def test_exponentialSmoothing_05(self):
ts_data = self.statsmodels_data_helper.getData1()
f_name = 'exponential_smoothing5.pmml'
model_obj = ExponentialSmoothing(ts_data,
trend='mul',
damped=True,
seasonal='add',
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(self.schema.is_valid(f_name), True)
def test_exponentialSmoothing_17(self):
ts_data = self.statsmodels_data_helper.getData3()
f_name = 'exponential_smoothing17.pmml'
model_obj = ExponentialSmoothing(ts_data,
trend=None,
damped=False,
seasonal=None,
seasonal_periods=None)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(self.schema.is_valid(f_name), True)
def test_exponentialSmoothing_05(self):
ts_data = self.getData1()
f_name = 'exponential_smoothing5.pmml'
model_obj = hw.ExponentialSmoothing(ts_data,
trend='mul',
damped=True,
seasonal='add',
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
def test_exponentialSmoothing_17(self):
ts_data = self.getData3()
f_name = 'exponential_smoothing17.pmml'
model_obj = hw.ExponentialSmoothing(ts_data,
trend=None,
damped=False,
seasonal=None,
seasonal_periods=None)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
def test_exponentialSmoothing_01(self):
ts_data = self.getData1()
f_name='exponential_smoothing1.pmml'
model_obj = ExponentialSmoothing(ts_data,
trend='add',
damped=True,
seasonal='add',
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(results_obj, f_name, model_name="Test2", description="test model")
self.assertEqual(os.path.isfile(f_name),True)
def test_exponentialSmoothing_03(self):
# data with no trend and no seasonality
# Oil production in Saudi Arabia
data = [
446.6565, 454.4733, 455.663, 423.6322, 456.2713, 440.5881,
425.3325, 485.1494, 506.0482, 526.792, 514.2689, 494.211
]
index = pd.DatetimeIndex(start='1996', end='2008', freq='A')
ts_data = pd.Series(data, index)
ts_data.index.name = 'datetime_index'
ts_data.name = 'oil_production'
f_name = 'exponential_smoothing.pmml'
model_obj = hw.ExponentialSmoothing(ts_data,
trend=None,
damped=False,
seasonal=None,
seasonal_periods=None)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend=None,
damped=False,
seasonal=None,
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
def test_keras_01(self):
def import_data(trend=False, seasonality=False):
"""
Returns a dataframe with time series values.
:param trend: boolean
If True, returns data with trend
:param seasonality: boolean
If True, returns data with seasonality
:return: ts_data: DataFrame
Index of the data frame is either a time-index or an integer index. First column has time series values
"""
if trend and seasonality:
# no of international visitors in Australia
data = [
41.7275, 24.0418, 32.3281, 37.3287, 46.2132, 29.3463,
36.4829, 42.9777, 48.9015, 31.1802, 37.7179, 40.4202,
51.2069, 31.8872, 40.9783, 43.7725, 55.5586, 33.8509,
42.0764, 45.6423, 59.7668, 35.1919, 44.3197, 47.9137
]
index = pd.DatetimeIndex(start='2005',
end='2010-Q4',
freq='QS')
ts_data = pd.Series(data, index)
ts_data.index.name = 'datetime_index'
ts_data.name = 'n_visitors'
return ts_data
elif trend:
# no. of annual passengers of air carriers registered in Australia
data = [
17.5534, 21.86, 23.8866, 26.9293, 26.8885, 28.8314,
30.0751, 30.9535, 30.1857, 31.5797, 32.5776, 33.4774,
39.0216, 41.3864, 41.5966
]
index = pd.DatetimeIndex(start='1990', end='2005', freq='A')
ts_data = pd.Series(data, index)
ts_data.index.name = 'datetime_index'
ts_data.name = 'n_passengers'
return ts_data
elif seasonality:
pass
else:
# Oil production in Saudi Arabia
data = [
446.6565, 454.4733, 455.663, 423.6322, 456.2713, 440.5881,
425.3325, 485.1494, 506.0482, 526.792, 514.2689, 494.211
]
index = pd.DatetimeIndex(start='1996', end='2008', freq='A')
ts_data = pd.Series(data, index)
ts_data.index.name = 'datetime_index'
ts_data.name = 'oil_production'
return ts_data
ts_data = import_data(trend=False, seasonality=False)
model_obj = hw.ExponentialSmoothing(ts_data,
trend=None,
damped=False,
seasonal=None,
seasonal_periods=None)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj,
'exponential_smoothing.pmml')
self.assertEqual(os.path.isfile("exponential_smoothing.pmml"), True)
ts_data = import_data(trend=True, seasonality=False)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=False,
seasonal=None,
seasonal_periods=None)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj,
'exponential_smoothing.pmml')
self.assertEqual(os.path.isfile("exponential_smoothing.pmml"), True)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='mul',
damped=False,
seasonal=None,
seasonal_periods=None)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj,
'exponential_smoothing.pmml')
self.assertEqual(os.path.isfile("exponential_smoothing.pmml"), True)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='mul',
damped=True,
seasonal=None,
seasonal_periods=None)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj,
'exponential_smoothing.pmml')
self.assertEqual(os.path.isfile("exponential_smoothing.pmml"), True)
ts_data = import_data(trend=True, seasonality=True)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=False,
seasonal='add',
seasonal_periods=4)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj,
'exponential_smoothing.pmml')
self.assertEqual(os.path.isfile("exponential_smoothing.pmml"), True)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=True,
seasonal='add',
seasonal_periods=4)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj,
'exponential_smoothing.pmml')
self.assertEqual(os.path.isfile("exponential_smoothing.pmml"), True)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=False,
seasonal='mul',
seasonal_periods=4)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj,
'exponential_smoothing.pmml')
self.assertEqual(os.path.isfile("exponential_smoothing.pmml"), True)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=False,
seasonal='mul',
seasonal_periods=4)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj,
'exponential_smoothing.pmml')
self.assertEqual(os.path.isfile("exponential_smoothing.pmml"), True)
def test_exponentialSmoothing_01(self):
# data with trend and seasonality present
# no of international visitors in Australia
data = [
41.7275, 24.0418, 32.3281, 37.3287, 46.2132, 29.3463, 36.4829,
42.9777, 48.9015, 31.1802, 37.7179, 40.4202, 51.2069, 31.8872,
40.9783, 43.7725, 55.5586, 33.8509, 42.0764, 45.6423, 59.7668,
35.1919, 44.3197, 47.9137
]
index = pd.DatetimeIndex(start='2005', end='2010-Q4', freq='QS')
ts_data = pd.Series(data, index)
ts_data.index.name = 'datetime_index'
ts_data.name = 'n_visitors'
f_name = 'exponential_smoothing.pmml'
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=True,
seasonal='add',
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=False,
seasonal='add',
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=True,
seasonal='mul',
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=False,
seasonal='mul',
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='mul',
damped=True,
seasonal='add',
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='mul',
damped=False,
seasonal='add',
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='mul',
damped=True,
seasonal='mul',
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='mul',
damped=False,
seasonal='mul',
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
def test_exponentialSmoothing_02(self):
# data with trend but no seasonality
# no. of annual passengers of air carriers registered in Australia
data = [
17.5534, 21.86, 23.8866, 26.9293, 26.8885, 28.8314, 30.0751,
30.9535, 30.1857, 31.5797, 32.5776, 33.4774, 39.0216, 41.3864,
41.5966
]
index = pd.DatetimeIndex(start='1990', end='2005', freq='A')
ts_data = pd.Series(data, index)
ts_data.index.name = 'datetime_index'
ts_data.name = 'n_passengers'
f_name = 'exponential_smoothing.pmml'
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=True,
seasonal=None,
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=True,
seasonal=None,
seasonal_periods=None)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=False,
seasonal=None,
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='add',
damped=False,
seasonal=None,
seasonal_periods=None)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='mul',
damped=True,
seasonal=None,
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='mul',
damped=True,
seasonal=None,
seasonal_periods=None)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='mul',
damped=False,
seasonal=None,
seasonal_periods=2)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
model_obj = hw.ExponentialSmoothing(ts_data,
trend='mul',
damped=False,
seasonal=None,
seasonal_periods=None)
results_obj = model_obj.fit(optimized=True)
ExponentialSmoothingToPMML(ts_data, model_obj, results_obj, f_name)
self.assertEqual(os.path.isfile(f_name), True)
os.remove(f_name)
