def make_stats_row_from_df(cur_data, include_power, effect_size = None, alpha = None):
'''Calculates output statistics given the data frame cur_data. If include_power, includes the power calculation.
efffect_size and alpha are only required/used if power is calculated
'''
sample_sizes = np.array([np.sum(cur_data[action_header] == i) for i in range(1,3)])
successes = np.array([np.sum(cur_data[cur_data[action_header] == 1][obs_reward_header]),
np.sum(cur_data[cur_data[action_header] == 2][obs_reward_header])])
#calculate sample size and mean
cur_row = {}
sample_size_1 = sample_sizes[0]
sample_size_2 = sample_sizes[1]
cur_row['sample_size_1'] = sample_size_1
cur_row['sample_size_2'] = sample_size_2
mean_1 = np.mean(cur_data[cur_data[action_header] == 1][obs_reward_header])# JN mean for arm 1
mean_2 = np.mean(cur_data[cur_data[action_header] == 2][obs_reward_header])#JN mean for arm 2
cur_row['mean_1'] = mean_1
cur_row['mean_2'] = mean_2
#SE = sqrt[(P^hat_A*(1-P^hat_A)/N_A + (P^hat_B*(1-P^hat_B)/N_B]
SE = np.sqrt(mean_1*(1 - mean_1)/sample_size_1 + mean_2*(1 - mean_2)/sample_size_2)
wald_type_stat = (mean_1 - mean_2)/SE #(P^hat_A - P^hat_b)/SE
#print('wald_type_stat:', wald_type_stat)
wald_pval = (1 - scipy.stats.norm.cdf(np.abs(wald_type_stat)))*2 #Two sided, symetric, so compare to 0.05
cur_row['wald_type_stat'] = wald_type_stat #TODO add in Delta!!!!
cur_row['wald_pval'] = wald_pval
#print("wald_pval", wald_pval)
#calculate total reward
cur_row['total_reward'] = np.sum(cur_data[obs_reward_header])
#calculate power
cur_row['ratio'] = sample_sizes[0] / sample_sizes[1]
if include_power:
cur_row['power'] = smp.GofChisquarePower().solve_power(effect_size, nobs = sum(sample_sizes), n_bins=(2-1)*(2-1) + 1, alpha = alpha)
cur_row['actual_es'] = calculate_effect_size(sample_sizes, successes)
#calculate chi squared contingency test
table = sms.Table(np.stack((successes,sample_sizes - successes)).T)
rslt = table.test_nominal_association()
cur_row['stat'] = rslt.statistic
cur_row['pvalue'] = rslt.pvalue
cur_row['df'] = rslt.df
# Added to match normal rewards
cur_row['statUnequalVar'],cur_row['pvalueUnequalVar'], cur_row['dfUnequalVar'] = cur_row['stat'],cur_row['pvalue'],cur_row['df']
return cur_row
def make_stats_row_from_df(simulations_df,
include_power,
action_count,
effect_size=None,
alpha=None):
'''Calculates output statistics given the data frame simulations_df. If include_power, includes the power calculation.
efffect_size and alpha are only required/used if power is calculated
'''
#REPLACE cur_data with simulations_df
#number of actions! action_count
step_size = max(simulations_df.index) + 1 #will be 4n for instance
n = step_size / 4 #assumes set to 4n
n_size_list = [math.ceil(n / 2), int(n), int(2 * n), int(4 * n)]
print("step_size", step_size)
print("n_size_list", n_size_list)
trials_count = len(simulations_df)
print("trials_count", trials_count) #will go to end, so 4n*num_sims
print(simulations_df.columns)
all_rows = []
for n_size in n_size_list:
sim_num = 0
for idx in range(0, trials_count, step_size):
one_sim_df = simulations_df[idx:idx + n_size].copy()
assert (len(one_sim_df) == n_size)
cur_row = {}
prop_exploring_ppd_cuml = np.sum(
one_sim_df[ppd_exp_header]) / n_size #cumulative
# print(one_sim_df["SampleNumber"])
# print("n_size", n_size, exploring_this_n)
exploring_this_n = one_sim_df[
one_sim_df["SampleNumber"] == n_size -
1][ppd_exp_header].iloc[0] #snap shot, conver to idx
sample_sizes = np.array([])
successes = np.array([])
means = np.array([])
np.array([])
np.array([])
for i in range(1, action_count + 1):
sample_sizes = np.append(
sample_sizes, np.sum(one_sim_df[action_header] == i))
successes = np.append(
successes,
np.sum(one_sim_df[one_sim_df[action_header] == i]
[obs_reward_header]))
means = np.append(
means,
np.mean(one_sim_df[one_sim_df[action_header] == i]
[obs_reward_header]))
#calculate sample size and mean
for i in range(action_count):
cur_row['sample_size_{}'.format(i + 1)] = sample_sizes[i]
cur_row['mean_{}'.format(i + 1)] = means[i]
if action_count == 2:
#SE = sqrt[(P^hat_A*(1-P^hat_A)/N_A + (P^hat_B*(1-P^hat_B)/N_B]
SE = np.sqrt(means[0] * (1 - means[0]) / sample_sizes[0] +
means[1] * (1 - means[1]) / sample_sizes[1])
wald_type_stat = (means[0] -
means[1]) / SE #(P^hat_A - P^hat_b)/SE
#print('wald_type_stat:', wald_type_stat)
#Two sided, symetric, so compare to 0.05
wald_pval = (1 -
scipy.stats.norm.cdf(np.abs(wald_type_stat))) * 2
cur_row['wald_type_stat'] = wald_type_stat
cur_row['wald_pval'] = wald_pval
#calculate total reward
cur_row['total_reward'] = np.sum(one_sim_df[obs_reward_header])
#calculate power
cur_row['ratio'] = sample_sizes[0] / sample_sizes[1]
if include_power:
cur_row['power'] = smp.GofChisquarePower().solve_power(
effect_size,
nobs=sum(sample_sizes),
n_bins=(2 - 1) * (2 - 1) + 1,
alpha=alpha)
cur_row['actual_es'] = calculate_effect_size(
sample_sizes, successes)
#calculate chi squared contingency test
table = sms.Table(
np.stack((successes, sample_sizes - successes)).T)
rslt = table.test_nominal_association()
cur_row['stat'] = rslt.statistic
cur_row['pvalue'] = rslt.pvalue
cur_row['df'] = rslt.df
# Added to match normal rewards
cur_row['statUnequalVar'] = cur_row['stat']
cur_row['pvalueUnequalVar'] = cur_row['pvalue']
cur_row['dfUnequalVar'] = cur_row['df']
cur_row['num_steps'] = max(one_sim_df.index) + 1
# cur_row['num_steps'] = n_size
cur_row['sim'] = sim_num
cur_row["prop_exploring_ppd_cuml"] = prop_exploring_ppd_cuml
cur_row["exploring_ppd_at_this_n"] = exploring_this_n
all_rows.append(cur_row)
sim_num += 1
return all_rows
def make_stats_row_from_df(simulations_df,
include_power,
action_count,
effect_size=None,
alpha=None):
'''Calculates output statistics given the data frame simulations_df. If include_power, includes the power calculation.
efffect_size and alpha are only required/used if power is calculated
'''
#REPLACE cur_data with simulations_df
#number of actions! action_count
step_size = max(simulations_df.index) + 1
trials_count = len(simulations_df)
sim_num = 0
all_rows = []
for idx in range(0, trials_count, step_size):
one_sim_df = simulations_df[idx:idx + step_size].copy()
sample_sizes = np.array([])
successes = np.array([])
means = np.array([])
np.array([])
np.array([])
for i in range(1, action_count + 1):
sample_sizes = np.append(sample_sizes,
np.sum(one_sim_df[action_header] == i))
successes = np.append(
successes,
np.sum(one_sim_df[one_sim_df[action_header] == i]
[obs_reward_header]))
means = np.append(
means,
np.mean(one_sim_df[one_sim_df[action_header] == i]
[obs_reward_header]))
#calculate sample size and mean
cur_row = {}
for i in range(action_count):
cur_row['sample_size_{}'.format(i + 1)] = sample_sizes[i]
cur_row['mean_{}'.format(i + 1)] = means[i]
if action_count == 2:
#SE = sqrt[(P^hat_A*(1-P^hat_A)/N_A + (P^hat_B*(1-P^hat_B)/N_B]
SE = np.sqrt(means[0] * (1 - means[0]) / sample_sizes[0] +
means[1] * (1 - means[1]) / sample_sizes[1])
wald_type_stat = (means[0] -
means[1]) / SE #(P^hat_A - P^hat_b)/SE
#print('wald_type_stat:', wald_type_stat)
#Two sided, symetric, so compare to 0.05
wald_pval = (1 - scipy.stats.norm.cdf(np.abs(wald_type_stat))) * 2
cur_row['wald_type_stat'] = wald_type_stat
cur_row['wald_pval'] = wald_pval
#calculate total reward
cur_row['total_reward'] = np.sum(one_sim_df[obs_reward_header])
#calculate power
cur_row['ratio'] = sample_sizes[0] / sample_sizes[1]
if include_power:
cur_row['power'] = smp.GofChisquarePower().solve_power(
effect_size,
nobs=sum(sample_sizes),
n_bins=(2 - 1) * (2 - 1) + 1,
alpha=alpha)
cur_row['actual_es'] = calculate_effect_size(sample_sizes, successes)
#calculate chi squared contingency test
table = sms.Table(np.stack((successes, sample_sizes - successes)).T)
rslt = table.test_nominal_association()
cur_row['stat'] = rslt.statistic
cur_row['pvalue'] = rslt.pvalue
cur_row['df'] = rslt.df
# Added to match normal rewards
cur_row['statUnequalVar'] = cur_row['stat']
cur_row['pvalueUnequalVar'] = cur_row['pvalue']
cur_row['dfUnequalVar'] = cur_row['df']
cur_row['num_steps'] = max(one_sim_df.index) + 1
cur_row['sim'] = sim_num
sim_num += 1
all_rows.append(cur_row)
return all_rows
