File size: 3,075 Bytes
919d971 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import numpy as np
import pandas as pd
import pickle
def get_adjacency_matrix(distance_df, sensor_ids):
"""
:param distance_df: data frame with three columns: [from, to, distance].
:param sensor_ids: list of sensor ids.
:param normalized_k: entries that become lower than normalized_k after normalization are set to zero for sparsity.
:return:
"""
num_sensors = len(sensor_ids)
dist_mx = np.zeros((num_sensors, num_sensors), dtype=np.float32)
# dist_mx[:] = np.inf
# Builds sensor id to index map.
sensor_id_to_ind = {}
for i, sensor_id in enumerate(sensor_ids):
sensor_id_to_ind[sensor_id] = i
# Fills cells in the matrix with distances.
for row in distance_df.values:
if row[0] not in sensor_id_to_ind or row[1] not in sensor_id_to_ind:
continue
dist_mx[sensor_id_to_ind[row[0]], sensor_id_to_ind[row[1]]] = row[2]
adj_mx = dist_mx
# calculate diagonal elements
for i in range(len(adj_mx)):
adj_mx[i, i] = 1 - np.sum(adj_mx[i])
# Calculates the standard deviation as theta.
# distances = dist_mx[~np.isinf(dist_mx)].flatten()
# std = distances.std()
# adj_mx = np.exp(-np.square(dist_mx / std))
#
# Make the adjacent matrix symmetric by taking the max.
# adj_mx = np.maximum.reduce([adj_mx, adj_mx.T])
#
# Sets entries that lower than a threshold, i.e., k, to zero for sparsity.
# adj_mx[adj_mx < normalized_k] = 0
return sensor_ids, sensor_id_to_ind, adj_mx
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--sensor_ids_filename",
type=str,
default="../data/sensor_graph/004_ca_commute_flows_nodelist.txt",
help="File containing sensor ids separated by lines.",
)
parser.add_argument(
"--distances_filename",
type=str,
default="../data/sensor_graph/004_ca_commute_flows_edgelist.csv",
help="CSV file containing sensor distances with three columns: [from, to, distance].",
)
# parser.add_argument('--normalized_k', type=float, default=0.0,
# help='Entries that become lower than normalized_k after normalization are set to zero for sparsity.')
parser.add_argument(
"--output_pkl_filename",
type=str,
default="../data/sensor_graph/adj_mx.pkl",
help="Path of the output file.",
)
args = parser.parse_args()
with open(args.sensor_ids_filename) as f:
sensor_ids = f.read().strip().splitlines()
distance_df = pd.read_csv(
args.distances_filename,
dtype={"source_county_fips_code": "str", "target_county_fips_code": "str"},
)
_, sensor_id_to_ind, adj_mx = get_adjacency_matrix(distance_df, sensor_ids)
# Save to pickle file.
with open(args.output_pkl_filename, "wb") as f:
pickle.dump([sensor_ids, sensor_id_to_ind, adj_mx], f, protocol=2)
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