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sklearn_NLP.html
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sklearn_NLP.html
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<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
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<title>sklearn_NLP</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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<p>chapter 13 文本数据处理</p>
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<h1 id="%E8%AF%8D%E8%A2%8B%E6%A8%A1%E5%9E%8B">词袋模型<a class="anchor-link" href="#%E8%AF%8D%E8%A2%8B%E6%A8%A1%E5%9E%8B">¶</a></h1>
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<h2 id="%E8%8B%B1%E8%AF%AD%E5%8F%A5%E5%AD%90%E5%88%86%E8%AF%8D">英语句子分词<a class="anchor-link" href="#%E8%8B%B1%E8%AF%AD%E5%8F%A5%E5%AD%90%E5%88%86%E8%AF%8D">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 导入向量化工具</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">CountVectorizer</span>
<span class="n">vect</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">()</span>
<span class="c1"># 拟合文本数据</span>
<span class="n">en</span><span class="o">=</span><span class="p">[</span><span class="s2">"The quick brown fox jumps over a lazy dog"</span><span class="p">]</span>
<span class="n">vect</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">en</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"words:</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="nb">len</span><span class="p">(</span><span class="n">vect</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span> <span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"words:</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">vect</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">))</span>
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<pre>words:8
words:{'the': 7, 'quick': 6, 'brown': 0, 'fox': 2, 'jumps': 3, 'over': 5, 'lazy': 4, 'dog': 1}
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<h2 id="%E4%B8%AD%E6%96%87%E5%8F%A5%E5%AD%90%E5%88%86%E8%AF%8D">中文句子分词<a class="anchor-link" href="#%E4%B8%AD%E6%96%87%E5%8F%A5%E5%AD%90%E5%88%86%E8%AF%8D">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 而中文不能自动分词</span>
<span class="n">cn</span><span class="o">=</span><span class="p">[</span><span class="s2">"那只敏捷的棕色狐狸跳过了一只懒惰的狗"</span><span class="p">]</span>
<span class="n">vect</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cn</span><span class="p">)</span>
<span class="n">vect</span><span class="o">.</span><span class="n">vocabulary_</span>
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<pre>{'那只敏捷的棕色狐狸跳过了一只懒惰的狗': 0}</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 中文使用 结巴分词 pip3 install jieba -i https://pypi.douban.com/simple/</span>
<span class="c1"># jieba-0.42.1</span>
<span class="kn">import</span> <span class="nn">jieba</span>
<span class="n">cn1</span><span class="o">=</span><span class="n">jieba</span><span class="o">.</span><span class="n">lcut</span><span class="p">(</span><span class="n">cn</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"cn1:"</span><span class="p">,</span> <span class="n">cn1</span><span class="p">)</span>
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<pre>cn1: ['那', '只', '敏捷', '的', '棕色', '狐狸', '跳过', '了', '一只', '懒惰', '的', '狗']
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 使用空格连起来</span>
<span class="n">cn2</span><span class="o">=</span> <span class="p">[</span><span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">cn1</span><span class="p">)]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"cn2:"</span><span class="p">,</span> <span class="n">cn2</span><span class="p">)</span>
<span class="n">vect</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cn2</span><span class="p">)</span>
<span class="n">vect</span><span class="o">.</span><span class="n">vocabulary_</span>
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<pre>cn2: ['那 只 敏捷 的 棕色 狐狸 跳过 了 一只 懒惰 的 狗']
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<pre>{'敏捷': 2, '棕色': 3, '狐狸': 4, '跳过': 5, '一只': 0, '懒惰': 1}</pre>
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<h2 id="%E5%8F%A5%E5%AD%90%E5%90%91%E9%87%8F%E5%8C%96%EF%BC%8C%E8%AF%8D%E8%A2%8B%E6%A8%A1%E5%9E%8B-bag-of-words">句子向量化，词袋模型 bag of words<a class="anchor-link" href="#%E5%8F%A5%E5%AD%90%E5%90%91%E9%87%8F%E5%8C%96%EF%BC%8C%E8%AF%8D%E8%A2%8B%E6%A8%A1%E5%9E%8B-bag-of-words">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 定义词袋模型</span>
<span class="nb">print</span><span class="p">(</span><span class="n">cn2</span><span class="p">)</span>
<span class="n">bag_of_words</span> <span class="o">=</span> <span class="n">vect</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span> <span class="n">cn2</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"2->"</span><span class="p">,</span> <span class="n">bag_of_words</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"3->"</span><span class="p">,</span> <span class="nb">repr</span><span class="p">(</span><span class="n">bag_of_words</span><span class="p">)</span> <span class="p">)</span>
<span class="c1"># 打印词袋模型的密度表达</span>
<span class="nb">print</span><span class="p">(</span> <span class="n">bag_of_words</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span> <span class="p">)</span>
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<pre>['那 只 敏捷 的 棕色 狐狸 跳过 了 一只 懒惰 的 狗']
2-> (0, 0) 1
(0, 1) 1
(0, 2) 1
(0, 3) 1
(0, 4) 1
(0, 5) 1
3-> <1x6 sparse matrix of type '<class 'numpy.int64'>'
with 6 stored elements in Compressed Sparse Row format>
[[1 1 1 1 1 1]]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## 话一句复杂的句子</span>
<span class="n">cn_1</span><span class="o">=</span><span class="n">jieba</span><span class="o">.</span><span class="n">lcut</span><span class="p">(</span><span class="s2">"懒惰的狐狸不如敏捷的狐狸敏捷，敏捷的狐狸不如懒惰的狐狸懒惰"</span><span class="p">)</span>
<span class="n">cn_2</span><span class="o">=</span><span class="p">[</span><span class="s2">" "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">cn_1</span><span class="p">)]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">cn_2</span><span class="p">)</span>
<span class="c1"># 建立新的词袋模型</span>
<span class="n">new_bag</span> <span class="o">=</span> <span class="n">vect</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">cn_2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"词袋特征:"</span><span class="p">,</span> <span class="nb">repr</span><span class="p">(</span><span class="n">new_bag</span><span class="p">)</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"词袋密度:"</span><span class="p">,</span> <span class="n">new_bag</span><span class="o">.</span><span class="n">toarray</span><span class="p">())</span>
<span class="c1"># 0位 一只 出现0次；1位 懒惰 出现3次；... 4位 狐狸 出现4次；...</span>
<span class="c1"># 词袋模型，仅仅是用数组表示每个单词出现的频率，并不表示每个单词的位置。</span>
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<pre>['懒惰 的 狐狸 不如 敏捷 的 狐狸 敏捷 ， 敏捷 的 狐狸 不如 懒惰 的 狐狸 懒惰']
词袋特征: <1x6 sparse matrix of type '<class 'numpy.int64'>'
with 3 stored elements in Compressed Sparse Row format>
词袋密度: [[0 3 3 0 4 0]]
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<h1 id="%E5%AF%B9%E6%96%87%E6%9C%AC%E6%95%B0%E6%8D%AE%E7%9A%84%E4%BC%98%E5%8C%96%E5%A4%84%E7%90%86">对文本数据的优化处理<a class="anchor-link" href="#%E5%AF%B9%E6%96%87%E6%9C%AC%E6%95%B0%E6%8D%AE%E7%9A%84%E4%BC%98%E5%8C%96%E5%A4%84%E7%90%86">¶</a></h1>
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<h2 id="%E4%BD%BF%E7%94%A8-n-Gram-%E6%94%B9%E5%96%84%E8%AF%8D%E8%A2%8B%E6%A8%A1%E5%9E%8B">使用 n-Gram 改善词袋模型<a class="anchor-link" href="#%E4%BD%BF%E7%94%A8-n-Gram-%E6%94%B9%E5%96%84%E8%AF%8D%E8%A2%8B%E6%A8%A1%E5%9E%8B">¶</a></h2>
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<p>除了词语的频率，词语的顺序也很重要。</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 随便写一句话</span>
<span class="kn">import</span> <span class="nn">jieba</span>
<span class="n">joke</span> <span class="o">=</span> <span class="n">jieba</span><span class="o">.</span><span class="n">lcut</span><span class="p">(</span><span class="s2">"道士看到和尚亲吻了尼姑的嘴唇"</span><span class="p">)</span>
<span class="c1"># 插入空格</span>
<span class="n">joke</span> <span class="o">=</span> <span class="p">[</span><span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">joke</span><span class="p">)]</span>
<span class="c1"># 转为向量</span>
<span class="c1"># 导入向量化工具</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">CountVectorizer</span>
<span class="n">vect</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">()</span>
<span class="n">vect</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">joke</span><span class="p">)</span>
<span class="n">joke_feature</span> <span class="o">=</span> <span class="n">vect</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">joke</span><span class="p">)</span>
<span class="c1"># 打印</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"单词表:"</span><span class="p">,</span> <span class="n">vect</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"特征表达:"</span><span class="p">,</span> <span class="n">joke_feature</span><span class="o">.</span><span class="n">toarray</span><span class="p">())</span>
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<pre>Building prefix dict from the default dictionary ...
Loading model from cache /tmp/jieba.cache
Loading model cost 0.460 seconds.
Prefix dict has been built successfully.
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<pre>单词表: {'道士': 5, '看到': 4, '和尚': 1, '亲吻': 0, '尼姑': 3, '嘴唇': 2}
特征表达: [[1 1 1 1 1 1]]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 调整顺序</span>
<span class="n">joke2</span> <span class="o">=</span> <span class="n">jieba</span><span class="o">.</span><span class="n">lcut</span><span class="p">(</span><span class="s2">"尼姑看到道士亲吻了和尚的嘴唇"</span><span class="p">)</span>
<span class="c1"># 插入空格</span>
<span class="n">joke2</span><span class="o">=</span><span class="p">[</span><span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">joke2</span><span class="p">)]</span>
<span class="c1"># 特征提取</span>
<span class="n">joke_feature2</span> <span class="o">=</span> <span class="n">vect</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">joke2</span><span class="p">)</span>
<span class="c1"># 打印</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"单词表:"</span><span class="p">,</span> <span class="n">vect</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"特征表达:"</span><span class="p">,</span> <span class="n">joke_feature2</span><span class="o">.</span><span class="n">toarray</span><span class="p">())</span>
<span class="c1"># 这2句对于人类是明显不同的，但对于这个机器模型是一模一样的。这肯定有问题。</span>
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<pre>单词表: {'道士': 5, '看到': 4, '和尚': 1, '亲吻': 0, '尼姑': 3, '嘴唇': 2}
特征表达: [[1 1 1 1 1 1]]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 可以使用 CountVectorizer 的 ngram_range 参数进行参数调节。</span>
<span class="c1"># n 是整型，</span>
<span class="c1"># n=2的模型叫做 bi-Gram，表示 n-Gram 会对相邻的2个单词进行配对；</span>
<span class="c1"># n=3的模型叫做 tri-Gram，表示 n-Gram 会对相邻的3个单词进行配对。</span>
<span class="n">vect</span><span class="o">=</span><span class="n">CountVectorizer</span><span class="p">(</span><span class="n">ngram_range</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="c1"># 重新进行文本数据的特征提取</span>
<span class="n">cv</span><span class="o">=</span><span class="n">vect</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">joke</span><span class="p">)</span>
<span class="n">joke_feature</span><span class="o">=</span><span class="n">cv</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">joke</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"单词表:"</span><span class="p">,</span> <span class="n">cv</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"特征表达:"</span><span class="p">,</span> <span class="n">joke_feature</span><span class="o">.</span><span class="n">toarray</span><span class="p">())</span>
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<pre>单词表: {'道士 看到': 4, '看到 和尚': 3, '和尚 亲吻': 1, '亲吻 尼姑': 0, '尼姑 嘴唇': 2}
特征表达: [[1 1 1 1 1]]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 调整顺序后</span>
<span class="c1"># 特征提取</span>
<span class="n">joke_feature2</span> <span class="o">=</span> <span class="n">cv</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">joke2</span><span class="p">)</span>
<span class="c1"># 打印</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"单词表:"</span><span class="p">,</span> <span class="n">cv</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"特征表达:"</span><span class="p">,</span> <span class="n">joke_feature2</span><span class="o">.</span><span class="n">toarray</span><span class="p">())</span>
<span class="c1"># 机器已经不认为这是同一句话了。</span>
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<pre>单词表: {'道士 看到': 4, '看到 和尚': 3, '和尚 亲吻': 1, '亲吻 尼姑': 0, '尼姑 嘴唇': 2}
特征表达: [[0 0 0 0 0]]
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<h2 id="%E4%BD%BF%E7%94%A8-tf-idf-%E7%AE%97%E6%B3%95%E5%A4%84%E7%90%86%E6%96%87%E6%9C%AC%E6%95%B0%E6%8D%AE">使用 tf-idf 算法处理文本数据<a class="anchor-link" href="#%E4%BD%BF%E7%94%A8-tf-idf-%E7%AE%97%E6%B3%95%E5%A4%84%E7%90%86%E6%96%87%E6%9C%AC%E6%95%B0%E6%8D%AE">¶</a></h2>
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<p>tf-idf: term frequency-inverse document frequency,翻译 “词频-逆向文件频率”。</p>
<ul>
<li>用来评估某个词对于一个语料库中某一份文件的重要程度。</li>
<li>如果一个词在A文件中频率很高，在其他文件中频率很低，那么 tf-idf 就认为这个词能区分文件，很重要。反之则认为该单词重要程度较低。</li>
</ul>
<p>公式：tf-idf 的计算公式有很多实现，这是其中一种。</p>
<ul>
<li>单词频率 tf= Nij / 求和(k, Nkj); 其中Nij表示某个词在语料库中某文件中出现的次数；分母表示该文件中所有单词出现的次数之和。</li>
<li>idf 的计算公式 idf = log( (N+1)/(Nw+1) ) + 1; 其中 N表示语料库中文件的总数；Nw表示语料库中包含上述单词的文件数量。</li>
<li>最终计算 tf-idf 值的公式: tf-idf = tf * idf</li>
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<h3 id="%E5%B8%B8%E8%A7%84-SVM-%E5%88%86%E7%B1%BB%E8%AF%84%E4%BB%B7%E6%96%87%E6%9C%AC">常规 SVM 分类评价文本<a class="anchor-link" href="#%E5%B8%B8%E8%A7%84-SVM-%E5%88%86%E7%B1%BB%E8%AF%84%E4%BB%B7%E6%96%87%E6%9C%AC">¶</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 这里使用一套英文 评价数据，一个是文本评论，一个是情感分类 positive or negative</span>
<span class="c1"># http://ai.stanford.edu/~amaas/data/sentiment/</span>
<span class="k">def</span> <span class="nf">readFiles2Array</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
<span class="n">arr</span><span class="o">=</span><span class="p">[];</span>
<span class="n">fr</span><span class="o">=</span><span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
<span class="k">for</span> <span class="n">lineR</span> <span class="ow">in</span> <span class="n">fr</span><span class="o">.</span><span class="n">readlines</span><span class="p">():</span>
<span class="n">line</span><span class="o">=</span><span class="n">lineR</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
<span class="n">arr</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
<span class="n">fr</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="k">return</span> <span class="n">arr</span><span class="p">;</span>
<span class="n">reviews</span><span class="o">=</span><span class="n">readFiles2Array</span><span class="p">(</span><span class="s2">"data/reviews.txt"</span><span class="p">)</span>
<span class="n">labels</span><span class="o">=</span><span class="n">readFiles2Array</span><span class="p">(</span><span class="s2">"data/labels.txt"</span><span class="p">)</span>
<span class="c1"># 查看一条评论</span>
<span class="nb">print</span><span class="p">(</span><span class="n">reviews</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="p">,</span> <span class="n">labels</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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<pre>bromwell high is a cartoon comedy . it ran at the same time as some other programs about school life such as teachers . my years in the teaching profession lead me to believe that bromwell high s satire is much closer to reality than is teachers . the scramble to survive financially the insightful students who can see right through their pathetic teachers pomp the pettiness of the whole situation all remind me of the schools i knew and their students . when i saw the episode in which a student repeatedly tried to burn down the school i immediately recalled . . . . . . . . . at . . . . . . . . . . high . a classic line inspector i m here to sack one of your teachers . student welcome to bromwell high . i expect that many adults of my age think that bromwell high is far fetched . what a pity that it isn t
positive
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 拆分数据</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="c1"># 为了快速，只取前1000条</span>
<span class="n">total_size</span><span class="o">=</span><span class="mi">2000</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">reviews</span><span class="p">)[:</span><span class="n">total_size</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">labels</span><span class="p">)[:</span><span class="n">total_size</span><span class="p">]</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="c1">#(18750,) (6250,)</span>
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<pre>(1500,) (500,)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 拟合</span>
<span class="n">vect</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="c1"># 文本转为向量</span>
<span class="n">X_train_vect</span> <span class="o">=</span> <span class="n">vect</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="c1"># </span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"训练集样本特征数量:</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">vect</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">())</span> <span class="p">)</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"最后10个训练集样本特征:</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">vect</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">()[</span><span class="o">-</span><span class="mi">10</span><span class="p">:]</span> <span class="p">))</span>
<span class="n">X_train_vect</span><span class="o">.</span><span class="n">shape</span> <span class="c1">#(750, 14330) 有1.4万特征</span>
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<pre>训练集样本特征数量:19861
最后10个训练集样本特征:['zorro' 'zsigmond' 'zu' 'zubeidaa' 'zucco' 'zuckerman' 'zuzz' 'zwick'
'zz' 'zzzz']
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<pre>(1500, 19861)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 有监督的学习算法，进行交叉验证评分</span>
<span class="c1"># 导入线性SVC分类模型</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">LinearSVC</span>
<span class="c1"># 导入交叉验证工具</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="c1"># 使用交叉验证对模型进行打分</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">LinearSVC</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">3000</span><span class="p">),</span> <span class="n">X_train_vect</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"mean score:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">()))</span>
<span class="c1"># 1k data: 0.836</span>
<span class="c1"># 2k data: 0.858</span>
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<pre>mean score:0.849
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 泛化到测试集的表现</span>
<span class="n">X_test_vect</span> <span class="o">=</span> <span class="n">vect</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># 使用 SVC 拟合训练数据集</span>
<span class="n">clf</span><span class="o">=</span><span class="n">LinearSVC</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">3000</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_vect</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"train score:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train_vect</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"test score:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_vect</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)))</span>
<span class="c1"># test score:</span>
<span class="c1"># 1k data: 0.844</span>
<span class="c1"># 2k data: 0.860</span>
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<pre>train score:1.000
test score:0.864
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<h3 id="tf-idf-%E5%88%86%E7%B1%BB">tf-idf 分类<a class="anchor-link" href="#tf-idf-%E5%88%86%E7%B1%BB">¶</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">TfidfTransformer</span>
<span class="c1"># 使用tf-idf工具转化训练集和测试集</span>
<span class="n">tfidf</span> <span class="o">=</span> <span class="n">TfidfTransformer</span><span class="p">(</span><span class="n">smooth_idf</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">tfidf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_vect</span><span class="p">)</span>
<span class="n">X_train_tfidf</span> <span class="o">=</span> <span class="n">tfidf</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train_vect</span><span class="p">)</span>
<span class="n">X_test_tfidf</span> <span class="o">=</span> <span class="n">tfidf</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test_vect</span><span class="p">)</span>
<span class="c1"># 将处理前后的特征打印进行比较</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"tf-idf前的特征:</span><span class="se">\n</span><span class="s2">"</span><span class="p">,</span> <span class="n">X_train_vect</span><span class="p">[:</span><span class="mi">6</span><span class="p">,</span> <span class="mi">20</span><span class="p">:</span><span class="mi">30</span><span class="p">]</span><span class="o">.</span><span class="n">toarray</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"tf-idf处理后的特征:"</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="n">display</span><span class="p">(</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">X_train_tfidf</span><span class="p">[:</span><span class="mi">6</span><span class="p">,</span> <span class="mi">20</span><span class="p">:</span><span class="mi">30</span><span class="p">]</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span> <span class="p">)</span> <span class="p">)</span>
<span class="c1"># 可见处理前是单词频数，处理后是词频乘以逆向文档频率，是一个浮点数。</span>
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<pre>tf-idf前的特征:
[[0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0]]
tf-idf处理后的特征:
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