From d15c1057c6d9f9f81f630611a9670d233a3731c3 Mon Sep 17 00:00:00 2001 From: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Date: Tue, 22 Oct 2024 11:01:14 -0400 Subject: [PATCH] Massive PR (#321) * info diversity tests * lexicon cleanup hotfix * Bump path-to-regexp and express in /website (#298) * Add Examples Notebook (#294) * Urgent fix to remove LIWC lexicons from public repo (#279) * delete small test lexicons * move .pkl files to assets and remove from GH * filesystem cleanup * update certainty pickle location * remove unpickling certainty * remove lexicons from pyproject * change lexical pkl path * add error handling when lexicons are not found * update warning message * add legal caveat and update name of certainty pkl to be correct * ensure lexicons are ignored * Update Documentation (Complete Conceptual Documentation, Document Assumptions) (#289) * new docs * lexicons hotfix * emilys doc edits * update deprecated github actions to latest * update last remaining text features * update index * update docs * update index * update docs * update docs and the feature dictionary * add basics.rst * add new basics page * update docs --------- Co-authored-by: Xinlan Emily Hu Co-authored-by: Xinlan Emily Hu * update torch requirements to resolve compatibility issue on torch end (#290) * Update Website (#291) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team --------- Co-authored-by: amytangzheng * update documentation for clarity and correct typos in positivity z-score and information exchange and liwc * add demo notebook * update notebook and add information to docs * update documentation --------- Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng * Bump path-to-regexp and express in /website Bumps [path-to-regexp](https://github.com/pillarjs/path-to-regexp) and [express](https://github.com/expressjs/express). These dependencies needed to be updated together. Updates `path-to-regexp` from 0.1.7 to 0.1.10 - [Release notes](https://github.com/pillarjs/path-to-regexp/releases) - [Changelog](https://github.com/pillarjs/path-to-regexp/blob/master/History.md) - [Commits](https://github.com/pillarjs/path-to-regexp/compare/v0.1.7...v0.1.10) Updates `express` from 4.19.2 to 4.21.0 - [Release notes](https://github.com/expressjs/express/releases) - [Changelog](https://github.com/expressjs/express/blob/4.21.0/History.md) - [Commits](https://github.com/expressjs/express/compare/4.19.2...4.21.0) --- updated-dependencies: - dependency-name: path-to-regexp dependency-type: indirect - dependency-name: express dependency-type: indirect ... Signed-off-by: dependabot[bot] --------- Signed-off-by: dependabot[bot] Co-authored-by: Xinlan Emily Hu Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng Co-authored-by: Xinlan Emily Hu Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump nltk from 3.8.1 to 3.9 (#297) * Add Examples Notebook (#294) * Urgent fix to remove LIWC lexicons from public repo (#279) * delete small test lexicons * move .pkl files to assets and remove from GH * filesystem cleanup * update certainty pickle location * remove unpickling certainty * remove lexicons from pyproject * change lexical pkl path * add error handling when lexicons are not found * update warning message * add legal caveat and update name of certainty pkl to be correct * ensure lexicons are ignored * Update Documentation (Complete Conceptual Documentation, Document Assumptions) (#289) * new docs * lexicons hotfix * emilys doc edits * update deprecated github actions to latest * update last remaining text features * update index * update docs * update index * update docs * update docs and the feature dictionary * add basics.rst * add new basics page * update docs --------- Co-authored-by: Xinlan Emily Hu Co-authored-by: Xinlan Emily Hu * update torch requirements to resolve compatibility issue on torch end (#290) * Update Website (#291) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team --------- Co-authored-by: amytangzheng * update documentation for clarity and correct typos in positivity z-score and information exchange and liwc * add demo notebook * update notebook and add information to docs * update documentation --------- Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng * Bump nltk from 3.8.1 to 3.9 Bumps [nltk](https://github.com/nltk/nltk) from 3.8.1 to 3.9. - [Changelog](https://github.com/nltk/nltk/blob/develop/ChangeLog) - [Commits](https://github.com/nltk/nltk/compare/3.8.1...3.9) --- updated-dependencies: - dependency-name: nltk dependency-type: direct:production ... Signed-off-by: dependabot[bot] * Update pyproject.toml * Update requirements.txt * Update download_resources.py --------- Signed-off-by: dependabot[bot] Co-authored-by: Xinlan Emily Hu Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng Co-authored-by: Xinlan Emily Hu Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump body-parser and express in /website (#296) * Add Examples Notebook (#294) * Urgent fix to remove LIWC lexicons from public repo (#279) * delete small test lexicons * move .pkl files to assets and remove from GH * filesystem cleanup * update certainty pickle location * remove unpickling certainty * remove lexicons from pyproject * change lexical pkl path * add error handling when lexicons are not found * update warning message * add legal caveat and update name of certainty pkl to be correct * ensure lexicons are ignored * Update Documentation (Complete Conceptual Documentation, Document Assumptions) (#289) * new docs * lexicons hotfix * emilys doc edits * update deprecated github actions to latest * update last remaining text features * update index * update docs * update index * update docs * update docs and the feature dictionary * add basics.rst * add new basics page * update docs --------- Co-authored-by: Xinlan Emily Hu Co-authored-by: Xinlan Emily Hu * update torch requirements to resolve compatibility issue on torch end (#290) * Update Website (#291) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team --------- Co-authored-by: amytangzheng * update documentation for clarity and correct typos in positivity z-score and information exchange and liwc * add demo notebook * update notebook and add information to docs * update documentation --------- Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng * Bump body-parser and express in /website Bumps [body-parser](https://github.com/expressjs/body-parser) and [express](https://github.com/expressjs/express). These dependencies needed to be updated together. Updates `body-parser` from 1.20.2 to 1.20.3 - [Release notes](https://github.com/expressjs/body-parser/releases) - [Changelog](https://github.com/expressjs/body-parser/blob/master/HISTORY.md) - [Commits](https://github.com/expressjs/body-parser/compare/1.20.2...1.20.3) Updates `express` from 4.19.2 to 4.21.0 - [Release notes](https://github.com/expressjs/express/releases) - [Changelog](https://github.com/expressjs/express/blob/4.21.0/History.md) - [Commits](https://github.com/expressjs/express/compare/4.19.2...4.21.0) --- updated-dependencies: - dependency-name: body-parser dependency-type: indirect - dependency-name: express dependency-type: indirect ... Signed-off-by: dependabot[bot] --------- Signed-off-by: dependabot[bot] Co-authored-by: Xinlan Emily Hu Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng Co-authored-by: Xinlan Emily Hu Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Check embedding update (#295) * Add Examples Notebook (#294) * Urgent fix to remove LIWC lexicons from public repo (#279) * delete small test lexicons * move .pkl files to assets and remove from GH * filesystem cleanup * update certainty pickle location * remove unpickling certainty * remove lexicons from pyproject * change lexical pkl path * add error handling when lexicons are not found * update warning message * add legal caveat and update name of certainty pkl to be correct * ensure lexicons are ignored * Update Documentation (Complete Conceptual Documentation, Document Assumptions) (#289) * new docs * lexicons hotfix * emilys doc edits * update deprecated github actions to latest * update last remaining text features * update index * update docs * update index * update docs * update docs and the feature dictionary * add basics.rst * add new basics page * update docs --------- Co-authored-by: Xinlan Emily Hu Co-authored-by: Xinlan Emily Hu * update torch requirements to resolve compatibility issue on torch end (#290) * Update Website (#291) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team --------- Co-authored-by: amytangzheng * update documentation for clarity and correct typos in positivity z-score and information exchange and liwc * add demo notebook * update notebook and add information to docs * update documentation --------- Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng * update check embeddings with tqdm loading bar and BERT tokenization update * (1) allow BERT sentiments to be generated from the messages with punctuation, rather than the preprocessed messages; (2) batch BERT sentiment generation to make it more efficient; (3) add loading bar for generation of chat-level features --------- Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng * Update README.md to remove col = "message" * Closes #302. * Rebase Shruti's testing changes (#303) * Urgent fix to remove LIWC lexicons from public repo (#279) (#280) * delete small test lexicons * move .pkl files to assets and remove from GH * filesystem cleanup * update certainty pickle location * remove unpickling certainty * remove lexicons from pyproject * change lexical pkl path * add error handling when lexicons are not found * update warning message * add legal caveat and update name of certainty pkl to be correct * ensure lexicons are ignored * ensure lexicons are ignored * Update Documentation (Complete Conceptual Documentation, Document Assumptions) (#289) * new docs * lexicons hotfix * emilys doc edits * update deprecated github actions to latest * update last remaining text features * update index * update docs * update index * update docs * update docs and the feature dictionary * add basics.rst * add new basics page * update docs --------- Co-authored-by: Xinlan Emily Hu Co-authored-by: Xinlan Emily Hu * update torch requirements to resolve compatibility issue on torch end (#290) * Update Website (#291) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team --------- Co-authored-by: amytangzheng * update documentation for clarity and correct typos in positivity z-score and information exchange and liwc * Patch Release 0.1.3 (#292) * Urgent fix to remove LIWC lexicons from public repo (#279) * delete small test lexicons * move .pkl files to assets and remove from GH * filesystem cleanup * update certainty pickle location * remove unpickling certainty * remove lexicons from pyproject * change lexical pkl path * add error handling when lexicons are not found * update warning message * add legal caveat and update name of certainty pkl to be correct * ensure lexicons are ignored * Update Documentation (Complete Conceptual Documentation, Document Assumptions) (#289) * new docs * lexicons hotfix * emilys doc edits * update deprecated github actions to latest * update last remaining text features * update index * update docs * update index * update docs * update docs and the feature dictionary * add basics.rst * add new basics page * update docs --------- Co-authored-by: Xinlan Emily Hu Co-authored-by: Xinlan Emily Hu * update torch requirements to resolve compatibility issue on torch end (#290) * Update Website (#291) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team --------- Co-authored-by: amytangzheng * update documentation for clarity and correct typos in positivity z-score and information exchange and liwc --------- Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng * update pyproject.toml * add demo notebook * update notebook and add information to docs * update documentation * Add Examples Notebook (#294) * Urgent fix to remove LIWC lexicons from public repo (#279) * delete small test lexicons * move .pkl files to assets and remove from GH * filesystem cleanup * update certainty pickle location * remove unpickling certainty * remove lexicons from pyproject * change lexical pkl path * add error handling when lexicons are not found * update warning message * add legal caveat and update name of certainty pkl to be correct * ensure lexicons are ignored * Update Documentation (Complete Conceptual Documentation, Document Assumptions) (#289) * new docs * lexicons hotfix * emilys doc edits * update deprecated github actions to latest * update last remaining text features * update index * update docs * update index * update docs * update docs and the feature dictionary * add basics.rst * add new basics page * update docs --------- Co-authored-by: Xinlan Emily Hu Co-authored-by: Xinlan Emily Hu * update torch requirements to resolve compatibility issue on torch end (#290) * Update Website (#291) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team --------- Co-authored-by: amytangzheng * update documentation for clarity and correct typos in positivity z-score and information exchange and liwc * add demo notebook * update notebook and add information to docs * update documentation --------- Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng * fix typo in demo * Bump path-to-regexp and express in /website (#298) * Add Examples Notebook (#294) * Urgent fix to remove LIWC lexicons from public repo (#279) * delete small test lexicons * move .pkl files to assets and remove from GH * filesystem cleanup * update certainty pickle location * remove unpickling certainty * remove lexicons from pyproject * change lexical pkl path * add error handling when lexicons are not found * update warning message * add legal caveat and update name of certainty pkl to be correct * ensure lexicons are ignored * Update Documentation (Complete Conceptual Documentation, Document Assumptions) (#289) * new docs * lexicons hotfix * emilys doc edits * update deprecated github actions to latest * update last remaining text features * update index * update docs * update index * update docs * update docs and the feature dictionary * add basics.rst * add new basics page * update docs --------- Co-authored-by: Xinlan Emily Hu Co-authored-by: Xinlan Emily Hu * update torch requirements to resolve compatibility issue on torch end (#290) * Update Website (#291) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team --------- Co-authored-by: amytangzheng * update documentation for clarity and correct typos in positivity z-score and information exchange and liwc * add demo notebook * update notebook and add information to docs * update documentation --------- Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng * Bump path-to-regexp and express in /website Bumps [path-to-regexp](https://github.com/pillarjs/path-to-regexp) and [express](https://github.com/expressjs/express). These dependencies needed to be updated together. Updates `path-to-regexp` from 0.1.7 to 0.1.10 - [Release notes](https://github.com/pillarjs/path-to-regexp/releases) - [Changelog](https://github.com/pillarjs/path-to-regexp/blob/master/History.md) - [Commits](https://github.com/pillarjs/path-to-regexp/compare/v0.1.7...v0.1.10) Updates `express` from 4.19.2 to 4.21.0 - [Release notes](https://github.com/expressjs/express/releases) - [Changelog](https://github.com/expressjs/express/blob/4.21.0/History.md) - [Commits](https://github.com/expressjs/express/compare/4.19.2...4.21.0) --- updated-dependencies: - dependency-name: path-to-regexp dependency-type: indirect - dependency-name: express dependency-type: indirect ... Signed-off-by: dependabot[bot] --------- Signed-off-by: dependabot[bot] Co-authored-by: Xinlan Emily Hu Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng Co-authored-by: Xinlan Emily Hu Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump nltk from 3.8.1 to 3.9 (#297) * Add Examples Notebook (#294) * Urgent fix to remove LIWC lexicons from public repo (#279) * delete small test lexicons * move .pkl files to assets and remove from GH * filesystem cleanup * update certainty pickle location * remove unpickling certainty * remove lexicons from pyproject * change lexical pkl path * add error handling when lexicons are not found * update warning message * add legal caveat and update name of certainty pkl to be correct * ensure lexicons are ignored * Update Documentation (Complete Conceptual Documentation, Document Assumptions) (#289) * new docs * lexicons hotfix * emilys doc edits * update deprecated github actions to latest * update last remaining text features * update index * update docs * update index * update docs * update docs and the feature dictionary * add basics.rst * add new basics page * update docs --------- Co-authored-by: Xinlan Emily Hu Co-authored-by: Xinlan Emily Hu * update torch requirements to resolve compatibility issue on torch end (#290) * Update Website (#291) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team --------- Co-authored-by: amytangzheng * update documentation for clarity and correct typos in positivity z-score and information exchange and liwc * add demo notebook * update notebook and add information to docs * update documentation --------- Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng * Bump nltk from 3.8.1 to 3.9 Bumps [nltk](https://github.com/nltk/nltk) from 3.8.1 to 3.9. - [Changelog](https://github.com/nltk/nltk/blob/develop/ChangeLog) - [Commits](https://github.com/nltk/nltk/compare/3.8.1...3.9) --- updated-dependencies: - dependency-name: nltk dependency-type: direct:production ... Signed-off-by: dependabot[bot] * Update pyproject.toml * Update requirements.txt * Update download_resources.py --------- Signed-off-by: dependabot[bot] Co-authored-by: Xinlan Emily Hu Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng Co-authored-by: Xinlan Emily Hu Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump body-parser and express in /website (#296) * Add Examples Notebook (#294) * Urgent fix to remove LIWC lexicons from public repo (#279) * delete small test lexicons * move .pkl files to assets and remove from GH * filesystem cleanup * update certainty pickle location * remove unpickling certainty * remove lexicons from pyproject * change lexical pkl path * add error handling when lexicons are not found * update warning message * add legal caveat and update name of certainty pkl to be correct * ensure lexicons are ignored * Update Documentation (Complete Conceptual Documentation, Document Assumptions) (#289) * new docs * lexicons hotfix * emilys doc edits * update deprecated github actions to latest * update last remaining text features * update index * update docs * update index * update docs * update docs and the feature dictionary * add basics.rst * add new basics page * update docs --------- Co-authored-by: Xinlan Emily Hu Co-authored-by: Xinlan Emily Hu * update torch requirements to resolve compatibility issue on torch end (#290) * Update Website (#291) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team --------- Co-authored-by: amytangzheng * update documentation for clarity and correct typos in positivity z-score and information exchange and liwc * add demo notebook * update notebook and add information to docs * update documentation --------- Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng * Bump body-parser and express in /website Bumps [body-parser](https://github.com/expressjs/body-parser) and [express](https://github.com/expressjs/express). These dependencies needed to be updated together. Updates `body-parser` from 1.20.2 to 1.20.3 - [Release notes](https://github.com/expressjs/body-parser/releases) - [Changelog](https://github.com/expressjs/body-parser/blob/master/HISTORY.md) - [Commits](https://github.com/expressjs/body-parser/compare/1.20.2...1.20.3) Updates `express` from 4.19.2 to 4.21.0 - [Release notes](https://github.com/expressjs/express/releases) - [Changelog](https://github.com/expressjs/express/blob/4.21.0/History.md) - [Commits](https://github.com/expressjs/express/compare/4.19.2...4.21.0) --- updated-dependencies: - dependency-name: body-parser dependency-type: indirect - dependency-name: express dependency-type: indirect ... Signed-off-by: dependabot[bot] --------- Signed-off-by: dependabot[bot] Co-authored-by: Xinlan Emily Hu Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng Co-authored-by: Xinlan Emily Hu Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Check embedding update (#295) * Add Examples Notebook (#294) * Urgent fix to remove LIWC lexicons from public repo (#279) * delete small test lexicons * move .pkl files to assets and remove from GH * filesystem cleanup * update certainty pickle location * remove unpickling certainty * remove lexicons from pyproject * change lexical pkl path * add error handling when lexicons are not found * update warning message * add legal caveat and update name of certainty pkl to be correct * ensure lexicons are ignored * Update Documentation (Complete Conceptual Documentation, Document Assumptions) (#289) * new docs * lexicons hotfix * emilys doc edits * update deprecated github actions to latest * update last remaining text features * update index * update docs * update index * update docs * update docs and the feature dictionary * add basics.rst * add new basics page * update docs --------- Co-authored-by: Xinlan Emily Hu Co-authored-by: Xinlan Emily Hu * update torch requirements to resolve compatibility issue on torch end (#290) * Update Website (#291) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team --------- Co-authored-by: amytangzheng * update documentation for clarity and correct typos in positivity z-score and information exchange and liwc * add demo notebook * update notebook and add information to docs * update documentation --------- Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng * update check embeddings with tqdm loading bar and BERT tokenization update * (1) allow BERT sentiments to be generated from the messages with punctuation, rather than the preprocessed messages; (2) batch BERT sentiment generation to make it more efficient; (3) add loading bar for generation of chat-level features --------- Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng * Update README.md to remove col = "message" * info diversity tests * intermediate info diversity changes rebasing! * intermediate changes * rebase dev * removed print * Update run_tests.py --------- Signed-off-by: dependabot[bot] Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: agshruti12 * mimicry tests + content word per conv feature * add call to content word accommodation per conv * spelling fix * mimicry change * Amy/website (#301) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team * gh actions and custom domain * deploy to custom url * deploy to custom url * updates to cname * changes to cname * cname updates * testing github actions * updates to github-actions-website * testing github actions * updates to gh actions * updates to github-actions * update home for testing gh actions * updates CNAME * update testing email * updates username/email * updates to email in github-actions-website * testing gh actions for feature_dict * testing github-actions feature_dict * updates to github-actions-feature_dict * Update github-actions-feature_dict.yaml * testing updates to feature_dict.py * testing feature_dict updates * testing updates to feature_dict.py * testing feature_dict deployment * Update github-actions-feature_dict.yaml * testing feature_dict updates * testing updates to feature_dict.py * updates to feature_dict * updates to github actions feature_dict * testing auto updates to feature_dict * Update feature_dict.py * testing feature_dict auto updates * testing feature_dict auto updates * Update feature_dict.py * testing feature_dict auto updates * remove commented code in feature_dict.py * Delete src/team_comm_tools/filtered_dict.json delete test json file * Update github-actions-website.yaml to deploy on update to dev * put 'dev' in quotes * Update github-actions-feature_dict.yaml to update upon dev * re-add filtered dict --------- Co-authored-by: Xinlan Emily Hu Co-authored-by: Xinlan Emily Hu * Update github-actions-website.yaml (#309) * Update github-actions-feature_dict.yaml (#308) * Package updates in Amy/website (#310) * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * deployed website * copyright and team * team headshots and footer * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * whitespace edits * homepage updates * feature table * website updates * renaming tpm-website to website * deploying via gh-pages * changed from tpm-website to website * edits to the pages * website updates * updated links * updated homepage * link updates * mobile compatibility * mobile adjustments * navbar mobile updates * homepage updates * add table of features * updated team page titles * include flask in requirements.txt * updates to table of features * load pages from top * fix to 404 issues * moved build under website folder * updates to package launch * hyperlink ./setup.sh * fix nav bar sizing and hamburger logo * include preprint * updates to "getting started" * update team * gh actions and custom domain * deploy to custom url * deploy to custom url * updates to cname * changes to cname * cname updates * testing github actions * updates to github-actions-website * testing github actions * updates to gh actions * updates to github-actions * update home for testing gh actions * updates CNAME * update testing email * updates username/email * updates to email in github-actions-website * testing gh actions for feature_dict * testing github-actions feature_dict * updates to github-actions-feature_dict * Update github-actions-feature_dict.yaml * testing updates to feature_dict.py * testing feature_dict updates * testing updates to feature_dict.py * testing feature_dict deployment * Update github-actions-feature_dict.yaml * testing feature_dict updates * testing updates to feature_dict.py * updates to feature_dict * updates to github actions feature_dict * testing auto updates to feature_dict * Update feature_dict.py * testing feature_dict auto updates * testing feature_dict auto updates * Update feature_dict.py * testing feature_dict auto updates * remove commented code in feature_dict.py * Delete src/team_comm_tools/filtered_dict.json delete test json file * Update github-actions-website.yaml to deploy on update to dev * put 'dev' in quotes * Update github-actions-feature_dict.yaml to update upon dev * re-add filtered dict * update packages for website --------- Co-authored-by: amytangzheng * Update package-lock.json to local version * Update package-lock.json * Update package.json * Update package-lock.json * Fix "@babel/plugin-proposal-private-property-in-object" error (#311) * Update package-lock.json * Update package.json * upgrade node packages * update team page + try to remove some of the deprecated packages * Revert "update team page + try to remove some of the deprecated packages" This reverts commit d04037df782639c3567cf1119cea627d3c6ad841. * revert attempts to upgrade packages * Denormalize liwc (#312) * address https://github.com/Watts-Lab/team_comm_tools/issues/306 * fix hedges reference and update dictionary * address https://github.com/Watts-Lab/team_comm_tools/issues/300 (#313) * Address issues with making feature names more clear; have cleaner defaults for output params (#314) * address https://github.com/Watts-Lab/team_comm_tools/issues/304 * address https://github.com/Watts-Lab/team_comm_tools/issues/286 and https://github.com/Watts-Lab/team_comm_tools/issues/299 * small fix to ensure filtered_dict does not generate in every run * PATCH FIX: Defaults in 0.1.4 were incorrectly specified * resolved merge conflicts * first_person_raw bug fix, dale-chale bug fix, updating test datasets * uncommenting featurebuilders * move aux files to notebooks folder * update pyproject.toml * edit docstrings in mimicry * update doucmentation * remove print statement: --------- Signed-off-by: dependabot[bot] Co-authored-by: Xinlan Emily Hu Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Xinlan Emily Hu Co-authored-by: amytangzheng Co-authored-by: amytangzheng <145236844+amytangzheng@users.noreply.github.com> --- .gitignore | 1 + docs/build/doctrees/environment.pickle | Bin 2268761 -> 324236 bytes docs/build/doctrees/examples.doctree | Bin 137145 -> 137166 bytes docs/build/doctrees/feature_builder.doctree | Bin 69652 -> 90730 bytes .../doctrees/features/basic_features.doctree | Bin 13498 -> 13636 bytes .../doctrees/features/burstiness.doctree | Bin 15820 -> 15930 bytes .../build/doctrees/features/certainty.doctree | Bin 9085 -> 9167 bytes .../features/discursive_diversity.doctree | Bin 15546 -> 15684 bytes docs/build/doctrees/features/fflow.doctree | Bin 9166 -> 9248 bytes .../features/get_all_DD_features.doctree | Bin 14943 -> 15053 bytes .../features/get_user_network.doctree | Bin 14291 -> 14401 bytes docs/build/doctrees/features/hedge.doctree | Bin 7611 -> 7693 bytes docs/build/doctrees/features/index.doctree | Bin 9897 -> 9918 bytes .../features/info_exchange_zscore.doctree | Bin 11721 -> 11803 bytes .../features/information_diversity.doctree | Bin 24690 -> 24856 bytes .../features/lexical_features_v2.doctree | Bin 15283 -> 15414 bytes .../named_entity_recognition_features.doctree | Bin 28074 -> 28281 bytes .../features/other_lexical_features.doctree | Bin 16292 -> 16430 bytes .../features/politeness_features.doctree | Bin 7924 -> 8006 bytes .../doctrees/features/politeness_v2.doctree | Bin 8637 -> 8719 bytes 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bytes docs/build/doctrees/utils/preprocess.doctree | Bin 54593 -> 54871 bytes .../doctrees/utils/summarize_features.doctree | Bin 55759 -> 56009 bytes .../zscore_chats_and_conversation.doctree | Bin 15809 -> 15919 bytes docs/build/html/.buildinfo | 2 +- .../content_word_accommodation.rst.txt | 8 +- docs/build/html/_static/searchtools.js | 7 +- docs/build/html/feature_builder.html | 2 +- docs/build/html/features/readability.html | 16 +- docs/build/html/features/word_mimicry.html | 50 +- .../content_word_accommodation.html | 7 +- docs/build/html/genindex.html | 4 + docs/build/html/objects.inv | Bin 4354 -> 4365 bytes docs/build/html/searchindex.js | 2 +- .../content_word_accommodation.rst | 8 +- pyproject.toml | 2 +- src/team_comm_tools/feature_dict.py | 6 +- .../features/info_exchange_zscore.py | 4 +- src/team_comm_tools/features/question_num.py | 2 +- src/team_comm_tools/features/readability.py | 48 +- src/team_comm_tools/features/word_mimicry.py | 325 +- .../utils/calculate_chat_level_features.py | 211 +- tests/data/cleaned_data/fflow.csv | 29 +- .../info_exchange_zscore_chats.csv | 7 + tests/data/cleaned_data/test_chat_level.csv | 1417 +++++---- .../cleaned_data/test_chat_level_complex.csv | 18 +- tests/data/cleaned_data/test_conv_level.csv | 32 +- .../cleaned_data/test_conv_level_complex.csv | 144 +- .../generate_liwc_tests.ipynb | 2 +- tests/ipython_notebooks/helper.ipynb | 2797 +++++++++++++++++ tests/ipython_notebooks/hi.csv | 30 + tests/ipython_notebooks/info_divvy.csv | 11 + tests/ipython_notebooks/info_divvy_LDA.csv | 89 + tests/ipython_notebooks/infodiv.ipynb | 860 +++++ tests/ipython_notebooks/output.csv | 15 + tests/run_tests.py | 39 +- tests/test_feature_metrics.py | 153 +- 86 files changed, 5336 insertions(+), 1012 deletions(-) create mode 100644 tests/data/cleaned_data/info_exchange_zscore_chats.csv create mode 100644 tests/ipython_notebooks/helper.ipynb create mode 100644 tests/ipython_notebooks/hi.csv create mode 100644 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52bb517003626668dcf6d90499328aef1e9b752b..42f1f4a4002427467ad26d288eb39475dabb5253 100644 GIT binary patch delta 395 zcmX?Dy}pLEfpw~i-A2}WM$H`k(BjmhV*QHLj8c7<)Z*-t`~v-u)WlqcfTH~5)Z$_V z-^7A~%)Io?GZ=lCH4hN0e6p_0l*t0zoSUbzUS=Up+hzgol}wB}n|JZwh}BAX2)UovT)Al5ybb!Bd{60aQS{H2@sD`hh=Wq@2Dx!FVY r5Eq24vf0?+1tXBXS;6QA<77*1kupWj8k}2{5x}RWdO!0F9U2yhim97lgaHz<`GdE~Bz}k { const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - for (const removalQuery of [".headerlinks", "script", "style"]) { + for (const removalQuery of [".headerlink", "script", "style"]) { htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); } if (anchor) { @@ -328,13 +328,14 @@ const Search = { for (const [title, foundTitles] of Object.entries(allTitles)) { if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { for (const [file, id] of foundTitles) { - let score = Math.round(100 * queryLower.length / title.length) + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles normalResults.push([ docNames[file], titles[file] !== title ? `${titles[file]} > ${title}` : title, id !== null ? "#" + id : "", null, - score, + score + boost, filenames[file], ]); } diff --git a/docs/build/html/feature_builder.html b/docs/build/html/feature_builder.html index 16fbb9a7..47407283 100644 --- a/docs/build/html/feature_builder.html +++ b/docs/build/html/feature_builder.html @@ -97,7 +97,7 @@

feature_builder module

-class feature_builder.FeatureBuilder(input_df: ~pandas.core.frame.DataFrame, vector_directory: ./vector_data/, output_file_base='output', output_file_path_chat_level=None, output_file_path_user_level=None, output_file_path_conv_level=None, custom_features: list = [], analyze_first_pct: list = [1.0], turns: bool = False, conversation_id_col: str = 'conversation_num', speaker_id_col: str = 'speaker_nickname', message_col: str = 'message', timestamp_col: str | tuple[str, str] = 'timestamp', grouping_keys: list = [], cumulative_grouping=False, within_task=False, ner_training_df: ~pandas.core.frame.DataFrame = None, ner_cutoff: int = 0.9, regenerate_vectors: bool = False, compute_vectors_from_preprocessed: bool = False)
+class feature_builder.FeatureBuilder(input_df: DataFrame, vector_directory: str = './vector_data/', output_file_base: str = 'output', output_file_path_chat_level: str = None, output_file_path_user_level: str = None, output_file_path_conv_level: str = None, custom_features: list = [], analyze_first_pct: list = [1.0], turns: bool = False, conversation_id_col: str = 'conversation_num', speaker_id_col: str = 'speaker_nickname', message_col: str = 'message', timestamp_col: str | tuple[str, str] = 'timestamp', grouping_keys: list = [], cumulative_grouping=False, within_task=False, ner_training_df: DataFrame = None, ner_cutoff: int = 0.9, regenerate_vectors: bool = False, compute_vectors_from_preprocessed: bool = False)

Bases: object

The FeatureBuilder is the main engine that reads in the user’s inputs and specifications and generates conversational features. The FeatureBuilder separately calls the classes (the ChatLevelFeaturesCalculator, diff --git a/docs/build/html/features/readability.html b/docs/build/html/features/readability.html index f389db3e..f2537a25 100644 --- a/docs/build/html/features/readability.html +++ b/docs/build/html/features/readability.html @@ -155,23 +155,11 @@

features.readability.count_syllables(word)
-

Count the number of syllables in a word.

-
-
Parameters:
-

word (str) – The input word.

-
-
Returns:
-

The number of syllables in the word.

-
-
Return type:
-

int

-
-
-
+
-features.readability.dale_chall_helper(text, **easy_words)
+features.readability.dale_chall_helper(text, easy_words)

Calculate the Dale-Chall readability score of a text. The Dale-Chall score are defined as:

0.1579 * ((difficult_words / words) * 100) + 0.0496 * (words / sentences)

diff --git a/docs/build/html/features/word_mimicry.html b/docs/build/html/features/word_mimicry.html index c64603c6..557a35b2 100644 --- a/docs/build/html/features/word_mimicry.html +++ b/docs/build/html/features/word_mimicry.html @@ -68,8 +68,10 @@
  • reddit_tags module
  • word_mimicry module
    • Content_mimicry_score()
      • +
    • Content_mimicry_score_per_conv()
    • computeTF()
    • compute_frequency()
      • +
    • compute_frequency_per_conv()
    • function_mimicry_score()
    • get_content_words_in_message()
    • get_function_words_in_message()
      • @@ -120,7 +122,33 @@
      features.word_mimicry.Content_mimicry_score(df, column_count_frequency, column_count_mimic)
      -

      Combine the steps to compute the content word mimicry score.

      +

      Combine the steps to compute the content word mimicry score. Normalizes +the frequency of words by how much they appear across the entire dataset.

      +
      +
      Parameters:
      +
        +

      • df (DataFrame) – The input dataframe.

        • +

      • column_count_frequency (str) – The column with content words to calculate frequency.

        • +

      • column_count_mimic (str) – The column with content word mimicry.

        • +
      +
      +
      Returns:
      +

      A series with content word accommodation scores.

      +
      +
      Return type:
      +

      Series

      +
      +
      +
      + +
      +
      +features.word_mimicry.Content_mimicry_score_per_conv(df, column_count_frequency, column_count_mimic, conversation_id)
      +

      Computes the content word mimicry score, but normalizes the term frequency of the words +by how often they appear within a given conversation. This version of the score may +be more useful in cases where different conversations in the dataset cover very +different subject matter, and therefore one may not wish to normalize across the +full dataset.

      Parameters:
        @@ -178,6 +206,26 @@
      +
      +
      +features.word_mimicry.compute_frequency_per_conv(df, on_column)
      +

      Compute the frequency of each content word across the whole dataset.

      +
      +
      Parameters:
      +
        +

      • df (DataFrame) – The input dataframe.

        • +

      • on_column (str) – The column with which we calculate content word frequency.

        • +
      +
      +
      Returns:
      +

      A dictionary with content words as keys and their frequencies as values.

      +
      +
      Return type:
      +

      dict

      +
      +
      +
      +
      features.word_mimicry.function_mimicry_score(function_mimic_words)
      diff --git a/docs/build/html/features_conceptual/content_word_accommodation.html b/docs/build/html/features_conceptual/content_word_accommodation.html index e04479ab..014d3e57 100644 --- a/docs/build/html/features_conceptual/content_word_accommodation.html +++ b/docs/build/html/features_conceptual/content_word_accommodation.html @@ -131,10 +131,15 @@

      Citation

      Implementation Basics

      -

      To compute the feature, we count the number of shared content words (defined as anything that is not on the function word list) between the current and previous utterance in a conversation, then normalize it by the frequency of the word across all inputs in the dataset. This follows the original authors’ method:

      +

      To compute the feature, we count the number of shared content words (defined as anything that is not on the function word list) between the current and previous utterance in a conversation, normalized by the frequency at which the word appears. This follows the original authors’ method:

      Content words are defined as any word that is not a function word. For each content word w in a given speaker’s turn, if w also occurs in the immediately preceding turn of the other, we count w as an accommodated content word. The raw count of accommodated content words is be the total number of these accommodated content words over every turn in the conversation side. Because content words vary widely in frequency, we normalized our counts by the frequency of each word.

      +

      For completeness, we interprete “the frequency of each word” in two distinct ways:

      +
        +

      1. The frequency of each word across the entire dataset (`content_word_accommodation`): here, we normalize non-function words with respect to the language used across all conversations in the dataset. This version of accommodation is useful if the entire dataset consists of similar conversations, or conversations about the same topic. Normalizing with respect to a larger dataset will be useful in establishing better estimates in identifying (and appropriately weigting) whichs words carry meaningful content in a particular domain.

        2. +

      2. The frequency of each word within a given conversation (`content_word_accommodation_per_conv`): here, we normalize non-function words with respect only to the language in a given conversation. This version of accommodation is useful if the dataset consists of very distinct conversations, for which it may not make sense to assume that the distribution of which words are “important” will hold across different domains.

        3. +

      The feature requires a reference list of function words, which are defined by the original authors as follows.

      Auxiliary and copular verbs

      able, am, are, aren’t, be, been, being, can, can’t, cannot, could, couldn’t, did, didn’t, do, don’t, get, got, gotta, had, hadn’t, hasn’t, have, haven’t, is, isn’t, may, should, should’ve, shouldn’t, was, were, will, won’t, would, would’ve, wouldn’t

      diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index bbb31849..79fe2257 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -177,6 +177,8 @@

      C

    • compress() (in module utils.preprocess)
    • compute_frequency() (in module features.word_mimicry) +
      • +
    • compute_frequency_per_conv() (in module features.word_mimicry)
    • computeTF() (in module features.word_mimicry)
      • @@ -187,6 +189,8 @@

      C

    • Content_mimicry_score() (in module features.word_mimicry) +
      • +
    • Content_mimicry_score_per_conv() (in module features.word_mimicry)
    • conv_level_features() (feature_builder.FeatureBuilder method)
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"Demo / Sample Code": [[0, "demo-sample-code"], [1, "demo-sample-code"]], "Discursive Diversity": [[34, "discursive-diversity"]], "Example:": [[41, "example"]], "FEATURE NAME": [[29, "feature-name"]], "Feature Column Names": [[1, "feature-column-names"], [61, "feature-column-names"]], "Feature Documentation": [[62, "feature-documentation"]], "Feature Information": [[1, "feature-information"], [61, "feature-information"]], "Features: Conceptual Documentation": [[39, "features-conceptual-documentation"]], "Features: Technical Documentation": [[11, "features-technical-documentation"]], "Forward Flow": [[35, "forward-flow"]], "Function Word Accommodation": [[36, "function-word-accommodation"]], "Generating Features: Utterance-, Speaker-, and Conversation-Level": [[62, "generating-features-utterance-speaker-and-conversation-level"]], "Getting Started": [[1, "getting-started"], [61, "getting-started"], [62, "getting-started"]], "Gini Coefficient": [[37, "gini-coefficient"]], "Hedge": [[38, 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"Implementation": [[32, "implementation"], [42, "implementation"], [52, "implementation"], [54, "implementation"]], "Implementation Basics": [[29, "implementation-basics"], [30, "implementation-basics"], [31, "implementation-basics"], [33, "implementation-basics"], [34, "implementation-basics"], [35, "implementation-basics"], [36, "implementation-basics"], [37, "implementation-basics"], [38, "implementation-basics"], [40, "implementation-basics"], [41, "implementation-basics"], [43, "implementation-basics"], [44, "implementation-basics"], [45, "implementation-basics"], [46, "implementation-basics"], [47, "implementation-basics"], [48, "implementation-basics"], [49, "implementation-basics"], [50, "implementation-basics"], [51, "implementation-basics"], [53, "implementation-basics"], [55, "implementation-basics"], [56, "implementation-basics"], [57, "implementation-basics"], [58, "implementation-basics"], [59, "implementation-basics"], [60, "implementation-basics"]], "Implementation Notes/Caveats": [[29, "implementation-notes-caveats"], [30, "implementation-notes-caveats"], [31, "implementation-notes-caveats"], [33, "implementation-notes-caveats"], [34, "implementation-notes-caveats"], [35, "implementation-notes-caveats"], [36, "implementation-notes-caveats"], [38, "implementation-notes-caveats"], [40, "implementation-notes-caveats"], [41, "implementation-notes-caveats"], [43, "implementation-notes-caveats"], [44, "implementation-notes-caveats"], [45, "implementation-notes-caveats"], [46, "implementation-notes-caveats"], [47, "implementation-notes-caveats"], [48, "implementation-notes-caveats"], [49, "implementation-notes-caveats"], [50, "implementation-notes-caveats"], [51, "implementation-notes-caveats"], [53, "implementation-notes-caveats"], [55, "implementation-notes-caveats"], [56, "implementation-notes-caveats"], [57, "implementation-notes-caveats"], [58, "implementation-notes-caveats"], [59, "implementation-notes-caveats"]], "Import Recommendations: Virtual 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[[45, "mimicry-bert"]], "Motivation": [[62, "motivation"]], "Moving Mimicry": [[46, "moving-mimicry"]], "Named Entity Recognition": [[47, "named-entity-recognition"]], "Named Entity Training Examples": [[47, "id2"]], "Online Discussion Tags": [[48, "online-discussion-tags"]], "Other Utilities": [[69, "other-utilities"]], "Ouput File": [[34, "id3"]], "Our Team": [[62, "our-team"]], "Output File": [[30, "id2"], [35, "id2"], [45, "id2"], [46, "id2"], [47, "id3"], [51, "id1"]], "Output File Naming Details": [[1, "output-file-naming-details"]], "Package Assumptions": [[0, "package-assumptions"]], "Politeness Strategies": [[50, "politeness-strategies"]], "Politeness/Receptiveness Markers": [[49, "politeness-receptiveness-markers"]], "Positivity Z-Score": [[52, "positivity-z-score"]], "Proportion of First Person Pronouns": [[53, "proportion-of-first-person-pronouns"]], "Question (Naive)": [[54, "question-naive"]], "Related Features": [[29, "related-features"], [30, "related-features"], [31, 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42, 60], "word_mimicri": 28, "work": 1, "your": 1, "z": [41, 52], "zscore_chats_and_convers": 73}}) \ No newline at end of file diff --git a/docs/source/features_conceptual/content_word_accommodation.rst b/docs/source/features_conceptual/content_word_accommodation.rst index d82a6b64..38d61559 100644 --- a/docs/source/features_conceptual/content_word_accommodation.rst +++ b/docs/source/features_conceptual/content_word_accommodation.rst @@ -13,10 +13,16 @@ Citation Implementation Basics ********************** -To compute the feature, we count the number of shared content words (defined as anything that is not on the function word list) between the current and previous utterance in a conversation, then normalize it by the frequency of the word across all inputs in the dataset. This follows the original authors' method: +To compute the feature, we count the number of shared content words (defined as anything that is not on the function word list) between the current and previous utterance in a conversation, normalized by the frequency at which the word appears. This follows the original authors' method: Content words are defined as any word that is not a function word. For each content word w in a given speaker’s turn, if w also occurs in the immediately preceding turn of the other, we count w as an accommodated content word. The raw count of accommodated content words is be the total number of these accommodated content words over every turn in the conversation side. Because content words vary widely in frequency, we normalized our counts by the frequency of each word. +For completeness, we interprete "the frequency of each word" in two distinct ways: + +1. **The frequency of each word across the entire dataset (`content_word_accommodation`)**: here, we normalize non-function words with respect to the language used across all conversations in the dataset. This version of accommodation is useful if the entire dataset consists of similar conversations, or conversations about the same topic. Normalizing with respect to a larger dataset will be useful in establishing better estimates in identifying (and appropriately weigting) whichs words carry meaningful content in a particular domain. + +2. **The frequency of each word within a given conversation (`content_word_accommodation_per_conv`)**: here, we normalize non-function words with respect only to the language in a given conversation. This version of accommodation is useful if the dataset consists of very distinct conversations, for which it may not make sense to assume that the distribution of which words are "important" will hold across different domains. + The feature requires a reference list of function words, which are defined by the original authors as follows. **Auxiliary and copular verbs** diff --git a/pyproject.toml b/pyproject.toml index 9461e0a2..22eef440 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta" [project] name = "team_comm_tools" -version = "0.1.4.post2" +version = "0.1.5" requires-python = ">= 3.10" dependencies = [ "chardet>=3.0.4", diff --git a/src/team_comm_tools/feature_dict.py b/src/team_comm_tools/feature_dict.py index 5b557091..63100e68 100644 --- a/src/team_comm_tools/feature_dict.py +++ b/src/team_comm_tools/feature_dict.py @@ -229,11 +229,11 @@ "bert_sentiment_data": False }, "Content Word Accommodation": { - "columns": ["content_word_accommodation"], + "columns": ["content_word_accommodation", "content_word_accommodation_per_conv"], "file": "./features/word_mimicry.py", "level": "Chat", "semantic_grouping": "Variance", - "description": "The total number of non-function words used in a given turn that were also used in the previous turn, normalized by the inverse document frequency of each content word.", + "description": "The total number of non-function words used in a given turn that were also used in the previous turn, normalized by the inverse document frequency of each content word. `content_word_accommodation` computes the frequency of the content words with respect to the entire dataset; `content_word_accommodation_per_conv` does so with respect to each conversation.", "references": "(Ranganath et al., 2013)", "wiki_link": "https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/content_word_accommodation.html", "function": ChatLevelFeaturesCalculator.calculate_word_mimicry, @@ -609,7 +609,7 @@ } def generate_filtered_dict(): - + keys_to_keep = ["columns", "file", "level", "semantic_grouping", "description", "references", "wiki_link"] filtered_dict = {feature_name: {key: value for key, value in feature_data.items() if key in keys_to_keep} diff --git a/src/team_comm_tools/features/info_exchange_zscore.py b/src/team_comm_tools/features/info_exchange_zscore.py index 9adaf771..b0a5b989 100644 --- a/src/team_comm_tools/features/info_exchange_zscore.py +++ b/src/team_comm_tools/features/info_exchange_zscore.py @@ -28,6 +28,8 @@ def get_info_exchange_wordcount(df, first_person, message_col): pd.Series: A column containing the difference in total words and first-person singular pronouns. ''' - first_person_regex = " | ".join(first_person) + first_person_regex = "\\b|\\b".join(first_person) + first_person_regex = "\\b" + first_person_regex + "\\b" + df['first_person_raw'] = df[message_col].apply(lambda chat: len(re.findall(first_person_regex, chat))) return (df["num_words"] - df["first_person_raw"]) \ No newline at end of file diff --git a/src/team_comm_tools/features/question_num.py b/src/team_comm_tools/features/question_num.py index c84fcd89..33b77d99 100644 --- a/src/team_comm_tools/features/question_num.py +++ b/src/team_comm_tools/features/question_num.py @@ -30,4 +30,4 @@ def calculate_num_question_naive(text, question_words): # Is a question if the sentence ends with "?" or starts with a word that is in the question_words list if sentence.endswith("?") or word_tokenize(sentence)[0] in question_words: num_q += 1 - return num_q \ No newline at end of file + return num_q diff --git a/src/team_comm_tools/features/readability.py b/src/team_comm_tools/features/readability.py index 8329f52e..ebf90e0f 100644 --- a/src/team_comm_tools/features/readability.py +++ b/src/team_comm_tools/features/readability.py @@ -5,20 +5,33 @@ from .basic_features import count_words +# Source: https://datascience.stackexchange.com/questions/23376/how-to-get-the-number-of-syllables-in-a-word/89312#89312 # Define the function to calculate the Dale-Chall score -def count_syllables(word): - """ - Count the number of syllables in a word. - - Args: - word(str): The input word. +VOWEL_RUNS = re.compile("[aeiouy]+", flags=re.I) +EXCEPTIONS = re.compile( + # fixes trailing e issues: + # smite, scared + "[^aeiou]e[sd]?$|" + # fixes adverbs: + # nicely + + "[^e]ely$", + flags=re.I +) +ADDITIONAL = re.compile( + # fixes incorrect subtractions from exceptions: + # smile, scarred, raises, fated + "[^aeioulr][lr]e[sd]?$|[csgz]es$|[td]ed$|" + # fixes miscellaneous issues: + # flying, piano, video, prism, fire, evaluate + + ".y[aeiou]|ia(?!n$)|eo|ism$|[^aeiou]ire$|[^gq]ua", + flags=re.I +) - Returns: - int: The number of syllables in the word. - """ - dic = pyphen.Pyphen(lang='en') - pyphen_result = dic.inserted(word) - return len(re.findall(r"-", pyphen_result)) +def count_syllables(word): + vowel_runs = len(VOWEL_RUNS.findall(word)) + exceptions = len(EXCEPTIONS.findall(word)) + additional = len(ADDITIONAL.findall(word)) + return max(1, vowel_runs - exceptions + additional) def count_difficult_words(text, easy_words): """ @@ -36,7 +49,6 @@ def count_difficult_words(text, easy_words): difficult_words = 0 # recall that words are already pre-processed; substitute punctuation here for words only words = re.sub(r"[^a-zA-Z0-9 ]+", '',text).split() - remaining_words = [i for i in words if not i in easy_words] for word in remaining_words: @@ -46,7 +58,7 @@ def count_difficult_words(text, easy_words): return difficult_words -def dale_chall_helper(text, **easy_words): +def dale_chall_helper(text, easy_words): """ Calculate the Dale-Chall readability score of a text. The Dale-Chall score are defined as: @@ -72,7 +84,13 @@ def dale_chall_helper(text, **easy_words): """ num_words = count_words(text) - num_sentences = len(re.split(r'[.?!]\s*', text)) + sentences = re.split(r'[.?!]\s*', text) + sentences = [x for x in sentences if x] + num_sentences = len(sentences) + + + if num_sentences == 0: + return 0 avg_sentence_length = num_words/num_sentences num_difficult_words = count_difficult_words(text, easy_words) diff --git a/src/team_comm_tools/features/word_mimicry.py b/src/team_comm_tools/features/word_mimicry.py index 545f9c70..d97bcd82 100644 --- a/src/team_comm_tools/features/word_mimicry.py +++ b/src/team_comm_tools/features/word_mimicry.py @@ -10,189 +10,240 @@ # To compute word mimicry, we use the dataset that removed all the punctuations # This is a *chat-level* feature in which order matters. # ''' + + def get_function_words_in_message(text, function_word_reference): - """ - Extract the function words & non-functions words from a message + """ + Extract the function words & non-functions words from a message - Args: - text (str): The input text to be analyzed. - function_word_reference (list): A list of function words to reference against. + Args: + text (str): The input text to be analyzed. + function_word_reference (list): A list of function words to reference against. - Returns: - list: A list of function words found in the input text. - """ + Returns: + list: A list of function words found in the input text. + """ - return [x for x in text.split() if x in function_word_reference] + return [x for x in text.split() if x in function_word_reference] -def get_content_words_in_message(text, function_word_reference): - """ - Extract the non-function words in a given message. +def get_content_words_in_message(text, function_word_reference): + """ + Extract the non-function words in a given message. - Args: - text (str): The input text to be analyzed. - function_word_reference (list): A list of function words to reference against. + Args: + text (str): The input text to be analyzed. + function_word_reference (list): A list of function words to reference against. - Returns: - list: A list of content words found in the input text. - """ - return [x for x in text.split() if x not in function_word_reference] + Returns: + list: A list of content words found in the input text. + """ + return [x for x in text.split() if x not in function_word_reference] def mimic_words(df, on_column, conversation_id): - """ - Return a list of words that are also used in the other's previous turn. - - Args: - df (DataFrame): The dataset that removed all punctuations. - on_column (str): The column that we want to find mimicry on. - conversation_id (str): The column name that should be selected as the conversation ID. + """ + Return a list of words that are also used in the other's previous turn. + + Args: + df (DataFrame): The dataset that removed all punctuations. + on_column (str): The column that we want to find mimicry on. + conversation_id (str): The column name that should be selected as the conversation ID. + + Returns: + list: A list of lists, where each sublist contains words mimicked from the previous turn. + """ + word_mimic = [[]] + for i in range(1, len(df)): + # only do this if they're in the same conversation + if df.loc[i, conversation_id] == df.loc[i-1, conversation_id]: + word_mimic.append([x for x in df.loc[i, on_column] + if x in df.loc[(i-1), on_column]]) + else: + word_mimic.append([]) + return word_mimic - Returns: - list: A list of lists, where each sublist contains words mimicked from the previous turn. - """ - word_mimic = [[]] - for i in range(1, len(df)): - if df.loc[i, conversation_id] == df.loc[i-1, conversation_id]: # only do this if they're in the same conversation - word_mimic.append([x for x in df.loc[i, on_column] if x in df.loc[(i-1),on_column]]) - else: - word_mimic.append([]) - return word_mimic def function_mimicry_score(function_mimic_words): - """ - Compute the number of mimic words for function words by simply counting the number of mimic words using len(). + """ + Compute the number of mimic words for function words by simply counting the number of mimic words using len(). - Args: - function_mimic_words (list): Each entry under the `function_word_mimicry` column. + Args: + function_mimic_words (list): Each entry under the `function_word_mimicry` column. + + Returns: + int: The number of function mimic words. + """ + return len(function_mimic_words) - Returns: - int: The number of function mimic words. - """ - return len(function_mimic_words) def compute_frequency(df, on_column): - """ - Compute the frequency of each content word across the whole dataset. + """ + Compute the frequency of each content word across the whole dataset. + + Args: + df (DataFrame): The input dataframe. + on_column (str): The column with which we calculate content word frequency. + + Returns: + dict: A dictionary with content words as keys and their frequencies as values. + """ + return (dict(pd.Series(np.concatenate(df[on_column])).value_counts())) - Args: - df (DataFrame): The input dataframe. - on_column (str): The column with which we calculate content word frequency. +def compute_frequency_per_conv(df, on_column): + """ + Compute the frequency of each content word across the whole dataset. - Returns: - dict: A dictionary with content words as keys and their frequencies as values. - """ - return(dict(pd.Series(np.concatenate(df[on_column])).value_counts())) + Args: + df (DataFrame): The input dataframe. + on_column (str): The column with which we calculate content word frequency. + Returns: + dict: A dictionary with content words as keys and their frequencies as values. + """ + df_temp = df.copy() + df_temp.reset_index(drop=True, inplace=True) + return (dict(pd.Series(np.concatenate(df_temp[on_column])).value_counts())) def computeTF(column_mimc, frequency_dict): - """ - Compute the term frequency of each content mimic word, then sum them up. + """ + Compute the term frequency of each content mimic word, then sum them up. - Args: - column_mimc (list): Each entry under the `content_word_mimicry` column. - frequency_dict (dict): A dictionary of content word frequency across the dataset. + Args: + column_mimc (list): Each entry under the `content_word_mimicry` column. + frequency_dict (dict): A dictionary of content word frequency across the dataset. - Returns: - float: The sum of term frequencies for the content mimic words. - """ - tfdict = {} - wf = pd.Series(column_mimc, dtype = 'str').value_counts() - for i in wf.index: - tfdict[i] = wf[i]/frequency_dict[i] - return sum(tfdict.values()) + Returns: + float: The sum of term frequencies for the content mimic words. + """ + tfdict = {} + wf = pd.Series(column_mimc, dtype='str').value_counts() + for i in wf.index: + tfdict[i] = wf[i]/frequency_dict[i] + return sum(tfdict.values()) def Content_mimicry_score(df, column_count_frequency, column_count_mimic): - """ - Combine the steps to compute the content word mimicry score. - - Args: - df (DataFrame): The input dataframe. - column_count_frequency (str): The column with content words to calculate frequency. - column_count_mimic (str): The column with content word mimicry. - - Returns: - Series: A series with content word accommodation scores. - - """ - # Compute the frequency of each content word across the whole dataset - ContWordFreq = compute_frequency(df, column_count_frequency) - # Compute the content_mimicry_score - return df[column_count_mimic].apply(lambda x:computeTF(x, ContWordFreq)) + """ + Combine the steps to compute the content word mimicry score. Normalizes + the frequency of words by how much they appear across the *entire dataset*. + + Args: + df (DataFrame): The input dataframe. + column_count_frequency (str): The column with content words to calculate frequency. + column_count_mimic (str): The column with content word mimicry. + + Returns: + Series: A series with content word accommodation scores. + + """ + # Compute the frequency of each content word across the whole dataset + ContWordFreq = compute_frequency(df, column_count_frequency) + # Compute the content_mimicry_score + return df[column_count_mimic].apply(lambda x:computeTF(x, ContWordFreq)) + +def Content_mimicry_score_per_conv(df, column_count_frequency, column_count_mimic, conversation_id): + """ + Computes the content word mimicry score, but normalizes the term frequency of the words + by how often they appear *within* a given conversation. This version of the score may + be more useful in cases where different conversations in the dataset cover very + different subject matter, and therefore one may not wish to normalize across the + full dataset. + + Args: + df (DataFrame): The input dataframe. + column_count_frequency (str): The column with content words to calculate frequency. + column_count_mimic (str): The column with content word mimicry. + + Returns: + Series: A series with content word accommodation scores. + + """ + content_mimic_scores = [] + for conv in df[conversation_id].unique(): + df_conv = df[df[conversation_id] == conv] + ContWordFreq = compute_frequency_per_conv(df_conv, column_count_frequency) + content_mimic_scores.append(df_conv[column_count_mimic].apply( + lambda x: computeTF(x, ContWordFreq)).tolist()) + return [item for sublist in content_mimic_scores for item in sublist] + def get_mimicry_bert(chat_data, vect_data, conversation_id): - """ - Uses SBERT vectors to get the cosine similarity between each message and the previous message. + """ + Uses SBERT vectors to get the cosine similarity between each message and the previous message. - Args: - chat_data (DataFrame): The input chat dataframe. - vect_data (DataFrame): The dataframe containing SBERT vectors. - conversation_id (str): The column name that should be selected as the conversation ID. + Args: + chat_data (DataFrame): The input chat dataframe. + vect_data (DataFrame): The dataframe containing SBERT vectors. + conversation_id (str): The column name that should be selected as the conversation ID. + + Returns: + list: A list of cosine similarity scores between each message and the previous message. + """ + + chat_df = chat_data.copy() + chat_df['message_embedding'] = conv_to_float_arr( + vect_data['message_embedding'].to_frame()) + + mimicry = [] - Returns: - list: A list of cosine similarity scores between each message and the previous message. - """ - - chat_df = chat_data.copy() - chat_df['message_embedding'] = conv_to_float_arr(vect_data['message_embedding'].to_frame()) + for num, conv in chat_df.groupby(conversation_id, sort=False): - mimicry = [] + # first chat has no zero mimicry score, nothing previous to compare it to + mimicry.append(0) + prev_embedding = conv.iloc[0]['message_embedding'] - for num, conv in chat_df.groupby(conversation_id, sort=False): + for index, row in conv[1:].iterrows(): - # first chat has no zero mimicry score, nothing previous to compare it to - mimicry.append(0) - prev_embedding = conv.iloc[0]['message_embedding'] + # last "pair" has only one element, safeguard against this + cur_embedding = row['message_embedding'] + cos_sim_matrix = cosine_similarity([cur_embedding, prev_embedding]) + cosine_sim = cos_sim_matrix[0, 1] - for index, row in conv[1:].iterrows(): - - # last "pair" has only one element, safeguard against this - cur_embedding = row['message_embedding'] - cos_sim_matrix = cosine_similarity([cur_embedding, prev_embedding]) - cosine_sim = cos_sim_matrix[0, 1] - - mimicry.append(cosine_sim) + mimicry.append(cosine_sim) - prev_embedding = row['message_embedding'] + prev_embedding = row['message_embedding'] - return mimicry + return mimicry def get_moving_mimicry(chat_data, vect_data, conversation_id): - """ - Calculate the moving average of mimicry scores using SBERT vectors. + """ + Calculate the moving average of mimicry scores using SBERT vectors. - Args: - chat_data (DataFrame): The input chat dataframe. - vect_data (DataFrame): The dataframe containing SBERT vectors. - conversation_id (str): The column name that should be selected as the conversation ID. + Args: + chat_data (DataFrame): The input chat dataframe. + vect_data (DataFrame): The dataframe containing SBERT vectors. + conversation_id (str): The column name that should be selected as the conversation ID. + + Returns: + list: A list of moving average mimicry scores for each message in the conversation. + """ + + chat_df = chat_data.copy() + chat_df['message_embedding'] = conv_to_float_arr( + vect_data['message_embedding'].to_frame()) - Returns: - list: A list of moving average mimicry scores for each message in the conversation. - """ + moving_mimicry = [] - chat_df = chat_data.copy() - chat_df['message_embedding'] = conv_to_float_arr(vect_data['message_embedding'].to_frame()) + for num, conv in chat_df.groupby(conversation_id, sort=False): - moving_mimicry = [] + prev_embedding = conv.iloc[0]["message_embedding"] + prev_mimicry = [] + # Start with 0; however, prev_mimicry is not stored so it is ignored from calculations + moving_mimicry.append(0) - for num, conv in chat_df.groupby(conversation_id, sort = False): + for index, row in conv[1:].iterrows(): + # find cosine similarity between current pair + cos_sim_matrix = cosine_similarity( + [row['message_embedding'], prev_embedding]) + cosine_sim = cos_sim_matrix[0, 1] - prev_embedding = conv.iloc[0]["message_embedding"] - prev_mimicry = [] - moving_mimicry.append(0) # Start with 0; however, prev_mimicry is not stored so it is ignored from calculations - - for index, row in conv[1:].iterrows(): - # find cosine similarity between current pair - cos_sim_matrix = cosine_similarity([row['message_embedding'], prev_embedding]) - cosine_sim = cos_sim_matrix[0, 1] + # get the running average + prev_mimicry.append(cosine_sim) + moving_mimicry.append(np.average(prev_mimicry)) - # get the running average - prev_mimicry.append(cosine_sim) - moving_mimicry.append(np.average(prev_mimicry)) + # update the previous embedding + prev_embedding = row['message_embedding'] - # update the previous embedding - prev_embedding = row['message_embedding'] - - return moving_mimicry \ No newline at end of file + return moving_mimicry diff --git a/src/team_comm_tools/utils/calculate_chat_level_features.py b/src/team_comm_tools/utils/calculate_chat_level_features.py index ef55d4be..3aa73645 100644 --- a/src/team_comm_tools/utils/calculate_chat_level_features.py +++ b/src/team_comm_tools/utils/calculate_chat_level_features.py @@ -14,7 +14,7 @@ from team_comm_tools.features.certainty import * from team_comm_tools.features.politeness_v2 import * from team_comm_tools.features.reddit_tags import * -from team_comm_tools.features.named_entity_recognition_features import* +from team_comm_tools.features.named_entity_recognition_features import * # Importing utils from .preload_word_lists import * @@ -41,31 +41,34 @@ class ChatLevelFeaturesCalculator: :param ner_cutoff: This is the cutoff value for the confidence of prediction for each named entity :type ner_cutoff: int """ + def __init__( - self, - chat_data: pd.DataFrame, - vect_data: pd.DataFrame, - bert_sentiment_data: pd.DataFrame, + self, + chat_data: pd.DataFrame, + vect_data: pd.DataFrame, + bert_sentiment_data: pd.DataFrame, ner_training: pd.DataFrame, ner_cutoff: int, conversation_id_col: str, message_col: str, timestamp_col: str | tuple[str, str] - ) -> None: + ) -> None: self.chat_data = chat_data self.vect_data = vect_data - self.bert_sentiment_data = bert_sentiment_data # Load BERT + self.bert_sentiment_data = bert_sentiment_data # Load BERT self.ner_training = ner_training self.ner_cutoff = ner_cutoff self.conversation_id_col = conversation_id_col self.timestamp_col = timestamp_col self.message_col = message_col - self.easy_dale_chall_words = get_dale_chall_easy_words() # load easy Dale-Chall words exactly once. - self.function_words = get_function_words() # load function words exactly once - self.question_words = get_question_words() # load question words exactly once - self.first_person = get_first_person_words() # load first person words exactly once - + # load easy Dale-Chall words exactly once. + self.easy_dale_chall_words = get_dale_chall_easy_words() + self.function_words = get_function_words() # load function words exactly once + self.question_words = get_question_words() # load question words exactly once + # load first person words exactly once + self.first_person = get_first_person_words() + def calculate_chat_level_features(self, feature_methods: list) -> pd.DataFrame: """ Main driver function for creating chat-level features. @@ -82,7 +85,7 @@ def calculate_chat_level_features(self, feature_methods: list) -> pd.DataFrame: # Return the input dataset with the chat level features appended (as columns) return self.chat_data - + def concat_bert_features(self) -> None: """ Concatenate RoBERTa sentiment features to the chat data. @@ -93,7 +96,8 @@ def concat_bert_features(self) -> None: :return: None :rtype: None """ - self.chat_data = pd.concat([self.chat_data, self.bert_sentiment_data], axis = 1) + self.chat_data = pd.concat( + [self.chat_data, self.bert_sentiment_data], axis=1) def text_based_features(self) -> None: """ @@ -108,14 +112,17 @@ def text_based_features(self) -> None: :rtype: None """ # Count Words - self.chat_data["num_words"] = self.chat_data[self.message_col].apply(count_words) - + self.chat_data["num_words"] = self.chat_data[self.message_col].apply( + count_words) + # Count Characters - self.chat_data["num_chars"] = self.chat_data[self.message_col].apply(count_characters) - - # Count Messages - self.chat_data["num_messages"] = self.chat_data[self.message_col].apply(count_messages) - + self.chat_data["num_chars"] = self.chat_data[self.message_col].apply( + count_characters) + + # Count Messages + self.chat_data["num_messages"] = self.chat_data[self.message_col].apply( + count_messages) + def info_exchange(self) -> None: """ Extract different types of z-scores from the chats. @@ -132,16 +139,20 @@ def info_exchange(self) -> None: """ # Get Modified Wordcount: Total word count - first_singular pronouns - self.chat_data["info_exchange_wordcount"] = get_info_exchange_wordcount(self.chat_data, self.first_person, self.message_col) - + self.chat_data["info_exchange_wordcount"] = get_info_exchange_wordcount( + self.chat_data, self.first_person, self.message_col) + # Get the z-score of each message across all chats - self.chat_data["info_exchange_zscore_chats"] = get_zscore_across_all_chats(self.chat_data, "info_exchange_wordcount") + self.chat_data["info_exchange_zscore_chats"] = get_zscore_across_all_chats( + self.chat_data, "info_exchange_wordcount") # Get the z-score within each conversation - self.chat_data["info_exchange_zscore_conversation"] = get_zscore_across_all_conversations(self.chat_data, "info_exchange_wordcount", self.conversation_id_col) + self.chat_data["info_exchange_zscore_conversation"] = get_zscore_across_all_conversations( + self.chat_data, "info_exchange_wordcount", self.conversation_id_col) # Drop the info exchange wordcount --- it's a linear combination of 2 columns and therefore useless - self.chat_data = self.chat_data.drop(columns=['info_exchange_wordcount']) + self.chat_data = self.chat_data.drop( + columns=['info_exchange_wordcount']) def positivity_zscore(self) -> None: """ @@ -155,10 +166,12 @@ def positivity_zscore(self) -> None: :rtype: None """ # Get the z-score of each message across all chats - self.chat_data["positivity_zscore_chats"] = get_zscore_across_all_chats(self.chat_data, "positive_bert") + self.chat_data["positivity_zscore_chats"] = get_zscore_across_all_chats( + self.chat_data, "positive_bert") # Get the z-score within each conversation - self.chat_data["positivity_zscore_conversation"] = get_zscore_across_all_conversations(self.chat_data, "positive_bert", self.conversation_id_col) + self.chat_data["positivity_zscore_conversation"] = get_zscore_across_all_conversations( + self.chat_data, "positive_bert", self.conversation_id_col) def lexical_features(self) -> None: """ @@ -169,8 +182,9 @@ def lexical_features(self) -> None: :return: None :rtype: None """ - self.chat_data = pd.concat([self.chat_data, liwc_features(self.chat_data, self.message_col)], axis = 1) - + self.chat_data = pd.concat( + [self.chat_data, liwc_features(self.chat_data, self.message_col)], axis=1) + def calculate_hedge_features(self) -> None: """ Calculate features related to expressing hesitation (or 'hedge'). @@ -195,8 +209,10 @@ def calculate_textblob_sentiment(self) -> None: :return: None :rtype: None """ - self.chat_data["textblob_subjectivity"] = self.chat_data[self.message_col].apply(get_subjectivity_score) - self.chat_data["textblob_polarity"] = self.chat_data[self.message_col].apply(get_polarity_score) + self.chat_data["textblob_subjectivity"] = self.chat_data[self.message_col].apply( + get_subjectivity_score) + self.chat_data["textblob_polarity"] = self.chat_data[self.message_col].apply( + get_polarity_score) def get_dale_chall_score_and_classfication(self) -> None: """ @@ -209,8 +225,10 @@ def get_dale_chall_score_and_classfication(self) -> None: :return: None :rtype: None """ - self.chat_data['dale_chall_score'] = self.chat_data[self.message_col].apply(lambda x: dale_chall_helper(x, easy_words = self.easy_dale_chall_words)) - self.chat_data['dale_chall_classification'] = self.chat_data['dale_chall_score'].apply(classify_text_dalechall) + self.chat_data['dale_chall_score'] = self.chat_data["message_lower_with_punc"].apply( + lambda x: dale_chall_helper(x, easy_words=self.easy_dale_chall_words)) + self.chat_data['dale_chall_classification'] = self.chat_data['dale_chall_score'].apply( + classify_text_dalechall) def other_lexical_features(self) -> None: """ @@ -230,20 +248,24 @@ def other_lexical_features(self) -> None: # Get the number of questions in each message # naive: Number of Question Marks + Sentences that start with question words - self.chat_data["num_question_naive"] = self.chat_data["message_lower_with_punc"].apply(lambda x: calculate_num_question_naive(x, question_words = self.question_words)) - + self.chat_data["num_question_naive"] = self.chat_data["message_lower_with_punc"].apply( + lambda x: calculate_num_question_naive(x, question_words=self.question_words)) + # Classify whether the message contains clarification questions - self.chat_data["NTRI"] = self.chat_data["message_lower_with_punc"].apply(classify_NTRI) - + self.chat_data["NTRI"] = self.chat_data["message_lower_with_punc"].apply( + classify_NTRI) + # Calculate the word type-to-token ratio - self.chat_data["word_TTR"] = self.chat_data[self.message_col].apply(get_word_TTR) - + self.chat_data["word_TTR"] = self.chat_data[self.message_col].apply( + get_word_TTR) + # Calculate the proportion of first person pronouns from the chats - self.chat_data["first_pronouns_proportion"] = get_proportion_first_pronouns(self.chat_data) + self.chat_data["first_pronouns_proportion"] = get_proportion_first_pronouns( + self.chat_data) # drop the raw number of first pronouns -- unnecessary given this is proportional to other first-pronoun columns self.chat_data = self.chat_data.drop(columns=['first_person_raw']) - + def calculate_word_mimicry(self) -> None: """ Calculate features related to word mimicry. @@ -256,7 +278,7 @@ def calculate_word_mimicry(self) -> None: - Identifies mimicry of function and content words from the immediate previous turn - Computes function word accommodation (number of mimicked function words) - Computes content word accommodation (sum of inverse frequency of mimicked content words) - + Drops the intermediate columns related to function and content words after calculation. :return: None @@ -264,21 +286,32 @@ def calculate_word_mimicry(self) -> None: """ # Extract function words / content words from a message - self.chat_data["function_words"] = self.chat_data[self.message_col].apply(lambda x: get_function_words_in_message(x, function_word_reference = self.function_words)) - self.chat_data["content_words"] = self.chat_data[self.message_col].apply(lambda x: get_content_words_in_message(x, function_word_reference = self.function_words)) - + self.chat_data["function_words"] = self.chat_data[self.message_col].apply( + lambda x: get_function_words_in_message(x, function_word_reference=self.function_words)) + self.chat_data["content_words"] = self.chat_data[self.message_col].apply( + lambda x: get_content_words_in_message(x, function_word_reference=self.function_words)) + # Extract the function words / content words that also appears in the immediate previous turn - self.chat_data["function_word_mimicry"] = mimic_words(self.chat_data, "function_words", self.conversation_id_col) - self.chat_data["content_word_mimicry"] = mimic_words(self.chat_data, "content_words", self.conversation_id_col) + self.chat_data["function_word_mimicry"] = mimic_words( + self.chat_data, "function_words", self.conversation_id_col) + self.chat_data["content_word_mimicry"] = mimic_words( + self.chat_data, "content_words", self.conversation_id_col) # Compute the number of function words that also appears in the immediate previous turn - self.chat_data["function_word_accommodation"] = self.chat_data["function_word_mimicry"].apply(function_mimicry_score) - + self.chat_data["function_word_accommodation"] = self.chat_data["function_word_mimicry"].apply( + function_mimicry_score) + # Compute the sum of inverse frequency of each content word that also occurred in the other’s immediately prior turn. - self.chat_data["content_word_accommodation"] = Content_mimicry_score(self.chat_data, "content_words","content_word_mimicry") + self.chat_data["content_word_accommodation"] = Content_mimicry_score( + self.chat_data, "content_words", "content_word_mimicry") + + # Compute the sum of inverse frequency of each content word that also occurred in the other’s immediately prior turn WITHIN CONVERSATIONS. + self.chat_data["content_word_accommodation_per_conv"] = Content_mimicry_score_per_conv( + self.chat_data, "content_words", "content_word_mimicry", self.conversation_id_col) # Drop the function / content word columns -- we don't need them in the output - self.chat_data = self.chat_data.drop(columns=['function_words', 'content_words', 'function_word_mimicry', 'content_word_mimicry']) + self.chat_data = self.chat_data.drop(columns=[ + 'function_words', 'content_words', 'function_word_mimicry', 'content_word_mimicry']) def calculate_vector_word_mimicry(self) -> None: """ @@ -288,16 +321,18 @@ def calculate_vector_word_mimicry(self) -> None: :rtype: None """ - self.chat_data["mimicry_bert"] = get_mimicry_bert(self.chat_data, self.vect_data, self.conversation_id_col) - self.chat_data["moving_mimicry"] = get_moving_mimicry(self.chat_data, self.vect_data, self.conversation_id_col) - + self.chat_data["mimicry_bert"] = get_mimicry_bert( + self.chat_data, self.vect_data, self.conversation_id_col) + self.chat_data["moving_mimicry"] = get_moving_mimicry( + self.chat_data, self.vect_data, self.conversation_id_col) + def get_temporal_features(self) -> None: """ Calculate features relevant to the timestamps of each chat. This function calculates and appends the following temporal feature to the chat data: - Time difference between messages sent - + It checks whether the 'timestamp' column is available. If not, it tries to calculate using 'timestamp_start' and 'timestamp_end' columns. @@ -305,12 +340,14 @@ def get_temporal_features(self) -> None: :rtype: None """ if type(self.timestamp_col) is str and {self.timestamp_col}.issubset(self.chat_data.columns): - self.chat_data["time_diff"] = get_time_diff(self.chat_data, self.timestamp_col, self.conversation_id_col) + self.chat_data["time_diff"] = get_time_diff( + self.chat_data, self.timestamp_col, self.conversation_id_col) elif type(self.timestamp_col) is tuple: timestamp_start, timestamp_end = self.timestamp_col if {timestamp_start, timestamp_end}.issubset(self.chat_data.columns): - self.chat_data["time_diff"] = get_time_diff_startend(self.chat_data, timestamp_start, timestamp_end, self.conversation_id_col) - + self.chat_data["time_diff"] = get_time_diff_startend( + self.chat_data, timestamp_start, timestamp_end, self.conversation_id_col) + def calculate_politeness_sentiment(self) -> None: """ Calculate politeness strategies using the Politeness module from Convokit. @@ -353,8 +390,9 @@ def get_forward_flow(self) -> None: :return: None :rtype: None """ - self.chat_data["forward_flow"] = get_forward_flow(self.chat_data, self.vect_data, self.conversation_id_col) - + self.chat_data["forward_flow"] = get_forward_flow( + self.chat_data, self.vect_data, self.conversation_id_col) + def get_certainty_score(self) -> None: """ Calculate the certainty score of a statement. @@ -367,9 +405,10 @@ def get_certainty_score(self) -> None: :return: None :rtype: None """ - + try: - self.chat_data["certainty_rocklage"] = self.chat_data["message_lower_with_punc"].apply(get_certainty) + self.chat_data["certainty_rocklage"] = self.chat_data["message_lower_with_punc"].apply( + get_certainty) except: print("WARNING: Certainty lexicon not found. Skipping feature...") @@ -394,28 +433,42 @@ def get_reddit_features(self) -> None: :return: None :rtype: None """ - self.chat_data["num_all_caps"] = self.chat_data[self.message_col + "_original"].apply(count_all_caps) - self.chat_data["num_links"] = self.chat_data["message_lower_with_punc"].apply(count_links) - self.chat_data["num_reddit_users"] = self.chat_data["message_lower_with_punc"].apply(count_user_references) - self.chat_data["num_emphasis"] = self.chat_data["message_lower_with_punc"].apply(count_emphasis) - self.chat_data["num_bullet_points"] = self.chat_data["message_lower_with_punc"].apply(count_bullet_points) - self.chat_data["num_numbered_points"] = self.chat_data["message_lower_with_punc"].apply(count_numbering) - self.chat_data["num_line_breaks"] = self.chat_data["message_lower_with_punc"].apply(count_line_breaks) - self.chat_data["num_quotes"] = self.chat_data["message_lower_with_punc"].apply(count_quotes) - self.chat_data["num_block_quote_responses"] = self.chat_data["message_lower_with_punc"].apply(count_responding_to_someone) - self.chat_data["num_ellipses"] = self.chat_data["message_lower_with_punc"].apply(count_ellipses) - self.chat_data["num_parentheses"] = self.chat_data["message_lower_with_punc"].apply(count_parentheses) - self.chat_data["num_emoji"] = self.chat_data["message_lower_with_punc"].apply(count_emojis) - + self.chat_data["num_all_caps"] = self.chat_data[self.message_col + + "_original"].apply(count_all_caps) + self.chat_data["num_links"] = self.chat_data["message_lower_with_punc"].apply( + count_links) + self.chat_data["num_reddit_users"] = self.chat_data["message_lower_with_punc"].apply( + count_user_references) + self.chat_data["num_emphasis"] = self.chat_data["message_lower_with_punc"].apply( + count_emphasis) + self.chat_data["num_bullet_points"] = self.chat_data["message_lower_with_punc"].apply( + count_bullet_points) + self.chat_data["num_numbered_points"] = self.chat_data["message_lower_with_punc"].apply( + count_numbering) + self.chat_data["num_line_breaks"] = self.chat_data["message_lower_with_punc"].apply( + count_line_breaks) + self.chat_data["num_quotes"] = self.chat_data["message_lower_with_punc"].apply( + count_quotes) + self.chat_data["num_block_quote_responses"] = self.chat_data["message_lower_with_punc"].apply( + count_responding_to_someone) + self.chat_data["num_ellipses"] = self.chat_data["message_lower_with_punc"].apply( + count_ellipses) + self.chat_data["num_parentheses"] = self.chat_data["message_lower_with_punc"].apply( + count_parentheses) + self.chat_data["num_emoji"] = self.chat_data["message_lower_with_punc"].apply( + count_emojis) + def get_named_entity(self) -> None: """ This function calculates the number of named entities in a chat. - + :return: None :rtype: None """ if self.ner_training is not None: train_spacy_ner(self.ner_training) - self.chat_data["num_named_entity"] = self.chat_data[self.message_col].apply(num_named_entity, cutoff=self.ner_cutoff) - self.chat_data["named_entities"] = self.chat_data[self.message_col].apply(named_entities, cutoff=self.ner_cutoff) + self.chat_data["num_named_entity"] = self.chat_data[self.message_col].apply( + num_named_entity, cutoff=self.ner_cutoff) + self.chat_data["named_entities"] = self.chat_data[self.message_col].apply( + named_entities, cutoff=self.ner_cutoff) diff --git a/tests/data/cleaned_data/fflow.csv b/tests/data/cleaned_data/fflow.csv index e70809f6..2810b950 100644 --- a/tests/data/cleaned_data/fflow.csv +++ b/tests/data/cleaned_data/fflow.csv @@ -82,4 +82,31 @@ X, B,Sports are a great way to stay active and healthy.,forward_flow,inv/dir X, C,Quesadillas are my favorite food in the whole world.,forward_flow,inv/dir X, D,Let's go shopping this weekend.,forward_flow,inv/dir X, D,Parties are so overrated.,forward_flow,inv/dir -X, D,Did you watch the latest political debate?,forward_flow,inv/dir \ No newline at end of file +X, D,Did you watch the latest political debate?,forward_flow,inv/dir +AA,A,I am going to walk my dog today.,mimicry_bert,inv/dir +AA,A,I am going to walk my pet today.,mimicry_bert,inv/dir +BB,A,The weather is gorgeous outside today.,mimicry_bert,inv/dir +BB,A,It's so sunny outside!,mimicry_bert,inv/dir +CC,A,The weather is gorgeous outside today.,mimicry_bert,inv/dir +CC,A,Does anyone want to play chess?,mimicry_bert,inv/dir +DD,A,I am going to walk my dog today.,mimicry_bert,unit_eq +DD,A,I am going to walk my pet today.,mimicry_bert,unit_eq +EE,A,I am going to walk my dog today.,mimicry_bert,unit_eq +EE,A,I am going to walk my pet today.,mimicry_bert,unit_eq +FF,A,The weather is gorgeous outside today.,mimicry_bert,unit_eq +FF,A,Does anyone want to play chess?,mimicry_bert,unit_eq +GG,A,This is the same sentence.,moving_mimicry,unit_eq +GG,A,This is the same sentence.,moving_mimicry,unit_eq +HH,A,This is the same sentence.,moving_mimicry,unit_eq +HH,A,This is the same sentence.,moving_mimicry,unit_eq +II,A,This is the same sentence.,moving_mimicry,unit_eq +II,A,I'm trying something new.,moving_mimicry,unit_eq +JJ,A,I want to eat something.,moving_mimicry,inv/dir +JJ,A,I've been meaning to try this restaurant.,moving_mimicry,inv/dir +JJ,A,Does the restaurant serve quesadillas?,moving_mimicry,inv/dir +KK,A,Let's go to the amusement park.,moving_mimicry,inv/dir +KK,A,Does it have roller coasters?,moving_mimicry,inv/dir +KK,A,I'm scared of roller coasters I can't go!,moving_mimicry,inv/dir +LL,A,Let me get on the dance floor.,moving_mimicry,inv/dir +LL,A,I've been meaning to try this restaurant.,moving_mimicry,inv/dir +LL,A,Bust some moves!,moving_mimicry,inv/dir \ No newline at end of file diff --git a/tests/data/cleaned_data/info_exchange_zscore_chats.csv b/tests/data/cleaned_data/info_exchange_zscore_chats.csv new file mode 100644 index 00000000..25deb2f0 --- /dev/null +++ b/tests/data/cleaned_data/info_exchange_zscore_chats.csv @@ -0,0 +1,7 @@ +conversation_num,speaker_nickname,message,expected_column,expected_value +D,1,I went to the store,info_exchange_zscore_chats,0 +D,2,Bought some groceries for dinner,info_exchange_zscore_chats,1.73205 +D,3,It's raining today,info_exchange_zscore_chats,-1.73205 +E,1,I went to the store,info_exchange_zscore_chats,0 +E,1,I went to the store,info_exchange_zscore_chats,0 +E,1,I went to the store,info_exchange_zscore_chats,0 \ No newline at end of file diff --git a/tests/data/cleaned_data/test_chat_level.csv b/tests/data/cleaned_data/test_chat_level.csv index e4c82b35..94fb7f45 100644 --- a/tests/data/cleaned_data/test_chat_level.csv +++ b/tests/data/cleaned_data/test_chat_level.csv @@ -1,681 +1,736 @@ -conversation_num,speaker_nickname,message,expected_column,expected_value -1,A,Hello I like fish.,num_words,4.0 -1,B,This sentence has five words.,num_words,5.0 -2,A,Hello??,num_words,1.0 -2,B,Is 4 a word?,num_words,4.0 -3,A,.,num_words,0.0 -4,test_A,"HELLO WORLD, THIS IS A TEST. hi HI. hi HI hi HI""",num_all_caps,9.0 -4,test_B,ONE TWO THREE. four five six. sEvEn EiGhT nInE.,num_all_caps,3.0 -4,test_A,Check out this [link](https://example.com) and this one http://example.org,num_links,2.0 -4,test_B,I like google.com and wikipedia.org but not amazon.com,num_links,3.0 -4,test_A,why don't you read everything at https://www.example.com and https://www.example.org and https://www.example.net and https://www.example.ca and https://www.example.co.uk,num_links,5.0 -4,test_B,"why don't you read everything at -- https://www.example.com -- https://www.example.org -- https://www.example.net -- https://www.example.ca -- https://www.example.co.uk",num_links,5.0 -4,test_A,"Hello u/user1 and u/user2, hi hi hi?",num_reddit_users,2.0 -4,test_B,I don't like u/user_1_test but I like u/user2Test,num_reddit_users,2.0 -4,test_A,"This is **bold**, *italics*, and this is not. This is ***bolded and italicized***",num_emphasis,3.0 -4,test_B,This is **uneven* in terms of *the emphasis**,num_emphasis,2.0 -4,test_A,* item 1\n* item 2\n- item 3,num_bullet_points,3.0 -4,test_B,"Here are all my arguments: -- point 1 -- point 2 -- point 3 -- point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines",num_bullet_points,4.0 -4,test_A,1. First\n2. Second\n3. Third,num_numbered_points,3.0 -4,test_B,This is the first line.\nThis is the second line.\nThis is the third line.,num_line_breaks,3.0 -4,test_A,"I have a line - - - - -here is a new line - - -here is a third line",num_line_breaks,3.0 -4,test_B,this is a line with\rA different kind of return value\rUsing carriage return instead of the newline character,num_line_breaks,3.0 -4,test_A,"""This is a quote."" She said, ""Here's another.""",num_quotes,2.0 -4,test_B,"""You miss 100% of the shots you don't take"" -- Wayne Gretzky",num_quotes,1.0 -4,test_A,"""I can't believe you use single quotes to quote people,"" she said. ""Well, he replied, 'sometimes single quotes are useful when you nest quotes inside other quotes,' according to my English teacher"" Then she said: 'okay'",num_quotes,4.0 -4,test_B,> Quoting someone else\nThis is my reply.,num_block_quote_responses,1.0 -4,test_A,> Quoting someone else\nThis is my reply.,num_block_quote_responses,1.0 -4,test_B,>>>> This is a quote but I went overboard with the carat character,num_block_quote_responses,1.0 -4,test_A,>> This is one where I put too many of the gt's,num_block_quote_responses,1.0 -4,test_B,"> Hello! -Goodbye!",num_block_quote_responses,1.0 -4,test_B,"> here I am making a quote -I respond to it -> I quote again -I respond to that too",num_block_quote_responses,2.0 -4,test_A,Well... I'm not sure... Maybe...,num_ellipses,3.0 -4,test_B,hm..what if I only use two periods.............or many periods............,num_ellipses,2.0 -4,test_B,This is a sentence (with some text in parentheses).,num_parentheses,1.0 -4,test_A,"""Sure,"" I said confidently (thiking to myself: no way!) This was definitely (not) one of my best moments.",num_parentheses,2.0 -4,test_B,(((((these parentheses are not properly closed.),num_parentheses,1.0 -4,test_B,((there are multiple parentheses here)),num_parentheses,2.0 -4,test_A,((1+(1+3+4)^2)+7+(9+8)),num_parentheses,4.0 -5,test1,I think that I think that I think,certainty_rocklage,4.5 -5,test2,I am a little confused,certainty_rocklage,2.47 -5,test2,I don't really know the answer,certainty_rocklage,1.33 -5,test3,I am sure that this is correct,certainty_rocklage,8.02 -5,test1,I am fairly certain in my response,certainty_rocklage,8.28 -5,test2,This is without a doubt the best movie I have ever seen,certainty_rocklage,4.5 -5,test2,I am not sure about how to how to approximately handle this,certainty_rocklage,2.69 -5,test3,I believe that he is guilty but I am not very certain,certainty_rocklage,6.56 -5,test1,I an open to you changing my mind on this issue,certainty_rocklage,4.5 -5,test2,I don't think the guy is the a$$hole. Thoughts?,certainty_rocklage,5.44 -5,test2,So who thinks the guy is an ass for asking his mother in law to learn english,certainty_rocklage,4.5 -5,test3,"I think that this person is not an asshole because, according to him, he was very polite while approaching the issue",certainty_rocklage,6.037 -5,test1,I can see how the family is upset because they feel the mother was disrespected but I can also understand the guy's feelings. Why should he have to work as interpreter for his mother in law?,certainty_rocklage,4.5 -5,test2,"Yes, I think his feeling makes sense to me to. Who doesn't want to be independent.",certainty_rocklage,4.89 -5,test2,I was conflicted because I could understand his frustration however I feel he should have maybe discussed strategies with how to approach the mother in law with his wife first.,certainty_rocklage,4.684 -5,test3,His MIL has been here for 8 years. You would think she'd pick up some English by now.,certainty_rocklage,4.5 -5,test1,I think he had every right to want to help his mother in law,certainty_rocklage,4.28 -5,test2,I also agree with culturedCow,certainty_rocklage,4.5 -5,test2,"I don't think he's an asshole. I think his request is reasonable. If you go to live in a foreign country, you should learn the language.",certainty_rocklage,5.125 -5,test3,"I think the guy is an asshole because for all his talk about how easy it is to use resources to learn a language, he didn't take the time to research WHY some people do not.",certainty_rocklage,5.505 -5,test1,I think he also tried to utilize other resources such as language learning apps to help her learn,certainty_rocklage,4.28 -5,test2,"I think also he needs to understand that language learning is not the same for everyone, not everyone has the same capacity to learn new languages quickly.",certainty_rocklage,6.09 -5,test2,"Maybe she does have a problem with learning languages, but she could at least try.",certainty_rocklage,3.79 -5,test3,After the edit he done it made it sound like he really loves his family,certainty_rocklage,6.175 -5,test1,"I don't think the guy is wrong in asking her to learn more english being that she lives in America, but he has to understand she is older and may not have the patience or capacity to learn a lot of english.",certainty_rocklage,5.472 -5,test2,"Learning a second language is easiest when you're a child for a reason. Your brain is wired differently then, which makes it easier.",certainty_rocklage,5.4 -5,test2,I think he tried to help her.� He gave her resources to use and she apparently didn't use them.,certainty_rocklage,4.32 -1,A,hello,Hello_receptiveness_yeomans,1.0 -1,B,So how should we answer this,Token_count_receptiveness_yeomans,6.0 -1,A,We can start here. What is the question?,YesNo_Questions_receptiveness_yeomans,0.0 -1,B,I am not sure. Where is the rest of our team?,WH_Questions_receptiveness_yeomans,1.0 -1,B,"Please help me figure this out, I really want to do well on this please",Please_receptiveness_yeomans,2.0 -2,C,Hey,Hello_receptiveness_yeomans,1.0 -2,C,Okay bro lets split it 50/50,Impersonal_Pronoun_receptiveness_yeomans,1.0 -2,D,Maybe but how about 60/40? I doubt its fair otherwise,Hedges_receptiveness_yeomans,2.0 -2,C,Seems fair,Hedges_receptiveness_yeomans,1.0 -1,B,I am not sure. Where is the rest of our team?,First_Person_Single_receptiveness_yeomans,1.0 -1,B,"Well please help me figure this out, I really want to do well on this please okay",factuality_politeness_convokit,1.0 -2,C,Seems possible,hashedge_politeness_convokit,1.0 -2,E,I see what youre thinking but I disagree,Acknowledgement_receptiveness_yeomans,1.0 -2,E,We get only one chance so we should understand how to split it,Acknowledgement_receptiveness_yeomans,2.0 -2,D,"I just don't agree, I'm making the 60/40 split",Adverb_Limiter_receptiveness_yeomans,1.0 -3,G,hey,indirect_greeting_politeness_convokit,1.0 -3,G,I think we should try something else,1st_person_start_politeness_convokit,1.0 -3,F,Ok whatever. You should leave the team then,2nd_person_start_politeness_convokit,1.0 -4,H,Honestly thank you so so much,factuality_politeness_convokit,1.0 -4,H,What's the plan?,direct_question_politeness_convokit,1.0 -4,I,That is the dumbest idea I've heard; youre actually dumb af,hasnegative_politeness_convokit,1.0 -4,H,What's ur problem here?,hasnegative_politeness_convokit,1.0 -5,J,Pleasure and an honor to meet you all,haspositive_politeness_convokit,1.0 -5,K,We should try that next,haspositive_politeness_convokit,0.0 -5,J,Could you please explain why? I don't really understand why you are thinking that,subjunctive_politeness_convokit,1.0 -5,K,Sorry sorry I didn't mean to,apologizing_politeness_convokit,1.0 -6,L,I don't really want to work with you all but let's get this over with,Impersonal_Pronoun_receptiveness_yeomans,1.0 -6,J,Fine by me,Affirmation_receptiveness_yeomans,1.0 -6,K,Ok so which part should we do first? the first or second?,YesNo_Questions_receptiveness_yeomans,1.0 -7,L,Please don't do that?,please_start_politeness_convokit,1.0 -7,L,I don't think that will work,hashedge_politeness_convokit,1.0 -7,M,I'm exhuasted rn,hasnegative_politeness_convokit,0.0 -7,M,i don't really care please just finish this up,haspositive_politeness_convokit,0.0 -7,N,Please don't do that?,Please_receptiveness_yeomans,1.0 -7,N,I don't think that will work,Hedges_receptiveness_yeomans,0.0 -7,O,I'm exhuasted rn,Negative_Emotion_receptiveness_yeomans,0.0 -7,O,i don't really care please just finish this up,Positive_Emotion_receptiveness_yeomans,0.0 -8,P,i appreciate all this from you,gratitude_politeness_convokit,1.0 -8,P,"well we should start rn, our part is long",1st_person_pl_politeness_convokit,1.0 -8,Q,ok forgive me for this error but,apologizing_politeness_convokit,1.0 -8,Q,you have to redo the whole thing,2nd_person_politeness_convokit,0.0 -8,R,ok so who will work with me? where should we begin?,direct_question_politeness_convokit,0.0 -8,S,i appreciate all this from you,Gratitude_receptiveness_yeomans,1.0 -8,S,"well we should start rn, our part is long",First_Person_Plural_receptiveness_yeomans,2.0 -8,T,ok forgive us for this error but,Apology_receptiveness_yeomans,0.0 -8,T,you have to redo the whole thing,Second_Person_receptiveness_yeomans,1.0 -8,U,ok so who will work with me? where should we begin?,WH_Questions_receptiveness_yeomans,2.0 -9,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Impersonal_Pronoun_receptiveness_yeomans,12.0 -10,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",First_Person_Single_receptiveness_yeomans,5.0 -11,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Hedges_receptiveness_yeomans,3.0 -12,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Negation_receptiveness_yeomans,3.0 -13,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Subjectivity_receptiveness_yeomans,3.0 -14,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Negative_Emotion_receptiveness_yeomans,3.0 -15,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Reasoning_receptiveness_yeomans,1.0 -16,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Agreement_receptiveness_yeomans,1.0 -17,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Second_Person_receptiveness_yeomans,1.0 -18,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Adverb_Limiter_receptiveness_yeomans,1.0 -19,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Disagreement_receptiveness_yeomans,1.0 -20,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Acknowledgement_receptiveness_yeomans,1.0 -21,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",First_Person_Plural_receptiveness_yeomans,1.0 -22,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",For_Me_receptiveness_yeomans,0.0 -23,A,And I will always love you,Conjunction_Start_receptiveness_yeomans,1.0 -23,B,"Can you help me, can you please?",Can_You_receptiveness_yeomans,2.0 -23,C,"Can you, will you, could you please be mine?",Could_You_receptiveness_yeomans,1.0 -23,D,"This land is your land, this land is my land; this land was made for you and for me",For_You_receptiveness_yeomans,1.0 -0,0,unneccessagf shoulds shouldve should'nt,discrepancies_lexical_wordcount,4 -0,0,wouldnt unneedofek want must've should'nt,discrepancies_lexical_wordcount,5 -0,0,hopes wish,discrepancies_lexical_wordcount,2 -0,0,must'nt rather wouldn't ought'nt,discrepancies_lexical_wordcount,4 -0,0,needn't unwantpotnw hopefulness oughta couldn't,discrepancies_lexical_wordcount,5 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other,nltk_english_stopwords_lexical_wordcount,4 -0,0,probably,hedge_words_lexical_wordcount,1 -0,0,maybe I guess possibly sort of a little,hedge_words_lexical_wordcount,5 -0,0,a little possibly I think sort of probably,hedge_words_lexical_wordcount,5 -0,0,probably sort of,hedge_words_lexical_wordcount,2 -0,0,I think,hedge_words_lexical_wordcount,1 \ No newline at end of file +conversation_num,speaker_nickname,message,expected_column,expected_value +1,A,Hello I like fish.,num_words,4 +1,B,This sentence has five words.,num_words,5 +2,A,Hello??,num_words,1 +2,B,Is 4 a word?,num_words,4 +3,A,.,num_words,0 +4,test_A,"HELLO WORLD, THIS IS A TEST. hi HI. hi HI hi HI""",num_all_caps,9 +4,test_B,ONE TWO THREE. four five six. sEvEn EiGhT nInE.,num_all_caps,3 +4,test_A,Check out this [link](https://example.com) and this one http://example.org,num_links,2 +4,test_B,I like google.com and wikipedia.org but not amazon.com,num_links,3 +4,test_A,why don't you read everything at https://www.example.com and https://www.example.org and https://www.example.net and https://www.example.ca and https://www.example.co.uk,num_links,5 +4,test_B,"why don't you read everything at +- https://www.example.com +- https://www.example.org +- https://www.example.net +- https://www.example.ca +- https://www.example.co.uk",num_links,5.0 +4,test_A,"Hello u/user1 and u/user2, hi hi hi?",num_reddit_users,2.0 +4,test_B,I don't like u/user_1_test but I like u/user2Test,num_reddit_users,2.0 +4,test_A,"This is **bold**, *italics*, and this is not. This is ***bolded and italicized***",num_emphasis,3.0 +4,test_B,This is **uneven* in terms of *the emphasis**,num_emphasis,2.0 +4,test_A,* item 1\n* item 2\n- item 3,num_bullet_points,3.0 +4,test_B,"Here are all my arguments: +- point 1 +- point 2 +- point 3 +- point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines point 4 is super long and takes up multiple lines",num_bullet_points,4.0 +4,test_A,1. First\n2. Second\n3. Third,num_numbered_points,3.0 +4,test_B,This is the first line.\nThis is the second line.\nThis is the third line.,num_line_breaks,3.0 +4,test_A,"I have a line + + + + +here is a new line + + +here is a third line",num_line_breaks,3.0 +4,test_B,this is a line with\rA different kind of return value\rUsing carriage return instead of the newline character,num_line_breaks,3.0 +4,test_A,"""This is a quote."" She said, ""Here's another.""",num_quotes,2.0 +4,test_B,"""You miss 100% of the shots you don't take"" -- Wayne Gretzky",num_quotes,1.0 +4,test_A,"""I can't believe you use single quotes to quote people,"" she said. ""Well, he replied, 'sometimes single quotes are useful when you nest quotes inside other quotes,' according to my English teacher"" Then she said: 'okay'",num_quotes,4.0 +4,test_B,> Quoting someone else\nThis is my reply.,num_block_quote_responses,1.0 +4,test_A,> Quoting someone else\nThis is my reply.,num_block_quote_responses,1.0 +4,test_B,>>>> This is a quote but I went overboard with the carat character,num_block_quote_responses,1.0 +4,test_A,>> This is one where I put too many of the gt's,num_block_quote_responses,1.0 +4,test_B,"> Hello! +Goodbye!",num_block_quote_responses,1.0 +4,test_B,"> here I am making a quote +I respond to it +> I quote again +I respond to that too",num_block_quote_responses,2.0 +4,test_A,Well... I'm not sure... Maybe...,num_ellipses,3.0 +4,test_B,hm..what if I only use two periods.............or many periods............,num_ellipses,2.0 +4,test_B,This is a sentence (with some text in parentheses).,num_parentheses,1.0 +4,test_A,"""Sure,"" I said confidently (thiking to myself: no way!) This was definitely (not) one of my best moments.",num_parentheses,2.0 +4,test_B,(((((these parentheses are not properly closed.),num_parentheses,1.0 +4,test_B,((there are multiple parentheses here)),num_parentheses,2.0 +4,test_A,((1+(1+3+4)^2)+7+(9+8)),num_parentheses,4.0 +5,test1,I think that I think that I think,certainty_rocklage,4.5 +5,test2,I am a little confused,certainty_rocklage,2.47 +5,test2,I don't really know the answer,certainty_rocklage,1.33 +5,test3,I am sure that this is correct,certainty_rocklage,8.02 +5,test1,I am fairly certain in my response,certainty_rocklage,8.28 +5,test2,This is without a doubt the best movie I have ever seen,certainty_rocklage,4.5 +5,test2,I am not sure about how to how to approximately handle this,certainty_rocklage,2.69 +5,test3,I believe that he is guilty but I am not very certain,certainty_rocklage,6.56 +5,test1,I an open to you changing my mind on this issue,certainty_rocklage,4.5 +5,test2,I don't think the guy is the a$$hole. Thoughts?,certainty_rocklage,5.44 +5,test2,So who thinks the guy is an ass for asking his mother in law to learn english,certainty_rocklage,4.5 +5,test3,"I think that this person is not an asshole because, according to him, he was very polite while approaching the issue",certainty_rocklage,6.037 +5,test1,I can see how the family is upset because they feel the mother was disrespected but I can also understand the guy's feelings. Why should he have to work as interpreter for his mother in law?,certainty_rocklage,4.5 +5,test2,"Yes, I think his feeling makes sense to me to. Who doesn't want to be independent.",certainty_rocklage,4.89 +5,test2,I was conflicted because I could understand his frustration however I feel he should have maybe discussed strategies with how to approach the mother in law with his wife first.,certainty_rocklage,4.684 +5,test3,His MIL has been here for 8 years. You would think she'd pick up some English by now.,certainty_rocklage,4.5 +5,test1,I think he had every right to want to help his mother in law,certainty_rocklage,4.28 +5,test2,I also agree with culturedCow,certainty_rocklage,4.5 +5,test2,"I don't think he's an asshole. I think his request is reasonable. If you go to live in a foreign country, you should learn the language.",certainty_rocklage,5.125 +5,test3,"I think the guy is an asshole because for all his talk about how easy it is to use resources to learn a language, he didn't take the time to research WHY some people do not.",certainty_rocklage,5.505 +5,test1,I think he also tried to utilize other resources such as language learning apps to help her learn,certainty_rocklage,4.28 +5,test2,"I think also he needs to understand that language learning is not the same for everyone, not everyone has the same capacity to learn new languages quickly.",certainty_rocklage,6.09 +5,test2,"Maybe she does have a problem with learning languages, but she could at least try.",certainty_rocklage,3.79 +5,test3,After the edit he done it made it sound like he really loves his family,certainty_rocklage,6.175 +5,test1,"I don't think the guy is wrong in asking her to learn more english being that she lives in America, but he has to understand she is older and may not have the patience or capacity to learn a lot of english.",certainty_rocklage,5.472 +5,test2,"Learning a second language is easiest when you're a child for a reason. Your brain is wired differently then, which makes it easier.",certainty_rocklage,5.4 +5,test2,I think he tried to help her.� He gave her resources to use and she apparently didn't use them.,certainty_rocklage,4.32 +1,A,hello,Hello_receptiveness_yeomans,1.0 +1,B,So how should we answer this,Token_count_receptiveness_yeomans,6.0 +1,A,We can start here. What is the question?,YesNo_Questions_receptiveness_yeomans,0.0 +1,B,I am not sure. Where is the rest of our team?,WH_Questions_receptiveness_yeomans,1.0 +1,B,"Please help me figure this out, I really want to do well on this please",Please_receptiveness_yeomans,2.0 +2,C,Hey,Hello_receptiveness_yeomans,1.0 +2,C,Okay bro lets split it 50/50,Impersonal_Pronoun_receptiveness_yeomans,1.0 +2,D,Maybe but how about 60/40? I doubt its fair otherwise,Hedges_receptiveness_yeomans,2.0 +2,C,Seems fair,Hedges_receptiveness_yeomans,1.0 +1,B,I am not sure. Where is the rest of our team?,First_Person_Single_receptiveness_yeomans,1.0 +1,B,"Well please help me figure this out, I really want to do well on this please okay",factuality_politeness_convokit,1.0 +2,C,Seems possible,hashedge_politeness_convokit,1.0 +2,E,I see what youre thinking but I disagree,Acknowledgement_receptiveness_yeomans,1.0 +2,E,We get only one chance so we should understand how to split it,Acknowledgement_receptiveness_yeomans,2.0 +2,D,"I just don't agree, I'm making the 60/40 split",Adverb_Limiter_receptiveness_yeomans,1.0 +3,G,hey,indirect_greeting_politeness_convokit,1.0 +3,G,I think we should try something else,1st_person_start_politeness_convokit,1.0 +3,F,Ok whatever. You should leave the team then,2nd_person_start_politeness_convokit,1.0 +4,H,Honestly thank you so so much,factuality_politeness_convokit,1.0 +4,H,What's the plan?,direct_question_politeness_convokit,1.0 +4,I,That is the dumbest idea I've heard; youre actually dumb af,hasnegative_politeness_convokit,1.0 +4,H,What's ur problem here?,hasnegative_politeness_convokit,1.0 +5,J,Pleasure and an honor to meet you all,haspositive_politeness_convokit,1.0 +5,K,We should try that next,haspositive_politeness_convokit,0.0 +5,J,Could you please explain why? I don't really understand why you are thinking that,subjunctive_politeness_convokit,1.0 +5,K,Sorry sorry I didn't mean to,apologizing_politeness_convokit,1.0 +6,L,I don't really want to work with you all but let's get this over with,Impersonal_Pronoun_receptiveness_yeomans,1.0 +6,J,Fine by me,Affirmation_receptiveness_yeomans,1.0 +6,K,Ok so which part should we do first? the first or second?,YesNo_Questions_receptiveness_yeomans,1.0 +7,L,Please don't do that?,please_start_politeness_convokit,1.0 +7,L,I don't think that will work,hashedge_politeness_convokit,1.0 +7,M,I'm exhuasted rn,hasnegative_politeness_convokit,0.0 +7,M,i don't really care please just finish this up,haspositive_politeness_convokit,0.0 +7,N,Please don't do that?,Please_receptiveness_yeomans,1.0 +7,N,I don't think that will work,Hedges_receptiveness_yeomans,0.0 +7,O,I'm exhuasted rn,Negative_Emotion_receptiveness_yeomans,0.0 +7,O,i don't really care please just finish this up,Positive_Emotion_receptiveness_yeomans,0.0 +8,P,i appreciate all this from you,gratitude_politeness_convokit,1.0 +8,P,"well we should start rn, our part is long",1st_person_pl_politeness_convokit,1.0 +8,Q,ok forgive me for this error but,apologizing_politeness_convokit,1.0 +8,Q,you have to redo the whole thing,2nd_person_politeness_convokit,0.0 +8,R,ok so who will work with me? where should we begin?,direct_question_politeness_convokit,0.0 +8,S,i appreciate all this from you,Gratitude_receptiveness_yeomans,1.0 +8,S,"well we should start rn, our part is long",First_Person_Plural_receptiveness_yeomans,2.0 +8,T,ok forgive us for this error but,Apology_receptiveness_yeomans,0.0 +8,T,you have to redo the whole thing,Second_Person_receptiveness_yeomans,1.0 +8,U,ok so who will work with me? where should we begin?,WH_Questions_receptiveness_yeomans,2.0 +9,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Impersonal_Pronoun_receptiveness_yeomans,12.0 +10,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",First_Person_Single_receptiveness_yeomans,5.0 +11,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Hedges_receptiveness_yeomans,3.0 +12,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Negation_receptiveness_yeomans,3.0 +13,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Subjectivity_receptiveness_yeomans,3.0 +14,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Negative_Emotion_receptiveness_yeomans,3.0 +15,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Reasoning_receptiveness_yeomans,1.0 +16,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Agreement_receptiveness_yeomans,1.0 +17,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Second_Person_receptiveness_yeomans,1.0 +18,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Adverb_Limiter_receptiveness_yeomans,1.0 +19,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Disagreement_receptiveness_yeomans,1.0 +20,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",Acknowledgement_receptiveness_yeomans,1.0 +21,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",First_Person_Plural_receptiveness_yeomans,1.0 +22,A,"I understand your perspective and agree that I would not want to have resentment in the workplace against women, as that would further compound the issue we are looking at. I do think that it is true that women are underrepresented in STEM careers and am a believer that something should be done to address this discrepancy, even if that is not implementing a priority for women in hiring decisions. While I don\'t think that companies should explicitly hire simply because of their gender, I do think that they should be mindful of the gender gap in STEM and look to address those issues through their hiring practices.",For_Me_receptiveness_yeomans,0.0 +23,A,And I will always love you,Conjunction_Start_receptiveness_yeomans,1.0 +23,B,"Can you help me, can you please?",Can_You_receptiveness_yeomans,2.0 +23,C,"Can you, will you, could you please be mine?",Could_You_receptiveness_yeomans,1.0 +23,D,"This land is your land, this land is my land; this land was made for you and for me",For_You_receptiveness_yeomans,1.0 +0,0,unneccessagf shoulds shouldve should'nt,discrepancies_lexical_wordcount,4 +0,0,wouldnt unneedofek want must've should'nt,discrepancies_lexical_wordcount,5 +0,0,hopes wish,discrepancies_lexical_wordcount,2 +0,0,must'nt rather wouldn't ought'nt,discrepancies_lexical_wordcount,4 +0,0,needn't unwantpotnw hopefulness oughta couldn't,discrepancies_lexical_wordcount,5 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awesome,hedge_naive,1 +E,3,It's raining today,hedge_naive,0 +F,1,what??,NTRI,1 +F,3,sorry excuse me what's your name again?,NTRI,1 +F,1,huh?,NTRI,1 +F,3,pardon?,NTRI,1 +F,3,pardon,NTRI,0 +F,3,say what again?,NTRI,1 +F,3,say again?,NTRI,1 +F,3,say again,NTRI,1 +F,3,what's that?,NTRI,1 +F,3,what is that,NTRI,1 +F,2,this is not funny,NTRI,0 +G,1,what is going on,word_TTR,1 +G,3,what what,word_TTR,0.5 +G,2,what what is that,word_TTR,0.75 +H,1,fantastic difficulty,dale_chall_score,19.5257 +H,3,discover direction different,dale_chall_score,0.1488 +H,3,trying quote random,dale_chall_score,0.1488 +H,3,erase eraser errand every even dig dim dime dine computer,dale_chall_score,5.7115 +H,2,even this is magnificent!,dale_chall_score,7.7824 +H,4,even this is magnificent! even this is magnificent!,dale_chall_score,7.7824 +H,1,fantastic difficulty,dale_chall_classification,difficult +H,3,trying quote random,dale_chall_classification,easy +H,3,erase eraser errand every even dig dim dime dine computer,dale_chall_classification,medium +H,3,discover direction different,dale_chall_classification,easy +H,4,even this is magnificent! even this is magnificent!,dale_chall_classification,difficult \ No newline at end of file diff --git a/tests/data/cleaned_data/test_chat_level_complex.csv b/tests/data/cleaned_data/test_chat_level_complex.csv index 7bc60c05..50c68700 100644 --- a/tests/data/cleaned_data/test_chat_level_complex.csv +++ b/tests/data/cleaned_data/test_chat_level_complex.csv @@ -1,19 +1,25 @@ conversation_num, speaker_nickname, message,feature A,A,The food was delicious and the service was great.,positive_bert A,A,The food was excellent and the service was impeccable.,positive_bert -A,A,The food was terrible and the service was awful.,negative_bert +A,A,The food was terrible and the service was awful.,positive_bert A,A,The movie was very entertaining and I highly recommend it.,positive_bert A,A,The movie was engaging and I would definitely recommend it.,positive_bert -A,A,The movie was awful and I wouldn't recommend it to anyone.,negative_bert +A,A,The movie was awful and I wouldn't recommend it to anyone.,positive_bert A,A,The weather was beautiful today.,positive_bert A,A,The weather was perfect today.,positive_bert -A,A,The weather was terrible today.,negative_bert +A,A,The weather was terrible today.,positive_bert A,A,I'm really enjoying this book.,positive_bert A,A,I'm captivated by this book.,positive_bert -A,A,I'm not enjoying this book at all.,negative_bert +A,A,I'm not enjoying this book at all.,positive_bert A,A,I love spending time with my family.,positive_bert A,A,I cherish the time I spend with my family.,positive_bert -A,A,I hate spending time with my family.,negative_bert +A,A,I hate spending time with my family.,positive_bert A,A,I'm so happy to see you.,positive_bert A,A,I'm overjoyed to see you.,positive_bert -A,A,I'm not happy to see you at all.,negative_bert \ No newline at end of file +A,A,I'm not happy to see you at all.,positive_bert +A,A,I do not like this example at all it is too boring,textblob_subjectivity +A,A,I prefer and strongly like chocolate ice cream,textblob_subjectivity +A,A,Textblob has two primary features,textblob_subjectivity +A,A,Today is the best day ever I am so happy,textblob_polarity +A,A,This restaurant is wonderful lovely great,textblob_polarity +A,A,The day is dark with sadness and depression,textblob_polarity diff --git a/tests/data/cleaned_data/test_conv_level.csv b/tests/data/cleaned_data/test_conv_level.csv index 34fde85a..d66dfa24 100644 --- a/tests/data/cleaned_data/test_conv_level.csv +++ b/tests/data/cleaned_data/test_conv_level.csv @@ -51,4 +51,34 @@ A, John,2020-04-20T18:30:40.125Z, I find that studying in short bursts is more e A, Mary,2020-04-20T18:31:00.125Z, I agree it's also important to take breaks and get enough sleep.,team_burstiness,-1.0 A, Jessica,2020-04-20T18:31:20.125Z, I'm always struggling to stay motivated any tips?,team_burstiness,-1.0 A, John,2020-04-20T18:31:40.125Z, I find it helpful to set realistic goals and break down large tasks into smaller ones.,team_burstiness,-1.0 -A, Mary,2020-04-20T18:32:00.125Z, It's also important to find a study buddy or group to help you stay accountable.,team_burstiness,-1.0 \ No newline at end of file +A, Mary,2020-04-20T18:32:00.125Z, It's also important to find a study buddy or group to help you stay accountable.,team_burstiness,-1.0 +B, B,, this is four words,gini_coefficient_sum_num_words,0 +B, B,, what about this also,gini_coefficient_sum_num_words,0 +B, B,, i think it is,gini_coefficient_sum_num_words,0 +B, A,, just some random words,gini_coefficient_sum_num_words,0 +B, A,, hello hello hello hello,gini_coefficient_sum_num_words,0 +B, A,, what is up ok,gini_coefficient_sum_num_words,0 +C, A,, this is same character count,gini_coefficient_sum_num_chars,0 +C, A,, this is same character count,gini_coefficient_sum_num_chars,0 +C, A,, this is same character count,gini_coefficient_sum_num_chars,0 +C, X,, this is same character count,gini_coefficient_sum_num_chars,0 +C, X,, this is same character count,gini_coefficient_sum_num_chars,0 +C, X,, this is same character count,gini_coefficient_sum_num_chars,0 +D, A,, this is same message count,gini_coefficient_sum_num_messages,0 +D, B,, this is same message count,gini_coefficient_sum_num_messages,0 +D, C,, this is same message count,gini_coefficient_sum_num_messages,0 +D, D,, this is same message count,gini_coefficient_sum_num_messages,0 +D, E,, this is same message count,gini_coefficient_sum_num_messages,0 +D, F,, this is same message count,gini_coefficient_sum_num_messages,0 +E, A,, i think im the only speaker in this conversation,gini_coefficient_sum_num_words,0.5 +E, A,, yeah i think we're trying to get the gini high,gini_coefficient_sum_num_words,0.5 +E, A,, ill just keep talking!,gini_coefficient_sum_num_words,0.5 +E, X,,,gini_coefficient_sum_num_words,0.5 +E, X,,,gini_coefficient_sum_num_words,0.5 +E, X,,,gini_coefficient_sum_num_words,0.5 +F, A,, i think im the only speaker in this conversation,gini_coefficient_sum_num_chars,0.75 +F, A,, yeah i think we're trying to get the gini high,gini_coefficient_sum_num_chars,0.75 +F, A,, ill just keep talking!,gini_coefficient_sum_num_ chars,0.75 +F, X,,,gini_coefficient_sum_num_chars,0.75 +F, Y,,,gini_coefficient_sum_num_chars,0.75 +F, Z,,,gini_coefficient_sum_num_chars,0.75 \ No newline at end of file diff --git a/tests/data/cleaned_data/test_conv_level_complex.csv b/tests/data/cleaned_data/test_conv_level_complex.csv index 02b3570e..858bb354 100644 --- a/tests/data/cleaned_data/test_conv_level_complex.csv +++ b/tests/data/cleaned_data/test_conv_level_complex.csv @@ -269,4 +269,146 @@ U,B, 2024-07-15T23:16:36.125Z, I think running is my favorite exercise.,incongru U,A, 2024-07-15T23:19:36.125Z, I want to go on a run.,incongruent_modulation U,B, 2024-07-15T23:20:36.125Z, I want to play some sports.,incongruent_modulation U,A, 2024-07-15T23:21:36.125Z, Let's go to the basketball courts.,incongruent_modulation -U,B, 2024-07-15T23:22:36.125Z, Let's go to the soccer fields.,incongruent_modulation \ No newline at end of file +U,B, 2024-07-15T23:22:36.125Z, Let's go to the soccer fields.,incongruent_modulation +conversation_1, speaker_1,, Have you checked out the latest developments in blockchain tech?,info_diversity +conversation_1, speaker_2,, Yeah the advancements are pretty incredible especially with DeFi.,info_diversity +conversation_1, speaker_3,, I’ve been really into how NFTs are being used for digital identity verification.,info_diversity +conversation_1, speaker_1,, NFTs are definitely evolving. It's amazing how they started just with art.,info_diversity +conversation_1, speaker_2,, Right? Now they’re branching out into real estate and even intellectual property.,info_diversity +conversation_1, speaker_3,, The possibilities are endless. I’m curious to see how governments will regulate it though.,info_diversity +conversation_1, speaker_1,, That’s a big question. Regulation could either stifle innovation or provide the structure it needs to grow.,info_diversity +conversation_1, speaker_2,, I think we’ll see a mix. Some countries are embracing it while others are more cautious.,info_diversity +conversation_1, speaker_3,, It’ll be interesting to see which approach works best. I’m also wondering about the environmental impact of all this.,info_diversity +conversation_1, speaker_1,, That’s a good point. The shift to more energy-efficient consensus algorithms is crucial.,info_diversity +conversation_2, speaker_1,, I just finished the machine learning course you recommended. It was awesome!,info_diversity +conversation_2, speaker_2,, That’s great to hear! Did you do all the assignments?,info_diversity +conversation_2, speaker_3,, I’m still working through it but the concepts are starting to click.,info_diversity +conversation_2, speaker_1,, The assignments really help solidify the knowledge. The neural networks part was a bit tough though.,info_diversity +conversation_2, speaker_2,, Neural networks can be tricky at first but once you get it it’s really powerful.,info_diversity +conversation_2, speaker_3,, I’m excited to apply what I’ve learned to some real-world data sets.,info_diversity +conversation_2, speaker_1,, Same here. I’m thinking of starting a side project to predict stock prices.,info_diversity +conversation_2, speaker_2,, That’s ambitious! But it’s a great way to put your skills to the test.,info_diversity +conversation_2, speaker_3,, I might join you on that! It’ll be cool to collaborate and see what we come up with.,info_diversity +conversation_2, speaker_1,, Let’s do it! We can brainstorm ideas this weekend.,info_diversity +conversation_5, speaker_1,, Did you catch the latest episode of that show everyone’s talking about?,info_diversity +conversation_5, speaker_2,, Yeah it was intense! But I had to pause halfway to finish my assignment.,info_diversity +conversation_5, speaker_3,, I haven’t started watching it yet. Is it really that good?,info_diversity +conversation_5, speaker_1,, Definitely worth it. But speaking of assignments how’s your machine learning course going?,info_diversity +conversation_5, speaker_2,, It’s going well! I’m almost done with the neural networks module.,info_diversity +conversation_5, speaker_3,, Neural networks? That’s tough. I’m still stuck on the earlier parts.,info_diversity +conversation_5, speaker_1,, You’ll get there. Just take it one step at a time. Also are you guys thinking about internships yet?,info_diversity +conversation_5, speaker_2,, I am! I’m applying to a few tech companies next week.,info_diversity +conversation_5, speaker_3,, I should probably start looking too. I’m just so caught up with other things.,info_diversity +conversation_5, speaker_1,, Same here. Between school shows and job applications it’s hard to find balance.,info_diversity +conversation_3, speaker_1,, Are you ready to start applying for internships?,info_diversity +conversation_3, speaker_2,, Almost. I’m just finalizing my resume and polishing up my GitHub.,info_diversity +conversation_3, speaker_3,, Same here. I’m focusing on tech companies that have strong mentorship programs.,info_diversity +conversation_3, speaker_1,, That’s smart. I’ve been looking at roles in fintech but I’m open to other sectors too.,info_diversity +conversation_3, speaker_2,, Fintech is hot right now. There’s so much innovation happening.,info_diversity +conversation_3, speaker_3,, I’m leaning towards something in AI or machine learning. There’s a lot of demand in those areas.,info_diversity +conversation_3, speaker_1,, Definitely. It’s a great time to be in tech. Have you guys started preparing for interviews?,info_diversity +conversation_3, speaker_2,, I’ve done a few Leetcode problems but I need to ramp up.,info_diversity +conversation_3, speaker_3,, Same I’m planning to do at least one problem a day until interview season.,info_diversity +conversation_3, speaker_1,, Sounds like a solid plan. We should do mock interviews together to practice.,info_diversity +conversation_4, speaker_1,, I’ve been using React for a new project and it’s going pretty well so far.,info_diversity +conversation_4, speaker_2,, Nice! What are you building?,info_diversity +conversation_4, speaker_3,, I’m curious too. React is great for building dynamic user interfaces.,info_diversity +conversation_4, speaker_1,, It’s a course review platform where students can rate and review their classes.,info_diversity +conversation_4, speaker_2,, That sounds really useful! Are you handling both the frontend and backend?,info_diversity +conversation_4, speaker_3,, I love that idea. What stack are you using for the backend?,info_diversity +conversation_4, speaker_1,, I’m using Node.js with Express for the backend and MySQL for the database.,info_diversity +conversation_4, speaker_2,, Solid choices. How are you planning to handle scalability if the platform grows?,info_diversity +conversation_4, speaker_3,, AWS could be a good option for scaling. Have you looked into that?,info_diversity +conversation_4, speaker_1,, Yeah I’m considering AWS for deployment. I want the platform to be scalable from the start.,info_diversity +conversation_6, speaker_1,, I just started a new book on AI ethics and it’s really eye-opening.,info_diversity +conversation_6, speaker_2,, That sounds interesting. What’s it about?,info_diversity +conversation_6, speaker_3,, AI ethics is such an important topic especially with all the recent developments.,info_diversity +conversation_6, speaker_1,, It explores how AI is impacting society both positively and negatively.,info_diversity +conversation_6, speaker_3,, I did! I’m planning to integrate one of them into my next project.,info_diversity +conversation_6, speaker_1,, That’s awesome. By the way are you still going to the gym regularly?,info_diversity +conversation_6, speaker_2,, I’ve been slacking off lately to be honest. Too much work.,info_diversity +conversation_6, speaker_3,, Same here. I’ve been prioritizing coding over everything else.,info_diversity +conversation_6, speaker_1,, We should do a workout session together. It’s good to take breaks from all the tech stuff.,info_diversity +conversation_15, speaker_1,, Have you ever thought about how artificial intelligence is changing our daily lives It’s everywhere now,info_diversity +conversation_15, speaker_2,, Artificial intelligence is integrated into so many aspects of life from virtual assistants to smart homes,info_diversity +conversation_15, speaker_3,, AI in smart homes is incredible It automates so many tasks and makes life easier,info_diversity +conversation_15, speaker_1,, The AI in virtual assistants like Siri and Alexa has gotten so much smarter too,info_diversity +conversation_15, speaker_2,, Virtual assistants are learning to understand natural language better thanks to advancements in AI,info_diversity +conversation_15, speaker_3,, And AI-driven recommendations on streaming services are spot on They know what we like,info_diversity +conversation_15, speaker_1,, AI really personalizes our experiences whether it’s shopping online or watching movies,info_diversity +conversation_15, speaker_2,, Exactly AI uses data to tailor services to our preferences which makes everything more convenient,info_diversity +conversation_15, speaker_3,, The impact of artificial intelligence on our daily lives is only going to grow It’s an exciting time,info_diversity +conversation_15, speaker_1,, Definitely AI is shaping the future in ways we’re only beginning to understand,info_diversity +conversation_16, speaker_1,, I’ve been really diving into cloud computing recently It’s amazing how it’s transforming businesses,info_diversity +conversation_16, speaker_2,, Cloud computing is revolutionizing how companies operate The scalability it offers is unparalleled,info_diversity +conversation_16, speaker_3,, The flexibility of cloud computing allows businesses to adapt quickly to changing demands,info_diversity +conversation_16, speaker_1,, And the cost savings with cloud computing are significant You only pay for what you use,info_diversity +conversation_16, speaker_2,, That’s a huge advantage for startups Cloud computing gives them access to resources they couldn’t afford otherwise,info_diversity +conversation_16, speaker_3,, Security in cloud computing has also improved a lot with encryption and other safeguards,info_diversity +conversation_16, speaker_1,, Cloud computing security is critical especially with sensitive data being stored remotely,info_diversity +conversation_16, speaker_2,, Exactly With the right cloud computing provider businesses can ensure their data is safe,info_diversity +conversation_16, speaker_3,, The future of cloud computing looks bright It’s becoming the backbone of modern IT infrastructure,info_diversity +conversation_16, speaker_1,, I agree Cloud computing is essential for any business looking to stay competitive,info_diversity +conversation_9, speaker_1,, I’ve been thinking about starting a garden but I have no idea where to begin,info_diversity +conversation_9, speaker_2,, Gardening is super relaxing You should start with some easy plants like herbs,info_diversity +conversation_9, speaker_3,, That sounds fun I’ve always wanted to grow my own vegetables but space is an issue,info_diversity +conversation_9, speaker_1,, I hear you I’m also limited on space but I’m thinking of trying container gardening,info_diversity +conversation_9, speaker_2,, That’s a great idea You can grow a lot in containers if you’re strategic,info_diversity +conversation_9, speaker_3,, Speaking of strategy has anyone been following the latest chess tournaments,info_diversity +conversation_9, speaker_1,, I have The level of play this year has been incredible,info_diversity +conversation_9, speaker_2,, I’m not a chess expert but I enjoy watching the matches They’re so intense,info_diversity +conversation_9, speaker_3,, It’s amazing how much thought goes into each move It’s like a mental workout,info_diversity +conversation_9, speaker_1,, Definitely I find that playing chess really sharpens my focus,info_diversity +AA, A,, this is four words,gini_coefficient_sum_num_words +AA, A,, what about this also i think,gini_coefficient_sum_num_words +AA, B,, i think it is,gini_coefficient_sum_num_words +AA, B,, just some random words,gini_coefficient_sum_num_words +AA, C,, hello hello hello hello helloooo,gini_coefficient_sum_num_words +AA, C,, what is up ok,gini_coefficient_sum_num_words +AAA, A,, this is six words i think,gini_coefficient_sum_num_words +AAA, A,, what about this being five,gini_coefficient_sum_num_words +AAA, B,, i only have four,gini_coefficient_sum_num_words +AAA, B,, but then i get seven words woohoo,gini_coefficient_sum_num_words +AAA, C,, i think this is even,gini_coefficient_sum_num_words +AAA, C,, lets see the results though,gini_coefficient_sum_num_words +AAAA, A,, im going to be talking the absolute most right now,gini_coefficient_sum_num_words +AAAA, A,, its because i want to raise our gini score woohooo,gini_coefficient_sum_num_words +AAAA, B,, ok,gini_coefficient_sum_num_words +AAAA, B,, i can talk,gini_coefficient_sum_num_words +AAAA, C,, hello,gini_coefficient_sum_num_words +AAAA, C,, i dont want to talk,gini_coefficient_sum_num_words +BB, A,, this is same character count,gini_coefficient_sum_num_chars +BB, A,, this similar similar counts,gini_coefficient_sum_num_chars +BB, B,, this is roughly same character,gini_coefficient_sum_num_chars +BB, B,, hello how are we doing blah,gini_coefficient_sum_num_chars +BB, C,, number of characteros,gini_coefficient_sum_num_chars +BB, C,, i want to get some good after,gini_coefficient_sum_num_chars +BBB, A,, what is your name,gini_coefficient_sum_num_chars +BBB, A,, my name is shruti,gini_coefficient_sum_num_chars +BBB, B,, his name is evan,gini_coefficient_sum_num_chars +BBB, B,, her name is priya dcosta,gini_coefficient_sum_num_chars +BBB, C,, we were on penn today,gini_coefficient_sum_num_chars +BBB, C,, team communication toolkit,gini_coefficient_sum_num_chars +BBBB, A,, this is same character count but an insane amount of characters,gini_coefficient_sum_num_chars +BBBB, A,, i am going to keep on saying things until i get fired,gini_coefficient_sum_num_chars +BBBB, B,, ok,gini_coefficient_sum_num_chars +BBBB, B,, lets see,gini_coefficient_sum_num_chars +BBBB, C,, curious for this,gini_coefficient_sum_num_chars +BBBB, C,, results should be good,gini_coefficient_sum_num_chars +CC, A,, this is same message count,gini_coefficient_sum_num_messages +CC, B,, this is same message count,gini_coefficient_sum_num_messages +CC, C,, this is same message count,gini_coefficient_sum_num_messages +CC, D,, this is same message count,gini_coefficient_sum_num_messages +CC, E,, this is same message count,gini_coefficient_sum_num_messages +CC, F,, this is same message count,gini_coefficient_sum_num_messages +CCC, A,, this is same message count,gini_coefficient_sum_num_messages +CCC, A,, this is same message count,gini_coefficient_sum_num_messages +CCC, C,, this is same message count,gini_coefficient_sum_num_messages +CCC, D,, this is same message count,gini_coefficient_sum_num_messages +CCC, E,, this is same message count,gini_coefficient_sum_num_messages +CCC, E,, this is same message count,gini_coefficient_sum_num_messages +CCCC, A,, this is different message count,gini_coefficient_sum_num_messages +CCCC, A,, this is different message count,gini_coefficient_sum_num_messages +CCCC, A,, this is different message count,gini_coefficient_sum_num_messages +CCCC, A,, this is different message count,gini_coefficient_sum_num_messages +CCCC, E,, this is different message count,gini_coefficient_sum_num_messages diff --git a/tests/ipython_notebooks/generate_liwc_tests.ipynb b/tests/ipython_notebooks/generate_liwc_tests.ipynb index 5c05794b..e140a992 100644 --- a/tests/ipython_notebooks/generate_liwc_tests.ipynb +++ b/tests/ipython_notebooks/generate_liwc_tests.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[],"source":["import pickle\n","import re\n","import pandas as pd\n","import os\n","from pathlib import Path\n","\n","lexicon_pkl_file_path = \"feature_engine/features/lexicons_dict.pkl\"\n","with open(lexicon_pkl_file_path, \"rb\") as lexicons_pickle_file:\n","\tlexicons_dict = pickle.load(lexicons_pickle_file)"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[],"source":["import os\n","import random\n","import csv\n","\n","alphabet = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\", \"k\", \"l\", \"m\", \"n\", \"o\", \"p\", \"q\", \"r\", \"s\", \"t\", \"u\", \"v\", \"w\", \"x\", \"y\", \"z\"]\n","filler = [\"Lorem\", \"ipsum\", \"dolor\", \"amet\", \"consectetur\", \"adipiscing\", \"sed\", \"euismod\", \"tempor\"]\n","\n","def generate_random_strings_from_wordlist(wordlist, num_strings, min_words=1, max_words=5):\n"," max_words = min(max_words, len(wordlist))\n"," strings = []\n"," expected_value = []\n"," for _ in range(num_strings):\n"," num_words = random.randint(min_words, max_words)\n"," expected_value.append(num_words/100)\n"," selected_words = random.sample(wordlist, num_words)\n","\n"," # for strings in selected_words that end with *, append a random number of letters to that string\n"," for i in range(len(selected_words)):\n"," selected_words[i] = selected_words[i].strip()\n"," if selected_words[i].endswith(\"*\"):\n"," selected_words[i] = selected_words[i][:-1] + ''.join(random.sample(alphabet, random.randint(1, 5)))\n","\n","\n"," # num_fillers = random.randint(1, 3)\n"," # filler_words = random.sample(filler, num_fillers)\n"," # final_words = random.choices(selected_words) + random.choices(filler_words)\n"," \n"," strings.append(' '.join(selected_words))\n"," return [strings, expected_value]\n","\n","def write_to_csv(strings, expected_vals, wordlist_name, filename='output.csv'):\n"," with open(filename, 'a', newline='') as csvfile:\n"," writer = csv.writer(csvfile)\n"," for string, expected_val in zip(strings, expected_vals):\n"," writer.writerow([0, 0, string, wordlist_name + \"_lexical_per_100\", expected_val])\n","\n","directory = ['feature_engine/features/lexicons/liwc_lexicons','feature_engine/features/lexicons/liwc_lexicons_small_test','feature_engine/features/lexicons/other_lexicons']\n","for d in directory:\n"," for filename in os.listdir(d):\n"," with open(d + \"/\" + filename, encoding=\"mac_roman\") as lexicons:\n"," if filename.startswith(\".\"):\n"," continue\n"," wordlist = lexicons.read().splitlines()\n"," output = generate_random_strings_from_wordlist(wordlist, num_strings=5)\n"," filename = filename[:-4] if filename.endswith(\".txt\") else filename\n"," write_to_csv(output[0], output[1], filename)\n","\n","# manually appended output.csv to test_chat_level.csv"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.6"}},"nbformat":4,"nbformat_minor":2} +{"cells":[{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/agshruti/Downloads/team-process-map/.venv/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n"]},{"ename":"FileNotFoundError","evalue":"[Errno 2] No such file or directory: 'feature_engine/features/lexicons_dict.pkl'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[8], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpathlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[1;32m 7\u001b[0m lexicon_pkl_file_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfeature_engine/features/lexicons_dict.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mlexicon_pkl_file_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m lexicons_pickle_file:\n\u001b[1;32m 9\u001b[0m \tlexicons_dict \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(lexicons_pickle_file)\n","File \u001b[0;32m~/Downloads/team-process-map/.venv/lib/python3.11/site-packages/IPython/core/interactiveshell.py:310\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m 304\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 305\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 306\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 307\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 308\u001b[0m )\n\u001b[0;32m--> 310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'feature_engine/features/lexicons_dict.pkl'"]}],"source":["import pickle\n","import re\n","import pandas as pd\n","import os\n","from pathlib import Path\n","\n","lexicon_pkl_file_path = \"feature_engine/features/lexicons_dict.pkl\"\n","with open(lexicon_pkl_file_path, \"rb\") as lexicons_pickle_file:\n","\tlexicons_dict = pickle.load(lexicons_pickle_file)"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["['able', 'am', 'are', 'arent', 'be', 'been', 'being', 'can', 'cant', 'cannot', 'could', 'couldnt', 'did', 'didnt', 'do', 'dont', 'get', 'got', 'gotta', 'had', 'hadnt', 'hasnt', 'have', 'havent', 'is', 'isnt', 'may', 'should', 'shouldve', 'shouldnt', 'was', 'were', 'will', 'wont', 'would', 'wouldve', 'wouldnt', 'although', 'and', 'as', 'because', 'cause', 'but', 'if', 'or', 'so', 'then', 'unless', 'whereas', 'while', 'a', 'an', 'each', 'every', 'all', 'lot', 'lots', 'the', 'this', 'those', 'anybody', 'anything', 'anywhere', 'everybodys', 'everyone', 'everything', 'everythings', 'everywhere', 'he', 'hed', 'hes', 'her', 'him', 'himself', 'herself', 'his', 'I', 'Id', 'Ill', 'Im', 'Ive', 'it', 'itd', 'itll', 'its', 'its', 'itself', 'me', 'my', 'mine', 'myself', 'nobody', 'nothing', 'nowhere', 'one', 'ones', 'ones', 'our', 'ours', 'she', 'shell', 'shes', 'shed', 'somebody', 'someone', 'someplace', 'that', 'thatd', 'thatll', 'thats', 'them', 'themselves', 'these', 'they', 'theyd', 'theyll', 'theyre', 'theyve', 'us', 'we', 'wed', 'well', 'were', 'weve', 'what', 'whatd', 'whats', 'whatever', 'when', 'where', 'whered', 'wheres', 'wherever', 'which', 'who', 'whos', 'whom', 'whose', 'why', 'you', 'youd', 'youll', 'youre', 'youve', 'your', 'yours', 'yourself', 'about', 'after', 'against', 'at', 'before', 'by', 'down', 'for', 'from', 'in', 'into', 'near', 'of', 'off', 'on', 'out', 'over', 'than', 'to', 'until', 'up', 'with', 'without', 'ah', 'hi', 'huh', 'like', 'mmhmm', 'oh', 'okay', 'right', 'uh', 'uhhuh', 'um', 'well', 'yeah', 'yup', 'just', 'no', 'not', 'really', 'too', 'very']\n","[['and huh be was itd', 'all anything anybody', 'get for which Id', 'from this then her to', 'herself before himself shes Ive'], [0.05, 0.03, 0.04, 0.05, 0.05]]\n"]}],"source":["import os\n","import random\n","import csv\n","\n","alphabet = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\", \"k\", \"l\", \"m\", \"n\", \"o\", \"p\", \"q\", \"r\", \"s\", \"t\", \"u\", \"v\", \"w\", \"x\", \"y\", \"z\"]\n","filler = [\"Lorem\", \"ipsum\", \"dolor\", \"amet\", \"consectetur\", \"adipiscing\", \"sed\", \"euismod\", \"tempor\"]\n","\n","def generate_random_strings_from_wordlist(wordlist, num_strings, min_words=1, max_words=5):\n"," max_words = min(max_words, len(wordlist))\n"," strings = []\n"," expected_value = []\n"," for _ in range(num_strings):\n"," num_words = random.randint(min_words, max_words)\n"," expected_value.append(num_words/100)\n"," selected_words = random.sample(wordlist, num_words)\n","\n"," # for strings in selected_words that end with *, append a random number of letters to that string\n"," for i in range(len(selected_words)):\n"," selected_words[i] = selected_words[i].strip()\n"," if selected_words[i].endswith(\"*\"):\n"," selected_words[i] = selected_words[i][:-1] + ''.join(random.sample(alphabet, random.randint(1, 5)))\n","\n"," # num_fillers = random.randint(1, 3)\n"," # filler_words = random.sample(filler, num_fillers)\n"," # final_words = random.choices(selected_words) + random.choices(filler_words)\n"," \n"," strings.append(' '.join(selected_words))\n"," return [strings, expected_value]\n","\n","def write_to_csv(strings, expected_vals, wordlist_name, filename='output.csv'):\n"," with open(filename, 'a', newline='') as csvfile:\n"," writer = csv.writer(csvfile)\n"," for string, expected_val in zip(strings, expected_vals):\n"," writer.writerow([0, 0, string, wordlist_name + \"_lexical_per_100\", expected_val])\n","\n","# directory = ['feature_engine/features/lexicons/liwc_lexicons','feature_engine/features/lexicons/liwc_lexicons_small_test','feature_engine/features/lexicons/other_lexicons']\n","parent = Path(os.getcwd()).parent\n","gparent = Path(parent).parent\n","function_words_path = os.path.join(gparent, \"src/team_comm_tools/features/lexicons/function_words.txt\")\n","\n","# directory = [function_words_path]\n","# for d in directory:\n","# for filename in os.listdir(d):\n","with open(function_words_path, encoding=\"mac_roman\") as lexicons:\n"," # if filename.startswith(\".\"):\n"," # continue\n"," wordlist = lexicons.read().splitlines()\n"," print(wordlist)\n"," output = generate_random_strings_from_wordlist(wordlist, num_strings=5)\n"," \n"," # filename = filename[:-4] if filename.endswith(\".txt\") else filename\n"," write_to_csv(output[0], output[1], \"hello.txt\")\n","\n","# manually appended output.csv to test_chat_level.csv"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["/Users/agshruti/Downloads/team-process-map/tests/ipython_notebooks\n"]},{"data":{"text/plain":["'/Users/agshruti/Downloads/team-process-map/src/features/lexicons/function_words.txt'"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# module Path not found\n","import os\n","\n","# print current directory\n","print(os.getcwd())\n","\n","# print parent directory\n","parent = Path(os.getcwd()).parent\n","gparent = Path(parent).parent\n","os.path.join(gparent, \"src/features/lexicons/function_words.txt\")\n","\n","# get parent directory\n","# parent_dir = Path(os.getcwd()).parent\n","\n","# # get file within parent directory\n","# file_path = os.path.join(parent_dir, \"output.csv\")\n","\n","# # get file within grandparent directory\n","# grandparent_dir = Path(parent_dir).parent\n","# file_path = os.path.join(grandparent_dir, \"output.csv\")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# generate function & content word accommodation tests\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.6"}},"nbformat":4,"nbformat_minor":2} diff --git a/tests/ipython_notebooks/helper.ipynb b/tests/ipython_notebooks/helper.ipynb new file mode 100644 index 00000000..b8667236 --- /dev/null +++ b/tests/ipython_notebooks/helper.ipynb @@ -0,0 +1,2797 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from team_comm_tools.feature_dict import feature_dict\n", + "import pytest\n", + "import pandas as pd\n", + "import numpy as np\n", + "from numpy import nan\n", + "import logging\n", + "import itertools\n", + "\n", + "test_chat_df = pd.read_csv(\"./output/chat/test_chat_level_chat.csv\")\n", + "test_conv_df = pd.read_csv(\"./output/conv/test_conv_level_conv.csv\")\n", + "test_chat_complex_df = pd.read_csv(\n", + " \"./output/chat/test_chat_level_chat_complex.csv\")\n", + "test_conv_complex_df = pd.read_csv(\n", + " \"./output/conv/test_conv_level_conv_complex.csv\")\n", + "test_conv_complex_df_ts = pd.read_csv(\n", + " \"./output/conv/test_conv_level_conv_complex_ts.csv\")\n", + "test_forward_flow_df = pd.read_csv(\"./output/chat/test_forward_flow_chat.csv\")\n", + "\n", + "# Import the Feature Dictionary\n", + "\n", + "chat_features = [feature_dict[feature][\"columns\"]\n", + " for feature in feature_dict.keys() if feature_dict[feature][\"level\"] == \"Chat\"]\n", + "conversation_features = [feature_dict[feature][\"columns\"] for feature in feature_dict.keys(\n", + ") if feature_dict[feature][\"level\"] == \"Conversation\"]\n", + "\n", + "num_features_chat = len(list(itertools.chain(*chat_features)))\n", + "num_features_conv = len(list(itertools.chain(*conversation_features))) - 3\n", + "\n", + "\n", + "num_tested_chat = test_chat_df['expected_column'].nunique() + test_chat_complex_df['feature'].nunique() + test_forward_flow_df['feature'].nunique()\n", + "test_chat = pd.concat([test_chat_df['expected_column'], test_chat_complex_df['feature'], test_forward_flow_df['feature']])\n", + "test_chat = test_chat.drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "hi = pd.concat([test_chat_df['expected_column'].drop_duplicates(), test_chat_complex_df['feature'].drop_duplicates(), test_forward_flow_df['feature'].drop_duplicates()])\n", + "hi.to_csv(\"hi.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/agshruti/Downloads/team-process-map/.venv/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n", + " from pandas.core import (\n" + ] + }, + { + "data": { + "text/html": [ + "
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      conversation_numgini_coefficient_sum_num_messages
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      " + ], + "text/plain": [ + " conversation_num gini_coefficient_sum_num_messages\n", + "0 A 0.041667\n", + "1 B 0.000000\n", + "2 C 0.297101\n", + "3 D 0.066667\n", + "4 E 0.000000\n", + "5 F 0.000000\n", + "6 G 0.000000\n", + "7 H 0.000000\n", + "8 I 0.000000\n", + "9 J 0.000000\n", + "10 K 0.000000\n", + "11 L 0.000000\n", + "12 M 0.000000\n", + "13 N 0.000000\n", + "14 O 0.000000\n", + "15 P 0.000000\n", + "16 Q 0.000000\n", + "17 R 0.000000\n", + "18 S 0.000000\n", + "19 T 0.000000\n", + "20 U 0.000000\n", + "21 conversation_1 0.066667\n", + "22 conversation_2 0.066667\n", + "23 conversation_5 0.066667\n", + "24 conversation_3 0.066667\n", + "25 conversation_4 0.066667\n", + "26 conversation_6 0.148148\n", + "27 conversation_15 0.066667\n", + "28 conversation_16 0.066667\n", + "29 conversation_9 0.066667\n", + "30 AA 0.000000\n", + "31 AAA 0.000000\n", + "32 AAAA 0.000000\n", + "33 BB 0.000000\n", + "34 BBB 0.000000\n", + "35 BBBB 0.000000\n", + "36 CC 0.166667\n", + "37 CCC 0.300000" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "test_conv_complex_df = pd.read_csv(\"./output/conv/test_conv_level_conv_complex.csv\")\n", + "test_conv_complex_df[['conversation_num', \"gini_coefficient_sum_num_messages\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "def get_function_words_in_message(text, function_word_reference):\n", + " \"\"\"\n", + " Extract the function words & non-functions words from a message\n", + "\n", + " Args:\n", + " text (str): The input text to be analyzed.\n", + " function_word_reference (list): A list of function words to reference against.\n", + "\n", + " Returns:\n", + " list: A list of function words found in the input text.\n", + " \"\"\"\n", + " if (pd.isna(text)):\n", + " return []\n", + " return [x for x in text.split() if x in function_word_reference]\n", + "\n", + "\n", + "def get_content_words_in_message(text, function_word_reference):\n", + " \"\"\"\n", + " Extract the non-function words in a given message.\n", + "\n", + " Args:\n", + " text (str): The input text to be analyzed.\n", + " function_word_reference (list): A list of function words to reference against.\n", + "\n", + " Returns:\n", + " list: A list of content words found in the input text.\n", + " \"\"\"\n", + " if (pd.isna(text)):\n", + " return []\n", + " return [x for x in text.split() if x not in function_word_reference]\n", + "\n", + "\n", + "def mimic_words(df, on_column, conversation_id):\n", + " \"\"\"\n", + " Return a list of words that are also used in the other's previous turn.\n", + "\n", + " Args:\n", + " df (DataFrame): The dataset that removed all punctuations.\n", + " on_column (str): The column that we want to find mimicry on.\n", + " conversation_id (str): The column name that should be selected as the conversation ID.\n", + "\n", + " Returns:\n", + " list: A list of lists, where each sublist contains words mimicked from the previous turn.\n", + " \"\"\"\n", + " word_mimic = [[]]\n", + " for i in range(1, len(df)):\n", + " # only do this if they're in the same conversation\n", + " if df.loc[i, conversation_id] == df.loc[i-1, conversation_id]:\n", + " word_mimic.append([x for x in df.loc[i, on_column]\n", + " if x in df.loc[(i-1), on_column]])\n", + " else:\n", + " word_mimic.append([])\n", + " return word_mimic\n", + "\n", + "\n", + "def function_mimicry_score(function_mimic_words):\n", + " \"\"\"\n", + " Compute the number of mimic words for function words by simply counting the number of mimic words using len().\n", + "\n", + " Args:\n", + " function_mimic_words (list): Each entry under the `function_word_mimicry` column.\n", + "\n", + " Returns:\n", + " int: The number of function mimic words.\n", + " \"\"\"\n", + " return len(function_mimic_words)\n", + "\n", + "\n", + "def compute_frequency(df, on_column):\n", + " \"\"\"\n", + " Compute the frequency of each content word across the whole dataset.\n", + "\n", + " Args:\n", + " df (DataFrame): The input dataframe.\n", + " on_column (str): The column with which we calculate content word frequency.\n", + "\n", + " Returns:\n", + " dict: A dictionary with content words as keys and their frequencies as values.\n", + " \"\"\"\n", + " df_temp = df.copy()\n", + " df_temp.reset_index(drop=True, inplace=True)\n", + " return (dict(pd.Series(np.concatenate(df_temp[on_column])).value_counts()))\n", + "\n", + "\n", + "def computeTF(column_mimc, frequency_dict):\n", + " \"\"\"\n", + " Compute the term frequency of each content mimic word, then sum them up.\n", + "\n", + " Args:\n", + " column_mimc (list): Each entry under the `content_word_mimicry` column.\n", + " frequency_dict (dict): A dictionary of content word frequency across the dataset.\n", + "\n", + " Returns:\n", + " float: The sum of term frequencies for the content mimic words.\n", + " \"\"\"\n", + " tfdict = {}\n", + " wf = pd.Series(column_mimc, dtype='str').value_counts()\n", + " for i in wf.index:\n", + " tfdict[i] = wf[i]/frequency_dict[i]\n", + " return sum(tfdict.values())\n", + "\n", + "\n", + "def Content_mimicry_score(df, column_count_frequency, column_count_mimic):\n", + " \"\"\"\n", + " Combine the steps to compute the content word mimicry score.\n", + "\n", + " Args:\n", + " df (DataFrame): The input dataframe.\n", + " column_count_frequency (str): The column with content words to calculate frequency.\n", + " column_count_mimic (str): The column with content word mimicry.\n", + "\n", + " Returns:\n", + " Series: A series with content word accommodation scores.\n", + "\n", + " \"\"\"\n", + " # Compute the frequency of each content word across the whole dataset\n", + " ContWordFreq = compute_frequency(df, column_count_frequency)\n", + " # Compute the content_mimicry_score\n", + " return df[column_count_mimic].apply(lambda x: computeTF(x, ContWordFreq)) \n", + " # content_mimic_scores = []\n", + " # for conv in df['conversation_num'].unique():\n", + " # df_conv = df[df['conversation_num'] == conv] \n", + " # ContWordFreq = compute_frequency(df_conv, column_count_frequency)\n", + " # content_mimic_scores.append(df_conv[column_count_mimic].apply(lambda x: computeTF(x, ContWordFreq)).tolist())\n", + " # return [item for sublist in content_mimic_scores for item in sublist]" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "\n", + "def get_function_words():\n", + " \"\"\"\n", + " Returns the list of function words according to Ranganath, Jurafsky, and McFarland (2013).\n", + "\n", + " Reference: https://web.stanford.edu/~jurafsky/pubs/ranganath2013.pdf\n", + "\n", + " :return: A list of function words.\n", + " :rtype: list\n", + " \"\"\"\n", + " # current_dir = os.path.dirname(__file__)\n", + " function_word_file_path = \"../src/team_comm_tools/features/lexicons/function_words.txt\"\n", + " # function_word_file_path = os.path.abspath(function_word_file_path)\n", + "\n", + " with open(function_word_file_path, 'r') as file:\n", + " function_word_list = [line.strip() for line in file]\n", + " return function_word_list\n", + "\n", + "func = get_function_words()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/agshruti/Downloads/team-process-map/.venv/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n", + " from pandas.core import (\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'hello': 1, 'i': 1, 'fish': 1, 'sentence': 1, 'has': 1, 'five': 1, 'words': 1}\n", + "{'hello': 1, 'i': 1, 'fish': 1, 'sentence': 1, 'has': 1, 'five': 1, 'words': 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      " + ], + "text/plain": [ + " conversation_num message \\\n", + "0 1 hello i like fish \n", + "1 1 this sentence has five words \n", + "2 2 hello \n", + "3 2 is 4 a word \n", + "4 3 NaN \n", + ".. ... ... \n", + "675 A get \n", + "676 B able am are \n", + "677 B able am are hello \n", + "678 B able am hello yoyo \n", + "679 B hello yoyo \n", + "\n", + " function_word_accommodation content_word_accommodation \n", + "0 0 0.000000 \n", + "1 0 0.000000 \n", + "2 0 0.000000 \n", + "3 0 0.000000 \n", + "4 0 0.000000 \n", + ".. ... ... \n", + "675 1 0.000000 \n", + "676 0 0.000000 \n", + "677 3 0.000000 \n", + "678 2 0.333333 \n", + "679 0 0.833333 \n", + "\n", + "[680 rows x 4 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from word_mimicry import *\n", + "\n", + "test_chat_df = pd.read_csv(\"./output/chat/test_chat_level_chat.csv\")\n", + "df = test_chat_df[['conversation_num', \"message\"]].reset_index(drop=True)\n", + "df\n", + "# df[-4:]\n", + "\n", + "df[\"function_words\"] = df[\"message\"].apply(\n", + " lambda x: get_function_words_in_message(x, function_word_reference=func))\n", + "df[\"content_words\"] = df[\"message\"].apply(\n", + " lambda x: get_content_words_in_message(x, function_word_reference=func))\n", + "\n", + "# Extract the function words / content words that also appears in the immediate previous turn\n", + "df[\"function_word_mimicry\"] = mimic_words(\n", + " df, \"function_words\", \"conversation_num\")\n", + "df[\"content_word_mimicry\"] = mimic_words(\n", + " df, \"content_words\", \"conversation_num\")\n", + "\n", + "# Compute the number of function words that also appears in the immediate previous turn\n", + "df[\"function_word_accommodation\"] = df[\"function_word_mimicry\"].apply(\n", + " function_mimicry_score)\n", + "\n", + "# Compute the sum of inverse frequency of each content word that also occurred in the other’s immediately prior turn.\n", + "df[\"content_word_accommodation\"] = Content_mimicry_score(\n", + " df, \"content_words\", \"content_word_mimicry\")\n", + "\n", + "# Drop the function / content word columns -- we don't need them in the output\n", + "df = df.drop(columns=['function_words', 'content_words', 'function_word_mimicry', 'content_word_mimicry'])\n", + "df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0.0\n", + "1 0.0\n", + "2 0.0\n", + "3 0.0\n", + "4 0.0\n", + " ... \n", + "675 0.0\n", + "676 0.0\n", + "677 0.0\n", + "678 0.1\n", + "679 0.6\n", + "Name: content_word_accommodation, Length: 680, dtype: float64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_chat_df = pd.read_csv(\"./output/chat/test_chat_level_chat.csv\")\n", + "test_chat_df['content_word_accommodation']" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " conversation_num message\n", + "0 1 Hello I like fish.\n", + "1 1 This sentence has five words.\n", + "{'Hello': 1, 'fish.': 1, 'This': 1, 'sentence': 1, 'has': 1, 'five': 1, 'words.': 1}\n", + "0 0\n", + "1 0\n", + "Name: content_word_accommodation, dtype: int64\n", + " conversation_num message\n", + "2 2 Hello??\n", + "3 2 Is 4 a word?\n", + "{'Hello??': 1, 'Is': 1, '4': 1, 'word?': 1}\n", + "0 0\n", + "1 0\n", + "Name: content_word_accommodation, dtype: int64\n", + " conversation_num message\n", + "4 3 .\n", + "{'.': 1}\n", + "0 0\n", + "Name: content_word_accommodation, dtype: int64\n", + " conversation_num message\n", + "5 4 HELLO WORLD, THIS IS A TEST. hi HI. hi HI hi HI\"\n", + "6 4 ONE TWO THREE. four five six. sEvEn EiGhT nInE.\n", + "7 4 Check out this [link](https://example.com) and...\n", + "8 4 I like google.com and wikipedia.org but not am...\n", + "9 4 why don't you read everything at https://www.e...\n", + "10 4 why don't you read everything at \\r\\n- https:/...\n", + "11 4 Hello u/user1 and u/user2, hi hi hi?\n", + "12 4 I don't like u/user_1_test but I like u/user2Test\n", + "13 4 This is **bold**, *italics*, and this is not. ...\n", + "14 4 This is **uneven* in terms of *the emphasis**\n", + "15 4 * item 1\\n* item 2\\n- item 3\n", + "16 4 Here are all my arguments:\\r\\n- point 1\\r\\n- p...\n", + "17 4 1. First\\n2. Second\\n3. Third\n", + "18 4 This is the first line.\\nThis is the second li...\n", + "19 4 I have a line\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nhere is a new...\n", + "20 4 this is a line with\\rA different kind of retur...\n", + "21 4 \"This is a quote.\" She said, \"Here's another.\"\n", + "22 4 \"You miss 100% of the shots you don't take\" --...\n", + "23 4 \"I can't believe you use single quotes to quot...\n", + "24 4 > Quoting someone else\\nThis is my reply.\n", + "25 4 > Quoting someone else\\nThis is my reply.\n", + "26 4 >>>> This is a quote but I went overboard with...\n", + "27 4 >> This is one where I put too many of t...\n", + "28 4 > Hello!\\r\\nGoodbye!\n", + "29 4 > here I am making a quote\\r\\nI respond to it\\...\n", + "30 4 Well... I'm not sure... Maybe...\n", + "31 4 hm..what if I only use two periods...............\n", + "32 4 This is a sentence (with some text in parenthe...\n", + "33 4 \"Sure,\" I said confidently (thiking to myself:...\n", + "34 4 (((((these parentheses are not properly closed.)\n", + "35 4 ((there are multiple parentheses here))\n", + "36 4 ((1+(1+3+4)^2)+7+(9+8))\n", + "{'point': 14, 'multiple': 12, 'long': 11, 'lines': 11, 'takes': 11, 'super': 11, '4': 11, '-': 9, 'This': 8, 'quote': 4, \"don't\": 4, 'line': 4, '>': 4, 'item': 3, 'quotes': 3, 'here': 3, 'line.\\\\nThis': 2, 'https://www.example.co.uk': 2, 'third': 2, 'reply.': 2, 'Quoting': 2, 'respond': 2, 'else\\\\nThis': 2, 'https://www.example.org': 2, 'many': 2, '3': 2, 'https://www.example.ca': 2, 'https://www.example.net': 2, 'read': 2, 'use': 2, 'single': 2, 'parentheses': 2, 'return': 2, 'https://www.example.com': 2, 'character': 2, '>>>>': 1, \"'sometimes\": 1, '>': 1, \"can't\": 1, 'believe': 1, 'people,\"': 1, 'said.': 1, '\"Well,': 1, 'replied,': 1, 'useful': 1, 'nest': 1, 'inside': 1, \"'okay'\": 1, 'other': 1, 'said:': 1, \"quotes,'\": 1, 'Then': 1, 'according': 1, 'English': 1, 'teacher\"': 1, 'HELLO': 1, 'put': 1, 'went': 1, 'overboard': 1, 'text': 1, 'parentheses).': 1, '\"Sure,\"': 1, 'said': 1, 'confidently': 1, '(thiking': 1, 'myself:': 1, 'way!)': 1, 'definitely': 1, '(not)': 1, 'best': 1, 'moments.': 1, '(((((these': 1, 'properly': 1, 'closed.)': 1, '((there': 1, 'here))': 1, 'some': 1, '(with': 1, 'sentence': 1, 'again': 1, 'carat': 1, '>>': 1, 'Gretzky': 1, \"gt's\": 1, 'Hello!': 1, 'Goodbye!': 1, 'making': 1, 'Well...': 1, 'periods............': 1, \"I'm\": 1, 'sure...': 1, 'Maybe...': 1, 'hm..what': 1, 'only': 1, 'two': 1, 'periods.............or': 1, '\"I': 1, '\"This': 1, 'Wayne': 1, 'terms': 1, 'http://example.org': 1, 'google.com': 1, 'wikipedia.org': 1, 'amazon.com': 1, 'Hello': 1, 'u/user1': 1, 'u/user2,': 1, 'hi?': 1, 'u/user_1_test': 1, 'u/user2Test': 1, '**bold**,': 1, '*italics*,': 1, 'not.': 1, '***bolded': 1, 'italicized***': 1, '[link](https://example.com)': 1, 'Check': 1, 'nInE.': 1, 'HI\"': 1, 'THIS': 1, 'IS': 1, 'A': 1, 'TEST.': 1, 'HI.': 1, 'HI': 1, 'ONE': 1, 'EiGhT': 1, 'TWO': 1, 'THREE.': 1, 'four': 1, 'five': 1, 'six.': 1, 'sEvEn': 1, '**uneven*': 1, '*the': 1, '--': 1, 'emphasis**': 1, 'value\\\\rUsing': 1, 'carriage': 1, 'instead': 1, 'newline': 1, 'WORLD,': 1, 'quote.\"': 1, 'She': 1, 'said,': 1, '\"Here\\'s': 1, 'another.\"': 1, '\"You': 1, 'miss': 1, '100%': 1, 'shots': 1, 'take\"': 1, 'kind': 1, 'different': 1, 'with\\\\rA': 1, '2': 1, '*': 1, '1\\\\n*': 1, '2\\\\n-': 1, 'Here': 1, 'arguments:': 1, '1': 1, '1.': 1, 'new': 1, 'First\\\\n2.': 1, 'Second\\\\n3.': 1, 'Third': 1, 'first': 1, 'second': 1, 'line.': 1, '((1+(1+3+4)^2)+7+(9+8))': 1}\n", + "0 0.000\n", + "1 0.000\n", + "2 0.000\n", + "3 0.000\n", + "4 0.000\n", + "5 3.250\n", + "6 0.000\n", + "7 0.000\n", + "8 0.000\n", + "9 0.125\n", + "10 0.000\n", + "11 0.500\n", + "12 0.000\n", + "13 0.000\n", + "14 0.500\n", + "15 0.250\n", + "16 0.000\n", + "17 0.000\n", + "18 0.000\n", + "19 0.000\n", + "20 1.500\n", + "21 0.000\n", + "22 0.125\n", + "23 0.000\n", + "24 0.500\n", + "25 0.000\n", + "26 0.000\n", + "27 0.000\n", + "28 0.125\n", + "29 0.000\n", + "30 0.500\n", + "31 0.000\n", + "Name: content_word_accommodation, dtype: float64\n", + " conversation_num message\n", + "37 5 I think that I think that I think\n", + "38 5 I am a little confused\n", + "39 5 I don't really know the answer\n", + "40 5 I am sure that this is correct\n", + "41 5 I am fairly certain in my response\n", + "42 5 This is without a doubt the best movie I have ...\n", + "43 5 I am not sure about how to how to approximatel...\n", + "44 5 I believe that he is guilty but I am not very ...\n", + "45 5 I an open to you changing my mind on this issue\n", + "46 5 I don't think the guy is the a$$hole. Thoughts?\n", + "47 5 So who thinks the guy is an ass for asking his...\n", + "48 5 I think that this person is not an asshole bec...\n", + "49 5 I can see how the family is upset because they...\n", + "50 5 Yes, I think his feeling makes sense to me to....\n", + "51 5 I was conflicted because I could understand hi...\n", + "52 5 His MIL has been here for 8 years. You would t...\n", + "53 5 I think he had every right to want to help his...\n", + "54 5 I also agree with culturedCow\n", + "55 5 I don't think he's an asshole. 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Where is the rest of our team?\n", + "74 8 Well please help me figure this out, I really ...\n", + "{'please': 2, 'sure.': 1, 'Where': 1, 'rest': 1, 'team?': 1, 'Well': 1, 'help': 1, 'figure': 1, 'out,': 1, 'want': 1}\n", + "0 0\n", + "1 0\n", + "Name: content_word_accommodation, dtype: int64\n", + " conversation_num message\n", + "75 9 Seems possible\n", + "76 9 I see what youre thinking but I disagree\n", + "77 9 We get only one chance so we should understand...\n", + "78 9 I just don't agree, I'm making the 60/40 split\n", + "{'split': 2, 'Seems': 1, 'possible': 1, 'see': 1, 'thinking': 1, 'disagree': 1, 'We': 1, 'only': 1, 'chance': 1, 'understand': 1, 'how': 1, \"don't\": 1, 'agree,': 1, \"I'm\": 1, 'making': 1, '60/40': 1}\n", + "0 0.0\n", + "1 0.0\n", + "2 0.0\n", + "3 0.5\n", + "Name: content_word_accommodation, dtype: float64\n", + " conversation_num message\n", + "79 10 hey\n", + "80 10 I think we should try something else\n", + "81 10 Ok whatever. You should leave the team then\n", + "112 10 I understand your perspective and agree that I...\n", + "{'think': 4, 'STEM': 2, 'something': 2, 'address': 2, 'women': 2, 'their': 2, 'hiring': 2, \"don\\\\'t\": 1, 'discrepancy,': 1, 'even': 1, 'implementing': 1, 'priority': 1, 'decisions.': 1, 'While': 1, 'explicitly': 1, 'companies': 1, 'believer': 1, 'hire': 1, 'simply': 1, 'gender,': 1, 'mindful': 1, 'gender': 1, 'gap': 1, 'look': 1, 'issues': 1, 'through': 1, 'done': 1, 'hey': 1, 'careers': 1, 'agree': 1, 'try': 1, 'else': 1, 'Ok': 1, 'whatever.': 1, 'You': 1, 'leave': 1, 'team': 1, 'understand': 1, 'perspective': 1, 'want': 1, 'underrepresented': 1, 'resentment': 1, 'workplace': 1, 'women,': 1, 'further': 1, 'compound': 1, 'issue': 1, 'looking': 1, 'at.': 1, 'true': 1, 'practices.': 1}\n", + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "Name: content_word_accommodation, dtype: int64\n", + " conversation_num message\n", + "82 11 Honestly thank you so so much\n", + "83 11 What's the plan?\n", + "84 11 That is the dumbest idea I've heard; youre act...\n", + "85 11 What's ur problem here?\n", + "113 11 I understand your perspective and agree that I...\n", + "{'think': 3, 'women': 2, 'STEM': 2, \"What's\": 2, 'address': 2, 'their': 2, 'hiring': 2, 'decisions.': 1, 'careers': 1, 'believer': 1, 'something': 1, 'done': 1, 'discrepancy,': 1, 'even': 1, 'implementing': 1, 'priority': 1, 'Honestly': 1, 'While': 1, 'underrepresented': 1, 'companies': 1, 'explicitly': 1, 'hire': 1, 'simply': 1, 'gender,': 1, 'mindful': 1, 'gender': 1, 'gap': 1, 'look': 1, 'issues': 1, 'through': 1, \"don\\\\'t\": 1, 'true': 1, 'thank': 1, 'problem': 1, 'much': 1, 'plan?': 1, 'That': 1, 'dumbest': 1, 'idea': 1, \"I've\": 1, 'heard;': 1, 'actually': 1, 'dumb': 1, 'af': 1, 'ur': 1, 'here?': 1, 'at.': 1, 'understand': 1, 'perspective': 1, 'agree': 1, 'want': 1, 'resentment': 1, 'workplace': 1, 'women,': 1, 'further': 1, 'compound': 1, 'issue': 1, 'looking': 1, 'practices.': 1}\n", + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0\n", + "Name: content_word_accommodation, dtype: int64\n", + " conversation_num message\n", + "86 12 Pleasure and an honor to meet you all\n", + "87 12 We should try that next\n", + "88 12 Could you please explain why? I don't really u...\n", + "89 12 Sorry sorry I didn't mean to\n", + "114 12 I understand your perspective and agree that I...\n", + "{'think': 3, 'women': 2, 'STEM': 2, 'address': 2, 'their': 2, 'understand': 2, 'hiring': 2, 'decisions.': 1, 'careers': 1, 'believer': 1, 'something': 1, 'done': 1, 'discrepancy,': 1, 'even': 1, 'implementing': 1, 'priority': 1, 'Pleasure': 1, 'While': 1, 'underrepresented': 1, 'companies': 1, 'explicitly': 1, 'hire': 1, 'simply': 1, 'gender,': 1, 'mindful': 1, 'gender': 1, 'gap': 1, 'look': 1, 'issues': 1, 'through': 1, \"don\\\\'t\": 1, 'true': 1, 'honor': 1, 'sorry': 1, 'meet': 1, 'We': 1, 'try': 1, 'next': 1, 'Could': 1, 'please': 1, 'explain': 1, 'why?': 1, \"don't\": 1, 'thinking': 1, 'Sorry': 1, \"didn't\": 1, 'at.': 1, 'mean': 1, 'perspective': 1, 'agree': 1, 'want': 1, 'resentment': 1, 'workplace': 1, 'women,': 1, 'further': 1, 'compound': 1, 'issue': 1, 'looking': 1, 'practices.': 1}\n", + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0\n", + 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'cuntn': 1, 'strengthe': 1, 'overwhelmfvlxi': 1, 'successqfhs': 1, 'herovqw': 1, 'overconfidence': 1, 'truly': 1, 'pushty': 1, 'phobii': 1, 'apprehensnmeyt': 1, 'obsesstusbg': 1, 'anguishuiy': 1, 'terrorkm': 1, 'restlesst': 1, 'tenseod': 1, 'timidfzbh': 1, 'cuntq': 1, 'stressetd': 1, 'apprehensrm': 1, 'dreadn': 1, 'oneself': 1, \"he'd\": 1, \"haven't\": 1, 'nobodashm': 1, 'never': 1, 'fuckerusz': 1, 'bitchnsl': 1, 'goddamomde': 1, 'fuckinoawys': 1, 'gianthy': 1, 'initiatoxtwa': 1, 'doghouseeastb': 1, 'follow': 1, 'meant': 1, 'hoped': 1, 'carry': 1, 'showed': 1, 'used': 1, 'theres': 1, 'care': 1, 'made': 1, 'misses': 1, 'kept': 1, 'gfjgevb': 1, 'exgirll': 1, 'mate': 1, 'loveru': 1, 'buddyv': 1, 'neighbord': 1, 'partnerzcvdj': 1, 'roomatel': 1, 'girlfriendt': 1, 'gft': 1, 'mean': 1, 'actually': 1, 'whod': 1, 'let': 1, 'ought': 1, \"wasn't\": 1, \"shan't\": 1, 'thank': 1, 'wished': 1, 'exclusxklz': 1, 'most': 1, 'driegnk': 1, 'touchlzf': 1, 'leatherjcf': 1, 'pressed': 1, 'squeezul': 1, 'silkjn': 1, 'brushtasj': 1, 'colde': 1, 'caressi': 1, 'hottjmuo': 1, 'mysterp': 1, 'ambigun': 1, 'vary': 1, 'versus': 1, 'fairly': 1, 'unresolvn': 1, 'hesitaq': 1, 'fuzznaxg': 1, 'hypotheticoqwzy': 1, 'barely': 1, 'indetermingifjl': 1, 'randomitf': 1, 'hypotheticvzkg': 1, 'supposed': 1, 'exclujvy': 1, 'somethingfxdq': 1, 'oughtve': 1, 'becomes': 1, 'done': 1, 'hushmqplh': 1, \"would've\": 1, 'problemo': 1, 'lacksuexg': 1, 'expectt': 1, 'hopeful': 1, 'desirva': 1, 'wishes': 1, 'wishing': 1, 'thundero': 1, 'thunderq': 1, 'inaudibln': 1, 'loudvh': 1, 'speak': 1, 'mistakwzl': 1, 'heard': 1, 'speakertphv': 1, 'shoutiaem': 1, 'audiblbohi': 1, 'whispervhxqk': 1, 'lawnxdi': 1, 'doghousemilk': 1, 'residenlwfxa': 1, 'vacuumk': 1, 'lawnjr': 1, 'residencpbs': 1, 'neighboregax': 1, 'hoping': 1, 'hope': 1, 'meaningi': 1, 'requirj': 1, 'wanted': 1, 'determining': 1, 'suppresshvw': 1, 'proof': 1, 'changes': 1, 'rearrangbqlg': 1, 'randomuer': 1, 'containn': 1, 'shoulds': 1, 'practically': 1, 'occasionalszxg': 1, 'hazy': 1, 'repressydvml': 1, 'tang': 1, 'ahead': 1, 'below': 1, 'under': 1, 'thru': 1, 'towardkhyrs': 1, 'between': 1, 'beside': 1, 'grabwktxr': 1, 'savourzw': 1, 'redness': 1, 'speaking': 1, 'souriau': 1, 'hardevtqef': 1, 'warmlvye': 1, 'hard': 1, 'somebodp': 1, 'missed': 1, 'unhappj': 1, \"son's\": 1, 'listenerqe': 1, 'girlfriendwsnya': 1, \"he'll\": 1, 'sisterqjtfk': 1, 'coworkertbpf': 1, 'mailing': 1, 'interrupm': 1, 'somethingjmcpi': 1, 'noboda': 1, 'somewhere': 1, 'pitiulpnv': 1, \"that'd\": 1, 'somethingosdqx': 1, 'somethingp': 1, 'someonecrhbd': 1, 'sinnzkc': 1, 'kosher': 1, \"qur'anpy\": 1, 'mercy': 1, 'templefydxz': 1, 'hinduejs': 1, 'muhammsla': 1, 'lutheranwv': 1, 'regretzkbrc': 1, 'tragickh': 1, 'catholickd': 1, 'boundlby': 1, 'hall': 1, 'bending': 1, 'widthxikw': 1, 'loverkfxo': 1, 'pornoiu': 1, 'virgindtrl': 1, 'humpysbwe': 1, 'prudishybiq': 1, 'virginc': 1, 'pubic': 1, 'prostatijvda': 1, 'curbijh': 1, 'deniaxi': 1, 'yearnlkw': 1, 'prudishxc': 1, 'deniabxp': 1, 'constrictimhr': 1, 'withholdyzaqb': 1, 'stops': 1, 'remorset': 1, 'ruinc': 1, 'grimwftle': 1, 'discouragnepqi': 1, 'tragict': 1, 'devastatu': 1, 'unimportant': 1, 'ministerlnfts': 1, 'sikhc': 1, \"I'm\": 1, 'threatxre': 1, 'wagerfv': 1, 'effectcaxgn': 1, 'leadmbhfe': 1, 'creatend': 1, 'deducgulp': 1, 'outcomecli': 1, 'hows': 1, 'producnbu': 1, 'solutionva': 1, 'launchvoap': 1, 'leadhbi': 1, 'provoku': 1, 'offencefhcog': 1, 'overtime': 1, 'ludicrouslf': 1, 'enragoqusn': 1, 'sucks': 1, 'brutals': 1, 'threatd': 1, 'destroyj': 1, 'sinister': 1, 'raping': 1, 'battliz': 1, 'warfareid': 1, 'defensjazn': 1, 'molestgv': 1, 'bucks': 1, 'mortgpzvi': 1, 'judaprfd': 1, 'classes': 1, 'muhammehcml': 1, 'sin': 1, 'laidoff': 1, 'tradejnx': 1, 'taxes': 1, 'freshmknd': 1, 'incorprhq': 1, 'negotiatjf': 1, 'presentationomwfa': 1, 'politics': 1, 'mda': 1, 'revieww': 1, 'owes': 1, 'spending': 1, 'cashk': 1, 'bankpsiar': 1, 'checks': 1, 'kronlwik': 1, 'dinarcsg': 1, 'businesszosw': 1, 'revenueocp': 1, 'costbjwta': 1, 'owe': 1, 'euro': 1, 'auditors': 1, 'store': 1, 'this?': 1}\n", + "0 0.00\n", + "1 0.20\n", + "2 0.00\n", + "3 0.00\n", + "4 0.00\n", + " ... \n", + "537 0.00\n", + "538 0.00\n", + "539 0.25\n", + "540 0.25\n", + "541 0.25\n", + "Name: content_word_accommodation, Length: 542, dtype: float64\n", + " conversation_num message\n", + "671 A able am are\n", + "672 A able am are hello\n", + "673 A able am hello\n", + "674 A able get got\n", + "675 A get\n", + "{'hello': 2}\n", + "0 0.0\n", + "1 0.0\n", + "2 0.5\n", + "3 0.0\n", + "4 0.0\n", + "Name: content_word_accommodation, dtype: float64\n", + " conversation_num message\n", + "676 B able am are\n", + "677 B able am are hello\n", + "678 B able am hello yoyo\n", + "679 B hello yoyo\n", + "{'hello': 3, 'yoyo': 2}\n", + "0 0.000000\n", + "1 0.000000\n", + "2 0.333333\n", + "3 0.833333\n", + "Name: content_word_accommodation, dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_46302/3956377500.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n" + ] + }, + { + "data": { + "text/html": [ + "
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      conversation_nummessagefunction_wordscontent_wordsfunction_word_mimicrycontent_word_mimicryfunction_word_accommodationcontent_word_accommodation
      0Bable am are[able, am, are][][][]00.000000
      1Bable am are hello[able, am, are][hello][able, am, are][]30.000000
      2Bable am hello yoyo[able, am][hello, yoyo][able, am][hello]20.000000
      3Bhello yoyo[][hello, yoyo][][hello, yoyo]00.000000
      4Aget[get][][get][]10.000000
      ...........................
      675Aget[get][][get][]10.000000
      676Bable am are[able, am, are][][][]00.000000
      677Bable am are hello[able, am, are][hello][able, am, are][]30.000000
      678Bable am hello yoyo[able, am][hello, yoyo][able, am][hello]20.333333
      679Bhello yoyo[][hello, yoyo][][hello, yoyo]00.833333
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      " + ], + "text/plain": [ + " conversation_num message function_words content_words \\\n", + "0 B able am are [able, am, are] [] \n", + "1 B able am are hello [able, am, are] [hello] \n", + "2 B able am hello yoyo [able, am] [hello, yoyo] \n", + "3 B hello yoyo [] [hello, yoyo] \n", + "4 A get [get] [] \n", + ".. ... ... ... ... \n", + "675 A get [get] [] \n", + "676 B able am are [able, am, are] [] \n", + "677 B able am are hello [able, am, are] [hello] \n", + "678 B able am hello yoyo [able, am] [hello, yoyo] \n", + "679 B hello yoyo [] [hello, yoyo] \n", + "\n", + " function_word_mimicry content_word_mimicry function_word_accommodation \\\n", + "0 [] [] 0 \n", + "1 [able, am, are] [] 3 \n", + "2 [able, am] [hello] 2 \n", + "3 [] [hello, yoyo] 0 \n", + "4 [get] [] 1 \n", + ".. ... ... ... \n", + "675 [get] [] 1 \n", + "676 [] [] 0 \n", + "677 [able, am, are] [] 3 \n", + "678 [able, am] [hello] 2 \n", + "679 [] [hello, yoyo] 0 \n", + "\n", + " content_word_accommodation \n", + "0 0.000000 \n", + "1 0.000000 \n", + "2 0.000000 \n", + "3 0.000000 \n", + "4 0.000000 \n", + ".. ... \n", + "675 0.000000 \n", + "676 0.000000 \n", + "677 0.000000 \n", + "678 0.333333 \n", + "679 0.833333 \n", + "\n", + "[680 rows x 8 columns]" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "test_chat_df = pd.read_csv(\"./data/cleaned_data/test_chat_level.csv\")\n", + "df = test_chat_df[['conversation_num', \"message\"]].reset_index(drop=True)\n", + "df\n", + "\n", + "df[\"function_words\"] = df[\"message\"].apply(\n", + " lambda x: get_function_words_in_message(x, function_word_reference=func))\n", + "df[\"content_words\"] = df[\"message\"].apply(\n", + " lambda x: get_content_words_in_message(x, function_word_reference=func))\n", + "\n", + "# Extract the function words / content words that also appears in the immediate previous turn\n", + "df[\"function_word_mimicry\"] = mimic_words(\n", + " df, \"function_words\", \"conversation_num\")\n", + "df[\"content_word_mimicry\"] = mimic_words(\n", + " df, \"content_words\", \"conversation_num\")\n", + "\n", + "# Compute the number of function words that also appears in the immediate previous turn\n", + "df[\"function_word_accommodation\"] = df[\"function_word_mimicry\"].apply(\n", + " function_mimicry_score)\n", + "\n", + "l = []\n", + "\n", + "for conv in df[\"conversation_num\"].unique():\n", + " # print(conv)\n", + " df_conv = df[df[\"conversation_num\"] == conv]\n", + " # Compute the sum of inverse frequency of each content word that also occurred in the other’s immediately prior turn.\n", + " df_conv[\"content_word_accommodation\"] = Content_mimicry_score(\n", + " df_conv, \"content_words\", \"content_word_mimicry\")\n", + " l.append(df_conv['content_word_accommodation'].tolist())\n", + " \n", + "# flatten the list\n", + "l = [item for sublist in l for item in sublist]\n", + "l\n", + "df['content_word_accommodation'] = l\n", + "df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      conversation_numspeaker_nicknamemessageexpected_columnexpected_valuemessage_originalmessage_lower_with_puncpositive_bertnegative_bertneutral_bert...num_numbered_pointsnum_line_breaksnum_quotesnum_block_quote_responsesnum_ellipsesnum_parenthesesnum_emojimimicry_bertmoving_mimicryforward_flow
      01Ahello i like fishnum_words4.00000Hello I like fish.hello i like fish.0.8970700.0044840.098446...01000000.0000000.0000000.000000e+00
      11Bthis sentence has five wordsnum_words5.00000This sentence has five words.this sentence has five words.0.0419380.1313700.826692...01000000.1134660.1134668.865345e-01
      22Ahellonum_words1.00000Hello??hello??0.1380020.0538700.808129...01000000.0000000.0000000.000000e+00
      32Bis 4 a wordnum_words4.00000Is 4 a word?is 4 a word?0.0424290.1166170.840954...01000000.1242320.1242328.757685e-01
      43ANaNnum_words0.00000..0.3023180.1205070.577175...01000000.0000000.0000000.000000e+00
      ..................................................................
      1D2bought some groceries for dinnerinfo_exchange_zscore_conversation1.73205Bought some groceries for dinnerbought some groceries for dinner0.5283250.0102190.461456...01000000.3787250.3787256.212753e-01
      2D3its raining todayinfo_exchange_zscore_chats-1.73205It's raining todayit's raining today0.2852140.1111690.603617...01000000.1792770.2790018.309010e-01
      3E1i went to the storeinfo_exchange_zscore_chats0.00000I went to the storei went to the store0.2660870.0291110.704801...01000000.0000000.0000000.000000e+00
      4E1i went to the storeinfo_exchange_zscore_chats0.00000I went to the storei went to the store0.2660870.0291110.704802...01000001.0000001.000000-2.220446e-16
      5E1i went to the storeinfo_exchange_zscore_chats0.00000I went to the storei went to the store0.2660870.0291110.704802...01000001.0000001.000000-2.220446e-16
      \n", + "

      695 rows × 160 columns

      \n", + "
      " + ], + "text/plain": [ + " conversation_num speaker_nickname message \\\n", + "0 1 A hello i like fish \n", + "1 1 B this sentence has five words \n", + "2 2 A hello \n", + "3 2 B is 4 a word \n", + "4 3 A NaN \n", + ".. ... ... ... \n", + "1 D 2 bought some groceries for dinner \n", + "2 D 3 its raining today \n", + "3 E 1 i went to the store \n", + "4 E 1 i went to the store \n", + "5 E 1 i went to the store \n", + "\n", + " expected_column expected_value \\\n", + "0 num_words 4.00000 \n", + "1 num_words 5.00000 \n", + "2 num_words 1.00000 \n", + "3 num_words 4.00000 \n", + "4 num_words 0.00000 \n", + ".. ... ... \n", + "1 info_exchange_zscore_conversation 1.73205 \n", + "2 info_exchange_zscore_chats -1.73205 \n", + "3 info_exchange_zscore_chats 0.00000 \n", + "4 info_exchange_zscore_chats 0.00000 \n", + "5 info_exchange_zscore_chats 0.00000 \n", + "\n", + " message_original message_lower_with_punc \\\n", + "0 Hello I like fish. hello i like fish. \n", + "1 This sentence has five words. this sentence has five words. \n", + "2 Hello?? hello?? \n", + "3 Is 4 a word? is 4 a word? \n", + "4 . . \n", + ".. ... ... \n", + "1 Bought some groceries for dinner bought some groceries for dinner \n", + "2 It's raining today it's raining today \n", + "3 I went to the store i went to the store \n", + "4 I went to the store i went to the store \n", + "5 I went to the store i went to the store \n", + "\n", + " positive_bert negative_bert neutral_bert ... num_numbered_points \\\n", + "0 0.897070 0.004484 0.098446 ... 0 \n", + "1 0.041938 0.131370 0.826692 ... 0 \n", + "2 0.138002 0.053870 0.808129 ... 0 \n", + "3 0.042429 0.116617 0.840954 ... 0 \n", + "4 0.302318 0.120507 0.577175 ... 0 \n", + ".. ... ... ... ... ... \n", + "1 0.528325 0.010219 0.461456 ... 0 \n", + "2 0.285214 0.111169 0.603617 ... 0 \n", + "3 0.266087 0.029111 0.704801 ... 0 \n", + "4 0.266087 0.029111 0.704802 ... 0 \n", + "5 0.266087 0.029111 0.704802 ... 0 \n", + "\n", + " num_line_breaks num_quotes num_block_quote_responses num_ellipses \\\n", + "0 1 0 0 0 \n", + "1 1 0 0 0 \n", + "2 1 0 0 0 \n", + "3 1 0 0 0 \n", + "4 1 0 0 0 \n", + ".. ... ... ... ... \n", + "1 1 0 0 0 \n", + "2 1 0 0 0 \n", + "3 1 0 0 0 \n", + "4 1 0 0 0 \n", + "5 1 0 0 0 \n", + "\n", + " num_parentheses num_emoji mimicry_bert moving_mimicry forward_flow \n", + "0 0 0 0.000000 0.000000 0.000000e+00 \n", + "1 0 0 0.113466 0.113466 8.865345e-01 \n", + "2 0 0 0.000000 0.000000 0.000000e+00 \n", + "3 0 0 0.124232 0.124232 8.757685e-01 \n", + "4 0 0 0.000000 0.000000 0.000000e+00 \n", + ".. ... ... ... ... ... \n", + "1 0 0 0.378725 0.378725 6.212753e-01 \n", + "2 0 0 0.179277 0.279001 8.309010e-01 \n", + "3 0 0 0.000000 0.000000 0.000000e+00 \n", + "4 0 0 1.000000 1.000000 -2.220446e-16 \n", + "5 0 0 1.000000 1.000000 -2.220446e-16 \n", + "\n", + "[695 rows x 160 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "test_chat_df = pd.read_csv(\"./output/chat/test_chat_level_chat.csv\")\n", + "test_info_exchange_zscore_df = pd.read_csv(\"./output/chat/info_exchange_zscore_chats.csv\")\n", + "pd.concat([test_chat_df, test_info_exchange_zscore_df], axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['-', '-']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import re\n", + "import nltk\n", + "import pyphen\n", + "\n", + "# Define the function to calculate the Dale-Chall score\n", + "def count_syllables(word):\n", + " \"\"\"\n", + " Count the number of syllables in a word.\n", + " \n", + " Args:\n", + " word(str): The input word.\n", + "\n", + " Returns:\n", + " int: The number of syllables in the word.\n", + " \"\"\"\n", + " dic = pyphen.Pyphen(lang='en')\n", + " pyphen_result = dic.inserted(word)\n", + " return re.findall(r\"-\", pyphen_result)\n", + "\n", + "count_syllables(\"difficulty\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import re\n", + "\n", + "VOWEL_RUNS = re.compile(\"[aeiouy]+\", flags=re.I)\n", + "EXCEPTIONS = re.compile(\n", + " # fixes trailing e issues:\n", + " # smite, scared\n", + " \"[^aeiou]e[sd]?$|\"\n", + " # fixes adverbs:\n", + " # nicely\n", + " + \"[^e]ely$\",\n", + " flags=re.I\n", + ")\n", + "ADDITIONAL = re.compile(\n", + " # fixes incorrect subtractions from exceptions:\n", + " # smile, scarred, raises, fated\n", + " \"[^aeioulr][lr]e[sd]?$|[csgz]es$|[td]ed$|\"\n", + " # fixes miscellaneous issues:\n", + " # flying, piano, video, prism, fire, evaluate\n", + " + \".y[aeiou]|ia(?!n$)|eo|ism$|[^aeiou]ire$|[^gq]ua\",\n", + " flags=re.I\n", + ")\n", + "\n", + "def count_syllables(word):\n", + " vowel_runs = len(VOWEL_RUNS.findall(word))\n", + " exceptions = len(EXCEPTIONS.findall(word))\n", + " additional = len(ADDITIONAL.findall(word))\n", + " return max(1, vowel_runs - exceptions + additional)\n", + "\n", + "count_syllables(\"difficulty\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import re\n", + "text = \"even this is magnificent! even this is magnificent!\"\n", + "num_words = len(text.split())\n", + "num_sentences = re.split(r'[.?!]\\s*', text)\n", + "\n", + "# remove empty strings\n", + "num_sentences = [x for x in num_sentences if x]\n", + "len(num_sentences)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\bi\\b|\\bme\\b|\\bmy\\b|\\bmine\\b|\\bwe\\b|\\bus\\b|\\bour\\b|\\bours\\b\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import re\n", + "first_person = [\"i\", \"me\", \"my\", \"mine\", \"we\", \"us\", \"our\", \"ours\"]\n", + "first_person_regex = \"\\\\b|\\\\b\".join(first_person)\n", + "first_person_regex = \"\\\\b\" + first_person_regex + \"\\\\b\"\n", + "print(first_person_regex)\n", + "re.findall(first_person_regex, \"hiii\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/ipython_notebooks/hi.csv b/tests/ipython_notebooks/hi.csv new file mode 100644 index 00000000..b9e52d04 --- /dev/null +++ b/tests/ipython_notebooks/hi.csv @@ -0,0 +1,30 @@ +,0 +5,num_all_caps +7,num_links +11,num_reddit_users +13,num_emphasis +15,num_bullet_points +17,num_numbered_points +18,num_line_breaks +21,num_quotes +24,num_block_quote_responses +30,num_ellipses +32,num_parentheses +37,certainty_rocklage +64,Hello +65,Token_count +66,YesNo_Questions +67,WH_Questions +70,Impersonal_Pronoun +71,Hedges +73,First_Person_Single +74,factuality +76,Acknowledgement +78,Adverb_Limiter +79,indirect_greeting +83,direct_question +84,hasnegative +86,haspositive +88,subjunctive +89,apologizing + diff --git a/tests/ipython_notebooks/info_divvy.csv b/tests/ipython_notebooks/info_divvy.csv new file mode 100644 index 00000000..92823f50 --- /dev/null +++ b/tests/ipython_notebooks/info_divvy.csv @@ -0,0 +1,11 @@ +conversation_num,speaker_nickname,timestamp,message,feature +conversation_5, speaker_1,, Did you catch the latest episode of that show everyone’s talking about?,info_diversity +conversation_5, speaker_2,, Yeah it was intense! But I had to pause halfway to finish my assignment.,info_diversity +conversation_5, speaker_3,, I haven’t started watching it yet. Is it really that good?,info_diversity +conversation_5, speaker_1,, Definitely worth it. But speaking of assignments how’s your machine learning course going?,info_diversity +conversation_5, speaker_2,, It’s going well! I’m almost done with the neural networks module.,info_diversity +conversation_5, speaker_3,, Neural networks? That’s tough. I’m still stuck on the earlier parts.,info_diversity +conversation_5, speaker_1,, You’ll get there. Just take it one step at a time. Also are you guys thinking about internships yet?,info_diversity +conversation_5, speaker_2,, I am! I’m applying to a few tech companies next week.,info_diversity +conversation_5, speaker_3,, I should probably start looking too. I’m just so caught up with other things.,info_diversity +conversation_5, speaker_1,, Same here. Between school shows and job applications it’s hard to find balance.,info_diversity \ No newline at end of file diff --git a/tests/ipython_notebooks/info_divvy_LDA.csv b/tests/ipython_notebooks/info_divvy_LDA.csv new file mode 100644 index 00000000..ee33004c --- /dev/null +++ b/tests/ipython_notebooks/info_divvy_LDA.csv @@ -0,0 +1,89 @@ +conversation_num,speaker_nickname,timestamp,message,feature +conversation_1, speaker_1,, Have you noticed the weather?, low +conversation_1, speaker_1,, Have you noticed the weather?, low +conversation_1, speaker_1,, Have you noticed the weather?, low +conversation_1, speaker_1,, Have you noticed the weather?, low +conversation_1, speaker_1,, Have you noticed the weather?, low +conversation_1, speaker_1,, Have you noticed the weather?, low +conversation_1, speaker_1,, Have you noticed the weather?, low +conversation_1, speaker_1,, Have you noticed the weather?, low +conversation_1, speaker_1,, Have you noticed the weather?, low +conversation_1, speaker_1,, Have you noticed the weather?, low +conversation_1, speaker_1,, Have you noticed the weather?, low +conversation_2, speaker_1,, Did you finish the book we were reading for class?, high +conversation_2, speaker_1,, Did you finish the book we were reading for class?, high +conversation_2, speaker_1,, Did you finish the book we were reading for class?, high +conversation_2, speaker_1,, Did you finish the book we were reading for class?, high +conversation_2, speaker_1,, Did you finish the book we were reading for class?, high +conversation_2, speaker_1,, Did you finish the book we were reading for class?, high +conversation_2, speaker_1,, Did you finish the book we were reading for class?, high +conversation_2, speaker_1,, Did you finish the book we were reading for class?, high +conversation_2, speaker_1,, Did you finish the book we were reading for class?, high +conversation_2, speaker_1,, Did you finish the book we were reading for class?, high +conversation_2, speaker_1,, Did you finish the book we were reading for class?, high +conversation_3, speaker_2,, Not yet but I did bake cookies yesterday!, high +conversation_3, speaker_1,, Oh that sounds fun! What kind of cookies?, high +conversation_3, speaker_2,, Chocolate chip! But I still need to find time for that book., high +conversation_3, speaker_1,, Yeah reading is tough with everything going on. Have you seen the new movie that came out last week?, high +conversation_3, speaker_2,, No I haven’t! I’ve been too busy reorganizing my room., high +conversation_3, speaker_1,, Reorganizing? I just bought some new plants for my room. Makes it feel fresh!, high +conversation_3, speaker_2,, That’s great! I’ve been thinking about getting a pet fish actually., high +conversation_3, speaker_1,, Fish are fun! Do you know what kind you want?, high +conversation_3, speaker_2,, Not sure yet but maybe something colorful. Oh speaking of colorful I’m painting my room too!, high +conversation_4, speaker_1,, Have you seen the new park they built nearby, low +conversation_4, speaker_2,, Yes I walked through it yesterday it’s so scenic, low +conversation_4, speaker_1,, I love how there’s so much space for picnics, low +conversation_4, speaker_2,, And the playground is perfect kids it was packed, low +conversation_4, speaker_1,, I noticed that too lots of families seem to love it, low +conversation_4, speaker_2,, It’s great to have such a community space close to home, low +conversation_4, speaker_2,, That’s a good idea there are plenty of shady spots under the trees, low +conversation_4, speaker_1,, I might even bring a picnic lunch next time, low +conversation_4, speaker_2,, Sounds perfect it’s such a peaceful place to relax, low +conversation_5, speaker_1,, Have you seen the new park they opened nearby, low +conversation_5, speaker_2,, Yes I went for a walk there the other day, low +conversation_5, speaker_1,, It’s so well designed with lots of walking paths, low +conversation_5, speaker_2,, I agree the flowers they planted are beautiful too, low +conversation_5, speaker_1,, I love how quiet it is despite being in the city, low +conversation_5, speaker_1,, I’m planning to take my bike there this weekend, low +conversation_5, speaker_2,, Good idea the paths are great for cycling too, low +conversation_5, speaker_1,, I might bring a bike and just hang out for a while, low +conversation_5, speaker_2,, Sounds like a perfect weekend plan, low +conversation_6, speaker_1,, Did you hear about the new sandwich shop downtown, high +conversation_6, speaker_2,, No but I just adopted a cat, high +conversation_6, speaker_2,, Her name is Luna she’s so playful, high +conversation_6, speaker_1,, I love that name I bet she’s cute, high +conversation_6, speaker_2,, She is and she’s already exploring every corner of the house, high +conversation_6, speaker_1,, I’ve been thinking about trying that new shop this weekend, high +conversation_6, speaker_2,, You should do it and let me know if it’s good I’ve been craving some clothes, high +conversation_6, speaker_1,, For sure I’ll grab one after my yoga class, high +conversation_6, speaker_2,, Oh you’re into yoga I’ve been thinking of starting it too, high +conversation_7, speaker_1,, Did you catch the game last night, low +conversation_7, speaker_2,, Yeah it was so close the whole time, low +conversation_7, speaker_1,, I know right the last quarter was so intense, low +conversation_7, speaker_2,, I thought our team had it for a second, low +conversation_7, speaker_1,, Same here that final play was crazy, low +conversation_7, speaker_2,, It’s been a while since a game kept me on the edge like that, low +conversation_7, speaker_1,, It makes you appreciate how skilled the players are, low +conversation_7, speaker_2,, Definitely they train so hard for moments like that, low +conversation_7, speaker_1,, I’m excited for the next game it’s going to be another tough one, low +conversation_7, speaker_2,, Me too can’t wait to see how it turns out, low +conversation_8, speaker_1,, Did you catch the game last night, low +conversation_8, speaker_2,, Yes it was so intense especially toward the end, low +conversation_8, speaker_1,, I know right I was on the edge of my seat, low +conversation_8, speaker_2,, The way our team came back in the second half was amazing, low +conversation_8, speaker_1,, That defense was on point they really stepped up, low +conversation_8, speaker_2,, For sure it was a great team effort, low +conversation_8, speaker_1,, I can’t wait for the next match it’s going to be exciting, low +conversation_8, speaker_2,, Me neither hopefully we’ll win this time, low +conversation_8, speaker_1,, Fingers crossed I’m already looking forward to it, low +conversation_8, speaker_2,, Same here I’m definitely going to watch, low +conversation_9, speaker_1,, Do you want to go to the concert this weekend, high +conversation_9, speaker_2,, I’m not sure but I did just finish a 500 piece puzzle, high +conversation_9, speaker_1,, That’s impressive puzzles are so relaxing, high +conversation_9, speaker_2,, They really are it took me a few hours but it was worth it, high +conversation_9, speaker_1,, Speaking of relaxing I’ve been getting into meditation, high +conversation_9, speaker_2,, Oh that sounds great I’ve always wanted to try that, high +conversation_9, speaker_1,, You should it really helps clear your mind, high +conversation_9, speaker_2,, Maybe I’ll give it a shot after finishing this new book I started, high +conversation_9, speaker_1,, What book are you reading, high +conversation_9, speaker_2,, It’s a mystery novel so good I can’t put it down, high \ No newline at end of file diff --git a/tests/ipython_notebooks/infodiv.ipynb b/tests/ipython_notebooks/infodiv.ipynb new file mode 100644 index 00000000..6b5c8814 --- /dev/null +++ b/tests/ipython_notebooks/infodiv.ipynb @@ -0,0 +1,860 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Previous LDA Model Results" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Attempt Number: 0\n", + "[FAILED] Test Case # conversation_7 : 0.7512651685958739\n", + "Attempt Number: 1\n", + "[FAILED] Test Case # conversation_4 : 0.5894661985718989\n", + "[FAILED] Test Case # conversation_7 : 0.34210148136901297\n", + "Attempt Number: 2\n", + "[FAILED] Test Case # conversation_7 : 0.23791709508394202\n", + "Attempt Number: 3\n", + "Attempt Number: 4\n", + "Attempt Number: 5\n", + "[FAILED] Test Case # conversation_4 : 0.004348320675631441\n", + "[FAILED] Test Case # conversation_7 : 0.8276635597113645\n", + "Attempt Number: 6\n", + "[FAILED] Test Case # conversation_4 : 0.09795153483568632\n", + "Attempt Number: 7\n", + "[FAILED] Test Case # conversation_7 : 0.2380256154675347\n", + "Attempt Number: 8\n", + "[FAILED] Test Case # conversation_7 : 0.5724496119873153\n", + "Attempt Number: 9\n", + "[FAILED] Test Case # conversation_4 : 0.6125460013915317\n", + "[FAILED] Test Case # conversation_7 : 0.7434659153438612\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from information_diversity import *\n", + "\n", + "df = pd.read_csv('info_divvy_LDA.csv')\n", + "\n", + "def get_batches(dataframe, batch_size=3):\n", + " batches = []\n", + " rows = list(dataframe.iterrows())\n", + " for i in range(0, len(rows), batch_size):\n", + " batches.append(rows[i:i + batch_size])\n", + " return batches\n", + "\n", + "for i in range(0, 10):\n", + " print(\"Attempt Number: \", i)\n", + " output_df = get_info_diversity(df, \"conversation_num\", \"message\")\n", + "\n", + " batches = get_batches(output_df)\n", + "\n", + " count = 0\n", + "\n", + " # print(\"ALL RESULTS SHOULD BE OVER \")\n", + "\n", + " for batch in batches:\n", + " feature = \"info_diversity\"\n", + "\n", + " og_result = batch[0][1][feature]\n", + " inv_result = batch[1][1][feature]\n", + " dir_result = batch[2][1][feature]\n", + "\n", + " inv_distance = abs(og_result - inv_result)\n", + " dir_distance = abs(og_result - dir_result)\n", + " count += 1\n", + "\n", + " if dir_distance < inv_distance:\n", + " print(\"[FAILED] Test Case #\", batch[0][1][\"conversation_num\"], \":\", dir_distance / inv_distance)\n", + " # print(\"Test Case #\", count, \":\", dir_distance / inv_distance)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preliminary BERT Model avec Distributions\n", + "\n", + "As we notice, the LDA model trends to be extremely stochastic in its implementation. We are going to pivot to BERTopic to create these topic representations and determine if that's giving us a more fruitful result. We are going to set a parameter to equal the minimum number of chats in a topics to indirectly affect the number of topic generates. With less chats permitted to be assigned a topic, we inherently generate more topics. " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "from bertopic import BERTopic\n", + "from bertopic.representation import KeyBERTInspired\n", + "\n", + "\n", + "docs = pd.read_csv('info_divvy.csv')['message'].to_list()\n", + "representation_model = KeyBERTInspired()\n", + "topic_model = BERTopic(representation_model=KeyBERTInspired(), min_topic_size=2)\n", + "# topics, probs = topic_model.fit_transform(docs)\n", + "topics = topic_model.fit(docs)" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      0Did you catch the latest episode of that show...-1-1_internships_assignments_learning_intense[internships, assignments, learning, intense, ...[ Yeah it was intense! But I had to pause half...internships - assignments - learning - intense...0.0False
      1Yeah it was intense! But I had to pause halfw...-1-1_internships_assignments_learning_intense[internships, assignments, learning, intense, ...[ Yeah it was intense! But I had to pause half...internships - assignments - learning - intense...0.0True
      2I haven’t started watching it yet. Is it real...-1-1_internships_assignments_learning_intense[internships, assignments, learning, intense, ...[ Yeah it was intense! But I had to pause half...internships - assignments - learning - intense...0.0False
      3Definitely worth it. But speaking of assignme...-1-1_internships_assignments_learning_intense[internships, assignments, learning, intense, ...[ Yeah it was intense! But I had to pause half...internships - assignments - learning - intense...0.0False
      4It’s going well! I’m almost done with the neu...-1-1_internships_assignments_learning_intense[internships, assignments, learning, intense, ...[ Yeah it was intense! But I had to pause half...internships - assignments - learning - intense...0.0True
      5Neural networks? That’s tough. I’m still stuc...-1-1_internships_assignments_learning_intense[internships, assignments, learning, intense, ...[ Yeah it was intense! But I had to pause half...internships - assignments - learning - intense...0.0False
      6You’ll get there. Just take it one step at a ...-1-1_internships_assignments_learning_intense[internships, assignments, learning, intense, ...[ Yeah it was intense! But I had to pause half...internships - assignments - learning - intense...0.0True
      7I am! I’m applying to a few tech companies ne...-1-1_internships_assignments_learning_intense[internships, assignments, learning, intense, ...[ Yeah it was intense! But I had to pause half...internships - assignments - learning - intense...0.0False
      8I should probably start looking too. I’m just...-1-1_internships_assignments_learning_intense[internships, assignments, learning, intense, ...[ Yeah it was intense! But I had to pause half...internships - assignments - learning - intense...0.0False
      9Same here. Between school shows and job appli...-1-1_internships_assignments_learning_intense[internships, assignments, learning, intense, ...[ Yeah it was intense! But I had to pause half...internships - assignments - learning - intense...0.0False
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Between school shows and job appli... -1 \n", + "\n", + " Name \\\n", + "0 -1_internships_assignments_learning_intense \n", + "1 -1_internships_assignments_learning_intense \n", + "2 -1_internships_assignments_learning_intense \n", + "3 -1_internships_assignments_learning_intense \n", + "4 -1_internships_assignments_learning_intense \n", + "5 -1_internships_assignments_learning_intense \n", + "6 -1_internships_assignments_learning_intense \n", + "7 -1_internships_assignments_learning_intense \n", + "8 -1_internships_assignments_learning_intense \n", + "9 -1_internships_assignments_learning_intense \n", + "\n", + " Representation \\\n", + "0 [internships, assignments, learning, intense, ... \n", + "1 [internships, assignments, learning, intense, ... \n", + "2 [internships, assignments, learning, intense, ... \n", + "3 [internships, assignments, learning, intense, ... \n", + "4 [internships, assignments, learning, intense, ... \n", + "5 [internships, assignments, learning, intense, ... \n", + "6 [internships, assignments, learning, intense, ... \n", + "7 [internships, assignments, learning, intense, ... \n", + "8 [internships, assignments, learning, intense, ... \n", + "9 [internships, assignments, learning, intense, ... \n", + "\n", + " Representative_Docs \\\n", + "0 [ Yeah it was intense! But I had to pause half... \n", + "1 [ Yeah it was intense! But I had to pause half... \n", + "2 [ Yeah it was intense! But I had to pause half... \n", + "3 [ Yeah it was intense! But I had to pause half... \n", + "4 [ Yeah it was intense! But I had to pause half... \n", + "5 [ Yeah it was intense! But I had to pause half... \n", + "6 [ Yeah it was intense! But I had to pause half... \n", + "7 [ Yeah it was intense! But I had to pause half... \n", + "8 [ Yeah it was intense! But I had to pause half... \n", + "9 [ Yeah it was intense! But I had to pause half... \n", + "\n", + " Top_n_words Probability \\\n", + "0 internships - assignments - learning - intense... 0.0 \n", + "1 internships - assignments - learning - intense... 0.0 \n", + "2 internships - assignments - learning - intense... 0.0 \n", + "3 internships - assignments - learning - intense... 0.0 \n", + "4 internships - assignments - learning - intense... 0.0 \n", + "5 internships - assignments - learning - intense... 0.0 \n", + "6 internships - assignments - learning - intense... 0.0 \n", + "7 internships - assignments - learning - intense... 0.0 \n", + "8 internships - assignments - learning - intense... 0.0 \n", + "9 internships - assignments - learning - intense... 0.0 \n", + "\n", + " Representative_document \n", + "0 False \n", + "1 True \n", + "2 False \n", + "3 False \n", + "4 True \n", + "5 False \n", + "6 True \n", + "7 False \n", + "8 False \n", + "9 False " + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "topic_model.get_document_info(docs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Topic Distribution per Chat" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics.pairwise import cosine_distances\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "def new_ID(docs_df):\n", + " docs = docs_df['message'].to_list()\n", + " representation_model = KeyBERTInspired()\n", + " topic_model = BERTopic(representation_model=KeyBERTInspired(), min_topic_size=2)\n", + " topic_model = topic_model.fit(docs)\n", + " # print(\"Conversation:\", docs_df[\"conversation_num\"].iloc[0], len(topic_model.get_topic_info()))\n", + " counter = 0\n", + " while (len(topic_model.get_topic_info()) == 1 and counter <= 5):\n", + " counter += 1\n", + " topic_model = topic_model.fit(docs)\n", + " # print(\"Retrying:\", len(topic_model.get_topic_info()))\n", + "\n", + " if len(topic_model.get_topic_info()) == 1:\n", + " return 0\n", + " \n", + " # TODO: This line FAILS when the number of topics is 1\n", + " topic_distr, _ = topic_model.approximate_distribution(docs)\n", + "\n", + " mean_vector = np.mean(topic_distr, axis=0)\n", + "\n", + " info_diversity = np.sum(cosine_distances(topic_distr, mean_vector.reshape(1, -1)))\n", + " info_diversity /= len(topic_distr)\n", + " return info_diversity\n", + "\n", + "def overhead_function(docs):\n", + " # print(\"Conversation number\", docs[\"conversation_num\"])\n", + " docs = docs['message'].to_list()\n", + " return new_ID(docs)\n", + "\n", + "# iterate through conversations, uniquely identified by \"conversation_num\" parameter\n", + "# group by conversation_num\n" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Attempt Number: 0\n", + "Attempt Number: 1\n", + "Attempt Number: 2\n", + "[FAILED] Test Case # conversation_4 : 0.9255120808110898\n", + "Attempt Number: 3\n", + "[FAILED] Test Case # conversation_4 : 0.1524170510975725\n", + "Attempt Number: 4\n", + "Attempt Number: 5\n", + "Attempt Number: 6\n", + "[FAILED] Test Case # conversation_4 : 0.8195466395333789\n", + "Attempt Number: 7\n", + "[FAILED] Test Case # conversation_4 : 0.7609662129271485\n", + "Attempt Number: 8\n", + "[FAILED] Test Case # conversation_4 : 0.9477367878580975\n", + "Attempt Number: 9\n" + ] + } + ], + "source": [ + "warnings.filterwarnings('ignore')\n", + "for i in range(0, 10):\n", + " print(\"Attempt Number:\", i)\n", + " output_df = df.groupby(\"conversation_num\").apply(lambda x: new_ID(x)).to_frame().reset_index()\n", + " output_df.columns = [\"conversation_num\", \"info_diversity\"]\n", + "\n", + " batches = get_batches(output_df)\n", + "\n", + " for batch in batches:\n", + " feature = \"info_diversity\"\n", + " \n", + " og_result = batch[0][1][feature]\n", + " inv_result = batch[1][1][feature]\n", + " dir_result = batch[2][1][feature]\n", + "\n", + " inv_distance = abs(og_result - inv_result)\n", + " dir_distance = abs(og_result - dir_result)\n", + "\n", + " if dir_distance < inv_distance:\n", + " print(\"[FAILED] Test Case #\", batch[0][1][\"conversation_num\"], \":\", dir_distance / inv_distance)" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "conversation_1\n", + "conversation_2\n", + "conversation_3\n", + "conversation_4\n", + "conversation_5\n", + "conversation_6\n", + "conversation_7\n", + "conversation_8\n", + "conversation_9\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/q6/rj1_hcnn4l1czvx3phrf8kbw0000gn/T/ipykernel_42521/3743902667.py:1: DeprecationWarning:\n", + "\n", + "DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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      DocumentTopicNameRepresentationRepresentative_DocsTop_n_wordsProbabilityRepresentative_document
      0I’ve been thinking about starting a garden bu...00_gardening_garden_vegetables_plants[gardening, garden, vegetables, plants, grow, ...[ I hear you I’m also limited on space but I’m...gardening - garden - vegetables - plants - gro...1.000000False
      1Gardening is super relaxing You should start ...00_gardening_garden_vegetables_plants[gardening, garden, vegetables, plants, grow, ...[ I hear you I’m also limited on space but I’m...gardening - garden - vegetables - plants - gro...0.937373False
      2That sounds fun I’ve always wanted to grow my...00_gardening_garden_vegetables_plants[gardening, garden, vegetables, plants, grow, ...[ I hear you I’m also limited on space but I’m...gardening - garden - vegetables - plants - gro...0.985573True
      3I hear you I’m also limited on space but I’m ...00_gardening_garden_vegetables_plants[gardening, garden, vegetables, plants, grow, ...[ I hear you I’m also limited on space but I’m...gardening - garden - vegetables - plants - gro...1.000000True
      4That’s a great idea You can grow a lot in con...00_gardening_garden_vegetables_plants[gardening, garden, vegetables, plants, grow, ...[ I hear you I’m also limited on space but I’m...gardening - garden - vegetables - plants - gro...0.817586True
      5Speaking of strategy has anyone been followin...11_chess_strategy_play_tournaments[chess, strategy, play, tournaments, intense, ...[ I’m not a chess expert but I enjoy watching ...chess - strategy - play - tournaments - intens...1.000000True
      6I have The level of play this year has been i...11_chess_strategy_play_tournaments[chess, strategy, play, tournaments, intense, ...[ I’m not a chess expert but I enjoy watching ...chess - strategy - play - tournaments - intens...0.848621True
      7I’m not a chess expert but I enjoy watching t...11_chess_strategy_play_tournaments[chess, strategy, play, tournaments, intense, ...[ I’m not a chess expert but I enjoy watching ...chess - strategy - play - tournaments - intens...1.000000True
      8It’s amazing how much thought goes into each ...11_chess_strategy_play_tournaments[chess, strategy, play, tournaments, intense, ...[ I’m not a chess expert but I enjoy watching ...chess - strategy - play - tournaments - intens...0.848621False
      9Definitely I find that playing chess really s...11_chess_strategy_play_tournaments[chess, strategy, play, tournaments, intense, ...[ I’m not a chess expert but I enjoy watching ...chess - strategy - play - tournaments - intens...1.000000False
      \n", + "
      " + ], + "text/plain": [ + " Document Topic \\\n", + "0 I’ve been thinking about starting a garden bu... 0 \n", + "1 Gardening is super relaxing You should start ... 0 \n", + "2 That sounds fun I’ve always wanted to grow my... 0 \n", + "3 I hear you I’m also limited on space but I’m ... 0 \n", + "4 That’s a great idea You can grow a lot in con... 0 \n", + "5 Speaking of strategy has anyone been followin... 1 \n", + "6 I have The level of play this year has been i... 1 \n", + "7 I’m not a chess expert but I enjoy watching t... 1 \n", + "8 It’s amazing how much thought goes into each ... 1 \n", + "9 Definitely I find that playing chess really s... 1 \n", + "\n", + " Name \\\n", + "0 0_gardening_garden_vegetables_plants \n", + "1 0_gardening_garden_vegetables_plants \n", + "2 0_gardening_garden_vegetables_plants \n", + "3 0_gardening_garden_vegetables_plants \n", + "4 0_gardening_garden_vegetables_plants \n", + "5 1_chess_strategy_play_tournaments \n", + "6 1_chess_strategy_play_tournaments \n", + "7 1_chess_strategy_play_tournaments \n", + "8 1_chess_strategy_play_tournaments \n", + "9 1_chess_strategy_play_tournaments \n", + "\n", + " Representation \\\n", + "0 [gardening, garden, vegetables, plants, grow, ... \n", + "1 [gardening, garden, vegetables, plants, grow, ... \n", + "2 [gardening, garden, vegetables, plants, grow, ... \n", + "3 [gardening, garden, vegetables, plants, grow, ... \n", + "4 [gardening, garden, vegetables, plants, grow, ... \n", + "5 [chess, strategy, play, tournaments, intense, ... \n", + "6 [chess, strategy, play, tournaments, intense, ... \n", + "7 [chess, strategy, play, tournaments, intense, ... \n", + "8 [chess, strategy, play, tournaments, intense, ... \n", + "9 [chess, strategy, play, tournaments, intense, ... \n", + "\n", + " Representative_Docs \\\n", + "0 [ I hear you I’m also limited on space but I’m... \n", + "1 [ I hear you I’m also limited on space but I’m... \n", + "2 [ I hear you I’m also limited on space but I’m... \n", + "3 [ I hear you I’m also limited on space but I’m... \n", + "4 [ I hear you I’m also limited on space but I’m... \n", + "5 [ I’m not a chess expert but I enjoy watching ... \n", + "6 [ I’m not a chess expert but I enjoy watching ... \n", + "7 [ I’m not a chess expert but I enjoy watching ... \n", + "8 [ I’m not a chess expert but I enjoy watching ... \n", + "9 [ I’m not a chess expert but I enjoy watching ... \n", + "\n", + " Top_n_words Probability \\\n", + "0 gardening - garden - vegetables - plants - gro... 1.000000 \n", + "1 gardening - garden - vegetables - plants - gro... 0.937373 \n", + "2 gardening - garden - vegetables - plants - gro... 0.985573 \n", + "3 gardening - garden - vegetables - plants - gro... 1.000000 \n", + "4 gardening - garden - vegetables - plants - gro... 0.817586 \n", + "5 chess - strategy - play - tournaments - intens... 1.000000 \n", + "6 chess - strategy - play - tournaments - intens... 0.848621 \n", + "7 chess - strategy - play - tournaments - intens... 1.000000 \n", + "8 chess - strategy - play - tournaments - intens... 0.848621 \n", + "9 chess - strategy - play - tournaments - intens... 1.000000 \n", + "\n", + " Representative_document \n", + "0 False \n", + "1 False \n", + "2 True \n", + "3 True \n", + "4 True \n", + "5 True \n", + "6 True \n", + "7 True \n", + "8 False \n", + "9 False " + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "topic_model.get_document_info(docs)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/ipython_notebooks/output.csv b/tests/ipython_notebooks/output.csv new file mode 100644 index 00000000..0d3e2885 --- /dev/null +++ b/tests/ipython_notebooks/output.csv @@ -0,0 +1,15 @@ +0,0,herself,hello.txt_lexical_per_100,0.01 +0,0,right Ive itself its,hello.txt_lexical_per_100,0.04 +0,0,over or when havent,hello.txt_lexical_per_100,0.04 +0,0,each his whereas yup yours,hello.txt_lexical_per_100,0.05 +0,0,ah where out,hello.txt_lexical_per_100,0.03 +0,0,whatd near about,hello.txt_lexical_per_100,0.03 +0,0,shouldnt wouldnt somebody oh,hello.txt_lexical_per_100,0.04 +0,0,us youll,hello.txt_lexical_per_100,0.02 +0,0,yourself,hello.txt_lexical_per_100,0.01 +0,0,youd,hello.txt_lexical_per_100,0.01 +0,0,and huh be was itd,hello.txt_lexical_per_100,0.05 +0,0,all anything anybody,hello.txt_lexical_per_100,0.03 +0,0,get for which Id,hello.txt_lexical_per_100,0.04 +0,0,from this then her to,hello.txt_lexical_per_100,0.05 +0,0,herself before himself shes Ive,hello.txt_lexical_per_100,0.05 diff --git a/tests/run_tests.py b/tests/run_tests.py index aadd767c..3b8103a7 100644 --- a/tests/run_tests.py +++ b/tests/run_tests.py @@ -20,6 +20,7 @@ conv_encoding = chardet.detect(file.read()) chat_df = pd.read_csv("data/cleaned_data/test_chat_level.csv", encoding=chat_encoding['encoding']) + info_exchange_df = pd.read_csv("data/cleaned_data/info_exchange_zscore_chats.csv", encoding=chat_encoding['encoding']) conv_df = pd.read_csv("data/cleaned_data/test_conv_level.csv", encoding=conv_encoding['encoding']) test_ner_df = pd.read_csv("data/cleaned_data/test_named_entity.csv", encoding='utf-8') test_ner_training_df = pd.read_csv("data/cleaned_data/train_named_entity.csv") @@ -43,10 +44,28 @@ "Forward Flow", "Discursive Diversity" ], - turns = False + turns = False, + regenerate_vectors = True ) testing_chat.featurize() + testing_info_exchange = FeatureBuilder( + input_df = info_exchange_df, + vector_directory = "./vector_data/", + output_file_path_chat_level = "./output/chat/info_exchange_zscore_chats.csv", + output_file_path_user_level = "./output/user/info_exchange_zscore_chats.csv", + output_file_path_conv_level = "./output/conv/info_exchange_zscore_chats.csv", + custom_features = [ # these require vect_data, so they now need to be explicitly included in order to calculate them + "(BERT) Mimicry", + "Moving Mimicry", + "Forward Flow", + "Discursive Diversity" + ], + turns = False, + regenerate_vectors = True + ) + testing_info_exchange.featurize() + testing_conv = FeatureBuilder( input_df = conv_df, vector_directory = "./vector_data/", @@ -59,7 +78,8 @@ "Forward Flow", "Discursive Diversity" ], - turns = False + turns = False, + regenerate_vectors = True ) testing_conv.featurize() @@ -76,7 +96,8 @@ "Forward Flow", "Discursive Diversity" ], - turns = False + turns = False, + regenerate_vectors = True ) test_ner_feature_builder.featurize() @@ -93,7 +114,8 @@ "Forward Flow", "Discursive Diversity" ], - turns = False + turns = False, + regenerate_vectors = True ) testing_chat_complex.featurize() @@ -110,7 +132,8 @@ "Forward Flow", "Discursive Diversity" ], - turns = False + turns = False, + regenerate_vectors = True ) testing_conv_complex.featurize() @@ -126,7 +149,8 @@ "Forward Flow", "Discursive Diversity" ], - turns = False + turns = False, + regenerate_vectors = True ) testing_conv_complex_ts.featurize() @@ -143,7 +167,8 @@ "Forward Flow", "Discursive Diversity" ], - turns = False + turns = False, + regenerate_vectors = True ) testing_forward_flow.featurize() \ No newline at end of file diff --git a/tests/test_feature_metrics.py b/tests/test_feature_metrics.py index 4c1844c4..e52e67d4 100644 --- a/tests/test_feature_metrics.py +++ b/tests/test_feature_metrics.py @@ -1,3 +1,4 @@ +from team_comm_tools.feature_dict import feature_dict import pytest import pandas as pd import numpy as np @@ -5,24 +6,37 @@ import logging import itertools -test_chat_df = pd.read_csv("./output/chat/test_chat_level_chat.csv") -test_conv_df = pd.read_csv("./output/conv/test_conv_level_conv.csv") -test_chat_complex_df = pd.read_csv("./output/chat/test_chat_level_chat_complex.csv") -test_conv_complex_df = pd.read_csv("./output/conv/test_conv_level_conv_complex.csv") -test_conv_complex_df_ts = pd.read_csv("./output/conv/test_conv_level_conv_complex_ts.csv") +test_chat_df = pd.read_csv("./output/chat/test_chat_level_chat.csv") +test_info_exchange_zscore_df = pd.read_csv("./output/chat/info_exchange_zscore_chats.csv") +test_chat_df = pd.concat([test_chat_df, test_info_exchange_zscore_df], axis=0) +test_conv_df = pd.read_csv("./output/conv/test_conv_level_conv.csv") +test_chat_complex_df = pd.read_csv( + "./output/chat/test_chat_level_chat_complex.csv") +test_conv_complex_df = pd.read_csv( + "./output/conv/test_conv_level_conv_complex.csv") +test_conv_complex_df_ts = pd.read_csv( + "./output/conv/test_conv_level_conv_complex_ts.csv") test_forward_flow_df = pd.read_csv("./output/chat/test_forward_flow_chat.csv") # Import the Feature Dictionary -from team_comm_tools.feature_dict import feature_dict -chat_features = [feature_dict[feature]["columns"] for feature in feature_dict.keys() if feature_dict[feature]["level"] == "Chat"] -conversation_features = [feature_dict[feature]["columns"] for feature in feature_dict.keys() if feature_dict[feature]["level"] == "Conversation"] +chat_features = [feature_dict[feature]["columns"] + for feature in feature_dict.keys() if feature_dict[feature]["level"] == "Chat"] +conversation_features = [feature_dict[feature]["columns"] for feature in feature_dict.keys( +) if feature_dict[feature]["level"] == "Conversation"] num_features_chat = len(list(itertools.chain(*chat_features))) num_features_conv = len(list(itertools.chain(*conversation_features))) -num_tested_chat = test_chat_df['expected_column'].nunique() + test_chat_complex_df['feature'].nunique() + test_forward_flow_df['feature'].nunique() -num_tested_conv = test_conv_df['expected_column'].nunique() + test_conv_complex_df['feature'].nunique() + +num_tested_chat = test_chat_df['expected_column'].nunique( +) + test_chat_complex_df['feature'].nunique() + test_forward_flow_df['feature'].nunique() +test_chat = test_chat_df['expected_column'] + \ + test_chat_complex_df['feature'] + test_forward_flow_df['feature'] +tested_chat = set(test_chat.tolist()) + + +num_tested_conv = len(set(test_conv_df['expected_column'].unique().tolist() + test_conv_complex_df['feature'].unique().tolist())) tested_features = {} @@ -33,6 +47,8 @@ pass # generate coverage for tests + + @pytest.mark.parametrize("row", test_chat_df.iterrows()) def test_chat_unit_equality(row): actual = row[1][row[1]['expected_column']] @@ -41,24 +57,28 @@ def test_chat_unit_equality(row): # if expected_column doesn't exist in tested_features, add an entry for it if row[1]['expected_column'] not in tested_features: tested_features[row[1]['expected_column']] = {'passed': 0, 'failed': 0} - + try: - assert round(float(actual), 3) == round(float(expected), 3) + if (type(actual) == str): + assert actual == expected + else: + assert round(float(actual), 3) == round(float(expected), 3) tested_features[row[1]['expected_column']]['passed'] += 1 except AssertionError: tested_features[row[1]['expected_column']]['failed'] += 1 with open('test.log', 'a') as file: file.write("\n") file.write("------TEST FAILED------\n") - file.write(f"Testing {row[1]['expected_column']} for message: {row[1]['message_original']}\n") + file.write( + f"Testing {row[1]['expected_column']} for message: {row[1]['message_original']}\n") file.write(f"Expected value: {expected}\n") file.write(f"Actual value: {actual}\n") - raise # Re-raise the AssertionError to mark the test as failed test_ner = pd.read_csv('./output/chat/test_named_entity_chat_level.csv') tested_features['Named Entity Recognition'] = {'passed': 0, 'failed': 0} + @pytest.mark.parametrize("row", test_ner.iterrows()) def test_named_entity_recognition(row): @@ -68,7 +88,8 @@ def test_named_entity_recognition(row): assert row[1]['named_entities'] == "()" tested_features['Named Entity Recognition']['passed'] += 1 except AssertionError: - parsed_actual = row[1]['named_entities'].replace(" ","").replace("(","").replace(")", "").split(',') + parsed_actual = row[1]['named_entities'].replace( + " ", "").replace("(", "").replace(")", "").split(',') actual = parsed_actual[0::2] if actual and actual[-1] == '': @@ -78,19 +99,21 @@ def test_named_entity_recognition(row): with open('test.log', 'a') as file: file.write("\n") file.write("------TEST FAILED------\n") - file.write(f"Testing NER for message: {row[1]['message_original']}\n") + file.write( + f"Testing NER for message: {row[1]['message_original']}\n") file.write(f"Expected value: {[]}\n") file.write(f"Actual value: {actual}\n") else: - expected = row[1]['expected_value'].split(',') - parsed_actual = row[1]['named_entities'].replace(" ","").replace("(","").replace(")", "").split(',') + expected = row[1]['expected_value'].split(',') + parsed_actual = row[1]['named_entities'].replace( + " ", "").replace("(", "").replace(")", "").split(',') actual = parsed_actual[0::2] if actual and actual[-1] == '': # Remove the last element - actual.pop() + actual.pop() - try: + try: assert len(expected) == len(actual) for named_entity in expected: assert named_entity.lower().strip() in actual @@ -100,14 +123,14 @@ def test_named_entity_recognition(row): with open('test.log', 'a') as file: file.write("\n") file.write("------TEST FAILED------\n") - file.write(f"Testing NER for message: {row[1]['message_original']}\n") + file.write( + f"Testing NER for message: {row[1]['message_original']}\n") file.write(f"Expected value: {expected}\n") file.write(f"Actual value: {actual}\n") # raise # Re-raise the AssertionError to mark the test as failed - @pytest.mark.parametrize("conversation_num, conversation_rows", test_conv_df.groupby('conversation_num')) def test_conv_unit_equality(conversation_num, conversation_rows): test_failed = False @@ -116,10 +139,11 @@ def test_conv_unit_equality(conversation_num, conversation_rows): for _, row in conversation_rows.iterrows(): if row['expected_column'] not in tested_features: - tested_features[row['expected_column']] = {'passed': 0, 'failed': 0} + tested_features[row['expected_column']] = { + 'passed': 0, 'failed': 0} actual = row[row['expected_column']] expected = row['expected_value'] - + try: assert round(actual, 3) == round(expected, 3) tested_features[row['expected_column']]['passed'] += 1 @@ -133,17 +157,19 @@ def test_conv_unit_equality(conversation_num, conversation_rows): with open('test.log', 'a') as file: file.write("\n") file.write("------TEST FAILED------\n") - file.write(f"Testing {row['expected_column']} for conversation_num: {conversation_num}\n") + file.write( + f"Testing {row['expected_column']} for conversation_num: {conversation_num}\n") file.write(f"Expected value: {expected_out}\n") file.write(f"Actual value: {actual_out}\n") - raise - -# testing complex features -test_chat_complex_df = pd.read_csv("./output/chat/test_chat_level_chat_complex.csv") +# testing complex features +test_chat_complex_df = pd.read_csv( + "./output/chat/test_chat_level_chat_complex.csv") # Helper function to generate batches of three rows + + def get_batches(dataframe, batch_size=3): batches = [] rows = list(dataframe.iterrows()) @@ -151,6 +177,7 @@ def get_batches(dataframe, batch_size=3): batches.append(rows[i:i + batch_size]) return batches + def get_conversation_batches(dataframe, batch_size=3): # group by conversation_num and get the last row from the group last_rows = dataframe.groupby('conversation_num').tail(1) @@ -162,15 +189,17 @@ def get_conversation_batches(dataframe, batch_size=3): batches.append(rows[i:i + batch_size]) return batches + # Assuming test_chat_complex_df is your DataFrame -batches = get_batches(test_chat_complex_df, batch_size=3) +batches = get_batches(test_chat_complex_df, batch_size=3) + @pytest.mark.parametrize("batch", batches) def test_chat_complex(batch): feature = batch[0][1]['feature'] if feature not in tested_features: tested_features[feature] = {'passed': 0, 'failed': 0} - + og_result = batch[0][1][feature] inv_result = batch[1][1][feature] dir_result = batch[2][1][feature] @@ -180,15 +209,16 @@ def test_chat_complex(batch): # calculate ratio between inv and dir ratio = dir_distance / inv_distance - + try: - + assert ratio > 1 tested_features[feature]['passed'] += 1 with open('test.log', 'a') as file: file.write("\n") file.write("------TEST PASSED------\n") - file.write(f"Testing {feature} for message: {batch[0][1]['message']}\n") + file.write( + f"Testing {feature} for message: {batch[0][1]['message']}\n") file.write(f"Inv message: {batch[1][1]['message']}\n") file.write(f"Dir message: {batch[2][1]['message']}\n") file.write(f"Ratio (DIR / INV): {ratio}\n") @@ -197,14 +227,18 @@ def test_chat_complex(batch): with open('test.log', 'a') as file: file.write("\n") file.write("------TEST FAILED------\n") - file.write(f"Testing {feature} for message: {batch[0][1]['message']}\n") + file.write( + f"Testing {feature} for message: {batch[0][1]['message']}\n") file.write(f"Inv message: {batch[1][1]['message']}\n") file.write(f"Dir message: {batch[2][1]['message']}\n") file.write(f"Ratio (DIR / INV): {ratio}\n") raise # Re-raise the AssertionError to mark the test as failed -batches = get_batches(test_conv_complex_df, batch_size=3) + get_conversation_batches(test_forward_flow_df, batch_size=3) + get_batches(test_conv_complex_df_ts, batch_size=3) + +batches = get_batches(test_conv_complex_df, batch_size=3) + get_conversation_batches( + test_forward_flow_df, batch_size=3) + get_batches(test_conv_complex_df_ts, batch_size=3) + @pytest.mark.parametrize("batch", batches) def test_conv_complex(batch): @@ -225,44 +259,52 @@ def test_conv_complex(batch): ratio = 2 else: ratio = dir_distance / inv_distance - + try: - + assert ratio > 1 tested_features[feature]['passed'] += 1 with open('test.log', 'a') as file: file.write("\n") file.write("------TEST PASSED------\n") - file.write(f"Testing {feature} for conversation: {batch[0][1]['conversation_num']}\n") - file.write(f"Inv conversation: {batch[1][1]['conversation_num']}\n") - file.write(f"Dir conversation: {batch[2][1]['conversation_num']}\n") + file.write( + f"Testing {feature} for conversation: {batch[0][1]['conversation_num']}\n") + file.write( + f"Inv conversation: {batch[1][1]['conversation_num']}\n") + file.write( + f"Dir conversation: {batch[2][1]['conversation_num']}\n") file.write(f"Ratio (DIR / INV): {ratio}\n") except AssertionError: tested_features[feature]['failed'] += 1 with open('test.log', 'a') as file: file.write("\n") file.write("------TEST FAILED------\n") - file.write(f"Testing {feature} for conversation: {batch[0][1]['conversation_num']}\n") - file.write(f"Inv conversation: {batch[1][1]['conversation_num']}\n") - file.write(f"Dir conversation: {batch[2][1]['conversation_num']}\n") + file.write( + f"Testing {feature} for conversation: {batch[0][1]['conversation_num']}\n") + file.write( + f"Inv conversation: {batch[1][1]['conversation_num']}\n") + file.write( + f"Dir conversation: {batch[2][1]['conversation_num']}\n") file.write(f"Ratio (DIR / INV): {ratio}\n") # raise # Re-raise the AssertionError to mark the test as failed + batches = get_conversation_batches(test_forward_flow_df, batch_size=3) + @pytest.mark.parametrize("batch", batches) def test_forward_flow_unit(batch): if (batch[0][1]['test_type'] != 'unit_eq'): return feature = batch[0][1]['feature'] - + if feature not in tested_features: tested_features[feature] = {'passed': 0, 'failed': 0} - og_result = batch[0][1][feature] - inv_result = batch[1][1][feature] - dir_result = batch[2][1][feature] + og_result = round(batch[0][1][feature], 3) + inv_result = round(batch[1][1][feature], 3) + dir_result = round(batch[2][1][feature], 3) inv_distance = abs(og_result - inv_result) dir_distance = abs(og_result - dir_result) @@ -273,13 +315,15 @@ def test_forward_flow_unit(batch): with open('test.log', 'a') as file: file.write("\n") file.write("------TEST PASSED------\n") - file.write(f"Testing {feature} for unit equality and perturbation across conversations: {batch[0][1]['conversation_num']}, {batch[1][1]['conversation_num']}, {batch[2][1]['conversation_num']}\n") + file.write( + f"Testing {feature} for unit equality and perturbation across conversations: {batch[0][1]['conversation_num']}, {batch[1][1]['conversation_num']}, {batch[2][1]['conversation_num']}\n") else: tested_features[feature]['failed'] += 1 with open('test.log', 'a') as file: file.write("\n") file.write("------TEST FAILED------\n") - file.write(f"Testing {feature} for conversation: {batch[0][1]['conversation_num']}\n") + file.write( + f"Testing {feature} for conversation: {batch[0][1]['conversation_num']}\n") file.write(f"OG Result: {batch[0][1][feature]}\n") file.write(f"UNIT TEST Result: {batch[1][1][feature]}\n") file.write(f"DIR Result: {batch[2][1][feature]}\n") @@ -290,10 +334,13 @@ def test_final_results(): with open('test.log', 'a') as file: for feature, results in tested_features.items(): - accuracy = results['passed'] / (results['passed'] + results['failed']) * 100 + accuracy = results['passed'] / \ + (results['passed'] + results['failed']) * 100 + # file.write(f"Feature: {feature}\n") if accuracy != 100: file.write("\n") file.write("------RESULTS------\n") file.write(f"Feature: {feature}\n") - file.write(f"Accuracy: {results['passed'] / (results['passed'] + results['failed']) * 100:.2f}%") - file.write("\n") \ No newline at end of file + file.write( + f"Accuracy: {results['passed'] / (results['passed'] + results['failed']) * 100:.2f}%") + file.write("\n")