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246 lines
5.0 KiB
Perl
Executable File
246 lines
5.0 KiB
Perl
Executable File
#!/usr/bin/env perl
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# This script is a very simple prototype to learn fann from rspamd logs
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# For now, it is intended for internal use only
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use strict;
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use warnings FATAL => 'all';
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use AI::FANN qw(:all);
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use Getopt::Std;
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my %sym_idx; # Symbols by index
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my %sym_names; # Symbols by name
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my $num = 1; # Number of symbols
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my @spam;
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my @ham;
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my $max_samples = -1;
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my $split = 1;
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my $preprocessed = 0; # ouptut is in format <score>:<0|1>:<SYM1,...SYMN>
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my $score_spam = 12;
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my $score_ham = -6;
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sub process {
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my ($input, $spam, $ham) = @_;
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my $samples = 0;
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while(<$input>) {
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if (!$preprocessed) {
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if (/^.*rspamd_task_write_log.*: \[(-?\d+\.?\d*)\/(\d+\.?\d*)\]\s*\[(.+)\].*$/) {
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if ($1 > $score_spam) {
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$_ = "$1:1: $3";
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}
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elsif ($1 < $score_ham) {
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$_ = "$1:0: $3\n";
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}
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else {
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# Out of boundary
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next;
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}
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}
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else {
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# Not our log message
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next;
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}
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}
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$_ =~ /^(-?\d+\.?\d*):([01]):\s*(\S.*)$/;
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my $is_spam = 0;
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if ($2 == 1) {
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$is_spam = 1;
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}
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my @ar = split /,/, $3;
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my %sample;
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foreach my $sym (@ar) {
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chomp $sym;
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if (!$sym_idx{$sym}) {
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$sym_idx{$sym} = $num;
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$sym_names{$num} = $sym;
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$num++;
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}
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$sample{$sym_idx{$sym}} = 1;
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}
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if ($is_spam) {
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push @{$spam}, \%sample;
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}
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else {
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push @{$ham}, \%sample;
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}
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$samples++;
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if ($max_samples > 0 && $samples > $max_samples) {
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return;
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}
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}
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}
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# Shuffle array
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sub fisher_yates_shuffle
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{
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my $array = shift;
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my $i = @$array;
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while ( --$i ) {
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my $j = int rand( $i + 1 );
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@$array[$i, $j] = @$array[$j, $i];
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}
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}
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# Train network
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sub train {
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my ($ann, $sample, $result) = @_;
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my @row;
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for (my $i = 1; $i < $num; $i++) {
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if ($sample->{$i}) {
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push @row, 1;
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}
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else {
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push @row, 0;
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}
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}
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#print "@row -> @{$result}\n";
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$ann->train(\@row, \@{$result});
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}
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sub test {
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my ($ann, $sample) = @_;
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my @row;
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for (my $i = 1; $i < $num; $i++) {
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if ($sample->{$i}) {
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push @row, 1;
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}
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else {
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push @row, 0;
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}
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}
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my $ret = $ann->run(\@row);
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return $ret;
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}
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my %opts;
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getopts('o:i:s:n:t:hpS:H:', \%opts);
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if ($opts{'h'}) {
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print "$0 [-i input] [-o output] [-s scores] [-n max_samples] [-S spam_score] [-H ham_score] [-ph]\n";
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exit;
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}
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my $input = *STDIN;
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if ($opts{'i'}) {
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open($input, '<', $opts{'i'}) or die "cannot open $opts{i}";
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}
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if ($opts{'n'}) {
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$max_samples = $opts{'n'};
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}
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if ($opts{'t'}) {
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# Test split
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$split = $opts{'t'};
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}
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if ($opts{'p'}) {
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$preprocessed = 1;
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}
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if ($opts{'H'}) {
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$score_ham = $opts{'H'};
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}
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if ($opts{'S'}) {
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$score_spam = $opts{'S'};
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}
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# ham_prob, spam_prob
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my @spam_out = (1);
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my @ham_out = (0);
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process($input, \@spam, \@ham);
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fisher_yates_shuffle(\@spam);
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fisher_yates_shuffle(\@ham);
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my $nspam = int(scalar(@spam) / $split);
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my $nham = int(scalar(@ham) / $split);
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my $ann = AI::FANN->new_standard($num - 1, ($num + 2) / 2, 1);
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my @train_data;
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# Train ANN
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for (my $i = 0; $i < $nham; $i++) {
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push @train_data, [ $ham[$i], \@ham_out ];
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}
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for (my $i = 0; $i < $nspam; $i++) {
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push @train_data, [ $spam[$i], \@spam_out ];
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}
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fisher_yates_shuffle(\@train_data);
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foreach my $train_row (@train_data) {
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train($ann, @{$train_row}[0], @{$train_row}[1]);
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}
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print "Trained $nspam SPAM and $nham HAM samples\n";
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# Now run fann
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if ($split > 1) {
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my $sample = 0.0;
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my $correct = 0.0;
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for (my $i = $nham; $i < $nham * $split; $i++) {
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my $ret = test($ann, $ham[$i]);
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#print "@{$ret}\n";
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if (@{$ret}[0] < 0.5) {
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$correct++;
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}
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$sample++;
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}
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print "Tested $sample HAM samples, correct matched: $correct, rate: ".($correct / $sample)."\n";
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$sample = 0.0;
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$correct = 0.0;
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for (my $i = $nspam; $i < $nspam * $split; $i++) {
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my $ret = test($ann, $spam[$i]);
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#print "@{$ret}\n";
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if (@{$ret}[0] > 0.5) {
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$correct++;
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}
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$sample++;
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}
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print "Tested $sample SPAM samples, correct matched: $correct, rate: ".($correct / $sample)."\n";
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}
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if ($opts{'o'}) {
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$ann->save($opts{'o'}) or die "cannot save ann into $opts{o}";
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}
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if ($opts{'s'}) {
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open(my $scores, '>',
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$opts{'s'}) or die "cannot open score file $opts{'s'}";
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print $scores "{";
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for (my $i = 1; $i < $num; $i++) {
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my $n = $i - 1;
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if ($i != $num - 1) {
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print $scores "\"$sym_names{$i}\":$n,";
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}
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else {
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print $scores "\"$sym_names{$i}\":$n}\n";
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}
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}
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}
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