至少一个指定的标签必须在y_true中 [英] At least one label specified must be in y_true

问题描述

我想根据y_test和pred_test得到一个混淆矩阵，但提出一个问题至少一个指定的标签必须在y_true中，我不知道为什么。

I want to get a confusion matrix according to y_test and pred_test, but raise a question "At least one label specified must be in y_true",i don't know why

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

我创建了卷积神经网络。建模并使用交叉验证进行估计，最后生成一个混淆矩阵。现在在生成混淆矩阵方面存在问题。

I create a convolutional neural network. model and use cross validation for estimate, finally generate a confusion matrix. Now there are problems in generating confusion matrix.

数据集为完整的代码如下：

The dataset is enter link description here.The complete code is as follows：

 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();

推荐答案

错误不在您的主代码中，而是在标志的定义中。当您将符号定义为

The error isn't in your main code but rather in the definition of sign. When you define sign as

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

系统在寻找标签0时无法读取您的标签， 1,2,3,4,5表示该错误试图说的内容，即在y_pred中找不到任何标签。
将符号更改为

the system cannot read your labels as it is looking for the labels 0,1,2,3,4,5 as what the error was trying to say i.e. it could not find any labels in sign in y_pred. changing sign to

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

应该解决该错误。至于现在要做的事情，它非常简单，只需将结果映射为该数组，然后在实际的预测（部署）过程中就将标签的数字值换掉。

should fix the error. As for what you do now , its rather simple just map your result as this array and then during the actual predictions(Deployment) just swap out the numeric values for the labels.

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