def main():
# Load arguments
args_path = join(dir_path, 'args.txt')
args = json.loads(open(args_path, 'r').read())
info = pe.get_info(args)
# Get path of model
model_dir_path = next((d for d in [x[0] for x in os.walk(dir_path)] if d.endswith('finish')), None)
os.makedirs(join(dir_path, 'figs'), exist_ok=True)
ending_eps = '_normalize.eps' if normalize else '.eps'
ending_png = '_normalize.png' if normalize else '.png'
# purchase_ratio(args, model_dir_path)
evaluate_policy_at_population_level_multiple_categories(args, model_dir_path, ending_eps, ending_png, info)
evaluate_policy_at_individual_level_multiple_categories(args, model_dir_path, ending_eps, ending_png, info)
evaluate_policy_at_population_level(args, model_dir_path, ending_eps, ending_png, info)
# evaluate_policy_at_individual_level(args, model_dir_path, ending_eps, ending_png, info)
compare_clusters(args, model_dir_path, ending_eps, ending_png, info)
# visualize_experts(n_experts=10)
fig_stats = plot_statistics(dir_path)
fig_path = os.getcwd() + '/' + dir_path + '/figs'
save_plt_as_png(fig_stats, fig_path + '/stats.png')
def eval_policy(
a_dir_path,
a_sample_length = 10000,
a_normalize = True,
a_n_demos_per_expert = 10,
a_n_last_days = 7,
a_max_n_purchases_per_n_last_days = 2,
a_show_info = True,
a_show_plots = False,
a_save_plots = True,
a_cluster_comparison = False
):
global dir_path, sample_length, normalize, n_demos_per_expert, n_last_days, max_n_purchases_per_n_last_days, show_info, show_plots, save_plots, cluster_comparison
dir_path = a_dir_path
sample_length = a_sample_length
normalize = a_normalize
n_demos_per_expert = a_n_demos_per_expert
n_last_days = a_n_last_days
max_n_purchases_per_n_last_days = a_max_n_purchases_per_n_last_days
show_info = a_show_info
show_plots = a_show_plots
save_plots = a_save_plots
cluster_comparison = a_cluster_comparison
# Load arguments
args_path = join(dir_path, 'args.txt')
args = json.loads(open(args_path, 'r').read())
info = pe.get_info(args)
# Get path of model
model_dir_path = next((d for d in [x[0] for x in os.walk(dir_path)] if d.endswith('finish')), None)
os.makedirs(join(dir_path, 'figs'), exist_ok=True)
ending_eps = '_normalize.eps' if normalize else '.eps'
ending_png = '_normalize.png' if normalize else '.png'
evaluate_policy_at_population_level(args, model_dir_path, ending_eps, ending_png, info)
# evaluate_policy_at_individual_level(args, model_dir_path, ending_eps, ending_png, info)
compare_clusters(args, model_dir_path, ending_eps, ending_png, info)
fig_stats = plot_statistics(dir_path)
fig_path = os.getcwd() + '/' + dir_path + '/figs'
save_plt_as_png(fig_stats, fig_path + '/stats.png')
if not show_plots: plt.close(fig_stats)
def main_new2():
import statistics
dir_path = 'ozzy_results/dummy/2020-05-08_14-50-32'
#experts = [2, 7, 8]
#rand_list = random.sample(range(11, 100), 5)
ls = [0, 1, 2, 3, 4]
model_path = None # join(os.getcwd(), dir_path, '12288000_checkpoint', 'model.npz')
n_runs = 10
sample_length = 5000
cutoff = 300
normalize = True
n_demos_per_expert = 1
n_last_days = 7
max_n_purchases_per_n_last_days = 2
show_info = True
show_plots = False
save_plots = True
cluster_comparison = False
args_path = join(dir_path, 'args.txt')
args = json.loads(open(args_path, 'r').read())
info = pe.get_info(args)
# Get path of model
model_dir_path = next(
(d for d in [x[0] for x in os.walk(dir_path)] if d.endswith('finish')),
None)
os.makedirs(join(dir_path, 'figs'), exist_ok=True)
ending_eps = '_normalize.eps' if normalize else '.eps'
ending_png = '_normalize.png' if normalize else '.png'
env, model, obs_normalizer = pe.get_env_and_model(args,
model_dir_path,
sample_length,
model_path=model_path)
data = []
expert_trajectories = env.generate_expert_trajectories(
out_dir=None, n_demos_per_expert=1, n_expert_time_steps=sample_length)
expert_states = expert_trajectories['states']
expert_actions = expert_trajectories['actions']
temp = []
for e in range(len(expert_actions)):
temp.append(get_purchase_ratio(expert_actions[e]))
mean_purchase_ratio = statistics.mean(temp)
print(mean_purchase_ratio)
dgm = DGM()
n_input_neurons = model.pi.model.in_size
for exp in ls:
agt_res = []
sample_res = []
dgm.spawn_new_customer(exp)
sample = dgm.sample(sample_length * n_runs)
sample = np.sum(sample, axis=0)
sample_chunks = [
sample[x:x + sample_length]
for x in range(0, len(sample), sample_length)
]
dummy = [0] * args['n_experts']
dummy[exp] = 1
#dgm.spawn_new_customer(exp)
#sample = dgm.sample((n_input_neurons-args['n_experts'])*n_runs)
#sample = np.sum(sample, axis=0)
#initial_chunks = [sample[x:x+(n_input_neurons-args['n_experts'])] for x in range(0, len(sample), (n_input_neurons-args['n_experts']))]
for chunk in sample_chunks:
initial_chunk = [0] * (n_input_neurons - args['n_experts'])
initial_state = np.concatenate((dummy, initial_chunk))
_, agent_actions = sample_probs_and_actions_from_policy(
env, model, obs_normalizer, initial_state=initial_state)
agt_res.append(get_purchase_ratio(agent_actions))
sample_res.append(get_purchase_ratio(chunk))
print('mean agent ' + str(exp))
print(statistics.mean(agt_res))
print('std')
print(statistics.stdev(agt_res))
print('mean expert ' + str(exp))
print(statistics.mean(sample_res))
print('std')
print(statistics.stdev(sample_res))
def main2(): #
dir_path = 'ozzy_results/dummy/2020-05-08_14-50-32'
#experts = [2, 7, 8]
rand_list = [33, 44]
model_path = None # join(os.getcwd(), dir_path, '12288000_checkpoint', 'model.npz')
n_runs = 10
sample_length = 5000
cutoff = 300
normalize = True
n_demos_per_expert = 1
n_last_days = 7
max_n_purchases_per_n_last_days = 2
show_info = True
show_plots = False
save_plots = True
cluster_comparison = False
args_path = join(dir_path, 'args.txt')
args = json.loads(open(args_path, 'r').read())
info = pe.get_info(args)
# Get path of model
model_dir_path = next(
(d for d in [x[0] for x in os.walk(dir_path)] if d.endswith('finish')),
None)
os.makedirs(join(dir_path, 'figs'), exist_ok=True)
ending_eps = '_normalize.eps' if normalize else '.eps'
ending_png = '_normalize.png' if normalize else '.png'
env, model, obs_normalizer = pe.get_env_and_model(args,
model_dir_path,
sample_length,
model_path=model_path)
data = []
expert_trajectories = env.generate_expert_trajectories(
out_dir=None, n_demos_per_expert=1, n_expert_time_steps=sample_length)
expert_states = expert_trajectories['states']
expert_actions = expert_trajectories['actions']
temp = []
for e in range(len(expert_actions)):
temp.append([
get_purchase_ratio(expert_actions[e][:i], gamma=0.3)
for i in range(len(expert_actions[e]))
])
temp = np.array(temp)
mean_purchase_ratio = np.mean(temp, axis=0)
for i, avg in enumerate(mean_purchase_ratio):
if i > cutoff:
if i % 10 == 0:
data.append([i, avg, 'Average expert', 'Ground truth'])
dgm = DGM()
for idx, rand in enumerate(rand_list):
dgm.spawn_new_customer(rand)
#dgm.spawn_new_customer(12)
sample = dgm.sample(sample_length * n_runs)
sample = np.sum(sample, axis=0)
sample_chunks = [
sample[x:x + sample_length]
for x in range(0, len(sample), sample_length)
]
for chunk in sample_chunks:
agent_actions, rand_list = get_probs_for_buys(env,
model,
obs_normalizer,
args,
model_dir_path,
expert=None,
init='rand',
dummi=False,
rand_seed=rand)
agt_purchase_ratio = [
get_purchase_ratio(agent_actions[:i], gamma=0.7)
for i in range(len(agent_actions))
]
sample_purchase_ratio = [
get_purchase_ratio(chunk[:i], gamma=0.7)
for i in range(len(chunk))
]
for i, (avg_agent, avg_sample) in enumerate(
zip(agt_purchase_ratio, sample_purchase_ratio)):
if i > cutoff:
if i % 10 == 0:
data.append([
i, avg_sample, 'New customer ' + str(idx + 1),
'Ground truth'
])
data.append([
i, avg_agent, 'New customer ' + str(idx + 1),
'Agent'
])
df = pd.DataFrame(data,
columns=['Day', 'Purchase ratio', 'Customer', 'Data'])
sns.set(style='darkgrid')
g = sns.relplot(x='Day',
y='Purchase ratio',
hue='Customer',
ci=95,
kind='line',
data=df,
facet_kws={'legend_out': True},
style='Data')
ax = g.axes[0][0]
handles, labels = ax.get_legend_handles_labels()
#labels[1] = "3"
#labels[2] = "8"
#labels[3] = "9"
#ax.legend(handles, labels)
#labels2, handles2 = zip(*sorted(zip(labels[1:], handles[1:]), key=lambda t: int(t[0].split(' ')[0])))
#labels2 = list(labels2)
#handles2 = list(handles2)
#labels2.insert(0, get_label_from_param(param))
#handles2.insert(0, handles[0])
#ax.legend(handles2, labels2)
#handles, labels = ax.get_legend_handles_labels()
#ax.legend(handles=handles[1:], labels=labels[1:])
plt.show()
g.fig.savefig(os.getcwd() + '/rand.pdf', format='pdf')
def main():
dir_path = 'ozzy_results/dummy/2020-05-08_14-50-32'
experts = [2, 7, 8]
model_path = None # join(os.getcwd(), dir_path, '12288000_checkpoint', 'model.npz')
n_runs = 10
sample_length = 5000
cutoff = 300
normalize = True
n_demos_per_expert = 1
n_last_days = 7
max_n_purchases_per_n_last_days = 2
show_info = True
show_plots = False
save_plots = True
cluster_comparison = False
args_path = join(dir_path, 'args.txt')
args = json.loads(open(args_path, 'r').read())
info = pe.get_info(args)
# Get path of model
model_dir_path = next(
(d for d in [x[0] for x in os.walk(dir_path)] if d.endswith('finish')),
None)
os.makedirs(join(dir_path, 'figs'), exist_ok=True)
ending_eps = '_normalize.eps' if normalize else '.eps'
ending_png = '_normalize.png' if normalize else '.png'
env, model, obs_normalizer = pe.get_env_and_model(args,
model_dir_path,
sample_length,
model_path=model_path)
data = []
#expert_trajectories = env.generate_expert_trajectories(out_dir=None, n_demos_per_expert=1, n_expert_time_steps=sample_length)
#expert_states = expert_trajectories['states']
#expert_actions = expert_trajectories['actions']
#temp = []
#for e in range(len(expert_actions)):
# temp.append([get_purchase_ratio(expert_actions[e][:i], gamma=0.3) for i in range(len(expert_actions[e]))])
#temp = np.array(temp)
#mean_purchase_ratio = np.mean(temp, axis=0)
for expert in experts:
if expert == 2:
lab = 'Two'
elif expert == 7:
lab = 'Five'
elif expert == 8:
lab = 'Nine'
e_actions = get_expert_actions(expert, sample_length * n_runs)
action_chunks = [
e_actions[x:x + sample_length]
for x in range(0, len(e_actions), sample_length)
]
for chunk in action_chunks:
agent_actions = get_probs_for_buys(env,
model,
obs_normalizer,
args,
model_dir_path,
expert=expert,
init='zeros',
dummi=True)
agt_purchase_ratio = [
get_purchase_ratio(agent_actions[:i], gamma=0.7)
for i in range(len(agent_actions))
]
exp_purchase_ratio = [
get_purchase_ratio(chunk[:i], gamma=0.7)
for i in range(len(chunk))
]
for i, (avg_agent, avg_expert) in enumerate(
zip(agt_purchase_ratio, exp_purchase_ratio)):
if i > cutoff:
if i % 10 == 0:
data.append([i, avg_agent, 'Agent', lab])
data.append([i, avg_expert, 'Expert', lab])
df = pd.DataFrame(data,
columns=['Days', 'Purchase ratio', 'Data', 'Customer'])
sns.set(style='darkgrid')
g = sns.relplot(x='Days',
y='Purchase ratio',
hue='Customer',
ci=95,
kind='line',
data=df,
facet_kws={'legend_out': False},
style='Data')
ax = g.axes[0][0]
handles, labels = ax.get_legend_handles_labels()
labels[1] = "3"
labels[2] = "8"
labels[3] = "9"
ax.legend(handles, labels)
#labels2, handles2 = zip(*sorted(zip(labels[1:], handles[1:]), key=lambda t: int(t[0].split(' ')[0])))
#labels2 = list(labels2)
#handles2 = list(handles2)
#labels2.insert(0, get_label_from_param(param))
#handles2.insert(0, handles[0])
#ax.legend(handles2, labels2)
#handles, labels = ax.get_legend_handles_labels()
#ax.legend(handles=handles[1:], labels=labels[1:])
plt.show()
g.fig.savefig(os.getcwd() + '/zeros_dummy_10.pdf', format='pdf')