python - 如何绘制多模型的计算时间？

Question

我想比较使用 bar 或其他东西的多个模型的计算时间。所以，我需要知道哪一个是最快的模型，也是最慢的一个，很容易使用数字而不是数字。

来自这里的完整代码：

from pandas import read_csv
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
import time
# Load dataset
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = read_csv(url, names=names)
# Split-out validation dataset
array = dataset.values
X = array[:,0:4]
y = array[:,4]
X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1, shuffle=True)
# Spot Check Algorithms
models = []
models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC(gamma='auto')))
# evaluate each model in turn
results = []
names = []
time_model = []
for name, model in models:
    start = time.time()
    kfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)
    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')
    results.append(cv_results)
    com_time = time.time() - start
    time_model.append(com_time)
    names.append(name)


    print('%s: %f (%f) '  % (name, cv_results.mean(), cv_results.std()))
    print ('time', time.time() - start)
    # print  time.mean
# Compare Algorithms
# pyplot.boxplot(results, labels=names)
# pyplot.title('Algorithm Comparison')
# pyplot.show()


# print time_model, names
pyplot.figure()
pyplot.title('Algorithm Comparison')
pyplot.bar(time_model, labels=names)

pyplot.show()

怎么做才能和下图这个图一样，顺序一样（升序）？

标签： pythonmatplotlibtimescikit-learn