def test_confidence_thresholding_2thresholds_3d_vis_api(csv_filename):
"""Ensure pdf and png figures can be saved via visualization API call.
:param csv_filename: csv fixture from tests.fixtures.filenames.csv_filename
:return: None
"""
input_features = [
text_feature(vocab_size=10, min_len=1, encoder='stacked_cnn'),
numerical_feature(),
category_feature(vocab_size=10, embedding_size=5),
set_feature(),
sequence_feature(vocab_size=10, max_len=10, encoder='embed')
]
output_features = [
category_feature(vocab_size=2, reduce_input='sum'),
category_feature(vocab_size=2, reduce_input='sum')
]
encoder = 'parallel_cnn'
# Generate test data
data_csv = generate_data(input_features, output_features, csv_filename)
input_features[0]['encoder'] = encoder
model = run_api_experiment(input_features, output_features)
test_df, train_df, val_df = obtain_df_splits(data_csv)
_, _, output_dir = model.train(training_set=train_df,
validation_set=val_df)
test_stats, predictions, _ = model.evaluate(dataset=test_df,
collect_predictions=True,
output_directory=output_dir)
output_feature_name1 = output_features[0]['name']
output_feature_name2 = output_features[1]['name']
# probabilities need to be list of lists containing each row data from the
# probability columns ref: https://ludwig-ai.github.io/ludwig-docs/api/#test - Return
probability1 = predictions.iloc[:, [2, 3, 4]].values
probability2 = predictions.iloc[:, [7, 8, 9]].values
ground_truth_metadata = model.training_set_metadata
target_predictions1 = test_df[output_feature_name1]
target_predictions2 = test_df[output_feature_name2]
ground_truth1 = np.asarray([
ground_truth_metadata[output_feature_name1]['str2idx'][prediction]
for prediction in target_predictions1
])
ground_truth2 = np.asarray([
ground_truth_metadata[output_feature_name2]['str2idx'][prediction]
for prediction in target_predictions2
])
viz_outputs = ('pdf', 'png')
for viz_output in viz_outputs:
vis_output_pattern_pdf = os.path.join(output_dir,
'*.{}'.format(viz_output))
visualize.confidence_thresholding_2thresholds_3d(
[probability1, probability2], [ground_truth1, ground_truth2],
[output_feature_name1, output_feature_name2],
labels_limit=0,
output_directory=output_dir,
file_format=viz_output)
figure_cnt = glob.glob(vis_output_pattern_pdf)
assert 1 == len(figure_cnt)
shutil.rmtree(output_dir, ignore_errors=True)
def test_confidence_thresholding_2thresholds_3d_vis_api(csv_filename):
"""Ensure pdf and png figures can be saved via visualization API call.
:param csv_filename: csv fixture from tests.fixtures.filenames.csv_filename
:return: None
"""
input_features = [
text_feature(vocab_size=10, min_len=1, encoder="stacked_cnn"),
numerical_feature(),
category_feature(vocab_size=10, embedding_size=5),
set_feature(),
sequence_feature(vocab_size=10, max_len=10, encoder="embed"),
]
output_features = [
category_feature(vocab_size=2, reduce_input="sum"),
category_feature(vocab_size=2, reduce_input="sum"),
]
encoder = "parallel_cnn"
with TemporaryDirectory() as tmpvizdir:
# Generate test data
data_csv = generate_data(input_features, output_features,
os.path.join(tmpvizdir, csv_filename))
input_features[0]["encoder"] = encoder
model = run_api_experiment(input_features, output_features)
test_df, train_df, val_df = obtain_df_splits(data_csv)
_, _, output_dir = model.train(training_set=train_df,
validation_set=val_df,
output_directory=os.path.join(
tmpvizdir, "results"))
test_stats, predictions, _ = model.evaluate(
dataset=test_df,
collect_predictions=True,
output_directory=output_dir)
output_feature_name1 = output_features[0]["name"]
output_feature_name2 = output_features[1]["name"]
ground_truth_metadata = model.training_set_metadata
feature1_cols = [
f"{output_feature_name1}_probabilities_{label}"
for label in ground_truth_metadata[output_feature_name1]["idx2str"]
]
feature2_cols = [
f"{output_feature_name2}_probabilities_{label}"
for label in ground_truth_metadata[output_feature_name2]["idx2str"]
]
# probabilities need to be list of lists containing each row data from the
# probability columns ref: https://ludwig-ai.github.io/ludwig-docs/api/#test - Return
probability1 = predictions.loc[:, feature1_cols].values
probability2 = predictions.loc[:, feature2_cols].values
target_predictions1 = test_df[output_feature_name1]
target_predictions2 = test_df[output_feature_name2]
ground_truth1 = np.asarray([
ground_truth_metadata[output_feature_name1]["str2idx"][prediction]
for prediction in target_predictions1
])
ground_truth2 = np.asarray([
ground_truth_metadata[output_feature_name2]["str2idx"][prediction]
for prediction in target_predictions2
])
viz_outputs = ("pdf", "png")
for viz_output in viz_outputs:
vis_output_pattern_pdf = os.path.join(output_dir,
f"*.{viz_output}")
visualize.confidence_thresholding_2thresholds_3d(
[probability1, probability2],
[ground_truth1, ground_truth2],
model.training_set_metadata,
[output_feature_name1, output_feature_name2],
labels_limit=0,
output_directory=output_dir,
file_format=viz_output,
)
figure_cnt = glob.glob(vis_output_pattern_pdf)
assert 1 == len(figure_cnt)