Create lr_test.py

Author: zhaozhiyong19890102

Chapter_1 Logistic Regression/lr_test.py

@@ -0,0 +1,90 @@
+# coding:UTF-8
+'''
+Date:20160901
+@author: zhaozhiyong
+'''
+import numpy as np
+from lr_train import sig
+
+def load_weight(w):
+    '''导入LR模型
+    input:  w(string)权重所在的文件位置
+    output: np.mat(w)(mat)权重的矩阵
+    '''
+    f = open(w)
+    w = []
+    for line in f.readlines():
+        lines = line.strip().split("\t")
+        w_tmp = []
+        for x in lines:
+            w_tmp.append(float(x))
+        w.append(w_tmp)    
+    f.close()
+    return np.mat(w)
+
+def load_data(file_name, n):
+    '''导入测试数据
+    input:  file_name(string)测试集的位置
+            n(int)特征的个数
+    output: np.mat(feature_data)(mat)测试集的特征
+    '''
+    f = open(file_name)
+    feature_data = []
+    for line in f.readlines():
+        feature_tmp = []
+        lines = line.strip().split("\t")
+        # print lines[2]
+        if len(lines) <> n - 1:
+            continue
+        feature_tmp.append(1)
+        for x in lines:
+            # print x
+            feature_tmp.append(float(x))
+        feature_data.append(feature_tmp)
+    f.close()
+    return np.mat(feature_data)
+
+def predict(data, w):
+    '''对测试数据进行预测
+    input:  data(mat)测试数据的特征
+            w(mat)模型的参数
+    output: h(mat)最终的预测结果
+    '''
+    h = sig(data * w.T)#sig
+    m = np.shape(h)[0]
+    for i in xrange(m):
+        if h[i, 0] < 0.5:
+            h[i, 0] = 0.0
+        else:
+            h[i, 0] = 1.0
+    return h
+
+def save_result(file_name, result):
+    '''保存最终的预测结果
+    input:  file_name(string):预测结果保存的文件名
+            result(mat):预测的结果
+    '''
+    m = np.shape(result)[0]
+    #输出预测结果到文件
+    tmp = []
+    for i in xrange(m):
+        tmp.append(str(h[i, 0]))
+    f_result = open(file_name, "w")
+    f_result.write("\t".join(tmp))
+    f_result.close()    
+
+if __name__ == "__main__":
+    # 1、导入LR模型
+    print "---------- 1.load model ------------"
+    w = load_weight("weights")
+    n = np.shape(w)[1]
+    # 2、导入测试数据
+    print "---------- 2.load data ------------"
+    testData = load_data("test_data", n)
+    # 3、对测试数据进行预测
+    print "---------- 3.get prediction ------------"
+    h = predict(testData, w)#进行预测
+    # 4、保存最终的预测结果
+    print "---------- 4.save prediction ------------"
+    save_result("result", h)
+