wehub-resource-sync
593b94c120
pytest / Unit Tests (push) Has been cancelled
pytest / Integration (integration_tests_a) (push) Has been cancelled
pytest / Integration (integration_tests_b) (push) Has been cancelled
pytest / Integration (integration_tests_c) (push) Has been cancelled
pytest / Integration (integration_tests_d) (push) Has been cancelled
pytest / Integration (integration_tests_e) (push) Has been cancelled
pytest / Integration (integration_tests_f) (push) Has been cancelled
pytest / Integration (integration_tests_g) (push) Has been cancelled
pytest / Integration (integration_tests_h) (push) Has been cancelled
pytest / Integration (integration_tests_i) (push) Has been cancelled
pytest / Integration (integration_tests_j) (push) Has been cancelled
pytest / Distributed (distributed_a) (push) Has been cancelled
pytest / Distributed (distributed_b) (push) Has been cancelled
pytest / Distributed (distributed_c) (push) Has been cancelled
pytest / Distributed (distributed_d) (push) Has been cancelled
pytest / Distributed (distributed_e) (push) Has been cancelled
pytest / Distributed (distributed_f) (push) Has been cancelled
pytest / Minimal Install (push) Has been cancelled
pytest / Event File (push) Has been cancelled
pytest (slow) / py-slow (push) Has been cancelled
Publish JSON Schema / publish-schema (push) Has been cancelled
937 lines
38 KiB
Python
937 lines
38 KiB
Python
# Copyright (c) 2023 Predibase, Inc., 2019 Uber Technologies, Inc.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
import glob
|
|
import logging
|
|
import os
|
|
from tempfile import TemporaryDirectory
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from ludwig import visualize
|
|
from ludwig.api import LudwigModel, TrainingStats
|
|
from ludwig.constants import BATCH_SIZE, ENCODER, NAME, PREDICTIONS, PROBABILITIES, PROBABILITY, TRAINER, TYPE
|
|
from ludwig.data.split import get_splitter
|
|
from ludwig.globals import HYPEROPT_STATISTICS_FILE_NAME
|
|
from ludwig.utils.data_utils import read_csv
|
|
from tests.integration_tests.utils import (
|
|
bag_feature,
|
|
binary_feature,
|
|
category_feature,
|
|
generate_data,
|
|
LocalTestBackend,
|
|
number_feature,
|
|
sequence_feature,
|
|
set_feature,
|
|
text_feature,
|
|
)
|
|
|
|
pytestmark = pytest.mark.integration_tests_g
|
|
|
|
|
|
def run_api_experiment(input_features, output_features):
|
|
"""Helper method to avoid code repetition in running an experiment.
|
|
|
|
:param input_features: input schema
|
|
:param output_features: output schema
|
|
:return: None
|
|
"""
|
|
config = {
|
|
"input_features": input_features,
|
|
"output_features": output_features,
|
|
"combiner": {"type": "concat", "output_size": 14},
|
|
TRAINER: {"epochs": 2, BATCH_SIZE: 128},
|
|
}
|
|
|
|
model = LudwigModel(config)
|
|
return model
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def experiment_to_use():
|
|
with TemporaryDirectory() as tmpdir:
|
|
experiment = Experiment("data_for_test.csv", tmpdir)
|
|
return experiment
|
|
|
|
|
|
class Experiment:
|
|
"""Helper class to create model test data, setup and run experiment.
|
|
|
|
Contain the needed model experiment statistics as class attributes.
|
|
"""
|
|
|
|
def __init__(self, csv_filename, tmpdir):
|
|
self.tmpdir = tmpdir
|
|
self.csv_file = os.path.join(tmpdir, csv_filename)
|
|
self.input_features = [category_feature(encoder={"vocab_size": 10})]
|
|
self.output_features = [category_feature(decoder={"vocab_size": 2}, reduce_input="sum")]
|
|
data_csv = generate_data(self.input_features, self.output_features, self.csv_file)
|
|
self.model = self._create_model()
|
|
test_df, train_df, val_df = obtain_df_splits(data_csv)
|
|
self.train_stats, self.preprocessed_data, self.output_dir = self.model.train(
|
|
training_set=train_df, validation_set=val_df, output_directory=os.path.join(tmpdir, "results")
|
|
)
|
|
self.test_stats_full, predictions, self.output_dir = self.model.evaluate(
|
|
dataset=test_df,
|
|
collect_overall_stats=True,
|
|
collect_predictions=True,
|
|
output_directory=self.output_dir,
|
|
return_type="dict",
|
|
)
|
|
self.output_feature_name = self.output_features[0][NAME]
|
|
self.ground_truth_metadata = self.preprocessed_data[3]
|
|
self.ground_truth = test_df[self.output_feature_name]
|
|
# probabilities need to be list of lists containing each row data
|
|
# from the probability columns
|
|
# ref: https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/api/LudwigModel#evaluate - Return
|
|
self.probability = predictions[self.output_feature_name][PROBABILITY]
|
|
self.probabilities = predictions[self.output_feature_name][PROBABILITIES]
|
|
self.predictions = predictions[self.output_feature_name][PREDICTIONS]
|
|
|
|
# numeric encoded values required for some visualizations
|
|
of_metadata = self.ground_truth_metadata[self.output_feature_name]
|
|
self.predictions_num = [of_metadata["str2idx"][x] for x in self.predictions]
|
|
|
|
def _create_model(self):
|
|
"""Configure and setup test model."""
|
|
config = {
|
|
"input_features": self.input_features,
|
|
"output_features": self.output_features,
|
|
"combiner": {"type": "concat", "output_size": 14},
|
|
TRAINER: {"epochs": 2, BATCH_SIZE: 128},
|
|
}
|
|
return LudwigModel(config, logging_level=logging.WARN)
|
|
|
|
|
|
def obtain_df_splits(data_csv):
|
|
"""Split input data csv file in to train, validation and test dataframes.
|
|
|
|
:param data_csv: Input data CSV file. :return test_df, train_df, val_df: Train, validation and test dataframe splits
|
|
"""
|
|
data_df = read_csv(data_csv)
|
|
# Obtain data split array mapping data rows to split type
|
|
# 0-train, 1-validation, 2-test
|
|
splitter = get_splitter("random")
|
|
train_df, val_df, test_df = splitter.split(data_df, LocalTestBackend())
|
|
return test_df, train_df, val_df
|
|
|
|
|
|
@pytest.mark.parametrize("training_only", [True, False])
|
|
def test_learning_curves_vis_api(experiment_to_use, training_only):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
viz_outputs = ("pdf", "png")
|
|
train_stats = experiment.train_stats
|
|
if training_only:
|
|
# ensure plot works with only training metrics
|
|
# Handle situation in Issue #1875
|
|
train_stats = TrainingStats(train_stats.training, {}, {})
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.learning_curves(
|
|
[train_stats], output_feature_name=None, output_directory=tmpvizdir, file_format=viz_output
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 3
|
|
|
|
|
|
def test_compare_performance_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
# extract test stats only
|
|
test_stats = experiment.test_stats_full
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.compare_performance(
|
|
[test_stats, test_stats],
|
|
output_feature_name=None,
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_compare_classifier_performance_from_prob_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
probability = experiment.probabilities
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.compare_classifiers_performance_from_prob(
|
|
[probability, probability],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
top_n_classes=[0],
|
|
labels_limit=0,
|
|
model_namess=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_compare_classifier_performance_from_pred_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
prediction = experiment.predictions
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.compare_classifiers_performance_from_pred(
|
|
[prediction, prediction],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
labels_limit=0,
|
|
model_namess=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_compare_classifiers_performance_subset_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
probabilities = experiment.probabilities
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.compare_classifiers_performance_subset(
|
|
[probabilities, probabilities],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
top_n_classes=[6],
|
|
labels_limit=0,
|
|
subset="ground_truth",
|
|
model_namess=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_compare_classifiers_performance_changing_k_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
probabilities = experiment.probabilities
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.compare_classifiers_performance_changing_k(
|
|
[probabilities, probabilities],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
top_k=3,
|
|
labels_limit=0,
|
|
model_namess=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_compare_classifiers_multiclass_multimetric_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
# extract test stats only
|
|
test_stats = experiment.test_stats_full
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.compare_classifiers_multiclass_multimetric(
|
|
[test_stats, test_stats],
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
top_n_classes=[6],
|
|
model_namess=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 4
|
|
|
|
|
|
def test_compare_classifiers_predictions_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
predictions = experiment.predictions
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.compare_classifiers_predictions(
|
|
[predictions, predictions],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
labels_limit=0,
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_compare_classifiers_predictions_distribution_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
predictions = experiment.predictions_num
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.compare_classifiers_predictions_distribution(
|
|
[predictions, predictions],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
labels_limit=0,
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_confidence_thresholding_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
probabilities = experiment.probabilities
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.confidence_thresholding(
|
|
[probabilities, probabilities],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
labels_limit=0,
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_confidence_thresholding_data_vs_acc_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
probabilities = experiment.probabilities
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.confidence_thresholding_data_vs_acc(
|
|
[probabilities, probabilities],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
labels_limit=0,
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_confidence_thresholding_data_vs_acc_subset_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
probabilities = experiment.probabilities
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.confidence_thresholding_data_vs_acc_subset(
|
|
[probabilities, probabilities],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
top_n_classes=[3],
|
|
labels_limit=0,
|
|
subset="ground_truth",
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_confidence_thresholding_data_vs_acc_subset_per_class_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
probabilities = experiment.probabilities
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.confidence_thresholding_data_vs_acc_subset_per_class(
|
|
[probabilities, probabilities],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
top_n_classes=[2],
|
|
labels_limit=0,
|
|
subset="ground_truth",
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
# 3 figures should be saved because experiment setting top_n_classes = 3
|
|
# hence one figure per class
|
|
assert len(figure_cnt) == 2
|
|
|
|
|
|
def test_confidence_thresholding_2thresholds_2d_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(encoder={"vocab_size": 10, "min_len": 1, "type": "stacked_cnn"}),
|
|
number_feature(),
|
|
category_feature(encoder={"vocab_size": 10, "embedding_size": 5}),
|
|
set_feature(),
|
|
sequence_feature(encoder={"vocab_size": 10, "max_len": 10, "type": "embed"}),
|
|
]
|
|
output_features = [
|
|
category_feature(decoder={"vocab_size": 2}, reduce_input="sum"),
|
|
category_feature(decoder={"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][TYPE] = 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_dir=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/latest/user_guide/api/LudwigModel#evaluate
|
|
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, "*.{}").format(viz_output)
|
|
visualize.confidence_thresholding_2thresholds_2d(
|
|
[probability1, probability2],
|
|
[ground_truth1, ground_truth2],
|
|
model.training_set_metadata,
|
|
[output_feature_name1, output_feature_name2],
|
|
labels_limit=0,
|
|
model_names=["Model1"],
|
|
output_directory=output_dir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 3
|
|
|
|
|
|
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(encoder={"vocab_size": 10, "min_len": 1, "type": "stacked_cnn"}),
|
|
number_feature(),
|
|
category_feature(encoder={"vocab_size": 10, "embedding_size": 5}),
|
|
set_feature(),
|
|
sequence_feature(encoder={"vocab_size": 10, "max_len": 10, "type": "embed"}),
|
|
]
|
|
output_features = [
|
|
category_feature(decoder={"vocab_size": 2}, reduce_input="sum"),
|
|
category_feature(decoder={"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][TYPE] = 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/latest/user_guide/api/LudwigModel#evaluate
|
|
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 len(figure_cnt) == 1
|
|
|
|
|
|
def test_binary_threshold_vs_metric_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
probabilities = experiment.probabilities
|
|
viz_outputs = ("pdf", "png")
|
|
metrics = ["accuracy"]
|
|
positive_label = 1
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.binary_threshold_vs_metric(
|
|
[probabilities, probabilities],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
metrics,
|
|
positive_label,
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_precision_recall_curves_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
probabilities = experiment.probabilities
|
|
viz_outputs = ("pdf", "png")
|
|
positive_label = 1
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.precision_recall_curves(
|
|
[probabilities, probabilities],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
positive_label,
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_precision_recall_curves_from_test_statistics_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 = [binary_feature(), bag_feature()]
|
|
output_features = [binary_feature()]
|
|
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
# Generate test data
|
|
data_csv = generate_data(
|
|
input_features, output_features, os.path.join(tmpvizdir, csv_filename), num_examples=20
|
|
)
|
|
output_feature_name = output_features[0]["name"]
|
|
|
|
model = run_api_experiment(input_features, output_features)
|
|
data_df = read_csv(data_csv)
|
|
_, _, output_dir = model.train(dataset=data_df, output_directory=os.path.join(tmpvizdir, "results"))
|
|
test_stats, _, _ = model.evaluate(dataset=data_df, collect_overall_stats=True, output_directory=output_dir)
|
|
viz_outputs = ("pdf", "png")
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = os.path.join(output_dir, f"*.{viz_output}")
|
|
visualize.precision_recall_curves_from_test_statistics(
|
|
[test_stats, test_stats],
|
|
output_feature_name,
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=output_dir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_roc_curves_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
probabilities = experiment.probabilities
|
|
viz_outputs = ("pdf", "png")
|
|
positive_label = 1
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.roc_curves(
|
|
[probabilities, probabilities],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
positive_label,
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_roc_curves_from_test_statistics_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 = [binary_feature(), bag_feature()]
|
|
output_features = [binary_feature()]
|
|
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
# Generate test data
|
|
data_csv = generate_data(input_features, output_features, os.path.join(tmpvizdir, csv_filename))
|
|
output_feature_name = output_features[0]["name"]
|
|
|
|
model = run_api_experiment(input_features, output_features)
|
|
data_df = read_csv(data_csv)
|
|
_, _, output_dir = model.train(dataset=data_df, output_directory=os.path.join(tmpvizdir, "results"))
|
|
# extract test metrics
|
|
test_stats, _, _ = model.evaluate(dataset=data_df, collect_overall_stats=True, output_directory=output_dir)
|
|
test_stats = test_stats
|
|
viz_outputs = ("pdf", "png")
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = os.path.join(output_dir, f"*.{viz_output}")
|
|
visualize.roc_curves_from_test_statistics(
|
|
[test_stats, test_stats],
|
|
output_feature_name,
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=output_dir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 1
|
|
|
|
|
|
def test_calibration_1_vs_all_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
probabilities = experiment.probabilities
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = os.path.join(tmpvizdir, f"*.{viz_output}")
|
|
visualize.calibration_1_vs_all(
|
|
[probabilities, probabilities],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
top_n_classes=[6],
|
|
labels_limit=0,
|
|
model_namess=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 5
|
|
|
|
|
|
def test_calibration_multiclass_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
probabilities = experiment.probabilities
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.calibration_multiclass(
|
|
[probabilities, probabilities],
|
|
experiment.ground_truth,
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
labels_limit=0,
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 2
|
|
|
|
|
|
def test_confusion_matrix_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
# extract test stats only
|
|
test_stats = experiment.test_stats_full
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.confusion_matrix(
|
|
[test_stats, test_stats],
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
top_n_classes=[0],
|
|
normalize=False,
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 4
|
|
|
|
|
|
def test_frequency_vs_f1_vis_api(experiment_to_use):
|
|
"""Ensure pdf and png figures can be saved via visualization API call.
|
|
|
|
:param experiment_to_use: Object containing trained model and results to test visualization
|
|
:return: None
|
|
"""
|
|
experiment = experiment_to_use
|
|
# extract test stats
|
|
test_stats = experiment.test_stats_full
|
|
viz_outputs = ("pdf", "png")
|
|
with TemporaryDirectory() as tmpvizdir:
|
|
for viz_output in viz_outputs:
|
|
vis_output_pattern_pdf = tmpvizdir + f"/*.{viz_output}"
|
|
visualize.frequency_vs_f1(
|
|
[test_stats, test_stats],
|
|
experiment.ground_truth_metadata,
|
|
experiment.output_feature_name,
|
|
top_n_classes=[0],
|
|
model_names=["Model1", "Model2"],
|
|
output_directory=tmpvizdir,
|
|
file_format=viz_output,
|
|
)
|
|
figure_cnt = glob.glob(vis_output_pattern_pdf)
|
|
assert len(figure_cnt) == 2
|
|
|
|
|
|
@pytest.mark.distributed
|
|
@pytest.mark.distributed_f
|
|
def test_hyperopt_report_vis_api(hyperopt_results_multiple_parameters, tmpdir):
|
|
vis_dir = os.path.join(tmpdir, "visualizations")
|
|
|
|
# Ensure visualizations directory is empty before creating plots
|
|
if os.path.exists(vis_dir):
|
|
for f in os.listdir(vis_dir):
|
|
os.remove(os.path.join(vis_dir, f))
|
|
|
|
visualize.hyperopt_report(
|
|
os.path.join(hyperopt_results_multiple_parameters, HYPEROPT_STATISTICS_FILE_NAME), output_directory=vis_dir
|
|
)
|
|
|
|
# test for creation of output directory
|
|
assert os.path.isdir(vis_dir)
|
|
|
|
figure_cnt = glob.glob(os.path.join(vis_dir, "*"))
|
|
assert len(figure_cnt) == 4
|
|
|
|
|
|
@pytest.mark.distributed
|
|
@pytest.mark.distributed_f
|
|
def test_hyperopt_hiplot_vis_api(hyperopt_results_multiple_parameters, tmpdir):
|
|
vis_dir = os.path.join(tmpdir, "visualizations")
|
|
|
|
# Ensure visualizations directory is empty before creating plots
|
|
if os.path.exists(vis_dir):
|
|
for f in os.listdir(vis_dir):
|
|
os.remove(os.path.join(vis_dir, f))
|
|
|
|
visualize.hyperopt_hiplot(
|
|
os.path.join(hyperopt_results_multiple_parameters, HYPEROPT_STATISTICS_FILE_NAME), output_directory=vis_dir
|
|
)
|
|
|
|
# test for creation of output directory
|
|
assert os.path.isdir(vis_dir)
|
|
|
|
# test for generatated html page
|
|
assert os.path.isfile(os.path.join(vis_dir, "hyperopt_hiplot.html"))
|
|
|
|
|
|
@pytest.mark.distributed
|
|
@pytest.mark.distributed_f
|
|
def test_hyperopt_report_vis_api_no_pairplot(hyperopt_results_single_parameter, tmpdir):
|
|
vis_dir = os.path.join(tmpdir, "visualizations")
|
|
|
|
# Ensure visualizations directory is empty before creating plots
|
|
if os.path.exists(vis_dir):
|
|
for f in os.listdir(vis_dir):
|
|
os.remove(os.path.join(vis_dir, f))
|
|
|
|
visualize.hyperopt_report(
|
|
os.path.join(hyperopt_results_single_parameter, HYPEROPT_STATISTICS_FILE_NAME), output_directory=vis_dir
|
|
)
|
|
|
|
figure_cnt = glob.glob(os.path.join(vis_dir, "*"))
|
|
|
|
# Only create plot for single parameter and skip pairplot creation
|
|
assert len(figure_cnt) == 1
|