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t/33-parallel-fit.t  view on Meta::CPAN

	like( $@, qr/parallel_fit/, 'non-numeric rejected' );
};

subtest 'parallel_fit=1 is equivalent to serial' => sub {
	# n_trees > 1 and parallel_fit == 1 hits the same serial branch as
	# parallel_fit undef.  Just verify it produces a working model.
	my $f = $CLASS->new(
		n_trees      => 20,
		sample_size  => 256,
		seed         => 7,
		parallel_fit => 1,
	);
	$f->fit( \@train );
	is( scalar @{ $f->{trees} }, 20, 'tree count matches n_trees' );
	my $s = $f->score_samples( \@query );
	cmp_ok( $s->[1], '>', $s->[0], 'model separates outlier from inlier' );
}; ## end 'parallel_fit=1 is equivalent to serial' => sub

subtest 'parallel_fit works for extended (EIF) mode' => sub {
	plan skip_all => 'no fork() on this platform' unless $can_fork;

	my $f = $CLASS->new(
		n_trees      => 40,
		sample_size  => 256,
		mode         => 'extended',
		seed         => 11,
		parallel_fit => 4,
	);
	$f->fit( \@train );

	is( scalar @{ $f->{trees} }, 40,         'tree count matches n_trees' );
	is( $f->{mode},              'extended', 'mode preserved across parallel fit' );

	# Sanity: extended-mode parallel-built model should still separate
	# the obvious outlier from the inlier.
	my $s = $f->score_samples( \@query );
	cmp_ok( $s->[1], '>',  $s->[0], 'outlier scores higher than inlier' );
	cmp_ok( $s->[0], '>=', 0,       'inlier score in [0,1]' );
	cmp_ok( $s->[1], '<=', 1,       'outlier score in [0,1]' );
}; ## end 'parallel_fit works for extended (EIF) mode' => sub

subtest 'parallel_fit + to_json/from_json round-trips' => sub {
	plan skip_all => 'no fork() on this platform' unless $can_fork;

	my $f1 = $CLASS->new(
		n_trees      => 25,
		sample_size  => 256,
		seed         => 17,
		parallel_fit => 3,
	)->fit( \@train );

	my $json = $f1->to_json;
	ok( length $json > 100, 'JSON has plausible body length' );

	my $f2 = $CLASS->from_json($json);
	is( scalar @{ $f2->{trees} }, 25, 'restored tree count matches' );

	my $s1    = $f1->score_samples( \@query );
	my $s2    = $f2->score_samples( \@query );
	my $diffs = grep { abs( $s1->[$_] - $s2->[$_] ) > 1e-9 } 0 .. $#$s1;
	is( $diffs, 0, 'restored model produces the same scores as the parallel-fit original' );
}; ## end 'parallel_fit + to_json/from_json round-trips' => sub

subtest 'parallel_fit with n_trees < workers caps workers at n_trees' => sub {
	plan skip_all => 'no fork() on this platform' unless $can_fork;

	# 3 trees, 8 workers requested -- only 3 workers should actually fork.
	my $f = $CLASS->new(
		n_trees      => 3,
		sample_size  => 64,
		seed         => 23,
		parallel_fit => 8,
	);
	$f->fit( \@train );
	is( scalar @{ $f->{trees} }, 3, 'tree count matches n_trees even when workers > n_trees' );

	my $s = $f->score_samples( \@query );
	is( scalar @$s, 2, 'two scores returned' );
}; ## end 'parallel_fit with n_trees < workers caps workers at n_trees' => sub

done_testing;