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//paip/script/weight/weightRegressor.py

'''
Created on Fri Dec 10 10:35
@author: han
'''
import pandas as pd
import pickle
import sys,os
import glob
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

HOME_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir)
DATA_DIR = 'data'
MODULE_DIR = 'util'
OUTPUT_DIR = 'out'

# set sys path to import PyDBconnector
sys.path.append(os.path.join(HOME_PATH, MODULE_DIR))
from PyDBconnector import PyDBconnector

class WeightRegressor():
    _pixelWeightModel = None
    _realWeightModel = None
    _conn = None
    def __init__(self, db_host_ip = '127.0.0.1'):
        with open('model/pixelWeightModel.pkl', 'rb') as f:
            self._pixelWeightModel = pickle.load(f)

with open('model/realWeightModel.pkl', 'rb') as f:
            self._realWeightModel = pickle.load(f)
        self._conn = PyDBconnector(host = db_host_ip)
    def getpixelWeight(self, pixelvalues):
        '''
        after load model, update model
        :return:
        '''
        return self._pixelWeightModel.predict(pixelvalues)

def getrealWeight(self, dayAge):
        '''
        after load model, update model
        :return:
        '''
        return self._realWeightModel.predict(dayAge)

def writeModel(self, isPixelWeightModel):
        '''
        write updated model to disk as pickle format
        :return:
        '''
        if isPixelWeightModel :
            with open('model/pixelWeightModel.pkl', 'wb') as f:
                pickle.dump(self._pixelWeightModel, f)
        else :
            with open('model/realWeightModel', 'wb') as f :
                pickle.dump(self._realWeightModel, f)

def predict(self):
        '''
        return predicted values
        :return:
        '''
        pass

def updatepixelWeightModel(self):
        # check if model-update is needed
        pass

def updaterealWeightModel(self):
        # check if model-update is needed
        sql_str = f'''select medianWeight,medianPixel \
                    from tbl_weight_stats \
                    where 1=1
                    order by create_time desc \
                        '''
        rows = self._conn.select_from_db(sql_str)

X, y = rows[['medianPixel']], rows['medianWeight']
        line_fitter = LinearRegression()
        line_fitter.fit(X, y)

with open('model/chery_linear_model.pkl', 'wb') as f:
            pickle.dump(line_fitter, f)

@classmethod
    def initCreateModel(self):
        # init pixel to weight model
        raw_df = pd.read_csv(glob.glob('../util/real_weight.csv')[0])

X, y = raw_df[['no_distortion_pixel_mean']], raw_df['real_weight']
        line_fitter = LinearRegression()
        line_fitter.fit(X, y)

with open('model/pixelWeightModel.pkl', 'wb') as f:
            pickle.dump(line_fitter, f)

# init weight trend model
        models_df = pd.read_csv(glob.glob('../util/dayAge_weight_trend.csv')[0])

poly_feature = PolynomialFeatures(degree=2, include_bias=False)
        A_poly = poly_feature.fit_transform(models_df.drop('predWeightMean', 1))

lin_reg = LinearRegression()  # LinearRegression 객체 생성
        lin_reg.fit(A_poly, models_df['predWeightMean'])

with open('model/realWeightModel.pkl', 'wb') as f:
            pickle.dump(line_fitter, f)

if __name__ == '__main__':

# init compile base model - just once
    #WeightRegressor.initCreateModel()

# update weight linear model
    WeightRegressor().updaterealWeightModel()