This project shows an algorithm selection library using ML based on the scikit-learn library.
This project arose after the writing of the paper Learning for Spatial Branching: An Algorithm Selection Approach
The use of machine learning techniques to improve the performance of branch-and-bound optimization algorithms is a very active area in the context of mixed integer linear problems, but little has been done for non-linear optimization. To bridge this gap, we develop a learning framework for spatial branching and show its efficacy in the context of the Reformulation-Linearization Technique for polynomial optimization problems.
This library is intended to generalize the analysis performed in the above-mentioned paper. It analyzes multiple algorithms on a set of problems, defined by their features, and seeks to provide a series of metrics and graphs to compare their performance. Not only compares the performance of the algorithms on the dataset, but also provides a learning-based solution of the optimal choice of algorithm based on the problem's features.
If you want to use this library on your Python code you can installing from PyPI as follow:
pip3 install algorithmselector-ML
Link to the PyPI project: https://pypi.org/project/algorithmselector-ML/
Thanks to this library you will be able to run the training and prediction of several models at once, preprocess the data and generate a performance measure based on the maximization or minimization of the generated output. In addition, three functions are included to generate comparison plots of the options to be analyzed. Therefore, starting from two data sets x and y (containing x the characteristics of the problems to be used and y the generated output) this library is able to:
- Create the test sets.
- Normalize the output (the user can choose between maximization or minimization).
- Train the necessary models.
- Create the prediction for the previously generated test sets and models.
- Create various plots that facilitate the understanding of the results.
The following is a list of the functions included in the ML module:
Preprocesses the data according to the parameters set by the user and returns the training sets and test sets.
The x parameter (dataframe) contains the problems (rows) and its features (columns) and the y parameter (dataframe) contains the outputs. Each column is an option. The segmentation will be done based on the test_size value, which indicates the percentage of instances in the test data. If normalize parameter is True the output (y_train and y_test) will take values between 0 and 1, being 1 the best option. The parameter better_smaller indicates the option to choose during normalization, minimize or maximize.
Trains multiple models using the method passed as parameter. It is based on sklearn library.
The output will have as many models as options to be evaluated. The x parameter (dataframe) contains the problems (rows) and its features (columns) and the y parameter (dataframe) contains the outputs. The method parameter indicates the name of the sklearn method to be used for the training. The user can introduce all the parameters accepted by the method, see sklearn documentation for more information.
Generates the predictions from the models passed as parameters. This function is based on the sklearn library.
The models parameter contains the path to the models saved during the training process. The data parameter contains the new data to use for the prediction (for example x_test). The option_names parameter contains the name of the options that the user is evaluating (headers of y_train).
Creates the potfolio with the best option for each instance (compared with the true optimal option). This function should be used with normalized data. If there is a tie all the optimal options will be shown.
The y_pred dataframe is used to create the ML_value and ML_option_name columns and the y_test dataframe is used for the Optimal_solution column. For each row the closest value to 1 will be selected.
The following is a list of the functions included in the plots module:
Represents the boxplot taking into account the number of times each option is selected as the best. It should be used with the normalized data. The ML box is also created by selecting the best option for each instance (i.e. the closest value to one after normalizing). Each box will represent the number of times that the option was selected as the best.
Creates the bar plot or the stacked bar plot that represent percentage of times (percentage of problems) that each option is selected by ML compared with the optimal solution. In other words, it compares the number of times that the ML selects the best possible option.
This function generates a plot where, for each instance, the branching rules are ranked from best to worst, according to the normalized output. Stacked bar graphs represent percentage of problems in each rank position per option. It should be used with the normalized data.
If you want to try the library an example python script and two csv files are provided. You just have to execute:
python3 example.py
The output will be the images of the three generated plots and will be stored in the path where the example.py file is located. In addition, a model will be generated for each option and stored in the models folder. If you wish to see the obtained results, it is enough to add the following lines of code:
Option 1 (prints the best solution for each problem obtained by ML):
res=ml.algorithm_portfolio(y_test,y_pred)
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
print(res)
Option 2 (prints the predictions generated by ML):
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
print(y_pred)
- Caseiro Arias, María
- Gómez Casares, Ignacio
- González Díaz, Julio
- González Rodríguez, Brais
- Pateiro López, Beatriz