def test_get_seasonality_matrix_month(self):
"""
Test that seasonality matrix works with month
:return: None
"""
dates = date_range(2013, 1, 2015, 8)
#This maps Jan -> 0, Dec -> 11 etc
seasonality_function = lambda date: date.month-1
matrix, compression = get_seasonality_matrix(dates, seasonality_function)
for key, val in compression.items():
self.assertEquals(val, key)
n_dates = len(dates)
n_months = 12
self.assertEquals(matrix.size, (n_dates, n_months))
# the first one which is Jan should be 1 for month index 0 and
# 0 for every other month index
self.assertEquals(matrix[0, 0], 1)
for month_num in xrange(n_months):
self.assertEquals(matrix[0, month_num], int(month_num == 0))
# Test that the whole matrix is as expected
for index in xrange(n_dates):
for month_num in xrange(n_months):
month_num_from_index = index % 12
self.assertEquals(matrix[index, month_num],
int(month_num == month_num_from_index))
def test_date_range(self):
expected = [datetime.date(2015, 1, 1), datetime.date(2015, 2, 1),
datetime.date(2015, 3, 1), datetime.date(2015, 4, 1),
datetime.date(2015, 5, 1)]
result = ds.date_range(2015, 1, 2015, 5)
self.assertEquals(result, expected)
def test_zaggy_predict_is_zero_on_dates_before_first_data_point(self):
self.model = ZaggyModel(self.dates, self.y)
self.model.fit()
old_dates = date_range(2013, 1, 2014, 12)
results = self.model.predict(old_dates)
for expected, result in zip(self.model.solution['model'], results):
self.assertEquals(result, 0.0)
def setUp(self):
self.mock = make_mock(do_plot=False)
self.y = self.mock['y']
self.num = len(self.y)
self.dates = date_range(2015, 1, 2029, 12)[0:self.num]
self.assertEquals(len(self.y), len(self.dates))
seas_func = lambda date: date.month-1
self.seasonality_matrix, self.compression_dict = \
get_seasonality_matrix(self.dates, seasonality_function=seas_func)
def make_extrapolatad_plot(params=None):
mock = get_mock_with_dates()
plot_mock(mock)
timescale = (1.0, 'month')
model = ZaggyModel(mock['dates'], mock['y'], timescale=timescale,
params=params)
model.fit()
plot_model(model)
dates = date_range(2022, 1, 2024, 12)
results = model.predict(dates)
plt.plot(dates, results, color='orange', alpha=0.7, marker='d',
label='predictions')
dates = date_range(2014, 1, 2016, 1)
results = model.predict(dates)
plt.plot(dates, results, color='orange', alpha=0.7, marker='s',
label='predictions (earlier)')
plt.legend(loc='upper left')
return model
def test_get_seasonality_matrix_year(self):
"""
Test that seasonality matrix works with year
:return: None
"""
dates = date_range(2013, 1, 2015, 8)
#This maps 2013->0, 2014->1, 2015->2
seasonality_function = lambda date: date.year-2013
matrix, compression = get_seasonality_matrix(dates, seasonality_function)
expected_compression = {0: 0, 1: 1, 2: 2}
self.assertDictEqual(compression, expected_compression)
n_dates = len(dates)
n_years = 3
self.assertEquals(matrix.size, (n_dates, n_years))
# Test that the whole matrix is as expected
for index in xrange(n_dates):
for year_num in xrange(n_years):
#integer division
year_num_from_index = index / 12
self.assertEquals(matrix[index, year_num],
int(year_num == year_num_from_index))
def get_mock_with_dates():
mock = make_mock()
num = len(mock['x'])
mock['dates'] = date_range(2015, 1, 2099, 12)[:num]
return mock
def setUp(self):
mock = make_mock()
self.y = mock['y']
num = len(self.y)
self.dates = date_range(2015, 1, 2029, 12)[0: num]
