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test_dataframe_regression.py
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test_dataframe_regression.py
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import numpy as np
import pandas as pd
import pytest
from pytest_regressions.testing import check_regression_fixture_workflow
@pytest.fixture
def no_regen(dataframe_regression, request):
if dataframe_regression._force_regen or request.config.getoption("force_regen"):
pytest.fail("--force-regen should not be used on this test.")
def test_usage_workflow(testdir, monkeypatch):
"""
:type testdir: _pytest.pytester.TmpTestdir
:type monkeypatch: _pytest.monkeypatch.monkeypatch
"""
import sys
monkeypatch.setattr(
sys, "testing_get_data", lambda: {"data": 1.1 * np.ones(50)}, raising=False
)
source = """
import sys
import pandas as pd
def test_1(dataframe_regression):
contents = sys.testing_get_data()
dataframe_regression.check(pd.DataFrame.from_dict(contents))
"""
def get_csv_contents():
filename = testdir.tmpdir / "test_file" / "test_1.csv"
frame = pd.read_csv(str(filename))
return {"data": frame["data"].values}
def compare_arrays(obtained, expected):
assert (obtained["data"] == expected["data"]).all()
check_regression_fixture_workflow(
testdir,
source=source,
data_getter=get_csv_contents,
data_modifier=lambda: monkeypatch.setattr(
sys, "testing_get_data", lambda: {"data": 1.2 * np.ones(50)}, raising=False
),
expected_data_1={"data": 1.1 * np.ones(50)},
expected_data_2={"data": 1.2 * np.ones(50)},
compare_fn=compare_arrays,
)
def test_common_cases(dataframe_regression, no_regen):
# Most common case: Data is valid, is present and should pass
data1 = 1.1 * np.ones(5000)
data2 = 2.2 * np.ones(5000)
dataframe_regression.check(pd.DataFrame.from_dict({"data1": data1, "data2": data2}))
# Assertion error case 1: Data has one invalid place
data1 = 1.1 * np.ones(5000)
data2 = 2.2 * np.ones(5000)
data1[500] += 0.1
with pytest.raises(AssertionError) as excinfo:
dataframe_regression.check(
pd.DataFrame.from_dict({"data1": data1, "data2": data2})
)
obtained_error_msg = str(excinfo.value)
expected = "\n".join(
[
"Values are not sufficiently close.",
"To update values, use --force-regen option.",
]
)
assert expected in obtained_error_msg
expected = "\n".join(
[
"data1:",
" obtained_data1 expected_data1 diff",
"500 1.20000000000000018 1.10000000000000009 0.10000000000000009",
]
)
assert expected in obtained_error_msg
# Assertion error case 2: More than one invalid data
data1 = 1.1 * np.ones(5000)
data2 = 2.2 * np.ones(5000)
data1[500] += 0.1
data1[600] += 0.2
data2[700] += 0.3
with pytest.raises(AssertionError) as excinfo:
dataframe_regression.check(
pd.DataFrame.from_dict({"data1": data1, "data2": data2})
)
obtained_error_msg = str(excinfo.value)
expected = "\n".join(
[
"Values are not sufficiently close.",
"To update values, use --force-regen option.",
]
)
assert expected in obtained_error_msg
expected = "\n".join(
[
"data1:",
" obtained_data1 expected_data1 diff",
"500 1.20000000000000018 1.10000000000000009 0.10000000000000009",
"600 1.30000000000000004 1.10000000000000009 0.19999999999999996",
]
)
assert expected in obtained_error_msg
expected = "\n".join(
[
"data2:",
" obtained_data2 expected_data2 diff",
"700 2.5 2.20000000000000018 0.29999999999999982",
]
)
assert expected in obtained_error_msg
# Assertion error case 3: More than one invalid data
data1 = 1.1 * np.ones(5000)
data2 = 2.2 * np.ones(5000)
data1[500] += 0.01
data2[500] += 0.01
with pytest.raises(AssertionError) as excinfo:
dataframe_regression.check(
pd.DataFrame.from_dict({"data1": data1, "data2": data2}),
tolerances={
"data1": dict(atol=1e-1, rtol=1e-17),
"data2": dict(atol=1e-17, rtol=1e-17),
},
)
obtained_error_msg = str(excinfo.value)
assert " data1:" not in obtained_error_msg
assert (
"\n".join(
[
"Values are not sufficiently close.",
"To update values, use --force-regen option.",
]
)
in obtained_error_msg
)
assert (
"\n".join(
[
"data2:",
" obtained_data2 expected_data2 diff",
"500 2.20999999999999996 2.20000000000000018 0.00999999999999979",
]
)
in obtained_error_msg
)
def test_different_data_types(dataframe_regression, no_regen):
# Original CSV file contains integer data
data1 = np.array([True] * 10)
with pytest.raises(
AssertionError,
match="Data type for data data1 of obtained and expected are not the same.",
):
dataframe_regression.check(pd.DataFrame.from_dict({"data1": data1}))
class Foo:
def __init__(self, bar):
self.bar = bar
@pytest.mark.parametrize(
"array", [[np.random.randint(10, 99, 6)] * 6, [Foo(i) for i in range(4)]]
)
def test_non_numeric_data(dataframe_regression, array, no_regen):
data1 = pd.DataFrame()
data1["data1"] = array
with pytest.raises(
AssertionError,
match="Only numeric data is supported on dataframe_regression fixture.\n"
" *Array with type '%s' was given." % (str(data1["data1"].dtype),),
):
dataframe_regression.check(data1)
def test_arrays_with_different_sizes(dataframe_regression, no_regen):
data1 = np.ones(10, dtype=np.float64)
with pytest.raises(
AssertionError, match="Obtained and expected data shape are not the same."
):
dataframe_regression.check(pd.DataFrame.from_dict({"data1": data1}))
def test_integer_values_smoke_test(dataframe_regression, no_regen):
data1 = np.ones(11, dtype=int)
dataframe_regression.check(pd.DataFrame.from_dict({"data1": data1}))
def test_number_formats(dataframe_regression, no_regen):
data1 = np.array([1.2345678e50, 1.2345678e-50, 0.0])
dataframe_regression.check(pd.DataFrame.from_dict({"data1": data1}))
def test_bool_array(dataframe_regression, no_regen):
data1 = np.array([True, True, True], dtype=bool)
with pytest.raises(AssertionError) as excinfo:
dataframe_regression.check(pd.DataFrame.from_dict({"data1": data1}))
obtained_error_msg = str(excinfo.value)
expected = "\n".join(
[
"Values are not sufficiently close.",
"To update values, use --force-regen option.",
]
)
assert expected in obtained_error_msg
expected = "\n".join(
[
"data1:",
" obtained_data1 expected_data1 diff",
"0 True False True",
"1 True False True",
"2 True False True",
]
)
assert expected in obtained_error_msg
def test_arrays_of_same_size(dataframe_regression):
same_size_int_arrays = {
"hello": np.zeros((1,), dtype=int),
"world": np.zeros((1,), dtype=int),
}
dataframe_regression.check(pd.DataFrame.from_dict(same_size_int_arrays))
def test_string_array(dataframe_regression):
data1 = {"potato": ["delicious", "nutritive", "yummy"]}
dataframe_regression.check(pd.DataFrame.from_dict(data1))
# TODO: The following fails with a confusing error message.
# Try wrong data
# data1 = {"potato": ["delicious", "nutritive", "yikes"]}
# dataframe_regression.check(pd.DataFrame.from_dict(data1))
def test_non_pandas_dataframe(dataframe_regression):
data = np.ones(shape=(10, 10))
with pytest.raises(
AssertionError,
match="Only pandas DataFrames are supported on dataframe_regression fixture.\n"
" *Object with type '%s' was given." % (str(type(data)),),
):
dataframe_regression.check(data)