def test_get_difference(source_stats, target_stats, expected_messages,
expected_stats):
result = cov.get_difference(Job(stats=source_stats, key="s"),
Job(stats=target_stats, key="t"))
assert result == create_result("Coverage Difference",
expected_messages,
stats=expected_stats)
def test_check_errors(get_job, error_count, expected_messages):
job = get_job
job.metadata = {"scrapystats": error_count}
job.key = "112358/13/21"
result = check_errors(job)
assert result == create_result("Job Errors", expected_messages)
def test_compare_prices_for_same_urls(source_data, target_data, tagged_fields,
expected_messages):
result = p.compare_prices_for_same_urls(pd.DataFrame(source_data),
pd.DataFrame(target_data),
tagged_fields)
assert result == create_result("Compare Prices For Same Urls",
expected_messages)
def test_show(mocker, capsys, message, stats, outputs):
mock_pio_show = mocker.patch("plotly.io.show", autospec=True)
mocked_md = mocker.patch("arche.report.display_markdown", autospec=True)
res = create_result("test show", message, stats=stats)
res.show()
mock_pio_show.assert_called_once_with(res.figures[0])
mocked_md.assert_has_calls(mocker.call(o) for o in outputs)
def test_validate_with_json_schema_fails(mocker, get_job_items, get_schema):
mocked_display = mocker.patch("arche.report.display_html", autospec=True)
url = f"{SH_URL}/112358/13/21/item/1"
res = create_result(
"JSON Schema Validation",
{
Level.ERROR: [(
"1 (25%) items have 1 errors",
None,
{
"'price' is a required property": {url}
},
)]
},
)
res.outcome = Outcome.FAILED
schema = {
"type": "object",
"required": ["price"],
"properties": {
"price": {}
}
}
a = Arche("source", schema=schema)
a._source_items = get_job_items
a.validate_with_json_schema()
assert len(a.report.results) == 1
assert a.report.results.get("JSON Schema Validation") == res
report_html = get_report_from_iframe(mocked_display.mock_calls[0][1][0])
assert "JSON Schema Validation - FAILED" in report_html
def test_validate_with_json_schema_fails(mocker, get_job_items, get_schema):
mocked_html = mocker.patch("arche.report.HTML", autospec=True)
key = f"112358/13/21"
url_base = f"{SH_URL}/{key}/item"
res = create_result(
"JSON Schema Validation",
{
Level.ERROR: [(
"4 items were checked, 1 error(s)",
None,
{
"'price' is a required property": {f"{key}/1"}
},
)]
},
)
schema = {"type": "object", "required": ["price"]}
a = Arche("source", schema=schema)
a._source_items = get_job_items
a.validate_with_json_schema()
assert len(a.report.results) == 1
assert a.report.results.get("JSON Schema Validation") == res
mocked_html.assert_any_call(
f"1 items affected - 'price' is a required property: <a href='{url_base}/1'>1</a>"
)
def test_compare_boolean_fields(source_data, target_data, expected_messages,
expected_stats):
source_df = pd.DataFrame(source_data)
target_df = pd.DataFrame(target_data)
rule_result = compare_boolean_fields(source_df, target_df)
assert rule_result == create_result("Boolean Fields", expected_messages,
expected_stats)
def test_validate_with_json_schema_fails(mocker, get_job_items, get_schema):
mocked_md = mocker.patch("arche.report.display_markdown", autospec=True)
url = f"{SH_URL}/112358/13/21/item/1"
res = create_result(
"JSON Schema Validation",
{
Level.ERROR: [(
"4 items were checked, 1 error(s)",
None,
{
"'price' is a required property": {url}
},
)]
},
)
schema = {
"type": "object",
"required": ["price"],
"properties": {
"price": {}
}
}
a = Arche("source", schema=schema)
a._source_items = get_job_items
a.validate_with_json_schema()
assert len(a.report.results) == 1
assert a.report.results.get("JSON Schema Validation") == res
mocked_md.assert_any_call(
f"1 items affected - 'price' is a required property: [1]({url})",
raw=True)
def test_find_by(data, columns, expected_messages, expected_err_items_count):
df = pd.DataFrame(data)
assert duplicates.find_by(df, columns) == create_result(
"Duplicates",
expected_messages,
items_count=len(df),
err_items_count=expected_err_items_count,
)
def test_garbage_symbols(raw_items, expected_messages, expected_items_count,
expected_err_items_count):
assert garbage_symbols(pd.DataFrame(raw_items)) == create_result(
"Garbage Symbols",
expected_messages,
items_count=expected_items_count,
err_items_count=expected_err_items_count,
)
def test_compare_response_ratio(source_stats, source_metadata, target_stats,
target_metadata, expected_messages):
source_job = Job(stats=source_stats, metadata=source_metadata)
target_job = Job(stats=target_stats, metadata=target_metadata)
result = compare_response_ratio(source_job, target_job)
assert result == create_result("Compare Responses Per Item Ratio",
expected_messages)
def test_garbage_symbols(get_job_items, expected_messages,
expected_items_count, expected_err_items_count):
assert garbage_symbols(get_job_items) == create_result(
"Garbage Symbols",
expected_messages,
items_count=expected_items_count,
err_items_count=expected_err_items_count,
)
def test_find_by_name_url(data, tagged_fields, expected_messages):
df = pd.DataFrame(data)
result = duplicates.find_by_name_url(df, tagged_fields)
assert result == create_result(
"Duplicates By **name_field, product_url_field** Tags",
expected_messages,
items_count=len(df),
)
def test_write_details(mocker, get_df, capsys, messages, expected_details):
mock_pio_show = mocker.patch("plotly.io.show", autospec=True)
r = Report()
for m in messages:
result = create_result(*m, stats=[get_df])
r.save(result)
r.write_details()
mock_pio_show.assert_called_with(result.figures[0])
assert capsys.readouterr().out == expected_details
def test_compare_finish_time(get_jobs, source_metadata, target_metadata,
expected_messages):
source_job, target_job = get_jobs
source_job.metadata = source_metadata
target_job.metadata = target_metadata
result = compare_finish_time(source_job, target_job)
assert result == create_result("Finish Time", expected_messages)
def test_report_call_arguments(mocker):
message = {Level.INFO: [("summary", "very detailed message")]}
mocked_display = mocker.patch("arche.report.display_html", autospec=True)
outcome = create_result("rule name here", message)
Report()(outcome)
report_html = get_report_from_iframe(mocked_display.mock_calls[0][1][0])
assert report_html.count("very detailed message") == 1
def test_show(mocker, capsys, message, stats, exp_md_output, exp_txt_outputs):
mock_pio_show = mocker.patch("plotly.io.show", autospec=True)
mocked_md = mocker.patch("arche.report.display_markdown", autospec=True)
mocked_print = mocker.patch("builtins.print", autospec=True)
res = create_result("rule name here", message, stats=stats)
res.show()
mock_pio_show.assert_called_once_with(res.figures[0])
mocked_md.assert_called_with(exp_md_output)
mocked_print.assert_has_calls(mocker.call(o) for o in exp_txt_outputs)
def test_find_by_unique(data, tagged_fields, expected_messages,
expected_err_items_count):
df = pd.DataFrame(data)
assert duplicates.find_by_unique(df, tagged_fields) == create_result(
"Duplicates By **unique** Tag",
expected_messages,
items_count=len(df),
err_items_count=expected_err_items_count,
)
def test_check_uniqueness(data, tagged_fields, expected_messages,
expected_err_items_count):
df = pd.DataFrame(data)
assert check_uniqueness(df, tagged_fields) == create_result(
"Uniqueness",
expected_messages,
items_count=len(df),
err_items_count=expected_err_items_count,
)
def test_compare_was_now(data, tagged_fields, expected_messages, expected_errors):
df = pd.DataFrame(data)
result = p.compare_was_now(df, tagged_fields)
assert result == create_result(
"Compare Price Was And Now",
expected_messages,
expected_errors,
items_count=len(df),
)
def test_anomalies(
mocker, jobs_stats: Dict, expected_messages: Dict, stats: pd.DataFrame
):
jobs = [
Job(key=key, stats={"counts": counts, "totals": {"input_values": input_values}})
for key, counts, input_values in jobs_stats
]
mocker.patch("arche.rules.coverage.api.get_jobs", return_value=jobs)
result = cov.anomalies(jobs_stats[-1][0], [key for key, *_ in jobs_stats[:-1]])
assert result == create_result("Anomalies", expected_messages, stats=stats)
def test_check_items(data, tagged_fields, expected_messages,
expected_err_items_count):
df = pd.DataFrame(data)
result = check_items(df, tagged_fields)
assert result == create_result(
"Duplicated Items",
expected_messages,
items_count=len(df),
err_items_count=expected_err_items_count,
)
def test_validate_with_json_schema(mocker, get_job_items, get_schema):
res = create_result("JSON Schema Validation", {})
mocked_call = mocker.patch("arche.report.Report.__call__", autospec=True)
a = Arche("source", schema=get_schema)
a._source_items = get_job_items
a.validate_with_json_schema()
mocked_call.assert_called_once_with(a.report, res)
assert len(a.report.results) == 1
assert a.report.results.get("JSON Schema Validation") == res
def test_write_details(mocker, get_df, capsys, messages, expected_details):
mock_pio_show = mocker.patch("plotly.io.show", autospec=True)
md_mock = mocker.patch("arche.report.display_markdown", autospec=True)
r = Report()
for m in messages:
result = create_result(*m, stats=[get_df])
r.save(result)
r.write_details()
mock_pio_show.assert_called_with(result.figures[0])
calls = [mocker.call(e) for e in expected_details]
md_mock.assert_has_calls(calls, any_order=True)
def test_validate_with_json_schema(mocker, get_job_items, get_schema):
res = create_result("JSON Schema Validation",
{Level.INFO: [("4 items were checked, 0 error(s)", )]})
mocked_show = mocker.patch("arche.rules.result.Result.show", autospec=True)
a = Arche("source", schema=get_schema)
a._source_items = get_job_items
a.validate_with_json_schema()
mocked_show.assert_called_once_with(res)
assert len(a.report.results) == 1
assert a.report.results.get("JSON Schema Validation") == res
def test_report_call(mocker, get_df, capsys, messages, expected_details):
mocked_display = mocker.patch("arche.report.display_html", autospec=True)
r = Report()
for m in messages:
result = create_result(*m, stats=[get_df])
r.save(result)
r()
report_html = get_report_from_iframe(mocked_display.mock_calls[0][1][0])
assert report_html.count("Plotly.newPlot") == 2
assert report_html.count("rule name here - INFO") == 2
assert report_html.count("other result there - INFO") == 2
def test_get_difference(source, target, categories, expected_messages, expected_stats):
assert c.get_difference(
"s", "t", pd.DataFrame(source), pd.DataFrame(target), categories
) == create_result(
"Category Coverage Difference", expected_messages, stats=expected_stats
)
def test_get_coverage_per_category(data, cat_names, expected_messages, expected_stats):
assert c.get_coverage_per_category(pd.DataFrame(data), cat_names) == create_result(
"Coverage For Scraped Categories", expected_messages, expected_stats
)
def test_check_outcome(get_job, metadata, expected_messages):
job = get_job
job.metadata = metadata
result = check_outcome(job)
assert result == create_result("Job Outcome", expected_messages)
def test_check_tags(source_columns, target_columns, tags, expected_messages):
result = check_tags(np.array(source_columns), np.array(target_columns),
tags)
print(result)
assert result == create_result("Tags", expected_messages)
