Python Sci-Kit Learn：多標簽分類ValueError：無法將字符串轉換為float：

Question

我正在嘗試使用sci-kit進行多標記分類學習0.17我的數據看起來像

訓練

Col1                  Col2
asd dfgfg             [1,2,3]
poioi oiopiop         [4]

測試

Col1                    
asdas gwergwger    
rgrgh hrhrh

我的代碼到目前為止

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)

但現在我明白了

ValueError: could not convert string to float: <value of Col1 here>

上

y_score = classifier.fit(X_train, y_train).decision_function(X_test) 

我是否也必須將X二進制化？ 為什么我需要將X維度轉換為浮點數？

Answer 1

是的，您必須將X轉換為數字表示（不是必需的二進制）以及y。 那是因為所有機器學習方法都在數字矩陣上運行。

怎么做到這一點？ 如果Col1中的每個樣本都可以包含不同的單詞（即它代表一些文本） - 您可以使用CountVectorizer轉換該列

from sklearn.feature_extraction.text import CountVectorizer

col1 = ["cherry banana", "apple appricote", "cherry apple", "banana apple appricote cherry apple"]

cv = CountVectorizer()
cv.fit_transform(col1) 
#<4x4 sparse matrix of type '<class 'numpy.int64'>'
#   with 10 stored elements in Compressed Sparse Row format>

cv.fit_transform(col1).toarray()
#array([[0, 0, 1, 1],
#       [1, 1, 0, 0],
#       [1, 0, 0, 1],
#       [2, 1, 1, 1]], dtype=int64)