Merge pull request #38 from gmrukwa/develop

Release v2.3.17

- Fixes bugs related to data exchange for parallel Pool, as Windows requires manual data sharing.
- Extends interface of `ParallelSampler` and allows to extend it.

.github/workflows/deploy.yml

@@ -12,7 +12,7 @@ on:
 env:
   MAJOR: ${{ 2 }}
   MINOR: ${{ 3 }}
-  FIXUP: ${{ 16 }}
+  FIXUP: ${{ 17 }}
   PACKAGE_INIT_FILE: ${{ 'divik/__init__.py' }}
   DOCKER_REPO: ${{ 'gmrukwa/divik' }}
   IS_ALPHA: ${{ github.event_name == 'pull_request' }}

.github/workflows/unittest.yml

@@ -3,7 +3,7 @@ name: Run unit tests
 on: push
 
 jobs:
-  tests:
+  docker_tests:
 
     runs-on: ubuntu-latest
 
@@ -14,3 +14,53 @@ jobs:
         run: docker build . --file docker/unittest.dockerfile --tag unittest
         env:
           ENABLE_SLOW_TESTS: True
+
+  windows_tests:
+
+    runs-on: windows-latest
+    strategy:
+      matrix:
+        python-version: [ '3.6', '3.7' ]
+      fail-fast: false
+    name: Python ${{ matrix.python-version }} Windows tests
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v1
+      - name: Setup Python
+        uses: actions/setup-python@v1
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: pip install -r requirements.txt
+      - name: Install native lib
+        run: python dev_setup.py install
+      - name: Test library
+        run: python -m unittest discover
+        env:
+          ENABLE_SLOW_TESTS: True
+
+  macos_tests:
+
+    runs-on: macos-latest
+    strategy:
+      matrix:
+        python-version: [ '3.6', '3.7' ]
+      fail-fast: false
+    name: Python ${{ matrix.python-version }} MacOS tests
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v1
+      - name: Setup Python
+        uses: actions/setup-python@v1
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: pip install -r requirements.txt
+      - name: Install native lib
+        run: python dev_setup.py install
+      - name: Test library
+        run: python -m unittest discover
+        env:
+          ENABLE_SLOW_TESTS: True

README.md

@@ -14,7 +14,7 @@ Python implementation of Divisive iK-means (DiviK) algorithm.
 > This section will be further developed soon.
 
 1) [`divik`](divik/_cli/divik.md) - runs DiviK in GAP-only scenario
-2) [`dunn-divik`](dunn-divik/_cli/dunn-divik.md) - runs DiviK in GAP & Dunn scenario
+2) [`dunn-divik`](dunn-divik/_cli/dunn_divik.md) - runs DiviK in GAP & Dunn scenario
 2) [`kmeans`](divik/_cli/auto_kmeans.md) - runs K-means with GAP statistic
 3) `linkage` - runs agglomerative clustering
 4) [`inspect`](divik/_cli/inspect.md) - visualizes DiviK result
@@ -40,7 +40,7 @@ docker pull gmrukwa/divik
 To install specific version, you can specify it in the command, e.g.:
 
 ```bash
-docker pull gmrukwa/divik:2.3.16
+docker pull gmrukwa/divik:2.3.17
 ```
 
 ## Python package
@@ -60,7 +60,7 @@ pip install divik
 or any stable tagged version, e.g.:
 
 ```bash
-pip install divik==2.3.16
+pip install divik==2.3.17
 ```
 
 If you want to have compatibility with

divik/__init__.py

@@ -1,17 +1,14 @@
-__version__ = '2.3.16'
+__version__ = '2.3.17'
 
-from ._seeding import seeded
-from ._utils import DivikResult
+from divik import core
 from divik import feature_selection
 from divik import feature_extraction
 from divik import cluster
 from divik import sampler
 from ._summary import plot, reject_split
-from ._gin_compat import (
-    configurable,
-    parse_gin_args
-)
 
+
+from .core import configurable
 for __estimator in [
     feature_extraction.KneePCA,
     feature_extraction.LocallyAdjustedRbfSpectralEmbedding,
@@ -32,14 +29,13 @@
 ]:
     configurable(__estimator)
 
+
 __all__ = [
     "__version__",
+    "core",
     "cluster",
     "feature_selection",
     "feature_extraction",
     "sampler",
-    "seeded",
-    "configurable", "parse_gin_args",
-    'DivikResult',
     "plot", "reject_split",
 ]

divik/_cli/_data_io.py

@@ -9,7 +9,7 @@
 import numpy as np
 from scipy import io as scio
 
-import divik._utils as u
+import divik.core as u
 
 
 def _load_mat_with(path: str, backend=scio.loadmat, ignore='__') -> np.ndarray:

divik/_cli/_utils.py

@@ -10,7 +10,7 @@
 import pandas as pd
 import tqdm
 
-import divik._utils as u
+import divik.core as u
 from divik import __version__
 from divik._cli._data_io import load_data
 

divik/_cli/auto_kmeans.py

@@ -10,7 +10,7 @@
 
 from divik.cluster import GAPSearch, KMeans
 import divik._cli._utils as scr
-import divik._utils as u
+import divik.core as u
 
 
 Segmentations = List[Tuple[u.IntLabels, u.Centroids]]

divik/_cli/divik.py

@@ -12,7 +12,7 @@
 from divik.cluster import DiviK
 import divik._summary as _smr
 import divik._cli._utils as sc
-import divik._utils as u
+import divik.core as u
 
 
 def _make_summary(result: typing.Optional[u.DivikResult]):

divik/_cli/linkage.py

@@ -12,7 +12,7 @@
 import scipy.io as sio
 from skimage.io import imsave
 import divik._cli._utils as scr
-import divik._utils as u
+import divik.core as u
 
 
 LinkageMatrix = NewType('LinkageMatrix', np.ndarray)

divik/_cli/visualize.py

@@ -3,7 +3,7 @@
 from skimage.io import imsave
 
 from divik._cli._data_io import load_data
-from divik._utils import visualize
+from divik.core import visualize
 
 
 def parse_args():

divik/_inspect/app.py

@@ -5,7 +5,7 @@
 import dash
 import numpy as np
 
-from divik._utils import DivikResult
+from divik.core import DivikResult
 from divik._cli._data_io import load_data
 
 

divik/_summary.py

@@ -22,7 +22,7 @@
 
 import numpy as np
 
-import divik._utils as u
+import divik.core as u
 
 
 def depth(tree, children_collection_name='subregions'):

divik/cluster/_divik/_base.py

@@ -9,8 +9,8 @@
 from sklearn.base import BaseEstimator, ClusterMixin, TransformerMixin
 from sklearn.utils.validation import check_is_fitted
 
-from divik import _summary as summary, feature_selection as fs, DivikResult
-from divik._utils import context_if, normalize_rows, maybe_pool
+from divik import _summary as summary, feature_selection as fs
+from divik.core import context_if, DivikResult, normalize_rows, maybe_pool
 
 
 class DiviKBase(BaseEstimator, ClusterMixin, TransformerMixin, metaclass=ABCMeta):