// CreateState creates a new state for AROW classifier.
func (c *AROWStateCreator) CreateState(ctx *core.Context, params data.Map) (core.SharedState, error) {
label, err := pluginutil.ExtractParamAsStringWithDefault(params, "label_field", "label")
if err != nil {
return nil, err
}
fv, err := pluginutil.ExtractParamAsStringWithDefault(params, "feature_vector_field", "feature_vector")
if err != nil {
return nil, err
}
rw, err := pluginutil.ExtractParamAndConvertToFloat(params, "regularization_weight")
if err != nil {
return nil, err
}
if rw <= 0 {
return nil, errors.New("regularization_weight parameter must be greater than zero")
}
a, err := NewAROW(float32(rw))
if err != nil {
return nil, fmt.Errorf("failed to initialize AROW: %v", err)
}
return &AROWState{
arow: a,
labelField: label,
featureVectorField: fv,
}, nil
}
func (c *PassiveAggressiveStateCreator) CreateState(ctx *core.Context, params data.Map) (core.SharedState, error) {
value, err := pluginutil.ExtractParamAsStringWithDefault(params, "value_field", "value")
if err != nil {
return nil, err
}
fv, err := pluginutil.ExtractParamAsStringWithDefault(params, "feature_vector_field", "feature_vector")
if err != nil {
return nil, err
}
rw, err := pluginutil.ExtractParamAndConvertToFloat(params, "regularization_weight")
if err != nil {
return nil, err
}
if rw <= 0 {
return nil, errors.New("regularization_weight parameter must be greater than zero")
}
sen, err := pluginutil.ExtractParamAndConvertToFloat(params, "sensitivity")
if err != nil {
return nil, err
}
if sen < 0 {
return nil, errors.New("sensitivity parameter must be not less than zero")
}
pa, err := NewPassiveAggressive(float32(rw), float32(sen))
if err != nil {
return nil, err
}
return &PassiveAggressiveState{
pa: pa,
valueField: value,
featureVectorField: fv,
}, nil
}
func (c *LightLOFStateCreator) CreateState(ctx *core.Context, params data.Map) (core.SharedState, error) {
fv, err := pluginutil.ExtractParamAsStringWithDefault(params, "feature_vector_field", "feature_vector")
if err != nil {
return nil, err
}
nnAlgoName, err := pluginutil.ExtractParamAsString(params, "nearest_neighbor_algorithm")
if err != nil {
return nil, err
}
var nnAlgo NNAlgorithm
switch strings.ToLower(nnAlgoName) {
case "lsh":
nnAlgo = LSH
case "minhash":
nnAlgo = Minhash
case "euclid_lsh":
nnAlgo = EuclidLSH
default:
return nil, fmt.Errorf("invalid nearest_neighbor_algorithm: %s", nnAlgoName)
}
hashNum, err := pluginutil.ExtractParamAsInt(params, "hash_num")
if err != nil {
return nil, err
}
nnNum, err := pluginutil.ExtractParamAsInt(params, "nearest_neighbor_num")
if err != nil {
return nil, err
}
rnnNum, err := pluginutil.ExtractParamAsInt(params, "reverse_nearest_neighbor_num")
if err != nil {
return nil, err
}
unlearn, err := pluginutil.ExtractParamAsStringWithDefault(params, "unlearner", "no")
if err != nil {
return nil, err
}
var maxSize int
var seed int64
switch unlearn {
case "no":
maxSize = 0
case "random":
m, err := pluginutil.ExtractParamAsInt(params, "max_size")
if err != nil {
return nil, err
}
maxSize = int(m)
seed, err = pluginutil.ExtractParamAsIntWithDefault(params, "seed", 0)
if err != nil {
return nil, err
}
default:
return nil, fmt.Errorf("invalid unlearner: %v", unlearn)
}
// TODO: check hashNum, nnNum, rnnNum <= INT_MAX
llof, err := NewLightLOF(nnAlgo, int(hashNum), int(nnNum), int(rnnNum), maxSize, seed)
if err != nil {
return nil, err
}
return &lightLOFState{
lightLOF: llof,
featureVectorField: fv,
}, nil
}