pytorch-California House Prices(Kaggle竞赛)

Kaggle 竞赛：California House Prices （使用MLP解决）

Step 1: 数据处理， num 和 obj 两类数据划分

import numpy as np
import pandas as pd
import seaborn as sns
import torch
from torch import nn
from d2l import torch as d2l
import matplotlib.pyplot as plt
%matplotlib inline

train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')

print(train_data.shape)
print(test_data.shape)

1.1 num数据处理

'''
将数据集分成 num 和 obj 两类
'''
numeric_features = train_data.dtypes[train_data.dtypes != 'object'].index
obj_features = train_data.dtypes[train_data.dtypes == 'object'].index

train_num = train_data[numeric_features]
train_obj = train_data[obj_features]

1.11 对数字特征的处理

相关性分析：皮尔森相关性分析 和 斯皮尔曼相关性分析
原因： 数据集特征太多，先进行相关性分析筛选出主要的特征

corrPearson = train_data.corr(method="pearson")    # 两种相关系数定义方法
corrSpearman = train_data.corr(method="spearman")

figure = plt.figure(figsize=(30,25))
sns.heatmap(corrPearson,annot=True,cmap='RdYlGn', vmin=-1, vmax=+1)
plt.title("PEARSON")
plt.xlabel("COLUMNS")
plt.ylabel("COLUMNS")

figure = plt.figure(figsize=(30,25))
sns.heatmap(corrSpearman,annot=True,cmap='RdYlGn', vmin=-1, vmax=+1)
plt.title("SPEARMAN")
plt.xlabel("COLUMNS")
plt.ylabel("COLUMNS")
plt.savefig('Spearman_corr.jpg')

1.12 异常值处理

main_num_features = ['Bathrooms', 'Full bathrooms', 'Tax assessed value', 'Annual tax amount', 
                 'Listed Price', 'Last Sold Price']

for main_num_feature in main_num_features:
    print(train_data[main_num_feature].value_counts())
    print("------"*20)

数据中存在部分异常数据，把离散点去除

train_data = train_data.drop(train_data[(train_data['Tax assessed value']>4 * 10000000) | (train_data['Sold Price']>5 * 10000000)].index)

1.2 object 数据

打印离散值特征

print(train_obj.shape)
print("------"*20)
print(train_obj.columns)
print("------"*20)
print(train_obj.info())
print("------"*20)
print(train_obj.describe())
print("------"*20)

ntrain = train_data.shape[0]
ntest = test_data.shape[0]
y_train = train_data['Sold Price'].values
all_features = main_num_features + main_obj_features

train_labels = torch.tensor(train_data['Sold Price'].values.reshape(-1, 1),
                               dtype=torch.float32)

train_data1 = train_data[all_features]
test_data1 = test_data[all_features]
all_data = pd.concat((train_data1, test_data1)).reset_index(drop=True)
# all_data.drop(['Sold Price'], axis=1, inplace=True)
print("all_data size is : {}".format(all_data.shape))

# 对于字符特征，使用独热编码
all_data = pd.get_dummies(all_data, dummy_na=True)
# 对于数值特征，用均值替代空值
all_data[main_num_features] = all_data[main_num_features].fillna(all_data[main_num_features].mean())

Step 2: 建立模型MLP

n_train = train_data.shape[0]

train_features = torch.tensor(all_data[:n_train].values,
                                dtype=torch.float32)
test_features = torch.tensor(all_data[n_train:].values,
                                dtype=torch.float32)
                                in_features = train_features.shape[1]

def get_net():
    net = nn.Sequential(nn.Linear(in_features, 64),
                        nn.ReLU(),
                        nn.Linear(64, 1))

    return net

loss = nn.MSELoss()

def log_rmse(net, features, labels):
    # 为了在取对数时进一步稳定该值，将小于1的值设置为1
    clipped_preds = torch.clamp(net(features), 1, float('inf'))
    rmse = torch.sqrt(loss(torch.log(clipped_preds),
                           torch.log(labels)))
    return rmse.item()
    
def train(net, train_features, train_labels, test_features, test_labels,
          num_epochs, learning_rate, weight_decay, batch_size):
    train_ls, test_ls = [], []
    train_iter = d2l.load_array((train_features, train_labels), batch_size)
    # 这里使用的是Adam优化算法
    optimizer = torch.optim.Adam(net.parameters(),
                                 lr = learning_rate,
                                 weight_decay = weight_decay)
    for epoch in range(num_epochs):
        for X, y in train_iter:
            optimizer.zero_grad()
            l = loss(net(X), y)
            l.backward()
            optimizer.step()
        train_ls.append(log_rmse(net, train_features, train_labels))
        if test_labels is not None:
            test_ls.append(log_rmse(net, test_features, test_labels))
    return train_ls, test_ls

def get_k_fold_data(k, i, X, y):
    assert k > 1
    fold_size = X.shape[0] // k
    X_train, y_train = None, None
    for j in range(k):
        idx = slice(j * fold_size, (j + 1) * fold_size)
        X_part, y_part = X[idx, :], y[idx]
        if j == i:
            X_valid, y_valid = X_part, y_part
        elif X_train is None:
            X_train, y_train = X_part, y_part
        else:
            X_train = torch.cat([X_train, X_part], 0)
            y_train = torch.cat([y_train, y_part], 0)
    return X_train, y_train, X_valid, y_valid
 
def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay,
           batch_size):
    train_l_sum, valid_l_sum = 0, 0
    for i in range(k):
        data = get_k_fold_data(k, i, X_train, y_train)
        net = get_net()
        train_ls, valid_ls = train(net, *data, num_epochs, learning_rate,
                                   weight_decay, batch_size)
        train_l_sum += train_ls[-1]
        valid_l_sum += valid_ls[-1]
        if i == 0:
            d2l.plot(list(range(1, num_epochs + 1)), [train_ls, valid_ls],
                     xlabel='epoch', ylabel='rmse', xlim=[1, num_epochs],
                     legend=['train', 'valid'], yscale='log')
        print(f'折{i + 1}，训练log rmse{float(train_ls[-1]):f}, '
              f'验证log rmse{float(valid_ls[-1]):f}')
    return train_l_sum / k, valid_l_sum / k
 
k, num_epochs, lr, weight_decay, batch_size = 5, 100, 0.01, 0.001, 64
train_l, valid_l = k_fold(k, train_features, train_labels, num_epochs, lr,
                          weight_decay, batch_size)
print(f'{k}-折验证: 平均训练log rmse: {float(train_l):f}, '
      f'平均验证log rmse: {float(valid_l):f}')

k, num_epochs, lr, weight_decay, batch_size = 5, 100, 0.01, 0.001, 64
def train_and_pred(train_features, test_features, train_labels, test_data,
                   num_epochs, lr, weight_decay, batch_size):
    net = get_net()
    train_ls, _ = train(net, train_features, train_labels, None, None,
                        num_epochs, lr, weight_decay, batch_size)
    d2l.plot(np.arange(1, num_epochs + 1), [train_ls], xlabel='epoch',
             ylabel='log rmse', xlim=[1, num_epochs], yscale='log')
    print(f'训练log rmse：{float(train_ls[-1]):f}')
    # 将网络应用于测试集。
    preds = net(test_features).detach().numpy()
    preds = pd.Series(preds.reshape(1,-1)[0])
    # 将其重新格式化以导出到Kaggle
#     test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])
#     submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)
#     submission.to_csv('submission.csv', index=False)
    return preds

Step 3 ：模型预测，导出

preds_1 = train_and_pred(train_features, test_features, train_labels, test_data,
               num_epochs, lr, weight_decay, batch_size)
sub_file = pd.read_csv('sample_submission.csv')
sub_file['Sold Price'] = preds_1
sub_file.to_csv('submission.csv')