def get_features(reviews, is_train):
end_date = datetime.date(2012, 1, 1) if is_train else datetime.date(2013, 1, 1)
# we multiply some values by constants as a hacky way of normalizing the features
return {
"age": 50.0 / ((end_date - get_date(reviews[0])).days + 30),
"age_0.5": 10.0 / (((end_date - get_date(reviews[0])).days + 30) ** 0.5),
"age_0.2": 3.0 / (((end_date - get_date(reviews[0])).days + 30) ** 0.2),
"stars": int(reviews[0]["stars"]) / 5.0,
"liked": 1 if int(reviews[0]["stars"]) > 3 else 0,
"bias": 1.0,
}
def get_features(reviews, is_train):
end_date = datetime.date(2012, 1, 1) if is_train else datetime.date(2013, 1, 1)
# we multiply some values by constants as a hacky way of normalizing the features
return {
"age": 50.0 / ((end_date - get_date(reviews[0])).days + 30),
"age_0.5": 10.0 / (((end_date - get_date(reviews[0])).days + 30) ** 0.5),
"age_0.2": 3.0 / (((end_date - get_date(reviews[0])).days + 30) ** 0.2),
"stars": int(reviews[0]["stars"]) / 5.0,
"liked": 1 if int(reviews[0]["stars"]) > 3 else 0,
"bias": 1.0
}
def run_random_walks(data_dir, weight_edges=False):
print "Loading data and building transition matrix..."
examples = util.load_json('./data/' + data_dir + '/examples.json')
G = nx.read_edgelist('./data/' + data_dir + '/graph.txt', nodetype=int)
if weight_edges:
reviews = util.load_json('./data/' + data_dir + '/review.json')
end_date = datetime.date(2012, 1, 1) if data_dir == 'train' else datetime.date(2013, 1, 1)
edges = G.edges()
for e in util.logged_loop(edges, util.LoopLogger(20000, len(edges), True)):
n1, n2 = str(e[0]), str(e[1])
if n1 not in reviews or n2 not in reviews[n1]:
n1, n2 = n2, n1
G[e[0]][e[1]]['weight'] = 1.0 / ((end_date - get_date(reviews[n1][n2][0])).days + 90)
del reviews # save some memory
adjacency_matrix = nx.adjacency_matrix(G)
inverse_degree_matrix = sparse.diags([[1.0 / adjacency_matrix.getrow(i).sum()
for i in range(adjacency_matrix.shape[0])]], [0])
transition_matrix = inverse_degree_matrix.dot(adjacency_matrix)
print "Running random walks..."
for u in util.logged_loop(examples, util.LoopLogger(10, len(examples), True)):
p = run_random_walk(transition_matrix, int(u), 10).todense()
for b in examples[u]:
examples[u][b] = p[0, int(b)]
util.write_json(examples, './data/' + data_dir
+ ('/weighted_random_walks.json' if weight_edges else '/random_walks.json'))
def run_random_walks(data_dir, weight_edges=False):
print "Loading data and building transition matrix..."
examples = util.load_json('./data/' + data_dir + '/examples.json')
G = nx.read_edgelist('./data/' + data_dir + '/graph.txt', nodetype=int)
if weight_edges:
reviews = util.load_json('./data/' + data_dir + '/review.json')
end_date = datetime.date(
2012, 1, 1) if data_dir == 'train' else datetime.date(2013, 1, 1)
edges = G.edges()
for e in util.logged_loop(edges,
util.LoopLogger(20000, len(edges), True)):
n1, n2 = str(e[0]), str(e[1])
if n1 not in reviews or n2 not in reviews[n1]:
n1, n2 = n2, n1
G[e[0]][e[1]]['weight'] = 1.0 / (
(end_date - get_date(reviews[n1][n2][0])).days + 90)
del reviews # save some memory
adjacency_matrix = nx.adjacency_matrix(G)
inverse_degree_matrix = sparse.diags([[
1.0 / adjacency_matrix.getrow(i).sum()
for i in range(adjacency_matrix.shape[0])
]], [0])
transition_matrix = inverse_degree_matrix.dot(adjacency_matrix)
print "Running random walks..."
for u in util.logged_loop(examples,
util.LoopLogger(10, len(examples), True)):
p = run_random_walk(transition_matrix, int(u), 10).todense()
for b in examples[u]:
examples[u][b] = p[0, int(b)]
util.write_json(
examples,
'./data/' + data_dir + ('/weighted_random_walks.json'
if weight_edges else '/random_walks.json'))
