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