sklearn_模型遍历

# _*_ coding = utf_8 _*_
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.metrics import accuracy_score, log_loss
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression
# from sklearn.cluster import KMeans

classifiers = [
    KNeighborsClassifier(3),
    SVC(probability=True),
    DecisionTreeClassifier(),
    RandomForestClassifier(),
    AdaBoostClassifier(),
    GradientBoostingClassifier(),
    GaussianNB(),
    LinearDiscriminantAnalysis(),
    QuadraticDiscriminantAnalysis(),
    LogisticRegression()]

log_cols = ["Classifier", "Accuracy"]
log      = pd.DataFrame(columns=log_cols)

sss = StratifiedShuffleSplit(n_splits=10, test_size=0.1, random_state=0)
# sss对象用于划分数据集
import sklearn.datasets as datasets
X,y=datasets.make_blobs(100,centers=10)

print(X.shape)
# X为特征集

# y为Label集

acc_dict = {}

for train_index, test_index in sss.split(X, y):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]

    for clf in classifiers:
        name = clf.__class__.__name__
        clf.fit(X_train, y_train)
        train_predictions = clf.predict(X_test)
        acc = accuracy_score(y_test, train_predictions)
        if name in acc_dict:
            acc_dict[name] += acc
        else:
            acc_dict[name] = acc
print(acc_dict)
for clf in acc_dict:
    acc_dict[clf] = acc_dict[clf] / 10.0
    # 计算平均准确率
    log_entry = pd.DataFrame([[clf, acc_dict[clf]]], columns=log_cols)
    log = log.append(log_entry)

plt.xlabel('Accuracy')
plt.title('Classifier Accuracy')

sns.set_color_codes("muted")
sns.barplot(x='Accuracy', y='Classifier', data=log, color="b")
# 画条形图分析
plt.show()