Python 至少有一个指定的标签必须为y_true

我想根据y_检验和pred_检验得到一个混淆矩阵，但提出了一个问题“至少有一个指定的标签必须是y_真的”，我不知道为什么

metrics.confusion_matrix(np.argmax(y_test,axis=1),pred_test)


  y_test =  [[0. 1. 0. 0. 0. 0.]
     [0. 0. 0. 0. 0. 1.]
     [0. 0. 0. 0. 1. 0.]
     ...
     [0. 0. 0. 1. 0. 0.]
     [0. 0. 1. 0. 0. 0.]
     [0. 0. 1. 0. 0. 0.]]

   pred_test = [1 4 5 ... 3 2 2]
   np.argmax(y_test,axis=1) = [1 5 4 ... 3 2 2]

  File "D:\Anaconda\lib\site-packages\sklearn\metrics\classification.py", line 259, in confusion_matrix
    raise ValueError("At least one label specified must be in y_true")
ValueError: At least one label specified must be in y_true

我创建了一个卷积神经网络。建模并使用交叉验证进行估计，最后生成混淆矩阵。现在在生成混淆矩阵时存在问题

数据集为，完整代码如下：

 import matplotlib
    #matplotlib.use('Agg')
    import timing
    from keras.layers import Input,Dense,Conv2D,MaxPooling2D,UpSampling2D,Flatten
    from keras.models import Model
    from keras import backend as K
    from keras.utils.np_utils import to_categorical
    import numpy as np
    import pandas as pd
    import seaborn as sns
    from keras.models import Sequential# 导入Sequential
    from keras.utils import np_utils, generic_utils
    from keras.callbacks import LearningRateScheduler
    import os
    from keras.layers import Dropout
    from keras.backend.tensorflow_backend import set_session
    import tensorflow as tf
    from sklearn.model_selection import train_test_split,  cross_val_score
    from sklearn.cross_validation import KFold, StratifiedKFold
    from keras.wrappers.scikit_learn import KerasClassifier
    from sklearn.preprocessing import LabelEncoder
    from sklearn import metrics
    import time
    from scipy import stats
    from keras import optimizers
    import matplotlib.pyplot as plt
    from keras import regularizers
    import keras
    from keras.callbacks import TensorBoard
    config = tf.ConfigProto(allow_soft_placement=True)
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
    config.gpu_options.allow_growth = True
    sess = tf.Session(config=config)
    os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
    time1 = time.time()
    class LossHistory(keras.callbacks.Callback):
        def on_train_begin(self, logs={}):
            self.losses = {'batch':[], 'epoch':[]}
            self.accuracy = {'batch':[], 'epoch':[]}
            self.val_loss = {'batch':[], 'epoch':[]}
            self.val_acc = {'batch':[], 'epoch':[]}

        def on_batch_end(self, batch, logs={}):
            self.losses['batch'].append(logs.get('loss'))
            self.accuracy['batch'].append(logs.get('acc'))
            self.val_loss['batch'].append(logs.get('val_loss'))
            self.val_acc['batch'].append(logs.get('val_acc'))

        def on_epoch_end(self, batch, logs={}):
            self.losses['epoch'].append(logs.get('loss'))
            self.accuracy['epoch'].append(logs.get('acc'))
            self.val_loss['epoch'].append(logs.get('val_loss'))
            self.val_acc['epoch'].append(logs.get('val_acc'))

        def loss_plot(self, loss_type):
            iters = range(len(self.losses[loss_type]))
            plt.figure()
            # acc
            plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')
            # loss
            plt.plot(iters, self.losses[loss_type], 'g', label='train loss')
            if loss_type == 'epoch':
                # val_acc
                plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')
                # val_loss
                plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')
            plt.grid(True)
            plt.xlabel(loss_type)
            plt.ylabel('acc-loss')
            plt.legend(loc="center")
            plt.show()
            #plt.savefig('common.png')


    #dataset
    RANDOM_SEED = 42
    def read_data(file_path):
        column_names = ['user-id', 'activity', 'timestamp', 'x-axis', 'y-axis', 'z-axis']
        m = pd.read_csv(file_path,names=column_names, header=None,sep=',')
        return m
    def feature_normalize(dataset):
        mu = np.mean(dataset,axis=0)
        sigma = np.std(dataset,axis=0)
        return (dataset-mu)/sigma

    dataset1 = read_data('ab.txt')
    dataset = pd.DataFrame(dataset1)
    dataset['x-axis'] = feature_normalize(dataset['x-axis'])
    dataset['y-axis'] = feature_normalize(dataset['y-axis'])
    dataset['z-axis'] = feature_normalize(dataset['z-axis'])

    N_TIME_STEPS = 200
    N_FEATURES = 3
    step = 200
    segments = []
    labels = []
    for i in range(0, len(dataset) - N_TIME_STEPS, step):
        xs = dataset['x-axis'].values[i: i + N_TIME_STEPS]
        ys = dataset['y-axis'].values[i: i + N_TIME_STEPS]
        zs = dataset['z-axis'].values[i: i + N_TIME_STEPS]
        label = stats.mode(dataset['activity'][i: i + N_TIME_STEPS])[0][0]
        segments.append([xs, ys, zs])
        labels.append(label)
    print("reduced size of data", np.array(segments).shape)
    reshaped_segments = np.asarray(segments,dtype=np.float32).reshape(-1,1, N_TIME_STEPS, 3)
    print("Reshape the segments", np.array(reshaped_segments).shape)
    #x_train1, x_val_test, y_train1, y_val_test = train_test_split(reshaped_segments, labels, test_size=0.25, random_state=RANDOM_SEED)

    batch_size = 128     
    num_classes =6

    def create_model():
        input_shape = Input(shape=(1,200,3))
        x = Conv2D(5, kernel_size=(1, 1), padding='valid')(input_shape)
        x1 = keras.layers.concatenate([input_shape, x], axis=-1)

        x = Conv2D(50, kernel_size=(1, 7),padding='valid',
                     kernel_initializer='glorot_uniform',
        kernel_regularizer = keras.regularizers.l2(0.0015))(x1)


        x = keras.layers.core.Activation('relu')(x)
        x = MaxPooling2D(pool_size=(1, 2))(x)
        x = Conv2D(50, kernel_size=(1, 7),padding='valid',kernel_initializer='glorot_uniform',
               kernel_regularizer=keras.regularizers.l2(0.0015))(x)
        x = keras.layers.core.Activation('relu')(x)
        x = MaxPooling2D(pool_size=(1, 2))(x)

        x = Flatten()(x)
        x = Dropout(0.9)(x)
        output = Dense(num_classes, activation='softmax',kernel_initializer='glorot_uniform',)(x)
        model = Model(inputs=input_shape,outputs=output)
        model.summary()

        sgd = optimizers.SGD(lr=0.005,decay=1e-6,momentum=0.9,nesterov=True)
        model.compile(loss=keras.losses.categorical_crossentropy,
                  optimizer=sgd,
                  metrics=['accuracy'])
        return model
    history = LossHistory()
    epochs = 4000


    #setting learning rate
    def scheduler(epoch):
        if epoch > 0.75 * epochs:
            lr = 0.0005
        elif epoch > 0.25 * epochs:
            lr = 0.001
        else:
            lr = 0.005
        return lr

    scheduler = LearningRateScheduler(scheduler)
    estimator = KerasClassifier(build_fn=create_model)
    #divide dataset

    scores = []
    confusions = []   
    sign = ['DOWNSTAIRS','JOGGING','SITTING','STANDING','UPSTAIRS','WALKING']
    encoder = LabelEncoder()
    encoder_y = encoder.fit_transform(labels)
    train_labels = to_categorical(encoder_y,num_classes=None)

    #kfold = StratifiedKFold(reshaped_segments.shape[0],n_folds=10,shuffle=True,random_state=42)
    kfold = StratifiedKFold(labels,n_folds=3,shuffle=True,random_state=42)
    for train_index,test_index in kfold:
        print(test_index)
        x_train, x_test = reshaped_segments[train_index], reshaped_segments[test_index]
        y_train, y_test = train_labels[train_index], train_labels[test_index]
        estimator.fit(x_train,y_train,callbacks=[scheduler,history],epochs=10,batch_size=128,verbose=0)
        scores.append(estimator.score(x_test,y_test))
        print(y_test)
        print(type(y_test))
        pred_test = estimator.predict(x_test)  
        print(pred_test)
        print(np.argmax(y_test,axis=1))
        confusions.append(metrics.confusion_matrix(np.argmax(y_test,axis=1),pred_test,sign))

    matrix = [[0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0]]

    for i in np.arange(n_folds-1):
        for j in len(confusions[0]):
            for k in len(confusions[0][0]):
                matrix[j][k] = matrix[j][k] + confusions[i][j][k] + confusions[i+1][j][k]  

    model.save('model.h5')  
    model.save_weights('my_model_weights.h5')
    print('score:',scores)
    scores = np.mean(scores)
    print('mean:',scores)

    plt.figure(figsize=(16,14))     
    sns.heatmap(matrix, xticklabels=sign, yticklabels=sign, annot=True, fmt="d");
    plt.title("CONFUSION MATRIX : ")
    plt.ylabel('True Label')
    plt.xlabel('Predicted label')
    plt.savefig('cmatrix.png')
    plt.show();

---

错误不在主代码中，而是在符号的定义中。当您将符号定义为

 sign = ['DOWNSTAIRS','JOGGING','SITTING','STANDING','UPSTAIRS','WALKING']

系统无法读取您的标签，因为它正在查找错误试图显示的标签0、1、2、3、4、5，即在登录y_pred中找不到任何标签。 将标志更改为

 sign = [1,2,3,4,5]

---

应该修复错误。至于您现在所做的，很简单，只需将结果映射为该数组，然后在实际预测（部署）过程中只需交换标签的数字值。

能否完整地包含测试矩阵，以便我们可以轻松地运行代码。还可以显示logit和labels@ashgetstazered我刚刚添加了数据集和完整的代码。谢谢您的帮助。@NiteyaShah我添加了数据集和完整的代码。非常感谢。