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Examples
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After installing the required dependencies as outlined in the `requirements <requirements.html>`_ file, yyou can utilize genetic algorithms for hyperparameter tuning in regression tasks using both ``tensorflow.keras`` and ``torch`` models. For example, the GitHub repository contains examples that demonstrate neural network reconstruction of the distance modulus using the Joint Light Analysis (JLA) compilation, similar to the IV.A section of the reference *Gómez-Vargas, I., Andrade, J. B., & Vázquez, J. A. (2023). Neural networks optimized by genetic algorithms in cosmology. Physical Review D, 107(4), 043509.*.

1) Using ``tensorflow.keras``:

.. code-block:: python

	import time
	from nnogada.Nnogada import Nnogada
	import pandas as pd
	from sklearn.preprocessing import StandardScaler
	import numpy as np


	df = pd.read_csv('data/jla.csv')
	N = len(df.values)
	randomize = np.random.permutation(N)
	data = df.values[randomize]
	N = len(df.values)
	z = data[:, 0]
	y = data[:, 1:3] ### toma el resto de variables a predecir
	y[:,1] = y[:, 1]**2+data[:,2]
	np.shape(y)


	dmag = df["dmb"]
	df2 = df['errors']+df['dmb']**2

	scalerz = StandardScaler()
	scalerz.fit(z.reshape(-1, 1))
	z = scalerz.transform(z.reshape(-1, 1))

	split = 0.75
	ntrain = int(split * len(z))
	indx = [ntrain]
	X_train, X_val = np.split(z, indx)
	Y_train, Y_val = np.split(y, indx)

	population_size = 11   # max of individuals per generation
	max_generations = 5    # number of generations
	gene_length = 4        # lenght of the gene, depends on how many hiperparameters are tested
	k = 1                  # num. of finalist individuals

	t = time.time()
	datos = []

	# Define the hyperparameters for the search
	hyperparams = {'deep': [1, 2], 'num_units': [1,2], 'batch_size': [256,1048]}

	# generate a Nnogada instance
	net_fit = Nnogada(hyp_to_find=hyperparams, X_train=X_train, Y_train=Y_train, X_val=X_val, Y_val=Y_val)
	# Set the possible values of hyperparameters and not use the default values from hyperparameters.py
	net_fit.set_hyperparameters()

	# best solution
	best_population = net_fit.ga_with_elitism(population_size, max_generations, gene_length, k)
	print(best_population)
	print("Total elapsed time:", (time.time()-t)/60, "minutes")



2) Using ``torch``:

.. code-block:: python

	import time
	from nnogada.Nnogada import Nnogada
	import pandas as pd
	from sklearn.preprocessing import StandardScaler
	import numpy as np


	df = pd.read_csv('data/jla.csv')
	N = len(df.values)
	randomize = np.random.permutation(N)
	data = df.values[randomize]
	N = len(df.values)
	z = data[:, 0]
	y = data[:, 1:3] ### toma el resto de variables a predecir
	y[:,1] = y[:, 1]**2+data[:,2]
	np.shape(y)


	dmag = df["dmb"]
	df2 = df['errors']+df['dmb']**2

	scalerz = StandardScaler()
	scalerz.fit(z.reshape(-1, 1))
	z = scalerz.transform(z.reshape(-1, 1))

	split = 0.75
	ntrain = int(split * len(z))
	indx = [ntrain]
	X_train, X_val = np.split(z, indx)
	Y_train, Y_val = np.split(y, indx)

	population_size = 11   # max of individuals per generation
	max_generations = 5    # number of generations
	gene_length = 4        # lenght of the gene, depends on how many hiperparameters are tested
	k = 1                  # num. of finalist individuals

	t = time.time()
	# Define the hyperparameters for the search
	hyperparams = {'deep': [1, 2], 'num_units': [1,2], 'batch_size': [2048,1048]}

	# generate a Nnogada instance
	net_fit = Nnogada(hyp_to_find=hyperparams, X_train=X_train, Y_train=Y_train, X_val=X_val, Y_val=Y_val,
		          neural_library='torch')
	# Set the possible values of hyperparameters and not use the default values from hyperparameters.py
	net_fit.set_hyperparameters()

	# Find best solutions
	net_fit.ga_with_elitism(population_size, max_generations, gene_length, k)
	# best solution
	print("best individual", net_fit.best)
	print("Best number of nodes:", net_fit.best['num_units'])
	print("Best number of layers:", net_fit.best['deep'])
	print("Best number of batch_size:", net_fit.best['batch_size'])
	print("Total elapsed time:", (time.time()-t)/60, "minutes")