calibrated classifier ValueError: could not convert string to float

Question

数据框：

id    review                                              name         label
1     it is a great product for turning lights on.        Ashley       
2     plays music and have a good sound.                  Alex        
3     I love it, lots of fun.                             Peter

我想使用概率分类器 (linear_svc) 根据评论预测标签（概率为 1）。我的代码：

from sklearn.svm import LinearSVC
from sklearn.calibration import CalibratedClassifierCV
from sklearn import datasets

#Load  dataset
X = training['review']
y = training['label']

linear_svc = LinearSVC()     #The base estimator

# This is the calibrated classifier which can give probabilistic classifier
calibrated_svc = CalibratedClassifierCV(linear_svc,
                                        method='sigmoid',  #sigmoid will use Platt's scaling. Refer to documentation for other methods.
                                        cv=3) 
calibrated_svc.fit(X, y)


# predict
prediction_data = predict_data['review']
predicted_probs = calibrated_svc.predict_proba(prediction_data)

它在 calibrated_svc.fit(X, y) 上给出以下错误：

ValueError: could not convert string to float: 'it is a great product for turning...'

非常感谢你的帮助。

Answer 1

试试这个：

from sklearn.feature_extraction.text import TfidfVectorizer

X = training['review']
y = training['label']    
prediction_data = predict_data['review']

tfv = TfidfVectorizer(min_df=1, stop_words = 'english')
tfv.fit(list(X) + list(prediction_data))
X =  tfv.transform(X) 
prediction_data = tfv.transform(prediction_data)

然后构建模型：

linear_svc = LinearSVC()    
calibrated_svc = CalibratedClassifierCV(linear_svc, method='sigmoid', cv=3) 
calibrated_svc.fit(X, y)

Answer 2

SVM 模型无法直接处理文本数据。您需要先从文本中提取一些数字特征。我推荐阅读一些关于 NLP 的内容，例如 Bag of Words 和 TF-IDF。无论如何，对于您建议的示例，功能最小的管道将是：

from sklearn.calibration import CalibratedClassifierCV
from sklearn import datasets
from sklearn.pipeline import make_pipeline
from sklearn.feature_extraction.text import TfidfVectorizer

#Load  dataset
X = training['review']
y = training['label']

linear_svc = make_pipeline(TfIdfVectorizer(), LinearSVC())

# This is the calibrated classifier which can give probabilistic classifier
calibrated_svc = CalibratedClassifierCV(linear_svc,
                                        method='sigmoid',
                                        cv=3) 
calibrated_svc.fit(X, y)


# predict
prediction_data = predict_data['review']
predicted_probs = calibrated_svc.predict_proba(prediction_data)

您可能还想通过删除特殊字符、小写字母、词干提取等来稍微清理一下文本。看看 spacy 文本处理库。

calibrated classifier ValueError: could not convert string to float

calibrated classifier ValueError: could not convert string to float

scikit-learn

text-classification

valueerror