random_state = np.random.RandomState(0)什么意思
时间: 2024-06-06 11:10:11 浏览: 11
这行代码是用来创建一个随机数生成器对象,其中参数0表示随机数种子。np.random.RandomState是numpy库中的一个随机数生成器类,它可以用于生成伪随机数序列。当使用相同的种子时,生成的随机数序列是相同的,这可以用于实验的可重复性。换句话说,如果你在一个程序中设置了相同的随机数种子,那么每次运行这个程序时,生成的随机数序列将是相同的。
相关问题
翻译这段程序并自行赋值调用:import matplotlib.pyplot as plt import numpy as np import sklearn import sklearn.datasets import sklearn.linear_model def plot_decision_boundary(model, X, y): # Set min and max values and give it some padding x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1 y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1 h = 0.01 # Generate a grid of points with distance h between them xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Predict the function value for the whole grid Z = model(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # Plot the contour and training examples plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) plt.ylabel('x2') plt.xlabel('x1') plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral) def sigmoid(x): s = 1/(1+np.exp(-x)) return s def load_planar_dataset(): np.random.seed(1) m = 400 # number of examples N = int(m/2) # number of points per class print(np.random.randn(N)) D = 2 # dimensionality X = np.zeros((m,D)) # data matrix where each row is a single example Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue) a = 4 # maximum ray of the flower for j in range(2): ix = range(Nj,N(j+1)) t = np.linspace(j3.12,(j+1)3.12,N) + np.random.randn(N)0.2 # theta r = anp.sin(4t) + np.random.randn(N)0.2 # radius X[ix] = np.c_[rnp.sin(t), rnp.cos(t)] Y[ix] = j X = X.T Y = Y.T return X, Y def load_extra_datasets(): N = 200 noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3) noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2) blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6) gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None) no_structure = np.random.rand(N, 2), np.random.rand(N, 2) return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure
这段程序是一个分类模型的辅助函数,包括了绘制决策边界、sigmoid函数和加载数据集的函数。具体实现如下:
```python
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import sklearn.datasets
import sklearn.linear_model
def plot_decision_boundary(model, X, y):
# 设置最小值和最大值,并给它们一些填充
x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
h = 0.01
# 生成一个网格,网格中点的距离为h
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# 对整个网格预测函数值
Z = model(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# 绘制轮廓和训练样本
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
plt.ylabel('x2')
plt.xlabel('x1')
plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)
def sigmoid(x):
s = 1 / (1 + np.exp(-x))
return s
def load_planar_dataset():
np.random.seed(1)
m = 400 # 样本数量
N = int(m / 2) # 每个类的样本数量
# 生成数据集
D = 2 # 特征维度
X = np.zeros((m, D)) # 特征矩阵
Y = np.zeros((m, 1), dtype='uint8') # 标签向量
a = 4 # 花的最大半径
for j in range(2):
ix = range(N*j, N*(j+1))
t = np.linspace(j*3.12, (j+1)*3.12, N) + np.random.randn(N)*0.2 # theta
r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius
X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
Y[ix] = j
X = X.T
Y = Y.T
return X, Y
def load_extra_datasets():
N = 200
noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3)
noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2)
blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6)
gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None)
no_structure = np.random.rand(N, 2), np.random.rand(N, 2)
return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure
```
这段程序中包含了以下函数:
- `plot_decision_boundary(model, X, y)`:绘制分类模型的决策边界,其中`model`是分类模型,`X`是特征矩阵,`y`是标签向量。
- `sigmoid(x)`:实现sigmoid函数。
- `load_planar_dataset()`:加载一个二维的花瓣数据集。
- `load_extra_datasets()`:加载五个其他数据集。
优化这段代码train_aucs=[] test_aucs=[]#train_aucs和test_aucs用来存储每次训练和测试的AUC值,AUC是一种常用的二分类模型性能评估指标 train_scores=[] test_scores=[]#train_scores和test_scores则是用来存储每次训练和测试的得分 loopn=5 #number of repetition while splitting train/test dataset with different random state. np.random.seed(10)#设置随机数生成器的种子,确保每次运行时生成的随机数一致。 random_states=np.random.choice(range(101), loopn, replace=False)#np.random.choice()用于从给定的范围内选择指定数量的随机数,range设置范围,loopn表示选择的随机数的数量,replace=False表示选择的随机数不可重复 scoring='f1'#设置性能指标 pca_comp=[]#设置空列表,储主成分分析(PCA)的组件 for i in range(loopn): train_X,test_X, train_y, test_y ,indices_train,indices_test= train_test_split(train, #通过train_test_split函数将数据集划分为训练集(train_X, train_y)和测试集(test_X, test_y),indices_train和indices_test返回索引 target,indices, test_size = 0.3,#数据集的70%,测试集占30% stratify=target, random_state=random_states[i]#随机状态(random_states[i])添加到random_states列表中 ) print("train_x.shpae:") print(train_X.shape) standardScaler = StandardScaler() standardScaler.fit(train_X) X_standard = standardScaler.transform(train_X) X_standard_test = standardScaler.transform(test_X) #calculate max n_components estimator = PCA(n_components=0.99,random_state=42) pca_X_train = estimator.fit_transform(X_standard) n_components=range(10,min(pca_X_train.shape),10) print(n_components) best_pca_train_aucs=[] best_pca_test_aucs=[] best_pca_train_scores=[] best_pca_test_scores=[]
下面是对代码进行优化的一些建议:
1. 使用列表推导式来简化train_aucs、test_aucs、train_scores和test_scores的初始化过程:
```python
train_aucs, test_aucs, train_scores, test_scores = [], [], [], []
```
2. 将循环次数和随机状态数量作为变量,使得代码更具可读性和可维护性:
```python
num_repetitions = 5
random_states = np.random.choice(range(101), num_repetitions, replace=False)
```
3. 使用enumerate函数来遍历循环中的索引和元素,消除对indices_train和indices_test的显式赋值:
```python
for i, random_state in enumerate(random_states):
train_X, test_X, train_y, test_y, _, _ = train_test_split(train, target, indices,
test_size=0.3,
stratify=target,
random_state=random_state)
# 其他代码...
```
4. 将数据标准化和PCA拟合与转换的过程合并为一步,减少冗余代码:
```python
standardScaler = StandardScaler()
X_standard = standardScaler.fit_transform(train_X)
X_standard_test = standardScaler.transform(test_X)
estimator = PCA(n_components=0.99, random_state=42)
pca_X_train = estimator.fit_transform(X_standard)
n_components = range(10, min(pca_X_train.shape), 10)
```
5. 使用空列表初始化变量的过程可以简化为一行代码:
```python
best_pca_train_aucs, best_pca_test_aucs, best_pca_train_scores, best_pca_test_scores = [], [], [], []
```
通过以上优化,可以使代码更加简洁和可读。请根据你的实际需要进行调整和修改。