利用事先训练好的模型预测不可见数据

Question

我正在使用Scikit学习执行有监督的机器学习。我有两个数据集。第一个数据集包含具有X要素和Y标签的数据。第二个数据集仅包含X要素，但不包含Y标签。我可以成功地对训练/测试数据执行LinearSVC，并获得测试数据集的Y标签

现在，我想使用为第一个数据集训练的模型来预测第二个数据集标签。如何在Scikit learn中使用从第一个数据集到第二个数据集（看不见的标签）的预训练模型

我尝试的代码片段： 更新了以下评论中的代码：

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import pandas as pd
import pickle


# ----------- Dataset 1: for training ----------- #
# Sample data ONLY
some_text = ['Books are amazing',
             'Harry potter book is awesome. It rocks',
             'Nutrition is very important',
             'Welcome to library, you can find as many book as you like',
             'Food like brocolli has many advantages']
y_variable = [1,1,0,1,0]

# books = 1 : y label
# food = 0 : y label

df = pd.DataFrame({'text':some_text,
                   'y_variable': y_variable
                          })

# ------------- TFIDF process -------------#
tfidf = TfidfVectorizer()
features = tfidf.fit_transform(df['text']).toarray()
labels = df.y_variable
features.shape


# ------------- Build Model -------------#
model = LinearSVC()
X_train, X_test, y_train, y_test= train_test_split(features,
                                                 labels,
                                                 train_size=0.5,
                                                 random_state=0)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)


# Export model
pickle.dump(model, open('model.pkl', 'wb'))
# Read the Model
model_pre_trained = pickle.load(open('model.pkl','rb'))


# ----------- Dataset 2: UNSEEN DATASET ----------- #

some_text2 = ['Harry potter books are amazing',
             'Gluten free diet is getting popular']

unseen_df = pd.DataFrame({'text':some_text2}) # Notice this doesn't have y_variable. This the is the data set I am trying to predict y_variable labels 1 or 0.


# This is where the ERROR occurs
X_unseen = tfidf.fit_transform(unseen_df['text']).toarray()
y_pred_unseen = model_pre_trained.predict(X_unseen) # error here: 
# ValueError: X has 11 features per sample; expecting 26


print(X_unseen.shape) # prints (2, 11)
print(X_train.shape) # prints (2, 26)


# Looking for an output like this for UNSEEN data
# Looking for results after predicting unseen and no label data. 
text                                   y_variable
Harry potter books are amazing         1
Gluten free diet is getting popular    0

它不必像我上面尝试的那样是pickle代码。我正在寻找是否有人有建议，或者是否有任何预构建功能可以从scikit中进行预测

Answer 1

如您所见，您的第一个tfidf将您的输入转换为26个功能，而第二个tfidf将它们转换为11个功能。因此发生错误是因为X_train与X_unseen的形状不同。提示告诉您X_unseen中的每个观察值的特征数少于model训练接收的特征数

在第二个脚本中加载model后，将为文本安装另一个矢量器。也就是说，来自第一个脚本的tfidf和来自第二个脚本的tfidf是不同的对象。为了使用model进行预测，需要使用原始tfidf转换X_unseen。为此，必须导出原始矢量器，将其加载到新脚本中，并在将其传递到model之前使用它转换新数据

### Do this in the first program
# Dump model and tfidf
pickle.dump(model, open('model.pkl', 'wb'))
pickle.dump(tfidf, open('tfidf.pkl', 'wb'))

### Do this in the second program
model = pickle.load(open('model.pkl', 'rb'))
tfidf = pickle.load(open('tfidf.pkl', 'rb'))

# Use `transform` instead of `fit_transform`
X_unseen = tfidf.transform(unseen_df['text']).toarray()

# Predict on `X_unseen`
y_pred_unseen = model_pre_trained.predict(X_unseen)

Answer 2

忽略第二个数据集，并使用train_test_split创建测试集

Answer 3

想象一下，你们训练了一个人工智能，用发动机、轮子、机翼和飞行员领结的图片来识别飞机。现在你调用同样的人工智能，你让它预测一架只有领结的飞机的模型。这就是scikit learn告诉您的：与X_train或X_test相比，X_unseen中的特性（=列）要少得多