WIP: Move from q_vec -> s; s -> q in the code.

The opt_broadcast.py still needs to be updated; issues with running
Karimi baseline.

Author: musically-ut

RedQueen-demo.ipynb

@@ -3,14 +3,12 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d6e0ca390>"
+       "<matplotlib.figure.Figure at 0x7f68dee2a160>"
       ]
      },
      "metadata": {},
@@ -31,10 +29,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import redqueen.utils as U\n",
@@ -101,17 +97,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "sim_opts_1_follower = OM.SimOpts(\n",
     "    src_id=0,\n",
     "    end_time=100,\n",
-    "    q_vec=np.array([1]),\n",
-    "    s=1.0,\n",
+    "    s=np.array([1]),\n",
+    "    q=1.0,\n",
     "    other_sources=[('Hawkes', {'src_id': 1, 'seed': 1, 'l_0': 1.0, 'alpha': 1.0, 'beta': 5.0})],\n",
     "    sink_ids=[1000],\n",
     "    edge_list=[(0, 1000), (1, 1000)]\n",
@@ -129,17 +123,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 83.3 ms, sys: 3.33 ms, total: 86.7 ms\n",
-      "Wall time: 84.6 ms\n"
+      "CPU times: user 66.7 ms, sys: 0 ns, total: 66.7 ms\n",
+      "Wall time: 63.1 ms\n"
      ]
     }
    ],
@@ -154,24 +146,22 @@
     "    'type': 'Opt',\n",
     "    'seed': seed,\n",
     "    'capacity': num_opt_tweets,\n",
-    "    's': sim_opts_1_follower.s\n",
+    "    'q': sim_opts_1_follower.q\n",
     "}\n",
     "OR.add_perf(perf_opt, opt_df, sim_opts_1_follower)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 73.3 ms, sys: 0 ns, total: 73.3 ms\n",
-      "Wall time: 72 ms\n"
+      "CPU times: user 100 ms, sys: 6.67 ms, total: 107 ms\n",
+      "Wall time: 103 ms\n"
      ]
     }
    ],
@@ -186,7 +176,7 @@
     "    'type': 'Poisson',\n",
     "    'seed': seed,\n",
     "    'capacity': num_poisson_tweets,\n",
-    "    's': sim_opts_1_follower.s\n",
+    "    'q': sim_opts_1_follower.q\n",
     "}\n",
     "OR.add_perf(perf_poisson, poisson_df, sim_opts_1_follower)"
    ]
@@ -203,9 +193,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -223,9 +211,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -244,9 +230,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -274,9 +258,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -324,9 +306,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -354,9 +334,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -607,10 +585,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
     "@Deco.optioned('opts')\n",
@@ -646,10 +622,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [],
    "source": [
     "example_1 = perf_to_json([opt_df, poisson_df], ['redqueen', 'poisson'], \n",
@@ -658,18 +632,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "43.482480948594869"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -681,9 +653,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1894,7 +1864,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,