混淆矩阵 - ValueError：发现样本数量不一致的输入变量

Question

为了重现性，我使用的训练和验证数据集是shared here

validation_dataset.csv 是 training_dataset.csv 的基本事实。

我在下面所做的是将数据集输入一个简单的 CNN 层，该层提取图像的有用特征并将其作为一维输入 LSTM 网络进行分类。

from keras.models import Sequential
from keras.layers import Dense, Flatten, Activation
from keras.layers.convolutional import Conv1D
from keras.layers import LSTM
from keras.layers.convolutional import MaxPooling1D
from keras.layers import TimeDistributed
from keras.layers import Dropout
from keras import optimizers
from keras.callbacks import EarlyStopping
import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
from confusion_matrix import plot_confusion_matrix
import scikitplot as skplt
from numpy import genfromtxt

train_set = genfromtxt('data/train/training_dataset.csv', delimiter=',') 
validation_set = genfromtxt('data/validation/validation_dataset.csv', delimiter=',') 


train_set = train_set[..., None] 
validation_set = validation_set[..., None]


X_train, X_test, y_train, y_test = train_test_split(train_set, validation_set, test_size=0.30, random_state=0)





    
batch_size=16
epochs=5

# Create the model
    
model = Sequential()

model.add(Conv1D(filters=5, kernel_size=3, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(LSTM(50, return_sequences=True))
model.add(Dropout(0.5))
model.add(LSTM(10))

model.add(Dense(1,kernel_initializer='random_normal'))
model.add(Activation('relu'))

adam = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0)
sgd = optimizers.SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)

model.compile(optimizer=adam, loss='mean_squared_error', metrics=['mae', 'mape', 'mean_squared_error', 'acc'])



model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs)



print(model.summary())


# Evaluate the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))

skplt.metrics.plot_confusion_matrix(y_test, scores, x_tick_rotation=50, title=' ', normalize=True)

最后，我想绘制模型的混淆矩阵

skplt.metrics.plot_confusion_matrix(y_test, scores, x_tick_rotation=50, title=' ', normalize=True)

但是，它会引发错误ValueError: Found input variables with inconsistent numbers of samples: [5394, 5]。

我们如何解决这个错误？

Answer 1

skplt.metrics.plot_confusion_matrix 的第二个参数必须是预测标签（请参阅https://scikit-plot.readthedocs.io/en/stable/metrics.html）。但是，您传递了scores，它不包含预测的标签。

解决办法是：

y_pred = model.predict(X_test)
skplt.metrics.plot_confusion_matrix(y_test,
                                    y_pred,
                                    x_tick_rotation=50,
                                    title=' ',
                                    normalize=True)

Answer 2

几天前我正在研究 SVM，当我尝试绘制混淆矩阵时，以下代码行对我有用。

predicted=model.predict(X_test) #predicted output
cm=metrics.confusion_matrix(y_test, predicted) 
df_cm = pd.DataFrame(cm, range(2), range(2))
sns.set(font_scale=1.4) 
sns.heatmap(df_cm, annot=True, annot_kws={"size": 16}) 
plt.title('CONFUSION MATRIX ',fontdict={'fontsize': 14, 'fontweight': 'bold'})
plt.show()