我想在SVC模型中执行GridSearchCV,但是它使用one-vs-all策略.对于后者,我可以这样做:
model_to_set = OneVsRestClassifier(SVC(kernel="poly"))
我的问题是参数.假设我想尝试以下值:
parameters = {"C":[1,2,4,8], "kernel":["poly","rbf"],"degree":[1,2,3,4]}
为了执行GridSearchCV,我应该做类似的事情:
cv_generator = StratifiedKFold(y, k=10)
model_tunning = GridSearchCV(model_to_set, param_grid=parameters, score_func=f1_score, n_jobs=1, cv=cv_generator)
但是,然后我执行它得到:
Traceback (most recent call last):
File "/.../main.py", line 66, in <module>
argclass_sys.set_model_parameters(model_name="SVC", verbose=3, file_path=PATH_ROOT_MODELS)
File "/.../base.py", line 187, in set_model_parameters
model_tunning.fit(self.feature_encoder.transform(self.train_feats), self.label_encoder.transform(self.train_labels))
File "/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.py", line 354, in fit
return self._fit(X, y)
File "/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.py", line 392, in _fit
for clf_params in grid for train, test in cv)
File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 473, in __call__
self.dispatch(function, args, kwargs)
File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line …使用交叉验证执行递归功能选择时出现以下错误:
Traceback (most recent call last):
File "/Users/.../srl/main.py", line 32, in <module>
argident_sys.train_classifier()
File "/Users/.../srl/identification.py", line 194, in train_classifier
feat_selector.fit(train_argcands_feats,train_argcands_target)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/feature_selection/rfe.py", line 298, in fit
ranking_ = rfe.fit(X[train], y[train]).ranking_
TypeError: only integer arrays with one element can be converted to an index
生成错误的代码如下:
def train_classifier(self):
# Get the argument candidates
argcands = self.get_argcands(self.reader)
# Extract the necessary features from the argument candidates
train_argcands_feats = []
train_argcands_target = []
for argcand in argcands:
train_argcands_feats.append(self.extract_features(argcand))
if argcand["info"]["label"] == "NULL":
train_argcands_target.append("NULL")
else: …我正在使用LogisticRegression作为模型来训练scikit-learn中的估算器.我使用的功能(大多数)是绝对的; 标签也是如此.因此,我分别使用DictVectorizer和LabelEncoder来正确编码值.
培训部分相当简单,但我遇到了测试部分的问题.简单的事情是使用训练模型的"预测"方法并获得预测标签.但是,对于我之后需要进行的处理,我需要每个特定实例的每个可能标签(类)的概率.我决定使用"predict_proba"方法.但是,对于同一个测试实例,我会得到不同的结果,无论我是在实例单独使用还是在其他实例的情况下使用此方法.
接下来,是一个重现问题的代码.
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction import DictVectorizer
from sklearn.preprocessing import LabelEncoder
X_real = [{'head': u'n\xe3o', 'dep_rel': u'ADVL'},
{'head': u'v\xe3o', 'dep_rel': u'ACC'},
{'head': u'empresa', 'dep_rel': u'SUBJ'},
{'head': u'era', 'dep_rel': u'ACC'},
{'head': u't\xeam', 'dep_rel': u'ACC'},
{'head': u'import\xe2ncia', 'dep_rel': u'PIV'},
{'head': u'balan\xe7o', 'dep_rel': u'SUBJ'},
{'head': u'ocupam', 'dep_rel': u'ACC'},
{'head': u'acesso', 'dep_rel': u'PRED'},
{'head': u'elas', 'dep_rel': u'SUBJ'},
{'head': u'assinaram', 'dep_rel': u'ACC'},
{'head': u'agredido', 'dep_rel': u'SUBJ'},
{'head': u'pol\xedcia', 'dep_rel': u'ADVL'},
{'head': u'se', 'dep_rel': u'ACC'}]
y_real = [u'AM-NEG', u'A1', u'A0', u'A1', …我开始使用scikit-learn做一些NLP.我已经使用了NLTK的一些分类器,现在我想尝试在scikit-learn中实现的分类器.
我的数据基本上是句子,我从这些句子的某些单词中提取特征来做一些分类任务.我的大多数功能都是名义上的:单词的词性(POS),左到右的单词,左到右的单词,右到右的单词,POS单词到单词. - 正确的,句法关系从一个词到另一个词的路径等.
当我使用NLTK分类器(决策树,朴素贝叶斯)进行一些实验时,特征集只是一个字典,其中包含特征的相应值:标称值.例如:[{"postag":"noun","wleft":"house","path":"VPNPNP",...},....].我只需将其传递给分类器,他们就完成了自己的工作.
这是使用的代码的一部分:
def train_classifier(self):
if self.reader == None:
raise ValueError("No reader was provided for accessing training instances.")
# Get the argument candidates
argcands = self.get_argcands(self.reader)
# Extract the necessary features from the argument candidates
training_argcands = []
for argcand in argcands:
if argcand["info"]["label"] == "NULL":
training_argcands.append( (self.extract_features(argcand), "NULL") )
else:
training_argcands.append( (self.extract_features(argcand), "ARG") )
# Train the appropriate supervised model
self.classifier = DecisionTreeClassifier.train(training_argcands)
return
以下是提取的一个功能集的示例:
[({'phrase': u'np', 'punct_right': 'NULL', 'phrase_left-sibling': 'NULL', 'subcat': 'fcl=np np vp np …python text-processing classification scikit-learn feature-engineering
我试图复制StratifiedShuffleSplitX 的例子,不是数组而是稀疏矩阵.在下面的示例中,此矩阵是通过DictVectorizer拟合混合名义和数字要素的数组创建的.
from sklearn.feature_extraction import DictVectorizer
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import StratifiedShuffleSplit
X = [{"a":1, "b":"xx"}, {"a":2, "b":"yx"}, {"a":2, "b":"yx"}, {"a":1, "b":"xx"}]
y = ["A", "B", "B", "A"]
X = DictVectorizer().fit_transform(X)
y = LabelEncoder().fit_transform(y)
sss = StratifiedShuffleSplit(y, 3, test_size=0.5, random_state=0)
for train_index, test_index in sss:
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
当我运行脚本时,抛出以下错误:
Traceback (most recent call last):
File ".../test.py", line 22, in <module>
X_train, X_test = X[train_index], X[test_index]
TypeError: only …