def execute(self):
parser = argparse.ArgumentParser()
parser.add_argument(
'CSVFILE', help='path to csv file with historical data.')
parser.add_argument(
'ESTVAL', help='an estimated value')
args = parser.parse_args()
file_path = args.CSVFILE
estimated_value = float(args.ESTVAL)
csv_data = io.read_csv_file(file_path)
if not csv_data:
print 'ERROR: No data'
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[y_column]]
print
x_data, y_data = probe.trim_to_equal_length(x_data, y_data)
print 'X DATA: {}'.format(x_data)
print 'Y DATA: {}'.format(y_data)
print
beta_0 = statistics.beta_0(x_data, y_data)
print u'\u03B20: {}'.format(beta_0)
beta_1 = statistics.beta_1(x_data, y_data)
print u'\u03B21: {}'.format(beta_1)
integ = integration.Integrator(20, 0.000001)
tdist = statistics.make_t_distribution(len(x_data) - 2)
itdist = lambda x: integ.integrate_minus_infinity_to(tdist, x)
std_dev = (
statistics.standard_deviation_around_regression(x_data, y_data)
)
print "StdDev: ", std_dev
projection = beta_0 + beta_1 * estimated_value
print 'Projection: ', projection
print 't(70 percent): ', integration.approximate_inverse(itdist, 0.85)
print 't(90 percent): ', integration.approximate_inverse(itdist, 0.95)
range70 = statistics.prediction_range(
estimated_value, 0.85, x_data, y_data
)
range90 = statistics.prediction_range(
estimated_value, 0.95, x_data, y_data
)
print 'Range(70 percent) =', projection + range70, \
'UPI =', projection - range70, 'LPI =', range70
print 'Range(90 percent) =', projection + range90, \
'UPI =', projection - range90, 'LPI =', range90
def get_regression(self):
"""Returns the linear regression for this estimation method.
Returns:
LinearRegression: A linear regression
"""
return statistics.LinearRegression(
statistics.beta_0(self.x_values, self.y_values), statistics.beta_1(self.x_values, self.y_values)
)
def execute(self):
parser = argparse.ArgumentParser()
parser.add_argument(
'CSVFILE', help='path to csv file with historical data.')
args = parser.parse_args()
file_path = args.CSVFILE
csv_data = io.read_csv_file(file_path)
if not csv_data:
print 'ERROR: No data'
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]
y_data = [float(each[y_column]) for each in csv_data]
#x_data, y_data = statistics.remove_outliers(x_data, y_data)
print
print 'X DATA: {}'.format(x_data)
print 'Y DATA: {}'.format(y_data)
print
beta_0 = statistics.beta_0(x_data, y_data)
print u'\u03B20: {}'.format(beta_0)
warnings = statistics.beta_0_warnings(beta_0)
if warnings:
print 'WARNINGS:'
print '\n'.join(warnings)
print
beta_1 = statistics.beta_1(x_data, y_data)
print u'\u03B21: {}'.format(beta_1)
warnings = statistics.beta_1_warnings(beta_1)
if warnings:
print 'WARNINGS:'
print '\n'.join(warnings)
print