scikit dask和dask-ml LogisticRegression的不同结果
scikit dask和dask-ml LogisticRegression的不同结果
提问于 2019-07-31 16:25:38
当使用相同的数据运行相同的LogisticRegression时，scikit-learn和dask实现之间的结果不应该不同。
版本: scikit-learn=0.21.2
达斯克-毫升=1.0.0
首先是dask：
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn import metrics
from dask_yarn import YarnCluster
from dask.distributed import Client
from dask_ml.linear_model import LogisticRegression
import dask.dataframe as dd
import dask.array as da
digits = load_digits()
x_train, x_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.25, random_state=0)
lr = LogisticRegression(solver_kwargs={"normalize":False})
lr.fit(x_train, y_train)
score = lr.score(x_test, y_test)
print(score)
predictions = lr.predict(x_test)
cm = metrics.confusion_matrix(y_test, predictions)
print(cm)现在使用sklearn：
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn import metrics
from dask_yarn import YarnCluster
from dask.distributed import Client
from sklearn.linear_model import LogisticRegression
import dask.dataframe as dd
import dask.array as da
digits = load_digits()
x_train, x_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.25, random_state=0)
lr = LogisticRegression()
lr.fit(x_train, y_train)
score = lr.score(x_test, y_test)
print(score)
predictions = lr.predict(x_test)
cm = metrics.confusion_matrix(y_test, predictions)
print(cm)scikit学习的分数和卷积矩阵
0.9533333333333334
[[37 0 0 0 0 0 0 0 0 0]
[ 0 39 0 0 0 0 2 0 2 0]
[ 0 0 41 3 0 0 0 0 0 0]
[ 0 0 1 43 0 0 0 0 0 1]
[ 0 0 0 0 38 0 0 0 0 0]
[ 0 1 0 0 0 47 0 0 0 0]
[ 0 0 0 0 0 0 52 0 0 0]
[ 0 1 0 1 1 0 0 45 0 0]
[ 0 3 1 0 0 0 0 0 43 1]
[ 0 0 0 1 0 1 0 0 1 44]]dask的记分和卷积矩阵
0.09555555555555556
[[ 0 37 0 0 0 0 0 0 0 0]
[ 0 43 0 0 0 0 0 0 0 0]
[ 0 44 0 0 0 0 0 0 0 0]
[ 0 45 0 0 0 0 0 0 0 0]
[ 0 38 0 0 0 0 0 0 0 0]
[ 0 48 0 0 0 0 0 0 0 0]
[ 0 52 0 0 0 0 0 0 0 0]
[ 0 48 0 0 0 0 0 0 0 0]
[ 0 48 0 0 0 0 0 0 0 0]
[ 0 47 0 0 0 0 0 0 0 0]]回答 1
Stack Overflow用户
回答已采纳
发布于 2019-08-06 02:41:58
dask_ml==1.0.0版本的Dask不支持多个类的logistic回归。使用原始示例的稍微修改的版本，如果您从贴合的dask predictions分类器中打印LogisticRegression，您将看到它给出了一个充满True的布尔数组。
from sklearn.datasets import load_digits
from dask_ml.linear_model import LogisticRegression
X, y = load_digits(return_X_y=True)
lr = LogisticRegression(solver_kwargs={"normalize": False})
lr.fit(X, y)
predictions = lr.predict(X)
print('predictions = {}'.format(predictions))输出
predictions = [ True True True ... True True True]这就是为什么达斯克-毫升和科学学习混淆矩阵彼此不同的原因。
在GitHub at https://github.com/dask/dask-ml/issues/386上有一个相关的未决问题
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/57295274
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