slim_gsgp.datasets

slim_gsgp.datasets.data_loader

slim_gsgp.datasets.data_loader.load_airfoil(X_y=True)

Loads and returns the Airfoil Self-Noise data set (regression)

NASA data set, obtained from a series of aerodynamic and acoustic tests of two and three-dimensional airfoil blade sections conducted in an anechoic wind tunnel. Downloaded from the UCI ML Repository. The file is located in slim_gsgp/datasets/data/airfoil.txt

Basic information: - Number of data instances: 1503; - Number of input features: 5; - Target’s range: [103.38-140.987].

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_bike_sharing(X_y=True)

Loads and returns the Bike Sharing data set (regression)

This dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information.

Basic information: - Number of data instances: 17389; - Number of input features: 13; - Target’s range: [22, 8714].

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

https://archive.ics.uci.edu/dataset/275/bike+sharing+dataset

slim_gsgp.datasets.data_loader.load_bioav(X_y=True)

Loads and returns the Oral Bioavailability data set (regression)

The data set consists in predicting the value of the percentage of the initial orally submitted drug dose that effectively reaches the systemic blood circulation after being filtered by the liver, as a function of drug’s molecular structure. For more details, consult the publication entitled as “Genetic programming for computational pharmacokinetics in drug discovery and development” by F. Archetti et al. (2007). The file is located in slim_gsgp/datasets/data/bioavailability.txt

Basic information: - Number of data instances: 358; - Number of input features: 241; - Target’s range: [0.4, 100.0].

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_boston(X_y=True)

Loads and returns the Boston Housing data set (regression)

This dataset contains information collected by the U.S. Census Service concerning housing in the area of Boston Massachusetts. Downloaded from the StatLib archive. The file is located in /slim_gsgp/datasets/data/boston.txt

Basic information: - Number of data instances: 506; - Number of input features: 13; - Target’s range: [5, 50].

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_breast_cancer(X_y=True)

Loads and returns the breast cancer data set (classification)

Breast Cancer Wisconsin (Diagnostic) dataset. Downloaded from the StatLib archive. The file is located in /slim_gsgp/datasets/data/boston.txt

Basic information: - Number of data instances: 569; - Number of input features: 30; - Target’s values: {0: “benign”, 1: “malign”}.

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_concrete_slump(X_y=True)

Loads and returns the Concrete Slump data set (regression)

Concrete is a highly complex material. The slump flow of concrete is not only determined by the water content, but that is also influenced by other concrete ingredients. Downloaded from the UCI ML Repository. The file is located in /slim_gsgp/datasets/data/concrete_slump.txt

Basic information: - Number of data instances: 103; - Number of input features: 7; - Target’s range: [0, 29].

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_concrete_strength(X_y=True)

Loads and returns the Concrete Strength data set (regression)

Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. Downloaded from the UCI ML Repository. The file is located in /slim_gsgp/datasets/data/concrete_strength.txt

Basic information: - Number of data instances: 1005; - Number of input features: 8; - Target’s range: [2.331807832, 82.5992248].

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_diabetes(X_y=True)

Loads and returns the Diabetes data set(regression)

The file is located in /slim_gsgp/datasets/data/diabetes.txt

Basic information: - Number of data instances: 442; - Number of input features: 10; - Target’s range: [25, 346].

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_efficiency_cooling(X_y=True)

Loads and returns the Cooling Efficiency data set(regression)

The data set regards cooling load assessment of buildings (that is, energy efficiency) as a function of building parameters. Downloaded from the UCI ML Repository. The file is located in /slim_gsgp/datasets/data/efficiency_cooling.txt

Basic information: - Number of data instances: 768; - Number of input features: 8; - Target’s range: [10.9, 48.03].

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X_y (bool, optional) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_efficiency_heating(X_y=True)

Loads and returns the Heating Efficiency data set(regression)

The data set regards heating load assessment of buildings (that is, energy efficiency) as a function of building parameters. Downloaded from the UCI ML Repository. The file is located in /slim_gsgp/datasets/data/efficiency_heating.txt

Basic information: - Number of data instances: 768; - Number of input features: 8; - Target’s range: [6.01, 43.1].

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_forest_fires(X_y=True)

Loads and returns the Forest Fires data set (regression)

The data set regards the prediction of the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data. Downloaded from the UCI ML Repository. The file is located in /slim_gsgp/datasets/data/forest_fires.txt

Basic information: - Number of data instances: 513; - Number of input features: 43; - Target’s range: [0.0, 6.995619625423205].

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_istanbul(X_y=True)

Loads and returns the Istanbul data set (regression). Taken from https://docs.1010data.com/MachineLearningExamples/IstanbulDataSet.html.

Parameters:

X_y (bool, optional) – Indicates if the data is to be returned as two objects of type torch.Tensor, otherwise as single Tensor.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

slim_gsgp.datasets.data_loader.load_ld50(X_y=True)

Loads and returns the LD50 data set(regression)

The data set consists in predicting the median amount of compound required to kill 50% of the test organisms (cavies), also called the lethal dose or LD50. For more details, consult the publication entitled as “Genetic programming for computational pharmacokinetics in drug discovery and development” by F. Archetti et al. (2007). The file is located in /slim_gsgp/datasets/data/ld50.txt

Basic information: - Number of data instances: 234; - Number of input features: 626; - Target’s range: [0.25, 8900.0].

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_pandas_df(df: DataFrame, X_y: bool = True)

Parameters:

• df (Pandas Dataframe) – The Pandas Dataframe that is to be used and turned into a torch.Tensor. Must contain the target variable in the last column if X_y is set to True.

• X_y (bool, optional) – Indicates if the data is to be returned as two objects of type torch.Tensor, otherwise as single Tensor.

Returns:

A tuple containing two torch.Tensors (X and the target variable, Y), if X_y is True or a single torch.Tensor if X_y is set to False

Return type:

tuple or torch.Tensor

slim_gsgp.datasets.data_loader.load_parkinson_updrs(X_y=True)

Loads and returns the Parkinsons Telemonitoring data set (regression)

The data set was created by A. Tsanas and M. Little of the Oxford’s university in collaboration with 10 medical centers in the US and Intel Corporation who developed the telemonitoring device to record the speech signals. The original study used a range of linear and nonlinear regression methods to predict the clinician’s Parkinson’s disease symptom score on the UPDRS scale (total UPDRS used here). Downloaded from the UCI ML Repository. The file is located in /slim_gsgp/datasets/data/parkinson_total_UPDRS.txt

Basic information: - Number of data instances: 5875; - Number of input features: 19; - Target’s range: [7.0, 54.992].

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_ppb(X_y=True)

Loads and returns the PPB data set(regression)

The data set consists in predicting the percentage of the initial drug dose which binds plasma proteins (also known as the plasma protein binding level). For more details, consult the publication entitled as “Genetic programming for computational pharmacokinetics in drug discovery and development” by F. Archetti et al. (2007). The file is located in /slim_gsgp/datasets/data/ppb.txt

Basic information: - Number of data instances: 131; - Number of input features: 626; - Target’s range: [0.5, 100.0]

Parameters:

X_y (bool, optional) – Return data as two objects of type torch.Tensor, otherwise as a pandas.DataFrame.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.

References

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in Python. Applied Sciences, 11(11), 4774.

slim_gsgp.datasets.data_loader.load_resid_build_sale_price(X_y=True)

Loads and returns the RESIDNAME data set (regression). Taken from https://archive.ics.uci.edu/dataset/437/residential+building+data+set

Parameters:

X_y (bool, optional) – Indicates if the data is to be returned as two objects of type torch.Tensor, otherwise as single Tensor.

Returns:

• X, y (torch.Tensor, torch.Tensor) – The input data (X) and the target of the prediction (y). The latter is extracted from the data set as the last column.

• df (pandas.DataFrame) – An object of type pandas.DataFrame which holds the data. The target is the last column.