def test_r2_evaluator():
predictions = pd.DataFrame({
'target': [0, 1, 2],
'prediction': [0.5, 0.9, 1.5]
})
result = r2_evaluator(predictions)
assert result['r2_evaluator__target'] == 0.745
def test_extract():
boston = load_boston()
df = pd.DataFrame(boston['data'], columns=boston['feature_names'])
df['target'] = boston['target']
df['time'] = pd.date_range(start='2015-01-01', periods=len(df))
np.random.seed(42)
df['space'] = np.random.randint(0, 100, size=len(df))
# Define train function
train_fn = linear_regression_learner(
features=boston['feature_names'].tolist(), target="target")
# Define evaluator function
base_evaluator = combined_evaluators(evaluators=[
r2_evaluator(target_column='target', prediction_column='prediction'),
spearman_evaluator(target_column='target',
prediction_column='prediction')
])
splitter = split_evaluator(eval_fn=base_evaluator,
split_col='RAD',
split_values=[4.0, 5.0, 24.0])
temporal_week_splitter = temporal_split_evaluator(eval_fn=base_evaluator,
time_col='time',
time_format='%Y-%W')
temporal_year_splitter = temporal_split_evaluator(eval_fn=base_evaluator,
time_col='time',
time_format='%Y')
eval_fn = combined_evaluators(evaluators=[base_evaluator, splitter])
temporal_week_eval_fn = combined_evaluators(
evaluators=[base_evaluator, temporal_week_splitter])
temporal_year_eval_fn = combined_evaluators(
evaluators=[base_evaluator, temporal_year_splitter])
# Define splitters
cv_split_fn = out_of_time_and_space_splitter(n_splits=5,
in_time_limit='2016-01-01',
time_column='time',
space_column='space')
tlc_split_fn = time_learning_curve_splitter(
training_time_limit='2016-01-01', time_column='time', min_samples=0)
sc_split_fn = stability_curve_time_splitter(
training_time_limit='2016-01-01', time_column='time', min_samples=0)
fw_sc_split_fn = forward_stability_curve_time_splitter(
training_time_start="2015-01-01",
training_time_end="2016-01-01",
holdout_gap=timedelta(days=30),
holdout_size=timedelta(days=30),
step=timedelta(days=30),
time_column='time')
# Validate results
cv_results = validator(df, cv_split_fn, train_fn, eval_fn)['validator_log']
tlc_results = validator(df, tlc_split_fn, train_fn,
eval_fn)['validator_log']
sc_results = validator(df, sc_split_fn, train_fn, eval_fn)['validator_log']
fw_sc_results = validator(df, fw_sc_split_fn, train_fn,
eval_fn)['validator_log']
# temporal evaluation results
predict_fn, _, _ = train_fn(df)
temporal_week_results = temporal_week_eval_fn(predict_fn(df))
temporal_year_results = temporal_year_eval_fn(predict_fn(df))
# Define extractors
base_extractors = combined_evaluator_extractor(base_extractors=[
evaluator_extractor(evaluator_name="r2_evaluator__target"),
evaluator_extractor(evaluator_name="spearman_evaluator__target")
])
splitter_extractor = split_evaluator_extractor(
split_col='RAD',
split_values=[4.0, 5.0, 24.0],
base_extractor=base_extractors)
temporal_week_splitter_extractor = temporal_split_evaluator_extractor(
time_col='time', time_format='%Y-%W', base_extractor=base_extractors)
temporal_year_splitter_extractor = temporal_split_evaluator_extractor(
time_col='time', time_format='%Y', base_extractor=base_extractors)
assert extract(cv_results, base_extractors).shape == (5, 9)
assert extract(cv_results, splitter_extractor).shape == (15, 10)
assert extract(tlc_results, base_extractors).shape == (12, 9)
assert extract(tlc_results, splitter_extractor).shape == (36, 10)
assert extract(sc_results, base_extractors).shape == (5, 9)
assert extract(sc_results, splitter_extractor).shape == (15, 10)
assert extract(fw_sc_results, base_extractors).shape == (3, 9)
assert extract(fw_sc_results, splitter_extractor).shape == (9, 10)
n_time_week_folds = len(df['time'].dt.strftime('%Y-%W').unique())
n_time_year_folds = len(df['time'].dt.strftime('%Y').unique())
assert temporal_week_splitter_extractor(temporal_week_results).shape == (
n_time_week_folds, 3)
assert temporal_year_splitter_extractor(temporal_year_results).shape == (
n_time_year_folds, 3)