A simple, multi-purpose genetic algorithm supporting both continuous and discrete design variables.


License
MIT
Install
pip install flexga==1.1.2

Documentation

A Simple, Multi-Purpose Genetic Algorithm

Build Status

flexga is a flexible, multi-purpose elitist genetic algorithm useful for single objective optimization problems. It can simultaneously support float, integer, categorical, boolean, and float vector arguments. As such, it is a versatile tool for hyperparemter optimization in machine learning models, among other things.

Getting Started

Installation

pip install flexga

Basic Usage

from flexga import flexga
from flexga.utils import inverted
from flexga.argmeta import FloatArgMeta

def rosenbrock(x: float, y: float) -> float:
    """A classsic continuous optimization problem"""
    return (1 - x) ** 2 + 100 * (y - x ** 2) ** 2

# The goal of rosenbrock is to minimize. The genetic
# algorithm maximizes, so we invert the function's output.
objective = inverted(rosenbrock)

fopt, args_opt, _ = flexga(
    objective,
    # We must specify annotations for rosenbrock's arguments,
    # in this case so the optimizer knows what the bounds are
    # for each input, and so it knows what distribution to
    # sample mutation values from.
    argsmeta = [
        FloatArgMeta(bounds=(-50, 50), mutation_std=1.0),
        FloatArgMeta(bounds=(-50, 50), mutation_std=1.0)
    ]
    iters = 500,
)

# The best value the optimizer found for the objective.
print(fopt) # 0.0

# The arguments that give the objective its optimal value
# i.e. `rosenbrock(*args_opt) == fopt`.
print(args_opt) # [1.0, 1.0]

Annotating Objective Arguments

flexga can handle objective functions that take positional arguments (as seen above via the argsmeta parameter), as well as key-word arguments (via the kwargsmeta parameter, as seen below in the machine learning model hyperparameter optimization example). It can handle mixed-type arguments of several datatypes, which is one of its best features. The supported data types, alongside their dedicated annotation classes, are:

Datatype Annotation Class
float flexga.argmeta.FloatArgMeta(bounds, mutation_std)
int flexga.argmeta.IntArgMeta(bounds, mutation_std)
numpy.ndarray vectors (must be 1D) flexga.argmeta.FloatVectorArgMeta(bounds, mutation_std)
bool flexga.argmeta.BoolArgMeta()
Categorical (one of a set of options, where the options can be of any type) flexga.argmeta.CategoricalArgMeta(options)

See the constructor definitions for each of these annotation classes inside the flexga.argmeta module for details on what values they need.

Example: Machine Learning Model Hyperparameter Optimization

Here is an example of using flexga to perform hyperparameter optimization of both continuous and integer hyperparameters for a decision tree classifier. flexga optimizes the F1 Macro metric as computed over a validation set on the digits problem.

from sklearn.metrics import f1_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from flexga import flexga
from flexga.argmeta import IntArgMeta, FloatArgMeta

# Make the train/validation split of the data.
dataset = load_digits()
X_train, X_val, y_train, y_val = train_test_split(
    dataset["data"], dataset["target"], test_size=0.33
)

# We use this model throughout the optimization process.
model = DecisionTreeClassifier()

# Get a baseline to compare the optimized result to.
model.fit(X_train, y_train)
y_pred = model.predict(X_val)
print(
    "baseline for default hyperparameters:",
    f1_score(y_val, y_pred, average="macro")
)

# This function accepts hyperparameter values, trains
# the model on the training set with those values,
# and returns F1 Macro computed over the validation
# set for the trained model. It is of the form
# `objective_function(design_variables) -> objective`,
# which is what the optimizer wants.
def objective(*args, **kwargs) -> float:
    # Set the hyperparameters - the things we're optimizing.
    model.set_params(**kwargs)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_val)
    return f1_score(y_val, y_pred, average="macro")


# Optimize the decision tree's hyperparameters over the
# validation set. `fopt`, the optimal F1 Macro found,
# should be higher than the baseline.
fopt, _, kwargs_opt = flexga(
    objective,
    kwargsmeta={
        # These are the things the genetic algorithm will optimize;
        # the decision tree's hyperparameters.
        "min_samples_split": IntArgMeta((2, 75), 3),
        "min_samples_leaf": IntArgMeta((1, 75), 3),
        "min_weight_fraction_leaf": FloatArgMeta((0.0, 0.5), 0.025),
        "min_impurity_decrease": FloatArgMeta((0.0, 1.0), 0.05),
        "ccp_alpha": FloatArgMeta((0.0, 1.0), 0.05),
    },
    # Will stop when the optimizer has not improved upon the best
    # fitness for 20 generations.
    patience=20,
    print_every=5,
)