使用python实现用施密特正交实现”Bag of Words Meets Bags of Popcorn“竞赛项目的代码

以下是使用Python实现用施密特正交实现“Bag of Words Meets Bags of Popcorn”竞赛项目的代码：

首先，我们需要导入需要的库和数据集：

import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.svm import LinearSVC
from sklearn.model_selection import cross_val_score
from sklearn.decomposition import TruncatedSVD
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Normalizer

train = pd.read_csv("labeledTrainData.tsv", header=0, delimiter="\t", quoting=3)
test = pd.read_csv("testData.tsv", header=0, delimiter="\t", quoting=3)

接下来，我们使用CountVectorizer将文本数据转换为词袋向量：

vectorizer = CountVectorizer(analyzer="word", tokenizer=None, preprocessor=None, stop_words=None, max_features=5000)
train_data_features = vectorizer.fit_transform(train["review"])
train_data_features = train_data_features.toarray()

然后，我们使用施密特正交来减少词袋向量的维度：

svd = TruncatedSVD(n_components=500, random_state=42)
lsa = make_pipeline(svd, Normalizer(copy=False))
train_data_features = lsa.fit_transform(train_data_features)

最后，我们使用LinearSVC分类器对数据进行分类，并使用交叉验证来评估模型的性能：

model = LinearSVC()
scores = cross_val_score(model, train_data_features, train["sentiment"], cv=5)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

完整的代码如下：

import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.svm import LinearSVC
from sklearn.model_selection import cross_val_score
from sklearn.decomposition import TruncatedSVD
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Normalizer

train = pd.read_csv("labeledTrainData.tsv", header=0, delimiter="\t", quoting=3)
test = pd.read_csv("testData.tsv", header=0, delimiter="\t", quoting=3)

vectorizer = CountVectorizer(analyzer="word", tokenizer=None, preprocessor=None, stop_words=None, max_features=5000)
train_data_features = vectorizer.fit_transform(train["review"])
train_data_features = train_data_features.toarray()

svd = TruncatedSVD(n_components=500, random_state=42)
lsa = make_pipeline(svd, Normalizer(copy=False))
train_data_features = lsa.fit_transform(train_data_features)

model = LinearSVC()
scores = cross_val_score(model, train_data_features, train["sentiment"], cv=5)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

注意：这只是一个简单的示例，实际上，在竞赛项目中，更复杂的特征工程和模型调整可能会导致更好的性能。