Improving Python notebooks (no ticket)

mldrugdiscovery · Feb 7, 2018 · d515b32 · d515b32
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diff --git a/python_bindings/notebooks/search_dense_nonoptim.ipynb b/python_bindings/notebooks/search_dense_nonoptim.ipynb
@@ -0,0 +1,376 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy \n",
+    "import sys \n",
+    "import nmslib \n",
+    "import time \n",
+    "import math \n",
+    "from sklearn.neighbors import NearestNeighbors\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Just read the data\n",
+    "all_data_matrix = numpy.loadtxt('../../sample_data/final128_10K.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Create a held-out query data set\n",
+    "(data_matrix, query_matrix) = train_test_split(all_data_matrix, test_size = 0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# of queries 1000, # of data points 9000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"# of queries %d, # of data points %d\"  % (query_matrix.shape[0], data_matrix.shape[0]) )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set index parameters\n",
+    "# These are the most important onese\n",
+    "M = 15\n",
+    "efC = 100\n",
+    "\n",
+    "num_threads = 4\n",
+    "index_time_params = {'M': M, 'indexThreadQty': num_threads, 'efConstruction': efC, 'post' : 0,\n",
+    "                     'skip_optimized_index' : 1 # using non-optimized index!\n",
+    "                    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Number of neighbors \n",
+    "K=100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Space name should correspond to the space name \n",
+    "# used for brute-force search\n",
+    "space_name='l2'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9000"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Intitialize the library, specify the space, the type of the vector and add data points \n",
+    "index = nmslib.init(method='hnsw', space=space_name, data_type=nmslib.DataType.DENSE_VECTOR) \n",
+    "index.addDataPointBatch(data_matrix) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index-time parameters {'M': 15, 'indexThreadQty': 4, 'efConstruction': 100, 'skip_optimized_index': 1, 'post': 0}\n",
+      "Indexing time = 0.291947\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create an index\n",
+    "start = time.time()\n",
+    "index.createIndex(index_time_params) \n",
+    "end = time.time() \n",
+    "print('Index-time parameters', index_time_params)\n",
+    "print('Indexing time = %f' % (end-start))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Setting query-time parameters {'efSearch': 100}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Setting query-time parameters\n",
+    "efS = 100\n",
+    "query_time_params = {'efSearch': efS}\n",
+    "print('Setting query-time parameters', query_time_params)\n",
+    "index.setQueryTimeParams(query_time_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kNN time total=0.037669 (sec), per query=0.000038 (sec), per query adjusted for thread number=0.000151 (sec)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Querying\n",
+    "query_qty = query_matrix.shape[0]\n",
+    "start = time.time() \n",
+    "nbrs = index.knnQueryBatch(query_matrix, k = K, num_threads = num_threads)\n",
+    "end = time.time() \n",
+    "print('kNN time total=%f (sec), per query=%f (sec), per query adjusted for thread number=%f (sec)' % \n",
+    "      (end-start, float(end-start)/query_qty, num_threads*float(end-start)/query_qty)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Computing gold-standard data\n",
+      "Brute-force preparation time 0.001139\n",
+      "brute-force kNN time total=0.319399 (sec), per query=0.000319 (sec)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Computing gold-standard data \n",
+    "print('Computing gold-standard data')\n",
+    "\n",
+    "start = time.time()\n",
+    "sindx = NearestNeighbors(n_neighbors=K, metric='l2', algorithm='brute').fit(data_matrix)\n",
+    "end = time.time()\n",
+    "\n",
+    "print('Brute-force preparation time %f' % (end - start))\n",
+    "\n",
+    "start = time.time() \n",
+    "gs = sindx.kneighbors(query_matrix)\n",
+    "end = time.time()\n",
+    "\n",
+    "print('brute-force kNN time total=%f (sec), per query=%f (sec)' % \n",
+    "      (end-start, float(end-start)/query_qty) )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kNN recall 0.993040\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Finally computing recall\n",
+    "recall=0.0\n",
+    "for i in range(0, query_qty):\n",
+    "  correct_set = set(gs[1][i])\n",
+    "  ret_set = set(nbrs[i][0])\n",
+    "  recall = recall + float(len(correct_set.intersection(ret_set))) / len(correct_set)\n",
+    "recall = recall / query_qty\n",
+    "print('kNN recall %f' % recall)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Save a meta index\n",
+    "index.saveIndex('dense_index_nonoptim.bin')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Re-intitialize the library, specify the space, the type of the vector.\n",
+    "newIndex = nmslib.init(method='hnsw', space=space_name, data_type=nmslib.DataType.DENSE_VECTOR) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9000"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For non-optimized indices or methods different from HNSW we DO need to re-add data points\n",
+    "newIndex.addDataPointBatch(data_matrix) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Re-load the index and re-run queries\n",
+    "newIndex.loadIndex('dense_index_nonoptim.bin')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Setting query-time parameters {'efSearch': 100}\n",
+      "kNN time total=0.031991 (sec), per query=0.000032 (sec), per query adjusted for thread number=0.000128 (sec)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Setting query-time parameters and querying\n",
+    "print('Setting query-time parameters', query_time_params)\n",
+    "newIndex.setQueryTimeParams(query_time_params)\n",
+    "\n",
+    "query_qty = query_matrix.shape[0]\n",
+    "start = time.time() \n",
+    "new_nbrs = newIndex.knnQueryBatch(query_matrix, k = K, num_threads = num_threads)\n",
+    "end = time.time() \n",
+    "print('kNN time total=%f (sec), per query=%f (sec), per query adjusted for thread number=%f (sec)' % \n",
+    "      (end-start, float(end-start)/query_qty, num_threads*float(end-start)/query_qty)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kNN recall 0.993040\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Finally computing recall for the new result set\n",
+    "recall=0.0\n",
+    "for i in range(0, query_qty):\n",
+    "  correct_set = set(gs[1][i])\n",
+    "  ret_set = set(new_nbrs[i][0])\n",
+    "  recall = recall + float(len(correct_set.intersection(ret_set))) / len(correct_set)\n",
+    "recall = recall / query_qty\n",
+    "print('kNN recall %f' % recall)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}