Dataset Generation

OptBench Dataset Generation¶

Dataset Generation

This page describes how to generate the OptBench dataset, which covers a wide range a wide range of different formats:

Text file based as *.csv and *.json
Binary file based as *.npy
Image file based as *.png for 2D, 3D, and gif animations. Furthermore, the image files can also contains series of different functions, if the used functions are 2D.

Text file based dataset generation

JSONCSVCode

JSON

{
    "x_0": [
        [
        -5.0, -4.9, -4.800000000000001, -4.700000000000001, -4.600000000000001,
        -4.500000000000002, -4.400000000000002, -4.3000000000000025,
        -4.200000000000003, -4.100000000000003, -4.0000000000000036,
        -3.900000000000004, -3.8000000000000043, -3.7000000000000046,
        -3.600000000000005, -3.5000000000000053, -3.4000000000000057,
        -3.300000000000006, -3.2000000000000064, -3.1000000000000068,
        -3.000000000000007, -2.9000000000000075, -2.800000000000008,
        -2.700000000000008, -2.6000000000000085, -2.500000000000009,
        -2.4000000000000092, -2.3000000000000096, -2.20000000000001,
        -2.1000000000000103, -2.0000000000000107, -1.900000000000011,
        -1.8000000000000114, -1.7000000000000117, -1.600000000000012,
        -1.5000000000000124, -1.4000000000000128, -1.3000000000000131,
        -1.2000000000000135, -1.1000000000000139, -1.0000000000000142,
        -0.9000000000000146, -0.8000000000000149, -0.7000000000000153,
        -0.6000000000000156, -0.500000000000016, -0.40000000000001634,
        -0.3000000000000167, -0.20000000000001705, -0.10000000000001741,
        -1.7763568394002505e-14, 0.09999999999998188, 0.19999999999998153,
        0.29999999999998117, 0.3999999999999808, 0.49999999999998046,
        0.5999999999999801, 0.6999999999999797, 0.7999999999999794,
        0.899999999999979, 0.9999999999999787, 1.0999999999999783,
        1.199999999999978, 1.2999999999999776, 1.3999999999999773,
        1.499999999999977, 1.5999999999999766, 1.6999999999999762,
        1.7999999999999758, 1.8999999999999755, 1.9999999999999751,
        2.0999999999999748, 2.1999999999999744, 2.299999999999974,
        2.3999999999999737, 2.4999999999999734, 2.599999999999973,
        2.6999999999999726, 2.7999999999999723, 2.899999999999972,
        2.9999999999999716, 3.0999999999999712, 3.199999999999971,
        3.2999999999999705, 3.39999999999997, 3.49999999999997,
        3.5999999999999694, 3.699999999999969, 3.7999999999999687,
        3.8999999999999684, 3.999999999999968, 4.099999999999968,
        4.199999999999967, 4.299999999999967, 4.399999999999967,
        4.499999999999966, 4.599999999999966, 4.6999999999999655,
        4.799999999999965, 4.899999999999965
        ], ...
    ]
}

Scdoc

x_0,x_1,x_2
-5.000000,-5.000000,12.642411
-5.000000,-4.900000,12.816406
-5.000000,-4.800000,13.289395
-5.000000,-4.700000,13.727453
-5.000000,-4.600000,13.966601
-5.000000,-4.500000,13.993625
-5.000000,-4.400000,13.820456
-5.000000,-4.300000,13.435255
-5.000000,-4.200000,12.851327
-5.000000,-4.100000,12.232743
-5.000000,-4.000000,11.913518
-5.000000,-3.900000,12.089881
...,...,...

For the generation of the dataset, the following code snippet can be used:

Python

es.save_as_csv(dir_name="csv")
es.save_as_json(dir_name="json")

Image Generation

Matplotlib res.plot_style = "matplot"Plotly res.plot_style = "plotly"

Example - I

Example - II

Example - I

Example - II

A class for optimizing and benchmarking mathematical functions.

About OptBench

This class inherits from the FunctionBorg, PlotBorg, and SaveBorg classes, which allows it to share state across multiple instances. It defines various properties and methods for managing settings related to plotting, such as the size and resolution of the plot, the axis labels, the color scheme, and the plot style (either Matplotlib or Plotly). It also defines a plot method that plots the shared states of a math function using either Matplotlib or Plotly, depending on the value of the _plot_style attribute.

Examples:

Python Console Session

>>> # 3D examples
>>> from umf.core.create import OptBench
>>> res = OptBench(["DeJongN5Function", "AckleyFunction"], dim=3)
>>> res.show = False
>>> res.plot_type_3d = "plot_surface"
>>> res.plot_style = "matplot"
>>> res.plot()
>>> res.save_as_image(dir_name="matplot")
>>> # As plotly
>>> res.plot_style = "plotly"
>>> res.plot()
>>> res.save_as_image(dir_name="plotly")
>>> res.save_as_csv(dir_name="csv")
>>> res.save_as_json(dir_name="json")

Python Console Session

>>> # 2D examples
>>> from umf.core.create import OptBench
>>> res = OptBench(
...     [
...         "CrystalBallDistribution",
...         "PseudoVoigtDistribution",
...         "AsymmetricRamanLineshape",
...     ],
...     [
...         {"mu": 1.0, "sigma":2, "alpha":0.5, "n":1.0},
...         {"mu": 1.0, "sigma":2, "eta":0.5},
...         {"mu": 1.0, "sigma":2, "eta":0.5, "gamma":0.5},
...     ],
...     start=-10,
...     stop=10,
...     step_size=0.1,
...     dim=2,
... )
>>> res.show = False
>>> res.plot_style = "matplot"
>>> res.plot_series()
>>> res.save_as_image(dir_name="matplot")
>>> # As plotly
>>> res.plot_style = "plotly"
>>> res.plot_series()
>>> res.save_as_image(dir_name="plotly")

Parameters:

Name	Type	Description	Default
`func_name`	`list[str] \| str`	Name of the function(s) to optimize and benchmark.	required
`func_args`	`list[dict[str, Any]] \| dict[str, Any] \| None`	Arguments for the function(s). Defaults to None.	`None`
`start`	`float`	Start value of the range. Defaults to -5.0.	`-5.0`
`stop`	`float`	Stop value of the range. Defaults to 5.0.	`5.0`
`step_size`	`float \| int`	Step value of the range or number of maximum steps. Defaults to 0.1.	`0.1`
`dim`	`int`	Number of dimensions. Defaults to 2.	`2`
`mode`	`str`	Mode for creating the range of values. Must be either 'arange' or 'linspace'. Defaults to "arange".	`'arange'`

Raises:

Type	Description
`ValueError`	Mode must be either 'arange' or 'linspace'.
`ValueError`	Number of dimensions must be greater than 1.

Attributes:

Name	Type	Description
`dim`	`int`	Number of dimensions.
`shared_result`	`dict`	Dictionary containing arrays for plotting and saving.

Methods:

Name	Description
`initializeshared_result`	Make arrays for plotting, saving and add them to share result.
`create_range`	float, stop: float,
`step_size`	float, dim: int = 2, mode: str = "arange")
`-> list[UniversalArray] \| MeshArray`	Create range of values for each dimension.