Merge branch 'master' into master

.github/workflows/linting.yml

@@ -0,0 +1,26 @@
+name: Libact linting
+
+on: [push, pull_request]
+
+jobs:
+  build:
+
+    runs-on: ${{ matrix.os }}
+    continue-on-error: True
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest]
+        python-version: [3.9]
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pylint
+      - run: pylint libact

.github/workflows/tests.yml

@@ -0,0 +1,44 @@
+name: Libact tests
+
+on: [push, pull_request]
+
+jobs:
+  build:
+
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-latest]
+        python-version: [2.7, 3.6, 3.7, 3.8, 3.9]
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          if [ "$RUNNER_OS" = "macOS" ]; then
+            brew update
+            brew install openblas
+            mkdir -p ~/.matplotlib
+            echo "backend: TkAgg" >> ~/.matplotlib/matplotlibrc
+          else
+            sudo apt-get update -qq
+            sudo apt-get install -y build-essential gfortran libatlas-base-dev liblapacke-dev
+            sudo apt-get install -y python3-dev
+          fi
+          python -m pip install --upgrade pip
+          pip install pylint coverage codecov
+          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+      - name: Install Libact
+        run: |
+          ./setup.py build_ext --inplace
+      - name: Unittests
+        run: |
+          python -m unittest -v
+      - run: coverage run --source libact --omit */tests/* setup.py test
+      - run: coverage report
+      - run: codecov

.pylintrc

@@ -38,7 +38,7 @@ bad-functions=map,filter
 # w, W - weight vector
 # N - number of instances
 # T, K - number of iterations
-good-names=i,j,k,_,X,Y,x,y,P,p,qs,w,W,N,T,K
+good-names=i,j,k,_,X,Y,Z,x,y,P,p,qs,w,W,N,T,K
 
 # Regular expression matching correct attribute names
 attr-rgx=[a-z_][a-z0-9_]{2,30}$
@@ -84,7 +84,7 @@ generated-members=
 [FORMAT]
 
 # Maximum number of characters on a single line.
-max-line-length=79
+max-line-length=120
 
 # List of optional constructs for which whitespace checking is disabled. `dict-
 # separator` is used to allow tabulation in dicts, etc.: {1  : 1,\n222: 2}.

libact/base/tests/test_dataset.py

@@ -75,7 +75,7 @@ def test_get_labeled_entries(self):
 
     def test_get_unlabeled_entries(self):
         dataset = self.setup_dataset()
-        idx, X = dataset.get_unlabeled_entries()
+        _, X = dataset.get_unlabeled_entries()
         self.assertTrue(np.array_equal(X[0], np.array([6, 7, 8])))
         self.assertTrue(np.array_equal(X[1], np.array([12, 13, 14])))
 

libact/models/sklearn_adapter.py

@@ -92,6 +92,7 @@ class SklearnProbaAdapter(ProbabilisticModel):
     """
 
     def __init__(self, clf):
+        super().__init__()
         self._model = clf
 
     def train(self, dataset, *args, **kwargs):

libact/models/tests/test_svm.py

@@ -29,7 +29,7 @@ def setUp(self):
     def test_svm(self):
         svc_clf = SVC(gamma="auto")
         svc_clf.fit(self.X_train, self.y_train)
-        svm = SVM()
+        svm = SVM(gamma="auto")
         svm.train(Dataset(self.X_train, self.y_train))
 
         assert_array_equal(

libact/query_strategies/multiclass/mdsp.py

@@ -9,13 +9,15 @@
 # Licence: BSD
 
 import numpy as np
+import sklearn
 
 import warnings
 
 from sklearn.base import BaseEstimator
 from sklearn.metrics import euclidean_distances
 from sklearn.utils import check_random_state, check_array, check_symmetric
 from joblib import Parallel, delayed
+
 from sklearn.isotonic import IsotonicRegression
 
 

libact/query_strategies/multiclass/tests/test_hierarchical_sampling.py

@@ -29,8 +29,8 @@ def test_hs_random_selecting(self):
         qseq = run_qs(ds, qs, self.y, len(self.y)-10)
         assert_array_equal(
             np.concatenate([qseq[:10], qseq[-10:]]),
-            np.array([48, 143, 13, 142, 88, 130, 29, 87, 36, 28,
-                      58, 137, 49, 105, 76, 71, 63, 47, 64, 55])
+            np.array([39, 126, 66, 135, 37, 33, 118, 132, 142, 144,
+                      71, 28, 63, 41, 140, 34, 20, 110, 136, 36])
             )
 
     def test_hs_active_selecting(self):
@@ -39,8 +39,8 @@ def test_hs_active_selecting(self):
         qseq = run_qs(ds, qs, self.y, len(self.y)-10)
         assert_array_equal(
             np.concatenate([qseq[:10], qseq[-10:]]),
-            np.array([48, 143, 13, 64, 101, 108, 51, 87, 36, 28,
-                      43, 118, 47, 25, 81, 82, 95, 40, 67, 120])
+            np.array([39, 126, 66, 135, 37, 33, 118, 132, 142, 144,
+                      89, 117, 48, 67, 75, 14, 79, 62, 105, 19])
             )
 
     def test_hs_subsampling(self):
@@ -52,7 +52,7 @@ def test_hs_subsampling(self):
         assert_array_equal(
             np.concatenate([qseq[:10], qseq[-10:]]),
             np.array([120, 50, 33, 28, 78, 133, 52, 124, 102, 109,
-                      81, 108, 12, 10, 89, 114, 92, 126, 48, 25])
+                      81, 108, 10, 89, 126, 114, 92, 48, 25, 13])
             )
 
     def test_hs_report_all_label(self):

libact/query_strategies/multilabel/adaptive_active_learning.py

@@ -6,8 +6,8 @@
 from joblib import Parallel, delayed
 
 from libact.base.dataset import Dataset
-from libact.base.interfaces import QueryStrategy, ContinuousModel
-from libact.utils import inherit_docstring_from, seed_random_state, zip
+from libact.base.interfaces import QueryStrategy
+from libact.utils import inherit_docstring_from, seed_random_state
 from libact.models.multilabel import BinaryRelevance
 
 
@@ -77,12 +77,13 @@ class AdaptiveActiveLearning(QueryStrategy):
 
     def __init__(self, dataset, base_clf, betas=None, n_jobs=1,
             random_state=None):
-        super(AdaptiveActiveLearning, self).__init__(dataset)
+        super().__init__(dataset)
 
         self.n_labels = len(self.dataset.data[0][1])
 
         self.base_clf = copy.deepcopy(base_clf)
 
+        # pyline: disable=fixme
         # TODO check beta value
         self.betas = betas
         if self.betas is None:

libact/query_strategies/multilabel/tests/test_multilabel_realdata.py

@@ -47,7 +47,7 @@ def test_mmc(self):
         qs = MMC(trn_ds, random_state=1126)
         qseq = run_qs(trn_ds, qs, self.y, self.quota)
         assert_array_equal(qseq,
-                np.array([117, 655, 1350, 909, 1003, 1116, 546, 1055, 165, 1441]))
+                np.array([117, 655, 234, 1419, 1350, 1224, 427, 890, 1447, 103]))
 
     def test_multilabel_with_auxiliary_learner_hlr(self):
         trn_ds = Dataset(self.X,

libact/query_strategies/src/variance_reduction/variance_reduction.c

@@ -4,11 +4,6 @@
 #include <numpy/arrayobject.h>
 #include <lapacke.h>
 
-/* DGESVD prototype */
-extern void LAPACK_dgesvd( char* jobu, char* jobvt, int* m, int* n, double* a,
-                    int* lda, double* s, double* u, int* ldu, double* vt, int* ldvt,
-                    double* work, int* lwork, int* info );
-
 double** An(double *pi, double *x, int labs, int dims);
 double** A(double **PI, double **X, int labs, int dims, int n_pool);
 double** Fisher(double *pi, double *x, double sigma, int labs, int dims);

libact/utils/multilabel/__init__.py

@@ -1,3 +1,5 @@
+"""Implementation of loss/score functions for multi-label evaluation."""
+
 import numpy as np
 
 def pairwise_rank_loss(Z, Y): #truth(Z), prediction(Y)
@@ -24,4 +26,4 @@ def pairwise_f1_score(Z, Y):
     down[down==0] = 1.
     up[down==0] = 1.
 
-    return up / down
+    return up / down

libact/utils/tests/test_criteria.py

@@ -16,4 +16,3 @@ def test_criteria(self):
                       [1, 0, 1, 1, 0],])
         assert_array_almost_equal(pairwise_rank_loss(a, b), [-2.5, -3.5, -1])
         assert_array_almost_equal(pairwise_f1_score(a, b), [0.5, 0.4, 0.8])
-

requirements.txt

@@ -1,7 +1,7 @@
 setuptools
 numpy
 scipy
-scikit-learn<=0.19.2
+scikit-learn>=0.24
 matplotlib
 Cython
 joblib

setup.py

@@ -26,9 +26,13 @@
         print("Platform Detection: Mac OS X. Link to openblas...")
         extra_link_args = []
         libraries = ['openblas']
-        library_dirs = ['/opt/local/lib']
+        library_dirs = [
+            '/opt/local/lib',
+            '/usr/local/opt/openblas/lib',  # for brew installs
+        ]
         include_dirs = (numpy.distutils.misc_util.get_numpy_include_dirs() +
-                        ['/opt/local/include'])
+                        ['/opt/local/include',
+                         '/usr/local/opt/openblas/include'])  # for brew installs
     else:
         # assume linux otherwise, unless we support Windows in the future...
         print("Platform Detection: Linux. Link to liblapacke...")