运行 受训机器学习模型出现错误
Getting error on running the trained Machine Learning model
我有一个包含列 'studentDetails' 和 'studentId' 的数据集。我在这个数据集上训练了我的模型并保存了它。当我训练模型并保存训练模型，然后加载训练模型进行预测时，它成功地为我提供了输出。但是当我独立加载保存的模型并使用它进行预测时，它给我一个错误 "CountVectorizer - Vocabulary wasn't fitted"
这是我使用的代码：
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
import pickle
from sklearn.svm import LinearSVC
X_train, X_test, y_train, y_test = train_test_split(df['studentDetails'], df['studentId'], random_state = 0)
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X_train)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
classificationModel = LinearSVC().fit(X_train_tfidf, y_train)
filename = 'finalized_model.sav'
pickle.dump(classificationModel, open(filename, 'wb'))
现在加载模型并预测：
from sklearn.feature_extraction.text import CountVectorizer
data_to_be_predicted="Alicia Scott is from United States"
filename = 'finalized_model.sav'
loaded_model = pickle.load(open(filename, 'rb'))
count_vect = CountVectorizer()
result = loaded_model.predict(count_vect.transform([data_to_be_predicted]))
print(result)
输出：
94120
当我 运行 只是第二个独立的代码片段时，它给我一个错误
错误：
CountVectorizer - Vocabulary wasn't fitted
我只是想知道，为什么我在第二种情况下会出错，因为我没有在第一种情况下的任何地方重新定义 count_vect = CountVectorizer() 当我得到正确的结果时。
第二个片段的问题是您没有使用合适的 CounVectorizer，它是一个新的所以没有合适。
我会建议你使用 fit 而不是 fit_transform，这 return 你已经是 CountVectorizer安装好，然后您可以像处理模型一样保存它。
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
import pickle
from sklearn.svm import LinearSVC
X_train, X_test, y_train, y_test = train_test_split(df['studentDetails'], df['studentId'], random_state = 0)
count_vect = CountVectorizer().fit(X_train)
X_train_counts = count_vect.transform(X_train)
tfidf_transformer = TfidfTransformer().fit(X_train_counts)
X_train_tfidf = tfidf_transformer.transform(X_train_counts)
classificationModel = LinearSVC().fit(X_train_tfidf, y_train)
filename = 'finalized_model.sav'
pickle.dump(classificationModel, open(filename, 'wb'))
pickle.dump(count_vect, open('count_vect, 'wb'))
pickle.dump(tfidf_transformer, open('tfidf_transformer, 'wb'))
现在您可以在想要进行预测时加载其中的 3 个：
from sklearn.feature_extraction.text import CountVectorizer
data_to_be_predicted="Alicia Scott is from United States"
filename = 'finalized_model.sav'
loaded_model = pickle.load(open(filename, 'rb'))
count_vect = pickle.load(open('count_vect', 'rb'))
result = loaded_model.predict(count_vect.transform([data_to_be_predicted]))
print(result)
我有一个包含列 'studentDetails' 和 'studentId' 的数据集。我在这个数据集上训练了我的模型并保存了它。当我训练模型并保存训练模型，然后加载训练模型进行预测时，它成功地为我提供了输出。但是当我独立加载保存的模型并使用它进行预测时，它给我一个错误 "CountVectorizer - Vocabulary wasn't fitted"
这是我使用的代码：
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
import pickle
from sklearn.svm import LinearSVC
X_train, X_test, y_train, y_test = train_test_split(df['studentDetails'], df['studentId'], random_state = 0)
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X_train)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
classificationModel = LinearSVC().fit(X_train_tfidf, y_train)
filename = 'finalized_model.sav'
pickle.dump(classificationModel, open(filename, 'wb'))
现在加载模型并预测：
from sklearn.feature_extraction.text import CountVectorizer
data_to_be_predicted="Alicia Scott is from United States"
filename = 'finalized_model.sav'
loaded_model = pickle.load(open(filename, 'rb'))
count_vect = CountVectorizer()
result = loaded_model.predict(count_vect.transform([data_to_be_predicted]))
print(result)
输出：
94120
当我 运行 只是第二个独立的代码片段时，它给我一个错误
错误：
CountVectorizer - Vocabulary wasn't fitted
我只是想知道，为什么我在第二种情况下会出错，因为我没有在第一种情况下的任何地方重新定义 count_vect = CountVectorizer() 当我得到正确的结果时。
第二个片段的问题是您没有使用合适的 CounVectorizer，它是一个新的所以没有合适。
我会建议你使用 fit 而不是 fit_transform，这 return 你已经是 CountVectorizer安装好，然后您可以像处理模型一样保存它。
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
import pickle
from sklearn.svm import LinearSVC
X_train, X_test, y_train, y_test = train_test_split(df['studentDetails'], df['studentId'], random_state = 0)
count_vect = CountVectorizer().fit(X_train)
X_train_counts = count_vect.transform(X_train)
tfidf_transformer = TfidfTransformer().fit(X_train_counts)
X_train_tfidf = tfidf_transformer.transform(X_train_counts)
classificationModel = LinearSVC().fit(X_train_tfidf, y_train)
filename = 'finalized_model.sav'
pickle.dump(classificationModel, open(filename, 'wb'))
pickle.dump(count_vect, open('count_vect, 'wb'))
pickle.dump(tfidf_transformer, open('tfidf_transformer, 'wb'))
现在您可以在想要进行预测时加载其中的 3 个：
from sklearn.feature_extraction.text import CountVectorizer
data_to_be_predicted="Alicia Scott is from United States"
filename = 'finalized_model.sav'
loaded_model = pickle.load(open(filename, 'rb'))
count_vect = pickle.load(open('count_vect', 'rb'))
result = loaded_model.predict(count_vect.transform([data_to_be_predicted]))
print(result)