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be4646b4b7
* Intermediate version * Fix yaml template & Successfully run rolling * Be compatible with benchmark * Get same results with previous linear model * Black formatting * Update black * Update the placeholder mechanism * Update CI * Update CI * Upgrade Black * Fix CI and simplify code * Fix CI * Move the data processing caching mechanism into utils. * Adjusting DDG-DA * Organize import
311 lines
9.4 KiB
Python
Executable File
311 lines
9.4 KiB
Python
Executable File
# 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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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 ...utils import get_or_create_path
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from ...log import get_module_logger
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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 .pytorch_utils import count_parameters
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from ...model.base import Model
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from ...data.dataset import DatasetH
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from ...data.dataset.handler import DataHandlerLP
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from .tcn import TemporalConvNet
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class TCN(Model):
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"""TCN 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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n_chans: int
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number of channels
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metric: str
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the evaluation 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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n_chans=128,
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kernel_size=5,
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num_layers=5,
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dropout=0.5,
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n_epochs=200,
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lr=0.0001,
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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("TCN")
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self.logger.info("TCN pytorch version...")
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# set hyper-parameters.
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self.d_feat = d_feat
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self.n_chans = n_chans
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self.kernel_size = kernel_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() and GPU >= 0 else "cpu")
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self.seed = seed
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self.logger.info(
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"TCN parameters setting:"
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"\nd_feat : {}"
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"\nn_chans : {}"
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"\nkernel_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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"\nuse_GPU : {}"
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"\nseed : {}".format(
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d_feat,
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n_chans,
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kernel_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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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.tcn_model = TCNModel(
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num_input=self.d_feat,
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output_size=1,
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num_channels=[self.n_chans] * self.num_layers,
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kernel_size=self.kernel_size,
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dropout=self.dropout,
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)
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self.logger.info("model:\n{:}".format(self.tcn_model))
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self.logger.info("model size: {:.4f} MB".format(count_parameters(self.tcn_model)))
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if optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.tcn_model.parameters(), lr=self.lr)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.tcn_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.tcn_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 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 in ("", "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, 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.tcn_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.tcn_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.tcn_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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x_values = data_x.values
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y_values = np.squeeze(data_y.values)
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self.tcn_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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with torch.no_grad():
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pred = self.tcn_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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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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save_path = get_or_create_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.tcn_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.tcn_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: DatasetH, segment: Union[Text, slice] = "test"):
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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(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
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index = x_test.index
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self.tcn_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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pred = self.tcn_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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class TCNModel(nn.Module):
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def __init__(self, num_input, output_size, num_channels, kernel_size, dropout):
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super().__init__()
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self.num_input = num_input
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self.tcn = TemporalConvNet(num_input, num_channels, kernel_size, dropout=dropout)
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self.linear = nn.Linear(num_channels[-1], output_size)
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def forward(self, x):
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x = x.reshape(x.shape[0], self.num_input, -1)
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output = self.tcn(x)
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output = self.linear(output[:, :, -1])
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return output.squeeze()
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