359 lines
13 KiB
Python
359 lines
13 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021 #
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##################################################
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from __future__ import division
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from __future__ import print_function
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import os, math, random
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from collections import OrderedDict
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import numpy as np
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import pandas as pd
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from typing import Text, Union
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import copy
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from functools import partial
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from typing import Optional, Text
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from qlib.utils import get_or_create_path
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from qlib.log import get_module_logger
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import torch
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import torch.nn.functional as F
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import torch.optim as optim
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import torch.utils.data as th_data
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from log_utils import AverageMeter
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from utils import count_parameters
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from xlayers import super_core
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from .transformers import DEFAULT_NET_CONFIG
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from .transformers import get_transformer
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from qlib.model.base import Model
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from qlib.data.dataset import DatasetH
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from qlib.data.dataset.handler import DataHandlerLP
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DEFAULT_OPT_CONFIG = dict(
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epochs=200,
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lr=0.001,
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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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num_workers=4,
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)
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def train_or_test_epoch(
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xloader, model, loss_fn, metric_fn, is_train, optimizer, device
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):
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if is_train:
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model.train()
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else:
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model.eval()
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score_meter, loss_meter = AverageMeter(), AverageMeter()
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for ibatch, (feats, labels) in enumerate(xloader):
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feats, labels = feats.to(device), labels.to(device)
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# forward the network
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preds = model(feats)
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loss = loss_fn(preds, labels)
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with torch.no_grad():
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score = metric_fn(preds, labels)
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loss_meter.update(loss.item(), feats.size(0))
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score_meter.update(score.item(), feats.size(0))
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# optimize the network
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if is_train and optimizer is not None:
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optimizer.zero_grad()
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loss.backward()
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torch.nn.utils.clip_grad_value_(model.parameters(), 3.0)
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optimizer.step()
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return loss_meter.avg, score_meter.avg
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class QuantTransformer(Model):
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"""Transformer-based Quant Model"""
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def __init__(
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self, net_config=None, opt_config=None, metric="", GPU=0, seed=None, **kwargs
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):
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# Set logger.
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self.logger = get_module_logger("QuantTransformer")
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self.logger.info("QuantTransformer PyTorch version...")
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# set hyper-parameters.
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self.net_config = net_config or DEFAULT_NET_CONFIG
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self.opt_config = opt_config or DEFAULT_OPT_CONFIG
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self.metric = metric
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self.device = torch.device(
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"cuda:{:}".format(GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu"
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)
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self.seed = seed
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self.logger.info(
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"Transformer parameters setting:"
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"\nnet_config : {:}"
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"\nopt_config : {:}"
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"\nmetric : {:}"
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"\ndevice : {:}"
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"\nseed : {:}".format(
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self.net_config,
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self.opt_config,
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self.metric,
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self.device,
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self.seed,
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)
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)
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if self.seed is not None:
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random.seed(self.seed)
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np.random.seed(self.seed)
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torch.manual_seed(self.seed)
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if self.use_gpu:
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torch.cuda.manual_seed(self.seed)
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torch.cuda.manual_seed_all(self.seed)
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self.model = get_transformer(self.net_config)
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self.model.set_super_run_type(super_core.SuperRunMode.FullModel)
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self.logger.info("model: {:}".format(self.model))
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self.logger.info("model size: {:.3f} MB".format(count_parameters(self.model)))
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if self.opt_config["optimizer"] == "adam":
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self.train_optimizer = optim.Adam(
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self.model.parameters(), lr=self.opt_config["lr"]
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)
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elif self.opt_config["optimizer"] == "adam":
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self.train_optimizer = optim.SGD(
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self.model.parameters(), lr=self.opt_config["lr"]
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)
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else:
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raise NotImplementedError(
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"optimizer {:} is not supported!".format(optimizer)
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)
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self.fitted = False
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self.model.to(self.device)
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@property
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def use_gpu(self):
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return self.device != torch.device("cpu")
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def to(self, device):
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if device is None:
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device = "cpu"
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self.device = device
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self.model.to(self.device)
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# move the optimizer
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for param in self.train_optimizer.state.values():
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# Not sure there are any global tensors in the state dict
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if isinstance(param, torch.Tensor):
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param.data = param.data.to(device)
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if param._grad is not None:
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param._grad.data = param._grad.data.to(device)
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elif isinstance(param, dict):
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for subparam in param.values():
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if isinstance(subparam, torch.Tensor):
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subparam.data = subparam.data.to(device)
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if subparam._grad is not None:
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subparam._grad.data = subparam._grad.data.to(device)
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def loss_fn(self, pred, label):
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mask = ~torch.isnan(label)
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if self.opt_config["loss"] == "mse":
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return F.mse_loss(pred[mask], label[mask])
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else:
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raise ValueError("unknown loss `{:}`".format(self.loss))
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def metric_fn(self, pred, label):
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# the metric score : higher is better
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if self.metric == "" or self.metric == "loss":
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return -self.loss_fn(pred, label)
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else:
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raise ValueError("unknown metric `{:}`".format(self.metric))
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def fit(
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self,
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dataset: DatasetH,
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save_dir: Optional[Text] = None,
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):
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def _prepare_dataset(df_data):
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return th_data.TensorDataset(
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torch.from_numpy(df_data["feature"].values).float(),
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torch.from_numpy(df_data["label"].values).squeeze().float(),
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)
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def _prepare_loader(dataset, shuffle):
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return th_data.DataLoader(
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dataset,
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batch_size=self.opt_config["batch_size"],
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drop_last=False,
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pin_memory=True,
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num_workers=self.opt_config["num_workers"],
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shuffle=shuffle,
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)
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df_train, df_valid, df_test = dataset.prepare(
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["train", "valid", "test"],
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col_set=["feature", "label"],
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data_key=DataHandlerLP.DK_L,
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)
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train_dataset, valid_dataset, test_dataset = (
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_prepare_dataset(df_train),
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_prepare_dataset(df_valid),
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_prepare_dataset(df_test),
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)
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train_loader, valid_loader, test_loader = (
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_prepare_loader(train_dataset, True),
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_prepare_loader(valid_dataset, False),
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_prepare_loader(test_dataset, False),
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)
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save_dir = get_or_create_path(save_dir, return_dir=True)
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self.logger.info(
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"Fit procedure for [{:}] with save path={:}".format(
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self.__class__.__name__, save_dir
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)
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)
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def _internal_test(ckp_epoch=None, results_dict=None):
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with torch.no_grad():
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shared_kwards = {
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"model": self.model,
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"loss_fn": self.loss_fn,
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"metric_fn": self.metric_fn,
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"is_train": False,
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"optimizer": None,
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"device": self.device,
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}
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train_loss, train_score = train_or_test_epoch(
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train_loader, **shared_kwards
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)
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valid_loss, valid_score = train_or_test_epoch(
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valid_loader, **shared_kwards
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)
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test_loss, test_score = train_or_test_epoch(
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test_loader, **shared_kwards
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)
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xstr = (
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"train-score={:.6f}, valid-score={:.6f}, test-score={:.6f}".format(
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train_score, valid_score, test_score
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)
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)
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if ckp_epoch is not None and isinstance(results_dict, dict):
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results_dict["train"][ckp_epoch] = train_score
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results_dict["valid"][ckp_epoch] = valid_score
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results_dict["test"][ckp_epoch] = test_score
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return dict(train=train_score, valid=valid_score, test=test_score), xstr
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# Pre-fetch the potential checkpoints
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ckp_path = os.path.join(save_dir, "{:}.pth".format(self.__class__.__name__))
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if os.path.exists(ckp_path):
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ckp_data = torch.load(ckp_path, map_location=self.device)
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stop_steps, best_score, best_epoch = (
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ckp_data["stop_steps"],
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ckp_data["best_score"],
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ckp_data["best_epoch"],
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)
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start_epoch, best_param = ckp_data["start_epoch"], ckp_data["best_param"]
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results_dict = ckp_data["results_dict"]
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self.model.load_state_dict(ckp_data["net_state_dict"])
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self.train_optimizer.load_state_dict(ckp_data["opt_state_dict"])
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self.logger.info("Resume from existing checkpoint: {:}".format(ckp_path))
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else:
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stop_steps, best_score, best_epoch = 0, -np.inf, -1
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start_epoch, best_param = 0, None
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results_dict = dict(
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train=OrderedDict(), valid=OrderedDict(), test=OrderedDict()
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)
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_, eval_str = _internal_test(-1, results_dict)
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self.logger.info(
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"Training from scratch, metrics@start: {:}".format(eval_str)
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)
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for iepoch in range(start_epoch, self.opt_config["epochs"]):
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self.logger.info(
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"Epoch={:03d}/{:03d} ::==>> Best valid @{:03d} ({:.6f})".format(
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iepoch, self.opt_config["epochs"], best_epoch, best_score
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)
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)
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train_loss, train_score = train_or_test_epoch(
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train_loader,
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self.model,
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self.loss_fn,
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self.metric_fn,
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True,
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self.train_optimizer,
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self.device,
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)
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self.logger.info(
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"Training :: loss={:.6f}, score={:.6f}".format(train_loss, train_score)
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)
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current_eval_scores, eval_str = _internal_test(iepoch, results_dict)
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self.logger.info("Evaluating :: {:}".format(eval_str))
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if current_eval_scores["valid"] > best_score:
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stop_steps, best_epoch, best_score = (
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0,
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iepoch,
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current_eval_scores["valid"],
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)
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best_param = copy.deepcopy(self.model.state_dict())
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else:
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stop_steps += 1
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if stop_steps >= self.opt_config["early_stop"]:
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self.logger.info(
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"early stop at {:}-th epoch, where the best is @{:}".format(
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iepoch, best_epoch
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)
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)
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break
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save_info = dict(
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net_config=self.net_config,
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opt_config=self.opt_config,
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net_state_dict=self.model.state_dict(),
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opt_state_dict=self.train_optimizer.state_dict(),
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best_param=best_param,
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stop_steps=stop_steps,
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best_score=best_score,
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best_epoch=best_epoch,
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results_dict=results_dict,
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start_epoch=iepoch + 1,
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)
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torch.save(save_info, ckp_path)
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self.logger.info(
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"The best score: {:.6f} @ {:02d}-th epoch".format(best_score, best_epoch)
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)
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self.model.load_state_dict(best_param)
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_, eval_str = _internal_test("final", results_dict)
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self.logger.info("Reload the best parameter :: {:}".format(eval_str))
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if self.use_gpu:
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with torch.cuda.device(self.device):
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torch.cuda.empty_cache()
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self.fitted = True
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def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
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if not self.fitted:
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raise ValueError("The model is not fitted yet!")
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x_test = dataset.prepare(
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segment, col_set="feature", data_key=DataHandlerLP.DK_I
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)
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index = x_test.index
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with torch.no_grad():
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self.model.eval()
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x_values = x_test.values
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sample_num, batch_size = x_values.shape[0], self.opt_config["batch_size"]
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preds = []
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for begin in range(sample_num)[::batch_size]:
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if sample_num - begin < batch_size:
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end = sample_num
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else:
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end = begin + batch_size
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x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
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with torch.no_grad():
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pred = self.model(x_batch).detach().cpu().numpy()
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preds.append(pred)
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return pd.Series(np.concatenate(preds), index=index)
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