Merge branch 'master' into master

Author: yangarbiter

.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}.

.travis.yml

@@ -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/base/tests/test_dataset.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/sklearn_adapter.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/models/tests/test_svm.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/mdsp.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/multiclass/tests/test_hierarchical_sampling.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: