{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9da8f0d7",
   "metadata": {},
   "source": [
    "# Jaccard evaluations\n",
    "In this notebook, we will evaluate the Jaccard similarity of the different methods. The first will be the implementation as provided from this Rust crate HyperLogLog couters, testing all of the available bits and precisions. Note that, since there are no Python bindings for the Rust crate (yet) we run the cose as one of the test in the crate test suite. \n",
    "\n",
    "The second one will be MinHash, using the implementation provided by the datasketch library. We will compare the performance of the two methods for the same amount of memory used.\n",
    "\n",
    "## What is Jaccard similarity?\n",
    "The Jaccard similarity is a measure of similarity between two sets. It is defined as the size of the intersection divided by the size of the union of the two sets.\n",
    "\n",
    "## What is an HyperLogLog counter?\n",
    "An HyperLogLog counter is a probabilistic data structure used to estimate the cardinality of a set. It is based on the observation that the cardinality of a set can be estimated by the maximum number of leading zeros in the binary representation of the hashes of the elements of the set. The HyperLogLog counter is a data structure that stores the maximum number of leading zeros for a set of hashes. The counter is initialized with a number of bits, which determines the maximum number of leading zeros that can be stored. The counter is then updated with the hashes of the elements of the set. The estimate of the cardinality is then given by the harmonic mean of the values stored in the counter. HyperLogLog counters can be used to compute the cardinality of the union of two sets by taking the maximum of the values stored in the two counters, and therefore we can also compute the Jaccard similarity of two sets.\n",
    "\n",
    "## What is MinHash?\n",
    "MinHash is a probabilistic data structure used to estimate the Jaccard similarity of two sets. It is based on the observation that the Jaccard similarity of two sets can be estimated by the fraction of the elements of the two sets that have the same hash. The MinHash data structure is a list of hashes of the elements of the set. The hashes are computed by applying a hash function to each element of the set. The estimate of the Jaccard similarity is then given by the fraction of the elements of the two sets that have the same hash. MinHash can be used to compute the Jaccard similarity of the union of two sets by taking the minimum of the values stored in the two MinHash data structures.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d09d3dd7",
   "metadata": {},
   "source": [
    "We will use the version of MinHash available from [datasketch](https://github.com/ekzhu/datasketch), which is the one used as reference in the paper [Graph Neural Networks for Link Prediction with Subgraph Sketching](https://openreview.net/pdf?id=m1oqEOAozQU)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "101c93fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install datasketch -q"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef8911b1",
   "metadata": {},
   "source": [
    "We run the test suite, specifically make sure you run the test called test_jaccard_perfs, which will run the Jaccard similarity evaluation for all the available bits and precisions. The test will output a csv file with the results, which we will use to plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b621e868",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m   Compiling\u001b[0m hyperloglog-rs v0.1.38 (/home/luca/Github/hyperloglog-rs)\n",
      "\u001b[K\u001b[0m\u001b[1m\u001b[33mwarning\u001b[0m\u001b[0m\u001b[1m: function `splitmix64` is never used\u001b[0mloglog-rs(test), tes...\n",
      "\u001b[0m  \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m--> \u001b[0m\u001b[0mtests/test_cardinalities.rs:22:4\u001b[0m\n",
      "\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\n",
      "\u001b[0m\u001b[1m\u001b[38;5;12m22\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0mfn splitmix64(mut x: u64) -> u64 {\u001b[0m\n",
      "\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m| \u001b[0m\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[33m^^^^^^^^^^\u001b[0m\n",
      "\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\n",
      "\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m= \u001b[0m\u001b[0m\u001b[1mnote\u001b[0m\u001b[0m: `#[warn(dead_code)]` on by default\u001b[0m\n",
      "\n",
      "\u001b[K\u001b[0m\u001b[1m\u001b[33mwarning\u001b[0m\u001b[0m\u001b[1m: function `xorshift` is never used\u001b[0merloglog-rs(test), tes...\n",
      "\u001b[0m  \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m--> \u001b[0m\u001b[0mtests/test_cardinalities.rs:29:4\u001b[0m\n",
      "\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\n",
      "\u001b[0m\u001b[1m\u001b[38;5;12m29\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0mfn xorshift(mut x: u64) -> u64 {\u001b[0m\n",
      "\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m| \u001b[0m\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[33m^^^^^^^^\u001b[0m\n",
      "\n",
      "\u001b[K\u001b[0m\u001b[1m\u001b[33mwarning\u001b[0m\u001b[0m\u001b[1m: function `write_line` is never used\u001b[0mloglog-rs(test), tes...\n",
      "\u001b[0m  \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m--> \u001b[0m\u001b[0mtests/test_cardinalities.rs:36:4\u001b[0m\n",
      "\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\n",
      "\u001b[0m\u001b[1m\u001b[38;5;12m36\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0mfn write_line<P: Precision + WordType<BITS>, const BITS: usize>(\u001b[0m\n",
      "\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m| \u001b[0m\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[33m^^^^^^^^^^\u001b[0m\n",
      "\n",
      "\u001b[K\u001b[0m\u001b[1m\u001b[33mwarning\u001b[0m\u001b[0m\u001b[1m: function `test_cardinality_perfs` is never used\u001b[0mst), tes...\n",
      "\u001b[0m  \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m--> \u001b[0m\u001b[0mtests/test_cardinalities.rs:57:4\u001b[0m\n",
      "\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\n",
      "\u001b[0m\u001b[1m\u001b[38;5;12m57\u001b[0m\u001b[0m \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m|\u001b[0m\u001b[0m \u001b[0m\u001b[0mfn test_cardinality_perfs() {\u001b[0m\n",
      "\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[38;5;12m| \u001b[0m\u001b[0m   \u001b[0m\u001b[0m\u001b[1m\u001b[33m^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n",
      "\n",
      "\u001b[K\u001b[0m\u001b[0m\u001b[1m\u001b[33mwarning\u001b[0m\u001b[1m:\u001b[0m `hyperloglog-rs` (test \"test_cardinalities\") generated 4 warnings\n",
      "\u001b[K\u001b[0m\u001b[0m\u001b[1m\u001b[32m    Finished\u001b[0m release [optimized] target(s) in 24.54sjaccard_perfs(test)    \n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m unittests src/lib.rs (target/release/deps/hyperloglog_rs-f644f710901a4c54)\n",
      "\n",
      "running 0 tests\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_cardinalities.rs (target/release/deps/test_cardinalities-6d5eb9a6b68fac38)\n",
      "\n",
      "running 0 tests\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_10.rs (target/release/deps/test_hll_10-b81f34b121759669)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_10_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_10_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_10_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.03s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_11.rs (target/release/deps/test_hll_11-7667de6704cece8e)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_11_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_11_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_11_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.03s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_12.rs (target/release/deps/test_hll_12-2ab4b2f7bae54709)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_12_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_12_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_12_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.04s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_13.rs (target/release/deps/test_hll_13-1887908b13d419d5)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_13_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_13_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_13_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.03s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_14.rs (target/release/deps/test_hll_14-343c1d0e316319a0)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_14_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_14_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_14_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.03s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_15.rs (target/release/deps/test_hll_15-35a8285f66fb8004)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_15_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_15_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_15_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.03s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_16.rs (target/release/deps/test_hll_16-31473672cf82ad0e)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_16_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_16_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_16_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.04s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_17.rs (target/release/deps/test_hll_17-e480e7aca2fe593f)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_17_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_17_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_17_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.04s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_4.rs (target/release/deps/test_hll_4-c8f3eb023b225afd)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_4_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_4_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_4_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.03s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_5.rs (target/release/deps/test_hll_5-c398b49acd86e1b4)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_5_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_5_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_5_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.03s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_6.rs (target/release/deps/test_hll_6-2f29c29bb0369b65)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_6_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_6_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_6_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.03s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_7.rs (target/release/deps/test_hll_7-c80f4de237ccc142)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_7_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_7_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_7_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.03s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_8.rs (target/release/deps/test_hll_8-e07fc61c4e326493)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_8_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_8_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_8_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.03s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_hll_9.rs (target/release/deps/test_hll_9-263e4015d4885f85)\n",
      "\n",
      "running 3 tests\n",
      "test test_hyper_log_log_at_precision_9_and_bits_4 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_9_and_bits_5 ... \u001b[32mok\u001b[m\n",
      "test test_hyper_log_log_at_precision_9_and_bits_6 ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.03s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m     Running\u001b[0m tests/test_jaccard_perfs.rs (target/release/deps/test_jaccard_perfs-2f6616ce4fff057e)\n",
      "\n",
      "running 1 test\n",
      "test test_jaccard_perfs ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 1.02s\n",
      "\n",
      "\u001b[0m\u001b[0m\u001b[1m\u001b[32m   Doc-tests\u001b[0m hyperloglog-rs\n",
      "\n",
      "running 71 tests\n",
      "test src/estimated_union_cardinalities.rs - estimated_union_cardinalities::EstimatedUnionCardinalities<F>::get_union_cardinality (line 70) ... \u001b[32mok\u001b[m\n",
      "test src/estimated_union_cardinalities.rs - estimated_union_cardinalities::EstimatedUnionCardinalities<F>::get_jaccard_index (line 168) ... \u001b[32mok\u001b[m\n",
      "test src/estimated_union_cardinalities.rs - estimated_union_cardinalities::EstimatedUnionCardinalities<F>::get_symmetric_difference_cardinality (line 147) ... \u001b[32mok\u001b[m\n",
      "test src/bitor.rs - bitor::HyperLogLog<P,BITS>::bitor (line 157) ... \u001b[32mok\u001b[m\n",
      "test src/estimated_union_cardinalities.rs - estimated_union_cardinalities::EstimatedUnionCardinalities<F>::get_right_cardinality (line 51) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::estimated_difference_cardinality_vector (line 1443) ... \u001b[32mok\u001b[m\n",
      "test src/estimated_union_cardinalities.rs - estimated_union_cardinalities::EstimatedUnionCardinalities<F>::get_intersection_cardinality (line 89) ... \u001b[32mok\u001b[m\n",
      "test src/estimated_union_cardinalities.rs - estimated_union_cardinalities::EstimatedUnionCardinalities<F>::get_left_cardinality (line 32) ... \u001b[32mok\u001b[m\n",
      "test src/bitor.rs - bitor::HyperLogLog<P,BITS>::bitor (line 225) ... \u001b[32mok\u001b[m\n",
      "test src/estimated_union_cardinalities.rs - estimated_union_cardinalities::EstimatedUnionCardinalities<F>::get_left_difference_cardinality (line 109) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::estimate_cardinality (line 268) ... \u001b[32mok\u001b[m\n",
      "test src/bitor.rs - bitor::HyperLogLog<P,BITS>::bitor (line 245) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS> (line 98) ... \u001b[32mok\u001b[m\n",
      "test src/estimated_union_cardinalities.rs - estimated_union_cardinalities::EstimatedUnionCardinalities<F>::get_right_difference_cardinality (line 128) ... \u001b[32mok\u001b[m\n",
      "test src/bitor.rs - bitor::HyperLogLog<P,BITS>::bitor (line 177) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::estimated_difference_cardinality_vector (line 1476) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::get_number_of_bits (line 643) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::get_hash_and_index (line 1557) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::from_registers (line 162) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::get_number_of_padding_registers (line 676) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::estimated_overlap_cardinality_matrix (line 1198) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::get_number_of_zero_registers (line 717) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::get_number_of_registers (line 658) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::get_number_of_padding_registers (line 698) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::estimated_overlap_cardinality_matrix (line 1139) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::estimate_jaccard_cardinality (line 473) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog (line 39) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::get_registers (line 753) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::estimate_difference_cardinality (line 508) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::estimated_overlap_cardinality_matrix (line 1105) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::estimate_union_cardinality (line 323) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::estimate_intersection_cardinality (line 436) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::is_empty (line 618) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog_array.rs - hyperloglog_array::HyperLogLogArray<P,BITS,N>::default (line 29) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::insert (line 1596) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::iter (line 549) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog_array.rs - hyperloglog_array::HyperLogLogArray<P,BITS,N>::from (line 335) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::get_registers (line 769) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog_array.rs - hyperloglog_array::HyperLogLogArray<P,BITS,N>::from (line 266) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::len (line 587) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog_array.rs - hyperloglog_array::HyperLogLogArray<P,BITS,N>::from (line 301) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog_array.rs - hyperloglog_array::HyperLogLogArray<P,BITS,N>::from (line 406) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog_array.rs - hyperloglog_array::HyperLogLogArray<P,BITS,N>::from (line 238) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::may_contain (line 794) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::may_contain_all (line 831) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog_array.rs - hyperloglog_array::HyperLogLogArray<P,BITS,N>::new (line 49) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::from_iter (line 1701) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog_array.rs - hyperloglog_array::HyperLogLogArray<P,BITS,N>::from (line 367) ... \u001b[32mok\u001b[m\n",
      "test src/bitor.rs - bitor::HyperLogLog<P,BITS>::bitor (line 207) ... \u001b[32mok\u001b[m\n",
      "test src/utils.rs - utils::ceil (line 22) ... \u001b[32mok\u001b[m\n",
      "test src/lib.rs - (line 33) ... \u001b[32mok\u001b[m\n",
      "test src/max_min.rs - max_min::MaxMin::get_min (line 22) ... \u001b[32mok\u001b[m\n",
      "test src/max_min.rs - max_min::MaxMin::get_max (line 8) ... \u001b[32mok\u001b[m\n",
      "test src/utils.rs - utils::linear_counting_threshold (line 128) ... \u001b[32mok\u001b[m\n",
      "test src/utils.rs - utils::ceil (line 30) ... \u001b[32mok\u001b[m\n",
      "test src/serde.rs - serde::HyperLogLog<P,BITS>::serialize (line 166) ... \u001b[32mok\u001b[m\n",
      "test src/utils.rs - utils::get_alpha (line 89) ... \u001b[32mok\u001b[m\n",
      "test src/utils.rs - utils::precompute_linear_counting (line 52) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog_array.rs - hyperloglog_array::HyperLogLogArray<P,BITS,N>::index_mut (line 197) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog_array.rs - hyperloglog_array::HyperLogLogArray<P,BITS,N>::index (line 153) ... \u001b[32mok\u001b[m\n",
      "test src/serde.rs - serde::HyperLogLog<P,BITS>::deserialize (line 202) ... \u001b[32mok\u001b[m\n",
      "test src/serde.rs - serde::HyperLogLogArray<P,BITS,N>::serialize (line 32) ... \u001b[32mok\u001b[m\n",
      "test src/bitor.rs - bitor::HyperLogLog<P,BITS>::bitor (line 139) ... \u001b[32mok\u001b[m\n",
      "test src/iter.rs - iter (line 16) ... \u001b[32mok\u001b[m\n",
      "test src/iter.rs - iter::HyperLogLogIterator::union (line 61) ... \u001b[32mok\u001b[m\n",
      "test src/iter.rs - iter (line 6) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::from (line 75) ... \u001b[32mok\u001b[m\n",
      "test src/lib.rs - (line 39) ... \u001b[32mok\u001b[m\n",
      "test src/hyperloglog.rs - hyperloglog::HyperLogLog<P,BITS>::estimated_overlap_and_differences_cardinality_matrices (line 905) ... \u001b[32mok\u001b[m\n",
      "test src/bitor.rs - bitor::HyperLogLog<P,BITS>::bitor_assign (line 63) ... \u001b[32mok\u001b[m\n",
      "test src/bitor.rs - bitor::HyperLogLog<P,BITS>::bitor_assign (line 12) ... \u001b[32mok\u001b[m\n",
      "\n",
      "test result: \u001b[32mok\u001b[m. 71 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 4.72s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!cargo test --release"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caefd364",
   "metadata": {},
   "source": [
    "and we load the results we have obtained:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd95a263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>bits</th>\n",
       "      <th>exact</th>\n",
       "      <th>approximation</th>\n",
       "      <th>set1</th>\n",
       "      <th>set2</th>\n",
       "      <th>memory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.451163</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>939,732,531,709,58,829,313,638,564,392,232,533...</td>\n",
       "      <td>262,650,964,940,716,554,476,209,957,212,474,17...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.451163</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>939,732,531,709,58,829,313,638,564,392,232,533...</td>\n",
       "      <td>262,650,964,940,716,554,476,209,957,212,474,17...</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.451163</td>\n",
       "      <td>0.582010</td>\n",
       "      <td>939,732,531,709,58,829,313,638,564,392,232,533...</td>\n",
       "      <td>262,650,964,940,716,554,476,209,957,212,474,17...</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.451163</td>\n",
       "      <td>0.521435</td>\n",
       "      <td>939,732,531,709,58,829,313,638,564,392,232,533...</td>\n",
       "      <td>262,650,964,940,716,554,476,209,957,212,474,17...</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.451163</td>\n",
       "      <td>0.521435</td>\n",
       "      <td>939,732,531,709,58,829,313,638,564,392,232,533...</td>\n",
       "      <td>262,650,964,940,716,554,476,209,957,212,474,17...</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8395</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>0.471412</td>\n",
       "      <td>0.469518</td>\n",
       "      <td>841,384,797,268,887,629,573,647,909,537,775,33...</td>\n",
       "      <td>153,878,830,311,443,43,227,496,460,32,765,874,...</td>\n",
       "      <td>262144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8396</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>0.471412</td>\n",
       "      <td>0.469518</td>\n",
       "      <td>841,384,797,268,887,629,573,647,909,537,775,33...</td>\n",
       "      <td>153,878,830,311,443,43,227,496,460,32,765,874,...</td>\n",
       "      <td>393216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8397</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0.471412</td>\n",
       "      <td>0.469518</td>\n",
       "      <td>841,384,797,268,887,629,573,647,909,537,775,33...</td>\n",
       "      <td>153,878,830,311,443,43,227,496,460,32,765,874,...</td>\n",
       "      <td>524288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8398</th>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>0.471412</td>\n",
       "      <td>0.469518</td>\n",
       "      <td>841,384,797,268,887,629,573,647,909,537,775,33...</td>\n",
       "      <td>153,878,830,311,443,43,227,496,460,32,765,874,...</td>\n",
       "      <td>655360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8399</th>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>0.471412</td>\n",
       "      <td>0.469518</td>\n",
       "      <td>841,384,797,268,887,629,573,647,909,537,775,33...</td>\n",
       "      <td>153,878,830,311,443,43,227,496,460,32,765,874,...</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8400 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      precision  bits     exact  approximation  \\\n",
       "0             4     1  0.451163       1.000000   \n",
       "1             4     2  0.451163       1.000000   \n",
       "2             4     3  0.451163       0.582010   \n",
       "3             4     4  0.451163       0.521435   \n",
       "4             4     5  0.451163       0.521435   \n",
       "...         ...   ...       ...            ...   \n",
       "8395         17     2  0.471412       0.469518   \n",
       "8396         17     3  0.471412       0.469518   \n",
       "8397         17     4  0.471412       0.469518   \n",
       "8398         17     5  0.471412       0.469518   \n",
       "8399         17     6  0.471412       0.469518   \n",
       "\n",
       "                                                   set1  \\\n",
       "0     939,732,531,709,58,829,313,638,564,392,232,533...   \n",
       "1     939,732,531,709,58,829,313,638,564,392,232,533...   \n",
       "2     939,732,531,709,58,829,313,638,564,392,232,533...   \n",
       "3     939,732,531,709,58,829,313,638,564,392,232,533...   \n",
       "4     939,732,531,709,58,829,313,638,564,392,232,533...   \n",
       "...                                                 ...   \n",
       "8395  841,384,797,268,887,629,573,647,909,537,775,33...   \n",
       "8396  841,384,797,268,887,629,573,647,909,537,775,33...   \n",
       "8397  841,384,797,268,887,629,573,647,909,537,775,33...   \n",
       "8398  841,384,797,268,887,629,573,647,909,537,775,33...   \n",
       "8399  841,384,797,268,887,629,573,647,909,537,775,33...   \n",
       "\n",
       "                                                   set2  memory  \n",
       "0     262,650,964,940,716,554,476,209,957,212,474,17...      16  \n",
       "1     262,650,964,940,716,554,476,209,957,212,474,17...      32  \n",
       "2     262,650,964,940,716,554,476,209,957,212,474,17...      48  \n",
       "3     262,650,964,940,716,554,476,209,957,212,474,17...      64  \n",
       "4     262,650,964,940,716,554,476,209,957,212,474,17...      80  \n",
       "...                                                 ...     ...  \n",
       "8395  153,878,830,311,443,43,227,496,460,32,765,874,...  262144  \n",
       "8396  153,878,830,311,443,43,227,496,460,32,765,874,...  393216  \n",
       "8397  153,878,830,311,443,43,227,496,460,32,765,874,...  524288  \n",
       "8398  153,878,830,311,443,43,227,496,460,32,765,874,...  655360  \n",
       "8399  153,878,830,311,443,43,227,496,460,32,765,874,...  786432  \n",
       "\n",
       "[8400 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"jaccard_benchmark.tsv\", sep=\"\\t\")\n",
    "df[\"memory\"] = 2**df.precision * df.bits\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5e9bfe8",
   "metadata": {},
   "source": [
    "We determine the number of u64 words to use for the MinHash to execute versions of MinHash with comparable memory usage to the HyperLogLog counters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef240523",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "bits = df.bits.unique()\n",
    "precision = df.precision.unique()\n",
    "\n",
    "number_of_words = {\n",
    "    # We divide again by 2 because the number of permutations used is a u64\n",
    "    # and by two again as \n",
    "    math.ceil(b * 2**p / 32) // 2 // 2\n",
    "    for b in bits\n",
    "    for p in precision\n",
    "}\n",
    "\n",
    "# Some HyperLogLog counters require less than 64 bits, so there will be\n",
    "# values in the list that are zero. We remove them.\n",
    "number_of_words = [\n",
    "    word\n",
    "    for word in number_of_words\n",
    "    if word > 0\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8c5238",
   "metadata": {},
   "source": [
    "We parallelize the computation of the Jaccard similarity using MinHash, which is significantly slower than the HyperLogLog counters as while the former is an extensively optimized Rust implementation, the latter is more didactical Python implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba90008f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List\n",
    "from datasketch import MinHash\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "def get_perfs(row) -> List:\n",
    "    row = row[1]\n",
    "    new_results = []\n",
    "    set1 = [\n",
    "        l.encode(\"utf8\")\n",
    "        for l in row.set1.split(\",\")\n",
    "    ]\n",
    "    set2 = [\n",
    "        r.encode(\"utf8\")\n",
    "        for r in row.set2.split(\",\")\n",
    "    ]\n",
    "    for number_of_permutations in number_of_words:\n",
    "        left_min_hash = MinHash(\n",
    "            num_perm=number_of_permutations\n",
    "        )\n",
    "        right_min_hash = MinHash(\n",
    "            num_perm=number_of_permutations\n",
    "        )\n",
    "        for left in set1:\n",
    "            left_min_hash.update(left)\n",
    "        for right in set2:\n",
    "            right_min_hash.update(right)\n",
    "        estimated = left_min_hash.jaccard(right_min_hash)\n",
    "        new_results.append({\n",
    "            \"exact\": row.exact,\n",
    "            \"set1\": row.set1,\n",
    "            \"set2\": row.set2,\n",
    "            \"approximation\": estimated,\n",
    "            \"number_of_permutations\": number_of_permutations\n",
    "        })\n",
    "    return new_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf90ca36",
   "metadata": {},
   "source": [
    "We compute the performance of the two methods for the same amount of memory used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5df58f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "89c07d515d924494ba9ce7beae10e0d6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8400 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from multiprocessing import Pool, cpu_count\n",
    "\n",
    "with Pool(cpu_count()) as p:\n",
    "    results = list(tqdm(\n",
    "        p.imap(\n",
    "            get_perfs,\n",
    "            df.iterrows()\n",
    "        ),\n",
    "        total=df.shape[0]\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bca25bb9",
   "metadata": {},
   "source": [
    "We convert the results into a pandas DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f65ed038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d46011f2b44c4f2e9142f9deea55c320",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8400 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exact</th>\n",
       "      <th>set1</th>\n",
       "      <th>set2</th>\n",
       "      <th>approximation</th>\n",
       "      <th>number_of_permutations</th>\n",
       "      <th>memory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.451163</td>\n",
       "      <td>939,732,531,709,58,829,313,638,564,392,232,533...</td>\n",
       "      <td>262,650,964,940,716,554,476,209,957,212,474,17...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.451163</td>\n",
       "      <td>939,732,531,709,58,829,313,638,564,392,232,533...</td>\n",
       "      <td>262,650,964,940,716,554,476,209,957,212,474,17...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.451163</td>\n",
       "      <td>939,732,531,709,58,829,313,638,564,392,232,533...</td>\n",
       "      <td>262,650,964,940,716,554,476,209,957,212,474,17...</td>\n",
       "      <td>0.468750</td>\n",
       "      <td>128</td>\n",
       "      <td>8192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.451163</td>\n",
       "      <td>939,732,531,709,58,829,313,638,564,392,232,533...</td>\n",
       "      <td>262,650,964,940,716,554,476,209,957,212,474,17...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.451163</td>\n",
       "      <td>939,732,531,709,58,829,313,638,564,392,232,533...</td>\n",
       "      <td>262,650,964,940,716,554,476,209,957,212,474,17...</td>\n",
       "      <td>0.464844</td>\n",
       "      <td>256</td>\n",
       "      <td>16384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302395</th>\n",
       "      <td>0.471412</td>\n",
       "      <td>841,384,797,268,887,629,573,647,909,537,775,33...</td>\n",
       "      <td>153,878,830,311,443,43,227,496,460,32,765,874,...</td>\n",
       "      <td>0.465007</td>\n",
       "      <td>6144</td>\n",
       "      <td>393216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302396</th>\n",
       "      <td>0.471412</td>\n",
       "      <td>841,384,797,268,887,629,573,647,909,537,775,33...</td>\n",
       "      <td>153,878,830,311,443,43,227,496,460,32,765,874,...</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>80</td>\n",
       "      <td>5120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302397</th>\n",
       "      <td>0.471412</td>\n",
       "      <td>841,384,797,268,887,629,573,647,909,537,775,33...</td>\n",
       "      <td>153,878,830,311,443,43,227,496,460,32,765,874,...</td>\n",
       "      <td>0.464453</td>\n",
       "      <td>2560</td>\n",
       "      <td>163840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302398</th>\n",
       "      <td>0.471412</td>\n",
       "      <td>841,384,797,268,887,629,573,647,909,537,775,33...</td>\n",
       "      <td>153,878,830,311,443,43,227,496,460,32,765,874,...</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>96</td>\n",
       "      <td>6144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302399</th>\n",
       "      <td>0.471412</td>\n",
       "      <td>841,384,797,268,887,629,573,647,909,537,775,33...</td>\n",
       "      <td>153,878,830,311,443,43,227,496,460,32,765,874,...</td>\n",
       "      <td>0.462891</td>\n",
       "      <td>3072</td>\n",
       "      <td>196608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>302400 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           exact                                               set1  \\\n",
       "0       0.451163  939,732,531,709,58,829,313,638,564,392,232,533...   \n",
       "1       0.451163  939,732,531,709,58,829,313,638,564,392,232,533...   \n",
       "2       0.451163  939,732,531,709,58,829,313,638,564,392,232,533...   \n",
       "3       0.451163  939,732,531,709,58,829,313,638,564,392,232,533...   \n",
       "4       0.451163  939,732,531,709,58,829,313,638,564,392,232,533...   \n",
       "...          ...                                                ...   \n",
       "302395  0.471412  841,384,797,268,887,629,573,647,909,537,775,33...   \n",
       "302396  0.471412  841,384,797,268,887,629,573,647,909,537,775,33...   \n",
       "302397  0.471412  841,384,797,268,887,629,573,647,909,537,775,33...   \n",
       "302398  0.471412  841,384,797,268,887,629,573,647,909,537,775,33...   \n",
       "302399  0.471412  841,384,797,268,887,629,573,647,909,537,775,33...   \n",
       "\n",
       "                                                     set2  approximation  \\\n",
       "0       262,650,964,940,716,554,476,209,957,212,474,17...       0.000000   \n",
       "1       262,650,964,940,716,554,476,209,957,212,474,17...       0.500000   \n",
       "2       262,650,964,940,716,554,476,209,957,212,474,17...       0.468750   \n",
       "3       262,650,964,940,716,554,476,209,957,212,474,17...       0.500000   \n",
       "4       262,650,964,940,716,554,476,209,957,212,474,17...       0.464844   \n",
       "...                                                   ...            ...   \n",
       "302395  153,878,830,311,443,43,227,496,460,32,765,874,...       0.465007   \n",
       "302396  153,878,830,311,443,43,227,496,460,32,765,874,...       0.575000   \n",
       "302397  153,878,830,311,443,43,227,496,460,32,765,874,...       0.464453   \n",
       "302398  153,878,830,311,443,43,227,496,460,32,765,874,...       0.531250   \n",
       "302399  153,878,830,311,443,43,227,496,460,32,765,874,...       0.462891   \n",
       "\n",
       "        number_of_permutations  memory  \n",
       "0                            1      64  \n",
       "1                            2     128  \n",
       "2                          128    8192  \n",
       "3                            4     256  \n",
       "4                          256   16384  \n",
       "...                        ...     ...  \n",
       "302395                    6144  393216  \n",
       "302396                      80    5120  \n",
       "302397                    2560  163840  \n",
       "302398                      96    6144  \n",
       "302399                    3072  196608  \n",
       "\n",
       "[302400 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "as_df = pd.DataFrame([\n",
    "    r\n",
    "    for result in tqdm(results)\n",
    "    for r in result\n",
    "])\n",
    "as_df[\"memory\"] = as_df.number_of_permutations * 64\n",
    "as_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "70b006e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>memory</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64</td>\n",
       "      <td>0.245700</td>\n",
       "      <td>0.038829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128</td>\n",
       "      <td>0.111834</td>\n",
       "      <td>0.119985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>192</td>\n",
       "      <td>0.066232</td>\n",
       "      <td>0.082245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>256</td>\n",
       "      <td>0.044520</td>\n",
       "      <td>0.058441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>320</td>\n",
       "      <td>0.036147</td>\n",
       "      <td>0.044544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>384</td>\n",
       "      <td>0.029469</td>\n",
       "      <td>0.043035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>512</td>\n",
       "      <td>0.026213</td>\n",
       "      <td>0.042820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>640</td>\n",
       "      <td>0.020692</td>\n",
       "      <td>0.030710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>768</td>\n",
       "      <td>0.016957</td>\n",
       "      <td>0.021557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1024</td>\n",
       "      <td>0.014456</td>\n",
       "      <td>0.024049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1280</td>\n",
       "      <td>0.013237</td>\n",
       "      <td>0.020518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1536</td>\n",
       "      <td>0.012405</td>\n",
       "      <td>0.018340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2048</td>\n",
       "      <td>0.007839</td>\n",
       "      <td>0.011935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2560</td>\n",
       "      <td>0.005947</td>\n",
       "      <td>0.009320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3072</td>\n",
       "      <td>0.005503</td>\n",
       "      <td>0.008481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4096</td>\n",
       "      <td>0.004550</td>\n",
       "      <td>0.008080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5120</td>\n",
       "      <td>0.002959</td>\n",
       "      <td>0.004933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6144</td>\n",
       "      <td>0.001980</td>\n",
       "      <td>0.002634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8192</td>\n",
       "      <td>0.001767</td>\n",
       "      <td>0.002578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10240</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>0.002184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12288</td>\n",
       "      <td>0.001101</td>\n",
       "      <td>0.001544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16384</td>\n",
       "      <td>0.000853</td>\n",
       "      <td>0.001230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20480</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>0.001090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24576</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.000989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>32768</td>\n",
       "      <td>0.000585</td>\n",
       "      <td>0.000822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>40960</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.000661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>49152</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>65536</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>81920</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.000280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>98304</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>131072</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>163840</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.000152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>196608</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.000127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>262144</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>327680</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>393216</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    memory      mean       std\n",
       "0       64  0.245700  0.038829\n",
       "1      128  0.111834  0.119985\n",
       "2      192  0.066232  0.082245\n",
       "3      256  0.044520  0.058441\n",
       "4      320  0.036147  0.044544\n",
       "5      384  0.029469  0.043035\n",
       "6      512  0.026213  0.042820\n",
       "7      640  0.020692  0.030710\n",
       "8      768  0.016957  0.021557\n",
       "9     1024  0.014456  0.024049\n",
       "10    1280  0.013237  0.020518\n",
       "11    1536  0.012405  0.018340\n",
       "12    2048  0.007839  0.011935\n",
       "13    2560  0.005947  0.009320\n",
       "14    3072  0.005503  0.008481\n",
       "15    4096  0.004550  0.008080\n",
       "16    5120  0.002959  0.004933\n",
       "17    6144  0.001980  0.002634\n",
       "18    8192  0.001767  0.002578\n",
       "19   10240  0.001470  0.002184\n",
       "20   12288  0.001101  0.001544\n",
       "21   16384  0.000853  0.001230\n",
       "22   20480  0.000844  0.001090\n",
       "23   24576  0.000760  0.000989\n",
       "24   32768  0.000585  0.000822\n",
       "25   40960  0.000469  0.000661\n",
       "26   49152  0.000337  0.000403\n",
       "27   65536  0.000261  0.000296\n",
       "28   81920  0.000223  0.000280\n",
       "29   98304  0.000156  0.000223\n",
       "30  131072  0.000124  0.000195\n",
       "31  163840  0.000104  0.000152\n",
       "32  196608  0.000099  0.000127\n",
       "33  262144  0.000064  0.000091\n",
       "34  327680  0.000048  0.000063\n",
       "35  393216  0.000039  0.000050"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "as_df[\"squared_error\"] = (as_df.exact - as_df.approximation)**2\n",
    "data_minhash = as_df.groupby(\"memory\")[\"squared_error\"].agg([\"mean\", \"std\"]).reset_index()\n",
    "data_minhash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b8106fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>bits</th>\n",
       "      <th>memory</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.288176</td>\n",
       "      <td>0.016250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>0.288176</td>\n",
       "      <td>0.016250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>0.026676</td>\n",
       "      <td>0.028074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>64</td>\n",
       "      <td>0.030784</td>\n",
       "      <td>0.032352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>0.030784</td>\n",
       "      <td>0.032352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>262144</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>393216</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>524288</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>655360</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>786432</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    precision  bits  memory      mean       std\n",
       "0           4     1      16  0.288176  0.016250\n",
       "1           4     2      32  0.288176  0.016250\n",
       "2           4     3      48  0.026676  0.028074\n",
       "3           4     4      64  0.030784  0.032352\n",
       "4           4     5      80  0.030784  0.032352\n",
       "..        ...   ...     ...       ...       ...\n",
       "79         17     2  262144  0.000001  0.000001\n",
       "80         17     3  393216  0.000001  0.000001\n",
       "81         17     4  524288  0.000001  0.000001\n",
       "82         17     5  655360  0.000001  0.000001\n",
       "83         17     6  786432  0.000001  0.000001\n",
       "\n",
       "[84 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"squared_error\"] = (df.exact - df.approximation)**2\n",
    "data_hll = df.groupby([\"precision\", \"bits\", \"memory\"])[\"squared_error\"].agg([\"mean\", \"std\"])\n",
    "data_hll = data_hll.reset_index()\n",
    "data_hll"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a53f306",
   "metadata": {},
   "source": [
    "We visualize two versions of the HyperLogLog counters, one including also **EXTREMELY SMALL** registers of 1 and 2 bits, which really push the limits of the HyperLogLog counters, and one without them, which is more representative of the performance of the HyperLogLog counters for the more common use cases. We include these tiny registers as exploring the limits of the HyperLogLog counters is one of the main goals of this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a370282",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for bits_to_discard in ((), (1, 2)):\n",
    "    for bits in data_hll[\"bits\"].unique():\n",
    "        if bits in bits_to_discard:\n",
    "            continue\n",
    "        filtered = data_hll[data_hll.bits == bits]\n",
    "        plt.errorbar(\n",
    "            filtered.memory,\n",
    "            filtered[\"mean\"],\n",
    "            filtered[\"std\"],\n",
    "            marker='^',\n",
    "            label=f\"HLL {bits} bits\"\n",
    "        )\n",
    "    plt.errorbar(\n",
    "        data_minhash.memory,\n",
    "        data_minhash[\"mean\"],\n",
    "        data_minhash[\"std\"],\n",
    "        marker='^',\n",
    "        label=\"MinHash\",\n",
    "        alpha=0.7\n",
    "    )\n",
    "    plt.legend()\n",
    "    plt.xscale(\"log\")\n",
    "    #plt.yscale(\"log\")\n",
    "    plt.ylabel(\"Jaccard MSE over 1000 random sets\")\n",
    "    plt.xlabel(\"Memory required (bits)\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc8a7c4",
   "metadata": {},
   "source": [
    "We do the same thing, but we multiply the error by the number of bits necessary by the data structure, so to better understand how the error scales with the number of bits used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "078ad21e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "for bits_to_discard in ((), (1, 2)):\n",
    "    for bits in data_hll[\"bits\"].unique():\n",
    "        if bits in bits_to_discard:\n",
    "            continue\n",
    "        filtered = data_hll[data_hll.bits == bits]\n",
    "        plt.errorbar(\n",
    "            filtered.memory,\n",
    "            filtered[\"mean\"] * filtered.memory,\n",
    "            filtered[\"std\"] * filtered.memory,\n",
    "            marker='^',\n",
    "            label=f\"HLL {bits} bits\"\n",
    "        )\n",
    "\n",
    "    plt.errorbar(\n",
    "        data_minhash.memory,\n",
    "        data_minhash[\"mean\"] * data_minhash.memory,\n",
    "        data_minhash[\"std\"] * data_minhash.memory,\n",
    "        marker='^',\n",
    "        label=\"MinHash\",\n",
    "        alpha=0.7\n",
    "    )\n",
    "    plt.legend()\n",
    "    plt.xscale(\"log\")\n",
    "    plt.ylabel(\"Jaccard MSE times number of bits\")\n",
    "    plt.xlabel(\"Memory required (bits)\")\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}