def test_predict_freq():
# test that predicted dates have same frequency
x = np.arange(1, 36.)
if _pandas_08x:
from pandas import date_range
# there's a bug in pandas up to 0.10.2 for YearBegin
#dates = date_range("1972-4-1", "2007-4-1", freq="AS-APR")
dates = date_range("1972-4-30", "2006-4-30", freq="A-APR")
series = Series(x, index=dates)
model = TimeSeriesModel(series)
#npt.assert_(model.data.freq == "AS-APR")
npt.assert_(model.data.freq == "A-APR")
start = model._get_predict_start("2006-4-30")
end = model._get_predict_end("2016-4-30")
model._make_predict_dates()
predict_dates = model.data.predict_dates
#expected_dates = date_range("2006-12-31", "2016-12-31",
# freq="AS-APR")
expected_dates = date_range("2006-4-30", "2016-4-30", freq="A-APR")
npt.assert_equal(predict_dates, expected_dates)
#ptesting.assert_series_equal(predict_dates, expected_dates)
else:
from pandas import DateRange, datetools
dates = DateRange("1972-1-1", "2007-1-1", offset=datetools.yearEnd)
series = Series(x, index=dates)
model = TimeSeriesModel(series)
npt.assert_(model.data.freq == "A")
start = model._get_predict_start("2006-12-31")
end = model._get_predict_end("2016-12-31")
model._make_predict_dates()
predict_dates = model.data.predict_dates
expected_dates = DateRange("2006-12-31",
"2016-12-31",
offset=datetools.yearEnd)
npt.assert_array_equal(predict_dates, expected_dates)
def test_predict_freq():
# test that predicted dates have same frequency
x = np.arange(1,36.)
if _pandas_08x:
from pandas import date_range
# there's a bug in pandas up to 0.10.2 for YearBegin
#dates = date_range("1972-4-1", "2007-4-1", freq="AS-APR")
dates = date_range("1972-4-30", "2006-4-30", freq="A-APR")
series = Series(x, index=dates)
model = TimeSeriesModel(series)
#npt.assert_(model.data.freq == "AS-APR")
npt.assert_(model.data.freq == "A-APR")
start = model._get_predict_start("2006-4-30")
end = model._get_predict_end("2016-4-30")
model._make_predict_dates()
predict_dates = model.data.predict_dates
#expected_dates = date_range("2006-12-31", "2016-12-31",
# freq="AS-APR")
expected_dates = date_range("2006-4-30", "2016-4-30", freq="A-APR")
npt.assert_equal(predict_dates, expected_dates)
#ptesting.assert_series_equal(predict_dates, expected_dates)
else:
from pandas import DateRange, datetools
dates = DateRange("1972-1-1", "2007-1-1", offset=datetools.yearEnd)
series = Series(x, index=dates)
model = TimeSeriesModel(series)
npt.assert_(model.data.freq == "A")
start = model._get_predict_start("2006-12-31")
end = model._get_predict_end("2016-12-31")
model._make_predict_dates()
predict_dates = model.data.predict_dates
expected_dates = DateRange("2006-12-31", "2016-12-31",
offset=datetools.yearEnd)
npt.assert_array_equal(predict_dates, expected_dates)
def test_get_predict_start_end():
index = pd.DatetimeIndex(start='1970-01-01', end='1990-01-01', freq='AS')
endog = pd.Series(np.zeros(10), index[:10])
model = TimeSeriesModel(endog)
predict_starts = [1, '1971-01-01', datetime(1971, 1, 1), index[1]]
predict_ends = [20, '1990-01-01', datetime(1990, 1, 1), index[-1]]
for start in predict_starts:
assert_equal(model._get_predict_start(start), 1)
for end in predict_ends:
assert_equal(model._get_predict_end(end), (9, 11))
def test_predict_freq():
# test that predicted dates have same frequency
x = np.arange(1, 36.)
# there's a bug in pandas up to 0.10.2 for YearBegin
#dates = date_range("1972-4-1", "2007-4-1", freq="AS-APR")
dates = date_range("1972-4-30", "2006-4-30", freq="A-APR")
series = Series(x, index=dates)
model = TimeSeriesModel(series)
#npt.assert_(model.data.freq == "AS-APR")
npt.assert_(model.data.freq == "A-APR")
start = model._get_predict_start("2006-4-30")
end = model._get_predict_end("2016-4-30")
model._make_predict_dates()
predict_dates = model.data.predict_dates
#expected_dates = date_range("2006-12-31", "2016-12-31",
# freq="AS-APR")
expected_dates = date_range("2006-4-30", "2016-4-30", freq="A-APR")
npt.assert_equal(predict_dates, expected_dates)
def test_pandas_nodates_index():
data = [988, 819, 964]
dates = ['a', 'b', 'c']
s = pd.Series(data, index=dates)
npt.assert_raises(ValueError, TimeSeriesModel, s)
# Test with a non-date index that doesn't raise an exception because it
# can be coerced into a nanosecond DatetimeIndex
# (This test doesn't make sense for Numpy < 1.7 since they don't have
# nanosecond support)
# (This test also doesn't make sense for Pandas < 0.14 since we don't
# support nanosecond index in Pandas < 0.14)
try:
# Check for Numpy < 1.7
_freq_to_pandas['N']
except:
pass
else:
data = [988, 819, 964]
# index=pd.date_range('1970-01-01', periods=3, freq='QS')
index = pd.to_datetime([100, 101, 102])
s = pd.Series(data, index=index)
# Alternate test for Pandas < 0.14
from distutils.version import LooseVersion
from pandas import __version__ as pd_version
if LooseVersion(pd_version) < '0.14':
assert_raises(NotImplementedError, TimeSeriesModel, s)
else:
actual_str = (index[0].strftime('%Y-%m-%d %H:%M:%S.%f') +
str(index[0].value))
assert_equal(actual_str, '1970-01-01 00:00:00.000000100')
mod = TimeSeriesModel(s)
start = mod._get_predict_start(0)
end, out_of_sample = mod._get_predict_end(4)
mod._make_predict_dates()
assert_equal(len(mod.data.predict_dates), 5)
def test_predict_freq():
# test that predicted dates have same frequency
x = np.arange(1,36.)
# there's a bug in pandas up to 0.10.2 for YearBegin
#dates = date_range("1972-4-1", "2007-4-1", freq="AS-APR")
dates = pd.date_range("1972-4-30", "2006-4-30", freq="A-APR")
series = pd.Series(x, index=dates)
model = TimeSeriesModel(series)
#npt.assert_(model.data.freq == "AS-APR")
npt.assert_(model.data.freq == "A-APR")
start = model._get_predict_start("2006-4-30")
end = model._get_predict_end("2016-4-30")
model._make_predict_dates()
predict_dates = model.data.predict_dates
#expected_dates = date_range("2006-12-31", "2016-12-31",
# freq="AS-APR")
expected_dates = pd.date_range("2006-4-30", "2016-4-30", freq="A-APR")
assert_equal(predict_dates, expected_dates)
def test_pandas_nodates_index():
data = [988, 819, 964]
dates = ['a', 'b', 'c']
s = pd.Series(data, index=dates)
npt.assert_raises(ValueError, TimeSeriesModel, s)
# Test with a non-date index that doesn't raise an exception because it
# can be coerced into a nanosecond DatetimeIndex
# (This test doesn't make sense for Numpy < 1.7 since they don't have
# nanosecond support)
# (This test also doesn't make sense for Pandas < 0.14 since we don't
# support nanosecond index in Pandas < 0.14)
try:
# Check for Numpy < 1.7
_freq_to_pandas['N']
except:
pass
else:
data = [988, 819, 964]
# index=pd.date_range('1970-01-01', periods=3, freq='QS')
index = pd.to_datetime([100, 101, 102])
s = pd.Series(data, index=index)
# Alternate test for Pandas < 0.14
from distutils.version import LooseVersion
from pandas import __version__ as pd_version
if LooseVersion(pd_version) < '0.14':
assert_raises(NotImplementedError, TimeSeriesModel, s)
else:
actual_str = (index[0].strftime('%Y-%m-%d %H:%M:%S.%f') +
str(index[0].value))
assert_equal(actual_str, '1970-01-01 00:00:00.000000100')
mod = TimeSeriesModel(s)
start = mod._get_predict_start(0)
end, out_of_sample = mod._get_predict_end(4)
mod._make_predict_dates()
assert_equal(len(mod.data.predict_dates), 5)