Python sklearn.cross_validation.train_test_split及混淆矩阵实现

sklearn.cross_validation.train_test_split随机划分训练集和测试集

一般形式：
train_test_split是交叉验证中常用的函数，功能是从样本中随机的按比例选取train data和testdata，形式为：

X_train,X_test, y_train, y_test =

cross_validation.train_test_split(train_data,train_target,test_size=0.4, random_state=0)

参数解释：

train_data：所要划分的样本特征集
train_target：所要划分的样本结果  
test_size：样本占比，如果是整数的话就是样本的数量
random_state：是随机数的种子。

随机数种子：其实就是该组随机数的编号，在需要重复试验的时候，保证得到一组一样的随机数。比如你每次都填1，其他参数一样的情况下你得到的随机数组是一样的。但填0或不填，每次都会不一样。随机数的产生取决于种子，随机数和种子之间的关系遵从以下两个规则：种子不同，产生不同的随机数；种子相同，即使实例不同也产生相同的随机数。
示例

fromsklearn.cross_validation import train_test_split
train= loan_data.iloc[0: 55596, :]
test= loan_data.iloc[55596:, :]
# 避免过拟合，采用交叉验证，验证集占训练集20%，固定随机种子（random_state)
train_X,test_X, train_y, test_y = train_test_split(train,
                                                   target,
                                                   test_size = 0.2,
                                                   random_state = 0)
train_y= train_y['label']
test_y= test_y['label']

plot_confusion_matrix.py（混淆矩阵实现实例）

print(__doc__)

import numpy as np
import matplotlib.pyplot as plt

from sklearn import svm, datasets
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix

# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)


def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(iris.target_names))
    plt.xticks(tick_marks, iris.target_names, rotation=45)
    plt.yticks(tick_marks, iris.target_names)
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')


# Compute confusion matrix
cm = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)
print('Confusion matrix, without normalization')
print(cm)
plt.figure()
plot_confusion_matrix(cm)

# Normalize the confusion matrix by row (i.e by the number of samples
# in each class)
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print('Normalized confusion matrix')
print(cm_normalized)
plt.figure()
plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')

plt.show()