请提供一段GAIN算法的代码
时间: 2023-03-21 11:03:47 浏览: 65
以下是一段基于GAIN算法的代码示例:
```python
# 导入相关库
import numpy as np
import pandas as pd
from fancyimpute import BiScaler, KNN, SoftImpute, IterativeImputer
# 定义GAIN算法类
class GAIN:
def __init__(self, data, miss_rate):
self.data = data
self.miss_rate = miss_rate
self.shape = data.shape
self.M = np.isnan(data) # 缺失值掩码
self.D = np.ones_like(data) - self.M # 数据完整值掩码
self.normalize_data()
def normalize_data(self):
scaler = BiScaler()
self.data = scaler.fit_transform(self.data)
def impute(self, model):
X = self.data.copy()
X[self.M] = np.nan
imputed = model.fit_transform(X)
imputed = self.data * self.M + imputed * self.D
return imputed
def gain(self, model, alpha=100, iterations=1000):
X = self.data.copy()
X[self.M] = np.nan
h = np.random.uniform(low=0.0, high=1.0, size=self.shape)
mask = np.random.binomial(n=1, p=1 - self.miss_rate, size=self.shape)
for i in range(iterations):
# Generator
h_prev = h
X[h == 0] = self.impute(model)[h == 0]
X_m = X * mask
H = np.concatenate((X_m, h_prev), axis=1)
Y_m = np.concatenate((self.data, np.zeros_like(self.data)), axis=1)
Y_m[self.M] = X_m[self.M]
model.fit(H, Y_m)
delta = model.predict(H) - Y_m
h = h_prev + alpha * (mask * delta - h_prev)
imputed = self.impute(model)
return imputed * (1 - self.M) + self.data * self.M
# 使用GAIN算法对数据进行填补
# 假设有一个10行5列的数据集,其中有10%的缺失值
data = np.random.normal(size=(10, 5))
missing_mask = np.random.binomial(n=1, p=0.1, size=(10, 5))
data[missing_mask == 1] = np.nan
# 创建GAIN对象并填补数据
gain = GAIN(data=data, miss_rate=0.1)
imputed_data = gain.gain(model=IterativeImputer())
```
这段代码可以用于基于GAIN算法对数据集进行缺失值填补。具体来说,它定义了一个GAIN类,该类在初始化时接收数据集和缺失率作为参数。它还实现了一个normalize_data方法,用于对数据进行规范化。然后,它提供了一个impute方法,该方法使用给定的模型填充缺失值。最后,它实现了一个gain方法,该方法使用GAIN算法对数据进行填充。