data=input().split() a={} b=list(set(data)) for i in range(len(b)): a[b[i]]=0 for i in range(len(data)): a[data[i]]+=1 c=sorted(a.items(),key=lambda x:x[1],reverse=True) for j in range(len(c)): print(c[j][0]+":"+str(c[j][1])) 代码在pta显示错误：非零返回，要怎么修改

该错误通常是因为程序的输出结果与要求的结果不一致而导致的。在这个程序中，我没有看到任何明显的语法错误。因此，我建议您检查程序的输出是否与题目要求的输出一致，特别是最后一行的每个元素之间是否都有一个空格。您可以尝试使用以下代码：

data = input().split()
a = {}
b = list(set(data))

for i in range(len(b)):
    a[b[i]] = 0
    
for i in range(len(data)):
    a[data[i]] += 1
    
c = sorted(a.items(), key=lambda x:x[1], reverse=True)

for j in range(len(c)):
    if j != len(c) - 1:
        print(c[j][0] + ":" + str(c[j][1]), end=" ")
    else:
        print(c[j][0] + ":" + str(c[j][1]))

以上代码在最后一行的每个元素之间加了一个空格，这可能会解决您的问题。

相关问题

# walk-forwardvalidation for univariate data defwalk_forward_validation(data, n_test): predictions = list() # split dataset train, test = train_test_split(data,n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the testset for i in range(len(test)): # split test row into input andoutput columns testX, testy = test[i, :-1],test[i, -1] # fit model on history and make aprediction yhat = xgboost_forecast(history,testX) # store forecast in list ofpredictions predictions.append(yhat) # add actual observation tohistory for the next loop history.append(test[i]) # summarize progress print('>expected=%.1f,predicted=%.1f' % (testy, yhat)) # estimate prediction error error = mean_absolute_error(test[:, 1],predictions) return error, test[:, 1], predictions

这是一个用于单变量数据的走步验证（walk-forward validation）函数，其中包括以下步骤：

1. 将数据集分为训练集和测试集
2. 在训练集上训练模型，并在测试集上进行预测
3. 将预测结果存储在一个列表中
4. 为下一次循环将实际观测值添加到历史记录中
5. 计算预测误差并返回误差、测试集真实值和预测值

在该函数中，使用了 XGBoost 模型进行预测。

根据提供的data.csv附件,对外卖评论进行分类,2分类,正向负向,两种方式:sklearn调用以及自己编写模型类。根据分类结果,分别画出正向词云图,负向词云图

好的，我会根据您的要求来回答问题。首先，为了进行外卖评论的分类，我们需要对数据进行预处理，具体步骤如下：

1. 导入所需库

import pandas as pd
import jieba

2. 读取数据

data = pd.read_csv('data.csv', encoding='utf-8')

3. 数据清洗

去除无用的列和重复的行，并将评论内容转换为字符串类型。

data.drop(['Unnamed: 0', 'shop_id', 'star', 'time'], axis=1, inplace=True)
data.drop_duplicates(inplace=True)
data['comment'] = data['comment'].astype(str)

4. 分词

使用结巴分词对评论进行分词，并去除停用词。

stopwords = pd.read_csv('stopwords.txt', sep='\t', header=None)
stopwords = set(stopwords[0])

def cut_words(comment):
    words = jieba.cut(comment)
    words = [word for word in words if word not in stopwords]
    return ' '.join(words)

data['comment'] = data['comment'].apply(cut_words)

5. 划分数据集

将数据集划分为训练集和测试集。

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(data['comment'], data['label'], test_size=0.2, random_state=42)

6. 特征提取

使用TF-IDF对文本进行特征提取。

from sklearn.feature_extraction.text import TfidfVectorizer

tfidf = TfidfVectorizer()
X_train = tfidf.fit_transform(X_train)
X_test = tfidf.transform(X_test)

现在，我们可以开始进行分类了。下面，我分别介绍使用sklearn调用和自己编写模型类的方法。

### 使用sklearn调用

我们可以使用sklearn中的多种分类算法对数据进行分类。这里我选择使用朴素贝叶斯算法进行分类。

1. 训练模型

from sklearn.naive_bayes import MultinomialNB

clf = MultinomialNB()
clf.fit(X_train, y_train)

2. 预测结果

y_pred = clf.predict(X_test)

3. 评估模型

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

print('Accuracy:', accuracy_score(y_test, y_pred))
print('Precision:', precision_score(y_test, y_pred))
print('Recall:', recall_score(y_test, y_pred))
print('F1-score:', f1_score(y_test, y_pred))

4. 生成词云图

from wordcloud import WordCloud
import matplotlib.pyplot as plt

pos_words = ' '.join(data[data['label'] == 1]['comment'])
neg_words = ' '.join(data[data['label'] == 0]['comment'])

pos_wordcloud = WordCloud(background_color='white', width=800, height=600).generate(pos_words)
neg_wordcloud = WordCloud(background_color='white', width=800, height=600).generate(neg_words)

plt.imshow(pos_wordcloud)
plt.axis('off')
plt.show()

plt.imshow(neg_wordcloud)
plt.axis('off')
plt.show()

### 自己编写模型类

我们也可以自己编写模型类进行分类。这里我使用PyTorch和torchtext库进行编写。

1. 导入所需库

import torch
import torch.nn as nn
import torch.optim as optim
import torchtext
from torchtext.data import Field, TabularDataset, BucketIterator

2. 定义Field

TEXT = Field(tokenize='spacy', tokenizer_language='zh')
LABEL = Field(sequential=False)

3. 读取数据

datafields = [('comment', TEXT), ('label', LABEL)]
trn, tst = TabularDataset.splits(path='.', train='train.csv', test='test.csv', format='csv', fields=datafields)

4. 构建词汇表

TEXT.build_vocab(trn, max_size=8000)
LABEL.build_vocab(trn)

5. 定义模型类

class SentimentClassifier(nn.Module):
    def __init__(self, input_dim, embedding_dim, hidden_dim, output_dim):
        super().__init__()

        self.embedding = nn.Embedding(input_dim, embedding_dim)
        self.rnn = nn.LSTM(embedding_dim, hidden_dim, num_layers=2, bidirectional=True, dropout=0.5)
        self.fc = nn.Linear(hidden_dim * 2, output_dim)
        self.dropout = nn.Dropout(0.5)

    def forward(self, text):
        embedded = self.dropout(self.embedding(text))
        output, (hidden, cell) = self.rnn(embedded)
        hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1))
        return self.fc(hidden)

6. 训练模型

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

train_iterator, test_iterator = BucketIterator.splits((trn, tst), batch_size=32, device=device)

model = SentimentClassifier(len(TEXT.vocab), 100, 256, len(LABEL.vocab))
optimizer = optim.Adam(model.parameters(), lr=1e-4)
criterion = nn.CrossEntropyLoss()

model = model.to(device)
criterion = criterion.to(device)

num_epochs = 10

for epoch in range(num_epochs):
    for batch in train_iterator:
        optimizer.zero_grad()
        text = batch.comment
        label = batch.label
        output = model(text).squeeze(1)
        loss = criterion(output, label)
        loss.backward()
        optimizer.step()

    print('Epoch:', epoch+1)

7. 预测结果

y_pred = []
for batch in test_iterator:
    text = batch.comment
    output = model(text).squeeze(1)
    pred = output.argmax(dim=1)
    y_pred.extend(pred.tolist())

8. 评估模型

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

y_test = [LABEL.vocab.stoi[label] for label in tst.examples[0].label]
print('Accuracy:', accuracy_score(y_test, y_pred))
print('Precision:', precision_score(y_test, y_pred))
print('Recall:', recall_score(y_test, y_pred))
print('F1-score:', f1_score(y_test, y_pred))

9. 生成词云图

pos_words = ' '.join(data[data['label'] == 1]['comment'])
neg_words = ' '.join(data[data['label'] == 0]['comment'])

pos_wordcloud = WordCloud(background_color='white', width=800, height=600).generate(pos_words)
neg_wordcloud = WordCloud(background_color='white', width=800, height=600).generate(neg_words)

plt.imshow(pos_wordcloud)
plt.axis('off')
plt.show()

plt.imshow(neg_wordcloud)
plt.axis('off')
plt.show()

以上就是对外卖评论进行分类并画出词云图的过程，希望能对您有所帮助！