array split does not result in an equal division报错怎么办
数据处理出现问题，除不开7，我该怎么修改数据呢？
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# 设置中文显示
plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei']
plt.rcParams['axes.unicode_minus'] = False
import math
import sklearn.metrics as skm
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Flatten, Input
from tensorflow.keras.layers import Conv1D, MaxPooling1D
from tensorflow.keras.layers import concatenate
from tensorflow.keras.utils import plot_model
def split_dataset(data):
'''
该函数实现以周为单位切分训练数据和测试数据
'''
# data为按天的耗电量统计数据，shape为(1442, 8)
# 测试集取最后一年的46周（322天）数据，剩下的159周（1113天）数据为训练集，以下的切片实现此功能。
train, test = data[1:-328], data[-328:-6]
train = np.array(np.split(train, len(train) / 7)) # 将数据划分为按周为单位的数据
test = np.array(np.split(test, len(test) / 7))
print('train.shape:{}, test.shape:{}\n'.format(train.shape, test.shape))
return train, test
def evaluate_forecasts(actual, predicted):
'''
该函数实现根据预期值评估一个或多个周预测损失
思路：统计所有单日预测的 RMSE
'''
scores = list()
for i in range(actual.shape[1]):
mse = skm.mean_squared_error(actual[:, i], predicted[:, i])
rmse = math.sqrt(mse)
scores.append(rmse)
s = 0 # 计算总的 RMSE
for row in range(actual.shape[0]):
for col in range(actual.shape[1]):
s += (actual[row, col] - predicted[row, col]) ** 2
score = math.sqrt(s / (actual.shape[0] * actual.shape[1]))
print('actual.shape[0]:{}, actual.shape[1]:{}'.format(actual.shape[0], actual.shape[1]))
return score, scores
def summarize_scores(name, score, scores):
s_scores = ', '.join(['%.1f' % s for s in scores])
print('%s: [%.3f] %s\n' % (name, score, s_scores))
def sliding_window(train, sw_width=7, n_out=7, in_start=0):
'''
该函数实现窗口宽度为7、滑动步长为1的滑动窗口截取序列数据
截取所有特征
'''
data = train.reshape((train.shape[0] * train.shape[1], train.shape[2])) # 将以周为单位的样本展平为以天为单位的序列
X, y = [], []
for _ in range(len(data)):
in_end = in_start + sw_width
out_end = in_end + n_out
# 保证截取样本完整，最大元素索引不超过原序列索引，则截取数据；否则丢弃该样本
if out_end <= len(data):
# 因为是for循环，所以滑动窗口的滑动步长为1；想调整滑动步长可以通过yield实现，后边的文章会讲；
X.append(data[in_start:in_end, :]) # 截取窗口宽度数量的采样点的全部8个特征
y.append(data[in_end:out_end, 0]) # 截取样本之后7个时间步长的总有功功耗（截取一个单列片段，有7个元素）
in_start += 1 # 实现简单的滑动窗口，滑动步长为1
return np.array(X), np.array(y)
def multi_head_cnn_model(train, sw_width, in_start=0, verbose=0, epochs=20, batch_size=16):
'''
该函数定义 Multi-head CNN 模型
'''
train_x, train_y = sliding_window(train, sw_width)
n_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]
in_layers, out_layers = [], [] # 用于存放每个特征序列的CNN子模型
for i in range(n_features):
inputs = Input(shape=(n_timesteps, 1))
conv1 = Conv1D(filters=32, kernel_size=3, activation='relu')(inputs)
conv2 = Conv1D(filters=32, kernel_size=3, activation='relu')(conv1)
pool1 = MaxPooling1D(pool_size=2)(conv2)
flat = Flatten()(pool1)
in_layers.append(inputs)
out_layers.append(flat)
merged = concatenate(out_layers) # 合并八个CNN子模型
dense1 = Dense(200, activation='relu')(merged) # 全连接层对上一层输出特征进行解释
dense2 = Dense(100, activation='relu')(dense1)
outputs = Dense(n_outputs)(dense2)
model = Model(inputs=in_layers, outputs=outputs)
model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])
print(model.summary())
plot_model(model, to_file='multi-head-cnn-energy-usage-prediction.png', show_shapes=True, show_layer_names=True,
dpi=300)
input_data = [train_x[:, :, i].reshape((train_x.shape[0], n_timesteps, 1)) for i in range(n_features)]
# 这里只是为了方便演示和输出loss曲线，不建议这么做，这样其实是训练了2倍的epoch；
# 可以保存模型，再加载预测；或者直接将预测函数定影在这里，省去调用步骤。
model.fit(input_data, train_y, epochs=epochs, batch_size=batch_size, verbose=verbose)
history = model.fit(input_data, train_y, epochs=epochs, batch_size=batch_size, verbose=verbose)
return model, history
def forecast(model, pred_seq, sw_width):
'''
该函数实现对输入数据的预测
多个特征
'''
data = np.array(pred_seq)
data = data.reshape((data.shape[0] * data.shape[1], data.shape[2]))
input_x = data[-sw_width:, :] # 获取输入数据的最后一周的数据
input_x = [input_x[:, i].reshape((1, input_x.shape[0], 1)) for i in
range(input_x.shape[1])] # 8个形状为[1, sw_width, 1]的列表
yhat = model.predict(input_x, verbose=0) # 预测下周数据
yhat = yhat[0] # 获取预测向量
return yhat
def evaluate_model(model, train, test, sd_width):
'''
该函数实现模型评估
'''
history_fore = [x for x in train]
predictions = list() # 用于保存每周的前向验证结果；
for i in range(len(test)):
yhat_sequence = forecast(model, history_fore, sd_width) # 预测下周的数据
predictions.append(yhat_sequence) # 保存预测结果
history_fore.append(test[i, :]) # 得到真实的观察结果并添加到历史中以预测下周
predictions = np.array(predictions) # 评估一周中每天的预测结果
score, scores = evaluate_forecasts(test[:, :, 0], predictions)
return score, scores
def model_plot(score, scores, days, name, history):
'''
该函数实现绘制RMSE曲线图和训练损失图
'''
plt.figure(figsize=(8, 6), dpi=150)
plt.subplot(2, 1, 1)
plt.plot(days, scores, marker='o', label=name)
plt.grid(linestyle='--', alpha=0.5)
plt.xlabel(r'$weekday$', size=15)
plt.ylabel(r'$RMSE$', size=15)
plt.title('Multi-head CNN 模型预测结果', size=18)
plt.subplot(2, 1, 2)
plt.plot(history.history['loss'], label='train')
plt.title('loss', y=0, loc='center')
plt.xlabel('$epochs$', size=10)
plt.ylabel('$loss$', size=10)
plt.legend()
plt.grid(linestyle='--', alpha=0.5)
plt.tight_layout()
plt.show()
def main_run(dataset, sw_width, days, name, in_start, verbose, epochs, batch_size):
'''
主函数：数据处理、模型训练流程
'''
# 划分训练集和测试集
train, test = split_dataset(dataset.values)
# 训练模型
model, history = multi_head_cnn_model(train, sw_width, in_start, verbose, epochs, batch_size)
# 计算RMSE
score, scores = evaluate_model(model, train, test, sw_width)
# 打印分数
summarize_scores(name, score, scores)
# 绘制RMSE图和训练损失图
model_plot(score, scores, days, name, history)
if __name__ == '__main__':
dataset = pd.read_csv('household_power_consumption.csv', header=0,
infer_datetime_format=True, engine='c',
parse_dates=['datetime'], index_col=['datetime'])
days = ['sun', 'mon', 'tue', 'wed', 'thr', 'fri', 'sat']
name = 'cnn'
sliding_window_width = 14
input_sequence_start = 0
epochs_num = 80 # 25
batch_size_set = 16
verbose_set = 0
main_run(dataset, sliding_window_width, days, name, input_sequence_start,
verbose_set, epochs_num, batch_size_set)
尝试使用np.array_split代替np.split