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ash08_xgb.py
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ash08_xgb.py
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import pickle
import pandas as pd
import numpy as np
import StatsFunctions as stats
import xgboost as xgb
from sklearn.preprocessing import StandardScaler
class ash08_xgb():
"""
This class represents the best model/configuration from Asheville (NCEI station) - aridity index = 1.1494.
It uses a XGB model and the following input configuration (being rs the predicted value):
['tx', 'tn', 'ra', 'deltat', 'energyt', 'hormin_tx', 'tx_prev', 'rs']
"""
def __init__(self):
# import model
filename = "models/xgb_ash08_tx_tn_ra_deltaT_energyt_hormin_tx_tx_prev_rs.json"
self.model = xgb.XGBRegressor()
self.model.load_model(filename)
# define required inputs
self.parameters = ['tx', 'tn', 'ra', 'deltat', 'energyt', 'hormin_tx', 'tx_prev', 'rs']
def import_dataset(self, fileLocation, fileType):
"""
This function import a dataset and convert it into a pandas Dataframe
Inputs:
fileLocation: "data/dataSet.csv"
fileType: string with csv, excel or txt
"""
if fileType == 'csv' or fileType == 'txt':
self.dfData = pd.read_csv(fileLocation)
elif fileType == 'excel':
self.dfData = pd.read_excel(fileLocation)
else:
AssertionError('this fileType does not exit, use excel, csv and txt instead')
#define deltat
self.dfData["deltat"] = self.dfData['tx'] - self.dfData['tn']
# convert rs from W/m2 to MJ/m2day-1
self.dfData["rs"] = self.dfData['rs']*0.0864
# filter nan values
self.dfData.dropna(inplace=True)
self.dfData.reset_index(drop=True, inplace=True)
# filter the parameters
self.dfData = self.dfData.filter(self.parameters)
def getStandardDataTest(self):
"""
Split data to train and test
"""
# from training original dataset
mean_list = [20.30, 6.67, 30.38, 13.62, 209.17, 14.49, 20.14]
std_list = [7.86, 8.31, 9.04, 4.93, 1033.36, 2.25, 8.03]
# we have the input data as x, and the output as y
self.x_test = np.array(self.dfData.iloc[:, :-1])
self.y_test = np.array(self.dfData.iloc[:, -1])
# standarization
scaler = StandardScaler()
scaler.mean_ = mean_list
scaler.scale_ = std_list
# x_train and x_test
self.x_test = np.array(scaler.transform(self.x_test))
return self.x_test, self.y_test
def predictValues(self):
self.y_pred = np.array(self.model.predict(self.x_test))
return self.y_pred
def statAnalysis(self):
rmse = stats.get_root_mean_square_error(self.y_test, self.y_pred)
rrmse = stats.get_root_mean_square_error(self.y_test, self.y_pred) / np.mean(self.y_test)
mbe = stats.get_mean_bias_error(self.y_test, self.y_pred)
r2 = stats.get_coefficient_of_determination(self.y_test, self.y_pred)
nse = stats.get_nash_suteliffe_efficiency(self.y_test, self.y_pred)
return rmse, rrmse, mbe, r2, nse
if __name__ == '__main__':
mlModel = ash08_xgb()
mlModel.import_dataset("data/ncei-asheville-example.csv", 'csv')
mlModel.getStandardDataTest()
mlModel.predictValues()
rmse, rrmse, mbe, r2, nse = mlModel.statAnalysis()
print(rmse, rrmse, mbe, r2, nse)