Random Forest Builder

from random import seed from random import randrange from csv import reader from math import sqrt import re import csv

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

1. Makes an array of the data. Each row is a point in time and
2. each column is a channel, except for the last column, which contains
3. the desired output.

def make_data(dataname, startname, labelname):

   data = load_csv(dataname)
   starttimes = load_csv(startname)
   labels = load_csv(labelname)
   numtrials = len(labels)
   regex = r"NaN\s+"
   
   #Convert the data, each row is one second and each column is one channel
   for i in range(0, len(data)):
       data[i] = [float(j) for j in data[i][0].split()]
       
   #Convert starttimes and labels. for labels, 0 indicates a test trial
   for i in range(0, numtrials):
       starttimes[i] = int(starttimes[i][0])
       if re.search(regex, labels[i][0]): labels[i] = 0 
       else: labels[i] = int(labels[i][0])
       
   #Add the labels to the data matrix
   for i in range(0,numtrials):
       if i == 0: begin, end = 0, starttimes[0]
       else: begin, end = starttimes[i-1], starttimes[i]
       for j in range(begin, end):
           if i == 0: data[j].append(0)
           else: data[j].append(labels[i])
   for j in range(starttimes[-1], len(data)):
       data[j].append(labels[-1])
   return data

1. Delete the rows with an unknown desired output

def delete_test_trials(data):

   new_data = list()
   for row in data:
       if row[-1] != 0: new_data.append(row)
   return new_data

1. Make a smaller set without replacement

def smaller_set(data, n_rows):

   data_copy = data
   new_data = list()
   while len(new_data) < n_rows:
       index = randrange(0, len(data_copy))
       new_data.append(data_copy[index])
       data_copy.remove(data_copy[index])
   return new_data

1. START OF THE RANDOM FOREST ALGORITHM

1. Convert string column to float

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

1. 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

1. 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

1. 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

1. 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

1. 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

1. 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

1. 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}

1. Create a terminal node value

def to_terminal(group): outcomes = [row[-1] for row in group] return max(set(outcomes), key=outcomes.count)

1. 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)

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

1. 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']

1. 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

1. 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)

1. 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)
       print("Tree " + str(i) + " is done!")

#predictions = [bagging_predict(trees, row) for row in test]

   return(trees)

1. Test the random forest algorithm

seed(2)

dataname = 'k3b_s.txt' startname = 'k3b_HDR_TRIG.txt' labelname = 'k3b_HDR_Classlabel.txt'

1. Creating the dataset

dataset = make_data(dataname, startname, labelname) print("Dataset is ready!") trainset = delete_test_trials(dataset) print("Trainset is ready!") smallset = smaller_set(trainset, 2000) print("Smallset is ready!")

1. Tried this part to see if it helps, but it raises it's own error

1. convert string attributes to floats
2. for i in range(0, len(smallset[0])-1):
3. str_column_to_float(smallset, i)

1. convert class column to integers
2. str_column_to_int(smallset, len(smallset[0])-1)

1. evaluate algorithm

n_folds = 5 max_depth = 10 min_size = 1 sample_size = 1.0 n_features = int(sqrt(len(trainset[0])-1)) n_trees = 10

1. scores = evaluate_algorithm(smallset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)
2. print('Trees: %d' % n_trees)
3. print('Scores: %s' % scores)
4. print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))

1. Make an output text file containing the random forest

forest = random_forest(smallset, smallset, max_depth, min_size, sample_size, n_trees, n_features) keys = forest[0].keys() with open('A forest.csv', 'w') as output_file:

   dict_writer = csv.DictWriter(output_file, keys)
   dict_writer.writeheader()
   dict_writer.writerows(forest)