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# Check AM constructor and acessors (which are related)
use strict;
use warnings;
use Algorithm::AM::Batch;
use Test::More 0.88;
use Test::Exception;
use Test::NoWarnings;
use Test::LongString;
plan tests => 12;
use t::TestAM qw(chapter_3_train chapter_3_test);

test_input_checking();
test_accessors();
test_classify();

sub test_input_checking {
    throws_ok {
        Algorithm::AM::Batch->new();
    } qr/Missing required parameter 'training_set'/,
    'dies when no training set provided';

    throws_ok {
        Algorithm::AM::Batch->new(
            training_set => 'stuff',
        );
    } qr/Parameter training_set should be an Algorithm::AM::DataSet/,
    'dies with bad training set';

    throws_ok {
        Algorithm::AM::Batch->new(
            training_set => Algorithm::AM::DataSet->new(
                cardinality => 3),
            test_set => Algorithm::AM::DataSet->new(
                cardinality => 3),
            foo => 'bar'
        );
    } qr/Invalid attributes for Algorithm::AM::Batch/,
    'dies with bad argument';

    throws_ok {
        my $batch = Algorithm::AM::Batch->new(
            training_set => Algorithm::AM::DataSet->new(
                cardinality => 3)
        );
        $batch->classify_all(Algorithm::AM::DataSet->new(
            cardinality => 4));
    } qr/Training and test sets do not have the same cardinality \(3 and 4\)/,
    'dies with mismatched dataset cardinalities';

    throws_ok {
        my $batch = Algorithm::AM::Batch->new(
            training_set =>
                Algorithm::AM::DataSet->new(cardinality => 3)
        );
        $batch->classify_all();
    } qr/Must provide a DataSet to classify_all/,
    'dies with no input to classify';

    throws_ok {
        my $batch = Algorithm::AM::Batch->new(
            training_set => Algorithm::AM::DataSet->new(
                cardinality => 3),
        );
        $batch->classify_all('foo');
    } qr/Must provide a DataSet to classify_all/,
    'dies with bad test set';
    return;
}

sub test_accessors {
    subtest 'Constructor saves data sets' => sub {
        plan tests => 4;
        my $batch = Algorithm::AM::Batch->new(
            training_set => Algorithm::AM::DataSet->new(
                cardinality => 3),
            test_set => Algorithm::AM::DataSet->new(
                cardinality => 3),
        );
        isa_ok($batch->training_set, 'Algorithm::AM::DataSet',
            'training_set returns correct object type');
        isa_ok($batch->test_set, 'Algorithm::AM::DataSet',
            'test_set returns correct object type');

        is($batch->training_set->cardinality, 3,
            'training set saved');
        is($batch->test_set->cardinality, 3,
            'test set saved');
    };

    subtest 'default configuration' => sub {
        plan tests => 5;
        my $batch = Algorithm::AM::Batch->new(
            training_set => Algorithm::AM::DataSet->new(
                cardinality => 3),
            test_set => Algorithm::AM::DataSet->new(
                cardinality => 3),
        );
        ok($batch->exclude_nulls, 'exclude nulls by default');
        ok($batch->exclude_given, 'exclude given by default');
        ok(!$batch->linear, 'pointer counting is quadratic by default');
        is($batch->probability, 1, 'probability is 1 by default');
        is($batch->repeat, 1, 'repeat is 1 by default');
    };

    subtest 'configuration via constructor' => sub {
        plan tests => 5;
        my $batch = Algorithm::AM::Batch->new(
            training_set => Algorithm::AM::DataSet->new(
                cardinality => 3),
            test_set => Algorithm::AM::DataSet->new(
                cardinality => 3),
            exclude_nulls => 0,
            exclude_given => 0,
            linear => 1,
            probability => .5,
            repeat => 2
        );
        ok(!$batch->exclude_nulls, 'exclude nulls turned off');
        ok(!$batch->exclude_given, 'exclude given turned off');