Exercise1

ML_1.py

@@ -0,0 +1,97 @@
+import numpy as np
+import torch
+from torch.autograd import Variable
+import pandas as pd
+
+#过采样函数
+def oversampling(data):
+    data0 = data[data[:,0]==0]
+    data1 = data[data[:,0]==1]#0、1分开
+    size0 = data0.shape[0]
+    size1 = data1.shape[0]
+    if size0 >= size1:#多的减少的
+        index= np.random.choice(size1,size0-size1,replace=False,p=None)#随机采样
+        data1 = np.concatenate((data1, data1[index,:]), axis=0)#合并
+    else:
+        index= np.random.choice(size0,size1-size0,replace=False,p=None)#随机采样
+        data0 = np.concatenate((data0, data0[index,:]), axis=0)#合并
+    x = np.concatenate((data0[:, 1:31], data1[:, 1:31]), axis=0)#合并
+    y = np.concatenate((data0[:, 0:1], data1[:, 0:1]), axis=0)#合并
+    return x, y
+
+#对数据进行预处理
+def pre_process(dataset):
+   data = dataset.iloc[:, 1:32].values
+   data[data=='M'] = 1
+   data[data=='B'] = 0      #MB分类
+   data = data.astype(np.float64)
+   x,y = oversampling(data)
+   size = x.shape[0]
+   index = np.random.permutation(size)       #随机排列
+   x = x[index]
+   y = y[index]
+   mu = np.mean(x, axis=0)
+   sigma = np.std(x, axis=0)
+   x=(x - mu)/sigma
+   split = int(len(y)*0.3)       #0.3
+   x_train = x[:split]
+   y_train = y[:split]
+   x_test = x[split:]
+   y_test = y[split:]
+   return x_train, y_train, x_test, y_test
+
+#精确度计算
+def cal_acc(x, y, y_pred):
+    size = x.shape[0]
+    y_pred = np.array([0 if y_pred[i] < 0.5 else 1 for i in range(size)])
+    acc = np.mean([1 if y[i] == y_pred[i] else 0 for i in range(size)])
+    return acc
+
+#模型
+class Model(torch.nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.linear = torch.nn.Linear(30, 1) 
+
+    def forward(self, x):
+        y_pred = torch.sigmoid(self.linear(x))
+        return y_pred
+
+#主体
+dataset= pd.read_csv('./data.csv')
+x_train, y_train, x_test, y_test = pre_process(dataset)#处理
+x_train = Variable(torch.Tensor(x_train))
+y_train = Variable(torch.Tensor(y_train))
+x_test = Variable(torch.Tensor(x_test))
+y_test = Variable(torch.Tensor(y_test))
+
+model = Model()
+criterion = torch.nn.BCELoss(reduction="mean")#损失（二分类交叉熵）
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)#优化器
+
+#进行循环训练
+i=50    #隔50个打印
+j=1    #计数器
+print('每',i,'个epoch一组')
+
+for epoch in range(1000):     #1000个
+    optimizer.zero_grad()
+    y_pred = model(x_train)
+    loss = criterion(y_pred, y_train)
+    loss.backward()    #反向传播
+    optimizer.step()    #更新优化器
+    if epoch % i == 0:    
+        print('第',j,'组','损失loss:', loss.data.item(), '精确度accuracy:', cal_acc(x_train, y_train, y_pred))
+        j=j+1
+
+y_test_pred = model(x_test)
+test_acc = cal_acc(x_test, y_test, y_test_pred)
+print('Test总精确度:', test_acc)
+
+for f in model.parameters():
+    print('具体数据：')
+    print(f.data)
+    print(f.grad)
+
+
+

README.md

@@ -1,3 +1,5 @@
-# SES2020spring
+# Exercise 1
 
-## Another day
+# The code is in ML_1.py, please let data.csv and ML_1.py in the same file.
+
+# A result for reference only is shown in figure named result.