Merge pull request #226 from bashtage/add-appveyor

TST: Add smoke decorator and appveyor

Author: bashtage

.travis.yml

@@ -9,7 +9,7 @@ env:
   global:
     - PANDAS=pandas
     - COVERAGE=true
-    - SKIPSLOW=""
+    - SKIP=""
     - DOCTR_PUSH_TYPE=""
     - XARRAY=
     - OPENBLAS=0
@@ -56,7 +56,7 @@ matrix:
     - SCIPY=1.0
     - PANDAS=0.23
     - STATSMODELS=0.9
-    - SKIPSLOW="--skip-slow"
+    - SKIP="--skip-slow --skip-smoke"
   - python: 3.6
     env:
     - PYTHON=3.6
@@ -66,7 +66,7 @@ matrix:
     - XARRAY=0.10
     - STATSMODELS=0.10
     - PIP_PACKAGES="xxhash"
-    - SKIPSLOW="--skip-slow"
+    - SKIP="--skip-slow --skip-smoke"
   - python: 3.7
     env:
     - PYTHON=3.7
@@ -79,13 +79,13 @@ matrix:
   - python: 3.8
     env:
     - PIP_PRE=true
-    - SKIPSLOW=--skip-slow
+    - SKIP="--skip-slow --skip-smoke"
 
   allow_failures:
   - python: 3.8
     env:
     - PIP_PRE=true
-    - SKIPSLOW=--skip-slow
+    - SKIP="--skip-slow --skip-smoke"
 
 
 before_install:
@@ -129,7 +129,7 @@ script:
       mkdir empty && cd empty
       python -c "import linearmodels;linearmodels.test()"
     else
-      pytest -n 2 --cov-config .coveragerc --cov=linearmodels linearmodels --durations=10 ${SKIPSLOW}
+      pytest -n 2 --cov-config .coveragerc --cov=linearmodels linearmodels --durations=10 ${SKIP}
     fi
   - flake8 linearmodels
   - set -e

appveyor.yml

@@ -0,0 +1,53 @@
+skip_tags: true
+clone_depth: 50
+
+matrix:
+    fast_finish: false
+
+environment:
+  global:
+    OMP_NUM_THREADS: 1
+    MKL_NUM_THREADS: 1
+    VML_NUM_THREADS: 1
+    PYTHONHASHSEED: 31211999
+
+  matrix:
+    - PYTHON: C:\Python38-x64
+      PYTHON_VERSION: 3.8
+      PYTHON_ARCH: 64
+
+    - PYTHON: C:\Python38
+      PYTHON_VERSION: 3.8
+      PYTHON_ARCH: 32
+
+
+# We always use a 64-bit machine, but can build x86 distributions
+# with the TARGET_ARCH variable.
+platform:
+    - x64
+
+install:
+    - git submodule update --init --recursive
+    - git fetch --all --tags
+
+    # Set path
+    - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PATH%
+
+    # Check that we have the expected version and architecture for Python
+    - python --version
+    - python -m pip install --upgrade pip wheel
+
+build_script:
+    # Install build requirements
+    - pip install cython numpy scipy statsmodels pandas xarray property_cached mypy-extensions pytest pytest-xdist
+    - python setup.py bdist_wheel
+    - ls dist/*
+
+test_script:
+    # install from wheel
+    - pip install --no-index --find-links dist/ linearmodels
+
+    # run tests from install wheel
+    - mkdir _for_testing
+    - cd _for_testing
+    - python -c "import linearmodels; linearmodels.test(['-n 2','--skip-slow','--skip-smoke','--durations=25'])"

linearmodels/__init__.py

@@ -81,7 +81,7 @@ def test(extra_args=None, exit=True, append=True):
     except ImportError:
         raise ImportError("Need pytest to run tests")
 
-    cmd = ["--tb=short", "--disable-pytest-warnings"]
+    cmd = ["--tb=auto"]
     if extra_args:
         if not isinstance(extra_args, list):
             extra_args = [extra_args]

linearmodels/conftest.py

@@ -1,9 +1,22 @@
 import pytest
 
 
+def pytest_configure(config):
+    # Minimal config to simplify running tests from lm.test()
+    config.addinivalue_line("markers", "slow: mark a test as slow")
+    config.addinivalue_line(
+        "markers", "smoke: mark a test as a coding error (smoke) test"
+    )
+    config.addinivalue_line(
+        "filterwarnings", "ignore:Method .ptp is deprecated:FutureWarning"
+    )
+
+
 def pytest_addoption(parser):
     parser.addoption("--skip-slow", action="store_true", help="skip slow tests")
     parser.addoption("--only-slow", action="store_true", help="run only slow tests")
+    parser.addoption("--skip-smoke", action="store_true", help="skip smoke tests")
+    parser.addoption("--only-smoke", action="store_true", help="run only smoke tests")
 
 
 def pytest_runtest_setup(item):
@@ -12,3 +25,9 @@ def pytest_runtest_setup(item):
 
     if "slow" not in item.keywords and item.config.getoption("--only-slow"):
         pytest.skip("skipping due to --only-slow")
+
+    if "smoke" in item.keywords and item.config.getoption("--skip-smoke"):
+        pytest.skip("skipping due to --skip-smoke")
+
+    if "smoke" not in item.keywords and item.config.getoption("--only-smoke"):
+        pytest.skip("skipping due to --only-smoke")

linearmodels/panel/utility.py

@@ -388,15 +388,18 @@ def check_absorbed(x: np.ndarray, variables: List[str]):
     variables : List[str]
         List of variable names
     """
-    if np.linalg.matrix_rank(x) < x.shape[1]:
+    rank = np.linalg.matrix_rank(x)
+    if rank < x.shape[1]:
         xpx = x.T @ x
         vals, vecs = np.linalg.eigh(xpx)
-        tol = vals.max() * x.shape[1] * np.finfo(np.float64).eps
-        absorbed = vals < tol
-        nabsorbed = absorbed.sum()
+        nabsorbed = x.shape[1] - rank
+        tol = np.sort(vals)[nabsorbed - 1]
+        absorbed = vals <= tol
         absorbed_vecs = vecs[:, absorbed]
         rows = []
         for i in range(nabsorbed):
+            abs_vec = np.abs(absorbed_vecs[:, i])
+            tol = abs_vec.max() * np.finfo(np.float64).eps * abs_vec.shape[0]
             vars_idx = np.where(np.abs(absorbed_vecs[:, i]) > tol)[0]
             rows.append(" " * 10 + ", ".join((variables[vi] for vi in vars_idx)))
         absorbed_variables = "\n".join(rows)

linearmodels/tests/asset_pricing/test_linear_factor_gmm.py

@@ -69,12 +69,14 @@ def test_linear_model_gmm_moments_jacobian(data):
     get_all(res)
 
 
+@pytest.mark.smoke
 def test_linear_model_gmm_smoke_iterate(data):
     mod = LinearFactorModelGMM(data.portfolios, data.factors)
     res = mod.fit(cov_type="robust", disp=5, steps=20)
     get_all(res)
 
 
+@pytest.mark.smoke
 def test_linear_model_gmm_smoke_risk_free(data):
     mod = LinearFactorModelGMM(data.portfolios, data.factors, risk_free=True)
     res = mod.fit(cov_type="robust", disp=10)
@@ -84,6 +86,7 @@ def test_linear_model_gmm_smoke_risk_free(data):
     str(res._cov_est.config)
 
 
+@pytest.mark.smoke
 def test_linear_model_gmm_kernel_smoke(data):
     mod = LinearFactorModelGMM(data.portfolios, data.factors)
     res = mod.fit(cov_type="kernel", disp=10)
@@ -93,12 +96,14 @@ def test_linear_model_gmm_kernel_smoke(data):
     str(res._cov_est.config)
 
 
+@pytest.mark.smoke
 def test_linear_model_gmm_kernel_bandwidth_smoke(data):
     mod = LinearFactorModelGMM(data.portfolios, data.factors)
     res = mod.fit(cov_type="kernel", bandwidth=10, disp=10)
     get_all(res)
 
 
+@pytest.mark.smoke
 def test_linear_model_gmm_cue_smoke(data):
     mod = LinearFactorModelGMM(data.portfolios, data.factors, risk_free=True)
     res = mod.fit(cov_type="robust", disp=10, use_cue=True)

linearmodels/tests/asset_pricing/test_model.py

@@ -22,36 +22,42 @@ def data(request):
     return generate_data(nportfolio=10, output=request.param)
 
 
+@pytest.mark.smoke
 def test_linear_model_gmm_smoke(data):
     mod = LinearFactorModelGMM(data.portfolios, data.factors)
     res = mod.fit(cov_type="robust", disp=5)
     get_all(res)
 
 
+@pytest.mark.smoke
 def test_linear_model_gmm_smoke_iterate(data):
     mod = LinearFactorModelGMM(data.portfolios, data.factors)
     res = mod.fit(cov_type="robust", disp=5, steps=20)
     get_all(res)
 
 
+@pytest.mark.smoke
 def test_linear_model_gmm_smoke_risk_free(data):
     mod = LinearFactorModelGMM(data.portfolios, data.factors, risk_free=True)
     res = mod.fit(cov_type="robust", disp=10)
     get_all(res)
 
 
+@pytest.mark.smoke
 def test_linear_model_gmm_kernel_smoke(data):
     mod = LinearFactorModelGMM(data.portfolios, data.factors)
     res = mod.fit(cov_type="kernel", disp=10)
     get_all(res)
 
 
+@pytest.mark.smoke
 def test_linear_model_gmm_kernel_bandwidth_smoke(data):
     mod = LinearFactorModelGMM(data.portfolios, data.factors)
     res = mod.fit(cov_type="kernel", bandwidth=10, disp=10)
     get_all(res)
 
 
+@pytest.mark.smoke
 def test_linear_model_gmm_cue_smoke(data):
     mod = LinearFactorModelGMM(data.portfolios, data.factors, risk_free=True)
     res = mod.fit(cov_type="robust", disp=10, use_cue=True)
@@ -112,6 +118,7 @@ def test_linear_model_time_series(data):
     assert_allclose(1.0 - stats.chi2.cdf(stat_direct, nport), res.j_statistic.pval)
 
 
+@pytest.mark.smoke
 def test_linear_model_time_series_kernel_smoke(data):
     mod = TradedFactorModel(data.portfolios, data.factors)
     mod.fit(cov_type="kernel")

linearmodels/tests/iv/test_absorbing.py

@@ -238,6 +238,7 @@ def ols_data(request):
     return generate_data(*request.param)
 
 
+@pytest.mark.smoke
 def test_smoke(data):
     mod = AbsorbingLS(
         data.y,

linearmodels/tests/iv/test_covariance.py

@@ -309,6 +309,7 @@ def test_unknown_kernel(self, data, kernel):
 
 
 class TestAutomaticBandwidth(object):
+    @pytest.mark.smoke
     def test_smoke(self, data, kernel):
         # TODO: This should be improved from a smoke test
         u = data.e.copy()

linearmodels/tests/iv/test_model.py

@@ -241,27 +241,31 @@ def test_2sls_just_identified(data):
     get_all(fs)
 
 
+@pytest.mark.smoke
 def test_durbin_smoke(data):
     mod = IV2SLS(data.dep, data.exog, data.endog, data.instr)
     res = mod.fit()
     res.durbin()
     res.durbin([mod.endog.cols[1]])
 
 
+@pytest.mark.smoke
 def test_wu_hausman_smoke(data):
     mod = IV2SLS(data.dep, data.exog, data.endog, data.instr)
     res = mod.fit()
     res.wu_hausman()
     res.wu_hausman([mod.endog.cols[1]])
 
 
+@pytest.mark.smoke
 def test_wooldridge_smoke(data):
     mod = IV2SLS(data.dep, data.exog, data.endog, data.instr)
     res = mod.fit()
     res.wooldridge_regression
     res.wooldridge_score
 
 
+@pytest.mark.smoke
 def test_model_summary_smoke(data):
     res = IV2SLS(data.dep, data.exog, data.endog, data.instr).fit()
     res.__repr__()

linearmodels/tests/iv/test_postestimation.py

@@ -108,6 +108,7 @@ def test_basmann_f(data):
     assert_allclose(res.basmann_f.pval, 0.6760, rtol=1e-3)
 
 
+@pytest.mark.smoke
 def test_c_stat_smoke(data):
     res = IVGMM(data.dep, data.exog, data.endog, data.instr).fit(cov_type="robust")
     c_stat = res.c_stat()

linearmodels/tests/panel/test_fama_macbeth.py

@@ -67,6 +67,7 @@ def test_unknown_cov_type(data):
         FamaMacBeth(data.y, data.x).fit(cov_type="unknown")
 
 
+@pytest.mark.smoke
 def test_fama_macbeth_kernel_smoke(data):
     FamaMacBeth(data.y, data.x).fit(cov_type="kernel")
     FamaMacBeth(data.y, data.x).fit(cov_type="kernel", kernel="bartlett")

linearmodels/tests/panel/test_model.py

@@ -216,8 +216,7 @@ def test_absorbing_effect(data, intercept):
         mod = PanelOLS(data.y, x, entity_effects=True)
         mod.fit()
     var_names = mod.exog.vars
-    print([var_names[i] in str(exc_info.value) for i in range(len(var_names))])
-    print(str(exc_info.value))
+
     assert var_names[3] in str(exc_info.value)
     assert (" " * (2 - intercept) + var_names[-1]) in str(exc_info.value)
 

linearmodels/tests/panel/test_panel_covariance.py

@@ -24,6 +24,7 @@ def setup_class(cls):
         cls.cluster4 = np.random.randint(0, 10, (cls.n * cls.t, 2))
         cls.cluster5 = np.random.randint(0, 10, (cls.n * cls.t, 3))
 
+    @pytest.mark.smoke
     def test_heteroskedastic_smoke(self):
         cov = HeteroskedasticCovariance(
             self.y, self.x, self.params, self.entity_ids, self.time_ids, extra_df=0
@@ -34,6 +35,7 @@ def test_heteroskedastic_smoke(self):
         ).cov
         assert cov.shape == (self.k, self.k)
 
+    @pytest.mark.smoke
     def test_homoskedastic_smoke(self):
         cov = HomoskedasticCovariance(
             self.y, self.x, self.params, self.entity_ids, self.time_ids, extra_df=0
@@ -44,6 +46,7 @@ def test_homoskedastic_smoke(self):
         ).cov
         assert cov.shape == (self.k, self.k)
 
+    @pytest.mark.smoke
     def test_clustered_covariance_smoke(self):
         cov = ClusteredCovariance(
             self.y, self.x, self.params, self.entity_ids, self.time_ids, extra_df=0
@@ -141,6 +144,7 @@ def test_clustered_covariance_error(self):
                 clusters=self.cluster4[::2],
             )
 
+    @pytest.mark.smoke
     def test_driscoll_kraay_smoke(self):
         cov = DriscollKraay(
             self.y, self.x, self.params, self.entity_ids, self.time_ids
@@ -155,6 +159,7 @@ def test_driscoll_kraay_smoke(self):
         ).cov
         assert cov.shape == (self.k, self.k)
 
+    @pytest.mark.smoke
     def test_ac_covariance_smoke(self):
         cov = ACCovariance(
             self.y, self.x, self.params, self.entity_ids, self.time_ids

linearmodels/tests/panel/test_panel_ols.py

@@ -1082,6 +1082,7 @@ def test_cov_equiv_cluster(data):
     assert_results_equal(res, res2)
 
 
+@pytest.mark.smoke
 def test_cluster_smoke(data):
     mod = PanelOLS(data.y, data.x, entity_effects=True)
     mod.fit(cov_type="clustered", cluster_time=True, debiased=False)
@@ -1180,6 +1181,7 @@ def test_entity_other(data):
     assert_results_equal(res, res2)
 
 
+@pytest.mark.smoke
 def test_other_weighted_smoke(data):
     mod = PanelOLS(data.y, data.x, weights=data.w, other_effects=data.c)
     mod.fit(debiased=False)

linearmodels/tests/system/test_gmm.py

@@ -207,6 +207,7 @@ def test_weight_options(data):
     res = mod.fit(cov_type="robust")
 
 
+@pytest.mark.smoke
 def test_no_constant_smoke():
     eqns = generate_3sls_data_v2(k=3, const=False)
     mod = IVSystemGMM(eqns)