rapids/tests/scripts/run_tests.py

97 lines
3.8 KiB
Python

import unittest
import yaml
import sys
import os
import pandas as pd
from snakemake.io import expand
class RapidsTests(unittest.TestCase):
unittest.TestLoader.sortTestMethodsUsing = lambda self, a, b: (a < b) - (a > b)
def generate_sensor_file_lists(self):
# Go through the configs and select those sensors with COMPUTE = True.
# Also get TIME_SEGMENTS, then create expected
# files. Return dictionary with list of file paths of expected and
# actual files for each sensor listed in the config file. Added for Travis.
# Initialize string of file path for both expected and actual metric values
segment = self.configs['TIME_SEGMENTS']['TYPE'].lower()
act_str = "data/processed/features/{pid}/{sensor_key}.csv"
exp_str = "tests/data/processed/features/"+segment+"/{pid}/{sensor_key}.csv"
# Build list of sensors to be tested.
sensors = []
for sensor in self.configs:
if "PROVIDERS" in self.configs[sensor] and self.configs[sensor]["PROVIDERS"] is not None:
for provider in self.configs[sensor]["PROVIDERS"]:
if self.configs[sensor]["PROVIDERS"][provider]["COMPUTE"]:
sensors.append(sensor.lower())
act_file_list = expand(act_str,pid=self.configs["PIDS"],sensor_key = sensors)
exp_file_list = expand(exp_str, pid=self.configs["PIDS"],sensor_key = sensors)
sensor_file_lists = list(zip(act_file_list,exp_file_list))
return sensor_file_lists
def test_sensors_files_exist(self):
# Loop through the file_list dictionary and check if the files exist.
file_lists = self.generate_sensor_file_lists()
for each in file_lists:
print("The actual output file should exist: {}".format(each[0]))
self.assertEqual(os.path.exists( each[0]), 1)
def test_sensors_features_calculations(self):
sensor_file_list = self.generate_sensor_file_lists()
for act_result, exp_result in sensor_file_list:
df_act = pd.read_csv(act_result)
df_exp = pd.read_csv(exp_result)
if df_act.empty:
print(act_result)
print("The expected output should be empty: {}".format(exp_result))
self.assertTrue(df_exp.empty)
else:
# The order in which the columns varies from time to time so
# the columns are sorted before doing the comparision
print("Comparing: {} and {}".format(act_result, exp_result))
df_exp = df_exp.reindex(sorted(df_exp.columns), axis=1)
df_act = df_act.reindex(sorted(df_act.columns), axis=1)
pd.testing.assert_frame_equal(df_exp, df_act, obj=df_exp)
class TestFrequency(RapidsTests):
@classmethod
def setUpClass(cls):
# Runs once to Setup env
# global configs
with open(r'tests/settings/frequency_config.yaml') as file:
cls.configs = yaml.full_load(file)
class TestPeriodic(RapidsTests):
@classmethod
def setUpClass(cls):
# Runs once to Setup env
# global configs
with open(r'tests/settings/periodic_config.yaml') as file:
cls.configs = yaml.full_load(file)
def run_some_tests(test_type):
# Run only the tests in the specified classes
if test_type == "frequency":
test_class = TestFrequency
elif test_type == "periodic":
test_class = TestPeriodic
else:
raise ValueError("Only frequency or periodic are valid arguments")
loader = unittest.TestLoader()
suite = loader.loadTestsFromTestCase(test_class)
big_suite = unittest.TestSuite(suite)
runner = unittest.TextTestRunner()
results = runner.run(big_suite)
if __name__ == '__main__':
run_some_tests(sys.argv[1])