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en-ems-0.2.2
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مشاهده بیشترتوضیحات
A package for selecting ensemble members using entropy theory
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | en-ems-0.2.2 |
| نام | en-ems |
| نسخه کتابخانه | 0.2.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Andre D. L. Zanchetta |
| ایمیل نویسنده | adlzanchetta@gmail.com |
| آدرس صفحه اصلی | https://github.com/adlzanchetta/en-ems |
| آدرس اینترنتی | https://pypi.org/project/en-ems/ |
| مجوز | - |
# En-EMS | *Entropy-based Ensemble Members Selection*
*en-ems* is a Python library for the selection of a set of mutually exclusive, collectivelly exaustive (MECE) ensemble members.
The library implements the approach presented by [Darbandsari and Coulibaly (2020)](http://doi.org/https://doi.org/10.1016/j.jhydrol.2020.125577) as step that antecedes the further merging of a set of ensemble forecasts.
The *en-ems* package is built over the [pyitlib](https://pypi.org/project/pyitlib/) package, which implements fundamental information theory methods.
## Installing
The library can be installed using the traditional pip:
```bash
pip install en-ems
```
And is listed on the Python Package Index (*pypi*) as [en-ems](https://pypi.org/project/en-ems/).
## Using
Suppose you have a file named ```example.csv``` with the following content:
```
Date, Memb_A, Memb_B, ..., Memb_Z, Obsv
2020/05/15, 1.12, 1.05, ..., 0.5, 1.01
2020/05/16, 1.15, 1.12, ..., 0.9, 1.10
2020/05/17, 1.13, 1.32, ..., 1.1, 1.29
... ... ... ..., ..., ...
2020/11/30, 1.22, 0.95, ..., 0.3, 0.87
```
In which the columns starting with "Memb_" hold the realization of one ensemble member for the time interval and "Obsv" holds the observed values for the same time interval.
If your our objective is to select a MECE set considering obaservations, it can be done using the standard parameters by:
```python
import pandas as pd
import enems
# read file
data_ensemble = pd.read_csv("example.csv").to_dict('list')
data_obsv = data_ensemble["Obsv"]
del data_ensemble["Obsv"], data_ensemble["Date"]
# perform selection
selection_log = enems.select_ensemble_members(data_ensemble, data_obsv)
```
The variable ```selection_log``` will be a dictionary containing a log of the *total correlation*, *joint antropy* and (if an observation was given) the *transinformation* of the given and selected datasets. It also contains, as expected, the ids of the selected ensemble members.
## Example 1: No observation data available
Mock data for a dataset with 75 supposed ensemble members and without observation records can be obtained with the function ```enems.load_data_75()```.
Here is a full example on how we can access the mock data, select a MECE subset and visualize the results using the popular ```matplotlib``` is given:
```python
import matplotlib.pyplot as plt
import enems
if __name__ == "__main__":
# ## LOAD DATA ################################################################################################### #
test_data_df = enems.load_data_75()
test_data = test_data_df.to_dict("list")
# ## SELECT MECE SUBSET ########################################################################################## #
selection_log = enems.select_ensemble_members(test_data, None, n_bins=10, bin_by="equal_intervals",
beta_threshold=0.95, n_processes=1, verbose=False)
# ## PLOT FUNCTIONS ############################################################################################## #
def plot_ensemble_members(all_series: dict, selected_series: set, plot_title: str, output_file_path: str) -> None:
_, axs = plt.subplots(1, 1, figsize=(7, 2.5))
axs.set_xlabel("Time")
axs.set_ylabel("Value")
axs.set_title(plot_title)
axs.set_xlim(0, 143)
axs.set_ylim(0, 5)
[axs.plot(all_series[series_id], color="#999999", zorder=3, alpha=0.33) for series_id in selected_series]
plt.tight_layout()
plt.savefig(output_file_path)
plt.close()
return None
def plot_log(n_total_members: int, log: dict, output_file_path: str) -> None:
_, axss = plt.subplots(1, 2, figsize=(7.0, 2.5))
x_values=[n_total_members-i-1 for i in range(len(log["history"]["total_correlation"]))]
axss[0].set_xlabel("Time")
axss[0].set_ylabel("Total correlation")
axss[0].plot(x_values, log["history"]["total_correlation"], color="#7777FF", zorder=3)
axss[0].set_ylim(70, 140)
axss[0].set_xlim(x_values[0], x_values[-1])
axss[1].set_xlabel("Time")
axss[1].set_ylabel("Joint entropy")
axss[1].axhline(log["original_ensemble_joint_entropy"], color="#FF7777", zorder=3, label="Full set")
axss[1].plot(x_values, log["history"]["joint_entropy"], color="#7777FF", zorder=3, label="Selected set")
axss[1].set_ylim(6.3, 6.9)
axss[1].set_xlim(x_values[0], x_values[-1])
axss[1].legend()
plt.tight_layout()
plt.savefig(output_file_path)
plt.close()
return None
# ## FUNCTIONS CALL ############################################################################################## #
plot_log(len(test_data.keys()), selection_log, "test/log.svg")
plot_ensemble_members(test_data, set(test_data.keys()),
"All members (%d)" % len(test_data.keys()),
"test/ensemble_all.svg")
plot_ensemble_members(test_data, selection_log["selected_members"],
"Selected members (%d)" % len(selection_log["selected_members"]),
"test/ensemble_selected.svg")
```
Which would give us the following plot:
*log.svg*
*ensemble_all.svg*
*ensemble_selected.svg*
## Example 2:
Additional mock observation data compatible with the mock ensemble members is distributed with the package. It can be accessed using the funcion ```enems.load_data_obs()```.
An example on how to use it to trigger the full version of the algorithm can is presented:
```python
import matplotlib.pyplot as plt
import numpy as np
import enems
if __name__ == "__main__":
# ## LOAD DATA ################################################################################################### #
test_data_obs = enems.load_data_obs().values
test_data_df = enems.load_data_75()
test_data = test_data_df.to_dict("list")
# ## PLOT FUNCTIONS ############################################################################################## #
def plot_ensemble_members([...]):
[...]
def plot_log([...]):
[...]
# ## FUNCTIONS CALL ############################################################################################## #
cur_selection_log = enems.select_ensemble_members(test_data, test_data_obs, n_bins=10, bin_by="equal_intervals",
beta_threshold=0.95, n_processes=1, verbose=False)
plot_log(len(test_data.keys()), cur_selection_log, "test/log_obs.svg")
plot_ensemble_members(test_data, test_data_obs, set(test_data.keys()),
"All members (%d)" % len(test_data.keys()),
"test/ensemble_all_obs.svg")
plot_ensemble_members(test_data, test_data_obs, cur_selection_log["selected_members"],
"Selected members (%d)" % len(cur_selection_log["selected_members"]),
"test/ensemble_selected_obs.svg")
del test_data_obs, cur_selection_log
```
Which would give us the following plot:
*log_obs.svg*
*ensemble_all_obs.svg*
*ensemble_selected_obs.svg*
## Further documentation
Further information about the library can be found in the *docs* folder of the Git repository of this project.
The users are can find the complete theoretical explanation and assessment of the method in the original work of [Darbandsari and Coulibaly (2020)](http://doi.org/https://doi.org/10.1016/j.jhydrol.2020.125577).
نیازمندی
| مقدار | نام |
|---|---|
| ==0.2.2 | pyitlib |
| <1,>=0.23 | scikit-learn |
| >=1.3 | pandas |
نحوه نصب
نصب پکیج whl en-ems-0.2.2:
pip install en-ems-0.2.2.whl
نصب پکیج tar.gz en-ems-0.2.2:
pip install en-ems-0.2.2.tar.gz