sparkregressionmetricmetadata.py

'''
Copyright (C) e2its - All Rights Reserved
 * Unauthorized copying of this file, via any medium is strictly prohibited
 * Proprietary and confidential
 *
 * This file is part of gDayF project.
 *
 * Written by Jose L. Sanchez <e2its.es@gmail.com>, 2016-2019
'''

from gdayf.metrics.regressionmetricmetadata import RegressionMetricMetadata
import time
from numpy import isnan



class SparkRegressionMetricMetadata(RegressionMetricMetadata):

    
    def __init__(self):
        RegressionMetricMetadata.__init__(self)

    
    def set_metrics(self, evaluator, data, objective_column=None):

        start = time.time()
        if evaluator is not None and data is not None:
                self['MSE'] = evaluator.evaluate(data, {evaluator.metricName: "mse"})
                self['mean_residual_deviance'] = None
                self['nobs'] = data.count()
                self['model_category'] = 'Regression'
                self['predictions'] = None
                self['rmsle'] = None
                self['r2'] = evaluator.evaluate(data, {evaluator.metricName: "r2"})
                self['RMSE'] = evaluator.evaluate(data, {evaluator.metricName: "rmse"})
                self['MAE'] = evaluator.evaluate(data, {evaluator.metricName: "mae"})
                self['scoring_time'] = int(time.time() - start)
                if isnan(self['RMSE']):
                    self['RMSE'] = 1e+16
                if isnan(self['r2']):
                    self['r2'] = 0