python3 support and auto autodetect so file

README.md

@@ -1,6 +1,6 @@
 ## compile libmf interface object files
 
-You just need a standard c++ compiler 
+You just need a standard c++ compiler
 ```
 $ g++ --std=c++11 *.cpp -shared -o python-libmf.so
 ```
@@ -25,3 +25,8 @@ if these work then you are good to go!
 `data.shape => (x, 3)` where x is the number of observations
 
 `ind` is a sparse numpy array of indices specifying where we want to predict unobserved values
+
+## for mac os 10.12
+```
+export MACOSX_DEPLOYMENT_TARGET=10.12
+```

libmf/lib_path.py

@@ -0,0 +1,11 @@
+import re
+import os
+import sys
+
+
+def find_lib_path():
+    path = os.environ["LIBMF_OBJ"] if "LIBMF_OBJ" in os.environ else sys.argv[1] if len(sys.argv) > 1 else \
+        os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    for i in os.listdir(path):
+        if os.path.isfile(os.path.join(path, i)) and re.match('python-libmf\.cpython(.*)\.so', i):
+            return os.path.join(path, i)

libmf/mf.py

@@ -2,9 +2,9 @@
 import ctypes
 import os
 import sys
+from . import lib_path
 
-compiled_src = os.environ["LIBMF_OBJ"] if "LIBMF_OBJ" in os.environ else sys.argv[1] if len(sys.argv) > 1 else \
-    os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + "/python-libmf.so"
+compiled_src = lib_path.find_lib_path()
 mf = ctypes.CDLL(compiled_src)
 c_float_p = ctypes.POINTER(ctypes.c_float)
 
@@ -73,7 +73,7 @@ def __init__(self, *args, **kwargs):
         self._options = MFParam()
         for kw in kwargs:
             if kw not in [i[0] for i in get_default_options()]:
-                print "Unrecognized keyword argument '{0}={1}'".format(kw, kwargs[kw])
+                print("Unrecognized keyword argument '{0}={1}'".format(kw, kwargs[kw]))
 
         for item in get_default_options():
             if item[0] not in kwargs:
@@ -183,4 +183,3 @@ def __generate_test_data(xs, ys, k, indices_only=False):
     ry = np.random.random_integers(0, ys, k)
     rv = np.random.rand(k)
     return np.vstack((rx, ry, rv)).transpose() if not indices_only else np.vstack((rx,ry)).transpose()
-

mf_tests.py

@@ -3,29 +3,29 @@
 
 test_data = mf.__generate_test_data(9000, 1000, 25000)
 
-print "testing fit method"
+print("testing fit method")
 mf_engine = mf.MF()
 try:
 	mf_engine.mf_fit(test_data)
-	print "OK"
+	print("OK")
 except Exception as e:
-	print e
-	print "FAILED"
+	print(e)
+	print("FAILED")
 
-print "testing predict method"
+print("testing predict method")
 try:
 	pred_data = mf.__generate_test_data(9000, 1000, 1000, indices_only=True)
-	print pred_data.shape
+	print(pred_data.shape)
 	mf_engine.mf_predict(pred_data)
-	print "OK"
+	print("OK")
 except Exception as e:
-	print "FAILED"
-	print e
+	print("FAILED")
+	print(e)
 
-print "testing cross validation"
+print("testing cross validation")
 try:
 	mf_engine.mf_cross_validation(test_data)
-	print "OK"
+	print("OK")
 except Exception as e:
-	print "FAILED"
-	print e
+	print("FAILED")
+	print(e)