Initial commit

readnpz.py

+import numpy as np
+
+data = np.load("./saved/results/dcrnn_predictions.npz")
+
+for key, value in data.items():
+    print(key)
+    print(value)
+    # np.savetxt("./saved/results" + key + ".csv", value)
\ No newline at end of file

test.py

@@ -13,9 +13,9 @@ import time
 def main(config):
     logger = config.get_logger('test')
 
-    graph_pkl_filename = 'data/sensor_graph/adj_mx_unix.pkl'
+    graph_pkl_filename = 'data/sensor_graph/adj_mx_2.pkl'
     _, _, adj_mat = utils.load_graph_data(graph_pkl_filename)
-    data = utils.load_dataset(dataset_dir='data/METR-LA',
+    data = utils.load_dataset(dataset_dir='data/metr-la-datasets-10',
                               batch_size=config["arch"]["args"]["batch_size"],
                               test_batch_size=config["arch"]["args"]["batch_size"])
     test_data_loader = data['test_loader']
@@ -57,6 +57,10 @@ def main(config):
     y_preds = torch.transpose(y_preds, 0, 1)
     y_preds = y_preds.detach().numpy()  # cast to numpy array
     print("--------test results--------")
+    clients_avg_mae = []
+    clients_avg_mape = []
+    clients_avg_rmse = []
+    clients_avg_pcc = []
     for horizon_i in range(y_truths.shape[1]):
         y_truth = np.squeeze(y_truths[:, horizon_i, :, 0])
 
@@ -67,13 +71,22 @@ def main(config):
         mae = metrics.masked_mae_np(y_pred[:y_truth.shape[0]], y_truth, null_val=0)
         mape = metrics.masked_mape_np(y_pred[:y_truth.shape[0]], y_truth, null_val=0)
         rmse = metrics.masked_rmse_np(y_pred[:y_truth.shape[0]], y_truth, null_val=0)
+        pcc= metrics.masked_pcc(y_pred[:y_truth.shape[0]], y_truth, null_val=0)
+        clients_avg_mae.append(mae)
+        clients_avg_mape.append(mape)
+        clients_avg_rmse.append(rmse)
+        clients_avg_pcc.append(pcc)
         print(
-            "Horizon {:02d}, MAE: {:.2f}, MAPE: {:.4f}, RMSE: {:.2f}".format(
-                horizon_i + 1, mae, mape, rmse
+            "Horizon {:02d}, MAE: {:.2f}, MAPE: {:.4f}, RMSE: {:.2f}, PCC: {:.4f}".format(
+                horizon_i + 1, mae, mape, rmse, pcc
             )
         )
-        log = {"Horizon": horizon_i+1, "MAE": mae, "MAPE": mape, "RMSE": rmse}
+        log = {"Horizon": horizon_i+1, "MAE": mae, "MAPE": mape, "RMSE": rmse, "PCC": pcc}
         logger.info(log)
+    printlog = 'On average over 12 horizons, Test MAE: {:.4f}, Test MAPE: {:.4f}, Test RMSE: {:.4f}, Test PCC: {:.4f}'
+    print(printlog.format(np.mean(clients_avg_mae),np.mean(clients_avg_mape),np.mean(clients_avg_rmse),np.mean(clients_avg_pcc)))
+    log = {"On average over 12 horizons, MAE": np.mean(clients_avg_mae), "MAPE":np.mean(clients_avg_mape), "RMSE": np.mean(clients_avg_rmse), "PCC": np.mean(clients_avg_pcc)}
+    logger.info(log)
     outputs = {
         'predictions': predictions,
         'groundtruth': groundtruth

train.py

@@ -12,9 +12,9 @@ import math
 def main(config):
     logger = config.get_logger('train')
 
-    graph_pkl_filename = 'data/sensor_graph/adj_mx_unix.pkl'
+    graph_pkl_filename = 'data/sensor_graph/adj_mx_bay_5.pkl'
     _, _, adj_mat = utils.load_graph_data(graph_pkl_filename)
-    data = utils.load_dataset(dataset_dir='data/METR-LA',
+    data = utils.load_dataset(dataset_dir='data/pems-bay-datasets-10',
                               batch_size=config["arch"]["args"]["batch_size"],
                               test_batch_size=config["arch"]["args"]["batch_size"])
     for k, v in data.items():