如何处理sklearn GradientBoostingClassifier中的分类变量？

Question

我正在尝试使用分类变量用GradientBoostingClassifier训练模型。

下面是一个原始代码示例，仅用于尝试将分类变量输入GradientBoostingClassifier。

from sklearn import datasets
from sklearn.ensemble import GradientBoostingClassifier
import pandas

iris = datasets.load_iris()
# Use only data for 2 classes.
X = iris.data[(iris.target==0) | (iris.target==1)]
Y = iris.target[(iris.target==0) | (iris.target==1)]

# Class 0 has indices 0-49. Class 1 has indices 50-99.
# Divide data into 80% training, 20% testing.
train_indices = list(range(40)) + list(range(50,90))
test_indices = list(range(40,50)) + list(range(90,100))
X_train = X[train_indices]
X_test = X[test_indices]
y_train = Y[train_indices]
y_test = Y[test_indices]

X_train = pandas.DataFrame(X_train)

# Insert fake categorical variable. 
# Just for testing in GradientBoostingClassifier.
X_train[0] = ['a']*40 + ['b']*40

# Model.
clf = GradientBoostingClassifier(learning_rate=0.01,max_depth=8,n_estimators=50).fit(X_train, y_train)

出现以下错误：

ValueError: could not convert string to float: 'b'

据我所知，在GradientBoostingClassifier构建模型之前，似乎需要对分类变量进行One Hot Encoding。

GradientBoostingClassifier是否可以使用分类变量构建模型，而不必进行一次热编码？

R-gbm包能够处理上述样本数据。我正在寻找一个具有同等功能的Python库。

Answer 1

当然它可以处理它，您只需要将分类变量编码为管道上的一个单独步骤。Sklearn完全能够处理分类变量以及R或任何其他ML包。R包仍然（可能）在后台执行一个热编码，它只是没有在这种情况下分离编码和拟合的关注点（可以说应该如此）。

Answer 2

pandas.get_dummies或statsmodels.tools.tools.categorical可用于将分类变量转换为虚拟矩阵。然后我们可以将虚拟矩阵合并回训练数据。

下面是执行上述过程后问题的示例代码。

from sklearn import datasets
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import roc_curve,auc
from statsmodels.tools import categorical
import numpy as np

iris = datasets.load_iris()
# Use only data for 2 classes.
X = iris.data[(iris.target==0) | (iris.target==1)]
Y = iris.target[(iris.target==0) | (iris.target==1)]

# Class 0 has indices 0-49. Class 1 has indices 50-99.
# Divide data into 80% training, 20% testing.
train_indices = list(range(40)) + list(range(50,90))
test_indices = list(range(40,50)) + list(range(90,100))
X_train = X[train_indices]
X_test = X[test_indices]
y_train = Y[train_indices]
y_test = Y[test_indices]


###########################################################################
###### Convert categorical variable to matrix and merge back with training
###### data.

# Fake categorical variable.
catVar = np.array(['a']*40 + ['b']*40)
catVar = categorical(catVar, drop=True)
X_train = np.concatenate((X_train, catVar), axis = 1)

catVar = np.array(['a']*10 + ['b']*10)
catVar = categorical(catVar, drop=True)
X_test = np.concatenate((X_test, catVar), axis = 1)
###########################################################################

# Model and test.
clf = GradientBoostingClassifier(learning_rate=0.01,max_depth=8,n_estimators=50).fit(X_train, y_train)

prob = clf.predict_proba(X_test)[:,1]   # Only look at P(y==1).

fpr, tpr, thresholds = roc_curve(y_test, prob)
roc_auc_prob = auc(fpr, tpr)

print(prob)
print(y_test)
print(roc_auc_prob)

感谢Andreas Muller指示熊猫数据帧不应用于scikit学习估计器。