def test_patsy_lazy_dict():
class LazyDict(dict):
def __init__(self, data):
self.data = data
def __missing__(self, key):
return np.array(self.data[key])
data = cpunish.load_pandas().data
data = LazyDict(data)
res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
res2 = res.predict(data)
npt.assert_allclose(res.fittedvalues, res2)
data = cpunish.load_pandas().data
data['INCOME'].loc[0] = None
data = LazyDict(data)
data.index = cpunish.load_pandas().data.index
res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
res2 = res.predict(data)
assert_equal(res.fittedvalues, res2) # Should lose a record
assert_equal(len(res2) + 1, len(cpunish.load_pandas().data))
def test_pandas_nodates_index():
data = [988, 819, 964]
dates = ['a', 'b', 'c']
s = pd.Series(data, index=dates)
# TODO: Remove this, this is now valid
# npt.assert_raises(ValueError, TimeSeriesModel, s)
# Test with a non-date index that does not raise an exception because it
# can be coerced into a nanosecond DatetimeIndex
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)
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')
with pytest.warns(ValueWarning, match="No frequency information"):
mod = TimeSeriesModel(s)
start, end, out_of_sample, _ = mod._get_prediction_index(0, 4)
assert_equal(len(mod.data.predict_dates), 5)
def test_pandas_nodates_index():
data = [988, 819, 964]
dates = ['a', 'b', 'c']
s = pd.Series(data, index=dates)
# TODO: Remove this, this is now valid
# 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
pd.to_offset('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)
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, end, out_of_sample, _ = mod._get_prediction_index(0, 4)
assert_equal(len(mod.data.predict_dates), 5)
def test_patsy_lazy_dict():
class LazyDict(dict):
def __init__(self, data):
self.data = data
def __missing__(self, key):
return np.array(self.data[key])
data = cpunish.load_pandas().data
data = LazyDict(data)
res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
res2 = res.predict(data)
npt.assert_allclose(res.fittedvalues, res2)
data = cpunish.load_pandas().data
data['INCOME'].loc[0] = None
data = LazyDict(data)
data.index = cpunish.load_pandas().data.index
res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
res2 = res.predict(data)
assert_equal(res.fittedvalues, res2) # Should lose a record
assert_equal(len(res2) + 1, len(cpunish.load_pandas().data))
def test_pandas_nodates_index():
data = [988, 819, 964]
dates = ['a', 'b', 'c']
s = pd.Series(data, index=dates)
# TODO: Remove this, this is now valid
# 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
pd.to_offset('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)
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, end, out_of_sample, _ = mod._get_prediction_index(0, 4)
assert_equal(len(mod.data.predict_dates), 5)
def test_formula_labels():
# make sure labels pass through patsy as expected
# data(Duncan) from car in R
dta = StringIO(""""type" "income" "education" "prestige"\n"accountant" "prof" 62 86 82\n"pilot" "prof" 72 76 83\n"architect" "prof" 75 92 90\n"author" "prof" 55 90 76\n"chemist" "prof" 64 86 90\n"minister" "prof" 21 84 87\n"professor" "prof" 64 93 93\n"dentist" "prof" 80 100 90\n"reporter" "wc" 67 87 52\n"engineer" "prof" 72 86 88\n"undertaker" "prof" 42 74 57\n"lawyer" "prof" 76 98 89\n"physician" "prof" 76 97 97\n"welfare.worker" "prof" 41 84 59\n"teacher" "prof" 48 91 73\n"conductor" "wc" 76 34 38\n"contractor" "prof" 53 45 76\n"factory.owner" "prof" 60 56 81\n"store.manager" "prof" 42 44 45\n"banker" "prof" 78 82 92\n"bookkeeper" "wc" 29 72 39\n"mail.carrier" "wc" 48 55 34\n"insurance.agent" "wc" 55 71 41\n"store.clerk" "wc" 29 50 16\n"carpenter" "bc" 21 23 33\n"electrician" "bc" 47 39 53\n"RR.engineer" "bc" 81 28 67\n"machinist" "bc" 36 32 57\n"auto.repairman" "bc" 22 22 26\n"plumber" "bc" 44 25 29\n"gas.stn.attendant" "bc" 15 29 10\n"coal.miner" "bc" 7 7 15\n"streetcar.motorman" "bc" 42 26 19\n"taxi.driver" "bc" 9 19 10\n"truck.driver" "bc" 21 15 13\n"machine.operator" "bc" 21 20 24\n"barber" "bc" 16 26 20\n"bartender" "bc" 16 28 7\n"shoe.shiner" "bc" 9 17 3\n"cook" "bc" 14 22 16\n"soda.clerk" "bc" 12 30 6\n"watchman" "bc" 17 25 11\n"janitor" "bc" 7 20 8\n"policeman" "bc" 34 47 41\n"waiter" "bc" 8 32 10""")
from pandas import read_table
dta = read_table(dta, sep=" ")
model = ols("prestige ~ income + education", dta).fit()
assert_equal(model.fittedvalues.index, dta.index)
def test_formula_labels():
# make sure labels pass through patsy as expected
# data(Duncan) from car in R
dta = StringIO(""""type","income","education","prestige"\n"accountant","prof",62,86,82\n"pilot","prof",72,76,83\n"architect","prof",75,92,90\n"author","prof",55,90,76\n"chemist","prof",64,86,90\n"minister","prof",21,84,87\n"professor","prof",64,93,93\n"dentist","prof",80,100,90\n"reporter","wc",67,87,52\n"engineer","prof",72,86,88\n"undertaker","prof",42,74,57\n"lawyer","prof",76,98,89\n"physician","prof",76,97,97\n"welfare.worker","prof",41,84,59\n"teacher","prof",48,91,73\n"conductor","wc",76,34,38\n"contractor","prof",53,45,76\n"factory.owner","prof",60,56,81\n"store.manager","prof",42,44,45\n"banker","prof",78,82,92\n"bookkeeper","wc",29,72,39\n"mail.carrier","wc",48,55,34\n"insurance.agent","wc",55,71,41\n"store.clerk","wc",29,50,16\n"carpenter","bc",21,23,33\n"electrician","bc",47,39,53\n"RR.engineer","bc",81,28,67\n"machinist","bc",36,32,57\n"auto.repairman","bc",22,22,26\n"plumber","bc",44,25,29\n"gas.stn.attendant","bc",15,29,10\n"coal.miner","bc",7,7,15\n"streetcar.motorman","bc",42,26,19\n"taxi.driver","bc",9,19,10\n"truck.driver","bc",21,15,13\n"machine.operator","bc",21,20,24\n"barber","bc",16,26,20\n"bartender","bc",16,28,7\n"shoe.shiner","bc",9,17,3\n"cook","bc",14,22,16\n"soda.clerk","bc",12,30,6\n"watchman","bc",17,25,11\n"janitor","bc",7,20,8\n"policeman","bc",34,47,41\n"waiter","bc",8,32,10""")
from pandas import read_csv
dta = read_csv(dta)
model = ols("prestige ~ income + education", dta).fit()
assert_equal(model.fittedvalues.index, dta.index)
def test_formula_labels():
# make sure labels pass through patsy as expected
# data(Duncan) from car in R
dta = StringIO(""""type" "income" "education" "prestige"\n"accountant" "prof" 62 86 82\n"pilot" "prof" 72 76 83\n"architect" "prof" 75 92 90\n"author" "prof" 55 90 76\n"chemist" "prof" 64 86 90\n"minister" "prof" 21 84 87\n"professor" "prof" 64 93 93\n"dentist" "prof" 80 100 90\n"reporter" "wc" 67 87 52\n"engineer" "prof" 72 86 88\n"undertaker" "prof" 42 74 57\n"lawyer" "prof" 76 98 89\n"physician" "prof" 76 97 97\n"welfare.worker" "prof" 41 84 59\n"teacher" "prof" 48 91 73\n"conductor" "wc" 76 34 38\n"contractor" "prof" 53 45 76\n"factory.owner" "prof" 60 56 81\n"store.manager" "prof" 42 44 45\n"banker" "prof" 78 82 92\n"bookkeeper" "wc" 29 72 39\n"mail.carrier" "wc" 48 55 34\n"insurance.agent" "wc" 55 71 41\n"store.clerk" "wc" 29 50 16\n"carpenter" "bc" 21 23 33\n"electrician" "bc" 47 39 53\n"RR.engineer" "bc" 81 28 67\n"machinist" "bc" 36 32 57\n"auto.repairman" "bc" 22 22 26\n"plumber" "bc" 44 25 29\n"gas.stn.attendant" "bc" 15 29 10\n"coal.miner" "bc" 7 7 15\n"streetcar.motorman" "bc" 42 26 19\n"taxi.driver" "bc" 9 19 10\n"truck.driver" "bc" 21 15 13\n"machine.operator" "bc" 21 20 24\n"barber" "bc" 16 26 20\n"bartender" "bc" 16 28 7\n"shoe.shiner" "bc" 9 17 3\n"cook" "bc" 14 22 16\n"soda.clerk" "bc" 12 30 6\n"watchman" "bc" 17 25 11\n"janitor" "bc" 7 20 8\n"policeman" "bc" 34 47 41\n"waiter" "bc" 8 32 10""")
from pandas import read_table
dta = read_table(dta, sep=" ")
model = ols("prestige ~ income + education", dta).fit()
assert_equal(model.fittedvalues.index, dta.index)
def test_formula_labels():
# make sure labels pass through patsy as expected
# data(Duncan) from car in R
dta = StringIO(""""type","income","education","prestige"\n"accountant","prof",62,86,82\n"pilot","prof",72,76,83\n"architect","prof",75,92,90\n"author","prof",55,90,76\n"chemist","prof",64,86,90\n"minister","prof",21,84,87\n"professor","prof",64,93,93\n"dentist","prof",80,100,90\n"reporter","wc",67,87,52\n"engineer","prof",72,86,88\n"undertaker","prof",42,74,57\n"lawyer","prof",76,98,89\n"physician","prof",76,97,97\n"welfare.worker","prof",41,84,59\n"teacher","prof",48,91,73\n"conductor","wc",76,34,38\n"contractor","prof",53,45,76\n"factory.owner","prof",60,56,81\n"store.manager","prof",42,44,45\n"banker","prof",78,82,92\n"bookkeeper","wc",29,72,39\n"mail.carrier","wc",48,55,34\n"insurance.agent","wc",55,71,41\n"store.clerk","wc",29,50,16\n"carpenter","bc",21,23,33\n"electrician","bc",47,39,53\n"RR.engineer","bc",81,28,67\n"machinist","bc",36,32,57\n"auto.repairman","bc",22,22,26\n"plumber","bc",44,25,29\n"gas.stn.attendant","bc",15,29,10\n"coal.miner","bc",7,7,15\n"streetcar.motorman","bc",42,26,19\n"taxi.driver","bc",9,19,10\n"truck.driver","bc",21,15,13\n"machine.operator","bc",21,20,24\n"barber","bc",16,26,20\n"bartender","bc",16,28,7\n"shoe.shiner","bc",9,17,3\n"cook","bc",14,22,16\n"soda.clerk","bc",12,30,6\n"watchman","bc",17,25,11\n"janitor","bc",7,20,8\n"policeman","bc",34,47,41\n"waiter","bc",8,32,10""")
from pandas import read_csv
dta = read_csv(dta)
model = ols("prestige ~ income + education", dta).fit()
assert_equal(model.fittedvalues.index, dta.index)
def test_ar_select_order_tstat():
rs = np.random.RandomState(123)
tau = 25
y = rs.randn(tau)
ts = Series(y, index=date_range(start='1/1/1990', periods=tau, freq='M'))
ar = AR(ts)
res = ar.select_order(maxlag=5, ic='t-stat')
assert_equal(res, 0)
def test_period_index():
# test 1285
dates = pd.PeriodIndex(start="1/1/1990", periods=20, freq="M")
x = np.arange(1, 21.)
model = TimeSeriesModel(pd.Series(x, index=dates))
assert_equal(model._index.freqstr, "M")
model = TimeSeriesModel(pd.Series(x, index=dates))
npt.assert_(model.data.freq == "M")
def test_ar_select_order_tstat():
rs = np.random.RandomState(123)
tau = 25
y = rs.randn(tau)
ts = Series(y, index=date_range(start='1/1/1990', periods=tau,
freq='M'))
ar = AR(ts)
res = ar.select_order(maxlag=5, ic='t-stat')
assert_equal(res, 0)
def test_ar_select_order_tstat():
rs = np.random.RandomState(123)
tau = 25
y = rs.randn(tau)
ts = Series(y, index=date_range(start="1/1/1990", periods=tau, freq="M"))
with pytest.warns(FutureWarning):
ar = AR(ts)
with pytest.warns(FutureWarning):
res = ar.select_order(maxlag=5, ic="t-stat")
assert_equal(res, 0)
def test_period_index():
# test 1285
dates = pd.PeriodIndex(start="1/1/1990", periods=20, freq="M")
x = np.arange(1, 21.)
model = TimeSeriesModel(pd.Series(x, index=dates))
assert_equal(model._index.freqstr, "M")
model = TimeSeriesModel(pd.Series(x, index=dates))
npt.assert_(model.data.freq == "M")
def test_ar_dates():
# just make sure they work
data = sm.datasets.sunspots.load(as_pandas=False)
dates = date_range(start='1700', periods=len(data.endog), freq='A')
endog = Series(data.endog, index=dates)
ar_model = sm.tsa.AR(endog, freq='A').fit(maxlag=9, method='mle', disp=-1)
pred = ar_model.predict(start='2005', end='2015')
predict_dates = date_range(start='2005', end='2016', freq='A')[:11]
assert_equal(ar_model.data.predict_dates, predict_dates)
assert_equal(pred.index, predict_dates)
def test_ar_dates():
# just make sure they work
data = sm.datasets.sunspots.load(as_pandas=False)
dates = date_range(start='1700', periods=len(data.endog), freq='A')
endog = Series(data.endog, index=dates)
ar_model = sm.tsa.AR(endog, freq='A').fit(maxlag=9, method='mle', disp=-1)
pred = ar_model.predict(start='2005', end='2015')
predict_dates = date_range(start='2005', end='2016', freq='A')[:11]
assert_equal(ar_model.data.predict_dates, predict_dates)
assert_equal(pred.index, predict_dates)
def test_ar_dates():
# just make sure they work
data = sm.datasets.sunspots.load()
dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog))
endog = Series(data.endog, index=dates)
ar_model = sm.tsa.AR(endog, freq='A').fit(maxlag=9, method='mle', disp=-1)
pred = ar_model.predict(start='2005', end='2015')
predict_dates = sm.tsa.datetools.dates_from_range('2005', '2015')
predict_dates = DatetimeIndex(predict_dates, freq='infer')
assert_equal(ar_model.data.predict_dates, predict_dates)
assert_equal(pred.index, predict_dates)
def test_ar_dates():
# just make sure they work
data = sm.datasets.sunspots.load()
dates = sm.tsa.datetools.dates_from_range("1700", length=len(data.endog))
endog = Series(data.endog, index=dates)
ar_model = sm.tsa.AR(endog, freq="A").fit(maxlag=9, method="mle", disp=-1)
pred = ar_model.predict(start="2005", end="2015")
predict_dates = sm.tsa.datetools.dates_from_range("2005", "2015")
predict_dates = DatetimeIndex(predict_dates, freq="infer")
assert_equal(ar_model.data.predict_dates, predict_dates)
assert_equal(pred.index, predict_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]]
desired = (1, 9, 11)
for start in predict_starts:
for end in predict_ends:
assert_equal(model._get_prediction_index(start, end)[:3], desired)
def test_ar_dates():
# just make sure they work
data = sm.datasets.sunspots.load(as_pandas=False)
dates = date_range(start="1700", periods=len(data.endog), freq="A")
endog = Series(data.endog, index=dates)
with pytest.warns(FutureWarning):
ar_model = AR(endog, freq="A").fit(maxlag=9, method="mle", disp=-1)
pred = ar_model.predict(start="2005", end="2015")
predict_dates = date_range(start="2005", end="2016", freq="A")[:11]
assert_equal(ar_model.data.predict_dates, predict_dates)
assert_equal(pred.index, predict_dates)
def test_get_predict_start_end():
index = pd.date_range(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]]
desired = (1, 9, 11)
for start in predict_starts:
for end in predict_ends:
assert_equal(model._get_prediction_index(start, end)[:3], desired)
def test_pandas_dates():
data = [988, 819, 964]
dates = ['2016-01-01 12:00:00', '2016-02-01 12:00:00', '2016-03-01 12:00:00']
datetime_dates = pd.to_datetime(dates)
result = pd.Series(data=data, index=datetime_dates, name='price')
df = pd.DataFrame(data={'price': data}, index=pd.DatetimeIndex(dates, freq='MS'))
model = TimeSeriesModel(df['price'])
assert_equal(model.data.dates, result.index)
def test_ar_dates():
# just make sure they work
data = sm.datasets.sunspots.load()
dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog))
endog = Series(data.endog, index=dates)
ar_model = sm.tsa.AR(endog, freq='A').fit(maxlag=9, method='mle', disp=-1)
pred = ar_model.predict(start='2005', end='2015')
predict_dates = sm.tsa.datetools.dates_from_range('2005', '2015')
from pandas import DatetimeIndex # pylint: disable-msg=E0611
predict_dates = DatetimeIndex(predict_dates, freq='infer')
assert_equal(ar_model.data.predict_dates, predict_dates)
assert_equal(pred.index, predict_dates)
def test_pandas_dates():
data = [988, 819, 964]
dates = ['2016-01-01 12:00:00', '2016-02-01 12:00:00', '2016-03-01 12:00:00']
datetime_dates = pd.to_datetime(dates)
result = pd.Series(data=data, index=datetime_dates, name='price')
df = pd.DataFrame(data={'price': data}, index=pd.DatetimeIndex(dates, freq='MS'))
model = TimeSeriesModel(df['price'])
assert_equal(model.data.dates, result.index)
def test_patsy_missing_data():
# Test pandas-style first
data = cpunish.load_pandas().data
data['INCOME'].loc[0] = None
res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
res2 = res.predict(data)
# First record will be dropped during fit, but not during predict
assert_equal(res.fittedvalues, res2[1:])
# Non-pandas version
data = cpunish.load_pandas().data
data['INCOME'].loc[0] = None
data = data.to_records(index=False)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
res2 = res.predict(data)
assert 'ValueWarning' in repr(w[-1].message)
assert 'nan values have been dropped' in repr(w[-1].message)
# Frist record will be dropped in both cases
assert_equal(res.fittedvalues, res2)
def test_patsy_missing_data():
# Test pandas-style first
data = cpunish.load_pandas().data
data['INCOME'].loc[0] = None
res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
res2 = res.predict(data)
# First record will be dropped during fit, but not during predict
assert_equal(res.fittedvalues, res2[1:])
# Non-pandas version
data = cpunish.load_pandas().data
data['INCOME'].loc[0] = None
data = data.to_records(index=False)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
res2 = res.predict(data)
assert 'ValueWarning' in repr(w[-1].message)
assert 'nan values have been dropped' in repr(w[-1].message)
# Frist record will be dropped in both cases
assert_equal(res.fittedvalues, res2)
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")
assert_equal(model._index.freqstr, "A-APR")
start, end, out_of_sample, _ = (model._get_prediction_index(
"2006-4-30", "2016-4-30"))
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_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")
assert_equal(model._index.freqstr, "A-APR")
start, end, out_of_sample, _ = (
model._get_prediction_index("2006-4-30", "2016-4-30"))
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_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")
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_pandas_nodates_index():
data = [988, 819, 964]
dates = ['a', 'b', 'c']
s = pd.Series(data, index=dates)
# TODO: Remove this, this is now valid
# 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
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)
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, end, out_of_sample, _ = mod._get_prediction_index(0, 4)
assert_equal(len(mod.data.predict_dates), 5)
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 = 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")
assert_equal(predict_dates, expected_dates)
def test_ar_select_order_tstat():
rs = np.random.RandomState(123)
tau = 25
y = rs.randn(tau)
ts = Series(y, index=DatetimeIndex(start="1/1/1990", periods=tau, freq="M"))
ar = AR(ts)
res = ar.select_order(maxlag=5, ic="t-stat")
assert_equal(res, 0)
