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[pre-commit.ci] pre-commit autoupdate (#6994)
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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pre-commit-ci[bot] committed Aug 1, 2023
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6 changes: 3 additions & 3 deletions .pre-commit-config.yaml
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Expand Up @@ -19,17 +19,17 @@ repos:
- id: trailing-whitespace

- repo: https://github.com/python-jsonschema/check-jsonschema
rev: 0.23.2
rev: 0.23.3
hooks:
- id: check-github-workflows

- repo: https://github.com/psf/black
rev: 23.3.0
rev: 23.7.0
hooks:
- id: black

- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.0.276
rev: v0.0.281
hooks:
- id: ruff
args: ["--fix"]
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import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

1. 数据加载和预处理

data = pd.read_csv('data.csv')
data['时间'] = pd.to_datetime(data['时间']) # 将时间列转换为日期格式
data.set_index('时间', inplace=True) # 将时间列设置为索引
data = data.dropna() # 去除缺失值

2. 特征归一化

scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(data)

3. 创建训练集和测试集

假设你有50条数据，可以将前40条作为训练集，后10条作为测试集

train_size = 40
train_data = scaled_data[:train_size, :]
test_data = scaled_data[train_size:, :]

4. 定义函数生成训练数据和标签

def create_dataset(dataset, time_steps=1):
X, Y = [], []
for i in range(len(dataset) - time_steps):
a = dataset[i:(i + time_steps), :]
X.append(a)
Y.append(dataset[i + time_steps, 5]) # 灌溉量是第六列数据，索引为5
return np.array(X), np.array(Y)

time_steps = 5 # 设定LSTM的时间步长，这里设为5
X_train, y_train = create_dataset(train_data, time_steps)
X_test, y_test = create_dataset(test_data, time_steps)

5. 构建LSTM模型

model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(time_steps, 5)))
model.add(LSTM(50, return_sequences=False))
model.add(Dense(1))

6. 编译模型

model.compile(optimizer='adam', loss='mean_squared_error')

7. 训练模型

model.fit(X_train, y_train, epochs=100, batch_size=16, verbose=2)

8. 预测并还原数据

train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

train_predict = scaler.inverse_transform(train_predict)
y_train = scaler.inverse_transform([y_train])
test_predict = scaler.inverse_transform(test_predict)
y_test = scaler.inverse_transform([y_test])

9. 可视化结果

import matplotlib.pyplot as plt

plt.plot(np.arange(0, len(y_train[0])), y_train[0], label="True Value")
plt.plot(np.arange(len(y_train[0]), len(y_train[0]) + len(y_test[0])), y_test[0], label="True Value(Test)")
plt.plot(np.arange(len(y_train[0])), train_predict[:, 0], label="Prediction")
plt.plot(np.arange(len(y_train[0]), len(y_train[0]) + len(y_test[0])), test_predict[:, 0], label="Prediction(Test)")
plt.legend()
plt.show()

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