Added numba to setup.py requirements (under demos) and requirements.txt

duanmuxiangyu · Sep 4, 2018 · 5ca6c0f · 5ca6c0f
1 parent 8eabe48
commit 5ca6c0f
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Showing 4 changed files with 44 additions and 42 deletions.
diff --git a/pyproject.toml b/pyproject.toml
@@ -0,0 +1,23 @@
+[tool.poetry]
+name = "stellargraph"
+version = "0.4.0"
+description = "Python library for machine learning on graphs"
+repository = "https://github.com/stellargraph/stellargraph"
+authors = ["Data61, CSIRO"]
+license = "Apache-2.0"
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "3.6"
+Keras = "^2.1"
+tensorflow = "^1.8"
+numpy = "^1.14"
+networkx = "^2.1"
+scikit-learn = "^0.18"
+gensim = "^3.4"
+matplotlib = "^2.2"
+pandas = "^0.20"
+numba = "^0.39"
+
+[tool.poetry.dev-dependencies]
+pytest = "^3.7"
diff --git a/requirements.txt b/requirements.txt
@@ -2,6 +2,7 @@ networkx==2.1
 setuptools==39.1.0
 Keras==2.2.2
 tensorflow==1.10.0
+numba==0.39.0
 numpy==1.14.5
 progressbar2==3.37.1
 scikit_learn==0.19.2

diff --git a/setup.py b/setup.py
@@ -22,18 +22,16 @@
 
 # Required packages
 REQUIRES = [
-    "keras",
-    "tensorflow",
-    "numpy",
-    "networkx",
-    "progressbar2",
-    "scikit_learn",
-    "matplotlib",
-    "gensim",
+    "keras>=2.2.0",
+    "tensorflow>=1.8",
+    "numpy>=1.14, <1.15",
+    "networkx>=2.1",
+    "scikit_learn>=0.18",
+    "matplotlib>=2.2",
+    "gensim>=3.4.0",
 ]
 
-
-EXTRAS_REQURES = {"demos": ["pandas"], "tests": ["pytest", "pytest-pep8", "pandas"]}
+EXTRAS_REQURES = {"demos": ["pandas", "numba"], "test": ["pytest", "pandas"]}
 
 # Long description
 with open("README.md", "r") as fh:
@@ -49,6 +47,7 @@
     long_description=LONG_DESCRIPTION,
     long_description_content_type="text/markdown",
     include_package_data=True,
+    python_requires='>=3.6.0, <3.7.0',
     install_requires=REQUIRES,
     extras_require=EXTRAS_REQURES,
     packages=setuptools.find_packages(exclude=("tests",)),

diff --git a/stellargraph/data/epgm.py b/stellargraph/data/epgm.py
@@ -27,7 +27,7 @@
 import multiprocessing
 from multiprocessing import Pool
 from functools import partial
-from progressbar import ProgressBar, SimpleProgress
+#from progressbar import ProgressBar, SimpleProgress
 from time import sleep
 
 
@@ -359,14 +359,14 @@ def to_nx_OLD(
                     n = len(nodes)
                     self.G_nx[graph_id] = []
 
-                    pbar = ProgressBar(
-                        widgets=[
-                            SimpleProgress(
-                                format="%(value_s)s of %(max_value_s)s nodes processed (%(percentage)3d%%)"
-                            )
-                        ],
-                        maxval=n,
-                    ).start()
+                    # pbar = ProgressBar(
+                    #     widgets=[
+                    #         SimpleProgress(
+                    #             format="%(value_s)s of %(max_value_s)s nodes processed (%(percentage)3d%%)"
+                    #         )
+                    #     ],
+                    #     maxval=n,
+                    # ).start()
                     # _ = [pool.apply_async(partial(node_neighbours, edges=edges), args=(v,),
                     #                       callback=self.G_nx[graph_id].append) for v in nodes]
                     # it seems that appending results using callback works much slower than either pool.map_async or pool.map
@@ -380,9 +380,9 @@ def to_nx_OLD(
                     # evaluate batches of imap, as the progress bar is being updated:
                     while len(self.G_nx[graph_id]) != n:
                         self.G_nx[graph_id].append(next(graph))
-                        pbar.update(len(self.G_nx[graph_id]))
+                        # pbar.update(len(self.G_nx[graph_id]))
 
-                    pbar.finish()
+                    # pbar.finish()
 
                     self.G_nx[graph_id] = dict(self.G_nx[graph_id])
 
@@ -395,27 +395,6 @@ def to_nx_OLD(
                 pool.join()
 
             else:  # sequential execution
-                if progress:
-                    n = len(nodes)
-                    # graph = {self._progress('node', n, progress_step, i, v): [e[1] for e in edges if e[0] == v] for i, v in
-                    #          enumerate(nodes)}  # this works ~2.5x faster (for cora dataset) than the above for loop
-                    pbar = ProgressBar(
-                        widgets=[
-                            SimpleProgress(
-                                format="%(value_s)s of %(max_value_s)s nodes processed"
-                            )
-                        ],
-                        maxval=n,
-                    ).start()
-                    self.G_nx[graph_id] = {
-                        self._progressbarUpdate(pbar, chunksize, v, i): [
-                            e[1] for e in edges if e[0] == v
-                        ]
-                        for i, v in enumerate(nodes)
-                    }  # this works ~2.5x faster (for cora dataset) than the above for loop
-                    pbar.finish()
-
-                else:
                     self.G_nx[graph_id] = {
                         v: [e[1] for e in edges if e[0] == v] for v in nodes
                     }  # this works ~2.5x faster (for cora dataset) than the above for loop