├── requirements.txt ├── data └── Evaluation of Jupyter Notebooks.csv ├── test_condorcet.py ├── README.md ├── condorcet.py ├── Run_ranking.ipynb └── LICENSE /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | pandas 3 | -------------------------------------------------------------------------------- /data/Evaluation of Jupyter Notebooks.csv: -------------------------------------------------------------------------------- 1 | Informazioni cronologiche,Who would you choose as winner?,Who would you choose for second place?,Who would you choose for third place? 2 | 2020/11/16 11:57:17 AM CET,marabernardi,rorondre,lordgrilo 3 | 2020/11/16 3:21:27 PM CET,Antonio-Leitao,lordgrilo,marabernardi 4 | 2020/11/17 10:40:44 PM CET,marabernardi,rorondre,kazuyamagiwa 5 | 2020/11/18 10:21:57 AM CET,lordgrilo,Antonio-Leitao,marabernardi 6 | 2020/11/18 2:29:42 PM CET,kazuyamagiwa,filco306,rorondre 7 | 2020/11/18 4:51:06 PM CET,filco306,rorondre,marabernardi 8 | 2020/11/18 7:52:16 PM CET,rorondre,filco306,marabernardi 9 | 2020/11/20 9:50:04 AM CET,Antonio-Leitao,filco306,rorondre 10 | 2020/11/21 6:47:42 PM CET,marabernardi,filco306,lordgrilo 11 | 2020/11/21 10:48:21 PM CET,rorondre,marabernardi,lordgrilo 12 | 2020/11/22 6:03:33 PM CET,marabernardi,kazuyamagiwa,rorondre 13 | 2020/11/22 6:19:20 PM CET,marabernardi,kazuyamagiwa,Antonio-Leitao 14 | 2020/11/22 7:18:54 PM CET,filco306,lordgrilo,rorondre -------------------------------------------------------------------------------- /test_condorcet.py: -------------------------------------------------------------------------------- 1 | """Ranks candidates by the Condorcet method with also unvoted candidates. 2 | 3 | For more information, please refer to https://en.wikipedia.org/wiki/Condorcet_method. 4 | """ 5 | 6 | __author__ = "Matteo Caorsi" 7 | ## I thank Michael G. Parker (http://omgitsmgp.com/) from whom I took the code skeleton 8 | 9 | import numpy as np 10 | import pandas as pd 11 | import condorcet 12 | 13 | def test_compute_ranks(): 14 | '''A simnple unit test 15 | ''' 16 | 17 | # create dataframe 18 | table = np.asarray([ 19 | ["b","c","a"], 20 | ["c","a","b"], 21 | ["c","a","b"], 22 | ["b","a","c"], 23 | ["d","a","c"], 24 | ["d","a","c"] 25 | ]) 26 | df = pd.DataFrame(table, columns = ["first_preference","second_preference","third_preference"]) 27 | try: 28 | # run the classification 29 | assert condorcet.compute_ranks(df)==[['a', 'c'], ['b'], ['d']] 30 | except: 31 | raise ValueError("The method is not working as expected!") 32 | 33 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # condorcet-method 2 | This repo implements the Schulze method, a Condorcet-type election method. Please refer to [Wikipedia](https://en.wikipedia.org/wiki/Schulze_method) for all the mathematical details. 3 | 4 | ## How to run the method 5 | 6 | __1. Prepare your data__ 7 | 8 | The first step is to import your data in a dataframe. The structure has to be the following: 9 | - Each voter has to correspond to a line 10 | - Each row contains, ordered from the left to the right columns, the preferences of the voter 11 | - Each entry corresponds to the name of teh candidates: make sure not to make spelling mistakes 12 | 13 | To see a concrete example, have a look at [Run_ranking.ipynb](https://github.com/giotto-ai/condorcet-method/blob/main/Run_ranking.ipynb). 14 | 15 | __2. Run the election method to discover the winning candidates__ 16 | 17 | To run the model once the dataframe `df` is ready, it is just one line: 18 | ``` 19 | condorcet.compute_ranks(df) 20 | ``` 21 | As in the example in the notebook, the output is a list of lists, where the position of the inner list corresponds to the ranking and if the inner lists are longer than one element, it is because there is a tie. 22 | 23 | __3. Dive deeper__ 24 | 25 | You can extract the *d[V,W]* and *p[V,W]* matrices with the methods: 26 | - `condorcet._compute_d(weighted_ranks, candidate_names)` 27 | - `condorcet._compute_p(dmat, candidate_names)` 28 | 29 | To extract the candidate names and the weighted ranks to be input to the above methods, please use: 30 | - `weighted_ranks = condorcet.weighted_ranks_from_df(df)` 31 | - `candidate_names = condorcet.candidate_names_from_df(df)` 32 | 33 | ## Code tests 34 | 35 | The [Wikipedia example](https://en.wikipedia.org/wiki/Schulze_method#Example) is reproduced in the example notebook and all the entries of the *p* and *d* matrices match the original ones. 36 | 37 | A short unit test can be run with the command `pytest` once in the repo root folder. To run the test, pytest is required. You can install it with `pip install pytest`. 38 | -------------------------------------------------------------------------------- /condorcet.py: -------------------------------------------------------------------------------- 1 | """Ranks candidates by the Condorcet method with also unvoted candidates. 2 | 3 | For more information, please refer to https://en.wikipedia.org/wiki/Condorcet_method. 4 | """ 5 | 6 | __author__ = "Matteo Caorsi" 7 | ## I thank Michael G. Parker (http://omgitsmgp.com/) from whom I took the code skeleton 8 | 9 | import numpy as np 10 | from collections import defaultdict 11 | 12 | def candidate_names_from_df(df): 13 | return list(np.unique(df.values.flatten())) 14 | 15 | def weighted_ranks_from_df(df): 16 | weighted_ranks = [] 17 | for row in df.values: 18 | weighted_ranks.append((list(row),1)) 19 | return weighted_ranks 20 | 21 | 22 | def _add_remaining_ranks(d, candidate_name, remaining_ranks, weight): 23 | for other_candidate_name in remaining_ranks: 24 | d[candidate_name, other_candidate_name] += weight 25 | 26 | 27 | def _add_ranks_to_d(d, ranks, weight, unvoted_candidates): 28 | for i, candidate_name in enumerate(ranks): 29 | remaining_ranks = ranks[i+1:] + unvoted_candidates 30 | _add_remaining_ranks(d, candidate_name, remaining_ranks, weight) 31 | 32 | 33 | def _compute_d(weighted_ranks, candidate_names): 34 | """Computes the d array in the Schulze method. 35 | 36 | d[V,W] is the number of voters who prefer candidate V over W. 37 | 38 | We consider unvoted candidates as being ranked less than any 39 | other candidate voted by the voter. 40 | """ 41 | d = defaultdict(int) 42 | for ranks, weight in weighted_ranks: 43 | unvoted_candidates = list(set(candidate_names)-set(ranks)) 44 | #print("unoted:", unvoted_candidates) 45 | _add_ranks_to_d(d, ranks, weight, unvoted_candidates) 46 | #print(d) 47 | return d 48 | 49 | 50 | def _compute_p(d, candidate_names): 51 | '''Computes the p array in the Schulze method. 52 | 53 | p[V,W] is the strength of the strongest path from candidate V to W. 54 | ''' 55 | 56 | # taken directly from wikipedia: https://en.wikipedia.org/wiki/Schulze_method#Implementation 57 | p = {} 58 | for candidate_name1 in candidate_names: 59 | for candidate_name2 in candidate_names: 60 | if candidate_name1 != candidate_name2: 61 | # get the value from the d matrix or default it to 0 62 | strength = d.get((candidate_name1, candidate_name2), 0) 63 | if strength > d.get((candidate_name2, candidate_name1), 0): 64 | p[candidate_name1, candidate_name2] = strength 65 | else: 66 | p[candidate_name1, candidate_name2] = 0 67 | 68 | for candidate_name1 in candidate_names: 69 | for candidate_name2 in candidate_names: 70 | if candidate_name1 != candidate_name2: 71 | for candidate_name3 in candidate_names: 72 | if (candidate_name1 != candidate_name3) and (candidate_name2 != candidate_name3): 73 | curr_value = p.get((candidate_name2, candidate_name3), 0) 74 | new_value = min( 75 | p.get((candidate_name2, candidate_name1), 0), 76 | p.get((candidate_name1, candidate_name3), 0)) 77 | p[candidate_name2, candidate_name3] = max(curr_value,new_value) 78 | return p 79 | 80 | 81 | def _rank_p(candidate_names, p): 82 | """Ranks the candidates by p.""" 83 | # how many times does a candidate wins against each of the others? 84 | candidate_wins = defaultdict(list) 85 | 86 | for candidate_name1 in candidate_names: 87 | num_wins = 0 88 | 89 | # Compute the number of wins this candidate has over all other candidates. 90 | for candidate_name2 in candidate_names: 91 | if candidate_name1 != candidate_name2: 92 | candidate1_score = p.get((candidate_name1, candidate_name2), 0) 93 | candidate2_score = p.get((candidate_name2, candidate_name1), 0) 94 | if candidate1_score > candidate2_score: 95 | num_wins += 1 96 | 97 | candidate_wins[num_wins].append(candidate_name1) 98 | #print(candidate_wins) 99 | sorted_wins = sorted(candidate_wins.keys(), reverse=True) 100 | return [candidate_wins[num_wins] for num_wins in sorted_wins] 101 | 102 | 103 | def compute_ranks(df): 104 | weighted_ranks = weighted_ranks_from_df(df) 105 | candidate_names = candidate_names_from_df(df) 106 | #print(candidate_names) 107 | d = _compute_d(weighted_ranks, candidate_names) 108 | p = _compute_p(d, candidate_names) 109 | return _rank_p(candidate_names, p) 110 | -------------------------------------------------------------------------------- /Run_ranking.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# The Condorcet-Schulze method\n", 8 | "\n", 9 | "The Schulze method is a refinemnet of the original Condorcet methods that are used for deciding the winning candidate of an election in which all the voters can express a number of different preferences.\n", 10 | "\n", 11 | "To read all about the method we have been using, have a look at [Wikipedia](https://en.wikipedia.org/wiki/Schulze_method).\n", 12 | "\n", 13 | "## How to use the condorcet module\n", 14 | "\n", 15 | "Create a dataframe with the ordered poreferences for each voter. The first column shall be the first preference for the i-th voter.\n", 16 | "\n", 17 | "The entries of each cell are the names of the candidates.\n", 18 | "\n", 19 | "Each voter can vote for all or part of the candidates. The non voted candidates are considered to be less preferred than the voted ones.\n" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 1, 25 | "metadata": {}, 26 | "outputs": [], 27 | "source": [ 28 | "import condorcet\n", 29 | "import numpy as np\n", 30 | "import pandas as pd" 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "metadata": {}, 36 | "source": [ 37 | "## Create or import your dataframe\n", 38 | "\n", 39 | "Make sure that the structure of the dataframe is a in the example below: each row represents the preferences of each voter (there are six voters in the exaple below). The names of the candidates are *a, b, c, d*." 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 2, 45 | "metadata": {}, 46 | "outputs": [ 47 | { 48 | "data": { 49 | "text/html": [ 50 | "
\n", 51 | "\n", 64 | "\n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | "
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" 113 | ], 114 | "text/plain": [ 115 | " first_preference second_preference third_preference\n", 116 | "0 b c a\n", 117 | "1 c a b\n", 118 | "2 c a b\n", 119 | "3 b a c\n", 120 | "4 d a c\n", 121 | "5 d a c" 122 | ] 123 | }, 124 | "execution_count": 2, 125 | "metadata": {}, 126 | "output_type": "execute_result" 127 | } 128 | ], 129 | "source": [ 130 | "# create dataframe\n", 131 | "table = np.asarray([\n", 132 | " [\"b\",\"c\",\"a\"],\n", 133 | " [\"c\",\"a\",\"b\"],\n", 134 | " [\"c\",\"a\",\"b\"],\n", 135 | " [\"b\",\"a\",\"c\"],\n", 136 | " [\"d\",\"a\",\"c\"],\n", 137 | " [\"d\",\"a\",\"c\"]\n", 138 | " ])\n", 139 | "df = pd.DataFrame(table, columns = [\"first_preference\",\"second_preference\",\"third_preference\"])\n", 140 | "df\n" 141 | ] 142 | }, 143 | { 144 | "cell_type": "markdown", 145 | "metadata": {}, 146 | "source": [ 147 | "## Run on the dataframe\n", 148 | "\n", 149 | "Just run the `condorcet.compute_ranks` method on the dataframe directly and the result will appear as a list of lists.\n", 150 | "\n", 151 | "In the example below, the output is ```[['a', 'c'], ['b'], ['d']]```, which means that there are two tie candidates in the first place, *a and c*, one candidate in the second place *b and* the least preferred candidate is *d*." 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": 3, 157 | "metadata": {}, 158 | "outputs": [ 159 | { 160 | "data": { 161 | "text/plain": [ 162 | "[['a', 'c'], ['b'], ['d']]" 163 | ] 164 | }, 165 | "execution_count": 3, 166 | "metadata": {}, 167 | "output_type": "execute_result" 168 | } 169 | ], 170 | "source": [ 171 | "# run the classification\n", 172 | "condorcet.compute_ranks(df)" 173 | ] 174 | }, 175 | { 176 | "cell_type": "markdown", 177 | "metadata": {}, 178 | "source": [ 179 | "## Dive deeper\n", 180 | "\n", 181 | "You can extract the *d[V,W]* and *p[V,W]* matrices with the methods:\n", 182 | " - ```condorcet._compute_d(weighted_ranks, candidate_names)```\n", 183 | " - ```condorcet._compute_p(dmat, candidate_names)```\n", 184 | " \n", 185 | "To extract the candidate names and the weighted ranks to be input to the above methods, please use:\n", 186 | " - ```weighted_ranks = condorcet.weighted_ranks_from_df(df)```\n", 187 | " - ```candidate_names = condorcet.candidate_names_from_df(df)```" 188 | ] 189 | }, 190 | { 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "## Test on Wikipedia example\n", 195 | "\n", 196 | "We test the method on [wikipedia example](https://en.wikipedia.org/wiki/Schulze_method#Example) to make sure that the algorithm works properly." 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": 4, 202 | "metadata": {}, 203 | "outputs": [ 204 | { 205 | "data": { 206 | "text/html": [ 207 | "
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" 550 | ], 551 | "text/plain": [ 552 | " first_column second_column third_column fourth_column\n", 553 | "0 A C B E\n", 554 | "1 A C B E\n", 555 | "2 A C B E\n", 556 | "3 A C B E\n", 557 | "4 A C B E\n", 558 | "5 A D E C\n", 559 | "6 A D E C\n", 560 | "7 A D E C\n", 561 | "8 A D E C\n", 562 | "9 A D E C\n", 563 | "10 B E D A\n", 564 | "11 B E D A\n", 565 | "12 B E D A\n", 566 | "13 B E D A\n", 567 | "14 B E D A\n", 568 | "15 B E D A\n", 569 | "16 B E D A\n", 570 | "17 B E D A\n", 571 | "18 C A B E\n", 572 | "19 C A B E\n", 573 | "20 C A B E\n", 574 | "21 C A E B\n", 575 | "22 C A E B\n", 576 | "23 C A E B\n", 577 | "24 C A E B\n", 578 | "25 C A E B\n", 579 | "26 C A E B\n", 580 | "27 C A E B\n", 581 | "28 C B A D\n", 582 | "29 C B A D\n", 583 | "30 D C E B\n", 584 | "31 D C E B\n", 585 | "32 D C E B\n", 586 | "33 D C E B\n", 587 | "34 D C E B\n", 588 | "35 D C E B\n", 589 | "36 D C E B\n", 590 | "37 E B A D\n", 591 | "38 E B A D\n", 592 | "39 E B A D\n", 593 | "40 E B A D\n", 594 | "41 E B A D\n", 595 | "42 E B A D\n", 596 | "43 E B A D\n", 597 | "44 E B A D" 598 | ] 599 | }, 600 | "execution_count": 4, 601 | "metadata": {}, 602 | "output_type": "execute_result" 603 | } 604 | ], 605 | "source": [ 606 | "# create dataframe\n", 607 | "df = pd.DataFrame()\n", 608 | "\n", 609 | "# populate with wikipedia preferences\n", 610 | "df[\"first_column\"] = 5*['A']+5*['A']+8*['B']+3*['C']+7*['C']+2*['C']+7*['D']+8*['E']\n", 611 | "df[\"second_column\"] = 5*['C']+5*['D']+8*['E']+3*['A']+7*['A']+2*['B']+7*['C']+8*['B']\n", 612 | "df[\"third_column\"] = 5*['B']+5*['E']+8*['D']+3*['B']+7*['E']+2*['A']+7*['E']+8*['A']\n", 613 | "df[\"fourth_column\"] = 5*['E']+5*['C']+8*['A']+3*['E']+7*['B']+2*['D']+7*['B']+8*['D']\n", 614 | "\n", 615 | "# we are not putting explicitly the last column, as it will be automatically inferred by the algorithm\n", 616 | "df" 617 | ] 618 | }, 619 | { 620 | "cell_type": "code", 621 | "execution_count": 5, 622 | "metadata": {}, 623 | "outputs": [ 624 | { 625 | "data": { 626 | "text/plain": [ 627 | "[['E'], ['A'], ['C'], ['B'], ['D']]" 628 | ] 629 | }, 630 | "execution_count": 5, 631 | "metadata": {}, 632 | "output_type": "execute_result" 633 | } 634 | ], 635 | "source": [ 636 | "# run the classification\n", 637 | "condorcet.compute_ranks(df)" 638 | ] 639 | }, 640 | { 641 | "cell_type": "code", 642 | "execution_count": 6, 643 | "metadata": {}, 644 | "outputs": [ 645 | { 646 | "name": "stdout", 647 | "output_type": "stream", 648 | "text": [ 649 | "d matrix: defaultdict(, {('A', 'C'): 26, ('A', 'B'): 20, ('A', 'E'): 22, ('A', 'D'): 30, ('C', 'B'): 29, ('C', 'E'): 24, ('C', 'D'): 17, ('B', 'E'): 18, ('B', 'D'): 33, ('E', 'D'): 31, ('D', 'E'): 14, ('D', 'C'): 28, ('D', 'B'): 12, ('E', 'C'): 21, ('E', 'B'): 27, ('B', 'A'): 25, ('B', 'C'): 16, ('E', 'A'): 23, ('D', 'A'): 15, ('C', 'A'): 19})\n", 650 | "p matrix: {('A', 'B'): 28, ('A', 'C'): 28, ('A', 'D'): 30, ('A', 'E'): 24, ('B', 'A'): 25, ('B', 'C'): 28, ('B', 'D'): 33, ('B', 'E'): 24, ('C', 'A'): 25, ('C', 'B'): 29, ('C', 'D'): 29, ('C', 'E'): 24, ('D', 'A'): 25, ('D', 'B'): 28, ('D', 'C'): 28, ('D', 'E'): 24, ('E', 'A'): 25, ('E', 'B'): 28, ('E', 'C'): 28, ('E', 'D'): 31}\n" 651 | ] 652 | } 653 | ], 654 | "source": [ 655 | "# checking the d and p matrix\n", 656 | "candidate_names = condorcet.candidate_names_from_df(df)\n", 657 | "weighted_ranks = condorcet.weighted_ranks_from_df(df)\n", 658 | "d = condorcet._compute_d(weighted_ranks, candidate_names)\n", 659 | "p = condorcet._compute_p(d, candidate_names)\n", 660 | "print(\"d matrix: \", d)\n", 661 | "print(\"p matrix: \", p)" 662 | ] 663 | }, 664 | { 665 | "cell_type": "markdown", 666 | "metadata": {}, 667 | "source": [ 668 | "## Test passed!\n", 669 | "The algorithmis sound and we were able to reproduce exaclty Wikipedia's example." 670 | ] 671 | }, 672 | { 673 | "cell_type": "markdown", 674 | "metadata": {}, 675 | "source": [ 676 | "## Application to the gtda-challenge-2020 \n", 677 | "\n", 678 | "We first import the csv file and then we run the method" 679 | ] 680 | }, 681 | { 682 | "cell_type": "code", 683 | "execution_count": 7, 684 | "metadata": {}, 685 | "outputs": [ 686 | { 687 | "data": { 688 | "text/html": [ 689 | "
\n", 690 | "\n", 703 | "\n", 704 | " \n", 705 | " \n", 706 | " \n", 707 | " \n", 708 | " \n", 709 | " \n", 710 | " \n", 711 | " \n", 712 | " \n", 713 | " \n", 714 | " \n", 715 | " \n", 716 | " \n", 717 | " \n", 718 | " \n", 719 | " \n", 720 | " \n", 721 | " \n", 722 | " \n", 723 | " \n", 724 | " \n", 725 | " \n", 726 | " \n", 727 | " \n", 728 | " \n", 729 | " \n", 730 | " \n", 731 | " \n", 732 | " \n", 733 | " \n", 734 | " \n", 735 | " \n", 736 | " \n", 737 | " \n", 738 | " \n", 739 | " \n", 740 | " \n", 741 | " \n", 742 | " \n", 743 | " \n", 744 | " \n", 745 | " \n", 746 | " \n", 747 | " \n", 748 | " \n", 749 | " \n", 750 | " \n", 751 | " \n", 752 | " \n", 753 | " \n", 754 | " \n", 755 | " \n", 756 | " \n", 757 | " \n", 758 | " \n", 759 | " \n", 760 | " \n", 761 | " \n", 762 | " \n", 763 | " \n", 764 | " \n", 765 | " \n", 766 | " \n", 767 | " \n", 768 | " \n", 769 | " \n", 770 | " \n", 771 | " \n", 772 | " \n", 773 | " \n", 774 | " \n", 775 | " \n", 776 | " \n", 777 | " \n", 778 | " \n", 779 | " \n", 780 | " \n", 781 | " \n", 782 | " \n", 783 | " \n", 784 | " \n", 785 | " \n", 786 | " \n", 787 | " \n", 788 | " \n", 789 | " \n", 790 | " \n", 791 | " \n", 792 | "
Who would you choose as winner?Who would you choose for second place?Who would you choose for third place?
0marabernardirorondrelordgrilo
1Antonio-Leitaolordgrilomarabernardi
2marabernardirorondrekazuyamagiwa
3lordgriloAntonio-Leitaomarabernardi
4kazuyamagiwafilco306rorondre
5filco306rorondremarabernardi
6rorondrefilco306marabernardi
7Antonio-Leitaofilco306rorondre
8marabernardifilco306lordgrilo
9rorondremarabernardilordgrilo
10marabernardikazuyamagiwarorondre
11marabernardikazuyamagiwaAntonio-Leitao
12filco306lordgrilororondre
\n", 793 | "
" 794 | ], 795 | "text/plain": [ 796 | " Who would you choose as winner? Who would you choose for second place? \\\n", 797 | "0 marabernardi rorondre \n", 798 | "1 Antonio-Leitao lordgrilo \n", 799 | "2 marabernardi rorondre \n", 800 | "3 lordgrilo Antonio-Leitao \n", 801 | "4 kazuyamagiwa filco306 \n", 802 | "5 filco306 rorondre \n", 803 | "6 rorondre filco306 \n", 804 | "7 Antonio-Leitao filco306 \n", 805 | "8 marabernardi filco306 \n", 806 | "9 rorondre marabernardi \n", 807 | "10 marabernardi kazuyamagiwa \n", 808 | "11 marabernardi kazuyamagiwa \n", 809 | "12 filco306 lordgrilo \n", 810 | "\n", 811 | " Who would you choose for third place? \n", 812 | "0 lordgrilo \n", 813 | "1 marabernardi \n", 814 | "2 kazuyamagiwa \n", 815 | "3 marabernardi \n", 816 | "4 rorondre \n", 817 | "5 marabernardi \n", 818 | "6 marabernardi \n", 819 | "7 rorondre \n", 820 | "8 lordgrilo \n", 821 | "9 lordgrilo \n", 822 | "10 rorondre \n", 823 | "11 Antonio-Leitao \n", 824 | "12 rorondre " 825 | ] 826 | }, 827 | "execution_count": 7, 828 | "metadata": {}, 829 | "output_type": "execute_result" 830 | } 831 | ], 832 | "source": [ 833 | "# import file as dataframe\n", 834 | "\n", 835 | "df_temp = pd.read_csv(\"data/Evaluation of Jupyter Notebooks.csv\")\n", 836 | "df = df_temp[df_temp.columns[-3:]]\n", 837 | "df" 838 | ] 839 | }, 840 | { 841 | "cell_type": "code", 842 | "execution_count": 8, 843 | "metadata": {}, 844 | "outputs": [ 845 | { 846 | "data": { 847 | "text/plain": [ 848 | "[['marabernardi'],\n", 849 | " ['filco306', 'rorondre'],\n", 850 | " ['lordgrilo'],\n", 851 | " ['kazuyamagiwa'],\n", 852 | " ['Antonio-Leitao']]" 853 | ] 854 | }, 855 | "execution_count": 8, 856 | "metadata": {}, 857 | "output_type": "execute_result" 858 | } 859 | ], 860 | "source": [ 861 | "# run the final classification\n", 862 | "\n", 863 | "condorcet.compute_ranks(df)" 864 | ] 865 | }, 866 | { 867 | "cell_type": "markdown", 868 | "metadata": {}, 869 | "source": [ 870 | "## Congratulations!!\n", 871 | "\n", 872 | "Congratulations to all the candidates that submitted a notebook to the challenge! All of the notebooks were original, well structured and clearly explained. Thank you for your efforts!\n", 873 | "\n", 874 | "I truly hope you enjoyed the challenge and do not hesitate to share your notebooks with your friends and colleagues!\n", 875 | "\n", 876 | "# The final classification is:\n", 877 | " - 1st place, **marabernardi**\n", 878 | " - 2nd place, **rorondre**\n", 879 | " - 2nd place ex aequo, **filco306**\n", 880 | " - 4th place, **lordgrilo**\n", 881 | " - 5th place, **kazuyamagiwa**\n", 882 | " - 6th place, **Antonio-Leitao**\n", 883 | " \n", 884 | "Bravo!! ;)" 885 | ] 886 | }, 887 | { 888 | "cell_type": "code", 889 | "execution_count": null, 890 | "metadata": {}, 891 | "outputs": [], 892 | "source": [] 893 | } 894 | ], 895 | "metadata": { 896 | "kernelspec": { 897 | "display_name": "Python 3", 898 | "language": "python", 899 | "name": "python3" 900 | }, 901 | "language_info": { 902 | "codemirror_mode": { 903 | "name": "ipython", 904 | "version": 3 905 | }, 906 | "file_extension": ".py", 907 | "mimetype": "text/x-python", 908 | "name": "python", 909 | "nbconvert_exporter": "python", 910 | "pygments_lexer": "ipython3", 911 | "version": "3.6.8" 912 | } 913 | }, 914 | "nbformat": 4, 915 | "nbformat_minor": 2 916 | } 917 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright 2020 L2F SA. 2 | 3 | If you need a different distribution license, please contact the L2F team 4 | at business@l2f.ch. 5 | 6 | Licensed under the GNU Affero General Public License (the "License"); 7 | you may not use this file except in compliance with the License. 8 | You may obtain a copy of the License below or at https://www.gnu.org/licenses/agpl-3.0.html 9 | 10 | Unless required by applicable law or agreed to in writing, software 11 | distributed under the License is distributed on an "AS IS" BASIS, 12 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | See the License for the specific language governing permissions and 14 | limitations under the License. 15 | 16 | 17 | GNU AFFERO GENERAL PUBLIC LICENSE 18 | Version 3, 19 November 2007 19 | 20 | Copyright (C) 2007 Free Software Foundation, Inc. 21 | Everyone is permitted to copy and distribute verbatim copies 22 | of this license document, but changing it is not allowed. 23 | 24 | Preamble 25 | 26 | The GNU Affero General Public License is a free, copyleft license for 27 | software and other kinds of works, specifically designed to ensure 28 | cooperation with the community in the case of network server software. 29 | 30 | The licenses for most software and other practical works are designed 31 | to take away your freedom to share and change the works. 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Patents. 476 | 477 | A "contributor" is a copyright holder who authorizes use under this 478 | License of the Program or a work on which the Program is based. The 479 | work thus licensed is called the contributor's "contributor version". 480 | 481 | A contributor's "essential patent claims" are all patent claims 482 | owned or controlled by the contributor, whether already acquired or 483 | hereafter acquired, that would be infringed by some manner, permitted 484 | by this License, of making, using, or selling its contributor version, 485 | but do not include claims that would be infringed only as a 486 | consequence of further modification of the contributor version. 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You may not convey a covered 529 | work if you are a party to an arrangement with a third party that is 530 | in the business of distributing software, under which you make payment 531 | to the third party based on the extent of your activity of conveying 532 | the work, and under which the third party grants, to any of the 533 | parties who would receive the covered work from you, a discriminatory 534 | patent license (a) in connection with copies of the covered work 535 | conveyed by you (or copies made from those copies), or (b) primarily 536 | for and in connection with specific products or compilations that 537 | contain the covered work, unless you entered into that arrangement, 538 | or that patent license was granted, prior to 28 March 2007. 539 | 540 | Nothing in this License shall be construed as excluding or limiting 541 | any implied license or other defenses to infringement that may 542 | otherwise be available to you under applicable patent law. 543 | 544 | 12. No Surrender of Others' Freedom. 545 | 546 | If conditions are imposed on you (whether by court order, agreement or 547 | otherwise) that contradict the conditions of this License, they do not 548 | excuse you from the conditions of this License. If you cannot convey a 549 | covered work so as to satisfy simultaneously your obligations under this 550 | License and any other pertinent obligations, then as a consequence you may 551 | not convey it at all. For example, if you agree to terms that obligate you 552 | to collect a royalty for further conveying from those to whom you convey 553 | the Program, the only way you could satisfy both those terms and this 554 | License would be to refrain entirely from conveying the Program. 555 | 556 | 13. Remote Network Interaction; Use with the GNU General Public License. 557 | 558 | Notwithstanding any other provision of this License, if you modify the 559 | Program, your modified version must prominently offer all users 560 | interacting with it remotely through a computer network (if your version 561 | supports such interaction) an opportunity to receive the Corresponding 562 | Source of your version by providing access to the Corresponding Source 563 | from a network server at no charge, through some standard or customary 564 | means of facilitating copying of software. This Corresponding Source 565 | shall include the Corresponding Source for any work covered by version 3 566 | of the GNU General Public License that is incorporated pursuant to the 567 | following paragraph. 568 | 569 | Notwithstanding any other provision of this License, you have 570 | permission to link or combine any covered work with a work licensed 571 | under version 3 of the GNU General Public License into a single 572 | combined work, and to convey the resulting work. The terms of this 573 | License will continue to apply to the part which is the covered work, 574 | but the work with which it is combined will remain governed by version 575 | 3 of the GNU General Public License. 576 | 577 | 14. Revised Versions of this License. 578 | 579 | The Free Software Foundation may publish revised and/or new versions of 580 | the GNU Affero General Public License from time to time. Such new versions 581 | will be similar in spirit to the present version, but may differ in detail to 582 | address new problems or concerns. 583 | 584 | Each version is given a distinguishing version number. If the 585 | Program specifies that a certain numbered version of the GNU Affero General 586 | Public License "or any later version" applies to it, you have the 587 | option of following the terms and conditions either of that numbered 588 | version or of any later version published by the Free Software 589 | Foundation. If the Program does not specify a version number of the 590 | GNU Affero General Public License, you may choose any version ever published 591 | by the Free Software Foundation. 592 | 593 | If the Program specifies that a proxy can decide which future 594 | versions of the GNU Affero General Public License can be used, that proxy's 595 | public statement of acceptance of a version permanently authorizes you 596 | to choose that version for the Program. 597 | 598 | Later license versions may give you additional or different 599 | permissions. However, no additional obligations are imposed on any 600 | author or copyright holder as a result of your choosing to follow a 601 | later version. 602 | 603 | 15. Disclaimer of Warranty. 604 | 605 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 606 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 607 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 608 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 609 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 610 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 611 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 612 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 613 | 614 | 16. Limitation of Liability. 615 | 616 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 617 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 618 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 619 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 620 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 621 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 622 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 623 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 624 | SUCH DAMAGES. 625 | 626 | 17. Interpretation of Sections 15 and 16. 627 | 628 | If the disclaimer of warranty and limitation of liability provided 629 | above cannot be given local legal effect according to their terms, 630 | reviewing courts shall apply local law that most closely approximates 631 | an absolute waiver of all civil liability in connection with the 632 | Program, unless a warranty or assumption of liability accompanies a 633 | copy of the Program in return for a fee. 634 | 635 | END OF TERMS AND CONDITIONS 636 | 637 | How to Apply These Terms to Your New Programs 638 | 639 | If you develop a new program, and you want it to be of the greatest 640 | possible use to the public, the best way to achieve this is to make it 641 | free software which everyone can redistribute and change under these terms. 642 | 643 | To do so, attach the following notices to the program. It is safest 644 | to attach them to the start of each source file to most effectively 645 | state the exclusion of warranty; and each file should have at least 646 | the "copyright" line and a pointer to where the full notice is found. 647 | 648 | 649 | Copyright (C) 650 | 651 | This program is free software: you can redistribute it and/or modify 652 | it under the terms of the GNU Affero General Public License as published 653 | by the Free Software Foundation, either version 3 of the License, or 654 | (at your option) any later version. 655 | 656 | This program is distributed in the hope that it will be useful, 657 | but WITHOUT ANY WARRANTY; without even the implied warranty of 658 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 659 | GNU Affero General Public License for more details. 660 | 661 | You should have received a copy of the GNU Affero General Public License 662 | along with this program. If not, see . 663 | 664 | Also add information on how to contact you by electronic and paper mail. 665 | 666 | If your software can interact with users remotely through a computer 667 | network, you should also make sure that it provides a way for users to 668 | get its source. For example, if your program is a web application, its 669 | interface could display a "Source" link that leads users to an archive 670 | of the code. There are many ways you could offer source, and different 671 | solutions will be better for different programs; see section 13 for the 672 | specific requirements. 673 | 674 | You should also get your employer (if you work as a programmer) or school, 675 | if any, to sign a "copyright disclaimer" for the program, if necessary. 676 | For more information on this, and how to apply and follow the GNU AGPL, see 677 | . 678 | --------------------------------------------------------------------------------