def test_exponentialSmoothing_04(self):
ts_data = self.getData1()
f_name = 'exponential_smoothing4.pmml'
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=False,
seasonal='mul',
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
statsmodels_to_pmml(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
def test_non_seasonal_arima(self):
fit_combin = [['c', 'css-mle', 'lbfgs'], ['c', 'css-mle', 'nm'],
['c', 'css-mle', 'bfgs'], ['c', 'css-mle', 'powell'],
['c', 'css-mle', 'cg'], ['c', 'css-mle', 'ncg'],
['c', 'mle', 'lbfgs'], ['c', 'mle', 'nm'],
['c', 'mle', 'bfgs'], ['c', 'mle', 'powell'],
['c', 'mle', 'cg'], ['c', 'mle', 'ncg'],
['c', 'css', 'lbfgs'], ['c', 'css', 'nm'],
['c', 'css', 'bfgs'], ['c', 'css', 'powell'],
['c', 'css', 'cg'], ['c', 'css', 'ncg']]
# no of cars sold
data = [
266, 146, 183, 119, 180, 169, 232, 225, 193, 123, 337, 186, 194,
150, 210, 273, 191, 287, 226, 304, 290, 422, 265, 342, 340, 440,
316, 439, 401, 390, 490, 408, 490, 420, 520, 480
]
index = pd.DatetimeIndex(start='2016-01-01',
end='2018-12-01',
freq='MS')
ts_data = pd.Series(data, index)
ts_data.index.name = 'date_index'
ts_data.name = 'cars_sold'
c = 0
for x in fit_combin:
try:
model = ARIMA(ts_data, order=(9, 2, 0))
result = model.fit(trend=x[0], method=x[1], solver=x[2])
try:
c = c + 1
file_name = 'non_seasonal_arima' + str(c) + '.pmml'
statsmodels_to_pmml(ts_data, model, result, file_name)
except:
continue
finally:
exported = os.path.isfile(file_name)
self.assertEqual(exported, True)
if (not exported):
break
except:
continue
def test_seasonal_arima(self):
model_combin = [[(3, 1, 1), (3, 1, 1, 12), 't', True, True, False,
True, False, False, True, False],
[(3, 1, 1), (3, 1, 1, 12), 't', True, True, False,
False, False, False, False, False],
[(3, 1, 1), (3, 1, 1, 12), 't', True, False, True,
True, False, False, True, False],
[(3, 1, 1), (3, 1, 1, 12), 't', True, False, True,
False, False, False, False, False],
[(3, 1, 1), (3, 1, 1, 12), 't', False, True, False,
True, False, False, True, False],
[(3, 1, 1), (3, 1, 1, 12), 't', False, True, False,
False, False, False, False, False],
[(3, 1, 1), (3, 1, 1, 12), 't', False, False, True,
True, False, False, True, False],
[(3, 1, 1), (3, 1, 1, 12), 't', False, False, True,
False, False, False, False, False]]
# no of cars sold
data = [
112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115,
126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140, 145, 150,
178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193,
181, 183, 218, 230, 242, 209, 191, 172, 194, 196, 196, 236, 235,
229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 227, 234,
264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315,
364, 347, 312, 274, 237, 278, 284, 277, 317, 313, 318, 374, 413,
405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467,
404, 347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404,
359, 310, 337, 360, 342, 406, 396, 420, 472, 548, 559, 463, 407,
362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390,
432
]
index = pd.DatetimeIndex(start='1949-01-01',
end='1960-12-01',
freq='MS')
ts_data = pd.Series(data, index)
ts_data.index.name = 'datetime_index'
ts_data.name = 'n_passengers'
c = 0
for x in model_combin:
try:
model = sarimax.SARIMAX(endog=ts_data,
exog=None,
order=x[0],
seasonal_order=x[1],
trend=x[2],
measurement_error=x[3],
time_varying_regression=x[4],
mle_regression=x[5],
simple_differencing=x[6],
enforce_stationarity=x[7],
enforce_invertibility=x[8],
hamilton_representation=x[9],
concentrate_scale=x[10])
result = model.fit()
try:
c = c + 1
file_name = 'seasonal_arima' + str(c) + '.pmml'
statsmodels_to_pmml(ts_data, model, result, file_name)
except:
continue
finally:
exported = os.path.isfile(file_name)
self.assertEqual(exported, True)
if (not exported):
break
except:
continue