def test_instantiation_valid_datetimeindex_periodindex():
# See long comment in test_instantiation valid
# Test pandas (Series, DataFrame); with index (no dates/freq argument)
for base_endog in dta[2:4]:
# DatetimeIndex and PeriodIndex, should not raise warnings
with warnings.catch_warnings():
warnings.simplefilter('error')
for ix, freq in date_indexes + period_indexes:
endog = base_endog.copy()
endog.index = ix
mod = tsa_model.TimeSeriesModel(endog)
if freq is None:
freq = ix.freq
if not isinstance(freq, str):
freq = freq.freqstr
assert isinstance(mod._index,
(pd.DatetimeIndex, pd.PeriodIndex))
assert mod._index_none is False
assert mod._index_dates is True
assert mod._index_generated is False
assert mod._index.freq == mod._index_freq
assert mod.data.dates.equals(mod._index)
assert mod.data.freq == freq
def test_prediction_increment_pandas_dates_nanosecond():
# upstream has a try/except that skips this test for pandas <= 0.14. Since
# that is several years old, we assume that case away.
# Date-based index
endog = dta[2].copy()
endog.index = pd.DatetimeIndex(start='1970-01-01',
periods=len(endog),
freq='N')
mod = tsa_model.TimeSeriesModel(endog)
# Basic prediction: [0, end]; the index is the date index
start_key = 0
end_key = None
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 0
assert end == nobs - 1
assert out_of_sample == 0
assert type(prediction_index) is type(endog.index) # noqa: E721
assert prediction_index.equals(mod._index)
# Negative index: [-2, end]
start_key = -2
end_key = -1
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 3
assert end == 4
assert out_of_sample == 0
assert type(prediction_index) is type(endog.index) # noqa: E721
assert prediction_index.equals(mod._index[3:])
# Forecasting: [1, 5]; the index is an extended version of the date index
start_key = 1
end_key = nobs
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 1
assert end == 4
assert out_of_sample == 1
desired_index = pd.DatetimeIndex(start='1970-01-01', periods=6,
freq='N')[1:]
assert prediction_index.equals(desired_index)
# Date-based keys
start_key = pd.Timestamp('1970-01-01')
end_key = pd.Timestamp(start_key.value + 7)
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 0
assert end == 4
assert out_of_sample == 3
desired_index = pd.DatetimeIndex(start='1970-01-01', periods=8, freq='N')
assert prediction_index.equals(desired_index)
def test_instantiation_no_index(endog):
# See long comment in test_instantiation valid
# Test list, numpy endog, pandas w/o index; with dates / freq argument
# Supported date indexes, should not raise warnings, can use valid freq
with warnings.catch_warnings():
warnings.simplefilter('error')
for ix, freq in date_indexes + period_indexes:
mod = tsa_model.TimeSeriesModel(endog, dates=ix, freq=freq)
if freq is None:
freq = ix.freq
if not isinstance(freq, str):
freq = freq.freqstr
assert isinstance(mod._index, (pd.DatetimeIndex, pd.PeriodIndex))
assert mod._index_none is False
assert mod._index_dates is True
assert mod._index_generated is False
assert mod._index.freq == mod._index_freq
assert mod.data.dates.equals(mod._index)
assert mod.data.freq == freq
# Other supported indexes, with valid freq, should not raise warnings
with warnings.catch_warnings():
warnings.simplefilter('error')
for ix, freq in supported_date_indexes:
mod = tsa_model.TimeSeriesModel(endog, dates=ix, freq=freq)
if freq is None:
freq = ix.freq
if not isinstance(freq, str):
freq = freq.freqstr
assert isinstance(mod._index, (pd.DatetimeIndex, pd.PeriodIndex))
assert mod._index_none is False
assert mod._index_dates is True
assert mod._index_generated is False
assert mod._index.freq == mod._index_freq
assert mod.data.dates.equals(mod._index)
assert mod.data.freq == freq
# Since only supported indexes are valid `dates` arguments, everything
# else is invalid here
for ix, freq in supported_increment_indexes + unsupported_indexes:
with pytest.raises(ValueError):
tsa_model.TimeSeriesModel(endog, dates=ix)
def test_range_index():
# GH#4457
tsa_model.__warningregistry__ = {}
endog = pd.Series(np.random.normal(size=5))
assert isinstance(endog.index, pd.RangeIndex)
# Warning should not be given
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
tsa_model.TimeSeriesModel(endog)
assert len(w) == 0
def test_prediction_increment_unsupported():
# a. Generated from unsupported index
endog = dta[2].copy()
endog.index = unsupported_indexes[-2][0]
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('ignore')
mod = tsa_model.TimeSeriesModel(endog)
# Basic prediction: [0, end]; notice that since this is an in-sample
# prediction, the index returned is the (unsupported) original index
start_key = 0
end_key = None
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 0
assert end == nobs - 1
assert out_of_sample == 0
assert prediction_index.equals(mod.data.row_labels)
# Negative index: [-2, end]; notice that since this is an in-sample
# prediction, the index returned is a piece of the (unsupported)
# original index
start_key = -2
end_key = -1
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 3
assert end == 4
assert out_of_sample == 0
assert prediction_index.equals(mod.data.row_labels[3:])
# Forecasting: [1, 5], notice that since an unsupported index was given,
# a warning will be issued
start_key = 1
end_key = nobs
message = ('No supported index is available.'
' Prediction results will be given with'
' an integer index beginning at `start`.')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
start, end, out_of_sample, prediction_index = (
mod._get_prediction_index(start_key, end_key))
# FIXME: 2018-09-21 this fails on appveyor, but inconsistently
# FIXME: dont comment-out code
#assert str(w[0].message) == message
assert start == 1
assert end == 4
assert out_of_sample == 1
assert prediction_index.equals(pd.Index(np.arange(1, 6)))
def test_prediction_increment_pandas_dates():
# Date-based index
endog = dta[2].copy()
endog.index = date_indexes[0][0] # Daily, 1950-01-01, 1950-01-02, ...
mod = tsa_model.TimeSeriesModel(endog)
# Basic prediction: [0, end]; the index is the date index
start_key = 0
end_key = None
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 0
assert end == nobs - 1
assert out_of_sample == 0
assert type(prediction_index) is type(endog.index) # noqa: E721
assert prediction_index.equals(mod._index)
# Negative index: [-2, end]
start_key = -2
end_key = -1
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 3
assert end == 4
assert out_of_sample == 0
assert type(prediction_index) is type(endog.index) # noqa: E721
assert prediction_index.equals(mod._index[3:])
# Forecasting: [1, 5]; the index is an extended version of the date index
start_key = 1
end_key = nobs
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 1
assert end == 4
assert out_of_sample == 1
desired_index = pd.DatetimeIndex(start='1950-01-02', periods=5, freq='D')
assert prediction_index.equals(desired_index)
# Date-based keys
start_key = '1950-01-01'
end_key = '1950-01-08'
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 0
assert end == 4
assert out_of_sample == 3
desired_index = pd.DatetimeIndex(start='1950-01-01', periods=8, freq='D')
assert prediction_index.equals(desired_index)
def test_instantiation_invalid():
# See long comment in test_instantiation_valid
# Test (invalid) freq with no index
endog = dta[0]
with pytest.raises(ValueError):
tsa_model.TimeSeriesModel(endog, freq=date_indexes[1][0].freq)
# Test conflicting index, freq specifications
endog = dta[2].copy()
endog.index = date_indexes[0][0]
with pytest.raises(ValueError):
tsa_model.TimeSeriesModel(endog, freq=date_indexes[1][0].freq)
# Test unsupported index, but a freq specification
endog = dta[2].copy()
endog.index = unsupported_indexes[0][0]
with pytest.raises(ValueError):
tsa_model.TimeSeriesModel(endog, freq=date_indexes[1][0].freq)
# Test index that can coerce to date time but incorrect freq
endog = dta[2].copy()
endog.index = numpy_datestr_indexes[0][0]
with pytest.raises(ValueError):
tsa_model.TimeSeriesModel(endog, freq=date_indexes[1][0].freq)
def test_instantiation_valid_range_index():
# GH#4457
# See long comment in `test_instantiation_valid`
for base_endog in dta[2:4]:
# RangeIndex (start=0, end=nobs, so equivalent to increment index)
endog = base_endog.copy()
endog.index = supported_increment_indexes[1][0]
mod = tsa_model.TimeSeriesModel(endog)
assert type(mod._index) is pd.RangeIndex # noqa:E721
assert mod._index_none is False
assert mod._index_dates is False
assert mod._index_generated is False
assert mod._index_freq is None
assert mod.data.dates is None
assert mod.data.freq is None
def test_instantiation_no_dates():
# See long comment in test_instantiation valid
# Baseline: list, numpy endog with no dates, no freq
for endog in dta[:2]:
# No indexes, should not raise warnings
with warnings.catch_warnings():
warnings.simplefilter('error')
mod = tsa_model.TimeSeriesModel(endog)
assert isinstance(mod._index, (pd.Int64Index, pd.RangeIndex))
assert mod._index_none is True
assert mod._index_dates is False
assert mod._index_generated is True
assert mod.data.dates is None
assert mod.data.freq is None
def test_custom_index():
tsa_model.__warningregistry__ = {}
endog = pd.Series(np.random.normal(size=5),
index=['a', 'b', 'c', 'd', 'e'])
message = ('An unsupported index was provided and will be ignored when'
' e.g. forecasting.')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
mod = tsa_model.TimeSeriesModel(endog)
assert str(w[0].message) == message
start_key = -2
end_key = -1
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
# Test the default output index
assert prediction_index.equals(pd.Index(['d', 'e']))
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key, index=['f', 'g']))
# Test custom output index
assert prediction_index.equals(pd.Index(['f', 'g']))
# Test out-of-sample
start_key = 4
end_key = 5
message = ('No supported index is available.'
' Prediction results will be given with'
' an integer index beginning at `start`.')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
start, end, out_of_sample, prediction_index = (
mod._get_prediction_index(start_key, end_key))
assert prediction_index.equals(pd.Index([4, 5]))
assert str(w[0].message) == message
# Test out-of-sample custom index
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key, index=['f', 'g']))
assert prediction_index.equals(pd.Index(['f', 'g']))
# Test invalid custom index
with pytest.raises(ValueError):
mod._get_prediction_index(start_key, end_key, index=['f', 'g', 'h'])
def test_prediction_rangeindex_withstep():
# GH#4457
index = supported_increment_indexes[3][0]
endog = pd.Series(dta[0], index=index)
mod = tsa_model.TimeSeriesModel(endog)
# Tests three common use cases: basic prediction, negative indexes, and
# out-of-sample indexes.
# Basic prediction: [0, end]
start_key = 0
end_key = None
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 0
assert end == nobs - 1
assert out_of_sample == 0
desired_index = pd.RangeIndex(start=0, stop=nobs * 6, step=6)
assert prediction_index.equals(desired_index)
# Negative index: [-2, end]
start_key = -2
end_key = -1
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 3
assert end == 4
assert out_of_sample == 0
desired_index = pd.RangeIndex(start=3 * 6, stop=nobs * 6, step=6)
assert prediction_index.equals(desired_index)
# Forecasting: [1, 5]
start_key = 1
end_key = nobs
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 1
assert end == 4
assert out_of_sample == 1
desired_index = pd.RangeIndex(start=1 * 6, stop=(nobs + 1) * 6, step=6)
assert prediction_index.equals(desired_index)
def test_prediction_increment_pandas_noindex():
endog = dta[2].copy()
mod = tsa_model.TimeSeriesModel(endog)
# Basic prediction: [0, end]; since there was no index and the data is
# Pandas, the index is the generated incrementing index, and no warning is
# issued
start_key = 0
end_key = None
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 0
assert end == nobs - 1
assert out_of_sample == 0
assert prediction_index.equals(mod._index)
# Negative index: [-2, end]; since there was no index and the data is
# Pandas, the index is the generated incrementing index, and no warning is
# issued
start_key = -2
end_key = -1
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 3
assert end == 4
assert out_of_sample == 0
assert prediction_index.equals(mod._index[3:])
# Forecasting: [1, 5]; since there was no index and the data is
# Pandas, the index is the generated incrementing index, and no warning is
# issued
start_key = 1
end_key = nobs
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 1
assert end == 4
assert out_of_sample == 1
assert prediction_index.equals(pd.Index(np.arange(1, 6)))
def test_prediction_increment_nonpandas():
endog = dta[0]
mod = tsa_model.TimeSeriesModel(endog)
# Basic prediction: [0, end]; since there was no index at all and the data
# is not Pandas, the returned prediction_index is None
start_key = 0
end_key = None
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 0
assert end == nobs - 1
assert out_of_sample == 0
assert prediction_index is None
# Negative index: [-2, end]; since there was no index at all and the data
# is not Pandas, the returned prediction_index is None
start_key = -2
end_key = -1
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 3
assert end == 4
assert out_of_sample == 0
assert prediction_index is None
# Forecasting: [1, 5]; since there was no index at all and the data
# is not Pandas, the returned prediction_index is None
start_key = 1
end_key = nobs
start, end, out_of_sample, prediction_index = (mod._get_prediction_index(
start_key, end_key))
assert start == 1
assert end == 4
assert out_of_sample == 1
assert prediction_index is None
def test_instantiation_no_index_dti_or_pi(endog):
# See long comment in test_instantiation valid
# Test list, numpy endog, pandas w/o index; with dates / freq argument
# dti --> DatetimeIndex
# pi --> PeriodIndex
# Supported date indexes, should not raise warnings, do not need freq
with warnings.catch_warnings():
warnings.simplefilter('error')
for ix, freq in date_indexes + period_indexes:
mod = tsa_model.TimeSeriesModel(endog, dates=ix)
if freq is None:
freq = ix.freq
if not isinstance(freq, str):
freq = freq.freqstr
assert isinstance(mod._index, (pd.DatetimeIndex, pd.PeriodIndex))
assert mod._index_none is False
assert mod._index_dates is True
assert mod._index_generated is False
assert mod._index.freq == mod._index_freq
assert mod.data.dates.equals(mod._index) is True
assert mod.data.freq == freq
def test_instantiation_valid_supported_with_freq():
# See long comment in test_instantiation valid
# Supported indexes *when a freq is given*, should not raise a warning
for base_endog in dta[2:4]:
with warnings.catch_warnings():
warnings.simplefilter('error')
for ix, freq in supported_date_indexes:
endog = base_endog.copy()
endog.index = ix
mod = tsa_model.TimeSeriesModel(endog, freq=freq)
if freq is None:
freq = ix.freq
if not isinstance(freq, str):
freq = freq.freqstr
assert isinstance(mod._index,
(pd.DatetimeIndex, pd.PeriodIndex))
assert mod._index_none is False
assert mod._index_dates is True
assert mod._index_generated is False
assert mod._index.freq == mod._index_freq
assert mod.data.dates.equals(mod._index) is True
assert mod.data.freq == freq
def test_instantiation_valid():
tsa_model.__warningregistry__ = {}
# The primary goal of this test function is to make sure the
# combinations that are supposed to be valid are actually valid, and
# that valid but unsupported options give the appropriate warning
# Secondarily, it also has some tests that invalid combinations raise
# exceptions, although it's not intended to be comprehensive.
#
# Each of `endog`, `exog` can be in the following categories:
# 0. None (only for exog)
# 1. list
# 2. numpy array
# 3. pandas series
# 4. pandas dataframe
#
# Each pandas index (of `endog`, `exog`, or passed to `dates`) can be:
# 0. None
# 1. RangeIndex (if applicable; i.e. if Pandas >= 0.18)
# 2. Int64Index with values exactly equal to 0, 1, ..., nobs-1
# 3. DatetimeIndex with frequency
# 4. PeriodIndex with frequency
# 5. Anything that doesn't fall into the above categories also should
# only raise an exception if it was passed to dates, and may trigger
# a warning otherwise.
#
# `date` can be one of the following:
# 0. None
# 2. Pandas index #2
# 3. Pandas index #3
# 4. List of date strings (requires freq)
# 5. List of datetime objects (requires freq)
# 6. Array of date strings (requires freq)
# 7. Array of datetime objects (requires freq)
# 8. Series of date strings (requires freq)
# 9. Series of datetime objects (requires freq)
# 10. Series of pandas timestamps (requires freq)
# 11. Anything that doesn't fall into the above categories should raise
# an exception.
#
# `freq` can be:
# 0. None
# 1. Something that can be passed to `pd.to_offset`
# 2. Anything that can't should raise an Exception
#
# Each test will be denoted by:
# endog.index:exog.index/date/freq where the corresponding
# location is the integer from above; e.g. 1.0:0.0/9/1 corresponds to
# - List endog (with no index)
# - No exog
# - Series of datetime objects
# - Something valid for `pd.to_offset` (e.g. 'D', if that works with
# dates)
#
# Notice that the endog.index:exog.index really collapses to a single
# element, which is the evaluated `row_label`. This is first the exog
# index, if exists, then the endog index, if it exists, or None
# otherwise. **Thus, we will not test `exog` here.**
#
# Example valid combinations of row_label/date/freq include:
# - */0/0 (i.e. anything is valid if date and freq are not passed)
# - */%/% where %/% denotes a valid date/freq combination (i.e. any
# row_label is valid if a valid date/freq combination is given)
#
# Example invalid combinations include:
# - [1-2],[3-4].4/0/[1-2] (i.e. if have freq, then must have, or
# coerce, a date index)
# - */[4-10]/0 (i.e. for some types of dates, freq must be passed)
# Test pandas (Series, DataFrame); with index (no dates/freq argument)
for base_endog in dta[2:4]:
# Increment index (this is a "supported" index in the sense that it
# doesn't raise a warning, but obviously not a date index)
endog = base_endog.copy()
endog.index = supported_increment_indexes[0][0]
mod = tsa_model.TimeSeriesModel(endog)
assert isinstance(mod._index, (pd.Int64Index, pd.RangeIndex))
assert mod._index_none is False
assert mod._index_dates is False
assert mod._index_generated is False
assert mod._index_freq is None
assert mod.data.dates is None
assert mod.data.freq is None
# Unsupported (or any) indexes to the given series, *when a supported
# date and freq is given*, should not raise a warning
with warnings.catch_warnings():
warnings.simplefilter('error')
for ix, freq in supported_date_indexes:
endog = base_endog.copy()
endog.index = unsupported_indexes[0][0]
mod = tsa_model.TimeSeriesModel(endog, dates=ix, freq=freq)
if freq is None:
freq = ix.freq
if not isinstance(freq, str):
freq = freq.freqstr
assert isinstance(mod._index,
(pd.DatetimeIndex, pd.PeriodIndex))
assert mod._index_none is False
assert mod._index_dates is True
assert mod._index_generated is False
assert mod._index.freq == mod._index_freq
assert mod.data.dates.equals(mod._index)
assert mod.data.freq == freq
# Date indexes with inferrable freq, but no given freq, should all give
# warnings
message = ('No frequency information was provided,'
' so inferred frequency %s will be used.')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
for ix, freq in supported_date_indexes:
endog = base_endog.copy()
endog.index = ix
mod = tsa_model.TimeSeriesModel(endog)
if freq is None:
freq = ix.freq
if not isinstance(freq, str):
freq = freq.freqstr
assert isinstance(mod._index, pd.DatetimeIndex)
assert mod._index_none is False
assert mod._index_dates is True
assert mod._index_generated is False
assert mod._index.freq == mod._index_freq
assert mod.data.dates.equals(mod._index)
# Note: here, we need to hedge the test a little bit because
# inferred frequencies aren't always the same as the original
# frequency. From the examples above, when the actual freq is
# 2QS-OCT, the inferred freq is 2QS-JAN. This is an issue with
# inferred frequencies, but since we are warning the user, it's
# not a failure of the code. Thus we only test the "major" part
# of the freq, and just test that the right message is given
# (even though it won't have the actual freq of the data in
# it).
assert mod.data.freq.split('-')[0] == freq.split('-')[0]
assert str(w[-1].message) == message % mod.data.freq
# Unsupported (but valid) indexes, should all give warnings
message = ('An unsupported index was provided and will be'
' ignored when e.g. forecasting.')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
for ix, freq in unsupported_indexes:
endog = base_endog.copy()
endog.index = ix
mod = tsa_model.TimeSeriesModel(endog)
assert isinstance(mod._index, (pd.Int64Index, pd.RangeIndex))
assert mod._index_none is False
assert mod._index_dates is False
assert mod._index_generated is True
assert mod._index_freq is None
assert mod.data.dates is None
assert mod.data.freq is None
assert str(w[0].message) == message
# Date indexes without inferrable freq, and with no given freq, should
# all give warnings
message = ('A date index has been provided, but it has no'
' associated frequency information and so will be'
' ignored when e.g. forecasting.')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
for ix, freq in unsupported_date_indexes:
endog = base_endog.copy()
endog.index = ix
mod = tsa_model.TimeSeriesModel(endog)
assert isinstance(mod._index, (pd.Int64Index, pd.RangeIndex))
assert mod._index_none is False
assert mod._index_dates is False
assert mod._index_generated is True
assert mod._index_freq is None
assert mod.data.dates is None
assert mod.data.freq is None
assert str(w[0].message) == message