Alg setup

Provides methods for the ATHENA project for setting DEAP structures used in software and functions used by DEAP during evolutionary algorithm.

Setup of DEAP structures and additional functions used in GENN algorithm

alt_fitness(fitness)

Return dict containing alternate fitness functions for reporting

Args:
fitness: name of fitness metric used

Returns:

Type	Description
dict	dict

Source code in src/athenage/genn/alg_setup.py

def alt_fitness(fitness: str) -> dict:
    """Return dict containing alternate fitness functions for reporting

        Args:
        fitness: name of fitness metric used 

    Returns:
        dict
    """

    methods={}
    if fitness != 'r-squared':
        methods['balanced_acc']=fitness_balacc
        methods['auprc']=fitness_auprc
        methods['auc']=fitness_auc
        methods['f1_score']=fitness_f1
        methods.pop(fitness, None)

    return methods

configure_toolbox(fitness, selection, crosstype='match', init='sensible')

Configure the DEAP toolbox for controlling GE algorithm

Parameters:

Name	Type	Description	Default
fitness	str	SNP values filename	required
selection	str	type of selection operator	required
crosstype	str	type of crossover operator	'match'
init	str	scale outcome values from 0 to 1.0	'sensible'

Returns:

Type	Description
Toolbox	DEAP base.Toolbox configured for a GE run

Source code in src/athenage/genn/alg_setup.py

def configure_toolbox(fitness: str, selection: str, crosstype:str ='match',
                      init:str ='sensible') -> base.Toolbox:
    """Configure the DEAP toolbox for controlling GE algorithm

    Args:
        fitness: SNP values filename
        selection: type of selection operator
        crosstype: type of crossover operator
        init: scale outcome values from 0 to 1.0

    Returns:
        DEAP base.Toolbox configured for a GE run
    """

    toolbox = base.Toolbox()
    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
    creator.create('Individual', grape.Individual, fitness=creator.FitnessMax)
    if init == 'sensible':
        toolbox.register("populationCreator", grape.sensible_initialization, creator.Individual) 
    else:
        toolbox.register("populationCreator", grape.random_initialization, creator.Individual) 

    if crosstype == 'onepoint':
        toolbox.register("mate", grape.crossover_onepoint)
    elif crosstype == 'match':
        toolbox.register("mate", grape.crossover_match)
    elif crosstype == 'block':
        toolbox.register("mate", grape.crossover_block)

    toolbox.register("mutate", grape.mutation_int_flip_per_codon)

    if fitness=='r-squared':
        if selection == 'epsilon_lexicase':
            toolbox.register("evaluate", fitness_rsquared_lexicase)
            toolbox.register("select", grape.selAutoEpsilonLexicase)#, tournsize=7)
        else:
            toolbox.register("evaluate", fitness_rsquared)
            toolbox.register("select", tools.selTournament, tournsize=2)
    elif fitness == 'balanced_acc':
        if selection=='lexicase':
            toolbox.register("evaluate", fitness_balacc_lexicase)
            toolbox.register("select", grape.selBalAccLexicase)
        else:
            toolbox.register("evaluate", fitness_balacc)
            toolbox.register("select", tools.selTournament, tournsize=2)
    elif fitness == 'auc':
        if selection=='lexicase':
            toolbox.register("evaluate", fitness_auc_lexicase)
            toolbox.register("select", grape.selBalAccLexicase)
        else:
            toolbox.register("evaluate", fitness_auc)
            toolbox.register("select", tools.selTournament, tournsize=2)
    elif fitness=='f1_score':
        if selection=='lexicase':
             toolbox.register("evaluate", fitness_f1_lexicase)
             toolbox.register("select", grape.selBalAccLexicase)
        else:
             toolbox.register("evaluate", fitness_f1)
             toolbox.register("select",tools.selTournament, tournsize=2 )
    elif fitness=='auprc':
        if selection=='lexicase':
             toolbox.register("evaluate", fitness_auprc_lexicase)
             toolbox.register("select", grape.selBalAccLexicase)
        else:
             toolbox.register("evaluate", fitness_auprc)
             toolbox.register("select",tools.selTournament, tournsize=2 )    
    else:
        raise ValueError("fitness must be fitness_rsquared or fitness_balacc")

    return toolbox

fitness_auc(individual, points)

Calculate area under the curve (AUC) as fitness for this individual using points passed

Parameters:

Name	Type	Description	Default
individual	Individual	solution being evaluated for fitness	required
points	list	2-D list containing inputs and outcome for calculating fitness points[0] contains 2-D np.ndarray of all inputs	required

Returns:

Type	Description
float	AUC fitness

Source code in src/athenage/genn/alg_setup.py

def fitness_auc(individual: 'deap.creator.Individual', points: list) -> float:
    """Calculate area under the curve (AUC)
    as fitness for this individual using points passed

    Args:
        individual: solution being evaluated for fitness
        points: 2-D list containing inputs and outcome for calculating fitness
            points[0] contains 2-D np.ndarray of all inputs

    Returns:
        AUC fitness
    """

    x = points[0]
    y = points[1]


    if individual.invalid == True:
        return INVALID_FITNESS,

    try:
        pred = eval(individual.phenotype)

#         pred2 = eval(compress_weights(individual.phenotype))
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        return INVALID_FITNESS,
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("evaluation error", err)
            raise
    assert np.isrealobj(pred)

    try:
        nan_mask = np.isnan(pred)
        # assign case/control status
        pred_nonan = np.where(pred[~nan_mask] < 0.5, 0, 1)
        fitness = roc_auc_score(y[~nan_mask],pred_nonan)
        individual.nmissing = np.count_nonzero(np.isnan(pred))

#         fitness_compressed = balanced_accuracy_score(y[~nan_mask],pred2)
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        fitness = INVALID_FITNESS
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("fitness error", err)
            raise

    if fitness == float("inf"):
        return INVALID_FITNESS,

    return fitness,

fitness_auc_lexicase(individual, points)

Calculate area under the curve (AUC) for this individual and store differences in predicted vs observed outcomes for use in lexicase selection

Parameters:

Name	Type	Description	Default
individual	Individual	solution being evaluated for fitness	required
points	list	2-D list containing inputs and outcome for calculating fitness points[0] contains 2-D np.ndarray of all inputs	required

Returns:

Type	Description
float	balanced accuracy fitness

Source code in src/athenage/genn/alg_setup.py

def fitness_auc_lexicase(individual: 'deap.creator.Individual', points: list) -> float:
    """Calculate area under the curve (AUC) for this individual and store differences in
        predicted vs observed outcomes for use in lexicase selection

    Args:
        individual: solution being evaluated for fitness
        points: 2-D list containing inputs and outcome for calculating fitness
            points[0] contains 2-D np.ndarray of all inputs

    Returns:
        balanced accuracy fitness
    """

    x = points[0]
    y = points[1]

    if individual.invalid == True:
        individual.ptscores = np.full(len(y), np.nan)
        return INVALID_FITNESS,

    try:
        pred = eval(individual.phenotype)
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        return INVALID_FITNESS,
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("evaluation error", err)
            raise
    assert np.isrealobj(pred)

    try:
        nan_mask = np.isnan(pred)
        # assign case/control status
        pred_nonan = np.where(pred[~nan_mask] < 0.5, 0, 1)
        fitness = roc_auc_score(y[~nan_mask],pred_nonan)
        individual.nmissing = np.count_nonzero(np.isnan(pred))

        # save individual point scores for use in lexicase selection
        full = np.copy(pred)
        full[~nan_mask] = np.where(pred[~nan_mask] < 0.5, 0, 1)
        individual.ptscores = np.absolute(y-full)


    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        fitness = INVALID_FITNESS
        individual.ptscores = np.full(len(y), np.nan)
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("fitness error", err)
            raise

    if fitness == float("inf"):
        individual.ptscores = np.full(len(y), np.nan)
        return INVALID_FITNESS,

    return fitness,

fitness_auprc(individual, points)

Calculate area under Precision-Recall (PR) curve as fitness for this individual using points passed

Parameters:

Name	Type	Description	Default
individual	Individual	solution being evaluated for fitness	required
points	list	2-D list containing inputs and outcome for calculating fitness points[0] contains 2-D np.ndarray of all inputs	required

Returns:

Type	Description
float	f1 score fitness

Source code in src/athenage/genn/alg_setup.py

def fitness_auprc(individual: 'deap.creator.Individual', points: list) -> float:
    """Calculate area under Precision-Recall (PR) curve as fitness for this individual using points passed

    Args:
        individual: solution being evaluated for fitness
        points: 2-D list containing inputs and outcome for calculating fitness
            points[0] contains 2-D np.ndarray of all inputs

    Returns:
        f1 score fitness
    """

    x = points[0]
    y = points[1]


    if individual.invalid == True:
        return INVALID_FITNESS,

    try:
        pred = eval(individual.phenotype)

    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        return INVALID_FITNESS,
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("evaluation error", err)
            raise
    assert np.isrealobj(pred)

    try:
        nan_mask = np.isnan(pred)
        # assign case/control status
        pred_nonan = np.where(pred[~nan_mask] < 0.5, 0, 1)
        fitness = average_precision_score(y[~nan_mask],pred_nonan)
        individual.nmissing = np.count_nonzero(np.isnan(pred))

#         fitness_compressed = balanced_accuracy_score(y[~nan_mask],pred2)
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        fitness = INVALID_FITNESS
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("fitness error", err)
            raise

    if fitness == float("inf"):
        return INVALID_FITNESS,

    return fitness,

fitness_auprc_lexicase(individual, points)

Calculate the AUPRC (area under Precision-Recall curve) for this individual and store differences in predicted vs observed outcomes for use in lexicase selection

Parameters:

Name	Type	Description	Default
individual	Individual	solution being evaluated for fitness	required
points	list	2-D list containing inputs and outcome for calculating fitness points[0] contains 2-D np.ndarray of all inputs	required

Returns:

Type	Description
float	auprc score fitness

Source code in src/athenage/genn/alg_setup.py

def fitness_auprc_lexicase(individual: 'deap.creator.Individual', points: list) -> float:
    """Calculate the AUPRC (area under Precision-Recall curve) for this individual and store differences in
        predicted vs observed outcomes for use in lexicase selection

    Args:
        individual: solution being evaluated for fitness
        points: 2-D list containing inputs and outcome for calculating fitness
            points[0] contains 2-D np.ndarray of all inputs

    Returns:
        auprc score fitness
    """

    x = points[0]
    y = points[1]

    if individual.invalid == True:
        individual.ptscores = np.full(len(y), np.nan)
        return INVALID_FITNESS,

    try:
        pred = eval(individual.phenotype)
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        return INVALID_FITNESS,
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("evaluation error", err)
            raise
    assert np.isrealobj(pred)

    try:
        nan_mask = np.isnan(pred)
        # assign case/control status
        pred_nonan = np.where(pred[~nan_mask] < 0.5, 0, 1)
        fitness = average_precision_score(y[~nan_mask],pred_nonan)
        individual.nmissing = np.count_nonzero(np.isnan(pred))

        # save individual point scores for use in lexicase selection
        full = np.copy(pred)
        full[~nan_mask] = np.where(pred[~nan_mask] < 0.5, 0, 1)
        individual.ptscores = np.absolute(y-full)


    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        fitness = INVALID_FITNESS
        individual.ptscores = np.full(len(y), np.nan)
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("fitness error", err)
            raise

    if fitness == float("inf"):
        individual.ptscores = np.full(len(y), np.nan)
        return INVALID_FITNESS,

    return fitness,

fitness_balacc(individual, points)

Calculate balanced accuracy as fitness for this individual using points passed

Parameters:

Name	Type	Description	Default
individual	Individual	solution being evaluated for fitness	required
points	list	2-D list containing inputs and outcome for calculating fitness points[0] contains 2-D np.ndarray of all inputs	required

Returns:

Type	Description
float	balanced accuracy fitness

Source code in src/athenage/genn/alg_setup.py

def fitness_balacc(individual: 'deap.creator.Individual', points: list) -> float:
    """Calculate balanced accuracy as fitness for this individual using points passed

    Args:
        individual: solution being evaluated for fitness
        points: 2-D list containing inputs and outcome for calculating fitness
            points[0] contains 2-D np.ndarray of all inputs

    Returns:
        balanced accuracy fitness
    """

    x = points[0]
    y = points[1]


    if individual.invalid == True:
        return INVALID_FITNESS,

    try:
        pred = eval(individual.phenotype)

#         pred2 = eval(compress_weights(individual.phenotype))
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        return INVALID_FITNESS,
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("evaluation error", err)
            raise
    assert np.isrealobj(pred)

    try:
        nan_mask = np.isnan(pred)
        # assign case/control status
        pred_nonan = np.where(pred[~nan_mask] < 0.5, 0, 1)
        fitness = balanced_accuracy_score(y[~nan_mask],pred_nonan)
        individual.nmissing = np.count_nonzero(np.isnan(pred))

#         fitness_compressed = balanced_accuracy_score(y[~nan_mask],pred2)
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        fitness = INVALID_FITNESS
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("fitness error", err)
            raise

    if fitness == float("inf"):
        return INVALID_FITNESS,

    return fitness,

fitness_balacc_lexicase(individual, points)

Calculate balanced accuracy fitness for this individual and store differences in predicted vs observed outcomes for use in lexicase selection

Parameters:

Name	Type	Description	Default
individual	Individual	solution being evaluated for fitness	required
points	list	2-D list containing inputs and outcome for calculating fitness points[0] contains 2-D np.ndarray of all inputs	required

Returns:

Type	Description
float	balanced accuracy fitness

Source code in src/athenage/genn/alg_setup.py

def fitness_balacc_lexicase(individual: 'deap.creator.Individual', points: list) -> float:
    """Calculate balanced accuracy fitness for this individual and store differences in
        predicted vs observed outcomes for use in lexicase selection

    Args:
        individual: solution being evaluated for fitness
        points: 2-D list containing inputs and outcome for calculating fitness
            points[0] contains 2-D np.ndarray of all inputs

    Returns:
        balanced accuracy fitness
    """

    x = points[0]
    y = points[1]

    if individual.invalid == True:
        individual.ptscores = np.full(len(y), np.nan)
        return INVALID_FITNESS,

    try:
        pred = eval(individual.phenotype)
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        return INVALID_FITNESS,
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("evaluation error", err)
            raise
    assert np.isrealobj(pred)

    try:
        nan_mask = np.isnan(pred)
        # assign case/control status
        pred_nonan = np.where(pred[~nan_mask] < 0.5, 0, 1)
        fitness = balanced_accuracy_score(y[~nan_mask],pred_nonan)
        individual.nmissing = np.count_nonzero(np.isnan(pred))

        # save individual point scores for use in lexicase selection
        full = np.copy(pred)
        full[~nan_mask] = np.where(pred[~nan_mask] < 0.5, 0, 1)
        individual.ptscores = np.absolute(y-full)


    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        fitness = INVALID_FITNESS
        individual.ptscores = np.full(len(y), np.nan)
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("fitness error", err)
            raise

    if fitness == float("inf"):
        individual.ptscores = np.full(len(y), np.nan)
        return INVALID_FITNESS,

    return fitness,

fitness_f1(individual, points)

Calculate F1 score (also known as balanced F-score or F-measure) as fitness for this individual using points passed

Parameters:

Name	Type	Description	Default
individual	Individual	solution being evaluated for fitness	required
points	list	2-D list containing inputs and outcome for calculating fitness points[0] contains 2-D np.ndarray of all inputs	required

Returns:

Type	Description
float	f1 score fitness

Source code in src/athenage/genn/alg_setup.py

def fitness_f1(individual: 'deap.creator.Individual', points: list) -> float:
    """Calculate F1 score (also known as balanced F-score or F-measure) as fitness for this individual using points passed

    Args:
        individual: solution being evaluated for fitness
        points: 2-D list containing inputs and outcome for calculating fitness
            points[0] contains 2-D np.ndarray of all inputs

    Returns:
        f1 score fitness
    """

    x = points[0]
    y = points[1]


    if individual.invalid == True:
        return INVALID_FITNESS,

    try:
        pred = eval(individual.phenotype)

    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        return INVALID_FITNESS,
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("evaluation error", err)
            raise
    assert np.isrealobj(pred)

    try:
        nan_mask = np.isnan(pred)
        # assign case/control status
        pred_nonan = np.where(pred[~nan_mask] < 0.5, 0, 1)
        fitness = f1_score(y[~nan_mask],pred_nonan)
        individual.nmissing = np.count_nonzero(np.isnan(pred))

#         fitness_compressed = balanced_accuracy_score(y[~nan_mask],pred2)
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        fitness = INVALID_FITNESS
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("fitness error", err)
            raise

    if fitness == float("inf"):
        return INVALID_FITNESS,

    return fitness,

fitness_f1_lexicase(individual, points)

Calculatethe F1 score (also known as balanced F-score or F-measure) for this individual and store differences in predicted vs observed outcomes for use in lexicase selection

Parameters:

Name	Type	Description	Default
individual	Individual	solution being evaluated for fitness	required
points	list	2-D list containing inputs and outcome for calculating fitness points[0] contains 2-D np.ndarray of all inputs	required

Returns:

Type	Description
float	f1 score fitness

Source code in src/athenage/genn/alg_setup.py

def fitness_f1_lexicase(individual: 'deap.creator.Individual', points: list) -> float:
    """Calculatethe F1 score (also known as balanced F-score or F-measure) for this individual and store differences in
        predicted vs observed outcomes for use in lexicase selection

    Args:
        individual: solution being evaluated for fitness
        points: 2-D list containing inputs and outcome for calculating fitness
            points[0] contains 2-D np.ndarray of all inputs

    Returns:
        f1 score fitness
    """

    x = points[0]
    y = points[1]

    if individual.invalid == True:
        individual.ptscores = np.full(len(y), np.nan)
        return INVALID_FITNESS,

    try:
        pred = eval(individual.phenotype)
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        return INVALID_FITNESS,
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("evaluation error", err)
            raise
    assert np.isrealobj(pred)

    try:
        nan_mask = np.isnan(pred)
        # assign case/control status
        pred_nonan = np.where(pred[~nan_mask] < 0.5, 0, 1)
        fitness = f1_score(y[~nan_mask],pred_nonan)
        individual.nmissing = np.count_nonzero(np.isnan(pred))

        # save individual point scores for use in lexicase selection
        full = np.copy(pred)
        full[~nan_mask] = np.where(pred[~nan_mask] < 0.5, 0, 1)
        individual.ptscores = np.absolute(y-full)


    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        fitness = INVALID_FITNESS
        individual.ptscores = np.full(len(y), np.nan)
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("fitness error", err)
            raise

    if fitness == float("inf"):
        individual.ptscores = np.full(len(y), np.nan)
        return INVALID_FITNESS,

    return fitness,

fitness_rsquared(individual, points)

Calculate r-squared fitness for this individual using points passed

Parameters:

Name	Type	Description	Default
individual	Individual	solution being evaluated for fitness	required
points	list	2-D list containing inputs and outcome for calculating fitness points[0] contains 2-D np.ndarray of all inputs	required

Returns:

Type	Description
float	r-squared fitness

Source code in src/athenage/genn/alg_setup.py

def fitness_rsquared(individual: 'deap.creator.Individual', points: list) -> float:
    """Calculate r-squared fitness for this individual using points passed

    Args:
        individual: solution being evaluated for fitness
        points: 2-D list containing inputs and outcome for calculating fitness
            points[0] contains 2-D np.ndarray of all inputs

    Returns:
        r-squared fitness
    """
    x = points[0]
    y = points[1]

    if individual.invalid == True:
        return INVALID_FITNESS,

    try:
        pred = eval(individual.phenotype)
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        return INVALID_FITNESS,
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("evaluation error", err)
            raise
    assert np.isrealobj(pred)

    try:
        fitness = r_squared(y,pred)
        individual.nmissing = np.count_nonzero(np.isnan(pred))
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        fitness = INVALID_FITNESS
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("fitness error", err)
            raise

    if fitness == float("inf"):
        return INVALID_FITNESS,

    return fitness,

fitness_rsquared_lexicase(individual, points)

Calculate r-squared fitness for this individual and store differences in predicted vs observed outcomes for use in lexicase selection

Parameters:

Name	Type	Description	Default
individual	Individual	solution being evaluated for fitness	required
points	list	2-D list containing inputs and outcome for calculating fitness points[0] contains 2-D np.ndarray of all inputs	required

Returns:

Type	Description
float	r-squared fitness

Source code in src/athenage/genn/alg_setup.py

def fitness_rsquared_lexicase(individual: 'deap.creator.Individual', points: list) -> float:
    """Calculate r-squared fitness for this individual and store differences in
        predicted vs observed outcomes for use in lexicase selection

    Args:
        individual: solution being evaluated for fitness
        points: 2-D list containing inputs and outcome for calculating fitness
            points[0] contains 2-D np.ndarray of all inputs

    Returns:
        r-squared fitness
    """

    x = points[0]
    y = points[1]

    if individual.invalid == True:
        individual.ptscores = np.full(len(y), np.nan)
        return INVALID_FITNESS,

    try:
        pred = eval(individual.phenotype)
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        return INVALID_FITNESS,
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("evaluation error", err)
            raise
    assert np.isrealobj(pred)

    try:
        fitness = r_squared(y,pred)
        individual.nmissing = np.count_nonzero(np.isnan(pred))

        # store individual differences for lexicase
        individual.ptscores = np.absolute(y-pred)
    except (FloatingPointError, ZeroDivisionError, OverflowError,
            MemoryError, ValueError):
        individual.ptscores = np.full(len(y), np.nan)
        fitness = INVALID_FITNESS
    except Exception as err:
            # Other errors should not usually happen (unless we have
            # an unprotected operator) so user would prefer to see them.
            print("fitness error", err)
            raise

    if fitness == float("inf"):
        individual.ptscores = np.full(len(y), np.nan)
        return INVALID_FITNESS,

    return fitness,

r_squared(y, y_hat)

Calculate r-squared values

Parameters:

Name	Type	Description	Default
y	ndarray	Observed values	required
y_hat	ndarray	Predicted values	required

Returns:

Type	Description
float	r-squared value

Source code in src/athenage/genn/alg_setup.py

def r_squared(y: np.ndarray, y_hat: np.ndarray) -> float:
    """Calculate r-squared values

    Args:
        y: Observed values
        y_hat: Predicted values

    Returns:
        r-squared value
    """

    nan_mask = np.isnan(y_hat)
    y_bar = y[~nan_mask].mean()
    ss_tot = ((y[~nan_mask]-y_bar)**2).sum()
    ss_res = ((y[~nan_mask]-y_hat[~nan_mask])**2).sum()
    return 1 - (ss_res/ss_tot)

set_crossover(toolbox, crosstype)

Sets crossover type for toolbox

Parameters:

Name	Type	Description	Default
toolbox	toolbox	DEAP toolbox	required
crosstype	str	specifies type to use	required

Returns:

Type	Description
None	None

Source code in src/athenage/genn/alg_setup.py

def set_crossover(toolbox: 'deap.base.toolbox', crosstype: str) -> None:
    """Sets crossover type for toolbox

    Args:
        toolbox: DEAP toolbox
        crosstype: specifies type to use

    Returns:
        None
    """
    if crosstype == 'onepoint':
        toolbox.register("mate", grape.crossover_onepoint)
    elif crosstype == 'match':
        toolbox.register("mate", grape.crossover_match)
    elif crosstype == 'block':
        toolbox.register("mate", grape.crossover_block)