Remote test and other stuff

NPoS/npos.py

@@ -0,0 +1,428 @@
+import unittest
+
+
+def print_list(ll):
+    for item in ll:
+        print(item)
+
+
+class edge:
+    def __init__(self, nominator_id, validator_id):
+        self.nominator_id = nominator_id
+        self.validator_id = validator_id
+        self.load = 0
+        self.weight = 0
+        self.candidate = None
+
+    def __str__(self):
+        return "Edge({}, weight = {:,})".format(
+            self.validator_id,
+            self.weight,
+        )
+
+
+class nominator:
+    def __init__(self, nominator_id, budget, targets):
+        self.nominator_id = nominator_id
+        self.budget = budget
+        self.edges = [edge(self.nominator_id, validator_id) for validator_id in targets]
+        self.load = 0
+
+    def __str__(self):
+        return "Nominator({}, budget = {:,}, load = {}, edges = {})".format(
+            self.nominator_id,
+            self.budget,
+            self.load,
+            [str(e) for e in self.edges]
+        )
+
+
+class candidate:
+    def __init__(self, validator_id, index):
+        self.validator_id = validator_id
+        self.valindex = index
+        self.approval_stake = 0
+        self.backed_stake = 0
+        self.elected = False
+        self.score = 0
+        self.scoredenom = 0
+
+    def __str__(self):
+        return "Candidate({}, approval = {:,}, backed_stake = {:,})".format(
+            self.validator_id,
+            self.approval_stake,
+            int(self.backed_stake),
+        )
+
+
+def seq_phragmen(votelist, num_to_elect):
+    nomlist, candidates = setuplists(votelist)
+    calculate_approval(nomlist)
+
+    elected_candidates = list()
+    for round in range(num_to_elect):
+        for candidate in candidates:
+            if not candidate.elected:
+                candidate.score = 1/candidate.approval_stake
+        for nom in nomlist:
+            for edge in nom.edges:
+                if not edge.candidate.elected:
+                    edge.candidate.score += nom.budget * nom.load / edge.candidate.approval_stake
+        best_candidate = 0
+        best_score = 1000  # should be infinite but I'm lazy
+        for candidate in candidates:
+            if not candidate.elected and candidate.score < best_score:
+                best_score = candidate.score
+                best_candidate = candidate.valindex
+        elected_candidate = candidates[best_candidate]
+        elected_candidate.elected = True
+        elected_candidate.electedpos = round
+        elected_candidates.append(elected_candidate)
+        for nom in nomlist:
+            for edge in nom.edges:
+                if edge.candidate.valindex == best_candidate:
+                    edge.load = elected_candidate.score - nom.load
+                    nom.load = elected_candidate.score
+
+    for candidate in elected_candidates:
+        candidate.backed_stake = 0
+
+    for nom in nomlist:
+        for edge in nom.edges:
+            if nom.load > 0.0:
+                edge.weight = nom.budget * edge.load/nom.load
+                edge.candidate.backed_stake += edge.weight
+            else:
+                edge.weight = 0
+    return (nomlist, elected_candidates)
+
+
+def equalise(nom, tolerance):
+    # Attempts to redistribute the nominators budget between elected validators. Assumes that all
+    # elected validators have backed_stake set correctly. Returns the max difference in stakes
+    # between sup.
+
+    elected_edges = [edge for edge in nom.edges if edge.candidate.elected]
+
+    if len(elected_edges) < 2:
+        return 0.0
+
+    stake_used = sum([edge.weight for edge in elected_edges])
+    backed_stakes = [edge.candidate.backed_stake for edge in elected_edges]
+    backingbacked_stakes = [
+        edge.candidate.backed_stake for edge in elected_edges if edge.weight > 0.0
+    ]
+
+    if len(backingbacked_stakes) > 0:
+        difference = max(backingbacked_stakes)-min(backed_stakes)
+        difference += nom.budget - stake_used
+        if difference < tolerance:
+            return difference
+    else:
+        difference = nom.budget
+
+    # remove all backing
+    for edge in nom.edges:
+        edge.candidate.backed_stake -= edge.weight
+        edge.weight = 0
+
+    elected_edges.sort(key=lambda x: x.candidate.backed_stake)
+    cumulative_backed_stake = 0
+    last_index = len(elected_edges) - 1
+
+    for i in range(len(elected_edges)):
+        backed_stake = elected_edges[i].candidate.backed_stake
+        if backed_stake * i - cumulative_backed_stake > nom.budget:
+            last_index = i-1
+            break
+        cumulative_backed_stake += backed_stake
+
+    last_stake = elected_edges[last_index].candidate.backed_stake
+    ways_to_split = last_index+1
+    excess = nom.budget + cumulative_backed_stake - last_stake*ways_to_split
+
+    for edge in elected_edges[0:ways_to_split]:
+        edge.weight = excess / ways_to_split + last_stake - edge.candidate.backed_stake
+        edge.candidate.backed_stake += edge.weight
+
+    return difference
+
+
+def equalise_all(nomlist, maxiterations, tolerance):
+    for i in range(maxiterations):
+        # for j in range(len(nomlist)):
+        #     nom = random.choice(nomlist)
+        #     equalise(nom, tolerance)
+        maxdifference = 0
+        for nom in nomlist:
+            difference = equalise(nom, tolerance)
+            maxdifference = max(difference, maxdifference)
+        if maxdifference < tolerance:
+            return
+
+
+def seq_phragmen_with_equalise(votelist, num_to_elect):
+    nomlist, elected_candidates = seq_phragmen(votelist, num_to_elect)
+    equalise_all(nomlist, 2, 0)
+    return nomlist, elected_candidates
+
+
+def calculateMaxScoreNoCutoff(nomlist, candidates):
+    # First we compute the denominator of the score
+    for candidate in candidates:
+        if not candidate.elected:
+            candidate.scoredenom = 1.0
+
+    for nom in nomlist:
+        denominator_contrib = 0
+
+        for edge in nom.edges:
+            if edge.candidate.elected:
+                denominator_contrib += edge.weight/edge.candidate.backed_stake
+
+        for edge in nom.edges:
+            if not edge.candidate.elected:
+                edge.candidate.scoredenom += denominator_contrib
+
+    # Then we divide. Not that score here is comparable to the recipricol of the score in
+    # seq-phragmen. In particular there low scores are good whereas here high scores are good.
+    best_candidate = 0
+    best_score = 0.0
+    for candidate in candidates:
+        if candidate.approval_stake > 0.0:
+            candidate.score = candidate.approval_stake / candidate.scoredenom
+            if not candidate.elected and candidate.score > best_score:
+                best_score = candidate.score
+                best_candidate = candidate
+        else:
+            candidate.score = 0.0
+
+    return (best_candidate, best_score)
+
+
+def electWithScore(nomlist, elected_candidate, cutoff):
+    for nom in nomlist:
+        for new_edge in nom.edges:
+            if new_edge.validator_id == elected_candidate.validator_id:
+                used_budget = sum([edge.weight for edge in nom.edges])
+
+                new_edge.weight = nom.budget - used_budget
+                elected_candidate.backed_stake += nom.budget - used_budget
+
+                for edge in nom.edges:
+                    if edge.validator_id != elected_candidate.validator_id and edge.weight > 0.0:
+                        if edge.candidate.backed_stake > cutoff:
+                            stake_to_take = edge.weight * cutoff / edge.candidate.backed_stake
+
+                            new_edge.weight += stake_to_take
+                            elected_candidate.backed_stake += stake_to_take
+
+                            edge.weight -= stake_to_take
+                            edge.candidate.backed_stake -= stake_to_take
+
+
+def phragmms(votelist, num_to_elect, tolerance=0.1):
+    nomlist, candidates = setuplists(votelist)
+    calculate_approval(nomlist)
+
+    elected_candidates = list()
+    for round in range(num_to_elect):
+        (elected_candidate, score) = calculateMaxScoreNoCutoff(nomlist, candidates)
+        electWithScore(nomlist, elected_candidate, score)
+
+        elected_candidate.elected = True
+        elected_candidates.append(elected_candidate)
+        elected_candidate.electedpos = round
+
+        equalise_all(nomlist, 10, tolerance)
+
+    return nomlist, elected_candidates
+
+
+def approval_voting(votelist, num_to_elect):
+    nomlist, candidates = setuplists(votelist)
+    # Compute the total possible stake for each candidate
+    for nom in nomlist:
+        for edge in nom.edges:
+            edge.candidate.approval_stake += nom.budget
+            edge.weight = nom.budget/min(len(nom.edges), num_to_elect)
+            edge.candidate.backed_stake += edge.weight
+    candidates.sort(key=lambda x: x.approval_stake, reverse=True)
+    elected_candidates = candidates[0:num_to_elect]
+    return nomlist, elected_candidates
+
+
+def calculate_approval(nomlist):
+    for nom in nomlist:
+        for edge in nom.edges:
+            edge.candidate.approval_stake += nom.budget
+
+
+def setuplists(votelist):
+    '''
+    Basically populates edge.candidate, and returns nomlist and candidate array. The former is a
+    flat list of nominators and the latter is a flat list of validator candidates.
+
+    Instead of Python's dict here, you can use anything with O(log n) addition and lookup. We can
+    also use a hashmap like dict, by generating a random constant r and useing H(canid+r) since the
+    naive thing is obviously attackable.
+    '''
+    nomlist = [nominator(votetuple[0], votetuple[1], votetuple[2]) for votetuple in votelist]
+    # Basically used as a cache.
+    candidate_dict = dict()
+    candidate_array = list()
+    num_candidates = 0
+    # Get an array of candidates.# We could reference these by index rather than pointer
+    for nom in nomlist:
+        for edge in nom.edges:
+            validator_id = edge.validator_id
+            if validator_id in candidate_dict:
+                index = candidate_dict[validator_id]
+                edge.candidate = candidate_array[index]
+            else:
+                candidate_dict[validator_id] = num_candidates
+                newcandidate = candidate(validator_id, num_candidates)
+                candidate_array.append(newcandidate)
+
+                edge.candidate = newcandidate
+                num_candidates += 1
+    return nomlist, candidate_array
+
+
+def run_and_print_all(votelist, to_elect):
+    print("######\nVotes ", votelist)
+
+    print("\nSequential Phragmén gives")
+    nomlist, elected_candidates = seq_phragmen(votelist, to_elect)
+    printresult(nomlist, elected_candidates)
+
+    print("\nApproval voting gives")
+    nomlist, elected_candidates = approval_voting(votelist, to_elect)
+    printresult(nomlist, elected_candidates)
+
+    print("\nSequential Phragmén with post processing gives")
+    nomlist, elected_candidates = seq_phragmen_with_equalise(votelist, to_elect)
+    printresult(nomlist, elected_candidates)
+
+    print("\nBalanced Heuristic (3.15 factor) gives")
+    nomlist, elected_candidates = phragmms(votelist, to_elect)
+    printresult(nomlist, elected_candidates)
+
+
+def printresult(nomlist, elected_candidates, verbose=True):
+    for candidate in elected_candidates:
+        print(candidate.validator_id, " is elected with stake ",
+              candidate.backed_stake, "and score ", candidate.score)
+    if verbose:
+        for nom in nomlist:
+            print(nom.nominator_id, " has load ", nom.load, "and supported ")
+            for edge in nom.edges:
+                print(edge.validator_id, " with stake ", edge.weight, end=", ")
+            print()
+        print()
+
+
+def example1():
+    votelist = [
+        ("A", 10.0, ["X", "Y"]),
+        ("B", 20.0, ["X", "Z"]),
+        ("C", 30.0, ["Y", "Z"]),
+    ]
+    run_and_print_all(votelist, 2)
+
+
+def example2():
+    votelist = [
+        ("10", 1000, ["10"]),
+        ("20", 1000, ["20"]),
+        ("30", 1000, ["30"]),
+        ("40", 1000, ["40"]),
+        ('2', 500, ['10', '20', '30']),
+        ('4', 500, ['10', '20', '40'])
+    ]
+    run_and_print_all(votelist, 2)
+
+
+class MaxScoreTest(unittest.TestCase):
+    def test_max_score_1(self):
+        votelist = [
+            (10, 10.0, [1, 2]),
+            (20, 20.0, [1, 3]),
+            (30, 30.0, [2, 3]),
+        ]
+        nomlist, candidates = setuplists(votelist)
+        calculate_approval(nomlist)
+
+        best, score = calculateMaxScoreNoCutoff(nomlist, candidates)
+        self.assertEqual(best.validator_id, 3)
+        self.assertEqual(score, 50)
+
+    def test_balance_heuristic_example_1(self):
+        votelist = [
+            (10, 10.0, [1, 2]),
+            (20, 20.0, [1, 3]),
+            (30, 30.0, [2, 3]),
+        ]
+        nomlist, winners = phragmms(votelist, 2, 0)
+        self.assertEqual(winners[0].validator_id, 3)
+        self.assertEqual(winners[1].validator_id, 2)
+
+        self.assertEqual(winners[0].backed_stake, 30)
+        self.assertEqual(winners[1].backed_stake, 30)
+
+    def test_balance_heuristic_example_linear(self):
+        votelist = [
+            (2, 2000, [11]),
+            (4, 1000, [11, 21]),
+            (6, 1000, [21, 31]),
+            (8, 1000, [31, 41]),
+            (110, 1000, [41, 51]),
+            (120, 1000, [51, 61]),
+            (130, 1000, [61, 71]),
+        ]
+
+        nomlist, winners = phragmms(votelist, 4, 0)
+        self.assertEqual(winners[0].validator_id, 11)
+        self.assertEqual(winners[0].backed_stake, 3000)
+
+        self.assertEqual(winners[1].validator_id, 31)
+        self.assertEqual(winners[1].backed_stake, 2000)
+
+        self.assertEqual(winners[2].validator_id, 51)
+        self.assertEqual(winners[2].backed_stake, 1500)
+
+        self.assertEqual(winners[3].validator_id, 61)
+        self.assertEqual(winners[3].backed_stake, 1500)
+
+
+
+class ElectionTest(unittest.TestCase):
+    def test_phragmen(self):
+        votelist = [
+            ("A", 10.0, ["X", "Y"]),
+            ("B", 20.0, ["X", "Z"]),
+            ("C", 30.0, ["Y", "Z"]),
+        ]
+        nomlist, elected_candidates = seq_phragmen(votelist, 2)
+        self.assertEqual(elected_candidates[0].validator_id, "Z")
+        self.assertAlmostEqual(elected_candidates[0].score, 0.02)
+        self.assertEqual(elected_candidates[1].validator_id, "Y")
+        self.assertAlmostEqual(elected_candidates[1].score, 0.04)
+
+    def test_approval(self):
+        votelist = [
+            ("A", 10.0, ["X", "Y"]),
+            ("B", 20.0, ["X", "Z"]),
+            ("C", 30.0, ["Y", "Z"]),
+        ]
+        nomlist, elected_candidates = approval_voting(votelist, 2)
+        self.assertEqual(elected_candidates[0].validator_id, "Z")
+        self.assertAlmostEqual(elected_candidates[0].approval_stake, 50.0)
+        self.assertEqual(elected_candidates[1].validator_id, "Y")
+        self.assertAlmostEqual(elected_candidates[1].approval_stake, 40.0)
+
+
+def main():
+    # example1()
+    example2()
+    # example3()