特征工程之特征预处理-文本特征_参见以下代码,构造语料文本数据集 corpus = ['the sky is blue and be

import pandas as pd
import numpy as np
import re
import nltk #pip install nltk
#jieba
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构造一个文本数据集

corpus = ['The sky is blue and beautiful.',
          'Love this blue and beautiful sky!',
          'The quick brown fox jumps over the lazy dog.',
          'The brown fox is quick and the blue dog is lazy!',
          'The sky is very blue and the sky is very beautiful today',
          'The dog is lazy but the brown fox is quick!'    
]
labels = ['weather', 'weather', 'animals', 'animals', 'weather', 'animals']
corpus = np.array(corpus)
corpus_df = pd.DataFrame({'Document': corpus, 
                          'Category': labels})
corpus_df = corpus_df[['Document', 'Category']]
corpus_df
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基本预处理

nltk.download()
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showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml





True
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#词频与停用词
wpt = nltk.WordPunctTokenizer()
stop_words = nltk.corpus.stopwords.words('english')
print (stop_words)
def normalize_document(doc):
    # lower case and remove special characters\whitespaces
    doc = re.sub(r'[^a-zA-Z0-9\s]', '', doc, re.I)
    doc = doc.lower()
    doc = doc.strip()
    # tokenize document
    tokens = wpt.tokenize(doc)
    # filter stopwords out of document
    filtered_tokens = [token for token in tokens if token not in stop_words]
    # re-create document from filtered tokens
    doc = ' '.join(filtered_tokens)
    return doc

normalize_corpus = np.vectorize(normalize_document)
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['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"]
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norm_corpus = normalize_corpus(corpus)
norm_corpus
#The sky is blue and beautiful.
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array(['sky blue beautiful', 'love blue beautiful sky',
       'quick brown fox jumps lazy dog', 'brown fox quick blue dog lazy',
       'sky blue sky beautiful today', 'dog lazy brown fox quick'],
      dtype='<U30')
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词袋模型

from sklearn.feature_extraction.text import CountVectorizer
print (norm_corpus)
cv = CountVectorizer(min_df=0., max_df=1.)
cv.fit(norm_corpus)
print (cv.get_feature_names())
cv_matrix = cv.fit_transform(norm_corpus)
cv_matrix = cv_matrix.toarray()
cv_matrix
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['sky blue beautiful' 'love blue beautiful sky'
 'quick brown fox jumps lazy dog' 'brown fox quick blue dog lazy'
 'sky blue sky beautiful today' 'dog lazy brown fox quick']
['beautiful', 'blue', 'brown', 'dog', 'fox', 'jumps', 'lazy', 'love', 'quick', 'sky', 'today']





array([[1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
       [1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0],
       [0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0],
       [0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0],
       [1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 1],
       [0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0]], dtype=int64)
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vocab = cv.get_feature_names()
pd.DataFrame(cv_matrix, columns=vocab)
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N-Grams模型

bv = CountVectorizer(ngram_range=(2,2))
bv_matrix = bv.fit_transform(norm_corpus)
bv_matrix = bv_matrix.toarray()
vocab = bv.get_feature_names()
pd.DataFrame(bv_matrix, columns=vocab)
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TF-IDF 模型

from sklearn.feature_extraction.text import TfidfVectorizer #中国 蜜蜂 养殖 它们的片频数都是20次
tv = TfidfVectorizer(min_df=0., max_df=1., use_idf=True)
tv_matrix = tv.fit_transform(norm_corpus)
tv_matrix = tv_matrix.toarray()

vocab = tv.get_feature_names()
pd.DataFrame(np.round(tv_matrix, 2), columns=vocab)
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Similarity特征

from sklearn.metrics.pairwise import cosine_similarity

similarity_matrix = cosine_similarity(tv_matrix)
similarity_df = pd.DataFrame(similarity_matrix)
similarity_df
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聚类特征

from sklearn.cluster import KMeans

km = KMeans(n_clusters=2)
km.fit_transform(similarity_df)
cluster_labels = km.labels_
cluster_labels = pd.DataFrame(cluster_labels, columns=['ClusterLabel'])
pd.concat([corpus_df, cluster_labels], axis=1)
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主题模型

from sklearn.decomposition import LatentDirichletAllocation

lda = LatentDirichletAllocation(n_topics=2, max_iter=100, random_state=42)
dt_matrix = lda.fit_transform(tv_matrix)
features = pd.DataFrame(dt_matrix, columns=['T1', 'T2'])
features
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e:\ProgramData\Anaconda3\lib\site-packages\sklearn\decomposition\online_lda.py:294: DeprecationWarning: n_topics has been renamed to n_components in version 0.19 and will be removed in 0.21
  DeprecationWarning)
e:\ProgramData\Anaconda3\lib\site-packages\sklearn\decomposition\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.
  DeprecationWarning)
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主题和词的权重

tt_matrix = lda.components_
for topic_weights in tt_matrix:
    topic = [(token, weight) for token, weight in zip(vocab, topic_weights)]
    topic = sorted(topic, key=lambda x: -x[1])
    topic = [item for item in topic if item[1] > 0.6]
    print(topic)
    print()

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[('fox', 1.7265536238698524), ('quick', 1.7264910761871224), ('dog', 1.7264019823624879), ('brown', 1.7263774760262807), ('lazy', 1.7263567668213813), ('jumps', 1.0326450363521607), ('blue', 0.7770158513472083)]

[('sky', 2.263185143458752), ('beautiful', 1.9057084998062579), ('blue', 1.7954559705805624), ('love', 1.1476805311187976), ('today', 1.0064979209198706)]
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词嵌入模型

from gensim.models import word2vec

wpt = nltk.WordPunctTokenizer()
tokenized_corpus = [wpt.tokenize(document) for document in norm_corpus]

# Set values for various parameters
feature_size = 10    # Word vector dimensionality  
window_context = 10          # Context window size                                                                                    
min_word_count = 1   # Minimum word count                        
sample = 1e-3   # Downsample setting for frequent words

w2v_model = word2vec.Word2Vec(tokenized_corpus, size=feature_size, 
                          window=window_context, min_count = min_word_count,
                          sample=sample)
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e:\ProgramData\Anaconda3\lib\site-packages\gensim\utils.py:860: UserWarning: detected Windows; aliasing chunkize to chunkize_serial
  warnings.warn("detected Windows; aliasing chunkize to chunkize_serial")
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w2v_model.wv['sky']
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array([-0.01302878, -0.0081328 ,  0.02701689,  0.03391293,  0.01191998,
       -0.00258705,  0.02996921,  0.01644186, -0.03398509, -0.00690445], dtype=float32)
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def average_word_vectors(words, model, vocabulary, num_features):
    
    feature_vector = np.zeros((num_features,),dtype="float64")
    nwords = 0.
    
    for word in words:
        if word in vocabulary: 
            nwords = nwords + 1.
            feature_vector = np.add(feature_vector, model[word])
    
    if nwords:
        feature_vector = np.divide(feature_vector, nwords)
        
    return feature_vector
    
   
def averaged_word_vectorizer(corpus, model, num_features):
    vocabulary = set(model.wv.index2word)
    features = [average_word_vectors(tokenized_sentence, model, vocabulary, num_features)
                    for tokenized_sentence in corpus]
    return np.array(features)
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w2v_feature_array = averaged_word_vectorizer(corpus=tokenized_corpus, model=w2v_model,
                                             num_features=feature_size)
pd.DataFrame(w2v_feature_array) #lstm
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e:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:9: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).
  if __name__ == '__main__':
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