def test_prediction_to_csv_class():
"""Test util.prediction_to_csv"""
with create_test_project() as p, create_test_dataset(p) as ds,\
create_test_featureset(p) as fs,\
create_test_model(fs, model_type='LinearSGDClassifier') as m,\
create_test_prediction(ds, m) as pred:
pred = featureset.from_netcdf(pred.file.uri)
assert util.prediction_to_csv(pred) ==\
[['ts_name', 'true_target', 'prediction'],
['0', 'Mira', 'Mira'],
['1', 'Classical_Cepheid', 'Classical_Cepheid'],
['2', 'Mira', 'Mira'],
['3', 'Classical_Cepheid', 'Classical_Cepheid'],
['4', 'Mira', 'Mira']]
def test_pred_results_table_rfc(driver):
driver.get('/')
with create_test_project() as p, create_test_dataset(p) as ds,\
create_test_featureset(p) as fs, create_test_model(fs) as m,\
create_test_prediction(ds, m):
_click_prediction_row(p.id, driver)
try:
rows = _grab_pred_results_table_rows(driver, 'Mira')
for row in rows:
probs = [
float(v.text)
for v in row.find_elements_by_tag_name('td')[3::2]
]
assert sorted(probs, reverse=True) == probs
driver.find_element_by_xpath(
"//th[contains(text(),'Time Series')]")
except:
driver.save_screenshot("/tmp/pred_click_tr_fail.png")
raise
def test_prediction_to_csv_regr():
"""Test util.prediction_to_csv"""
with create_test_project() as p, create_test_dataset(p, label_type='regr') as ds,\
create_test_featureset(p, label_type='regr') as fs,\
create_test_model(fs, model_type='LinearRegressor') as m,\
create_test_prediction(ds, m) as pred:
pred = featureset.from_netcdf(pred.file.uri)
results = util.prediction_to_csv(pred)
assert results[0] == ['ts_name', 'true_target', 'prediction']
npt.assert_array_almost_equal(
[[float(e) for e in row] for row in results[1:]],
[[0, 2.2, 2.2],
[1, 3.4, 3.4],
[2, 4.4, 4.4],
[3, 2.2, 2.2],
[4, 3.1, 3.1]])
def test_download_prediction_csv_class(driver):
driver.get('/')
with create_test_project() as p, create_test_dataset(p) as ds,\
create_test_featureset(p) as fs,\
create_test_model(fs, model_type='LinearSGDClassifier') as m,\
create_test_prediction(ds, m):
_click_download(p.id, driver)
assert os.path.exists('/tmp/cesium_prediction_results.csv')
try:
npt.assert_equal(
np.genfromtxt('/tmp/cesium_prediction_results.csv',
dtype='str'),
[
'ts_name,true_target,prediction', '0,Mira,Mira',
'1,Classical_Cepheid,Classical_Cepheid', '2,Mira,Mira',
'3,Classical_Cepheid,Classical_Cepheid', '4,Mira,Mira'
])
finally:
os.remove('/tmp/cesium_prediction_results.csv')
def test_download_prediction_csv_regr(driver):
driver.get('/')
with create_test_project() as p, create_test_dataset(p, label_type='regr') as ds,\
create_test_featureset(p, label_type='regr') as fs,\
create_test_model(fs, model_type='LinearRegressor') as m,\
create_test_prediction(ds, m):
_click_download(p.id, driver)
assert os.path.exists('/tmp/cesium_prediction_results.csv')
try:
results = np.genfromtxt('/tmp/cesium_prediction_results.csv',
dtype='str',
delimiter=',')
npt.assert_equal(results[0],
['ts_name', 'true_target', 'prediction'])
npt.assert_array_almost_equal(
[[float(e) for e in row] for row in results[1:]],
[[0, 2.2, 2.2], [1, 3.4, 3.4], [2, 4.4, 4.4], [3, 2.2, 2.2],
[4, 3.1, 3.1]])
finally:
os.remove('/tmp/cesium_prediction_results.csv')
def test_add_prediction_lsgdc(driver):
driver.get('/')
with create_test_project() as p, create_test_dataset(p) as ds,\
create_test_featureset(p) as fs,\
create_test_model(fs, model_type='LinearSGDClassifier') as m:
_add_prediction(p.id, driver)
def test_add_prediction_rfc(driver):
driver.get('/')
with create_test_project() as p, create_test_dataset(p) as ds,\
create_test_featureset(p) as fs, create_test_model(fs) as m:
_add_prediction(p.id, driver)
def test_add_prediction_rfr(driver):
driver.get('/')
with create_test_project() as p, create_test_dataset(p, label_type='regr') as ds,\
create_test_featureset(p, label_type='regr') as fs,\
create_test_model(fs, model_type='RandomForestRegressor') as m:
_add_prediction(p.id, driver)