Python 学习算法的计算核心

我对ML python环境非常陌生，我需要绘制精度/召回图，如本文所述：[您需要计算y_分数：

    # Create a simple classifier
classifier = svm.LinearSVC(random_state=random_state)
classifier.fit(X_train, y_train)
y_score = classifier.decision_function(X_test)

所以问题是：如何使用多项式NaiveBayes或LearningTree计算分数？在我的代码中，我有：

 print("MultinomialNB - countVectorizer")

    xTrain, xTest, yTrain, yTest=countVectorizer(db)

    classifier = MultinomialNB()
    model = classifier.fit(xTrain, yTrain)
    yPred = model.predict(xTest)

    print("confusion Matrix of MNB/ cVectorizer:\n")
    print(confusion_matrix(yTest, yPred))
    print("\n")
    print("classificationReport Matrix of MNB/ cVectorizer:\n")
    print(classification_report(yTest, yPred))

    elapsed_time = time.time() - start_time
    print("elapsed Time: %.3fs" %elapsed_time) 

绘图功能：

def plotLearningAlgorithm(yTest,yScore,algName):

    precision, recall, _ = precision_recall_curve(yTest, yScore)

    plt.step(recall, precision, color='b', alpha=0.2,
         where='post')
    plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)

    plt.xlabel('Recall')
    plt.ylabel('Precision')
    plt.ylim([0.0, 1.05])
    plt.xlim([0.0, 1.0])
    plt.title('2-class Precision-Recall'+ algName +'curve: AP={0:0.2f}'.format(average_precision))

绘图错误：

<ipython-input-43-d07c3365bfc2> in MultinomialNaiveBayesOPT()
     11     yPred = model.predict(xTest)
     12 
---> 13     plotLearningAlgorithm(yTest,model.predict_proba(xTest),"MultinomialNB - countVectorizer")
     14 
     15     print("confusion Matrix of MNB/ cVectorizer:\n")

<ipython-input-42-260aac9918f2> in plotLearningAlgorithm(yTest, yScore, algName)
      1 def plotLearningAlgorithm(yTest,yScore,algName):
      2 
----> 3     precision, recall, _ = precision_recall_curve(yTest, yScore)
      4 
      5     step_kwargs = ({'step': 'post'}

/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/ranking.py in precision_recall_curve(y_true, probas_pred, pos_label, sample_weight)
    522     fps, tps, thresholds = _binary_clf_curve(y_true, probas_pred,
    523                                              pos_label=pos_label,
--> 524                                              sample_weight=sample_weight)
    525 
    526     precision = tps / (tps + fps)

/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/ranking.py in _binary_clf_curve(y_true, y_score, pos_label, sample_weight)
    398     check_consistent_length(y_true, y_score, sample_weight)
    399     y_true = column_or_1d(y_true)
--> 400     y_score = column_or_1d(y_score)
    401     assert_all_finite(y_true)
    402     assert_all_finite(y_score)

/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in column_or_1d(y, warn)
    758         return np.ravel(y)
    759 
--> 760     raise ValueError("bad input shape {0}".format(shape))
    761 
    762 

ValueError: bad input shape (9000, 2)

因此，y_Pred需要通过以下方式进行转换：

yPred_probability = yPred_probability[:,1];

---

非常感谢@ignoring_gravity为我提供了正确的解决方案，我还打印了无技能线，以增加图表的可读性。

他们称之为

y_score

的就是您的ML算法输出的预测概率

在多项式nb和决策树（我想这就是你所说的LearningTree？）中，你可以使用方法

。predict\u proba

：

    classifier = MultinomialNB()
    model = classifier.fit(xTrain, yTrain)
    yPred = model.predict_proba(xTest)

---

这个问题与jupyter笔记本无关-请不要发送不相关的标签（删除并替换为

scikit learn

和

naivebayes

）。谢谢你的回复，但我得到了上面的错误，有什么提示吗？除非你发布了一个可复制的示例，否则没有-请参阅提供的解决方案带有一点线索-从你的解决方案开始出现错误；）

    classifier = MultinomialNB()
    model = classifier.fit(xTrain, yTrain)
    yPred = model.predict_proba(xTest)

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split

# countvectorizer is not used for train and test split, instead use train_test_split
train_x, test_x, train_y, test_y = train_test_split(X, y, test_size=0.33, random_state=42)  # here x is going to be your textual data, whereas y will be your target 

countMatrix_train = vect.fit_transform(train_x)  # you have to fit with your train data


countMatrix_test =  vect.transform(test_x)  # now have to transform( and not fit_transform) according to your train data


classifier = MultinomialNB()
classifier.fit(countMatrix_train, train_y)


ypred = classifier.predict(countMatrix_test)   # this will give you class for your test data, now use this for making classification report