#!/usr/bin/perl -w
## classify_by_asking_questions.pl
use lib '../blib/lib', '../blib/arch';
use strict;
use Algorithm::DecisionTree;
#my $training_datafile = "training.dat";
my $training_datafile = "stage3cancer.csv";
my $dt = Algorithm::DecisionTree->new(
training_datafile => $training_datafile,
csv_class_column_index => 2,
csv_columns_for_features => [3,4,5,6,7,8],
entropy_threshold => 0.01,
max_depth_desired => 8,
);
$dt->get_training_data();
$dt->calculate_first_order_probabilities();
$dt->calculate_class_priors();
### UNCOMMENT THE NEXT STATEMENT if you would like to see
### the training data that was read from the disk file:
#$dt->show_training_data();
#print "\nStarting construction of the decision tree:\n\n";
my $root_node = $dt->construct_decision_tree_classifier();
### UNCOMMENT THE NEXT STATEMENT if you would like to see
### the decision tree displayed in your terminal window:
$root_node->display_decision_tree(" ");
### The classifiy() in the call below returns a reference to
### a hash whose keys are the class labels and the values
### the associated probabilities:
my %classification = %{$dt->classify_by_asking_questions($root_node)};
my @solution_path = @{$classification{'solution_path'}};
delete $classification{'solution_path'};
my @which_classes = keys %classification;
@which_classes = sort {$classification{$b} <=> $classification{$a}}
@which_classes;
print "\nClassification:\n\n";
print " class probability\n";
print " ---------- -----------\n";
foreach my $which_class (@which_classes) {
my $classstring = sprintf("%-30s", $which_class);
my $valuestring = sprintf("%-30s", $classification{$which_class});
print " $classstring $valuestring\n";
}
print "\nSolution path in the decision tree: @solution_path\n";
print "\nNumber of nodes created: " . $root_node->how_many_nodes() . "\n";