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https://github.com/wassname/torch-neuralpointprocess.git
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wassname
File diff suppressed because one or more lines are too long
@@ -3,7 +3,6 @@ from torch import nn
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from torch.autograd import grad
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from torch.optim import Adam
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from torch.nn import functional as F
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# from optimization import BertAdam
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from matplotlib import pyplot as plt
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@@ -61,7 +60,7 @@ class IntensityNet(nn.Module):
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return [nll, log_lmbda_mean, int_lmbda_mean, lmbda]
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LEAK=1
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class GTPP(nn.Module):
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def __init__(self, config):
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@@ -70,27 +69,16 @@ class GTPP(nn.Module):
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self.batch_size = config.batch_size
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self.lr = config.lr
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self.log_mode = config.log_mode # TODO mean to be used here?
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self.log_mode = config.log_mode # TODO meant to be used here?
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self.embedding = nn.Embedding(num_embeddings=config.event_class, embedding_dim=config.emb_dim)
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self.emb_drop = nn.Dropout(p=config.dropout)
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self.lstm = nn.LSTM(input_size=LEAK+config.emb_dim,
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self.lstm = nn.LSTM(input_size=1+config.emb_dim,
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hidden_size=config.hid_dim,
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batch_first=True,
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bidirectional=False)
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self.intensity_net = IntensityNet(config)
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# self.set_optimizer(total_step=1)
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# def set_optimizer(self, total_step, use_bert=False):
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# if use_bert:
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# self.set_optimizer = BertAdam(params=self.parameters(),
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# lr=self.lr,
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# warmup=0.1,
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# t_total=total_step)
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# else:
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# self.set_optimizer = Adam(self.parameters(), lr=self.lr)
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def forward(self, batch):
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@@ -98,27 +86,10 @@ class GTPP(nn.Module):
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event_seq = event_seq.long()
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emb = self.embedding(event_seq)
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emb = self.emb_drop(emb)
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if LEAK:
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lstm_input = torch.cat([emb[:, :-1], time_seq[:, :-1].unsqueeze(-1)], dim=-1)
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else:
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lstm_input = emb
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lstm_input = torch.cat([emb[:, :-1], time_seq[:, :-1].unsqueeze(-1)], dim=-1)
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hidden_state, _ = self.lstm(lstm_input)
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# FIXME wait we pass the target time into the LSTM. Is this data leakage?
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nll, log_lmbda, int_lmbda, lmbda = self.intensity_net(hidden_state, time_seq[:, -1])
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return [nll, log_lmbda.detach(), int_lmbda.detach(), lmbda.detach()]
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# def train_batch(self, batch):
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# self.set_optimizer.zero_grad()
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# nll, log_lmbda, int_lmbda, lmbda = self.forward(batch)
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# loss = nll
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# loss.backward()
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# self.set_optimizer.step()
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# return nll.item(), log_lmbda.item(), int_lmbda.item(), lmbda
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-304
@@ -1,304 +0,0 @@
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# # coding=utf-8
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# # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# #
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# # Licensed under the Apache License, Version 2.0 (the "License");
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# # you may not use this file except in compliance with the License.
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# # You may obtain a copy of the License at
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# #
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# # http://www.apache.org/licenses/LICENSE-2.0
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# #
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# # Unless required by applicable law or agreed to in writing, software
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# # distributed under the License is distributed on an "AS IS" BASIS,
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# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# # See the License for the specific language governing permissions and
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# # limitations under the License.
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# """PyTorch optimization for BERT model."""
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# import math
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# import torch
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# from torch.optim import Optimizer
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# from torch.optim.optimizer import required
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# from torch.nn.utils import clip_grad_norm_
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# import logging
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# import abc
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# import sys
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# logger = logging.getLogger(__name__)
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# if sys.version_info >= (3, 4):
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# ABC = abc.ABC
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# else:
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# ABC = abc.ABCMeta('ABC', (), {})
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# class _LRSchedule(ABC):
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# """ Parent of all LRSchedules here. """
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# warn_t_total = False # is set to True for schedules where progressing beyond t_total steps doesn't make sense
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# def __init__(self, warmup=0.002, t_total=-1, **kw):
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# """
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# :param warmup: what fraction of t_total steps will be used for linear warmup
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# :param t_total: how many training steps (updates) are planned
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# :param kw:
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# """
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# super(_LRSchedule, self).__init__(**kw)
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# if t_total < 0:
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# logger.warning("t_total value of {} results in schedule not being applied".format(t_total))
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# if not 0.0 <= warmup < 1.0 and not warmup == -1:
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# raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup))
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# warmup = max(warmup, 0.)
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# self.warmup, self.t_total = float(warmup), float(t_total)
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# self.warned_for_t_total_at_progress = -1
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# def get_lr(self, step, nowarn=False):
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# """
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# :param step: which of t_total steps we're on
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# :param nowarn: set to True to suppress warning regarding training beyond specified 't_total' steps
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# :return: learning rate multiplier for current update
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# """
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# if self.t_total < 0:
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# return 1.
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# progress = float(step) / self.t_total
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# ret = self.get_lr_(progress)
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# # warning for exceeding t_total (only active with warmup_linear
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# if not nowarn and self.warn_t_total and progress > 1. and progress > self.warned_for_t_total_at_progress:
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# logger.warning(
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# "Training beyond specified 't_total'. Learning rate multiplier set to {}. Please set 't_total' of {} correctly."
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# .format(ret, self.__class__.__name__))
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# self.warned_for_t_total_at_progress = progress
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# # end warning
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# return ret
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# @abc.abstractmethod
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# def get_lr_(self, progress):
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# """
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# :param progress: value between 0 and 1 (unless going beyond t_total steps) specifying training progress
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# :return: learning rate multiplier for current update
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# """
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# return 1.
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# class ConstantLR(_LRSchedule):
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# def get_lr_(self, progress):
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# return 1.
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# class WarmupCosineSchedule(_LRSchedule):
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# """
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# Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
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# Decreases learning rate from 1. to 0. over remaining `1 - warmup` steps following a cosine curve.
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# If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup.
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# """
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# warn_t_total = True
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# def __init__(self, warmup=0.002, t_total=-1, cycles=.5, **kw):
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# """
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# :param warmup: see LRSchedule
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# :param t_total: see LRSchedule
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# :param cycles: number of cycles. Default: 0.5, corresponding to cosine decay from 1. at progress==warmup and 0 at progress==1.
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# :param kw:
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# """
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# super(WarmupCosineSchedule, self).__init__(warmup=warmup, t_total=t_total, **kw)
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# self.cycles = cycles
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# def get_lr_(self, progress):
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# if progress < self.warmup:
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# return progress / self.warmup
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# else:
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# progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
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# return 0.5 * (1. + math.cos(math.pi * self.cycles * 2 * progress))
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# class WarmupCosineWithHardRestartsSchedule(WarmupCosineSchedule):
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# """
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# Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
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# If `cycles` (default=1.) is different from default, learning rate follows `cycles` times a cosine decaying
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# learning rate (with hard restarts).
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# """
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# def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
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# super(WarmupCosineWithHardRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
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# assert(cycles >= 1.)
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# def get_lr_(self, progress):
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# if progress < self.warmup:
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# return progress / self.warmup
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# else:
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# progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
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# ret = 0.5 * (1. + math.cos(math.pi * ((self.cycles * progress) % 1)))
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# return ret
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# class WarmupCosineWithWarmupRestartsSchedule(WarmupCosineWithHardRestartsSchedule):
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# """
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# All training progress is divided in `cycles` (default=1.) parts of equal length.
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# Every part follows a schedule with the first `warmup` fraction of the training steps linearly increasing from 0. to 1.,
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# followed by a learning rate decreasing from 1. to 0. following a cosine curve.
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# """
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# def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
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# assert(warmup * cycles < 1.)
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# warmup = warmup * cycles if warmup >= 0 else warmup
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# super(WarmupCosineWithWarmupRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
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# def get_lr_(self, progress):
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# progress = progress * self.cycles % 1.
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# if progress < self.warmup:
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# return progress / self.warmup
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# else:
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# progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
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# ret = 0.5 * (1. + math.cos(math.pi * progress))
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# return ret
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# class WarmupConstantSchedule(_LRSchedule):
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# """
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# Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
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# Keeps learning rate equal to 1. after warmup.
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# """
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# def get_lr_(self, progress):
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# if progress < self.warmup:
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# return progress / self.warmup
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# return 1.
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# class WarmupLinearSchedule(_LRSchedule):
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# """
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# Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
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# Linearly decreases learning rate from 1. to 0. over remaining `1 - warmup` steps.
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# """
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# warn_t_total = True
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# def get_lr_(self, progress):
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# if progress < self.warmup:
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# return progress / self.warmup
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# return max((progress - 1.) / (self.warmup - 1.), 0.)
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# SCHEDULES = {
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# None: ConstantLR,
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# "none": ConstantLR,
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# "warmup_cosine": WarmupCosineSchedule,
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# "warmup_constant": WarmupConstantSchedule,
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# "warmup_linear": WarmupLinearSchedule
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# }
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# class BertAdam(Optimizer):
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# """Implements BERT version of Adam algorithm with weight decay fix.
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# Params:
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# lr: learning rate
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# warmup: portion of t_total for the warmup, -1 means no warmup. Default: -1
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# t_total: total number of training steps for the learning
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# rate schedule, -1 means constant learning rate of 1. (no warmup regardless of warmup setting). Default: -1
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# schedule: schedule to use for the warmup (see above).
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# Can be `'warmup_linear'`, `'warmup_constant'`, `'warmup_cosine'`, `'none'`, `None` or a `_LRSchedule` object (see below).
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# If `None` or `'none'`, learning rate is always kept constant.
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# Default : `'warmup_linear'`
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# b1: Adams b1. Default: 0.9
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# b2: Adams b2. Default: 0.999
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# e: Adams epsilon. Default: 1e-6
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# weight_decay: Weight decay. Default: 0.01
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# max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0
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# """
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# def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear',
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# b1=0.9, b2=0.999, e=1e-6, weight_decay=0.01, max_grad_norm=1.0, **kwargs):
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# if lr is not required and lr < 0.0:
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# raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
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# if not isinstance(schedule, _LRSchedule) and schedule not in SCHEDULES:
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# raise ValueError("Invalid schedule parameter: {}".format(schedule))
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# if not 0.0 <= b1 < 1.0:
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# raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1))
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# if not 0.0 <= b2 < 1.0:
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# raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2))
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# if not e >= 0.0:
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# raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e))
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# # initialize schedule object
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# if not isinstance(schedule, _LRSchedule):
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# schedule_type = SCHEDULES[schedule]
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# schedule = schedule_type(warmup=warmup, t_total=t_total)
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# else:
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# if warmup != -1 or t_total != -1:
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# logger.warning("warmup and t_total on the optimizer are ineffective when _LRSchedule object is provided as schedule. "
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# "Please specify custom warmup and t_total in _LRSchedule object.")
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# defaults = dict(lr=lr, schedule=schedule,
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# b1=b1, b2=b2, e=e, weight_decay=weight_decay,
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# max_grad_norm=max_grad_norm)
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# super(BertAdam, self).__init__(params, defaults)
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# def get_lr(self):
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# lr = []
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# for group in self.param_groups:
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# for p in group['params']:
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# state = self.state[p]
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# if len(state) == 0:
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# return [0]
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# lr_scheduled = group['lr']
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# lr_scheduled *= group['schedule'].get_lr(state['step'])
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# lr.append(lr_scheduled)
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# return lr
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# def step(self, closure=None):
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# """Performs a single optimization step.
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# Arguments:
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# closure (callable, optional): A closure that reevaluates the model
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# and returns the loss.
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# """
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# loss = None
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# if closure is not None:
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# loss = closure()
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# for group in self.param_groups:
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# for p in group['params']:
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# if p.grad is None:
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# continue
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# grad = p.grad.data
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# if grad.is_sparse:
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# raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
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# state = self.state[p]
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# # State initialization
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# if len(state) == 0:
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# state['step'] = 0
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# # Exponential moving average of gradient values
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# state['next_m'] = torch.zeros_like(p.data)
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# # Exponential moving average of squared gradient values
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# state['next_v'] = torch.zeros_like(p.data)
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# next_m, next_v = state['next_m'], state['next_v']
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# beta1, beta2 = group['b1'], group['b2']
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# # Add grad clipping
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# if group['max_grad_norm'] > 0:
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# clip_grad_norm_(p, group['max_grad_norm'])
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# # Decay the first and second moment running average coefficient
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# # In-place operations to update the averages at the same time
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# next_m.mul_(beta1).add_(1 - beta1, grad)
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# next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad)
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# update = next_m / (next_v.sqrt() + group['e'])
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# # Just adding the square of the weights to the loss function is *not*
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# # the correct way of using L2 regularization/weight decay with Adam,
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# # since that will interact with the m and v parameters in strange ways.
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# #
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# # Instead we want to decay the weights in a manner that doesn't interact
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# # with the m/v parameters. This is equivalent to adding the square
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# # of the weights to the loss with plain (non-momentum) SGD.
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# if group['weight_decay'] > 0.0:
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# update += group['weight_decay'] * p.data
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# lr_scheduled = group['lr']
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# lr_scheduled *= group['schedule'].get_lr(state['step'])
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# update_with_lr = lr_scheduled * update
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# p.data.add_(-update_with_lr)
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# state['step'] += 1
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# # step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1
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# # No bias correction
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# # bias_correction1 = 1 - beta1 ** state['step']
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# # bias_correction2 = 1 - beta2 ** state['step']
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# return loss
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@@ -38,80 +38,6 @@ def get_parser():
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parser.add_argument("--mean_first", action="store_true", help="in model take mean first")
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return parser
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# if __name__ == "__main__":
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# parser = get_parser()
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# config = parser.parse_args()
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# path = 'data/'
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# if config.data == 'exponential_hawkes':
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# train_data = read_timeseries(path + config.data + '_training.csv')
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# val_data = read_timeseries(path + config.data + '_validation.csv')
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# test_data = read_timeseries(path + config.data + '_testing.csv')
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# else:
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# raise NotImplemented('only exponential_hawkes')
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# train_timeseq, train_eventseq = generate_sequence(train_data, config.seq_len, log_mode=config.log_mode)
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# train_loader = DataLoader(torch.utils.data.TensorDataset(train_timeseq, train_eventseq), shuffle=True, batch_size=config.batch_size)
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# val_timeseq, val_eventseq = generate_sequence(val_data, config.seq_len, log_mode=config.log_mode)
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# val_loader = DataLoader(torch.utils.data.TensorDataset(val_timeseq, val_eventseq), shuffle=False, batch_size=len(val_data))
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# model = GTPP(config)
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# best_loss = 1e3
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# patients = 0
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# tol = 30
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# for epoch in range(config.epochs):
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|
||||
# model.train()
|
||||
|
||||
# loss1 = loss2 = loss3 = 0
|
||||
|
||||
# for batch in train_loader:
|
||||
# loss, log_lmbda, int_lmbda, lmbda = model.train_batch(batch)
|
||||
|
||||
# loss1 += loss
|
||||
# loss2 += log_lmbda
|
||||
# loss3 += int_lmbda
|
||||
|
||||
|
||||
# model.eval()
|
||||
|
||||
# for batch in val_loader:
|
||||
# val_loss, val_log_lmbda, val_int_lmbda, _ = model(batch)
|
||||
|
||||
# if best_loss > val_loss:
|
||||
# best_loss = val_loss.item()
|
||||
# else:
|
||||
# patients += 1
|
||||
# if patients >= tol:
|
||||
# print("Early Stop")
|
||||
# print("epoch", epoch)
|
||||
# plt_lmbda(train_data[0], model=model, seq_len=config.seq_len, log_mode=config.log_mode)
|
||||
# break
|
||||
|
||||
# if epoch % config.prt_evry == 0:
|
||||
# print("Epochs:{}".format(epoch))
|
||||
# print("Training Negative Log Likelihood:{} Log Lambda:{}: Integral Lambda:{}".format(loss1/train_timeseq.size(0), -loss2 / train_timeseq.size(0), loss3 / train_timeseq.size(0)))
|
||||
# print("Validation Negative Log Likelihood:{} Log Lambda:{}: Integral Lambda:{}".format(val_loss / val_timeseq.size(0),
|
||||
# -val_log_lmbda / val_timeseq.size(0),
|
||||
# val_int_lmbda/val_timeseq.size(0)))
|
||||
# plt_lmbda(train_data[0], model=model, seq_len=config.seq_len, log_mode=config.log_mode)
|
||||
# # plt_lmbda(test_data[0], model=model, seq_len=config.seq_len, log_mode=config.log_mode)
|
||||
|
||||
|
||||
# print("end")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user