如何在 python 中包含 KNN 的混淆矩阵？

Question

我试图为这个 KNN 算法包含一个混淆矩阵。而且由于它已经很复杂了，使用嵌套交叉验证和网格搜索最优参数，我不知道在哪里包括混淆矩阵部分。

print(__doc__)

# Number of random trials
NUM_TRIALS = 30

# Load the dataset

X_iris = X.values
y_iris = y

# Set up possible values of parameters to optimize over
p_grid = {"n_neighbors": [1, 5, 10, 15]}

# We will use a Support Vector Classifier with "rbf" kernel
svm = KNeighborsClassifier()

# Arrays to store scores
non_nested_scores = np.zeros(NUM_TRIALS)
nested_scores = np.zeros(NUM_TRIALS)

# Loop for each trial
for i in range(NUM_TRIALS):

    # Choose cross-validation techniques for the inner and outer loops,
    # independently of the dataset.
    # E.g "GroupKFold", "LeaveOneOut", "LeaveOneGroupOut", etc.
    inner_cv = KFold(n_splits=4, shuffle=True, random_state=i)
    outer_cv = KFold(n_splits=4, shuffle=True, random_state=i)

    # Non_nested parameter search and scoring
    clf = GridSearchCV(estimator=svm, param_grid=p_grid, cv=inner_cv)
    clf.fit(X_iris, y_iris)
    non_nested_scores[i] = clf.best_score_

    # Nested CV with parameter optimization
    nested_score = cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv)
    nested_scores[i] = nested_score.mean()

score_difference = non_nested_scores - nested_scores

print("Average difference of {:6f} with std. dev. of {:6f}."
      .format(score_difference.mean(), score_difference.std()))

# Plot scores on each trial for nested and non-nested CV
plt.figure()
plt.subplot(211)
non_nested_scores_line, = plt.plot(non_nested_scores, color='r')
nested_line, = plt.plot(nested_scores, color='b')
plt.ylabel("score", fontsize="14")
plt.legend([non_nested_scores_line, nested_line],
           ["Non-Nested CV", "Nested CV"],
           bbox_to_anchor=(0, .4, .5, 0))
plt.title("Non-Nested and Nested Cross Validation on Touch Classification Data set KNN",
          x=.5, y=1.1, fontsize="15")

# Plot bar chart of the difference.
plt.subplot(212)
difference_plot = plt.bar(range(NUM_TRIALS), score_difference)
plt.xlabel("Individual Trial #")
plt.legend([difference_plot],
           ["Non-Nested CV - Nested CV Score"],
           bbox_to_anchor=(0, 1, .8, 0))
plt.ylabel("score difference", fontsize="14")

并且我试图包括这部分以包括我的算法的混淆矩阵：

cm = confusion_matrix(y_test, preds)
tn, fp, fn, tp = confusion_matrix(y_test, preds).ravel()
cm = [[tp,fp],[fn,tn]]



ax= plt.subplot()
sns.heatmap(cm, annot=True, fmt = "d", cmap="Spectral"); #annot=True to annotate cells

# labels, title and ticks
ax.set_xlabel('ACTUAL LABELS');ax.set_ylabel('PREDICTED LABELS'); 
ax.set_title('KNN Confusion Matrix'); 
ax.xaxis.set_ticklabels(['11', '12','13','21','22','23','31','32','33']); ax.yaxis.set_ticklabels(['Soft', 'Tough']);

我对混淆矩阵的算法理解不够透彻，我不确定如何为我的 KNN 算法正确实现它。在我的数据集中，我有

y = ['11', '12','13','21','22','23','31','32','33'] #my labels

      Duration  Grand Mean  Max Mean Activation
0           64  136.772461           178.593750
1           67  193.445196           258.515625
2           67  112.382929           145.765625
3           88  156.530717           238.734375

#head of my feature matrix

Answer 1

您首先需要使用 GridSearchCV 的最佳估计器进行预测。

preds=clf.best_estimator_.predict(X_test)

然后使用 sklearn.metrics

中的 confusion_matrix 函数打印混淆矩阵

from sklearn.metrics import confusion_matrix
print confusion_matrix(y_test, preds)

一旦有了混淆矩阵，就可以绘制它了。编辑：由于您没有单独的测试数据，因此您将在 X_iris 上进行测试。但总是首选拆分数据。在

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