段落向量模型的交叉验证

Question

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from gensim.sklearn_api import D2VTransformer

data = pd.read_csv('https://pastebin.com/raw/bSGWiBfs')
np.random.seed(0)

X_train = data.apply(lambda r: simple_preprocess(r['text'], min_len=2), axis=1)
y_train = data.label

model = D2VTransformer(size=10, min_count=1, iter=5, seed=1)
clf = LogisticRegression(random_state=0)

pipeline = Pipeline([
        ('vec', model),
        ('clf', clf)
    ])

pipeline.fit(X_train, y_train)

score = pipeline.score(X_train, y_train)
print("Score:", score) # This works
cval = cross_val_score(pipeline, X_train, y_train, scoring='accuracy', cv=3)
print("Cross-Validation:", cval) # This doesn't work

密钥错误：0

我通过将cross_val_score中的X_train替换为model.transform(X_train)或model.fit_transform(X_train)来进行实验。此外，我尝试了对原始输入数据(data.text)进行同样的处理，而不是预处理文本。我怀疑用于交叉验证的x_train的格式一定有问题，而用于管道的.score函数工作得很好。我还注意到cross_val_score与countvectorizer()一起工作。

有人发现错误了吗？

Answer 1

不，这与从model转换无关。它与cross_val_score有关。

cross_val_score将根据cv参数拆分提供的数据。为此，它会做这样的事情：

for train, test in splitter.split(X_train, y_train):
    new_X_train, new_y_train = X_train[train], y_train[train]

但是您的x_train是一个pandas.series对象，其中基于索引的选择不能像这样工作。请参阅:https://pandas.pydata.org/pandas-docs/stable/indexing.html#selection-by-position

X_train = data.apply(lambda r: simple_preprocess(r['text'], min_len=2), axis=1)

# Access the internal numpy array
X_train = data.apply(lambda r: simple_preprocess(r['text'], min_len=2), axis=1).values

OR

# Convert series to list
X_train = data.apply(lambda r: simple_preprocess(r['text'], min_len=2), axis=1).tolist()