def point_1():
model_1 = load_stan_model('04-assignment/model_1.stan')
model_2 = load_stan_model('04-assignment/model_2.stan')
model_3 = load_stan_model('04-assignment/model_3.stan')
y = [1] * 17 + [0] * 3
data = {'N': len(y), 'y': y}
fit_1, summary_1, _ = fit_stan_model(model_1,
data,
iter=iterations,
seed=random_seed)
fit_2, summary_2, _ = fit_stan_model(model_2,
data,
iter=iterations,
seed=random_seed)
fit_3, summary_3, _ = fit_stan_model(model_3,
data,
iter=iterations,
seed=random_seed)
recreate_figure_64([fit_1, fit_2, fit_3], y)
plt.tight_layout()
sns.despine()
plt.savefig('04-assignment/figure-64.png')
def main():
with open('06-assignment/data.json') as json_file:
data = json.load(json_file)
data_df = pd.read_csv('06-assignment/data.csv')
#plot_data(data_df)
model = load_stan_model('06-assignment/hierarchical_model_student.stan')
fit, fit_summary, fit_model = fit_stan_model(model, data, iter=10000, warmup=1000)
fit_summary.to_csv('06-assignment/student/fit_summary.csv')
point_1(fit, fit_summary)
point_2(fit, fit_summary)
point_3()
point_4(fit, fit_summary)
def point_1(data):
model = load_stan_model('05-assignment/hierarchical_model.stan', True)
fit, summary, fit_model = fit_stan_model(model,
data,
iter=10000,
warmup=1000)
the_dude_posterior = fit.extract('exp_theta')['exp_theta'][:, 3]
mu_posterior = fit.extract('exp_mu')['exp_mu']
random_posteriors = fit.extract('exp_theta')['exp_theta'][:, [10, 1]]
sns.distplot(the_dude_posterior,
bins=30,
norm_hist=True,
label='The Dude θ')
sns.distplot(mu_posterior, bins=30, norm_hist=True, label='Group θ')
sns.distplot(random_posteriors[:, 0], bins=30, norm_hist=True)
sns.distplot(random_posteriors[:, 1], bins=30, norm_hist=True)
print(f'The Dude\'s mean: {np.mean(the_dude_posterior):.2f}')
print(f'The group mean: {np.mean(mu_posterior):.2f}')
plt.axvline(np.mean(the_dude_posterior),
color='b',
lw=2,
linestyle='--',
label='The Dude mean')
plt.axvline(np.mean(mu_posterior),
color='r',
lw=2,
linestyle='--',
label='Group mean')
the_dude_hdi = hdi(the_dude_posterior, 0.95)
mu_hdi = hdi(mu_posterior, 0.95)
print(f'The Dude\'s HDI: {the_dude_hdi}')
print(f'The group HDI: {mu_hdi}')
plt.legend()
sns.despine()
plt.savefig('05-assignment/point_1.png')
pickle.dump(fit.extract('tau'),
open('05-assignment/tau_posterior_for_assignment_6.pkl', 'wb'))
return fit, summary, fit_model
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
sns.set_context('paper')
from utils.utils import load_stan_model, fit_stan_model, \
plot_trace, plot_posterior, \
compare_distributions, roc_curve
from utils.hdi import hdi_from_book as hdi
from pprint import pprint
import json
if __name__ == '__main__':
with open('07-assignment/data.json') as json_file:
data = json.load(json_file)
data_df = pd.read_csv('07-assignment/data.csv')
model_directory = Path('07-assignment/normal_model/')
model = load_stan_model(model_directory / 'normal_model.stan')
fit, fit_summary, fit_model = fit_stan_model(model,
data,
iter=35000,
warmup=5000)
fit_summary.to_csv(model_directory / 'fit_summary.csv',
float_format='%.5f')
pickle.dump(fit.extract(), open(model_directory / 'fit.pkl', 'wb'))
def point_2():
model = load_stan_model('04-assignment/model_3.stan')
y = [1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1]
z = [1, 0, 0, 0, 0, 0, 0, 1, 1, 0]
data_y = {'N': len(y), 'y': y}
data_z = {'N': len(z), 'y': z}
fit_y, summary_y, _ = fit_stan_model(model,
data_y,
iter=iterations,
seed=random_seed)
fit_z, summary_z, _ = fit_stan_model(model,
data_z,
iter=iterations,
seed=random_seed)
hdi_y = hdi(fit_y['theta'])
hdi_z = hdi(fit_z['theta'])
print(
f'Expected probability for getting heads with data y: {summary_y.loc["theta", "mean"]:.2f}'
)
print(f'HDI of y: {hdi_y[0]:.2f} - {hdi_y[1]:.2f}')
print(f'Probability of θ > 0.5: {np.mean(fit_y["theta"] > 0.5)}')
d_theta = fit_y['theta'] - fit_z['theta']
hdi_d_theta = hdi(d_theta)
probability_higher, point_of_even = compare_distributions(
fit_y['theta'], fit_z['theta'])
plt.figure()
sns.distplot(fit_y['theta'], bins=60, hist=True, label='θ_y')
sns.distplot(fit_z['theta'], bins=60, hist=True, label='θ_z')
plt.plot(hdi_y, [0, 0], 'b', alpha=0.5, linewidth=5, label='y 95% HDI')
plt.plot(hdi_z, [0, 0], 'r', alpha=0.5, linewidth=5, label='z 95% HDI')
plt.axvline(point_of_even, color='r', linestyle='--', alpha=0.5)
plt.xlabel('θ')
plt.ylabel('Density')
plt.legend()
plt.tight_layout()
sns.despine()
plt.savefig('04-assignment/comparison.png')
print(
f'Expected probability that y and z are drawn from the same coin: {1 - np.mean(d_theta > 0):.2f}'
)
print(
f'True positive rate of choosing coin y: {1 - probability_higher:.2f}')
print(
f'HDI of difference between y and z: {hdi_d_theta[0]:.2f} - {hdi_d_theta[1]:.2f}'
)
plt.figure()
sns.distplot(d_theta, hist=True)
plt.plot(hdi_d_theta, [0, 0], 'k', linewidth=5, label='95% HDI')
plt.xlabel('dθ')
plt.ylabel('Density')
plt.legend()
plt.tight_layout()
sns.despine()
plt.savefig('04-assignment/figure-point-2.png')
plt.show()