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lib/Algorithm/LibLinear.pm  view on Meta::CPAN

    L2R_LR => 0.01,
    L2R_L2LOSS_SVC_DUAL => 0.1,
    L2R_L2LOSS_SVC => 0.01,
    L2R_L1LOSS_SVC_DUAL => 0.1,
    MCSVM_CS => 0.1,
    L1R_L2LOSS_SVC => 0.01,
    L1R_LR => 0.01,
    L2R_LR_DUAL => 0.1,
    L2R_L2LOSS_SVR => 0.0001,
    L2R_L2LOSS_SVR_DUAL => 0.1,
    L2R_L1LOSS_SVR_DUAL => 0.1,
    ONECLASS_SVM => 0.01,
);

my %solvers = (
    L2R_LR => 0,
    L2R_L2LOSS_SVC_DUAL => 1,
    L2R_L2LOSS_SVC => 2,
    L2R_L1LOSS_SVC_DUAL => 3,
    MCSVM_CS => 4,
    L1R_L2LOSS_SVC => 5,
    L1R_LR => 6,
    L2R_LR_DUAL => 7,
    L2R_L2LOSS_SVR => 11,
    L2R_L2LOSS_SVR_DUAL => 12,
    L2R_L1LOSS_SVR_DUAL => 13,
    ONECLASS_SVM => 21,
);

sub new {
    args
        my $class => ClassName,
        my $bias => +{ isa => Num, default => -1.0, },
        my $cost => +{ isa => Num, default => 1, },
        my $epsilon => +{ isa => Num, optional => 1, },
        my $loss_sensitivity => +{ isa => Num, default => 0.1, },
        my $nu => +{ isa => Num, default => 0.5, },
        my $regularize_bias => +{ isa => Bool, default => 1, },
        my $solver => +{
            isa => $SolverDescriptor,
            default => 'L2R_L2LOSS_SVC_DUAL',
        },
        my $weights => +{ isa => ArrayRef[$ClassWeight], default => [], };

    $epsilon //= $default_eps{$solver};
    my (@weight_labels, @weights);
    for my $weight (@$weights) {
        push @weight_labels, $weight->{label};
        push @weights, $weight->{weight};
    }
    my $training_parameter = Algorithm::LibLinear::TrainingParameter->new(
        $solvers{$solver},
        $epsilon,
        $cost,
        \@weight_labels,
        \@weights,
        $loss_sensitivity,
        $nu,
        $regularize_bias,
    );
    bless +{
      bias => $bias,
      training_parameter => $training_parameter,
    } => $class;
}

sub bias { $_[0]->{bias} }

sub cost { $_[0]->training_parameter->cost }

sub cross_validation {
    args
        my $self => $InstanceOfPackage,
        my $data_set => InstanceOf['Algorithm::LibLinear::DataSet'],
        my $num_folds => Int;

    my $targets = $self->training_parameter->cross_validation(
        $data_set->as_problem(bias => $self->bias),
        $num_folds,
    );
    my @labels = map { $_->{label} } @{ $data_set->as_arrayref };
    if ($self->is_regression_solver) {
        my $total_square_error = sum map {
            ($targets->[$_] - $labels[$_]) ** 2;
        } (0 .. $data_set->size - 1);
        # Returns mean squared error.
        # TODO: Squared correlation coefficient (see train.c in LIBLINEAR.)
        return $total_square_error / $data_set->size;
    } else {
        my $num_corrects =
            grep { $targets->[$_] == $labels[$_] } (0 .. $data_set->size - 1);
        return $num_corrects / $data_set->size;
    }
}

sub epsilon { $_[0]->training_parameter->epsilon }

sub find_cost_parameter {
    args
        my $self => $InstanceOfPackage,
        my $data_set => InstanceOf['Algorithm::LibLinear::DataSet'],
        my $initial => +{ isa => Num, default => -1.0 },
        my $num_folds => Int,
        my $update => +{ isa => Bool, default => 0, };

    my ($cost, undef, $accuracy) = @{
        $self->find_parameters(
            data_set => $data_set,
            initial_cost => $initial,
            initial_loss_sensitivity => -1.0,
            num_folds => $num_folds,
            update => $update,
        )
    };
    return [ $cost, $accuracy ];
  }

sub find_parameters {
    args
        my $self => $InstanceOfPackage,
        my $data_set => InstanceOf['Algorithm::LibLinear::DataSet'],