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ExamplesRandomizedTrees/classify_database_records.pl  view on Meta::CPAN

print "\nReading the training data ...\n";
$rt->get_training_data_for_N_trees();

##   UNCOMMENT the following statement if you want to see the training data used for each tree::
$rt->show_training_data_for_all_trees();


print "\nCalculating first order probabilities...\n";
$rt->calculate_first_order_probabilities();

print "\nCalculating class priors...\n";
$rt->calculate_class_priors();

print "\nConstructing all decision trees ....\n";
$rt->construct_all_decision_trees();

##   UNCOMMENT the following statement if you want to see all decision trees individually:
$rt->display_all_decision_trees();

####################  Extract test samples from the database file  ######################

my @records_to_classify_local = @records_to_classify;

my %record_ids_with_class_labels;
my %record_ids_with_features_and_vals;
my @all_fields;
my $record_index = 0;
open FILEIN, $database_file || die "unable to open $database_file: $!";        
while (<FILEIN>) {
    next if /^[ ]*\r?\n?$/;
    $_ =~ s/\r?\n?$//;
    my $record = cleanup_csv($_);
    if ($record_index == 0) {
        @all_fields = split /,/, $record;
        $record_index++;
        next;
    }
    my @parts = split /,/, $record;
    my $record_lbl = $parts[0];
    $record_lbl  =~ s/^\s*\"|\"\s*$//g;
    if (contained_in($record_lbl, @records_to_classify_local)) {
        @records_to_classify_local = grep {$_ ne $record_lbl} @records_to_classify_local;
        $record_ids_with_class_labels{$record_lbl} = $parts[$csv_class_column_index];
        my @fields_of_interest = map {$all_fields[$_]} @{$csv_columns_for_features};
        my @feature_vals_of_interest = map {$parts[$_]} @{$csv_columns_for_features};
        my @interleaved = ( @fields_of_interest, @feature_vals_of_interest )[ map { $_, $_ + @fields_of_interest } ( 0 .. $#fields_of_interest ) ];
        my @features_and_vals = pairmap { "$a=$b" } @interleaved;
        $record_ids_with_features_and_vals{$record_lbl} = \@features_and_vals;
    }
    last if @records_to_classify_local == 0;
    $record_index++; 
}
close FILEIN;

# Now classify all the records extracted from the database file:    
my %original_classifications;
my %calculated_classifications;
foreach my $record_index (sort {$a <=> $b} keys %record_ids_with_features_and_vals) {
    my @test_sample = @{$record_ids_with_features_and_vals{$record_index}};
    # Let's now get rid of those feature=value combos when value is 'NA'    
    my $unknown_value_for_a_feature_flag;
    map {$unknown_value_for_a_feature_flag = 1 if $_ =~ /=NA$/} @test_sample;
    next if $unknown_value_for_a_feature_flag;
    $rt->classify_with_all_trees( \@test_sample );
    my $classification = $rt->get_majority_vote_classification();
    printf("\nclassification for %5d: %10s       original classification: %s", $record_index, $classification, $record_ids_with_class_labels{$record_index});
    $original_classifications{$record_index} = $record_ids_with_class_labels{$record_index};
    $classification =~ /=(.+)$/;
    $calculated_classifications{$record_index} = $1;
}

my $total_errors = 0;
my @confusion_matrix_row1 = (0,0);
my @confusion_matrix_row2 = (0,0);

print "\n\nCalculating the error rate and the confusion matrix:\n";
foreach my $record_index (sort keys %calculated_classifications) {
    $total_errors += 1 if $original_classifications{$record_index} ne $calculated_classifications{$record_index};    
    if ($original_classifications{$record_index} eq $class_names_used_in_database[1]) {
        if ($calculated_classifications{$record_index} eq $class_names_used_in_database[1]) {
            $confusion_matrix_row1[0] += 1;
        } else {
            $confusion_matrix_row1[1] += 1;
        }
    }
    if ($original_classifications{$record_index} eq $class_names_used_in_database[0]) {
        if ($calculated_classifications{$record_index} eq $class_names_used_in_database[1]) {
            $confusion_matrix_row2[0] += 1;
        } else {
            $confusion_matrix_row2[1] += 1;
        }
    }
}

my $percentage_errors =  ($total_errors * 100.0) / scalar keys %calculated_classifications;
print "\n\nClassification error rate: $percentage_errors\n";
print "\nConfusion Matrix:\n\n";
printf("%50s          %25s\n", "classified as NOT at risk", "classified as at risk");
printf("Known to be NOT at risk: %10d  %35d\n\n", @confusion_matrix_row1);                       #(G)
printf("Known to be at risk:%15d  %35d\n\n", @confusion_matrix_row2);                            #(H)


#============== Now interact with the user for classifying additional records  ==========

if ($interaction_needed) {
    while (1) {
        print "\n\nWould you like to see classification for a particular record: ";
        my $input = <STDIN>;
        if ($input =~ /^\s*n/) {
            die "goodbye";
        } elsif ($input =~ /^\s*y/) {
            print "\nEnter record numbers whose classifications you want to see (multiple entries allowed): ";
            $input = <STDIN>;        
            my @records_to_classify = map {int($_)} split /\s+/, $input;
            my @records_to_classify_local =  @records_to_classify;
            my %record_ids_with_class_labels;
            my %record_ids_with_features_and_vals;
            my @all_fields;
            open FILEIN, $database_file || die "unable to open $database_file: $!";        
            my $record_index = 0;
            while (<FILEIN>) {
                next if /^[ ]*\r?\n?$/;
                $_ =~ s/\r?\n?$//;