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plot_MTNet.py
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plot_MTNet.py
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import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from dataset import *
from sklearn.cluster import KMeans
def plot_tree(g):
# this plot requires pygraphviz package
pos = nx.nx_agraph.graphviz_layout(g, prog="dot")
nx.draw_networkx(g,
pos,
with_labels=False,
node_size=20,
node_color=[[0.5, 0.5, 0.5]],
arrowsize=8)
# node_labels = nx.get_node_attributes(g, 'x')
# nx.draw_networkx_labels(g, pos, labels=node_labels, font_color='blue')
node_labels = nx.get_node_attributes(g, 'y')
nx.draw_networkx_labels(g, pos, labels=node_labels, font_color='red')
# node_labels = nx.get_node_attributes(g, 'time')
# nx.draw_networkx_labels(g, pos, labels=node_labels, font_color='green')
plt.show()
def add_children(tree, trajectory, index, idx2idx_dict, flag_dict, nary):
"""
Using DFS to construct the tree for N-ary TreeLSTM
"""
node = trajectory[index]
idx2idx_dict[index] = tree.number_of_nodes()
if index > 0:
tree.add_node(idx2idx_dict[index], u=node['features'][0], x=node['features'][1], time=node['time'],
y=node['labels'], mask=1, type=0) # add parent node
else:
tree.add_node(idx2idx_dict[index], u=node['features'][0], x=node['features'][1], time=node['time'],
y=node['labels'], mask=1, type=5) # add parent node
if index > 0 and flag_dict[index] > 0:
flag_dict[index] -= 1 # already play as parent node
for i in range(nary, 0, -1):
if index - i >= 0:
add_children(tree, trajectory, index - i, idx2idx_dict, flag_dict, nary)
tree.add_edge(idx2idx_dict[index - i], idx2idx_dict[index]) # src -> dst
else: # fictitious node
node_id = tree.number_of_nodes()
tree.add_node(node_id, u=node['features'][0], x=trajectory[0]['features'][1], time=node['time'],
y=[-1] * 3, mask=0, type=-1)
tree.add_edge(node_id, idx2idx_dict[index]) # src -> dst
node_id = tree.number_of_nodes()
tree.add_node(node_id, u=node['features'][0], x=node['features'][1], time=node['time'],
y=node['labels'], mask=1, type=3) # add parent node
tree.add_edge(node_id, idx2idx_dict[index]) # src -> dst
for i in range(1, 3):
node_id = tree.number_of_nodes()
tree.add_node(node_id, u=node['features'][0], x=node['features'][i + 1], time=node['time'],
y=node['labels'], mask=1, type=i) # 1 POI, 2 cat, 3 coo
tree.add_edge(node_id, idx2idx_dict[index]) # src -> dst
return
def add_children_out(tree, trajectory, index, idx2idx_dict, flag_dict, nary):
re_index = len(trajectory) - 1 - index
max_index = re_index
node = trajectory[re_index]
idx2idx_dict[index] = tree.number_of_nodes()
if index > 0:
tree.add_node(idx2idx_dict[index], u=node['features'][0], x=node['features'][1], time=node['time'],
y=node['labels'], mask=1, type=0)
else:
tree.add_node(idx2idx_dict[index], u=node['features'][0], x=node['features'][1], time=node['time'],
y=node['labels'], mask=1, type=5)
if index > 0 and flag_dict[index] > 0:
flag_dict[index] -= 1 # already play as parent node
for i in range(nary, 0, -1):
if index - i < 0: # fictitious node
node_id = tree.number_of_nodes()
tree.add_node(node_id, u=node['features'][0], x=trajectory[-1]['features'][1], time=node['time'],
y=[-1] * 3, mask=0, type=-1)
tree.add_edge(node_id, idx2idx_dict[index]) # src -> dst
max_index = len(trajectory) - 1
else:
child_idx = add_children_out(tree, trajectory, index - i, idx2idx_dict, flag_dict, nary)
max_index = child_idx if max_index < child_idx else max_index
tree.add_edge(idx2idx_dict[index - i], idx2idx_dict[index]) # src -> dst
node_id = tree.number_of_nodes()
tree.add_node(node_id, u=node['features'][0], x=node['features'][1], time=node['time'],
y=node['labels'], mask=1, type=3)
tree.add_edge(node_id, idx2idx_dict[index]) # src -> dst
for i in range(1, 3):
node_id = tree.number_of_nodes()
tree.add_node(node_id, u=node['features'][0], x=node['features'][i + 1], time=node['time'],
y=node['labels'], mask=1, type=i)
tree.add_edge(node_id, idx2idx_dict[index]) # src -> dst
# change node label
tree.add_node(idx2idx_dict[index], u=node['features'][0], x=node['features'][1], time=node['time'],
y=trajectory[max_index]['labels'], mask=1, type=0)
return max_index
def add_true_node(tree, trajectory, index, parent_node_id, nary):
for i in range(nary - 1, 0, -1):
if index - i >= 0:
node_id = tree.number_of_nodes()
node = trajectory[index - i]
tree.add_node(node_id, x=node['features'], time=node['time'], y=node['labels'], mask=1, mask2=0, type=1)
tree.add_edge(node_id, parent_node_id)
else: # empty node
node_id = tree.number_of_nodes()
tree.add_node(node_id, x=[0] * 4, time=0, y=[-1] * 3, mask=0, mask2=0, type=-1)
tree.add_edge(node_id, parent_node_id)
sub_parent_node_id = tree.number_of_nodes()
tree.add_node(sub_parent_node_id, x=[0] * 4, time=0, y=[-1] * 3, mask=0, mask2=0, type=-1)
tree.add_edge(sub_parent_node_id, parent_node_id)
if index - (nary - 1) > 0:
add_true_node(tree, trajectory, index - (nary - 1), sub_parent_node_id, nary)
tree.add_node(sub_parent_node_id, x=[0] * 4, time=0, y=trajectory[index - (nary - 1)]['labels'], mask=0,
mask2=0, type=-1)
def add_period_node(tree, trajectory, nary):
node_id = tree.number_of_nodes()
period_label = trajectory[len(trajectory) - 1]['labels'] if len(trajectory) > 0 else [-1] * 3
tree.add_node(node_id, x=[0] * 4, time=0, y=period_label, mask=0, mask2=1, type=-1)
if len(trajectory) > 0:
add_true_node(tree, trajectory, len(trajectory), node_id, nary)
return node_id
def add_day_node(tree, trajectory, labels, index, nary):
node_id = tree.number_of_nodes()
tree.add_node(node_id, x=[0] * 4, time=0, y=labels[index], mask=0, mask2=1, type=0)
if index > 0: # recursion
child_node_id = add_day_node(tree, trajectory, labels, index - 1, nary)
tree.add_edge(child_node_id, node_id)
else:
fake_node_id = tree.number_of_nodes()
tree.add_node(fake_node_id, x=[0] * 4, time=0, y=[-1] * 3, mask=0, mask2=0, type=-1)
tree.add_edge(fake_node_id, node_id)
day_trajectory = trajectory[index]
for i in range(len(day_trajectory)): # Four time periods， 0-6， 6-12， 12-18， 18-24
period_node_id = add_period_node(tree, day_trajectory[i], nary)
tree.add_edge(period_node_id, node_id)
return node_id
def construct_MobilityTree(trajectory, labels, nary, need_plot):
tree = nx.DiGraph()
add_day_node(tree, trajectory, labels, len(trajectory) - 1, nary)
if need_plot:
plot_tree(tree) # optional
if __name__ == "__main__":
# dataset = 'NYC'
# train_df = pd.read_csv(f'dataset/{dataset}/{dataset}_train.csv')
#
# # User id to index
# uid_list = [str(uid) for uid in list(set(train_df['user_id'].to_list()))]
# user_id2idx_dict = dict(zip(uid_list, range(len(uid_list))))
# # POI id to index
# POI_list = list(set(train_df['POI_id'].tolist()))
# POI_list.sort()
# POI_id2idx_dict = dict(zip(POI_list, range(len(POI_list))))
# # Cat id to index
# cat_list = list(set(train_df['POI_catid'].tolist()))
# cat_list.sort()
# cat_id2idx_dict = dict(zip(cat_list, range(len(cat_list))))
#
# data_train = np.column_stack((train_df['longitude'], train_df['latitude']))
# kmeans_train = KMeans(n_clusters=50)
# kmeans_train.fit(data_train)
# train_df['coo_label'] = kmeans_train.labels_
#
# # Build dataset
# map_set = (user_id2idx_dict, POI_id2idx_dict, cat_id2idx_dict)
# train_dataset = TrajectoryTrainDataset(train_df, map_set)
# train_dataloader = DataLoader(train_dataset, batch_size=128, shuffle=True, drop_last=False,
# pin_memory=True, num_workers=0, collate_fn=lambda x: x)
#
# for b_idx, batch in tqdm(enumerate(train_dataloader), total=len(train_dataloader), desc="Training"):
# in_tree_batcher, out_tree_batcher = [], []
# for trajectory, label in batch:
# construct_MobilityTree(trajectory, label, 5, True, 'in')
# break
f = [0] * 4
trajectory = [[[{'features': f, 'time': 0, 'labels': [1]}, {'features': f, 'time': 0, 'labels': [2]},
{'features': f, 'time': 0, 'labels': [3]}, {'features': f, 'time': 0, 'labels': [4]},
{'features': f, 'time': 0, 'labels': [5]}, {'features': f, 'time': 0, 'labels': [6]}],
[],
[],
[{'features': f, 'time': 0, 'labels': [4, 1]}]],
[[],
[{'features': f, 'time': 0, 'labels': [1]}, {'features': f, 'time': 0, 'labels': [2]},
{'features': f, 'time': 0, 'labels': [3]}, {'features': f, 'time': 0, 'labels': [4]},
{'features': f, 'time': 0, 'labels': [5]}, {'features': f, 'time': 0, 'labels': [6]}],
[{'features': f, 'time': 0, 'labels': [1]}, {'features': f, 'time': 0, 'labels': [2]},
{'features': f, 'time': 0, 'labels': [3]}, {'features': f, 'time': 0, 'labels': [4]},
{'features': f, 'time': 0, 'labels': [5]}, {'features': f, 'time': 0, 'labels': [6]}],
[]],
[[],
[],
[],
[]],
[[],
[],
[],
[]],
[[],
[],
[],
[]],
[[],
[],
[],
[]]]
label = [[[1] * 3], [[2] * 3], [[3] * 3], [[4] * 3], [[5] * 3], [[6] * 3]]
construct_MobilityTree(trajectory, label, 5, True, 'in')