tensorflow - 使用机器学习或深度学习在给出节点到节点映射的情况下预测两个节点之间的链接概率

Question

有人可以指导我看一个教程，为下面给出的问题提供一个初步的想法。

我有一个作者到共同作者的映射，如下所示:

mapping
>>
{0: [2860, 3117],
 1: [318, 1610, 1776, 1865, 2283, 2507, 3076, 3108, 3182, 3357, 3675, 4040],
 2: [164, 413, 1448, 1650, 3119, 3238],
} # this is just sample


link_attributes.iloc[:5,:7]
>>

first   id  keyword_0   keyword_10  keyword_13  keyword_15  keyword_2
0        4      0        1.0             1.0    1.0         1.0
1        9      1        1.0             1.0    1.0         1.0
2        7      2        1.0             NaN    1.0         1.0
3        6      3        1.0             1.0    NaN         1.0
4        9      4        1.0             1.0    1.0         1.0

我必须预测在 Source 和 Sink 之间建立链接的概率

例如，如果我得到一个 Source=13 和 Sink=31，那么我必须找到在 13 和 31 之间建立链接的概率。所有链接是无向的。

Answer 1

import json
import numpy
from keras import Sequential
from keras.layers import Dense

def get_keys(data, keys):  # get all keys from json file
    if isinstance(data, list):
        for item in data:
            get_keys(item, keys)
    if isinstance(data, dict):
        sub_keys = data.keys()
        for sub_key in sub_keys:
            keys.append(sub_key)

# get all keys, each key is a feature of instances
json_data = open("nodes.json")  # read 4016 instances
jdata = json.load(json_data)
keys = []
get_keys(jdata, keys)
keys = set(keys)
print(set(keys))

def build_instance(json_object):  # use to build instance from json object, ex: instance = [f0,f1,f2,f3,....f404]
    features = []
    features.append(json_object.get('id'))
    for key in keys:
        value = json_object.get(key)
        if value is None:
            value = 0
        elif key == 'id':
            continue
        features.append(value)
    return features

# read all instances and format them, each instance will be [f0,f1, f2,...], as i read from json file, each instance will have 405 features
instances = []
num_of_instances = 0
for item in jdata:
    features = build_instance(item)
    instances.append(features)
    num_of_instances = num_of_instances + 1
    print(num_of_instances)

# read "author_id - co author ids" file
traintxt = open('train.txt', 'r')
lines = traintxt.readlines()

au_vs_co_auth_list = []
for line in lines:
    line = line.split('\t', 200)
    print(line)
    # convert value from string to int
    string = line[0]  # example line[0] = '14 445'
    id_vs_coauthor = string.split(" ", 200)
    id = id_vs_coauthor[0]
    co_author = id_vs_coauthor[1]

    line[0:1] = [int(id), int(co_author)]
    for i in range(2, len(line)):
        line[i] = int(line[i])
    au_vs_co_auth_list.append(line)

print(len(au_vs_co_auth_list))  # we have 4016 authors

X_train = []
Y_train = []
generated_train_pairs = []
train_num = 30000  # choose 30000 random training instances
for i in range(train_num):
    print(i)
    index1 = numpy.random.randint(0, len(au_vs_co_auth_list), 1)[0]
    co_authors_of_index1 = au_vs_co_auth_list[index1]
    author_id_of_index_1 = au_vs_co_auth_list[index1][0]

    if index1 % 2 == 0:  # try to create a sample that two author is not related
        index2 = numpy.random.randint(0, len(au_vs_co_auth_list), 1)[0]
        author_id_of_index_2 = au_vs_co_auth_list[index2][0]

        # make sure id1 != id2 and auth 1 and auth2 are not related
        while (index1 == index2) or (author_id_of_index_2 in co_authors_of_index1):
            index2 = numpy.random.randint(0, len(au_vs_co_auth_list), 1)[0]
            author_id_of_index_2 = au_vs_co_auth_list[index2][0]
        y = [0, 1]  # [relative=FALSE,non-related = TRUE]
    else:  # try to create a sample that two author is related
        author_id_of_index_2 = numpy.random.randint(1, len(co_authors_of_index1),size=1)[0]
        y = [1, 0]  # [relative=TRUE,non-related = FALSE]

    x = instances[author_id_of_index_1][1:] + instances[author_id_of_index_2][
                                              1:]  # x = [feature1, feature2,...feature404',feature1', feature2',...feature404']
    X_train.append(x)
    Y_train.append(y)
X_train = numpy.asarray(X_train)
Y_train = numpy.asarray(Y_train)
print(X_train.shape)
print(Y_train.shape)

# now we have x_train, y_train, build model right now
model = Sequential()
model.add(Dense(512, input_shape=X_train[0].shape, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(2, activation='sigmoid'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=512, epochs=3, verbose=2)
model.save("model.h5")
# now to predict probability of linking between two author ids

id1 = 11  # just random
id2 = 732  # just random

author1 = None
author2 = None
for item in jdata:
    if item.get('id') == id1:
        author1 = build_instance(item)
    if item.get('id') == id2:
        author2 = build_instance(item)
    if author1 is not None and author2 is not None:
        break

x_test = author1[1:] + author2[1:]
x_test = numpy.expand_dims(numpy.asarray(x_test), axis=0)
probability = model.predict(x_test)

print("author id ", id1, " and author id ", id2, end=" ")
if probability[0][1] > probability[0][0]:
    print("Not related")
else:
    print("Related")
print(probability)

输出:

author id  11  and author id  732 related