i am trying to use scikit learn 0.17 with anaconda 2.7 for a multilabel classification problem. here is my code
我正在尝试使用scikit learn 0.17和anaconda 2.7来解决多标签分类问题。这是我的代码
import pandas as pd
import pickle
import re
from sklearn.cross_validation import train_test_split
from sklearn.metrics.metrics import classification_report, accuracy_score, confusion_matrix
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB as MNB
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV
traindf = pickle.load(open("train.pkl","rb"))
X, y = traindf['colC'], traindf['colB'].as_matrix()
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=0.7)
pip = Pipeline([
('vect', TfidfVectorizer(
analyzer='word',
binary=False,
decode_error='ignore',
dtype=<type 'numpy.int64'>,
encoding=u'utf-8',
input=u'content',
lowercase=True,
max_df=0.25,
max_features=None,
min_df=1,
ngram_range=(1, 1),
norm=u'l2',
preprocessor=None,
smooth_idf=True,
stop_words='english',
strip_accents=None,
sublinear_tf=True,
token_pattern=u'(?u)\\b\\w\\w+\\b',
tokenizer=nltk.data.load('tokenizers/punkt/english.pickle'),
use_idf=True, vocabulary=None)),
('clf', LogisticRegression(
C=10,
class_weight=None,
dual=False,
fit_intercept=True,
intercept_scaling=1,
max_iter=100,
multi_class='multinomial',
n_jobs=1,
penalty='l2',
random_state=None,
solver='lbfgs',
tol=0.0001,
verbose=0,
warm_start=False))
])
parameters = {}
gridSearchTS = GridSearchCV(pip,parameters,n_jobs=3, verbose=1, scoring='accuracy')
gridSearchTS.fit(Xtrain, ytrain)
predictions = gridSearchTS.predict(Xtest)
print ('Accuracy:', accuracy_score(ytest, predictions))
print ('Confusion Matrix:', confusion_matrix(ytest, predictions))
print ('Classification Report:', classification_report(ytest, predictions))
testdf = pickle.load(open("test.pkl","rb"))
predictions=gridSearchTS.predict(testdf['colC'])
testdf['colB'] = predictions
print(testdf.info())
testdf.to_csv("res.csv")
and here is what my data looks like
这是我的数据
training
培训
colC colB
some text [list of tags]
some text [list of tags]
test
测试
colC
some text
some text
but i get the error
但我得到了误差
raise ValueError('You appear to be using a legacy multi-label data'
ValueError: You appear to be using a legacy multi-label data representation. Sequence of sequences are no longer supported; use a binary array or sparse matrix instead.
what does this mean?
这是什么意思?
here is the full stacktrace
这是完整的堆栈跟踪
Traceback (most recent call last):
File "X:\asd.py", line 34, in getTags
gridSearchTS.fit(Xtrain, ytrain)
File "X:\popol\Continuum\Anaconda2\lib\site-packages\sklearn\grid_search.py", line 804, in fit
return self._fit(X, y, ParameterGrid(self.param_grid))
File "X:\popol\Continuum\Anaconda2\lib\site-packages\sklearn\grid_search.py", line 532, in _fit
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
File "X:\popol\Continuum\Anaconda2\lib\site-packages\sklearn\cross_validation.py", line 1676, in check_cv
if type_of_target(y) in ['binary', 'multiclass']:
File "X:\popol\Continuum\Anaconda2\lib\site-packages\sklearn\utils\multiclass.py", line 251, in type_of_target
raise ValueError('You appear to be using a legacy multi-label data'
ValueError: You appear to be using a legacy multi-label data representation. Sequence of sequences are no longer supported; use a binary array or sparse matrix instead.
how do i fix this? do i need to change the format of my data? why does gridSearchTS.fit(Xtrain, ytrain) fail? how do i make X and y suitable for the fit function?
我怎么修复这个?我是否需要更改我的数据格式?为什么gridSearchTS。fit(Xtrain ytrain)失败?如何使X和y适合这个拟合函数?
Edit
编辑
I tried
我试着
from sklearn.preprocessing import MultiLabelBinarizer
y=MultiLabelBinarizer().fit_transform(y)
random_state = np.random.RandomState(0)
# Split into training and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
random_state=random_state)
# Run classifier
from sklearn import svm, datasets
classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
random_state=random_state))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)
but now i get
但是现在我得到
ValueError: could not convert string to float: <value of ColC here>
on
在
y_score = classifier.fit(X_train, y_train).decision_function(X_test)
do i have to binarize X as well? why do i need to convert the X dimension to float?
我还需要对X进行二进制化吗?为什么我要把X维变换成浮点数?
1 个解决方案
#1
10
The documents give this example:
文件给出了这个例子:
>>> from sklearn.preprocessing import MultiLabelBinarizer
>>> y = [[2, 3, 4], [2], [0, 1, 3], [0, 1, 2, 3, 4], [0, 1, 2]]
>>> MultiLabelBinarizer().fit_transform(y)
array([[0, 0, 1, 1, 1],
[0, 0, 1, 0, 0],
[1, 1, 0, 1, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 0, 0]])
MultiLabelBinarizer.fit_transform takes in your labeled sets and can output the binary array. The output should then be alright to pass to your fit function.
MultiLabelBinarizer。fit_transform接受您的标记集并可以输出二进制数组。输出应该可以传递给fit函数。
#1
10
The documents give this example:
文件给出了这个例子:
>>> from sklearn.preprocessing import MultiLabelBinarizer
>>> y = [[2, 3, 4], [2], [0, 1, 3], [0, 1, 2, 3, 4], [0, 1, 2]]
>>> MultiLabelBinarizer().fit_transform(y)
array([[0, 0, 1, 1, 1],
[0, 0, 1, 0, 0],
[1, 1, 0, 1, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 0, 0]])
MultiLabelBinarizer.fit_transform takes in your labeled sets and can output the binary array. The output should then be alright to pass to your fit function.
MultiLabelBinarizer。fit_transform接受您的标记集并可以输出二进制数组。输出应该可以传递给fit函数。