def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
np.random.seed(100)
group_0_prob = 0.5
maximize_reward_result = lending.Experiment(
group_0_prob=group_0_prob,
interest_rate=1.0,
bank_starting_cash=10000,
seed=200,
num_steps=FLAGS.num_steps,
burnin=200,
cluster_shift_increment=0.01,
include_cumulative_loans=True,
return_json=False,
threshold_policy=MAXIMIZE_REWARD).run()
equality_of_opportunity_result = lending.Experiment(
group_0_prob=group_0_prob,
interest_rate=1.0,
bank_starting_cash=10000,
seed=200,
num_steps=FLAGS.num_steps,
burnin=200,
cluster_shift_increment=0.01,
include_cumulative_loans=True,
return_json=False,
threshold_policy=EQUALIZE_OPPORTUNITY).run()
lending_plots.do_plotting(maximize_reward_result,
equality_of_opportunity_result,
equality_of_opportunity_result,
# static_equality_of_opportunity_result,
FLAGS.plots_directory,
options=None)
def test_short_run_recall_is_perfect(self):
# Run for fewer steps than the burnin - this should give 100% recall
# since during the burnin period, all loans are accepted.
result = lending.Experiment(num_steps=10).run()
result = json.loads(result)
self.assertEqual(result['metric_results']['recall'], {
'0': 1.0,
'1': 1.0
})
def test_plotting(self):
plotting_dir = self.create_tempdir().full_path
self.assert_contains_no_pdfs(plotting_dir)
# Run a single experiment to create the results.
result = lending.Experiment(
num_steps=100, include_cumulative_loans=True, return_json=False).run()
# Tests that all_plots can execute.
lending_plots.do_plotting(result, result, result, plotting_dir)
# There should be at least one .pdf file created.
self.assert_contains_pdfs(plotting_dir)
def test_experiment_runs_with_default_parameters(self):
# Tests that the experiment can run.
result = lending.Experiment(num_steps=100).run()
# Tests that the result is a valid json string.
result = json.loads(result)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
np.random.seed(100)
group_0_prob = 0.5
result = lending.Experiment(
group_0_prob=group_0_prob,
interest_rate=1.0,
bank_starting_cash=10000,
seed=200,
num_steps=FLAGS.num_steps,
burnin=200,
cluster_shift_increment=0.01,
include_cumulative_loans=True,
return_json=False,
threshold_policy=(EQUALIZE_OPPORTUNITY if FLAGS.equalize_opportunity
else MAXIMIZE_REWARD)).run()
title = ('Eq. opportunity' if FLAGS.equalize_opportunity else 'Max reward')
metrics = result['metric_results']
# Standalone figure of initial credit distribution
fig = plt.figure(figsize=(4, 4))
lending.plot_credit_distribution(
metrics['initial_credit_distribution'],
'Initial',
path=os.path.join(FLAGS.plots_directory,
'initial_credit_distribution.png')
if FLAGS.plots_directory else None,
include_median=True,
figure=fig)
# Initial and final credit distributions next to each other.
fig = plt.figure(figsize=(8, 4))
plt.subplot(1, 2, 1)
lending.plot_credit_distribution(metrics['initial_credit_distribution'],
'Initial',
path=None,
include_median=True,
figure=fig)
plt.subplot(1, 2, 2)
lending.plot_credit_distribution(
metrics['final_credit_distributions'],
'Final - %s' % title,
path=os.path.join(FLAGS.plots_directory,
'final_credit_distribution.png')
if FLAGS.plots_directory else None,
include_median=True,
figure=fig)
fig = plt.figure()
lending.plot_bars(metrics['recall'],
title='Recall - %s' % title,
path=os.path.join(FLAGS.plots_directory, 'recall.png')
if FLAGS.plots_directory else None,
figure=fig)
fig = plt.figure()
lending.plot_bars(metrics['precision'],
title='Precision - %s' % title,
ylabel='Precision',
path=os.path.join(FLAGS.plots_directory, 'precision.png')
if FLAGS.plots_directory else None,
figure=fig)
fig = plt.figure()
lending.plot_cumulative_loans(
{'demo - %s' % title: metrics['cumulative_loans']},
path=os.path.join(FLAGS.plots_directory, 'cumulative_loans.png')
if FLAGS.plots_directory else None,
figure=fig)
print('Profit %s %f' % (title, result['metric_results']['profit rate']))
plt.show()
if FLAGS.outfile:
with open(FLAGS.outfile, 'w') as f:
f.write(result)