mirror of
https://github.com/wassname/torch-neuralpointprocess.git
synced 2026-06-27 16:32:26 +08:00
wassname
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
import torch
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import numpy as np
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from matplotlib import pyplot as plt
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def read_timeseries(path):
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with open(path) as f:
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seqs = f.readlines()
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return [[(float(t), 0) for t in seq.split(';')[0].split()] for seq in seqs]
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def generate_sequence(timeseries, seq_len, log_mode=False):
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## For the case that Each time_sequence has different length of time-series data.
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TIME_SEQS = []
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EVENT_SEQS = []
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for time_seq in timeseries:
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if not log_mode:
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for idx in range(len(time_seq)-seq_len+1):
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seq = time_seq[idx:idx+seq_len]
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times = [t for (t, e) in seq]
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times = [0] + np.diff(times).tolist()
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events = [e for (t, e) in seq]
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TIME_SEQS.append(times)
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EVENT_SEQS.append(events)
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else:
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for idx in range(len(time_seq) - seq_len + 1):
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seq = time_seq[idx:idx + seq_len]
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times = [t for (t, e) in seq]
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mu = np.mean(times)
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std = np.std(times)
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times = (times-mu)/std
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times = [0] + np.diff(times).tolist()
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events = [e for (t, e) in seq]
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TIME_SEQS.append(times)
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EVENT_SEQS.append(events)
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TIME_SEQS = torch.Tensor(TIME_SEQS)
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EVENT_SEQS = torch.Tensor(EVENT_SEQS)
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return TIME_SEQS, EVENT_SEQS
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def plt_lmbda(timeseries, model, seq_len, log_mode=False, dt=0.01, lmbda0=0., alpha=0.01, beta=1.0):
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lmbda_dict = dict()
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pred_dict = dict()
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t_span = np.arange(start=timeseries[0][0], stop=timeseries[-1][0]+dt, step=dt)
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# exponential_hwakes : lmbda0, alpha, beta: 0.2, 0.8, 1.0
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# lmbda = lambda0 + alpha*sum(exp{-beta*(t-t_i)})
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lmbda_dict[0] = np.zeros(t_span.shape)
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for t, e in timeseries:
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target = (t_span > t)
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lmbda_dict[0][target] += alpha*np.exp(-beta*(t_span[target]-t))
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lmbda_dict[0] += lmbda0
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# pred_dict[0] = np.zeros(t_span.shape)
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pred_dict[0] = np.zeros(len(timeseries)-seq_len+1)
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test_timeseq, test_eventseq = generate_sequence([timeseries], seq_len, log_mode=log_mode)
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_, _, _, pred_dict[0] = model((test_timeseq, test_eventseq))
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plt.plot(t_span, lmbda_dict[0], color='green', label='true prob')
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plt.plot([t for t, e in timeseries][seq_len-1:], np.array(pred_dict[0].detach()), color='olive', label='pred prob')
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plt.scatter([t for t, e in timeseries], [-.01 for _ in timeseries], color='blue', label='events')
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plt.legend()
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plt.show()
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