dmlc--dgl
ziqiaomeng
ddeb86f91c
* Prepare for metapath sampler This file is just for reviewing metapath sampling algorithm (Python version). * Delete metapath_sampler * Prepare for metapath sampler This file is just for reviewing metapath sampling algorithm (Python code). * Add files via upload * Create metapath2vec.md * Add files via upload * Delete data_handler.py * Delete word2vec.py * Delete word_train.py * Add files via upload Metapath2vec implementations. Metapath2vec++ needs negative sampler optimization. * Delete shuffle_training.py * Delete test.py * Add files via upload * Delete sampler.py * Delete metapath_sampler.md * Add files via upload * Update and rename shuffle_training.py to metapath2vec.py * Update reading_data.py * Update metapath2vec.md * Update metapath2vec.md * Update metapath2vec.md * Update metapath2vec.md * Update metapath2vec.md * Create label 2 * Delete label 2 * Create testing.md * Add files via upload * Create sample.md * Add files via upload * Delete sampler.py * Add files via upload * Delete googlescholar.8area.author.label.txt * Delete googlescholar.8area.venue.label.txt * Delete testing.md * Delete id_author.txt * Delete id_conf.txt * Delete paper.txt * Delete paper_author.txt * Delete paper_conf.txt * Delete sample.md * Delete sampler.py * Add files via upload * Add files via upload * Add files via upload * Delete reading_data.py * Add files via upload * Add files via upload * Delete metapath2vec.py * Add files via upload * Rename shuffle_training.py to metapath2vec.py * Update metapath2vec.md * Delete reading_data.py * add comments and remov e commented codes
111 行
4.6 KiB
Python
111 行
4.6 KiB
Python
import numpy as np
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import f1_score
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if __name__ == "__main__":
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venue_count = 133
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author_count = 246678
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experiment_times = 1
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percent = 0.05
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file = open(".../output_file_path/...")
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file_2 = open(".../label 2/googlescholar.8area.author.label.txt")
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check_venue = {}
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check_author = {}
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for line in file_1:
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venue_label = line.strip().split(" ")
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check_venue[venue_label[0]] = int(venue_label[1])
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for line in file_2:
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author_label = line.strip().split(" ")
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check_author[author_label[0]] = int(author_label[1])
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venue_embed_dict = {}
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author_embed_dict = {}
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# collect embeddings separately in dictionary form
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file.readline()
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print("read line by line")
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for line in file:
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embed = line.strip().split(' ')
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if embed[0] in check_venue:
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venue_embed_dict[embed[0]] = []
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for i in range(1, len(embed), 1):
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venue_embed_dict[embed[0]].append(float(embed[i]))
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if embed[0] in check_author:
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author_embed_dict[embed[0]] = []
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for j in range(1, len(embed), 1):
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author_embed_dict[embed[0]].append(float(embed[j]))
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#get venue embeddings
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print("reading finished")
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venues = list(venue_embed_dict.keys())
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authors = list(author_embed_dict.keys())
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macro_average_venue = 0
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micro_average_venue = 0
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macro_average_author = 0
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micro_average_author = 0
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for time in range(experiment_times):
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print("one more time")
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np.random.shuffle(venues)
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np.random.shuffle(authors)
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venue_embedding = np.array([])
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author_embedding = np.array([])
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print("collecting venue embeddings")
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for venue in venues:
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temp = np.array(venue_embed_dict[venue])
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if len(venue_embedding) == 0:
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venue_embedding = temp
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else:
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venue_embedding = np.vstack((venue_embedding, temp))
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print("collecting author embeddings")
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count = 0
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for author in authors:
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count += 1
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print("one more author " + str(count))
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temp_1 = np.array(author_embed_dict[author])
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if len(author_embedding) == 0:
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author_embedding = temp_1
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else:
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author_embedding = np.vstack((author_embedding, temp_1))
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# split data into training and testing
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author_split = int(author_count * 0.8)
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author_training = author_embedding[:author_split+1,:]
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author_testing = author_embedding[author_split+1:,:]
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print("splitting")
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venue_split = int(venue_count * percent)
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venue_training = venue_embedding[:venue_split,:]
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venue_testing = venue_embedding[venue_split:,:]
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author_split = int(author_count * percent)
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author_training = author_embedding[:author_split,:]
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author_testing = author_embedding[author_split:,:]
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# split label into training and testing
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venue_label = []
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venue_true = []
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author_label = []
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author_true = []
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for i in range(len(venues)):
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if i < venue_split:
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venue_label.append(check_venue[venues[i]])
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else:
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venue_true.append(check_venue[venues[i]])
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venue_label = np.array(venue_label)
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venue_true = np.array(venue_true)
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for j in range(len(authors)):
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if j < author_split:
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author_label.append(check_author[authors[j]])
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else:
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author_true.append(check_author[authors[j]])
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author_label = np.array(author_label)
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author_true = np.array(author_true)
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file.close()
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print("beging predicting")
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clf_venue = LogisticRegression(random_state=0, solver="lbfgs", multi_class="multinomial").fit(venue_training,venue_label)
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y_pred_venue = clf_venue.predict(venue_testing)
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clf_author = LogisticRegression(random_state=0, solver="lbfgs", multi_class="multinomial").fit(author_training,author_label)
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y_pred_author = clf_author.predict(author_testing)
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macro_average_venue += f1_score(venue_true, y_pred_venue, average="macro")
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micro_average_venue += f1_score(venue_true, y_pred_venue, average="micro")
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macro_average_author += f1_score(author_true, y_pred_author, average="macro")
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micro_average_author += f1_score(author_true, y_pred_author, average="micro")
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print(macro_average_venue/float(experiment_times))
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print(micro_average_venue/float(experiment_times))
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print(macro_average_author / float(experiment_times))
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print(micro_average_author / float(experiment_times))
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