分类变量

分类变量（categorical variable）是说明事物类别的一个名称，其取值是分类数据。如“性别”就是一个分类变量，其变量值为“男”或“女”；“行业”也是一个分类变量，其变量值可以为“零售业”、“旅游业”、“汽车制造业”等。

方法一：
直接丢弃分类变量。

drop_X_train = X_train.select_dtypes(exclude=['object'])
drop_X_valid = X_valid.select_dtypes(exclude=['object'])

print("MAE from Approach 1 (Drop categorical variables):")
print(score_dataset(drop_X_train, drop_X_valid, y_train, y_valid))

方法二：
给分类变量进行标签编码。

from sklearn.preprocessing import LabelEncoder

# All categorical columns
object_cols = [col for col in X_train.columns if X_train[col].dtype == "object"]

# Columns that can be safely label encoded
good_label_cols = [col for col in object_cols if set(X_train[col]) == set(X_valid[col])]
        
# Problematic columns that will be dropped from the dataset
bad_label_cols = list(set(object_cols)-set(good_label_cols))

# Drop categorical columns that will not be encoded
label_X_train = X_train.drop(bad_label_cols, axis=1)
label_X_valid = X_valid.drop(bad_label_cols, axis=1)

# Apply label encoder to each column with categorical data
label_encoder = LabelEncoder()

for col in set(good_label_cols):
    label_X_train[col] = label_encoder.fit_transform(X_train[col])
    label_X_valid[col] = label_encoder.transform(X_valid[col])

print("MAE from Approach 2 (Label Encoding):") 
print(score_dataset(label_X_train, label_X_valid, y_train, y_valid))

方法三：
独热编码。

# Get number of unique entries in each column with categorical data
object_nunique = list(map(lambda col: X_train[col].nunique(), object_cols))
d = dict(zip(object_cols, object_nunique))

# Print number of unique entries by column, in ascending order
sorted(d.items(), key=lambda x: x[1])

# Columns that will be one-hot encoded
low_cardinality_cols = [col for col in object_cols if X_train[col].nunique() < 10]

# Columns that will be dropped from the dataset
high_cardinality_cols = list(set(object_cols)-set(low_cardinality_cols))

print('Categorical columns that will be one-hot encoded:', low_cardinality_cols)
print('\nCategorical columns that will be dropped from the dataset:', high_cardinality_cols)

from sklearn.preprocessing import OneHotEncoder

# 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("MAE from Approach 3 (One-Hot Encoding):") 
print(score_dataset(OH_X_train, OH_X_valid, y_train, y_valid))