tfidfVectorizer(input='content', encoding='utf-8',
                 decode_error='strict', strip_accents=None, lowercase=True,
                 preprocessor=None, tokenizer=None, analyzer='word',
                 stop_words=None, token_pattern=r"(?u)\b\w\w+\b",
                 ngram_range=(1, 1), max_df=1.0, min_df=1,
                 max_features=None, vocabulary=None, binary=False,
                 dtype=np.float64, norm='l2', use_idf=True, smooth_idf=True,
                 sublinear_tf=False)

  Class  ...                                            Content
0  news  ...  中廣網唐山６月１２日消息（記者湯一亮　莊勝春）據中國之聲《新聞晚高峰》報道，今天（１２日）上...
1  news  ...  天津衛(wèi)視求職節(jié)目《非你莫屬》“暈倒門”事件余波未了，主持人張紹剛前日通過《非你莫屬》節(jié)目組發(fā)...
2  news  ...  臨沂（山東），２０１２年６月４日　夫妻“麥客”忙麥收　６月４日，在山東省臨沂市郯城縣郯城街道...
3  news  ...  中廣網北京６月１３日消息（記者王宇）據中國之聲《新聞晚高峰》報道，明天凌晨兩場歐洲杯的精彩比...
4  news  ...  環(huán)球網記者李亮報道，正在意大利度蜜月的“臉譜”創(chuàng)始人扎克伯格與他華裔妻子的一舉一動都處于媒體...

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report
from sklearn.naive_bayes import MultinomialNB
import jieba
def load_data():
    """讀取數據/停用詞"""
    # 讀取數據
    data = pd.read_csv("test.txt", sep="\t", names=["Class", "Title", "Content"])
    print(data.head())
    # 讀取停用詞
    stop_words = pd.read_csv("stopwords.txt", names=["stop_words"], encoding="utf-8")
    stop_words = stop_words["stop_words"].values.tolist()
    print(stop_words)
    return data, stop_words
def main():
    """主函數"""
    # 讀取數據
    data, stop_words = load_data()
    # 分詞
    segs = data["Content"].apply(lambda x: ' '.join(jieba.cut(x)))
    # Tf-Idf
    tf_idf = TfidfVectorizer(stop_words=stop_words, max_features=1000, lowercase=False)
    # 擬合
    tf_idf.fit(segs)
    # 轉換
    X = tf_idf.transform(segs)
    # 分割數據
    X_train, X_test, y_train, y_test = train_test_split(X, data["Class"], random_state=0)
    # 調試輸出
    print(X_train[:2])
    print(y_train[:2])
    # 實例化樸素貝葉斯
    classifier = MultinomialNB()
    # 擬合
    classifier.fit(X_train, y_train)
    # 計算分數
    acc = classifier.score(X_test, y_test)
    print("準確率:", acc)
    # 報告
    report = classification_report(y_test, classifier.predict(X_test))
    print(report)
if __name__ == '__main__':
    main()

  Class  ...                                            Content
0  news  ...  中廣網唐山６月１２日消息（記者湯一亮　莊勝春）據中國之聲《新聞晚高峰》報道，今天（１２日）上...
1  news  ...  天津衛(wèi)視求職節(jié)目《非你莫屬》“暈倒門”事件余波未了，主持人張紹剛前日通過《非你莫屬》節(jié)目組發(fā)...
2  news  ...  臨沂（山東），２０１２年６月４日　夫妻“麥客”忙麥收?。对拢慈?，在山東省臨沂市郯城縣郯城街道...
3  news  ...  中廣網北京６月１３日消息（記者王宇）據中國之聲《新聞晚高峰》報道，明天凌晨兩場歐洲杯的精彩比...
4  news  ...  環(huán)球網記者李亮報道，正在意大利度蜜月的“臉譜”創(chuàng)始人扎克伯格與他華裔妻子的一舉一動都處于媒體...
[5 rows x 3 columns]
['?', '、', '。', '“', '”', '《', '》', '！', '，', '：', '；', '？', '啊', '阿', '哎', '哎呀', '哎喲', '唉', '俺', '俺們', '按', '按照', '吧', '吧噠', '把', '罷了', '被', '本', '本著', '比', '比方', '比如', '鄙人', '彼', '彼此', '邊', '別', '別的', '別說', '并', '并且', '不比', '不成', '不單', '不但', '不獨', '不管', '不光', '不過', '不僅', '不拘', '不論', '不怕', '不然', '不如', '不特', '不惟', '不問', '不只', '朝', '朝著', '趁', '趁著', '乘', '沖', '除', '除此之外', '除非', '除了', '此', '此間', '此外', '從', '從而', '打', '待', '但', '但是', '當', '當著', '到', '得', '的', '的話', '等', '等等', '地', '第', '叮咚', '對', '對于', '多', '多少', '而', '而況', '而且', '而是', '而外', '而言', '而已', '爾后', '反過來', '反過來說', '反之', '非但', '非徒', '否則', '嘎', '嘎登', '該', '趕', '個', '各', '各個', '各位', '各種', '各自', '給', '根據', '跟', '故', '故此', '固然', '關于', '管', '歸', '果然', '果真', '過', '哈', '哈哈', '呵', '和', '何', '何處', '何況', '何時', '嘿', '哼', '哼唷', '呼哧', '乎', '嘩', '還是', '還有', '換句話說', '換言之', '或', '或是', '或者', '極了', '及', '及其', '及至', '即', '即便', '即或', '即令', '即若', '即使', '幾', '幾時', '己', '既', '既然', '既是', '繼而', '加之', '假如', '假若', '假使', '鑒于', '將', '較', '較之', '叫', '接著', '結果', '借', '緊接著', '進而', '盡', '盡管', '經', '經過', '就', '就是', '就是說', '據', '具體地說', '具體說來', '開始', '開外', '靠', '咳', '可', '可見', '可是', '可以', '況且', '啦', '來', '來著', '離', '例如', '哩', '連', '連同', '兩者', '了', '臨', '另', '另外', '另一方面', '論', '嘛', '嗎', '慢說', '漫說', '冒', '么', '每', '每當', '們', '莫若', '某', '某個', '某些', '拿', '哪', '哪邊', '哪兒', '哪個', '哪里', '哪年', '哪怕', '哪天', '哪些', '哪樣', '那', '那邊', '那兒', '那個', '那會兒', '那里', '那么', '那么些', '那么樣', '那時', '那些', '那樣', '乃', '乃至', '呢', '能', '你', '你們', '您', '寧', '寧可', '寧肯', '寧愿', '哦', '嘔', '啪達', '旁人', '呸', '憑', '憑借', '其', '其次', '其二', '其他', '其它', '其一', '其余', '其中', '起', '起見', '起見', '豈但', '恰恰相反', '前后', '前者', '且', '然而', '然后', '然則', '讓', '人家', '任', '任何', '任憑', '如', '如此', '如果', '如何', '如其', '如若', '如上所述', '若', '若非', '若是', '啥', '上下', '尚且', '設若', '設使', '甚而', '甚么', '甚至', '省得', '時候', '什么', '什么樣', '使得', '是', '是的', '首先', '誰', '誰知', '順', '順著', '似的', '雖', '雖然', '雖說', '雖則', '隨', '隨著', '所', '所以', '他', '他們', '他人', '它', '它們', '她', '她們', '倘', '倘或', '倘然', '倘若', '倘使', '騰', '替', '通過', '同', '同時', '哇', '萬一', '往', '望', '為', '為何', '為了', '為什么', '為著', '喂', '嗡嗡', '我', '我們', '嗚', '嗚呼', '烏乎', '無論', '無寧', '毋寧', '嘻', '嚇', '相對而言', '像', '向', '向著', '噓', '呀', '焉', '沿', '沿著', '要', '要不', '要不然', '要不是', '要么', '要是', '也', '也罷', '也好', '一', '一般', '一旦', '一方面', '一來', '一切', '一樣', '一則', '依', '依照', '矣', '以', '以便', '以及', '以免', '以至', '以至于', '以致', '抑或', '因', '因此', '因而', '因為', '喲', '用', '由', '由此可見', '由于', '有', '有的', '有關', '有些', '又', '于', '于是', '于是乎', '與', '與此同時', '與否', '與其', '越是', '云云', '哉', '再說', '再者', '在', '在下', '咱', '咱們', '則', '怎', '怎么', '怎么辦', '怎么樣', '怎樣', '咋', '照', '照著', '者', '這', '這邊', '這兒', '這個', '這會兒', '這就是說', '這里', '這么', '這么點兒', '這么些', '這么樣', '這時', '這些', '這樣', '正如', '吱', '之', '之類', '之所以', '之一', '只是', '只限', '只要', '只有', '至', '至于', '諸位', '著', '著呢', '自', '自從', '自個兒', '自各兒', '自己', '自家', '自身', '綜上所述', '總的來看', '總的來說', '總的說來', '總而言之', '總之', '縱', '縱令', '縱然', '縱使', '遵照', '作為', '兮', '呃', '唄', '咚', '咦', '喏', '啐', '喔唷', '嗬', '嗯', '噯', 'a', 'able', 'about', 'above', 'abroad', 'according', 'accordingly', 'across', 'actually', 'adj', 'after', 'afterwards', 'again', 'against', 'ago', 'ahead', "ain't", 'all', 'allow', 'allows', 'almost', 'alone', 'along', 'alongside', 'already', 'also', 'although', 'always', 'am', 'amid', 'amidst', 'among', 'amongst', 'an', 'and', 'another', 'any', 'anybody', 'anyhow', 'anyone', 'anything', 'anyway', 'anyways', 'anywhere', 'apart', 'appear', 'appreciate', 'appropriate', 'are', "aren't", 'around', 'as', "a's", 'aside', 'ask', 'asking', 'associated', 'at', 'available', 'away', 'awfully', 'b', 'back', 'backward', 'backwards', 'be', 'became', 'because', 'become', 'becomes', 'becoming', 'been', 'before', 'beforehand', 'begin', 'behind', 'being', 'believe', 'below', 'beside', 'besides', 'best', 'better', 'between', 'beyond', 'both', 'brief', 'but', 'by', 'c', 'came', 'can', 'cannot', 'cant', "can't", 'caption', 'cause', 'causes', 'certain', 'certainly', 'changes', 'clearly', "c'mon", 'co', 'co.', 'com', 'come', 'comes', 'concerning', 'consequently', 'consider', 'considering', 'contain', 'containing', 'contains', 'corresponding', 'could', "couldn't", 'course', "c's", 'currently', 'd', 'dare', "daren't", 'definitely', 'described', 'despite', 'did', "didn't", 'different', 'directly', 'do', 'does', "doesn't", 'doing', 'done', "don't", 'down', 'downwards', 'during', 'e', 'each', 'edu', 'eg', 'eight', 'eighty', 'either', 'else', 'elsewhere', 'end', 'ending', 'enough', 'entirely', 'especially', 'et', 'etc', 'even', 'ever', 'evermore', 'every', 'everybody', 'everyone', 'everything', 'everywhere', 'ex', 'exactly', 'example', 'except', 'f', 'fairly', 'far', 'farther', 'few', 'fewer', 'fifth', 'first', 'five', 'followed', 'following', 'follows', 'for', 'forever', 'former', 'formerly', 'forth', 'forward', 'found', 'four', 'from', 'further', 'furthermore', 'g', 'get', 'gets', 'getting', 'given', 'gives', 'go', 'goes', 'going', 'gone', 'got', 'gotten', 'greetings', 'h', 'had', "hadn't", 'half', 'happens', 'hardly', 'has', "hasn't", 'have', "haven't", 'having', 'he', "he'd", "he'll", 'hello', 'help', 'hence', 'her', 'here', 'hereafter', 'hereby', 'herein', "here's", 'hereupon', 'hers', 'herself', "he's", 'hi', 'him', 'himself', 'his', 'hither', 'hopefully', 'how', 'howbeit', 'however', 'hundred', 'i', "i'd", 'ie', 'if', 'ignored', "i'll", "i'm", 'immediate', 'in', 'inasmuch', 'inc', 'inc.', 'indeed', 'indicate', 'indicated', 'indicates', 'inner', 'inside', 'insofar', 'instead', 'into', 'inward', 'is', "isn't", 'it', "it'd", "it'll", 'its', "it's", 'itself', "i've", 'j', 'just', 'k', 'keep', 'keeps', 'kept', 'know', 'known', 'knows', 'l', 'last', 'lately', 'later', 'latter', 'latterly', 'least', 'less', 'lest', 'let', "let's", 'like', 'liked', 'likely', 'likewise', 'little', 'look', 'looking', 'looks', 'low', 'lower', 'ltd', 'm', 'made', 'mainly', 'make', 'makes', 'many', 'may', 'maybe', "mayn't", 'me', 'mean', 'meantime', 'meanwhile', 'merely', 'might', "mightn't", 'mine', 'minus', 'miss', 'more', 'moreover', 'most', 'mostly', 'mr', 'mrs', 'much', 'must', "mustn't", 'my', 'myself', 'n', 'name', 'namely', 'nd', 'near', 'nearly', 'necessary', 'need', "needn't", 'needs', 'neither', 'never', 'neverf', 'neverless', 'nevertheless', 'new', 'next', 'nine', 'ninety', 'no', 'nobody', 'non', 'none', 'nonetheless', 'noone', 'no-one', 'nor', 'normally', 'not', 'nothing', 'notwithstanding', 'novel', 'now', 'nowhere', 'o', 'obviously', 'of', 'off', 'often', 'oh', 'ok', 'okay', 'old', 'on', 'once', 'one', 'ones', "one's", 'only', 'onto', 'opposite', 'or', 'other', 'others', 'otherwise', 'ought', "oughtn't", 'our', 'ours', 'ourselves', 'out', 'outside', 'over', 'overall', 'own', 'p', 'particular', 'particularly', 'past', 'per', 'perhaps', 'placed', 'please', 'plus', 'possible', 'presumably', 'probably', 'provided', 'provides', 'q', 'que', 'quite', 'qv', 'r', 'rather', 'rd', 're', 'really', 'reasonably', 'recent', 'recently', 'regarding', 'regardless', 'regards', 'relatively', 'respectively', 'right', 'round', 's', 'said', 'same', 'saw', 'say', 'saying', 'says', 'second', 'secondly', 'see', 'seeing', 'seem', 'seemed', 'seeming', 'seems', 'seen', 'self', 'selves', 'sensible', 'sent', 'serious', 'seriously', 'seven', 'several', 'shall', "shan't", 'she', "she'd", "she'll", "she's", 'should', "shouldn't", 'since', 'six', 'so', 'some', 'somebody', 'someday', 'somehow', 'someone', 'something', 'sometime', 'sometimes', 'somewhat', 'somewhere', 'soon', 'sorry', 'specified', 'specify', 'specifying', 'still', 'sub', 'such', 'sup', 'sure', 't', 'take', 'taken', 'taking', 'tell', 'tends', 'th', 'than', 'thank', 'thanks', 'thanx', 'that', "that'll", 'thats', "that's", "that've", 'the', 'their', 'theirs', 'them', 'themselves', 'then', 'thence', 'there', 'thereafter', 'thereby', "there'd", 'therefore', 'therein', "there'll", "there're", 'theres', "there's", 'thereupon', "there've", 'these', 'they', "they'd", "they'll", "they're", "they've", 'thing', 'things', 'think', 'third', 'thirty', 'this', 'thorough', 'thoroughly', 'those', 'though', 'three', 'through', 'throughout', 'thru', 'thus', 'till', 'to', 'together', 'too', 'took', 'toward', 'towards', 'tried', 'tries', 'truly', 'try', 'trying', "t's", 'twice', 'two', 'u', 'un', 'under', 'underneath', 'undoing', 'unfortunately', 'unless', 'unlike', 'unlikely', 'until', 'unto', 'up', 'upon', 'upwards', 'us', 'use', 'used', 'useful', 'uses', 'using', 'usually', 'v', 'value', 'various', 'versus', 'very', 'via', 'viz', 'vs', 'w', 'want', 'wants', 'was', "wasn't", 'way', 'we', "we'd", 'welcome', 'well', "we'll", 'went', 'were', "we're", "weren't", "we've", 'what', 'whatever', "what'll", "what's", "what've", 'when', 'whence', 'whenever', 'where', 'whereafter', 'whereas', 'whereby', 'wherein', "where's", 'whereupon', 'wherever', 'whether', 'which', 'whichever', 'while', 'whilst', 'whither', 'who', "who'd", 'whoever', 'whole', "who'll", 'whom', 'whomever', "who's", 'whose', 'why', 'will', 'willing', 'wish', 'with', 'within', 'without', 'wonder', "won't", 'would', "wouldn't", 'x', 'y', 'yes', 'yet', 'you', "you'd", "you'll", 'your', "you're", 'yours', 'yourself', 'yourselves', "you've", 'z', 'zero']
Building prefix dict from the default dictionary ...
Loading model from cache C:\Users\Windows\AppData\Local\Temp\jieba.cache
Loading model cost 0.797 seconds.
Prefix dict has been built successfully.
C:\Users\Windows\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py:300: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ain', 'aren', 'couldn', 'daren', 'didn', 'doesn', 'don', 'hadn', 'hasn', 'haven', 'isn', 'll', 'mayn', 'mightn', 'mon', 'mustn', 'needn', 'oughtn', 'shan', 'shouldn', 've', 'wasn', 'weren', 'won', 'wouldn'] not in stop_words.
  'stop_words.' % sorted(inconsistent))
C:\Users\Windows\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
  (0, 1)	0.172494787172401
  (0, 13)	0.03617578927683419
  (0, 19)	0.044685283861169885
  (0, 24)	0.04378669110667244
  (0, 32)	0.04770060616202845
  (0, 37)	0.08714906173699981
  (0, 42)	0.03262617791847282
  (0, 79)	0.03598272479613044
  (0, 80)	0.03384787551537572
  (0, 83)	0.105263111952599
  (0, 88)	0.1963277784717525
  (0, 89)	0.04008970433219022
  (0, 90)	0.1412328663779052
  (0, 98)	0.03994454517190565
  (0, 134)	0.04594067399577792
  (0, 142)	0.04422553495980068
  (0, 147)	0.05830540319790606
  (0, 149)	0.02851184938909845
  (0, 163)	0.03588762154160368
  (0, 166)	0.11695593718680143
  (0, 168)	0.06847746933720501
  (0, 170)	0.08047415949662211
  (0, 176)	0.03776179057073174
  (0, 185)	0.03924979201525634
  (0, 200)	0.04184963649844074
  :	:
  (0, 855)	0.04946215984804544
  (0, 865)	0.04649241857748127
  (0, 869)	0.138252930106818
  (0, 870)	0.21384828173878706
  (0, 873)	0.1667028225043511
  (0, 876)	0.09298483715496254
  (0, 885)	0.19784215331311594
  (0, 888)	0.04008970433219022
  (0, 896)	0.04985458905113395
  (0, 897)	0.49416003160549193
  (0, 902)	0.03938479984191792
  (0, 909)	0.03885574129240138
  (0, 910)	0.03911664642497605
  (0, 917)	0.05050266101265748
  (0, 922)	0.03966061581314387
  (0, 933)	0.02711876416903459
  (0, 947)	0.0329697338326223
  (0, 955)	0.15044205095521537
  (0, 972)	0.03255891003473477
  (0, 998)	0.1578946679288985
  (1, 196)	0.37511948551579166
  (1, 224)	0.21966463546937165
  (1, 420)	0.24197656045436386
  (1, 460)	0.49133294291978213
  (1, 787)	0.7148930709574247
1394    news.cn
1365    news.cn
Name: Class, dtype: object
準確率: 0.7176165803108808
               precision    recall  f1-score   support
1688.autos.cn       0.00      0.00      0.00         3
     autos.cn       1.00      0.22      0.36         9
       biz.cn       0.63      0.73      0.68        45
          cpc       0.00      0.00      0.00         8
      dangshi       0.48      0.77      0.59        13
       ent.cn       0.86      0.57      0.68        44
        henan       0.98      0.77      0.86        69
         news       0.00      0.00      0.00        16
      news.cn       0.64      0.93      0.76       131
      society       0.00      0.00      0.00         5
    sports.cn       0.86      0.86      0.86        37
       theory       0.00      0.00      0.00         6
     accuracy                           0.72       386
    macro avg       0.45      0.40      0.40       386
 weighted avg       0.69      0.72      0.68       386