reflame 1.0.1

Description:

reflame 1.0.1

Reflame (REvolutionizing Functional Link Artificial neural networks by MEtaheuristic algorithms) is a Python library that
implements a framework for training Functional Link Neural Network (FLNN) networks using Metaheuristic Algorithms. It
provides a comparable alternative to the traditional FLNN network and is compatible with the Scikit-Learn library.
With Reflame, you can perform searches and hyperparameter tuning using the functionalities provided by the Scikit-Learn library.

Free software: GNU General Public License (GPL) V3 license
Provided Estimator: FlnnRegressor, FlnnClassifier, MhaFlnnRegressor, MhaFlnnClassifier
Total Official Metaheuristic-based Flnn Regression: > 200 Models
Total Official Metaheuristic-based Flnn Classification: > 200 Models
Supported performance metrics: >= 67 (47 regressions and 20 classifications)
Supported objective functions (as fitness functions or loss functions): >= 67 (47 regressions and 20 classifications)
Documentation: https://reflame.readthedocs.io
Python versions: >= 3.8.x
Dependencies: numpy, scipy, scikit-learn, pandas, mealpy, permetrics, torch, skorch

Citation Request
If you want to understand how Metaheuristic is applied to Functional Link Neural Network, you need to read the paper
titled "A resource usage prediction system using functional-link and genetic algorithm neural network for multivariate cloud metrics".
The paper can be accessed at the following this link
Please include these citations if you plan to use this library:
@software{nguyen_van_thieu_2023_8249046,
author = {Nguyen Van Thieu},
title = {Revolutionizing Functional Link Neural Network by Metaheuristic Algorithms: reflame - A Python Library},
month = 11,
year = 2023,
publisher = {Zenodo},
doi = {10.5281/zenodo.8249045},
url = {https://github.com/thieu1995/reflame}
}

@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}
}

@inproceedings{nguyen2019building,
author = {Thieu Nguyen and Binh Minh Nguyen and Giang Nguyen},
booktitle = {International Conference on Theory and Applications of Models of Computation},
organization = {Springer},
pages = {501--517},
title = {Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization},
year = {2019},
url={https://doi.org/10.1007/978-3-030-14812-6_31},
doi={10.1007/978-3-030-14812-6_31}
}

@inproceedings{nguyen2018resource,
author = {Thieu Nguyen and Nhuan Tran and Binh Minh Nguyen and Giang Nguyen},
booktitle = {2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA)},
organization = {IEEE},
pages = {49--56},
title = {A Resource Usage Prediction System Using Functional-Link and Genetic Algorithm Neural Network for Multivariate Cloud Metrics},
year = {2018},
url={https://doi.org/10.1109/SOCA.2018.00014},
doi={10.1109/SOCA.2018.00014}
}

Installation

Install the current PyPI release:

$ pip install reflame==1.0.1


Install directly from source code

$ git clone https://github.com/thieu1995/reflame.git
$ cd reflame
$ python setup.py install


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

$ pip install git+https://github.com/thieu1995/reflame

After installation, you can import Reflame as any other Python module:
$ python
>>> import reflame
>>> reflame.__version__

Examples
In this section, we will explore the usage of the Reflame model with the assistance of a dataset. While all the
preprocessing steps mentioned below can be replicated using Scikit-Learn, we have implemented some utility functions
to provide users with convenience and faster usage.
Combine Reflame library like a normal library with scikit-learn.
### Step 1: Importing the libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
from reflame import FlnnRegressor, FlnnClassifier, MhaFlnnRegressor, MhaFlnnClassifier

#### Step 2: Reading the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values

#### Step 3: Next, split dataset into train and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True, random_state=100)

#### Step 4: Feature Scaling
scaler_X = MinMaxScaler()
scaler_X.fit(X_train)
X_train = scaler_X.transform(X_train)
X_test = scaler_X.transform(X_test)

le_y = LabelEncoder() # This is for classification problem only
le_y.fit(y)
y_train = le_y.transform(y_train)
y_test = le_y.transform(y_test)

#### Step 5: Fitting FLNN-based model to the dataset

##### 5.1: Use standard FLNN model for regression problem
regressor = FlnnRegressor(expand_name="chebyshev", n_funcs=4, act_name="elu",
obj_name="MSE", max_epochs=100, batch_size=32, optimizer="SGD", verbose=True)
regressor.fit(X_train, y_train)

##### 5.2: Use standard FLNN model for classification problem
classifer = FlnnClassifier(expand_name="chebyshev", n_funcs=4, act_name="sigmoid",
obj_name="BCEL", max_epochs=100, batch_size=32, optimizer="SGD", verbose=True)
classifer.fit(X_train, y_train)

##### 5.3: Use Metaheuristic-based FLNN model for regression problem
print(MhaFlnnClassifier.SUPPORTED_OPTIMIZERS)
print(MhaFlnnClassifier.SUPPORTED_REG_OBJECTIVES)
opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
model = MhaFlnnRegressor(expand_name="chebyshev", n_funcs=3, act_name="elu",
obj_name="RMSE", optimizer="BaseGA", optimizer_paras=opt_paras, verbose=True)
regressor.fit(X_train, y_train)

##### 5.4: Use Metaheuristic-based FLNN model for classification problem
print(MhaFlnnClassifier.SUPPORTED_OPTIMIZERS)
print(MhaFlnnClassifier.SUPPORTED_CLS_OBJECTIVES)
opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
classifier = MhaFlnnClassifier(expand_name="chebyshev", n_funcs=4, act_name="sigmoid",
obj_name="NPV", optimizer="BaseGA", optimizer_paras=opt_paras, verbose=True)
classifier.fit(X_train, y_train)

#### Step 6: Predicting a new result
y_pred = regressor.predict(X_test)

y_pred_cls = classifier.predict(X_test)
y_pred_label = le_y.inverse_transform(y_pred_cls)

#### Step 7: Calculate metrics using score or scores functions.
print("Try my AS metric with score function")
print(regressor.score(X_test, y_test, method="AS"))

print("Try my multiple metrics with scores function")
print(classifier.scores(X_test, y_test, list_methods=["AS", "PS", "F1S", "CEL", "BSL"]))

Utilities everything that Reflame provided
### Step 1: Importing the libraries
from reflame import Data, FlnnRegressor, FlnnClassifier, MhaFlnnRegressor, MhaFlnnClassifier
from sklearn.datasets import load_digits

#### Step 2: Reading the dataset
X, y = load_digits(return_X_y=True)
data = Data(X, y)

#### Step 3: Next, split dataset into train and test set
data.split_train_test(test_size=0.2, shuffle=True, random_state=100)

#### Step 4: Feature Scaling
data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("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)

#### Step 5: Fitting FLNN-based model to the dataset

##### 5.1: Use standard FLNN model for regression problem
regressor = FlnnRegressor(expand_name="chebyshev", n_funcs=4, act_name="tanh",
obj_name="MSE", max_epochs=100, batch_size=32, optimizer="SGD", verbose=True)
regressor.fit(data.X_train, data.y_train)

##### 5.2: Use standard FLNN model for classification problem
classifer = FlnnClassifier(expand_name="chebyshev", n_funcs=4, act_name="tanh",
obj_name="BCEL", max_epochs=100, batch_size=32, optimizer="SGD", verbose=True)
classifer.fit(data.X_train, data.y_train)

##### 5.3: Use Metaheuristic-based FLNN model for regression problem
print(MhaFlnnClassifier.SUPPORTED_OPTIMIZERS)
print(MhaFlnnClassifier.SUPPORTED_REG_OBJECTIVES)
opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
model = MhaFlnnRegressor(expand_name="chebyshev", n_funcs=3, act_name="elu",
obj_name="RMSE", optimizer="BaseGA", optimizer_paras=opt_paras, verbose=True)
regressor.fit(data.X_train, data.y_train)

##### 5.4: Use Metaheuristic-based FLNN model for classification problem
print(MhaFlnnClassifier.SUPPORTED_OPTIMIZERS)
print(MhaFlnnClassifier.SUPPORTED_CLS_OBJECTIVES)
opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
classifier = MhaFlnnClassifier(expand_name="chebyshev", n_funcs=4, act_name="sigmoid",
obj_name="NPV", optimizer="BaseGA", optimizer_paras=opt_paras, verbose=True)
classifier.fit(data.X_train, data.y_train)

#### Step 6: Predicting a new result
y_pred = regressor.predict(data.X_test)

y_pred_cls = classifier.predict(data.X_test)
y_pred_label = scaler_y.inverse_transform(y_pred_cls)

#### Step 7: Calculate metrics using score or scores functions.
print("Try my AS metric with score function")
print(regressor.score(data.X_test, data.y_test, method="AS"))

print("Try my multiple metrics with scores function")
print(classifier.scores(data.X_test, data.y_test, list_methods=["AS", "PS", "F1S", "CEL", "BSL"]))

A real-world dataset contains features that vary in magnitudes, units, and range. We would suggest performing
normalization when the scale of a feature is irrelevant or misleading. Feature Scaling basically helps to normalize
the data within a particular range.

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

from reflame import MhaFlnnClassifier, MhaFlnnRegressor

print(MhaFlnnRegressor.SUPPORTED_REG_OBJECTIVES)
print(MhaFlnnClassifier.SUPPORTED_CLS_OBJECTIVES)


I got this type of error

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
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.


import pandas as pd
from imblearn.over_sampling import SMOTE
from reflame import Data

dataset = pd.read_csv('examples/dataset.csv', index_col=0).values
X, y = dataset[:, 0:-1], dataset[:, -1]

X_new, y_new = SMOTE().fit_resample(X, y)
data = Data(X_new, y_new)


2nd: Use different random_state numbers in split_train_test() function.

import pandas as pd
from reflame import Data

dataset = pd.read_csv('examples/dataset.csv', index_col=0).values
X, y = dataset[:, 0:-1], dataset[:, -1]
data = Data(X, y)
data.split_train_test(test_size=0.2, random_state=10) # Try different random_state value

Support (questions, problems)
Official Links


Official source code repo: https://github.com/thieu1995/reflame


Official document: https://reflame.readthedocs.io/


Download releases: https://pypi.org/project/reflame/


Issue tracker: https://github.com/thieu1995/reflame/issues


Notable changes log: https://github.com/thieu1995/reflame/blob/master/ChangeLog.md


Official chat group: https://t.me/+fRVCJGuGJg1mNDg1


This project also related to our another projects which are "optimization" and "machine learning", check it here:

https://github.com/thieu1995/mealpy
https://github.com/thieu1995/metaheuristics
https://github.com/thieu1995/opfunu
https://github.com/thieu1995/enoppy
https://github.com/thieu1995/permetrics
https://github.com/thieu1995/MetaCluster
https://github.com/thieu1995/pfevaluator
https://github.com/thieu1995/intelelm
https://github.com/aiir-team