def setUpClass(cls):
config = FormatAnalyzer._load_train_config(merge_dicts(
get_train_config(), {
"javascript": {
"feature_extractor": {
"left_siblings_window": 1,
"right_siblings_window": 1,
"parents_depth": 1,
"node_features": ["start_line", "reserved", "roles"],
},
},
}))
base = Path(__file__).parent
with lzma.open(str(base / "benchmark.js.xz"), mode="rt") as fin:
contents = fin.read()
with lzma.open(str(base / "benchmark.uast.xz")) as fin:
uast = bblfsh.Node.FromString(fin.read())
file = File(content=bytes(contents, "utf-8"), uast=uast)
files = [file, file]
cls.fe = FeatureExtractor(language="javascript",
**config["javascript"]["feature_extractor"])
cls.fe.extract_features(files)
cls.class_representations = cls.fe.composite_class_representations
cls.n_classes = len(cls.fe.labels_to_class_sequences)
cls.ordinal = cls.return_node_feature(FeatureId.start_line)
cls.categorical = cls.return_node_feature(FeatureId.reserved)
cls.bag = cls.return_node_feature(FeatureId.roles)
def setUpClass(cls):
base = Path(__file__).parent
# str() is needed for Python 3.5
with lzma.open(str(base / "benchmark.js.xz"), mode="rt") as fin:
contents = fin.read()
with lzma.open(str(base / "benchmark.uast.xz")) as fin:
uast = bblfsh.Node.FromString(fin.read())
file = File(content=bytes(contents, "utf-8"),
uast=uast)
cls.files = [file]
config = FormatAnalyzer._load_train_config(get_train_config())
cls.config = config["javascript"]
cls.extractor = FeatureExtractor(language="javascript", **cls.config["feature_extractor"])
def setUpClass(cls):
cls.maxDiff = None
base = Path(__file__).parent
# str() is needed for Python 3.5
with lzma.open(str(base / "benchmark_small.js.xz"), mode="rt") as fin:
contents = fin.read()
with lzma.open(str(base / "benchmark_small.js.uast.xz")) as fin:
uast = bblfsh.Node.FromString(fin.read())
config = FormatAnalyzer._load_train_config(get_train_config())
fe_config = config["javascript"]
cls.feature_extractor = FeatureExtractor(
language="javascript",
label_composites=label_composites,
**fe_config["feature_extractor"])
cls.file = File(content=bytes(contents, "utf-8"), uast=uast)
cls.X, cls.y, (cls.vnodes_y, cls.vnodes, cls.vnode_parents, cls.node_parents) = \
cls.feature_extractor.extract_features([cls.file])
def test_train_review_analyzer_integration(self):
"""Integration test for review event."""
with TestAnalyzer(
port=self.port,
db=self.db.name,
fs=self.fs.name,
analyzer="lookout.style.format.benchmarks.general_report"):
server.run("push",
FROM_COMMIT,
TO_COMMIT,
port=self.port,
git_dir=self.jquery_dir,
config_json=json.dumps(
{QualityReportAnalyzer.name: get_train_config()}))
server.run("review",
FROM_COMMIT,
TO_COMMIT,
port=self.port,
git_dir=self.jquery_dir,
config_json=json.dumps(
{QualityReportAnalyzer.name: get_analyze_config()}))
def test_evaluate_smoke_entry_integration(self):
input_path = os.path.join(os.path.dirname(generate_smoke.__file__),
"data", "js_smoke_init.tar.xz")
generate_smoke.js_format_rules = {"equal_no_space_style": (" = ", "=")}
with tempfile.TemporaryDirectory(
prefix="test-smoke-eval-") as outputpath:
generate_smoke.generate_smoke_entry(input_path,
outputpath,
force=True)
with open(os.path.join(outputpath, "index.csv")) as index:
index_content = index.read().splitlines()
self.assertEqual(len(index_content), 5)
self.assertEqual(
set(os.listdir(outputpath)),
{"index.csv", "nodejs", "jsquery", "freeCodeCamp", "react"})
with open(os.path.join(outputpath, "index.csv"), "w") as index:
index.write("\n".join(index_content[::3]))
report_dir = os.path.join(outputpath, "report")
evaluate_smoke_entry(outputpath, report_dir, None, "0.0.0.0:9432",
get_train_config(), get_analyze_config())
report = pandas.read_csv(os.path.join(report_dir, "report.csv"))
self.assertEqual(len(report), 11)
self.assertEqual(len(report.columns), 15)
def setUp(self):
config = FormatAnalyzer._load_train_config(get_train_config())
self.extractor = FeatureExtractor(
language="javascript", **config["javascript"]["feature_extractor"])
