Computational Cultural Understanding (CCU) Evaluation Validation and Scoring Toolkit

Version: 1.3.5

Date: December 17, 2024

Table of Content

Overview

Setup

Directory Structure and File Format

Overview

This package contains the tools to validate and score the TA1 evaluation tasks: ND (norm discovery), ED (emotion detection), VD (valence diarization), AD (arousal diarization), CD (change detection) and scoring tools for the Hidden Norms (NDMAP). Please refer to the CCU Evaluation Plan for more information about CCU, the evaluation tasks, and the file formats.

This README file describes the reference annotation validation tool, system output validation tool, scoring tool, reference statistics computing tool and random submission generation tool. OpenCCU evaluation uses a subset of these tools and has its own README.

Reference Validation Tool: confirms that a reference annotation set follows the LDC (Linguistic Data Consortium) CCU annotation package directory structure.
System Output Validation Tool: confirms that a submission of system output follows the rules set in the CCU Evaluation Plan.
Scoring Tool: scores a system output submission against a reference with a scoring index file.
Reference Statistics Computing Tool: computes basic statistics on the reference data for the ND or ED task.
Random Submission Generation Tool: generates a random submission for the ND or ED task.

Setup

The tools mentioned above are included as a Python package. They can be run under a shell terminal and have been confirmed to work under OS X and Ubuntu.

Prerequisites

Installation

Install the Python package using the following commands:

git clone https://github.com/usnistgov/ccu_validation_scoring

cd ./CCU_validation_scoring

python3 -m pip install -e ./

Directory Structure and File Format

The CCU validation and scoring toolkit expects input directories and/or files to have specific structures and formats. This section gives more information on these structures and formats that are referred in subsequent sections.

The reference directory mentioned validation and scoring sections must follow the LDC annotation data package directory structure and at a minimum must contain the following files in the given directory structure to pass validation:

<reference_directory>/
     ./data/
          norms.tab
          emotions.tab
          valence_arousal.tab
          changepoint.tab
     ./docs/
          segments.tab
          file_info.tab
     ./index_files/
          <DATASET>.system_input.index.tab

where <DATASET> is the name of dataset.

Please refer to the LDC CCU annotation data package README for the formats of the above .tab files.

The toolkit includes several sample reference datasets for testing. See ccu_validation_scoring/test/reference/LDC_reference_sample or other sibling directories.

The toolkit uses different index files for various purposes:

system input index file - tells the scorer which files are available for the system to process. This file is included in the CCU source data package and is used in validation and generation of a sample submission. The format is described in the CCU Evaluation Plan.
system output index file - tells the scorer which files were processed by the system. This file is generated by the users and is used in validation and scoring. It must be located inside the submission directory. The format is described in the CCU Evaluation Plan.
scoring index file - tells the scorer which files to score to facilitate subset scoring. This file is generated by the users and is used in scoring. The scoring index file has one column with the header file_id containing file IDs to score, one per row.

An example of a system input index file can be found in the sample reference datasets:

ccu_validation_scoring/test/reference/LDC_reference_sample/index_files/LC1-SimulatedMiniEvalP1.20220909.system_input.index.tab

An example of a system output index file can be found in the sample submissions:

ccu_validation_scoring/test/pass_submissions/pass_submissions_LDC_
reference_sample/ED/CCU_P1_TA1_ED_NIST_mini-eval1_20220816_050236/system_output.index.tab

Usage

In the CCU_validation_scoring-x.x.x/ directory, run the following to get the version and usage:

CCU_scoring version

CCU_scoring is an umbrella tool that has several subcommands, each with its own set of command line options. To get a list of subcommands, execute:

CCU_scoring -h

Use the -h flag on the subcommand to get the subcommand help manual. For example:

CCU_scoring score-nd -h

Reference Validation Subcommand

Validate a reference annotation directory to make sure the reference directory have the required files.

CCU_scoring validate-ref -ref <reference_directory>

Required Arguments

-ref: reference directory

# an example of reference validation
CCU_scoring validate-ref -ref test/reference/LDC_reference_sample

Submission Validation Subcommands

Norm Discovery (ND) Validation Subcommand

Use the command below to validate an ND submission directory against a reference directory. The submission directory must include a system output index file.

CCU_scoring validate-nd -s <submission_directory> -ref <reference_directory>

Required Arguments

-s: submission directory
-ref: reference directory

Optional Arguments

-sf: string to define the submission format. Default "regular" is for CCU evaluation while "open" is for OpenCCU.
-n: file containing the known norm to validate if norms are either known norms or team define norms.

# an example of submission validation
CCU_scoring validate-nd \
-s test/pass_submissions/pass_submissions_LDC_reference_sample/ND/CCU_P1_TA1_ND_NIST_mini-eval1_20220815_164235 \
-ref test/reference/LDC_reference_sample

Norm Discovery Mapping Validation Subcommand

Use the command below to validate the format of an NDMAP submission directory with a hidden norm list. This validation only applies to the mapping file, not the original system. The hidden norm list has one column (no header) containing norm IDs, one per row.

CCU_scoring validate-ndmap -s <submission_directory> -n <hidden_norm_list_file>

Required Arguments

-s: submission directory
-n: file containing the hidden norm mapping

# an example of ndmap submission validation
CCU_scoring validate-ndmap \
-s test/pass_submissions/pass_submissions_LDC_reference_sample/NDMAP/CCU_P1_TA1_NDMAP_NIST_mini-eval1_20220605_050236 \
-n test/hidden_norms.txt

Emotion Detection (ED) and Change Detection (CD) Validation Subcommands

Use the command below to validate an ED or CD submission directory against a reference directory. The submission directory must include a system output index file.

CCU_scoring validate-ed -s <submission_directory> -ref <reference_directory>
CU_scoring validate-cd -s <submission_directory> -ref <reference_directory>

Required Arguments

-s: submission directory
-ref: reference directory

# an example of ed submission validation
CCU_scoring validate-ed \
-s test/pass_submissions/pass_submissions_LDC_reference_sample/ED/CCU_P1_TA1_ED_NIST_mini-eval1_20220531_050236 \
-ref test/reference/LDC_reference_sample

# an example of cd submission validation
CCU_scoring validate-cd \
-s test/pass_submissions/pass_submissions_LDC_reference_sample/CD/CCU_P1_TA1_CD_NIST_mini-eval1_20220531_050236 \
-ref test/reference/LDC_reference_sample

Valence Diarization (VD) and Arousal Diarization (AD) Validation Subcommands

Use the command below to validate an VD or AD submission directory against a reference directory. The submission directory must include a system output index file.

CCU_scoring validate-vd -s <submission_directory> -ref <reference_directory>
CU_scoring validate-ad -s <submission_directory> -ref <reference_directory>

Required Arguments

-s: submission directory
-ref: reference directory

Optional Arguments

-g: allow gap in system output of valence and arousal

# an example of ed submission validation
CCU_scoring validate-vd \
-s test/pass_submissions/pass_submissions_LDC_reference_sample/VD/CCU_P1_TA1_VD_NIST_mini-eval1_20220531_050236 \
-ref test/reference/LDC_reference_sample

# an example of cd submission validation
CCU_scoring validate-ad \
-s test/pass_submissions/pass_submissions_LDC_reference_sample/AD/CCU_P1_TA1_AD_NIST_mini-eval1_20220531_050236 \
-ref test/reference/LDC_reference_sample

Submission Scoring Subcommands

Norm Discovery (ND) Scoring Subcommand

Use the command below to score an ND submission directory against a reference directory with a scoring index file. The submission directory must include a system output index file.

CCU_scoring score-nd -s <norm_submission_directory> -ref <reference_directory> -i <scoring_index_file>

Norm Discovery Mapping Scoring Subcommand

Use the command below to score an NDMAP submission directory and an ND submission against a reference directory with a scoring index file. The submission directory must include a system output index file.

CCU_scoring score-nd -s <norm_submission_directory> -m <norm_mapping_submission_directory> -ref <reference_directory> -i <scoring_index_file>

Required Arguments

-s: norm submission directory
-ref: reference directory
-i: file containing the file id of scoring datasets

Optional Arguments

-sf: string to define the submission format. Default "regular" is for CCU evaluation while "open" is for OpenCCU.
-m: norm mapping submission directory
-n: file containing the norm to filter norm from scoring
-t: comma separated list of IoU thresholds
-aC: forgiveness collar (in seconds) to determine true positive or false positive in scale metric alignment
-xC: forgiveness collar (in characters) to determine true positive or false positive in scale metric alignment
-o: output directory containing the score and alignment file
-xR: gap (in characters) for the text reference instances merging
-aR: gap (in seconds) for the audio/video reference instances merging
-vR: define how to handle the adhere/violate labels for the reference norm instances merging. "class" is to use the class label only (ignoring status) to merge and "class-status" is to use the class and status label to merge
-xS: gap (in characters) for the text system instances merging
-aS: gap (in seconds) for the audio/video system instances merging
-lS: choose "min_llr" or "max_llr" to combine system llrs for the system instances merging
-vS: choose "class" or "class-status" to define how to handle the adhere/violate labels for the system instances merging. "class" is to use the class label only to merge and "class-status" is to use the class and status label to merge
-f: change reference annotation to "noann" when there are the same annotations but different status
-d: dump reference and system inputs as they are processed during scoring.
-q: do not dump ther final alignment and scores to stdout.
-lf: Filter system output by LLRs. The option requires the form :by_value:. is one of: 'after_read|after_transforms'. is the floating point threshold to retain detections with values >=

# an example of norm scoring
CCU_scoring score-nd \
-s test/pass_submissions/pass_submissions_LDC_reference_sample/ND/CCU_P1_TA1_ND_NIST_mini-eval1_20220815_164235 \
-ref test/reference/LDC_reference_sample \
-i test/reference/LDC_reference_sample/index_files/LC1-SimulatedMiniEvalP1.20220909.ND.scoring.index.tab

# an example of ndmap scoring
CCU_scoring score-nd \
-s test/pass_submissions/pass_submissions_LDC_reference_sample/ND/CCU_P1_TA1_ND_NIST_mini-eval1_20220531_050236 \
-m test/pass_submissions/pass_submissions_LDC_reference_sample/NDMAP/CCU_P1_TA1_NDMAP_NIST_mini-eval1_20220605_050236 \
-ref test/reference/LDC_reference_sample \
-i test/reference/LDC_reference_sample/index_files/LC1-SimulatedMiniEvalP1.20220909.ND.scoring.index.tab

Emotion Detection (ED) Scoring Subcommand

Use the command below to score an ED submission directory against a reference directory with a scoring index file. The submission directory must include a system output index file.

CCU_scoring score-ed -s <emotion_submission_directory> -ref <reference_directory> -i <scoring_index_file>

Required Arguments

-s: emotion submission directory
-ref: reference directory
-i: file containing the file id of scoring datasets

Optional Arguments

-e: file containing the emotion to filter emotion from scoring
-t: comma separated list of IoU thresholds
-aC: forgiveness collar (in seconds) to determine true positive or false positive in scale metric alignment
-xC: forgiveness collar (in characters) to determine true positive or false positive in scale metric alignment
-o: output directory containing the score and alignment file
-xR: gap (in characters) for the text reference instances merging
-aR: gap (in seconds) for the audio/video reference instances merging
-xS: gap (in characters) for the text system instances merging
-aS: gap (in seconds) for the audio/video system instances merging
-lS: choose "min_llr" or "max_llr" to combine system llrs for the system instances merging
-mv: set the mimimum agreement for voting between annotators. default is 2 agreeing annotators per segment.
-d: dump reference and system inputs as they are processed during scoring
-q: do not dump ther final alignment and scores to stdout
--align_hacks: Provide 'ManyRef:ManyHyp' to apply Many-to-Many instance matching alignment algorithm
-lf: Filter system output by LLRs. The option requires the form :by_value:. is one of: 'after_read|after_transforms'. is the floating point threshold to retain detections with values >=
-fm: choose one from [1/4, 1/3, 1/2, 1] to define Filemerge proportion

# an example of ed scoring
CCU_scoring score-ed \
-s test/pass_submissions/pass_submissions_LDC_reference_sample/ED/CCU_P1_TA1_ED_NIST_mini-eval1_20220531_050236 \
-ref test/reference/LDC_reference_sample \
-i test/reference/LDC_reference_sample/index_files/LC1-SimulatedMiniEvalP1.20220909.ED.scoring.index.tab

Valence Diarization (VD) and Arousal Diarization (AD) Scoring Subcommands

Use the commands below to score an VD or AD submission directory against a reference directory with a scoring index file. The submission directory must include a system output index file.

CCU_scoring score-vd -s <valence_submission_directory> -ref <reference_directory> -i <scoring_index_file>
CCU_scoring score-ad -s <arousal_submission_directory> -ref <reference_directory> -i <scoring_index_file>

Required Arguments

-s: valence or arousal submission directory
-ref: reference directory
-i: file containing the file id of scoring datasets

Optional Arguments

-o: output directory containing the score and diarization file
-q: do not dump ther final alignment and scores to stdout
-g: allow gap in system output of valence and arousal

# an example of vd scoring
CCU_scoring score-vd \
-s test/pass_submissions/pass_submissions_LDC_reference_sample/VD/CCU_P1_TA1_VD_NIST_mini-eval1_20220531_050236 \
-ref test/reference/LDC_reference_sample \
-i test/reference/LDC_reference_sample/index_files/LC1-SimulatedMiniEvalP1.20220909.VD.scoring.index.tab

# an example of ad scoring
CCU_scoring score-ad \
-s test/pass_submissions/pass_submissions_LDC_reference_sample/AD/CCU_P1_TA1_AD_NIST_mini-eval1_20220531_050236 \
-ref test/reference/LDC_reference_sample \
-i test/reference/LDC_reference_sample/index_files/LC1-SimulatedMiniEvalP1.20220909.AD.scoring.index.tab

Change Detection (CD) Scoring Subcommand

Use the command below to score a CD submission directory against a reference directory with a scoring index file. The submission directory must include a system output index file.

CCU_scoring score-cd -s <change_submission_directory> -ref <reference_directory> -i <scoring_index_file>

Required Arguments

-s: change detection submission directory
-ref: reference directory
-i: file containing the file id of scoring datasets

Optional Arguments

-e: comma separated list of delta CP (change point) text thresholds
-m: comma separated list of delta CP (change point) time thresholds
-o: output directory containing the score and alignment file
-d: dump reference and system inputs as they are processed during scoring
-q: do not dump ther final alignment and scores to stdout
-lf: Filter system output by LLRs. The option requires the form :by_value:. is one of: 'after_read|after_transforms'. is the floating point threshold to retain detections with values >=

# an example of cd scoring
CCU_scoring score-cd \
-s test/pass_submissions/pass_submissions_LDC_reference_sample/CD/CCU_P1_TA1_CD_NIST_mini-eval1_20220531_050236 \
-ref test/reference/LDC_reference_sample \
-i test/reference/LDC_reference_sample/index_files/LC1-SimulatedMiniEvalP1.20220909.CD.scoring.index.tab

Reference Statistics Computing Tool

The following command should be run within the CCU_validation_scoring-x.x.x/ directory.

python3 scripts/ccu_ref_analysis.py -r <reference_directory> -t <task_string> -i <scoring_index_file> -o <output_file>

Required Arguments

-r: reference directory
-t: norms or emotions
-i: file containing the file id of scoring datasets
-o: file where the statistics will be output

Optional Arguments

-xR: gap (in characters) for the text reference instances merging
-aR: gap (in seconds) for the audio/video reference instances merging
-vR: define how to handle the adhere/violate labels for the reference norm instances merging. "class" is to use the class label only (ignoring status) to merge and "class-status" is to use the class and status label to merge
-f: change reference annotation to "noann" when there are the same annotations but different status

# an example of statistics computing
python3 scripts/ccu_ref_analysis.py -r test/reference/LDC_reference_sample \
-t norms \
-i test/reference/LDC_reference_sample/index_files/LC1-SimulatedMiniEvalP1.20220909.ND.scoring.index.tab \
-o tmp.tab

Random Submission Generation Tool

The following command should be run within the CCU_validation_scoring-x.x.x/ directory.

python3 scripts/generate_ccu_random_submission.py -ref <reference_directory> -t <task_string> -i <scoring_index_file> -o <output_directory>

Required Arguments

-ref: reference directory
-t: norms or emotions
-i: file containing the file id of scoring datasets
-o: output directory containing a random submission

Optional Arguments

-xR: gap (in characters) for the text reference instances merging
-aR: gap (in seconds) for the audio/video reference instances merging
-vR: define how to handle the adhere/violate labels for the reference norm instances merging. "class" is to use the class label only (ignoring status) to merge and "class-status" is to use the class and status label to merge
-f: change reference annotation to "noann" when there are the same annotations but different status

# an example of random submission generation
python3 scripts/generate_ccu_random_submission.py -ref test/reference/LDC_reference_sample \
-t norms \
-i test/reference/LDC_reference_sample/index_files/LC1-SimulatedMiniEvalP1.20220909.ND.scoring.index.tab \
-o tmp

Report a Bug

Please send bug reports to nist_ccu@nist.gov

For the bug report to be useful, please include the command line, files and text output, including the error message in your email.

Test case bug report

A test suite has been developed and is runnable using the following command within the CCU_validation_scoring-x.x.x/ directory:

This will run the tests against a set of submissions and reference files available under test.

pytest

Authors

Jennifer Yu <yan.yu@nist.gov>

Clyburn Cunningham <clyburn.cunningham@nist.gov>

Lukas Diduch <lukas.diduch@nist.gov>

Jonathan Fiscus <jonathan.fiscus@nist.gov>

Audrey Tong <audrey.tong@nist.gov>

Licensing Statement

Full details can be found at: http://nist.gov/data/license.cfm

NIST-developed software is provided by NIST as a public service. You may use,
copy, and distribute copies of the software in any medium, provided that you
keep intact this entire notice. You may improve, modify, and create derivative
works of the software or any portion of the software, and you may copy and
distribute such modifications or works. Modified works should carry a notice
stating that you changed the software and should note the date and nature of
any such change. Please explicitly acknowledge the National Institute of
Standards and Technology as the source of the software. 

NIST-developed software is expressly provided "AS IS." NIST MAKES NO WARRANTY
OF ANY KIND, EXPRESS, IMPLIED, IN FACT, OR ARISING BY OPERATION OF LAW,
INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTY OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE, NON-INFRINGEMENT, AND DATA ACCURACY. NIST NEITHER
REPRESENTS NOR WARRANTS THAT THE OPERATION OF THE SOFTWARE WILL BE
UNINTERRUPTED OR ERROR-FREE, OR THAT ANY DEFECTS WILL BE CORRECTED. NIST DOES
NOT WARRANT OR MAKE ANY REPRESENTATIONS REGARDING THE USE OF THE SOFTWARE OR
THE RESULTS THEREOF, INCLUDING BUT NOT LIMITED TO THE CORRECTNESS, ACCURACY,
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You are solely responsible for determining the appropriateness of using and
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to property. The software developed by NIST employees is not subject to
copyright protection within the United States.

Name		Name	Last commit message	Last commit date
Latest commit History 353 Commits
CCU_validation_scoring		CCU_validation_scoring
scripts		scripts
test		test
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
LICENSE.txt		LICENSE.txt
Makefile		Makefile
OpenCCU_README.md		OpenCCU_README.md
README.md		README.md
__init__.py		__init__.py
pytest.ini		pytest.ini
setup.py		setup.py
test.py		test.py

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Computational Cultural Understanding (CCU) Evaluation Validation and Scoring Toolkit

Table of Content

Overview

Setup

Prerequisites

Installation

Directory Structure and File Format

Usage

Reference Validation Subcommand

Submission Validation Subcommands

Submission Scoring Subcommands

Reference Statistics Computing Tool

Random Submission Generation Tool

Report a Bug

Test case bug report

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Licensing Statement

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Computational Cultural Understanding (CCU) Evaluation Validation and Scoring Toolkit

Table of Content

Reference Validation Subcommand

Submission Validation Subcommands

Submission Scoring Subcommands

Reference Statistics Computing Tool

Random Submission Generation Tool

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