Merge branch 'master' into feature/844-cross

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 - [#867](https://github.com/helmholtz-analytics/heat/pull/867) Upgraded to support torch 1.9.0
 - [#876](https://github.com/helmholtz-analytics/heat/pull/876) Make examples work (Lasso and kNN)
+- [#894](https://github.com/helmholtz-analytics/heat/pull/894) Change inclusion of license file
 - [#884](https://github.com/helmholtz-analytics/heat/pull/884) Added capabilities for PyTorch 1.10.0, this is now the recommended version to use.
 
 ## Bug Fixes

examples/classification/demo_knn.py

@@ -2,15 +2,17 @@
 import os
 import random
 
-# Fix python path if run from terminal
-curdir = os.path.dirname(os.path.abspath(__file__))
-sys.path.insert(0, os.path.abspath(os.path.join(curdir, "../../")))
-
 import heat as ht
 from heat.classification.kneighborsclassifier import KNeighborsClassifier
 
+import pkg_resources
+
 # Load dataset from hdf5 file
-X = ht.load_hdf5("../../heat/datasets/iris.h5", dataset="data", split=0)
+iris_path = pkg_resources.resource_filename(
+    pkg_resources.Requirement.parse("heat"), "heat/datasets/iris.h5"
+)
+
+X = ht.load_hdf5(iris_path, dataset="data", split=0)
 
 # Generate keys for the iris.h5 dataset
 keys = []

examples/lasso/demo.py

@@ -3,22 +3,25 @@
 import sys
 import os
 
-# Fix python path if run from terminal
-curdir = os.path.dirname(os.path.abspath(__file__))
-sys.path.insert(0, os.path.abspath(os.path.join(curdir, "../../")))
-
 import heat as ht
 from matplotlib import pyplot as plt
 from sklearn import datasets
 import heat.regression.lasso as lasso
+
 import plotfkt
 
+import pkg_resources
+
 # read scikit diabetes data set
 diabetes = datasets.load_diabetes()
 
 # load diabetes dataset from hdf5 file
-X = ht.load_hdf5("../../heat/datasets/diabetes.h5", dataset="x", split=0)
-y = ht.load_hdf5("../../heat/datasets/diabetes.h5", dataset="y", split=0)
+diabetes_path = pkg_resources.resource_filename(
+    pkg_resources.Requirement.parse("heat"), "heat/datasets/diabetes.h5"
+)
+
+X = ht.load_hdf5(diabetes_path, dataset="x", split=0)
+y = ht.load_hdf5(diabetes_path, dataset="y", split=0)
 
 # normalize dataset #DoTO this goes into the lasso fit routine soon as issue #106 is solved
 X = X / ht.sqrt((ht.mean(X ** 2, axis=0)))

heat/datasets/__init__.py

@@ -0,0 +1,3 @@
+"""
+Make heat.datasets available as a module.
+"""

setup.py

@@ -12,7 +12,7 @@
 setup(
     name="heat",
     packages=find_packages(exclude=("*tests*", "*benchmarks*")),
-    data_files=["README.md", "LICENSE"],
+    package_data={"heat.datasets": ["*.csv", "*.h5", "*.nc"]},
     version=__version__,
     description="A framework for high-performance data analytics and machine learning.",
     long_description=long_description,