Script for ensemble optimization using SMACdown. The script can be used for both classification and regression ensembles, provided you select the appropriate metric to be optimized (see below).

Classification metrics:

• average_recall

• average_phi

• accuracy

• average_precision

• average_f_measure

Regression metrics:

• r_squared.

In this package, you will find some utilities to apply fuzzy logic t-norms and t-conorms to datasets.

You can find all the information about the T-norms and T-conorms in the following links:

• T-norms and T-conorms

• Construction of T-norms

Fields used as operands must contain real values between 0 and 1. As they are logical values it doesn't make sense having values outside this range. In this case, the script will raise an error.

The script will create a new column in the dataset for each one of the norms specified in the norms list.

T-norms and T-conorms

Below, you can find an example of the content of the norms input:

["min-tnorm","product","lukasiewicz", "drastic-tnorm", "nilpotent-min",["schweizer-sklar", 0.5], ["hamacher", 0,5]]

As you can see there are some norms that need a parameter that has to be specified in this way.

These are the possibles t-norms:

• min-tnorm: Minimum t-norm. Also called the Gödel t-norm. No parameters.
• product: Product t-norm. The ordinary product of real numbers. No parameters.
• lukasiewicz: Łukasiewicz t-norm. No parameters.
• drastic-tnorm: Drastic t-norm. No parameters.
• nilpotent-min: Nilpotent minimum t-norm. No parameters.
• [schweizer-sklar p]: Schweizer–Sklar t-norms. Parameter p in the range [-∞, ∞]
• [hamacher p]: Hamacher t-norms. Parameter p in the range [0, ∞]
• [yager p]: Yager t-norms. Parameter p in the range [0, ∞]
• [aczel-alsina p]: Aczél–Alsina t-norms. Parameter p in the range [0, ∞]
• [dombi p]: Dombi t-norms. Parameter p in the range [0, ∞]
• [sugeno-weber p]: Sugeno–Weber t-norms. Parameter p in the range [-1, ∞]

These are the possible t-conorms:

• max-tconorm: Maximum t-norm. Dual to the minimum t-norm, is the smallest t-conorm. No parameters
• probabilistic: Probabilistic t-norm. It's dual to the product t-norm. No parameters.
• bounded: Bounded t-norm. It's dual to the Łukasiewicz t-norm. No parameters.
• drastic-tconorm: Drastic t-conorm. It's dual to the drastic t-norm. No paramters.
• nilpotent-max: Nilpotent maximum t-conorm. It's dual to the nilpotent minimum.
• einstein-sum: Einstein t-conorm. It's a dual to one of the Hamacher t-norms.

Find more info in readme

Find the closest cluster rows to a given one

The objective of this script is to perform a k-fold cross validation of a logistic regression built from a dataset. The algorithm:

• Divides the dataset in k parts
• Holds out the data in one of the parts and builds a logistic regression with the rest of data
• Evaluates the logistic regression with the hold out data
• The second and third steps are repeated with each of the k parts, so that k evaluations are generated
• Finally, the evaluation metrics are averaged to provide the cross-validation metrics.

The output of the script will be an evaluation ID. This evaluation is a cross-validation, meaning that its metrics are averages of the k evaluations created in the cross-validation process.

For more information, please see the readme.

The objective of this script is to perform a k-fold cross validation of an ensemble built from a dataset. The algorithm:

• Divides the dataset in k parts
• Holds out the data in one of the parts and builds an ensemble with the rest of data
• Evaluates the ensemble with the hold out data
• The second and third steps are repeated with each of the k parts, so that k evaluations are generated
• Finally, the evaluation metrics are averaged to provide the cross-validation metrics.

The output of the script will be an evaluation ID. This evaluation is a cross-validation, meaning that its metrics are averages of the k evaluations created in the cross-validation process.

For more information, please see the readme.

5-fold cross-validation of a model created with default parameters

The objective of this script is to perform a k-fold cross validation of an already built Supervised Learning Model. The algorithm:

• Retrives the dataset and configuration of the model
• Divides the dataset in k parts
• Holds out the data in one of the parts and builds a supervised model with the rest of data
• Evaluates the model with the hold out data
• The second and third steps are repeated with each of the k parts, so that k evaluations are generated
• Finally, the evaluation metrics are averaged to provide the cross-validation metrics.

The output of the script will be an evaluation ID. This evaluation is a cross-validation, meaning that its metrics are averages of the k evaluations created in the cross-validation process.

For more information, please see the readme.

The objective of this script is to perform a k-fold cross validation of a linear regression built from a dataset. The algorithm:

• Divides the dataset in k parts
• Holds out the data in one of the parts and builds a linear regression with the rest of data
• Evaluates the linear regression with the hold out data
• The second and third steps are repeated with each of the k parts, so that k evaluations are generated
• Finally, the evaluation metrics are averaged to provide the cross-validation metrics.

The output of the script will be an evaluation ID. This evaluation is a cross-validation, meaning that its metrics are averages of the k evaluations created in the cross-validation process.

For more information, please see the readme.

The objective of this script is to perform a k-fold cross validation of a deepnet built from a dataset. The algorithm:

• Divides the dataset in k parts
• Holds out the data in one of the parts and builds a deepnet with the rest of data
• Evaluates the deepnet with the hold out data
• The second and third steps are repeated with each of the k parts, so that k evaluations are generated
• Finally, the evaluation metrics are averaged to provide the cross-validation metrics.

The output of the script will be an evaluation ID. This evaluation is a cross-validation, meaning that its metrics are averages of the k evaluations created in the cross-validation process.

For more information, please see the readme.