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package Algorithm::Classifier::IsolationForest::App::Command::csv2plot;

use strict;
use warnings;
use Algorithm::Classifier::IsolationForest ();
use Algorithm::Classifier::IsolationForest::App -command;
use File::Slurp qw(read_file write_file);
use File::Spec  ();
use File::Temp  qw(tempfile);

sub opt_spec {
	return (
		[ 'i=s', 'Input CSV for processing.',                { 'completion' => 'files' } ],
		[ 'o=s', 'PNG file to output to. Default: plot.png', { 'default'    => 'plot.png', 'completion' => 'files' } ],
		[ 'w',   'If the file specified via -o exists, over write it.' ],
		[
			'p=s',
			'Type of plot. Default: auto',
			{ 'default' => 'auto', completion => [ 'auto', '2heat', '3range', '3binary' ] }
		],
		[ 'print',        'Print what would be used with gnuplot instead of calling gnuplot' ],
		[ 'open',         'Call xdg-open to open the generated graph.' ],
		[ 'small-points', 'Use smaller points (ps 0.8) for dense 3range datasets.' ],
	);
} ## end sub opt_spec

sub abstract { 'Plot the CSV data used with iforest via gnuplot.' }

sub description {
	'Plot the CSV data used with iforest via gnuplot.

Plot types are as below.

auto: If there are two columns, 2heat. If there are 4 or more columns, 3range.
2heat: Use column 1 and 2 to generate a splatter plot over a heat map.
3range: Use columns 1 and 2 for x/y, and the second-to-last column for the
        score gradient. Suitable for predict -d output (any number of features).
3binary: Use columns 1 and 2 for x/y, and the last column for normal/abnormal.
         Suitable for predict -d output or gblob output.

3range and 3binary require data outputted from predict with the -d flag.
For N-dimensional data, columns 1 and 2 are always used for the x/y axes.
';
} ## end sub description

sub validate {
	my ( $self, $opt, $args ) = @_;

	if ( !defined( $opt->{'i'} ) ) {
		$self->usage_error('-i has not been specified for a file to process');
	} elsif ( !-f $opt->{'i'} ) {
		$self->usage_error( '-i, "' . $opt->{'i'} . '", is not a file or does not exist' );
	} elsif ( !-r $opt->{'i'} ) {
		$self->usage_error( '-i, "' . $opt->{'i'} . '", is not readable' );
	}

	if ( -e $opt->{'o'} && !$opt->{'w'} ) {
		$self->usage_error( '-o, "' . $opt->{'o'} . '", already exists and -w is not given' );
	} elsif ( -e $opt->{'o'} && !-f $opt->{'o'} ) {
		$self->usage_error( '-o, "' . $opt->{'o'} . '", already exists and -w given but file is not a file' );
	} elsif ( -e $opt->{'o'} && !-w $opt->{'o'} ) {
		$self->usage_error( '-o, "' . $opt->{'o'} . '", already exists and -w given but file is not writable' );
	}

	if (   ( $opt->{'p'} ne 'auto' )
		&& ( $opt->{'p'} ne '2heat' )
		&& ( $opt->{'p'} ne '3range' )
		&& ( $opt->{'p'} ne '3binary' ) )
	{
		$self->usage_error( '-p, "' . $opt->{'p'} . '", is not set to auto, 2heat, 3range, or 3binary' );
	}

	return 1;
} ## end sub validate

sub execute {
	my ( $self, $opt, $args ) = @_;

	my @raw_csv      = read_file( $opt->{'i'} );
	my @first_fields = split( /,/, $raw_csv[0] );
	my $ncols        = scalar @first_fields;

	if ( $opt->{'p'} eq 'auto' && $ncols >= 4 ) {
		$opt->{'p'} = '3range';
	} elsif ( $opt->{'p'} eq 'auto' && $ncols >= 2 ) {
		$opt->{'p'} = '2heat';
	} elsif ( $opt->{'p'} eq 'auto' ) {
		die('-p is set to auto and the specified CSV does not have enough columns');
	} elsif ( $opt->{'p'} eq '2heat' && $ncols < 2 ) {
		die('2heat specified but there is no column for y');
	} elsif ( $opt->{'p'} eq '3range' && $ncols < 3 ) {
		die('3range specified but there is no column for score');
	} elsif ( $opt->{'p'} eq '3binary' && $ncols < 3 ) {
		die('3binary specified but there is no column for truth');
	}

	# For predict -d output: score is the second-to-last column, prediction is last.
	my $score_col = $ncols - 1;
	my $pred_col  = $ncols;

	$opt->{'i'} = File::Spec->rel2abs( $opt->{'i'} );