stacking的實現-mxtend庫

原創 Jack_kun 2019-01-12 14:36

Table of Contents

關於Stacked的實現庫-mlxtend的學習

環境情況：
##################################################################
python 3.6
mlxtend 0.13.0
scikit-learn 0.19.0
##################################################################

該mlxtend庫的的stacking實現，通過結合sklearn中的模型進行。
推薦使用StackingCV方法。

StackingClassifier

該函數主要介紹stacking基本原理，是簡單實現。

  • 基本算法說明
    算法圖解

算法僞代碼

例子1 簡單實現stacking

from sklearn import datasets

iris = datasets.load_iris()
X, y = iris.data[:, 1:3], iris.target

from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB 
from sklearn.ensemble import RandomForestClassifier
from mlxtend.classifier import StackingClassifier
import numpy as np

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()
sclf = StackingClassifier(classifiers=[clf1, clf2, clf3], 
                          meta_classifier=lr)

print('3-fold cross validation:\n')

for clf, label in zip([clf1, clf2, clf3, sclf], 
                      ['KNN', 
                       'Random Forest', 
                       'Naive Bayes',
                       'StackingClassifier']):

    scores = model_selection.cross_val_score(clf, X, y, 
                                              cv=3, scoring='accuracy')
    print("Accuracy: %0.2f (+/- %0.2f) [%s]" 
          % (scores.mean(), scores.std(), label))

3-fold cross validation:

Accuracy: 0.91 (+/- 0.01) [KNN]
Accuracy: 0.91 (+/- 0.06) [Random Forest]
Accuracy: 0.92 (+/- 0.03) [Naive Bayes]
Accuracy: 0.95 (+/- 0.03) [StackingClassifier]

例子2 sub-model的輸出是probabilities

基於第一層的概率，進行第二層的計算

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()
sclf = StackingClassifier(classifiers=[clf1, clf2, clf3],
                          use_probas=True,
                          average_probas=False,
                          meta_classifier=lr)

print('3-fold cross validation:\n')

for clf, label in zip([clf1, clf2, clf3, sclf], 
                      ['KNN', 
                       'Random Forest', 
                       'Naive Bayes',
                       'StackingClassifier']):

    scores = model_selection.cross_val_score(clf, X, y, 
                                              cv=3, scoring='accuracy')
    print("Accuracy: %0.2f (+/- %0.2f) [%s]" 
          % (scores.mean(), scores.std(), label))

3-fold cross validation:

Accuracy: 0.91 (+/- 0.01) [KNN]
Accuracy: 0.91 (+/- 0.06) [Random Forest]
Accuracy: 0.92 (+/- 0.03) [Naive Bayes]
Accuracy: 0.94 (+/- 0.03) [StackingClassifier]

例子3 結合GridSearch

結合網格搜索交叉驗證

  • 各個模型參數
  • 各個基學習器+參數
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB 
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from mlxtend.classifier import StackingClassifier

# Initializing models

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()
sclf = StackingClassifier(classifiers=[clf1, clf2, clf3], 
                          meta_classifier=lr)

params = {'kneighborsclassifier__n_neighbors': [1, 5],
          'randomforestclassifier__n_estimators': [10, 50],
          'meta-logisticregression__C': [0.1, 10.0]}

grid = GridSearchCV(estimator=sclf, 
                    param_grid=params, 
                    cv=5,
                    refit=True)
grid.fit(X, y)

cv_keys = ('mean_test_score', 'std_test_score', 'params')

for r, _ in enumerate(grid.cv_results_['mean_test_score']):
    print("%0.3f +/- %0.2f %r"
          % (grid.cv_results_[cv_keys[0]][r],
             grid.cv_results_[cv_keys[1]][r] / 2.0,
             grid.cv_results_[cv_keys[2]][r]))

print('Best parameters: %s' % grid.best_params_)
print('Accuracy: %.2f' % grid.best_score_)

0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 10}
0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 50}
0.927 +/- 0.02 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 10}
0.913 +/- 0.03 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 50}
0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 10}
0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 50}
0.933 +/- 0.02 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 10}
0.940 +/- 0.02 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 50}
Best parameters: {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 50}
Accuracy: 0.94

from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB 
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from mlxtend.classifier import StackingClassifier

# Initializing models

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()
sclf = StackingClassifier(classifiers=[clf1, clf2, clf3], 
                          meta_classifier=lr)

params = {'kneighborsclassifier__n_neighbors': [1, 5],
          'classifiers':[(clf1,clf1),(clf2, clf3)]}

grid = GridSearchCV(estimator=sclf, 
                    param_grid=params, 
                    cv=5,
                    refit=True)
grid.fit(X, y)

cv_keys = ('mean_test_score', 'std_test_score', 'params')

for r, _ in enumerate(grid.cv_results_['mean_test_score']):
    print("%0.3f +/- %0.2f %r"
          % (grid.cv_results_[cv_keys[0]][r],
             grid.cv_results_[cv_keys[1]][r] / 2.0,
             grid.cv_results_[cv_keys[2]][r]))

print('Best parameters: %s' % grid.best_params_)
print('Accuracy: %.2f' % grid.best_score_)

0.907 +/- 0.03 {'classifiers': (KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=1, p=2,
           weights='uniform'), KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=1, p=2,
           weights='uniform')), 'kneighborsclassifier__n_neighbors': 1}
0.907 +/- 0.03 {'classifiers': (KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=1, p=2,
           weights='uniform'), KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=1, p=2,
           weights='uniform')), 'kneighborsclassifier__n_neighbors': 5}
0.933 +/- 0.02 {'classifiers': (RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
            oob_score=False, random_state=1, verbose=0, warm_start=False), GaussianNB(priors=None)), 'kneighborsclassifier__n_neighbors': 1}
0.933 +/- 0.02 {'classifiers': (RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
            oob_score=False, random_state=1, verbose=0, warm_start=False), GaussianNB(priors=None)), 'kneighborsclassifier__n_neighbors': 5}
Best parameters: {'classifiers': (RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
            oob_score=False, random_state=1, verbose=0, warm_start=False), GaussianNB(priors=None)), 'kneighborsclassifier__n_neighbors': 1}
Accuracy: 0.93

列子4-針對不同整體選用不同模型

from sklearn.datasets import load_iris
from mlxtend.classifier import StackingClassifier
from mlxtend.feature_selection import ColumnSelector
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.linear_model import LogisticRegression

iris = load_iris()
X = iris.data
y = iris.target

pipe1 = make_pipeline(ColumnSelector(cols=(0, 2)),
                      LogisticRegression())
pipe2 = make_pipeline(ColumnSelector(cols=(1, 2, 3)),
                      LogisticRegression())

sclf = StackingClassifier(classifiers=[pipe1, pipe2], 
                          meta_classifier=LogisticRegression())

sclf.fit(X, y)

StackingClassifier(average_probas=False,
          classifiers=[Pipeline(memory=None,
     steps=[('columnselector', ColumnSelector(cols=(0, 2), drop_axis=False)), ('logisticregression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          pen...='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False))])],
          meta_classifier=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False),
          store_train_meta_features=False, use_clones=True,
          use_features_in_secondary=False, use_probas=False, verbose=0)

StackingClassifier(average_probas=False,
          classifiers=[Pipeline(steps=[('columnselector', ColumnSelector(cols=(0, 2))), ('logisticregression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False))])],
          meta_classifier=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False),
          use_features_in_secondary=False, use_probas=False, verbose=0)

StackingClassifier(average_probas=False,
          classifiers=[Pipeline(memory=None,
     steps=[('columnselector', ColumnSelector(cols=(0, 2), drop_axis=False)), ('logisticregression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False))])],
          meta_classifier=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False),
          store_train_meta_features=False, use_clones=True,
          use_features_in_secondary=False, use_probas=False, verbose=0)

StackingCVClassifier

在一般應用當中，常常需要將數據集劃分幾份，即採用K折樣本劃分的方式。下面是對k折交叉驗證的方式的說明
算法圖解

算法僞代碼

例子1 簡單stackingCV 分類

from sklearn import datasets

iris = datasets.load_iris()
X, y = iris.data[:, 1:3], iris.target

from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB 
from sklearn.ensemble import RandomForestClassifier
from mlxtend.classifier import StackingCVClassifier
import numpy as np

RANDOM_SEED = 42

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=RANDOM_SEED)
clf3 = GaussianNB()
lr = LogisticRegression()

# The StackingCVClassifier uses scikit-learn's check_cv
# internally, which doesn't support a random seed. Thus
# NumPy's random seed need to be specified explicitely for
# deterministic behavior
np.random.seed(RANDOM_SEED)
sclf = StackingCVClassifier(classifiers=[clf1, clf2, clf3], 
                            meta_classifier=lr)

print('3-fold cross validation:\n')

for clf, label in zip([clf1, clf2, clf3, sclf], 
                      ['KNN', 
                       'Random Forest', 
                       'Naive Bayes',
                       'StackingClassifier']):

    scores = model_selection.cross_val_score(clf, X, y, 
                                              cv=3, scoring='accuracy')
    print("Accuracy: %0.2f (+/- %0.2f) [%s]" 
          % (scores.mean(), scores.std(), label))

3-fold cross validation:

Accuracy: 0.91 (+/- 0.01) [KNN]
Accuracy: 0.90 (+/- 0.03) [Random Forest]
Accuracy: 0.92 (+/- 0.03) [Naive Bayes]
Accuracy: 0.93 (+/- 0.02) [StackingClassifier]

import matplotlib.pyplot as plt
from mlxtend.plotting import plot_decision_regions
import matplotlib.gridspec as gridspec
import itertools

gs = gridspec.GridSpec(2, 2)

fig = plt.figure(figsize=(10,8))

for clf, lab, grd in zip([clf1, clf2, clf3, sclf], 
                         ['KNN', 
                          'Random Forest', 
                          'Naive Bayes',
                          'StackingCVClassifier'],
                          itertools.product([0, 1], repeat=2)):

    clf.fit(X, y)
    ax = plt.subplot(gs[grd[0], grd[1]])
    fig = plot_decision_regions(X=X, y=y, clf=clf)
    plt.title(lab)
plt.show()

在這裏插入圖片描述

例子2 sub-model的輸出是probabilities

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()

# The StackingCVClassifier uses scikit-learn's check_cv
# internally, which doesn't support a random seed. Thus
# NumPy's random seed need to be specified explicitely for
# deterministic behavior
np.random.seed(RANDOM_SEED)
sclf = StackingCVClassifier(classifiers=[clf1, clf2, clf3],
                            use_probas=True,
                            meta_classifier=lr)

print('3-fold cross validation:\n')

for clf, label in zip([clf1, clf2, clf3, sclf], 
                      ['KNN', 
                       'Random Forest', 
                       'Naive Bayes',
                       'StackingClassifier']):

    scores = model_selection.cross_val_score(clf, X, y, 
                                              cv=3, scoring='accuracy')
    print("Accuracy: %0.2f (+/- %0.2f) [%s]" 
          % (scores.mean(), scores.std(), label))

3-fold cross validation:

Accuracy: 0.91 (+/- 0.01) [KNN]
Accuracy: 0.91 (+/- 0.06) [Random Forest]
Accuracy: 0.92 (+/- 0.03) [Naive Bayes]
Accuracy: 0.95 (+/- 0.04) [StackingClassifier]

例子3 結合GridSearch

結合sklearn中GridSearchCV功能。
param中的sub-model分類器參數名稱，“sklearn分類器名稱__參數”。例如’kneighborsclassifier__n_neighbors’。
meta-model參數名稱，“meta-sklearn分類器名稱__參數”。例如’meta-logisticregression__C’。

from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB 
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from mlxtend.classifier import StackingCVClassifier

# Initializing models

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=RANDOM_SEED)
clf3 = GaussianNB()
lr = LogisticRegression()

# The StackingCVClassifier uses scikit-learn's check_cv
# internally, which doesn't support a random seed. Thus
# NumPy's random seed need to be specified explicitely for
# deterministic behavior
np.random.seed(RANDOM_SEED)
sclf = StackingCVClassifier(classifiers=[clf1, clf2, clf3], 
                            meta_classifier=lr)

params = {'kneighborsclassifier__n_neighbors': [1, 5],
          'randomforestclassifier__n_estimators': [10, 50],
          'meta-logisticregression__C': [0.1, 10.0]}

grid = GridSearchCV(estimator=sclf, 
                    param_grid=params, 
                    cv=5,
                    refit=True)
grid.fit(X, y)

cv_keys = ('mean_test_score', 'std_test_score', 'params')

for r, _ in enumerate(grid.cv_results_['mean_test_score']):
    print("%0.3f +/- %0.2f %r"
          % (grid.cv_results_[cv_keys[0]][r],
             grid.cv_results_[cv_keys[1]][r] / 2.0,
             grid.cv_results_[cv_keys[2]][r]))

print('Best parameters: %s' % grid.best_params_)
print('Accuracy: %.2f' % grid.best_score_)

0.673 +/- 0.01 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 10}
0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 50}
0.920 +/- 0.02 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 10}
0.893 +/- 0.02 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 50}
0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 10}
0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 50}
0.947 +/- 0.02 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 10}
0.947 +/- 0.02 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 50}
Best parameters: {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 10}
Accuracy: 0.95

例子4 在不同特徵子集上用分類算法

from sklearn.datasets import load_iris
from mlxtend.classifier import StackingCVClassifier
from mlxtend.feature_selection import ColumnSelector
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression

iris = load_iris()
X = iris.data
y = iris.target

pipe1 = make_pipeline(ColumnSelector(cols=(0, 2)),
                      LogisticRegression())
pipe2 = make_pipeline(ColumnSelector(cols=(1, 2, 3)),
                      LogisticRegression())

sclf = StackingCVClassifier(classifiers=[pipe1, pipe2], 
                            meta_classifier=LogisticRegression())

sclf.fit(X, y)

StackingCVClassifier(classifiers=[Pipeline(memory=None,
     steps=[('columnselector', ColumnSelector(cols=(0, 2), drop_axis=False)), ('logisticregression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          pen...='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False))])],
           cv=2,
           meta_classifier=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False),
           shuffle=True, store_train_meta_features=False, stratify=True,
           use_clones=True, use_features_in_secondary=False,
           use_probas=False, verbose=0)

sclf = StackingCVClassifier(classifiers=[Pipeline(steps=[('columnselector', ColumnSelector(cols=(0, 2))), ('logisticregression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False))])],
           cv=2,
           meta_classifier=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False),
           shuffle=True, stratify=True, use_features_in_secondary=False,
           use_probas=False, verbose=0)

scores = model_selection.cross_val_score(sclf, X, y, 
                                              cv=3, scoring='accuracy')
print(scores.mean())

0.920343137255

該庫是機器學習的輔助工具包，提供機器學習流程的豐富功能，待深入瞭解。同時，上面涉及的兩個方法的具體各個參數，可以查看該庫的官網。

參考來源：

  1. StackingClassifier
  2. StackingClassifierCV
發表評論
所有評論
還沒有人評論，想成為第一個評論的人麼? 請在上方評論欄輸入並且點擊發布.
相關文章

HTML頁面關於高分屏的設置

記錄一個HTML頁面關於高分屏的踩到的坑。 所謂高分屏，就是在同樣大小的屏幕面積上顯示更多的像素點，這樣可以呈現更好的可視效果的屏幕。例如，我的筆記本是15.6寸，理論上它的屏幕分辨率應該是1920 x 1080像素，但實際上我的筆記本屏幕

harlee44 2024-05-04 14:31:10

DAPPER 事務 TRANSACTION

https://www.cnblogs.com/friend/p/16754184.html\   public async Task<int> Save(long moldProducedProductId, List<MoldStan

老飛飛 2024-05-04 14:29:20

[MDP.AspNetCore] 實作OAuth協定SSO Server/Client專案範例

團隊負責的系統變多的時候，使用SSO Server提供統一身分驗證，讓團隊只需要維護一份用戶資料及一個身分驗證服務。除了減少團隊維護成本之外，也讓使用者不用記憶多個站臺的帳號密碼，提供更好的使用者體驗。 本篇文章，介紹使用MDP.AspNe

Clark159 2024-05-04 14:23:49

Redis官方開源的可視化管理工具 - RedisInsight

前言 今天大姚給大家推薦一款Redis官方開源的可視化管理工具：RedisInsight。 Redis介紹 Redis (Remote Dictionary Server) 是一個使用 C 語言編寫的，開源的 (遵守 BSD 協議) 高性

追逐時光 2024-05-04 14:21:49

Python 潮流週刊#49：谷歌裁員 Python 團隊，微軟開源 MS-DOS 4.0

本週刊由 Python貓 出品，精心篩選國內外的 250+ 信息源，爲你挑選最值得分享的文章、教程、開源項目、軟件工具、播客和視頻、熱門話題等內容。願景：幫助所有讀者精進 Python 技術，並增長職業和副業的收入。 本期週刊分享了 12

豌豆花下貓 2024-05-04 14:19:09

HarmonyOS 垂直方向內容滾動條實現

概述 Swiper組件是一個用戶界面元素，用於在垂直方向上滾動內容。它通過遍歷一個數據集合，爲每一項創建一個可滾動的文本項。 代碼實現 以下是Swiper組件的實現代碼： Swiper(){ ForEach(searchSwiper,

西北野狼 2024-05-04 14:05:08

基於SSM的倉庫進銷存系統畢業設計論文【範文】

摘要 隨着信息技術的不斷髮展，企業對於倉儲管理的要求日益提高。爲了提升倉庫管理的自動化和智能化水平，本研究設計並實現了一個基於Spring、Spring MVC和MyBatis (SSM) 框架的在倉庫進銷存系統。該系統旨在爲企業提供一個高

Lucky帥小武 2024-05-04 14:03:17

基於SSM的在線考試系統畢業設計論文【範文】

摘要 隨着信息技術的飛速發展，網絡教學逐漸成爲教育行業的重要組成部分。在線考試系統作爲網絡教學平臺的關鍵模塊之一，其便捷性、高效性和公正性受到廣泛關注，基於SSM框架的在線考試系統旨在提供一個穩定、可靠並且易於維護的在線考試環境，以滿足現代

Lucky帥小武 2024-05-04 14:03:17

CSS & JS Effect – 用 wheel 模擬 scroll

前言 在 用 JavaScript 實現 position sticky 文章中，我提到了用 wheel 來模擬 scroll 效果。 這篇來說說具體怎麼實現，挺簡單的哦。   Preparation table.html <div c

興傑 2024-05-04 13:59:16

python包：torchsummary

利用torchsummary觀察每一層的情況   1）按照方式    pip install torchsummary     2）

wenluderen 2024-05-04 13:56:56

Windows使用WSL2及docker(Ubuntu22.04 LTS)

WSL2初始化 1.換源 #1 cp /etc/apt/sources.list /etc/apt/sources.list.bak #2 vim /etc/apt/sources.list # 清空原源並替換成以下源 # deb-src

臨冬城城主 2024-05-04 13:52:56

學習Mysql 你應該懂得

  1、日誌系統：平時在設計系統時可以借鑑一下 參考下面文章 https://www.cnblogs.com/ScarecrowAnBird/p/18163444 2、索引：提高性能利器   3、鎖：提高併發能力小絕招 https://ww

落葉已歸根 2024-05-04 13:45:25

mysql 鎖，和加鎖機制

背景間隙鎖是MySQL在RR可重複讀隔離級別下用來修復幻讀才引入的一種鎖，間隙鎖也只有在RR可重複讀隔離級別下才會存在，如果是在RC讀已提交隔離級別下，是沒有間隙鎖的存在的。另外，我們也知道，幻讀這種現象也只有在當前讀的時候纔會發生，在一致

落葉已歸根 2024-05-04 13:45:25

深入 Django 模型層：數據庫設計與 ORM 實踐指南

title: 深入 Django 模型層：數據庫設計與 ORM 實踐指南 date: 2024/5/3 18:25:33 updated: 2024/5/3 18:25:33 categories: 後端開發 tags: Djang

Mifen 2024-05-04 13:38:24

[轉帖]Introducing Exadata Cloud@Customer X10M

https://blogs.oracle.com/database/post/introducing-exadata-cloudcustomer-x10m     Extreme Scale with Dramatically

濟南小老虎 2024-05-04 13:29:33