![在这里插入图片描述](/static/img/images/00329.png)





```python
def get_EMC(temperature_c, humidity1):
    """
    temperature_c  unit C
    humidity1 unit %
    """
    humidity = humidity1 / 100
    temperature_f = 32 + (9.0 / 5.0 * temperature_c)
    EMC_W = 330 + 0.0452 * temperature_f + 0.00415 * temperature_f
    EMC_K = 0.791 + 0.000463 * temperature_f - 0.000000844 * temperature_f * temperature_f
    EMC_K1 = 6.34 + 0.000775 * temperature_f - 0.0000935 * temperature_f * temperature_f
    EMC_K2 = 1.09 + 0.0284 * temperature_f - 0.0000904 * temperature_f * temperature_f
    EMC = 1800 / EMC_W * ((EMC_K * humidity / (1 - EMC_K * humidity)) + (
            EMC_K1 * EMC_K * humidity + 2 * EMC_K1 * EMC_K2 * EMC_K * EMC_K * humidity * humidity) / (
                                  1 + EMC_K1 * EMC_K * humidity + EMC_K1 * EMC_K2 * EMC_K * EMC_K * humidity * humidity))
    return EMC
```

python 平衡含水率计算公式

# 解决办法





一句话，给搜索路径，代码在调用路人甲，就把路人甲的父亲路径加入即可，可以采取的办法1是绝对路径：

```python
import os
import sys
sys.path.append("/home/kevin_xie/yifeinfs/pycharm_k/python_test/")
```

办法2是相对路径+sys的一些方法：

```python
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) # to get "/home/kevin_xie/yifeinfs/pycharm_k/python_test/"
```

# 详细说明

python提示找不到包怎么办？

比如我有如下的工程，里面有reco1包和reco2包，每个包里面还有sub包。在prfunction.py中有一个pr函数，我想在test1.py中去运行这个函数。

![在这里插入图片描述](/static/img/images/00330.png)

如果在pythcharm中，你可以这么写，没有问题，能找到包。

```powershell
from reco1.sub.prfunction import pr
pr()
```

但你在终端执行，在/home/kevin_xie/yifeinfs/pycharm_k/python_test/reco2/sub2执行是执行不了的：

```powershell
(py37) [kevin_xie@localhost sub2]$ pwd
/home/kevin_xie/yifeinfs/pycharm_k/python_test/reco2/sub2
(py37) [kevin_xie@localhost sub2]$ python test1.py
Traceback (most recent call last):
  File "test1.py", line 2, in <module>
    from reco1.sub.prfunction import pr
ModuleNotFoundError: No module named 'reco1'
```

或者你可以去/home/kevin_xie/yifeinfs/pycharm_k/python_test执行，也是不行的：

```powershell
(py37) [kevin_xie@localhost python_test]$ pwd
/home/kevin_xie/yifeinfs/pycharm_k/python_test
(py37) [kevin_xie@localhost python_test]$ python reco2/sub2/test1.py
Traceback (most recent call last):
  File "reco2/sub2/test1.py", line 2, in <module>
    from reco1.sub.prfunction import pr
ModuleNotFoundError: No module named 'reco1'
```

推出方法，如果这么写，将想调用的包的父亲路径加入，就能寻找到了。


```powershell
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) # to get "/home/kevin_xie/yifeinfs/pycharm_k/python_test/"
from reco1.sub.prfunction import pr
pr()
```

python 导包找不到路径终极解决办法 ModuleNotFoundError: No module named ‘xxx’

# 问题





预测模型执行代码过慢，想要快速预测。

每次都是将历史数据作为训练数据喂给模型，然后让模型输出预测数据。这个模型的训练过程太慢。

# 解决

（1）讲训练的模型存储起来，每次预测的时候直接用存储的模型进行预测，躲开训练环节；

（2）有新数据的时候，可以在不紧急的时候训练模型并存储起来；依照执行速度，可以设定更新频率，比如2个小时更新一次模型，1天更新一次模型。

# 实现

建立路径：

![在这里插入图片描述](/static/img/images/00331.png)

quikly_predict.py 内容
```python
import os
from fbprophet import Prophet
import pandas as pd
import matplotlib.pyplot as plt
import math
import pickle
class data_quikly_predict:
    pwd = os.path.dirname(os.path.abspath(__file__))
    data_dir = os.path.join(pwd, "data")
    def __init__(self):
        if not os.path.exists(self.data_dir):
            os.mkdir(self.data_dir)
        self.model_list = self.find_all_file(self.data_dir)
    def update_model_list(self):
        self.model_list = self.find_all_file(self.data_dir)
    def find_all_file(self, dirname):
        result = []
        for maindir, subdir, file_name_list in os.walk(dirname):
            for filename in file_name_list:
                apath = os.path.join(maindir, filename)
                result.append(apath)
        return result
    def create_new_model(self, df: pd.DataFrame, new_model_name: str):
        """
        :param df: 训练新模型所需要的数据
        :param new_model_name: 模型名称
        :return:
        """
        assert ("ds" in df.columns) and ("y" in df.columns)
        df['ds'] = df['ds'].astype('datetime64[ns]')
        model = Prophet(yearly_seasonality=True, weekly_seasonality=True)
        model.fit(df)  # 训练模型m
        model_file = os.path.join(self.data_dir, new_model_name)
        if model_file in self.model_list:
            print("模型名称已经被使用,但即将被置换\r\n")
        with open(model_file, 'wb') as f:
            pickle.dump(model, f)
        self.update_model_list()
    def predict(self, future_time: pd.DataFrame, model_name: str):
        """
        :param future_time:未来需要预测的时间点DataFrame，只有一行'ds'
        :param model_name:想要使用的模型名称
        :return:
        """
        model_file = os.path.join(self.data_dir, model_name)
        if model_file not in self.model_list:
            raise print("没有在data里找到这个模型文件，请确保用数据训练出了可使用的模型\r\n")
        with open(model_file, 'rb') as f:
            model = pickle.load(f)
            forecast = model.predict(future_time)  # 开始预测
            forecast = forecast[['ds', 'yhat']]
        return forecast
    def dummy_predict(self, periods, freq: str, model_name: str):
        """
        傻瓜化预测
        :param periods: 需要预测多少个数据结果
        :param freq: 从训练数据末尾调用频率
        :param model_name: 模型名称
        :return:
        """
        model_file = os.path.join(self.data_dir, model_name)
        if model_file not in self.model_list:
            raise print("没有在data里找到这个模型文件，请确保用数据训练出了可使用的模型\r\n")
        with open(model_file, 'rb') as f:
            model = pickle.load(f)
        future_time = model.make_future_dataframe(periods=periods, freq=freq, include_history=False)  # 预测的设置 还没预测
        forecast = model.predict(future_time)  # 开始预测
        forecast = forecast[['ds', 'yhat']]
        return forecast
    def quikly_plot_forecast(self, forecast: pd.DataFrame):
        plt.plot(list(forecast['ds']), list(forecast['yhat']), color='b')
        plt.show()
if __name__ == '__main__':
    dataqp = data_quikly_predict()
    timelist = list(pd.date_range(start='2021-01-01 00:00:00', end='2022-01-01 00:00:00', freq='H'))
    y = [math.sin(data.hour) for k, data in enumerate(timelist)]
    data_df = pd.DataFrame({'ds': timelist, 'y': y})
    dataqp.create_new_model(data_df, "camera1")
    future = pd.DataFrame({'ds': list(pd.date_range(start='2022-05-01 00:00:00',
                                                    end='2022-05-05 01:00:00',
                                                    freq='H'))})  # 预测的设置 还没预测
    forecast_res = dataqp.predict(future, model_name="camera1")
    dataqp.quikly_plot_forecast(forecast_res)
```

# 如何使用

下面代码是正常过程，先执行训练，然后预测数据。
```python
import math
import pandas as pd
from fbprophet_persistent_prodict.quikly_predict import data_quikly_predict
dataqp = data_quikly_predict()
timelist = list(pd.date_range(start='2021-01-01 00:00:00', end='2022-01-01 00:00:00', freq='H'))
y = [math.sin(data.hour) for k, data in enumerate(timelist)]
data_df = pd.DataFrame({'ds': timelist, 'y': y})
dataqp.create_new_model(data_df, "camera6")
future = pd.DataFrame({'ds': list(pd.date_range(start='2022-05-01 00:00:00',
                                            end='2022-05-05 01:00:00',
                                            freq='H'))})  # 预测的设置 还没预测
forecast_res = dataqp.predict(future, model_name="camera6")
dataqp.quikly_plot_forecast(forecast_res)
```

先预测，后用训练更新模型：

```csharp
import math
import pandas as pd
from fbprophet_persistent_prodict.quikly_predict import data_quikly_predict
dataqp = data_quikly_predict()
future = pd.DataFrame({'ds': list(pd.date_range(start='2022-05-01 00:00:00',
                                            end='2022-05-05 01:00:00',
                                            freq='H'))})  # 预测的设置 还没预测
forecast_res = dataqp.predict(future, model_name="camera6")
timelist = list(pd.date_range(start='2021-01-01 00:00:00', end='2022-01-01 00:00:00', freq='H'))
y = [math.sin(data.hour) for k, data in enumerate(timelist)]
data_df = pd.DataFrame({'ds': timelist, 'y': y})
dataqp.create_new_model(data_df, "camera6")
```

python 时序预测模型 fbprophet 持久化存储 大数据快速预测

利用python解释器对文件内置的__file__属性获取：




```python
os.path.dirname(os.path.abspath(__file__))
```

os.path.abspath(__file__)是当前py文件在操作系统的绝对路径文件名，os.path.dirname获取文件所在路径。

python 获取当前py文件的绝对路径

# 安装Prophet





python: 3.7.9

pystan: 2.19.0.0

pandas

fbprophet: 0.6.0

anaconda方式：

conda install pystan=2.19.0.0

conda install -c conda-forge fbprophet=0.6.0

# Prophet介绍

https://www.cnblogs.com/bonelee/p/9577432.html

https://facebook.github.io/prophet/docs/quick_start.html

可以预测数据，也可以给出趋势。时间序列预测上，充满专家经验：周期趋势、离群点、突变点、突变。

# 时间跨度

make_future_dataframe中的fre是Offset aliases形式的，用的pandas时间跨度

https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases

比如1hour1min可以表示时间跨度是61分钟

# 预测未来1

利用函数make_future_dataframe生成未来时间DataFrame对象future ：

```python
from fbprophet import Prophet
import pandas as pd
import matplotlib.pyplot as plt
import math
timelist = list(pd.date_range(start='2021-01-01 00:00:00', end='2022-01-01 00:00:00', freq='H'))
y = [math.sin(data.hour) for k, data in enumerate(timelist)]
data_df = pd.DataFrame({'ds': timelist, 'y': y})
data_df['ds'] = data_df['ds'].astype('datetime64[ns]')
m = Prophet()
m.fit(data_df)  # 训练模型m
future = m.make_future_dataframe(periods=50, freq='H',include_history=False)  # 预测的设置 还没预测
forecast = m.predict(future)  # 开始预测
# plt.plot(data_df['ds'][-50:], data_df['y'][-50:], color='b')
# plt.plot(forecast['ds'], forecast['yhat'], color='r')
# plt.show()
plt.plot(list(data_df['ds'][-50:])+list(forecast['ds']),
         list(data_df['y'][-50:])+list(forecast['yhat']), color='b')
plt.show()
```

![在这里插入图片描述](/static/img/images/00332.png)

# 预测未来2

未来任意时间：

```python
# 1 取数据
# https://www.kesci.com/mw/dataset/5d64a35a8499bc002c07bb1b
from fbprophet import Prophet
import pandas as pd
import matplotlib.pyplot as plt
import math
timelist = list(pd.date_range(start='2021-01-01 00:00:00', end='2022-01-01 00:00:00', freq='H'))
y = [math.sin(data.hour) for k, data in enumerate(timelist)]
data_df = pd.DataFrame({'ds': timelist, 'y': y})
data_df['ds'] = data_df['ds'].astype('datetime64[ns]')
m = Prophet()
m.fit(data_df)  # 训练模型m
future = pd.DataFrame({'ds': list(pd.date_range(start='2022-05-01 00:00:00', end='2022-05-05 00:00:00', freq='H'))})  # 预测的设置 还没预测
forecast = m.predict(future)  # 开始预测
# plt.plot(data_df['ds'][-50:], data_df['y'][-50:], color='b')
plt.plot(forecast['ds'], forecast['yhat'], color='r')
plt.show()
# plt.plot(list(data_df['ds'][-50:])+list(forecast['ds']),
#          list(data_df['y'][-50:])+list(forecast['yhat']), color='b')
# plt.show()
```


![在这里插入图片描述](/static/img/images/00333.png)

# 预测模型持久化存储

时序预测模型 fbprophet

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