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在我的 sklearn 分类模型中，当我手动设置 TfidfVectorizer 的“ngram_range=(4,4)”参数时，我得到 0.58 作为 f1_macro 的结果 .. 例如对于 unigram (1,1) 结果是 0.49 ..

问题是，当我使用 GridSearchCv 方法选择最佳参数时，它没有给我最佳参数，而是返回参数集中第一个元素的结果，请看一下我的代码以使其更清楚：

import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, average_precision_score
import re
from os import walk
import csv
import operator

# variables --
co = dict()
lex = []

def tokenizeManu(txt):
    txt = clean_str(txt)
    return txt.split()

def tokenizeTfidf(txt):
    return txt.split() return txt.split() # It just split the text without any operation

def repAllTxt(txt):
    out = re.sub("[a-z]|[A-Z]", '*', txt)
    out = re.sub("[0-9]", '#', out)
    return out

def corpDict(x):
    count = CountVectorizer(ngram_range=(1, 1), tokenizer=tokenizeManu, lowercase=False)
    countFit = count.fit_transform(x)
    vocab = count.get_feature_names()
    dist = np.sum(countFit.toarray(), axis=0)
    for tag, count in zip(vocab, dist):
        co[tag] = count

    # print(len(co))

def clean_str(string):
    string = re.sub(r"[^A-Za-z0-9()\-\":.$,!?\'\`]", r" ", string)
    string = re.sub(r"([()\-\":.,!?\'\`])", r" \1 ", string)
    string = re.sub(r"\'s", r" \'s", string)
    string = re.sub(r"\'m", r" \'m", string)
    string = re.sub(r"\'ve", r" \'ve", string)
    string = re.sub(r"n\'t", r" n\'t", string)
    string = re.sub(r"\'re", r" \'re", string)
    string = re.sub(r"\'d", r" \'d", string)
    string = re.sub(r"\'ll", r" \'ll", string)
    string = re.sub(r"\s{2,}", r" ", string)
    return string.strip()

def readLexicons():
    path = 'lexicons'
    # Load data from files
    f = []
    for (dirpath, dirnames, filenames) in walk(path):
        for i in filenames:
            f.append(str(dirpath+'\\'+i))

    lexList = []
    for pa in f:
        if pa.endswith('txt') == True:
            with open(pa, encoding="utf8") as inf:
                reader = csv.reader(inf, delimiter='\n',quoting=csv.QUOTE_NONE)
                col = list(zip(*reader))
                lexList.extend(col[0])
        else:
            file_object = open(pa, "r")
            file_object = file_object.read()
            file_object = re.findall(r'((?<=word1=)\w+)', file_object)
            lexList.extend(file_object)
    lex.extend(lexList)

def prepTxtStar(X, kValue, maintainLex):
    sorted_co = sorted(co.items(), key=operator.itemgetter(1), reverse=True)[:kValue]
    sorted_co = list([i[0] for i in sorted_co])

    for row in range(len(X)):
        c = str(X[row]).split()
        for i in range(len(c)):
            if c[i] in co.keys():
                if not sorted_co.__contains__(c[i]):
                    if maintainLex == 0:
                        c[i] = repAllTxt(c[i])
                    else:
                        if not lex.__contains__(c[i]):
                            c[i] = repAllTxt(c[i])
        X[row] = ' '.join(c)
    for x in X[:3]:
        print(x)
    return X

def readFiles():
    path = 'datasetpaaaaaaaaaaath/ds.txt'

    f = []
    for (dirpath, dirnames, filenames) in walk(path):
        for i in filenames:
            f.append(str(dirpath+'\\'+i))
    x = []
    y = []
    lexList = []
    for pa in f:
        if pa.endswith('txt') == True:
            with open(pa, encoding="utf8") as inf:
                reader = csv.reader(inf, delimiter='\t',quoting=csv.QUOTE_NONE)
                col = list(zip(*reader))
                x.extend(col[2])
                y.extend(col[3])
    return x,y





if __name__ == "__main__":
    xOri, yOri = readFiles()
    xOri = [clean_str(i) for i in xOri]
    readLexicons()
    corpDict(xOri)
    xOri = prepTxtStar(xOri, kValue=10000000, maintainLex=0)


    x, xTest, y, yTest = train_test_split(xOri, yOri, test_size=0.32, random_state=42)
    model = Pipeline([
                        ('tfidf', TfidfVectorizer( analyzer='char_wb', min_df=0.0007,lowercase=False,tokenizer=tokenizeTfidf)),
                        ('clf', SGDClassifier(tol=None, loss='hinge', random_state=38, max_iter=5))
                     ])

    # Grid search
    parameters = {
                    'tfidf__ngram_range': [(1,1),(2,2),(3,3),(4,4),(5,5),(6,6)]
                 }
    gs_clf = GridSearchCV(model, parameters, n_jobs=-1, scoring='f1_macro')
    gs_clf = gs_clf.fit(x, y)

    predicted = gs_clf.predict(xTest)
    for param_name in sorted(parameters.keys()):
        print("%s: %r" % (param_name, gs_clf.best_params_[param_name]))
    print('F1 Macro: ', f1_score(yTest, predicted, average='macro'))

在这个例子中，我得到了以下结果：

tfidf__ngram_range: (1, 1)
F1 Macro:  0.4927875243664717

因此，它选择参数集的第一个元素 (1,1) 而根据 f1_score 的最佳元素是 (4,4) ！

有什么问题，我错过了什么吗？

已编辑：完整的源代码与数据集一起添加：数据集

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