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0625459 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | """Unit & integration test suite for locked research dataset ingestion and preprocessing."""
import unittest
import os
import pandas as pd
import numpy as np
from pathlib import Path
from ml_rl.preprocessing.dataset_loader import load_raw_dataset, create_test_fixture
from ml_rl.preprocessing.schema import (
TRIAGE_MODEL_INPUT_FEATURES,
METADATA_IDENTIFIERS,
ALL_TRIAGE_SAFE_FIELDS,
TARGET_COLUMN,
LLM_INPUT_SAFE_FIELDS,
)
from ml_rl.preprocessing.feature_availability import get_triage_model_inputs, get_llm_input_features, audit_dataframe_leakage
from ml_rl.preprocessing.cleaning import clean_text, build_combined_text
from ml_rl.preprocessing.normalization import normalize_priority_value, normalize_target_column
from ml_rl.preprocessing.validation import handle_missing_values, validate_schema, check_data_quality
from ml_rl.preprocessing.splitting import select_reproducible_subset, split_group_aware_by_project, verify_zero_project_leakage
from ml_rl.preprocessing.manifest import generate_dataset_manifest, MANIFEST_PATH
from ml_rl.preprocessing.reporting import generate_quality_report, REPORT_PATH
from ml_rl.preprocessing.runner import run_preprocessing_pipeline
class TestPreprocessingPipeline(unittest.TestCase):
"""Comprehensive test suite verifying official ground truth, schema, splitting, and leakage guards."""
def setUp(self):
self.df_raw = create_test_fixture(num_rows=100)
def test_1_target_comes_exclusively_from_priority_score(self):
"""1. Verifies research target comes strictly from priority_score in {1,2,3,4,5}."""
self.assertEqual(normalize_priority_value(1), 0) # Lowest
self.assertEqual(normalize_priority_value(2), 1) # Low
self.assertEqual(normalize_priority_value(3), 2) # Medium
self.assertEqual(normalize_priority_value(4), 3) # High
self.assertEqual(normalize_priority_value(5), 4) # Highest
df_norm = normalize_target_column(self.df_raw)
self.assertTrue((df_norm["priority_normalized"].isin([0, 1, 2, 3, 4])).all())
def test_2_heuristic_aliases_cannot_become_research_labels(self):
"""2. Verifies heuristic priority aliases (Major, Minor, Blocker, etc.) are ignored if priority_score is missing."""
df_alias = self.df_raw.copy()
df_alias["priority_score"] = np.nan
df_alias["Priority"] = "Major"
df_alias["priority_name"] = "Blocker"
df_norm = normalize_target_column(df_alias)
self.assertEqual(len(df_norm), 0, "Heuristic aliases must not become research labels!")
def test_3_all_research_labels_in_0_to_4(self):
"""3. Verifies all valid normalized labels fall strictly in range 0..4."""
for val in [1, 2, 3, 4, 5]:
lbl = normalize_priority_value(val)
self.assertIn(lbl, [0, 1, 2, 3, 4])
for invalid_val in [0, 6, -1, "Major", "Blocker", None, np.nan]:
self.assertIsNone(normalize_priority_value(invalid_val))
def test_4_identifiers_excluded_from_model_inputs(self):
"""4. Verifies metadata identifiers (issue_key, project_id, etc.) are excluded from model input features."""
df_inputs = get_triage_model_inputs(self.df_raw)
for ident in METADATA_IDENTIFIERS:
self.assertNotIn(ident, df_inputs.columns, f"Identifier {ident} must be excluded from model inputs!")
for input_feat in TRIAGE_MODEL_INPUT_FEATURES:
self.assertIn(input_feat, df_inputs.columns)
def test_5_project_splits_are_completely_disjoint(self):
"""5. Verifies project splits are 100% disjoint (train_projects ∩ val_projects ∩ test_projects = ∅)."""
norm_df = normalize_target_column(self.df_raw)
t, v, te = split_group_aware_by_project(norm_df, seed=42)
is_clean, info = verify_zero_project_leakage(t, v, te)
self.assertTrue(is_clean)
self.assertEqual(len(info["train_val_intersection"]), 0)
self.assertEqual(len(info["train_test_intersection"]), 0)
self.assertEqual(len(info["val_test_intersection"]), 0)
def test_6_research_subset_comes_only_from_priority_score_1_to_5(self):
"""6. Verifies subset population originates exclusively from priority_score in {1..5}."""
norm_df = normalize_target_column(self.df_raw)
subset_df = select_reproducible_subset(norm_df, target_issue_count=50, seed=42)
self.assertTrue((subset_df["priority_raw"].isin([1, 2, 3, 4, 5])).all())
def test_7_dataset_manifest_records_authoritative_target(self):
"""7. Verifies dataset manifest declares priority_score as authoritative target."""
manifest = generate_dataset_manifest(
dataset_name="TEST FIXTURE",
is_real_dataset=False,
total_rows=100,
usable_rows=100,
missing_target_rows=0,
num_projects=3,
selected_issue_count=100,
train_projects_count=1,
val_projects_count=1,
test_projects_count=1,
train_issue_count=34,
val_issue_count=33,
test_issue_count=33,
zero_leakage_verified=True,
)
self.assertEqual(manifest["authoritative_target_column"], "priority_score")
self.assertEqual(manifest["feature_summary"]["candidate_model_input_features_count"], 20)
self.assertEqual(manifest["feature_summary"]["metadata_identifiers_count"], 5)
def test_8_schema_and_llm_input_isolation(self):
"""8. Verifies LLM prompt input features strictly exclude targets and post-triage metrics."""
df_llm = get_llm_input_features(self.df_raw)
self.assertNotIn("priority_score", df_llm.columns)
self.assertNotIn("priority_name", df_llm.columns)
self.assertNotIn("Cycle Time (hours)", df_llm.columns)
self.assertIn("issue_summary", df_llm.columns)
def test_9_end_to_end_pipeline_runner(self):
"""9. Verifies full pipeline execution produces valid splits and manifests."""
train_df, val_df, test_df, summary = run_preprocessing_pipeline()
self.assertTrue(summary["zero_leakage_verified"])
self.assertTrue(MANIFEST_PATH.exists())
self.assertTrue(REPORT_PATH.exists())
if __name__ == "__main__":
unittest.main()
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