“config=wandb.config”的含义是什么？

Question

我试图为我的Logistic回归模型做一个扫描设置。我阅读了wandb的教程，无法理解如何进行配置，特别是教程中config=wandb.config的含义。如果有人能给我一个很好的解释，我会非常感激的。以下是我所做的：

sweep_config = {
    'method': 'grid'
}

metric = {
    'name': 'f1-score',
    'goal': 'maximize'
}

sweep_config['metric'] = metric

parameters = {
    'penalty': {
        'values': ['l2']
    },
    'C': {
        'values': [0.01, 0.1, 1.0, 10.0, 100.0]
    }
}

sweep_config['parameters'] = parameters

然后创建yaml文件：

stream = open('config.yaml', 'w')
yaml.dump(sweep_config, stream) 

那么是时候训练了：

with wandb.init(project=WANDB_PROJECT_NAME):
    config = wandb.config
    
    features = pd.read_csv('data/x_features.csv')
    vectorizer = TfidfVectorizer(ngram_range=(1,2))

    X_features = features = vectorizer.fit_transform(features['lemmatized_reason'])

    y_labels = pd.read_csv('data/y_labels.csv')

    split_data = train_test_split(X_features, y_labels, train_size = 0.85, test_size = 0.15, stratify=y_labels)
    features_train, labels_train = split_data[0], split_data[2]
    features_test, labels_test = split_data[1], split_data[3]
    
    config = wandb.config
    log_reg = LogisticRegression(
        penalty=config.penalty,
        C = config.C
    )
    
    log_reg.fit(features_train, labels_train)
    
    labels_pred = log_reg.predict(features_test)
    labels_proba = log_reg.predict_proba(features_test)
    labels=list(map(str,y_labels['label'].unique()))
    
    # Visualize single plot
    cm = wandb.sklearn.plot_confusion_matrix(labels_test, labels_pred, labels)
    
    score_f1 = f1_score(labels_test, labels_pred, average='weighted')
    
    sm = wandb.sklearn.plot_summary_metrics(
    log_reg, features_train, labels_train, features_test, labels_test)
    
    roc = wandb.sklearn.plot_roc(labels_test, labels_proba)
    
    wandb.log({
        "f1-weighted-log-regression-tfidf-skf": score_f1, 
        "roc-log-regression-tfidf-skf": roc, 
        "conf-mat-logistic-regression-tfidf-skf": cm,
        "summary-metrics-logistic-regression-tfidf-skf": sm
        })

最后是sweep_id和with语句之外的代理：

sweep_id = wandb.sweep(sweep_config, project="multiple-classifiers")
wandb.agent(sweep_id)

在这个配置方面，我遗漏了一些重要的东西，我只是无法理解。

Answer 1

我的工作是举重和偏见。使用wandb清理，想法是wandb需要能够改变扫描中的超参数。

将超参数传递给LogisticRegression的下面部分也可以重写。

config = wandb.config
log_reg = LogisticRegression(
    penalty=config.penalty,
    C = config.C
)

就像这样：

log_reg = LogisticRegression(
    penalty=wandb.config.penalty,
    C = wandb.config.C
)

但是，我认为您缺少定义列车函数或列车脚本，这也需要传递给wandb。没有它，上面的例子就行不通了。

下面是一个最低限度的例子，应该有帮助。希望清扫文件也能提供帮助。

import numpy as np 
import random
import wandb

#  Step 1: Define sweep config
sweep_configuration = {
    'method': 'random',
    'name': 'sweep',
    'metric': {'goal': 'maximize', 'name': 'val_acc'},
    'parameters': 
    {
        'batch_size': {'values': [16, 32, 64]},
        'epochs': {'values': [5, 10, 15]},
        'lr': {'max': 0.1, 'min': 0.0001}
     }
}

#  Step 2: Initialize sweep by passing in config
sweep_id = wandb.sweep(sweep_configuration)

def train_one_epoch(epoch, lr, bs): 
  acc = 0.25 + ((epoch/30) +  (random.random()/10))
  loss = 0.2 + (1 - ((epoch-1)/10 +  random.random()/5))
  return acc, loss

def evaluate_one_epoch(epoch): 
  acc = 0.1 + ((epoch/20) +  (random.random()/10))
  loss = 0.25 + (1 - ((epoch-1)/10 +  random.random()/6))
  return acc, loss

def train():
    run = wandb.init()

    #  Step 3: Use hyperparameter values from `wandb.config`
    lr  =  wandb.config.lr
    bs = wandb.config.batch_size
    epochs = wandb.config.epochs

    for epoch in np.arange(1, epochs):
      train_acc, train_loss = train_one_epoch(epoch, lr, bs)
      val_acc, val_loss = evaluate_one_epoch(epoch)

      wandb.log({
        'epoch': epoch, 
        'train_acc': train_acc,
        'train_loss': train_loss, 
        'val_acc': val_acc, 
        'val_loss': val_loss
      })

#  Step 4: Launch sweep by making a call to `wandb.agent`
wandb.agent(sweep_id, function=train, count=4)

最后，您能分享上面找到代码的链接吗？也许我们需要更新一些例子:)