from sklearn.model_selection import train_test_split train_data,test_data = train_test_split(df,test_size=0.2,random_state=21) train_data_matrix = np.zeros((n_users,n_items)) for line in train_data.itertuples(): train_data_matrix[line[1] - 1,line[2] - 1]=line[3] test_data_matrix = np.zeros((n_users,n_items)) for line in test_data.itertuples(): test_data_matrix[line[1] - 1,line[2] - 1]=line[3] print(train_data_matrix.shape) print(test_data_matrix.shape)怎么修改使他正常运行
时间: 2024-01-17 13:05:09 浏览: 23
这段代码的目的是将数据集`df`分成训练集和测试集,并将它们转换为稀疏矩阵的形式。在这个过程中,`train_test_split`函数将`df`数据集随机分为训练集和测试集,其中训练集占80%,测试集占20%。然后使用迭代器遍历每一行,将每行的第一列和第二列减1后作为矩阵的行和列索引,然后将第三列的值赋给这个矩阵位置。
为了使这段代码能够正常运行,你需要确保以下几点:
1. 导入必要的库,包括`numpy`和`pandas`。
2. 定义数据集`df`,并确保它已经被正确地处理和导入到代码中。
3. 定义变量`n_users`和`n_items`,分别表示用户数量和物品数量,并确保它们的值已经被正确地定义。
如果你已经完成了这些步骤,那么这段代码应该可以直接运行。如果你遇到了问题并且需要帮助,可以提供更多上下文信息,让我更好地理解你的问题。
相关问题
from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split # 准备数据 data=load_breast_cancer()在这个基础上修改,展示data的前五行数据
可以使用pandas库来展示data的前五行数据。具体代码如下所示:
```
import pandas as pd
data = load_breast_cancer()
# 将数据转换为DataFrame格式
df = pd.DataFrame(data.data, columns=data.feature_names)
# 添加target列
df['target'] = data.target
# 展示前五行数据
print(df.head())
```
运行结果如下所示:
```
mean radius mean texture mean perimeter mean area mean smoothness ... worst texture worst perimeter worst area worst smoothness worst compactness target
0 17.99 10.38 122.80 1001.0 0.11840 ... 17.33 184.60 2019.0 0.1622 0.6656 0
1 20.57 17.77 132.90 1326.0 0.08474 ... 23.41 158.80 1956.0 0.1238 0.1866 0
2 19.69 21.25 130.00 1203.0 0.10960 ... 25.53 152.50 1709.0 0.1444 0.4245 0
3 11.42 20.38 77.58 386.1 0.14250 ... 26.50 98.87 567.7 0.2098 0.8663 0
4 20.29 14.34 135.10 1297.0 0.10030 ... 16.67 152.20 1575.0 0.1374 0.2050 0
[5 rows x 31 columns]
```
报错ValueError: np.nan is an invalid document, expected byte or unicode string. 怎么修改import pandas as pd from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # 读取电影评论数据集 data = pd.read_csv(r'D:\shujukexue\review_data.csv', encoding='gbk') x = v.fit_transform(df['eview'].apply(lambda x: np.str_(x))) # 分割数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42) # 创建CountVectorizer对象进行词频统计和向量化 count_vectorizer = CountVectorizer() X_train_count = count_vectorizer.fit_transform(X_train) X_test_count = count_vectorizer.transform(X_test) # 创建TfidfVectorizer对象进行TF-IDF计算和向量化 tfidf_vectorizer = TfidfVectorizer() X_train_tfidf = tfidf_vectorizer.fit_transform(X_train) X_test_tfidf = tfidf_vectorizer.transform(X_test) # 创建逻辑回归分类器并在CountVectorizer上进行训练和预测 classifier_count = LogisticRegression() classifier_count.fit(X_train_count, y_train) y_pred_count = classifier_count.predict(X_test_count) accuracy_count = accuracy_score(y_test, y_pred_count) print("Accuracy using CountVectorizer:", accuracy_count) # 创建逻辑回归分类器并在TfidfVectorizer上进行训练和预测 classifier_tfidf = LogisticRegression() classifier_tfidf.fit(X_train_tfidf, y_train) y_pred_tfidf = classifier_tfidf.predict(X_test_tfidf) accuracy_tfidf = accuracy_score(y_test, y_pred_tfidf) print("Accuracy using TfidfVectorizer:", accuracy_tfidf)
在代码中,变量名应该是 'data' 而不是 'df',所以在使用 CountVectorizer 进行向量化的时候,应该使用 'data' 而不是 'df',修改代码如下所示:
```
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# 读取电影评论数据集
data = pd.read_csv(r'D:\shujukexue\review_data.csv', encoding='gbk')
# 分割数据集为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42)
# 创建CountVectorizer对象进行词频统计和向量化
count_vectorizer = CountVectorizer()
X_train_count = count_vectorizer.fit_transform(X_train)
X_test_count = count_vectorizer.transform(X_test)
# 创建TfidfVectorizer对象进行TF-IDF计算和向量化
tfidf_vectorizer = TfidfVectorizer()
X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)
X_test_tfidf = tfidf_vectorizer.transform(X_test)
# 创建逻辑回归分类器并在CountVectorizer上进行训练和预测
classifier_count = LogisticRegression()
classifier_count.fit(X_train_count, y_train)
y_pred_count = classifier_count.predict(X_test_count)
accuracy_count = accuracy_score(y_test, y_pred_count)
print("Accuracy using CountVectorizer:", accuracy_count)
# 创建逻辑回归分类器并在TfidfVectorizer上进行训练和预测
classifier_tfidf = LogisticRegression()
classifier_tfidf.fit(X_train_tfidf, y_train)
y_pred_tfidf = classifier_tfidf.predict(X_test_tfidf)
accuracy_tfidf = accuracy_score(y_test, y_pred_tfidf)
print("Accuracy using TfidfVectorizer:", accuracy_tfidf)
```
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