Create gen_adj_mx.py

spatial_first_tutorial/DCRNN/gen_adj_mx.py

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+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, normalized_k=0.1):
+    """
+
+    :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]
+
+    # 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/graph_sensor_ids.txt',
+                        help='File containing sensor ids separated by comma.')
+    parser.add_argument('--distances_filename', type=str, default='data/sensor_graph/distances_la_2012.csv',
+                        help='CSV file containing sensor distances with three columns: [from, to, distance].')
+    parser.add_argument('--normalized_k', type=float, default=0.1,
+                        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_mat.pkl',
+                        help='Path of the output file.')
+    args = parser.parse_args()
+
+    with open(args.sensor_ids_filename) as f:
+        sensor_ids = f.read().strip().split(',')
+    distance_df = pd.read_csv(args.distances_filename, dtype={'from': 'str', 'to': '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)