Question

我正在用Naive Bayes来实现sklearn，数据不平衡。我的数据有超过16000条记录和6个输出类别。

我尝试用sample_weight计算的sklearn.utils.class_weight拟合模型

sample_weight收到以下信息：

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))

我收到此消息： ValueError: Found input variables with inconsistent numbers of samples: [13212, 6]

谁能告诉我我做错了什么以及如何解决？

非常感谢。

Answer 1

sample_weight和class_weight是两回事。

顾名思义，

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

您正在使用class_weight计算sample_weight而不是sklearn.utils.class_weight，但是尝试将其传递给sample_weight。因此，尺寸不匹配错误。

请参见以下问题，以进一步了解这两个权重在内部如何相互作用：

Answer 2

通过这种方式，我能够计算权重以解决类别不平衡问题。

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)

如何纠正sklearn.naive_bayes中的sample_weight？

2 个答案: