def test_fails_on_duplicate_input_data(input_data_file_with_duplicates,
output_data_file):
with pytest.raises(ValueError):
data_model = ModelFromData(input_data_file_with_duplicates,
output_data_file, 1.)
data_model.evaluate([1, 2, 3, 4, 5])
def test_fails_on_unmatched_input(input_data_file, output_data_file):
"""
Ensure an exception is raised if an input supplied to evaluate does not
have a match in the input data.
"""
data_model = ModelFromData(input_data_file, output_data_file, 1.)
with pytest.raises(ValueError):
data_model.evaluate(-1)
def test_single_row_2d_input_data(input_data_file_2d, output_data_file_2d):
"""
Test ability to evaluate one row of input data passed in as 2d array.
"""
# Initialize model from spring-mass example data files:
data_model = ModelFromData(input_data_file_2d, output_data_file_2d, 1.)
# Should be able to do this without an exception.
one_row_2d_input = np.array([data_model._inputs[0]])
data_model.evaluate(one_row_2d_input)
def test_fails_on_duplicate_input_data(input_data_file_with_duplicates,
output_data_file):
"""
Ensure an exception is raised if there are duplicate inputs found in the
input data.
"""
with pytest.raises(ValueError):
data_model = ModelFromData(input_data_file_with_duplicates,
output_data_file, 1.)
data_model.evaluate([1, 2, 3, 4, 5])
def test_evaluate_fails_on_invalid_input(input_data_file, output_data_file):
data_model = ModelFromData(input_data_file, output_data_file, 1.)
bogus_input = "five"
with pytest.raises(TypeError):
data_model.evaluate(bogus_input)
bogus_input = [[1, 2], [3, 4]]
with pytest.raises(ValueError):
data_model.evaluate(bogus_input)
def test_evaluate_with_cost_delay(cost, input_data_file, output_data_file):
model = ModelFromData(input_data_file, output_data_file, cost=cost,
wait_cost_duration=True)
sample = model._inputs[0]
start_time = timeit.default_timer()
model.evaluate(sample)
evaluation_time = timeit.default_timer() - start_time
# Ensure evaluation time was close to specified cost.
assert np.abs(evaluation_time - cost) < .01
def test_evaluate_fails_on_invalid_input(input_data_file, output_data_file):
"""
Ensure that an invalid input triggers an exception in evaluate.
"""
data_model = ModelFromData(input_data_file, output_data_file, 1.)
bogus_input = "five"
with pytest.raises(TypeError):
data_model.evaluate(bogus_input)
bogus_input = [[1, 2], [3, 4]]
with pytest.raises(ValueError):
data_model.evaluate(bogus_input)
def test_evaluate_with_cost_delay(cost, input_data_file, output_data_file):
"""
Test ability to wait for the duration of the cost specified for the model
when evaluating a sample.
"""
model = ModelFromData(input_data_file,
output_data_file,
cost=cost,
wait_cost_duration=True)
sample = model._inputs[0]
start_time = timeit.default_timer()
model.evaluate(sample)
evaluation_time = timeit.default_timer() - start_time
# Ensure evaluation time was close to specified cost.
assert np.abs(evaluation_time - cost) < .01
def test_evaluate_2d_data(input_data_file_2d, output_data_file_2d, index):
# Initialize model from spring-mass example data files:
data_model = ModelFromData(input_data_file_2d, output_data_file_2d, 1.)
input_data = np.genfromtxt(input_data_file_2d)
output_data = np.genfromtxt(output_data_file_2d)
# Model expects arrays as inputs/outputs
model_output = data_model.evaluate(input_data[index])
true_output = output_data[index]
assert np.all(np.isclose(model_output, true_output))
def test_evaluate_1d_data(input_data_file, output_data_file, index):
"""
Test ability to evaluate one dimensional data.
"""
# Initialize model from spring-mass example data files:
data_model = ModelFromData(input_data_file, output_data_file, 1.)
input_data = np.genfromtxt(input_data_file)
output_data = np.genfromtxt(output_data_file)
# Model expects arrays as inputs/outputs.
model_output = data_model.evaluate(input_data[index])
true_output = output_data[index]
assert np.all(np.isclose(model_output, true_output))
