def check_result(got):
try:
self.assertLen(got, 1)
for item in got:
# We can't verify the actual predictions, but we can verify the keys
self.assertIn(constants.BATCHED_PREDICTIONS_KEY, item)
for pred in item[constants.BATCHED_PREDICTIONS_KEY]:
for model_name in ('model1', 'model2'):
self.assertIn(model_name, pred)
for output_name in ('chinese_head',
'english_head',
'other_head'):
for pred_key in ('logistic',
'probabilities',
'all_classes'):
self.assertIn(
output_name + '/' + pred_key,
pred[model_name])
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got): # pylint: disable=invalid-name
try:
self.assertEqual(1, len(got), 'got: %s' % got)
(slice_key, value) = got[0]
self.assertEqual((), slice_key)
self.assertIn(metric_keys.CALIBRATION_PLOT_MATRICES, value)
buckets = value[metric_keys.CALIBRATION_PLOT_MATRICES]
self.assertSequenceAlmostEqual(buckets[0], [-19.0, -17.0, 2.0])
self.assertSequenceAlmostEqual(buckets[1], [0.0, 1.0, 1.0])
self.assertSequenceAlmostEqual(buckets[11],
[0.00303, 3.00303, 3.0])
self.assertSequenceAlmostEqual(buckets[10000],
[1.99997, 3.99997, 2.0])
self.assertSequenceAlmostEqual(buckets[10001],
[28.0, 32.0, 4.0])
self.assertIn(metric_keys.CALIBRATION_PLOT_BOUNDARIES, value)
boundaries = value[metric_keys.CALIBRATION_PLOT_BOUNDARIES]
self.assertAlmostEqual(0.0, boundaries[0])
self.assertAlmostEqual(0.001, boundaries[10])
self.assertAlmostEqual(0.005, boundaries[50])
self.assertAlmostEqual(0.010, boundaries[100])
self.assertAlmostEqual(0.100, boundaries[1000])
self.assertAlmostEqual(0.800, boundaries[8000])
self.assertAlmostEqual(1.000, boundaries[10000])
plot_data = metrics_for_slice_pb2.PlotData()
calibration_plot.populate_plots_and_pop(value, plot_data)
self.assertProtoEquals(
"""lower_threshold_inclusive:1.0
upper_threshold_exclusive: inf
num_weighted_examples {
value: 4.0
}
total_weighted_label {
value: 32.0
}
total_weighted_refined_prediction {
value: 28.0
}""", plot_data.calibration_histogram_buckets.buckets[10001])
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
self.assertEqual(got_slice_key, ())
self.assertLen(got_metrics, 1)
key = metric_types.MetricKey(name='confusion_matrix_at_thresholds')
self.assertIn(key, got_metrics)
got_metric = got_metrics[key]
self.assertProtoEquals(
"""
matrices {
threshold: 0.3
false_negatives: 1.0
true_negatives: 1.0
false_positives: 1.0
true_positives: 1.0
precision: 0.5
recall: 0.5
}
matrices {
threshold: 0.5
false_negatives: 1.0
true_negatives: 2.0
true_positives: 1.0
precision: 1.0
recall: 0.5
}
matrices {
threshold: 0.8
false_negatives: 1.0
true_negatives: 2.0
true_positives: 1.0
precision: 1.0
recall: 0.5
}
""", got_metric)
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got): # pylint: disable=invalid-name
try:
self.assertEqual(1, len(got), 'got: %s' % got)
(slice_key, value) = got[0]
self.assertEqual((), slice_key)
expected_values_dict = {
metric_keys.base_key('[email protected]'): 2.0 / 3.0,
metric_keys.base_key('[email protected]'):
1.0 / 2.0,
metric_keys.base_key('[email protected]'): 3.0 / 5.0,
metric_keys.base_key('[email protected]'): 1.0 / 2.0,
metric_keys.base_key('[email protected]'):
1.0 / 3.0,
metric_keys.base_key('[email protected]'): 2.0 / 5.0,
metric_keys.base_key('[email protected]'): 2.0 / 3.0,
metric_keys.base_key('[email protected]'): 2.0 / 3.0,
metric_keys.base_key('[email protected]'): 2.0 / 3.0,
metric_keys.base_key('[email protected]'): 2.0 / 3.0,
metric_keys.base_key('[email protected]'): 2.0 / 3.0,
metric_keys.base_key('[email protected]'): 1.0 / 3.0,
metric_keys.base_key('[email protected]'):
1.0 / 2.0,
metric_keys.base_key('[email protected]'): 2.0 / 5.0,
metric_keys.base_key('[email protected]'): 1.0 / 2.0,
metric_keys.base_key('[email protected]'):
2.0 / 3.0,
metric_keys.base_key('[email protected]'): 3.0 / 5.0,
metric_keys.base_key('[email protected]'): 1.0 / 5.0,
metric_keys.base_key('[email protected]'): 1.0 / 3.0,
metric_keys.base_key('[email protected]'):
0.0 / 2.0,
metric_keys.base_key('[email protected]'): 1.0 / 5.0,
metric_keys.base_key('[email protected]'): 2.0 / 2.0,
metric_keys.base_key('[email protected]'):
2.0 / 3.0,
metric_keys.base_key('[email protected]'): 4.0 / 5.0,
}
self.assertDictElementsAlmostEqual(value, expected_values_dict)
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 2)
# We can't verify the actual predictions, but we can verify the keys.
for item in got:
self.assertIn(constants.PREDICTIONS_KEY, item)
if multi_model:
self.assertIn('model1', item[constants.PREDICTIONS_KEY])
self.assertIn('model2', item[constants.PREDICTIONS_KEY])
if multi_output:
self.assertIn('Identity',
item[constants.PREDICTIONS_KEY]['model1'])
self.assertIn('Identity_1',
item[constants.PREDICTIONS_KEY]['model1'])
elif multi_output:
self.assertIn('Identity', item[constants.PREDICTIONS_KEY])
self.assertIn('Identity_1', item[constants.PREDICTIONS_KEY])
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
self.assertEqual(got_slice_key, ())
self.assertLen(got_metrics, 1)
key = metric_types.MetricKey(
name='_binary_confusion_matrices_[-inf]',
sub_key=metric_types.SubKey(top_k=3))
self.assertIn(key, got_metrics)
got_matrices = got_metrics[key]
self.assertEqual(
got_matrices,
binary_confusion_matrices.Matrices(
thresholds=[float('-inf')],
tp=[2.0],
fp=[10.0],
tn=[6.0],
fn=[2.0]))
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
self.assertEqual(got_slice_key, ())
expected = {}
for output_name, per_output_values in expected_values.items(
):
for name, value in per_output_values.items():
sub_key = None
if '@' in name:
sub_key = metric_types.SubKey(
top_k=int(name.split('@')[1]))
key = metric_types.MetricKey(
name=name,
output_name=output_name,
sub_key=sub_key)
expected[key] = value
self.assertDictElementsAlmostEqual(got_metrics, expected)
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got): # pylint: disable=invalid-name
try:
self.assertEqual(1, len(got), 'got: %s' % got)
(slice_key, value) = got[0]
self.assertEqual((), slice_key)
self.assertIn(
metric_keys.CONFUSION_MATRIX_AT_THRESHOLDS_MATRICES, value)
matrices = value[
metric_keys.CONFUSION_MATRIX_AT_THRESHOLDS_MATRICES]
# | | --------- Threshold -----------
# true label | pred | wt | -1e-6 | 0.0 | 0.7 | 0.8 | 1.0
# - | 0.0 | 1.0 | FP | TN | TN | TN | TN
# + | 0.0 | 1.0 | TP | FN | FN | FN | FN
# + | 0.7 | 3.0 | TP | TP | FN | FN | FN
# - | 0.8 | 2.0 | FP | FP | FP | TN | TN
# + | 1.0 | 3.0 | TP | TP | TP | TP | FN
self.assertSequenceAlmostEqual(
matrices[0], [0.0, 0.0, 3.0, 7.0, 7.0 / 10.0, 1.0])
self.assertSequenceAlmostEqual(
matrices[1], [1.0, 1.0, 2.0, 6.0, 6.0 / 8.0, 6.0 / 7.0])
self.assertSequenceAlmostEqual(
matrices[2], [4.0, 1.0, 2.0, 3.0, 3.0 / 5.0, 3.0 / 7.0])
self.assertSequenceAlmostEqual(
matrices[3], [4.0, 3.0, 0.0, 3.0, 1.0, 3.0 / 7.0])
self.assertSequenceAlmostEqual(
matrices[4],
[7.0, 3.0, 0.0, 0.0, float('nan'), 0.0])
self.assertIn(
metric_keys.CONFUSION_MATRIX_AT_THRESHOLDS_THRESHOLDS,
value)
thresholds = value[
metric_keys.CONFUSION_MATRIX_AT_THRESHOLDS_THRESHOLDS]
self.assertAlmostEqual(-1e-6, thresholds[0])
self.assertAlmostEqual(0.0, thresholds[1])
self.assertAlmostEqual(0.7, thresholds[2])
self.assertAlmostEqual(0.8, thresholds[3])
self.assertAlmostEqual(1.0, thresholds[4])
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
(got_key, got_trigger_labels_pairs_list) = got[0]
self.assertEqual(b"Patient/14", got_key)
self.assertLen(got_trigger_labels_pairs_list, 2)
# Sort got_trigger_labels_pairs_list by trigger.event_time, so that
# the ordering is always consistent in ordering.
sorted_list = sorted(
got_trigger_labels_pairs_list,
key=lambda x: x[0].event_time.value_us)
(got_trigger1, got_label_list1) = sorted_list[0]
self.assertProtoEqual(got_trigger1, trigger1)
self.assertLen(got_label_list1, 1)
self.assertProtoEqual(got_label_list1[0], label1)
(got_trigger2, got_label_list2) = sorted_list[1]
self.assertProtoEqual(got_trigger2, trigger2)
self.assertLen(got_label_list2, 1)
self.assertProtoEqual(got_label_list2[0], label2)
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
self.assertEqual(got_slice_key, ())
self.assertLen(got_metrics, 1)
key = metric_types.MetricKey(
name='multi_class_confusion_matrix_at_thresholds')
got_matrix = got_metrics[key]
self.assertEqual(
multi_class_confusion_matrix_metrics.Matrices({
0.5: {
multi_class_confusion_matrix_metrics.MatrixEntryKey(actual_class_id=0,
predicted_class_id=0):
0.5,
multi_class_confusion_matrix_metrics.MatrixEntryKey(actual_class_id=2,
predicted_class_id=-1):
1.0
}
}), got_matrix)
except AssertionError as err:
raise util.BeamAssertException(err)
def _matcher(actual):
"""Matcher function for comparing the example dicts."""
try:
# Check number of examples.
test.assertLen(actual, len(expected))
for i in range(len(actual)):
for key in actual[i]:
# Check each feature value.
if isinstance(expected[i][key], np.ndarray):
test.assertEqual(
expected[i][key].dtype, actual[i][key].dtype,
'Expected dtype {}, found {} in actual[{}][{}]: {}'.format(
expected[i][key].dtype, actual[i][key].dtype, i, key,
actual[i][key]))
np.testing.assert_equal(actual[i][key], expected[i][key])
else:
test.assertEqual(
expected[i][key], actual[i][key],
'Unexpected value of actual[{}][{}]'.format(i, key))
except AssertionError:
raise util.BeamAssertException(traceback.format_exc())
def check_result(got):
try:
self.assertLen(got, 2)
for item in got:
self.assertIn(constants.FEATURES_KEY, item)
for feature in ('language', 'age'):
for features_dict in item[constants.FEATURES_KEY]:
self.assertIn(feature, features_dict)
self.assertIn(constants.LABELS_KEY, item)
self.assertIn(constants.PREDICTIONS_KEY, item)
for model in ('model1', 'model2'):
for predictions_dict in item[
constants.PREDICTIONS_KEY]:
self.assertIn(model, predictions_dict)
self.assertIn(constants.EXAMPLE_WEIGHTS_KEY, item)
for i in range(len(item[constants.FEATURES_KEY])):
self.assertAlmostEqual(
item[constants.FEATURES_KEY][i]['age'],
item[constants.EXAMPLE_WEIGHTS_KEY][i])
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
self.assertDictElementsAlmostEqual(got[0][constants.FEATURES_KEY][0],
{
'fixed_int': np.array([1]),
'fixed_float': np.array([1.0]),
})
self.assertEqual(got[0][constants.FEATURES_KEY][0]['fixed_string'],
np.array([b'fixed_string1']))
self.assertAlmostEqual(got[0][constants.LABELS_KEY][0],
np.array([1.0]))
self.assertAlmostEqual(got[0][constants.EXAMPLE_WEIGHTS_KEY][0],
np.array([0.5]))
self.assertDictElementsAlmostEqual(got[0][constants.FEATURES_KEY][1],
{
'fixed_int': np.array([1]),
'fixed_float': np.array([1.0]),
})
self.assertEqual(got[0][constants.FEATURES_KEY][1]['fixed_string'],
np.array([b'fixed_string2']))
self.assertAlmostEqual(got[0][constants.LABELS_KEY][1],
np.array([0.0]))
self.assertAlmostEqual(got[0][constants.EXAMPLE_WEIGHTS_KEY][1],
np.array([0.0]))
self.assertDictElementsAlmostEqual(got[0][constants.FEATURES_KEY][2],
{
'fixed_int': np.array([2]),
'fixed_float': np.array([0.0]),
})
self.assertEqual(got[0][constants.FEATURES_KEY][2]['fixed_string'],
np.array([b'fixed_string3']))
self.assertAlmostEqual(got[0][constants.LABELS_KEY][2],
np.array([0.0]))
self.assertAlmostEqual(got[0][constants.EXAMPLE_WEIGHTS_KEY][2],
np.array([1.0]))
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_plots = got[0]
self.assertEqual(got_slice_key, ())
self.assertLen(got_plots, 1)
key = metric_types.PlotKey(
name='multi_class_confusion_matrix_plot')
got_matrix = got_plots[key]
self.assertProtoEquals(
"""
matrices {
threshold: 0.0
entries {
actual_class_id: 0
predicted_class_id: 2
num_weighted_examples: 1.0
}
entries {
actual_class_id: 1
predicted_class_id: 1
num_weighted_examples: 2.0
}
entries {
actual_class_id: 1
predicted_class_id: 2
num_weighted_examples: 0.25
}
entries {
actual_class_id: 2
predicted_class_id: 2
num_weighted_examples: 1.5
}
}
""", got_matrix)
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
self.assertEqual(got_slice_key, ())
self.assertLen(got_metrics, 6) # 1 threshold * 6 metrics
self.assertTrue(
math.isnan(got_metrics[metric_types.MetricKey(
name=
'fairness_indicators_metrics/[email protected]',
model_name='',
output_name='',
sub_key=None)]))
self.assertTrue(
math.isnan(got_metrics[metric_types.MetricKey(
name=
'fairness_indicators_metrics/[email protected]',
model_name='',
output_name='',
sub_key=None)]))
except AssertionError as err:
raise util.BeamAssertException(err)
def check_metrics(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
example_count_key = metric_types.MetricKey(
name='example_count')
weighted_example_count_key = metric_types.MetricKey(
name='weighted_example_count')
label_key = metric_types.MetricKey(name='mean_label')
pred_key = metric_types.MetricKey(name='mean_prediction')
self.assertEqual(got_slice_key, ())
self.assertDictElementsAlmostEqual(
got_metrics, {
example_count_key: 3,
weighted_example_count_key: 4.0,
label_key:
(1.0 + 0.0 + 2 * 0.0) / (1.0 + 1.0 + 2.0),
pred_key:
(0.2 + 0.8 + 2 * 0.5) / (1.0 + 1.0 + 2.0),
})
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
self.assertEqual(got_slice_key, ())
total_queries_key = metric_types.MetricKey(
name='total_queries')
total_documents_key = metric_types.MetricKey(
name='total_documents')
min_documents_key = metric_types.MetricKey(
name='min_documents')
max_documents_key = metric_types.MetricKey(
name='max_documents')
self.assertDictElementsAlmostEqual(got_metrics, {
total_queries_key: 3,
total_documents_key: 6,
min_documents_key: 1,
max_documents_key: 3
},
places=5)
except AssertionError as err:
raise util.BeamAssertException(err)
def _matcher(actual):
"""Matcher function for comparing DatasetFeatureStatisticsList proto."""
try:
test.assertEqual(len(actual), 1)
# Get the dataset stats from DatasetFeatureStatisticsList proto.
actual_stats = actual[0].datasets[0]
expected_stats = expected_result.datasets[0]
test.assertEqual(actual_stats.num_examples, expected_stats.num_examples)
test.assertEqual(len(actual_stats.features), len(expected_stats.features))
expected_features = {}
for feature in expected_stats.features:
expected_features[feature.name] = feature
for feature in actual_stats.features:
compare.assertProtoEqual(
test,
feature,
expected_features[feature.name],
normalize_numbers=True)
except AssertionError, e:
raise util.BeamAssertException('Failed assert: ' + str(e))
def check_result(got): # pylint: disable=invalid-name
try:
self.assertEqual(1, len(got), 'got: %s' % got)
(slice_key, value) = got[0]
self.assertEqual((), slice_key)
self.assertIn(
metric_keys.base_key('[email protected]'), value)
self.assertIn(
metric_keys.base_key('[email protected]'), value)
self.assertIn(
metric_keys.base_key('[email protected]'), value)
self.assertIn(
metric_keys.base_key('[email protected]'), value)
self.assertIn(
metric_keys.base_key('[email protected]'), value)
self.assertIn(
metric_keys.base_key('[email protected]'), value)
self.assertIn(
metric_keys.base_key('[email protected]'), value)
self.assertIn(
metric_keys.base_key('[email protected]'), value)
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
self.assertEqual(got_slice_key, ())
self.assertDictElementsAlmostEqual(
got_metrics, {
metric_types.MetricKey(name='accuracy',
example_weighted=None):
1.0,
metric_types.MetricKey(name='label/mean',
example_weighted=None):
0.5,
metric_types.MetricKey(name='my_mean_age',
example_weighted=None):
3.75,
metric_types.MetricKey(name='my_mean_age_times_label',
example_weighted=None):
1.75
})
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got = got[0]
self.assertIn(constants.PREDICTIONS_KEY, got)
self.assertLen(got[constants.PREDICTIONS_KEY], 2)
for item in got[constants.PREDICTIONS_KEY]:
if multi_model:
self.assertIn('model1', item)
self.assertIn('model2', item)
if multi_output:
self.assertIn('Identity', item['model1'])
self.assertIn('Identity_1', item['model1'])
elif multi_output:
self.assertIn('Identity', item)
self.assertIn('Identity_1', item)
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(result, check_result, label='result')
def check_metrics(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
example_count_key = metric_types.MetricKey(
name='example_count')
weighted_example_count_key = metric_types.MetricKey(
name='weighted_example_count')
label_key_class_0 = metric_types.MetricKey(
name='mean_label',
sub_key=metric_types.SubKey(class_id=0))
label_key_class_1 = metric_types.MetricKey(
name='mean_label',
sub_key=metric_types.SubKey(class_id=1))
label_key_class_2 = metric_types.MetricKey(
name='mean_label',
sub_key=metric_types.SubKey(class_id=2))
self.assertEqual(got_slice_key, ())
self.assertDictElementsAlmostEqual(
got_metrics, {
example_count_key:
4,
weighted_example_count_key:
(1.0 + 2.0 + 3.0 + 4.0),
label_key_class_0:
(1 * 1.0 + 0 * 2.0 + 0 * 3.0 + 0 * 4.0) /
(1.0 + 2.0 + 3.0 + 4.0),
label_key_class_1:
(0 * 1.0 + 1 * 2.0 + 0 * 3.0 + 1 * 4.0) /
(1.0 + 2.0 + 3.0 + 4.0),
label_key_class_2:
(0 * 1.0 + 0 * 2.0 + 1 * 3.0 + 0 * 4.0) /
(1.0 + 2.0 + 3.0 + 4.0)
})
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got): # pylint: disable=invalid-name
try:
self.assertEqual(1, len(got), 'got: %s' % got)
(slice_key, value) = got[0]
self.assertEqual((), slice_key)
self.assertIn(metric_keys.AUC_PLOTS_MATRICES, value)
matrices = value[metric_keys.AUC_PLOTS_MATRICES]
# | | --------- Threshold -----------
# true label | pred | -1e-6 | 0.0 | 0.7 | 0.8 | 1.0
# - | 0.0 | FP | TN | TN | TN | TN
# + | 0.0 | TP | FN | FN | FN | FN
# + | 0.7 | TP | TP | FN | FN | FN
# - | 0.8 | FP | FP | FP | TN | TN
# + | 1.0 | TP | TP | TP | TP | FN
self.assertSequenceAlmostEqual(matrices[0],
[0, 0, 2, 3, 3.0 / 5.0, 1.0])
self.assertSequenceAlmostEqual(
matrices[1], [1, 1, 1, 2, 2.0 / 3.0, 2.0 / 3.0])
self.assertSequenceAlmostEqual(
matrices[7001], [2, 1, 1, 1, 1.0 / 2.0, 1.0 / 3.0])
self.assertSequenceAlmostEqual(
matrices[8001], [2, 2, 0, 1, 1.0 / 1.0, 1.0 / 3.0])
self.assertSequenceAlmostEqual(
matrices[10001],
[3, 2, 0, 0, float('nan'), 0.0])
self.assertIn(metric_keys.AUC_PLOTS_THRESHOLDS, value)
thresholds = value[metric_keys.AUC_PLOTS_THRESHOLDS]
self.assertAlmostEqual(0.0, thresholds[1])
self.assertAlmostEqual(0.001, thresholds[11])
self.assertAlmostEqual(0.005, thresholds[51])
self.assertAlmostEqual(0.010, thresholds[101])
self.assertAlmostEqual(0.100, thresholds[1001])
self.assertAlmostEqual(0.800, thresholds[8001])
self.assertAlmostEqual(1.000, thresholds[10001])
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
self.assertEqual(got_slice_key, ())
key = metric.keys[0]
# 1: prediction = 1, label = 2
# 2: prediction = 2, label = 1
# 3: prediction = 3, label = 2
# 4: prediction = 4, label = 3
#
# pred_x_labels = 2 + 2 + 6 + 12 = 22
# labels = 2 + 1 + 2 + 3 = 8
# preds = 1 + 2 + 3 + 4 = 10
# sq_labels = 4 + 1 + 4 + 9 = 18
# sq_preds = 1 + 4 + 9 + 16 = 30
# examples = 4
#
# r^2 = (22 - 8 * 10 / 4)^2 / (30 - 10^2 / 4) * (18 - 8^2 / 4)
# r^2 = 4 / (5 * 2) = 0.4
self.assertDictElementsAlmostEqual(got_metrics, {key: 0.4}, places=5)
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
self.assertEqual(got_slice_key, ())
self.assertLen(got_metrics, 6)
self.assertDictElementsAlmostEqual(
got_metrics, {
metric_types.MetricKey(
name='flip_count/[email protected]',
example_weighted=True):
5.0,
metric_types.MetricKey(
name='flip_count/[email protected]',
example_weighted=True):
7.0,
metric_types.MetricKey(
name='flip_count/[email protected]',
example_weighted=True):
6.0,
metric_types.MetricKey(
name='flip_count/[email protected]',
example_weighted=True):
7.0,
})
self.assertAllEqual(
got_metrics[metric_types.MetricKey(
name='flip_count/[email protected]',
example_weighted=True)], np.array([['id_2'], ['id_3']]))
self.assertAllEqual(
got_metrics[metric_types.MetricKey(
name='flip_count/[email protected]',
example_weighted=True)],
np.array([['id_2'], ['id_3'], ['id_4']]))
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertEqual(got, [{'a': 1, 'c': 3}])
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_plots = got[0]
self.assertEqual(got_slice_key, ())
self.assertLen(got_plots, 1)
key = metric_types.PlotKey(
name='multi_label_confusion_matrix_plot')
got_matrix = got_plots[key]
self.assertProtoEquals(
"""
matrices {
threshold: 0.5
entries {
actual_class_id: 0
predicted_class_id: 0
false_negatives: 0.0
true_negatives: 0.0
false_positives: 0.0
true_positives: 2.0
}
entries {
actual_class_id: 0
predicted_class_id: 1
false_negatives: 1.0
true_negatives: 1.0
false_positives: 0.0
true_positives: 0.0
}
entries {
actual_class_id: 0
predicted_class_id: 2
false_negatives: 0.0
true_negatives: 2.0
false_positives: 0.0
true_positives: 0.0
}
entries {
actual_class_id: 1
predicted_class_id: 0
false_negatives: 0.0
true_negatives: 1.0
false_positives: 0.0
true_positives: 1.0
}
entries {
actual_class_id: 1
predicted_class_id: 1
false_negatives: 1.0
true_negatives: 0.0
false_positives: 0.0
true_positives: 1.0
}
entries {
actual_class_id: 1
predicted_class_id: 2
false_negatives: 0.0
false_positives: 0.0
true_negatives: 2.0
true_positives: 0.0
}
}
""", got_matrix)
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 2)
self.assertDictElementsAlmostEqual(
got[0][constants.FEATURES_KEY], {
'fixed_int': np.array([1]),
})
self.assertEqual(
got[0][constants.FEATURES_KEY]['fixed_string'],
np.array([b'fixed_string1']))
for model_name in ('model1', 'model2'):
self.assertIn(model_name, got[0][constants.LABELS_KEY])
self.assertIn(model_name,
got[0][constants.EXAMPLE_WEIGHTS_KEY])
self.assertIn(model_name,
got[0][constants.PREDICTIONS_KEY])
self.assertAlmostEqual(
got[0][constants.LABELS_KEY]['model1'],
np.array([1.0]))
self.assertDictElementsAlmostEqual(
got[0][constants.LABELS_KEY]['model2'], {
'output1': np.array([1.0]),
'output2': np.array([0.0])
})
self.assertAlmostEqual(
got[0][constants.EXAMPLE_WEIGHTS_KEY]['model1'],
np.array([0.5]))
self.assertDictElementsAlmostEqual(
got[0][constants.EXAMPLE_WEIGHTS_KEY]['model2'], {
'output1': np.array([0.5]),
'output2': np.array([0.5])
})
self.assertAlmostEqual(
got[0][constants.PREDICTIONS_KEY]['model1'],
np.array([1.0]))
self.assertDictElementsAlmostEqual(
got[0][constants.PREDICTIONS_KEY]['model2'], {
'output1': np.array([1.0]),
'output2': np.array([1.0])
})
self.assertDictElementsAlmostEqual(
got[1][constants.FEATURES_KEY], {
'fixed_int': np.array([1]),
})
self.assertEqual(
got[1][constants.FEATURES_KEY]['fixed_string'],
np.array([b'fixed_string2']))
for model_name in ('model1', 'model2'):
self.assertIn(model_name, got[1][constants.LABELS_KEY])
self.assertIn(model_name,
got[1][constants.EXAMPLE_WEIGHTS_KEY])
self.assertIn(model_name,
got[1][constants.PREDICTIONS_KEY])
self.assertAlmostEqual(
got[1][constants.LABELS_KEY]['model1'],
np.array([1.0]))
self.assertDictElementsAlmostEqual(
got[1][constants.LABELS_KEY]['model2'], {
'output1': np.array([1.0]),
'output2': np.array([1.0])
})
self.assertAlmostEqual(
got[1][constants.EXAMPLE_WEIGHTS_KEY]['model1'],
np.array([0.0]))
self.assertDictElementsAlmostEqual(
got[1][constants.EXAMPLE_WEIGHTS_KEY]['model2'], {
'output1': np.array([0.0]),
'output2': np.array([1.0])
})
self.assertAlmostEqual(
got[1][constants.PREDICTIONS_KEY]['model1'],
np.array([2.0]))
self.assertDictElementsAlmostEqual(
got[1][constants.PREDICTIONS_KEY]['model2'], {
'output1': np.array([2.0]),
'output2': np.array([2.0])
})
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_plots = got[0]
self.assertEqual(got_slice_key, ())
self.assertLen(got_plots, 1)
key = metric_types.PlotKey(name='calibration_plot')
self.assertIn(key, got_plots)
got_plot = got_plots[key]
self.assertProtoEquals(
"""
buckets {
lower_threshold_inclusive: -inf
upper_threshold_exclusive: 0.0
total_weighted_label {
value: 4.0
}
total_weighted_refined_prediction {
value: -0.4
}
num_weighted_examples {
value: 4.0
}
}
buckets {
lower_threshold_inclusive: 0.0
upper_threshold_exclusive: 0.1
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 0.1
upper_threshold_exclusive: 0.2
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 0.2
upper_threshold_exclusive: 0.3
total_weighted_label {
}
total_weighted_refined_prediction {
value: 1.6
}
num_weighted_examples {
value: 8.0
}
}
buckets {
lower_threshold_inclusive: 0.3
upper_threshold_exclusive: 0.4
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 0.4
upper_threshold_exclusive: 0.5
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 0.5
upper_threshold_exclusive: 0.6
total_weighted_label {
value: 5.0
}
total_weighted_refined_prediction {
value: 4.0
}
num_weighted_examples {
value: 8.0
}
}
buckets {
lower_threshold_inclusive: 0.6
upper_threshold_exclusive: 0.7
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 0.7
upper_threshold_exclusive: 0.8
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 0.8
upper_threshold_exclusive: 0.9
total_weighted_label {
value: 8.0
}
total_weighted_refined_prediction {
value: 6.4
}
num_weighted_examples {
value: 8.0
}
}
buckets {
lower_threshold_inclusive: 0.9
upper_threshold_exclusive: 1.0
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 1.0
upper_threshold_exclusive: inf
total_weighted_label {
value: 8.0
}
total_weighted_refined_prediction {
value: 8.8
}
num_weighted_examples {
value: 8.0
}
}
""", got_plot)
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertEqual(got, [{constants.INPUT_KEY: 'input', 'other': 2}])
except AssertionError as err:
raise util.BeamAssertException(err)
