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log = MultiOutputClassifier(SGDClassifier(loss="log"), n_jobs=10)
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log.fit(train_embeds, train_labels)
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print("F1 score", f1_score(test_labels, log.predict(test_embeds), average="micro"))
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print("Random baseline F1 score", f1_score(test_labels, dummy.predict(test_embeds), average="micro"))
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if __name__ == '__main__':
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parser = ArgumentParser("Run evaluation on PPI data.")
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parser.add_argument("dataset_dir", "Path to directory containing the dataset.")
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parser.add_argument("data_dir", "Path to directory containing the learned node embeddings. Set to 'feat' for raw features.")
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parser.add_argument("setting", "Either val or test.")
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args = parser.parse_args()
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dataset_dir = args.dataset_dir
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data_dir = args.data_dir
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setting = args.setting
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print("Loading data...")
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G = json_graph.node_link_graph(json.load(open(dataset_dir + "/ppi-G.json")))
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labels = json.load(open("/dfs/scratch0/graphnet/ppi/ppi-class_map.json"))
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labels = {int(i):l for i, l in labels.iteritems()}
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train_ids = [n for n in G.nodes() if not G.node[n]['val'] and not G.node[n]['test']]
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test_ids = [n for n in G.nodes() if G.node[n][setting]]
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train_labels = np.array([labels[i] for i in train_ids])
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test_labels = np.array([labels[i] for i in test_ids])
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print("running", data_dir)
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if data_dir == "feat":
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print("Using only features..")
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feats = np.load(data_dir + "/ppi-feats.npy")
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## Logistic gets through off by big counts, so log transform num comments and score
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feats[:,0] = np.log(feats[:,0]+1.0)
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feats[:,1] = np.log(feats[:,1]-min(np.min(feats[:,1]), -1))
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feat_id_map = json.load(open("/dfs/scratch0/graphnet/ppi/ppi-id_map.json"))
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feat_id_map = {int(id):val for id,val in feat_id_map.iteritems()}
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train_feats = feats[[feat_id_map[id] for id in train_ids]]
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test_feats = feats[[feat_id_map[id] for id in test_ids]]
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print("Running regression..")
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from sklearn.preprocessing import StandardScaler
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scaler = StandardScaler()
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scaler.fit(train_feats)
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train_feats = scaler.transform(train_feats)
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test_feats = scaler.transform(test_feats)
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run_regression(train_feats, train_labels, test_feats, test_labels)
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else:
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embeds = np.load(data_dir + "/val.npy")
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id_map = {}
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with open(data_dir + "/val.txt") as fp:
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for i, line in enumerate(fp):
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id_map[int(line.strip())] = i
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train_embeds = embeds[[id_map[id] for id in train_ids]]
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test_embeds = embeds[[id_map[id] for id in test_ids]]
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print("Running regression..")
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run_regression(train_embeds, train_labels, test_embeds, test_labels)
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eval_scripts/reddit_eval.py
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eval_scripts/reddit_eval.py
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from __future__ import print_function
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import json
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import numpy as np
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from networkx.readwrite import json_graph
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from argparse import ArgumentParser
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def run_regression(train_embeds, train_labels, test_embeds, test_labels):
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np.random.seed(1)
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from sklearn.linear_model import SGDClassifier
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from sklearn.dummy import DummyClassifier
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from sklearn.metrics import f1_score
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dummy = DummyClassifier()
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dummy.fit(train_embeds, train_labels)
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log = SGDClassifier(loss="log", n_jobs=55, n_iter=50)
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log.fit(train_embeds, train_labels)
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print("Test scores")
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print(f1_score(test_labels, log.predict(test_embeds), average="micro"))
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print(f1_score(test_labels, log.predict(test_embeds), average="macro"))
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print("Train scores")
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print(f1_score(train_labels, log.predict(train_embeds), average="micro"))
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print(f1_score(train_labels, log.predict(train_embeds), average="macro"))
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print("Random baseline")
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print(f1_score(test_labels, dummy.predict(test_embeds), average="micro"))
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print(f1_score(test_labels, dummy.predict(test_embeds), average="macro"))
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if __name__ == '__main__':
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parser = ArgumentParser("Run evaluation on Reddit data.")
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parser.add_argument("dataset_dir", "Path to directory containing the dataset.")
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parser.add_argument("data_dir", "Path to directory containing the learned node embeddings. Set to 'feat' for raw features.")
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parser.add_argument("setting", "Either val or test.")
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args = parser.parse_args()
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dataset_dir = args.dataset_dir
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data_dir = args.data_dir
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setting = args.setting
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print("Loading data...")
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G = json_graph.node_link_graph(json.load(open(dataset_dir + "/reddit-G.json")))
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labels = json.load(open(dataset_dir + "/reddit-class_map.json"))
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data_dir = sys.argv[1]
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setting = sys.argv[2]
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train_ids = [n for n in G.nodes() if not G.node[n]['val'] and not G.node[n]['test']]
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test_ids = [n for n in G.nodes() if G.node[n][setting]]
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train_labels = [labels[i] for i in train_ids]
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test_labels = [labels[i] for i in test_ids]
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if data_dir == "feat":
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print("Using only features..")
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feats = np.load(dataset_dir + "/reddit-feats.npy")
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## Logistic gets through off by big counts, so log transform num comments and score
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feats[:,0] = np.log(feats[:,0]+1.0)
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feats[:,1] = np.log(feats[:,1]-min(np.min(feats[:,1]), -1))
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feat_id_map = json.load(open(dataset_dir + "reddit-id_map.json"))
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feat_id_map = {id:val for id,val in feat_id_map.iteritems()}
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train_feats = feats[[feat_id_map[id] for id in train_ids]]
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test_feats = feats[[feat_id_map[id] for id in test_ids]]
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print("Running regression..")
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from sklearn.preprocessing import StandardScaler
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scaler = StandardScaler()
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scaler.fit(train_feats)
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train_feats = scaler.transform(train_feats)
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test_feats = scaler.transform(test_feats)
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run_regression(train_feats, train_labels, test_feats, test_labels)
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elif "n2v" in data_dir:
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print("Doing it N2V style.")
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base_embeds = np.load(data_dir + "/val.npy")
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base_id_map = {}
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with open(data_dir + "/val.txt") as fp:
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for i, line in enumerate(fp):
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base_id_map[line.strip()] = i
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tuned_embeds = np.load(data_dir + "/val-test.npy")
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tuned_id_map = {}
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with open(data_dir + "/val-test.txt") as fp:
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for i, line in enumerate(fp):
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tuned_id_map[line.strip()] = i
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train_embeds = base_embeds[[base_id_map[id] for id in train_ids]]
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test_embeds = tuned_embeds[[tuned_id_map[id] for id in test_ids]]
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print("Running regression..")
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run_regression(train_embeds, train_labels, test_embeds, test_labels)
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# loading feats
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feats = np.load(dataset_dir + "/reddit-feats.npy")
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feat_id_map = json.load(open(dataset_dir + "/reddit-id_map.json"))
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feat_id_map = {id:val for id,val in feat_id_map.iteritems()}
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train_feats = feats[[feat_id_map[id] for id in train_ids]]
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test_feats = feats[[feat_id_map[id] for id in test_ids]]
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train_embeds = np.hstack([train_feats, train_embeds])
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test_embeds = np.hstack([test_feats, test_embeds])
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from sklearn.preprocessing import StandardScaler
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scaler = StandardScaler()
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scaler.fit(train_embeds)
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train_embeds = scaler.transform(train_embeds)
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test_embeds = scaler.transform(test_embeds)
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print("Running regression with feats..")
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run_regression(train_embeds, train_labels, test_embeds, test_labels)
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else:
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embeds = np.load(data_dir + "/val.npy")
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id_map = {}
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with open(data_dir + "/val.txt") as fp:
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for i, line in enumerate(fp):
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id_map[line.strip()] = i
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train_embeds = embeds[[id_map[id] for id in train_ids]]
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test_embeds = embeds[[id_map[id] for id in test_ids]]
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print("Running regression..")
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run_regression(train_embeds, train_labels, test_embeds, test_labels)
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