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提交 aa75893a 编写于 作者: S ShusenTang
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fix bug(#46) in feature preprocessing

上级 62bee2ff
隐藏空白更改
内联 并排
Showing with 1828 addition and 368 deletion
code/chapter03_DL-basics/3.16_kaggle-house-price.ipynb
浏览文件 @ aa75893a
......@@ -16,7 +16,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"0.4.1\n"
"1.2.0\n"
]
}
],
......@@ -222,8 +222,8 @@
"numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index\n",
"all_features[numeric_features] = all_features[numeric_features].apply(\n",
" lambda x: (x - x.mean()) / (x.std()))\n",
"# 标准化后，每个特征的均值变为0，所以可以直接用0来替换缺失值\n",
"all_features = all_features.fillna(0)"
"# 标准化后，每个数值特征的均值变为0，所以可以直接用0来替换缺失值\n",
"all_features[numeric_features] = all_features[numeric_features].fillna(0)"
]
},
{
......@@ -234,7 +234,7 @@
{
"data": {
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"execution_count": 8,
......@@ -405,12 +405,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
"fold 0, train rmse 0.241054, valid rmse 0.221462\n",
"fold 1, train rmse 0.229857, valid rmse 0.268489\n",
"fold 2, train rmse 0.231413, valid rmse 0.238157\n",
"fold 3, train rmse 0.237733, valid rmse 0.218747\n",
"fold 4, train rmse 0.230720, valid rmse 0.258712\n",
"5-fold validation: avg train rmse 0.234155, avg valid rmse 0.241113\n"
"fold 0, train rmse 0.240939, valid rmse 0.221437\n",
"fold 1, train rmse 0.229326, valid rmse 0.267492\n",
"fold 2, train rmse 0.231815, valid rmse 0.237722\n",
"fold 3, train rmse 0.237550, valid rmse 0.219035\n",
"fold 4, train rmse 0.230578, valid rmse 0.258887\n",
"5-fold validation: avg train rmse 0.234042, avg valid rmse 0.240915\n"
]
},
{
......@@ -450,10 +450,10 @@
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docs/chapter03_DL-basics/3.16_kaggle-house-price.md
浏览文件 @ aa75893a
......@@ -83,8 +83,8 @@ all_features = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))
numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index
all_features[numeric_features] = all_features[numeric_features].apply(
lambda x: (x - x.mean()) / (x.std()))
# 标准化后，每个特征的均值变为0，所以可以直接用0来替换缺失值
all_features = all_features.fillna(0)
# 标准化后，每个数值特征的均值变为0，所以可以直接用0来替换缺失值
all_features[numeric_features] = all_features[numeric_features].fillna(0)
```
接下来将离散数值转成指示特征。举个例子，假设特征MSZoning里面有两个不同的离散值RL和RM，那么这一步转换将去掉MSZoning特征，并新加两个特征MSZoning\_RL和MSZoning\_RM，其值为0或1。如果一个样本原来在MSZoning里的值为RL，那么有MSZoning\_RL=1且MSZoning\_RM=0。
......@@ -92,12 +92,12 @@ all_features = all_features.fillna(0)
``` python
# dummy_na=True将缺失值也当作合法的特征值并为其创建指示特征
all_features = pd.get_dummies(all_features, dummy_na=True)
all_features.shape # (2919, 354)
all_features.shape # (2919, 331)
```
可以看到这一步转换将特征数从79增加到了354。
可以看到这一步转换将特征数从79增加到了331。
最后，通过`values`属性得到NumPy格式的数据，并转成`NDArray`方便后面的训练。
最后，通过`values`属性得到NumPy格式的数据，并转成`Tensor`方便后面的训练。
``` python
n_train = train_data.shape[0]
......
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