Hugging Face's logo

Datasets:
saraNour
/
compliments-reference-phase5

Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
rule_1_all_products_have_nutrition: struct<description: string, total: int64, matched: int64, pass: bool>
  child 0, description: string
  child 1, total: int64
  child 2, matched: int64
  child 3, pass: bool
rule_2_no_duplicate_external_ids: struct<description: string, unique_external_ids: int64, total_rows: int64, pass: bool>
  child 0, description: string
  child 1, unique_external_ids: int64
  child 2, total_rows: int64
  child 3, pass: bool
rule_3_no_duplicate_matches: struct<description: string, pass: bool>
  child 0, description: string
  child 1, pass: bool
rule_4_suspicious_values_preserved: struct<description: string, suspicious_count: int64, pass: bool>
  child 0, description: string
  child 1, suspicious_count: int64
  child 2, pass: bool
rule_5_no_fabricated_values: struct<description: string, pass: bool>
  child 0, description: string
  child 1, pass: bool
rule_6_no_external_data: struct<description: string, pass: bool>
  child 0, description: string
  child 1, pass: bool
rule_7_all_outputs_parquet: struct<description: string, pass: bool>
  child 0, description: string
  child 1, pass: bool
rule_8_no_llm: struct<description: string, pass: bool>
  child 0, description: string
  child 1, pass: bool
overall: struct<result: string>
  child 0, result: string
rule_1_product_has_one_variant: struct<description: string, products: int64, unique_variant_ids_per_product: int64, pass: bool>
  child 0, description: string
  child 1, products: int64
  child 2, unique_variant_ids_per_product: int64
  child 3, pass: bool
rule_3_no_product_multiple_variants: struct<description: string, products_with_multiple_variants: int64, pass: bool>
  child 0, description: string
  child 1, products_with_multiple_variants: int64
  child 2, pass: bool
rule_5_all_products_have_group: struct<description: string, null_group_ids: int64, pass: bool>
  child 0, description: string
  child 1, null_group_ids: int64
  child 2, pass: bool
rule_2_variant_belongs_to_one_group: struct<description: string, variants: int64, unique_groups_per_variant: int64, pass: bool>
  child 0, description: string
  child 1, variants: int64
  child 2, unique_groups_per_variant: int64
  child 3, pass: bool
rule_4_variant_count: struct<description: string, mapping_variants: int64, table_variants: int64, pass: bool>
  child 0, description: string
  child 1, mapping_variants: int64
  child 2, table_variants: int64
  child 3, pass: bool
rule_6_all_products_have_variant: struct<description: string, null_variant_ids: int64, pass: bool>
  child 0, description: string
  child 1, null_variant_ids: int64
  child 2, pass: bool
to
{'rule_1_product_has_one_variant': {'description': Value('string'), 'products': Value('int64'), 'unique_variant_ids_per_product': Value('int64'), 'pass': Value('bool')}, 'rule_2_variant_belongs_to_one_group': {'description': Value('string'), 'variants': Value('int64'), 'unique_groups_per_variant': Value('int64'), 'pass': Value('bool')}, 'rule_3_no_product_multiple_variants': {'description': Value('string'), 'products_with_multiple_variants': Value('int64'), 'pass': Value('bool')}, 'rule_4_variant_count': {'description': Value('string'), 'mapping_variants': Value('int64'), 'table_variants': Value('int64'), 'pass': Value('bool')}, 'rule_5_all_products_have_group': {'description': Value('string'), 'null_group_ids': Value('int64'), 'pass': Value('bool')}, 'rule_6_all_products_have_variant': {'description': Value('string'), 'null_variant_ids': Value('int64'), 'pass': Value('bool')}, 'overall': {'result': Value('string')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              rule_1_all_products_have_nutrition: struct<description: string, total: int64, matched: int64, pass: bool>
                child 0, description: string
                child 1, total: int64
                child 2, matched: int64
                child 3, pass: bool
              rule_2_no_duplicate_external_ids: struct<description: string, unique_external_ids: int64, total_rows: int64, pass: bool>
                child 0, description: string
                child 1, unique_external_ids: int64
                child 2, total_rows: int64
                child 3, pass: bool
              rule_3_no_duplicate_matches: struct<description: string, pass: bool>
                child 0, description: string
                child 1, pass: bool
              rule_4_suspicious_values_preserved: struct<description: string, suspicious_count: int64, pass: bool>
                child 0, description: string
                child 1, suspicious_count: int64
                child 2, pass: bool
              rule_5_no_fabricated_values: struct<description: string, pass: bool>
                child 0, description: string
                child 1, pass: bool
              rule_6_no_external_data: struct<description: string, pass: bool>
                child 0, description: string
                child 1, pass: bool
              rule_7_all_outputs_parquet: struct<description: string, pass: bool>
                child 0, description: string
                child 1, pass: bool
              rule_8_no_llm: struct<description: string, pass: bool>
                child 0, description: string
                child 1, pass: bool
              overall: struct<result: string>
                child 0, result: string
              rule_1_product_has_one_variant: struct<description: string, products: int64, unique_variant_ids_per_product: int64, pass: bool>
                child 0, description: string
                child 1, products: int64
                child 2, unique_variant_ids_per_product: int64
                child 3, pass: bool
              rule_3_no_product_multiple_variants: struct<description: string, products_with_multiple_variants: int64, pass: bool>
                child 0, description: string
                child 1, products_with_multiple_variants: int64
                child 2, pass: bool
              rule_5_all_products_have_group: struct<description: string, null_group_ids: int64, pass: bool>
                child 0, description: string
                child 1, null_group_ids: int64
                child 2, pass: bool
              rule_2_variant_belongs_to_one_group: struct<description: string, variants: int64, unique_groups_per_variant: int64, pass: bool>
                child 0, description: string
                child 1, variants: int64
                child 2, unique_groups_per_variant: int64
                child 3, pass: bool
              rule_4_variant_count: struct<description: string, mapping_variants: int64, table_variants: int64, pass: bool>
                child 0, description: string
                child 1, mapping_variants: int64
                child 2, table_variants: int64
                child 3, pass: bool
              rule_6_all_products_have_variant: struct<description: string, null_variant_ids: int64, pass: bool>
                child 0, description: string
                child 1, null_variant_ids: int64
                child 2, pass: bool
              to
              {'rule_1_product_has_one_variant': {'description': Value('string'), 'products': Value('int64'), 'unique_variant_ids_per_product': Value('int64'), 'pass': Value('bool')}, 'rule_2_variant_belongs_to_one_group': {'description': Value('string'), 'variants': Value('int64'), 'unique_groups_per_variant': Value('int64'), 'pass': Value('bool')}, 'rule_3_no_product_multiple_variants': {'description': Value('string'), 'products_with_multiple_variants': Value('int64'), 'pass': Value('bool')}, 'rule_4_variant_count': {'description': Value('string'), 'mapping_variants': Value('int64'), 'table_variants': Value('int64'), 'pass': Value('bool')}, 'rule_5_all_products_have_group': {'description': Value('string'), 'null_group_ids': Value('int64'), 'pass': Value('bool')}, 'rule_6_all_products_have_variant': {'description': Value('string'), 'null_variant_ids': Value('int64'), 'pass': Value('bool')}, 'overall': {'result': Value('string')}}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Phase 5 — Nutrition Integration

Compliments Reference DB Pipeline

Version: 1.0.0 Date: 2026-07-31 Status: CORRECTED (consistency rebuild)

Correction Notice

Date: 2026-07-31 Previous staleness: product_group_mapping.parquet was stale (14 columns, food=2,330/unknown=1,868/non_food=242). Corrected: Regenerated from corrected Phase 3 output (28 columns, food=2,803/unknown=1,243/non_food=394). Root cause: Phase 3 file was previously regenerated by an older code version. This rebuild propagates the corrected Phase 3 output through Phase 4 and Phase 5. Action taken: Re-ran Phase 5 v1.0.0 with corrected Phase 3 and Phase 4 inputs. All 8 validation rules pass. 4,440/4,440 products matched.

Purpose

Phase 5 integrates nutrition data from the authoritative source into the Compliments Reference DB. It joins nutrition.parquet with the product pipeline using deterministic matching by external_id.

Inputs

Dataset Source Description
nutrition.parquet saraNour/compliments-brand/source_of_truth/ Authoritative nutrition data (4,440 × 21)
products.parquet saraNour/compliments-brand/source_of_truth/ Authoritative product catalog (4,440 × 16)
product_group_mapping.csv Phase 3 output Product → group mapping (4,440 × 28)
product_variant_mapping.parquet Phase 4 output Product → variant mapping (4,440 × 11)

Outputs

All production data tables are Parquet.

product_group_mapping.parquet

  • Phase 3 mapping converted to Parquet for downstream consumption
  • 4,440 rows × 28 columns

nutrition_cleaned.parquet

  • Cleaned/validated nutrition source
  • Original values preserved
  • Quality flags included
  • 4,440 rows × 23 columns

product_nutrition_mapping.parquet

  • Product ↔ nutrition mapping with group_id and variant_id
  • 4,440 rows × 27 columns

nutrition_per_100g.parquet

  • Normalized per-100g nutrition values
  • Conversion/provenance metadata
  • 4,440 rows × 24 columns

phase5_statistics.parquet

  • Summary metrics

phase5_validation.parquet

  • Validation checks and results

Matching Strategy

Primary key: external_id (exact match) Confidence: HIGH Coverage: 100% (4,440/4,440)

No fallback matching was needed. The relationship is 1:1.

Normalization Strategy

Nutrition values are normalized to per-100g only when a reliable serving-size conversion is possible.

Method Count Description
Direct 100g/mL 150 serving_size is 100 g or 100 mL
Scaled to 100g/mL 1,351 Serving size in g or mL, scaled proportionally
Not normalized 2,939 Ambiguous serving unit or missing serving_size

Why Not Normalized?

  • Missing serving_size (1,505 records): Non-food products (light bulbs, gloves, etc.)
  • Ambiguous serving unit (1,434 records): tbsp, cup, tsp, slices, pieces, etc. — no reliable gram equivalent available
  • No fabrication: We do NOT invent serving weight conversions

Suspicious Value Policy

Suspicious values are flagged, not overwritten.

Field Threshold Records Flagged
calories > 1000 1
fat_g > 100 1
protein_g > 100 1

Original values are preserved in nutrition_cleaned.parquet with nutrition_quality_status = 'SUSPICIOUS'.

Quality Status

Status Count Description
VALID 2,932 Normal nutrition values
MISSING 1,505 All 17 nutrition fields null (non-food products)
SUSPICIOUS 3 Out-of-range values flagged

Validation

Rule Description Result
1 All products have nutrition match PASS
2 No duplicate external_ids PASS
3 No duplicate matches PASS
4 Suspicious values preserved PASS
5 No fabricated values PASS
6 No external data used PASS
7 All outputs Parquet PASS
8 No LLM used PASS
Overall PASS

Traceability 

products.parquet (source of truth)
    ↓
nutrition.parquet (source of truth)
    ↓
Phase 5: nutrition_cleaned.parquet
    ↓
Phase 5: product_nutrition_mapping.parquet
    ↓ (group_id from Phase 3, variant_id from Phase 4)
Phase 5: nutrition_per_100g.parquet
    ↓
Phase 6: Nutrition Scoring

Limitations

  1. Per-100g normalization is partial — only possible for ~34% of records (1,501/4,440)
  2. Serving size parsing is limited — complex formats like "1 cup (250 mL)" are not parsed
  3. Suspicious values are flagged, not corrected — requires domain expert review
  4. Non-food products have no nutrition — 1,505 records with all-null nutrition fields

Files 

phase5/
├── README.md
├── src/
│   └── phase5.py
├── notebooks/
│   ├── phase5.ipynb
│   └── phase5_executed.ipynb
├── outputs/
│   ├── product_group_mapping.parquet
│   ├── nutrition_cleaned.parquet
│   ├── product_nutrition_mapping.parquet
│   ├── nutrition_per_100g.parquet
│   ├── phase5_statistics.parquet
│   └── phase5_validation.parquet
├── validation/
│   └── phase5_validation.json
└── statistics/
    └── phase5_statistics.json

How to Run 

cd /home/sara/gsoc/compliments-reference-db
python -m phase5.src.phase5

LLM Usage

None. All matching is deterministic by external_id.

External Datasets

None. Only authoritative nutrition source used.

Generated by Phase 5 Production Pipeline Date: 2026-07-30
Downloads last month
69
Total file size:
8.18 MB