Question

我正在尝试从pandas数据帧中训练不同特征的元分类器。

这些功能本质上是文本或分类。

我在拟合模型时遇到问题，出现以下错误：“找到输入变量的样本数不一致：[1,48678]”。我理解错误的含义，但不知道如何解决它。非常感谢！

我使用的代码如下：

import pandas as pd
from sklearn import preprocessing
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.feature_extraction.text import TfidfVectorizer

# set target label
target_label = ['target']
features = ['cat_1', 'cat_2', 'cat_3', 'cat_4', 'cat_5', 
'text_1']

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(cleansed_data[features], 
cleansed_data[target_label], test_size=0.2, random_state=0)

text_features = ['text_1']
categorical_features = ['cat_1', 'cat_2', 'cat_3', 'cat_4', 'cat_5']

# encoder
le = preprocessing.LabelEncoder()

# vectoriser
vectoriser = TfidfVectorizer()

# classifiers
mlp_clf = MLPClassifier()
rf_clf = RandomForestClassifier()

from sklearn.base import TransformerMixin, BaseEstimator
class SelectColumnsTransfomer(BaseEstimator, TransformerMixin):

    def __init__(self, columns=[]):
    self.columns = columns

    def transform(self, X, **transform_params):
        trans = X[self.columns].copy()
        return trans

    def fit(self, X, y=None, **fit_params):
    return self

# text pipeline
text_steps = [('feature extractor', SelectColumnsTransfomer(text_features)),
          ('tf-idf', vectoriser),
          ('classifier', mlp_clf)]

# categorical pipeline
categorical_steps = [('feature extractor', 
SelectColumnsTransfomer(categorical_features)),
                 ('label encode', le),
                 ('classifier', rf_clf)]

pl_text = Pipeline(text_steps)
pl_categorical = Pipeline(categorical_steps)

pl_text.fit(X_train, y_train)

from mlxtend.classifier import StackingCVClassifier
sclf = StackingCVClassifier(classifiers=[pl_text, pl_categorical],
                      use_probas=True,
                      meta_classifier=LogisticRegression())

编辑：这是一些重新创建问题的代码。 'ValueError：找到样本数不一致的输入变量：[1,3]'

d = {'cat_1': ['A', 'A', 'B'], 'cat_2': [1, 2, 3], 
'cat_2': ['G', 'H', 'I'], 'cat_3': ['AA', 'DD', 'PP'], 
'cat_4': ['X', 'B', 'V'], 
'text_1': ['the cat sat on the mat', 'the mat sat on the cat', 'sat on the cat mat']} 
features = pd.DataFrame(data=d)

t = [0, 1, 0]
target = pd.DataFrame(data=t)

text_features = ['text_1']
categorical_features = ['cat_1', 'cat_2', 'cat_3', 'cat_4', 'cat_5']

# text pipeline
text_steps = [('feature extractor', SelectColumnsTransfomer(text_features)),
              ('tf-idf', vectoriser),
              ('classifier', mlp_clf)]

# categorical pipeline
categorical_steps = [('feature extractor', 
SelectColumnsTransfomer(categorical_features)),
                 ('label encode', le),
                 ('classifier', rf_clf)]

pl_text = Pipeline(text_steps)
pl_categorical = Pipeline(categorical_steps)

pl_text.fit(features, target)

from mlxtend.classifier import StackingCVClassifier
sclf = StackingCVClassifier(classifiers=[pl_text, pl_categorical],
                          use_probas=True,
                          meta_classifier=LogisticRegression())

sclf.fit(features, target)

Answer 1

好的，我设法通过替换text_features = [＆＃39; text_1＆＃39;]让它工作 with text_features =＆＃39; text_1＆＃39;

基本上，当您将[＆＃39; text_1＆＃39;]传递给SelectColumnsTransfomer类时，它会返回一个DataFrame对象，tfidf向量生成器将其视为一个单一输入。 vectoriser在您的管道中应用fit_transform并返回单个值。此单个值不能用于预测三个目标值。

如果您传入＆＃39; text_1＆＃39;，这将为您提供一个系列，矢量图将正确识别您有三个字符串作为功能。您的文本管道现在可以正常工作。

带有元分类器的管道

1 个答案: