num_features_for_split = sqrt(total_input_features)

# 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 = 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 =a 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, class_values):
 gini = 0.0
 for class_value in class_values:
  for group in groups:
   size = len(group)
   if size == 0:
    continue
   proportion = [row[-1] for row in group].count(class_value) / float(size)
   gini += (proportion * (1.0 - proportion))
 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(dataset, 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)

# Test the random forest algorithm
seed(1)
# load and prepare data
filename = 'sonar.all-data.csv'
dataset = load_csv(filename)
# convert string attributes to integers
for i in range(0, len(dataset[0])-1):
 str_column_to_float(dataset, i)
# convert class column to integers
str_column_to_int(dataset, len(dataset[0])-1)
# evaluate algorithm
n_folds = 5
max_depth = 10
min_size = 1
sample_size = 1.0
n_features = int(sqrt(len(dataset[0])-1))
for n_trees in [1, 5, 10]:
 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))))

Trees: 1
Scores: [68.29268292682927, 75.60975609756098, 70.73170731707317, 63.41463414634146, 65.85365853658537]
Mean Accuracy: 68.780%
 
Trees: 5
Scores: [68.29268292682927, 68.29268292682927, 78.04878048780488, 65.85365853658537, 68.29268292682927]
Mean Accuracy: 69.756%
 
Trees: 10
Scores: [68.29268292682927, 78.04878048780488, 75.60975609756098, 70.73170731707317, 70.73170731707317]
Mean Accuracy: 72.683%