def build_good_partner_matrix(self): """ Returns: -------- An n by n matrix of good partner ratings for n players i.e. an n by n matrix where n is the number of players. Each row (i) and column (j) represents an individual player and the value Pij is the sum of the number of repetitions where player i cooperated as often or more than opponent j. """ plist = list(range(self.nplayers)) good_partner_matrix = [[0 for opponent in plist] for player in plist] for player in plist: for opponent in plist: if player != opponent: for index_pair, repetitions in self.interactions.items(): if (player, opponent) == index_pair: for interaction in repetitions: coops = iu.compute_cooperations(interaction) if coops[0] >= coops[1]: good_partner_matrix[player][opponent] += 1 if (opponent, player) == index_pair: for interaction in repetitions: coops = iu.compute_cooperations(interaction) if coops[0] <= coops[1]: good_partner_matrix[player][opponent] += 1 return good_partner_matrix
def build_good_partner_matrix(self): """ Returns: -------- An n by n matrix of good partner ratings for n players i.e. an n by n matrix where n is the number of players. Each row (i) and column (j) represents an individual player and the value Pij is the sum of the number of repetitions where player i cooperated as often or more than opponent j. """ plist = list(range(self.nplayers)) good_partner_matrix = [[0 for opponent in plist] for player in plist] for player in plist: for opponent in plist: if player != opponent: for index_pair, repetitions in self.interactions.items(): if (player, opponent) == index_pair: for interaction in repetitions: coops = iu.compute_cooperations(interaction) if coops[0] >= coops[1]: good_partner_matrix[player][opponent] += 1 elif (opponent, player) == index_pair: for interaction in repetitions: coops = iu.compute_cooperations(interaction) if coops[0] <= coops[1]: good_partner_matrix[player][opponent] += 1 return good_partner_matrix
def build_cooperation(self): """ Returns: -------- The list of cooperation counts. List of the form: [ML1, ML2, ML3..., MLn] Where n is the number of players and MLi is a list of the form: [pi1, pi2, pi3, ..., pim] Where pij is the total number of cooperations over all repetitions played by player i against player j. """ plist = list(range(self.nplayers)) cooperations = [[0 for opponent in plist] for player in plist] for player in plist: for opponent in plist: if player != opponent: for index_pair, repetitions in self.interactions.items(): coop_count = 0 if (player, opponent) == index_pair: for interaction in repetitions: coop_count += iu.compute_cooperations( interaction)[0] elif (opponent, player) == index_pair: for interaction in repetitions: coop_count += iu.compute_cooperations( interaction)[1] cooperations[player][opponent] += coop_count return cooperations
def _calculate_results(self, interactions): results = [] scores = iu.compute_final_score(interactions, self.game) results.append(scores) score_diffs = scores[0] - scores[1], scores[1] - scores[0] results.append(score_diffs) turns = len(interactions) results.append(turns) score_per_turns = iu.compute_final_score_per_turn( interactions, self.game) results.append(score_per_turns) score_diffs_per_turns = score_diffs[0] / turns, score_diffs[1] / turns results.append(score_diffs_per_turns) initial_coops = tuple( map(bool, iu.compute_cooperations(interactions[:1]))) results.append(initial_coops) cooperations = iu.compute_cooperations(interactions) results.append(cooperations) state_distribution = iu.compute_state_distribution(interactions) results.append(state_distribution) state_to_action_distributions = iu.compute_state_to_action_distribution( interactions) results.append(state_to_action_distributions) winner_index = iu.compute_winner_index(interactions, self.game) results.append(winner_index) return results
def build_cooperation(self): """ Returns: -------- The list of cooperation counts. List of the form: [ML1, ML2, ML3..., MLn] Where n is the number of players and MLi is a list of the form: [pi1, pi2, pi3, ..., pim] Where pij is the total number of cooperations over all repetitions played by player i against player j. """ plist = list(range(self.nplayers)) cooperations = [[0 for opponent in plist] for player in plist] for player in plist: for opponent in plist: if player != opponent: for index_pair, repetitions in self.interactions.items(): coop_count = 0 if (player, opponent) == index_pair: for interaction in repetitions: coop_count += iu.compute_cooperations(interaction)[0] if (opponent, player) == index_pair: for interaction in repetitions: coop_count += iu.compute_cooperations(interaction)[1] cooperations[player][opponent] += coop_count return cooperations
def _calculate_results(self, interactions): results = [] scores = iu.compute_final_score(interactions, self.game) results.append(scores) score_diffs = scores[0] - scores[1], scores[1] - scores[0] results.append(score_diffs) turns = len(interactions) results.append(turns) score_per_turns = iu.compute_final_score_per_turn(interactions, self.game) results.append(score_per_turns) score_diffs_per_turns = score_diffs[0] / turns, score_diffs[1] / turns results.append(score_diffs_per_turns) initial_coops = tuple(map(bool, iu.compute_cooperations(interactions[:1]))) results.append(initial_coops) cooperations = iu.compute_cooperations(interactions) results.append(cooperations) state_distribution = iu.compute_state_distribution(interactions) results.append(state_distribution) state_to_action_distributions = iu.compute_state_to_action_distribution( interactions ) results.append(state_to_action_distributions) winner_index = iu.compute_winner_index(interactions, self.game) results.append(winner_index) return results
def test_compute_cooperations(self): for inter, coop in zip(self.interactions, self.cooperations): self.assertEqual(coop, iu.compute_cooperations(inter))
def cooperation(self): """Returns the count of cooperations by each player""" return iu.compute_cooperations(self.result)
def _build_score_related_metrics(self, progress_bar=False, keep_interactions=False): """ Read the data and carry out all relevant calculations. Parameters ---------- progress_bar : bool Whether or not to display a progress bar keep_interactions : bool Whether or not to lad the interactions in to memory """ match_chunks = self.read_match_chunks(progress_bar) for match in match_chunks: p1, p2 = int(match[0][0]), int(match[0][1]) for repetition, record in enumerate(match): interaction = record[4:] if keep_interactions: try: self.interactions[(p1, p2)].append(interaction) except KeyError: self.interactions[(p1, p2)] = [interaction] scores_per_turn = iu.compute_final_score_per_turn( interaction, game=self.game) cooperations = iu.compute_cooperations(interaction) state_counter = iu.compute_state_distribution(interaction) self._update_match_lengths(repetition, p1, p2, interaction) self._update_payoffs(p1, p2, scores_per_turn) self._update_score_diffs(repetition, p1, p2, scores_per_turn) self._update_normalised_cooperation(p1, p2, interaction) if p1 != p2: # Anything that ignores self interactions for player in [p1, p2]: self.total_interactions[player] += 1 self._update_match_lengths(repetition, p2, p1, interaction) self._update_wins(repetition, p1, p2, interaction) self._update_scores(repetition, p1, p2, interaction) self._update_normalised_scores(repetition, p1, p2, scores_per_turn) self._update_cooperation(p1, p2, cooperations) initial_coops = iu.compute_cooperations(interaction[:1]) self._update_initial_cooperation_count( p1, p2, initial_coops) self._update_state_distribution(p1, p2, state_counter) self._update_good_partner_matrix(p1, p2, cooperations) if progress_bar: self.progress_bar = tqdm.tqdm(total=12 + 2 * self.nplayers, desc="Finishing") self._summarise_normalised_scores() self._summarise_normalised_cooperation() self.ranking = self._build_ranking() self.normalised_state_distribution = self._build_normalised_state_distribution( ) self.ranked_names = self._build_ranked_names() self.payoff_matrix = self._build_payoff_matrix() self.payoff_stddevs = self._build_payoff_stddevs() self.payoff_diffs_means = self._build_payoff_diffs_means() self.vengeful_cooperation = self._build_vengeful_cooperation() self.cooperating_rating = self._build_cooperating_rating() self.initial_cooperation_rate = self._build_initial_cooperation_rate() self.good_partner_rating = self._build_good_partner_rating() self.eigenjesus_rating = self._build_eigenjesus_rating() self.eigenmoses_rating = self._build_eigenmoses_rating() if progress_bar: self.progress_bar.close()
def _build_score_related_metrics(self, progress_bar=False, keep_interactions=False): """ Read the data and carry out all relevant calculations. Parameters ---------- progress_bar : bool Whether or not to display a progress bar keep_interactions : bool Whether or not to lad the interactions in to memory """ match_chunks = self.read_match_chunks(progress_bar) for match in match_chunks: p1, p2 = int(match[0][0]), int(match[0][1]) for repetition, record in enumerate(match): interaction = record[4:] if keep_interactions: try: self.interactions[(p1, p2)].append(interaction) except KeyError: self.interactions[(p1, p2)] = [interaction] scores_per_turn = iu.compute_final_score_per_turn(interaction, game=self.game) cooperations = iu.compute_cooperations(interaction) state_counter = iu.compute_state_distribution(interaction) self._update_match_lengths(repetition, p1, p2, interaction) self._update_payoffs(p1, p2, scores_per_turn) self._update_score_diffs(repetition, p1, p2, scores_per_turn) self._update_normalised_cooperation(p1, p2, interaction) if p1 != p2: # Anything that ignores self interactions for player in [p1, p2]: self.total_interactions[player] += 1 self._update_match_lengths(repetition, p2, p1, interaction) self._update_wins(repetition, p1, p2, interaction) self._update_scores(repetition, p1, p2, interaction) self._update_normalised_scores(repetition, p1, p2, scores_per_turn) self._update_cooperation(p1, p2, cooperations) self._update_state_distribution(p1, p2, state_counter) self._update_good_partner_matrix(p1, p2, cooperations) if progress_bar: self.progress_bar = tqdm.tqdm(total=11 + 2 * self.nplayers, desc="Finishing") self._summarise_normalised_scores() self._summarise_normalised_cooperation() self.ranking = self._build_ranking() self.normalised_state_distribution = self._build_normalised_state_distribution() self.ranked_names = self._build_ranked_names() self.payoff_matrix = self._build_payoff_matrix() self.payoff_stddevs = self._build_payoff_stddevs() self.payoff_diffs_means = self._build_payoff_diffs_means() self.vengeful_cooperation = self._build_vengeful_cooperation() self.cooperating_rating = self._build_cooperating_rating() self.good_partner_rating = self._build_good_partner_rating() self.eigenjesus_rating = self._build_eigenjesus_rating() self.eigenmoses_rating = self._build_eigenmoses_rating() if progress_bar: self.progress_bar.close()