我正在尝试使用 sci-kit learn 0.17 进行多标签分类,我的数据看起来像
训练
Col1 Col2
asd dfgfg [1,2,3]
poioi oiopiop [4]
测试
Col1
asdas gwergwger
rgrgh hrhrh
到目前为止我的代码
import numpy as np
from sklearn import svm, datasets
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
def getLabels():
traindf = pickle.load(open("train.pkl","rb"))
X = traindf['Col1']
y = traindf['Col2']
# Binarize the output
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)
但现在我明白了
ValueError: could not convert string to float: <value of Col1 here>
在
y_score = classifier.fit(X_train, y_train).decision_function(X_test)
我也必须对 X 进行二值化吗?为什么我需要将 X 维度转换为浮点数?