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univariate_power_analysis_comparison.py
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import matplotlib.pyplot as plt
import numpy as np
import os
import seaborn as sns
import shutil
# =========== HYPERPARAMETERS ==========
UNIVARIATE_DISTRIBUTIONS = ['chi_square_9', 'exp_9']
NUM_SAMPLES = 20000
NUM_TRIALS = 5
# ========== OUTPUT DIRECTORIES ==========
OUTPUT_DIR = 'examples/power_analyses/univariate_output/'
MODELS_OUTPUT_DIR = OUTPUT_DIR + 'MODELS/'
SYN_DATA_OUTPUT_DIR = OUTPUT_DIR + 'SYN_DATA/'
REAL_DATA_OUTPUT_DIR = OUTPUT_DIR + 'REAL_DATA/'
POWER_OUTPUT_DIR = OUTPUT_DIR + 'POWER/'
RESULTS_DIR = 'RESULTS/'
# shutil.rmtree(OUTPUT_DIR, ignore_errors=True)
# os.makedirs(MODELS_OUTPUT_DIR)
# os.makedirs(SYN_DATA_OUTPUT_DIR)
# os.makedirs(REAL_DATA_OUTPUT_DIR)
# os.makedirs(POWER_OUTPUT_DIR)
os.makedirs(RESULTS_DIR)
# ========== RUN PIPELINE ==========
def generate_real_cmd(dist, num_samples, output_dir):
return 'python3 sample_prob_dist.py {0} {1} {2}/'.format(dist, num_samples, output_dir)
def train_gan_cmd(real_data_dir, output_dir):
return 'python3 train_prob_gan.py {0}data.npy {1}'.format(real_data_dir, output_dir)
def generate_syn_cmd(gen_dir, num_samples, output_dir):
return 'python3 generate_prob_gan.py {0}generator {1} {2}'.format(gen_dir, num_samples, output_dir)
def power_analysis_cmd(real_data_1_dir, real_data_2_dir, syn_data_1_dir, syn_data_2_dir, output_dir):
return 'python3 univariate_power_analysis.py {0}data.npy {1}data.npy {2}data.npy {3}data.npy {4}'.format(real_data_1_dir, syn_data_1_dir, real_data_2_dir, syn_data_2_dir, output_dir)
def output_dirs(dist, k):
model_tag_base = '[{0}]_[k={1}]'.format(dist, k)
model_1_tag = model_tag_base + '_[v=1]'
model_2_tag = model_tag_base + '_[v=2]'
real_data_1_dir = '{0}{1}/'.format(REAL_DATA_OUTPUT_DIR, model_1_tag)
real_data_2_dir = '{0}{1}/'.format(REAL_DATA_OUTPUT_DIR, model_2_tag)
model_1_dir = '{0}{1}/'.format(MODELS_OUTPUT_DIR, model_1_tag)
model_2_dir = '{0}{1}/'.format(MODELS_OUTPUT_DIR, model_2_tag)
syn_data_1_dir = '{0}{1}/'.format(SYN_DATA_OUTPUT_DIR, model_1_tag)
syn_data_2_dir = '{0}{1}/'.format(SYN_DATA_OUTPUT_DIR, model_2_tag)
return real_data_1_dir, real_data_2_dir, model_1_dir, model_2_dir, syn_data_1_dir, syn_data_2_dir
def run_cmd_sequence(cmds):
for cmd in cmds:
os.system(cmd)
def generate_real_data_samples():
for i in range(len(UNIVARIATE_DISTRIBUTIONS)):
for k in range(NUM_TRIALS):
dist_i = UNIVARIATE_DISTRIBUTIONS[i]
real_data_1_dir, real_data_2_dir, _, _, _, _ = output_dirs(dist_i, k)
sample_real_1 = generate_real_cmd(dist_i, NUM_SAMPLES, real_data_1_dir)
sample_real_2 = generate_real_cmd(dist_i, NUM_SAMPLES, real_data_2_dir)
run_cmd_sequence([sample_real_1, sample_real_2])
def train_gans():
for i in range(len(UNIVARIATE_DISTRIBUTIONS)):
for k in range(NUM_TRIALS):
dist_i = UNIVARIATE_DISTRIBUTIONS[i]
real_data_1_dir, real_data_2_dir, model_1_dir, model_2_dir, _, _ = output_dirs(dist_i, k)
train_gan_1 = train_gan_cmd(real_data_1_dir, model_1_dir)
train_gan_2 = train_gan_cmd(real_data_2_dir, model_2_dir)
run_cmd_sequence([train_gan_1, train_gan_2])
def generate_syn_data_samples():
for i in range(len(UNIVARIATE_DISTRIBUTIONS)):
for k in range(NUM_TRIALS):
dist_i = UNIVARIATE_DISTRIBUTIONS[i]
_, _, model_1_dir, model_2_dir, syn_data_1_dir, syn_data_2_dir = output_dirs(dist_i, k)
sample_syn_1 = generate_syn_cmd(model_1_dir, NUM_SAMPLES, syn_data_1_dir)
sample_syn_2 = generate_syn_cmd(model_2_dir, NUM_SAMPLES, syn_data_2_dir)
run_cmd_sequence([sample_syn_1, sample_syn_2])
def run_power_analyses():
for i in range(len(UNIVARIATE_DISTRIBUTIONS)):
for j in range(i, len(UNIVARIATE_DISTRIBUTIONS)):
for k in range(NUM_TRIALS):
dist_i = UNIVARIATE_DISTRIBUTIONS[i]
dist_j = UNIVARIATE_DISTRIBUTIONS[j]
real_data_1_dir_i, real_data_2_dir_i, _, _, syn_data_1_dir_i, syn_data_2_dir_i = output_dirs(dist_i, k)
real_data_1_dir_j, real_data_2_dir_j, _, _, syn_data_1_dir_j, syn_data_2_dir_j = output_dirs(dist_j, k)
output_dir = '{0}[{1}_VS_{2}]_[k={3}]/'.format(POWER_OUTPUT_DIR, dist_i, dist_j, k)
cmd = power_analysis_cmd(real_data_1_dir_i, real_data_2_dir_j, syn_data_1_dir_i, syn_data_2_dir_j, output_dir)
run_cmd_sequence([cmd])
def visualize():
for i in range(len(UNIVARIATE_DISTRIBUTIONS)):
for j in range(i, len(UNIVARIATE_DISTRIBUTIONS)):
figure, axes = plt.subplots(nrows=1, ncols=1)
n = None
t_test_real_power = []
mmd_test_real_power = []
t_test_syn_power = []
mmd_test_syn_power = []
for k in range(NUM_TRIALS):
dist_i = UNIVARIATE_DISTRIBUTIONS[i]
dist_j = UNIVARIATE_DISTRIBUTIONS[j]
power_dir_k = '{0}[{1}_VS_{2}]_[k={3}]/'.format(POWER_OUTPUT_DIR, dist_i, dist_j, k)
if n is None:
n = np.load(power_dir_k+'n.npy')
t_test_real_power.append(np.load(power_dir_k+'t_test_real_power.npy'))
mmd_test_real_power.append(np.load(power_dir_k+'mmd_test_real_power.npy'))
t_test_syn_power.append(np.load(power_dir_k+'t_test_syn_power.npy'))
mmd_test_syn_power.append(np.load(power_dir_k+'mmd_test_syn_power.npy'))
n = np.array(n)
t_test_real_power = np.array(t_test_real_power)
mmd_test_real_power = np.array(mmd_test_real_power)
t_test_syn_power = np.array(t_test_syn_power)
mmd_test_syn_power = np.array(mmd_test_syn_power)
# Plot curve of n vs power
# sns.tsplot(data=t_test_real_power, time=n, ci=[68, 95], color='blue', condition='Real', ax=axes[0])
# sns.tsplot(data=t_test_syn_power, time=n, ci=[68, 95], color='orange', condition='Synthetic', ax=axes[0])
# axes[0].set_title('Sample Size vs T Test Power')
# axes[0].set_xlabel('Sample Size')
# axes[0].set_ylabel('Power')
# axes[0].set_ylim([-0.1, 1.1])
# axes[0].legend(loc="upper right")
sns.tsplot(data=mmd_test_real_power, time=n, ci=[68, 95], color='blue', condition='Real', ax=axes)
sns.tsplot(data=mmd_test_syn_power, time=n, ci=[68, 95], color='orange', condition='Synthetic', ax=axes)
axes.set_title('Sample Size vs MMD Test Power')
axes.set_xlabel('Sample Size')
axes.set_ylabel('Power')
axes.set_ylim([-0.1, 1.1])
axes.legend(loc="upper right")
# Save results
figure.tight_layout()
figure.savefig('{0}{1}_VS_{2}'.format(RESULTS_DIR, dist_i, dist_j), format='eps')
# ========== MAIN ==========
# generate_real_data_samples()
# train_gans()
# generate_syn_data_samples()
# run_power_analyses()
visualize()