我正在尝试使用scikit-learn和随机森林分类器进行递归特征消除，使用OOB ROC作为递归过程中创建的每个子集的评分方法。
然而，当我尝试使用RFECV方法时，我得到一个错误说AttributeError: 'RandomForestClassifier' object has no attribute 'coef_'
随机森林本身没有系数，但它们确实有基尼分数排名。所以，我想知道如何解决这个问题。
请注意，我想使用一种方法，该方法将明确地告诉我在最佳分组中选择了pandas DataFrame中的哪些特征，因为我正在使用递归特征选择来尽量减少我将输入到最终分类器中的数据量。
下面是一些示例代码:
from sklearn import datasets
import pandas as pd
from pandas import Series
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFECV
iris = datasets.load_iris()
x=pd.DataFrame(iris.data, columns=['var1','var2','var3', 'var4'])
y=pd.Series(iris.target, name='target')
rf = RandomForestClassifier(n_estimators=500, min_samples_leaf=5, n_jobs=-1)
rfecv = RFECV(estimator=rf, step=1, cv=10, scoring='ROC', verbose=2)
selector=rfecv.fit(x, y)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/bbalin/anaconda/lib/python2.7/site-packages/sklearn/feature_selection/rfe.py", line 336, in fit
ranking_ = rfe.fit(X_train, y_train).ranking_
File "/Users/bbalin/anaconda/lib/python2.7/site-packages/sklearn/feature_selection/rfe.py", line 148, in fit
if estimator.coef_.ndim > 1:
AttributeError: 'RandomForestClassifier' object has no attribute 'coef_'
以下是我为使RandomForestClassifier与RFECV一起工作所做的工作:
class RandomForestClassifierWithCoef(RandomForestClassifier):
def fit(self, *args, **kwargs):
super(RandomForestClassifierWithCoef, self).fit(*args, **kwargs)
self.coef_ = self.feature_importances_
如果您使用'accuracy'或'f1' score，则仅使用此类即可。对于'roc_auc'， RFECV抱怨不支持多类格式。使用下面的代码将其更改为两类分类，'roc_auc'评分就可以工作了。(使用Python 3.4.1和scikit-learn 0.15.1)
y=(pd.Series(iris.target, name='target')==2).astype(int)
插入你的代码:
from sklearn import datasets
import pandas as pd
from pandas import Series
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFECV
class RandomForestClassifierWithCoef(RandomForestClassifier):
def fit(self, *args, **kwargs):
super(RandomForestClassifierWithCoef, self).fit(*args, **kwargs)
self.coef_ = self.feature_importances_
iris = datasets.load_iris()
x=pd.DataFrame(iris.data, columns=['var1','var2','var3', 'var4'])
y=(pd.Series(iris.target, name='target')==2).astype(int)
rf = RandomForestClassifierWithCoef(n_estimators=500, min_samples_leaf=5, n_jobs=-1)
rfecv = RFECV(estimator=rf, step=1, cv=2, scoring='roc_auc', verbose=2)
selector=rfecv.fit(x, y)
这是我的代码，我已经整理了一下，使它与您的任务相关:
features_to_use = fea_cols # this is a list of features
# empty dataframe
trim_5_df = DataFrame(columns=features_to_use)
run=1
# this will remove the 5 worst features determined by their feature importance computed by the RF classifier
while len(features_to_use)>6:
print('number of features:%d' % (len(features_to_use)))
# build the classifier
clf = RandomForestClassifier(n_estimators=1000, random_state=0, n_jobs=-1)
# train the classifier
clf.fit(train[features_to_use], train['OpenStatusMod'].values)
print('classifier score: %fn' % clf.score(train[features_to_use], df['OpenStatusMod'].values))
# predict the class and print the classification report, f1 micro, f1 macro score
pred = clf.predict(test[features_to_use])
print(classification_report(test['OpenStatusMod'].values, pred, target_names=status_labels))
print('micro score: ')
print(metrics.precision_recall_fscore_support(test['OpenStatusMod'].values, pred, average='micro'))
print('macro score:n')
print(metrics.precision_recall_fscore_support(test['OpenStatusMod'].values, pred, average='macro'))
# predict the class probabilities
probs = clf.predict_proba(test[features_to_use])
# rescale the priors
new_probs = kf.cap_and_update_priors(priors, probs, private_priors, 0.001)
# calculate logloss with the rescaled probabilities
print('log loss: %fn' % log_loss(test['OpenStatusMod'].values, new_probs))
row={}
if hasattr(clf, "feature_importances_"):
# sort the features by importance
sorted_idx = np.argsort(clf.feature_importances_)
# reverse the order so it is descending
sorted_idx = sorted_idx[::-1]
# add to dataframe
row['num_features'] = len(features_to_use)
row['features_used'] = ','.join(features_to_use)
# trim the worst 5
sorted_idx = sorted_idx[: -5]
# swap the features list with the trimmed features
temp = features_to_use
features_to_use=[]
for feat in sorted_idx:
features_to_use.append(temp[feat])
# add the logloss performance
row['logloss']=[log_loss(test['OpenStatusMod'].values, new_probs)]
print('')
# add the row to the dataframe
trim_5_df = trim_5_df.append(DataFrame(row))
run +=1
所以我在这里做的是，我有一个我想要训练的特征列表，然后根据特征的重要性进行预测，然后剔除最差的5个，然后重复。在每次运行期间，我添加一行来记录预测性能，以便以后可以进行一些分析。
原始代码要大得多，我有不同的分类器和数据集，我正在分析，但我希望你能从上面得到图片。我注意到的是，对于随机森林，我在每次运行中删除的特征数量会影响性能，因此每次减少1、3和5个特征会产生不同的最佳特征集。
我发现使用gradientboosting classifier更具可预测性和可重复性，因为最终的最佳特征集同意我一次裁剪1个特征，还是3个或5个特征。
我希望我在这里不是教你吸吮鸡蛋，你可能比我知道的更多，但我的方法是使用快速分类器来获得最佳特征集的大致概念，然后使用性能更好的分类器，然后开始超参数调优，再次进行粗粒度比较，然后细粒度一旦我得到最好的参数是什么。
我提交了添加coef_的请求，以便RandomForestClassifier可以与RFECV一起使用。然而，改变已经发生了。此更改将在0.17版本。
如果你想现在使用最新的开发版本，你可以拉出它。
这是我的想法。这是一个非常简单的解决方案，并且依赖于自定义精度度量(称为weightedAccuracy)，因为我正在对高度不平衡的数据集进行分类。但是，如果需要的话，它应该很容易扩展。
from sklearn import datasets
import pandas
from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation
from sklearn.metrics import confusion_matrix
def get_enhanced_confusion_matrix(actuals, predictions, labels):
""""enhances confusion_matrix by adding sensivity and specificity metrics"""
cm = confusion_matrix(actuals, predictions, labels = labels)
sensitivity = float(cm[1][1]) / float(cm[1][0]+cm[1][1])
specificity = float(cm[0][0]) / float(cm[0][0]+cm[0][1])
weightedAccuracy = (sensitivity * 0.9) + (specificity * 0.1)
return cm, sensitivity, specificity, weightedAccuracy
iris = datasets.load_iris()
x=pandas.DataFrame(iris.data, columns=['var1','var2','var3', 'var4'])
y=pandas.Series(iris.target, name='target')
response, _ = pandas.factorize(y)
xTrain, xTest, yTrain, yTest = cross_validation.train_test_split(x, response, test_size = .25, random_state = 36583)
print "building the first forest"
rf = RandomForestClassifier(n_estimators = 500, min_samples_split = 2, n_jobs = -1, verbose = 1)
rf.fit(xTrain, yTrain)
importances = pandas.DataFrame({'name':x.columns,'imp':rf.feature_importances_
}).sort(['imp'], ascending = False).reset_index(drop = True)
cm, sensitivity, specificity, weightedAccuracy = get_enhanced_confusion_matrix(yTest, rf.predict(xTest), [0,1])
numFeatures = len(x.columns)
rfeMatrix = pandas.DataFrame({'numFeatures':[numFeatures],
'weightedAccuracy':[weightedAccuracy],
'sensitivity':[sensitivity],
'specificity':[specificity]})
print "running RFE on %d features"%numFeatures
for i in range(1,numFeatures,1):
varsUsed = importances['name'][0:i]
print "now using %d of %s features"%(len(varsUsed), numFeatures)
xTrain, xTest, yTrain, yTest = cross_validation.train_test_split(x[varsUsed], response, test_size = .25)
rf = RandomForestClassifier(n_estimators = 500, min_samples_split = 2,
n_jobs = -1, verbose = 1)
rf.fit(xTrain, yTrain)
cm, sensitivity, specificity, weightedAccuracy = get_enhanced_confusion_matrix(yTest, rf.predict(xTest), [0,1])
print("n"+str(cm))
print('the sensitivity is %d percent'%(sensitivity * 100))
print('the specificity is %d percent'%(specificity * 100))
print('the weighted accuracy is %d percent'%(weightedAccuracy * 100))
rfeMatrix = rfeMatrix.append(
pandas.DataFrame({'numFeatures':[len(varsUsed)],
'weightedAccuracy':[weightedAccuracy],
'sensitivity':[sensitivity],
'specificity':[specificity]}), ignore_index = True)
print("n"+str(rfeMatrix))
maxAccuracy = rfeMatrix.weightedAccuracy.max()
maxAccuracyFeatures = min(rfeMatrix.numFeatures[rfeMatrix.weightedAccuracy == maxAccuracy])
featuresUsed = importances['name'][0:maxAccuracyFeatures].tolist()
print "the final features used are %s"%featuresUsed