TfidfVectorizer算法的测试模型
时间: 2023-12-01 19:02:23 浏览: 29
TfidfVectorizer算法是一种文本特征提取方法,它可以将文本转换为数值特征向量。在使用TfidfVectorizer算法时,我们通常会将数据集分为训练集和测试集,然后使用训练集中的词列表进行每篇文章的重要性统计,最后使用朴素贝叶斯算法进行预测。在预测时,我们可以使用模型的score方法来评估模型的准确率,也可以使用predict_log_proba方法来预测某篇文章属于某个类别的概率。
相关问题
利用Rocchio算法实现测试文档分类的python代码
Rocchio算法是一种基于向量空间模型的文本分类算法,其思想是将测试文档的向量表示与已知类别的训练文档的向量表示进行比较,根据最相似的训练文档的类别来预测测试文档的类别。以下是一个基于Rocchio算法的测试文档分类的Python代码示例:
```python
import numpy as np
class RocchioClassifier:
def __init__(self, alpha=1, beta=0.75, threshold=0):
self.alpha = alpha # 加权因子
self.beta = beta # 减权因子
self.threshold = threshold # 判断阈值
def fit(self, X, y):
# 计算各个类别的文档向量的平均值
self.class_means = {}
for label in np.unique(y):
self.class_means[label] = np.mean(X[y == label], axis=0)
def predict(self, X):
y_pred = []
for x in X:
# 计算测试文档向量与各个类别的文档向量的余弦相似度
similarities = {}
for label, mean in self.class_means.items():
similarities[label] = np.dot(x, mean) / (np.linalg.norm(x) * np.linalg.norm(mean))
# 根据余弦相似度最大的类别来预测测试文档的类别
max_label = max(similarities, key=similarities.get)
if similarities[max_label] >= self.threshold:
y_pred.append(max_label)
else:
y_pred.append(None)
return y_pred
def fit_predict(self, X_train, y_train, X_test):
self.fit(X_train, y_train)
return self.predict(X_test)
```
使用示例:
```python
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
# 加载数据集
newsgroups = fetch_20newsgroups(subset='all')
# 特征提取
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(newsgroups.data)
y = newsgroups.target
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 训练并预测
clf = RocchioClassifier()
y_pred = clf.fit_predict(X_train, y_train, X_test)
# 评估分类器性能
print(classification_report(y_test, y_pred, target_names=newsgroups.target_names))
```
knn算法过滤邮件代码
以下是使用KNN算法过滤邮件的Python代码:
```python
import numpy as np
from collections import Counter
import re
def clean_text(text):
"""
将文本转换为小写并去除非字母字符
"""
text = text.lower()
text = re.sub(r'[^a-z]', ' ', text)
return text
def get_word_counts(text):
"""
计算文本中每个单词的出现次数
"""
words = text.split()
return Counter(words)
def get_cosine_similarity(a, b):
"""
计算两个向量之间的余弦相似度
"""
dot_product = np.dot(a, b)
norm_a = np.linalg.norm(a)
norm_b = np.linalg.norm(b)
return dot_product / (norm_a * norm_b)
class KNN:
def __init__(self, k=5):
self.k = k
def fit(self, X, y):
self.X = X
self.y = y
def predict(self, X):
y_pred = []
for x in X:
# 计算与训练集中每个样本的余弦相似度
similarities = []
for i in range(len(self.X)):
xi = self.X[i]
yi = self.y[i]
xi_counts = get_word_counts(clean_text(xi))
x_counts = get_word_counts(clean_text(x))
similarity = get_cosine_similarity(list(xi_counts.values()), list(x_counts.values()))
similarities.append((similarity, yi))
# 根据相似度排序,并选取前k个最相似的样本
similarities = sorted(similarities, key=lambda x: x[0], reverse=True)
k_neighbors = similarities[:self.k]
# 统计k个样本中类别出现的次数,选取出现次数最多的类别作为预测结果
k_neighbors_labels = [x[1] for x in k_neighbors]
most_common = Counter(k_neighbors_labels).most_common(1)
y_pred.append(most_common[0][0])
return y_pred
```
使用示例:
```python
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
# 加载20个新闻组数据集
newsgroups = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))
# 将文本转换为TF-IDF向量
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(newsgroups.data)
y = newsgroups.target
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 构建KNN分类器,并使用Pipeline进行封装
knn = KNN(k=5)
clf = Pipeline([
('vectorizer', vectorizer),
('knn', knn)
])
# 训练模型并预测测试集
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)
```
在这个示例中,我们使用20个新闻组数据集进行了测试,使用TF-IDF向量化文本,并将KNN算法封装在Pipeline中。最终,我们得到了一个准确率为约0.54的模型。
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