mirror of
https://github.com/wassname/torch-neuralpointprocess.git
synced 2026-06-27 17:50:52 +08:00
kanghoon
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
import torch
|
|
import numpy as np
|
|
from matplotlib import pyplot as plt
|
|
|
|
|
|
def read_timeseries(path):
|
|
|
|
with open(path) as f:
|
|
seqs = f.readlines()
|
|
|
|
return [[(float(t), 0) for t in seq.split(';')[0].split()] for seq in seqs]
|
|
|
|
|
|
def generate_sequence(timeseries, seq_len, log_mode=False):
|
|
|
|
## For the case that Each time_sequence has different length of time-series data.
|
|
TIME_SEQS = []
|
|
EVENT_SEQS = []
|
|
|
|
for time_seq in timeseries:
|
|
|
|
if not log_mode:
|
|
for idx in range(len(time_seq)-seq_len+1):
|
|
seq = time_seq[idx:idx+seq_len]
|
|
times = [t for (t, e) in seq]
|
|
times = [0] + np.diff(times).tolist()
|
|
events = [e for (t, e) in seq]
|
|
TIME_SEQS.append(times)
|
|
EVENT_SEQS.append(events)
|
|
|
|
else:
|
|
for idx in range(len(time_seq) - seq_len + 1):
|
|
seq = time_seq[idx:idx + seq_len]
|
|
times = [t for (t, e) in seq]
|
|
mu = np.mean(times)
|
|
std = np.std(times)
|
|
times = (times-mu)/std
|
|
times = [0] + np.diff(times).tolist()
|
|
events = [e for (t, e) in seq]
|
|
TIME_SEQS.append(times)
|
|
EVENT_SEQS.append(events)
|
|
|
|
TIME_SEQS = torch.Tensor(TIME_SEQS)
|
|
EVENT_SEQS = torch.Tensor(EVENT_SEQS)
|
|
|
|
return TIME_SEQS, EVENT_SEQS
|
|
|
|
|
|
|
|
def plt_lmbda(timeseries, model, seq_len, log_mode=False, dt=0.01, lmbda0=0.2, alpha=0.8, beta=1.0):
|
|
|
|
lmbda_dict = dict()
|
|
pred_dict = dict()
|
|
t_span = np.arange(start=timeseries[0][0], stop=timeseries[-1][0]+dt, step=dt)
|
|
|
|
# exponential_hwakes : lmbda0, alpha, beta: 0.2, 0.8, 1.0
|
|
# lmbda = lambda0 + alpha*sum(exp{-beta*(t-t_i)})
|
|
|
|
|
|
lmbda_dict[0] = np.zeros(t_span.shape)
|
|
|
|
|
|
for t, e in timeseries:
|
|
target = (t_span > t)
|
|
lmbda_dict[0][target] += alpha*np.exp(-beta*(t_span[target]-t))
|
|
lmbda_dict[0] += lmbda0
|
|
|
|
# pred_dict[0] = np.zeros(t_span.shape)
|
|
pred_dict[0] = np.zeros(len(timeseries)-seq_len+1)
|
|
test_timeseq, test_eventseq = generate_sequence([timeseries], seq_len, log_mode=log_mode)
|
|
_, _, _, pred_dict[0] = model((test_timeseq, test_eventseq))
|
|
|
|
|
|
plt.plot(t_span, lmbda_dict[0], color='green')
|
|
plt.plot([t for t, e in timeseries][seq_len-1:], np.array(pred_dict[0].detach()), color='olive')
|
|
plt.scatter([t for t, e in timeseries], [-1 for _ in timeseries], color='blue')
|
|
plt.show() |