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python - 心脏病预测的反向传播 - 结果不准确?

转载 作者:行者123 更新时间:2023-11-30 08:58:35 24 4
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我正在使用具有反向传播功能的多层感知器来预测心脏病,并且我正在使用此处链接的数据集: https://drive.google.com/file/d/1LGSDCODkoj-bqhvdCLMeJu7BbTyzyjyE/view?usp=sharing

它有 303 条记录和 5 个输出类别 (0、1、2、3、4、5),以 0 到 5 的等级表示心脏病的严重程度。

我通过用平均值调整缺失值来处理数据集中的缺失数据,然后我什至尝试删除缺失值的行,以防影响准确性。

我已经通过 K 折交叉验证 (n_folds=5) 和 sigmoid 激活函数实现了反向传播(代码如下)。我计算了每次折叠的准确性,然后取平均值以获得最终的总体准确性。

但无论我做什么 - 调整学习率,改变 no。褶皱,没有。隐藏层 - 准确度保持在 55% 到 60% 之间,我需要它 >85%我不知道出了什么问题!代码或数据集有问题吗?

# Backprop
from random import seed
from random import randrange
from random import random
from csv import reader
from math import exp

# Load a CSV file
def load_csv(filename):
dataset = list()
with open(filename, 'r') as file:
csv_reader = reader(file)
for row in csv_reader:
if not row:
continue
dataset.append(row)
return dataset

# Convert string column to float
def str_column_to_float(dataset, column):
for row in dataset:
row[column] = float(row[column].strip())

# Convert string column to integer
def str_column_to_int(dataset, column):
class_values = [row[column] for row in dataset]
unique = set(class_values)
lookup = dict()
for i, value in enumerate(unique):
lookup[value] = i
for row in dataset:
row[column] = lookup[row[column]]
return lookup

# Find the min and max values for each column
def dataset_minmax(dataset):
minmax = list()
stats = [[min(column), max(column)] for column in zip(*dataset)]
return stats

# Rescale dataset columns to the range 0-1
def normalize_dataset(dataset, minmax):
for row in dataset:
for i in range(len(row)-1):
row[i] = (row[i] - minmax[i][0]) / (minmax[i][1] - minmax[i][0])

# Split a dataset into k folds
def cross_validation_split(dataset, n_folds):
dataset_split = list()
dataset_copy = list(dataset)
fold_size = int(len(dataset) / n_folds)
for i in range(n_folds):
fold = list()
while len(fold) < fold_size:
index = randrange(len(dataset_copy))
fold.append(dataset_copy.pop(index))
dataset_split.append(fold)
return dataset_split

# Calculate accuracy percentage
def accuracy_metric(actual, predicted):
correct = 0
for i in range(len(actual)):
if actual[i] == predicted[i]:
correct += 1
return correct / float(len(actual)) * 100.0

# Evaluate an algorithm using a cross validation split
def evaluate_algorithm(dataset, algorithm, n_folds, *args):
folds = cross_validation_split(dataset, n_folds)
scores = list()
for fold in folds:
train_set = list(folds)
train_set.remove(fold)
train_set = sum(train_set, [])
test_set = list()
for row in fold:
row_copy = list(row)
test_set.append(row_copy)
row_copy[-1] = None
predicted = algorithm(train_set, test_set, *args)
actual = [row[-1] for row in fold]
accuracy = accuracy_metric(actual, predicted)
scores.append(accuracy)
return scores

# Calculate neuron activation for an input
def activate(weights, inputs):
activation = weights[-1]
for i in range(len(weights)-1):
activation += weights[i] * inputs[i]
return activation

# Transfer neuron activation
def transfer(activation):
return 1.0 / (1.0 + exp(-activation))

# Forward propagate input to a network output
def forward_propagate(network, row):
inputs = row
for layer in network:
new_inputs = []
for neuron in layer:
activation = activate(neuron['weights'], inputs)
neuron['output'] = transfer(activation)
new_inputs.append(neuron['output'])
inputs = new_inputs
return inputs

# Calculate the derivative of an neuron output
def transfer_derivative(output):
return output * (1.0 - output)

# Backpropagate error and store in neurons
def backward_propagate_error(network, expected):
for i in reversed(range(len(network))):
layer = network[i]
errors = list()
if i != len(network)-1:
for j in range(len(layer)):
error = 0.0
for neuron in network[i + 1]:
error += (neuron['weights'][j] * neuron['delta'])
errors.append(error)
else:
for j in range(len(layer)):
neuron = layer[j]
errors.append(expected[j] - neuron['output'])
for j in range(len(layer)):
neuron = layer[j]
neuron['delta'] = errors[j] * transfer_derivative(neuron['output'])

# Update network weights with error
def update_weights(network, row, l_rate):
for i in range(len(network)):
inputs = row[:-1]
if i != 0:
inputs = [neuron['output'] for neuron in network[i - 1]]
for neuron in network[i]:
for j in range(len(inputs)):
neuron['weights'][j] += l_rate * neuron['delta'] * inputs[j]
neuron['weights'][-1] += l_rate * neuron['delta']

# Train a network for a fixed number of epochs
def train_network(network, train, l_rate, n_epoch, n_outputs):
for epoch in range(n_epoch):
for row in train:
outputs = forward_propagate(network, row)
expected = [0 for i in range(n_outputs)]
expected[row[-1]] = 1
backward_propagate_error(network, expected)
update_weights(network, row, l_rate)

# Initialize a network
def initialize_network(n_inputs, n_hidden, n_outputs):
network = list()
hidden_layer = [{'weights':[random() for i in range(n_inputs + 1)]} for i in range(n_hidden)]
network.append(hidden_layer)
output_layer = [{'weights':[random() for i in range(n_hidden + 1)]} for i in range(n_outputs)]
network.append(output_layer)
return network

# Make a prediction with a network
def predict(network, row):
outputs = forward_propagate(network, row)
return outputs.index(max(outputs))

# Backpropagation Algorithm With Stochastic Gradient Descent
def back_propagation(train, test, l_rate, n_epoch, n_hidden):
n_inputs = len(train[0]) - 1
n_outputs = len(set([row[-1] for row in train]))
network = initialize_network(n_inputs, n_hidden, n_outputs)
train_network(network, train, l_rate, n_epoch, n_outputs)
predictions = list()
for row in test:
prediction = predict(network, row)
predictions.append(prediction)
return(predictions)

# Test Backprop on Seeds dataset
seed(1)
# load and prepare data
filename = 'heartdisease.csv'
dataset = load_csv(filename)
for i in range(len(dataset[0])-1):
str_column_to_float(dataset, i)
# convert class column to integers
str_column_to_int(dataset, len(dataset[0])-1)
# normalize input variables
minmax = dataset_minmax(dataset)
normalize_dataset(dataset, minmax)
# evaluate algorithm
n_folds = 5
l_rate = 0.3
n_epoch = 500
n_hidden = 5
scores = evaluate_algorithm(dataset, back_propagation, n_folds, l_rate, n_epoch, n_hidden)
print('Scores: %s' % scores)
print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))

最佳答案

首先,这个问题不是针对 stackoverflow 的。其次,你准确率低的主要原因是记录数量少。如您所知,神经网络需要大量数据来学习模式。 6 个类的 303 条记录(即每个类大约 50 条记录)不会将其减少到 85% 或更多。你应该尝试增强树方法或其他方法。

关于python - 心脏病预测的反向传播 - 结果不准确?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/49717740/

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