def from_previous_reduction(cls, input_dir):
parent = gl.load_sgraph(input_dir+'parent')
verticy_descriptions = gl.load_sframe(input_dir+'verticy_descriptions')
child = gl.load_sgraph(input_dir+'child')
gw = cls()
gw.g = parent
gw.verticy_descriptions = verticy_descriptions
gw.child = cls()
gw.child.g = child
return gw
def from_previous_reduction(cls, input_dir):
parent = gl.load_sgraph(input_dir + 'parent')
verticy_descriptions = gl.load_sframe(input_dir +
'verticy_descriptions')
child = gl.load_sgraph(input_dir + 'child')
gw = cls()
gw.g = parent
gw.verticy_descriptions = verticy_descriptions
gw.child = cls()
gw.child.g = child
return gw
def create_initial_bayesian_network():
'''
Start from a randomly generated Bayesian network where there is no edge between the variables of the same type.
First, create a blacklist.
'''
g = load_sgraph('data_graph')
edges = g.get_edges()
features = edges[['__dst_id', 'relation']].unique()
features.rename({'__dst_id': 'feature_id', 'relation': 'feature_type'})
bn = SGraph()
bn = bn.add_vertices(features, vid_field='feature_id')
n_features = features.num_rows()
edges_data_graph = g.get_edges()
n_patients = edges_data_graph['__src_id'].unique().size()
random.seed(1234)
for i in range(20):
src = features['feature_id'][random.randint(0, n_features - 1)]
dst = 'E8498'
#dst = features['feature_id'][random.randint(0, n_features-1)]
bn = bn.add_edges(Edge(src, dst))
print "Added edge between " + src + " and " + dst
bic = get_bic_score(g, bn, n_patients)
return g
def create_initial_bayesian_network():
'''
Start from a randomly generated Bayesian network where there is no edge between the variables of the same type.
First, create a blacklist.
'''
g = load_sgraph('data_graph')
edges = g.get_edges()
features = edges[['__dst_id', 'relation']].unique()
features.rename({'__dst_id': 'feature_id', 'relation': 'feature_type'})
bn = SGraph()
bn = bn.add_vertices(features, vid_field = 'feature_id')
n_features = features.num_rows()
edges_data_graph = g.get_edges()
n_patients = edges_data_graph['__src_id'].unique().size()
random.seed(1234)
for i in range(20):
src = features['feature_id'][random.randint(0, n_features-1)]
dst = 'E8498'
#dst = features['feature_id'][random.randint(0, n_features-1)]
bn = bn.add_edges(Edge(src, dst))
print "Added edge between " + src + " and " + dst
bic = get_bic_score(g, bn, n_patients)
return g
def __load_data_structure__(self, filepath):
"""Return data structure if can be loaded, otherwise returns None and logs warning"""
# try to load different supported types, since don't know what type just try all and swallow exceptions
obj = None
try:
obj = _gl.load_sframe(filepath)
return obj
except:
pass
try:
obj = _gl.load_sgraph(filepath)
return obj
except:
pass
try:
obj = _gl.load_model(filepath)
return obj
except:
pass
try:
obj = _gl.SArray(data=filepath)
return obj
except:
pass
__LOGGER__.debug("Unable to load dependency, unsupported type at path: %s" % filepath)
return None
def main():
args = parse_args('Test the result of the CommunityDetection algorithm')
result_graph = gl.load_sgraph(args.graph_name)
expected = gl.SFrame.read_csv(args.expected_output, delimiter=' ', header=False, column_type_hints=long)
for node in result_graph.vertices:
test = expected.apply(lambda x: node['label'] != x['X2'] and node['__id'] == x['X1'])
if test.sum() > 0:
print('Not all values match, invalid algorithm')
exit(1)
def load_nxgraph_from_sgraph(graph_path):
sg = gl.load_sgraph(graph_path)
import networkx as nx
g = nx.Graph()
# Put the nodes and edges from the SGraph into a NetworkX graph
g.add_nodes_from(list(sg.vertices['__id']))
g.add_edges_from([(e['__src_id'], e['__dst_id'], e['attr'])
for e in sg.edges])
return g
def get_pr_result_from_input(input_file):
g = gl.load_sgraph(input_file, format='csv')
pr = gl.pagerank.create(g)
pr_out = pr['pagerank']
csvfile_pr_movie_start_result = open("output/pr_movie_start_result.csv", "w")
writer_pr_movie_start_result = csv.writer(csvfile_pr_movie_start_result)
for pr_out_item in pr_out:
writer_pr_movie_start_result.writerow([pr_out_item['__id'], pr_out_item['pagerank'], pr_out_item['delta']])
csvfile_pr_movie_start_result.close()
def _load_graphlab_object(cls, obj_type, obj_path):
if obj_type == 'model':
return graphlab.load_model(obj_path)
elif obj_type == 'sarray':
return graphlab.SArray(obj_path)
elif obj_type == 'sframe':
return graphlab.load_sframe(obj_path)
elif obj_type == 'sgraph':
return graphlab.load_sgraph(obj_path)
else:
raise RuntimeError(str(obj_type) + ' is not supported')
def main():
args = parse_args('Test the result of the CommunityDetection algorithm')
result_graph = gl.load_sgraph(args.graph_name)
expected = gl.SFrame.read_csv(args.expected_output,
delimiter=' ',
header=False,
column_type_hints=long)
for node in result_graph.vertices:
test = expected.apply(
lambda x: node['label'] != x['X2'] and node['__id'] == x['X1'])
if test.sum() > 0:
print('Not all values match, invalid algorithm')
exit(1)
def __init__(self, sf_path=None, g_path=None, cache_max=0.75):
self.sf = None
self.label = None
self.bin_sfs = None
self.reps = gl.SArray(dtype=str)
self.hier_graph = None
self.num_bins = 0
self.features = None
self.distance = None
self.cache_max = cache_max
if g_path:
self.g = gl.load_sgraph(g_path)
self.sf = self.g.vertices
elif sf_path:
self.sf = gl.load_sframe(sf_path)
def main():
args = parse_args('Test the result of the CommunityDetection algorithm')
result_graph = gl.load_sgraph(args.graph_name)
expected = gl.SFrame.read_csv(args.expected_output, delimiter=' ', header=False, column_type_hints=long)
for node in result_graph.vertices.sort('__id'):
test = expected.apply(
lambda x: float_not_equals(x['X2'], node['local_clustering_coefficient']) and node['__id'] == x['X1'])
if test.sum() > 0:
print('Not all values match, invalid algorithm')
exit(1)
expected_average_cc = expected.filter_by([0], 'X1')['X2'][0]
actual_average_cc = result_graph.vertices['average_clustering_coefficient'][0]
if float_not_equals(expected_average_cc, actual_average_cc):
print('Average Clustering Coefficient is wrong: expected: "%s", but got: "%s"' % (
expected_average_cc, actual_average_cc))
def load_gl_object(filename):
"""
Load a GLC datastructure from a filename.
Parameters
----------
filename : Filename for the archive
Returns
----------
The GLC object.
"""
obj = None
if not os.path.exists(filename):
raise "Loading error: %s is not a valid filename." % filename
try:
obj = _gl.load_sframe(filename)
return obj
except:
pass
try:
obj = _gl.load_sgraph(filename)
return obj
except:
pass
try:
obj = _gl.load_model(filename)
return obj
except:
pass
try:
obj = _gl.SArray(data=filename)
return obj
except:
pass
return obj
def main():
args = parse_args('Test the result of the CommunityDetection algorithm')
result_graph = gl.load_sgraph(args.graph_name)
expected = gl.SFrame.read_csv(args.expected_output,
delimiter=' ',
header=False,
column_type_hints=long)
for node in result_graph.vertices.sort('__id'):
test = expected.apply(
lambda x: float_not_equals(x['X2'], node[
'local_clustering_coefficient']) and node['__id'] == x['X1'])
if test.sum() > 0:
print('Not all values match, invalid algorithm')
exit(1)
expected_average_cc = expected.filter_by([0], 'X1')['X2'][0]
actual_average_cc = result_graph.vertices[
'average_clustering_coefficient'][0]
if float_not_equals(expected_average_cc, actual_average_cc):
print(
'Average Clustering Coefficient is wrong: expected: "%s", but got: "%s"'
% (expected_average_cc, actual_average_cc))
def get_nodes_and_edges(self):
directory_names = os.listdir(self._input_directory_path)
graph_tuples = []
for directory_name in directory_names:
file_names = os.listdir(self._input_directory_path +
directory_name)
for file_name in file_names:
if ".sgraph" in file_name:
print("File name is: {0}".format(file_name))
pattern = "^([^\.]+)__([^\.]+).[^\.]+.([^\.]+).sgraph$"
match = re.match(pattern, file_name)
group_tuple = match.groups()
category = group_tuple[0]
sub_category = group_tuple[1]
timestamp = group_tuple[2]
sub_graph = gl.load_sgraph(self._input_directory_path +
directory_name + "/" +
file_name)
sub_graph.save(self._output_directory_path + file_name +
".csv",
format='csv')
summary_dict = sub_graph.summary()
num_vertices = summary_dict['num_vertices']
num_edges = summary_dict['num_edges']
tuple = (category, sub_category, timestamp, num_vertices,
num_edges)
graph_tuples.append(tuple)
df = pd.DataFrame(
graph_tuples,
columns=['category', 'sub_category', 'date', 'nodes', 'edges'])
df.to_csv(self._output_directory_path + "graph_summary.csv")
if numpy.linalg.norm(numpy.array(dst['memVector']), ord=2) > 0.0:
if numpy.linalg.norm(
numpy.array(dst['prev']) - numpy.array(dst['memVector']),
ord=2) < CONVERGENCE_VALUE:
dst['isSuperNode'] = 1
return (src, edge, dst)
def updatePrev(src, edge, dst):
if src['isSuperNode'] == 0:
src['prev'] = src['memVector']
if dst['isSuperNode'] == 0:
dst['prev'] = dst['memVector']
return (src, edge, dst)
if __name__ == '__main__':
graph = gl.load_sgraph("s3://sdurgam/GraphLab/Graph")
graph = graph.triple_apply(initialise, mutated_fields=['prev'])
convergence = graph.vertices['isSuperNode'].sum()
while (convergence < NUM_NON_SUPERNODES):
graph = graph.triple_apply(propagate, mutated_fields=['memVector'])
graph = graph.triple_apply(l2Norm, mutated_fields=['isSuperNode'])
graph = graph.triple_apply(updatePrev, mutated_fields=['prev'])
graph.vertices['memVector'] = graph.vertices['memVector'].apply(
lambda x: [0.0] * 92000)
convergence = graph.vertices['isSuperNode'].sum()
graph = graph.save("s3://sdurgam/GraphLab/Graph")
import graphlab as gl
from graphlab.data_structures.sgraph import SGraph as _SGraph
import graphlab.aggregate as _Aggregate
from graphlab import SArray
from graphlab import SFrame
from graphlab import Vertex
from graphlab import SGraph
from graphlab import Edge
g = gl.load_sgraph('/home/tweninge/wiki.graph')
def initVertex(g):
g.vertices['dist'] = 8888
g.vertices['sent'] = 0
#g.vertices['from_last_art'] = 0
#g.vertices['count'] =0
g.vertices['isDead'] = 0
#g.vertices['vid_set'] = SArray.from_const({}, g.summary()['num_vertices'])
#seen here have two function, for the cat, it is used to remember the articles, for art, it is used as the vid_set
#in fact, it is a dict with the form of {'id':[dist, from_last_art]}
g.vertices['seen'] = SArray.from_const({}, g.summary()['num_vertices'])
#g.vertices['msg_q'] = SArray.from_const([], g.summary()['num_vertices']) we donnot need it any more
#g = gl.load_graph('/Users/liuzuozhu/Downloads/web-Google.txt', format='snap')
initVertex(g)
#print g.get_vertices()
# def initEdge(g):
# #g.edges.head(5)
import os
import graphlab as gl
data_file = 'US_business_links'
if os.path.exists(data_file):
sg = gl.load_sgraph(data_file)
# sg.save('1', format='csv')
else:
url = 'https://static.turi.com/datasets/' + data_file
sg = gl.load_sgraph(url)
sg.save(data_file)
print sg.summary()
pr = gl.pagerank.create(sg, max_iterations=10)
# print pr['pagerank']
print pr.summary()
pr_out = pr['pagerank']
print pr_out.topk('pagerank', k=10)
##Triangle counting
##The number of triangles in a vertex's immediate neighborhood is a measure of the "density" of the vertex's neighborhood.
tri = gl.triangle_counting.create(sg)
print tri.summary()
tri_out = tri['triangle_count']
print tri_out.topk('triangle_count', k=10)
##Because GraphLab Create SGraphs use directed edges, the shortest path toolkit also finds the shortest directed paths to a source vertex.
sssp = gl.shortest_path.create(sg, source_vid='Microsoft')
sssp.get_path(vid='Weyerhaeuser',
import graphlab as gl
from graphlab import SFrame, SGraph, Vertex, Edge
edge_data = SFrame.read_csv(
'http://s3.amazonaws.com/dato-datasets/bond/bond_edges.csv')
g = SGraph()
g = g.add_edges(edge_data, src_field='src', dst_field='dst')
print g
g.save('james_bond')
new_graph = gl.load_sgraph('james_bond')
g.show(vlabel='id', highlight=['James Bond', 'Moneypenny'], arrows=True)
results.save('buckets/' + b)
print 'Nearest Neighbors Complete!\n'
# Create Graphs
# reload the SFrame
sf = gl.load_sframe('sydney_sf')
print 'Creating Graphs...'
for i in range(1, 671):
print 'Creating Graph for B' + str(i)
cur_edges = gl.load_sframe('buckets/B' + str(i))
edge_verts = cur_edges['query_label'].append(cur_edges['reference_label'])
edge_verts = edge_verts.unique()
cur_verts = sf.filter_by(edge_verts, 'mongo_id')
g = gl.SGraph(cur_verts, cur_edges,
vid_field='mongo_id',
src_field='query_label',
dst_field='reference_label')
g.save('graphs/B' + str(i))
print 'Graph Creation Complete!\n'
# Calculate Components
print 'Calculating Components...'
for i in range(1, 671):
print 'Calculating Components for for B' + str(i)
g = gl.load_sgraph('graphs/B' + str(i))
cc = gl.connected_components.create(g)
cc.save('components/B' + str(i))
print 'Success!'
exit()
def sgraph_to_csv(sgraph_path, output_path):
sg = gl.load_sgraph(sgraph_path)
sg.save(output_path, 'csv')
import graphlab as gl
g = gl.load_sgraph('input/test.txt', format='snap')
pr = gl.pagerank.create(g)
pr_out = pr['pagerank']
print "#########"
for pr_out_index in pr_out:
print pr_out_index
import graphlab as gl
g = gl.load_sgraph('page_graph')
N = len(g.vertices)
beta = 0.02
epsilon = 150
f = open('topk_weight', 'w')
g.vertices['weight'] = 1.0
g.vertices['degree'] = 0
def increment_degree(src, edge, dst):
src['degree'] += 1
return (src, edge, dst)
def increment_weight(src, edge, dst):
dst['weight_new'] += src['weight'] / src['degree']
return (src, edge, dst)
g = g.triple_apply(increment_degree, mutated_fields=['degree'])
while True:
g.vertices['weight_new'] = 0
g.triple_apply(increment_weight, mutated_fields=['weight_new'])
g.vertices['weight_new'] = beta / N + (
1 - beta) * (g.vertices['weight_new'] +
itr = 0
for i in range(0, 10):
vector.append(array.index(max(array)) + NUM_FIRST + itr)
array.remove(max(array))
itr += 1
return vector
def getNumRecos(src, edge, dst):
src['rightRecos'] = len(set(src['recos']).intersection(src['groundTruth']))
dst['rightRecos'] = len(set(dst['recos']).intersection(dst['groundTruth']))
return (src, edge, dst)
if __name__ == "__main__":
graph = gl.load_sgraph("s3://sank/GraphLab/Graph")
groundTruth = gl.SArray("GroundTruth")
graph.vertices['recos'] = graph.vertices['prev'].apply(
lambda x: getRecos(x))
graph.vertices['groundTruth'] = groundTruth
graph.vertices['rightRecos'] = 0
graph = graph.triple_apply(getNumRecos, mutated_fields=['rightRecos'])
r1 = list(
graph.vertices.sort('__id')['rightRecos'][NUM_SUPERNODES:]).count(1)
r2 = list(
graph.vertices.sort('__id')['rightRecos'][NUM_SUPERNODES:]).count(2)
r3 = list(
graph.vertices.sort('__id')['rightRecos'][NUM_SUPERNODES:]).count(3)
