diff --git a/docs/notebooks/annoytutorial.ipynb b/docs/notebooks/annoytutorial.ipynb index 3bc2bc9938..3da331df03 100644 --- a/docs/notebooks/annoytutorial.ipynb +++ b/docs/notebooks/annoytutorial.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -42,7 +42,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Word2Vec(vocab=10186, size=100, alpha=0.025)\n" + "Word2Vec(vocab=6981, size=100, alpha=0.025)\n" ] } ], @@ -59,12 +59,10 @@ " def __iter__(self):\n", " for line in open(lee_train_file):\n", " # assume there's one document per line, tokens separated by whitespace\n", - " yield line.lower().split()\n", + " yield gensim.utils.simple_preprocess(line)\n", "\n", "sentences = MyText()\n", - " \n", "model = Word2Vec(sentences, min_count=1)\n", - "\n", "print(model)" ] }, @@ -73,14 +71,19 @@ "metadata": {}, "source": [ "\n", - "#### Comparing the traditional implementation and the Annoy \n", - "\n", - "N.B. Running the timing cells below more than once gives subsequent timings close to zero, as cached objects are used. To get accurate timings, always run these cells from a freshly started kernel." + "#### Comparing the traditional implementation and the Annoy \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These benchmarks are run on a 2.4GHz 4 core i7 processor " ] }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -93,50 +96,41 @@ " raise ValueError(\"SKIP: Please install the annoy indexer\")\n", "\n", "model.init_sims()\n", - "vector = model.wv.syn0norm[0]\n", - "annoy_index = AnnoyIndexer(model, 500)" + "annoy_index = AnnoyIndexer(model, 300)" ] }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 3, "metadata": { - "collapsed": false, - "scrolled": false + "collapsed": false }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n", - "Wall time: 1.07 ms\n" - ] - }, { "data": { "text/plain": [ - "[('the', 0.9999999403953552),\n", - " ('a', 0.9999854564666748),\n", - " ('in', 0.9999842643737793),\n", - " ('on', 0.9999839067459106),\n", - " ('from', 0.9999836683273315)]" + "[(u'the', 1.0),\n", + " (u'on', 0.999976396560669),\n", + " (u'in', 0.9999759197235107),\n", + " (u'two', 0.9999756217002869),\n", + " (u'after', 0.9999749660491943)]" ] }, - "execution_count": 108, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "%%time\n", - "#Traditional implementation:\n", + "# Dry run to make sure both indices are fully in RAM\n", + "vector = model.wv.syn0norm[0]\n", + "model.most_similar([vector], topn=5, indexer=annoy_index)\n", "model.most_similar([vector], topn=5)" ] }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -145,22 +139,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "('a', 0.9973033787682652)\n", - "('in', 0.9971976953092963)\n", - "('an', 0.9971216244157404)\n", - "('which', 0.9964874279685318)\n", - "('has', 0.996418114984408)\n", - "CPU times: user 4 ms, sys: 0 ns, total: 4 ms\n", - "Wall time: 3.22 ms\n" + "Gensim: 0.002638526\n", + "Annoy: 0.001149898\n", + "\n", + "Annoy is 2.29 times faster on average over 1000 random queries on this particular run\n" ] } ], "source": [ - "%%time\n", - "#Annoy implementation:\n", - "neighbors = model.most_similar([vector], topn=5, indexer=annoy_index)\n", - "for neighbor in neighbors:\n", - " print(neighbor)" + "import time, numpy\n", + "\n", + "def avg_query_time(annoy_index=None):\n", + " \"\"\"\n", + " Average query time of a most_similar method over 1000 random queries,\n", + " uses annoy if given an indexer\n", + " \"\"\"\n", + " total_time = 0\n", + " for _ in range(1000):\n", + " rand_vec = model.wv.syn0norm[numpy.random.randint(0, len(model.vocab))]\n", + " start_time = time.clock()\n", + " model.most_similar([rand_vec], topn=5, indexer=annoy_index)\n", + " total_time += time.clock() - start_time\n", + " return total_time / 1000\n", + "\n", + "gensim_time = avg_query_time()\n", + "annoy_time = avg_query_time(annoy_index)\n", + "print \"Gensim: {}\".format(gensim_time) \n", + "print \"Annoy: {}\".format(annoy_time)\n", + "print \"\\nAnnoy is {} times faster on average over 1000 random queries on \\\n", + "this particular run\".format(numpy.round(gensim_time / annoy_time, 2))" ] }, { @@ -168,7 +175,8 @@ "metadata": {}, "source": [ "\n", - "A similarity query using Annoy is significantly faster than using the traditional brute force method\n", + "**This speedup factor is by no means constant** and will vary greatly from run to run and is particular to this data set, BLAS setup, Annoy parameters(as tree size increases speedup factor decreases), machine specifications, among other factors.\n", + "\n", ">**Note**: Initialization time for the annoy indexer was not included in the times. The optimal knn algorithm for you to use will depend on how many queries you need to make and the size of the corpus. If you are making very few similarity queries, the time taken to initialize the annoy indexer will be longer than the time it would take the brute force method to retrieve results. If you are making many queries however, the time it takes to initialize the annoy indexer will be made up for by the incredibly fast retrieval times for queries once the indexer has been initialized" ] }, @@ -206,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -240,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -248,7 +256,7 @@ "source": [ "from gensim.similarities.index import AnnoyIndexer\n", "# 100 trees are being used in this example\n", - "annoy_index = AnnoyIndexer(model,100)" + "annoy_index = AnnoyIndexer(model, 100)" ] }, { @@ -260,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -269,16 +277,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "('orbits', 0.6323515474796295)\n", - "('changes,', 0.6309329569339752)\n", - "('dr.', 0.6305853128433228)\n", - "('terrorism,', 0.6300898194313049)\n", - "('creditors', 0.6264415979385376)\n" + "(u'science', 0.9998273665260058)\n", + "(u'rates', 0.9086664393544197)\n", + "(u'insurance', 0.9080813005566597)\n", + "(u'north', 0.9077721834182739)\n", + "(u'there', 0.9076579436659813)\n" ] } ], "source": [ - "# Derive the vector for the word \"army\" in our model\n", + "# Derive the vector for the word \"science\" in our model\n", "vector = model[\"science\"]\n", "# The instance of AnnoyIndexer we just created is passed \n", "approximate_neighbors = model.most_similar([vector], topn=5, indexer=annoy_index)\n", @@ -298,7 +306,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The closer the cosine similarity of a vector is to 1, the more similar that word is to our query, which was the vector for \"army\"." + "The closer the cosine similarity of a vector is to 1, the more similar that word is to our query, which was the vector for \"science\"." ] }, { @@ -311,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -331,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -340,11 +348,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "('orbits', 0.6323515176773071)\n", - "('changes,', 0.6309329569339752)\n", - "('dr.', 0.630585253238678)\n", - "('terrorism,', 0.6300898790359497)\n", - "('creditors', 0.6264415681362152)\n" + "(u'science', 0.9998273665260058)\n", + "(u'rates', 0.9086666032671928)\n", + "(u'insurance', 0.9080811440944672)\n", + "(u'north', 0.9077721834182739)\n", + "(u'there', 0.9076577797532082)\n" ] } ], @@ -381,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -390,22 +398,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "Process Id: 12223\n", - "('pioline', 0.6537479162216187)\n", - "('benares', 0.6409106850624084)\n", - "('test,', 0.63045534491539)\n", - "('looked', 0.6301617622375488)\n", - "('terrorism,', 0.6300898194313049)\n", - "Memory used by process 12223= pmem(rss=213467136, vms=1341652992, shared=8622080, text=3051520, lib=0, data=1030971392, dirty=0)\n", - "Process Id: 12239\n", - "('pioline', 0.6537479162216187)\n", - "('benares', 0.6409106850624084)\n", - "('test,', 0.63045534491539)\n", - "('looked', 0.6301617622375488)\n", - "('terrorism,', 0.6300898194313049)\n", - "Memory used by process 12239= pmem(rss=213467136, vms=1341652992, shared=8622080, text=3051520, lib=0, data=1030971392, dirty=0)\n", - "CPU times: user 92 ms, sys: 32 ms, total: 124 ms\n", - "Wall time: 7.05 s\n" + "Process Id: 6216\n", + "(u'klusener', 0.9090957194566727)\n", + "(u'started', 0.908975400030613)\n", + "(u'gutnick', 0.908865213394165)\n", + "(u'ground', 0.9084076434373856)\n", + "(u'interest', 0.9074432477355003)\n", + "Memory used by process 6216= pmem(rss=126914560, vms=1385103360, shared=9273344, text=3051520, lib=0, data=1073524736, dirty=0)\n", + "Process Id: 6231\n", + "(u'klusener', 0.9090957194566727)\n", + "(u'started', 0.908975400030613)\n", + "(u'gutnick', 0.908865213394165)\n", + "(u'ground', 0.9084076434373856)\n", + "(u'interest', 0.9074432477355003)\n", + "Memory used by process 6231= pmem(rss=126496768, vms=1385103360, shared=8835072, text=3051520, lib=0, data=1073524736, dirty=0)\n", + "CPU times: user 64 ms, sys: 12 ms, total: 76 ms\n", + "Wall time: 2.86 s\n" ] } ], @@ -444,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -453,22 +461,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "Process Id: 12262\n", - "('orbits', 0.6323515474796295)\n", - "('changes,', 0.6309329569339752)\n", - "('dr.', 0.6305853128433228)\n", - "('terrorism,', 0.6300898194313049)\n", - "('creditors', 0.6264415979385376)\n", - "Memory used by process 12262 pmem(rss=231096320, vms=1358987264, shared=26247168, text=3051520, lib=0, data=1030971392, dirty=0)\n", - "Process Id: 12277\n", - "('orbits', 0.6323515474796295)\n", - "('changes,', 0.6309329569339752)\n", - "('dr.', 0.6305853128433228)\n", - "('terrorism,', 0.6300898194313049)\n", - "('creditors', 0.6264415979385376)\n", - "Memory used by process 12277 pmem(rss=231096320, vms=1358987264, shared=26247168, text=3051520, lib=0, data=1030971392, dirty=0)\n", - "CPU times: user 128 ms, sys: 28 ms, total: 156 ms\n", - "Wall time: 479 ms\n" + "Process Id: 6246\n", + "(u'science', 0.9998273665260058)\n", + "(u'rates', 0.9086664393544197)\n", + "(u'insurance', 0.9080813005566597)\n", + "(u'north', 0.9077721834182739)\n", + "(u'there', 0.9076579436659813)\n", + "Memory used by process 6246 pmem(rss=125091840, vms=1382862848, shared=22179840, text=3051520, lib=0, data=1058062336, dirty=0)\n", + "Process Id: 6261\n", + "(u'science', 0.9998273665260058)\n", + "(u'rates', 0.9086664393544197)\n", + "(u'insurance', 0.9080813005566597)\n", + "(u'north', 0.9077721834182739)\n", + "(u'there', 0.9076579436659813)\n", + "Memory used by process 6261 pmem(rss=125034496, vms=1382862848, shared=22122496, text=3051520, lib=0, data=1058062336, dirty=0)\n", + "CPU times: user 44 ms, sys: 16 ms, total: 60 ms\n", + "Wall time: 202 ms\n" ] } ], @@ -516,16 +524,16 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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M7AV3f7y6g0RVTc8Cl7j7z5We/gho4e6LzOxw4EVg+9q/FREpdN9+Cy+8ENobPvooNEZ/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\nEKk/XUgzWVrKFBEieUUXrhS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eM2YMHHUUXHhhGCW9996wySZZDVOk4OVq0aHjarrokEgyAwfCBhvAU1XN/hWZ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\n90FnVv7+bwJnwe8zYiwsr5Z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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -566,16 +574,16 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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