Question

我想绘制一个混淆矩阵来显示分类器的性能，但它只显示标签的数量，而不是标签本身：

from sklearn.metrics import confusion_matrix
import pylab as pl
y_test=['business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business']

pred=array(['health', 'business', 'business', 'business', 'business',
       'business', 'health', 'health', 'business', 'business', 'business',
       'business', 'business', 'business', 'business', 'business',
       'health', 'health', 'business', 'health'], 
      dtype='|S8')

cm = confusion_matrix(y_test, pred)
pl.matshow(cm)
pl.title('Confusion matrix of the classifier')
pl.colorbar()
pl.show()

如何将标签（health，business..etc）添加到混淆矩阵？

Answer 1

正如在this question中暗示的那样，您必须通过存储您调用的matplotlib函数传递的图形和轴对象（fig，{{1}来“打开”lower-level artist API。下面有{}}和ax个变量。然后，您可以使用cax / set_xticklabels替换默认的x轴和y轴刻度线：

set_yticklabels

请注意，我已将from sklearn.metrics import confusion_matrix labels = ['business', 'health'] cm = confusion_matrix(y_test, pred, labels) print(cm) fig = plt.figure() ax = fig.add_subplot(111) cax = ax.matshow(cm) plt.title('Confusion matrix of the classifier') fig.colorbar(cax) ax.set_xticklabels([''] + labels) ax.set_yticklabels([''] + labels) plt.xlabel('Predicted') plt.ylabel('True') plt.show()列表传递给labels函数，以确保它已正确排序，与刻度匹配。

这导致下图：

enter image description here

Answer 2

我认为值得一提的是这里使用seaborn.heatmap。

import seaborn as sns
import matplotlib.pyplot as plt     

ax= plt.subplot()
sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells

# labels, title and ticks
ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); 
ax.set_title('Confusion Matrix'); 
ax.xaxis.set_ticklabels(['business', 'health']); ax.yaxis.set_ticklabels(['health', 'business']);

Answer 3

您可能会对此感兴趣 https://github.com/pandas-ml/pandas-ml/

实现了混淆矩阵的Python Pandas实现。

一些功能：

情节混淆矩阵
绘制归一化混淆矩阵
班级统计
整体统计

以下是一个例子：

In [1]: from pandas_ml import ConfusionMatrix
In [2]: import matplotlib.pyplot as plt

In [3]: y_test = ['business', 'business', 'business', 'business', 'business',
        'business', 'business', 'business', 'business', 'business',
        'business', 'business', 'business', 'business', 'business',
        'business', 'business', 'business', 'business', 'business']

In [4]: y_pred = ['health', 'business', 'business', 'business', 'business',
       'business', 'health', 'health', 'business', 'business', 'business',
       'business', 'business', 'business', 'business', 'business',
       'health', 'health', 'business', 'health']

In [5]: cm = ConfusionMatrix(y_test, y_pred)

In [6]: cm
Out[6]:
Predicted  business  health  __all__
Actual
business         14       6       20
health            0       0        0
__all__          14       6       20

In [7]: cm.plot()
Out[7]: <matplotlib.axes._subplots.AxesSubplot at 0x1093cf9b0>

In [8]: plt.show()

In [9]: cm.print_stats()
Confusion Matrix:

Predicted  business  health  __all__
Actual
business         14       6       20
health            0       0        0
__all__          14       6       20


Overall Statistics:

Accuracy: 0.7
95% CI: (0.45721081772371086, 0.88106840959427235)
No Information Rate: ToDo
P-Value [Acc > NIR]: 0.608009812201
Kappa: 0.0
Mcnemar's Test P-Value: ToDo


Class Statistics:

Classes                                 business health
Population                                    20     20
P: Condition positive                         20      0
N: Condition negative                          0     20
Test outcome positive                         14      6
Test outcome negative                          6     14
TP: True Positive                             14      0
TN: True Negative                              0     14
FP: False Positive                             0      6
FN: False Negative                             6      0
TPR: (Sensitivity, hit rate, recall)         0.7    NaN
TNR=SPC: (Specificity)                       NaN    0.7
PPV: Pos Pred Value (Precision)                1      0
NPV: Neg Pred Value                            0      1
FPR: False-out                               NaN    0.3
FDR: False Discovery Rate                      0      1
FNR: Miss Rate                               0.3    NaN
ACC: Accuracy                                0.7    0.7
F1 score                               0.8235294      0
MCC: Matthews correlation coefficient        NaN    NaN
Informedness                                 NaN    NaN
Markedness                                     0      0
Prevalence                                     1      0
LR+: Positive likelihood ratio               NaN    NaN
LR-: Negative likelihood ratio               NaN    NaN
DOR: Diagnostic odds ratio                   NaN    NaN
FOR: False omission rate                       1      0

Answer 4

我找到了一个可以绘制从sklearn生成的混淆矩阵的函数。

import numpy as np


def plot_confusion_matrix(cm,
                          target_names,
                          title='Confusion matrix',
                          cmap=None,
                          normalize=True):
    """
    given a sklearn confusion matrix (cm), make a nice plot

    Arguments
    ---------
    cm:           confusion matrix from sklearn.metrics.confusion_matrix

    target_names: given classification classes such as [0, 1, 2]
                  the class names, for example: ['high', 'medium', 'low']

    title:        the text to display at the top of the matrix

    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm
                  see http://matplotlib.org/examples/color/colormaps_reference.html
                  plt.get_cmap('jet') or plt.cm.Blues

    normalize:    If False, plot the raw numbers
                  If True, plot the proportions

    Usage
    -----
    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by
                                                              # sklearn.metrics.confusion_matrix
                          normalize    = True,                # show proportions
                          target_names = y_labels_vals,       # list of names of the classes
                          title        = best_estimator_name) # title of graph

    Citiation
    ---------
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

    """
    import matplotlib.pyplot as plt
    import numpy as np
    import itertools

    accuracy = np.trace(cm) / float(np.sum(cm))
    misclass = 1 - accuracy

    if cmap is None:
        cmap = plt.get_cmap('Blues')

    plt.figure(figsize=(8, 6))
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()

    if target_names is not None:
        tick_marks = np.arange(len(target_names))
        plt.xticks(tick_marks, target_names, rotation=45)
        plt.yticks(tick_marks, target_names)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]


    thresh = cm.max() / 1.5 if normalize else cm.max() / 2
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        if normalize:
            plt.text(j, i, "{:0.4f}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")
        else:
            plt.text(j, i, "{:,}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")


    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
    plt.show()

看起来像这样

Answer 5

from sklearn import model_selection
test_size = 0.33
seed = 7
X_train, X_test, y_train, y_test = model_selection.train_test_split(feature_vectors, y, test_size=test_size, random_state=seed)

from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix

model = LogisticRegression()
model.fit(X_train, y_train)
result = model.score(X_test, y_test)
print("Accuracy: %.3f%%" % (result*100.0))
y_pred = model.predict(X_test)
print("F1 Score: ", f1_score(y_test, y_pred, average="macro"))
print("Precision Score: ", precision_score(y_test, y_pred, average="macro"))
print("Recall Score: ", recall_score(y_test, y_pred, average="macro")) 

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix

def cm_analysis(y_true, y_pred, labels, ymap=None, figsize=(10,10)):
    """
    Generate matrix plot of confusion matrix with pretty annotations.
    The plot image is saved to disk.
    args: 
      y_true:    true label of the data, with shape (nsamples,)
      y_pred:    prediction of the data, with shape (nsamples,)
      filename:  filename of figure file to save
      labels:    string array, name the order of class labels in the confusion matrix.
                 use `clf.classes_` if using scikit-learn models.
                 with shape (nclass,).
      ymap:      dict: any -> string, length == nclass.
                 if not None, map the labels & ys to more understandable strings.
                 Caution: original y_true, y_pred and labels must align.
      figsize:   the size of the figure plotted.
    """
    if ymap is not None:
        y_pred = [ymap[yi] for yi in y_pred]
        y_true = [ymap[yi] for yi in y_true]
        labels = [ymap[yi] for yi in labels]
    cm = confusion_matrix(y_true, y_pred, labels=labels)
    cm_sum = np.sum(cm, axis=1, keepdims=True)
    cm_perc = cm / cm_sum.astype(float) * 100
    annot = np.empty_like(cm).astype(str)
    nrows, ncols = cm.shape
    for i in range(nrows):
        for j in range(ncols):
            c = cm[i, j]
            p = cm_perc[i, j]
            if i == j:
                s = cm_sum[i]
                annot[i, j] = '%.1f%%\n%d/%d' % (p, c, s)
            elif c == 0:
                annot[i, j] = ''
            else:
                annot[i, j] = '%.1f%%\n%d' % (p, c)
    cm = pd.DataFrame(cm, index=labels, columns=labels)
    cm.index.name = 'Actual'
    cm.columns.name = 'Predicted'
    fig, ax = plt.subplots(figsize=figsize)
    sns.heatmap(cm, annot=annot, fmt='', ax=ax)
    #plt.savefig(filename)
    plt.show()

cm_analysis(y_test, y_pred, model.classes_, ymap=None, figsize=(10,10))

使用https://gist.github.com/hitvoice/36cf44689065ca9b927431546381a3f7

请注意，如果您使用rocket_r，它将使颜色反转，并且某种程度上看起来更自然，更好，如下所示：

Answer 6

要添加到@ akilat90关于sklearn.metrics.plot_confusion_matrix的更新中：

您可以直接在ConfusionMatrixDisplay中使用sklearn.metrics类，而无需将分类器传递给plot_confusion_matrix。它还具有display_labels参数，可让您根据需要指定显示在图形中的标签。

ConfusionMatrixDisplay的构造函数无法提供对图表进行更多自定义的方法，但是您可以在调用ax_属性后通过plot()属性访问对象的matplotlib轴。方法。我添加了第二个示例来显示这一点。

我发现不得不对大量数据重新运行分类器只是为了生成带有plot_confusion_matrix的图，这很烦人。我正在根据预测数据生成其他图，所以我不想浪费我的时间进行每次重新预测。这也是解决该问题的简便方法。

示例：

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay

cm = confusion_matrix(y_true, y_preds, normalize='all')
cmd = ConfusionMatrixDisplay(cm, display_labels=['business','health'])
cmd.plot()

使用ax_的示例：

cm = confusion_matrix(y_true, y_preds, normalize='all')
cmd = ConfusionMatrixDisplay(cm, display_labels=['business','health'])
cmd.plot()
cmd.ax_.set(xlabel='Predicted', ylabel='True')

Answer 7

    from sklearn.metrics import confusion_matrix
    import seaborn as sns
    import matplotlib.pyplot as plt
    model.fit(train_x, train_y,validation_split = 0.1, epochs=50, batch_size=4)
    y_pred=model.predict(test_x,batch_size=15)
    cm =confusion_matrix(test_y.argmax(axis=1), y_pred.argmax(axis=1))  
    index = ['neutral','happy','sad']  
    columns = ['neutral','happy','sad']  
    cm_df = pd.DataFrame(cm,columns,index)                      
    plt.figure(figsize=(10,6))  
    sns.heatmap(cm_df, annot=True)

Answer 8

给定模型，validx，validy。在其他答案的大力帮助下，这符合我的需求。

sklearn.metrics.plot_confusion_matrix

import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(26,26))
sklearn.metrics.plot_confusion_matrix(model, validx, validy, ax=ax, cmap=plt.cm.Blues)
ax.set(xlabel='Predicted', ylabel='Actual', title='Confusion Matrix Actual vs Predicted')

sklearn情节混淆矩阵与标签

8 个答案: