def test_prediction_intervals_actually_work():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, predict_intervals=[0.05, 0.95])
df_boston_test = df_boston_test.reset_index(drop=True)
intervals = ml_predictor.predict_intervals(df_boston_test)
actuals = df_boston_test.MEDV
count_under = 0
count_over = 0
# print(intervals)
for idx, row in intervals.iterrows():
actual = actuals.iloc[idx]
if actual < row['interval_0.05']:
count_under += 1
if actual > row['interval_0.95']:
count_over += 1
len_intervals = len(intervals)
pct_under = count_under * 1.0 / len_intervals
pct_over = count_over * 1.0 / len_intervals
# There's a decent bit of noise since this is such a small dataset
assert pct_under < 0.2
assert pct_over < 0.1
def test_predict_uncertainty_true():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, predict_intervals=True)
intervals = ml_predictor.predict_intervals(df_boston_test)
assert isinstance(intervals, pd.DataFrame)
assert intervals.shape[0] == df_boston_test.shape[0]
result_list = ml_predictor.predict_intervals(df_boston_test,
return_type='list')
assert isinstance(result_list, list)
assert len(result_list) == df_boston_test.shape[0]
for idx, row in enumerate(result_list):
assert isinstance(row, list)
assert len(row) == 3
singles = df_boston_test.head().to_dict('records')
for row in singles:
result = ml_predictor.predict_intervals(row)
assert isinstance(result, dict)
assert 'prediction' in result
assert 'interval_0.05' in result
assert 'interval_0.95' in result
for row in singles:
result = ml_predictor.predict_intervals(row, return_type='list')
assert isinstance(result, list)
assert len(result) == 3
df_intervals = ml_predictor.predict_intervals(df_boston_test,
return_type='df')
assert isinstance(df_intervals, pd.DataFrame)
try:
ml_predictor.predict_intervals(df_boston_test,
return_type='this will not work')
assert False
except ValueError:
assert True
def test_predict_intervals_should_fail_if_not_trained():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train)
try:
intervals = ml_predictor.predict_intervals(df_boston_test)
assert False
except ValueError:
assert True
def test_prediction_intervals_lets_the_user_specify_number_of_intervals():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train,
predict_intervals=True,
prediction_intervals=[.2])
intervals = ml_predictor.predict_intervals(df_boston_test,
return_type='list')
assert len(intervals[0]) == 2
def test_predict_intervals_takes_in_custom_intervals():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
# df_boston_train = pd.concat([df_boston_train, df_boston_train, df_boston_train])
ml_predictor.train(df_boston_train, predict_intervals=[0.4, 0.6])
custom_intervals = ml_predictor.predict_intervals(df_boston_test,
return_type='list')
assert isinstance(custom_intervals, list)
singles = df_boston_test.head().to_dict('records')
acceptable_keys = set(['prediction', 'interval_0.4', 'interval_0.6'])
for row in singles:
result = ml_predictor.predict_intervals(row)
assert isinstance(result, dict)
assert 'prediction' in result
assert 'interval_0.4' in result
assert 'interval_0.6' in result
for key in result.keys():
assert key in acceptable_keys
for row in singles:
result = ml_predictor.predict_intervals(row, return_type='list')
assert isinstance(result, list)
assert len(result) == 3
df_intervals = ml_predictor.predict_intervals(df_boston_test,
return_type='df')
assert df_intervals.shape[0] == df_boston_test.shape[0]
assert isinstance(df_intervals, pd.DataFrame)
# Now make sure that the interval values are actually different
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, predict_intervals=True)
default_intervals = ml_predictor.predict_intervals(df_boston_test,
return_type='list')
# This is a super flaky test, because we've got such a small datasize, and we're trying to get distributions from it
len_intervals = len(custom_intervals)
num_failures = 0
for idx, custom_row in enumerate(custom_intervals):
default_row = default_intervals[idx]
if custom_row[1] <= default_row[1]:
num_failures += 1
print('{} should be higher than {}'.format(custom_row[1],
default_row[1]))
if custom_row[2] >= default_row[2]:
print('{} should be lower than {}'.format(custom_row[1],
default_row[1]))
num_failures += 1
assert num_failures < 0.18 * len_intervals