import logging
import os
from logging import config
import numpy as np
from sklearn.metrics import *
import pandas as pd
BLUEMIST_PATH = os.environ["BLUEMIST_PATH"]
config.fileConfig(BLUEMIST_PATH + '/' + 'logging.config')
logger = logging.getLogger("bluemist")
default_regression_metrics = [mean_absolute_error, mean_squared_error, r2_score]
all_regression_metrics = [*default_regression_metrics, explained_variance_score, max_error, mean_squared_log_error,
median_absolute_error, mean_absolute_percentage_error, mean_poisson_deviance,
mean_gamma_deviance, mean_tweedie_deviance, d2_tweedie_score, mean_pinball_loss]
classification_metrics = []
all_classification_metrics = []
[docs]class scoringStrategy:
y_true = None
y_pred = None
metrics_requested = None
metrics_to_be_returned = None
def __init__(self, y_true, y_pred, metrics):
self.y_true = y_true
self.y_pred = y_pred
self.metrics_requested = metrics
self.metrics_to_be_returned = []
[docs] def getStats(self):
rows = []
row = []
logger.info('Metrics requested :: {}'.format(self.metrics_requested))
indexes_with_nan_predictions = np.argwhere(np.isnan(self.y_pred))
self.y_true = np.delete(self.y_true, indexes_with_nan_predictions)
self.y_pred = np.delete(self.y_pred, indexes_with_nan_predictions)
logger.info('Indexes with NaN predictions to be removed :: {}'.format(indexes_with_nan_predictions))
if isinstance(self.metrics_requested, str) and self.metrics_requested == 'default':
for metric in default_regression_metrics:
self.metrics_to_be_returned.append(metric.__name__)
try:
row.append(metric(self.y_true, self.y_pred))
except Exception as e:
row.append(str(e))
logger.error('Exception occurred while computing metric :: {}'.format(str(e)), exc_info=True)
elif isinstance(self.metrics_requested, str) and self.metrics_requested == 'all':
for metric in all_regression_metrics:
self.metrics_to_be_returned.append(metric.__name__)
try:
row.append(metric(self.y_true, self.y_pred))
except Exception as e:
row.append(str(e))
logger.error('Exception occurred while computing metric :: {}'.format(str(e)), exc_info=True)
elif isinstance(self.metrics_requested, list) and len(self.metrics_requested) > 0:
for metric in list:
self.metrics_to_be_returned.append(metric.__name__)
try:
row.append(metric(self.y_true, self.y_pred))
except Exception as e:
row.append(str(e))
logger.error('Exception occurred while computing metric :: {}'.format(str(e)), exc_info=True)
rows.append(row)
stats_df = pd.DataFrame(rows, columns=self.metrics_to_be_returned)
return stats_df