def _test(self):
y_cur = deepcopy(self.y)
for i, yi in zip(y_indexes, y_pred):
y_cur[i] = yi
code_generator = CodeGenerator(self.feature_extractor)
pred_vnodes = code_generator.apply_predicted_y(
self.vnodes, self.vnodes_y, list(range(len(self.vnodes_y))),
FakeRules(y_cur))
generated_file = code_generator.generate(pred_vnodes, "local")
self.assertEqual(generated_file, result_local)
def _generate_token_fixes(
self,
file: File,
fe: FeatureExtractor,
feature_extractor_output,
bblfsh_stub: "bblfsh.aliases.ProtocolServiceStub",
rules: Rules,
) -> Tuple[List[LineFix], List[VirtualNode], numpy.ndarray, numpy.ndarray]:
X, y, (vnodes_y, vnodes, vnode_parents,
node_parents) = feature_extractor_output
y_pred_pure, rule_winners, new_rules, grouped_quote_predictions = rules.predict(
X=X, vnodes_y=vnodes_y, vnodes=vnodes, feature_extractor=fe)
y_pred = rules.fill_missing_predictions(y_pred_pure, y)
if self.config["uast_break_check"]:
y, y_pred, vnodes_y, rule_winners, safe_preds = filter_uast_breaking_preds(
y=y,
y_pred=y_pred,
vnodes_y=vnodes_y,
vnodes=vnodes,
files={file.path: file},
feature_extractor=fe,
stub=bblfsh_stub,
vnode_parents=vnode_parents,
node_parents=node_parents,
rule_winners=rule_winners,
grouped_quote_predictions=grouped_quote_predictions)
y_pred_pure = y_pred_pure[safe_preds]
assert len(y) == len(y_pred)
assert len(y) == len(rule_winners)
code_generator = CodeGenerator(fe, skip_errors=True)
new_vnodes = code_generator.apply_predicted_y(vnodes, vnodes_y,
rule_winners, new_rules)
token_fixes = []
for line_number, line in self._group_line_nodes(
y, y_pred, vnodes_y, new_vnodes, rule_winners):
line_ys, line_ys_pred, line_vnodes_y, new_line_vnodes, line_winners = line
new_code_line = code_generator.generate(
new_line_vnodes, "local").lstrip("\n").splitlines()[0]
confidence = self._get_comment_confidence(line_ys, line_ys_pred,
line_winners, new_rules)
fixed_vnodes = [
vnode for vnode in new_line_vnodes
if hasattr(vnode, "y_old") and vnode.y_old != vnode.y
]
token_fixes.append(
LineFix(
line_number=line_number, # line number for the comment
suggested_code=
new_code_line, # code line suggested by our model
fixed_vnodes=fixed_vnodes, # VirtualNode-s with changed y
confidence=
confidence, # overall confidence in the prediction, 0-100
))
return token_fixes, new_vnodes, y_pred_pure, y
def _generate_token_fixes(
self, file: File, fe: FeatureExtractor, feature_extractor_output,
bblfsh_stub: "bblfsh.aliases.ProtocolServiceStub", rules: Rules,
) -> Tuple[List[LineFix], List[VirtualNode], numpy.ndarray, numpy.ndarray]:
X, y, (vnodes_y, vnodes, vnode_parents, node_parents) = feature_extractor_output
y_pred_pure, rule_winners, new_rules, grouped_quote_predictions = rules.predict(
X=X, vnodes_y=vnodes_y, vnodes=vnodes, feature_extractor=fe)
y_pred = rules.fill_missing_predictions(y_pred_pure, y)
if self.analyze_config[file.language.lower()]["uast_break_check"]:
checker = UASTStabilityChecker(fe)
y, y_pred, vnodes_y, rule_winners, safe_preds = checker.check(
y=y, y_pred=y_pred, vnodes_y=vnodes_y, vnodes=vnodes, files=[file],
stub=bblfsh_stub, vnode_parents=vnode_parents, node_parents=node_parents,
rule_winners=rule_winners, grouped_quote_predictions=grouped_quote_predictions)
y_pred_pure = y_pred_pure[safe_preds]
assert len(y) == len(y_pred)
assert len(y) == len(rule_winners)
code_generator = CodeGenerator(fe, skip_errors=True)
new_vnodes = code_generator.apply_predicted_y(vnodes, vnodes_y, rule_winners, new_rules)
token_fixes = []
newline_index = CLASS_INDEX[CLS_NEWLINE]
for line_number, line in self._group_line_nodes(
y, y_pred, vnodes_y, new_vnodes, rule_winners):
line_ys, line_ys_pred, line_vnodes_y, new_line_vnodes, line_winners = line
try:
new_code_line = code_generator.generate_new_line(new_line_vnodes)
except Exception:
self._log.exception(
"Failed to generate new line suggestion for line %d in %s. line vnodes:\n%s",
line_number, file.path, "\n".join(
"%s, y_old=%s" % (repr(vn), getattr(vn, "y_old", "N/A"))
for vn in new_line_vnodes))
new_code_line = None
if (new_line_vnodes and hasattr(new_line_vnodes[0], "y_old") and newline_index in
new_line_vnodes[0].y_old):
lines_num_diff = new_line_vnodes[0].y.count(newline_index) - \
new_line_vnodes[0].y_old.count(newline_index)
if lines_num_diff < 0:
# Some lines were removed. This means that several original lines should be
# modified. GitHub Suggested Change feature cannot handle such cases right now.
# To not confuse the user we do not provide any code suggestion.
new_code_line = None
confidence = self._get_comment_confidence(line_ys, line_ys_pred, line_winners,
new_rules)
fixed_vnodes = [vnode for vnode in new_line_vnodes if
hasattr(vnode, "y_old") and vnode.y_old != vnode.y]
token_fixes.append(LineFix(
line_number=line_number, # line number for the comment
suggested_code=new_code_line, # code line suggested by our model
fixed_vnodes=fixed_vnodes, # VirtualNode-s with changed y
confidence=confidence, # overall confidence in the prediction, 0-100
))
return token_fixes, new_vnodes, y_pred_pure, y
def _test(self):
y_cur = deepcopy(self.y)
for offset, yi in zip(offsets, y_pred):
i = None
for i, vnode in enumerate(vnodes_y): # noqa: B007
if offset == vnode.start.offset:
break
y_cur[i] = yi
code_generator = CodeGenerator(self.feature_extractor)
pred_vnodes = code_generator.apply_predicted_y(
self.vnodes, self.vnodes_y, list(range(len(self.vnodes_y))),
FakeRules(y_cur))
generated_file = code_generator.generate(pred_vnodes)
self.assertEqual(generated_file, result)
def test_generate_new_line(self):
self.maxDiff = None
expected_res = {
"nothing changed": [],
"remove new line in the end of 4th line":
None,
"indentation in the beginning":
[" import { makeToast } from '../../common/app/Toasts/redux';"],
"remove indentation in the 4th line till the end":
[" return Object.keys(flash)", " }"],
"new line between 6th and 7th regular code lines":
["\n return messages.map(message => ({"],
"new line in the middle of the 7th code line with indentation increase":
[" return messages\n .map(message => ({", " })"],
"new line in the middle of the 7th code line with indentation decrease":
[" return messages\n .map(message => ({", " })"],
"new line in the middle of the 7th code line without indentation increase":
[" return messages\n .map(message => ({"],
"change quotes":
['import { makeToast } from "../../common/app/Toasts/redux";'],
"remove indentation decrease 11th line": [" }));"],
"change indentation decrease to indentation increase 11th line":
[" }));"],
"change indentation decrease to indentation increase 11th line but keep the rest":
[" }));", "})"],
}
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_config(get_config())
fe_config = config["train"]["javascript"]
for case in expected_res:
offsets, y_pred, _ = cases[case]
feature_extractor = FeatureExtractor(
language="javascript",
label_composites=label_composites,
**fe_config["feature_extractor"])
file = UnicodeFile(content=contents,
uast=uast,
path="",
language="")
X, y, (vnodes_y, vnodes, vnode_parents, node_parents) = \
feature_extractor.extract_features([file])
y_cur = deepcopy(y)
for offset, yi in zip(offsets, y_pred):
i = None
for i, vnode in enumerate(vnodes_y): # noqa: B007
if offset == vnode.start.offset:
break
y_cur[i] = yi
code_generator = CodeGenerator(feature_extractor)
pred_vnodes = code_generator.apply_predicted_y(
vnodes, vnodes_y, list(range(len(vnodes_y))), FakeRules(y_cur))
res = []
for gln in FormatAnalyzer._group_line_nodes(
y, y_cur, vnodes_y, pred_vnodes, [1] * len(y)):
line, (line_y, line_y_pred, line_vnodes_y, line_vnodes,
line_rule_winners) = gln
new_code_line = code_generator.generate_new_line(line_vnodes)
res.append(new_code_line)
if expected_res[case] is not None:
# None means that we delete some lines. We are not handle this properly now.
self.assertEqual(res, expected_res[case], case)