# -*- coding: utf-8 -*-
"""
Created on 2017-9-5

@author: cheng.li
"""

from alphamind.model.modelbase import ModelBase
from alphamind.model.linearmodel import ConstLinearModel
from alphamind.model.linearmodel import LinearRegression
from alphamind.model.linearmodel import LassoRegression
from alphamind.model.linearmodel import LogisticRegression
from alphamind.model.treemodel import RandomForestRegressor
from alphamind.model.treemodel import RandomForestClassifier
from alphamind.model.treemodel import XGBRegressor
from alphamind.model.treemodel import XGBClassifier
from alphamind.model.treemodel import XGBTrainer


def load_model(model_desc: dict) -> ModelBase:

    model_name = model_desc['model_name']
    model_name_parts = set(model_name.split('.'))

    if 'ConstLinearModel' in model_name_parts:
        return ConstLinearModel.load(model_desc)
    elif 'LinearRegression' in model_name_parts:
        return LinearRegression.load(model_desc)
    elif 'LassoRegression' in model_name_parts:
        return LassoRegression.load(model_desc)
    elif 'LogisticRegression' in model_name_parts:
        return LogisticRegression.load(model_desc)
    elif 'RandomForestRegressor' in model_name_parts:
        return RandomForestRegressor.load(model_desc)
    elif 'RandomForestClassifier' in model_name_parts:
        return RandomForestClassifier.load(model_desc)
    elif 'XGBRegressor' in model_name_parts:
        return XGBRegressor.load(model_desc)
    elif 'XGBClassifier' in model_name_parts:
        return XGBClassifier.load(model_desc)
    elif 'XGBTrainer' in model_name_parts:
        return XGBTrainer.load(model_desc)
    else:
        raise ValueError('{0} is not currently supported in model loader.'.format(model_name))

# -*- coding: utf-8 -*-
"""
Created on 2017-9-5

@author: cheng.li
"""

from alphamind.model.modelbase import ModelBase
from alphamind.model.linearmodel import ConstLinearModel
from alphamind.model.linearmodel import LinearRegression
from alphamind.model.linearmodel import LassoRegression
from alphamind.model.linearmodel import LogisticRegression
from alphamind.model.treemodel import RandomForestRegressor
from alphamind.model.treemodel import RandomForestClassifier
from alphamind.model.treemodel import XGBRegressor
from alphamind.model.treemodel import XGBClassifier
from alphamind.model.treemodel import XGBTrainer
from alphamind.model.svm import NvSVRModel


def load_model(model_desc: dict) -> ModelBase:

    model_name = model_desc['model_name']
    model_name_parts = set(model_name.split('.'))

    if 'ConstLinearModel' in model_name_parts:
        return ConstLinearModel.load(model_desc)
    elif 'LinearRegression' in model_name_parts:
        return LinearRegression.load(model_desc)
    elif 'LassoRegression' in model_name_parts:
        return LassoRegression.load(model_desc)
    elif 'LogisticRegression' in model_name_parts:
        return LogisticRegression.load(model_desc)
    elif 'RandomForestRegressor' in model_name_parts:
        return RandomForestRegressor.load(model_desc)
    elif 'RandomForestClassifier' in model_name_parts:
        return RandomForestClassifier.load(model_desc)
    elif 'XGBRegressor' in model_name_parts:
        return XGBRegressor.load(model_desc)
    elif 'XGBClassifier' in model_name_parts:
        return XGBClassifier.load(model_desc)
    elif 'XGBTrainer' in model_name_parts:
        return XGBTrainer.load(model_desc)
    elif 'NvSVR' in model_name_parts:
        return NvSVRModel.load(model_desc)
    else:
        raise ValueError('{0} is not currently supported in model loader.'.format(model_name))

update loader