Merge pull request #5934 from bashtage/pr-5933-5784

Backport #5933 and #5784

azure-pipelines.yml

@@ -1,7 +1,7 @@
 # https://docs.microsoft.com/en-us/azure/devops/pipelines/process/templates?view=azure-devops#passing-parameters
 
 trigger:
-- master
+- maintenance/0.10.x
 
 variables:
   MKL_NUM_THREADS: 1

docs/source/release/version0.10.1.rst

@@ -29,9 +29,9 @@ providing feedback.
 
 Thanks to all of the contributors for the 0.10 release (based on git log):
 
+.. TODO: Remove if none
 
-* Someone
-* Someone Else
+* Someone??
 
 These lists of names are automatically generated based on git log, and may not
 be complete.
@@ -41,6 +41,7 @@ Merged Pull Requests
 
 The following Pull Requests were merged since the last release:
 
+* :pr:`5784`: MAINT: implement parts of #5220, deprecate ancient aliases
 * :pr:`5892`: BUG: fix pandas compat
 * :pr:`5893`: BUG: exponential smoothing - damped trend gives incorrect param, predictions
 * :pr:`5895`: DOC: improvements to BayesMixedGLM docs, argument checking
@@ -49,3 +50,4 @@ The following Pull Requests were merged since the last release:
 * :pr:`5917`: BUG: TVTP for Markov regression
 * :pr:`5921`: BUG: Ensure exponential smoothers has continuous double data
 * :pr:`5930`: BUG: Limit lags in KPSS
+* :pr:`5933`: MAINT: Fix test that fails with positive probability

statsmodels/sandbox/distributions/tests/test_multivariate.py

@@ -6,8 +6,9 @@
 import numpy as np
 from numpy.testing import (assert_almost_equal, assert_array_almost_equal,
                            assert_allclose)
+
 from statsmodels.sandbox.distributions.multivariate import (
-                mvstdtprob, mvstdnormcdf)
+    mvstdtprob, mvstdnormcdf)
 from statsmodels.sandbox.distributions.mv_normal import MVT, MVNormal
 
 
@@ -123,52 +124,53 @@ def setup_class(cls):
         cls.mvn3 = mvn3
         cls.mvn3c = mvn3c
 
-
     def test_mvn_pdf(self):
         cov3 = self.cov3
         mvn3 = self.mvn3
-        mvn3c = self.mvn3c
 
         r_val = [-7.667977543898155, -6.917977543898155, -5.167977543898155]
-        assert_array_almost_equal( mvn3.logpdf(cov3), r_val, decimal = 14)
-        #decimal 18
-        r_val = [0.000467562492721686, 0.000989829804859273, 0.005696077243833402]
-        assert_array_almost_equal( mvn3.pdf(cov3), r_val, decimal = 17)
-        #cheating new mean, same cov, too dangerous, got wrong instance in tests
-        #mvn3.mean = np.array([0,0,0])
-        mvn3b = MVNormal(np.array([0,0,0]), cov3)
+        assert_array_almost_equal(mvn3.logpdf(cov3), r_val, decimal=14)
+        # decimal 18
+        r_val = [0.000467562492721686, 0.000989829804859273,
+                 0.005696077243833402]
+        assert_array_almost_equal(mvn3.pdf(cov3), r_val, decimal=17)
+
+        mvn3b = MVNormal(np.array([0, 0, 0]), cov3)
         r_val = [0.02914269740502042, 0.02269635555984291, 0.01767593948287269]
-        assert_array_almost_equal( mvn3b.pdf(cov3), r_val, decimal = 16)
+        assert_array_almost_equal(mvn3b.pdf(cov3), r_val, decimal=16)
 
-    def test_mvt_pdf(self):
+    def test_mvt_pdf(self, reset_randomstate):
         cov3 = self.cov3
         mu3 = self.mu3
 
-        mvt = MVT((0,0), 1, 5)
-        assert_almost_equal(mvt.logpdf(np.array([0.,0.])), -1.837877066409345,
+        mvt = MVT((0, 0), 1, 5)
+        assert_almost_equal(mvt.logpdf(np.array([0., 0.])), -1.837877066409345,
                             decimal=15)
-        assert_almost_equal(mvt.pdf(np.array([0.,0.])), 0.1591549430918953,
+        assert_almost_equal(mvt.pdf(np.array([0., 0.])), 0.1591549430918953,
                             decimal=15)
 
-        mvt.logpdf(np.array([1.,1.]))-(-3.01552989458359)
+        mvt.logpdf(np.array([1., 1.])) - (-3.01552989458359)
 
-        mvt1 = MVT((0,0), 1, 1)
-        mvt1.logpdf(np.array([1.,1.]))-(-3.48579549941151) #decimal=16
+        mvt1 = MVT((0, 0), 1, 1)
+        mvt1.logpdf(np.array([1., 1.])) - (-3.48579549941151)  # decimal=16
 
         rvs = mvt.rvs(100000)
-        assert_almost_equal(np.cov(rvs, rowvar=0), mvt.cov, decimal=1)
+        assert_almost_equal(np.cov(rvs, rowvar=False), mvt.cov, decimal=1)
 
         mvt31 = MVT(mu3, cov3, 1)
         assert_almost_equal(mvt31.pdf(cov3),
-            [0.0007276818698165781, 0.0009980625182293658, 0.0027661422056214652],
-            decimal=17)
+                            [0.0007276818698165781, 0.0009980625182293658,
+                             0.0027661422056214652],
+                            decimal=17)
 
         mvt = MVT(mu3, cov3, 3)
         assert_almost_equal(mvt.pdf(cov3),
-            [0.000863777424247410, 0.001277510788307594, 0.004156314279452241],
-            decimal=17)
+                            [0.000863777424247410, 0.001277510788307594,
+                             0.004156314279452241],
+                            decimal=17)
 
 
 if __name__ == '__main__':
     import pytest
+
     pytest.main([__file__, '-vvs', '-x', '--pdb'])

statsmodels/tools/decorators.py

@@ -2,7 +2,65 @@
 from statsmodels.tools.sm_exceptions import CacheWriteWarning
 import warnings
 
-__all__ = ['cache_readonly', 'cache_writable']
+__all__ = ['cache_readonly', 'cache_writable', 'deprecated_alias']
+
+
+def deprecated_alias(old_name, new_name, remove_version=None, msg=None,
+                     warning=FutureWarning):
+    """
+    Deprecate attribute in favor of alternative name.
+
+    Parameters
+    ----------
+    old_name : str
+        Old, deprecated name
+    new_name : str
+        New name
+    remove_version : str, optional
+        Version that the alias will be removed
+    msg : str, optional
+        Message to show.  Default is
+        `old_name` is a deprecated alias for `new_name`
+    warning : Warning, optional
+        Warning class to give.  Default is FutureWarning.
+
+    Notes
+    -----
+    Older or less-used classes may not conform to statsmodels naming
+    conventions.  `deprecated_alias` lets us bring them into conformance
+    without breaking backward-compatibility.
+
+    Example
+    -------
+    Instances of the `Foo` class have a `nvars` attribute, but it _should_
+    be called `neqs`:
+
+    class Foo(object):
+        nvars = deprecated_alias('nvars', 'neqs')
+        def __init__(self, neqs):
+            self.neqs = neqs
+
+    >>> foo = Foo(3)
+    >>> foo.nvars
+    __main__:1: FutureWarning: nvars is a deprecated alias for neqs
+    3
+    """
+
+    if msg is None:
+        msg = '%s is a deprecated alias for %s' % (old_name, new_name)
+        if remove_version is not None:
+            msg += ', will be removed in version %s' % remove_version
+
+    def fget(self):
+        warnings.warn(msg, warning, stacklevel=2)
+        return getattr(self, new_name)
+
+    def fset(self, value):
+        warnings.warn(msg, warning, stacklevel=2)
+        setattr(self, new_name, value)
+
+    res = property(fget=fget, fset=fset)
+    return res
 
 
 class CachedAttribute(object):

statsmodels/tools/tests/test_decorators.py

@@ -4,7 +4,7 @@
 from numpy.testing import assert_equal
 
 from statsmodels.tools.decorators import (
-    cache_readonly, CacheWriteWarning)
+    cache_readonly, CacheWriteWarning, deprecated_alias)
 
 
 def test_cache_readonly():
@@ -40,3 +40,43 @@ def f(self):
         ex.b = -1
 
     assert_equal(ex._cache, dict(b=1,))
+
+
+def dummy_factory(msg, remove_version, warning):
+    class Dummy(object):
+        y = deprecated_alias('y', 'x',
+                             remove_version=remove_version,
+                             msg=msg,
+                             warning=warning)
+
+        def __init__(self, y):
+            self.x = y
+
+    return Dummy(1)
+
+
+@pytest.mark.parametrize('warning', [FutureWarning, UserWarning])
+@pytest.mark.parametrize('remove_version', [None, '0.11'])
+@pytest.mark.parametrize('msg', ['test message', None])
+def test_deprecated_alias(msg, remove_version, warning):
+    dummy_set = dummy_factory(msg, remove_version, warning)
+    with pytest.warns(warning) as w:
+        dummy_set.y = 2
+        assert dummy_set.x == 2
+
+    assert warning.__class__ is w[0].category.__class__
+
+    dummy_get = dummy_factory(msg, remove_version, warning)
+    with pytest.warns(warning) as w:
+        x = dummy_get.y
+        assert x == 1
+
+    assert warning.__class__ is w[0].category.__class__
+    message = str(w[0].message)
+    if not msg:
+        if remove_version:
+            assert 'will be removed' in message
+        else:
+            assert 'will be removed' not in message
+    else:
+        assert msg in message

statsmodels/tsa/vector_ar/svar_model.py

@@ -13,6 +13,7 @@
 import numpy.linalg as npl
 from numpy.linalg import slogdet
 
+from statsmodels.tools.decorators import deprecated_alias
 from statsmodels.tools.numdiff import approx_hess, approx_fprime
 from statsmodels.tsa.vector_ar.irf import IRAnalysis
 from statsmodels.tsa.vector_ar.var_model import VARProcess, VARResults
@@ -54,13 +55,15 @@ class SVAR(tsbase.TimeSeriesModel):
     ----------
     Hamilton (1994) Time Series Analysis
     """
+
+    y = deprecated_alias("y", "endog", remove_version="0.11.0")
+
     def __init__(self, endog, svar_type, dates=None,
                  freq=None, A=None, B=None, missing='none'):
         super(SVAR, self).__init__(endog, None, dates, freq, missing=missing)
         #(self.endog, self.names,
         # self.dates) = data_util.interpret_data(endog, names, dates)
 
-        self.y = self.endog #keep alias for now
         self.neqs = self.endog.shape[1]
 
         types = ['A', 'B', 'AB']
@@ -560,16 +563,20 @@ class SVARResults(SVARProcess, VARResults):
 
     _model_type = 'SVAR'
 
+    y = deprecated_alias("y", "endog", remove_version="0.11.0")
+    ys_lagged = deprecated_alias("ys_lagged", "endog_lagged",
+                                 remove_version="0.11.0")
+
     def __init__(self, endog, endog_lagged, params, sigma_u, lag_order,
                  A=None, B=None, A_mask=None, B_mask=None, model=None,
                  trend='c', names=None, dates=None):
 
         self.model = model
-        self.y = self.endog = endog  #keep alias for now
-        self.ys_lagged = self.endog_lagged = endog_lagged #keep alias for now
+        self.endog = endog
+        self.endog_lagged = endog_lagged
         self.dates = dates
 
-        self.n_totobs, self.neqs = self.y.shape
+        self.n_totobs, self.neqs = self.endog.shape
         self.nobs = self.n_totobs - lag_order
         k_trend = util.get_trendorder(trend)
         if k_trend > 0: # make this the polynomial trend order
@@ -630,7 +637,7 @@ def sirf_errband_mc(self, orth=False, repl=1000, T=10,
         Parameters
         ----------
         orth: bool, default False
-            Compute orthoganalized impulse response error bands
+            Compute orthogonalized impulse response error bands
         repl: int
             number of Monte Carlo replications to perform
         T: int, default 10

statsmodels/tsa/vector_ar/tests/test_var.py

@@ -3,7 +3,6 @@
 Test VAR Model
 """
 import warnings
-# pylint: disable=W0612,W0231
 from statsmodels.compat.python import (iteritems, StringIO, lrange, BytesIO,
                                        range)
 
@@ -39,6 +38,24 @@
 DECIMAL_2 = 2
 
 
+@pytest.fixture()
+def bivariate_var_data(reset_randomstate):
+    """A bivariate dataset for VAR estimation"""
+    e = np.random.standard_normal((252, 2))
+    y = np.zeros_like(e)
+    y[:2] = e[:2]
+    for i in range(2, 252):
+        y[i] = .2 * y[i - 1] + .1 * y[i - 2] + e[i]
+    return y
+
+
+@pytest.fixture()
+def bivariate_var_result(bivariate_var_data):
+    """A bivariate VARResults for reuse"""
+    mod = VAR(bivariate_var_data)
+    return mod.fit()
+
+
 class CheckVAR(object):
     # just so pylint won't complain
     res1 = None
@@ -400,10 +417,10 @@ def test_acorr(self):
         acorrs = self.res.acorr(10)
 
     def test_forecast(self):
-        point = self.res.forecast(self.res.y[-5:], 5)
+        point = self.res.forecast(self.res.endog[-5:], 5)
 
     def test_forecast_interval(self):
-        y = self.res.y[:-self.p:]
+        y = self.res.endog[:-self.p:]
         point, lower, upper = self.res.forecast_interval(y, 5)
 
     @pytest.mark.matplotlib
@@ -767,3 +784,9 @@ def test_exog(self):
         assert_allclose(fci2, fci1, rtol=1e-12)
         assert_allclose(fci3, fci1, rtol=1e-12)
         assert_allclose(fci3, fci2, rtol=1e-12)
+
+
+@pytest.mark.parametrize('attr', ['y', 'ys_lagged'])
+def test_deprecated_attributes_varresults(bivariate_var_result, attr):
+    with pytest.warns(FutureWarning):
+        getattr(bivariate_var_result, attr)