如何纠正sample_weight中的sklearn.naive_bayes？

Question

我正在用不平衡的数据通过Naive Bayes实现sklearn。我的数据有超过16k的记录和6个输出类别。

我试着用由sample_weight计算的sklearn.utils.class_weight来拟合这个模型

sample_weight收到的消息如下：

sample_weight = 11.77540107 1.82284768 0.64688602 2.47138047 0.38577435 1.21389195

import numpy as np

data_set = np.loadtxt("./data/_vector21.csv", delimiter=",")

inp_vec = data_set[:, 1:22]
out_vec = data_set[:, 22:]
#
# # Split dataset into training set and test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(inp_vec, out_vec, test_size=0.2)    # 80% training and 20% test
#
# class weight
from keras.utils.np_utils import to_categorical
output_vec_categorical = to_categorical(y_train)
from sklearn.utils import class_weight
y_ints = [y.argmax() for y in output_vec_categorical]
c_w = class_weight.compute_class_weight('balanced', np.unique(y_ints), y_ints)
cw = {}
for i in set(y_ints):
    cw[i] = c_w[i]

# Create a Gaussian Classifier
from sklearn.naive_bayes import *
model = GaussianNB()

# Train the model using the training sets
print(c_w)

model.fit(X_train, y_train, c_w)

# Predict the response for test dataset
y_pred = model.predict(X_test)

# Import scikit-learn metrics module for accuracy calculation
from sklearn import metrics

# Model Accuracy, how often is the classifier correct?
print("\nClassification Report: \n", (metrics.classification_report(y_test, y_pred)))
print("\nAccuracy: %.3f%%" % (metrics.accuracy_score(y_test, y_pred)*100))

我收到一条消息：ValueError: Found input variables with inconsistent numbers of samples: [13212, 6]

有人能告诉我我做错了什么吗?怎么解决呢？

非常感谢。

Answer 1

sample_weight和class_weight是两种不同的东西。

顾名思义：

• sample_weight将应用于单个样本(数据中的行)。因此，sample_weight的长度必须与X中的样本数相匹配。
• class_weight是使分类器对类给予更多的重视和重视。因此，class_weight的长度必须与目标中的类数相匹配。

您正在通过使用class_weight计算sample_weight，而不是通过sklearn.utils.class_weight计算，但随后尝试将其传递给sample_weight。因此，尺寸不匹配误差。

请参阅以下问题，以更好地了解这两个权重如何在内部相互作用：

• What is the difference between sample weight and class weight options in scikit learn?
• https://stats.stackexchange.com/questions/244630/difference-between-sample-weight-and-class-weight-randomforest-classifier

Answer 2

这样我就能计算出权重来处理班级的不平衡。

from sklearn.utils import class_weight

sample = class_weight.compute_sample_weight('balanced', y_train)

#Classifier Naive Bayes
naive = naive_bayes.MultinomialNB()
naive.fit(X_train,y_train, sample_weight=sample)
predictions_NB = naive.predict(X_test)