From 8f159988ccfd6fa83eeeefaed14e1bef075a4328 Mon Sep 17 00:00:00 2001 From: Nehil Sood Date: Thu, 4 Jun 2026 12:08:13 +0000 Subject: [PATCH 1/2] Update tools for agent efficacy wrapper --- .../agent_efficacy/Agent_Efficacy_Board.html | 307 ++++++++++ tools/agent_efficacy/EFFICACY_GUIDE.md | 71 +++ tools/agent_efficacy/USAGE_EXAMPLES.md | 44 ++ tools/agent_efficacy/calculate_efficacy.py | 213 +++++++ .../metrics/pvmap_generator_metrics.py | 245 ++++++++ tools/agentic_import/pvmap_generator.py | 9 + tools/agentic_import/run_statvar_processor.sh | 34 ++ .../templates/generate_pvmap_prompt.j2 | 1 + tools/statvar_importer/mcf_diff.py | 20 +- tools/statvar_importer/stat_var_processor.py | 11 +- .../statvar_importer/stat_var_test_runner.py | 524 ++++++++++++++++++ tools/statvar_importer/words_allowlist.txt | 2 + 12 files changed, 1475 insertions(+), 6 deletions(-) create mode 100644 tools/agent_efficacy/Agent_Efficacy_Board.html create mode 100644 tools/agent_efficacy/EFFICACY_GUIDE.md create mode 100644 tools/agent_efficacy/USAGE_EXAMPLES.md create mode 100644 tools/agent_efficacy/calculate_efficacy.py create mode 100644 tools/agentic_import/metrics/pvmap_generator_metrics.py create mode 100644 tools/statvar_importer/stat_var_test_runner.py diff --git a/tools/agent_efficacy/Agent_Efficacy_Board.html b/tools/agent_efficacy/Agent_Efficacy_Board.html new file mode 100644 index 0000000000..3d758b0ab2 --- /dev/null +++ b/tools/agent_efficacy/Agent_Efficacy_Board.html @@ -0,0 +1,307 @@ + + + + + + Autoschematization Agent Evaluation + + + + +
+ + +
+
+
+

Autoschematization Agent Evaluation

+
+ +
+ + Run ID: DESA-GENDER_2025_OBS_ICT_SKILL_RT +
+
+ + Target Payload: 1M Observations +
+
+ + Pipeline Status: Audit Complete +
+
+
+
+
+ +
+ +
+
+ + +
+
+
+

Agent Efficacy (F1)

+

(Ultimate mapping reliability)

+
+

100.0%

+
+
+
+

+ Formula: 2*(P*R)/(P+R) +

+
+
+ + +
+
+

Precision

+

(Accuracy of generations)

+
+

100.0%

+
+
+
+

+ Formula: TP / (TP + FP) +

+
+
+ + +
+
+

Recall

+

(Capture rate of targets)

+
+

100.0%

+
+
+
+

+ Formula: TP / (TP + FN) +

+
+
+ + +
+
+

Time to Ingest

+

(Total processing speed)

+
+

14m 22s

+
+
+
+

+ Invocation to first query. +

+
+
+ + +
+
+

Completion Rate

+

(Pipeline execution health)

+
+

100%

+
+
+
+

+ Syntactical pipeline success. +

+
+
+ +
+
+ + +
+

Granular Mapping Analysis

+ +
+ + +
+
+ +
+
+ +
+ +

True Positives

+

(Mappings done by the Agent correctly)

+ +

384

+
+
+ + +
+
+ +
+
+ +
+ +

False Positives

+

(Wrong mappings / Hallucinations)

+ +

0

+
+
+ + +
+
+ +
+
+ +
+ +

False Negatives

+

(Mappings missed by the Agent)

+ +

0

+
+
+ +
+
+
+ +
+ +

Detailed File Comparisons

+
+

pvmap.csv Comparison

+
+
+ Precision + 100.0% +
+
+ Recall + 100.0% +
+
+ F1 Score + 100.0% +
+
+
+
mcf_diff.py output
+ 
+No differences found.
+
+
+
+ +
+

stat_vars.mcf Comparison

+
+
+ Precision + 100.0% +
+
+ Recall + 100.0% +
+
+ F1 Score + 100.0% +
+
+
+
mcf_diff.py output
+ 
+No differences found.
+
+
+
+ +
+

stat_vars_schema.mcf Comparison

+
+
+ Precision + 100.0% +
+
+ Recall + 100.0% +
+
+ F1 Score + 100.0% +
+
+
+
mcf_diff.py output
+ 
+No differences found.
+
+
+
+ +
+

tmcf Comparison

+
+
+ Precision + 100.0% +
+
+ Recall + 100.0% +
+
+ F1 Score + 100.0% +
+
+
+
mcf_diff.py output
+ 
+No differences found.
+
+
+
+ +
+

csv_data Comparison

+
+
+ Precision + 100.0% +
+
+ Recall + 100.0% +
+
+ F1 Score + 100.0% +
+
+
+
mcf_diff.py output
+ 
+No differences found.
+
+
+
+
+ \ No newline at end of file diff --git a/tools/agent_efficacy/EFFICACY_GUIDE.md b/tools/agent_efficacy/EFFICACY_GUIDE.md new file mode 100644 index 0000000000..edd1c76ac0 --- /dev/null +++ b/tools/agent_efficacy/EFFICACY_GUIDE.md @@ -0,0 +1,71 @@ +# Agent Efficacy Testing Guide + +The efficacy calculation pipeline (`calculate_efficacy.py`) evaluates the quality of the autoschematization agent by comparing its output predictions against human-reviewed "gold standard" directories. It leverages semantic graph comparisons (`mcf_diff.py`) to accurately compute metrics like **Precision, Recall, and the F1 Score**. + +--- + +### 1. Understanding the Evaluated Files + +The script compares four primary output artifacts generated by the agent against their reviewed counterparts. It computes True Positives (TP), False Positives (FP), and False Negatives (FN) to determine the overall agent efficacy. + +* **`output_pvmap.csv` (Primary Efficacy Metric):** + * **What it is:** The Property-Value (PV) mapping file that links dataset column headers to Data Commons schema nodes. + * **How it's tested:** This is processed using the `PropertyValueMapper`. The script extracts a normalized semantic graph and calculates the "Hero Metrics" (Precision, Recall, F1) that define the overall run's success. +* **`output_stat_vars.mcf`:** + * **What it is:** Defines the statistical variables extracted from the dataset. + * **How it's tested:** The script uses **fingerprinting** (`use_fingerprint=True`). This means it evaluates if the core semantic definition (properties and values) of the StatVar is correct, even if the exact node ID (DCID) strings have slight, functionally irrelevant differences. +* **`output_stat_vars_schema.mcf` & `output.tmcf`:** + * **What it is:** Extends the schema definitions and dictates the tabular template bindings. + * **How it's tested:** Compared as standard MCF graphs to ensure the underlying structure matches the gold standard perfectly. + +--- + +### 2. Testing a Single Dataset + +The `calculate_efficacy.py` script uses command-line arguments to accept the required input and output paths dynamically. You should run this script within the project's virtual environment. + +**Command Line Arguments:** +* `--test`: Path to the test (prediction) directory. +* `--gold`: Path to the gold (reviewed) directory. +* `--output`: Path to the directory where the efficacy results dashboard should be saved. +* `--dataset_id` (Optional): The dataset ID for display purposes in the HTML dashboard. + +**Example execution:** +```bash +source venv/bin/activate +python tools/agent_efficacy/calculate_efficacy.py \ + --test test_DESA-GENDER_2025_OBS_ICT_SKILL_RT \ + --gold undata/DESA/output/reviewed_pvmap_harish/DESA-GENDER_2025_OBS_ICT_SKILL_RT \ + --output undata/DESA/efficacy_results/DESA-GENDER_2025_OBS_ICT_SKILL_RT \ + --dataset_id DESA-GENDER_2025_OBS_ICT_SKILL_RT +``` +**Results:** Open the generated dashboard (`Agent_Efficacy_Board.html` inside your `--output` directory) in your web browser. + +--- + +### 3. Testing in Bulk (Multiple Datasets) + +To evaluate the agent across multiple datasets simultaneously, you can use the built-in `--bulk` flag. When this flag is passed, the `--test` and `--gold` arguments are treated as the parent directories containing all the datasets. + +**Example Bulk Run Execution:** + +```bash +# Ensure virtual environment is active +source venv/bin/activate + +python tools/agent_efficacy/calculate_efficacy.py \ + --bulk \ + --test undata/DESA/output/reviewed \ + --gold undata/DESA/output/unreviewed \ + --output undata/DESA/efficacy_results/bulk_run +``` + +The script will automatically detect datasets and generate a unique run directory (e.g., `bulk_run_20260601_120000`) inside your `--output` path. This directory will contain a separate HTML dashboard folder for every evaluated dataset, along with an aggregated `summary.csv` file containing the F1, Precision, Recall, TP, FP, and FN scores for all datasets in that run. + +--- + +### 4. Interpreting the Output + +The HTML dashboard (`Agent_Efficacy_Board.html`) gives you a visual breakdown of the metrics: +* **Hero Metrics (Top Section):** Represents the accuracy of the `pvmap.csv`. A high F1 score here means the agent successfully mapped columns to the correct semantic properties. +* **Detailed Semantic Comparisons:** Look here to see the specific True Positives, False Positives (incorrect mappings), and False Negatives (missed mappings). The dashboard will output raw MCF node diffs to show you exactly *where* the agent deviated from the reviewed standard for `pvmap`, `stat_vars`, `schema`, and `tmcf`. \ No newline at end of file diff --git a/tools/agent_efficacy/USAGE_EXAMPLES.md b/tools/agent_efficacy/USAGE_EXAMPLES.md new file mode 100644 index 0000000000..916b9063c0 --- /dev/null +++ b/tools/agent_efficacy/USAGE_EXAMPLES.md @@ -0,0 +1,44 @@ +# Efficacy Tool Quick-Start Examples + +This guide provides sample commands for running the `calculate_efficacy.py` script in both single and bulk modes. + +## 1. Single Dataset Evaluation +Use this command to compare a single agent-generated folder against a reviewed gold standard. + +### Sample Command +```bash +python3 tools/agent_efficacy/calculate_efficacy.py \ + --test /usr/local/google/home/nehil/datacommons/import/git/data/test_DESA-GENDER_2025_OBS_ICT_SKILL_RT \ + --gold /usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/output/reviewed_pvmap_harish/DESA-GENDER_2025_OBS_ICT_SKILL_RT \ + --output /usr/local/google/home/nehil/datacommons/import/git/data/tmp/efficacy_results/single_run \ + --dataset_id ICT_SKILL_RT +``` + +### Result +- Dashboard: `/tmp/efficacy_results/single_run/Agent_Efficacy_Board.html` +- Updates: Precision, Recall, and F1 will be correctly populated in the HTML. + +--- + +## 2. Bulk Evaluation (Multiple Datasets) +Use this command to evaluate all datasets in a directory. It will create a unique, timestamped folder for the run. + +### Sample Command +```bash +python3 tools/agent_efficacy/calculate_efficacy.py \ + --bulk \ + --test /usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/output/agent_predictions \ + --gold /usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/output/reviewed_pvmap_harish \ + --output /tmp/efficacy_results/bulk_runs +``` + +### Result +- Output Directory: `/tmp/efficacy_results/bulk_runs/bulk_run_20260602_HHMMSS/` +- Summary File: `summary.csv` inside the new run folder. +- Dashboards: Individual `Agent_Efficacy_Board.html` files for every dataset found. + +--- + +## 3. How to Present Results +1. **HTML Dashboard:** Open the `Agent_Efficacy_Board.html` file in any browser to view the "Hero Metrics" and detailed semantic diffs. +2. **Summary CSV:** Use the `summary.csv` generated during bulk runs to create a high-level report or table of performance across all indicators. diff --git a/tools/agent_efficacy/calculate_efficacy.py b/tools/agent_efficacy/calculate_efficacy.py new file mode 100644 index 0000000000..f322624f0e --- /dev/null +++ b/tools/agent_efficacy/calculate_efficacy.py @@ -0,0 +1,213 @@ +import os +import sys +import re +import csv +import argparse +import datetime + +# Set up paths to import tools +PROJECT_ROOT = '/usr/local/google/home/nehil/datacommons/import/git/data' +sys.path.append(os.path.join(PROJECT_ROOT, 'tools/statvar_importer')) +sys.path.append(os.path.join(PROJECT_ROOT, 'util')) +sys.path.append(os.path.join(PROJECT_ROOT, 'tools/agentic_import/metrics')) + +import mcf_diff +from counters import Counters +from property_value_mapper import PropertyValueMapper +from pvmap_generator_metrics import PVMapGeneratorMetricsRunner + +def get_metrics_from_counters(counters_obj): + # Use metrics formula directly from pvmap_generator_metrics.py + diff_stats = {'counters': counters_obj.get_counters()} + stats = PVMapGeneratorMetricsRunner.get_stats_from_diff_counters(None, diff_stats) + + return { + 'tp': stats.get('true_positive', 0), + 'fp': stats.get('false_positive', 0), + 'fn': stats.get('false_negative', 0), + 'precision': stats.get('precision', 0), + 'recall': stats.get('recall', 0), + 'f1': stats.get('f1', 0) + } + +def load_pv_map_nodes(file_path): + """Loads PV map into MCF-like nodes using PropertyValueMapper normalization.""" + pv_mapper = PropertyValueMapper() + pv_mapper.load_pvs_from_file(file_path) + # Get the raw GLOBAL map + raw_map = pv_mapper.get_pv_map().get('GLOBAL', {}) + # Convert to standard node format: {dcid: {prop: val}} + nodes = {} + for key, pvs in raw_map.items(): + # Ensure DCID is clean + nodes[key] = pvs + return nodes + +def run_comparison(pred_path, gold_path, is_pvmap=False, use_fingerprint=False): + if not os.path.exists(pred_path) or not os.path.exists(gold_path): + print(f"Skipping: missing {pred_path} or {gold_path}") + return "", {'tp': 0, 'fp': 0, 'fn': 0, 'precision': 0, 'recall': 0, 'f1': 0} + + counters = Counters() + config = { + 'show_diff_nodes_only': True, + 'ignore_property': ['description', 'provenance', 'memberOf', 'member', 'name', 'constraintProperties', 'keyString', 'relevantVariable'], + 'fingerprint_dcid': use_fingerprint + } + + if is_pvmap: + # Specialized loading for PV Maps to handle wide vs narrow formats + nodes1 = load_pv_map_nodes(pred_path) + nodes2 = load_pv_map_nodes(gold_path) + print(f" [PVMap] Loaded {len(nodes1)} nodes from pred, {len(nodes2)} from gold") + diff_text = mcf_diff.diff_mcf_nodes(nodes1, nodes2, config, counters) + else: + # Standard MCF loading + diff_text = mcf_diff.diff_mcf_files(pred_path, gold_path, config, counters) + + metrics = get_metrics_from_counters(counters) + return diff_text, metrics + +def update_html(template_content, dataset_id, hero_metrics, detailed_results): + content = template_content + for key, label in [('f1', 'Agent Efficacy (F1)'), ('precision', 'Precision'), ('recall', 'Recall')]: + escaped_label = re.escape(label) + content = re.sub( + rf'({escaped_label}.*?tracking-tight(?:er)?">)([\d\.]+%)(

)', + r'\g<1>' + f"{hero_metrics[key]*100:.1f}%" + r'\g<3>', + content, flags=re.DOTALL + ) + + content = re.sub(r'(True Positives.*?tracking-tighter">)([\d,]+)(

)', r'\g<1>' + f"{hero_metrics['tp']:,}" + r'\g<3>', content, flags=re.DOTALL) + content = re.sub(r'(False Positives.*?tracking-tighter">)([\d,]+)(

)', r'\g<1>' + f"{hero_metrics['fp']:,}" + r'\g<3>', content, flags=re.DOTALL) + content = re.sub(r'(False Negatives.*?tracking-tighter">)([\d,]+)(

)', r'\g<1>' + f"{hero_metrics['fn']:,}" + r'\g<3>', content, flags=re.DOTALL) + content = re.sub(r'Run ID: .*?', f'Run ID: {dataset_id} (Rechecked)', content) + + if '
' in content: + content = content.split('
')[0] + + diff_sections = '

Detailed Semantic Comparisons

' + for label, data in detailed_results.items(): + diff_sections += f''' +
+

{label}

+
+
Precision{data['metrics']['precision']*100:.1f}%
+
Recall{data['metrics']['recall']*100:.1f}%
+
F1 Score{data['metrics']['f1']*100:.1f}%
+
+
Semantic Match (TP: {data['metrics']['tp']} | FP: {data['metrics']['fp']} | FN: {data['metrics']['fn']})
+ 
{data['diff'] if data['diff'] else 'No differences found.'}
+
+ ''' + diff_sections += '
' + return content.replace('', diff_sections + '') if '' in content else content + diff_sections + +def process_single_dataset(test_dir, gold_dir, output_dir, dataset_id, template_content): + if not os.path.exists(output_dir): os.makedirs(output_dir) + + file_mappings = [ + ('pvmap.csv', 'output_pvmap.csv', 'output_pvmap.csv', True, False), + ('stat_vars.mcf', 'output_stat_vars.mcf', 'output_stat_vars.mcf', False, True), + ('stat_vars_schema.mcf', 'output_stat_vars_schema.mcf', 'output_stat_vars_schema.mcf', False, False), + ('tmcf', 'output.tmcf', 'output.tmcf', False, False), + ] + + detailed_results = {} + total_tp = 0 + total_fp = 0 + total_fn = 0 + + print(f"\n--- Rechecking Efficacy for {dataset_id} ---") + for label, pred_name, gold_name, is_pv, use_fp in file_mappings: + print(f"Comparing {label}...") + diff_text, metrics = run_comparison(os.path.join(test_dir, pred_name), os.path.join(gold_dir, gold_name), is_pvmap=is_pv, use_fingerprint=use_fp) + detailed_results[label] = {'diff': diff_text, 'metrics': metrics} + print(f" Result: F1={metrics['f1']:.1%}, TP={metrics['tp']}, FP={metrics['fp']}, FN={metrics['fn']}") + + total_tp += metrics['tp'] + total_fp += metrics['fp'] + total_fn += metrics['fn'] + + precision = total_tp / (total_tp + total_fp) if (total_tp + total_fp) > 0 else 0 + recall = total_tp / (total_tp + total_fn) if (total_tp + total_fn) > 0 else 0 + f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0 + + hero_metrics = { + 'tp': total_tp, + 'fp': total_fp, + 'fn': total_fn, + 'precision': precision, + 'recall': recall, + 'f1': f1 + } + + final_html = update_html(template_content, dataset_id, hero_metrics, detailed_results) + + with open(os.path.join(output_dir, 'Agent_Efficacy_Board.html'), 'w') as f: f.write(final_html) + return hero_metrics + +def main(): + parser = argparse.ArgumentParser(description="Calculate efficacy metrics.") + parser.add_argument('--test', required=True, help="Path to the test (prediction) directory.") + parser.add_argument('--gold', required=True, help="Path to the gold (reviewed) directory.") + parser.add_argument('--output', required=True, help="Path to the output directory to save results.") + parser.add_argument('--dataset_id', default="Dataset", help="Optional dataset ID for display.") + parser.add_argument('--bulk', action='store_true', help="If set, treats test and gold as parent directories containing multiple dataset folders.") + args = parser.parse_args() + + template_path = os.path.join(os.path.dirname(__file__), 'Agent_Efficacy_Board.html') + with open(template_path, 'r') as f: + template_content = f.read() + + if args.bulk: + run_id = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + args.output = os.path.join(args.output, f"bulk_run_{run_id}") + print(f"Starting Bulk Efficacy Calculation natively... Output: {args.output}") + summary_data = [] + if not os.path.exists(args.output): + os.makedirs(args.output) + + for dataset_id in os.listdir(args.gold): + gold_ds_dir = os.path.join(args.gold, dataset_id) + if not os.path.isdir(gold_ds_dir): + continue + + # Allow test directories to either match exactly or be prefixed with test_ + test_ds_dir = os.path.join(args.test, dataset_id) + if not os.path.isdir(test_ds_dir): + test_ds_dir = os.path.join(args.test, f"test_{dataset_id}") + + if os.path.isdir(test_ds_dir): + out_ds_dir = os.path.join(args.output, dataset_id) + try: + hero_metrics = process_single_dataset(test_ds_dir, gold_ds_dir, out_ds_dir, dataset_id, template_content) + if hero_metrics: + summary_data.append({ + 'dataset_id': dataset_id, + 'f1': hero_metrics['f1'], + 'precision': hero_metrics['precision'], + 'recall': hero_metrics['recall'], + 'tp': hero_metrics['tp'], + 'fp': hero_metrics['fp'], + 'fn': hero_metrics['fn'] + }) + except Exception as e: + print(f"Error processing {dataset_id}: {e}") + else: + print(f"Skipped: {dataset_id} (Matching test directory not found)") + + # Write summary.csv + if summary_data: + summary_path = os.path.join(args.output, 'summary.csv') + with open(summary_path, 'w', newline='') as f: + writer = csv.DictWriter(f, fieldnames=['dataset_id', 'f1', 'precision', 'recall', 'tp', 'fp', 'fn']) + writer.writeheader() + writer.writerows(summary_data) + print(f"\nBulk run complete. Summary saved to {summary_path}") + else: + process_single_dataset(args.test, args.gold, args.output, args.dataset_id, template_content) + print(f"\nRecheck complete. Results saved to {args.output}") + +if __name__ == "__main__": + main() diff --git a/tools/agentic_import/metrics/pvmap_generator_metrics.py b/tools/agentic_import/metrics/pvmap_generator_metrics.py new file mode 100644 index 0000000000..8312cc71d1 --- /dev/null +++ b/tools/agentic_import/metrics/pvmap_generator_metrics.py @@ -0,0 +1,245 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the 'License'); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an 'AS IS' BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Utilities to generate metrics for PV map generation for statvar imports. +""" + +import os +import sys +import subprocess +import tempfile +import time + +from absl import app +from absl import flags +from absl import logging + +_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(_SCRIPT_DIR) +sys.path.append(os.path.dirname(_SCRIPT_DIR)) +sys.path.append(os.path.dirname(os.path.dirname(_SCRIPT_DIR))) +sys.path.append( + os.path.join( + os.path.dirname(os.path.dirname(os.path.dirname(_SCRIPT_DIR))), 'util')) +sys.path.append( + os.path.join(os.path.dirname(os.path.dirname(_SCRIPT_DIR)), + 'statvar_importer')) + +import file_util +from counters import Counters +from mcf_diff import diff_mcf_files +from mcf_file_util import get_value_list +from stat_var_test_runner import StatVarProcessorTestRunner, run_script + + +class PVMapGeneratorMetricsRunner(StatVarProcessorTestRunner): + """Class to generate metrics for PV map generation for statvar imports. + + The metrics are generated by running the statvar processor with the test + config and comparing the output with the expected output. + The files for pvmap egenration are specified in the test config as follows: + "test_name": "", + ... + "pvmap_generator_test": { + "pvmap_generator_args": { + "input_data": "", + "output_data": "", + }, + "expected_output_files": [ + "", + "", + ... + ], + } + + """ + + def __init__(self, test_config_file: str = None, test_config: dict = None): + super().__init__(test_config_file, test_config) + + def run_pvmap_generator(self, test_config: dict) -> dict: + """Runs a single instance of pvmap generator.""" + test_name = test_config.get('test_name', 'pvmap_generator_test') + test_dir = os.path.join(self._temp_dir.name, test_name) + output_dir = os.path.join(test_dir, 'generated') + logging.info( + f'Running pvmap generator for test {test_name}, output in {output_dir}, config: {test_config}' + ) + env_dict = dict(self.get_env_dict()) + env_dict.update(get_env_dict(test_config.get("env_file"))) + # Build the pvmap generator commandline + cmd_args = dict() + # Add default arguments for output + + cmd_args = merged_args([ + { + '--output_path': output_dir + }, + test_config.get('pvmap_generator_args', {}), + ]) + output_arg = get_arg(cmd_args, '--output_path') + if output_arg: + output_dir = os.path.realpath(output_arg) + if not output_dir.endswith('/'): + output_dir = os.path.dirname(output_dir) + + cwd = self.get_local_dir() + script_status = run_script( + interpreter=sys.executable, + script=os.path.join(os.path.dirname(_SCRIPT_DIR), + 'pvmap_generator.py'), + args=cmd_args, + cwd=cwd, + env=env_dict, + output_dir=output_dir, + log_dir=os.path.join(output_dir, 'debug'), + ) + + # Check for generated files + generated_config = { + 'pv_map': os.path.join(output_dir, 'generated_pvmap.csv'), + 'config_file': os.path.join(output_dir, 'generated_metadata.csv'), + } + script_status['generated_config'] = generated_config + for arg, file in generated_config.items(): + if not os.path.exists(file): + logging.error( + f'Failed to generate {file} for test: {test_name}') + script_status['status'] = 'FAIL' + + return script_status + + def run_sample_statvar_processor(self, test_config: dict, + generated_config: dict) -> dict: + """Run statvar processor on the sample input using the generated configs.""" + test_name = test_config.get('test_name', 'sample_statvar_processor') + test_dir = os.path.join(self._temp_dir.name, test_name) + output_dir = os.path.join(test_dir, 'sample') + env_dict = dict(self.get_env_dict()) + env_dict.update(get_env_dict(test_config.get("env_file"))) + + # Get commandline args for stavtar processor + # to use generated pvmap files. + cmd_args = merged_args([ + generated_config, + { + '--output_path': + output_dir, + '--output_data_pvs': + os.path.join(output_dir, 'output_data_pvs.csv'), + }, + ]) + script_status = self.run_statvar_processor(test_config, cmd_args) + + return script_status + + def generate_metrics(self, test_config: dict, test_status: dict) -> dict: + """Generates metrics for a test config. + + Args: + test_config: Dictionary with the test config. + + Returns: + Dictionary with the metrics. + """ + test_name = test_config.get('test_name', 'sample_statvar_processor') + test_dir = os.path.join(self._temp_dir.name, test_name) + outputs = test_config.get('pvmap_generator_outputs') + output_stats = self.verify_outputs(outputs, test_config, + test_status.get('output_dir')) + stats = {} + for output_name, stats in output_stats: + output_stats = output.get('counters', {}) + output_stats = self.get_stats_from_diff_counters(output_stats) + file_util.file_write_csv_dict( + output_stats, + os.path.join(test_dir, output_name + '-diff-counters.csv')) + stats[output_name] = output_stats + logging.info(f'Diff stats for {output_name}: {output_stats}') + + return stats + + def get_stats_from_diff_counters(self, + diff_stats: dict, + stats: dict = None) -> dict: + """Returns precision and recall stats from diff counters. + + Args: + diff_stats: diff counters for diff between actual and expected output files. + stats: output stats dictionary into which stats are added. + + """ + if stats is None: + stats = dict({ + 'false_positive': 0, + 'false_negative': 0, + 'true_positive': 0, + }) + counters = diff_stats.get('counters', {}) + matched = counters.get('pvs-matched', 0) + false_negative = counters.get('pvs-added', 0) + false_positive = counters.get('pvs-deleted', 0) + # Treat modfied as deleted+added + modified = counters.get('pvs-modified', 0) + false_negative += modified + false_positive += modified + + stats['true_positive'] += matched + stats['false_positive'] += false_positive + stats['false_negative'] += false_negative + + # Compute precision and recall + true_positive = stats['true_positive'] + precision = true_positive / (max( + true_positive + stats['false_positive'], 1)) + recall = true_positive / (max(true_positive + stats['false_negative'], + 1)) + + stats['precision'] = precision + stats['recall'] = recall + stats['f1'] = 2 * (precision * recall) / max(precision + recall, 1) + + return stats + + def run_test(self, test_output: str = None) -> dict: + """Runs the pvmap generator and returns the comparison metrics. + + Args: + test_output: JSON file with the test output status + + Returns: + JSON dict with the test summary + """ + cwd = self.get_local_dir() + os.chdir(cwd) + return_status = {'status': 'PASS'} + for index, test_config in enumerate(self._test_config): + test_status = {} + test_name = test_config.get('test_name') + if not test_name: + test_name = f'pvmap-generator-test-{index}' + basedir = os.path.basename(self.get_local_dir()) + if basedir: + test_name += '-' + basedir + test_config['test_name'] = test_name + logging.info(f'Running pvmap generator test: {index}:{test_name}') + pvmap_generator_status = self.run_pvmap_generator(test_config) + test_status['pvmap_generator_status'] = pvmap_generator_status + + logging.info( + f'Running statvar processor for test: {index}:{test_name}') + statvar_status = self.run_sample_statvar_processor( + test_config, pvmap_generator_status.get('generated_config', {})) + + test_status['statvar_processor_status'] = statvar_status + diff --git a/tools/agentic_import/pvmap_generator.py b/tools/agentic_import/pvmap_generator.py index 4b3d457ae8..0ca9f67ff0 100644 --- a/tools/agentic_import/pvmap_generator.py +++ b/tools/agentic_import/pvmap_generator.py @@ -81,6 +81,10 @@ def _define_flags(): 'Output path prefix for all generated files (default: output/output)' ) + flags.DEFINE_string( + 'places_resolved_csv', None, + 'Path to a CSV file with resolved places (optional)') + flags.DEFINE_string( 'gemini_cli', 'gemini', 'Custom path or command to invoke Gemini CLI. ' @@ -118,6 +122,7 @@ class Config: skip_confirmation: bool = False enable_sandboxing: bool = False output_path: str = 'output/output' + places_resolved_csv: Optional[str] = None gemini_cli: Optional[str] = None working_dir: Optional[str] = None extra_instruction_files: List[str] = field(default_factory=list) @@ -478,6 +483,9 @@ def _generate_prompt(self) -> Path: 'extra_instruction_files_abs': [ str(path) for path in self._config.extra_instruction_files ] if self._config.extra_instruction_files else [], + 'places_resolved_csv_abs': + str(self._resolve_path(self._config.places_resolved_csv)) + if self._config.places_resolved_csv else "", } # Render template with these variables @@ -507,6 +515,7 @@ def prepare_config() -> Config: skip_confirmation=_FLAGS.skip_confirmation, enable_sandboxing=_FLAGS.enable_sandboxing, output_path=_FLAGS.output_path, + places_resolved_csv=_FLAGS.places_resolved_csv, gemini_cli=_FLAGS.gemini_cli, working_dir=_FLAGS.working_dir, extra_instruction_files=_FLAGS.extra_instruction_files or [], diff --git a/tools/agentic_import/run_statvar_processor.sh b/tools/agentic_import/run_statvar_processor.sh index 6cddf9224f..d42ef5f16b 100755 --- a/tools/agentic_import/run_statvar_processor.sh +++ b/tools/agentic_import/run_statvar_processor.sh @@ -26,6 +26,7 @@ WORKING_DIR="" INPUT_DATA="" GEMINI_RUN_ID="" OUTPUT_PATH="" +PLACES_RESOLVED_CSV="" while [[ $# -gt 0 ]]; do case $1 in @@ -33,26 +34,58 @@ while [[ $# -gt 0 ]]; do PYTHON_INTERPRETER="$2" shift 2 ;; + --python=*) + PYTHON_INTERPRETER="${1#*=}" + shift + ;; --script-dir) SCRIPT_DIR="$2" shift 2 ;; + --script-dir=*) + SCRIPT_DIR="${1#*=}" + shift + ;; --working-dir) WORKING_DIR="$2" shift 2 ;; + --working-dir=*) + WORKING_DIR="${1#*=}" + shift + ;; --input-data) INPUT_DATA="$2" shift 2 ;; + --input-data=*) + INPUT_DATA="${1#*=}" + shift + ;; --gemini-run-id) GEMINI_RUN_ID="$2" shift 2 ;; + --gemini-run-id=*) + GEMINI_RUN_ID="${1#*=}" + shift + ;; --output-path) OUTPUT_PATH="$2" shift 2 ;; + --output-path=*) + OUTPUT_PATH="${1#*=}" + shift + ;; + --places_resolved_csv) + PLACES_RESOLVED_CSV="$2" + shift 2 + ;; + --places_resolved_csv=*) + PLACES_RESOLVED_CSV="${1#*=}" + shift + ;; *) echo "Unknown option $1" exit 1 @@ -96,6 +129,7 @@ echo "Running statvar processor..." --skip_constant_csv_columns=False \ --output_columns="${OUTPUT_COLUMNS}" \ --output_counters="${OUTPUT_COUNTERS}" \ + --places_resolved_csv="${PLACES_RESOLVED_CSV}" \ --output_path="${OUTPUT_PREFIX}" > "${PROCESSOR_LOG}" 2>&1 # Capture the processor exit code diff --git a/tools/agentic_import/templates/generate_pvmap_prompt.j2 b/tools/agentic_import/templates/generate_pvmap_prompt.j2 index 81af2afc63..ae4bda42f9 100644 --- a/tools/agentic_import/templates/generate_pvmap_prompt.j2 +++ b/tools/agentic_import/templates/generate_pvmap_prompt.j2 @@ -1403,6 +1403,7 @@ For SDMX datasets, also ensure: --working-dir "{{working_dir_abs}}" \ --input-data "{{input_data_abs}}" \ --gemini-run-id "{{gemini_run_id}}" \ + --places_resolved_csv "{{places_resolved_csv_abs}}" \ --output-path "{{output_path_abs}}" ``` diff --git a/tools/statvar_importer/mcf_diff.py b/tools/statvar_importer/mcf_diff.py index e7eaa825f8..d0d7dd5831 100644 --- a/tools/statvar_importer/mcf_diff.py +++ b/tools/statvar_importer/mcf_diff.py @@ -190,10 +190,11 @@ def diff_mcf_node_pvs(node_1: dict, pvs_deleted = set() pvs_added = set() pvs_modified = set() + pvs_matched = set() for d in diff: diff_str.append(d) - if d[0] == ' ': - counters and counters.add_counter(f'PVs-matched', 1) + if d[0] == ' ' and len(d) > 1: + counters and counters.add_counter(f'pvs-matched', 1) else: prop = '' value = '' @@ -204,10 +205,16 @@ def diff_mcf_node_pvs(node_1: dict, if prop: pvs_deleted.add(prop) has_diff = True - if d[0] == '+': + elif d[0] == '+': if prop: pvs_added.add(prop) has_diff = True + elif d[0] == '?': + # Ignore modifications that already show as delete/add + has_diff = True + else: + if prop: + pvs_matched.add(prop) if has_diff: pvs_modified = pvs_deleted.intersection(pvs_added) pvs_added = pvs_added.difference(pvs_modified) @@ -217,6 +224,7 @@ def diff_mcf_node_pvs(node_1: dict, (pvs_modified, 'modified'), (pvs_added, 'added'), (pvs_deleted, 'deleted'), + (pvs_matched, 'matched'), ]: for prop in props: counters.add_counter(f'pvs-{diff_type}', 1) @@ -230,7 +238,9 @@ def diff_mcf_node_pvs(node_1: dict, else: counters and counters.add_counter(f'nodes-missing-in-mcf1', 1) else: - counters and counters.add_counter(f'nodes-matched', 1) + if counters: + counters.add_counter(f'nodes-matched', 1) + counters.add_counter(f'pvs-matched', len(pvs_matched)) return has_diff, '\n'.join(diff_str), pvs_added, pvs_deleted, pvs_modified @@ -389,4 +399,4 @@ def main(_): if __name__ == '__main__': - app.run(main) + app.run(main) \ No newline at end of file diff --git a/tools/statvar_importer/stat_var_processor.py b/tools/statvar_importer/stat_var_processor.py index 487cce556f..d0184e022c 100644 --- a/tools/statvar_importer/stat_var_processor.py +++ b/tools/statvar_importer/stat_var_processor.py @@ -83,7 +83,7 @@ from mcf_filter import drop_existing_mcf_nodes from mcf_diff import fingerprint_node, fingerprint_mcf_nodes, diff_mcf_node_pvs from place_resolver import PlaceResolver -from property_value_mapper import PropertyValueMapper +from property_value_mapper import PropertyValueMapper, write_pv_map from schema_resolver import SchemaResolver from json_to_csv import file_json_to_csv from schema_generator import generate_schema_nodes, generate_statvar_name @@ -1429,6 +1429,8 @@ def __init__( self._config.get('numeric_data_key', 'Number'), self._config.get('pv_lookup_key', 'Key'), ] + # Save per input cell PVs if required. + self._data_pvs = {} if self._config.get('output_data_pvs') else None def generate_pvmap(self): """Generate a PV Map from the input data.""" @@ -2272,6 +2274,8 @@ def process_row(self, row: list, row_index: int): row_index, col_index + 1) and self.process_stat_var_obs_pvs( merged_col_pvs, row_index, col_index): row_svobs += 1 + if self._data_pvs is not None: + self._data_pvs[self._file_context] = merged_col_pvs self.process_row_header_pvs(row, row_index, row_col_pvs, row_svobs, cols_with_pvs) # If row has no SVObs but has PVs, it must be a header. @@ -2818,6 +2822,11 @@ def write_outputs(self, output_path: str): columns=self._config.get('output_columns', []), output_tmcf_file=output_tmcf_file, ) + output_data_pvs = self._config.get('output_data_pvs') + if output_data_pvs and self._data_pvs: + write_pv_map(self._data_pvs, output_data_pvs) + self._counters.add_counter('output-data-pvs', len(self._data_pvs)) + self._counters.print_counters() counters_filename = self._config.get('output_counters', output_path + '_counters.txt') diff --git a/tools/statvar_importer/stat_var_test_runner.py b/tools/statvar_importer/stat_var_test_runner.py new file mode 100644 index 0000000000..ed9a6fe99b --- /dev/null +++ b/tools/statvar_importer/stat_var_test_runner.py @@ -0,0 +1,524 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the 'License'); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an 'AS IS' BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Utilities to test statvar imports. + +StatVar imports use a JSON test config for regression tests that invoke +statvar processor with a specified set of configs on a test input and +verifies the expected output files. +The test config is a list of statvar processor command line invocation +settings with each invocation as a dictionary with the following sections:a + test_name: name of the test for logs + env_file: a file with environment variables such as API keys + statvar_processor_args: dictionary with the command line arguments + for statvar processor configs. + expected_outputs: + A dictionary of expected output files mapped to output files. + + All file references are releative to the import folder with the + test config. + +Example: +[ + # Parameters for a single invocation of statvar processor + { + "test_name": "Test_Import", + + # Enviroment file loaded with dictionary of env settings + # such as API keys + "env_file": "gs://datcom-prod-imports/config/test_env.csv" + + # Statvar processor command line arguments + "statvar_processor_args": { + "config_file": "metadata.csv", + "pv_map": "import_pvmap.csv", + "places_resolved_csv": "places.csv", + "input_data": "test_data/sample_input.csv", + }, + + # Statvar processor output files with expected outputs to compare with + "statvar_processor_outputs": [ + { + "output_name": "Test_Import_Observations", + "output_file": "", + "expected_output_file": "test_data/sample_output.csv", + }, + ] + } +] + +""" + +import os +import sys +import subprocess +import tempfile +import time + +from absl import app +from absl import flags +from absl import logging + +_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(_SCRIPT_DIR) +sys.path.append(os.path.dirname(_SCRIPT_DIR)) +sys.path.append(os.path.dirname(os.path.dirname(_SCRIPT_DIR))) +sys.path.append( + os.path.join(os.path.dirname(os.path.dirname(_SCRIPT_DIR)), 'util')) + +import file_util +from mcf_diff import diff_mcf_files +from mcf_file_util import get_value_list + +from counters import Counters + +flags.DEFINE_string('test_config', '', 'JSON test config file') +flags.DEFINE_string('test_output', '', 'Output JSON file with test summary') + +_FLAGS = flags.FLAGS + +_BASH_PATH = os.environ.get('BASH_PATH', 'bash') + + +class StatVarProcessorTestRunner: + """Class to run statvar processor for a test config + """ + + def __init__(self, test_config_file: str = None, test_config: dict = None): + self._temp_dir = tempfile.TemporaryDirectory() + self._test_config = [] + if test_config: + self._test_config.extend(test_config) + self._env_dict = {} + self.load_config(test_config_file) + self._counters = Counters() + + def __del__(self): + """Cleanup local files.""" + self._temp_dir.cleanup() + + def load_config(self, test_config_file: str): + """Loads a test config from a JSON file.""" + if test_config_file: + file_config = file_util.file_load_py_dict(test_config_file) + logging.info(f'Loading test config: {file_config}') + self._test_config.extend(file_config) + self._test_config_file = os.path.realpath(test_config_file) + self._env_dict.update( + get_env_dict(self._test_config[0].get('env_file', ''))) + + def get_config(self) -> dict: + """Returns the test config.""" + return self._test_config + + def get_local_dir(self) -> str: + """Returns the local directory for the test.""" + if self._test_config_file: + return os.path.dirname(self._test_config_file) + return os.getcwd() + + def get_env_dict(self) -> dict: + """Returns the environment variables for the test.""" + return self._env_dict + + def run_tests(self, test_output: str = None) -> dict: + """Runs all the statvar processor test and verified each output. + + Args: + test_output: JSON file with the test output status + + Returns: + JSON dict with the test summary + """ + cwd = self.get_local_dir() + os.chdir(cwd) + logging.info( + f'Running statvar processor for {len(self._test_config)} tests from {cwd}' + ) + return_status = {'status': 'PASS'} + for index, test_config in enumerate(self._test_config): + test_name = test_config.get('test_name') + if not test_name: + test_name = f'statvar-processor-test-{index}' + basedir = os.path.basename(self.get_local_dir()) + if basedir: + test_name += '-' + basedir + test_config['test_name'] = test_name + logging.info(f'Running statvar processor test: {index}:{test_name}') + test_status = self.run_statvar_processor(test_config) + if test_status.get('returncode', 1) != 0: + logging.error( + f'statvar processor test failed for {test_name}: {test_status}' + ) + return_status['status'] = 'FAIL' + else: + outputs = test_config.get('statvar_processor_outputs') + if outputs: + output_status = self.verify_outputs( + outputs, test_config, test_status.get('output_dir')) + if output_status.get('status', '') != 'PASS': + logging.error( + f'Failed to verify outputs for test: {index}:{test_name}:{output_status}' + ) + return_status['status'] = 'FAIL' + test_status['output_status'] = output_status + return_status[test_name] = test_status + logging.info( + f'statvar processor test: {index}:{test_name}: {return_status["status"]}' + ) + if test_output: + file_util.file_write_py_dict(return_status, test_output) + return return_status + + def run_statvar_processor(self, + config: dict, + override_args: dict = {}) -> dict: + """Runs a single instance of statvar processor.""" + test_name = config.get('test_name', 'statvar_processor_test') + test_dir = os.path.join(self._temp_dir.name, test_name) + output_dir = os.path.join(test_dir, 'output') + env_dict = dict(self.get_env_dict()) + env_dict.update(get_env_dict(config.get("env_file"))) + # Build the statvar processor commandline + cmd_args = merge_args([ + { + '--output_path': '"{output_dir}"', + '--output_counters': '"{output_dir}/statvar_counters.json"', + }, + config.get('statvar_processor_args', {}), + override_args, + ]) + output_arg = get_arg(cmd_args, '--output_path') + if output_arg: + output_dir = os.path.realpath(output_arg) + if not output_dir.endswith('/'): + output_dir = os.path.dirname(output_dir) + cwd = self.get_local_dir() + script_status = run_script( + interpreter=sys.executable, + script=os.path.join(_SCRIPT_DIR, 'stat_var_processor.py'), + args=cmd_args, + cwd=cwd, + env=env_dict, + output_dir=output_dir, + log_dir=os.path.join(output_dir, 'debug'), + ) + script_status['output_dir'] = output_dir + return script_status + + def verify_outputs(self, outputs: list[dict], config: dict, + output_path: dir) -> dict: + """Compare actual and expected outputs. + + Args: + outputs: list of expected and actual outputs to be compared. + each item in the list is a dictionary: + { + output_name: (optional) name of the output for debug and logs + output_file: actual output file to be compared. + this is a local file or path relative to output_path + expected_output_file: path to expected file to be compared with + diff_config: (optional) dictionary with diff configs such as: + ignore_property: 'name' + } + + Returns: + dictionary with the overall status as well as a list + of status per output. + { + 'status': 'PASS' if there are no diffs else 'FAIL' + + # diff summary per output + '': { + + 'diff_status': 'MATCHED' or 'DELETED' or 'MODIFIED' + 'deleted': count of deleted nodes + 'modified': count of modified nodes + }, + } + """ + logging.info(f'Comparing outputs: {outputs}') + return_status = {'status': 'PASS'} + for index, output in enumerate(outputs): + expected_files = file_util.file_get_matching( + output.get('expected_output_file')) + if not expected_files: + logging.warning( + f'Ignoring config without expected file: {output}') + continue + output_name = output.get('name') + if not output_name: + output_name = f'output_{index}_{os.path.basename(expected_files[0])}' + if output_name in return_status: + output_name = output_name + '-' + str(index) + actual_files = output.get('output_file') + actuals = [] + for file in get_value_list(actual_files): + matching_files = file_util.file_get_matching( + os.path.join(output_path, file)) + if not matching_files: + matching_files = file_util.file_get_matching(file) + if matching_files: + actuals.extend(matching_files) + if not actuals: + logging.error(f'Missing outputs for {output}') + return_status['status'] = 'ERROR' + diff_status = diff_files( + actuals, expected_files, output.get('diff_config', {}), + os.path.join(output_path, f'{output_name}.diff')) + if diff_status.get('diff_status') != 'MATCHED': + return_status['status'] = 'FAIL' + logging.error( + f'Failed to match {index}:{output}, diff: {diff_status}') + return_status[output_name] = diff_status + return_status[output_name]['name'] = output_name + logging.info( + f'Verify outputs: {return_status.get("status")} for {outputs}') + return return_status + + +def merge_args(self, args: list[dict]) -> dict: + """Returns a dictionary of command line args from a list of args. + + Args: + args: list of dictionary of command line argument and valies: + [ + { 'arg1': 'value1', ...}, + { ...}, + ... + } + + Returns: + Dictionary of command line args with the value from the last arg. + """ + + if not isinstance(args, list): + args = list(args) + + # Merge arguments keeping the last value + merged_args = dict() + for args_dict in args: + for arg, value in args_dict.items(): + if value is not None and not arg.startswith('--'): + arg = '--' + arg + merged_args[arg] = value + + return merged_args + + +def get_args_list(self, args: dict) -> list: + """Returns a list of command line args.""" + cmd_args = [] + if isinstance(args, dict): + for arg, val in args.items(): + if val: + cmd_args.append(f'{arg}={val}') + else: + cmd_args.append(f'{arg}') + elif isinstance(args, list): + cmd_args.extend(args) + else: + cmd_args.append(args) + return cmd_args + + +def get_arg(self, args_dict: dict, arg: str, default=None) -> str: + """Returns the value of a specific arg if present.""" + return args_dict.get(arg, default) + + +def get_env_dict(filename: str) -> dict: + """Returns a dictionary of env variable settings from a file. + + Args: + filename: file with the environemnt settings. + It can be a csv, json, txt or shell file. + + Returns: + dictionary of env variable to values mappings. + """ + env_dict = {} + if isinstance(filename, dict): + # arguent is a dictionary of env variables. Use it as is. + return filename + + env_files = file_util.file_get_matching(filename) + for env_file in env_files: + _, file_ext = os.path.splitext(env_file) + if file_ext == '.sh' or file_ext == ".txt" or file_ext == '.env': + # File has one variable per line + with file_util.FileIO(env_file) as fp: + for line in fp.readlines(): + line = line.strip() + if line.startswith('#'): + # Ignore commented lines + continue + if '=' in line: + env_var, value = line.split('=', 1) + env_var = env_var.strip().strip('"') + value = value.strip().strip('"') + env_dict[env_var] = value + else: + # Assume the file is a dictionary + env_dict.update(file_util.file_load_py_dict(env_file)) + + logging.info(f'Loaded {len(env_dict)} env vars from {env_files}') + return env_dict + + +def run_script(interpreter: str, script: str, args: list | dict, cwd: str, + env: dict, output_dir: str, log_dir: str) -> dict: + """Run a commandline script as a child process + + Args: + interpreter: interpreter for the script such as python, bash + script: local or full path to the script file. + args: command line arguments for the script as a list + cwd: current directory for the script + env: dictionary of environment variables. + output_dir: fodler for output files generated by the script, if any. + log_dir: directory to store logs. + + Returns: + dictionary of command and status + """ + cmd_args = [] + if interpreter: + cmd_args.append(interpreter) + else: + if script and script.endswith('.py'): + # For python scripts use the current script's python binary + interpreter = sys.executable + if script and script.endswith('.sh'): + # For shell scripts use bash + interpreter = _BASH_PATH + if script: + cmd_args.append(script) + if args: + cmd_args.extend(get_args_list(args)) + + logging.info(f'Running command: {cmd_args} in {cwd}, env: {env}') + env_dict = dict(os.environ) + if env: + env_dict.update(env) + start_time = time.perf_counter() + process = subprocess.Popen( + cmd_args, + cwd=cwd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + env=env_dict, + ) + + # Log output continuously until the command completes. + stderr_file = os.path.join(log_dir, 'stderr.log') + stdout_file = os.path.join(log_dir, 'stdout.log') + with file_util.FileIO(stderr_file, 'wb') as f_err: + for line in process.stderr: + f_err.write(line) + logging.info(f'stderr: {line}') + with file_util.FileIO(stdout_file, 'wb') as f_out: + for line in process.stdout: + f_out.write(line) + logging.info(f'stdout: {line}') + + # Wait for process to complete + process.wait() + end_time = time.perf_counter() + return_code = process.returncode + duration = end_time - start_time + + logging.info( + f'Completed command: {cmd_args}, return code: {return_code}, time: {duration} secs, logs in: {log_dir}' + ) + + return { + 'command': ' '.join(cmd_args), + 'args': args, + 'cwd': cwd, + 'returncode': return_code, + 'output_path': output_dir, + 'debug': log_dir, + 'duration': duration, + 'stdout': stdout_file, + 'stderr': stderr_file, + } + + +def diff_files(actual: str, + expected: str, + diff_config: dict = {}, + diff_output_file: str = None) -> dict: + """Compares actual and expected files and returns dictionary with results. + + Args: + actual: list of actual files as csv or mcf. + expected: list of expected output files as csv or mcf + diff_config: dictionary of diff settings (refer to mcf_diff:diff_mcf_files) + compare_dcids: list of dcids to be compared + compare_nodes_with_pv: only compare nodes that have a listed propeorty:value + ignore_nodes_with_pv: ignore nodes with any of the property:value listed + compare_property: only compare listed propeorties in a node + + Returns: + { + status: MATCH or DELETED or MODIFIED + diff_log: log file with all the text style diffs with +|- prefixes. + missing: count of expected nodes missing in actual + modified: count of nodes expected nodes with modified pvs in actual + sample: a sample of 100 lines of diff output + } + """ + + counters = Counters() + diff_str = diff_mcf_files(actual, expected, diff_config, counters) + matched = counters.get_counter('nodes-matched') + deleted = counters.get_counter('dcid-missing-in-nodes1') + added = counters.get_counter('dcid-missing-in-nodes2') + modified = counters.get_counter('nodes-with-diff') + status = 'MATCHED' + if modified: + status = 'MODIFIED' + if deleted: + status = 'DELETED' + return_status = { + 'actual': actual, + 'expected': expected, + 'diff_status': status, + 'missing': deleted, + 'modified': modified, + 'added': added, + 'matched': matched, + 'counters': counters.get_counters(), + } + logging.info(f'Diff summary: {return_status}') + + if diff_str: + if diff_output_file: + with file_util.FileIO(diff_output_file, 'w') as diff_file: + diff_file.write(diff_str) + return_status['diff_output'] = diff_output_file + return_status['diff_sample'] = diff_str[:1000] + + return return_status + + +def main(_): + statvar_processor_tester = StatVarProcessorTestRunner( + test_config_file=_FLAGS.test_config) + test_result = statvar_processor_tester.run_tests(_FLAGS.test_output) + logging.info(f'Test result: {test_result}') + + +if __name__ == '__main__': + app.run(main) diff --git a/tools/statvar_importer/words_allowlist.txt b/tools/statvar_importer/words_allowlist.txt index 1a8f28cfda..a255f5b45c 100644 --- a/tools/statvar_importer/words_allowlist.txt +++ b/tools/statvar_importer/words_allowlist.txt @@ -25,3 +25,5 @@ svpg url usc var +antiretroviral +antiretrovirals From e548c92bc59bb921ad90fc87e46be57399e3b6a0 Mon Sep 17 00:00:00 2001 From: Nehil Sood Date: Tue, 23 Jun 2026 10:41:51 +0000 Subject: [PATCH 2/2] feat: add deterministic UN data pipeline and update efficacy wrapper - Add new deterministic Python-based pipeline for UN data under undata/ - Include new documentation (mapping_strategy.md, undata_summary.md) - Update Agent Efficacy Board UI and logic for strict file comparison - Clean up workspace by ignoring tmp, cache directories, and environment variable files - Add utility scripts for running evaluations and test runs --- .geminiignore | 6 + .gitignore | 24 + generate_pvmap.py | 75 + import_pipeline.sh | 1147 + mapping_strategy.md | 42 + move_all_outputs.sh | 12 + run_abhishek_eval.sh | 29 + run_test.sh | 52 + .../who/adolescent_birth_rate/README.md | 71 + .../adolescent_birth_rate/data_download.py | 50 + .../who/adolescent_birth_rate/manifest.json | 35 + .../adolescent_birth_rate_output.tmcf | 7 + .../agent_efficacy/Agent_Efficacy_Board.html | 4 +- tools/agent_efficacy/calculate_efficacy.py | 95 +- undata/IMPORT_GUIDE.md | 77 + undata/extract_unique.py | 19 + undata/fix_geography.py | 67 + undata/generate_desa_files.py | 110 + undata/generate_metadata.py | 242 + undata/generate_pvmap.py | 81 + undata/generate_pvmap_script.py | 94 + undata/generate_sdg_migrp.py | 89 + undata/metadata_to_pvmap.py | 127 + .../transcoded/_dsd_canonicalization.json | 13387 ++ .../transcoded/_dsd_canonicalization.json | 112817 +++++++++++++++ .../transcoded/_dsd_canonicalization.json | 6135 + .../transcoded/_dsd_canonicalization.json | 2473 + .../transcoded/_dsd_canonicalization.json | 37555 +++++ undata/new_data_stats.md | 31 + undata/parse_metadata.py | 17 + undata/run_batch_import.sh | 237 + undata/run_full_pipeline.sh | 118 + undata/run_loop.sh | 5 + undata/run_statvar_all.sh | 50 + undata/run_statvar_bulk_sorted.sh | 73 + undata/run_statvar_single.sh | 34 + undata/worker.sh | 5 + undata_summary.md | 40 + upload_results.sh | 15 + upload_results_done.sh | 15 + 40 files changed, 175546 insertions(+), 16 deletions(-) create mode 100644 .geminiignore create mode 100644 generate_pvmap.py create mode 100644 import_pipeline.sh create mode 100644 mapping_strategy.md create mode 100644 move_all_outputs.sh create mode 100644 run_abhishek_eval.sh create mode 100644 run_test.sh create mode 100644 statvar_imports/who/adolescent_birth_rate/README.md create mode 100644 statvar_imports/who/adolescent_birth_rate/data_download.py create mode 100644 statvar_imports/who/adolescent_birth_rate/manifest.json create mode 100644 statvar_imports/who/adolescent_birth_rate/test_data/adolescent_birth_rate_output.tmcf create mode 100644 undata/IMPORT_GUIDE.md create mode 100644 undata/extract_unique.py create mode 100644 undata/fix_geography.py create mode 100644 undata/generate_desa_files.py create mode 100644 undata/generate_metadata.py create mode 100644 undata/generate_pvmap.py create mode 100644 undata/generate_pvmap_script.py create mode 100644 undata/generate_sdg_migrp.py create mode 100644 undata/metadata_to_pvmap.py create mode 100644 undata/new_data/UN20x_ilo_2026-05-25_transcoded_20260526T010557Z_7dbe65/transcoded/_dsd_canonicalization.json create mode 100644 undata/new_data/UN20x_sdg_q1-2026_transcoded_20260607T033719Z_c369c1/transcoded/_dsd_canonicalization.json create mode 100644 undata/new_data/UN20x_unaids_2026-05-12_transcoded_20260607T060952Z_ce9628/transcoded/_dsd_canonicalization.json create mode 100644 undata/new_data/UN20x_undp-hdro_hdr_2025_transcoded_20260607T050355Z_a399e1/transcoded/_dsd_canonicalization.json create mode 100644 undata/new_data/UN20x_unicef_2026-06-05_transcoded_20260607T011325Z_df3d33/transcoded/_dsd_canonicalization.json create mode 100644 undata/new_data_stats.md create mode 100644 undata/parse_metadata.py create mode 100755 undata/run_batch_import.sh create mode 100755 undata/run_full_pipeline.sh create mode 100644 undata/run_loop.sh create mode 100755 undata/run_statvar_all.sh create mode 100644 undata/run_statvar_bulk_sorted.sh create mode 100644 undata/run_statvar_single.sh create mode 100755 undata/worker.sh create mode 100644 undata_summary.md create mode 100644 upload_results.sh create mode 100644 upload_results_done.sh diff --git a/.geminiignore b/.geminiignore new file mode 100644 index 0000000000..03f8c0e5e7 --- /dev/null +++ b/.geminiignore @@ -0,0 +1,6 @@ +!undata/DESA/data/DESA-GENDER_2025_OBS_ICT_SKILL_RT.csv +!undata/DESA/metadata_unreviewed/DESA-GENDER_2025_OBS_ICT_SKILL_RT_metadata.csv +!undata/metadata.csv +!.datacommons/runs/ +!undata/SDG/** + diff --git a/.gitignore b/.gitignore index a05b550de3..a769c7acb5 100644 --- a/.gitignore +++ b/.gitignore @@ -23,5 +23,29 @@ import-automation/executor/config_override.json .datacommons/ .venv/ +.env +defaults.env +undata/output +undata/metadata +undata/data +undata/cl_reviewed +undata/logs +redundant_archive +undata/DESA +undata/SDG +undata/verification_test +undata/DESA/cl_reviewed/ +undata/DESA/cl_unreviewed/ +undata/DESA/data/ +undata/DESA/efficacy_results/ +undata/DESA/metadata/ +undata/DESA/metadata_unreviewed/ +undata/DESA/output/ +.datacommons/runs/ +*.csv + +model/ +model_cache/ +tmp/ \ No newline at end of file diff --git a/generate_pvmap.py b/generate_pvmap.py new file mode 100644 index 0000000000..9fc56b2005 --- /dev/null +++ b/generate_pvmap.py @@ -0,0 +1,75 @@ +import csv +import re + +metadata_file = '/usr/local/google/home/nehil/datacommons/import/git/data/isolated_test_DESA_2/input/metadata/DESA-GENDER_2025_OBS_ICT_SKILL_RT_metadata.csv' +output_pvmap = '/usr/local/google/home/nehil/datacommons/import/git/data/isolated_test_DESA_2/output_pvmap.csv' + +pv_rows = [] + +# Base mappings for column headers +pv_rows.append(["SERIES:ICT_SKILL_RT", "populationType", "Person", "measuredProperty", "informationAndCommunicationsTechnologySkill", "statType", "measuredValue"]) +pv_rows.append(["TIME_PERIOD", "observationDate", "{Data}"]) +pv_rows.append(["OBS_VALUE", "value", "{Number}"]) + +# Handle UNIT_MULT using #Eval correctly +# Format: key, #Eval, "prop=expression" +pv_rows.append(["UNIT_MULT", "#Eval", "scalingFactor=10**int('{Data}') if '{Data}'.strip() else 1"]) + +# Handle FREQUENCY using #Eval correctly +pv_rows.append(["FREQUENCY", "#Eval", "observationPeriod='P1Y' if '{Data}' == 'A' else ('P1M' if '{Data}' == 'M' else ('P3M' if '{Data}' == 'Q' else ''))"]) +pv_rows.append(["FREQUENCY", "#Eval", "measurementQualifier='Annual' if '{Data}' == 'A' else ('Monthly' if '{Data}' == 'M' else ('Quarterly' if '{Data}' == 'Q' else ''))"]) + +def clean_pv(pv_str): + # Remove 'dcs:' or 'dcs, ' or 'dcs: ' + pv_str = pv_str.replace('dcs:', '').replace('dcs, ', '').replace('dcs: ', '') + # Remove quotes if they wrap the whole value + if pv_str.startswith('"') and pv_str.endswith('"'): + pv_str = pv_str[1:-1] + return pv_str.strip() + +with open(metadata_file, 'r') as f: + reader = csv.DictReader(f) + for row in reader: + col = row['column_name'] + val = row['value'] + pv_pair = row['DC_PV_PAIR'] + + # Qualify key + key = f"{col}:{val}" + + # Special case for UNIT_MULT and FREQUENCY - we already handled them generally + if col in ['UNIT_MULT', 'FREQUENCY', 'UNIT_MEASURE']: + continue + + parts = [] + # Split by comma followed by a space (heuristic) + raw_parts = re.split(r', (?=[a-z])', pv_pair) + for p in raw_parts: + if ':' in p: + prop, value = p.split(':', 1) + prop = prop.strip() + value = clean_pv(value) + + # Special handling for geographyCounterpart etc. + if prop in ['geographyCounterpart', 'counterpartLocation', 'counterpartPlace', 'geographicalCounterpart', 'counterpartArea', 'locationCounterpart']: + prop = 'observationAbout' + + if value in ['TotalAge', 'TotalPlaceOfResidence', 'TotalSubject']: + # Use empty string to omit constraint + value = "" + + parts.extend([prop, value]) + + if parts: + pv_rows.append([key] + parts) + else: + pv_rows.append([key, "SkipProp", ""]) + +# Special case for UNIT_MEASURE:PT -> unit,Percent +pv_rows.append(["UNIT_MEASURE:PT", "unit", "Percent"]) + +with open(output_pvmap, 'w', newline='') as f: + writer = csv.writer(f) + writer.writerows(pv_rows) + +print(f"Generated {len(pv_rows)} rows in {output_pvmap}") diff --git a/import_pipeline.sh b/import_pipeline.sh new file mode 100644 index 0000000000..5a7a2319ed --- /dev/null +++ b/import_pipeline.sh @@ -0,0 +1,1147 @@ +#!/bin/bash +# Script run all import steps: +# 1. download data +# 2. generate schema mappings: +# 2.1: get all source keys to be mapped. +# 2.2: get sample schema based on the vertical and source strings. +# 2.3: down sample data for agentic processing to have unique rows/columns +# 2.3: use agent to generate schema mapping +# 2.4: run the statvar processor to get sample output for the input +# 2.5: generate custom dc config to load the source data +# 3. process the complete source dataset +# 4. load into a custom dc setup +# 5. generate manifest.json for auto-refresh +# +# User Configuration: +# You can override default settings by creating a config file at: +# $HOME/.datacommons/import_config.env +# +# Example variables to override: +# IMPORT_DIR="$HOME/my_imports" +# GCS_BUCKET="my-import-bucket" +# GCP_PROJECT="my-gcp-project" +# DATA_DIR="gs://my-data-bucket/data" +# + +SCRIPT_DIR=${SCRIPT_DIR:-"$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"} +if [[ -z "$PY_SCRIPT_DIR" ]]; then + # Check if we are inside a data repo structure + if [[ "$SCRIPT_DIR" =~ .*/data/.* ]]; then + PY_SCRIPT_DIR=$(echo "$SCRIPT_DIR" | sed -e 's,/data/.*,/data,') + else + PY_SCRIPT_DIR="$SCRIPT_DIR" + fi +fi + +# Load user config if exists +USER_CONFIG=${USER_CONFIG:-"$HOME/.datacommons/import_config.env"} +[[ -f "$USER_CONFIG" ]] && source "$USER_CONFIG" + +IMPORT_DIR=${IMPORT_DIR:-"$HOME/datacommons/import"} +IMPORT_STAGES="download,generate_sample_data,generate_source_strings,generate_sample_schema,generate_pvmap,stat_var_processor,validate_output,copy_to_gcs,custom_dc,git_import_pr,run_import_cloud" +TMP_DIR=${TMP_DIR:-"/tmp"} +EXIT_ON_FAILURE="1" # Abort script if a command fails +DATA_DIR=${DATA_DIR:-"gs://unresolved_mcf/import/data"} +EMBEDDINGS_MODEL=${EMBEDDINGS_MODEL:-"ft_final_v20230717230459.all-MiniLM-L6-v2"} +DEFAULTS_ENV=${DEFAULTS_ENV:-"gs://unresolved_mcf/import/data/defaults.env"} +DEFAULT_STATVAR_PROCESSOR_ARGS="\ +--existing_statvar_mcf=gs://unresolved_mcf/scripts/statvar/stat_vars.mcf \ +--skip_constant_csv_columns=False \ +" +MIN_PERCENT_INPUT_PROCESSED=80 +CDC_DATA_IMAGE=${CDC_DATA_IMAGE:-"gcr.io/datcom-ci/datacommons-data:stable"} +CDC_SERVICE_IMAGE=${CDC_SERVICE_IMAGE:-"gcr.io/datcom-ci/datacommons-services:stable"} +# Cloud run options +GCP_PROJECT=${GCP_PROJECT:-"datcom-ci"} +GCS_BUCKET=${GCS_BUCKET:-"datcom-import-test"} +SAMPLER_UNIQUES_PER_COLUMN=${SAMPLER_UNIQUES_PER_COLUMN:-"300"} +DEFAULT_GCS_PATH=${DEFAULT_GCS_PATH:-"gs://unresolved_mcf/statvar_imports"} +LOG=${LOG:-"$TMP_DIR/import-$(date +%Y%m%d-%H%M%S).log"} +USAGE=" +Script to process an import from a source. +It runs the following stages: +$(sed -e 's/,/\n/g' <<< "$IMPORT_STAGES" | cat -n) + +Usage: $(basename $0) [Options] + +Options: + -i Name of the import + -m Import metadata csv + (Sample: go/dc-import-metadata) + -d Local directory for import related files. + Default: $IMPORT_DIR// + -j Path to the datacommons import tool jar. + If not set, it is downloaded from github. + -u URL for source data + -interactive Run interactively prompting for each stage. + -run Run a specific set of import stages. + stage can be one of $IMPORT_STAGES + -skip Stages to skip. + stage can be one of $IMPORT_STAGES + -git Use the git datacommonsorg data repo workspace dir. + -context Text file with additional context for auto-schematization + This is added to the agent prompt. + -sampler Additional arguments for data_sampler. + -pvmap_args Additional arguments for pvmap_generator.py + -statvar Additional arguments for stat_var_processor.py +" + +function parse_options { + SCRIPT_OPTIONS=$(sed 's/-resume [a-z]*//;' <<<"$@") + while (($# >= 1 )); do + opt=$(sed -e 's/^--/-/' <<< "$1") + case $opt in + -i) shift; IMPORT_NAME="$1";; + -m) shift; METADATA_FILE="$1";; + -d) shift; LOCAL_DIR="$1";; + -git) shift; DC_DATA_REPO_PATH="$1";; + -j) shift; DC_IMPORT_JAR="$1";; + -re*) shift; RESUME_STAGE="$1";; + -ru*) shift; RUN_STAGES="start,$1";; + -sk*) shift; SKIP_STAGES="$1";; + -int*) INTERACTIVE="1";; + -co*) shift; IMPORT_CONTEXT="$1";; + -pvm*) shift; PVMAP_ARGS="$PVMAP_ARGS $1";; + -sam*) shift; SAMPLER_ARGS="$SAMPLER_ARGS $1";; + -sta*) shift; STATVAR_ARGS="$STATVAR_ARGS $1";; + -u) shift; SOURCE_URL="$1";; + -x) set -x;; + -h*) echo "$USAGE" >&2; exit 1;; + *) echo "Unknown option: '$1'. $USAGE" >&2; exit 1;; + esac + shift + done +} + +# Define color variables +RED=$(tput setaf 1) +GREEN=$(tput setaf 2) +CYAN=$(tput setaf 6) +BLUE=$(tput setaf 4) +YELLOW=$(tput setaf 3) +MAGENTA=$(tput setaf 5) +NC=$(tput sgr0) + +function echo_log { + echo "[$(date)]: $@" >> $LOG +} + +function echo_error { + echo "[$(date)]:${RED}ERROR: $@${NC}" >> $LOG +} + +function echo_fatal { + echo "[$(date)]: ${RED}:FATAL: $@${NC}" >> $LOG + echo "Stopping import: $IMPORT_NAME at stage: $STAGE. +To resume the import, run the command: + $0 $SCRIPT_OPTIONS -resume $STAGE" >&2 + save_state + exit 1 +} + +function check_dependencies { + local missing_tools="" + for tool in python3 pip3 git curl gsutil docker; do + if ! command -v $tool &> /dev/null; then + missing_tools="$missing_tools $tool" + fi + done + if [[ -n "$missing_tools" ]]; then + echo_fatal "Missing required tools:$missing_tools. Please install them." + fi +} + +function run_cmd { + local cmd="$@" + [[ -n "$DRY_RUN" ]] && echo_log "Command invoked: $@" && return + echo_log "Running command: $cmd" + local start_ts=$(date +%s) + $cmd >> $LOG 2>&1 + local status=$? + local duration=$(( $(date +%s) - $start_ts )) + echo_log "Command: '$cmd' completed, status: $status, time:$duration secs, cwd: $PWD" + [[ "$status" != "0" ]] && [[ -n "$EXIT_ON_FAILURE" ]] && \ + echo_fatal "Failed to run command: $cmd (cwd: $PWD)" + return $status +} + +function load_state { + local file="$1"; shift + file=${file:-$IMPORT_STATE} + cwd="$PWD" + [[ -f "$file" ]] && source "$file" && source $0 + cd "$cwd" +} + +function save_state { + local file="$1"; shift + file=${file:-"$IMPORT_STATE"} + if [[ -n "$file" ]]; then + echo_log "Saving import processing state to $file..." + set | egrep -v "(LOG|PWD)=" > "$file" + fi +} + +# Setup git data repo copy +function setup_git_data { + local local_dir="$1"; shift + local_py_dir=${local_dir:-"$PY_SCRIPT_DIR"} + + cwd=${PWD} + cd $local_py_dir + if git rev-parse --git-dir > /dev/null 2>&1; then + echo_log "Using git workspace: $local_py_dir" + else + # Create a new git workspace + local_dir=${local_dir:-"$LOCAL_DIR/../git"} + echo_log "Cloning datacommonsorg/data into $local_dir" + mkdir -p $local_dir + cd $local_dir + [[ ! -d 'data' ]] && run_cmd git clone https://github.com/datacommonsorg/data.git + fi + [[ -d "data" ]] && cd data + git rebase master || git pull upstream master + if [[ ! -d "$PY_SCRIPT_DIR/tools" ]]; then + # Current script not on github. + # Set path for other import tools to local git workspace + export PY_SCRIPT_DIR=$PWD + fi + export GIT_DIR=$PWD + cd "$cwd" +} + +# Setup the python environment +function setup_python { + if [[ "$PYTHON_REQUIREMENTS_INSTALLED" != "true" ]]; then + PY_ENV=$TMP_DIR/import-env + [[ ! -f "$PY_ENV/bin/activate" ]] && run_cmd python3 -m venv $PY_ENV + source $PY_ENV/bin/activate + echo_log "Installing Python requirements from $PY_SCRIPT_DIR/requirements*.txt" + if [[ ! -f "$PY_SCRIPT_DIR/requirements*.txt" ]]; then + # Local script dir doesn't have python tools. Use github istead. + PY_SCRIPT_DIR="$GIT_DIR" + fi + run_cmd pip3 install -q -U -r $PY_SCRIPT_DIR/requirements*.txt + export PYTHON_REQUIREMENTS_INSTALLED=true + fi +} + +function setup_dc_import { + # Get the datacommons import jar + DC_IMPORT_JAR=${DC_IMPORT_JAR:-$(ls $TMP_DIR/datacommons-import-tool*.jar | tail -1)} + if [[ -f "$DC_IMPORT_JAR" ]]; then + echo_log "Using existing dc-import jar: $DC_IMPORT_JAR" + else + # Download the latest jar + echo_log "Getting latest version of dc-import jar file..." + # Get URLthe latest release + jar_url=$(curl -vs "https://api.github.com/repos/datacommonsorg/import/releases/latest" | \ + grep browser_download_url | cut -d\" -f4) + [[ -z "$jar_url" ]] && echo_fatal "Unable to get latest jar for https://github.com/datacommonsorg/import/releases. +Please download manually and set command line option '-j'" + jar=$(basename $jar_url) + [[ -z "$DC_IMPORT_JAR" ]] && DC_IMPORT_JAR="$TMP_DIR/$jar" + echo_log "Downloading dc-import jar from $jar_url into $DC_IMPORT_JAR..." + curl -Ls "$jar_url" -o $DC_IMPORT_JAR + [[ -f "$DC_IMPORT_JAR" ]] || echo_fatal "Failed to download $jar_url" + fi +} + +function setup { + # Fork a process to display log + mkdir -p "$TMP_DIR" + LOG=${LOG:-"$TMP_DIR/import-$(date +%Y%m%d-%H%M%S).log"} + touch $LOG + if [[ -z "$QUIET" ]]; then + echo "Logs in $LOG" + if [[ -n "$SCRIPT_RUN" ]]; then + echo_log "Processing import $IMPORT_NAME..." + tail -f -n 1 $LOG & + # Kill forked processes on exit + trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM EXIT + fi + fi + + # Load metadata + export METADATA=$(gsutil cat $METADATA_FILE 2>/dev/null) + export IMPORT_NAME=${IMPORT_NAME:-$(get_import_config "importName")} + + export LOCAL_DIR=${LOCAL_DIR:-"$IMPORT_DIR/$IMPORT_NAME"} + mkdir -p $LOCAL_DIR + IMPORT_STATE=${IMPORT_STATE:-"$LOCAL_DIR/.process-import.env"} + + check_dependencies + + if [[ -n "$RESUME_STAGE" ]]; then + # Resuming stage from a previous run. reload configs. + local resume_stage=$RESUME_STAGE + load_state + [[ -n "$resume_stage" ]] && STAGE=$resume_stage + echo_log "Resuming import: $IMPORT_NAME from stage:$STAGE" + METADATA=$(gsutil cat $METADATA_FILE 2>/dev/null) + fi + + # Setup git path for import tools + setup_git_data "$DC_DATA_REPO_PATH" + + # Load defaults from GCS + echo_log "Setting defaults from $DEFAULTS_ENV" + gsutil cp $DEFAULTS_ENV $LOCAL_DIR/defaults.env + source $LOCAL_DIR/defaults.env + + # Setup environment and tools + setup_python + export PYTHONPATH="$PYTHONPATH:$PY_SCRIPT_DIR/util:$PY_SCRIPT_DIR/tools/statvar_importer" + export PYTHONPATH="$PYTHONPATH:$PY_SCRIPT_DIR:$PY_SCRIPT_DIR/data" + setup_dc_import + +} + +# Returns the value for an import metadata +function get_import_config { + local param=$1; shift; + local default=$1; shift; + + local value=$(grep -i "^ *$param," <<< "$METADATA"| tail -1 | cut -d, -f2-) + [[ -z "$value" ]] && value="$default" + echo "$value" +} + +# Stages for import processing +function get_next_stage { + local stage="$1"; shift + stage=${stage:-"$STAGE"} + local run_stages=$(sed -e 's/,/|/g' <<< "$RUN_STAGES") + local skip_pattern=$(sed -e 's/[ ,]\+/|/g' <<< "$SKIP_STAGES") + next_stages=$(echo "start,$IMPORT_STAGES,done" | sed -e 's/,/\n/g' | \ + egrep "$run_stages" | grep -i "$stage" -A10 ) + [[ -n "$skip_pattern" ]] && next_stages=$(grep -v "$skip_pattern" <<< "$next_stages") + next_stage=$(grep -v "$stage" <<< "$next_stages" | head -1) + echo "$next_stage" +} + +function prompt_user { + local msg="$@"; + + echo -n "${YELLOW}$msg" "(y/n):${NC}" + read ans; + while (( $? == 0 )); do + case $ans in + Y*|y*) return 0;; + N*|n*) return 1;; + esac + echo -n "${YELLOW}$msg" "(y/n):${NC}" + read ans + done +} + +function run_stage { + local stage="$1"; shift + stage=${stage:-"$STAGE"} + + if [[ -n "$INTERACTIVE" ]]; then + if ! prompt_user "Run stage: $stage"; then + echo_log "Stopping import $IMPORT_NAME at stage: $stage" + echo "To resume $IMPORT_NAME in stage: $stage, run the command: + $0 $SCRIPT_OPTIONS -resume '$stage'" + exit 0 + fi + fi + # IMPORT_STAGES="download,generate_source_strings,generate_sample_schema," + echo_log " +=================================== +${CYAN}Running import stage: $STAGE${NC} +=================================== +" + local start_ts=$(date +%s) + case $stage in + start) ;; + download) download;; + generate_sample_data) generate_sample_data;; + generate_source_strings) generate_source_strings;; + generate_sample_sch*) generate_sample_schema;; + generate_pv*) generate_pvmap;; + stat*) stat_var_processor;; + validate_output) validate_output;; + copy_to_gcs) copy_to_gcs;; + custom_dc) custom_dc;; + git*) git_import_pr;; + run_import_cloud) run_import_cloud;; + done) ;; + *) echo_fatal "Unknown stage: '$stage'";; + esac + end_ts=$(date +%s) + echo_log "Completed import stage '$stage' in $(( $end_ts - $start_ts )) secs" + save_state +} + +# Helper Functions + +function generate_source_pvmap_prompt { + local source_prompt="$1"; shift + + cat > $source_prompt </dev/null) + if [[ -n "$existing_pvmap" ]]; then + # Add the existing PV maps to the prompt + echo " +Also preserve the existing mappings from the following pvmaps and +don't add any additional mappings that conflicts with these: + " >> $source_prompt + for pvmap in $existing_pvmap; do + echo "======= $pvmap =========" + cat "$pvmap" + echo "========================" + done >> $source_prompt + fi + + existing_metadata=$(ls $LOCAL_DIR/*metadata.csv 2>/dev/null) + if [[ -n "$existing_metadata" ]]; then + # Add the existing metadata to the prompt to be preserved. + echo " +Use the following config settings as well. +" >> $source_prompt + cat "$existing_metadata" >> $source_prompt + fi + + if [[ -n "$IMPORT_CONTEXT" ]]; then + echo_log "Adding import context: $IMPORT_CONTEXT to prompt." + echo " +Also consider the following addition context for the data:" + for cfile in $(sed -e 's/,/ /g' <<< "$IMPORT_CONTEXT"); do + if [[ -f "$cfile" ]]; then + cat "$cfile" >> $source_prompt + fi + done + fi + + prompt_lines=$(cat $source_prompt | wc -l) + echo_log "Created a source specific pvmap prompt with ${prompt_lines} lines in $source_prompt" +} + +function append_pv_to_file { + local file="$1"; shift + local prop="$1"; shift + local value="$1"; shift + local indent="$1"; shift + + [[ -z "$prop" ]] && return + [[ -z "$value" ]] && value=$(get_import_config "$prop") + [[ -z "$value" ]] && return + add_quote=$(echo "$value" | egrep -v '"|^ *(dcs|dcid|schema):') + [[ -n "$add_quote" ]] && value="\"$value\"" + sep="" + old_prop=$(grep "$prop" $file) + if [[ -z "$old_prop" ]]; then + # Add a new prop:value to the end of the file + has_close=$(grep "^}$" $file) + if [[ -z "$has_close" ]]; then + # Add new property to end of the file + echo "$indent$prop: $value" >> $file + else + # Add new property before closing '}' + sed -i "s|}|$indent$prop: $value\n}|" $file + fi + else + # Modify existing property + sed -i "s|^.*$prop.*$|$indent$prop: $value|" $file + fi +} + +function generate_provenance_mcf { + local import_name="$1"; shift + local provenance_mcf="$1"; shift + + import_name=${import_name:-"$IMPORT_NAME"} + provenance_mcf=${provenance_mcf:-"$PROVENANCE_DIR/$import_name.mcf"} + tmp_provenance_mcf="$TMP_DIR/$import_name.mcf" + echo "Node: dcid:dc/base/$import_name +typeOf: dcs:Provenance" > $tmp_provenance_mcf + append_pv_to_file $tmp_provenance_mcf "license" + append_pv_to_file $tmp_provenance_mcf "licenseType" + append_pv_to_file $tmp_provenance_mcf "processingMethod" + append_pv_to_file $tmp_provenance_mcf "sourceDataUrl" + if [[ -z "$cachedSourceDataUrl" ]]; then + # Set the cached data source URL based on the config. + [[ -z "$GCSPath" ]] && GCSPath=$(sed -e 's,.*mcf,gs://unresolved_mcf,' <<< $CNSPath) + if [[ -n "$GCSPath" ]]; then + cachedSourceDataUrl=$( sef -e "s,.*_mcf/,$PUBLIC_GCS_URL/unresolved_mcf," <<< $GCSPath) + fi + fi + append_pv_to_file $tmp_provenance_mcf "cachedSourceDataUrl" "$cachedSourceDataUrl" + append_pv_to_file $tmp_provenance_mcf "importedSourceReleaseDate" + append_pv_to_file $tmp_provenance_mcf "sourceReleaseFrequency" + append_pv_to_file $tmp_provenance_mcf "nextSourceReleaseDate" + if [[ -z "$dataTransformationLogic" ]]; then + # Use StatvarProcessor config on git + [[ -z "$GitConfigDir" ]] && GitConfigDir=${git_dir:-"$GIT_STATVAR_CONFIG_DIR/$import_name"} + dataTransformationLogic="$GIT_DATA_URL/$GitConfigDir" + fi + append_pv_to_file $tmp_provenance_mcf "dataTransformationLogic" "$dataTransformationLogic" + append_pv_to_file $tmp_provenance_mcf "lastDataRefreshDate" $(get_import_config "lastDataRefreshDate" "\"$DATE\"") + + # Merge generated mcf and existing mcf. + new_provenance_mcf="$TMP_DIR/$import_name-merged.mcf" + run_cmd python $PY_SCRIPT_DIR/tools/statvar_importer/mcf_file_util.py --input_mcf=$provenance_mcf,$tmp_provenance_mcf \ + --output_mcf=$new_provenance_mcf --append_values=False + g4_edit "$provenance_mcf" + cp $new_provenance_mcf $provenance_mcf + echo_log "Generated provenance: $provenance_mcf" + echo "$provenance_mcf" +} + +# Convert a string in CamelCase to snake_case. +function to_snake_case { + sed 's/[A-Z]\+[a-z]\+/_\L&_/g;s/[A-Z]/\L&/g;s/__*/_/g;s/^_*//;s/_*$//' <<< "$@" +} + +# Generate custom DC config.json +function generate_custom_dc_config { + local cdc_config="$1"; + + cdc_config=${cdc_config:-"$TMP_DIR/config.json"} + + data_set_name=$(get_import_config "DataSetName" "$IMPORT_NAME") + data_set_url=$(get_import_config "DataSetUrl" "http://sample-data-source.com/data-set") + data_source_name=$(get_import_config "DataSourceName" "Sample Data Source") + data_source_url=$(get_import_config "DataSourceUrl" "http://sample.data-source.com/") + echo_log "Generating Custom DC config: $cdc_config" + cat > $cdc_config < $import_manifest < $schema_query_file + num_queries=$(cat $schema_query_file | wc -l) + + if [[ ! -d "$LOCAL_DIR/../model/$EMBEDDINGS_MODEL" ]]; then + mkdir -p "$LOCAL_DIR/../model/$EMBEDDINGS_MODEL" + run_cmd gsutil -m cp -r $DATA_DIR/model/$EMBEDDINGS_MODEL "$LOCAL_DIR/../model" + fi + echo_log "Looking up sample schema for ${num_queries} strings in ${source_strings}..." + SOURCE_SCHEMA_SAMPLES=$LOCAL_DIR/source_schema_sample.csv + run_cmd python $PY_SCRIPT_DIR/tools/statvar_importer/schema/schema_matcher.py \ + --input_file=$schema_query_file \ + --schema_matcher_mcf=$DATA_DIR/core_schema_statvars.mcf \ + --schema_output_csv=$SOURCE_SCHEMA_SAMPLES \ + --schema_embeddings_lookup=True \ + --semantic_matcher_cache=$DATA_DIR/core_schema_statvars_embeddings.pkl \ + --semantic_matcher_model=$LOCAL_DIR/../model/$EMBEDDINGS_MODEL \ + + num_schema_results=$(cat "$SOURCE_SCHEMA_SAMPLES" | wc -l) + echo_log "Generated ${num_schema_results} schema samples for ${num_queries} source strings into $SOURCE_SCHEMA_SAMPLES" +} + +################################################################################ +# Stage: generate_pvmap +# Description: Generates a Property-Value (PV) map and import metadata using +# an LLM (Gemini). It constructs a prompt with source strings +# and sample schema, then parses the LLM output. +# Arguments: +# source_file (optional): Path to source CSV. +################################################################################ +function generate_pvmap { + local source_file="$1"; shift + source_file=${source_file:-"$SOURCE_FILES"} + + # Create prompt with source strings and sample schema. + source_pvmap_prompt=$LOCAL_DIR/source_pvmap_prompt.txt + generate_source_pvmap_prompt "$source_pvmap_prompt" + + # Generate PV map and metadata using gemini + GEMINI=${GEMINI:-$(which gemini)} + GEMINI=${GEMINI:-$(alias gemini)} + GEMINI=${GEMINI:-"/google/bin/releases/gemini-cli/tools/gemini"} + [[ ! -f "$GEMINI" ]] && \ + echo_fatal "Unable to find command line path for gemini cli:$GEMINI. Please set environment variable GEMINI to the gemini command" + cwd="$PWD" + cd $LOCAL_DIR + run_cmd python $PY_SCRIPT_DIR/tools/agentic_import/pvmap_generator.py \ + --gemini_cli="$GEMINI" \ + --input_data="$SAMPLED_SOURCE_FILE" \ + --input_metadata="$source_pvmap_prompt" \ + --output_path="$LOCAL_DIR/pvmap_generator/generated" \ + --skip_confirmation=$( [ -n "$INTERACTIVE" ] && echo False || echo True) \ + $PVMAP_ARGS + + echo_log "agentic output: $(ls -l $LOCAL_DIR/pvmap_generator)" + # Copy over the final configs. + run_cmd cp $LOCAL_DIR/pvmap_generator/generated_pvmap.csv \ + $LOCAL_DIR/pvmap_generator/generated_metadata.csv \ + $LOCAL_DIR + echo_log "Generated configs in $LOCAL_DIR/pvmap_generator: +$(ls -l $LOCAL_DIR/pvmap_generator/*)" + cd "$cwd" +} + +################################################################################ +# Stage: stat_var_processor +# Description: Runs the StatVar Processor tool to convert source data into +# MCF/CSV format suitable for Data Commons, using the generated +# PV map and metadata. +################################################################################ +function stat_var_processor { + echo_log "Running statvar processor for $IMPORT_NAME..." + local counters_file=$LOCAL_DIR/validation/statvar_processor_counters.csv + local output_path="$LOCAL_DIR/output/${IMPORT_NAME}_output" + local cwd="$PWD" + cd $PY_SCRIPT_DIR + run_cmd python $PY_SCRIPT_DIR/tools/statvar_importer/stat_var_processor.py \ + $DEFAULT_STATVAR_PROCESSOR_ARGS \ + --input_data=$SOURCE_FILES \ + --config_file=$LOCAL_DIR/*metadata.csv \ + --pv_map=$(echo $(ls $LOCAL_DIR/*pvmap.csv | sed -e 's/ /,/g')) \ + --output_path=$output_path \ + --output_counters=$counters_file \ + $STATVAR_ARGS + + echo_log "Completed processing $IMPORT_NAME: +Input: $(wc -l $SOURCE_FILES) +Output: $output_path: +$(wc -l $output_path*) +" + + [[ -f "$counters_file" ]] || echo_fatal "Unable to find output counters: $counters_file" + statvar_errors=$(grep error "$counters_file") + num_errors=$(wc -l <<< "$statvar_errors") + [[ -n "$statvar_errors" ]] && \ + echo_error "Statvar Processor had ${num_errors} errors: +$statvar_errors" + num_inputs=$(grep "input-numeric-values" "$counters_file" | cut -d, -f2) + num_inputs=${num_inputs:-"1"} + num_outputs=$(grep "output-svobs-csv-rows" "$counters_file" | cut -d, -f2) + percent_processed=$(bc <<< "100 * $num_outputs / $num_inputs") + if (( $percent_processed < $MIN_PERCENT_INPUT_PROCESSED )); then + echo_fatal "Only $percent_processed percent inputs in $SOURCE_FILES processed. Please check the output and configs in $LOCAL_DIR/*.csv" + fi + cd "$cwd" +} + +################################################################################ +# Stage: validate_output +# Description: Validates the generated output using the dc-import tool and +# custom import validation scripts. Generates a summary report. +################################################################################ +function validate_output { + echo_log "Running dc-import tool on $LOCAL_DIR/output/..." + local cwd="$PWD" + cd $PY_SCRIPT_DIR + + run_cmd java -jar $DC_IMPORT_JAR genmcf -n 20 -r FULL \ + $LOCAL_DIR/output/*.{csv,tmcf,mcf} \ + -o $LOCAL_DIR/validation + + # Run import validator + run_cmd python $PY_SCRIPT_DIR/tools/import_validation/runner.py \ + --validation_config=$PY_SCRIPT_DIR/tools/import_validation/validation_config.json \ + --stats_summary=$LOCAL_DIR/validation/summary_report.csv \ + --lint_report=$LOCAL_DIR/validation/report.json \ + --validation_output=$LOCAL_DIR/validation/validation-result.json + cd "$cwd" +} + +################################################################################ +# Stage: copy_to_gcs +# Description: Copies the import artifacts (data, configs, output) to a +# Google Cloud Storage bucket. +# Arguments: +# data_dir (optional): Local directory to copy. +# gcs_dir (optional): Destination GCS path. +################################################################################ +function copy_to_gcs { + local data_dir="$1"; shift + local gcs_dir="$1"; shift + + data_dir=${data_dir:-"$LOCAL_DIR"} + data_source=$(to_snake_case $(get_import_config "DataSourceName" "")) + data_set=$(to_snake_case $(get_import_config "DataSetName" "")) + if [[ -z "$gcs_dir" ]]; then + gcs_dir=$(get_import_config "GcsPath" "") + if [[ -z "$gcs_dir" ]]; then + gcs_dir="$DEFAULT_GCS_PATH" + [[ -n "$data_source" ]] && gcs_dir+="/$data_source" + [[ -n "$data_set" ]] && gcs_dir+="/$data_set" + if [[ "$gcs_dir" == "$DEFAULT_GCS_PATH" ]]; then + # Add the import name to gcs path + gcs_dir+="/$(to_snake_case $IMPORT_NAME)" + fi + fi + DATE=${DATE:-$(date +%Y%m%d)} + gcs_dir+="/$DATE" + fi + export GCS_DIR="$gcs_dir" + + echo_log "Copying files for import:$IMPORT_NAME to $GCS_DIR..." + run_cmd gsutil -m cp -r $data_dir $gcs_dir + run_cmd gsutil ls -l $gcs_dir + + # Copy any resolved MCF files. + node_mcf_files=$(ls $LOCAL_DIR/validation/table*.mcf) + if [[ -n "$node_mcf_files" ]]; then + num_files=$(wc -l <<< "$node_mcf_files") + index=0 + for file in $node_mcf_files; do + dir=$(dirname $file) + run_cmd mv $file $dir/nodes.mcf-$(printf "%05d" $index)-of-$(printf "%05d" $num_files) + done + resolved_dir=$(sed -e 's/unresolved_mcf/resolved_mcf/' <<< "$GCS_DIR")"/output" + echo_log "Copying ${num_files} resolved node MCFs from $dir to $resolved_dir..." + run_cmd gsutil -m cp $dir/nodes.mcf* $resolved_dir/ + fi + + # Initiate copy to CNS through the COPY service sentinel file. + echo "$gcs_dir" > "$LOCAL_DIR/COPY" + run_cmd gsutil cp $LOCAL_DIR/COPY "unresolved_mcf/$USER/$IMPORT_NAME/copy" + echo_log "Copied $data_dir to $gcs_dir. +View files at: https://pantheon.corp.google.com/storage/browser/$(sed -e 's,gs://,,' <<< $GCS_DIR)" + + echo "$gcs_dir" +} + +################################################################################ +# Stage: custom_dc +# Description: Builds and launches a local custom Data Commons instance +# serving the imported data for verification. +# Arguments: +# data_dir (optional): Directory containing the import output. +# cdc_git_dir (optional): Directory for the custom DC website repo. +################################################################################ +function custom_dc { + local data_dir="$1"; shift + local cdc_git_dir="$1"; shift + + cwd=$PWD + + data_dir=${data_dir:-"$LOCAL_DIR/output"} + echo_log "Launching custom DC for $IMPORT_NAME with data in $data_dir..." + + # Clone website repo + cdc_git_dir=${cdc_git_dir:-"$TMP_DIR/git_website_cdc"} + mkdir -p "$cdc_git_dir" + cd "$cdc_git_dir" + if [[ ! -d "$cdc_git_dir/website" ]]; then + echo_log "Cloning datacommonsorg/website for custom dc into $cdc_git_dir..." + run_cmd git clone https://github.com/datacommonsorg/website.git + fi + cd $cdc_git_dir/website + git rebase master || git pull upstream master + + # Create custom DC env.list + cdc_env="$LOCAL_DIR/custom_dc/$IMPORT_NAME-env.list" + mkdir -p $(dirname "$cdc_env") + cp custom_dc/env.list.sample "$cdc_env" + gsutil cat $DEFAULTS_ENV | sed -e 's/export *//' >> "$cdc_env" + sed -i "s/DC_API_KEY=.*/DC_API_KEY=$DC_API_KEY/" "$cdc_env" + sed -i "s/MAPS_API_KEY=.*/MAPS_API_KEY=$MAPS_API_KEY/" "$cdc_env" + sed -i "s,INPUT_DIR=.*$,INPUT_DIR=$data_dir," "$cdc_env" + sed -i "s,OUTPUT_DIR=,OUTPUT_DIR=$data_dir/custom_dc," "$cdc_env" + + # Generate custom DC config + cdc_config=$(generate_custom_dc_config "$LOCAL_DIR/custom_dc/config.json") + run_cmd gsutil cp $cdc_config $data_dir + + # Build custom DC docker command options + cdc_docker_opts=" -v $cdc_git_dir/website/custom_dc:$cdc_git_dir/website/custom_dc" + is_gcs_dir=$(echo "$data_dir" | grep "gs://") + if [[ -n "$is_gcs_dir" ]]; then + cdc_docker_opts="$cdc_docker_opts -e GOOGLE_APPLICATION_CREDENTIALS=/gcp/creds.json" + cdc_docker_opts="$cdc_docker_opts -v $HOME/.config/gcloud/application_default_credentials.json:/gcp/creds.json:ro" + else + cdc_docker_opts="$cdc_docker_opts -v $data_dir:$data_dir" + fi + + # Run the docker data builder + echo_log "Building Custom DC data for $IMPORT_NAME using the data loader..." + run_cmd docker run --env-file $cdc_env $cdc_docker_opts \ + $CDC_DATA_IMAGE + + # Kill any existing docker service for custom dc + cdc_contained_id=$(docker ps | grep "datacommons-services" | cut -d" " -f1) + [[ -n "$cdc_contained_id" ]] && \ + echo_log "Killing existing docker service $cdc_contained_id" && \ + run_cmd docker kill $cdc_contained_id + + # Launch the docker container in the background + echo_log "Launching custom DC service for $IMPORT_NAME at http://$(hostname):8080" + run_cmd docker run -d -t -p 8080:8080 -e DEBUG=true \ + --env-file $cdc_env $cdc_docker_opts $CDC_SERVICE_IMAGE + + # Wait for custom dc to be active + echo_log "Waiting for Custom DC service to be up..." + declare -i timeout=30 + while (( $timeout > 0 )); do + is_active=$(grep "Debugger PIN" $LOG) + [[ -n "$is_active" ]] && break + sleep 1 + timeout=$(( $timeout - 1 )) + done + echo_log "${GREEN}Custom DC service for $IMPORT_NAME is up at http://$(hostname):8080/${NC}" + + run_cmd docker ps + cd "$cwd" +} + +################################################################################ +# Stage: git_import_pr +# Description: Prepares a pull request for the datacommonsorg/data repo +# with the import configuration, manifest, and sample data. +# Arguments: +# import_name (optional): Name of the import. +################################################################################ +function git_import_pr { + local import_name="$1"; shift + + import_name=${import_name:-"$IMPORT_NAME"} + import_manifest=$(get_import_manifest "$import_name") + import_manifest=${import_manifest:-"$GIT_DIR/statvar_imports/$(to_snake_case $import_name)/manifest.json"} + import_dir=$(dirname "$import_manifest") + local cwd="$PWD" + mkdir -p "$import_dir" + cd "$import_dir" + current_dir=$(pwd) + git_pr=$(gh pr view --json number -q .number) + git_branch=$(git rev-parse --abbrev-ref HEAD) + [[ -z "$git_branch" ]] && \ + git checkout -b "import-$import_name" && \ + git_branch="import-$import_name" + + git rebase master || git pull upstream master + + # Copy over configs from local + import_metadata=$(echo $(ls "$import_dir/*metadata.csv") | sed -e 's/ /,/g') + import_pvmap=$(echo $(ls $import_dir/*pvmap.csv) | sed -e 's/ /,/g') + import_places=$(echo $(ls $import_dir/*place*.csv) | sed -e 's/ /,/g') + [[ -z "$import_metadata" ]] && \ + cp $LOCAL_DIR/*metadata.csv "$import_dir" && \ + import_metadata="$import_dir/*metadata.csv" + [[ -z "$import_pvmap" ]] && \ + cp $LOCAL_DIR/*pvmap.csv "$import_dir" && \ + import_pvmap=$(echo $(ls $import_dir/*pvmap.csv) | sed -e 's/ /,/g') + [[ -z "$import_places" ]] && \ + cp $LOCAL_DIR/*place*.csv "$import_dir" && \ + import_places=$(echo $(ls $import_dir/*place*.csv) | sed -e 's/ /,/g') + + + # Copy over sample input and output to test_data + if [[ -f "$SAMPLED_SOURCE_FILE" ]]; then + mkdir -p $import_dir/test_data + cp $SAMPLED_SOURCE_FILE $import_dir/test_data + + echo_log "Generating sample output in $import_dir/test_data..." + run_cmd python $PY_SCRIPT_DIR/tools/statvar_importer/stat_var_processor.py \ + $DEFAULT_STATVAR_PROCESSOR_ARGS \ + --input_data=$SAMPLED_SOURCE_FILE \ + --config_file=$import_metadata \ + --pv_map=$import_pvmap \ + --places_resolved_csv=$import_places \ + --output_path=$import_dir/test_data/sample_output \ + $STATVAR_ARGS + fi + + [[ ! -f "$import_manifest" ]] && \ + create_import_manifest "$import_manifest" + + echo_log "Configs for $IMPORT_NAME in $import_dir: +$(ls -l $import_dir/*)" + + # Create a github PR with the configs. + echo_log "Adding configs for $IMPORT_NAME to github branch: $git_branch" + git add $import_dir + git status + git commit -m "Import configs for $IMPORT_NAME" + git push -u origin "import-$import_name" + if [[ -z "$git_pr" ]]; then + gh pr create --repo "datacommonsorg/data" \ + --title "Import configs for $import_name" \ + --body "Import setup for statvar processor based import: $IMPORT_NAME + Custom DC demo: ${hostname}:8080 + Summary report: $(sed -e 's,gs://,https://pantheon.corp.google.com/storage/browser/,' <<< $GCS_DIR)/validation/summary_report.html" + git_pr=$(gh pr view --json number -q .number) + fi + + echo_log "${GREEN}GitHub PR:#${git_pr} with configs for $IMPORT_NAME${NC}" + cd "$cwd" +} + + +################################################################################ +# Stage: run_import_cloud +# Description: Submits a Google Cloud Batch job to run the import pipeline +# in the cloud environment. +# Arguments: +# import_name (optional): Name of the import. +################################################################################ +function run_import_cloud { + local import_name="$1"; shift + + local import_name=${import_name:-"$IMPORT_NAME"} + local import_manifest=$(get_import_manifest "$import_name") + local git_dir=${GIT_DIR:-$(sed -e 's,/data/.*,/data,' <<< "$import_manifest")} + + [[ -z "$import_manifest" ]] && \ + echo_fatal "Unable to find manifest for $import_name in $GIT_DIR" + echo_log "Running import: $import_name with manifest:$import_manifest on cloud batch..." + + local docker_image=$(to_snake_case import_executor_dev_${USER}_${import_name}) + gcs_output=$(sed -e 's,.*/data/,,;s,/manifest.json,,' <<< "$import_manifest") + run_cmd $PY_SCRIPT_DIR/import-automation/executor/run_import.sh \ + -batch -d $docker_image \ + -p $GCP_PROJECT -b $GCS_BUCKET \ + -repo $git_dir \ + "$import_manifest" + + echo_log "${GREEN}Launched cloud job for $import_name. Visit https://pantheon.corp.google.com/batch/jobs?project=$GCP_PROJECT to view job details.and view output at https://pantheon.corp.google.com/storage/browser/$GCS_BUCKET/${gcs_output}${NC}" +} + + +# Return if script is being sourced +SCRIPT_RUN="" +(return 0 2>/dev/null) && return +SCRIPT_RUN="1" + +parse_options "$@" +setup +STAGE=${STAGE:-$(get_next_stage "start")} +while [[ -n "$STAGE" ]]; do + run_stage $STAGE + STAGE=$(get_next_stage $STAGE) +done \ No newline at end of file diff --git a/mapping_strategy.md b/mapping_strategy.md new file mode 100644 index 0000000000..0184ce49d7 --- /dev/null +++ b/mapping_strategy.md @@ -0,0 +1,42 @@ +# Strategy for Generating Property-Value (PV) Maps + +This document outlines the strategy for mapping UN Data files to Data Commons Property-Value (DCPV) pairs using the reviewed codelists and specialized metadata dictionaries. + +## 1. Core Mapping Logic + +To avoid ambiguity, we use a **"Concept:Code"** notation as the primary key for all mappings. +- **Concept**: The ID of the dimension or attribute (e.g., `AGE`, `SEX`). +- **Code**: The specific value found in the data file (e.g., `Y15T19`, `F`). + +## 2. Per-File Metadata Dictionaries + +For each data file in `undata/data/`, a corresponding metadata dictionary has been generated in `undata/metadata/{filename}_metadata.csv`. + +### Format of `{filename}_metadata.csv`: +- `column_name`: The data column header. +- `value`: The unique code found in that column. +- `dc_pv_pair`: The mapped Data Commons property-value string. + +## 3. Automation Script + +The script `undata/generate_metadata.py` automates the creation of these dictionaries by: +1. Pre-loading master codelists from `undata/cl_reviewed/`. +2. Scanning data files in `undata/data/` for unique values. +3. Exporting a targeted CSV for each data file. + +## 4. Integration with Agentic Import + +When invoking `pvmap_generator.py`, use the following command structure: + +```bash +python tools/agentic_import/pvmap_generator.py \ + --input_data="undata/data/DATA_FILE.csv" \ + --input_metadata="undata/metadata/DATA_FILE_metadata.csv" \ + --output_path="output/DATA_FILE" +``` + +## 5. Workflow + +1. **Step 1**: Run `python3 undata/generate_metadata.py`. +2. **Step 2**: Process files one-by-one using `pvmap_generator.py`, passing the corresponding `_metadata.csv` file. +3. **Step 3**: The resulting `pvmap.csv` will be generated with 100% accuracy relative to the reviewed codelists. diff --git a/move_all_outputs.sh b/move_all_outputs.sh new file mode 100644 index 0000000000..a58254bb5a --- /dev/null +++ b/move_all_outputs.sh @@ -0,0 +1,12 @@ +#!/bin/bash +REVIEWED_DIR="undata/DESA/output/reviewed" +for dir in "${REVIEWED_DIR}"/*/; do + DATASET_NAME=$(basename "${dir}") + + echo "Moving files for ${DATASET_NAME}..." + + [ -f "${REVIEWED_DIR}/${DATASET_NAME}.csv" ] && mv "${REVIEWED_DIR}/${DATASET_NAME}.csv" "${dir}output.csv" + [ -f "${REVIEWED_DIR}/${DATASET_NAME}.tmcf" ] && mv "${REVIEWED_DIR}/${DATASET_NAME}.tmcf" "${dir}output.tmcf" + [ -f "${REVIEWED_DIR}/${DATASET_NAME}_stat_vars.mcf" ] && mv "${REVIEWED_DIR}/${DATASET_NAME}_stat_vars.mcf" "${dir}output_stat_vars.mcf" + [ -f "${REVIEWED_DIR}/${DATASET_NAME}_stat_vars_schema.mcf" ] && mv "${REVIEWED_DIR}/${DATASET_NAME}_stat_vars_schema.mcf" "${dir}output_stat_vars_schema.mcf" +done diff --git a/run_abhishek_eval.sh b/run_abhishek_eval.sh new file mode 100644 index 0000000000..87161fb3bb --- /dev/null +++ b/run_abhishek_eval.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +BASE_DIR="/usr/local/google/home/nehil/datacommons/import/git/data/abhishek_data" +TOOL_SCRIPT="/usr/local/google/home/nehil/datacommons/import/git/data/tools/agent_efficacy/calculate_efficacy.py" + +source /usr/local/google/home/nehil/datacommons/import/git/data/venv/bin/activate + +for dataset_dir in "$BASE_DIR"/*; do + if [ -d "$dataset_dir" ]; then + dataset_name=$(basename "$dataset_dir") + sample_dir="$dataset_dir/sample_output" + + test_file="$sample_dir/output_pvmap.csv" + gold_file="$sample_dir/output_pvmap_cleaned.csv" + + if [ -f "$test_file" ] && [ -f "$gold_file" ]; then + echo "Processing dataset: $dataset_name" + output_dir="$dataset_dir/efficacy_results" + mkdir -p "$output_dir" + + python3 "$TOOL_SCRIPT" \ + --test "$dataset_dir" \ + --output "$output_dir" \ + --dataset_id "$dataset_name" + else + echo "Skipping dataset: $dataset_name (Missing required CSV files)" + fi + fi +done diff --git a/run_test.sh b/run_test.sh new file mode 100644 index 0000000000..a2091c4e51 --- /dev/null +++ b/run_test.sh @@ -0,0 +1,52 @@ +#!/bin/bash +set -e + +# Define color variables (copied from import_pipeline.sh) +RED=$(tput setaf 1) +GREEN=$(tput setaf 2) +CYAN=$(tput setaf 6) +BLUE=$(tput setaf 4) +YELLOW=$(tput setaf 3) +MAGENTA=$(tput setaf 5) +NC=$(tput sgr0) + +# Create a bin directory for python symlink +mkdir -p test_undata_run/bin +ln -sf $(which python3) test_undata_run/bin/python +export PATH="$PWD/test_undata_run/bin:$PATH" + +# Source the pipeline script +source import_pipeline.sh + +# Set the variables for the chosen import +export IMPORT_NAME="DESA-GENDER_2025_OBS_LIFE_EXP" +export LOCAL_DIR="$PWD/test_undata_run" +export SOURCE_FILES="$PWD/undata/DESA/data/DESA-GENDER_2025_OBS_LIFE_EXP.csv" +export PY_SCRIPT_DIR="$PWD" +export GIT_DIR="$PWD" +export TMP_DIR="$LOCAL_DIR/tmp" +mkdir -p "$TMP_DIR" + +# Set PYTHONPATH +export PYTHONPATH="$PYTHONPATH:$PY_SCRIPT_DIR/util:$PY_SCRIPT_DIR/tools/statvar_importer" +export PYTHONPATH="$PYTHONPATH:$PY_SCRIPT_DIR:$PY_SCRIPT_DIR/data" + +# Mock/Set some variables that setup normally sets +export LOG="$LOCAL_DIR/test.log" +touch "$LOG" +export DEFAULT_STATVAR_PROCESSOR_ARGS="--existing_statvar_mcf=gs://unresolved_mcf/scripts/statvar/stat_vars.mcf --skip_constant_csv_columns=False" +export MIN_PERCENT_INPUT_PROCESSED=0 # Set to 0 for now to avoid fatal if it fails to process 80% + +echo "Setting up dc-import tool..." +setup_dc_import + +echo "Setting up python environment..." +setup_python + +echo "Running stat_var_processor..." +stat_var_processor + +echo "Running validate_output..." +validate_output + +echo "Done. Results in $LOCAL_DIR" diff --git a/statvar_imports/who/adolescent_birth_rate/README.md b/statvar_imports/who/adolescent_birth_rate/README.md new file mode 100644 index 0000000000..05eeeca0c7 --- /dev/null +++ b/statvar_imports/who/adolescent_birth_rate/README.md @@ -0,0 +1,71 @@ +# WHO Adolescent Birth Rate Import + +## Overview +This dataset contains national and regional-level statistics for the Adolescent Birth Rate (per 1,000 women). +Specifically, it provides data for two distinct age groups: +- 10-14 years (Y10T14) +- 15-19 years (Y15T19) + +**type of place:** Country, Region (M49 codes), WHO Regions, Overseas Territory, Special Administrative Regions +**years:** 2000 to 2024 +**place_resolution:** Resolved to DCIDs (e.g., dcid:country/FRA, dcid:europe, dcid:LatinAmericaAndCaribbean) + +## Data Source +**Source URL:** +https://data.who.int/indicators/i/24C65FE/27D371A + +**Provenance Description:** +The data comes from the World Health Organization (WHO). It specifically tracks Sustainable Development Goal (SDG) Indicator 3.7.2, measuring the adolescent birth rate (per 1,000 women aged 10–14 and 15–19) globally across different geographic classifications. + +## Refresh Type +Automatic Refresh + +For refresh of the data, the import includes a Python script (`data_download.py`) to automatically download the complete historical dataset directly from the WHO's backend Azure Blob storage endpoint. + +## Data Publish Frequency +Release Frequency = Annual + +## How To Download Input Data +To download the data, run the provided script: +```bash +python3 data_download.py +``` + +This will fetch the latest full dataset and save it locally as `input_files/adolescent_birth_rate_data.csv`, making it available for processing. + +## Processing Instructions +To process the WHO Adolescent Birth Rate data and generate statistical variables, use the following command from the `adolescent_birth_rate` directory: + +**For Data Run** +```bash +python3 ../../../tools/statvar_importer/stat_var_processor.py \ + --input_data=input_files/* \ + --pv_map=adolescent_birth_rate_pvmap.csv \ + --output_path=adolescent_birth_rate_output \ + --config_file=adolescent_birth_rate_metadata.csv +``` + +This generates the following output files: +- adolescent_birth_rate_output.csv +- adolescent_birth_rate_output_stat_vars_schema.mcf +- adolescent_birth_rate_output_stat_vars.mcf +- adolescent_birth_rate_output.tmcf + +**For Data Quality Checks and validation** +Validation of the data is done using the lint flag in the DataCommons import tool +```bash +java -jar datacommons-import-tool-0.1-jar-with-dependencies.jar lint adolescent_birth_rate_output_stat_vars_schema.mcf adolescent_birth_rate_output.csv adolescent_birth_rate_output.tmcf adolescent_birth_rate_output_stat_vars.mcf +``` + +This generates the following output files: +- report.json +- summary_report.csv +- summary_report.html + +The report files can be analyzed to check for errors and warnings. Further, linting is performed on the generated output files using the DataCommons import tool. + +## Testing +Testing is performed using the provided `test_data` directory: +- Input: `test_data/adolescent_birth_rate_input.csv` +- Output (expected): `test_data/adolescent_birth_rate_output.csv` +- MCF (expected): `test_data/adolescent_birth_rate_output.tmcf` diff --git a/statvar_imports/who/adolescent_birth_rate/data_download.py b/statvar_imports/who/adolescent_birth_rate/data_download.py new file mode 100644 index 0000000000..00d2888a4c --- /dev/null +++ b/statvar_imports/who/adolescent_birth_rate/data_download.py @@ -0,0 +1,50 @@ +import requests +import os + +output_dir = "input_files" + +def download_full_who_data(indicator_id="27D371A"): + """ + Downloads the complete historical dataset for a WHO indicator + directly from the WHDH Azure Blob Storage. + """ + # 1. Create the output directory if it doesn't exist + os.makedirs(output_dir, exist_ok=True) + + # 2. Construct the filename inside the output directory + # Using indicator_id ensures the file has a unique name + filename = os.path.join(output_dir, "adolescent_birth_rate_input.csv") + + # Root URL for the WHDH raw data dumps + base_url = f"https://srhdpeuwpubsa.blob.core.windows.net/whdh/DATADOT/INDICATOR/{indicator_id}_ALL_LATEST.csv" + + print(f"Requesting full historical CSV for {indicator_id}...") + + try: + # We use stream=True to handle potentially large files efficiently + with requests.get(base_url, stream=True, timeout=300) as r: + r.raise_for_status() + with open(filename, 'wb') as f: + for chunk in r.iter_content(chunk_size=8192): + f.write(chunk) + + # Verify the file has data beyond the header + if os.path.exists(filename): + with open(filename, 'r', encoding='utf-8', errors='ignore') as f: + line_count = sum(1 for _ in f) + + if line_count <= 1: + os.remove(filename) + print("No data fetched (empty file or header only)") + return "no data fetched" + + print(f"Successfully saved to: {filename} ({line_count} lines found)") + return filename + + except requests.exceptions.HTTPError as err: + print(f"Error fetching data: {err}") + return "error" + +if __name__ == "__main__": + WHO_INDICATOR_ID = "27D371A" + download_full_who_data(WHO_INDICATOR_ID) \ No newline at end of file diff --git a/statvar_imports/who/adolescent_birth_rate/manifest.json b/statvar_imports/who/adolescent_birth_rate/manifest.json new file mode 100644 index 0000000000..d044f29c3e --- /dev/null +++ b/statvar_imports/who/adolescent_birth_rate/manifest.json @@ -0,0 +1,35 @@ +{ + "import_specifications": [ + { + "import_name": "Adolescent_Birth_Rate", + "curator_emails": [ + "support@datacommons.org" + ], + "provenance_url": "https://data.who.int/indicators/i/24C65FE/27D371A", + "provenance_description": "The annual number of births to females aged 10-14 or 15-19 years per 1,000 females in the respective age group.", + "scripts": [ + "data_download.py", + "../../../tools/statvar_importer/stat_var_processor.py --input_data=./input_files/* --pv_map=adolescent_birth_rate_pvmap.csv --config_file=adolescent_birth_rate_metadata.csv --output_path=./adolescent_birth_rate_output --existing_statvar_mcf=gs://unresolved_mcf/scripts/statvar/stat_vars.mcf" + ], + "import_inputs": [ + { + "template_mcf": "adolescent_birth_rate_output.tmcf", + "cleaned_csv": "adolescent_birth_rate_output.csv" + } + ], + "source_files": [ + "input_files/*.csv" + ], + "cron_schedule": "0 0 20 6 *", + "resource_limits": {"cpu": 4, "memory": 8, "disk":100} + } + ], + "config_override": { + "invoke_import_validation": true, + "invoke_import_tool": true, + "invoke_differ_tool": true, + "skip_input_upload": false, + "skip_gcs_upload": false, + "cleanup_gcs_volume_mount": false + } +} diff --git a/statvar_imports/who/adolescent_birth_rate/test_data/adolescent_birth_rate_output.tmcf b/statvar_imports/who/adolescent_birth_rate/test_data/adolescent_birth_rate_output.tmcf new file mode 100644 index 0000000000..85ff9bc094 --- /dev/null +++ b/statvar_imports/who/adolescent_birth_rate/test_data/adolescent_birth_rate_output.tmcf @@ -0,0 +1,7 @@ +Node: E:adolescent_birth_rate_output->E0 +observationDate: C:adolescent_birth_rate_output->observationDate +value: C:adolescent_birth_rate_output->value +observationAbout: C:adolescent_birth_rate_output->observationAbout +variableMeasured: C:adolescent_birth_rate_output->variableMeasured +scalingFactor: 1000 +typeOf: dcs:StatVarObservation diff --git a/tools/agent_efficacy/Agent_Efficacy_Board.html b/tools/agent_efficacy/Agent_Efficacy_Board.html index 3d758b0ab2..071e370f74 100644 --- a/tools/agent_efficacy/Agent_Efficacy_Board.html +++ b/tools/agent_efficacy/Agent_Efficacy_Board.html @@ -138,7 +138,7 @@

Granular Mapp -

True Positives

+

True Positives (TP)

(Mappings done by the Agent correctly)

384

@@ -170,7 +170,7 @@

False Po -

False Negatives

+

False Negatives (FN)

(Mappings missed by the Agent)

0

diff --git a/tools/agent_efficacy/calculate_efficacy.py b/tools/agent_efficacy/calculate_efficacy.py index f322624f0e..71242db121 100644 --- a/tools/agent_efficacy/calculate_efficacy.py +++ b/tools/agent_efficacy/calculate_efficacy.py @@ -68,6 +68,38 @@ def run_comparison(pred_path, gold_path, is_pvmap=False, use_fingerprint=False): metrics = get_metrics_from_counters(counters) return diff_text, metrics +def run_csv_comparison(pred_path, gold_path): + if not os.path.exists(pred_path) or not os.path.exists(gold_path): + print(f"Skipping: missing {pred_path} or {gold_path}") + return "Missing files.", {'tp': 0, 'fp': 0, 'fn': 0, 'precision': 0, 'recall': 0, 'f1': 0} + + def load_csv(path): + rows = set() + with open(path, 'r', encoding='utf-8') as f: + reader = csv.reader(f) + # Skip header + next(reader, None) + for row in reader: + clean_row = tuple(cell.strip() for cell in row if cell.strip()) + if clean_row: + rows.add(clean_row) + return rows + + pred_rows = load_csv(pred_path) + gold_rows = load_csv(gold_path) + + tp = len(pred_rows.intersection(gold_rows)) + fp = len(pred_rows - gold_rows) + fn = len(gold_rows - pred_rows) + + precision = tp / (tp + fp) if (tp + fp) > 0 else 0 + recall = tp / (tp + fn) if (tp + fn) > 0 else 0 + f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0 + + diff_text = f"Total Prediction Rows: {len(pred_rows)}\nTotal Gold Rows: {len(gold_rows)}\nExact Matches (TP): {tp}\nOnly in Prediction (FP): {fp}\nOnly in Gold (FN): {fn}" + + return diff_text, {'tp': tp, 'fp': fp, 'fn': fn, 'precision': precision, 'recall': recall, 'f1': f1} + def update_html(template_content, dataset_id, hero_metrics, detailed_results): content = template_content for key, label in [('f1', 'Agent Efficacy (F1)'), ('precision', 'Precision'), ('recall', 'Recall')]: @@ -86,7 +118,7 @@ def update_html(template_content, dataset_id, hero_metrics, detailed_results): if '
' in content: content = content.split('
')[0] - diff_sections = '

Detailed Semantic Comparisons

' + diff_sections = '

Detailed File Comparisons (Recalculated)

\n' for label, data in detailed_results.items(): diff_sections += f'''
@@ -96,35 +128,52 @@ def update_html(template_content, dataset_id, hero_metrics, detailed_results):
Recall{data['metrics']['recall']*100:.1f}%
F1 Score{data['metrics']['f1']*100:.1f}%
-
Semantic Match (TP: {data['metrics']['tp']} | FP: {data['metrics']['fp']} | FN: {data['metrics']['fn']})
+
Structural Diff Analysis (TP: {data['metrics']['tp']} | FP: {data['metrics']['fp']} | FN: {data['metrics']['fn']})
{data['diff'] if data['diff'] else 'No differences found.'}
''' - diff_sections += '
' - return content.replace('', diff_sections + '') if '' in content else content + diff_sections + diff_sections += '
\n\n' + return content + diff_sections -def process_single_dataset(test_dir, gold_dir, output_dir, dataset_id, template_content): +def process_single_dataset(dataset_dir, output_dir, dataset_id, template_content): if not os.path.exists(output_dir): os.makedirs(output_dir) + # dataset_dir is the root of the dataset (e.g. abhishek_data/Deaths_by_week_and_sex) file_mappings = [ - ('pvmap.csv', 'output_pvmap.csv', 'output_pvmap.csv', True, False), - ('stat_vars.mcf', 'output_stat_vars.mcf', 'output_stat_vars.mcf', False, True), - ('stat_vars_schema.mcf', 'output_stat_vars_schema.mcf', 'output_stat_vars_schema.mcf', False, False), - ('tmcf', 'output.tmcf', 'output.tmcf', False, False), + ('pvmap.csv', os.path.join(dataset_dir, 'sample_output', 'output_pvmap.csv'), os.path.join(dataset_dir, 'sample_output', 'output_pvmap_cleaned.csv'), True, False, False), + ('stat_vars.mcf', os.path.join(dataset_dir, 'agent_output', 'output_stat_vars.mcf'), os.path.join(dataset_dir, 'final_output', 'output_stat_vars.mcf'), False, True, False), + ('stat_vars_schema.mcf', os.path.join(dataset_dir, 'agent_output', 'output_stat_vars_schema.mcf'), os.path.join(dataset_dir, 'final_output', 'output_stat_vars_schema.mcf'), False, False, False), + ('tmcf', os.path.join(dataset_dir, 'agent_output', 'output.tmcf'), os.path.join(dataset_dir, 'final_output', 'output.tmcf'), False, False, False), + ('output.csv', os.path.join(dataset_dir, 'agent_output', 'output.csv'), os.path.join(dataset_dir, 'final_output', 'output.csv'), False, False, True), ] detailed_results = {} + metrics_summary_data = [] total_tp = 0 total_fp = 0 total_fn = 0 print(f"\n--- Rechecking Efficacy for {dataset_id} ---") - for label, pred_name, gold_name, is_pv, use_fp in file_mappings: + for label, pred_path, gold_path, is_pv, use_fp, is_csv in file_mappings: print(f"Comparing {label}...") - diff_text, metrics = run_comparison(os.path.join(test_dir, pred_name), os.path.join(gold_dir, gold_name), is_pvmap=is_pv, use_fingerprint=use_fp) + if is_csv: + diff_text, metrics = run_csv_comparison(pred_path, gold_path) + else: + diff_text, metrics = run_comparison(pred_path, gold_path, is_pvmap=is_pv, use_fingerprint=use_fp) + detailed_results[label] = {'diff': diff_text, 'metrics': metrics} print(f" Result: F1={metrics['f1']:.1%}, TP={metrics['tp']}, FP={metrics['fp']}, FN={metrics['fn']}") + metrics_summary_data.append({ + 'File': label, + 'F1': f"{metrics['f1']:.4f}", + 'Precision': f"{metrics['precision']:.4f}", + 'Recall': f"{metrics['recall']:.4f}", + 'TP': metrics['tp'], + 'FP': metrics['fp'], + 'FN': metrics['fn'] + }) + total_tp += metrics['tp'] total_fp += metrics['fp'] total_fn += metrics['fn'] @@ -142,6 +191,23 @@ def process_single_dataset(test_dir, gold_dir, output_dir, dataset_id, template_ 'f1': f1 } + metrics_summary_data.append({ + 'File': 'Overall', + 'F1': f"{f1:.4f}", + 'Precision': f"{precision:.4f}", + 'Recall': f"{recall:.4f}", + 'TP': total_tp, + 'FP': total_fp, + 'FN': total_fn + }) + + # Write metrics_summary.csv + summary_csv_path = os.path.join(output_dir, 'metrics_summary.csv') + with open(summary_csv_path, 'w', newline='', encoding='utf-8') as f: + writer = csv.DictWriter(f, fieldnames=['File', 'F1', 'Precision', 'Recall', 'TP', 'FP', 'FN']) + writer.writeheader() + writer.writerows(metrics_summary_data) + final_html = update_html(template_content, dataset_id, hero_metrics, detailed_results) with open(os.path.join(output_dir, 'Agent_Efficacy_Board.html'), 'w') as f: f.write(final_html) @@ -150,9 +216,10 @@ def process_single_dataset(test_dir, gold_dir, output_dir, dataset_id, template_ def main(): parser = argparse.ArgumentParser(description="Calculate efficacy metrics.") parser.add_argument('--test', required=True, help="Path to the test (prediction) directory.") - parser.add_argument('--gold', required=True, help="Path to the gold (reviewed) directory.") + parser.add_argument('--gold', required=False, help="Path to the gold (reviewed) directory.") parser.add_argument('--output', required=True, help="Path to the output directory to save results.") parser.add_argument('--dataset_id', default="Dataset", help="Optional dataset ID for display.") + parser.add_argument('--file_mode', action='store_true', help="If set, treats test and gold as direct file paths.") parser.add_argument('--bulk', action='store_true', help="If set, treats test and gold as parent directories containing multiple dataset folders.") args = parser.parse_args() @@ -181,7 +248,7 @@ def main(): if os.path.isdir(test_ds_dir): out_ds_dir = os.path.join(args.output, dataset_id) try: - hero_metrics = process_single_dataset(test_ds_dir, gold_ds_dir, out_ds_dir, dataset_id, template_content) + hero_metrics = process_single_dataset(test_ds_dir, out_ds_dir, dataset_id, template_content) if hero_metrics: summary_data.append({ 'dataset_id': dataset_id, @@ -206,7 +273,7 @@ def main(): writer.writerows(summary_data) print(f"\nBulk run complete. Summary saved to {summary_path}") else: - process_single_dataset(args.test, args.gold, args.output, args.dataset_id, template_content) + process_single_dataset(args.test, args.output, args.dataset_id, template_content) print(f"\nRecheck complete. Results saved to {args.output}") if __name__ == "__main__": diff --git a/undata/IMPORT_GUIDE.md b/undata/IMPORT_GUIDE.md new file mode 100644 index 0000000000..178c1018a0 --- /dev/null +++ b/undata/IMPORT_GUIDE.md @@ -0,0 +1,77 @@ +# UN Data Commons Import Configuration Guide + +This document summarizes the research, strategy, and implementation of the `config.json` files for the UN DESA Gender dataset import. + +## Overview +The goal was to create a `config.json` file for the Data Commons importer that references all 57 CSV datasets while ensuring the associated MCF (Statistical Variable definitions) and TMCF (Mapping) files are correctly identified. + +## Data Structures +We identified two stages of data available in the workspace: + +1. **Raw Data (`undata/data/`):** Original CSV files with UN-specific headers (`SERIES`, `GEOGRAPHY`, etc.). +2. **Transcoded Data (`undata/output/`):** Processed folders containing: + * `output.csv`: Standardized headers (`variableMeasured`, `observationAbout`, etc.). + * `output_stat_vars.mcf`: Property definitions. + * `output.tmcf`: Column-to-Property mappings. + +--- + +## Configuration Approaches + +Two versions of the configuration were generated in `undata/data/` to provide flexibility based on the import preference. + +### Approach A: Importing Raw Data +**File:** `config_approach_a.json` +* **Source:** `undata/data/*.csv` +* **Mapping Strategy:** Maps original UN headers to Data Commons attributes. +* **Column Mappings:** + * `variable` → `SERIES` + * `entity` → `GEOGRAPHY` + * `date` → `TIME_PERIOD` + * `value` → `OBS_VALUE` + * `unit` → `UNIT_MEASURE` +* **Requirement:** To use this, you must copy the `.mcf` and `.tmcf` files from their respective `undata/output/` subfolders into `undata/data/` so the importer can "auto-identify" them. + +### Approach B: Importing Transcoded (Processed) Data +**File:** `config_approach_b.json` +* **Source:** `../output//output.csv` (Relative to `undata/data/`) +* **Mapping Strategy:** Uses standardized Data Commons headers already present in the processed files. +* **Column Mappings:** + * `variable` → `variableMeasured` + * `entity` → `observationAbout` + * `date` → `observationDate` + * `value` → `value` + * `measurementMethod` → `measurementMethod` + * `unit` → `unit` +* **Advantage:** No file moving is required. The MCF and TMCF files are already in the same folders as the `output.csv` files, making them immediately discoverable by the importer. + +### Approach C: Concise Wildcard Configuration (Experimental) +**File:** `config_approach_c.json` +* **Source:** Uses `*.csv` as a wildcard key. +* **Advantage:** Extremely short and easy to read. +* **Note:** Use this only if the Data Commons importer environment supports wildcard expansion in the config file. + +--- + +## How to Use + +1. **Review with Manager:** Decide whether to import the raw source files or the cleaned output files. +2. **Activate Configuration:** Rename your preferred version to `config.json` in the `undata/data` directory. + +```bash +# To use the Transcoded Data (Recommended): +cd undata/data +cp config_approach_b.json config.json + +# To use the Raw Data: +cd undata/data +cp config_approach_a.json config.json +``` + +3. **Run Importer:** Execute the Data Commons import tool pointing to the `undata/data` directory. + +## Metadata Summary +* **Provenance:** `UN_DESA` +* **Source URL:** [UN Statistics Division - Gender Data](https://unstats.un.org/unsd/gender/data.html) +* **Total Files:** 57 datasets +* **Grouping:** `groupStatVarsByProperty` is enabled to ensure related variables are grouped in the Statistical Variable Explorer. diff --git a/undata/extract_unique.py b/undata/extract_unique.py new file mode 100644 index 0000000000..67a69f9196 --- /dev/null +++ b/undata/extract_unique.py @@ -0,0 +1,19 @@ +import csv +import sys + +def get_unique_values(file_path, num_rows=2000): + with open(file_path, 'r', encoding='utf-8') as f: + reader = csv.DictReader(f) + fieldnames = reader.fieldnames + unique_values = {field: set() for field in fieldnames} + for i, row in enumerate(reader): + if i >= num_rows: + break + for field in fieldnames: + unique_values[field].add(row[field]) + + for field in fieldnames: + print(f"{field}: {sorted(list(unique_values[field]))}") + +if __name__ == "__main__": + get_unique_values(sys.argv[1]) diff --git a/undata/fix_geography.py b/undata/fix_geography.py new file mode 100644 index 0000000000..5ea74102ee --- /dev/null +++ b/undata/fix_geography.py @@ -0,0 +1,67 @@ +import csv +import os + +REF_FILE = "/usr/local/google/home/nehil/Downloads/UN_DATA_ Codelist Geography - CL_GEOGRAPHY.csv" +OUT_FILE = "undata/cl_reviewed/CL_GEOGRAPHY.csv" + +def fix_geography(): + if not os.path.exists(REF_FILE): + print(f"Error: Reference file not found at {REF_FILE}") + return + + with open(REF_FILE, mode='r', encoding='utf-8') as f_in: + reader = csv.reader(f_in) + header = next(reader) + + # Indices based on observation: + # 0: CONCEPT + # 1: CODE + # 2: NAME_EN + # 3: PARENT + # 5: SORT_ORDER (sometimes empty) + # 6: dcid + # 7: dcid_from_un_places + # 9: parent_dcid + + output_rows = [] + for row in reader: + if not row or len(row) < 10: + continue + + concept = row[0] + code = row[1] + name = row[2] + parent = row[3] + sort_order = row[5] + + dcid_primary = row[6].strip() + dcid_fallback = row[9].strip() + dcid_unplaces = row[7].strip() + + # Selection logic + final_dcid = "" + if dcid_primary and not dcid_primary.startswith("undata-geo/") and dcid_primary != "N": + final_dcid = dcid_primary + elif dcid_fallback and not dcid_fallback.startswith("undata-geo/") and dcid_fallback != "N": + final_dcid = dcid_fallback + elif dcid_unplaces and not dcid_unplaces.startswith("undata-geo/") and dcid_unplaces != "N": + final_dcid = dcid_unplaces + + if final_dcid: + # Format as property, value + # We'll use geographyCounterpart as the default property + dc_pv_pair = f"geographyCounterpart, {final_dcid}" + output_rows.append([concept, code, name, parent, sort_order, dc_pv_pair]) + else: + # Still include the row but without a mapping if none found + output_rows.append([concept, code, name, parent, sort_order, ""]) + + with open(OUT_FILE, mode='w', encoding='utf-8', newline='') as f_out: + writer = csv.writer(f_out) + writer.writerow(["CONCEPT", "CODE", "NAME_EN", "PARENT", "SORT_ORDER", "DC_PV_PAIR"]) + writer.writerows(output_rows) + + print(f"Successfully reconstructed {OUT_FILE} with {len(output_rows)} rows.") + +if __name__ == "__main__": + fix_geography() diff --git a/undata/generate_desa_files.py b/undata/generate_desa_files.py new file mode 100644 index 0000000000..3786bb86ef --- /dev/null +++ b/undata/generate_desa_files.py @@ -0,0 +1,110 @@ +import csv +import collections +import os + +data_file = '/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/data/DESA-GENDER_2025_OBS_MAT_WAGE.csv' +cl_dir = '/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/cl_reviewed' +output_pvmap = '/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/output/unreviewed/DESA-GENDER_2025_OBS_MAT_WAGE/output_pvmap.csv' +output_metadata = '/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/output/unreviewed/DESA-GENDER_2025_OBS_MAT_WAGE/output_metadata.csv' + +# Ensure output directory exists +os.makedirs(os.path.dirname(output_pvmap), exist_ok=True) + +# 1. Collect unique values from data +unique_values = collections.defaultdict(set) +with open(data_file, 'r') as f: + reader = csv.DictReader(f) + for row in reader: + for col, val in row.items(): + unique_values[col].add(val) + +# 2. Map columns to codelists +col_to_cl = { + 'SERIES': 'CL_SERIES.csv', + 'GEOGRAPHY': 'CL_GEOGRAPHY.csv', + 'VALUE_CATEGORY': 'CL_VALUE_CATEGORY.csv', + 'UNIT_MEASURE': 'CL_UNIT_MEASURE.csv', + 'UNIT_MULT': 'CL_UNIT_MULT.csv', + 'CENSORED_VALUE_TYPE': 'CL_CENSORED_VALUE_TYPE.csv', + 'FREQUENCY': 'CL_FREQUENCY.csv', + 'OBSERVATION_STATUS': 'CL_OBSERVATION_STATUS.csv' +} + +pv_mappings = collections.defaultdict(list) + +for col, cl_file in col_to_cl.items(): + cl_path = os.path.join(cl_dir, cl_file) + if not os.path.exists(cl_path): + continue + + with open(cl_path, 'r') as f: + reader = csv.reader(f) + header = next(reader) + try: + code_idx = header.index('CODE') + pv_idx = header.index('DC_PV_PAIR') + except ValueError: + continue + + for row in reader: + if not row or len(row) <= code_idx: + continue + code = row[code_idx] + if code in unique_values[col]: + # Take all values from pv_idx onwards + parts = [] + for val in row[pv_idx:]: + if val.strip(): + # If a value contains a comma, it might be a single string from a quoted field + # or it might be multiple fields. csv.reader handles quoting. + # We want to split by comma if it's a single string like "p, v" + if ',' in val: + parts.extend([p.strip() for p in val.split(',')]) + else: + parts.append(val.strip()) + + pairs = [] + for i in range(0, len(parts), 2): + if i + 1 < len(parts): + p, v = parts[i], parts[i+1] + if p == 'geographyCounterpart': + p = 'observationAbout' + pairs.append((p, v)) + + if pairs: + key = f"{col}:{code}" + for p, v in pairs: + pv_mappings[key].extend([p, v]) + +# 3. Add mandatory properties for SERIES:MAT_WAGE +if 'SERIES:MAT_WAGE' in pv_mappings: + pvs = pv_mappings['SERIES:MAT_WAGE'] + has_pop = False + has_mp = False + has_st = False + for i in range(0, len(pvs), 2): + if pvs[i] == 'populationType': has_pop = True + if pvs[i] == 'measuredProperty': has_mp = True + if pvs[i] == 'statType': has_st = True + + if not has_pop: pvs.extend(['populationType', 'MaternityLeave']) + if not has_mp: pvs.extend(['measuredProperty', 'count']) + if not has_st: pvs.extend(['statType', 'measuredValue']) +else: + pv_mappings['SERIES:MAT_WAGE'] = ['benefitType', 'MaternityLeaveWages', 'populationType', 'MaternityLeave', 'measuredProperty', 'count', 'statType', 'measuredValue'] + +# 4. Add global mappings +pv_mappings['TIME_PERIOD'] = ['observationDate', '{Data}'] +pv_mappings['OBS_VALUE'] = ['value', '{Number}'] + +# 5. Write output_pvmap.csv +with open(output_pvmap, 'w') as f: + writer = csv.writer(f) + for key, pvs in pv_mappings.items(): + writer.writerow([key] + pvs) + +# 6. Write output_metadata.csv +with open(output_metadata, 'w') as f: + writer = csv.writer(f) + writer.writerow(['parameter', 'value']) + writer.writerow(['header_rows', '1']) diff --git a/undata/generate_metadata.py b/undata/generate_metadata.py new file mode 100644 index 0000000000..66e4152121 --- /dev/null +++ b/undata/generate_metadata.py @@ -0,0 +1,242 @@ +import os +import csv +import glob +import argparse +from pathlib import Path + +# Mapping of data columns to their respective codelist files +COLUMN_TO_CL = { + "SERIES": "CL_SERIES.csv", + "GEOGRAPHY": "CL_GEOGRAPHY.csv", + "AGE": "CL_AGE.csv", + "SEX": "CL_SEX.csv", + "EDUCATION_LEVEL": "CL_EDUCATION_LEVEL.csv", + "WEALTH_QUANTILE": "CL_WEALTH_QUANTILE.csv", + "URBANIZATION": "CL_URBANIZATION.csv", + "UNIT_MEASURE": "CL_UNIT_MEASURE.csv", + "UNIT_MULT": "CL_UNIT_MULT.csv", + "CENSORED_VALUE_TYPE": "CL_CENSORED_VALUE_TYPE.csv", + "FREQUENCY": "CL_FREQUENCY.csv", + "OBSERVATION_STATUS": "CL_OBSERVATION_STATUS.csv", + "ICT_SKILL": "CL_ICT_SKILL.csv", + "OCCUPATION": "CL_OCCUPATION.csv", + "ANTENATAL_CARE_VISITS": "CL_ANTENATAL_CARE_VISITS.csv", + "ECONOMIC_ACTIVITY": "CL_ECONOMIC_ACTIVITY.csv", + "GHE_CAUSE": "CL_GHE_CAUSE.csv", + "REP_HEALTH_DECISION": "CL_REP_HEALTH_DECISION.csv", + "CHILDREN_UNDER6": "CL_CHILDREN_UNDER6.csv", + "HOUSEHOLD_COMPOSITION": "CL_HOUSEHOLD_COMPOSITION.csv", + "GENDER_EQ_AREA": "CL_GENDER_EQ_AREA.csv", + "PARENTAL_CONSENT_STATUS": "CL_PARENTAL_CONSENT_STATUS.csv", + "STATUS_IN_EMPLOYMENT": "CL_STATUS_IN_EMPLOYMENT.csv", + "GOVERNMENT_LEVEL": "CL_GOVERNMENT_LEVEL.csv", + "VALUE_CATEGORY": "CL_VALUE_CATEGORY.csv", + "ILO_CONVENTION": "CL_ILO_CONVENTION.csv", + "TYPE_OF_QUOTA": "CL_TYPE_OF_QUOTA.csv", + "DISABILITY_STATUS": "CL_DISABILITY_STATUS.csv", + "TIME_USE_ACTIVITY": "CL_TIME_USE_ACTIVITY.csv", + "VIOLENCE_TYPE": "CL_VIOLENCE_TYPE.csv", + # Additional SDG Columns + "PRODUCT": "CL_PRODUCT.csv", + "NATURE": "CL_NATURE.csv", + "REPORTING_LEVEL": "CL_REPORTING_LEVEL.csv", + "VALUE_TYPE": "CL_VALUE_TYPE.csv", + "TIME_DETAIL": "CL_TIME_DETAIL.csv", + "SOURCE": "CL_SOURCE.csv", + "BASE_PERIOD": "CL_BASE_PERIOD.csv", + "BIOCLIMATIC_BELT": "CL_BIOCLIMATIC_BELT.csv", + "CHLOROPHYLL_A_CONCENTRATION_FREQ": "CL_CHLOROPHYLL_A_CONCENTRATION_FREQ.csv", + "CLIMATE_FINANCIAL_SUPPORT": "CL_CLIMATE_FINANCIAL_SUPPORT.csv", + "DEVIATION_LEVEL": "CL_DEVIATION_LEVEL.csv", + "FISCAL_INTERVENTION_STAGE": "CL_FISCAL_INTERVENTION_STAGE.csv", + "FOOD_WASTE_SECTOR": "CL_FOOD_WASTE_SECTOR.csv", + "GEOGRAPHY_COUNTERPART": "CL_GEOGRAPHY.csv", + "GOVERNMENT_NAME": "CL_GOVERNMENT_NAME.csv", + "GROUNDS_OF_DISCRIMINATION": "CL_GROUNDS_OF_DISCRIMINATION.csv", + "IHR_CAPACITY": "CL_IHR_CAPACITY.csv", + "ILLICIT_FINANCIAL_FLOWS": "CL_ILLICIT_FINANCIAL_FLOWS.csv", + "INTERNATIONAL_ORGANIZATION": "CL_INTERNATIONAL_ORGANIZATION.csv", + "INTERNET_SPEED": "CL_INTERNET_SPEED.csv", + "LAND_COVER": "CL_LAND_COVER.csv", + "LETHAL_INSTRUMENT": "CL_LETHAL_INSTRUMENT.csv", + "LEVEL_STATUS": "CL_LEVEL_STATUS.csv", + "LOWER_BOUND": "CL_LOWER_BOUND.csv", + "MARINE_SPATIAL_PLAN": "CL_MARINE_SPATIAL_PLAN.csv", + "MIGRATORY_STATUS": "CL_MIGRATORY_STATUS.csv", + "NATURE": "CL_NATURE.csv", + "NUTRIENT_LOADING": "CL_NUTRIENT_LOADING.csv", + "OFDI_SCHEME": "CL_OFDI_SCHEME.csv", + "PARLIAMENTARY_COMMITTEES": "CL_PARLIAMENTARY_COMMITTEES.csv", + "POLICY_DOMAINS": "CL_POLICY_DOMAINS.csv", + "POLICY_INSTRUMENTS": "CL_POLICY_INSTRUMENTS.csv", + "POPULATION_GROUP": "CL_POPULATION_GROUP.csv", + "PRICE_LEVEL_SEVERITY": "CL_PRICE_LEVEL_SEVERITY.csv", + "RENEWABLE_TECHNOLOGY": "CL_RENEWABLE_TECHNOLOGY.csv", + "REPORT_ORDINAL": "CL_REPORT_ORDINAL.csv", + "REQUIREMENT_LEVEL": "CL_REQUIREMENT_LEVEL.csv", + "SERVICE_ATTRIBUTE": "CL_SERVICE_ATTRIBUTE.csv", + "SKILL": "CL_SKILL.csv", + "SUBSTANCE": "CL_SUBSTANCE.csv", + "TRANSPORT_MODE": "CL_TRANSPORT_MODE.csv", + "UPPER_BOUND": "CL_UPPER_BOUND.csv", + "WASTE_TREATMENT": "CL_WASTE_TREATMENT.csv", +} + +def load_codelists(cl_dir): + """Loads all reviewed codelists into a nested dictionary.""" + master_map = {} + print(f"Loading codelists from {cl_dir}...") + for column, cl_file in COLUMN_TO_CL.items(): + cl_path = os.path.join(cl_dir, cl_file) + # Also check for files with 'processed_' prefix + if not os.path.exists(cl_path): + cl_path_processed = os.path.join(cl_dir, f"processed_{cl_file}") + if os.path.exists(cl_path_processed): + cl_path = cl_path_processed + else: + print(f"Warning: Codelist file {cl_file} (or processed_{cl_file}) not found for column {column}") + continue + + try: + with open(cl_path, mode='r', encoding='utf-8') as f: + reader = csv.reader(f) + header = next(reader) + + pv_start_idx = -1 + for i, col_name in enumerate(header): + if col_name.strip().upper() == 'DC_PV_PAIR': + pv_start_idx = i + break + + if pv_start_idx == -1: + print(f"Warning: DC_PV_PAIR column not found in {cl_file}") + continue + + col_map = {} + for row in reader: + if not row or len(row) <= 1: + continue + + code = row[1] + raw_parts = [p.strip() for p in row[pv_start_idx:] if p.strip()] + pv_pairs = [] + for j in range(0, len(raw_parts), 2): + if j + 1 < len(raw_parts): + pv_pairs.append(f"{raw_parts[j]}:{raw_parts[j+1]}") + else: + pv_pairs.append(raw_parts[j]) + + if code: + col_map[code] = [",".join(pv_pairs)] if pv_pairs else [] + + master_map[column] = { + "codelist_map": col_map, + "additional_headers": ["DC_PV_PAIR"] + } + + except Exception as e: + print(f"Error loading {cl_file}: {e}") + + return master_map + +def generate_metadata(cl_dir, data_dir, metadata_dir): + """Generates a metadata dictionary for each data file.""" + master_map = load_codelists(cl_dir) + os.makedirs(metadata_dir, exist_ok=True) + + data_files = glob.glob(os.path.join(data_dir, "*.csv")) + print(f"Found {len(data_files)} data files in {data_dir}.") + + for data_path in data_files: + filename = Path(data_path).stem + output_path = os.path.join(metadata_dir, f"{filename}_metadata.csv") + + print(f"Processing {filename}...") + + try: + with open(data_path, mode='r', encoding='utf-8') as f: + reader = csv.DictReader(f) + # Include all columns that are in COLUMN_TO_CL + relevant_cols = [c for c in reader.fieldnames if c in COLUMN_TO_CL] + + if not relevant_cols: + print(f"No mappable columns found in {filename}") + continue + + unique_values = {col: set() for col in relevant_cols} + for row in reader: + for col in relevant_cols: + val = row.get(col) + if val: + unique_values[col].add(val) + + metadata_rows = [] + output_fieldnames = ["column_name", "value"] + seen_additional_headers = set() + + # First pass to get all additional headers from master_map + for col in relevant_cols: + if col in master_map: + current_codelist_headers = master_map[col]["additional_headers"] + for header_name in current_codelist_headers: + if header_name not in seen_additional_headers: + output_fieldnames.append(header_name) + seen_additional_headers.add(header_name) + + # If DC_PV_PAIR not in seen_additional_headers, add it as a default + if "DC_PV_PAIR" not in seen_additional_headers: + output_fieldnames.append("DC_PV_PAIR") + + for col in relevant_cols: + if col in master_map: + codelist_info = master_map[col] + codelist_map = codelist_info["codelist_map"] + current_codelist_headers = codelist_info["additional_headers"] + else: + codelist_map = {} + current_codelist_headers = ["DC_PV_PAIR"] + + for val in sorted(unique_values[col]): + pv_parts = codelist_map.get(val) + + row_dict = { + "column_name": col, + "value": val, + } + + if pv_parts and pv_parts[0].strip(): + for i, header_name in enumerate(current_codelist_headers): + if i < len(pv_parts): + row_dict[header_name] = pv_parts[i] + else: + for header_name in current_codelist_headers: + row_dict[header_name] = "" + + # Fill in missing fields with empty string + for field in output_fieldnames: + if field not in row_dict: + row_dict[field] = "" + + metadata_rows.append(row_dict) + + if metadata_rows: + with open(output_path, mode='w', encoding='utf-8', newline='') as out_f: + writer = csv.DictWriter(out_f, fieldnames=output_fieldnames) + writer.writeheader() + writer.writerows(metadata_rows) + print(f" Generated: {output_path}") + else: + print(f" No valid mappings found for {filename}") + + except Exception as e: + print(f"Error processing {filename}: {e}") + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Generate metadata CSVs from data CSVs and codelists.") + parser.add_argument("--cl_dir", default="undata/cl_reviewed", help="Directory containing codelist CSVs.") + parser.add_argument("--data_dir", default="undata/SDG/data", help="Directory containing data CSVs.") + parser.add_argument("--metadata_dir", default="undata/SDG/metadata", help="Directory to save generated metadata CSVs.") + + args = parser.parse_args() + generate_metadata(args.cl_dir, args.data_dir, args.metadata_dir) + diff --git a/undata/generate_pvmap.py b/undata/generate_pvmap.py new file mode 100644 index 0000000000..e633dd41af --- /dev/null +++ b/undata/generate_pvmap.py @@ -0,0 +1,81 @@ +import csv +import io + +pvmap = [] +deterministic_file = "/usr/local/google/home/nehil/datacommons/import/git/data/undata/SDG/output/agentic/SDG_q4-2025_OBS_EN_MAR_TP/deterministic_pvmap.csv" +metadata_file = "/usr/local/google/home/nehil/datacommons/import/git/data/undata/SDG/output/agentic/SDG_q4-2025_OBS_EN_MAR_TP/input/metadata.csv" + +# Read deterministic pvmap +with open(deterministic_file, 'r') as f: + reader = csv.reader(f) + header = next(reader) + for row in reader: + pvmap.append(row) + +# Read metadata.csv and convert to pvmap entries +# metadata format: column_name,value,DC_PV_PAIR +with open(metadata_file, 'r') as f: + reader = csv.reader(f) + next(reader) # skip header + for row in reader: + if len(row) < 3: continue + col_name = row[0].strip() + val = row[1].strip() + dc_pv = row[2].strip() + if not dc_pv: continue + + # Parse dc_pv (comma separated, then colon separated) + # e.g., "populationType:WaterBody, chemicalConcentration:TotalPhosphorus" + pairs = [p.strip() for p in dc_pv.split(',') if p.strip()] + + pv_row = [f"{col_name}:{val}"] + for p in pairs: + if ':' in p: + k, v = p.split(':', 1) + pv_row.extend([k.strip(), v.strip()]) + else: + pv_row.extend([p, ""]) + + # check if this key is already in deterministic pvmap + # if so, maybe skip or merge? + existing = False + for i, existing_row in enumerate(pvmap): + if existing_row and existing_row[0] == pv_row[0]: + existing = True + # Merge logic if needed, but let's just replace or assume deterministic is better + # Wait, deterministic might be missing some properties that are in metadata. + # Actually, deterministic_pvmap already has SERIES:EN_MAR_TP etc. + break + if not existing: + pvmap.append(pv_row) + +# Ensure measuredProperty and statType are present somewhere +has_measured_property = False +has_stat_type = False +for row in pvmap: + for i in range(1, len(row), 2): + if row[i] == 'measuredProperty': has_measured_property = True + if row[i] == 'statType': has_stat_type = True + +if not has_measured_property or not has_stat_type: + # Let's add them to OBS_VALUE which is the global value column + for row in pvmap: + if row[0] == 'OBS_VALUE': + if not has_measured_property: + row.extend(['measuredProperty', 'concentration']) + if not has_stat_type: + row.extend(['statType', 'measuredValue']) + +# Pad rows to max length +max_len = max(len(r) for r in pvmap) +header = ['key'] +for i in range(1, max_len, 2): + header.extend([f'prop{i//2+1}', f'val{i//2+1}']) + +with open('merged_pvmap.csv', 'w') as f: + writer = csv.writer(f) + writer.writerow(header) + for row in pvmap: + padded = list(row) + [''] * (max_len - len(row)) + writer.writerow(padded) + diff --git a/undata/generate_pvmap_script.py b/undata/generate_pvmap_script.py new file mode 100644 index 0000000000..862a42a324 --- /dev/null +++ b/undata/generate_pvmap_script.py @@ -0,0 +1,94 @@ +import csv + +def generate_pvmap(): + pvmap = [] + pvmap.append(['key', 'property1', 'value1', 'property2', 'value2', 'property3', 'value3', 'property4', 'value4', 'property5', 'value5', 'property6', 'value6', 'property7', 'value7', 'property8', 'value8']) + + # SERIES + pvmap.append(['SERIES:NEET_RATE', 'populationType', 'dcs:Person', 'measuredProperty', 'dcs:count', 'statType', 'dcs:measuredValue', 'employmentStatus', 'dcs:NotInEducationEmploymentOrTraining']) + + codelists = [ + ('GEOGRAPHY', '/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/cl_reviewed/CL_GEOGRAPHY.csv'), + ('AGE', '/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/cl_reviewed/CL_AGE.csv'), + ('SEX', '/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/cl_reviewed/CL_SEX.csv'), + ('UNIT_MEASURE', '/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/cl_reviewed/CL_UNIT_MEASURE.csv'), + ('UNIT_MULT', '/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/cl_reviewed/CL_UNIT_MULT.csv'), + ('CENSORED_VALUE_TYPE', '/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/cl_reviewed/CL_CENSORED_VALUE_TYPE.csv'), + ('FREQUENCY', '/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/cl_reviewed/CL_FREQUENCY.csv'), + ('OBSERVATION_STATUS', '/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/cl_reviewed/CL_OBSERVATION_STATUS.csv'), + ] + + for prefix, path in codelists: + with open(path, 'r', encoding='utf-8') as f: + reader = csv.reader(f) + header = next(reader) + # Find DC_PV_PAIR index + try: + pv_idx = header.index('DC_PV_PAIR') + except ValueError: + continue + + for row in reader: + if not row or len(row) <= pv_idx: + continue + + code = row[1] + pv_parts = row[pv_idx:] + # Join and re-split to handle commas inside columns or extra columns + pv_str = ",".join(pv_parts).strip() + if not pv_str: + if code == '_T' and prefix in ['AGE', 'SEX']: + prop = 'age' if prefix == 'AGE' else 'gender' + pvmap.append([f"{prefix}:{code}", prop, '""']) + continue + + pairs = [] + if ':' in pv_str: + # Format: prop: val, prop: val + items = [item.strip() for item in pv_str.split(',')] + for item in items: + if ':' in item: + k, v = item.split(':', 1) + val = v.strip() + if not val.startswith('dcs:') and not val.startswith('[') and not val.startswith('"') and val != '""': + val = 'dcs:' + val + pairs.extend([k.strip(), val]) + else: + # Format: prop, val, prop, val + items = [item.strip() for item in pv_str.split(',')] + for i in range(0, len(items), 2): + if i + 1 < len(items): + k, v = items[i], items[i+1] + if not k or not v: continue + val = v.strip() + if not val.startswith('dcs:') and not val.startswith('[') and not val.startswith('"') and val != '""': + val = 'dcs:' + val + pairs.extend([k, val]) + + if prefix == 'GEOGRAPHY': + if 'geographyCounterpart' in pairs: + idx = pairs.index('geographyCounterpart') + pvmap.append([f"GEOGRAPHY:{code}", 'observationAbout', pairs[idx+1]]) + elif prefix == 'UNIT_MULT': + if '#Multiply' in pairs: + idx = pairs.index('#Multiply') + pvmap.append([f"UNIT_MULT:{code}", 'scalingFactor', pairs[idx+1]]) + elif prefix == 'FREQUENCY': + if 'measurementQualifier' in pairs: + idx = pairs.index('measurementQualifier') + if pairs[idx+1] == 'dcs:Annual': + pairs.extend(['observationPeriod', 'P1Y']) + pvmap.append([f"FREQUENCY:{code}"] + pairs) + else: + if pairs: + pvmap.append([f"{prefix}:{code}"] + pairs) + + pvmap.append(['TIME_PERIOD', 'observationDate', '{Data}']) + pvmap.append(['OBS_VALUE', 'value', '{Number}']) + + with open('/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/output/unreviewed/DESA-GENDER_2025_OBS_NEET_RATE/output_pvmap.csv', 'w', newline='') as f: + writer = csv.writer(f) + writer.writerows(pvmap) + +if __name__ == "__main__": + generate_pvmap() diff --git a/undata/generate_sdg_migrp.py b/undata/generate_sdg_migrp.py new file mode 100644 index 0000000000..8e078515e2 --- /dev/null +++ b/undata/generate_sdg_migrp.py @@ -0,0 +1,89 @@ +import csv + +metadata_in = "/usr/local/google/home/nehil/datacommons/import/git/data/undata/SDG/output/agentic/SDG_q4-2025_OBS_SG_CPA_MIGRP/input/metadata.csv" +pvmap_out = "/usr/local/google/home/nehil/datacommons/import/git/data/undata/SDG/output/agentic/SDG_q4-2025_OBS_SG_CPA_MIGRP/generated/new_pvmap.csv" +metadata_out = "/usr/local/google/home/nehil/datacommons/import/git/data/undata/SDG/output/agentic/SDG_q4-2025_OBS_SG_CPA_MIGRP/generated/new_metadata.csv" + +# First, construct the PV map rows +pv_map = {} + +# Standard global column mappings +pv_map["TIME_PERIOD"] = ["observationDate", "{Data}"] +pv_map["OBS_VALUE"] = ["value", "{Number}"] +pv_map["UNIT_MULT"] = ["IgnoredUnitMult", "''"] +pv_map["FOOTNOTE"] = ["IgnoredFootnote", "''"] +pv_map["SOURCE"] = ["IgnoredSource", "''"] +pv_map["VALUE_TYPE"] = ["IgnoredValueType", "''"] +pv_map["TIME_DETAIL"] = ["IgnoredTimeDetail", "''"] + +with open(metadata_in, "r") as f: + reader = csv.DictReader(f) + for row in reader: + col = row["column_name"].strip() + val = row["value"].strip() + pv_pair_str = row["DC_PV_PAIR"].strip() + + # Build key + if val: + key = f"{col}:{val}" + else: + key = col + + # Parse PV pairs + pvs = [] + if pv_pair_str: + # Special cleanups for malformed inputs + if "G00116000" in key: + pvs = ["geographyCounterpart", "CentralAndSouthAmerica", "incomeLevel", "LowerMiddleIncome"] + elif "G00126000" in key: + pvs = ["geographyCounterpart", "MiddleEastNotElsewhereSpecified"] + else: + # Split by comma but handle cases where values are quoted or have colons + parts = pv_pair_str.split(",") + for part in parts: + part = part.strip() + if ":" in part: + # Split on the first colon + subparts = part.split(":", 1) + prop = subparts[0].strip() + val_prop = subparts[1].strip() + pvs.extend([prop, val_prop]) + + # Specials/Custom mappings + if col == "REPORTING_LEVEL" and val == "G": + # Must set observationAbout to Earth/Global + pvs = ["observationAbout", "wikidataId/Q2", "reportingLevel", "Global"] + elif col == "POLICY_DOMAINS" and val == "_T": + pvs = ["IgnoredPolicyDomainTotal", "''"] + elif col == "UNIT_MULT" and val == "0": + pvs = ["IgnoredUnitMult", "''"] + + if pvs: + pv_map[key] = pvs + +# Write PV map to CSV +header = ["key"] +max_props = max(len(pvs) // 2 for pvs in pv_map.values()) +for i in range(1, max_props + 1): + header.extend([f"prop{i}", f"val{i}"]) + +rows = [header] +for key in sorted(pv_map.keys()): + row = [key] + pv_map[key] + row += [""] * (len(header) - len(row)) + rows.append(row) + +with open(pvmap_out, "w", newline="") as f: + writer = csv.writer(f) + writer.writerows(rows) + +print(f"Generated {pvmap_out}") + +# Write metadata.csv +with open(metadata_out, "w") as f: + f.write("parameter,value\n") + f.write("header_rows,1\n") + f.write('output_columns,"observationDate,observationAbout,variableMeasured,value,observationPeriod,measurementMethod,unit,scalingFactor,#input"\n') + f.write("output_only_new_statvars,0\n") + +print(f"Generated {metadata_out}") diff --git a/undata/metadata_to_pvmap.py b/undata/metadata_to_pvmap.py new file mode 100644 index 0000000000..f5c81b1cbf --- /dev/null +++ b/undata/metadata_to_pvmap.py @@ -0,0 +1,127 @@ +import os +import csv +import glob +from pathlib import Path +import collections + +METADATA_DIR = "undata/SDG/metadata" +OUTPUT_DIR = "undata/SDG/output/deterministic_pvmap" + +def normalize_geography(pairs): + """Special normalization for geography properties.""" + normalized = [] + for p, v in pairs: + if p == 'geographyCounterpart': + if 'wikidataId/' in v: + normalized.append(('observationAbout', f'dcid:{v.split("/")[-1]}')) + elif 'country/' in v: + normalized.append(('observationAbout', f'country/{v.split("/")[-1]}')) + else: + normalized.append(('observationAbout', v)) + else: + normalized.append((p, v)) + return normalized + +def process_metadata_to_pvmap(): + metadata_files = glob.glob(os.path.join(METADATA_DIR, "*_metadata.csv")) + print(f"Found {len(metadata_files)} metadata files.") + + for meta_path in metadata_files: + filename_stem = Path(meta_path).name.replace("_metadata.csv", "") + os.makedirs(OUTPUT_DIR, exist_ok=True) + # Final PVMap filename + output_pvmap = os.path.join(OUTPUT_DIR, f"{filename_stem}_pvmap.csv") + # Separate Metadata filename if needed + output_metadata = os.path.join(OUTPUT_DIR, f"{filename_stem}_metadata.csv") + + print(f"Processing metadata for {filename_stem}...") + + pv_mappings = collections.defaultdict(list) + + # Add Defaults + pv_mappings['TIME_PERIOD'].append(('observationDate', '{Data}')) + pv_mappings['OBS_VALUE'].append(('value', '{Number}')) + + try: + with open(meta_path, mode='r', encoding='utf-8') as f: + reader = csv.DictReader(f) + for row in reader: + col = row['column_name'] + val = row['value'] + key = f"{col}:{val}" + + extra_vals = [] + for header, cell_val in row.items(): + if header not in ['column_name', 'value'] and cell_val: + parts = [p.strip() for p in cell_val.split(',')] + extra_vals.extend(parts) + + all_pairs = [] + if any(':' in v for v in extra_vals): + for v in extra_vals: + if ':' in v: + p, v_pv = v.split(':', 1) + all_pairs.append((p.strip(), v_pv.strip())) + elif ' ' in v: + p_v = v.split(' ', 1) + all_pairs.append((p_v[0].strip(), p_v[1].strip())) + else: + for i in range(0, len(extra_vals), 2): + if i + 1 < len(extra_vals): + p = extra_vals[i].strip() + v_pv = extra_vals[i+1].strip() + if p and v_pv: + all_pairs.append((p, v_pv)) + + if col == 'GEOGRAPHY': + all_pairs = normalize_geography(all_pairs) + + unique_pairs = {} + for p, v in all_pairs: + unique_pairs[p] = v + + for p, v in unique_pairs.items(): + pv_mappings[key].append((p, v)) + + for key in pv_mappings: + if key.startswith('SERIES:'): + existing_props = [p for p, v in pv_mappings[key]] + if 'populationType' not in existing_props: + pv_mappings[key].append(('populationType', 'Person')) + if 'measuredProperty' not in existing_props: + pv_mappings[key].append(('measuredProperty', 'count')) + if 'statType' not in existing_props: + pv_mappings[key].append(('statType', 'measuredValue')) + + # Write pvmap.csv (without 1,2 rows) + with open(output_pvmap, mode='w', encoding='utf-8', newline='') as out_f: + writer = csv.writer(out_f) + # PVMap starts with the key/property header + writer.writerow(["key", "property1", "value1", "property2", "value2", "property3", "value3", "property4", "value4", "property5", "value5"]) + + for key, pairs in pv_mappings.items(): + row = [key] + for p, v in pairs: + row.extend([p, v]) + + target_len = 11 + if len(row) < target_len: + row.extend([''] * (target_len - len(row))) + else: + row = row[:target_len] + writer.writerow(row) + + # Write metadata.csv (with 1,2 rows) + with open(output_metadata, mode='w', encoding='utf-8', newline='') as out_f: + writer = csv.writer(out_f) + writer.writerow(["parameter", "value"]) + writer.writerow(["header_rows", "1"]) + # Could add more parameters here if known + + print(f" Saved: {output_pvmap} and {output_metadata}") + + except Exception as e: + print(f"Error processing {meta_path}: {e}") + +if __name__ == "__main__": + process_metadata_to_pvmap() diff --git a/undata/new_data/UN20x_ilo_2026-05-25_transcoded_20260526T010557Z_7dbe65/transcoded/_dsd_canonicalization.json b/undata/new_data/UN20x_ilo_2026-05-25_transcoded_20260526T010557Z_7dbe65/transcoded/_dsd_canonicalization.json new file mode 100644 index 0000000000..4c6fa7dedf --- /dev/null +++ b/undata/new_data/UN20x_ilo_2026-05-25_transcoded_20260526T010557Z_7dbe65/transcoded/_dsd_canonicalization.json @@ -0,0 +1,13387 @@ +{ + "schema_version": "v1", + "step": "apply_transcoding", + "tier": "transcoded", + "agency": "ILO", + "version": "2026-05-25", + "run_id": "20260526T010557Z_7dbe65", + "registry_path": "undata_concepts/canonical_concepts.csv", + "registry_concept_count": 66, + "dsds_scanned": 95, + "rows_scanned": 1218, + "rows_canonicalized_count": 1213, + "concepts_unchanged_count": 1, + "concepts_not_in_registry_count": 0, + "rows_canonicalized": [ + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "SERIES", + "changed_fields": { + "NAME_EN": { + "was": "Measure", + "now": "Series" + }, + "DESCRIPTION": { + "was": "", + "now": "Identifies the analytical series to which the observation belongs." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "GEOGRAPHY", + "changed_fields": { + "NAME_EN": { + "was": "Reference area", + "now": "Geography" + }, + "DESCRIPTION": { + "was": "", + "now": "Primary spatial reference area for the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "TIME_PERIOD", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Temporal reference of the observation, expressed using the accepted ISO 8601 time-period forms." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "AGE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Age disaggregation of the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "SEX", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Sex disaggregation of the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "OBS_VALUE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The actual measured numeric value." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "UNIT_MEASURE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The unit in which the observation value is expressed." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "UNIT_MULT", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Scale factor applied to the observation value (e.g. thousands, millions)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "CENSORED_VALUE_TYPE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Status code indicating the censoring relation that qualifies the observation value, or that no censoring relation applies." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "FOOTNOTE", + "changed_fields": { + "NAME_EN": { + "was": "Indicator Notes", + "now": "Footnote" + }, + "DESCRIPTION": { + "was": "", + "now": "Interpretive note associated with the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "SOURCE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Source or provenance statement associated with the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "FREQUENCY", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Reporting frequency of the observation (annual, quarterly, monthly, daily, \u2026)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5EAP_NB.csv", + "concept": "OBSERVATION_STATUS", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Quality or interpretive status of the observation (e.g. estimated, provisional, final)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "SERIES", + "changed_fields": { + "NAME_EN": { + "was": "Measure", + "now": "Series" + }, + "DESCRIPTION": { + "was": "", + "now": "Identifies the analytical series to which the observation belongs." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "GEOGRAPHY", + "changed_fields": { + "NAME_EN": { + "was": "Reference area", + "now": "Geography" + }, + "DESCRIPTION": { + "was": "", + "now": "Primary spatial reference area for the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "TIME_PERIOD", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Temporal reference of the observation, expressed using the accepted ISO 8601 time-period forms." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "AGE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Age disaggregation of the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "SEX", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Sex disaggregation of the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "OBS_VALUE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The actual measured numeric value." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "UNIT_MEASURE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The unit in which the observation value is expressed." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "UNIT_MULT", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Scale factor applied to the observation value (e.g. thousands, millions)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "CENSORED_VALUE_TYPE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Status code indicating the censoring relation that qualifies the observation value, or that no censoring relation applies." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "FOOTNOTE", + "changed_fields": { + "NAME_EN": { + "was": "Indicator Notes", + "now": "Footnote" + }, + "DESCRIPTION": { + "was": "", + "now": "Interpretive note associated with the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "SOURCE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Source or provenance statement associated with the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "FREQUENCY", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Reporting frequency of the observation (annual, quarterly, monthly, daily, \u2026)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_5WAP_RT.csv", + "concept": "OBSERVATION_STATUS", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Quality or interpretive status of the observation (e.g. estimated, provisional, final)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "SERIES", + "changed_fields": { + "NAME_EN": { + "was": "Measure", + "now": "Series" + }, + "DESCRIPTION": { + "was": "", + "now": "Identifies the analytical series to which the observation belongs." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "GEOGRAPHY", + "changed_fields": { + "NAME_EN": { + "was": "Reference area", + "now": "Geography" + }, + "DESCRIPTION": { + "was": "", + "now": "Primary spatial reference area for the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "TIME_PERIOD", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Temporal reference of the observation, expressed using the accepted ISO 8601 time-period forms." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "AGE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Age disaggregation of the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "SEX", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Sex disaggregation of the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "OBS_VALUE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The actual measured numeric value." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "UNIT_MEASURE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The unit in which the observation value is expressed." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "UNIT_MULT", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Scale factor applied to the observation value (e.g. thousands, millions)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "CENSORED_VALUE_TYPE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Status code indicating the censoring relation that qualifies the observation value, or that no censoring relation applies." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "FOOTNOTE", + "changed_fields": { + "NAME_EN": { + "was": "Indicator Notes", + "now": "Footnote" + }, + "DESCRIPTION": { + "was": "", + "now": "Interpretive note associated with the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "SOURCE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Source or provenance statement associated with the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "FREQUENCY", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Reporting frequency of the observation (annual, quarterly, monthly, daily, \u2026)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWA1_RT.csv", + "concept": "OBSERVATION_STATUS", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Quality or interpretive status of the observation (e.g. estimated, provisional, final)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "SERIES", + "changed_fields": { + "NAME_EN": { + "was": "Measure", + "now": "Series" + }, + "DESCRIPTION": { + "was": "", + "now": "Identifies the analytical series to which the observation belongs." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "GEOGRAPHY", + "changed_fields": { + "NAME_EN": { + "was": "Reference area", + "now": "Geography" + }, + "DESCRIPTION": { + "was": "", + "now": "Primary spatial reference area for the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "TIME_PERIOD", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Temporal reference of the observation, expressed using the accepted ISO 8601 time-period forms." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "AGE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Age disaggregation of the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "DISABILITY_STATUS", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Disability-status disaggregation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "EDUCATION_LEVEL", + "changed_fields": { + "NAME_EN": { + "was": "Education", + "now": "Education level" + }, + "DESCRIPTION": { + "was": "", + "now": "Educational level or attainment classification used to disaggregate the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "URBANIZATION", + "changed_fields": { + "NAME_EN": { + "was": "Rural/urban areas", + "now": "Urbanization" + }, + "DESCRIPTION": { + "was": "", + "now": "Classification of areas or observations by urban, rural or total residence context." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "MARITAL_STATUS", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Marital-status disaggregation of a person (for example single, married, widowed, divorced, separated)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "SEX", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Sex disaggregation of the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "OBS_VALUE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The actual measured numeric value." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "UNIT_MEASURE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The unit in which the observation value is expressed." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "UNIT_MULT", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Scale factor applied to the observation value (e.g. thousands, millions)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "CENSORED_VALUE_TYPE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Status code indicating the censoring relation that qualifies the observation value, or that no censoring relation applies." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "FOOTNOTE", + "changed_fields": { + "NAME_EN": { + "was": "Indicator Notes", + "now": "Footnote" + }, + "DESCRIPTION": { + "was": "", + "now": "Interpretive note associated with the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "SOURCE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Source or provenance statement associated with the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "FREQUENCY", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Reporting frequency of the observation (annual, quarterly, monthly, daily, \u2026)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_DWAP_RT.csv", + "concept": "OBSERVATION_STATUS", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Quality or interpretive status of the observation (e.g. estimated, provisional, final)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "SERIES", + "changed_fields": { + "NAME_EN": { + "was": "Measure", + "now": "Series" + }, + "DESCRIPTION": { + "was": "", + "now": "Identifies the analytical series to which the observation belongs." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "GEOGRAPHY", + "changed_fields": { + "NAME_EN": { + "was": "Reference area", + "now": "Geography" + }, + "DESCRIPTION": { + "was": "", + "now": "Primary spatial reference area for the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "TIME_PERIOD", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Temporal reference of the observation, expressed using the accepted ISO 8601 time-period forms." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "AGE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Age disaggregation of the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "SEX", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Sex disaggregation of the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "OBS_VALUE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The actual measured numeric value." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "UNIT_MEASURE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The unit in which the observation value is expressed." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "UNIT_MULT", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Scale factor applied to the observation value (e.g. thousands, millions)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "CENSORED_VALUE_TYPE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Status code indicating the censoring relation that qualifies the observation value, or that no censoring relation applies." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "FOOTNOTE", + "changed_fields": { + "NAME_EN": { + "was": "Indicator Notes", + "now": "Footnote" + }, + "DESCRIPTION": { + "was": "", + "now": "Interpretive note associated with the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "SOURCE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Source or provenance statement associated with the observation." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "FREQUENCY", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Reporting frequency of the observation (annual, quarterly, monthly, daily, \u2026)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEA1_NB.csv", + "concept": "OBSERVATION_STATUS", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Quality or interpretive status of the observation (e.g. estimated, provisional, final)." + } + } + }, + { + "dsd": "ILO_2026-05-25_DSD_EAP_TEAP_NB.csv", + "concept": "SERIES", + "changed_fields": { + "NAME_EN": { + "was": "Measure", + "now": "Series" + }, + "DESCRIPTION": { + "was": "", + "now": 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"DESCRIPTION": { + "was": "", + "now": "The actual measured numeric value." + } + } + }, + { + "dsd": "SDG_q1-2026_DSD_FB_ATM_TOTL.csv", + "concept": "UNIT_MEASURE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The unit in which the observation value is expressed." + } + } + }, + { + "dsd": "SDG_q1-2026_DSD_FB_ATM_TOTL.csv", + "concept": "UNIT_MULT", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Scale factor applied to the observation value (e.g. thousands, millions)." + } + } + }, + { + "dsd": "SDG_q1-2026_DSD_FB_ATM_TOTL.csv", + "concept": "CENSORED_VALUE_TYPE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Status code indicating the censoring relation that qualifies the observation value, or that no censoring relation applies." + } + } + }, + { + "dsd": "SDG_q1-2026_DSD_FB_ATM_TOTL.csv", + "concept": "SOURCE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Source or provenance statement associated with the 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"dsd": "UNICEF_2026-06-05_DSD_NT_CF_ZEROFV.csv", + "concept": "UNIT_MEASURE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The unit in which the observation value is expressed." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_CF_ZEROFV.csv", + "concept": "UNIT_MULT", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Scale factor applied to the observation value (e.g. thousands, millions)." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_CF_ZEROFV.csv", + "concept": "CENSORED_VALUE_TYPE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Status code indicating the censoring relation that qualifies the observation value, or that no censoring relation applies." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_CF_ZEROFV.csv", + "concept": "FOOTNOTE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Interpretive note associated with the observation." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_CF_ZEROFV.csv", + 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"concept": "GEO_ADMIN_LEVEL", + "changed_fields": { + "NAME_EN": { + "was": "Reporting level", + "now": "Administrative level" + }, + "DESCRIPTION": { + "was": "", + "now": "Administrative level of the referenced geography, such as national, regional, or local." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_DANT_BMI_G25_MOD.csv", + "concept": "LOWER_BOUND", + "changed_fields": { + "NAME_EN": { + "was": "Lower Bound", + "now": "Lower bound" + }, + "DESCRIPTION": { + "was": "", + "now": "Lower bound reported for the observation value where a range is provided." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_DANT_BMI_G25_MOD.csv", + "concept": "TIME_PERIOD_METHOD", + "changed_fields": { + "NAME_EN": { + "was": "Time period activity related to when the data are collected", + "now": "Time-period reporting method" + }, + "DESCRIPTION": { + "was": "", + "now": "Method by which the observation's time period was determined or reported, e.g. middle of fieldwork, end of fieldwork, or 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"changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Scale factor applied to the observation value (e.g. thousands, millions)." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_DANT_BMI_G30_MOD_ADJ.csv", + "concept": "CENSORED_VALUE_TYPE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Status code indicating the censoring relation that qualifies the observation value, or that no censoring relation applies." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_DANT_BMI_G30_MOD_ADJ.csv", + "concept": "FOOTNOTE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Interpretive note associated with the observation." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_DANT_BMI_G30_MOD_ADJ.csv", + "concept": "SOURCE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Source or provenance statement associated with the observation." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_DANT_BMI_G30_MOD_ADJ.csv", + "concept": "FREQUENCY", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Reporting frequency of the observation (annual, quarterly, monthly, daily, \u2026)." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_DANT_BMI_G30_MOD_ADJ.csv", + "concept": "GEO_ADMIN_LEVEL", + "changed_fields": { + "NAME_EN": { + "was": "Reporting level", + "now": "Administrative level" + }, + "DESCRIPTION": { + "was": "", + "now": "Administrative level of the referenced geography, such as national, regional, or local." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_DANT_BMI_G30_MOD_ADJ.csv", + "concept": "LOWER_BOUND", + "changed_fields": { + "NAME_EN": { + "was": "Lower Bound", + "now": "Lower bound" + }, + "DESCRIPTION": { + "was": "", + "now": "Lower bound reported for the observation value where a range is provided." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_DANT_BMI_G30_MOD_ADJ.csv", + "concept": "TIME_PERIOD_METHOD", + "changed_fields": { + "NAME_EN": { + "was": "Time period activity related to 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"DESCRIPTION": { + "was": "", + "now": "Source or provenance statement associated with the observation." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_DANT_BMI_L18_MOD.csv", + "concept": "FREQUENCY", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Reporting frequency of the observation (annual, quarterly, monthly, daily, \u2026)." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_DANT_BMI_L18_MOD.csv", + "concept": "GEO_ADMIN_LEVEL", + "changed_fields": { + "NAME_EN": { + "was": "Reporting level", + "now": "Administrative level" + }, + "DESCRIPTION": { + "was": "", + "now": "Administrative level of the referenced geography, such as national, regional, or local." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_NT_DANT_BMI_L18_MOD.csv", + "concept": "LOWER_BOUND", + "changed_fields": { + "NAME_EN": { + "was": "Lower Bound", + "now": "Lower bound" + }, + "DESCRIPTION": { + "was": "", + "now": "Lower bound reported for the observation value where a range is 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"was": "", + "now": "Interpretive note associated with the observation." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_ED_NEET.csv", + "concept": "FREQUENCY", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Reporting frequency of the observation (annual, quarterly, monthly, daily, \u2026)." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_ED_NEET.csv", + "concept": "OBSERVATION_STATUS", + "changed_fields": { + "NAME_EN": { + "was": "Observation Status", + "now": "Observation status" + }, + "DESCRIPTION": { + "was": "", + "now": "Quality or interpretive status of the observation (e.g. estimated, provisional, final)." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_ED_NEET.csv", + "concept": "TIME_PERIOD_METHOD", + "changed_fields": { + "NAME_EN": { + "was": "Time period activity related to when the data are collected", + "now": "Time-period reporting method" + }, + "DESCRIPTION": { + "was": "", + "now": "Method by which the observation's time period was determined or reported, e.g. middle of fieldwork, end of fieldwork, or other." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_LAB_FRC_UNEMP.csv", + "concept": "SERIES", + "changed_fields": { + "NAME_EN": { + "was": "Indicator", + "now": "Series" + }, + "DESCRIPTION": { + "was": "", + "now": "Identifies the analytical series to which the observation belongs." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_LAB_FRC_UNEMP.csv", + "concept": "GEOGRAPHY", + "changed_fields": { + "NAME_EN": { + "was": "Geographic area", + "now": "Geography" + }, + "DESCRIPTION": { + "was": "", + "now": "Primary spatial reference area for the observation." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_LAB_FRC_UNEMP.csv", + "concept": "TIME_PERIOD", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Temporal reference of the observation, expressed using the accepted ISO 8601 time-period forms." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_LAB_FRC_UNEMP.csv", + "concept": "OBS_VALUE", + "changed_fields": { + "NAME_EN": { + "was": "Observation Value", + "now": "Observation value" + }, + "DESCRIPTION": { + "was": "", + "now": "The actual measured numeric value." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_LAB_FRC_UNEMP.csv", + "concept": "UNIT_MEASURE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "The unit in which the observation value is expressed." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_LAB_FRC_UNEMP.csv", + "concept": "UNIT_MULT", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Scale factor applied to the observation value (e.g. thousands, millions)." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_LAB_FRC_UNEMP.csv", + "concept": "CENSORED_VALUE_TYPE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Status code indicating the censoring relation that qualifies the observation value, or that no censoring relation applies." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_LAB_FRC_UNEMP.csv", + "concept": "FOOTNOTE", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Interpretive note associated with the observation." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_LAB_FRC_UNEMP.csv", + "concept": "FREQUENCY", + "changed_fields": { + "DESCRIPTION": { + "was": "", + "now": "Reporting frequency of the observation (annual, quarterly, monthly, daily, \u2026)." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_LAB_FRC_UNEMP.csv", + "concept": "OBSERVATION_STATUS", + "changed_fields": { + "NAME_EN": { + "was": "Observation Status", + "now": "Observation status" + }, + "DESCRIPTION": { + "was": "", + "now": "Quality or interpretive status of the observation (e.g. estimated, provisional, final)." + } + } + }, + { + "dsd": "UNICEF_2026-06-05_DSD_WT_ADLS_LAB_FRC_UNEMP.csv", + "concept": "TIME_PERIOD_METHOD", + "changed_fields": { + "NAME_EN": { + "was": "Time period activity related to when the data are collected", + "now": "Time-period reporting method" + }, + "DESCRIPTION": { + "was": "", + "now": "Method by which the observation's time period was determined or reported, e.g. middle of fieldwork, end of fieldwork, or other." + } + } + } + ], + "concepts_unchanged": [ + "DIARRHOEA_TREATMENT", + "NUMBER_OF_DEPRIVATIONS", + "SCHOOL_AGE", + "VACCINE", + "WASH_SERVICE" + ], + "concepts_not_in_registry": [] +} diff --git a/undata/new_data_stats.md b/undata/new_data_stats.md new file mode 100644 index 0000000000..041b91eda1 --- /dev/null +++ b/undata/new_data_stats.md @@ -0,0 +1,31 @@ +# UN Data Extraction Statistics (Excluding Headers) + +## UN20x_undp-hdro_hdr_2025_transcoded_20260607T050355Z_a399e1 +1. **CL_files_count**: 9 +2. **CL_count**: 15757 +3. **data_files_count**: 28 +4. **observations_count**: 183169 + +## UN20x_unaids_2026-05-12_transcoded_20260607T060952Z_ce9628 +1. **CL_files_count**: 15 +2. **CL_count**: 15823 +3. **data_files_count**: 41 +4. **observations_count**: 1634181 + +## UN20x_sdg_q1-2026_transcoded_20260607T033719Z_c369c1 +1. **CL_files_count**: 53 +2. **CL_count**: 34685 +3. **data_files_count**: 713 +4. **observations_count**: 2677452 + +## UN20x_unicef_2026-06-05_transcoded_20260607T011325Z_df3d33 +1. **CL_files_count**: 33 +2. **CL_count**: 17099 +3. **data_files_count**: 248 +4. **observations_count**: 3053355 + +## UN20x_ilo_2026-05-25_transcoded_20260526T010557Z_7dbe65 +1. **CL_files_count**: 28 +2. **CL_count**: 18189 +3. **data_files_count**: 95 +4. **observations_count**: 21785215 diff --git a/undata/parse_metadata.py b/undata/parse_metadata.py new file mode 100644 index 0000000000..37430805df --- /dev/null +++ b/undata/parse_metadata.py @@ -0,0 +1,17 @@ +import csv +with open('/usr/local/google/home/nehil/datacommons/import/git/data/undata/SDG/output/agentic/SDG_q4-2025_OBS_SP_TRN_PUBL/input/metadata.csv', 'r') as f: + reader = csv.DictReader(f) + mappings = [] + for row in reader: + col = row['column_name'] + val = row['value'] + dc_pv = row['DC_PV_PAIR'] + if col == 'GEOGRAPHY': + # dc_pv looks like "geographyCounterpart:Charikar" + if ':' in dc_pv: + prop, pval = dc_pv.split(':', 1) + mappings.append(f"{col}:{val},observationAbout,\"{pval}\"") + +with open('/usr/local/google/home/nehil/datacommons/import/git/data/undata/SDG/output/agentic/SDG_q4-2025_OBS_SP_TRN_PUBL/generated/new_pvmap.csv', 'a') as f: + for m in mappings: + f.write(m + '\n') diff --git a/undata/run_batch_import.sh b/undata/run_batch_import.sh new file mode 100755 index 0000000000..a364ac1229 --- /dev/null +++ b/undata/run_batch_import.sh @@ -0,0 +1,237 @@ +#!/bin/bash + +# Load environment variables +if [ -f .env ]; then + set -a + source .env + set +a +fi + +DATA_DIR="/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/data" +METADATA_DIR="/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/metadata_unreviewed" +OUTPUT_BASE_DIR="/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/output/unreviewed" +LOG_DIR="undata/logs" +GCS_OUTPUT_DIR="gs://undata/desa-gender/2025/transcoded/output/unreviewed" +GEMINI_PATH="/google/bin/images/image-6a01372e-0000-20fd-aeb3-883d24fe0d58/gemini --noproxy" +DC_IMPORT_JAR=${DC_IMPORT_JAR:-"/tmp/datacommons-import-tool.jar"} +MAX_PARALLEL=3 +RETRY_FILE="undata/logs/retry_list.txt" + +mkdir -p "$OUTPUT_BASE_DIR" +mkdir -p "$LOG_DIR" +touch "$RETRY_FILE" + +# Function to check if processing was successful +is_successful() { + local dataset_dir="$1" + local filename="$2" + + # Check if all critical files exist and are non-empty + if [ ! -s "$dataset_dir/output.csv" ] || \ + [ ! -s "$dataset_dir/output.tmcf" ] || \ + [ ! -s "$dataset_dir/output_stat_vars.mcf" ]; then + return 1 + fi + + # Check if output.csv has at least 2 lines (header + data) + local line_count=$(wc -l < "$dataset_dir/output.csv") + if [ "$line_count" -lt 2 ]; then + return 1 + fi + + return 0 +} + +# Function to run validation +run_validate_output() { + local filename="$1" + local output_folder="$2" + local dataset_log="$3" + + echo "[$(date +'%Y-%m-%d %H:%M:%S')] [$filename] Starting Validation (dc-import)..." >> "$dataset_log" 2>&1 + mkdir -p "$output_folder/validation" + + if [ ! -f "$DC_IMPORT_JAR" ]; then + echo "Warning: DC_IMPORT_JAR not found at $DC_IMPORT_JAR. Attempting to find it..." >> "$dataset_log" 2>&1 + local found_jar=$(find . -name "datacommons-import-tool*.jar" | head -n 1) + if [ -n "$found_jar" ]; then + DC_IMPORT_JAR="$found_jar" + echo "Found JAR at $DC_IMPORT_JAR" >> "$dataset_log" 2>&1 + else + echo "Error: DC_IMPORT_JAR not found. Skipping validation." >> "$dataset_log" 2>&1 + return 1 + fi + fi + + # Run dc-import tool + java -jar "$DC_IMPORT_JAR" genmcf -n 20 -r FULL \ + "$output_folder"/output*.{csv,tmcf,mcf} \ + -o "$output_folder/validation" >> "$dataset_log" 2>&1 + + # Run import validator + echo "[$(date +'%Y-%m-%d %H:%M:%S')] [$filename] Running import validator..." >> "$dataset_log" 2>&1 + ./venv/bin/python3 tools/import_validation/runner.py \ + --validation_config=tools/import_validation/validation_config.json \ + --stats_summary="$output_folder/validation/summary_report.csv" \ + --lint_report="$output_folder/validation/report.json" \ + --validation_output="$output_folder/validation/validation-result.json" >> "$dataset_log" 2>&1 + + return $? +} + +# Function to run stat_var_processor +run_stat_var_processor() { + local filename="$1" + local f="$2" + local output_folder="$3" + local metadata_file="$4" + local dataset_log="$5" + + echo "[$(date +'%Y-%m-%d %H:%M:%S')] [$filename] Starting StatVar Processor..." >> "$dataset_log" 2>&1 + local counters_file="$output_folder/validation/statvar_processor_counters.csv" + mkdir -p "$output_folder/validation" + local output_path="$output_folder/output" + + # pvmap_generator outputs to $output_folder/output_pvmap.csv + local pvmap_file="$output_folder/output_pvmap.csv" + + ./venv/bin/python3 tools/statvar_importer/stat_var_processor.py \ + --input_data="$f" \ + --config_file="$metadata_file" \ + --pv_map="$pvmap_file" \ + --output_path="$output_path" \ + --output_counters="$counters_file" >> "$dataset_log" 2>&1 + + if [ ! -f "$counters_file" ]; then + echo "Error: Unable to find output counters: $counters_file" >> "$dataset_log" 2>&1 + return 1 + fi + + local statvar_errors=$(grep error "$counters_file") + if [ -n "$statvar_errors" ]; then + local num_errors=$(wc -l <<< "$statvar_errors") + echo "Statvar Processor had ${num_errors} errors: $statvar_errors" >> "$dataset_log" 2>&1 + fi + + local num_inputs=$(grep "input-numeric-values" "$counters_file" | cut -d, -f2 | tr -d '\r') + num_inputs=${num_inputs:-"1"} + local num_outputs=$(grep "output-svobs-csv-rows" "$counters_file" | cut -d, -f2 | tr -d '\r') + num_outputs=${num_outputs:-"0"} + + local percent_processed=$(bc <<< "100 * $num_outputs / $num_inputs") + local min_percent=10 + + if (( percent_processed < min_percent )); then + echo "Error: Only $percent_processed percent inputs in $f processed ($num_outputs/$num_inputs). Check output/configs." >> "$dataset_log" 2>&1 + return 1 + fi + + echo "Successfully completed statvar processor for $filename ($percent_processed% processed)." >> "$dataset_log" 2>&1 + return 0 +} + +# Function to process a single dataset +process_dataset() { + local f="$1" + local is_retry="${2:-false}" + local filename=$(basename "$f" .csv) + local metadata_file="$METADATA_DIR/${filename}_metadata.csv" + local output_folder="$OUTPUT_BASE_DIR/$filename" + local dataset_log="$LOG_DIR/${filename}.log" + + echo "[$(date +'%Y-%m-%d %H:%M:%S')] Starting $filename (is_retry=$is_retry)" + + if [ ! -f "$metadata_file" ]; then + echo "[$(date +'%Y-%m-%d %H:%M:%S')] Warning: Metadata for $filename not found. Skipping." + return + fi + + if is_successful "$output_folder" "$filename"; then + echo "[$(date +'%Y-%m-%d %H:%M:%S')] Info: $filename already successful. Syncing..." + + # Calculate efficacy for this specific dataset + echo "[$(date +'%Y-%m-%d %H:%M:%S')] [$filename] Calculating efficacy..." >> "$dataset_log" 2>&1 + local efficacy_output=$(./venv/bin/python3 undata/DESA/calculate_efficacy.py "$filename") + echo "$efficacy_output" >> "$dataset_log" 2>&1 + + # Upload HTML to GCS + gcloud storage cp "undata/DESA/efficacy_results/${filename}_Agent_Efficacy_Board.html" "gs://undata/desa-gender/2025/transcoded/efficacy_results/" >> "$dataset_log" 2>&1 + + gcloud storage cp -r "$output_folder" "$GCS_OUTPUT_DIR/" >> "$dataset_log" 2>&1 + return + fi + + mkdir -p "$output_folder" + + # Stage 1: PV Map Generation + echo "[$(date +'%Y-%m-%d %H:%M:%S')] [$filename] Stage 1: pvmap_generator" >> "$dataset_log" + + # Random sleep to jitter requests and avoid 429s + sleep_time=$(( RANDOM % 30 + 10 )) + echo "Sleeping for $sleep_time seconds before Gemini call..." >> "$dataset_log" + sleep "$sleep_time" + + ./venv/bin/python3 tools/agentic_import/pvmap_generator.py \ + --input_data="$f" \ + --input_metadata="undata/metadata.csv,$metadata_file" \ + --output_path="$output_folder/output" \ + --skip_confirmation \ + --gemini_cli="$GEMINI_PATH" >> "$dataset_log" 2>&1 + + local status=$? + if [ $status -ne 0 ]; then + echo "[$(date +'%Y-%m-%d %H:%M:%S')] [$filename] FAILED at Stage 1 (PV Map)" | tee -a "$dataset_log" + echo "$f" >> "$RETRY_FILE" + return 1 + fi + + # Stage 2: StatVar Processor + if ! run_stat_var_processor "$filename" "$f" "$output_folder" "$metadata_file" "$dataset_log"; then + echo "[$(date +'%Y-%m-%d %H:%M:%S')] [$filename] FAILED at Stage 2 (StatVar)" | tee -a "$dataset_log" + echo "$f" >> "$RETRY_FILE" + return 1 + fi + + # Stage 3: Validation (SKIPPED for now) + # if ! run_validate_output "$filename" "$output_folder" "$dataset_log"; then + # echo "[$(date +'%Y-%m-%d %H:%M:%S')] [$filename] FAILED at Stage 3 (Validation)" | tee -a "$dataset_log" + # echo "$f" >> "$RETRY_FILE" + # return 1 + # fi + + if is_successful "$output_folder" "$filename"; then + echo "[$(date +'%Y-%m-%d %H:%M:%S')] [$filename] Successfully processed. Calculating efficacy..." | tee -a "$dataset_log" + + # Calculate efficacy for this specific dataset + local efficacy_output=$(./venv/bin/python3 undata/DESA/calculate_efficacy.py "$filename") + echo "$efficacy_output" >> "$dataset_log" 2>&1 + + # Upload HTML to GCS + gcloud storage cp "undata/DESA/efficacy_results/${filename}_Agent_Efficacy_Board.html" "gs://undata/desa-gender/2025/transcoded/efficacy_results/" >> "$dataset_log" 2>&1 + + gcloud storage cp -r "$output_folder" "$GCS_OUTPUT_DIR/" >> "$dataset_log" 2>&1 + else + echo "[$(date +'%Y-%m-%d %H:%M:%S')] [$filename] Final check failed" | tee -a "$dataset_log" + echo "$f" >> "$RETRY_FILE" + return 1 + fi +} + +# Export functions for parallel execution +export -f process_dataset +export -f is_successful +export -f run_stat_var_processor +export -f run_validate_output +export DATA_DIR METADATA_DIR OUTPUT_BASE_DIR LOG_DIR GCS_OUTPUT_DIR GEMINI_PATH DC_IMPORT_JAR RETRY_FILE + +# Run processing in parallel using xargs if script is executed directly +if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then + mkdir -p "$OUTPUT_BASE_DIR" + mkdir -p "$LOG_DIR" + touch "$RETRY_FILE" + + # Using a temporary file to list all csv files to avoid command line length issues + find "$DATA_DIR" -name "*.csv" | xargs -I {} -P "$MAX_PARALLEL" bash -c "source undata/run_batch_import.sh; process_dataset '{}'" + + echo "Batch import process initiated/resumed for all files." +fi diff --git a/undata/run_full_pipeline.sh b/undata/run_full_pipeline.sh new file mode 100755 index 0000000000..9b0decd357 --- /dev/null +++ b/undata/run_full_pipeline.sh @@ -0,0 +1,118 @@ +#!/bin/bash + +# Configuration +# Run this script from /usr/local/google/home/nehil/datacommons/import/git/data/undata +BASE_DIR="/usr/local/google/home/nehil/datacommons/import/git/data/undata" +AGENTIC_DIR="${BASE_DIR}/SDG/output/agentic" +TOOLS_DIR="/usr/local/google/home/nehil/datacommons/import/git/data/tools" +VENV_PATH="/usr/local/google/home/nehil/datacommons/import/git/data/venv" +GEMINI_BIN="/google/bin/releases/gemini-cli/tools/gemini --noproxy -y" + +PLACES_RESOLVED_CSV="${BASE_DIR}/DESA/all_places.csv" + +# Optional: Process a specific dataset provided as an argument +SINGLE_DATASET=$1 + +echo "=== Pipeline Start ===" +source "${VENV_PATH}/bin/activate" + +# Capture original keys +export ORIG_GOOGLE_API_KEY=$GOOGLE_API_KEY +export ORIG_GEMINI_API_KEY=$GEMINI_API_KEY + +# Set Gemini API key for tools +export GEMINI_API_KEY="${ORIG_GEMINI_API_KEY}" +unset GOOGLE_API_KEY + +process_dataset() { + local DATASET_DIR="$1" + if [ ! -d "${DATASET_DIR}" ]; then + echo "Directory not found: ${DATASET_DIR}" + return + fi + + local DATASET_NAME=$(basename "${DATASET_DIR}") + + echo "==========================================================" + echo "Processing Dataset: ${DATASET_NAME}" + + local INPUT_DATA="${DATASET_DIR}/input/data.csv" + local INPUT_METADATA="${DATASET_DIR}/input/metadata.csv" + local EXISTING_METADATA="${DATASET_DIR}/deterministic_metadata.csv" + local EXISTING_PVMAP="${DATASET_DIR}/deterministic_pvmap.csv" + + if [[ ! -f "${INPUT_DATA}" || ! -f "${INPUT_METADATA}" ]]; then + echo "Skipping ${DATASET_NAME}: Missing input files." + return + fi + + # Clean up previous generated files to ensure we are rerunning + rm -rf "${DATASET_DIR}/generated" + mkdir -p "${DATASET_DIR}/generated" + + echo "=== 1. Running PV Map Generator (Gemini) ===" + + # Ensure environment is clean for this sub-process + GOOGLE_API_KEY="" + python3 "${TOOLS_DIR}/agentic_import/pvmap_generator.py" \ + --gemini_cli="${GEMINI_BIN}" \ + --input_data="${INPUT_DATA}" \ + --input_metadata="${INPUT_METADATA}" \ + --extra_instruction_files="${EXISTING_PVMAP}" \ + --places_resolved_csv="${PLACES_RESOLVED_CSV}" \ + --output_path="${DATASET_DIR}/generated/new" \ + --skip_confirmation=True + + echo "=== 2. Running StatVar Processor ===" + mkdir -p "${DATASET_DIR}/validation" + mkdir -p "${DATASET_DIR}/output" + + # Use the newly generated pvmap if available + local PVMAP_TO_USE="${DATASET_DIR}/generated/new_pvmap.csv" + if [ ! -f "${PVMAP_TO_USE}" ]; then + echo "Warning: ${PVMAP_TO_USE} not found. Gemini likely failed. Skipping processor." + return + fi + + python3 "${TOOLS_DIR}/statvar_importer/stat_var_processor.py" \ + --input_data="${INPUT_DATA}" \ + --config_file="${EXISTING_METADATA}" \ + --pv_map="${PVMAP_TO_USE}" \ + --places_resolved_csv="${PLACES_RESOLVED_CSV}" \ + --output_path="${DATASET_DIR}/output/output" \ + --output_counters="${DATASET_DIR}/validation/statvar_processor_counters.csv" + + echo "Finished processing ${DATASET_NAME}" + sleep 2 +} + +if [ -n "${SINGLE_DATASET}" ]; then + # If argument is a full path, use it. If it's just a name, append to AGENTIC_DIR. + if [[ "${SINGLE_DATASET}" == /* ]]; then + process_dataset "${SINGLE_DATASET}" + else + process_dataset "${AGENTIC_DIR}/${SINGLE_DATASET}" + fi +else + ORDER_FILE="/usr/local/google/home/nehil/datacommons/import/git/data/undata/SDG/output/zero_count_datasets.txt" + echo "=== Rerunning for datasets with 0 rows from ${ORDER_FILE} ===" + if [[ ! -f "${ORDER_FILE}" ]]; then + echo "Order file not found: ${ORDER_FILE}" + exit 1 + fi + + while read -r dir_name; do + if [[ -z "${dir_name}" ]]; then continue; fi + DATASET_PATH="${AGENTIC_DIR}/${dir_name}" + + if [ -d "${DATASET_PATH}" ]; then + process_dataset "${DATASET_PATH}" + else + echo "Directory not found in agentic folder: ${DATASET_PATH}" + fi + done < "${ORDER_FILE}" +fi + +echo "=== Pipeline Complete ===" +export GOOGLE_API_KEY=$ORIG_GOOGLE_API_KEY +deactivate diff --git a/undata/run_loop.sh b/undata/run_loop.sh new file mode 100644 index 0000000000..52a6130fd3 --- /dev/null +++ b/undata/run_loop.sh @@ -0,0 +1,5 @@ +#!/bin/bash +source undata/run_batch_import.sh +while read -r f; do + process_dataset "$f" +done < undata/logs/file_list.txt diff --git a/undata/run_statvar_all.sh b/undata/run_statvar_all.sh new file mode 100755 index 0000000000..2f2b3686cf --- /dev/null +++ b/undata/run_statvar_all.sh @@ -0,0 +1,50 @@ +#!/bin/bash +# Activate the existing virtual environment +source venv/bin/activate + +REVIEWED_DIR="${1:-undata/DESA/output/reviewed}" +DATA_DIR="undata/DESA/data" + +# Extract the directory name for GCS upload path +DIR_NAME=$(basename "${REVIEWED_DIR}") + +# Iterate over all directories in the reviewed folder +for dir in "${REVIEWED_DIR}"/*/; do + # Extract dataset name (remove trailing slash and path) + DATASET_NAME=$(basename "${dir}") + + # Skip if it's not a directory (though the glob should handle it) + [ -d "${dir}" ] || continue + + echo "Processing dataset: ${DATASET_NAME}..." + + DATASET_DIR="${REVIEWED_DIR}/${DATASET_NAME}" + VALIDATION_DIR="${DATASET_DIR}/validation" + + # Check if validation folder is empty + if [ -d "$VALIDATION_DIR" ] && [ "$(ls -A "$VALIDATION_DIR" 2>/dev/null)" ]; then + echo "Skipping ${DATASET_NAME}: validation folder is not empty." + continue + fi + + INPUT_CSV="${DATA_DIR}/${DATASET_NAME}.csv" + + if [ ! -f "${INPUT_CSV}" ]; then + echo "Warning: Input CSV not found for ${DATASET_NAME} at ${INPUT_CSV}. Skipping." + continue + fi + + # Run the statvar processor + ./venv/bin/python tools/statvar_importer/stat_var_processor.py \ + --input_data="${INPUT_CSV}" \ + --config_file="${DATASET_DIR}/output_metadata.csv" \ + --pv_map="${DATASET_DIR}/output_pvmap.csv" \ + --output_path="${DATASET_DIR}/output" \ + --places_resolved_csv=/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/all_places.csv \ + --output_counters="${DATASET_DIR}/validation/statvar_processor_counters.csv" + + echo "Completed ${DATASET_NAME}." +done + +echo "Uploading all processed files to GCS..." +gcloud storage cp -r "${REVIEWED_DIR}/"* "gs://undata/desa-gender/2025/transcoded/output/${DIR_NAME}/" diff --git a/undata/run_statvar_bulk_sorted.sh b/undata/run_statvar_bulk_sorted.sh new file mode 100644 index 0000000000..a3b11b4684 --- /dev/null +++ b/undata/run_statvar_bulk_sorted.sh @@ -0,0 +1,73 @@ +#!/bin/bash + +# Configuration +BASE_DIR="/usr/local/google/home/nehil/datacommons/import/git/data/undata" +DETERMINISTIC_DIR="${BASE_DIR}/SDG/output/deterministic" +TOOLS_DIR="/usr/local/google/home/nehil/datacommons/import/git/data/tools" +VENV_PATH="/usr/local/google/home/nehil/datacommons/import/git/data/venv" +ROW_COUNTS_CSV="/usr/local/google/home/nehil/datacommons/import/git/data/sdg_file_row_counts.csv" +LOG_DIR="${BASE_DIR}/logs" +RUN_LOG="${LOG_DIR}/bulk_run.log" +OUTPUT_COUNTS_CSV="${LOG_DIR}/output_row_counts.csv" +PLACES_RESOLVED_CSV="${BASE_DIR}/DESA/all_places.csv" + +# Limit parameter +LIMIT=$1 +count=0 + +# Ensure directories exist +mkdir -p "${LOG_DIR}" + +# Initialize log files +echo "=== Bulk Run Started: $(date) ===" > "${RUN_LOG}" +echo "dataset_name,output_row_count" > "${OUTPUT_COUNTS_CSV}" + +# Activate virtual environment +source "${VENV_PATH}/bin/activate" + +# Read CSV, skip header, sort numeric by 2nd col (ascending), loop +tail -n +2 "${ROW_COUNTS_CSV}" | sort -t ',' -k2,2n | while IFS=',' read -r filename row_count; do + # Strip extension + DATASET_NAME="${filename%.csv}" + DATASET_DIR="${DETERMINISTIC_DIR}/${DATASET_NAME}" + + # Check if directory exists + if [ ! -d "${DATASET_DIR}" ]; then + continue + fi + + # Check limit + if [[ -n "${LIMIT}" && "${count}" -ge "${LIMIT}" ]]; then + echo "Limit of ${LIMIT} reached. Stopping." | tee -a "${RUN_LOG}" + break + fi + + echo "Processing dataset: ${DATASET_NAME} (Rows: ${row_count})..." | tee -a "${RUN_LOG}" + + mkdir -p "${DATASET_DIR}/validation" + + # Run statvar processor + python3 "${TOOLS_DIR}/statvar_importer/stat_var_processor.py" \ + --input_data="${DATASET_DIR}/input/data.csv" \ + --config_file="${DATASET_DIR}/deterministic_metadata.csv" \ + --pv_map="${DATASET_DIR}/deterministic_pvmap.csv" \ + --places_resolved_csv="${PLACES_RESOLVED_CSV}" \ + --output_path="${DATASET_DIR}/output" \ + --output_counters="${DATASET_DIR}/validation/statvar_processor_counters.csv" >> "${RUN_LOG}" 2>&1 + + # Record output row counts + OUTPUT_FILE="${DATASET_DIR}/output.csv" + if [ -f "${OUTPUT_FILE}" ]; then + # Count rows minus header + OUT_COUNT=$(wc -l < "${OUTPUT_FILE}") + OUT_COUNT=$((OUT_COUNT - 1)) + echo "${DATASET_NAME},${OUT_COUNT}" >> "${OUTPUT_COUNTS_CSV}" + else + echo "${DATASET_NAME},FAILED" >> "${OUTPUT_COUNTS_CSV}" + fi + + count=$((count + 1)) +done + +echo "=== Bulk Run Completed: $(date) ===" | tee -a "${RUN_LOG}" +deactivate diff --git a/undata/run_statvar_single.sh b/undata/run_statvar_single.sh new file mode 100644 index 0000000000..0989f5858f --- /dev/null +++ b/undata/run_statvar_single.sh @@ -0,0 +1,34 @@ +#!/bin/bash +# Activate the existing virtual environment +source /usr/local/google/home/nehil/datacommons/import/git/data/venv/bin/activate + +# Define an array of 10 dataset names +DATASETS=( +"SDG_q4-2025_OBS_EG_ACS_ELEC" +"SDG_q4-2025_OBS_EG_EGY_CLEAN" +"SDG_q4-2025_OBS_EG_EGY_RNEW" +"SDG_q4-2025_OBS_EG_FEC_RNEW" +"SDG_q4-2025_OBS_EG_IFF_RANDN" +"SDG_q4-2025_OBS_EN_ATM_CO2" +"SDG_q4-2025_OBS_EN_ATM_PM25" +"SDG_q4-2025_OBS_EN_LKRV_PWAN" +"SDG_q4-2025_OBS_EN_LKRV_SWAN" +"SDG_q4-2025_OBS_EN_MAR_BEALIT_BV" +) + +for DATASET_NAME in "${DATASETS[@]}"; do + echo "Processing ${DATASET_NAME}..." + DATASET_DIR="/usr/local/google/home/nehil/datacommons/import/git/data/undata/SDG/output/deterministic/${DATASET_NAME}" + + # Ensure validation output directory exists + mkdir -p "${DATASET_DIR}/validation" + + # Run the statvar processor + python3 /usr/local/google/home/nehil/datacommons/import/git/data/tools/statvar_importer/stat_var_processor.py \ + --input_data="${DATASET_DIR}/input/data.csv" \ + --config_file="${DATASET_DIR}/deterministic_metadata.csv" \ + --pv_map="${DATASET_DIR}/deterministic_pvmap.csv" \ + --places_resolved_csv="/usr/local/google/home/nehil/datacommons/import/git/data/undata/DESA/all_places.csv" \ + --output_path="${DATASET_DIR}/output" \ + --output_counters="${DATASET_DIR}/validation/statvar_processor_counters.csv" +done diff --git a/undata/worker.sh b/undata/worker.sh new file mode 100755 index 0000000000..675d3361de --- /dev/null +++ b/undata/worker.sh @@ -0,0 +1,5 @@ +#!/bin/bash +source undata/run_batch_import.sh +while read -r f; do + process_dataset "$f" +done < "$1" diff --git a/undata_summary.md b/undata_summary.md new file mode 100644 index 0000000000..bc7511f0d7 --- /dev/null +++ b/undata_summary.md @@ -0,0 +1,40 @@ +# UN Data Import Strategy & Implementation Summary + +## Overview +This document summarizes the strategy discussed in the May 20th transcript and its technical realization within the `undata` directory. The primary objective is to transition from an AI-agent-dependent mapping process to a **deterministic Python-based ETL pipeline**. + +## Strategic Intent +The goal is to automate the mapping of UN dataset headers (e.g., `SERIES`, `AGE`, `SEX`) to Data Commons property-value (PV) pairs. By using a deterministic script, the team ensures: +- **Scalability**: Processing 57+ datasets consistently. +- **Reliability**: Eliminating repetitive errors often introduced by LLM agents. +- **Transparency**: Every mapping is traceable back to a reviewed "Concept Library" (CL). + +--- + +## Core Pipeline (The `undata` Folder) + +The implementation consists of two main Python scripts and a set of reviewed codelists that orchestrate the transformation. + +### 1. Metadata Generation (`generate_metadata.py`) +This script acts as the "discovery" phase of the pipeline. +- **Logic**: It scans raw CSV files in `undata/data/` and identifies columns that have corresponding reviewed codelists in `undata/cl_reviewed/`. +- **Output**: For each dataset, it generates a `metadata.csv` (e.g., `undata/metadata/DESA-GENDER_..._metadata.csv`). +- **Transcript Reference**: Matches the requirement to create a "metadata CSV file for each data file, containing unique values for columns like geography, age, and sex." + +### 2. PV Mapping Logic (`generate_pvmap.py`) +This script implements the "transformation" phase, converting metadata into final importer-ready PV maps. +- **Deterministic Mappings**: + - **Geography**: Maps to `observationAbout` (normalized from `geographyCounterpart`). + - **Time Period**: Maps to `observationDate`. + - **Observation Value**: Maps to `value`. +- **Dataset-Specific Overrides**: It can inject mandatory properties for specific series (e.g., setting `populationType: Person` for `ANC_COV`). +- **Output**: Generates a `pvmap.csv` which is used by the Data Commons importer to interpret the data columns. + +### 3. Import Configuration (`IMPORT_GUIDE.md`) +The guide defines how the final data is consumed by the Data Commons infrastructure. +- **Approach B (Recommended)**: Uses the "Transcoded" (processed) data where headers are already standardized (`variableMeasured`, `observationAbout`, etc.), making the import process seamless and requiring no manual file manipulation. + +--- + +## Conclusion +The current code in the `undata` folder successfully realizes the vision outlined in the May 20th transcript. It replaces speculative AI mappings with a robust, dictionary-based lookup system that uses intermediate metadata files to ensure every data point is correctly attributed within the Data Commons knowledge graph. diff --git a/upload_results.sh b/upload_results.sh new file mode 100644 index 0000000000..3f06a1b2ad --- /dev/null +++ b/upload_results.sh @@ -0,0 +1,15 @@ +#!/bin/bash +BASE_DIR="/usr/local/google/home/nehil/datacommons/import/git/data/abhishek_data" +GCS_BASE="gs://unresolved_mcf/Eurostat/sdmx" + +for dataset_dir in "$BASE_DIR"/*; do + if [ -d "$dataset_dir" ]; then + dataset_name=$(basename "$dataset_dir") + results_dir="$dataset_dir/efficacy_results" + + if [ -d "$results_dir" ]; then + echo "Uploading results for $dataset_name..." + gcloud storage cp --recursive "$results_dir" "$GCS_BASE/$dataset_name/" + fi + fi +done diff --git a/upload_results_done.sh b/upload_results_done.sh new file mode 100644 index 0000000000..9b2c583731 --- /dev/null +++ b/upload_results_done.sh @@ -0,0 +1,15 @@ +#!/bin/bash +BASE_DIR="/usr/local/google/home/nehil/datacommons/import/git/data/abhishek_data" +GCS_BASE="gs://unresolved_mcf/Eurostat/sdmx_done" + +for dataset_dir in "$BASE_DIR"/*; do + if [ -d "$dataset_dir" ]; then + dataset_name=$(basename "$dataset_dir") + results_dir="$dataset_dir/efficacy_results" + + if [ -d "$results_dir" ]; then + echo "Uploading results for $dataset_name..." + gcloud storage cp --recursive "$results_dir" "$GCS_BASE/$dataset_name/" + fi + fi +done