def test_non_seasonal_arima18(self):
ts_data = self.getData4()
f_name = 'non_seasonal_arima18.pmml'
model = ARIMA(ts_data, order=(9, 2, 0))
result = model.fit(trend='c', method='css', solver='ncg')
ArimaToPMML(result, f_name)
self.assertEqual(os.path.isfile(f_name), True)
def test_varmax_without_intercept(self):
ts_data = self.get_data_for_varmax()
f_name = 'varmax_without_intercept.pmml'
model = VARMAX(ts_data, order=(1, 1), trend=None)
result = model.fit()
ArimaToPMML(result, f_name)
self.assertEqual(os.path.isfile(f_name), True)
def test_non_seasonal_arima5(self):
ts_data = self.getData4()
f_name = 'non_seasonal_arima5.pmml'
model = StateSpaceARIMA(ts_data, order=(1, 1, 1))
result = model.fit()
ArimaToPMML(result, f_name, description="A test model")
self.assertEqual(os.path.isfile(f_name), True)
def test_non_seasonal_arima18(self):
ts_data = self.statsmodels_data_helper.getData4()
f_name = 'non_seasonal_arima18.pmml'
model = ARIMA(ts_data, order=(9, 2, 0))
result = model.fit(trend='c', method='css', solver='ncg')
ArimaToPMML(ts_data, model, result, f_name)
self.assertEqual(self.schema.is_valid(f_name), True)
def test_varmax_without_intercept(self):
ts_data = self.statsmodels_data_helper.get_data_for_varmax()
f_name = 'varmax_without_intercept.pmml'
model = VARMAX(ts_data, order=(1, 1), trend=None)
result = model.fit()
ArimaToPMML(result, f_name)
self.assertEqual(self.schema.is_valid(f_name), True)
def test_non_seasonal_arima3(self):
ts_data = self.getData4()
f_name = 'non_seasonal_arima3.pmml'
model = ARIMA(ts_data, order=(1, 0, 1))
result = model.fit(trend='c', method='css-mle')
ArimaToPMML(result, f_name, description="A test model")
self.assertEqual(os.path.isfile(f_name), True)
def test_non_seasonal_arima8(self):
ts_data = self.statsmodels_data_helper.get_non_seasonal_data()
f_name = 'non_seasonal_arima8.pmml'
model = ARIMA(ts_data, order=(5, 1, 2))
result = model.fit(trend='c', method='mle')
ArimaToPMML(result, f_name, conf_int=[80, 95])
self.assertEqual(self.schema.is_valid(f_name), True)
def test_non_seasonal_arima2(self):
ts_data = self.statsmodels_data_helper.get_non_seasonal_data()
f_name = 'non_seasonal_arima1.pmml'
model = StateSpaceARIMA(ts_data, order=(3, 1, 2), trend='c')
result = model.fit()
ArimaToPMML(result, f_name)
self.assertEqual(self.schema.is_valid(f_name), True)
def test_seasonal_arima2(self):
ts_data = self.statsmodels_data_helper.get_seasonal_data()
f_name = 'seasonal_arima2.pmml'
model = SARIMAX(endog=ts_data,
exog=None,
order=(3, 1, 1),
seasonal_order=(3, 1, 1, 12))
result = model.fit()
ArimaToPMML(result, f_name, conf_int=[80])
self.assertEqual(self.schema.is_valid(f_name), True)
def test_seasonal_arima4(self):
ts_data = self.getData5()
f_name = 'seasonal_arima4.pmml'
model = SARIMAX(
endog=ts_data,
order=(1, 0, 1),
seasonal_order=(1, 0, 1, 12),
)
result = model.fit(disp=False)
ArimaToPMML(result, f_name)
self.assertEqual(os.path.isfile(f_name), True)
def test_01(self):
ts_data = getData()
f_name='arima201_c_car_sold.pmml'
model = ARIMA(ts_data,order=(2,0,1))
result = model.fit(trend = 'c', method = 'css')
ArimaToPMML(result, f_name)
model_name = self.adapaUtilities.upload_to_zserver(f_name)
z_pred = self.adapaUtilities.score_single_record(model_name)
model_pred = result.forecast()[0][0]
self.assertEqual(model_pred, z_pred['predicted_cars_sold'])
z_pred = self.adapa_utility.score_in_zserver(model_name, 'nyoka/tests/test_car_sold.csv','TS')
model_pred = result.forecast(5)[0][-1]
self.assertEqual(model_pred, z_pred)
def test_03(self):
ts_data = self.getData()
f_name='arima212_c_car_sold.pmml'
model = ARIMA(ts_data,order=(2,1,2))
result = model.fit(trend = 'c', method = 'mle')
ArimaToPMML(result, f_name)
model_name = self.adapa_utility.upload_to_zserver(f_name)
z_pred = self.adapa_utility.score_single_record(model_name)
model_pred = result.forecast()[0][0]
self.assertEqual("{:.9f}".format(model_pred), "{:.9f}".format(z_pred['predicted_cars_sold']))
z_pred = self.adapa_utility.score_in_zserver(model_name, 'nyoka/tests/test_car_sold.csv','TS')
model_pred = result.forecast(5)[0][-1]
self.assertEqual("{:.9f}".format(z_pred), "{:.9f}".format(model_pred))
def test_02(self):
data=pd.read_csv("nyoka/tests/JohnsonJohnsonWithDate.csv")
data['index']=pd.to_datetime(data['index'], format='%Y-%m-%d')
data.set_index(['index'], inplace=True)
mod = SARIMAX(data,order=(1,1,1),seasonal_order=(1,1,1, 4))
result = mod.fit(disp=False)
ArimaToPMML(result, 'jnj_seasonal_arima.pmml')
model_name = self.adapa_utility.upload_to_zserver('jnj_seasonal_arima.pmml')
z_pred = self.adapa_utility.score_single_record(model_name)
model_pred = result.forecast()[0]
self.assertEqual("{:.9f}".format(model_pred), "{:.9f}".format(z_pred['predicted_value']))
z_pred = self.adapa_utility.score_in_zserver(model_name, 'nyoka/tests/test_jnj.csv','TS')
model_pred = result.forecast(5)[-1]
self.assertEqual("{:.9f}".format(z_pred), "{:.9f}".format(model_pred))
def test_seasonal_arima7(self):
ts_data = self.getData5()
f_name = 'seasonal_arima7.pmml'
model = sarimax.SARIMAX(endog=ts_data,
exog=None,
order=(3, 1, 1),
seasonal_order=(3, 1, 1, 12),
trend='t',
measurement_error=False,
time_varying_regression=False,
mle_regression=True,
simple_differencing=True,
enforce_stationarity=False,
enforce_invertibility=False,
hamilton_representation=True,
concentrate_scale=False)
result = model.fit(disp=False)
ArimaToPMML(result, f_name)
self.assertEqual(os.path.isfile(f_name), True)
def test_seasonal_arima8(self):
ts_data = self.statsmodels_data_helper.getData5()
f_name = 'seasonal_arima8.pmml'
model = SARIMAX(endog=ts_data,
exog=None,
order=(3, 1, 1),
seasonal_order=(3, 1, 1, 12),
trend='t',
measurement_error=False,
time_varying_regression=False,
mle_regression=True,
simple_differencing=False,
enforce_stationarity=False,
enforce_invertibility=False,
hamilton_representation=False,
concentrate_scale=False)
result = model.fit()
ArimaToPMML(ts_data, model, result, f_name)
self.assertEqual(self.schema.is_valid(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'
ArimaToPMML(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'
ArimaToPMML(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
