def satisfies_preconditions(cls, historical_data, proxy_value):
"""Indicates whether or not the historical data allows this method to
be used for the given proxy value.
Arguments:
historical_data(HistoricalData): The historical estimation data
proxy_value(float): The proxy size estimate
Returns:
bool: True if this method can be used, False otherwise.
"""
planned_sizes, actual_times = trim_to_equal_length(historical_data.proxy_sizes, historical_data.actual_times)
if len(planned_sizes) < 3:
return False
regression = cls(historical_data).get_regression()
expected_time = regression.estimate(proxy_value)
if regression.beta0 > 0.25 * expected_time:
return False
productivity = 1.0 / (sum(planned_sizes) / sum(actual_times))
beta1_range = 0.5 * productivity
if regression.beta1 < (productivity - beta1_range) or regression.beta > (productivity + beta1_range):
return False
if statistics.correlation(planned_sizes, actual_times) ** 2 < 0.5:
return False
if statistics.significance(planned_sizes, actual_times) > 0.05:
return False
return True
def satisfies_preconditions(cls, historical_data, proxy_value):
"""Indicates whether or not the historical data allows this method to
be used for the given proxy value.
Arguments:
historical_data(HistoricalData): The historical estimation data
proxy_value(float): The proxy size estimate
Returns:
bool: True if this method can be used, False otherwise.
"""
proxy_sizes, actual_sizes = trim_to_equal_length(historical_data.proxy_sizes, historical_data.actual_sizes)
# Too few data points
if len(actual_sizes) < 3:
return False
regression = cls(historical_data).get_regression()
estimated_size = regression.estimate(proxy_value)
# Beta0 is not close to zero
if regression.beta0 > 0.25 * estimated_size:
return False
# Beta1 is out of bounds
if regression.beta1 < 0.5 or regression.beta1 > 2.0:
return False
# Weakly correlated
if statistics.correlation(proxy_sizes, actual_sizes) ** 2 < 0.5:
return False
# Weak statistical significance
if statistics.significance(proxy_sizes, actual_sizes) > 0.05:
return False
return True
def get_significance(self):
"""Returns the correlation significance.
Returns:
float: The percent chance that values were generated randomly.
"""
return statistics.significance(self.x_values, self.y_values)
def execute(self):
"""Run the program"""
parser = argparse.ArgumentParser()
parser.add_argument('CSVFILE', help='path to csv file with data.')
args = parser.parse_args()
csv_data = io.read_csv_file(args.CSVFILE)
if not csv_data:
print 'ERROR: Invalid csv data file.'
sys.exit(1)
columns = csv_data[0].keys()
x_column = io.choose_from_list('X Column:', columns)
y_column = io.choose_from_list('Y Column:', columns)
x_data = [float(each[x_column]) for each in csv_data if each[x_column]]
y_data = [float(each[y_column]) for each in csv_data if each[x_column]]
print 'R:', statistics.correlation(x_data, y_data)
print 'T:', statistics.t_value(x_data, y_data)
print 'Significance:', statistics.significance(x_data, y_data)
def test_should_correctly_compute_significance(self):
result = statistics.significance(self.x_data, self.y_data)
self.assertAlmostEqual(result, 2 * (1 - 0.99999), 4)