Merge branch 'main' into dev

.github/workflows/pypi.yaml

@@ -0,0 +1,27 @@
+# Setting up github action for pypi
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [created]
+
+jobs:
+  deploy:
+    runs-on: ubuntu-20.04
+
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install setuptools wheel twine
+    - name: Build and publish
+      env:
+        TWINE_USERNAME: __token__
+        TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+      run: |
+        python setup.py sdist bdist_wheel
+        twine upload dist/*
+ 

requirements.txt

@@ -1,28 +1,19 @@
+###### Requirements without Version Specifiers ######
+iminuit
+lightgbm
+scikit-learn
+seaborn
+setuptools_scm
+
+###### Requirements with Version Specifiers ######
 astropy>=1.1.2
-jupyter>=1.0.0
+future>=0.16
+george>=0.3.0
 matplotlib>=1.5.1
+nose>=1.3.7
 numpy>=1.18.4
-scikit-learn>=0.20.0
-scipy>=0.17.0
-george>=0.3.0
-iminuit>=1.2
 pandas>=0.23.0
-extinction>=0.3.0
-imbalanced-learn>=0.4.3
-# python==3.7
-pip>=20.1
-emcee>=2.1.0
-numpydoc>=0.6.0
-pytest-remotedata>=0.3.1
+pyyaml>=3.13
 pywavelets>=0.4.0
+scipy>=0.17.0
 sncosmo>=2.1.0
-nose>=1.3.7
-future>=0.16
-pyyaml>=3.13
-pytest-xdist>=1.26.1
-seaborn
-schwimmbad
-# cesium
-tqdm
-lightgbm
-ipympl

setup.py

@@ -1,4 +1,4 @@
-from setuptools import setup
+from setuptools import setup, find_packages
 from setuptools.command.install import install
 import sys
 import os
@@ -7,7 +7,7 @@
 
 
 PACKAGENAME = 'snmachine'
-__FALLBACK_VERSION__ = '2.0.0'
+__FALLBACK_VERSION__ = '2.1.0'
 
 
 class ExtractExampleData(install):
@@ -37,22 +37,40 @@ def run(self):
 
 setup(
     name='snmachine',
+    author='Michelle Lochner et al.',
+    author_email='[email protected]',
+    description='Machine learning code for photometric supernova '
+                'classification',
+    url='https://github.com/LSSTDESC/snmachine',
+    license='BSD-3-Clause License',
+    description='Machine learning code for photometric supernova '
+                'classification',
     use_scm_version={
         "root": ".",
         "relative_to": __file__,
         "fallback_version": __FALLBACK_VERSION__},
     setup_requires=['setuptools_scm>=3.2.0'],
-    packages=['snmachine', 'utils'],
+    packages=find_packages(),
     include_package_data=True,
     package_data={'snmachine': ['example_data/SPCC_SUBSET.tar.gz',
                                 'example_data/output_spcc_no_z/features/*.dat',
                                 'example_data/example_data_for_tests.pckl']},
     exclude_package_data={'utils': ['archive/*']},
     cmdclass={'install': ExtractExampleData},
-    url='https://github.com/LSSTDESC/snmachine',
-    license='BSD-3-Clause License',
-    author='Michelle Lochner et al.',
-    author_email='[email protected]',
-    description='Machine learning code for photometric supernova '
-                'classification'
+    install_requires=['astropy>=1.1.2',
+                      'matplotlib>=3.0.0',
+                      'numpy>=1.18.4',
+                      'scikit-learn',
+                      'scipy>=1.4.0',
+                      'george>=0.3.0',
+                      'iminuit',
+                      'pandas>=0.23.0',
+                      'pywavelets>=0.4.0',
+                      'sncosmo>=2.1.0',
+                      'nose>=1.3.7',
+                      'future>=0.16',
+                      'pyyaml>=3.13',
+                      'seaborn',
+                      'lightgbm',
+                      'setuptools_scm']
 )

snmachine/snclassifier.py

@@ -27,7 +27,7 @@
 from sklearn import model_selection
 from sklearn.model_selection import PredefinedSplit, StratifiedKFold
 from sklearn.preprocessing import StandardScaler
-from utils import plasticc_utils
+from snmachine.utils import plasticc_utils
 
 # This allows the user to easily loop through all possible classifiers
 choice_of_classifiers = ['svm', 'knn', 'random_forest', 'decision_tree',

utils/plasticc_pipeline.py → snmachine/utils/plasticc_pipeline.py

@@ -24,11 +24,10 @@
 
 from argparse import ArgumentParser
 # not used at the moment
-from imblearn.metrics import classification_report_imbalanced  
-# from imblearn.over_sampling import SMOTE  
-from imblearn.pipeline import make_pipeline  
-# from utils.plasticc_utils import plasticc_log_loss
-from utils.plasticc_utils import plot_confusion_matrix
+#from imblearn.metrics import classification_report_imbalanced  
+#from imblearn.over_sampling import SMOTE  
+#from imblearn.pipeline import make_pipeline
+from snmachine.utils.plasticc_utils import plot_confusion_matrix
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import train_test_split