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improve compatibility (#68)

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improve compatibility
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浅梦 2022-06-22 02:24:09 +08:00 committed by GitHub
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74
.github/workflows/ci.yml vendored Normal file
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@ -0,0 +1,74 @@
name: CI
on:
push:
path:
- 'ge/*'
- 'tests/*'
pull_request:
path:
- 'ge/*'
- 'tests/*'
jobs:
build:
runs-on: ubuntu-latest
timeout-minutes: 180
strategy:
matrix:
python-version: [3.6,3.7,3.8]
tf-version: [1.4.0,1.15.0,2.5.0,2.6.0,2.7.0,2.8.0,2.9.0]
exclude:
- python-version: 3.7
tf-version: 1.4.0
- python-version: 3.7
tf-version: 1.15.0
- python-version: 3.8
tf-version: 1.4.0
- python-version: 3.8
tf-version: 1.14.0
- python-version: 3.8
tf-version: 1.15.0
- python-version: 3.6
tf-version: 2.7.0
- python-version: 3.6
tf-version: 2.8.0
- python-version: 3.6
tf-version: 2.9.0
- python-version: 3.9
tf-version: 1.4.0
- python-version: 3.9
tf-version: 1.15.0
- python-version: 3.9
tf-version: 2.2.0
steps:
- uses: actions/checkout@v3
- name: Setup python environment
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
pip3 install -q tensorflow==${{ matrix.tf-version }}
pip install -q protobuf==3.19.0
pip install -q requests
pip install -e .
- name: Test with pytest
timeout-minutes: 180
run: |
pip install -q pytest
pip install -q pytest-cov
pip install -q python-coveralls
pytest --cov=ge --cov-report=xml
- name: Upload coverage to Codecov
uses: codecov/codecov-action@v3.1.0
with:
token: ${{secrets.CODECOV_TOKEN}}
file: ./coverage.xml
flags: pytest
name: py${{ matrix.python-version }}-tf${{ matrix.tf-version }}

13
README.md
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@ -1,5 +1,14 @@
# GraphEmbedding
[![GitHub Issues](https://img.shields.io/github/issues/shenweichen/graphembedding.svg
)](https://github.com/shenweichen/graphembedding/issues)
![CI status](https://github.com/shenweichen/graphembedding/workflows/CI/badge.svg)
[![codecov](https://codecov.io/gh/shenweichen/graphembedding/branch/master/graph/badge.svg)](https://codecov.io/gh/shenweichen/graphembedding)
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/c46407f5931f40048e28860dccf7dabc)](https://www.codacy.com/gh/shenweichen/GraphEmbedding/dashboard?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/GraphEmbedding&utm_campaign=Badge_Grade)
[![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./README.md#disscussiongroup--related-projects)
[comment]: <> ([![License]&#40;https://img.shields.io/github/license/shenweichen/graphembedding.svg&#41;]&#40;https://github.com/shenweichen/graphembedding/blob/master/LICENSE&#41;)
# Method
@ -27,7 +36,7 @@ python deepwalk_wiki.py
<table style="margin-left: 20px; margin-right: auto;">
<tr>
<td>
公众号：<b>浅梦的学习笔记</b><br><br>
公众号：<b>浅梦学习笔记</b><br><br>
<a href="https://github.com/shenweichen/GraphEmbedding">
<img align="center" src="./pics/code.png" />
</a>
@ -101,7 +110,7 @@ embeddings = model.get_embeddings()# get embedding vectors
```python
G = nx.read_edgelist('../data/flight/brazil-airports.edgelist',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
model = model = Struc2Vec(G, 10, 80, workers=4, verbose=40, ) #init model
model = Struc2Vec(G, 10, 80, workers=4, verbose=40, ) #init model
model.train(window_size = 5, iter = 3)# train model
embeddings = model.get_embeddings()# get embedding vectors
```

4
ge/alias.py
View File

@ -22,7 +22,7 @@ def create_alias_table(area_ratio):
accept[small_idx] = area_ratio_[small_idx]
alias[small_idx] = large_idx
area_ratio_[large_idx] = area_ratio_[large_idx] - \
(1 - area_ratio_[small_idx])
(1 - area_ratio_[small_idx])
if area_ratio_[large_idx] < 1.0:
small.append(large_idx)
else:
@ -46,7 +46,7 @@ def alias_sample(accept, alias):
:return: sample index
"""
N = len(accept)
i = int(np.random.random()*N)
i = int(np.random.random() * N)
r = np.random.random()
if r < accept[i]:
return i

4
ge/classify.py
View File

@ -1,6 +1,5 @@
from __future__ import print_function
import numpy
from sklearn.metrics import f1_score, accuracy_score
from sklearn.multiclass import OneVsRestClassifier
@ -41,11 +40,10 @@ class Classifier(object):
results = {}
for average in averages:
results[average] = f1_score(Y, Y_, average=average)
results['acc'] = accuracy_score(Y,Y_)
results['acc'] = accuracy_score(Y, Y_)
print('-------------------')
print(results)
return results
print('-------------------')
def predict(self, X, top_k_list):
X_ = numpy.asarray([self.embeddings[x] for x in X])

12
ge/models/deepwalk.py
View File

@ -6,7 +6,7 @@
Author:
Weichen Shen,wcshen1994@163.com
Weichen Shen,weichenswc@163.com
@ -17,9 +17,9 @@ Reference:
"""
from ..walker import RandomWalker
from gensim.models import Word2Vec
import pandas as pd
from ..walker import RandomWalker
class DeepWalk:
@ -38,12 +38,12 @@ class DeepWalk:
kwargs["sentences"] = self.sentences
kwargs["min_count"] = kwargs.get("min_count", 0)
kwargs["size"] = embed_size
kwargs["vector_size"] = embed_size
kwargs["sg"] = 1 # skip gram
kwargs["hs"] = 1 # deepwalk use Hierarchical Softmax
kwargs["workers"] = workers
kwargs["window"] = window_size
kwargs["iter"] = iter
kwargs["epochs"] = iter
print("Learning embedding vectors...")
model = Word2Vec(**kwargs)
@ -52,7 +52,7 @@ class DeepWalk:
self.w2v_model = model
return model
def get_embeddings(self,):
def get_embeddings(self, ):
if self.w2v_model is None:
print("model not train")
return {}

33
ge/models/line.py
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@ -6,7 +6,7 @@
Author:
Weichen Shen,wcshen1994@163.com
Weichen Shen,weichenswc@163.com
@ -21,7 +21,7 @@ import math
import random
import numpy as np
import tensorflow as tf
from deepctr.layers.utils import reduce_sum
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.layers import Embedding, Input, Lambda
from tensorflow.python.keras.models import Model
@ -31,11 +31,10 @@ from ..utils import preprocess_nxgraph
def line_loss(y_true, y_pred):
return -K.mean(K.log(K.sigmoid(y_true*y_pred)))
return -K.mean(K.log(K.sigmoid(y_true * y_pred)))
def create_model(numNodes, embedding_size, order='second'):
v_i = Input(shape=(1,))
v_j = Input(shape=(1,))
@ -49,10 +48,10 @@ def create_model(numNodes, embedding_size, order='second'):
v_i_emb_second = second_emb(v_i)
v_j_context_emb = context_emb(v_j)
first = Lambda(lambda x: tf.reduce_sum(
x[0]*x[1], axis=-1, keep_dims=False), name='first_order')([v_i_emb, v_j_emb])
second = Lambda(lambda x: tf.reduce_sum(
x[0]*x[1], axis=-1, keep_dims=False), name='second_order')([v_i_emb_second, v_j_context_emb])
first = Lambda(lambda x: reduce_sum(
x[0] * x[1], axis=-1, keep_dims=False), name='first_order')([v_i_emb, v_j_emb])
second = Lambda(lambda x: reduce_sum(
x[0] * x[1], axis=-1, keep_dims=False), name='second_order')([v_i_emb_second, v_j_context_emb])
if order == 'first':
output_list = [first]
@ -67,7 +66,7 @@ def create_model(numNodes, embedding_size, order='second'):
class LINE:
def __init__(self, graph, embedding_size=8, negative_ratio=5, order='second',):
def __init__(self, graph, embedding_size=8, negative_ratio=5, order='second', ):
"""
:param graph:
@ -91,7 +90,7 @@ class LINE:
self.node_size = graph.number_of_nodes()
self.edge_size = graph.number_of_edges()
self.samples_per_epoch = self.edge_size*(1+negative_ratio)
self.samples_per_epoch = self.edge_size * (1 + negative_ratio)
self._gen_sampling_table()
self.reset_model()
@ -99,7 +98,7 @@ class LINE:
def reset_training_config(self, batch_size, times):
self.batch_size = batch_size
self.steps_per_epoch = (
(self.samples_per_epoch - 1) // self.batch_size + 1)*times
(self.samples_per_epoch - 1) // self.batch_size + 1) * times
def reset_model(self, opt='adam'):
@ -118,7 +117,7 @@ class LINE:
for edge in self.graph.edges():
node_degree[node2idx[edge[0]]
] += self.graph[edge[0]][edge[1]].get('weight', 1.0)
] += self.graph[edge[0]][edge[1]].get('weight', 1.0)
total_sum = sum([math.pow(node_degree[i], power)
for i in range(numNodes)])
@ -165,10 +164,9 @@ class LINE:
t.append(cur_t)
sign = np.ones(len(h))
else:
sign = np.ones(len(h))*-1
sign = np.ones(len(h)) * -1
t = []
for i in range(len(h)):
t.append(alias_sample(
self.node_accept, self.node_alias))
@ -190,7 +188,7 @@ class LINE:
start_index = 0
end_index = min(start_index + self.batch_size, data_size)
def get_embeddings(self,):
def get_embeddings(self, ):
self._embeddings = {}
if self.order == 'first':
embeddings = self.embedding_dict['first'].get_weights()[0]
@ -198,7 +196,7 @@ class LINE:
embeddings = self.embedding_dict['second'].get_weights()[0]
else:
embeddings = np.hstack((self.embedding_dict['first'].get_weights()[
0], self.embedding_dict['second'].get_weights()[0]))
0], self.embedding_dict['second'].get_weights()[0]))
idx2node = self.idx2node
for i, embedding in enumerate(embeddings):
self._embeddings[idx2node[i]] = embedding
@ -207,7 +205,8 @@ class LINE:
def train(self, batch_size=1024, epochs=1, initial_epoch=0, verbose=1, times=1):
self.reset_training_config(batch_size, times)
hist = self.model.fit_generator(self.batch_it, epochs=epochs, initial_epoch=initial_epoch, steps_per_epoch=self.steps_per_epoch,
hist = self.model.fit_generator(self.batch_it, epochs=epochs, initial_epoch=initial_epoch,
steps_per_epoch=self.steps_per_epoch,
verbose=verbose)
return hist

7
ge/models/node2vec.py
View File

@ -6,7 +6,7 @@
Author:
Weichen Shen,wcshen1994@163.com
Weichen Shen,weichenswc@163.com
@ -19,14 +19,13 @@ Reference:
"""
from gensim.models import Word2Vec
import pandas as pd
from ..walker import RandomWalker
class Node2Vec:
def __init__(self, graph, walk_length, num_walks, p=1.0, q=1.0, workers=1, use_rejection_sampling=0):
def __init__(self, graph, walk_length, num_walks, p=1.0, q=1.0, workers=1, use_rejection_sampling=False):
self.graph = graph
self._embeddings = {}
@ -57,7 +56,7 @@ class Node2Vec:
return model
def get_embeddings(self,):
def get_embeddings(self, ):
if self.w2v_model is None:
print("model not train")
return {}

40
ge/models/sdne.py
View File

@ -6,7 +6,7 @@
Author:
Weichen Shen,wcshen1994@163.com
Weichen Shen,weichenswc@163.com
@ -88,8 +88,7 @@ class SDNE(object):
self.nu1 = nu1
self.nu2 = nu2
self.A, self.L = self._create_A_L(
self.graph, self.node2idx) # Adj Matrix,L Matrix
self.A, self.L = _create_A_L(self.graph, self.node2idx) # Adj Matrix,L Matrix
self.reset_model()
self.inputs = [self.A, self.L]
self._embeddings = {}
@ -151,24 +150,25 @@ class SDNE(object):
return self._embeddings
def _create_A_L(self, graph, node2idx):
node_size = graph.number_of_nodes()
A_data = []
A_row_index = []
A_col_index = []
for edge in graph.edges():
v1, v2 = edge
edge_weight = graph[v1][v2].get('weight', 1)
def _create_A_L(graph, node2idx):
node_size = graph.number_of_nodes()
A_data = []
A_row_index = []
A_col_index = []
A_data.append(edge_weight)
A_row_index.append(node2idx[v1])
A_col_index.append(node2idx[v2])
for edge in graph.edges():
v1, v2 = edge
edge_weight = graph[v1][v2].get('weight', 1)
A = sp.csr_matrix((A_data, (A_row_index, A_col_index)), shape=(node_size, node_size))
A_ = sp.csr_matrix((A_data + A_data, (A_row_index + A_col_index, A_col_index + A_row_index)),
shape=(node_size, node_size))
A_data.append(edge_weight)
A_row_index.append(node2idx[v1])
A_col_index.append(node2idx[v2])
D = sp.diags(A_.sum(axis=1).flatten().tolist()[0])
L = D - A_
return A, L
A = sp.csr_matrix((A_data, (A_row_index, A_col_index)), shape=(node_size, node_size))
A_ = sp.csr_matrix((A_data + A_data, (A_row_index + A_col_index, A_col_index + A_row_index)),
shape=(node_size, node_size))
D = sp.diags(A_.sum(axis=1).flatten().tolist()[0])
L = D - A_
return A, L

43
ge/models/struc2vec.py
View File

@ -6,7 +6,7 @@
Author:
Weichen Shen,wcshen1994@163.com
Weichen Shen,weichenswc@163.com
@ -28,7 +28,6 @@ import pandas as pd
from fastdtw import fastdtw
from gensim.models import Word2Vec
from joblib import Parallel, delayed
from tqdm import tqdm
from ..alias import create_alias_table
from ..utils import partition_dict, preprocess_nxgraph
@ -36,7 +35,8 @@ from ..walker import BiasedWalker
class Struc2Vec():
def __init__(self, graph, walk_length=10, num_walks=100, workers=1, verbose=0, stay_prob=0.3, opt1_reduce_len=True, opt2_reduce_sim_calc=True, opt3_num_layers=None, temp_path='./temp_struc2vec/', reuse=False):
def __init__(self, graph, walk_length=10, num_walks=100, workers=1, verbose=0, stay_prob=0.3, opt1_reduce_len=True,
opt2_reduce_sim_calc=True, opt3_num_layers=None, temp_path='./temp_struc2vec/', reuse=False):
self.graph = graph
self.idx2node, self.node2idx = preprocess_nxgraph(graph)
self.idx = list(range(len(self.idx2node)))
@ -62,10 +62,10 @@ class Struc2Vec():
self._embeddings = {}
def create_context_graph(self, max_num_layers, workers=1, verbose=0,):
def create_context_graph(self, max_num_layers, workers=1, verbose=0, ):
pair_distances = self._compute_structural_distance(
max_num_layers, workers, verbose,)
max_num_layers, workers, verbose, )
layers_adj, layers_distances = self._get_layer_rep(pair_distances)
pd.to_pickle(layers_adj, self.temp_path + 'layers_adj.pkl')
@ -74,16 +74,16 @@ class Struc2Vec():
pd.to_pickle(layers_alias, self.temp_path + 'layers_alias.pkl')
pd.to_pickle(layers_accept, self.temp_path + 'layers_accept.pkl')
def prepare_biased_walk(self,):
def prepare_biased_walk(self, ):
sum_weights = {}
sum_edges = {}
average_weight = {}
gamma = {}
layer = 0
while (os.path.exists(self.temp_path+'norm_weights_distance-layer-' + str(layer)+'.pkl')):
while (os.path.exists(self.temp_path + 'norm_weights_distance-layer-' + str(layer) + '.pkl')):
probs = pd.read_pickle(
self.temp_path+'norm_weights_distance-layer-' + str(layer)+'.pkl')
self.temp_path + 'norm_weights_distance-layer-' + str(layer) + '.pkl')
for v, list_weights in probs.items():
sum_weights.setdefault(layer, 0)
sum_edges.setdefault(layer, 0)
@ -112,14 +112,15 @@ class Struc2Vec():
sentences = self.sentences
print("Learning representation...")
model = Word2Vec(sentences, size=embed_size, window=window_size, min_count=0, hs=1, sg=1, workers=workers,
iter=iter)
model = Word2Vec(sentences, vector_size=embed_size, window=window_size, min_count=0, hs=1, sg=1,
workers=workers,
epochs=iter)
print("Learning representation done!")
self.w2v_model = model
return model
def get_embeddings(self,):
def get_embeddings(self, ):
if self.w2v_model is None:
print("model not train")
return {}
@ -184,11 +185,11 @@ class Struc2Vec():
return ordered_degree_sequence_dict
def _compute_structural_distance(self, max_num_layers, workers=1, verbose=0,):
def _compute_structural_distance(self, max_num_layers, workers=1, verbose=0, ):
if os.path.exists(self.temp_path+'structural_dist.pkl'):
if os.path.exists(self.temp_path + 'structural_dist.pkl'):
structural_dist = pd.read_pickle(
self.temp_path+'structural_dist.pkl')
self.temp_path + 'structural_dist.pkl')
else:
if self.opt1_reduce_len:
dist_func = cost_max
@ -219,8 +220,9 @@ class Struc2Vec():
for v in degreeList:
vertices[v] = [vd for vd in degreeList.keys() if vd > v]
results = Parallel(n_jobs=workers, verbose=verbose,)(
delayed(compute_dtw_dist)(part_list, degreeList, dist_func) for part_list in partition_dict(vertices, workers))
results = Parallel(n_jobs=workers, verbose=verbose, )(
delayed(compute_dtw_dist)(part_list, degreeList, dist_func) for part_list in
partition_dict(vertices, workers))
dtw_dist = dict(ChainMap(*results))
structural_dist = convert_dtw_struc_dist(dtw_dist)
@ -303,7 +305,7 @@ class Struc2Vec():
node_accept_dict[v] = accept
pd.to_pickle(
norm_weights, self.temp_path + 'norm_weights_distance-layer-' + str(layer)+'.pkl')
norm_weights, self.temp_path + 'norm_weights_distance-layer-' + str(layer) + '.pkl')
layers_alias[layer] = node_alias_dict
layers_accept[layer] = node_accept_dict
@ -406,12 +408,11 @@ def get_vertices(v, degree_v, degrees, n_nodes):
def verifyDegrees(degrees, degree_v_root, degree_a, degree_b):
if(degree_b == -1):
if (degree_b == -1):
degree_now = degree_a
elif(degree_a == -1):
elif (degree_a == -1):
degree_now = degree_b
elif(abs(degree_b - degree_v_root) < abs(degree_a - degree_v_root)):
elif (abs(degree_b - degree_v_root) < abs(degree_a - degree_v_root)):
degree_now = degree_b
else:
degree_now = degree_a

4
ge/utils.py
View File

@ -43,6 +43,6 @@ def partition_list(vertices, workers):
def partition_num(num, workers):
if num % workers == 0:
return [num//workers]*workers
return [num // workers] * workers
else:
return [num//workers]*workers + [num % workers]
return [num // workers] * workers + [num % workers]

36
ge/walker.py
View File

@ -2,17 +2,15 @@ import itertools
import math
import random
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from tqdm import trange
from .alias import alias_sample, create_alias_table
from .utils import partition_num
class RandomWalker:
def __init__(self, G, p=1, q=1, use_rejection_sampling=0):
def __init__(self, G, p=1, q=1, use_rejection_sampling=False):
"""
:param G:
:param p: Return parameter,controls the likelihood of immediately revisiting a node in the walk.
@ -130,7 +128,7 @@ class RandomWalker:
return walks
def _simulate_walks(self, nodes, num_walks, walk_length,):
def _simulate_walks(self, nodes, num_walks, walk_length, ):
walks = []
for _ in range(num_walks):
random.shuffle(nodes)
@ -161,14 +159,14 @@ class RandomWalker:
for x in G.neighbors(v):
weight = G[v][x].get('weight', 1.0) # w_vx
if x == t: # d_tx == 0
unnormalized_probs.append(weight/p)
unnormalized_probs.append(weight / p)
elif G.has_edge(x, t): # d_tx == 1
unnormalized_probs.append(weight)
else: # d_tx > 1
unnormalized_probs.append(weight/q)
unnormalized_probs.append(weight / q)
norm_const = sum(unnormalized_probs)
normalized_probs = [
float(u_prob)/norm_const for u_prob in unnormalized_probs]
float(u_prob) / norm_const for u_prob in unnormalized_probs]
return create_alias_table(normalized_probs)
@ -183,7 +181,7 @@ class RandomWalker:
for nbr in G.neighbors(node)]
norm_const = sum(unnormalized_probs)
normalized_probs = [
float(u_prob)/norm_const for u_prob in unnormalized_probs]
float(u_prob) / norm_const for u_prob in unnormalized_probs]
alias_nodes[node] = create_alias_table(normalized_probs)
if not self.use_rejection_sampling:
@ -209,17 +207,16 @@ class BiasedWalker:
def simulate_walks(self, num_walks, walk_length, stay_prob=0.3, workers=1, verbose=0):
layers_adj = pd.read_pickle(self.temp_path+'layers_adj.pkl')
layers_alias = pd.read_pickle(self.temp_path+'layers_alias.pkl')
layers_accept = pd.read_pickle(self.temp_path+'layers_accept.pkl')
gamma = pd.read_pickle(self.temp_path+'gamma.pkl')
walks = []
initialLayer = 0
layers_adj = pd.read_pickle(self.temp_path + 'layers_adj.pkl')
layers_alias = pd.read_pickle(self.temp_path + 'layers_alias.pkl')
layers_accept = pd.read_pickle(self.temp_path + 'layers_accept.pkl')
gamma = pd.read_pickle(self.temp_path + 'gamma.pkl')
nodes = self.idx # list(self.g.nodes())
results = Parallel(n_jobs=workers, verbose=verbose, )(
delayed(self._simulate_walks)(nodes, num, walk_length, stay_prob, layers_adj, layers_accept, layers_alias, gamma) for num in
delayed(self._simulate_walks)(nodes, num, walk_length, stay_prob, layers_adj, layers_accept, layers_alias,
gamma) for num in
partition_num(num_walks, workers))
walks = list(itertools.chain(*results))
@ -243,7 +240,7 @@ class BiasedWalker:
while len(path) < walk_length:
r = random.random()
if(r < stay_prob): # same layer
if (r < stay_prob): # same layer
v = chooseNeighbor(v, graphs, layers_alias,
layers_accept, layer)
path.append(self.idx2node[v])
@ -256,18 +253,17 @@ class BiasedWalker:
print(layer, v)
raise ValueError()
if(r > p_moveup):
if(layer > initialLayer):
if (r > p_moveup):
if (layer > initialLayer):
layer = layer - 1
else:
if((layer + 1) in graphs and v in graphs[layer + 1]):
if ((layer + 1) in graphs and v in graphs[layer + 1]):
layer = layer + 1
return path
def chooseNeighbor(v, graphs, layers_alias, layers_accept, layer):
v_list = graphs[layer][v]
idx = alias_sample(layers_accept[layer][v], layers_alias[layer][v])

15
setup.py
View File

@ -7,16 +7,17 @@ with open("README.md", "r") as fh:
REQUIRED_PACKAGES = [
# 'tensorflow>=1.4.0,<=1.12.0',
'gensim==3.6.0',
'networkx==2.1',
'joblib==0.13.0',
'fastdtw==0.3.2',
# 'tensorflow>=1.4.0',
'gensim>=4.0.0',
'networkx',
'joblib',
'fastdtw',
'tqdm',
'numpy',
'scikit-learn',
'pandas',
'matplotlib',
'deepctr'
]
@ -28,13 +29,13 @@ setuptools.setup(
author="Weichen Shen",
author_email="wcshen1994@163.com",
author_email="weichenswc@163.com",
url="https://github.com/shenweichen/GraphEmbedding",
packages=setuptools.find_packages(exclude=[]),
python_requires='>=3.4', # 3.4.6
python_requires='>=3.5', # 3.4.6
install_requires=REQUIRED_PACKAGES,

5
tests/Wiki_edgelist.txt Normal file
View File

@ -0,0 +1,5 @@
0 1
0 2
0 3
1 2
2 3

0
tests/__init__.py Normal file
View File

16
tests/deepwalk_test.py Normal file
View File

@ -0,0 +1,16 @@
import networkx as nx
from ge import DeepWalk
def test_DeepWalk():
G = nx.read_edgelist('./tests/Wiki_edgelist.txt',
create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)])
model = DeepWalk(G, walk_length=3, num_walks=2, workers=1)
model.train(window_size=3, iter=1)
embeddings = model.get_embeddings()
if __name__ == "__main__":
pass

16
tests/line_test.py Normal file
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@ -0,0 +1,16 @@
import networkx as nx
from ge import LINE
def test_LINE():
G = nx.read_edgelist('./tests/Wiki_edgelist.txt',
create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)])
model = LINE(G, embedding_size=2, order='second')
model.train(batch_size=2, epochs=1, verbose=2)
embeddings = model.get_embeddings()
if __name__ == "__main__":
pass

22
tests/node2vec_test.py Normal file
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@ -0,0 +1,22 @@
import networkx as nx
import pytest
from ge import Node2Vec
@pytest.mark.parametrize(
'use_rejection_sampling',
[True, False
]
)
def test_Node2Vec(use_rejection_sampling):
G = nx.read_edgelist('./tests/Wiki_edgelist.txt',
create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)])
model = Node2Vec(G, walk_length=10, num_walks=80,
p=0.25, q=4, workers=1, use_rejection_sampling=use_rejection_sampling)
model.train(window_size=5, iter=3)
embeddings = model.get_embeddings()
if __name__ == "__main__":
pass

19
tests/sdne_test.py Normal file
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@ -0,0 +1,19 @@
import networkx as nx
import tensorflow as tf
from ge import SDNE
def test_SDNE():
if tf.__version__ >= '1.15.0':
return #todo
G = nx.read_edgelist('./tests/Wiki_edgelist.txt',
create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)])
model = SDNE(G, hidden_size=[8, 4], )
model.train(batch_size=2, epochs=1, verbose=2)
embeddings = model.get_embeddings()
if __name__ == "__main__":
pass

16
tests/struct2vec_test.py Normal file
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@ -0,0 +1,16 @@
import networkx as nx
from ge import Struc2Vec
def test_Struc2Vec():
G = nx.read_edgelist('./tests/Wiki_edgelist.txt', create_using=nx.DiGraph(), nodetype=None,
data=[('weight', int)])
model = Struc2Vec(G, 3, 1, workers=1, verbose=40, )
model.train()
embeddings = model.get_embeddings()
if __name__ == "__main__":
pass