使用以下代码时，n_folds必须为 2 或更多。如何更改它将适用于 n_folds = 1？但是，当将其更改为 1 时，对于 n_folds = 2 及更多，它可以工作。似乎有些东西在函数中不起作用。对于 n_folds = 1，存在以下错误：
Traceback (most recent call last):
File "GX.py", line 266, in <module>
scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)
File "GX.py", line 92, in evaluate_algorithm
predicted = algorithm(train_set, test_set, *args)
File "GX.py", line 223, in random_forest
tree = build_tree(sample, max_depth, min_size, n_features)
File "GX.py", line 183, in build_tree
root = get_split(train, n_features)
File "GX.py", line 137, in get_split
index = randrange(len(dataset[0]) - 1)
IndexError: list index out of range
这是代码：
# Select the best split point for a dataset
def get_split(dataset, n_features):
class_values = list(set(row[-1] for row in dataset))
b_index, b_value, b_score, b_groups = 999, 999, 999, None
features = list()
while len(features) < n_features:
index = randrange(len(dataset[0])-1)
if index not in features:
features.append(index)
for index in features:
for row in dataset:
groups = test_split(index, row[index], dataset)
gini = gini_index(groups, class_values)
if gini < b_score:
b_index, b_value, b_score, b_groups = index, row[index], gini, groups
return {'index':b_index, 'value':b_value, 'groups':b_groups}
# Random Forest Algorithm on Sonar Dataset
from random import seed
from random import randrange
from csv import reader
from math import sqrt
# 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
# 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
# Split a dataset based on an attribute and an attribute value
def test_split(index, value, dataset):
left, right = list(), list()
for row in dataset:
if row[index] < value:
left.append(row)
else:
right.append(row)
return left, right
# Calculate the Gini index for a split dataset
def gini_index(groups, classes):
# count all samples at split point
n_instances = float(sum([len(group) for group in groups]))
# sum weighted Gini index for each group
gini = 0.0
for group in groups:
size = float(len(group))
# avoid divide by zero
if size == 0:
continue
score = 0.0
# score the group based on the score for each class
for class_val in classes:
p = [row[-1] for row in group].count(class_val) / size
score += p * p
# weight the group score by its relative size
gini += (1.0 - score) * (size / n_instances)
return gini
# Select the best split point for a dataset
def get_split(dataset, n_features):
class_values = list(set(row[-1] for row in dataset))
b_index, b_value, b_score, b_groups = 999, 999, 999, None
features = list()
while len(features) < n_features:
index = randrange(len(dataset[0]) - 1)
if index not in features:
features.append(index)
for index in features:
for row in dataset:
groups = test_split(index, row[index], dataset)
gini = gini_index(groups, class_values)
if gini < b_score:
b_index, b_value, b_score, b_groups = index, row[index], gini, groups
return {'index': b_index, 'value': b_value, 'groups': b_groups}
# Create a terminal node value
def to_terminal(group):
outcomes = [row[-1] for row in group]
return max(set(outcomes), key=outcomes.count)
# Create child splits for a node or make terminal
def split(node, max_depth, min_size, n_features, depth):
left, right = node['groups']
del (node['groups'])
# check for a no split
if not left or not right:
node['left'] = node['right'] = to_terminal(left + right)
return
# check for max depth
if depth >= max_depth:
node['left'], node['right'] = to_terminal(left), to_terminal(right)
return
# process left child
if len(left) <= min_size:
node['left'] = to_terminal(left)
else:
node['left'] = get_split(left, n_features)
split(node['left'], max_depth, min_size, n_features, depth + 1)
# process right child
if len(right) <= min_size:
node['right'] = to_terminal(right)
else:
node['right'] = get_split(right, n_features)
split(node['right'], max_depth, min_size, n_features, depth + 1)
# Build a decision tree
def build_tree(train, max_depth, min_size, n_features):
root = get_split(train, n_features)
split(root, max_depth, min_size, n_features, 1)
return root
# Make a prediction with a decision tree
def predict(node, row):
if row[node['index']] < node['value']:
if isinstance(node['left'], dict):
return predict(node['left'], row)
else:
return node['left']
else:
if isinstance(node['right'], dict):
return predict(node['right'], row)
else:
return node['right']
# Create a random subsample from the dataset with replacement
def subsample(dataset, ratio):
sample = list()
n_sample = round(len(dataset) * ratio)
while len(sample) < n_sample:
index = randrange(len(dataset))
sample.append(dataset[index])
return sample
# Make a prediction with a list of bagged trees
def bagging_predict(trees, row):
predictions = [predict(tree, row) for tree in trees]
return max(set(predictions), key=predictions.count)
# Random Forest Algorithm
def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features):
trees = list()
for i in range(n_trees):
sample = subsample(train, sample_size)
tree = build_tree(sample, max_depth, min_size, n_features)
trees.append(tree)
predictions = [bagging_predict(trees, row) for row in test]
return (predictions)
seed(1)
import pandas as pd
file_path ='https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data'
dataset2 = pd.read_csv(file_path, header=None, sep=',')
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
dataset1 = pd.DataFrame(dataset2)
dataset1 = dataset1.drop(0, axis=1)
train, test = train_test_split(dataset1, test_size=0.1, random_state = 0) ###<-----
df = dataset1.astype('str')
dataset = df.values.tolist()
train1 = train.astype('str')
train = train1.values.tolist()
test1 = test.astype('str')
test = test1.values.tolist()
target_index = 0
for i in range(0, len(dataset[0])):
if i != target_index:
str_column_to_float(dataset, i)
# convert class column to integers
str_column_to_int(dataset, target_index)
# evaluate algorithm
n_folds = 1 ##<----
max_depth = 10
min_size = 1
sample_size = 1.0
n_features = int(sqrt(len(dataset[0]) - 1))
for n_trees in [5]:
scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)
print('Trees: %d' % n_trees)
print('Scores: %s' % scores)
print('Mean Accuracy: %.3f%%' % (sum(scores) / float(len(scores))))
你了解交叉验证的概念吗？ 在 k 折叠交叉验证中，数据被拆分为 k 个样本，一个样本用于测试模型，k-1 个样本用于训练模型。
如果将 k = 1（或示例中的 n = 1）设置为"n" ，则无法对数据进行采样并执行交叉验证。