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 _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_state_distribution(self):
for inter, dist in zip(self.interactions, self.state_distribution):
self.assertEqual(dist, iu.compute_state_distribution(inter))
def state_distribution(self):
"""
Returns the count of each state for a set of interactions.
"""
return iu.compute_state_distribution(self.result)
def test_compute_state_distribution(self):
for inter, dist in zip(self.interactions, self.state_distribution):
self.assertEqual(dist, iu.compute_state_distribution(inter))
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()
def state_distribution(self):
"""
Returns the count of each state for a set of interactions.
"""
return iu.compute_state_distribution(self.result)