def main(unused_arg):
# Construct meta-game payoff tables
# payoff_tables = get_kuhn_poker_data()
payoff_tables = [
np.array([[1.1, -10.0], [1.0, -1.0], [-1.0, 1.0]]),
np.array([[-1.1, 10.0], [-1.0, 1.0], [1.0, -1.0]])
]
payoffs_are_hpt_format = utils.check_payoffs_are_hpt(payoff_tables)
strat_labels = utils.get_strat_profile_labels(payoff_tables,
payoffs_are_hpt_format)
# Run AlphaRank
rhos, rho_m, pi, _, _ = alpharank.compute(payoff_tables, alpha=1e1)
# Report & plot results
alpharank.print_results(payoff_tables,
payoffs_are_hpt_format,
rhos=rhos,
rho_m=rho_m,
pi=pi)
utils.print_rankings_table(payoff_tables, pi, strat_labels)
m_network_plotter = alpharank_visualizer.NetworkPlot(payoff_tables,
rhos,
rho_m,
pi,
strat_labels,
num_top_profiles=8)
m_network_plotter.compute_and_draw_network()
def compute_and_report_alpharank(payoff_tables,
m=50,
alpha=100,
verbose=False,
num_top_strats_to_print=8):
"""Computes and visualizes Alpha-Rank outputs.
Args:
payoff_tables: List of game payoff tables, one for each agent identity. Each
payoff_table may be either a numpy array, or a _PayoffTableInterface
object.
m: Finite population size.
alpha: Fermi distribution temperature parameter.
verbose: Set to True to print intermediate results.
num_top_strats_to_print: Number of top strategies to print.
Returns:
pi: AlphaRank stationary distribution/rankings.
"""
payoffs_are_hpt_format = utils.check_payoffs_are_hpt(payoff_tables)
rhos, rho_m, pi, _, _ = compute(payoff_tables, m=m, alpha=alpha)
strat_labels = utils.get_strat_profile_labels(payoff_tables,
payoffs_are_hpt_format)
if verbose:
print_results(payoff_tables, payoffs_are_hpt_format, pi=pi)
utils.print_rankings_table(payoff_tables,
pi,
strat_labels,
num_top_strats_to_print=num_top_strats_to_print)
m_network_plotter = NetworkPlot(payoff_tables,
rhos,
rho_m,
pi,
strat_labels,
num_top_profiles=8)
m_network_plotter.compute_and_draw_network()
return pi
# Import the csv
df = pd.read_csv(csv_path, index_col=False)
# Convert to payoff_tables
payoff_tables, strat_labels = payoff_tables_from_df(df)
# payoff_tables = heuristic_payoff_table.from_elo_scores([1286, 1322, 1401, 1440, 1457, 1466, 1470])
payoffs_are_hpt_format = utils.check_payoffs_are_hpt(payoff_tables)
# strat_labels = utils.get_strat_profile_labels(payoff_tables(), payoffs_are_hpt_format)
# Run AlphaRank
rhos, rho_m, pi, _, _ = alpharank.compute(payoff_tables, alpha=1e-1)
# Report & plot results
alpharank.print_results(payoff_tables,
payoffs_are_hpt_format,
rhos=rhos,
rho_m=rho_m,
pi=pi)
utils.print_rankings_table(payoff_tables, pi, strat_labels)
m_network_plotter = alpharank_visualizer.NetworkPlot(payoff_tables,
rhos,
rho_m,
pi,
strat_labels,
num_top_profiles=7)
m_network_plotter.compute_and_draw_network()
# alpharank.sweep_pi_vs_alpha(payoff_tables, visualize=True)