Merge branch 'release/v0.5.1'

README.md

@@ -7,7 +7,7 @@ The framework also features efficient tensor-based implementation of an algorith
 
 
 ## Prerequisites
-**Note:** Currently runs on python 2 only (python 3 support in plans).
+Current version of Polara supports both Python 2 and Python 3 environments. Future versions are likely to drop support of Python 2 to make a better use of Python 3 features.
 
 The framework heavily depends on `Pandas, Numpy, Scipy` and `Numba` packages. Better performance can be achieved with `mkl` (optional). It's also recommended to use `jupyter notebook` for experimentation. Visualization of results can be done with help of `matplotlib` and optionally `seaborn`. The easiest way to get all those at once is to use the latest [Anaconda distribution](https://www.continuum.io/downloads).
 
@@ -17,7 +17,7 @@ If you use a separate `conda` environment for testing, the following command can
 
 Alternatively, a new conda environment with all required packages can be created by:
 
-`conda create -n <your_environment_name> python=2.7 --file conda_req.txt`
+`conda create -n <your_environment_name> python=3.6 --file conda_req.txt`
 
 
 ## Installation

examples/Custom evaluation.ipynb

@@ -45,6 +45,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import print_function\n",
     "import numpy as np\n",
     "from polara.datasets.movielens import get_movielens_data"
    ]
@@ -176,7 +177,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Preparing data...\n"
+      "Preparing data...\n",
+      "Done.\n"
      ]
     }
    ],
@@ -464,7 +466,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PureSVD training time: 0.259858550909s\n"
+      "PureSVD training time: 0.0981479023320091s\n"
      ]
     }
    ],
@@ -602,7 +604,7 @@
     {
      "data": {
       "text/plain": [
-       "Relevance(precision=0.46719986760687032, recall=0.18790260795025798, fallout=0.058754565239814198, specifity=0.70752554180746507, miss_rate=0.7362722359391265)"
+       "Relevance(precision=0.4671998676068703, recall=0.18790260795025798, fallout=0.0587545652398142, specifity=0.7075255418074651, miss_rate=0.7362722359391265)"
       ]
      },
      "execution_count": 21,
@@ -682,7 +684,7 @@
     {
      "data": {
       "text/plain": [
-       "(5L, 10L)"
+       "(5, 10)"
       ]
      },
      "execution_count": 24,
@@ -708,7 +710,7 @@
     }
    ],
    "source": [
-    "print (len(test_users), svd.topk)"
+    "print((len(test_users), svd.topk))"
    ]
   },
   {
@@ -732,7 +734,7 @@
     }
    ],
    "source": [
-    "print data_model.test.holdout"
+    "print(data_model.test.holdout)"
    ]
   },
   {
@@ -902,7 +904,7 @@
    ],
    "source": [
     "assert unseen_data.userid.nunique() == len(unseen_users)\n",
-    "print len(unseen_users)"
+    "print(len(unseen_users))"
    ]
   },
   {
@@ -971,7 +973,7 @@
     {
      "data": {
       "text/plain": [
-       "(1208L, 10L)"
+       "(1208, 10)"
       ]
      },
      "execution_count": 33,
@@ -1642,7 +1644,7 @@
     {
      "data": {
       "text/plain": [
-       "Relevance(precision=0.48771352650892064, recall=0.19621777241472779, fallout=0.058711254774068439, specifity=0.68088130501335409, miss_rate=0.741780456106087)"
+       "Relevance(precision=0.48771352650892064, recall=0.1962177724147278, fallout=0.05871125477406844, specifity=0.6808813050133541, miss_rate=0.741780456106087)"
       ]
      },
      "execution_count": 43,
@@ -1657,21 +1659,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2 [sc2]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.14"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   },
   "toc": {
    "nav_menu": {},