Actions CI

Author: fabioferreira

.github/workflows/ci.yml

@@ -0,0 +1,51 @@
+name: CI
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: macos-latest
+    strategy:
+      matrix:
+        python-version: ["3.11"]
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: "pip"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip wheel
+          python -m pip install -r requirements.txt
+          python -m pip install --break-system-packages -e .
+
+      - name: Run estimator tests
+        run: |
+          python tests/unittests_estimators.py
+          python tests/unittests_evaluations.py
+          python tests/unittests_simulations.py
+          python tests/unittests_utils.py
+
+      #- name: Run torch regression smoke tests
+      #  run: |
+      #    python -m pytest tests/test_nf_torch.py
+      #    python -m pytest tests/test_kmn_torch.py
+
+      - name: Build distributions
+        run: |
+          python -m pip install build
+          python -m build --sdist --wheel
+
+

.travis.yml

@@ -9,14 +9,18 @@ install:
   - pip install -q -r requirements.txt
   - pip install --break-system-packages -e .
 
+before_deploy:
+  - python -m pip install build
+  - python -m build --sdist --wheel
+
 # command to run tests
 script:
   - python tests/unittests_estimators.py
   - python tests/unittests_evaluations.py
   - python tests/unittests_simulations.py
   - python tests/unittests_utils.py
-  #- python -m pytest tests/test_nf_torch.py
-  #- python -m pytest tests/test_kmn_torch.py
+  - python tests/test_nf_torch.py
+  - python tests/test_kmn_torch.py
 
 notifications:
   email:
@@ -25,3 +29,13 @@ notifications:
       - fabioferreira@mailbox.org
     on_success: never
     on_failure: never
+
+deploy:
+  provider: pypi
+  user: "__token__"
+  password:
+    secure: "REPLACE_WITH_ENCRYPTED_TOKEN"
+  distributions: "sdist bdist_wheel"
+  on:
+    branch: main
+    tags: true

cde/utils/async_executor.py

@@ -4,6 +4,11 @@
 import numpy as np
 from progressbar.bar import ProgressBar
 
+try:
+    multiprocessing.set_start_method('fork')
+except RuntimeError:
+    pass
+
 class AsyncExecutor:
 
     def __init__(self, n_jobs=1):

tests/unittests_simulations.py

@@ -204,7 +204,7 @@ def test_pdf(self):
     p_sim = sim_model.pdf(x,y)
     p_true = stats.norm.pdf(y, loc=1, scale=2)
     diff = np.sum(np.abs(p_sim - p_true))
-    self.assertAlmostEquals(diff, 0.0, places=2)
+    self.assertAlmostEqual(diff, 0.0, places=2)
 
   def test_cdf(self):
     sim_model = EconDensity()
@@ -213,7 +213,7 @@ def test_cdf(self):
     p_sim = sim_model.cdf(x,y)
     p_true = stats.norm.cdf(y, loc=1, scale=2)
     diff = np.sum(np.abs(p_sim - p_true))
-    self.assertAlmostEquals(diff, 0.0, places=2)
+    self.assertAlmostEqual(diff, 0.0, places=2)
 
   def test_value_at_risk(self):
     sim_model = EconDensity()
@@ -252,8 +252,8 @@ def test_random_seed(self):
 
     diff_x = np.sum(np.abs(X1[:100] - X2[:]))
     diff_y = np.sum(np.abs(Y1[:100] - Y2[:]))
-    self.assertAlmostEquals(diff_x, 0, places=2)
-    self.assertAlmostEquals(diff_y, 0, places=2)
+    self.assertAlmostEqual(diff_x, 0, places=2)
+    self.assertAlmostEqual(diff_y, 0, places=2)
 
 class TetsSkewNormal(unittest.TestCase):
   def setUp(self):
@@ -518,7 +518,9 @@ def test_covariance(self):
     self.assertAlmostEqual(cov_est[0][1][0], sigma[1][0], places=2)
 
 
-def mean_pdf(density, x_cond, n_samples=10 ** 6):
+_DEFAULT_MC_SAMPLES = 5 * 10 ** 6
+
+def mean_pdf(density, x_cond, n_samples=_DEFAULT_MC_SAMPLES):
   means = np.zeros((x_cond.shape[0], density.ndim_y))
   for i in range(x_cond.shape[0]):
     x = x = np.tile(x_cond[i].reshape((1, x_cond[i].shape[0])), (n_samples, 1))
@@ -527,7 +529,7 @@ def mean_pdf(density, x_cond, n_samples=10 ** 6):
     means[i] = integral
   return means
 
-def covariance_pdf(density, x_cond, n_samples=10 ** 6):
+def covariance_pdf(density, x_cond, n_samples=_DEFAULT_MC_SAMPLES):
   covs = np.zeros((x_cond.shape[0], density.ndim_y, density.ndim_y))
   mean = density.mean_(x_cond)
   for i in range(x_cond.shape[0]):