"split_id",
"model",
"metric",
"value",
"config",
])
for split_id, res in enumerate(results):
for metric_name, val in res.items():
df.loc[len(df)] = (
time,
dataset,
split_id,
"Groundtruth",
metric_name,
val,
vars(args),
)
export_csv(df, "data/output/results.csv", append=True)
if args.fit:
with Timer("Fitting a hawkes process"):
learner = HawkesExpKern(
decays=np.full((args.n_types, args.n_types), args.exp_decay))
learner.fit(timestamps)
print(learner.baseline)
print(learner.adjacency)
results[metric_name] = eval_fns[metric_name](A_true, A_pred)
# export evaluation results
time = pd.Timestamp.now()
df = pd.DataFrame(
columns=[
"timestamp",
"dataset",
"split_id",
"model",
"metric",
"value",
"config",
]
)
for metric_name, val in results.items():
df.loc[len(df)] = (
time,
args.dataset,
args.split_id,
args.model,
metric_name,
val,
vars(args),
)
logger.info(df)
export_csv(df, osp.join(args.output_dir, "results.csv"), append=True)