tiandiao123

import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
from sklearn.model_selection import train_test_split

def feature_selection_lgb(X, y, feature_names, threshold=0.01, use_shap=False):
    """
    使用 LightGBM 进行特征选择
    

tiandiao123

import os
import joblib
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.metrics import precision_score, recall_score, accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, StandardScaler

tiandiao123

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from torch.distributions import Categorical

# Define the policy network
class PolicyNetwork(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(PolicyNetwork, self).__init__()

tiandiao123

// concurrent_queue.cpp
#include "concurrent_queue.h"
#include <iostream>

template <typename T>
void ConcurrentQueue<T>::push(T value) {
    std::lock_guard<std::mutex> lock(mutex);
    queue.push(std::move(value));
    cv.notify_one(); // Notify one waiting thread if it's waiting
}

tiandiao123

// concurrent_queue.h
#ifndef CONCURRENT_QUEUE_H
#define CONCURRENT_QUEUE_H

#include <torch/torch.h>
#include <queue>
#include <mutex>
#include <condition_variable>

template <typename T>

tiandiao123

import torch
from torch import nn
import torch.nn.functional as F
from torch_geometric.data import Data
from torch_geometric.nn.conv import DNAConv
import numpy as np

class DynamicGraphNN(torch.nn.Module):
    def __init__(self, in_channels, out_channels, num_layers = 2, heads=1, groups=1):
        super().__init__()

tiandiao123

import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from torch.utils.data import DataLoader, Dataset, TensorDataset

# Assuming the model is a simple neural network for regression/classification
class SimpleNN(nn.Module):
    def __init__(self, input_size, output_size):
        super(SimpleNN, self).__init__()

tiandiao123

import torch
import torch.nn.functional as F

def sample_reweight(loss_curve, loss_values, k_th, alpha1=1.0, alpha2=1.0, bins_sr=10, decay=0.9):
    """
    The SR module of Double Ensemble using PyTorch.

    Args:
    - loss_curve: Tensor, shape (N, T), the loss curve for each sample over training iterations.
    - loss_values: Tensor, shape (N,), the loss of the current ensemble on each sample.

tiandiao123

import argparse
import time

import torch
import torch.distributed as dist
import transformers
from transformers import LlamaForCausalLM, LlamaTokenizer

import colossalai
from colossalai.inference import CaiInferEngine

tiandiao123

from vllm import LLM, SamplingParams
import torch 
from torch import distributed as dist 
import time
from tqdm import tqdm
import numpy as np

# # Create an LLM.
llm = LLM(
        # model="/home/lclcq/share/llama-7b",