#!/usr/bin/env perl
### boosting_for_classifying_one_test_sample_2.pl
## This script demonstrates how you can use boosting to classify a single
## test sample.
## The most important thing to keep in mind if you want to use boosting is
## the constructor parameters:
##
## how_many_stages
## As its name implies, this parameter controls how many decision trees
## will cascaded together for the boosted classifier. Recall that the
## training set for each decision tree in a cascade is heavily influenced
## by what gets misclassified by the previous decision tree. At the same
## time, the trust we place in each decision tree is based on its overall
## performance for classifying the entire training dataset.
use strict;
use warnings;
use Algorithm::BoostedDecisionTree;
my $training_datafile = "training6.csv";
my $boosted = Algorithm::BoostedDecisionTree->new(
training_datafile => $training_datafile,
csv_class_column_index => 1,
csv_columns_for_features => [2,3],
entropy_threshold => 0.01,
max_depth_desired => 8,
symbolic_to_numeric_cardinality_threshold => 10,
how_many_stages => 4,
csv_cleanup_needed => 1,
);
print "Reading and processing training data...\n";
$boosted->get_training_data_for_base_tree();
## UNCOMMENT THE FOLLOWING STATEMENT if you want to see the training data used for
## just the base tree:
$boosted->show_training_data_for_base_tree();
# This is a required call:
print "Calculating first-order probabilities...\n";
$boosted->calculate_first_order_probabilities_and_class_priors();
# This is a required call:
print "Constructing base decision tree...\n";
$boosted->construct_base_decision_tree();
# UNCOMMENT THE FOLLOWING TWO STATEMENTS if you would like to see the base decision
# tree displayed in your terminal window:
#print "\n\nThe Decision Tree:\n\n";
$boosted->display_base_decision_tree();
# This is a required call:
print "Constructing the rest of the decision trees....\n";
$boosted->construct_cascade_of_trees();
# UNCOMMENT the following statement if you wish to see the class labels for the
# samples misclassified by any particular stage. The integer argument in the call
# you see below is the stage index. Whe set to 0, that means the base classifier.
#$boosted->show_class_labels_for_misclassified_samples_in_stage(0);
## UNCOMMENT the next statement if you want to see the decision trees constructed
## for each stage of the cascade:
print "\nDisplaying the decision trees for all stages:\n\n";
$boosted->display_decision_trees_for_different_stages();
print "Reading the test sample ...\n";
my $test_sample = ['gdp = 50.0',
'return_on_invest = 45'];
# This is a required call:
print "Classifying with all the decision trees ....\n";
$boosted->classify_with_boosting($test_sample);
# UNCOMMENT THE FOLLOWING TWO STATEMENTS if you would like to see the classification
# results obtained with all the decision trees in the cascade:
print "\nDisplaying the classification results with all stages:\n\n";
$boosted->display_classification_results_for_each_stage();
my $final_classification = $boosted->trust_weighted_majority_vote_classifier();
print "\nFinal classification: $final_classification\n";
$boosted->display_trust_weighted_decision_for_test_sample();