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# Copyright 2022 Baler Contributors
# 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 os
import numpy as np
import pytest
import torch
from sklearn.preprocessing import MinMaxScaler
from baler.modules import data_processing
from baler.modules import helper
def test_import_config_success():
# Call the import_config function with the sample config file path
config = helper.Config
config.Foo = "Bar"
config.Baz = 10
# Assert that the result is equal to the expected config
# This checks that the import_config function correctly loads the JSON file and returns the expected dictionary
assert config.Foo == "Bar"
def test_save_model():
# Test data
model = torch.nn.Linear(3, 2)
model_path = "test_model.pt"
# Save the model
data_processing.save_model(model, model_path)
# Check that the model file has been created
assert os.path.exists(model_path)
# Clean up
os.remove(model_path)
@pytest.fixture
def minmax_test_data():
return [
(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), np.array([[1, 2, 3], [6, 6, 6]])),
(
np.array([[-1, -2, -3], [-4, -5, -6], [-7, -8, -9]]),
np.array([[-7, -8, -9], [6, 6, 6]]),
),
(np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]), np.array([[0, 0, 0], [2, 2, 2]])),
]
def test_find_minmax_success(minmax_test_data):
for data, expected_result in minmax_test_data:
result = data_processing.find_minmax(data)
assert np.array_equal(result, expected_result)
def test_normalize():
# Test data
data = [1, 2, 3, 4, 5]
# Test configuration 1
custom_norm1 = False
expected_result1 = np.array([0.0, 0.25, 0.5, 0.75, 1.0])
# Test configuration 2
custom_norm2 = True
expected_result2 = np.array([1, 2, 3, 4, 5])
# Test the normalize function with the test data and configuration 1
result1 = data_processing.normalize(data, custom_norm1)
np.testing.assert_almost_equal(result1, expected_result1)
# Test the normalize function with the test data and configuration 2
result2 = data_processing.normalize(data, custom_norm2)
np.testing.assert_almost_equal(result2, expected_result2)
def test_renormalize_std():
# Test data
data = np.array([0.1, 0.2, 0.3, 0.4, 0.5])
true_min = 1
feature_range = 2
# Renormalize the data using the renormalize_std function
renormalized_data = data_processing.renormalize_std(data, true_min, feature_range)
# Check that the renormalized data is correct
expected_renormalized_data = np.array([1.2, 1.4, 1.6, 1.8, 2.0])
np.testing.assert_array_equal(renormalized_data, expected_renormalized_data)
def test_renormalize_func():
# Test data
scaler = MinMaxScaler()
data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
scaler.fit(data)
norm_data = scaler.transform(data)
true_min = [-1, 2]
feature_range = [2, 16]
# Renormalize the data using the renormalize_std function
renormalized_data = data_processing.renormalize_func(
norm_data, true_min, feature_range
)
# Check that the renormalized data is correct
expected_renormalized_data = np.array([[-1, 2], [-0.5, 6], [0, 10], [1, 18]])
np.testing.assert_array_equal(renormalized_data, expected_renormalized_data)
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