Merge remote-tracking branch 'upstream/master' into feat-rename-vars

Author: mvargas33

.github/workflows/main.yml

@@ -0,0 +1,68 @@
+name: CI
+
+# Controls when the workflow will run
+on:
+  # Triggers the workflow on push or pull request events but only for the master branch
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+    
+jobs:
+
+  # Checks compatibility with an old version of sklearn (0.20.3)
+  compatibility:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ['3.6', '3.7', '3.8']
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Run Tests with skggm + scikit-learn 0.20.3
+        env:
+          SKGGM_VERSION: a0ed406586c4364ea3297a658f415e13b5cbdaf8
+        run:  |
+          sudo apt-get install liblapack-dev
+          pip install --upgrade pip pytest
+          pip install wheel cython numpy scipy codecov pytest-cov
+          pip install scikit-learn==0.20.3
+          pip install git+https://github.com/skggm/skggm.git@${SKGGM_VERSION}
+          pytest test --cov
+          bash <(curl -s https://codecov.io/bash)
+          
+  # Run normal testing with the latests versions of all dependencies
+  build:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ['3.6', '3.7', '3.8', '3.9']
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Run Tests without skggm
+        run:  |
+          sudo apt-get install liblapack-dev
+          pip install --upgrade pip pytest
+          pip install wheel cython numpy scipy codecov pytest-cov scikit-learn
+          pytest test --cov
+          bash <(curl -s https://codecov.io/bash)
+      - name: Run Tests with skggm
+        env:
+          SKGGM_VERSION: a0ed406586c4364ea3297a658f415e13b5cbdaf8
+        run:  |
+          pip install git+https://github.com/skggm/skggm.git@${SKGGM_VERSION}
+          pytest test --cov
+          bash <(curl -s https://codecov.io/bash)
+      - name: Syntax checking with flake8
+        run: |
+          pip install flake8
+          flake8 --extend-ignore=E111,E114 --show-source;

.travis.yml

@@ -1,4 +1,4 @@
-|Travis-CI Build Status| |License| |PyPI version| |Code coverage|
+|GitHub Actions Build Status| |License| |PyPI version| |Code coverage|
 
 metric-learn: Metric Learning in Python
 =======================================
@@ -67,8 +67,8 @@ Bibtex entry::
 
 .. _sphinx documentation: http://contrib.scikit-learn.org/metric-learn/
 
-.. |Travis-CI Build Status| image:: https://api.travis-ci.org/scikit-learn-contrib/metric-learn.svg?branch=master
-   :target: https://travis-ci.org/scikit-learn-contrib/metric-learn
+.. |GitHub Actions Build Status| image:: https://github.com/scikit-learn-contrib/metric-learn/workflows/CI/badge.svg
+   :target: https://github.com/scikit-learn-contrib/metric-learn/actions?query=event%3Apush+branch%3Amaster
 .. |License| image:: http://img.shields.io/:license-mit-blue.svg?style=flat
    :target: http://badges.mit-license.org
 .. |PyPI version| image:: https://badge.fury.io/py/metric-learn.svg

README.rst

@@ -292,6 +292,11 @@ same class are not imposed to be close.
     lfda = LFDA(k=2, dim=2)
     lfda.fit(X, Y)
 
+.. note::
+    LDFA suffers from a problem called “sign indeterminacy”, which means the sign of the ``components`` and the output from transform depend on a random state. This is directly related to the calculation of eigenvectors in the algorithm. The same input ran in different times might lead to different transforms, but both valid.
+    
+    To work around this, fit instances of this class to data once, then keep the instance around to do transformations.
+
 .. topic:: References:
 
     .. [1] Sugiyama. `Dimensionality Reduction of Multimodal Labeled Data by Local

build_tools/travis/flake8_diff.sh

@@ -79,12 +79,17 @@ def test_singular_returns_pseudo_inverse(self):
 class TestSCML(object):
   @pytest.mark.parametrize('basis', ('lda', 'triplet_diffs'))
   def test_iris(self, basis):
+    """
+    SCML applied to Iris dataset should give better results when
+    computing class separation.
+    """
     X, y = load_iris(return_X_y=True)
+    before = class_separation(X, y)
     scml = SCML_Supervised(basis=basis, n_basis=85, k_genuine=7, k_impostor=5,
                            random_state=42)
     scml.fit(X, y)
-    csep = class_separation(scml.transform(X), y)
-    assert csep < 0.24
+    after = class_separation(scml.transform(X), y)
+    assert before > after + 0.03  # It's better by a margin of 0.03
 
   def test_big_n_features(self):
     X, y = make_classification(n_samples=100, n_classes=3, n_features=60,
@@ -929,7 +934,7 @@ def test_singleton_class(self):
       X = X[[ind_0[0], ind_1[0], ind_2[0]]]
       y = y[[ind_0[0], ind_1[0], ind_2[0]]]
 
-      A = make_spd_matrix(X.shape[1], X.shape[1])
+      A = make_spd_matrix(n_dim=X.shape[1], random_state=X.shape[1])
       nca = NCA(init=A, max_iter=30, n_components=X.shape[1])
       nca.fit(X, y)
       assert_array_equal(nca.components_, A)
@@ -940,7 +945,7 @@ def test_one_class(self):
       X = self.iris_points[self.iris_labels == 0]
       y = self.iris_labels[self.iris_labels == 0]
 
-      A = make_spd_matrix(X.shape[1], X.shape[1])
+      A = make_spd_matrix(n_dim=X.shape[1], random_state=X.shape[1])
       nca = NCA(init=A, max_iter=30, n_components=X.shape[1])
       nca.fit(X, y)
       assert_array_equal(nca.components_, A)

doc/supervised.rst

@@ -503,12 +503,12 @@ def test_init_mahalanobis(estimator, build_dataset):
       model.fit(input_data, labels)
 
       # Initialize with a random spd matrix
-      init = make_spd_matrix(X.shape[1], random_state=rng)
+      init = make_spd_matrix(n_dim=X.shape[1], random_state=rng)
       model.set_params(**{param: init})
       model.fit(input_data, labels)
 
       # init.shape[1] must match X.shape[1]
-      init = make_spd_matrix(X.shape[1] + 1, X.shape[1] + 1)
+      init = make_spd_matrix(n_dim=X.shape[1] + 1, random_state=rng)
       model.set_params(**{param: init})
       msg = ('The input dimensionality {} of the given '
              'mahalanobis matrix `{}` must match the '