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from pathlib import Path
import traceback
from typing import List
from deepchecks.tabular import Dataset, Suite
from deepchecks.tabular.checks import (
ConflictingLabels,
DataDuplicates,
LabelDrift,
OutlierSampleDetection,
TrainTestSamplesMix,
)
import numpy as np
import pandas as pd
from turing.config import LABEL_COLUMN, LABELS_MAP
try:
from deepchecks.nlp import TextData
from deepchecks.nlp.checks import (
PropertyDrift,
TextEmbeddingsDrift,
)
NLP_AVAILABLE = True
except ImportError:
NLP_AVAILABLE = False
def _encode_labels_for_validation(
series: pd.Series, class_names: List[str]
) -> pd.Series:
def encode(lbl):
active_labels = []
for idx, is_active in enumerate(lbl):
if is_active:
if idx < len(class_names):
active_labels.append(class_names[idx])
else:
active_labels.append(f"Class_{idx}")
if not active_labels:
return "No_Label"
return " & ".join(active_labels)
return series.apply(encode)
def _calculate_code_specific_properties(text_series: List[str]) -> pd.DataFrame:
props = []
for text in text_series:
s = str(text)
length = len(s)
non_alnum = sum(1 for c in s if not c.isalnum() and not c.isspace())
props.append(
{
"Text_Length": length,
"Symbol_Ratio": non_alnum / length if length > 0 else 0.0,
}
)
return pd.DataFrame(props)
def _nuke_rogue_files():
"""
delete .npy files
"""
rogue_filenames = [
"embeddings.npy"
]
for fname in rogue_filenames:
p = Path(fname)
if p.exists():
try:
p.unlink()
except Exception:
pass
def run_custom_deepchecks(
df_train: pd.DataFrame,
df_test: pd.DataFrame,
output_dir: Path,
stage: str,
language: str,
):
print(f" [Deepchecks] Running Integrity Suite ({stage})...")
output_dir.mkdir(parents=True, exist_ok=True)
class_names = LABELS_MAP.get(language, [])
cols = ["f_length", "f_word_count", "f_starts_verb", "text_hash"]
for c in cols:
if c not in df_train.columns:
df_train[c] = 0
if c not in df_test.columns:
df_test[c] = 0
train_ds_df = df_train[cols].copy()
train_ds_df["target"] = _encode_labels_for_validation(
df_train[LABEL_COLUMN], class_names
)
test_ds_df = df_test[cols].copy()
test_ds_df["target"] = _encode_labels_for_validation(
df_test[LABEL_COLUMN], class_names
)
cat_features = ["text_hash", "f_starts_verb"]
train_ds = Dataset(train_ds_df, label="target", cat_features=cat_features)
test_ds = Dataset(test_ds_df, label="target", cat_features=cat_features)
check_conflicts = ConflictingLabels(columns=["text_hash"])
if hasattr(check_conflicts, "add_condition_ratio_of_conflicting_labels_not_greater_than"):
check_conflicts.add_condition_ratio_of_conflicting_labels_not_greater_than(0)
else:
check_conflicts.add_condition_ratio_of_conflicting_labels_less_or_equal(0)
check_duplicates = DataDuplicates()
if hasattr(check_duplicates, "add_condition_ratio_not_greater_than"):
check_duplicates.add_condition_ratio_not_greater_than(0.05)
else:
check_duplicates.add_condition_ratio_less_or_equal(0.05)
check_leakage = TrainTestSamplesMix(columns=["text_hash"])
try:
if hasattr(check_leakage, "add_condition_ratio_not_greater_than"):
check_leakage.add_condition_ratio_not_greater_than(0)
except Exception:
pass
check_outliers = OutlierSampleDetection()
try:
if hasattr(check_outliers, "add_condition_outlier_ratio_less_or_equal"):
check_outliers.add_condition_outlier_ratio_less_or_equal(0.05)
except Exception:
pass
custom_suite = Suite(
"Code Quality & Integrity",
check_conflicts,
check_duplicates,
check_leakage,
LabelDrift(),
check_outliers,
)
try:
result = custom_suite.run(train_dataset=train_ds, test_dataset=test_ds)
report_path = output_dir / f"1_Integrity_{stage}.html"
result.save_as_html(str(report_path), as_widget=False)
print(f" [Deepchecks] Report Saved: {report_path}")
except Exception as e:
print(f" [Deepchecks] Error: {e}")
traceback.print_exc()
def run_targeted_nlp_checks(
df_train: pd.DataFrame,
df_test: pd.DataFrame,
output_dir: Path,
stage: str,
language: str = "english",
):
if not NLP_AVAILABLE:
print(" [Skip] NLP Suite skipped (libs not installed).")
return
from deepchecks.nlp import Suite as NLPSuite
print(f" [NLP Check] Running Semantic Analysis ({stage})...")
output_dir.mkdir(parents=True, exist_ok=True)
# Clean up any existing garbage before starting
_nuke_rogue_files()
DRIFT_THRESHOLD = 0.20
PROP_THRESHOLD = 0.35
SAMPLE_SIZE = 2000
df_tr = (
df_train.sample(n=SAMPLE_SIZE, random_state=42)
if len(df_train) > SAMPLE_SIZE
else df_train
)
df_te = (
df_test.sample(n=SAMPLE_SIZE, random_state=42)
if len(df_test) > SAMPLE_SIZE
else df_test
)
try: # START MAIN TRY BLOCK
y_tr = np.vstack(df_tr[LABEL_COLUMN].tolist())
y_te = np.vstack(df_te[LABEL_COLUMN].tolist())
train_ds = TextData(
df_tr["comment_sentence"].tolist(),
label=y_tr,
task_type="text_classification",
)
test_ds = TextData(
df_te["comment_sentence"].tolist(),
label=y_te,
task_type="text_classification",
)
print(" [NLP Check] Calculating custom code properties...")
train_props = _calculate_code_specific_properties(
df_tr["comment_sentence"].tolist()
)
test_props = _calculate_code_specific_properties(
df_te["comment_sentence"].tolist()
)
train_ds.set_properties(train_props)
test_ds.set_properties(test_props)
# In-memory calculation only.
train_ds.calculate_builtin_embeddings()
test_ds.calculate_builtin_embeddings()
check_embeddings = TextEmbeddingsDrift()
if hasattr(check_embeddings, "add_condition_drift_score_not_greater_than"):
check_embeddings.add_condition_drift_score_not_greater_than(DRIFT_THRESHOLD)
elif hasattr(check_embeddings, "add_condition_drift_score_less_than"):
check_embeddings.add_condition_drift_score_less_than(DRIFT_THRESHOLD)
check_len = PropertyDrift(custom_property_name="Text_Length")
if hasattr(check_len, "add_condition_drift_score_not_greater_than"):
check_len.add_condition_drift_score_not_greater_than(PROP_THRESHOLD)
elif hasattr(check_len, "add_condition_drift_score_less_than"):
check_len.add_condition_drift_score_less_than(PROP_THRESHOLD)
check_sym = PropertyDrift(custom_property_name="Symbol_Ratio")
if hasattr(check_sym, "add_condition_drift_score_not_greater_than"):
check_sym.add_condition_drift_score_not_greater_than(PROP_THRESHOLD)
elif hasattr(check_sym, "add_condition_drift_score_less_than"):
check_sym.add_condition_drift_score_less_than(PROP_THRESHOLD)
suite = NLPSuite(
"Code Comment Semantic Analysis",
check_embeddings,
check_len,
check_sym
)
res = suite.run(train_ds, test_ds)
report_path = output_dir / f"2_Semantic_{stage}.html"
res.save_as_html(str(report_path), as_widget=False)
print(f" [NLP Check] Report saved: {report_path}")
try:
passed = res.get_passed_checks()
n_passed = len(passed)
n_total = len(res.results)
print(f" [NLP Result] {n_passed}/{n_total} checks passed.")
if n_passed < n_total:
print(" [NLP Warning] Failed Checks details:")
for result in res.results:
if not result.passed_conditions():
print(f" - {result.check.name}: {result.conditions_results[0].details}")
except Exception:
pass
except Exception as e:
print(f" [NLP Check] Failed: {e}")
import traceback
traceback.print_exc()
finally:
_nuke_rogue_files() |