Random Forest Implementation in Python: Supervised Learning Ensemble Technique

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Random Forest Implementation in Python: Supervised Learning Ensemble Technique

 

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Since DataIntel offers DAaaS & SSaS to its clients – here I am sharing a snippet of our work. This is a broad generic theme about implementing Random Forest Ensemble Technique. For every situation code may vary but the code below covers the coordinates of this ensemble technique in a best possible way.
[sourcecode language ="python"  wraplines="true" collapse="false" firstline (1)]
# The dataset is first loaded, 
# the string values converted to numeric and the output column is converted from strings to the integer values of 0 and 1. 
# This is achieved with helper functions load_csv(), 
# str_column_to_float() and str_column_to_int() to load and prepare the dataset.

# I will use k-fold cross validation to estimate the performance of the learned model on unseen data. 
# This means that I will construct and evaluate k models and estimate the performance as the mean model error. 
# Classification accuracy will be used to evaluate each model. 
# These behaviors are provided in the cross_validation_split(), accuracy_metric() and evaluate_algorithm() helper functions.

# I will also use an implementation of the Classification and Regression Trees (CART) algorithm 
# adapted for bagging including the helper functions test_split() to split a dataset into groups, 
# gini_index() to evaluate a split point, 
# our modified get_split() function discussed in the previous step, 
# to_terminal(), split() and build_tree() used to create a single decision tree, 
# predict() to make a prediction with a decision tree, 
# subsample() to make a subsample of the training dataset and bagging_predict() to make a prediction with a list of decision trees.

# A new function name random_forest() is developed that first creates a list of decision trees 
# from subsamples of the training dataset and then uses them to make predictions.


# 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 **** 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
= float(row
.strip()) # Convert string column to integer def str_column_to_int(dataset, column): class_values =
for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i for row in dataset: row
= lookup
] 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 =
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 =
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 =
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) # Test the random forest algorithm seed(2) # load and prepare data filename = '****.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)))) [/sourcecode] To be continued

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