我想将我从一个字典和一个独立数组中获取的数据分割成训练数据和测试数据。我尝试了各种方法，但没有成功。由于这些数据在我处理流程中的预处理方式，我需要最初将特征保持为字典的形式。社区中有谁对此有建议吗？

字典（特征值）：

{'input1': array([42., 50., 68., ..., 60., 46., 60.]), 'input2': array([[-2.00370455, -2.35689664, -1.96147382, ...,  2.11014128,          2.59383321,  1.24209607],        [-1.97130549, -2.19063663, -2.02996445, ...,  2.32125568,          2.27316046,  1.48600614],        [-2.01526666, -2.40440917, -1.94321752, ...,  2.15266657,          2.68460488,  1.23534095],        ...,        [-2.1359458 , -2.52428007, -1.75701785, ...,  2.25480819,          2.68114281,  1.75468981],        [-1.95868206, -2.23297167, -1.96401751, ...,  2.07427239,          2.60306072,  1.28556955],        [-1.80507278, -2.62199521, -2.08697271, ...,  2.34080577,          2.48254585,  1.52028871]])}

目标值

y = array([0.83, 0.4 , 0.53, ..., 0.  , 0.94, 1. ])Shape: (3000,)

创建字典

#字典值input1 = embeddings.numpy()input2 = df['feature'].valuesy = df['target'].valuesfull_model_inputs = [input1 , embeddings]original_model_inputs = dict(input1 = input1 , input2 = input2 )

分割数据

x_train, x_test, y_train, y_test = train_test_split([original_model_inputs['input1'],                                                      original_model_inputs['input2']], y, test_size = 0.2, random_state = 6)

或

x_train, x_test, y_train, y_test = train_test_split(original_model_inputs, y, test_size = 0.2, random_state = 6)

错误信息

ValueError: Found input variables with inconsistent numbers of samples: [2, 3000]

Input1:

[55., 46., 46., ..., 60., 60., 45.]Shape: (3000,)

Input2:

[[-2.00370455, -2.35689664, -1.96147382, ...,  2.11014128,         2.59383321,  1.24209607],       [-1.97130549, -2.19063663, -2.02996445, ...,  2.32125568,         2.27316046,  1.48600614],       [-2.01526666, -2.40440917, -1.94321752, ...,  2.15266657,         2.68460488,  1.23534095],       ...,       [-2.1359458 , -2.52428007, -1.75701785, ...,  2.25480819,         2.68114281,  1.75468981],       [-1.95868206, -2.23297167, -1.96401751, ...,  2.07427239,         2.60306072,  1.28556955],       [-1.80507278, -2.62199521, -2.08697271, ...,  2.34080577,         2.48254585,  1.52028871]]Shape: (3000, 3840)

构建模型

input1= Input(shape = (1, ))input2= Input(shape = (3840, ))# 第一分支处理第一个输入x = Dense(units = 128, activation="relu")(input1)x = BatchNormalization()(x)x = Dense(units = 128, activation="relu")(x)x = BatchNormalization()(x)x = Model(inputs=input1, outputs=x)# 第二分支处理第二个输入（嵌入）y = Dense(units = 128, activation="relu")(input2)y = BatchNormalization()(y)y = Dense(units = 128, activation="relu")(y)y = BatchNormalization()(y)  y = Model(inputs=input2, outputs=y)# 结合两个分支的输出combined = Concatenate()([x.output, y.output])out = Dense(128, activation='relu')(combined)out = Dropout(0.5)(out)out = Dense(1)(out)# 模型将接受两个分支的输入，然后输出一个单一值model = Model(inputs = [x.input, y.input], outputs = out)model.compile(loss='mse', optimizer = Adam(lr = 0.001), metrics = ['mse'])model.fit([X1,X2], Y, epochs=3)

回答：

将你的字典放入pandas数据框中，这将保留数据的维度，并按你希望的方式分割数据：

df = pd.DataFrame({"input1":original_model_inputs["input1"],                     "input2":list(original_model_inputs["input2"])})X_train, X_test, y_train, y_test = train_test_split(df,y)

转换回原始格式：

X_train = X_train.to_dict("list")X_test = X_test.to_dict("list")

编辑

为了保持你的处理流程的功能性，你可能需要添加以下两行代码：

X_train = {k:np.array(v) for k,v in X_train.items()}X_test = {k:np.array(v) for k,v in X_test.items()}