def test_compute_eval_from_predictions():
"""
Test compute_eval_from_predictions function console script
"""
pred_path = join(_my_dir, 'other',
'test_compute_eval_from_predictions.predictions')
input_path = join(_my_dir, 'other',
'test_compute_eval_from_predictions.jsonlines')
# we need to capture stdout since that's what main() writes to
compute_eval_from_predictions_cmd = [input_path, pred_path, 'pearson',
'unweighted_kappa']
try:
old_stdout = sys.stdout
old_stderr = sys.stderr
sys.stdout = mystdout = StringIO()
sys.stderr = mystderr = StringIO()
cefp.main(compute_eval_from_predictions_cmd)
score_rows = mystdout.getvalue().strip().split('\n')
err = mystderr.getvalue()
print(err)
finally:
sys.stdout = old_stdout
sys.stderr = old_stderr
scores = {}
for score_row in score_rows:
score, metric_name, pred_path = score_row.split('\t')
scores[metric_name] = float(score)
assert_almost_equal(scores['pearson'], 0.6197797868009122)
assert_almost_equal(scores['unweighted_kappa'], 0.2)
def test_warning_when_prediction_method_and_no_probabilities():
"""
Test compute_eval_from_predictions logs a warning if a prediction method is provided
but the predictions file doesn't contain probabilities.
"""
lc = LogCapture()
lc.begin()
pred_path = join(_my_dir, 'other',
'test_compute_eval_from_predictions_predictions.tsv')
input_path = join(_my_dir, 'other',
'test_compute_eval_from_predictions.jsonlines')
# we need to capture stdout since that's what main() writes to
compute_eval_from_predictions_cmd = [input_path, pred_path, 'pearson',
'unweighted_kappa', '--method', 'highest']
try:
old_stdout = sys.stdout
old_stderr = sys.stderr
sys.stdout = mystdout = StringIO()
sys.stderr = mystderr = StringIO()
cefp.main(compute_eval_from_predictions_cmd)
score_rows = mystdout.getvalue().strip().split('\n')
err = mystderr.getvalue()
print(err)
finally:
sys.stdout = old_stdout
sys.stderr = old_stderr
log_msg = ("skll.utilities.compute_eval_from_predictions: WARNING: A prediction "
"method was provided, but the predictions file doesn't contain "
"probabilities. Ignoring prediction method 'highest'.")
eq_(lc.handler.buffer[-1], log_msg)
def test_conflicting_prediction_and_example_ids():
"""
Make sure compute_eval_from_predictions breaks with ValueError when predictions and
examples don't have the same id set in 'compute_eval_from_predictions'.
"""
pred_path = join(_my_dir, 'other',
'test_compute_eval_from_predictions_probs_predictions.tsv')
input_path = join(_my_dir, 'other',
'test_compute_eval_from_predictions_different_ids.jsonlines')
compute_eval_from_predictions_cmd = [input_path, pred_path, 'pearson']
cefp.main(compute_eval_from_predictions_cmd)
def test_compute_eval_from_predictions_breaks_with_expval_and_nonnumeric_classes():
"""
Make sure compute_eval_from_predictions breaks with ValueError when predictions are
calculated via expected_value and the classes are non numeric.
"""
pred_path = join(_my_dir, 'other',
'test_compute_eval_from_predictions_nonnumeric_classes_predictions.tsv')
input_path = join(_my_dir, 'other',
'test_compute_eval_from_predictions_nonnumeric_classes.jsonlines')
compute_eval_from_predictions_cmd = [input_path, pred_path, 'explained_variance',
'r2', '--method', 'expected_value']
cefp.main(compute_eval_from_predictions_cmd)
def test_compute_eval_from_predictions_with_probs():
"""
Test compute_eval_from_predictions function console script, with probabilities in
the predictions file.
"""
pred_path = join(_my_dir, 'other',
'test_compute_eval_from_predictions_probs_predictions.tsv')
input_path = join(_my_dir, 'other',
'test_compute_eval_from_predictions_probs.jsonlines')
# we need to capture stdout since that's what main() writes to
compute_eval_from_predictions_cmd = [input_path, pred_path, 'pearson',
'unweighted_kappa']
try:
old_stdout = sys.stdout
old_stderr = sys.stderr
sys.stdout = mystdout = StringIO()
sys.stderr = mystderr = StringIO()
cefp.main(compute_eval_from_predictions_cmd)
score_rows = mystdout.getvalue().strip().split('\n')
err = mystderr.getvalue()
print(err)
finally:
sys.stdout = old_stdout
sys.stderr = old_stderr
scores = {}
for score_row in score_rows:
score, metric_name, pred_path = score_row.split('\t')
scores[metric_name] = float(score)
assert_almost_equal(scores['pearson'], 0.6197797868009122)
assert_almost_equal(scores['unweighted_kappa'], 0.2)
#
# Test expected value predictions method
#
compute_eval_from_predictions_cmd = [input_path, pred_path, 'explained_variance',
'r2' ,'--method', 'expected_value']
try:
old_stdout = sys.stdout
old_stderr = sys.stderr
sys.stdout = mystdout = StringIO()
sys.stderr = mystderr = StringIO()
cefp.main(compute_eval_from_predictions_cmd)
score_rows = mystdout.getvalue().strip().split('\n')
err = mystderr.getvalue()
print(err)
finally:
sys.stdout = old_stdout
sys.stderr = old_stderr
scores = {}
for score_row in score_rows:
score, metric_name, pred_path = score_row.split('\t')
scores[metric_name] = float(score)
assert_almost_equal(scores['r2'], 0.19999999999999996)
assert_almost_equal(scores['explained_variance'], 0.23809523809523792)