DimlpPred¶

Description¶

The Discretized Interpretable Multi-Layer Perceptron (DIMLP) is a neural network architecture that combines the predictive power of a traditional Multi-Layer Perceptron (MLP) with the ability to extract interpretable rules. Unlike conventional neural networks, DIMLP uses a staircase activation function in the first hidden layer, creating a grid of hyper-rectangles in the feature space. These hyper-rectangles help define discriminant decision boundaries between classes, facilitating the extraction of symbolic rules.

DimlpPred generates predictions on a test dataset using the model trained by dimlpTrn.

For more details on the Dimlp algorithm, you can refer to this paper.

Arguments list¶

The dimlpPred algorithm works with both required and optional arguments. Each argument has specific properties:

• Is required means whether an argument must be specified when calling the program or not.
• Type specifies the argument datatype.
• CLI argument syntax is the exact name to use if you are writing the argument along with the program call.
• JSON identifier is the exact name to use if you are writing the argument inside a JSON configuration file.
• Default value is the value that will be used by the program if the argument is not specified. If None, it could mean that the argument is not used at all during the algorithm execution or could also mean that you have to specify it yourself.

Show help¶

Display parameters and other helpful information concerning the program usage and terminate it when done.

JSON configuration file¶

File containing the configuration for the algorithm in JSON format (see more about JSON configuration files).

Root folder path¶

Default path from where all the other arguments related to file paths are going to be based. Using this allows you to work with paths relative from this location and avoid writing absolute paths or lengthy relative paths.

Test data file¶

File containing the test portion of the dataset.

Weights file¶

Path to the file containing the weights of the model trained with dimlpTrn.

Hidden layers file¶

Path to the file containing hidden layers sizes.

Number of attributes¶

Number of attributes in the dataset (should be equal to the number of inputs of the model). Takes values in the range [1,∞[.

Number of classes¶

Number of classes in the dataset (should be equal to the number of outputs of the model). Takes values in the range [2,∞[.

Test predictions output file¶

Path to the file where the test predictions will be stored.

Logs output file¶

Name of file containing every feedback made by the algorithm during its execution. If not specified, the feedback is displayed into the terminal.

Number of stairs¶

Number of stairs in the staircase activation function used in the Dimlp layer during training. Takes values in the range [3,∞[.

Usage example¶

from dimlpfidex.dimlp import dimlpPred

dimlpPred(
"""--test_data_file test_data.txt 
--weights_file weights.wts 
--nb_attributes 16 
--hidden_layers_file hidden_layers.out 
--nb_classes 2 
--test_pred_outfile predTest.out 
--root_folder dimlp/datafiles"""
)

./dimlpPred --test_data_file test_data.txt --weights_file weights.wts --nb_attributes 16 --hidden_layers_file hidden_layers.out --nb_classes 2 --test_pred_outfile predTest.out --root_folder ../dimlp/datafiles

Output interpretation¶

Test prediction file¶

This file contains the predicted probabilities for each possible class for each test sample. Each row corresponds to the prediction for a single sample, with N values representing the probability that the sample belongs to class 0, 1, ... or class N. The values in each row sum to 1. The class with the highest probability is considered the predicted class for that sample, unless a decision threshold is applied for a specific class. In that case, if the predicted probability for that class exceeds the threshold, the sample is classified as belonging to that class.

For example:

0.000718874 0.999281
0.949143 0.050857

In the first row, the model predicts a probability of approximately 0.0007 that the sample belongs to class 0, and 0.9993 that it belongs to class 1. Therefore, the model predicts class 1 for this sample. In the second row, the model predicts a probability of 0.949 that the sample belongs to class 0, and 0.051 that it belongs to class 1. Hence, the model predicts class 0 for this sample.

Each row of probabilities allows you to interpret the model's confidence in its predictions, enabling you to understand the likelihood of each sample belonging to a particular class.