doenut.designer

Module Contents

Functions

_check_is_input_dict(→ None)

Validate an input dictionary's type.

get_ranges(→ Dict[Any, List[float]])

Find the ranges of data in an input dictionary

full_fact(→ pandas.DataFrame)

Generate a full factorial model from the supplied parameters

frac_fact(→ pandas.DataFrame)

build a 2-level fractional factorial design

experiment_designer(levels, res[, do_midpoints, ...])

levels is a dictionary of factor name and levels

frac_fact_res_designer(levels, res[, do_midpoints, ...])

fact_designer(levels[, do_midpoints, shuffle, ...])

Attributes

logger

doenut.designer.logger
doenut.designer._check_is_input_dict(data: Dict[Any, Iterable]) → None[source]

Validate an input dictionary’s type. Most of these functions require a dictionary of lists as their input data This is a helper function that will throw an appropriate assert if needed.

Parameters:

data (Dict[Any, Iterable]) – The data dictionary to validate

Raises:

TypeError – If the data is of the wrong type, or if any of the values in the dictionary are not iterable

doenut.designer.get_ranges(data: Dict[Any, Iterable[float]]) → Dict[Any, List[float]][source]

Find the ranges of data in an input dictionary

Go through a dictionary of value lists, and return the same, but with only the min / max value from each in each.

Parameters:

data (Dict[Any, Iterable[float]]) – The input dictionary to parse

Returns:

A dictionary of the same keys, but each value has now been replaced with a list of min and max of that value list

Return type:

Dict[Any, List[float]]

doenut.designer.full_fact(data: Dict[Any, List[float]]) → pandas.DataFrame[source]

Generate a full factorial model from the supplied parameters

Parameters:

data (Dict[Any, List[float]]) – dict of lists of allowed values for each parameter

Returns:

A dataframe of all the generated experiments

Return type:

pd.DataFrame

doenut.designer.frac_fact(data: Dict[Any, List[float]], resolution: int = None) → pandas.DataFrame[source]

build a 2-level fractional factorial design

Parameters:

  • data (Dict[Any, List[float]]) – dictionary to design from

  • resolution (float, optional) – what resolution model to build. Default is param_count/2

Returns:

A dataframe of all the experiments

Return type:

pd.DataFrame

doenut.designer.experiment_designer(levels, res, do_midpoints=True, shuffle=True, repeats=1, num_midpoints=3)[source]

levels is a dictionary of factor name and levels res is the resolution (for frac fact) - shouldn’t be in class do_midpoints whether to add in the mid points shuffle whether to shuffle repeats how many repeats you’re doing of the NON-MIDPOINTS num_midpoints, how many midpoints to do

Parameters:

  • levels –

  • res –

  • do_midpoints – (Default value = True)

  • shuffle – (Default value = True)

  • repeats – (Default value = 1)

  • num_midpoints – (Default value = 3)

doenut.designer.frac_fact_res_designer(levels, res, do_midpoints=True, shuffle=True, repeats=1, num_midpoints=3)[source]
doenut.designer.fact_designer(levels, do_midpoints=True, shuffle=True, repeats=1, num_midpoints=3)[source]