celeba_folds[i] += images[i * count_per_fold: (i + 1) * count_per_fold]
时间: 2024-06-01 15:12:06 浏览: 12
This line of code is creating a list of folds for a given set of images. The variable "count_per_fold" represents the number of images to include in each fold.
The "i" variable is used as an index for each fold. The code is using list slicing to extract the appropriate range of images for each fold.
For example, if there are 100 images and count_per_fold is set to 20, then there will be 5 folds. The first fold will contain images 0-19, the second fold will contain images 20-39, and so on.
The "celeba_folds" list will contain each fold as a separate element.
相关问题
for k in k_choices: k_to_accuracies[k] = [] for i in range(num_folds): X_train_fold = np.concatenate([ fold for j, fold in enumerate(X_train_folds) if i != j ]) y_train_fold = np.concatenate([ fold for j, fold in enumerate(y_train_folds) if i != j ]) X_val = X_train_folds[i] y_val = y_train_folds[i] classifier.train(X_train_fold, y_train_fold) y_pred_fold = classifier.predict(X_val, k=k, num_loops=0) num_correct = np.sum(y_pred_fold == y_val) accuracy = float(num_correct) / X_val.shape[0] k_to_accuracies[k].append(accuracy)
这段代码是一个 k-fold 交叉验证的过程,用于评估分类器在不同 k 值下的准确率。其中,k_choices 是一个包含不同 k 值的列表,k_to_accuracies 是一个字典,用于存储每个 k 值对应的准确率列表。
在每个 k 值的循环中,首先将当前 k 值对应的准确率列表初始化为空。然后,在每个折叠循环中,通过 np.concatenate 将除了当前折叠之外的所有折叠样本合并为训练集 X_train_fold 和 y_train_fold。同时,将当前折叠样本作为验证集 X_val 和 y_val。
接下来,使用分类器的 train 方法在训练集上进行训练。然后,使用分类器的 predict 方法在验证集上进行预测,设置 k 值为当前循环的 k 值,num_loops 为 0。
计算预测正确的数量 num_correct,然后通过除以验证集的样本数量 X_val.shape[0] 得到准确率,并将其添加到当前 k 值对应的准确率列表中。
最终,返回包含不同 k 值对应准确率列表的字典 k_to_accuracies。
详细分析代码“from sklearn.cross_validation import StratifiedKFold from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score,precision_score #from sklearn.model_selection import train_test_split x,y=zip(*sentences) from sklearn.feature_extraction.text import CountVectorizer vec = CountVectorizer( analyzer='word', # tokenise by character ngrams ngram_range=(1,4), # use ngrams of size 1 and 2 max_features=20000, # keep the most common 1000 ngrams ) vec.fit(x) def stratifiedkfold_cv(x,y,clf_class,shuffle=True,n_folds=5,**kwargs): stratifiedk_fold = StratifiedKFold(y, n_folds=n_folds, shuffle=shuffle) y_pred = y[:] for train_index, test_index in stratifiedk_fold: X_train, X_test = x[train_index], x[test_index] y_train = y[train_index] clf = clf_class(**kwargs) clf.fit(X_train,y_train) y_pred[test_index] = clf.predict(X_test) return y_pred NB = MultinomialNB print(precision_score(y ,stratifiedkfold_cv(vec.transform(x) ,np.array(y),NB) , average='macro'))”并添加注释,每段代码的作用,参数代表什么
```
# 导入StratifiedKFold交叉验证、MultinomialNB朴素贝叶斯分类器和评估指标accuracy_score、precision_score
from sklearn.cross_validation import StratifiedKFold
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, precision_score
# 从sentences中分离出x和y
x, y = zip(*sentences)
# 导入CountVectorizer词袋模型,并对x进行处理
from sklearn.feature_extraction.text import CountVectorizer
vec = CountVectorizer(
analyzer='word', # 按照单词进行分割
ngram_range=(1,4), # 使用1~4个单词的组合方式
max_features=20000, # 保留最常见的20000个单词组合
)
vec.fit(x)
# 对数据进行StratifiedKFold交叉验证
def stratifiedkfold_cv(x,y,clf_class,shuffle=True,n_folds=5,**kwargs):
stratifiedk_fold = StratifiedKFold(y, n_folds=n_folds, shuffle=shuffle) # 将数据划分为5折,每一折中正负样本比例都相同
y_pred = y[:] # 复制y
for train_index, test_index in stratifiedk_fold: # 循环5次,每一次使用其中4折作为训练数据,1折作为测试数据
X_train, X_test = x[train_index], x[test_index]
y_train = y[train_index]
clf = clf_class(**kwargs) # 使用朴素贝叶斯分类器
clf.fit(X_train,y_train) # 训练模型
y_pred[test_index] = clf.predict(X_test) # 预测测试数据
return y_pred
NB = MultinomialNB # 定义朴素贝叶斯分类器
print(precision_score(y, # 计算精确率
stratifiedkfold_cv(vec.transform(x), # 对数据进行词袋模型转换
np.array(y), # 将y转换为数组形式
NB), # 使用朴素贝叶斯分类器进行分类
average='macro')) # 按照宏平均计算
```
参数说明:
- x: 输入的文本数据,类型为tuple或list
- y: 对应的标签,类型为tuple或list
- analyzer: 按照什么方式进行分割,默认为'word',即按照单词进行分割
- ngram_range: 使用几个单词的组合,默认为(1,1),即只使用单个单词
- max_features: 保留最常见的单词组合数量,默认为None,即保留所有单词组合
- shuffle: 是否对数据进行随机排序,默认为True
- n_folds: 将数据划分为几折,默认为5
- clf_class: 指定分类器的类别,默认为MultinomialNB
- kwargs: 指定分类器的其他参数
此段代码的作用是:对文本数据进行朴素贝叶斯分类器的精确率评估,并使用StratifiedKFold交叉验证对数据进行划分,保证每一折中正负样本的比例相同。首先将文本数据进行词袋模型转换,然后使用stratifiedkfold_cv函数对数据进行交叉验证和分类,最后计算并输出精确率值。
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