mirror of
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synced 2026-06-06 05:51:17 +08:00
Update models.
This commit is contained in:
lwwang1995
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@@ -57,7 +57,7 @@ task:
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num_layers: 2
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dropout: 0.0
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n_epochs: 200
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lr: 1e-3
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lr: 1e-2
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early_stop: 10
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batch_size: 800
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metric: loss
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@@ -21,6 +21,7 @@ from ...log import get_module_logger, TimeInspector
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader
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from ...model.base import Model
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from ...data.dataset import DatasetH
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@@ -62,7 +63,9 @@ class GATs(Model):
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model_path=None,
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optimizer="adam",
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GPU="0",
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n_jobs=10,
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seed=None,
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batch_size=800,
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**kwargs
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):
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# Set logger.
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@@ -84,8 +87,10 @@ class GATs(Model):
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self.with_pretrain = with_pretrain
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self.model_path = model_path
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self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
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self.n_jobs = n_jobs
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self.use_gpu = torch.cuda.is_available()
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self.seed = seed
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self.batch_size = batch_size
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self.logger.info(
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"GATs parameters setting:"
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@@ -218,7 +223,7 @@ class GATs(Model):
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def fit(
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self,
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dataset: DatasetH,
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dataset,
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evals_result=dict(),
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verbose=True,
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save_path=None,
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@@ -1,7 +1,6 @@
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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from __future__ import division
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from __future__ import print_function
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@@ -21,266 +20,15 @@ from ...log import get_module_logger, TimeInspector
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import torch
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import torch.nn as nn
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import torch.nn.init as init
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import torch.optim as optim
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from torch.utils.data import DataLoader
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from ...model.base import Model
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from ...data.dataset import DatasetH, TSDatasetH
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from ...data.dataset import DatasetH
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from ...data.dataset.handler import DataHandlerLP
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class SFM(Model):
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"""SFM Model
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Parameters
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----------
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d_feat : int
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input dimension for each time step
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metric: str
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the evaluate metric used in early stop
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optimizer : str
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optimizer name
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GPU : str
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the GPU ID(s) used for training
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"""
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def __init__(
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self,
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d_feat=6,
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hidden_size=64,
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num_layers=2,
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dropout=0.0,
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n_epochs=200,
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lr=0.001,
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metric="",
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batch_size=2000,
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early_stop=20,
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loss="mse",
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optimizer="adam",
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n_jobs=10,
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GPU="0",
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seed=None,
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**kwargs
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):
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# Set logger.
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self.logger = get_module_logger("SFM")
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self.logger.info("SFM pytorch version...")
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# set hyper-parameters.
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self.d_feat = d_feat
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.dropout = dropout
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self.n_epochs = n_epochs
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self.lr = lr
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self.metric = metric
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self.batch_size = batch_size
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self.early_stop = early_stop
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self.optimizer = optimizer.lower()
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self.loss = loss
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self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
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self.n_jobs = n_jobs
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self.use_gpu = torch.cuda.is_available()
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self.seed = seed
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self.logger.info(
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"SFM parameters setting:"
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"\nd_feat : {}"
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"\nhidden_size : {}"
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"\nnum_layers : {}"
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"\ndropout : {}"
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"\nn_epochs : {}"
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"\nlr : {}"
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"\nmetric : {}"
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"\nbatch_size : {}"
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"\nearly_stop : {}"
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"\noptimizer : {}"
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"\nloss_type : {}"
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"\nvisible_GPU : {}"
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"\nn_jobs : {}"
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"\nuse_GPU : {}"
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"\nseed : {}".format(
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d_feat,
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hidden_size,
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num_layers,
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dropout,
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n_epochs,
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lr,
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metric,
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batch_size,
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early_stop,
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optimizer.lower(),
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loss,
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GPU,
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n_jobs,
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self.use_gpu,
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seed,
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)
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)
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if self.seed is not None:
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np.random.seed(self.seed)
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torch.manual_seed(self.seed)
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self.SFM_model = SFM_Model(
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d_feat=self.d_feat,
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hidden_size=self.hidden_size,
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num_layers=self.num_layers,
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dropout=self.dropout,
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).to(self.device)
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if optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.SFM_model.parameters(), lr=self.lr)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.SFM_model.parameters(), lr=self.lr)
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else:
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raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
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self._fitted = False
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self.SFM_model.to(self.device)
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def mse(self, pred, label):
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loss = (pred - label) ** 2
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return torch.mean(loss)
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def loss_fn(self, pred, label):
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mask = ~torch.isnan(label)
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if self.loss == "mse":
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return self.mse(pred[mask], label[mask])
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raise ValueError("unknown loss `%s`" % self.loss)
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def metric_fn(self, pred, label):
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mask = torch.isfinite(label)
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if self.metric == "" or self.metric == "loss":
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return -self.loss_fn(pred[mask], label[mask])
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raise ValueError("unknown metric `%s`" % self.metric)
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def train_epoch(self, data_loader):
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self.SFM_model.train()
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for data in data_loader:
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feature = data[:, :, 0:-1].to(self.device)
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label = data[:, -1, -1].to(self.device)
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pred = self.SFM_model(feature.float())
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loss = self.loss_fn(pred, label)
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self.train_optimizer.zero_grad()
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loss.backward()
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torch.nn.utils.clip_grad_value_(self.SFM_model.parameters(), 3.0)
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self.train_optimizer.step()
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def test_epoch(self, data_loader):
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self.SFM_model.eval()
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scores = []
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losses = []
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for data in data_loader:
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feature = data[:, :, 0:-1].to(self.device)
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# feature[torch.isnan(feature)] = 0
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label = data[:, -1, -1].to(self.device)
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pred = self.SFM_model(feature.float())
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loss = self.loss_fn(pred, label)
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losses.append(loss.item())
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score = self.metric_fn(pred, label)
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scores.append(score.item())
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return np.mean(losses), np.mean(scores)
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def fit(
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self,
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dataset,
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evals_result=dict(),
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verbose=True,
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save_path=None,
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):
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dl_train = dataset.prepare("train", data_key=DataHandlerLP.DK_L)
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dl_valid = dataset.prepare("valid", data_key=DataHandlerLP.DK_L)
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dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader
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dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader
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train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs)
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valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs)
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if save_path == None:
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save_path = create_save_path(save_path)
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stop_steps = 0
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train_loss = 0
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best_score = -np.inf
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best_epoch = 0
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evals_result["train"] = []
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evals_result["valid"] = []
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# train
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self.logger.info("training...")
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self._fitted = True
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for step in range(self.n_epochs):
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self.logger.info("Epoch%d:", step)
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self.logger.info("training...")
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self.train_epoch(train_loader)
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self.logger.info("evaluating...")
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train_loss, train_score = self.test_epoch(train_loader)
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val_loss, val_score = self.test_epoch(valid_loader)
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self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
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evals_result["train"].append(train_score)
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evals_result["valid"].append(val_score)
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if val_score > best_score:
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best_score = val_score
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stop_steps = 0
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best_epoch = step
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best_param = copy.deepcopy(self.SFM_model.state_dict())
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else:
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stop_steps += 1
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if stop_steps >= self.early_stop:
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self.logger.info("early stop")
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break
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self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
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self.SFM_model.load_state_dict(best_param)
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torch.save(best_param, save_path)
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if self.use_gpu:
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torch.cuda.empty_cache()
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def predict(self, dataset):
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if not self._fitted:
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raise ValueError("model is not fitted yet!")
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dl_test = dataset.prepare("test", data_key=DataHandlerLP.DK_I)
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dl_test.config(fillna_type="ffill+bfill")
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test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=20)
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self.SFM_model.eval()
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preds = []
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for data in test_loader:
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feature = data[:, :, 0:-1].to(self.device)
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with torch.no_grad():
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if self.use_gpu:
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pred = self.SFM_model(feature.float()).detach().cpu().numpy()
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else:
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pred = self.SFM_model(feature.float()).detach().numpy()
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preds.append(pred)
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return pd.Series(np.concatenate(preds), index=dl_test.get_index())
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class SFM_Model(nn.Module):
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def __init__(
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self,
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@@ -334,6 +82,8 @@ class SFM_Model(nn.Module):
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self.states = []
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def forward(self, input):
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input = input.reshape(len(input), self.input_dim, -1) # [N, F, T]
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input = input.permute(0, 2, 1) # [N, T, F]
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time_step = input.shape[1]
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for ts in range(time_step):
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@@ -432,3 +182,290 @@ class SFM_Model(nn.Module):
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constants.append(torch.tensor(array).to(self.device))
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self.states[5:] = constants
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class SFM(Model):
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"""SFM Model
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Parameters
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----------
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input_dim : int
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input dimension
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output_dim : int
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output dimension
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lr : float
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learning rate
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optimizer : str
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optimizer name
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GPU : str
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the GPU ID(s) used for training
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"""
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def __init__(
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self,
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d_feat=6,
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hidden_size=64,
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output_dim=1,
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freq_dim=10,
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dropout_W=0.0,
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dropout_U=0.0,
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n_epochs=200,
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lr=0.001,
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metric="",
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batch_size=2000,
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early_stop=20,
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eval_steps=5,
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loss="mse",
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optimizer="gd",
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n_jobs=10,
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GPU="0",
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seed=None,
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**kwargs
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):
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# Set logger.
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self.logger = get_module_logger("SFM")
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self.logger.info("SFM pytorch version...")
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# set hyper-parameters.
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self.d_feat = d_feat
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self.hidden_size = hidden_size
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self.output_dim = output_dim
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self.freq_dim = freq_dim
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self.dropout_W = dropout_W
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self.dropout_U = dropout_U
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self.n_epochs = n_epochs
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self.lr = lr
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self.metric = metric
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self.batch_size = batch_size
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self.early_stop = early_stop
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self.eval_steps = eval_steps
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self.optimizer = optimizer.lower()
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self.loss = loss
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self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
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self.n_jobs = n_jobs
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self.use_gpu = torch.cuda.is_available()
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self.seed = seed
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self.logger.info(
|
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"SFM parameters setting:"
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"\nd_feat : {}"
|
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"\nhidden_size : {}"
|
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"\noutput_size : {}"
|
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"\nfrequency_dimension : {}"
|
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"\ndropout_W: {}"
|
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"\ndropout_U: {}"
|
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"\nn_epochs : {}"
|
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"\nlr : {}"
|
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"\nmetric : {}"
|
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"\nbatch_size : {}"
|
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"\nearly_stop : {}"
|
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"\neval_steps : {}"
|
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"\noptimizer : {}"
|
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"\nloss_type : {}"
|
||||
"\nvisible_GPU : {}"
|
||||
"\nuse_GPU : {}"
|
||||
"\nseed : {}".format(
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d_feat,
|
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hidden_size,
|
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output_dim,
|
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freq_dim,
|
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dropout_W,
|
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dropout_U,
|
||||
n_epochs,
|
||||
lr,
|
||||
metric,
|
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batch_size,
|
||||
early_stop,
|
||||
eval_steps,
|
||||
optimizer.lower(),
|
||||
loss,
|
||||
GPU,
|
||||
self.use_gpu,
|
||||
seed,
|
||||
)
|
||||
)
|
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|
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if self.seed is not None:
|
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np.random.seed(self.seed)
|
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torch.manual_seed(self.seed)
|
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|
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self.sfm_model = SFM_Model(
|
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d_feat=self.d_feat,
|
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output_dim=self.output_dim,
|
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hidden_size=self.hidden_size,
|
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freq_dim=self.freq_dim,
|
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dropout_W=self.dropout_W,
|
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dropout_U=self.dropout_U,
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device=self.device,
|
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)
|
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if optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.sfm_model.parameters(), lr=self.lr)
|
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elif optimizer.lower() == "gd":
|
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self.train_optimizer = optim.SGD(self.sfm_model.parameters(), lr=self.lr)
|
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else:
|
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raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
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|
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self._fitted = False
|
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self.sfm_model.to(self.device)
|
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|
||||
def train_epoch(self, data_loader):
|
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|
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self.sfm_model.train()
|
||||
|
||||
for data in data_loader:
|
||||
feature = data[:, :, 0:-1].to(self.device)
|
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label = data[:, -1, -1].to(self.device)
|
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|
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pred = self.sfm_model(feature.float())
|
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loss = self.loss_fn(pred, label)
|
||||
|
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self.train_optimizer.zero_grad()
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_value_(self.sfm_model.parameters(), 3.0)
|
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self.train_optimizer.step()
|
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|
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def test_epoch(self, data_loader):
|
||||
|
||||
self.sfm_model.eval()
|
||||
|
||||
scores = []
|
||||
losses = []
|
||||
|
||||
for data in data_loader:
|
||||
|
||||
feature = data[:, :, 0:-1].to(self.device)
|
||||
# feature[torch.isnan(feature)] = 0
|
||||
label = data[:, -1, -1].to(self.device)
|
||||
|
||||
pred = self.sfm_model(feature.float())
|
||||
loss = self.loss_fn(pred, label)
|
||||
losses.append(loss.item())
|
||||
|
||||
score = self.metric_fn(pred, label)
|
||||
scores.append(score.item())
|
||||
|
||||
return np.mean(losses), np.mean(scores)
|
||||
|
||||
def fit(
|
||||
self,
|
||||
dataset: DatasetH,
|
||||
evals_result=dict(),
|
||||
verbose=True,
|
||||
save_path=None,
|
||||
):
|
||||
|
||||
dl_train = dataset.prepare("train", data_key=DataHandlerLP.DK_L)
|
||||
dl_valid = dataset.prepare("valid", data_key=DataHandlerLP.DK_L)
|
||||
|
||||
dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader
|
||||
dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader
|
||||
|
||||
train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs)
|
||||
valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs)
|
||||
|
||||
if save_path == None:
|
||||
save_path = create_save_path(save_path)
|
||||
|
||||
stop_steps = 0
|
||||
train_loss = 0
|
||||
best_score = -np.inf
|
||||
best_epoch = 0
|
||||
evals_result["train"] = []
|
||||
evals_result["valid"] = []
|
||||
|
||||
# train
|
||||
self.logger.info("training...")
|
||||
self._fitted = True
|
||||
|
||||
for step in range(self.n_epochs):
|
||||
self.logger.info("Epoch%d:", step)
|
||||
self.logger.info("training...")
|
||||
self.train_epoch(train_loader)
|
||||
self.logger.info("evaluating...")
|
||||
train_loss, train_score = self.test_epoch(train_loader)
|
||||
val_loss, val_score = self.test_epoch(valid_loader)
|
||||
self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
|
||||
evals_result["train"].append(train_score)
|
||||
evals_result["valid"].append(val_score)
|
||||
|
||||
if val_score > best_score:
|
||||
best_score = val_score
|
||||
stop_steps = 0
|
||||
best_epoch = step
|
||||
best_param = copy.deepcopy(self.sfm_model.state_dict())
|
||||
else:
|
||||
stop_steps += 1
|
||||
if stop_steps >= self.early_stop:
|
||||
self.logger.info("early stop")
|
||||
break
|
||||
|
||||
self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
|
||||
self.sfm_model.load_state_dict(best_param)
|
||||
torch.save(best_param, save_path)
|
||||
|
||||
if self.use_gpu:
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def mse(self, pred, label):
|
||||
loss = (pred - label) ** 2
|
||||
return torch.mean(loss)
|
||||
|
||||
def loss_fn(self, pred, label):
|
||||
mask = ~torch.isnan(label)
|
||||
|
||||
if self.loss == "mse":
|
||||
return self.mse(pred[mask], label[mask])
|
||||
|
||||
raise ValueError("unknown loss `%s`" % self.loss)
|
||||
|
||||
def metric_fn(self, pred, label):
|
||||
|
||||
mask = torch.isfinite(label)
|
||||
|
||||
if self.metric == "" or self.metric == "loss":
|
||||
return -self.loss_fn(pred[mask], label[mask])
|
||||
|
||||
raise ValueError("unknown metric `%s`" % self.metric)
|
||||
|
||||
def predict(self, dataset):
|
||||
if not self._fitted:
|
||||
raise ValueError("model is not fitted yet!")
|
||||
|
||||
dl_test = dataset.prepare("test", data_key=DataHandlerLP.DK_I)
|
||||
dl_test.config(fillna_type="ffill+bfill")
|
||||
test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=20)
|
||||
self.sfm_model.eval()
|
||||
preds = []
|
||||
|
||||
for data in test_loader:
|
||||
|
||||
feature = data[:, :, 0:-1].to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
if self.use_gpu:
|
||||
pred = self.sfm_model(feature.float()).detach().cpu().numpy()
|
||||
else:
|
||||
pred = self.sfm_model(feature.float()).detach().numpy()
|
||||
|
||||
preds.append(pred)
|
||||
|
||||
return pd.Series(np.concatenate(preds), index=dl_test.get_index())
|
||||
|
||||
|
||||
class AverageMeter(object):
|
||||
"""Computes and stores the average and current value"""
|
||||
|
||||
def __init__(self):
|
||||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
self.val = 0
|
||||
self.avg = 0
|
||||
self.sum = 0
|
||||
self.count = 0
|
||||
|
||||
def update(self, val, n=1):
|
||||
self.val = val
|
||||
self.sum += val * n
|
||||
self.count += n
|
||||
self.avg = self.sum / self.count
|
||||
|
||||
Reference in New Issue
Block a user