Question

我有一个脚本，可以随机生成一组数据并训练几个分类器，将它们相互比较（它与http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html非常相似）：

from itertools import product

import numpy as np

from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

from sklearn.datasets import make_classification
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split

names = ["Linear SVM", "Decision Tree",
     "Random Forest", "AdaBoost", "Naive Bayes", "Linear Discriminant Analysis",
     "Quadratic Discriminant Analysis"]

def griddy_mcsearchface(num_samples, num_feats, num_feats_to_remove): 
    classifiers = [
        SVC(kernel="linear", C=0.025),
        DecisionTreeClassifier(max_depth=5),
        RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
        AdaBoostClassifier(), GaussianNB(),
        LinearDiscriminantAnalysis(),
        QuadraticDiscriminantAnalysis()]

    classifiers2 = [
        SVC(kernel="linear", C=0.025),
        DecisionTreeClassifier(max_depth=5),
        RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
        AdaBoostClassifier(), GaussianNB(),
        LinearDiscriminantAnalysis(),
        QuadraticDiscriminantAnalysis()]

    X, y = make_classification(n_samples=num_samples, n_features=num_feats, n_redundant=0, n_informative=2,
                           random_state=1, n_clusters_per_class=1)
    X = StandardScaler().fit_transform(X)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)

    for name, clf, clf2 in zip(names, classifiers, classifiers2):
        clf.fit(X_train, y_train)
        score = clf.score(X_test, y_test)
        # Remove 40% of the features.
        clf2.fit(X_train[:,:-num_feats_to_remove], y_train)
        score2 = clf2.score(X_test[:,:-num_feats_to_remove], y_test)
        yield (num_samples, num_feats, num_feats_to_remove, name, score, score2)

并运行它：

_samples = [100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000]
_feats = [10, 20, 50, 100, 200, 500, 10000]
_feats_to_rm = [5, 10, 25, 50, 100, 250]
for num_samples, num_feats, num_feats_to_remove in product(_samples, _feats, _feats_to_rm):
    if num_feats <= num_feats_to_remove:
        continue
    for i in griddy_mcsearchface(num_samples, num_feats, num_feats_to_remove):
        print (i)

脚本输出如下内容：

(100, 10, 5, 'Linear SVM', 1.0, 0.40000000000000002)
(100, 10, 5, 'Decision Tree', 1.0, 0.65000000000000002)
(100, 10, 5, 'Random Forest', 1.0, 0.90000000000000002)
(100, 10, 5, 'AdaBoost', 1.0, 0.65000000000000002)
(100, 10, 5, 'Naive Bayes', 1.0, 0.75)
(100, 10, 5, 'Linear Discriminant Analysis', 1.0, 0.40000000000000002)
(100, 10, 5, 'Quadratic Discriminant Analysis', 1.0, 0.84999999999999998)
(100, 20, 5, 'Linear SVM', 1.0, 1.0)
(100, 20, 5, 'Decision Tree', 0.94999999999999996, 0.94999999999999996)
(100, 20, 5, 'Random Forest', 0.80000000000000004, 0.75)
(100, 20, 5, 'AdaBoost', 1.0, 0.94999999999999996)
(100, 20, 5, 'Naive Bayes', 1.0, 1.0)
(100, 20, 5, 'Linear Discriminant Analysis', 1.0, 1.0)
(100, 20, 5, 'Quadratic Discriminant Analysis', 0.84999999999999998, 0.94999999999999996)
(100, 20, 10, 'Linear SVM', 0.94999999999999996, 0.65000000000000002)
(100, 20, 10, 'Decision Tree', 0.94999999999999996, 0.59999999999999998)
(100, 20, 10, 'Random Forest', 0.75, 0.69999999999999996)
(100, 20, 10, 'AdaBoost', 0.94999999999999996, 0.69999999999999996)
(100, 20, 10, 'Naive Bayes', 0.94999999999999996, 0.75)

但clf.fit()现在是单线程的。

假设我有足够的线程来为每次迭代运行所有分类器，我如何能够为for num_samples, num_feats, num_feats_to_remove in product(_samples, _feats, _feats_to_rm)的每次迭代使用不同的线程训练分类器？

如果我被限制为4或8个线程，但我需要为每次迭代训练＆gt; 4或＆gt; 8个分类器，它是如何完成的？

Answer 1

这不是一个答案，更多的是对第一个问题的答案粗略草图，

我如何能够使用不同的线程训练分类器 magistrate_id

我假设您的意思是，for every iteration of for num_samples, num_feats, num_feats_to_remove in product(_samples, _feats, _feats_to_rm)的每次迭代都需要for name, clf, clf2 in zip(names, classifiers, classifiers2):和clf在不同的处理器上进行培训。

以下是一些工作代码作为起点（实施起来很差，但总体思路是存在的）：

clf2

如果我误解了你的问题，那么上面代码中的一般原则可以根据你的需要进行修改。我从上面代码中接受的答案here中提取。

每次迭代在多个CPU上训练不同的scikit-learn分类器

1 个答案: