Performance Python-将GridSearchCV与NLTK结合使用

我有点不确定如何将SKLearn的GridSearchCV应用到我与NLTK一起使用的随机林中。讨论了如何正常使用GridSearchCV，但是我的数据格式与标准的x和y分割不同。这是我的密码：

import nltk
import numpy as np
from nltk.classify.scikitlearn import SklearnClassifier
from nltk.corpus.reader import CategorizedPlaintextCorpusReader
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC


reader_train = CategorizedPlaintextCorpusReader('C:/Users/User/Documents/Sentiment/machine_learning/amazon/amazon/', r'.*\.txt', cat_pattern=r'(\w+)/*', encoding='latin1')

documents_train = [ (list(reader_train.words(fileid)), category)
                 for category in reader_train.categories()
                 for fileid in reader_train.fileids(category) ]

all_words = []

for w in reader_train.words():
    all_words.append(w.lower())

all_words = nltk.FreqDist(all_words)

word_features = list(all_words.keys())[:3500]

def find_features(documents):
    words = set(documents)
    features = {}
    for w in word_features:
        features[w] = (w in words)

return features

featuresets_train = [(find_features(rev), category) for (rev, category) in documents_train]

np.random.shuffle(featuresets_train)

training_set = featuresets_train[:1600]
testing_set = featuresets_train[:400]

RandFor = SklearnClassifier(RandomForestClassifier())
RandFor.train(training_set)
print("RandFor accuracy:", (nltk.classify.accuracy(RandFor, testing_set)) *100)

此代码不是生成常规的x和y拆分，而是生成一个元组列表，其中每个元组的格式如下：

({'i': True, 'am': False, 'conflicted': False ... 'about': False}, neg)

---

有没有办法将GridSearchCV应用于此格式的数据？

要将

培训集

转换为scikit可用表单，只需执行以下操作

from sklearn.feature_extraction import DictVectorizer
vectorizer = DictVectorizer()

X_train, y_train = list(zip(*training_set))
X_train = vectorizer.fit_transform(X_train)

X_test, y_test = list(zip(*testing_set))
X_test = vectorizer.transform(X_test)

之后，你可以很容易地打电话

clf = RandomForestClassifier()
clf.fit(X_train, y_train)

accuracy = clf.score(X_test, y_test)
print("RandFor accuracy:", (accuracy) * 100)

---

您正在将已经非常完美的scikit学习估计器（

RandomForestClassifier

）包装为与nltk兼容的估计器。你需要使用

RandomForestClassifier

和

GridSearchCV

吗？GridSearchCV不是必须的，我很乐意尝试其他优化算法。不过，这确实需要改进。谢谢你，伙计，非常感谢，而且似乎工作得很好！祝您今天过得愉快。