将 Scikit-Learn OneHotEncoder 与 Pandas DataFrame 一起使用

Question

我正在尝试使用 Scikit-Learn 的 OneHotEncoder 将 Pandas DataFrame 中包含字符串的列替换为单热编码等效项。我的以下代码不起作用：

from sklearn.preprocessing import OneHotEncoder
# data is a Pandas DataFrame

jobs_encoder = OneHotEncoder()
jobs_encoder.fit(data['Profession'].unique().reshape(1, -1))
data['Profession'] = jobs_encoder.transform(data['Profession'].to_numpy().reshape(-1, 1))

它产生以下错误（列表中的字符串被省略）：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-91-3a1f568322f5> in <module>()
      3 jobs_encoder = OneHotEncoder()
      4 jobs_encoder.fit(data['Profession'].unique().reshape(1, -1))
----> 5 data['Profession'] = jobs_encoder.transform(data['Profession'].to_numpy().reshape(-1, 1))

/usr/local/anaconda3/envs/ml/lib/python3.6/site-packages/sklearn/preprocessing/_encoders.py in transform(self, X)
    730                                        copy=True)
    731         else:
--> 732             return self._transform_new(X)
    733 
    734     def inverse_transform(self, X):

/usr/local/anaconda3/envs/ml/lib/python3.6/site-packages/sklearn/preprocessing/_encoders.py in _transform_new(self, X)
    678         """New implementation assuming categorical input"""
    679         # validation of X happens in _check_X called by _transform
--> 680         X_int, X_mask = self._transform(X, handle_unknown=self.handle_unknown)
    681 
    682         n_samples, n_features = X_int.shape

/usr/local/anaconda3/envs/ml/lib/python3.6/site-packages/sklearn/preprocessing/_encoders.py in _transform(self, X, handle_unknown)
    120                     msg = ("Found unknown categories {0} in column {1}"
    121                            " during transform".format(diff, i))
--> 122                     raise ValueError(msg)
    123                 else:
    124                     # Set the problematic rows to an acceptable value and

ValueError: Found unknown categories ['...', ..., '...'] in column 0 during transform

下面是一些示例数据：

data['Profession'] =

0         unkn
1         safe
2         rece
3         unkn
4         lead
          ... 
111988    indu
111989    seni
111990    mess
111991    seni
111992    proj
Name: Profession, Length: 111993, dtype: object

我到底做错了什么？

Answer 1

OneHotEncoder 将分类整数特征编码为单热数值数组。它的 Transform 方法 returns 如果 sparse=True 是一个稀疏矩阵，否则它 returns 一个二维数组。

您不能将 二维数组 （或稀疏矩阵）转换为 Pandas 系列 。您必须为每个类别.

创建一个 Pandas 系列（Pandas 数据帧中的一列）

我建议改为 pandas.get_dummies：

data = pd.get_dummies(data,prefix=['Profession'], columns = ['Profession'], drop_first=True)

编辑：

使用 Sklearn OneHotEncoder：

transformed = jobs_encoder.transform(data['Profession'].to_numpy().reshape(-1, 1))
#Create a Pandas DataFrame of the hot encoded column
ohe_df = pd.DataFrame(transformed, columns=jobs_encoder.get_feature_names())
#concat with original data
data = pd.concat([data, ohe_df], axis=1).drop(['Profession'], axis=1)

其他选项： 如果您使用 GridSearch it's recommanded to use ColumnTransformer and FeatureUnion with Pipeline or directly make_column_transformer

进行超参数调整

Answer 2

原来是Scikit-LearnsLabelBinarizer gave me better luck in converting the data to one-hot encoded format, with help from ，我的最终代码如下

import pandas as pd
from sklearn.preprocessing import LabelBinarizer

jobs_encoder = LabelBinarizer()
jobs_encoder.fit(data['Profession'])
transformed = jobs_encoder.transform(data['Profession'])
ohe_df = pd.DataFrame(transformed)
data = pd.concat([data, ohe_df], axis=1).drop(['Profession'], axis=1)

Answer 3

以下是 Kaggle Learn 推荐的一种方法。不要认为目前有更简单的方法可以从原始 pandas DataFrame 到单热编码 DataFrame.

# Apply one-hot encoder to each column with categorical data
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[low_cardinality_cols]))
OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[low_cardinality_cols]))

# One-hot encoding removed index; put it back
OH_cols_train.index = X_train.index
OH_cols_valid.index = X_valid.index

# Remove categorical columns (will replace with one-hot encoding)
num_X_train = X_train.drop(object_cols, axis=1)
num_X_valid = X_valid.drop(object_cols, axis=1)

# Add one-hot encoded columns to numerical features
OH_X_train = pd.concat([num_X_train, OH_cols_train], axis=1)
OH_X_valid = pd.concat([num_X_valid, OH_cols_valid], axis=1)
print(OH_X_train)

Answer 4

这样就可以了。如果您对可视化不感兴趣，请删除 plotly 部分。如果不需要降价，也可以将 printmd 更改为打印。

def fn_cat_onehot(df):

    """Generate onehoteencoded features for all categorical columns in df"""

    printmd(f"df shape: {df.shape}")

    # NaN handing
    nan_count = df.isna().sum().sum()
    if nan_count > 0:
        printmd(f"NaN = **{nan_count}** will be categorized under feature_nan columns")

    # generation
    from sklearn.preprocessing import OneHotEncoder

    model_oh = OneHotEncoder(handle_unknown="ignore", sparse=False)
    for c in df.select_dtypes("category").columns:
        printmd(f"Encoding **{c}**")  # which column
        matrix = model_oh.fit_transform(
            df[[c]]
        )  # get a matrix of new features and values
        names = model_oh.get_feature_names_out()  # get names for these features
        df_oh = pd.DataFrame(
            data=matrix, columns=names, index=df.index
        )  # create df of these new features
        display(df_oh.plot.hist())
        df = pd.concat([df, df_oh], axis=1)  # concat with existing df
        df.drop(
            c, axis=1, inplace=True
        )  # drop categorical column so that it is all numerical for modelling

    printmd(f"#### New df shape: **{df.shape}**")
    return df

将 Scikit-Learn OneHotEncoder 与 Pandas DataFrame 一起使用

Using Scikit-Learn OneHotEncoder with a Pandas DataFrame

python

machine-learning

pandas

scikit-learn

one-hot-encoding