isanet.datasets

isanet.datasets.monk module

isanet.datasets.monk.load_monk(dataset='1', type='train')

Load and return the one of the Monk dataset (Classification Task). The monk datasets are a classic and very easy class classification dataset.

Parameters

• dataset (string ("1", "2", "3"), default = "1") – define which monks dataset load

• type (string ("train", "test"), default = "train) – define which type of dataset you want to load: train or test

Note

The one hot encoder is applied to the data variable: each row is a feature of X Data of a monk dataset and and its related encoding.:

{
    0: [[1, 0, 0], [0, 1, 0], [0, 0, 1]],
    1: [[1, 0, 0], [0, 1, 0], [0, 0, 1]],
    2: [[1,0],[0,1]],
    3: [[1, 0, 0], [0, 1, 0], [0, 0, 1]],
    4: [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
    5: [[1,0],[0,1]]
}

Monk 1

Classes	2	2
Samples total	124	432
Data X Dimensionality	17	17
Data X Type	bool	bool
Data Y Dimensionality	1	1
Data Y Type	bool	bool

Monk 2

Classes	2	2
Samples total	169	432
Data X Dimensionality	17	17
Data X Type	bool	bool
Data Y Dimensionality	1	1
Data Y Type	bool	bool

Monk 3

Classes	2	2
Samples total	122	432
Data X Dimensionality	17	17
Data X Type	bool	bool
Data Y Dimensionality	1	1
Data Y Type	bool	bool

isanet.datasets.iris module

isanet.datasets.iris.load_iris()

Load and return the iris dataset (Classification Task). The iris dataset is a classic and very easy multi-class classification dataset.

Note

The one hot encoder is applied to the target variable:

{
    "Iris-setosa": [1, 0, 0],
    "Iris-versicolor": [0, 1, 0],
    "Iris-virginica": [0, 0, 1]
}

Classes	3
Samples per class	50
Samples total	150
Data X Dimensionality	4
Data X Type	real
Data Y Dimensionality	3
Data Y Type	bool