forked from ippossebon/neural-nets
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main.py
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main.py
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import csv
import random
import math
import statistics
import jsonpickle
import pprint
# import matplotlib.pyplot as plt
from utils import FileUtils
from neuralnetwork import NeuralNetwork
"""
./backpropagation network.txt initial_weights.txt dataset.txt
"""
print_multi_array = pprint.PrettyPrinter(indent=4)
def main():
config_file = './data/configs/network.txt'
initial_weights_file = './data/configs/initial_weights.txt'
dataset_file = './data/datasets/wine.txt'
fileUtils = FileUtils(dataset_file=dataset_file, config_file=config_file)
dataset = fileUtils.getDataset()
#normalized_dataset = normalizeDataset(dataset)
neurons_per_layer = [1, 2, 1]
network = NeuralNetwork(
config_file=config_file,
dataset=dataset,
initial_weights_file=initial_weights_file,
neurons_per_layer=neurons_per_layer
)
network.backpropagation()
def runCrossValidation():
results_accuracy = []
results_precision = []
results_recall = []
results_f1measure = []
k = 10
x = range(1,50)
x_axis = [1, 5, 10, 20, 25, 30, 35, 40, 45, 50]
for i in x_axis:
folds = getKStratifiedFolds(instances, target_class, k=k)
results = crossValidation(attributes,
attributes_types,
target_class,
folds,
b=1,
k=k)
results_accuracy.append(results[0])
results_precision.append(results[1])
results_recall.append(results[2])
results_f1measure.append(results[3])
plt.xticks(np.arange(min(x), max(x)+1, 5.0))
plt.plot(x_axis, results_accuracy, label = "Accuracy")
plt.plot(x_axis, results_precision, label = "Precision")
plt.plot(x_axis, results_recall, label = "Recall")
plt.plot(x_axis, results_f1measure, label = "F1-Measure")
plt.ylabel('Values')
plt.xlabel('Number of trees')
plt.title('Results for' + file_name)
plt.legend()
plt.show()
# Normaliza as features dado o limite [0,1]
def normalizeDataset(dataset):
for i in range(len(dataset)):
dataset_line = dataset[i].attributes
min_value = np.min(dataset_line)
max_value = np.max(dataset_line)
# calcula novo valor normalizado para cada parametro
for j in range(len(dataset_line)):
dataset_line[j] = (dataset_line[j] - min_value)/(max_value - min_value)
for i in range(len(dataset)):
dataset_line = dataset[i].attributes
# calcula novo valor normalizado para cada parametro
for j in range(len(dataset_line)):
#print(len(dataset_line))
ex1 = Instance(attributes=dataset_line[j], classification=dataset[i].classification)
#print(self.dataset[i].classification)
training_data.append(ex1)
#print(training_data)
# Cria o conjunto de bootstrap
def getBootstrap(data_set, size):
bootstrap = []
for i in range(size):
index = random.randint(0, len(data_set)-1)
bootstrap.append(data_set[index])
return bootstrap
# Executa a validacao cruzada estratificada
def crossValidation(attributes, attributes_types, target_class, folds, b, k):
config_file = './data/configs/network.txt'
initial_weights_file = './data/configs/initial_weights.txt'
dataset_file = './data/datasets/wine.txt'
accuracy_values = []
precision_values = []
recall_values = []
fmeasure_values = []
for i in range(k):
training_set_folds = list(folds)
training_set_folds.remove(folds[i])
training_set = transformToList(training_set_folds)
# bootstrap tem o tamanho do conjunto de treinamento
bootstrap_size = len(training_set)
test_set = folds[i]
forest = []
for j in range(b):
bootstrap = getBootstrap(training_set, bootstrap_size)
neurons_per_layer = [1, 2, 1]
network = NeuralNetwork(
config_file=config_file,
dataset_file=dataset_file,
initial_weights_file=initial_weights_file,
neurons_per_layer=neurons_per_layer
)
network.backpropagation()
forest.append(network)
# Usa o ensemble de B arvores para prever as instancias do fold i
# (fold de teste) e avaliar desempenho do algoritmo
true_positives, false_positives, false_negatives, true_negatives = evaluateForest(forest, test_set, target_class)
accuracy_values.append(calculateAccuracy(true_positives, true_negatives, false_positives, false_negatives))
precision_value = calculatePrecision(true_positives, false_positives)
precision_values.append(precision_value)
recall_value = calculateRecall(true_positives, false_negatives)
recall_values.append(recall_value)
fmeasure_values.append(calculateF1Measure(precision_value, recall_value))
accuracy = sum(accuracy_values)/len(accuracy_values)
precision = sum(precision_values)/len(precision_values)
recall = sum(recall_values)/len(recall_values)
fmeasure = sum(fmeasure_values)/len(fmeasure_values)
return accuracy, precision, recall, fmeasure
def calculateAccuracy(true_positives, true_negatives, false_positives, false_negatives):
return float((true_positives + true_negatives)/(true_positives + true_negatives + false_positives + false_negatives))
def calculateRecall(true_positives, false_negatives):
return float((true_positives)/(true_positives + false_negatives))
def calculatePrecision(true_positives, false_positives):
return float((true_positives)/(true_positives + false_positives))
def calculateF1Measure(precision, recall):
return float((2*precision*recall)/(precision+recall))
if __name__ == '__main__':
main()