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Expand Up @@ -5,7 +5,6 @@ src="https://thieu1995.github.io/post/2023-08/mafese-02.png"
alt="MAFESE"/>
</p>


---

[![GitHub release](https://img.shields.io/badge/release-0.1.9-yellow.svg)](https://github.com/thieu1995/mafese/releases)
Expand Down Expand Up @@ -41,26 +40,55 @@ problem using meta-heuristic algorithms.
* **📦 Dependencies:** `numpy`, `scipy`, `scikit-learn`, `pandas`, `mealpy`, `permetrics`, `plotly`, `kaleido`


# Installation
### Citation Request

* Install the [current PyPI release](https://pypi.python.org/pypi/mafese):
```sh
$ pip install mafese==0.1.9
```
Please include these citations if you plan to use this incredible library:


```code
@article{van2024feature,
title={Feature selection using metaheuristics made easy: Open source MAFESE library in Python},
author={Van Thieu, Nguyen and Nguyen, Ngoc Hung and Heidari, Ali Asghar},
journal={Future Generation Computer Systems},
year={2024},
publisher={Elsevier}
}
@article{van2023mealpy,
title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
author={Van Thieu, Nguyen and Mirjalili, Seyedali},
journal={Journal of Systems Architecture},
year={2023},
publisher={Elsevier},
doi={10.1016/j.sysarc.2023.102871}
}
* Install directly from source code
```sh
$ git clone https://github.com/thieu1995/mafese.git
$ cd mafese
$ python setup.py install
```

* In case, you want to install the development version from Github:
# Usage

## Goals

- **Our library provides all state-of-the-art feature selection methods**:
+ Unsupervised-based FS
+ Filter-based FS
+ Embedded-based FS
+ Regularization (Lasso-based)
+ Tree-based methods
+ Wrapper-based FS
+ Sequential-based: forward and backward
+ Recursive-based
+ MHA-based: Metaheuristic Algorithms

## Installation

* Install the [current PyPI release](https://pypi.python.org/pypi/mafese):
```sh
$ pip install git+https://github.com/thieu1995/mafese
$ pip install mafese
```

After installation, you can import MAFESE as any other Python module:
After installation, you can import MAFESE and check its installed version:

```sh
$ python
Expand All @@ -69,9 +97,10 @@ $ python
```


### Lib's structure

```code
<details><summary><h2>Lib's structure</h2></summary>

```code
docs
examples
mafese
Expand Down Expand Up @@ -107,13 +136,16 @@ README.md
setup.py
```

### Examples
</details>


## Examples

Let's go through some examples.

#### 1. First, load dataset. You can use the available datasets from Mafese:
### 1. First, load dataset. You can use the available datasets from Mafese:

```python
```python
# Load available dataset from MAFESE
from mafese import get_dataset

Expand All @@ -137,20 +169,30 @@ X, y = dataset[:, 0:-1], dataset[:, -1]
data = Data(X, y)
```

#### 2. Next, split dataset into train and test set
### 2. Next, prepare your dataset


#### 2.1 Split dataset into train and test set

```python
```python
data.split_train_test(test_size=0.2, inplace=True)
print(data.X_train[:2].shape)
print(data.y_train[:2].shape)
```

**You should confirm that your dataset is scaled and normalized for some problem or estimator such as Neural Network**
#### 2.2 Feature Scaling

```python
data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("standard", "minmax"))
data.X_test = scaler_X.transform(data.X_test)

data.y_train, scaler_y = data.encode_label(data.y_train) # This is for classification problem only
data.y_test = scaler_y.transform(data.y_test)
```

#### 3. Next, choose the Selector that you want to use by first import them:
### 3. Next, choose the Selector that you want to use by first import them:

```python
```python
## First way, we recommended
from mafese import UnsupervisedSelector, FilterSelector, LassoSelector, TreeSelector
from mafese import SequentialSelector, RecursiveSelector, MhaSelector, MultiMhaSelector
Expand All @@ -165,9 +207,9 @@ from mafese.wrapper.recursive import RecursiveSelector
from mafese.wrapper.mha import MhaSelector, MultiMhaSelector
```

#### 4. Next, create an instance of Selector class you want to use:
### 4. Next, create an instance of Selector class you want to use:

```python
```python
feat_selector = UnsupervisedSelector(problem='classification', method='DR', n_features=5)

feat_selector = FilterSelector(problem='classification', method='SPEARMAN', n_features=5)
Expand All @@ -191,15 +233,15 @@ feat_selector = MultiMhaSelector(problem="classification", estimator="knn",
transfer_func="vstf_01", obj_name="AS")
```

#### 5. Fit the model to X_train and y_train
### 5. Fit the model to X_train and y_train

```python
```python
feat_selector.fit(data.X_train, data.y_train)
```

#### 6. Get the information
### 6. Get the information

```python
```python
# check selected features - True (or 1) is selected, False (or 0) is not selected
print(feat_selector.selected_feature_masks)
print(feat_selector.selected_feature_solution)
Expand All @@ -208,64 +250,75 @@ print(feat_selector.selected_feature_solution)
print(feat_selector.selected_feature_indexes)
```

#### 7. Call transform() on the X that you want to filter it down to selected features
### 7. Call transform() on the X that you want to filter it down to selected features

```python
```python
X_train_selected = feat_selector.transform(data.X_train)
X_test_selected = feat_selector.transform(data.X_test)
```

#### 8.You can build your own evaluating method or use our method.
### 8.You can build your own evaluating method or use our method.

**If you use our method, don't transform the data.**

i) You can use difference estimator than the one used in feature selection process
```python
#### 8.1 You can use difference estimator than the one used in feature selection process
```python
feat_selector.evaluate(estimator="svm", data=data, metrics=["AS", "PS", "RS"])

## Here, we pass the data that was loaded above. So it contains both train and test set. So, the results will look
like this:
{'AS_train': 0.77176, 'PS_train': 0.54177, 'RS_train': 0.6205, 'AS_test': 0.72636, 'PS_test': 0.34628, 'RS_test': 0.52747}
```

ii) You can use the same estimator in feature selection process
```python
#### 8.2 You can use the same estimator in feature selection process
```python
X_test, y_test = data.X_test, data.y_test
feat_selector.evaluate(estimator=None, data=data, metrics=["AS", "PS", "RS"])
```

1) Where do I find the supported metrics like above ["AS", "PS", "RS"]. What is that?
For more usage examples please look at [examples](/examples) folder.


# Support

## Some popular questions

1. Where do I find the supported metrics like above ["AS", "PS", "RS"]. What is that?

You can find it here: https://github.com/thieu1995/permetrics or use this
```python

```python
from mafese import MhaSelector

print(MhaSelector.SUPPORTED_REGRESSION_METRICS)
print(MhaSelector.SUPPORTED_CLASSIFICATION_METRICS)
```

3) How do I know my Selector support which estimator? which methods?
```python
2. How do I know my Selector support which estimator? which methods?

```python
print(feat_selector.SUPPORT)
```
Or you better read the document from: https://mafese.readthedocs.io/en/latest/

3) I got this type of error
```python
3. I got this type of error. How to solve it?

```python
raise ValueError("Existed at least one new label in y_pred.")
ValueError: Existed at least one new label in y_pred.
```
How to solve this?
```

+ This occurs only when you are working on a classification problem with a small dataset that has many classes. For
> This occurs only when you are working on a classification problem with a small dataset that has many classes. For
instance, the "Zoo" dataset contains only 101 samples, but it has 7 classes. If you split the dataset into a
training and testing set with a ratio of around 80% - 20%, there is a chance that one or more classes may appear
in the testing set but not in the training set. As a result, when you calculate the performance metrics, you may
encounter this error. You cannot predict or assign new data to a new label because you have no knowledge about the
new label. There are several solutions to this problem.


+ 1st: Use the SMOTE method to address imbalanced data and ensure that all classes have the same number of samples.

```python
```python
from imblearn.over_sampling import SMOTE
import pandas as pd
from mafese import Data
Expand All @@ -289,14 +342,9 @@ data.split_train_test(test_size=0.2, random_state=10) # Try different random_s
```


For more usage examples please look at [examples](/examples) folder.


# Support (questions, problems)
<details><summary><h2>Official Links</h2></summary>

### Official Links

* Official source code repo: https://github.com/thieu1995/mafese
* Official source code repository: https://github.com/thieu1995/mafese
* Official document: https://mafese.readthedocs.io/
* Download releases: https://pypi.org/project/mafese/
* Issue tracker: https://github.com/thieu1995/mafese/issues
Expand All @@ -314,6 +362,28 @@ For more usage examples please look at [examples](/examples) folder.
* https://github.com/thieu1995/pfevaluator
* https://github.com/aiir-team

</details>



<details><summary><h2>Related Documents</h2></summary>

1. https://neptune.ai/blog/feature-selection-methods
2. https://www.blog.trainindata.com/feature-selection-machine-learning-with-python/
3. https://github.com/LBBSoft/FeatureSelect
4. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2754-0
5. https://github.com/scikit-learn-contrib/boruta_py
6. https://elki-project.github.io/
7. https://sci2s.ugr.es/keel/index.php
8. https://archive.ics.uci.edu/datasets
9. https://python-charts.com/distribution/box-plot-plotly/
10. https://plotly.com/python/box-plots/?_ga=2.50659434.2126348639.1688086416-114197406.1688086416#box-plot-styling-mean--standard-deviation

</details>




### Citation Request

Please include these citations if you plan to use this library:
Expand All @@ -338,18 +408,3 @@ Please include these citations if you plan to use this library:
doi={10.1016/j.sysarc.2023.102871}
}
```



### Related Documents

1. https://neptune.ai/blog/feature-selection-methods
2. https://www.blog.trainindata.com/feature-selection-machine-learning-with-python/
3. https://github.com/LBBSoft/FeatureSelect
4. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2754-0
5. https://github.com/scikit-learn-contrib/boruta_py
6. https://elki-project.github.io/
7. https://sci2s.ugr.es/keel/index.php
8. https://archive.ics.uci.edu/datasets
9. https://python-charts.com/distribution/box-plot-plotly/
10. https://plotly.com/python/box-plots/?_ga=2.50659434.2126348639.1688086416-114197406.1688086416#box-plot-styling-mean--standard-deviation

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