怎么用concat连接int和object类型序列

要连接 int 和 object 类型的序列，您可以使用 pandas 库中的 pd.concat 函数。以下是使用 pd.concat 连接 int 和 object 类型序列的示例代码：

import pandas as pd

# 创建 int 类型的序列
int_series = pd.Series([1, 2, 3])

# 创建 object 类型的序列
object_series = pd.Series(['a', 'b', 'c'])

# 使用 pd.concat 连接两个序列
concatenated_series = pd.concat([int_series, object_series])

# 打印连接后的序列
print(concatenated_series)

运行上述代码将输出以下结果：

0    1
1    2
2    3
0    a
1    b
2    c
dtype: object

这样，您就成功地将 int 和 object 类型的序列连接在一起。请注意，连接后的序列将自动转换为 object 类型。

相关问题

基于lstm-crf的序列标注的模型代码

以下是使用TensorFlow实现基于LSTM-CRF的序列标注的模型代码示例：

import tensorflow as tf
from tensorflow.contrib.crf import crf_log_likelihood, viterbi_decode

class BiLSTM_CRF(object):
    def __init__(self, num_chars, num_tags, embedding_dim, hidden_dim, batch_size=64, learning_rate=0.001):
        self.num_chars = num_chars
        self.num_tags = num_tags
        self.embedding_dim = embedding_dim
        self.hidden_dim = hidden_dim
        self.batch_size = batch_size
        self.learning_rate = learning_rate
        
        self.inputs = tf.placeholder(tf.int32, shape=[None, None], name='inputs')
        self.targets = tf.placeholder(tf.int32, shape=[None, None], name='targets')
        self.seq_length = tf.placeholder(tf.int32, shape=[None], name='seq_length')
        
        self._build_model()
        self._build_loss()
        self._build_optimizer()
        
        self.sess = tf.Session()
        self.sess.run(tf.global_variables_initializer())
        
    def _build_model(self):
        # 定义词嵌入层
        self.word_embeddings = tf.Variable(tf.random_uniform([self.num_chars, self.embedding_dim], -1.0, 1.0), name='word_embeddings')
        embeddings = tf.nn.embedding_lookup(self.word_embeddings, self.inputs)
        
        # 定义双向LSTM层
        cell_fw = tf.contrib.rnn.LSTMCell(self.hidden_dim)
        cell_bw = tf.contrib.rnn.LSTMCell(self.hidden_dim)
        (output_fw, output_bw), _ = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, embeddings, dtype=tf.float32, sequence_length=self.seq_length)
        output = tf.concat([output_fw, output_bw], axis=-1)
        
        # 定义全连接层
        W = tf.get_variable('W', shape=[2*self.hidden_dim, self.num_tags], initializer=tf.contrib.layers.xavier_initializer())
        b = tf.get_variable('b', shape=[self.num_tags], initializer=tf.zeros_initializer())
        
        output = tf.reshape(output, [-1, 2*self.hidden_dim])
        logits = tf.matmul(output, W) + b
        self.logits = tf.reshape(logits, [-1, tf.shape(self.inputs)[1], self.num_tags])
        
        # 定义CRF层
        self.transition_params = tf.get_variable('transition_params', shape=[self.num_tags, self.num_tags], initializer=tf.contrib.layers.xavier_initializer())
        
    def _build_loss(self):
        log_likelihood, self.transition_params = crf_log_likelihood(inputs=self.logits, tag_indices=self.targets, sequence_lengths=self.seq_length, transition_params=self.transition_params)
        self.loss = tf.reduce_mean(-log_likelihood)
    
    def _build_optimizer(self):
        self.optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)
        
    def train(self, inputs, targets, seq_length):
        feed_dict = {self.inputs: inputs, self.targets: targets, self.seq_length: seq_length}
        _, loss = self.sess.run([self.optimizer, self.loss], feed_dict=feed_dict)
        return loss
    
    def predict(self, inputs, seq_length):
        feed_dict = {self.inputs: inputs, self.seq_length: seq_length}
        logits, transition_params = self.sess.run([self.logits, self.transition_params], feed_dict=feed_dict)
        viterbi_sequences = []
        for logit, length in zip(logits, seq_length):
            logit = logit[:length]
            viterbi_seq, _ = viterbi_decode(logit, transition_params)
            viterbi_sequences.append(viterbi_seq)
        return viterbi_sequences

这里实现了一个包含词嵌入、双向LSTM、全连接和CRF层的模型，并使用Adam优化器进行训练。在训练过程中，需要传入输入序列、目标序列和序列长度；在预测过程中，只需要传入输入序列和序列长度即可得到预测的标注序列。

Tansformer时间序列预测模型代码

以下是使用 Transformer 模型进行时间序列预测的 Python 代码示例：

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import MinMaxScaler

# 加载数据
data = pd.read_csv('data.csv', header=None)
dataset = data.values.astype('float32')

# 数据归一化
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# 定义函数获取输入输出数据
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset) - look_back - 1):
        a = dataset[i:(i + look_back), 0]
        X.append(a)
        Y.append(dataset[i + look_back, 0])
    return np.array(X), np.array(Y)

# 创建训练和测试数据集
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :]

look_back = 3
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# 转换数据为张量
trainX = tf.convert_to_tensor(trainX)
trainY = tf.convert_to_tensor(trainY)
testX = tf.convert_to_tensor(testX)
testY = tf.convert_to_tensor(testY)

# 定义 Transformer 模型
class Transformer(tf.keras.Model):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, maximum_position_encoding, rate=0.1):
        super(Transformer, self).__init__()

        self.d_model = d_model
        self.embedding = tf.keras.layers.Dense(d_model, activation='relu')
        self.pos_encoding = positional_encoding(maximum_position_encoding, d_model)

        self.encoder = Encoder(num_layers, d_model, num_heads, dff, rate)

        self.final_layer = tf.keras.layers.Dense(1)

    def call(self, x, training):
        seq_len = tf.shape(x)[1]
        x = self.embedding(x)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.pos_encoding[:, :seq_len, :]
        x = self.encoder(x, training)
        x = self.final_layer(x)
        return x

def positional_encoding(position, d_model):
    angle_rads = get_angles(np.arange(position)[:, np.newaxis], np.arange(d_model)[np.newaxis, :], d_model)
    # 对数组中的偶数元素使用 sin 函数
    sines = np.sin(angle_rads[:, 0::2])
    # 对数组中的奇数元素使用 cos 函数
    cosines = np.cos(angle_rads[:, 1::2])
    pos_encoding = np.concatenate([sines, cosines], axis=-1)
    pos_encoding = pos_encoding[np.newaxis, ...]
    return tf.cast(pos_encoding, dtype=tf.float32)

def get_angles(pos, i, d_model):
    angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(d_model))
    return pos * angle_rates

class MultiHeadAttention(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model

        assert d_model % self.num_heads == 0

        self.depth = d_model // self.num_heads

        self.wq = tf.keras.layers.Dense(d_model)
        self.wk = tf.keras.layers.Dense(d_model)
        self.wv = tf.keras.layers.Dense(d_model)

        self.dense = tf.keras.layers.Dense(d_model)

    def split_heads(self, x, batch_size):
        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
        return tf.transpose(x, perm=[0, 2, 1, 3])

    def call(self, v, k, q, mask):
        batch_size = tf.shape(q)[0]

        q = self.wq(q)
        k = self.wk(k)
        v = self.wv(v)

        q = self.split_heads(q, batch_size)
        k = self.split_heads(k, batch_size)
        v = self.split_heads(v, batch_size)

        scaled_attention, attention_weights = scaled_dot_product_attention(q, k, v, mask)

        scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])
        concat_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model))

        output = self.dense(concat_attention)

        return output, attention_weights

def scaled_dot_product_attention(q, k, v, mask):
    matmul_qk = tf.matmul(q, k, transpose_b=True)

    dk = tf.cast(tf.shape(k)[-1], tf.float32)
    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)

    if mask is not None:
        scaled_attention_logits += (mask * -1e9)

    attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)

    output = tf.matmul(attention_weights, v)

    return output, attention_weights

class PointWiseFeedForwardNetwork(tf.keras.layers.Layer):
    def __init__(self, d_model, dff):
        super(PointWiseFeedForwardNetwork, self).__init__()
        self.dense1 = tf.keras.layers.Dense(dff, activation='relu')
        self.dense2 = tf.keras.layers.Dense(d_model)

    def call(self, x):
        x = self.dense1(x)
        x = self.dense2(x)
        return x

class EncoderLayer(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(EncoderLayer, self).__init__()

        self.mha = MultiHeadAttention(d_model, num_heads)
        self.ffn = PointWiseFeedForwardNetwork(d_model, dff)

        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)

        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)

    def call(self, x, training, mask=None):
        attn_output, _ = self.mha(x, x, x, mask)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(x + attn_output)

        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = self.layernorm2(out1 + ffn_output)

        return out2

class Encoder(tf.keras.layers.Layer):
    def __init__(self, num_layers, d_model, num_heads, dff, maximum_position_encoding, rate=0.1):
        super(Encoder, self).__init__()

        self.d_model = d_model
        self.num_layers = num_layers

        self.embedding = tf.keras.layers.Dense(d_model, activation='relu')
        self.pos_encoding = positional_encoding(maximum_position_encoding, d_model)

        self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate) for _ in range(num_layers)]

        self.dropout = tf.keras.layers.Dropout(rate)

    def call(self, x, training, mask=None):
        seq_len = tf.shape(x)[1]

        x = self.embedding(x)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.pos_encoding[:, :seq_len, :]

        x = self.dropout(x, training=training)

        for i in range(self.num_layers):
            x = self.enc_layers[i](x, training, mask)

        return x

# 定义模型参数
num_layers = 4
d_model = 128
dff = 512
num_heads = 8
input_vocab_size = len(trainX)
dropout_rate = 0.1
maximum_position_encoding = len(trainX)

# 创建 Transformer 模型
model = Transformer(num_layers, d_model, num_heads, dff, input_vocab_size, maximum_position_encoding, dropout_rate)

# 定义优化器和损失函数
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
loss_object = tf.keras.losses.MeanSquaredError()

# 定义评估指标
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.MeanAbsoluteError(name='train_mae')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.MeanAbsoluteError(name='test_mae')

# 定义训练函数
@tf.function
def train_step(inputs, targets):
    with tf.GradientTape() as tape:
        predictions = model(inputs, True)
        loss = loss_object(targets, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss(loss)
    train_accuracy(targets, predictions)

# 定义测试函数
@tf.function
def test_step(inputs, targets):
    predictions = model(inputs, False)
    t_loss = loss_object(targets, predictions)

    test_loss(t_loss)
    test_accuracy(targets, predictions)

# 训练模型
EPOCHS = 100
for epoch in range(EPOCHS):
    for i in range(len(trainX)):
        train_step(trainX[i], trainY[i])

    for i in range(len(testX)):
        test_step(testX[i], testY[i])

    template = 'Epoch {}, Loss: {}, MAE: {}, Test Loss: {}, Test MAE: {}'
    print(template.format(epoch + 1, train_loss.result(), train_accuracy.result(), test_loss.result(), test_accuracy.result()))

# 使用模型进行预测
predictions = []
for i in range(len(testX)):
    prediction = model(testX[i], False)
    predictions.append(prediction.numpy()[0][0])

# 反归一化预测结果
predictions = scaler.inverse_transform(np.array(predictions).reshape(-1, 1))

# 反归一化真实结果
testY = scaler.inverse_transform(testY.reshape(-1, 1))

# 输出预测结果和真实结果
for i in range(len(predictions)):
    print('Predicted:', predictions[i][0], 'Actual:', testY[i][0])

在上述代码中，我们首先加载了时间序列数据，然后对数据进行归一化处理。接着，我们定义了一个函数来生成输入输出数据，用于训练模型。然后，我们将数据转换为张量，并定义了一个 Transformer 模型。接着，我们定义了训练和测试函数，使用 Adam 优化器和均方误差损失函数进行模型训练。在训练完成后，我们使用模型对测试数据进行预测，并将预测结果反归一化，以得到真实的预测结果。最后，我们输出了预测结果和真实结果，以便进行比较和评估。