optuna和 lightgbm

文章目录

optuna使用

1.导入相关包

python 复制代码
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from fvcore.nn import FlopCountAnalysis

import optuna


DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
DIR = ".."
BATCHSIZE = 128
N_TRAIN_EXAMPLES = BATCHSIZE * 30   # 128 * 30个训练
N_VALID_EXAMPLES = BATCHSIZE * 10   # 128 * 10个预测

2.定义模型可选参数

optuna支持很多种搜索方式:

(1)trial.suggest_categorical('optimizer', ['MomentumSGD', 'Adam']):表示从SGD和adam里选一个使用;

(2)trial.suggest_int('num_layers', 1, 3):从1~3范围内的int里选;

(3)trial.suggest_uniform('dropout_rate', 0.0, 1.0):从0~1内的uniform分布里选;

(4)trial.suggest_loguniform('learning_rate', 1e-5, 1e-2):从1e-5~1e-2的log uniform分布里选;

(5)trial.suggest_discrete_uniform('drop_path_rate', 0.0, 1.0, 0.1):从0~1且step为0.1的离散uniform分布里选;

python 复制代码
def define_model(trial):
    n_layers = trial.suggest_int("n_layers", 1, 3) # 从[1,3]范围里面选一个
    layers = []

    in_features = 28 * 28
    for i in range(n_layers):
        out_features = trial.suggest_int("n_units_l{}".format(i), 4, 128)
        layers.append(nn.Linear(in_features, out_features))
        layers.append(nn.ReLU())
        p = trial.suggest_float("dropout_{}".format(i), 0.2, 0.5)
        layers.append(nn.Dropout(p))

        in_features = out_features

    layers.append(nn.Linear(in_features, 10))
    layers.append(nn.LogSoftmax(dim=1))

    return nn.Sequential(*layers)

3.定义训练代码和评估代码

python 复制代码
# Defines training and evaluation.
def train_model(model, optimizer, train_loader):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.view(-1, 28 * 28).to(DEVICE), target.to(DEVICE)
        optimizer.zero_grad()
        F.nll_loss(model(data), target).backward()
        optimizer.step()


def eval_model(model, valid_loader):
    model.eval()
    correct = 0
    with torch.no_grad():
        for batch_idx, (data, target) in enumerate(valid_loader):
            data, target = data.view(-1, 28 * 28).to(DEVICE), target.to(DEVICE)
            pred = model(data).argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    accuracy = correct / N_VALID_EXAMPLES

    flops = FlopCountAnalysis(model, inputs=(torch.randn(1, 28 * 28).to(DEVICE),)).total()
    return flops, accuracy

4.定义目标函数

python 复制代码
def objective(trial):
    train_dataset = torchvision.datasets.FashionMNIST(
        DIR, train=True, download=True, transform=torchvision.transforms.ToTensor()
    )
    train_loader = torch.utils.data.DataLoader(
        torch.utils.data.Subset(train_dataset, list(range(N_TRAIN_EXAMPLES))),
        batch_size=BATCHSIZE,
        shuffle=True,
    )

    val_dataset = torchvision.datasets.FashionMNIST(
        DIR, train=False, transform=torchvision.transforms.ToTensor()
    )
    val_loader = torch.utils.data.DataLoader(
        torch.utils.data.Subset(val_dataset, list(range(N_VALID_EXAMPLES))),
        batch_size=BATCHSIZE,
        shuffle=True,
    )
    model = define_model(trial).to(DEVICE)

    optimizer = torch.optim.Adam(
        model.parameters(), trial.suggest_float("lr", 1e-5, 1e-1, log=True)
    )

    for epoch in range(10):
        train_model(model, optimizer, train_loader)
    flops, accuracy = eval_model(model, val_loader)
    return flops, accuracy

5.运行程序

运行30次实验,每次实验返回 flops,accuracy

python 复制代码
study = optuna.create_study(directions=["minimize", "maximize"]) # flops 最小化, accuracy 最大化
study.optimize(objective, n_trials=30, timeout=300)

print("Number of finished trials: ", len(study.trials))

6.可视化

flops, accuracy 二维图

optuna.visualization.plot_pareto_front(study, target_names=["FLOPS", "accuracy"])

7.超参数的重要性

对于flops

optuna.visualization.plot_param_importances(

study, target=lambda t: t.values[0], target_name="flops"

)

对于accuracy

optuna.visualization.plot_param_importances(

study, target=lambda t: t.values[1], target_name="accuracy"

)

8.查看相关信息

python 复制代码
# https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/002_multi_objective.html
# 利用pytorch mnist 识别
# 设置了一些超参数,lr, layer number, feature_number等
# 然后目标是 flops 和 accurary

# 最后是可视化:
# 显示试验的一些结果:
# optuna.visualization.plot_pareto_front(study, target_names=["FLOPS", "accuracy"])
# 左上角是最好的

# 显示重要性:
# optuna.visualization.plot_param_importances(
#     study, target=lambda t: t.values[0], target_name="flops"
# )
# optuna.visualization.plot_param_importances(
#     study, target=lambda t: t.values[1], target_name="accuracy"
# )


# trials的属性:
print(f"Number of trials on the Pareto front: {len(study.best_trials)}")

trial_with_highest_accuracy = max(study.best_trials, key=lambda t: t.values[1])
print(f"Trial with highest accuracy: ")
print(f"\tnumber: {trial_with_highest_accuracy.number}")
print(f"\tparams: {trial_with_highest_accuracy.params}")
print(f"\tvalues: {trial_with_highest_accuracy.values}")

9.可视化的一个完整示例

python 复制代码
# You can use Matplotlib instead of Plotly for visualization by simply replacing `optuna.visualization` with
# `optuna.visualization.matplotlib` in the following examples.
from optuna.visualization import plot_contour
from optuna.visualization import plot_edf
from optuna.visualization import plot_intermediate_values
from optuna.visualization import plot_optimization_history
from optuna.visualization import plot_parallel_coordinate
from optuna.visualization import plot_param_importances
from optuna.visualization import plot_rank
from optuna.visualization import plot_slice
from optuna.visualization import plot_timeline

def objective(trial):
    train_dataset = torchvision.datasets.FashionMNIST(
        DIR, train=True, download=True, transform=torchvision.transforms.ToTensor()
    )
    train_loader = torch.utils.data.DataLoader(
        torch.utils.data.Subset(train_dataset, list(range(N_TRAIN_EXAMPLES))),
        batch_size=BATCHSIZE,
        shuffle=True,
    )

    val_dataset = torchvision.datasets.FashionMNIST(
        DIR, train=False, transform=torchvision.transforms.ToTensor()
    )
    val_loader = torch.utils.data.DataLoader(
        torch.utils.data.Subset(val_dataset, list(range(N_VALID_EXAMPLES))),
        batch_size=BATCHSIZE,
        shuffle=True,
    )
    model = define_model(trial).to(DEVICE)

    optimizer = torch.optim.Adam(
        model.parameters(), trial.suggest_float("lr", 1e-5, 1e-1, log=True)
    )

    for epoch in range(10):
        train_model(model, optimizer, train_loader)

        val_accuracy = eval_model(model, val_loader)
        trial.report(val_accuracy, epoch)

        if trial.should_prune():
            raise optuna.exceptions.TrialPruned()

    return val_accuracy

study = optuna.create_study(
    direction="maximize",
    sampler=optuna.samplers.TPESampler(seed=SEED),
    pruner=optuna.pruners.MedianPruner(),
)
study.optimize(objective, n_trials=30, timeout=300)

运行之后可视化:


10.lightgbm实验

python 复制代码
"""
Optuna example that optimizes a classifier configuration for cancer dataset using LightGBM.

In this example, we optimize the validation accuracy of cancer detection using LightGBM.
We optimize both the choice of booster model and their hyperparameters.

"""

import numpy as np
import optuna

import lightgbm as lgb
import sklearn.datasets
import sklearn.metrics
from sklearn.model_selection import train_test_split


# FYI: Objective functions can take additional arguments
# (https://optuna.readthedocs.io/en/stable/faq.html#objective-func-additional-args).
def objective(trial):
    data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)
    train_x, valid_x, train_y, valid_y = train_test_split(data, target, test_size=0.25)
    dtrain = lgb.Dataset(train_x, label=train_y)

    param = {
        "objective": "binary",
        "metric": "binary_logloss",
        "verbosity": -1,
        "boosting_type": "gbdt",
        "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True),
        "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True),
        "num_leaves": trial.suggest_int("num_leaves", 2, 256),
        "feature_fraction": trial.suggest_float("feature_fraction", 0.4, 1.0),
        "bagging_fraction": trial.suggest_float("bagging_fraction", 0.4, 1.0),
        "bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
        "min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
    }

    gbm = lgb.train(param, dtrain)
    preds = gbm.predict(valid_x)
    pred_labels = np.rint(preds)
    accuracy = sklearn.metrics.accuracy_score(valid_y, pred_labels)
    return accuracy


if __name__ == "__main__":
    study = optuna.create_study(direction="maximize")
    study.optimize(objective, n_trials=100)

    print("Number of finished trials: {}".format(len(study.trials)))

    print("Best trial:")
    trial = study.best_trial

    print("  Value: {}".format(trial.value))

    print("  Params: ")
    for key, value in trial.params.items():
        print("    {}: {}".format(key, value))

运行结果:

https://github.com/microsoft/LightGBM/tree/master/examples

https://blog.csdn.net/yang1015661763/article/details/131364826

相关推荐
倔强青铜三5 分钟前
苦练Python第23天:元组秘籍与妙用
人工智能·python·面试
Norvyn_729 分钟前
LeetCode|Day18|20. 有效的括号|Python刷题笔记
笔记·python·leetcode
AndrewHZ36 分钟前
【图像处理基石】如何入门色彩评估?
图像处理·人工智能·深度学习·色彩科学·hvs·色彩评估·颜色工程
chao_7891 小时前
更灵活方便的初始化、清除方法——fixture【pytest】
服务器·自动化测试·python·pytest
心情好的小球藻1 小时前
Python应用进阶DAY9--类型注解Type Hinting
开发语言·python
都叫我大帅哥1 小时前
LangChain加载HTML内容全攻略:从入门到精通
python·langchain
静心问道1 小时前
TrOCR: 基于Transformer的光学字符识别方法,使用预训练模型
人工智能·深度学习·transformer·多模态
亲持红叶1 小时前
GLU 变种:ReGLU 、 GEGLU 、 SwiGLU
人工智能·深度学习·神经网络·激活函数
惜.己1 小时前
使用python读取json数据,简单的处理成元组数组
开发语言·python·测试工具·json
都叫我大帅哥3 小时前
Python的Optional:让你的代码优雅处理“空值”危机
python