def test_calculate_metrics_with_populated_channels_full_of_zeroes_should_raise_a_zero_division_error(
self):
channels = {'X': np.array([0])}
parameters = {'c': 2, 'm': 2}
with pytest.raises(ZeroDivisionError):
_, _, _ = MetricsCalculation.calculate_metrics(
channels, parameters, None)
def test_calculate_metrics_with_None_performance_metrics_should_return_None_performance_metrics(
self):
_, _, metrics = MetricsCalculation.calculate_metrics(
{'X': np.array([1])}, {
'c': 0,
'm': 0
}, None)
assert metrics is None
def test_calculate_metrics_with_not_None_performance_metrics_should_return_more_elements_in_metrics(
self):
metrics = pd.DataFrame()
start = metrics.shape[0]
_, _, metrics = MetricsCalculation.calculate_metrics(
{'X': np.array([1])}, {
'c': 0,
'm': 0
}, metrics)
end = metrics.shape[0]
assert start < end
def test_calculate_metrics_with_populated_parameters_should_return_correct_calculations(
self):
channels = {'X': np.array([1])}
parameters = {'c': 0, 'm': 0}
channels, parameters, _ = MetricsCalculation.calculate_metrics(
channels, parameters, None)
assert sum(channels.get('Y')) == 0
assert sum(channels.get('A')) == 1
assert sum(channels.get('B')) == 1
assert sum(channels.get('C')) == 2
assert parameters.get('b') == 1
def test_calculate_metrics_with_populated_channels_and_parameters_should_return_more_elements_in_both_channels_and_parameters(
self):
channels = {'X': np.array([1])}
parameters = {'c': 0, 'm': 0}
channels_start_keycount = len(channels)
parameters_start_keycount = len(parameters)
channels, parameters, _ = MetricsCalculation.calculate_metrics(
channels, parameters, None)
channels_end_keycount = len(channels)
parameters_end_keycount = len(parameters)
assert channels_start_keycount < channels_end_keycount
assert parameters_start_keycount < parameters_end_keycount
def test_calculate_metrics_with_empty_dictionaries_should_return_exception(
self):
with pytest.raises(AttributeError):
MetricsCalculation.calculate_metrics({}, {})
from app.ingest_data import DataIngestor
from app.calculate_metrics import MetricsCalculation
import pandas as pd
import time
import os
# Metrics collection
if os.path.isfile('./data/performance/performance_metrics.csv'):
metrics = pd.read_csv('./data/performance/performance_metrics.csv')
else:
metrics = pd.DataFrame()
channels, parameters, metrics = DataIngestor.ingest_data(
'./data/channels.txt', './data/parameters.txt', metrics)
channels, parameters, metrics = MetricsCalculation.calculate_metrics(
channels, parameters, metrics)
# Performance metrics gathering for data persistence
if metrics is not None:
start = time.time()
MetricsCalculation.persist_data(channels, 'channels')
MetricsCalculation.persist_data(parameters, 'parameters')
# Performance metrics gathering for data persistence
if metrics is not None:
end = time.time()
metrics = metrics.append(
{
'key': 'metrics_persisting',
'value': end - start