457 lines
13 KiB
Python
Executable File
457 lines
13 KiB
Python
Executable File
##################################################
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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
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import numpy as np
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import pandas as pd
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import copy
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from functools import partial
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from sklearn.metrics import roc_auc_score, mean_squared_error
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from typing import Optional
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import logging
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from qlib.utils import (
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unpack_archive_with_buffer,
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save_multiple_parts_file,
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create_save_path,
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drop_nan_by_y_index,
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)
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from qlib.log import get_module_logger, TimeInspector
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import layers as xlayers
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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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class QuantTransformer(Model):
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"""Transformer-based Quant Model
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"""
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def __init__(
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self,
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d_feat=6,
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hidden_size=64,
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num_layers=2,
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dropout=0.0,
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n_epochs=200,
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lr=0.001,
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metric="",
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batch_size=2000,
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early_stop=20,
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loss="mse",
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optimizer="adam",
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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.d_feat = d_feat
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.dropout = dropout
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self.n_epochs = n_epochs
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self.lr = lr
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self.metric = metric
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self.batch_size = batch_size
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self.early_stop = early_stop
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self.optimizer = optimizer.lower()
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self.loss = loss
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self.device = torch.device("cuda:{:}".format(GPU) if torch.cuda.is_available() else "cpu")
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self.use_gpu = torch.cuda.is_available()
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self.seed = seed
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self.logger.info(
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"GRU parameters setting:"
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"\nd_feat : {}"
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"\nhidden_size : {}"
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"\nnum_layers : {}"
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"\ndropout : {}"
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"\nn_epochs : {}"
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"\nlr : {}"
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"\nmetric : {}"
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"\nbatch_size : {}"
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"\nearly_stop : {}"
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"\noptimizer : {}"
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"\nloss_type : {}"
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"\nvisible_GPU : {}"
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"\nuse_GPU : {}"
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"\nseed : {}".format(
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d_feat,
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hidden_size,
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num_layers,
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dropout,
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n_epochs,
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lr,
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metric,
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batch_size,
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early_stop,
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optimizer.lower(),
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loss,
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GPU,
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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.model = TransformerModel(d_feat=self.d_feat)
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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 mse(self, pred, label):
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loss = (pred - label) ** 2
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return torch.mean(loss)
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def loss_fn(self, pred, label):
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mask = ~torch.isnan(label)
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if self.loss == "mse":
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return self.mse(pred[mask], label[mask])
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raise ValueError("unknown loss `%s`" % self.loss)
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def metric_fn(self, pred, label):
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mask = torch.isfinite(label)
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if self.metric == "" or self.metric == "loss":
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return -self.loss_fn(pred[mask], label[mask])
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raise ValueError("unknown metric `%s`" % self.metric)
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def train_epoch(self, 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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pred = self.model(x_batch).detach().numpy()
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preds.append(pred)
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return pd.Series(np.concatenate(preds), index=index)
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# Real Model
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class MLP(nn.Module):
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
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super(MLP, self).__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class Attention(nn.Module):
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def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
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super(Attention, self).__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
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self.scale = qk_scale or head_dim ** -0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class Block(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
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super(Block, self).__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = xlayers.DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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def forward(self, x):
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x = x + self.drop_path(self.attn(self.norm1(x)))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class SimpleEmbed(nn.Module):
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def __init__(self, d_feat, embed_dim):
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super(SimpleEmbed, self).__init__()
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self.d_feat = d_feat
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self.proj = nn.Linear(d_feat, embed_dim)
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def forward(self, x):
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x = x.reshape(len(x), self.d_feat, -1) # [N, F*T] -> [N, F, T]
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x = x.permute(0, 2, 1) # [N, F, T] -> [N, T, F]
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out = self.proj(x)
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return out
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class TransformerModel(nn.Module):
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def __init__(self,
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d_feat: int,
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embed_dim: int = 64,
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depth: int = 4,
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num_heads: int = 4,
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mlp_ratio: float = 4.,
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qkv_bias: bool = True,
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qk_scale: Optional[float] = None,
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drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None):
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"""
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Args:
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d_feat (int, tuple): input image size
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embed_dim (int): embedding dimension
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depth (int): depth of transformer
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num_heads (int): number of attention heads
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mlp_ratio (int): ratio of mlp hidden dim to embedding dim
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qkv_bias (bool): enable bias for qkv if True
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qk_scale (float): override default qk scale of head_dim ** -0.5 if set
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drop_rate (float): dropout rate
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attn_drop_rate (float): attention dropout rate
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drop_path_rate (float): stochastic depth rate
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norm_layer: (nn.Module): normalization layer
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"""
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super(TransformerModel, self).__init__()
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self.embed_dim = embed_dim
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self.num_features = embed_dim
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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self.input_embed = SimpleEmbed(d_feat, embed_dim=embed_dim)
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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self.pos_embed = xlayers.PositionalEncoder(d_model=embed_dim, max_seq_len=65)
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self.pos_drop = nn.Dropout(p=drop_rate)
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
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self.blocks = nn.ModuleList([
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Block(
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dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
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for i in range(depth)])
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self.norm = norm_layer(embed_dim)
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# regression head
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self.head = nn.Linear(self.num_features, 1)
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xlayers.trunc_normal_(self.cls_token, std=.02)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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xlayers.trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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def forward_features(self, x):
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batch, flatten_size = x.shape
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feats = self.input_embed(x) # batch * 60 * 64
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cls_tokens = self.cls_token.expand(batch, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
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feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
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feats_w_tp = self.pos_embed(feats_w_ct)
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feats_w_tp = self.pos_drop(feats_w_tp)
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xfeats = feats_w_tp
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for block in self.blocks:
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xfeats = block(xfeats)
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xfeats = self.norm(xfeats)[:, 0]
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return xfeats
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def forward(self, x):
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feats = self.forward_features(x)
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predicts = self.head(feats).squeeze(-1)
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return predicts
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