我试图使用rfecv缩小与我的分类器真正相关的功能数量。这是我写的代码
import sklearn
import pandas as p
import numpy as np
import scipy as sp
import pylab as pl
from sklearn import linear_model, cross_validation, metrics
from sklearn.svm import SVC
from sklearn.feature_selection import RFECV
from sklearn.metrics import zero_one_loss
from sklearn import preprocessing
#from sklearn.feature_extraction.text import CountVectorizer
#from sklearn.feature_selection import SelectKBest, chi2
modelType = "notext"
# ----------------------------------------------------------
# Prepare the Data
# ----------------------------------------------------------
training_data = np.array(p.read_table('F:/NYC/NYU/SM/3/SNLP/Project/Data/train.tsv'))
print ("Read Data\n")
# get the target variable and set it as Y so we can predict it
Y = training_data[:,-1]
print(Y)
# not all data is numerical, so we'll have to convert those fields
# fix "is_news":
training_data[:,17] = [0 if x == "?" else 1 for x in training_data[:,17]]
# fix -1 entries in hasDomainLink
training_data[:,14] = [0 if x =="-1" else x for x in training_data[:,10]]
# fix "news_front_page":
training_data[:,20] = [999 if x == "?" else x for x in training_data[:,20]]
training_data[:,20] = [1 if x == "1" else x for x in training_data[:,20]]
training_data[:,20] = [0 if x == "0" else x for x in training_data[:,20]]
# fix "alchemy category":
training_data[:,3] = [0 if x=="arts_entertainment" else x for x in training_data[:,3]]
training_data[:,3] = [1 if x=="business" else x for x in training_data[:,3]]
training_data[:,3] = [2 if x=="computer_internet" else x for x in training_data[:,3]]
training_data[:,3] = [3 if x=="culture_politics" else x for x in training_data[:,3]]
training_data[:,3] = [4 if x=="gaming" else x for x in training_data[:,3]]
training_data[:,3] = [5 if x=="health" else x for x in training_data[:,3]]
training_data[:,3] = [6 if x=="law_crime" else x for x in training_data[:,3]]
training_data[:,3] = [7 if x=="recreation" else x for x in training_data[:,3]]
training_data[:,3] = [8 if x=="religion" else x for x in training_data[:,3]]
training_data[:,3] = [9 if x=="science_technology" else x for x in training_data[:,3]]
training_data[:,3] = [10 if x=="sports" else x for x in training_data[:,3]]
training_data[:,3] = [11 if x=="unknown" else x for x in training_data[:,3]]
training_data[:,3] = [12 if x=="weather" else x for x in training_data[:,3]]
training_data[:,3] = [999 if x=="?" else x for x in training_data[:,3]]
print ("Corrected outliers data\n")
# ----------------------------------------------------------
# Models
# ----------------------------------------------------------
if modelType == "notext":
print ("no text model\n")
#ignore features which are useless
X = training_data[:,list([3, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 19, 20, 22, 25])]
scaler = preprocessing.StandardScaler()
print("initialized scaler \n")
scaler.fit(X,Y)
print("fitted train data and labels\n")
X = scaler.transform(X)
print("Transformed train data\n")
svc = SVC(kernel = "linear")
print("Initialized SVM\n")
rfecv = RFECV(estimator = svc, cv = 5, loss_func = zero_one_loss, verbose = 1)
print("Initialized RFECV\n")
rfecv.fit(X,Y)
print("Fitted train data and label\n")
rfecv.support_
print ("Optimal Number of features : %d" % rfecv.n_features_)
savetxt('rfecv.csv', rfecv.ranking_, delimiter=',', fmt='%f')
在调用“rfecv.fit(X,Y)”时,我的代码从metrices.py文件中抛出一个错误“ValueError:不支持unknown”
错误在sklearn.metrics.metrics
:
# No metrics support "multiclass-multioutput" format
if (y_type not in ["binary", "multiclass", "multilabel-indicator", "multilabel-sequences"]):
raise ValueError("{0} is not supported".format(y_type))
这是一个分类问题,目标值只有0或1。 数据集可在Kaggle Competition Data
找到如果有人能够指出我哪里出错了,我将不胜感激。
答案 0 :(得分:8)
RFECV
检查目标/列车数据是binary
,multiclass
,multilabel-indicator
或multilabel-sequences
类型之一:
y
包含< = 2个离散值,是1d或列
矢量。y
包含两个以上的离散值,不是a
序列序列,是1d或列向量。y
是一个包含更多内容的二维数组
比两个离散值,不是序列序列,两者都是
尺寸大小> 1。y
是一个标签指示符矩阵,一个数组
两个维度,至少有两列,最多2个唯一
值。,而Y
为unknown
,即
y
类似于数组,但不是上述数组,例如3d数组或非序列对象数组。原因是您的目标数据是字符串(格式"0"
和"1"
)并且加载了read_table
作为对象:
>>> training_data[:, -1].dtype
dtype('O')
>>> type_of_target(training_data[:, -1])
'unknown'
要解决此问题,您可以转换为int
:
>>> Y = training_data[:, -1].astype(int)
>>> type_of_target(Y)
'binary'