Reformulate Q-Transformer
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@ -4,7 +4,7 @@
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# Refer to:
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# Refer to:
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# - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.ipynb
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# - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.ipynb
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# - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.py
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# - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.py
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# python exps/trading/workflow_tt.py --market all --gpu 1
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# python exps/trading/workflow_tt.py --gpu 1 --market csi300
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#####################################################
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#####################################################
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import sys, argparse
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import sys, argparse
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from pathlib import Path
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from pathlib import Path
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@ -63,7 +63,8 @@ def main(xargs):
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"class": "QuantTransformer",
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"class": "QuantTransformer",
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"module_path": "trade_models",
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"module_path": "trade_models",
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"kwargs": {
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"kwargs": {
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"loss": "mse",
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"net_config": None,
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"opt_config": None,
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"GPU": "0",
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"GPU": "0",
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"metric": "loss",
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"metric": "loss",
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},
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},
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@ -107,20 +108,23 @@ def main(xargs):
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provider_uri = "~/.qlib/qlib_data/cn_data"
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provider_uri = "~/.qlib/qlib_data/cn_data"
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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save_dir = "{:}-{:}".format(xargs.save_dir, xargs.market)
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dataset = init_instance_by_config(dataset_config)
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dataset = init_instance_by_config(dataset_config)
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for irun in range(xargs.times):
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for irun in range(xargs.times):
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xmodel_config = model_config.copy()
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xmodel_config = model_config.copy()
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xmodel_config = update_gpu(xmodel_config, xags.gpu)
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xmodel_config = update_gpu(xmodel_config, xargs.gpu)
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task = dict(model=xmodel_config, dataset=dataset_config, record=record_config)
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task_config = dict(model=xmodel_config, dataset=dataset_config, record=record_config)
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run_exp(task_config, dataset, "Transformer", "recorder-{:02d}-{:02d}".format(irun, xargs.times), xargs.save_dir)
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run_exp(task_config, dataset, xargs.name, "recorder-{:02d}-{:02d}".format(irun, xargs.times), save_dir)
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if __name__ == "__main__":
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Vanilla Transformable Transformer")
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parser = argparse.ArgumentParser("Vanilla Transformable Transformer")
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parser.add_argument("--save_dir", type=str, default="./outputs/tt-ml-runs", help="The checkpoint directory.")
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parser.add_argument("--save_dir", type=str, default="./outputs/vtt-runs", help="The checkpoint directory.")
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parser.add_argument("--name", type=str, default="Transformer", help="The experiment name.")
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parser.add_argument("--times", type=int, default=10, help="The repeated run times.")
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parser.add_argument("--times", type=int, default=10, help="The repeated run times.")
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parser.add_argument("--gpu", type=int, default=0, help="The GPU ID used for train / test.")
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parser.add_argument("--gpu", type=int, default=0, help="The GPU ID used for train / test.")
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parser.add_argument("--market", type=str, default="csi300", help="The market indicator.")
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parser.add_argument("--market", type=str, default="all", help="The market indicator.")
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args = parser.parse_args()
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args = parser.parse_args()
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main(args)
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main(args)
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@ -14,10 +14,10 @@ from typing import Optional
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import logging
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import logging
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from qlib.utils import (
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from qlib.utils import (
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unpack_archive_with_buffer,
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unpack_archive_with_buffer,
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save_multiple_parts_file,
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save_multiple_parts_file,
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create_save_path,
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create_save_path,
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drop_nan_by_y_index,
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drop_nan_by_y_index,
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)
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)
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from qlib.log import get_module_logger, TimeInspector
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from qlib.log import get_module_logger, TimeInspector
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@ -25,6 +25,7 @@ import torch
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import torch.nn as nn
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import torch.nn as nn
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import torch.nn.functional as F
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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.optim as optim
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import torch.utils.data as th_data
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import layers as xlayers
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import layers as xlayers
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from utils import count_parameters
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from utils import count_parameters
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@ -34,409 +35,399 @@ from qlib.data.dataset import DatasetH
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from qlib.data.dataset.handler import DataHandlerLP
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from qlib.data.dataset.handler import DataHandlerLP
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default_net_config = dict(d_feat=6, hidden_size=48, depth=5, pos_drop=0.1)
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default_opt_config = dict(epochs=200, lr=0.001, batch_size=2000, early_stop=20, loss="mse", optimizer="adam")
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class QuantTransformer(Model):
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class QuantTransformer(Model):
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"""Transformer-based Quant Model
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"""Transformer-based Quant Model"""
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"""
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def __init__(self, net_config=None, opt_config=None, metric="", GPU=0, seed=None, **kwargs):
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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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def __init__(
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# set hyper-parameters.
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self,
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self.net_config = net_config or default_net_config
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d_feat=6,
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self.opt_config = opt_config or default_opt_config
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hidden_size=48,
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self.metric = metric
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depth=5,
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self.device = torch.device("cuda:{:}".format(GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
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pos_dropout=0.1,
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self.seed = seed
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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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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("QuantTransformer")
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self.logger.info("QuantTransformer pytorch version...")
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# set hyper-parameters.
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self.logger.info(
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self.d_feat = d_feat
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"Transformer parameters setting:"
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self.hidden_size = hidden_size
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"\nnet_config : {:}"
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self.depth = depth
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"\nopt_config : {:}"
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self.pos_dropout = pos_dropout
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"\nmetric : {:}"
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self.n_epochs = n_epochs
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"\ndevice : {:}"
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self.lr = lr
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"\nseed : {:}".format(
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self.metric = metric
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self.net_config,
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self.batch_size = batch_size
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self.opt_config,
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self.early_stop = early_stop
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self.metric,
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self.optimizer = optimizer.lower()
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self.device,
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self.loss = loss
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self.seed,
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self.device = torch.device("cuda:{:}".format(GPU) if torch.cuda.is_available() else "cpu")
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)
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self.use_gpu = torch.cuda.is_available()
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)
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self.seed = seed
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self.logger.info(
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if self.seed is not None:
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"Transformer parameters setting:"
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np.random.seed(self.seed)
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"\nd_feat : {}"
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torch.manual_seed(self.seed)
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"\nhidden_size : {}"
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"\ndepth : {}"
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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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"\nuse_GPU : {}"
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"\nseed : {}".format(
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d_feat,
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hidden_size,
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depth,
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pos_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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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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self.model = TransformerModel(
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np.random.seed(self.seed)
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d_feat=self.net_config["d_feat"],
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torch.manual_seed(self.seed)
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embed_dim=self.net_config["hidden_size"],
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depth=self.net_config["depth"],
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pos_drop=self.net_config["pos_drop"],
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)
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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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self.model = TransformerModel(d_feat=self.d_feat,
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if self.opt_config["optimizer"] == "adam":
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embed_dim=self.hidden_size,
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self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.opt_config["lr"])
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depth=self.depth,
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elif self.opt_config["optimizer"] == "adam":
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pos_dropout=pos_dropout)
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self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.opt_config["lr"])
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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 optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.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.model.to(self.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.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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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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else:
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raise ValueError("unknown metric `{:}`".format(self.metric))
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def train_epoch(self, x_train, y_train):
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x_train_values = x_train.values
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y_train_values = np.squeeze(y_train.values)
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self.model.train()
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indices = np.arange(len(x_train_values))
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np.random.shuffle(indices)
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for i in range(len(indices))[:: self.batch_size]:
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if len(indices) - i < self.batch_size:
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break
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feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
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label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
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pred = self.model(feature)
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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.model.parameters(), 3.0)
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self.train_optimizer.step()
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def test_epoch(self, data_x, data_y):
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# prepare training data
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x_values = data_x.values
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y_values = np.squeeze(data_y.values)
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self.model.eval()
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scores = []
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losses = []
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indices = np.arange(len(x_values))
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for i in range(len(indices))[:: self.batch_size]:
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if len(indices) - i < self.batch_size:
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break
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feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
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label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
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pred = self.model(feature)
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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: DatasetH,
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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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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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x_train, y_train = df_train["feature"], df_train["label"]
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x_valid, y_valid = df_valid["feature"], df_valid["label"]
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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(x_train, y_train)
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self.logger.info("evaluating...")
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train_loss, train_score = self.test_epoch(x_train, y_train)
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val_loss, val_score = self.test_epoch(x_valid, y_valid)
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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.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.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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x_test = dataset.prepare("test", col_set="feature")
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index = x_test.index
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self.model.eval()
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x_values = x_test.values
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sample_num = x_values.shape[0]
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preds = []
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for begin in range(sample_num)[:: self.batch_size]:
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if sample_num - begin < self.batch_size:
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end = sample_num
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else:
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end = begin + self.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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if self.use_gpu:
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pred = self.model(x_batch).detach().cpu().numpy()
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else:
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else:
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pred = self.model(x_batch).detach().numpy()
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raise NotImplementedError("optimizer {:} is not supported!".format(optimizer))
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preds.append(pred)
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self.fitted = False
|
||||||
|
self.model.to(self.device)
|
||||||
|
|
||||||
return pd.Series(np.concatenate(preds), index=index)
|
@property
|
||||||
|
def use_gpu(self):
|
||||||
|
self.device == torch.device("cpu")
|
||||||
|
|
||||||
|
def loss_fn(self, pred, label):
|
||||||
|
mask = ~torch.isnan(label)
|
||||||
|
if self.opt_config["loss"] == "mse":
|
||||||
|
return F.mse_loss(pred[mask], label[mask])
|
||||||
|
else:
|
||||||
|
raise ValueError("unknown loss `{:}`".format(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])
|
||||||
|
else:
|
||||||
|
raise ValueError("unknown metric `{:}`".format(self.metric))
|
||||||
|
|
||||||
|
def train_epoch(self, xloader, model, loss_fn, optimizer):
|
||||||
|
model.train()
|
||||||
|
scores, losses = [], []
|
||||||
|
for ibatch, (feats, labels) in enumerate(xloader):
|
||||||
|
feats = feats.to(self.device, non_blocking=True)
|
||||||
|
labels = labels.to(self.device, non_blocking=True)
|
||||||
|
# forward the network
|
||||||
|
preds = model(feats)
|
||||||
|
loss = loss_fn(preds, labels)
|
||||||
|
with torch.no_grad():
|
||||||
|
score = self.metric_fn(preds, labels)
|
||||||
|
losses.append(loss.item())
|
||||||
|
scores.append(loss.item())
|
||||||
|
# optimize the network
|
||||||
|
optimizer.zero_grad()
|
||||||
|
loss.backward()
|
||||||
|
torch.nn.utils.clip_grad_value_(model.parameters(), 3.0)
|
||||||
|
optimizer.step()
|
||||||
|
return np.mean(losses), np.mean(scores)
|
||||||
|
|
||||||
|
def test_epoch(self, xloader, model, loss_fn, metric_fn):
|
||||||
|
model.eval()
|
||||||
|
scores, losses = [], []
|
||||||
|
with torch.no_grad():
|
||||||
|
for ibatch, (feats, labels) in enumerate(xloader):
|
||||||
|
feats = feats.to(self.device, non_blocking=True)
|
||||||
|
labels = labels.to(self.device, non_blocking=True)
|
||||||
|
# forward the network
|
||||||
|
preds = model(feats)
|
||||||
|
loss = loss_fn(preds, labels)
|
||||||
|
score = self.metric_fn(preds, labels)
|
||||||
|
losses.append(loss.item())
|
||||||
|
scores.append(loss.item())
|
||||||
|
return np.mean(losses), np.mean(scores)
|
||||||
|
|
||||||
|
def fit(
|
||||||
|
self,
|
||||||
|
dataset: DatasetH,
|
||||||
|
evals_result=dict(),
|
||||||
|
verbose=True,
|
||||||
|
save_path=None,
|
||||||
|
):
|
||||||
|
def _prepare_dataset(df_data):
|
||||||
|
return th_data.TensorDataset(
|
||||||
|
torch.from_numpy(df_data["feature"].values).float(),
|
||||||
|
torch.from_numpy(df_data["label"].values).squeeze().float(),
|
||||||
|
)
|
||||||
|
|
||||||
|
df_train, df_valid, df_test = dataset.prepare(
|
||||||
|
["train", "valid", "test"],
|
||||||
|
col_set=["feature", "label"],
|
||||||
|
data_key=DataHandlerLP.DK_L,
|
||||||
|
)
|
||||||
|
train_dataset, valid_dataset, test_dataset = (
|
||||||
|
_prepare_dataset(df_train),
|
||||||
|
_prepare_dataset(df_valid),
|
||||||
|
_prepare_dataset(df_test),
|
||||||
|
)
|
||||||
|
|
||||||
|
train_loader = th_data.DataLoader(
|
||||||
|
train_dataset, batch_size=self.opt_config["batch_size"], shuffle=True, drop_last=False, pin_memory=True
|
||||||
|
)
|
||||||
|
valid_loader = th_data.DataLoader(
|
||||||
|
valid_dataset, batch_size=self.opt_config["batch_size"], shuffle=False, drop_last=False, pin_memory=True
|
||||||
|
)
|
||||||
|
test_loader = th_data.DataLoader(
|
||||||
|
test_dataset, batch_size=self.opt_config["batch_size"], shuffle=False, drop_last=False, pin_memory=True
|
||||||
|
)
|
||||||
|
|
||||||
|
if save_path == None:
|
||||||
|
save_path = create_save_path(save_path)
|
||||||
|
stop_steps, best_score, best_epoch = 0, -np.inf, 0
|
||||||
|
train_loss = 0
|
||||||
|
evals_result["train"] = []
|
||||||
|
evals_result["valid"] = []
|
||||||
|
|
||||||
|
# train
|
||||||
|
self.logger.info("Fit procedure for [{:}] with save path={:}".format(self.__class__.__name__, save_path))
|
||||||
|
|
||||||
|
def _internal_test():
|
||||||
|
train_loss, train_score = self.test_epoch(train_loader, self.model, self.loss_fn, self.metric_fn)
|
||||||
|
valid_loss, valid_score = self.test_epoch(valid_loader, self.model, self.loss_fn, self.metric_fn)
|
||||||
|
test_loss, test_score = self.test_epoch(test_loader, self.model, self.loss_fn, self.metric_fn)
|
||||||
|
xstr = "train-score={:.6f}, valid-score={:.6f}, test-score={:.6f}".format(
|
||||||
|
train_score, valid_score, test_score
|
||||||
|
)
|
||||||
|
return dict(train=train_score, valid=valid_score, test=test_score), xstr
|
||||||
|
|
||||||
|
_, eval_str = _internal_test()
|
||||||
|
self.logger.info("Before Training: {:}".format(eval_str))
|
||||||
|
for iepoch in range(self.opt_config["epochs"]):
|
||||||
|
self.logger.info("Epoch={:03d}/{:03d} ::==>>".format(iepoch, self.opt_config["epochs"]))
|
||||||
|
|
||||||
|
train_loss, train_score = self.train_epoch(train_loader, self.model, self.loss_fn, self.train_optimizer)
|
||||||
|
self.logger.info("Training :: loss={:.6f}, score={:.6f}".format(train_loss, train_score))
|
||||||
|
|
||||||
|
eval_score_dict, eval_str = _internal_test()
|
||||||
|
self.logger.info("Evaluating :: {:}".format(eval_str))
|
||||||
|
evals_result["train"].append(eval_score_dict["train"])
|
||||||
|
evals_result["valid"].append(eval_score_dict["valid"])
|
||||||
|
|
||||||
|
if eval_score_dict["valid"] > best_score:
|
||||||
|
stop_steps, best_epoch, best_score = 0, iepoch, eval_score_dict["valid"]
|
||||||
|
best_param = copy.deepcopy(self.model.state_dict())
|
||||||
|
else:
|
||||||
|
stop_steps += 1
|
||||||
|
if stop_steps >= self.opt_config["early_stop"]:
|
||||||
|
self.logger.info("early stop at {:}-th epoch, where the best is @{:}".format(iepoch, best_epoch))
|
||||||
|
break
|
||||||
|
|
||||||
|
self.logger.info("The best score: {:.6f} @ {:02d}-th epoch".format(best_score, best_epoch))
|
||||||
|
self.model.load_state_dict(best_param)
|
||||||
|
torch.save(best_param, save_path)
|
||||||
|
|
||||||
|
if self.use_gpu:
|
||||||
|
torch.cuda.empty_cache()
|
||||||
|
self.fitted = True
|
||||||
|
|
||||||
|
def predict(self, dataset):
|
||||||
|
|
||||||
|
if not self.fitted:
|
||||||
|
raise ValueError("model is not fitted yet!")
|
||||||
|
|
||||||
|
x_test = dataset.prepare("test", col_set="feature")
|
||||||
|
index = x_test.index
|
||||||
|
self.model.eval()
|
||||||
|
x_values = x_test.values
|
||||||
|
sample_num = x_values.shape[0]
|
||||||
|
preds = []
|
||||||
|
|
||||||
|
for begin in range(sample_num)[:: self.batch_size]:
|
||||||
|
|
||||||
|
if sample_num - begin < self.batch_size:
|
||||||
|
end = sample_num
|
||||||
|
else:
|
||||||
|
end = begin + self.batch_size
|
||||||
|
|
||||||
|
x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
if self.use_gpu:
|
||||||
|
pred = self.model(x_batch).detach().cpu().numpy()
|
||||||
|
else:
|
||||||
|
pred = self.model(x_batch).detach().numpy()
|
||||||
|
|
||||||
|
preds.append(pred)
|
||||||
|
|
||||||
|
return pd.Series(np.concatenate(preds), index=index)
|
||||||
|
|
||||||
|
|
||||||
# Real Model
|
# Real Model
|
||||||
|
|
||||||
|
|
||||||
class Attention(nn.Module):
|
class Attention(nn.Module):
|
||||||
|
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0):
|
||||||
|
super(Attention, self).__init__()
|
||||||
|
self.num_heads = num_heads
|
||||||
|
head_dim = dim // num_heads
|
||||||
|
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
|
||||||
|
self.scale = qk_scale or math.sqrt(head_dim)
|
||||||
|
|
||||||
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
|
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||||
super(Attention, self).__init__()
|
self.attn_drop = nn.Dropout(attn_drop)
|
||||||
self.num_heads = num_heads
|
self.proj = nn.Linear(dim, dim)
|
||||||
head_dim = dim // num_heads
|
self.proj_drop = nn.Dropout(proj_drop)
|
||||||
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
|
|
||||||
self.scale = qk_scale or math.sqrt(head_dim)
|
|
||||||
|
|
||||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
def forward(self, x):
|
||||||
self.attn_drop = nn.Dropout(attn_drop)
|
B, N, C = x.shape
|
||||||
self.proj = nn.Linear(dim, dim)
|
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||||
self.proj_drop = nn.Dropout(proj_drop)
|
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
||||||
|
|
||||||
def forward(self, x):
|
attn = (q @ k.transpose(-2, -1)) * self.scale
|
||||||
B, N, C = x.shape
|
attn = attn.softmax(dim=-1)
|
||||||
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
attn = self.attn_drop(attn)
|
||||||
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
|
||||||
|
|
||||||
attn = (q @ k.transpose(-2, -1)) * self.scale
|
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
||||||
attn = attn.softmax(dim=-1)
|
x = self.proj(x)
|
||||||
attn = self.attn_drop(attn)
|
x = self.proj_drop(x)
|
||||||
|
return x
|
||||||
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
|
||||||
x = self.proj(x)
|
|
||||||
x = self.proj_drop(x)
|
|
||||||
return x
|
|
||||||
|
|
||||||
|
|
||||||
class Block(nn.Module):
|
class Block(nn.Module):
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
dim,
|
||||||
|
num_heads,
|
||||||
|
mlp_ratio=4.0,
|
||||||
|
qkv_bias=False,
|
||||||
|
qk_scale=None,
|
||||||
|
attn_drop=0.0,
|
||||||
|
mlp_drop=0.0,
|
||||||
|
drop_path=0.0,
|
||||||
|
act_layer=nn.GELU,
|
||||||
|
norm_layer=nn.LayerNorm,
|
||||||
|
):
|
||||||
|
super(Block, self).__init__()
|
||||||
|
self.norm1 = norm_layer(dim)
|
||||||
|
self.attn = Attention(
|
||||||
|
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=mlp_drop
|
||||||
|
)
|
||||||
|
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||||
|
self.drop_path = xlayers.DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
||||||
|
self.norm2 = norm_layer(dim)
|
||||||
|
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||||
|
self.mlp = xlayers.MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=mlp_drop)
|
||||||
|
|
||||||
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None,
|
def forward(self, x):
|
||||||
attn_drop=0., mlp_drop=0., drop_path=0.,
|
x = x + self.drop_path(self.attn(self.norm1(x)))
|
||||||
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||||
super(Block, self).__init__()
|
return x
|
||||||
self.norm1 = norm_layer(dim)
|
|
||||||
self.attn = Attention(
|
|
||||||
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=mlp_drop)
|
|
||||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
|
||||||
self.drop_path = xlayers.DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
|
||||||
self.norm2 = norm_layer(dim)
|
|
||||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
|
||||||
self.mlp = xlayers.MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=mlp_drop)
|
|
||||||
|
|
||||||
def forward(self, x):
|
|
||||||
x = x + self.drop_path(self.attn(self.norm1(x)))
|
|
||||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
|
||||||
return x
|
|
||||||
|
|
||||||
|
|
||||||
class SimpleEmbed(nn.Module):
|
class SimpleEmbed(nn.Module):
|
||||||
|
def __init__(self, d_feat, embed_dim):
|
||||||
|
super(SimpleEmbed, self).__init__()
|
||||||
|
self.d_feat = d_feat
|
||||||
|
self.embed_dim = embed_dim
|
||||||
|
self.proj = nn.Linear(d_feat, embed_dim)
|
||||||
|
|
||||||
def __init__(self, d_feat, embed_dim):
|
def forward(self, x):
|
||||||
super(SimpleEmbed, self).__init__()
|
x = x.reshape(len(x), self.d_feat, -1) # [N, F*T] -> [N, F, T]
|
||||||
self.d_feat = d_feat
|
x = x.permute(0, 2, 1) # [N, F, T] -> [N, T, F]
|
||||||
self.embed_dim = embed_dim
|
out = self.proj(x) * math.sqrt(self.embed_dim)
|
||||||
self.proj = nn.Linear(d_feat, embed_dim)
|
return out
|
||||||
|
|
||||||
def forward(self, x):
|
|
||||||
x = x.reshape(len(x), self.d_feat, -1) # [N, F*T] -> [N, F, T]
|
|
||||||
x = x.permute(0, 2, 1) # [N, F, T] -> [N, T, F]
|
|
||||||
out = self.proj(x) * math.sqrt(self.embed_dim)
|
|
||||||
return out
|
|
||||||
|
|
||||||
|
|
||||||
class TransformerModel(nn.Module):
|
class TransformerModel(nn.Module):
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
d_feat: int,
|
||||||
|
embed_dim: int = 64,
|
||||||
|
depth: int = 4,
|
||||||
|
num_heads: int = 4,
|
||||||
|
mlp_ratio: float = 4.0,
|
||||||
|
qkv_bias: bool = True,
|
||||||
|
qk_scale: Optional[float] = None,
|
||||||
|
pos_drop=0.0,
|
||||||
|
mlp_drop_rate=0.0,
|
||||||
|
attn_drop_rate=0.0,
|
||||||
|
drop_path_rate=0.0,
|
||||||
|
norm_layer=None,
|
||||||
|
):
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
d_feat (int, tuple): input image size
|
||||||
|
embed_dim (int): embedding dimension
|
||||||
|
depth (int): depth of transformer
|
||||||
|
num_heads (int): number of attention heads
|
||||||
|
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
|
||||||
|
qkv_bias (bool): enable bias for qkv if True
|
||||||
|
qk_scale (float): override default qk scale of head_dim ** -0.5 if set
|
||||||
|
pos_drop (float): dropout rate for the positional embedding
|
||||||
|
mlp_drop_rate (float): the dropout rate for MLP layers in a block
|
||||||
|
attn_drop_rate (float): attention dropout rate
|
||||||
|
drop_path_rate (float): stochastic depth rate
|
||||||
|
norm_layer: (nn.Module): normalization layer
|
||||||
|
"""
|
||||||
|
super(TransformerModel, self).__init__()
|
||||||
|
self.embed_dim = embed_dim
|
||||||
|
self.num_features = embed_dim
|
||||||
|
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
||||||
|
|
||||||
def __init__(self,
|
self.input_embed = SimpleEmbed(d_feat, embed_dim=embed_dim)
|
||||||
d_feat: int,
|
|
||||||
embed_dim: int = 64,
|
|
||||||
depth: int = 4,
|
|
||||||
num_heads: int = 4,
|
|
||||||
mlp_ratio: float = 4.,
|
|
||||||
qkv_bias: bool = True,
|
|
||||||
qk_scale: Optional[float] = None,
|
|
||||||
pos_dropout=0., mlp_drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None):
|
|
||||||
"""
|
|
||||||
Args:
|
|
||||||
d_feat (int, tuple): input image size
|
|
||||||
embed_dim (int): embedding dimension
|
|
||||||
depth (int): depth of transformer
|
|
||||||
num_heads (int): number of attention heads
|
|
||||||
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
|
|
||||||
qkv_bias (bool): enable bias for qkv if True
|
|
||||||
qk_scale (float): override default qk scale of head_dim ** -0.5 if set
|
|
||||||
pos_dropout (float): dropout rate for the positional embedding
|
|
||||||
mlp_drop_rate (float): the dropout rate for MLP layers in a block
|
|
||||||
attn_drop_rate (float): attention dropout rate
|
|
||||||
drop_path_rate (float): stochastic depth rate
|
|
||||||
norm_layer: (nn.Module): normalization layer
|
|
||||||
"""
|
|
||||||
super(TransformerModel, self).__init__()
|
|
||||||
self.embed_dim = embed_dim
|
|
||||||
self.num_features = embed_dim
|
|
||||||
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
|
||||||
|
|
||||||
self.input_embed = SimpleEmbed(d_feat, embed_dim=embed_dim)
|
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
||||||
|
self.pos_embed = xlayers.PositionalEncoder(d_model=embed_dim, max_seq_len=65, dropout=pos_drop)
|
||||||
|
|
||||||
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
||||||
self.pos_embed = xlayers.PositionalEncoder(d_model=embed_dim, max_seq_len=65, dropout=pos_dropout)
|
self.blocks = nn.ModuleList(
|
||||||
|
[
|
||||||
|
Block(
|
||||||
|
dim=embed_dim,
|
||||||
|
num_heads=num_heads,
|
||||||
|
mlp_ratio=mlp_ratio,
|
||||||
|
qkv_bias=qkv_bias,
|
||||||
|
qk_scale=qk_scale,
|
||||||
|
attn_drop=attn_drop_rate,
|
||||||
|
mlp_drop=mlp_drop_rate,
|
||||||
|
drop_path=dpr[i],
|
||||||
|
norm_layer=norm_layer,
|
||||||
|
)
|
||||||
|
for i in range(depth)
|
||||||
|
]
|
||||||
|
)
|
||||||
|
self.norm = norm_layer(embed_dim)
|
||||||
|
|
||||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
# regression head
|
||||||
self.blocks = nn.ModuleList([
|
self.head = nn.Linear(self.num_features, 1)
|
||||||
Block(
|
|
||||||
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
|
||||||
attn_drop=attn_drop_rate, mlp_drop=mlp_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
|
|
||||||
for i in range(depth)])
|
|
||||||
self.norm = norm_layer(embed_dim)
|
|
||||||
|
|
||||||
# regression head
|
xlayers.trunc_normal_(self.cls_token, std=0.02)
|
||||||
self.head = nn.Linear(self.num_features, 1)
|
self.apply(self._init_weights)
|
||||||
|
|
||||||
xlayers.trunc_normal_(self.cls_token, std=.02)
|
def _init_weights(self, m):
|
||||||
self.apply(self._init_weights)
|
if isinstance(m, nn.Linear):
|
||||||
|
xlayers.trunc_normal_(m.weight, std=0.02)
|
||||||
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||||
|
nn.init.constant_(m.bias, 0)
|
||||||
|
elif isinstance(m, nn.LayerNorm):
|
||||||
|
nn.init.constant_(m.bias, 0)
|
||||||
|
nn.init.constant_(m.weight, 1.0)
|
||||||
|
|
||||||
def _init_weights(self, m):
|
def forward_features(self, x):
|
||||||
if isinstance(m, nn.Linear):
|
batch, flatten_size = x.shape
|
||||||
xlayers.trunc_normal_(m.weight, std=.02)
|
feats = self.input_embed(x) # batch * 60 * 64
|
||||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
||||||
nn.init.constant_(m.bias, 0)
|
|
||||||
elif isinstance(m, nn.LayerNorm):
|
|
||||||
nn.init.constant_(m.bias, 0)
|
|
||||||
nn.init.constant_(m.weight, 1.0)
|
|
||||||
|
|
||||||
def forward_features(self, x):
|
cls_tokens = self.cls_token.expand(batch, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
||||||
batch, flatten_size = x.shape
|
feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
|
||||||
feats = self.input_embed(x) # batch * 60 * 64
|
feats_w_tp = self.pos_embed(feats_w_ct)
|
||||||
|
|
||||||
cls_tokens = self.cls_token.expand(batch, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
xfeats = feats_w_tp
|
||||||
feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
|
for block in self.blocks:
|
||||||
feats_w_tp = self.pos_embed(feats_w_ct)
|
xfeats = block(xfeats)
|
||||||
|
|
||||||
xfeats = feats_w_tp
|
xfeats = self.norm(xfeats)[:, 0]
|
||||||
for block in self.blocks:
|
return xfeats
|
||||||
xfeats = block(xfeats)
|
|
||||||
|
|
||||||
xfeats = self.norm(xfeats)[:, 0]
|
def forward(self, x):
|
||||||
return xfeats
|
feats = self.forward_features(x)
|
||||||
|
predicts = self.head(feats).squeeze(-1)
|
||||||
def forward(self, x):
|
return predicts
|
||||||
feats = self.forward_features(x)
|
|
||||||
predicts = self.head(feats).squeeze(-1)
|
|
||||||
return predicts
|
|
||||||
|
Loading…
Reference in New Issue
Block a user