From 8af5f67583d03deb7cfa088e6d93223e710a05d2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?V=C3=ADt=20Novotn=C3=BD?= <witiko@mail.muni.cz>
Date: Sun, 28 Jan 2018 17:53:05 +0100
Subject: [PATCH] Replace explicit timers with magic %time in Soft Cosine
 Measure notebook

---
 docs/notebooks/soft_cosine_tutorial.ipynb | 35 +++++++----------------
 1 file changed, 10 insertions(+), 25 deletions(-)

diff --git a/docs/notebooks/soft_cosine_tutorial.ipynb b/docs/notebooks/soft_cosine_tutorial.ipynb
index 36d68276bf..fbc239a142 100644
--- a/docs/notebooks/soft_cosine_tutorial.ipynb
+++ b/docs/notebooks/soft_cosine_tutorial.ipynb
@@ -40,8 +40,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from time import time\n",
-    "\n",
     "# Initialize logging.\n",
     "import logging\n",
     "logging.basicConfig(format='%(asctime)s | %(levelname)s : %(message)s')"
@@ -140,7 +138,7 @@
     }
    ],
    "source": [
-    "start = time()\n",
+    "%%time\n",
     "import os\n",
     "\n",
     "from gensim.models import KeyedVectors\n",
@@ -149,9 +147,7 @@
     "    \n",
     "model = KeyedVectors.load_word2vec_format('/data/GoogleNews-vectors-negative300.bin.gz', binary=True)\n",
     "similarity_matrix = model.similarity_matrix(dictionary)\n",
-    "del model\n",
-    "\n",
-    "print('Cell took %.2f seconds to run.' % (time() - start))"
+    "del model"
    ]
   },
   {
@@ -278,8 +274,7 @@
     }
    ],
    "source": [
-    "start = time()\n",
-    "\n",
+    "%%time\n",
     "import json\n",
     "\n",
     "# Business IDs of the restaurants.\n",
@@ -312,8 +307,6 @@
     "            # Add to corpus for similarity queries.\n",
     "            scm_corpus.append(text)\n",
     "            documents.append(json_line['text'])"
-    "\n",
-    "print('Cell took %.2f seconds to run.' %(time() - start))"
    ]
   },
   {
@@ -340,6 +333,7 @@
     }
    ],
    "source": [
+    "%%time\n",
     "from matplotlib import cycler, pyplot as plt\n",
     "%matplotlib inline\n",
     "\n",
@@ -387,8 +381,7 @@
     }
    ],
    "source": [
-    "start = time()\n",
-    "\n",
+    "%%time\n",
     "from gensim.models import Word2Vec\n",
     "\n",
     "# Train Word2Vec on all the restaurants.\n",
@@ -401,9 +394,7 @@
     "dictionary = corpora.Dictionary(scm_corpus)\n",
     "scm_corpus = [dictionary.doc2bow(document) for document in scm_corpus]\n",
     "similarity_matrix = model.wv.similarity_matrix(dictionary)\n",
-    "instance = SoftCosineSimilarity(scm_corpus, similarity_matrix, num_best=num_best)\n",
-    "\n",
-    "print('Cell took %.2f seconds to run.' %(time() - start))"
+    "instance = SoftCosineSimilarity(scm_corpus, similarity_matrix, num_best=num_best)"
    ]
   },
   {
@@ -431,14 +422,11 @@
     }
    ],
    "source": [
-    "start = time()\n",
-    "\n",
+    "%%time\n",
     "sent = 'Yummy! Great view of the Bellagio Fountain show.'\n",
     "query = dictionary.doc2bow(preprocess(sent))\n",
     "\n",
-    "sims = instance[query]  # A query is simply a \"look-up\" in the similarity class.\n",
-    "\n",
-    "print('Cell took %.2f seconds to run.' %(time() - start))"
+    "sims = instance[query]  # A query is simply a \"look-up\" in the similarity class."
    ]
   },
   {
@@ -559,8 +547,7 @@
     }
    ],
    "source": [
-    "start = time()\n",
-    "\n",
+    "%%time\n",
     "sent = 'Great view of the Bellagio Fountain show.'\n",
     "query = dictionary.doc2bow(preprocess(sent))\n",
     "\n",
@@ -571,9 +558,7 @@
     "for i in range(num_best):\n",
     "    print()\n",
     "    print('sim = %.4f' % sims[i][1])\n",
-    "    print(documents[sims[i][0]])\n",
-    "\n",
-    "print('Cell took %.2f seconds to run.' %(time() - start))"
+    "    print(documents[sims[i][0]])"
    ]
   },
   {