Autoencoder

.github/workflows/docbuild.yml

@@ -8,7 +8,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: [3.7, 3.8, 3.9, "3.10"]
+        python-version: ["3.10", "3.11"]
 
     steps:
       - name: Checkout repo

.github/workflows/test.yml

@@ -8,7 +8,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: [3.7, 3.8, 3.9, "3.10"]
+        python-version: ["3.10", "3.11"]
 
     steps:
       - name: Checkout repo
@@ -30,12 +30,11 @@ jobs:
           pydocstyle
       - name: Test
         run: |
-          cp $(python -c 'import site; print(site.getsitepackages()[0])')/afqinsight/_version.py afqinsight/_version.py
-          tox
+          cd && mkdir for_test && cd for_test && pytest --pyargs afqinsight --cov-report term-missing --cov=afqinsight
       - name: Coveralls
         run: |
           coveralls
-        if: matrix.python-version == 3.8
+        if: matrix.python-version == 3.10
         env:
           COVERALLS_REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/python/black
-    rev: 22.3.0
+    rev: 24.2.0
     hooks:
       - id: black
-        language_version: python3.8
+        language_version: python

afqinsight/__init__.py

@@ -1,4 +1,5 @@
 """AFQ-Insight is a Python library for statistical learning of tractometry data."""
+
 from . import datasets  # noqa
 from . import utils  # noqa
 from .cross_validate import *  # noqa

afqinsight/_serial_bagging.py

@@ -9,6 +9,7 @@
 parallelism when using a dask.distributed backend, I will gladly remove this
 private module. @richford
 """
+
 import itertools
 import numbers
 import numpy as np
@@ -383,7 +384,7 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
         # Validate max_features
         if isinstance(self.max_features, numbers.Integral):
             max_features = self.max_features
-        elif isinstance(self.max_features, np.float):
+        elif isinstance(self.max_features, float):
             max_features = self.max_features * self.n_features_in_
         else:
             raise ValueError("max_features must be int or float")
@@ -897,7 +898,7 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
         # Validate max_features
         if isinstance(self.max_features, numbers.Integral):
             max_features = self.max_features
-        elif isinstance(self.max_features, np.float):  # pragma: no cover
+        elif isinstance(self.max_features, float):  # pragma: no cover
             max_features = self.max_features * self.n_features_in_
         else:  # pragma: no cover
             raise ValueError("max_features must be int or float")

afqinsight/augmentation/__init__.py

@@ -13,4 +13,5 @@
 augmentation for time series classification with neural networks," PLOS ONE
 16(7): e0254841. DOI: https://doi.org/10.1371/journal.pone.0254841
 """
+
 from .augmentation import *  # noqa: F401,F403

afqinsight/augmentation/augmentation.py

@@ -13,6 +13,7 @@
 augmentation for time series classification with neural networks," PLOS ONE
 16(7): e0254841. DOI: https://doi.org/10.1371/journal.pone.0254841
 """
+
 import numpy as np
 from tqdm import tqdm
 

afqinsight/augmentation/dtw.py

@@ -11,6 +11,7 @@
 augmentation for time series classification with neural networks," PLOS ONE
 16(7): e0254841. DOI: https://doi.org/10.1371/journal.pone.0254841
 """
+
 import numpy as np
 import sys