02. 对数几率回归

冷不防 2022-01-14 11:07 204阅读 0赞

[2019独角兽企业重金招聘Python工程师标准>>> ][2019_Python_] ![hot3.png][]

# 对数几率回归
    
    # 对数几率回归回答是或否的问题
    
    # sigmoid函数： 输入一个值，返回一个从0到1的值
    
    # 对于分类问题使用的损失函数时交叉熵， 交叉熵能够输出一个更大的损失值，从而使梯度下降法做出更大的优化
    
    
    import tensorflow as tf
    import numpy as np
    import pandas as pd
    
    data = pd.read_csv('./data/titanic/train.csv')
    
    # 列名
    print(data.columns)
    
    # 选出比较重要的数据
    data = data[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]
    
    # 对空值使用0填充
    data = data.fillna(0)
    
    # print(data)
    
    # 将string转为数值
    data['Sex'] = pd.factorize(data.Sex)[0]
    
    # Pclass值为1，2，3，会对机器学习造成困扰，进行独热编码，将三个等级转换为三维，去掉潜在的线性关系
    data['p1'] = np.array(data['Pclass'] == 1).astype(np.float32)
    data['p2'] = np.array(data['Pclass'] == 2).astype(np.float32)
    data['p3'] = np.array(data['Pclass'] == 3).astype(np.float32)
    del data['Pclass']
    # print(data)
    
    # Embarked独热编码
    data['e1'] = np.array(data['Embarked'] == 'S').astype(np.float32)
    data['e2'] = np.array(data['Embarked'] == 'C').astype(np.float32)
    data['e3'] = np.array(data['Embarked'] == 'Q').astype(np.float32)
    del data['Embarked']
    # print(data)
    
    # 将数据转换为np.array
    # np.stack: 把每一列的数据放到一个array中，如Sex列放入一个数组中
    data_data = np.stack(
        [data.Sex.values.astype(np.float32), data.Age.values.astype(np.float32), data.SibSp.values.astype(np.float32),
         data.Parch.values.astype(np.float32), data.Fare.values.astype(np.float32), data.p1.values,
         data.p2.values, data.p3.values, data.e1.values, data.e2.values, data.e3.values]).T
    
    # data.Survived是一个894长度的向量，需要变成894行一列，与特征值对应
    # print(np.shape(data_data))
    # print(np.shape(data.Survived))
    data_target = np.reshape(data.Survived.values.astype(np.float32), (891, 1))
    # print(np.shape(data_target))
    
    # 定义网络
    
    # 输入, 不设定一次放多少行，但一定时11列
    x = tf.placeholder('float', shape=[None, 11])
    # 输出
    y = tf.placeholder('float', shape=[None, 1])
    
    # 矩阵相乘，行数必须与前面的列数一样, weight输出值是一个标量值，所以时一列
    weight = tf.Variable(tf.random_normal([11, 1]))
    bias = tf.Variable(tf.random_normal([1]))
    
    # 定义输出： 矩阵相乘
    output = tf.matmul(x, weight) + bias
    
    # 推断: sigmoid输出概率值，大于0.5判断为1， 小于0.5判断为0， 使用tf.cast把布尔值转换为floag32
    pred = tf.cast(tf.sigmoid(output) > 0.5, tf.float32)
    
    # 使用交叉熵计算损失， 实际值labels为y, 推断值logits为output, 计算交叉熵会进行sigmoid处理
    loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=output))
    
    # 梯段下降法训练
    train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
    
    # 计算正确率, 判断推断值和实际值是否相等。 reduce_mean：求均值
    accuracy = tf.reduce_mean(tf.cast(tf.equal(pred, y), tf.float32))
    
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    
    for i in range(10000):
        # 尽量乱序输入，没10000步所有数据都进行一次循环。
        # 每100行输入进行
        for n in range(len(data_target) // 100):
            # 乱序
            index = np.random.permutation(len(data_target))
    
            # 打乱顺序
            data_data = data_data[index]
            data_target = data_target[index]
    
            batch_xs = data_data[n: n + 100]
            batch_ys = data_target[n: n + 100]
    
            sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys})
        if i % 1000 == 0:
            print(sess.run((loss, accuracy), feed_dict={x: batch_xs, y: batch_ys}))
    
    data_test = pd.read_csv('./data/titanic/test.csv')
    data_test = data_test.fillna(0)
    data_test['Sex'] = pd.factorize(data_test.Sex)[0]
    data_test['p1'] = np.array(data_test['Pclass'] == 1).astype(np.float32)
    data_test['p2'] = np.array(data_test['Pclass'] == 2).astype(np.float32)
    data_test['p3'] = np.array(data_test['Pclass'] == 3).astype(np.float32)
    data_test['e1'] = np.array(data_test['Embarked'] == 'S').astype(np.float32)
    data_test['e2'] = np.array(data_test['Embarked'] == 'C').astype(np.float32)
    data_test['e3'] = np.array(data_test['Embarked'] == 'Q').astype(np.float32)
    test_data = np.stack([data_test.Sex.values.astype(np.float32), data_test.Age.values.astype(np.float32),
                          data_test.SibSp.values.astype(np.float32),
                          data_test.Parch.values.astype(np.float32), data_test.Fare.values.astype(np.float32),
                          data_test.p1.values,
                          data_test.p2.values, data_test.p3.values, data_test.e1.values, data_test.e2.values,
                          data_test.e3.values]).T
    
    test_lable = pd.read_csv('./data/titanic/gender_submission.csv')
    
    test_lable = np.reshape(test_lable.Survived.values.astype(np.float32), (418, 1))
    
    sess.run(accuracy, feed_dict={x: test_data, y: test_lable})
    
    sess.close()

转载于:https://my.oschina.net/pasenger/blog/3014253

[2019_Python_]: https://my.oschina.net/u/2663968/blog/3061697
[hot3.png]: /images/20220114/86f24253a65c43bbbdde0b7af4211103.png