machine-learning - 如何查看每个单词的tf-idf分数

我试图知道文档中每个单词的tf-idf分数。但是，它仅返回矩阵中的值，但是我看到针对每个单词的tf-idf分数的特定表示形式。

我使用了处理过的代码，但是我想更改它的显示方式：

码：

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer

bow_transformer = CountVectorizer(analyzer=text_process).fit(df["comments"].head())
print(len(bow_transformer.vocabulary_))

tfidf_transformer = CountVectorizer(analyzer=text_process).fit(messages['message'])
bow_transformer.vocabulary_transformer().fit(message_bow)

message_tfidf = tfidf_transformer.transform(message_bow)

我得到这样的结果(39028,01),(1393,1672)。但是，我希望结果像

features    tfidf
fruit       0.00344
excellent   0.00289

最佳答案

您可以通过使用以下代码来实现以上结果：

def extract_topn_from_vector(feature_names, sorted_items, topn=5):
    """
      get the feature names and tf-idf score of top n items in the doc,
      in descending order of scores.
    """

    # use only top n items from vector.
    sorted_items = sorted_items[:topn]

    results= {}
    # word index and corresponding tf-idf score
    for idx, score in sorted_items:
        results[feature_names[idx]] = round(score, 3)

    # return a sorted list of tuples with feature name and tf-idf score as its element(in descending order of tf-idf scores).
    return sorted(results.items(), key=lambda kv: kv[1], reverse=True)

feature_names = count_vect.get_feature_names()
coo_matrix = message_tfidf.tocoo()
tuples = zip(coo_matrix.col, coo_matrix.data)
sorted_items = sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)

# extract only the top n elements.
# Here, n is 10.
word_tfidf = extract_topn_from_vector(feature_names, sorted_items, 10)

print("{}  {}".format("features", "tfidf"))
for k in word_tfidf:
    print("{} - {}".format(k[0], k[1]))

查看下面的完整代码，以更好地了解上述代码段。
以下代码是不言自明的。

完整代码：

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from nltk.corpus import stopwords
import string
import nltk
import pandas as pd

data = pd.read_csv('yourfile.csv')

stops = set(stopwords.words("english"))
wl = nltk.WordNetLemmatizer()

def clean_text(text):
    """
      - Remove Punctuations
      - Tokenization
      - Remove Stopwords
      - stemming/lemmatizing
    """
    text_nopunct = "".join([char for char in text if char not in string.punctuation])
    tokens = re.split("\W+", text)
    text = [word for word in tokens if word not in stops]
    text = [wl.lemmatize(word) for word in text]
    return text

def extract_topn_from_vector(feature_names, sorted_items, topn=5):
    """
      get the feature names and tf-idf score of top n items in the doc,
      in descending order of scores.
    """

    # use only top n items from vector.
    sorted_items = sorted_items[:topn]

    results= {}
    # word index and corresponding tf-idf score
    for idx, score in sorted_items:
        results[feature_names[idx]] = round(score, 3)

    # return a sorted list of tuples with feature name and tf-idf score as its element(in descending order of tf-idf scores).
    return sorted(results.items(), key=lambda kv: kv[1], reverse=True)

count_vect = CountVectorizer(analyzer=clean_text, tokenizer = None, preprocessor = None, stop_words = None, max_features = 5000)
freq_term_matrix = count_vect.fit_transform(data['text_body'])

tfidf = TfidfTransformer(norm="l2")
tfidf.fit(freq_term_matrix)

feature_names = count_vect.get_feature_names()

# sample document
doc = 'watched horrid thing TV. Needless say one movies watch see much worse get.'

tf_idf_vector = tfidf.transform(count_vect.transform([doc]))

coo_matrix = tf_idf_vector.tocoo()
tuples = zip(coo_matrix.col, coo_matrix.data)
sorted_items = sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)

# extract only the top n elements.
# Here, n is 10.
word_tfidf = extract_topn_from_vector(feature_names,sorted_items,10)

print("{}  {}".format("features", "tfidf"))
for k in word_tfidf:
    print("{} - {}".format(k[0], k[1]))

样本输出：

features  tfidf
Needless - 0.515
horrid - 0.501
worse - 0.312
watched - 0.275
TV - 0.272
say - 0.202
watch - 0.199
thing - 0.189
much - 0.177
see - 0.164