From 9b9ce1614be3a9d5b87016927c0e0e37020a3a79 Mon Sep 17 00:00:00 2001 From: Xinlan Emily Hu Date: Tue, 8 Oct 2024 00:02:13 -0400 Subject: [PATCH 1/3] v.0.1.4 Release (#315) * 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. * 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 --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Shruti Agarwal <46203852+agshruti12@users.noreply.github.com> Co-authored-by: amytangzheng Co-authored-by: amytangzheng <145236844+amytangzheng@users.noreply.github.com> --- .../github-actions-feature_dict.yaml | 60 + .github/workflows/github-actions-website.yaml | 46 + .gitignore | 1 + README.md | 4 +- docs/build/doctrees/environment.pickle | Bin 317452 -> 2268761 bytes docs/build/doctrees/examples.doctree | Bin 121540 -> 137145 bytes docs/build/doctrees/feature_builder.doctree | Bin 85973 -> 69652 bytes .../doctrees/features/basic_features.doctree | Bin 13636 -> 13498 bytes .../doctrees/features/burstiness.doctree | Bin 15930 -> 15820 bytes .../build/doctrees/features/certainty.doctree | Bin 9167 -> 9085 bytes .../features/discursive_diversity.doctree | Bin 15684 -> 15546 bytes docs/build/doctrees/features/fflow.doctree | Bin 9248 -> 9166 bytes .../features/get_all_DD_features.doctree | Bin 15053 -> 14943 bytes .../features/get_user_network.doctree | Bin 14401 -> 14291 bytes docs/build/doctrees/features/hedge.doctree | Bin 7693 -> 7611 bytes docs/build/doctrees/features/index.doctree | Bin 9918 -> 9897 bytes .../features/info_exchange_zscore.doctree | Bin 11803 -> 11721 bytes .../features/information_diversity.doctree | Bin 24856 -> 24690 bytes .../features/lexical_features_v2.doctree | Bin 16310 -> 15283 bytes 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docs/build/html/searchindex.js | 2 +- docs/build/html/utils/check_embeddings.html | 13 +- docs/source/basics.rst | 12 +- docs/source/examples.rst | 100 +- docs/source/features_conceptual/liwc.rst | 12 +- docs/source/index.rst | 76 +- examples/demo.ipynb | 1974 +++++--- examples/featurize.py | 32 +- pyproject.toml | 7 +- requirements.txt | 5 +- setup.sh | 2 +- src/team_comm_tools/feature_builder.py | 114 +- src/team_comm_tools/feature_dict.py | 254 +- .../features/lexical_features_v2.py | 17 +- src/team_comm_tools/lambda_function.py | 29 + .../utils/calculate_chat_level_features.py | 13 +- src/team_comm_tools/utils/check_embeddings.py | 68 +- .../utils/download_resources.py | 14 +- tests/data/cleaned_data/help.ipynb | 47 - tests/data/cleaned_data/helper.ipynb | 1474 ------ tests/data/cleaned_data/test_chat_level.csv | 1362 ++--- .../cleaned_data/test_vector_edge_cases.csv | 6 + tests/run_package_grouping_tests.py | 52 +- tests/run_tests.py | 28 +- tests/test_feature_metrics.py | 4 +- tests/test_package.py | 48 +- website/package-lock.json | 4489 +++++++++++------ website/package.json | 13 +- website/public/CNAME | Bin 0 -> 58 bytes website/src/components/pages/Team.js | 16 +- 96 files changed, 6198 insertions(+), 4579 deletions(-) create mode 100644 .github/workflows/github-actions-feature_dict.yaml create mode 100644 .github/workflows/github-actions-website.yaml create mode 100644 src/team_comm_tools/lambda_function.py delete mode 100644 tests/data/cleaned_data/help.ipynb delete mode 100644 tests/data/cleaned_data/helper.ipynb create mode 100644 tests/data/cleaned_data/test_vector_edge_cases.csv create mode 100644 website/public/CNAME diff --git a/.github/workflows/github-actions-feature_dict.yaml b/.github/workflows/github-actions-feature_dict.yaml new file mode 100644 index 00000000..afe17ff4 --- /dev/null +++ b/.github/workflows/github-actions-feature_dict.yaml @@ -0,0 +1,60 @@ +name: Deploy feature_dict to AWS Lambda +run-name: ${{ github.actor }} is deploying the feature dictionary to AWS + +on: + push: + branches: + - 'dev' + paths: + - 'src/team_comm_tools/feature_dict.py' + +jobs: + deploy: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set Up Python + uses: actions/setup-python@v4 + with: + python-version: "3.11" + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + ./setup.sh + pip install flask + pip install awscli + + - name: Install package + run: pip install . + + # Run the feature_dict.py file to generate filtered_dict.json + - name: Run feature_dict.py + run: | + cd src + cd team_comm_tools + python feature_dict.py run + + - name: Package Lambda function + run: | + mkdir package + pip install --target ./package flask + cp src/team_comm_tools/feature_dict.py ./package # Copies feature_dict.py + cp src/team_comm_tools/lambda_function.py ./package # Copies lambda_function.py + cp src/team_comm_tools/filtered_dict.json ./package # Copies filtered_dict.json + cd package + zip -r ../function.zip . # Packages the Lambda function + + - name: Configure AWS credentials + uses: aws-actions/configure-aws-credentials@v1 + with: + aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }} + aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }} + aws-region: ${{ secrets.AWS_REGION }} + + - name: Update Lambda function + run: | + aws lambda update-function-code --function-name ${{ secrets.LAMBDA_FUNCTION_NAME }} --zip-file fileb://function.zip diff --git a/.github/workflows/github-actions-website.yaml b/.github/workflows/github-actions-website.yaml new file mode 100644 index 00000000..0d36ce4b --- /dev/null +++ b/.github/workflows/github-actions-website.yaml @@ -0,0 +1,46 @@ +name: Deploy Website on Commit +run-name: ${{ github.actor }} is deploying the website + +on: + push: + branches: + - 'dev' + paths: + - 'website/**' # Only trigger when changes occur in the website folder + +jobs: + deploy: + + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Set up Node.js + uses: actions/setup-node@v4 + with: + node-version: '20.15.0' + + - name: Install dependencies + run: npm ci + working-directory: ./website # Navigate to the website folder + + - name: Build the project + run: npm run build + working-directory: ./website + + - name: Add CNAME file + run: echo 'teamcommtools.seas.upenn.edu' > ./website/build/CNAME + + - name: Configure Git + run: | + git config --global user.email "team_comm_tools_admin@gmail.com" + git config --global user.name "team_comm_tools_admin" + working-directory: ./website + + - name: Deploy + run: | + git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/${{ github.repository }}.git + npm run deploy + working-directory: ./website # Run deploy inside the website folder diff --git a/.gitignore b/.gitignore index bfb8aeb6..96b04f41 100644 --- a/.gitignore +++ b/.gitignore @@ -31,6 +31,7 @@ MANIFEST .DS_Store # unwanted files +*/filtered_dict.json src/team_comm_tools/features/lexicons/liwc_lexicons/* src/team_comm_tools/features/lexicons/liwc_lexicons_small_test/* src/team_comm_tools/features/lexicons/certainty.txt diff --git a/README.md b/README.md index 4a0cf58d..3ddc232d 100644 --- a/README.md +++ b/README.md @@ -85,7 +85,7 @@ my_feature_builder = FeatureBuilder( ) # this line of code runs the FeatureBuilder on your data -my_feature_builder.featurize(col="message") +my_feature_builder.featurize() ``` ### Data Format @@ -112,4 +112,4 @@ For more information, please refer to the [Introduction on our Read the Docs Pag Please visit our website, [https://teamcommtools.seas.upenn.edu/](https://teamcommtools.seas.upenn.edu/), for general information about our project and research. For more detailed documentation on our features and examples, please visit our [Read the Docs Page](https://conversational-featurizer.readthedocs.io/en/latest/). # Becoming a Contributor -If you would like to make pull requests to this open-sourced repository, please read our [GitHub Repo Getting Started Guide](/github_repo_getting_started.md). We welcome new feature contributions or improvements to our framework. \ No newline at end of file +If you would like to make pull requests to this open-sourced repository, please read our [GitHub Repo Getting Started Guide](/github_repo_getting_started.md). 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These processes take time, and we cache the outputs so that subsequent runs of the FeatureBuilder on the same dataset will not take as much time. Therefore, we require you to pass in a location where you'd like us to save these outputs. + * By default, the directory is named "vector_data/." + * **Note that we do not require the name of the vector directory to be a folder that already exists**; if it doesn't exist, we will create it for you. * Inside the folder, we will store the RoBERTa outputs in a subfolder called "sentiment", and the SBERT vectors in a subfolder called "sentence." We will create both of these subfolders for you. * The **turns** parameter, which we will discuss later, controls whether or not you'd like the FeatureBuilder to treat successive utterances by the same individual as a single "turn," or whether you'd like them to be treated separately. We will cache different versions of outputs based on this parameter; we use a subfolder called "chats" (when **turns=False**) or "turns" (when **turns=True**). -* There are three output files for each run of the FeatureBuilder, which mirror the three levels of analysis: utterance-, speaker-, and conversation-level. (Please see the section on `Generating Features: Utterance-, Speaker-, and Conversation-Level `_ for more details.) However, this means that we require you to provide a path for where you would like us to store each of the output files; **output_file_path_chat_level** (Utterance- or Chat-Level Features), **output_file_path_user_level** (Speaker- or User-Level Features), and **output_file_path_conv_level** (Conversation-Level Features). +.. _output_file_details: + +Output File Naming Details +"""""""""""""""""""""""""""" + +* There are three output files for each run of the FeatureBuilder, which mirror the three levels of analysis: utterance-, speaker-, and conversation-level. (Please see the section on `Generating Features: Utterance-, Speaker-, and Conversation-Level `_ for more details.) These are generated using the **output_file_base** parameter. + + * **All of the outputs will be generated in a folder called "output."** + + * Within the "output" folder, **we generate sub-folders such that the three files will be located in subfolders called "chat," "user," and "conv," respectively.** + + * Similar to the **vector_directory** parameter, the "chat" directory will be renamed to "turn" depending on the value of the **turns** parameter. + +* It is possible to generate different names for each of the three output files, rather than using the same base file path by modifying **output_file_path_chat_level** (Utterance- or Chat-Level Features), **output_file_path_user_level** (Speaker- or User-Level Features), and **output_file_path_conv_level** (Conversation-Level Features). However, because outputs are organized in the specific locations described above, **we have specific requirements for inputting the output paths, and we will modify the path under the hood to match our file naming schema,** rather than saving the file directly to the specified location. * We expect that you pass in a **path**, not just a filename. For example, the path needs to be "./my_file.csv", and not just "my_file.csv"; you will get an error if you pass in only a name without the "/". - * Regardless of your path location, we will automatically append the name "output" to the fornt of your file path, such that **all of the outputs will be generated in a folder called "output."** + * Regardless of your path location, we will automatically append the name "output" to the fornt of your file path. - * Within the "output" folder, **we will also generate sub-folders such that the three files will be located in subfolders called "chat," "user," and "conv," respectively.** + * Within the "output" folder, **we will also generate the chat/user/conv sub-folders.** * If you pass in a path that already contains the above automatically-generated elements (for example, "./output/chat/my_chat_features.csv"), we will skip these steps and directly save it in the relevant folder. @@ -153,7 +174,7 @@ Basic Input Columns output_file_path_chat_level = "./output/chat/jury_output_chat_level.csv" - * And these two ways of specifying an output path are equivalent, assumign that turns=True: + * And these two ways of specifying an output path are equivalent, assuming that turns=True: .. code-block:: python @@ -161,6 +182,10 @@ Basic Input Columns output_file_path_chat_level = "./output/turn/jury_output_turn_level.csv" + +Turns +"""""" + * The **turns** parameter controls whether we want to treat successive messages from the same person as a single turn. For example, in a text conversation, sometimes individuals will send many message in rapid succession, as follows: * **John**: Hey Michael @@ -277,3 +302,62 @@ Here are some additional design details of the FeatureBuilder that you may wish * The only caveat to this rule is if you happen to have a column that is named exactly the same as one of the conversation features that we generate. In that case, your column will be overwritten. Please refer to ``_ for a list of all the features we generate, along with their column names. * **When summarizing features from the utterance level to the conversation and speaker level, we only consider numeric features.** This is perhaps a simplifying assumption more than anything else; although we do extract non-numeric information (for example, a Dale-Chall label of whether an utterance is "Easy" to ready or not; a list of named entities identified), we cannot summarize these efficiently, so they are not considered. + +Inspecting Generated Features +++++++++++++++++++++++++++++++ + +Feature Information +^^^^^^^^^^^^^^^^^^^^^ +Every FeatureBuilder object has an underlying property called the **feature_dict**, which lists information and references about the features included in the toolkit. Assuming that **jury_feature_builder** is the name of your FeatureBuilder, you can access the feature dictionary as follows: + +.. code-block:: python + + jury_feature_builder.feature_dict + +The keys of this dictionary are the formal feature names, and the value is a JSON blob with information about the feature or collection of features. A more nicely-displayed version of this dictionary is also available on our `website `_. + +**New in v.0.1.4**: To access a list of the formal feature names that a FeatureBuilder will generate, you can use the **feature_names** property: + +.. code-block:: python + + jury_feature_builder.feature_names # a list of formal feature names included in featurization (e.g., "Team Burstiness") + +You can also use the **feature_names** property in tandem with the **feature_dict** to learn more about a specific feature; for example, the following code will show the dictionary entry for the first feature in **feature_names**: + +.. code-block:: python + + jury_feature_builder.feature_dict[jury_feature_builder.feature_names[0]] + +Here is some example output (for the RoBERTa sentiment feature): + +.. code-block:: text + + {'columns': ['positive_bert', 'negative_bert', 'neutral_bert'], + 'file': './utils/check_embeddings.py', + 'level': 'Chat', + 'semantic_grouping': 'Emotion', + 'description': 'The extent to which a statement is positive, negative, or neutral, as assigned by Cardiffnlp/twitter-roberta-base-sentiment-latest. The total scores (Positive, Negative, Neutral) sum to 1.', + 'references': '(Hugging Face, 2023)', + 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/positivity_bert.html', + 'function': None>, + 'dependencies': [], + 'preprocess': [], + 'vect_data': False, + 'bert_sentiment_data': True} + +Feature Column Names +^^^^^^^^^^^^^^^^^^^^^ + +Once you call **.featurize()**, you can also obtain a convenient list of the feature columns generated by the toolkit: + +.. code-block:: python + + jury_feature_builder.chat_features # a list of the feature columns generated at the chat (utterance) level + jury_feature_builder.conv_features_base # a list of the base (non-aggregated) feature columns at the conversation level + jury_feature_builder.conv_features_all # a list of all feature columns at the conversation level, including aggregates + +These lists may be useful to you if you'd like to inspect which features in the output dataframe come from the FeatureBuilder; for example: + +.. code-block:: python + + jury_output_chat_level[jury_feature_builder.chat_features] \ No newline at end of file diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt index e3a9e994..9e4be9bf 100644 --- a/docs/build/html/_sources/index.rst.txt +++ b/docs/build/html/_sources/index.rst.txt @@ -44,6 +44,8 @@ After you import the package and install dependencies, you can then use our tool Using the Package ****************** +Declaring a FeatureBuilder ++++++++++++++++++++++++++++ Once you import the tool, you will be able to declare a FeatureBuilder object, which is the heart of our tool. Here is some sample syntax: .. code-block:: python @@ -60,11 +62,10 @@ Once you import the tool, you will be able to declare a FeatureBuilder object, w timestamp_col= "timestamp", # this is where we'll cache things like sentence vectors; this directory doesn't have to exist; we'll create it for you! vector_directory = "./vector_data/", - # give us names for the utterance (chat), speaker (user), and conversation-level outputs - output_file_path_chat_level = "./my_output_chat_level.csv", - output_file_path_user_level = "./my_output_user_level.csv", - output_file_path_conv_level = "./my_output_conversation_level.csv", - # if true, this will combine successive turns by the same speaker. + # this will be the base file path for which we generate the three outputs; + # you will get your outputs in output/chat/my_output_chat_level.csv; output/conv/my_output_conv_level.csv; and output/user/my_output_user_level. + output_file_base = "my_output" + # it will also store the output into output/turns/my_output_chat_level.csv turns = False, # these features depend on sentence vectors, so they take longer to generate on larger datasets. Add them in manually if you are interested in adding them to your output! custom_features = [ @@ -76,7 +77,70 @@ Once you import the tool, you will be able to declare a FeatureBuilder object, w ) # this line of code runs the FeatureBuilder on your data - my_feature_builder.featurize(col="message") + my_feature_builder.featurize() + +Inspecting Generated Features +++++++++++++++++++++++++++++++ + +Feature Information +^^^^^^^^^^^^^^^^^^^^^ +Every FeatureBuilder object has an underlying property called the **feature_dict**, which lists information and references about the features included in the toolkit. Assuming that **my_feature_builder** is the name of your FeatureBuilder, you can access the feature dictionary as follows: + +.. code-block:: python + + my_feature_builder.feature_dict + +The keys of this dictionary are the formal feature names, and the value is a JSON blob with information about the feature or collection of features. A more nicely-displayed version of this dictionary is also available on our `website `_. + +**New in v.0.1.4**: To access a list of the formal feature names that a FeatureBuilder will generate, you can use the **feature_names** property: + +.. code-block:: python + + my_feature_builder.feature_names # a list of formal feature names included in featurization (e.g., "Team Burstiness") + +You can also use the **feature_names** property in tandem with the **feature_dict** to learn more about a specific feature; for example, the following code will show the dictionary entry for the first feature in **feature_names**: + +.. code-block:: python + + my_feature_builder.feature_dict[my_feature_builder.feature_names[0]] + +Here is some example output (for the RoBERTa sentiment feature): + +.. code-block:: text + + {'columns': ['positive_bert', 'negative_bert', 'neutral_bert'], + 'file': './utils/check_embeddings.py', + 'level': 'Chat', + 'semantic_grouping': 'Emotion', + 'description': 'The extent to which a statement is positive, negative, or neutral, as assigned by Cardiffnlp/twitter-roberta-base-sentiment-latest. The total scores (Positive, Negative, Neutral) sum to 1.', + 'references': '(Hugging Face, 2023)', + 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/positivity_bert.html', + 'function': None>, + 'dependencies': [], + 'preprocess': [], + 'vect_data': False, + 'bert_sentiment_data': True} + +Feature Column Names +^^^^^^^^^^^^^^^^^^^^^ + +Once you call **.featurize()**, you can also obtain a convenient list of the feature columns generated by the toolkit: + +.. code-block:: python + + my_feature_builder.chat_features # a list of the feature columns generated at the chat (utterance) level + my_feature_builder.conv_features_base # a list of the base (non-aggregated) feature columns at the conversation level + my_feature_builder.conv_features_all # a list of all feature columns at the conversation level, including aggregates + +These lists may be useful to you if you'd like to inspect which features in the output dataframe come from the FeatureBuilder; for example: + +.. code-block:: python + + jury_output_chat_level[my_feature_builder.chat_features] + + +Table of Contents +****************** Use the Table of Contents below to learn more about our tool. We recommend that you begin in the "Introduction" section, then explore other sections of the documentation as they become relevant to you. We recommend reading :ref:`basics` for a high-level overview of the requirements and parameters, and then reading through the :ref:`examples` for a detailed walkthrough and discussion of considerations. diff --git a/docs/build/html/_static/searchtools.js b/docs/build/html/_static/searchtools.js index b08d58c9..92da3f8b 100644 --- a/docs/build/html/_static/searchtools.js +++ b/docs/build/html/_static/searchtools.js @@ -178,7 +178,7 @@ const Search = { htmlToText: (htmlString, anchor) => { const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - for (const removalQuery of [".headerlink", "script", "style"]) { + for (const removalQuery of [".headerlinks", "script", "style"]) { htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); } if (anchor) { @@ -328,14 +328,13 @@ 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) { - const score = Math.round(Scorer.title * queryLower.length / title.length); - const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + let score = Math.round(100 * queryLower.length / title.length) normalResults.push([ docNames[file], titles[file] !== title ? `${titles[file]} > ${title}` : title, id !== null ? "#" + id : "", null, - score + boost, + score, filenames[file], ]); } diff --git a/docs/build/html/examples.html b/docs/build/html/examples.html index 87206ae3..f91818c9 100644 --- a/docs/build/html/examples.html +++ b/docs/build/html/examples.html @@ -66,6 +66,11 @@

  • Additional FeatureBuilder Considerations
    • +
  • Inspecting Generated Features +
    • @@ -155,16 +160,16 @@

    Configuring the FeatureBuildertimestamp_col = "timestamp", grouping_keys = ["batch_num", "round_num"], vector_directory = "./vector_data/", - output_file_path_chat_level = "./jury_output_chat_level.csv", - output_file_path_user_level = "./jury_output_user_level.csv", - output_file_path_conv_level = "./jury_output_conversation_level.csv", + output_file_base = "jury_output", turns = True ) -jury_feature_builder.featurize(col="message") +jury_feature_builder.featurize()

    Basic Input Columns

    +
    +
    Conversation Parameters
    @@ -203,21 +209,41 @@

    Basic Input Columns +
    Vector Directory
    +

    • The vector_directory is the name of a directory in which we will store some pre-processed information. Some features require running inference from HuggingFace’s RoBERTa-based sentiment model, and others require generating SBERT vectors. These processes take time, and we cache the outputs so that subsequent runs of the FeatureBuilder on the same dataset will not take as much time. Therefore, we require you to pass in a location where you’d like us to save these outputs.

        +

      • By default, the directory is named “vector_data/.”

      • Note that we do not require the name of the vector directory to be a folder that already exists; if it doesn’t exist, we will create it for you.

      • Inside the folder, we will store the RoBERTa outputs in a subfolder called “sentiment”, and the SBERT vectors in a subfolder called “sentence.” We will create both of these subfolders for you.

      • The turns parameter, which we will discuss later, controls whether or not you’d like the FeatureBuilder to treat successive utterances by the same individual as a single “turn,” or whether you’d like them to be treated separately. We will cache different versions of outputs based on this parameter; we use a subfolder called “chats” (when turns=False) or “turns” (when turns=True).

      • -

    • There are three output files for each run of the FeatureBuilder, which mirror the three levels of analysis: utterance-, speaker-, and conversation-level. (Please see the section on Generating Features: Utterance-, Speaker-, and Conversation-Level for more details.) However, this means that we require you to provide a path for where you would like us to store each of the output files; output_file_path_chat_level (Utterance- or Chat-Level Features), output_file_path_user_level (Speaker- or User-Level Features), and output_file_path_conv_level (Conversation-Level Features).

      +
    +

    +
    +
    Output File Naming Details
    +
      +

    • There are three output files for each run of the FeatureBuilder, which mirror the three levels of analysis: utterance-, speaker-, and conversation-level. (Please see the section on Generating Features: Utterance-, Speaker-, and Conversation-Level for more details.) These are generated using the output_file_base parameter.

      +
      +
        +

      • All of the outputs will be generated in a folder called “output.”

        • +

      • Within the “output” folder, we generate sub-folders such that the three files will be located in subfolders called “chat,” “user,” and “conv,” respectively.

        • +

      • Similar to the vector_directory parameter, the “chat” directory will be renamed to “turn” depending on the value of the turns parameter.

        • +
      +
      +
      • +

    • It is possible to generate different names for each of the three output files, rather than using the same base file path by modifying output_file_path_chat_level (Utterance- or Chat-Level Features), output_file_path_user_level (Speaker- or User-Level Features), and output_file_path_conv_level (Conversation-Level Features). However, because outputs are organized in the specific locations described above, we have specific requirements for inputting the output paths, and we will modify the path under the hood to match our file naming schema, rather than saving the file directly to the specified location.

      • We expect that you pass in a path, not just a filename. For example, the path needs to be “./my_file.csv”, and not just “my_file.csv”; you will get an error if you pass in only a name without the “/”.

        • -

      • Regardless of your path location, we will automatically append the name “output” to the fornt of your file path, such that all of the outputs will be generated in a folder called “output.”

        • -

      • Within the “output” folder, we will also generate sub-folders such that the three files will be located in subfolders called “chat,” “user,” and “conv,” respectively.

        • +

      • Regardless of your path location, we will automatically append the name “output” to the fornt of your file path.

        • +

      • Within the “output” folder, we will also generate the chat/user/conv sub-folders.

      • If you pass in a path that already contains the above automatically-generated elements (for example, “./output/chat/my_chat_features.csv”), we will skip these steps and directly save it in the relevant folder.

      • Similar to the vector_directory parameter, the “chat” directory will be renamed to “turn” depending on the value of the turns parameter.

      • This means that the following two ways of specifying an output path are equivalent, assuming that turns=False:

        • @@ -228,7 +254,7 @@

        Basic Input Columns -

      • And these two ways of specifying an output path are equivalent, assumign that turns=True:

        • +

      • And these two ways of specifying an output path are equivalent, assuming that turns=True:

      output_file_path_chat_level = "./jury_output_turn_level.csv"
 
@@ -237,6 +263,11 @@ 
      Basic Input Columns
+
      Turns
      
+
        
 
      • The turns parameter controls whether we want to treat successive messages from the same person as a single turn. For example, in a text conversation, sometimes individuals will send many message in rapid succession, as follows:
        
 
        
 
          
@@ -255,6 +286,7 @@ 
          Basic Input Columns
 
          Advanced Configuration Columns
          
 
          More advanced users of the FeatureBuilder should consider the following optional parameters, depending on their needs.
          
@@ -373,6 +405,53 @@ 
          Additional FeatureBuilder Considerations
+
          Inspecting Generated Features
          
+
          
+
          Feature Information
          
+
          Every FeatureBuilder object has an underlying property called the feature_dict, which lists information and references about the features included in the toolkit. Assuming that jury_feature_builder is the name of your FeatureBuilder, you can access the feature dictionary as follows:
          
+
          jury_feature_builder.feature_dict
+
          
+
          
+
          The keys of this dictionary are the formal feature names, and the value is a JSON blob with information about the feature or collection of features. A more nicely-displayed version of this dictionary is also available on our website.
          
+
          New in v.0.1.4: To access a list of the formal feature names that a FeatureBuilder will generate, you can use the feature_names property:
          
+
          jury_feature_builder.feature_names # a list of formal feature names included in featurization (e.g., "Team Burstiness")
+
          
+
          
+
          You can also use the feature_names property in tandem with the feature_dict to learn more about a specific feature; for example, the following code will show the dictionary entry for the first feature in feature_names:
          
+
          jury_feature_builder.feature_dict[jury_feature_builder.feature_names[0]]
+
          
+
          
+
          Here is some example output (for the RoBERTa sentiment feature):
          
+
          {'columns': ['positive_bert', 'negative_bert', 'neutral_bert'],
+ 'file': './utils/check_embeddings.py',
+ 'level': 'Chat',
+ 'semantic_grouping': 'Emotion',
+ 'description': 'The extent to which a statement is positive, negative, or neutral, as assigned by Cardiffnlp/twitter-roberta-base-sentiment-latest. The total scores (Positive, Negative, Neutral) sum to 1.',
+ 'references': '(Hugging Face, 2023)',
+ 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/positivity_bert.html',
+ 'function': <function team_comm_tools.utils.calculate_chat_level_features.ChatLevelFeaturesCalculator.concat_bert_features(self) -> None>,
+ 'dependencies': [],
+ 'preprocess': [],
+ 'vect_data': False,
+ 'bert_sentiment_data': True}
+
          
+
          
+
          
+
          
+
          Feature Column Names
          
+
          Once you call .featurize(), you can also obtain a convenient list of the feature columns generated by the toolkit:
          
+
          jury_feature_builder.chat_features # a list of the feature columns generated at the chat (utterance) level
+jury_feature_builder.conv_features_base # a list of the base (non-aggregated) feature columns at the conversation level
+jury_feature_builder.conv_features_all # a list of all feature columns at the conversation level, including aggregates
+
          
+
          
+
          These lists may be useful to you if you’d like to inspect which features in the output dataframe come from the FeatureBuilder; for example:
          
+
          jury_output_chat_level[jury_feature_builder.chat_features]
+
          
+
          
+
          
+
    diff --git a/docs/build/html/feature_builder.html b/docs/build/html/feature_builder.html index d74e306f..16fbb9a7 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: DataFrame, vector_directory: str, output_file_path_chat_level: str, output_file_path_user_level: str, output_file_path_conv_level: str, 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)
    +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)

    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, @@ -107,10 +107,11 @@

    Parameters:

    • input_df (pd.DataFrame) – A pandas DataFrame containing the conversation data that you wish to featurize.

      • -

    • vector_directory (str) – Directory path where the vectors are to be cached.

      • -

    • output_file_path_chat_level (str) – Path where the chat (utterance)-level output csv file is to be generated.

      • -

    • output_file_path_user_level (str) – Path where the user (speaker)-level output csv file is to be generated.

      • -

    • output_file_path_conv_level (str) – Path where the conversation-level output csv file is to be generated.

      • +

    • vector_directory (str) – Directory path where the vectors are to be cached. Defaults to “./vector_data/”

      • +

    • output_file_base (str) – Base name for the output files, which will be used to auto-generate filenames for each of the three levels. Defaults to “output.”

      • +

    • output_file_path_chat_level (str) – Path where the chat (utterance)-level output csv file is to be generated. (This parameter will override the base name.)

      • +

    • output_file_path_user_level (str) – Path where the user (speaker)-level output csv file is to be generated. (This parameter will override the base name.)

      • +

    • output_file_path_conv_level (str) – Path where the conversation-level output csv file is to be generated. (This parameter will override the base name.)

    • custom_features (list, optional) – A list of additional features outside of the default features that should be calculated. Defaults to an empty list (i.e., no additional features beyond the defaults will be computed).

    • analyze_first_pct (list(float), optional) – Analyze the first X% of the data. This parameter is useful because the earlier stages of the conversation may be more predictive than the later stages. Thus, researchers may wish to analyze only the first X% of the conversation data and compare the performance with using the full dataset. Defaults to [1.0].

      • @@ -130,6 +131,7 @@

    • ner_training_df (pd.DataFrame) – This is a pandas dataframe of training data for named entity recognition feature. Defaults to None, and will not generate named entity featuers if it does not exist.

    • ner_cutoff (int) – This is the cutoff value for the confidence of prediction for each named entity. Defaults to 0.9.

    • regenerate_vectors (bool, optional) – If true, will regenerate vector data even if it already exists. Defaults to False.

      • +

    • compute_vectors_from_preprocessed (bool, optional) – If true, computes vectors using preprocessed text (that is, with capitalization and punctuation removed). This was the default behavior for v.0.1.3 and earlier, but we now default to computing metrics on the unpreprocessed text (which INCLUDES capitalization and punctuation). Defaults to False.

    Returns:
    @@ -174,22 +176,19 @@
    -featurize(col: str = 'message') → None
    +featurize() → None

    Main driver function for feature generation.

    This function creates chat-level features, generates features for different truncation percentages of the data if specified, and produces user-level and conversation-level features. Finally, the features are saved into the designated output files.

    -
    Parameters:
    -

    col (str, optional) – Column to preprocess, defaults to “message”

    +
    Returns:
    +

    None

    -
    Returns:
    +
    Return type:

    None

    -
    Return type:
    -

    None

    -
    diff --git a/docs/build/html/features/lexical_features_v2.html b/docs/build/html/features/lexical_features_v2.html index 249171b1..3ad8fd6b 100644 --- a/docs/build/html/features/lexical_features_v2.html +++ b/docs/build/html/features/lexical_features_v2.html @@ -54,7 +54,7 @@
  • basic_features module
  • certainty module
  • lexical_features_v2 module
    • @@ -114,12 +114,10 @@ — A faster version of the lexical_features.py file.

    -
    -features.lexical_features_v2.get_liwc_rate(regex, chat)
    +
    +features.lexical_features_v2.get_liwc_count(regex, chat)

    ” -Computes the LIWC features as a rate per 100 words, per best practice (Yeomans et al. 2023; https://www.mikeyeomans.info/papers/PGCR_yeomans.pdf, p. 42)

    -

    We apply the following formula: -Rate of word use / 100 words = count / chat length * (chat length / 100)

    +Count the number of LIWC lexicon words

    Parameters:
      @@ -128,7 +126,7 @@
    Returns:
    -

    The rate at which the message uses words from a given lexicon.

    +

    The number of lexicon words present in the message

    Return type:

    float

    @@ -141,7 +139,7 @@ features.lexical_features_v2.liwc_features(chat_df: DataFrame, message_col) → DataFrame

    This function takes in the chat level input dataframe and computes lexical features -(rates at which the message contains contains words from a given lexicon, such as LIWC).

    +(the number of words from a given lexicon, such as LIWC).

    Parameters:
    diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 32100a3c..bbb31849 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -512,7 +512,7 @@

    G

  • get_info_exchange_wordcount() (in module features.info_exchange_zscore)
    • -
  • get_liwc_rate() (in module features.lexical_features_v2) +
  • get_liwc_count() (in module features.lexical_features_v2)
  • get_max() (in module utils.summarize_features)
    • diff --git a/docs/build/html/index.html b/docs/build/html/index.html index 5353d11e..a522400c 100644 --- a/docs/build/html/index.html +++ b/docs/build/html/index.html @@ -110,6 +110,8 @@

    Import Recommendations: Virtual Environment and Pip

    Using the Package

    +
    +

    Declaring a FeatureBuilder

    Once you import the tool, you will be able to declare a FeatureBuilder object, which is the heart of our tool. Here is some sample syntax:

    my_feature_builder = FeatureBuilder(
    input_df = my_pandas_dataframe,
@@ -123,11 +125,10 @@ 
    Using the Packagetimestamp_col= "timestamp",
    # this is where we'll cache things like sentence vectors; this directory doesn't have to exist; we'll create it for you!
    vector_directory = "./vector_data/",
-   # give us names for the utterance (chat), speaker (user), and conversation-level outputs
-   output_file_path_chat_level = "./my_output_chat_level.csv",
-   output_file_path_user_level = "./my_output_user_level.csv",
-   output_file_path_conv_level = "./my_output_conversation_level.csv",
-   # if true, this will combine successive turns by the same speaker.
+   # this will be the base file path for which we generate the three outputs;
+   # you will get your outputs in output/chat/my_output_chat_level.csv; output/conv/my_output_conv_level.csv; and output/user/my_output_user_level.
+   output_file_base = "my_output"
+   # it will also store the output into output/turns/my_output_chat_level.csv
    turns = False,
    # these features depend on sentence vectors, so they take longer to generate on larger datasets. Add them in manually if you are interested in adding them to your output!
    custom_features = [
@@ -139,9 +140,60 @@ 
    Using the Package)
 
 # this line of code runs the FeatureBuilder on your data
-my_feature_builder.featurize(col="message")
+my_feature_builder.featurize()
    +
    +
    +

    Inspecting Generated Features

    +
    +

    Feature Information

    +

    Every FeatureBuilder object has an underlying property called the feature_dict, which lists information and references about the features included in the toolkit. Assuming that my_feature_builder is the name of your FeatureBuilder, you can access the feature dictionary as follows:

    +
    my_feature_builder.feature_dict
+
    +
    +

    The keys of this dictionary are the formal feature names, and the value is a JSON blob with information about the feature or collection of features. A more nicely-displayed version of this dictionary is also available on our website.

    +

    New in v.0.1.4: To access a list of the formal feature names that a FeatureBuilder will generate, you can use the feature_names property:

    +
    my_feature_builder.feature_names # a list of formal feature names included in featurization (e.g., "Team Burstiness")
+
    +
    +

    You can also use the feature_names property in tandem with the feature_dict to learn more about a specific feature; for example, the following code will show the dictionary entry for the first feature in feature_names:

    +
    my_feature_builder.feature_dict[my_feature_builder.feature_names[0]]
+
    +
    +

    Here is some example output (for the RoBERTa sentiment feature):

    +
    {'columns': ['positive_bert', 'negative_bert', 'neutral_bert'],
+ 'file': './utils/check_embeddings.py',
+ 'level': 'Chat',
+ 'semantic_grouping': 'Emotion',
+ 'description': 'The extent to which a statement is positive, negative, or neutral, as assigned by Cardiffnlp/twitter-roberta-base-sentiment-latest. The total scores (Positive, Negative, Neutral) sum to 1.',
+ 'references': '(Hugging Face, 2023)',
+ 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/positivity_bert.html',
+ 'function': <function team_comm_tools.utils.calculate_chat_level_features.ChatLevelFeaturesCalculator.concat_bert_features(self) -> None>,
+ 'dependencies': [],
+ 'preprocess': [],
+ 'vect_data': False,
+ 'bert_sentiment_data': True}
+
    +
    +
    +
    +

    Feature Column Names

    +

    Once you call .featurize(), you can also obtain a convenient list of the feature columns generated by the toolkit:

    +
    my_feature_builder.chat_features # a list of the feature columns generated at the chat (utterance) level
+my_feature_builder.conv_features_base # a list of the base (non-aggregated) feature columns at the conversation level
+my_feature_builder.conv_features_all # a list of all feature columns at the conversation level, including aggregates
+
    +
    +

    These lists may be useful to you if you’d like to inspect which features in the output dataframe come from the FeatureBuilder; for example:

    +
    jury_output_chat_level[my_feature_builder.chat_features]
+
    +
    +
    +
    +
    +
    +

    Table of Contents

    Use the Table of Contents below to learn more about our tool. We recommend that you begin in the “Introduction” section, then explore other sections of the documentation as they become relevant to you. We recommend reading The Basics for a high-level overview of the requirements and parameters, and then reading through the Worked Example for a detailed walkthrough and discussion of considerations.

    Contents:

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69], "start": [1, 61, 62], "strategi": 50, "subject": 57, "summarize_featur": 72, "tabl": 61, "tag": 48, "take": 59, "team": [55, 61, 62], "technic": 11, "temporal_featur": 23, "textblob": [56, 57], "textblob_sentiment_analysi": 24, "time": 58, "token": 60, "toolkit": 61, "touch": 0, "train": 47, "troubleshoot": [1, 61], "turn": [1, 59], "turn_taking_featur": 25, "type": 60, "us": 61, "user": 11, "util": 69, "utter": [11, 39, 62, 69], "variance_in_dd": 26, "vector": 1, "virtual": [1, 61], "walkthrough": 1, "within_person_discursive_rang": 27, "word": [31, 36, 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/build/html/utils/check_embeddings.html b/docs/build/html/utils/check_embeddings.html index 8f98f7b0..ba19ea73 100644 --- a/docs/build/html/utils/check_embeddings.html +++ b/docs/build/html/utils/check_embeddings.html @@ -132,7 +132,7 @@
    -utils.check_embeddings.generate_bert(chat_data, output_path, message_col)
    +utils.check_embeddings.generate_bert(chat_data, output_path, message_col, batch_size=64)

    Generates RoBERTa sentiment scores for the given chat data and saves them to a CSV file.

    Parameters:
    @@ -140,6 +140,7 @@

  • chat_data (pd.DataFrame) – Contains message data to be analyzed for sentiments.

  • output_path (str) – Path to save the CSV file containing sentiment scores.

  • message_col (str, optional) – A string representing the column name that should be selected as the message. Defaults to “message”.

    • +

  • batch_size (int) – The size of each batch for processing sentiment analysis. Defaults to 64.

    • Raises:
    @@ -224,17 +225,17 @@
    -utils.check_embeddings.get_sentiment(text)
    -

    Analyzes the sentiment of the given text using a BERT model and returns the scores for positive, negative, and neutral sentiments.

    +utils.check_embeddings.get_sentiment(texts) +

    Analyzes the sentiment of the given list of texts using a BERT model and returns a DataFrame with scores for positive, negative, and neutral sentiments.

    Parameters:
    -

    text (str or None) – The input text to analyze.

    +

    texts (list of str) – The list of input texts to analyze.

    Returns:
    -

    A dictionary with sentiment scores.

    +

    A DataFrame with sentiment scores.

    Return type:
    -

    dict

    +

    pd.DataFrame

    diff --git a/docs/source/basics.rst b/docs/source/basics.rst index 6f5d1c9e..87221375 100644 --- a/docs/source/basics.rst +++ b/docs/source/basics.rst @@ -48,10 +48,14 @@ Package Assumptions 7. **Additional Columns**: Columns not required as inputs (conversation identifier, speaker identifier, message, and timestamp column(s)) are assumed to be metadata and won't be summarized in the featurization process. 8. **Vector Data Cache**: Your data's vector data will be cached in **vector_directory**. This directory will be created if it doesn’t exist, but its contents should be reserved for cached vector files. + + * This parameter defaults to "vector_data/". -9. **Output Files**: We generate three outputs: **output_file_path_chat_level** (Utterance- or Chat-Level Features), **output_file_path_user_level** (Speaker- or User-Level Features), and **output_file_path_conv_level** (Conversation-Level Features). + * Note: v0.1.3 and earlier compute vectors using _preprocessed_ text by default, which drops capitalization and punctuation. However, this can affect the interpretation of sentiment vectors; for example, "Hello!" has more positive sentiment than "hello." Consequently, from v0.1.4 onwards, we compute vectors using the raw input text, including punctuation and capitalization. To restore this behavior, please set **compute_vectors_from_preprocessed** to True. - * This should be a *path*, not just a filename. For example, "./my_file.csv", not just "my_file.csv." + * Additionally, we assume that empty messages are equivalent to "NaN vector," defined `here `_. + +9. **Output File Base**: We generate three output files at different levels of analysis. (Utterance/Chat, Speaker/User, and Conversation). We recommend using the **output_file_base** parameter to give them all a common naming scheme (a string that will be used to automatically name all files). You can also name each of them individually, but there's some complexity (for now) that we explain in :ref:`output_file_details`. 10. **Custom Features**: To save time, we exclude features that require computing sentence vectors by default. To access these features, use the **custom_features** parameter in your FeatureBuilder: @@ -79,4 +83,6 @@ Here are some parameters that can be customized. For more details, refer to the 4. **ner_training_df** and **ner_cutoff**: Measure the number of named entities in each utterance (see :ref:`named_entity_recognition`). -5. **regenerate_vectors**: Force-regenerate vector data even if it already exists. \ No newline at end of file +5. **regenerate_vectors**: Force-regenerate vector data even if it already exists. + +6. **compute_vectors_from_preprocessed**: Computes vectors using preprocessed text (that is, with capitalization and punctuation removed). This was the default behavior for v.0.1.3 and earlier, but we now default to computing metrics on the unpreprocessed text (which INCLUDES capitalization and punctuation), and this parameter now defaults to False. \ No newline at end of file diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 8d4e89e1..b7bc948d 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -85,16 +85,17 @@ Now we are ready to call the FeatureBuilder on our data. All we need to do is de timestamp_col = "timestamp", grouping_keys = ["batch_num", "round_num"], vector_directory = "./vector_data/", - output_file_path_chat_level = "./jury_output_chat_level.csv", - output_file_path_user_level = "./jury_output_user_level.csv", - output_file_path_conv_level = "./jury_output_conversation_level.csv", + output_file_base = "jury_output", turns = True ) - jury_feature_builder.featurize(col="message") + jury_feature_builder.featurize() Basic Input Columns ^^^^^^^^^^^^^^^^^^^^ +Conversation Parameters +""""""""""""""""""""""""" + * The **input_df** parameter is where you pass in your dataframe. In this case, we want to run the FeatureBuilder on the juries data that we read in! * The **speaker_id_col** refers to the name of the column containing a unique identifier for each speaker / participant in the conversation. Here, in the data, the name of our columns is called "speaker_nickname." @@ -105,6 +106,8 @@ Basic Input Columns * If you do not pass anything in, "message" is the default value for this parameter. + * We assume that all messages are ordered chronologically. + * The **timestamp_col** refers to the name of the column containing when each utterance was said. In this case, we have exactly one timestamp for each message, stored in "timestamp." * If you do not pass anything in, "timestamp" is the default value for this parameter. @@ -125,21 +128,39 @@ Basic Input Columns conversation_id_col = "batch_num" +Vector Directory +"""""""""""""""""" + * The **vector_directory** is the name of a directory in which we will store some pre-processed information. Some features require running inference from HuggingFace's `RoBERTa-based sentiment model `_, and others require generating `SBERT vectors `_. These processes take time, and we cache the outputs so that subsequent runs of the FeatureBuilder on the same dataset will not take as much time. Therefore, we require you to pass in a location where you'd like us to save these outputs. + * By default, the directory is named "vector_data/." + * **Note that we do not require the name of the vector directory to be a folder that already exists**; if it doesn't exist, we will create it for you. * Inside the folder, we will store the RoBERTa outputs in a subfolder called "sentiment", and the SBERT vectors in a subfolder called "sentence." We will create both of these subfolders for you. * The **turns** parameter, which we will discuss later, controls whether or not you'd like the FeatureBuilder to treat successive utterances by the same individual as a single "turn," or whether you'd like them to be treated separately. We will cache different versions of outputs based on this parameter; we use a subfolder called "chats" (when **turns=False**) or "turns" (when **turns=True**). -* There are three output files for each run of the FeatureBuilder, which mirror the three levels of analysis: utterance-, speaker-, and conversation-level. (Please see the section on `Generating Features: Utterance-, Speaker-, and Conversation-Level `_ for more details.) However, this means that we require you to provide a path for where you would like us to store each of the output files; **output_file_path_chat_level** (Utterance- or Chat-Level Features), **output_file_path_user_level** (Speaker- or User-Level Features), and **output_file_path_conv_level** (Conversation-Level Features). +.. _output_file_details: + +Output File Naming Details +"""""""""""""""""""""""""""" + +* There are three output files for each run of the FeatureBuilder, which mirror the three levels of analysis: utterance-, speaker-, and conversation-level. (Please see the section on `Generating Features: Utterance-, Speaker-, and Conversation-Level `_ for more details.) These are generated using the **output_file_base** parameter. + + * **All of the outputs will be generated in a folder called "output."** + + * Within the "output" folder, **we generate sub-folders such that the three files will be located in subfolders called "chat," "user," and "conv," respectively.** + + * Similar to the **vector_directory** parameter, the "chat" directory will be renamed to "turn" depending on the value of the **turns** parameter. + +* It is possible to generate different names for each of the three output files, rather than using the same base file path by modifying **output_file_path_chat_level** (Utterance- or Chat-Level Features), **output_file_path_user_level** (Speaker- or User-Level Features), and **output_file_path_conv_level** (Conversation-Level Features). However, because outputs are organized in the specific locations described above, **we have specific requirements for inputting the output paths, and we will modify the path under the hood to match our file naming schema,** rather than saving the file directly to the specified location. * We expect that you pass in a **path**, not just a filename. For example, the path needs to be "./my_file.csv", and not just "my_file.csv"; you will get an error if you pass in only a name without the "/". - * Regardless of your path location, we will automatically append the name "output" to the fornt of your file path, such that **all of the outputs will be generated in a folder called "output."** + * Regardless of your path location, we will automatically append the name "output" to the fornt of your file path. - * Within the "output" folder, **we will also generate sub-folders such that the three files will be located in subfolders called "chat," "user," and "conv," respectively.** + * Within the "output" folder, **we will also generate the chat/user/conv sub-folders.** * If you pass in a path that already contains the above automatically-generated elements (for example, "./output/chat/my_chat_features.csv"), we will skip these steps and directly save it in the relevant folder. @@ -153,7 +174,7 @@ Basic Input Columns output_file_path_chat_level = "./output/chat/jury_output_chat_level.csv" - * And these two ways of specifying an output path are equivalent, assumign that turns=True: + * And these two ways of specifying an output path are equivalent, assuming that turns=True: .. code-block:: python @@ -161,6 +182,10 @@ Basic Input Columns output_file_path_chat_level = "./output/turn/jury_output_turn_level.csv" + +Turns +"""""" + * The **turns** parameter controls whether we want to treat successive messages from the same person as a single turn. For example, in a text conversation, sometimes individuals will send many message in rapid succession, as follows: * **John**: Hey Michael @@ -277,3 +302,62 @@ Here are some additional design details of the FeatureBuilder that you may wish * The only caveat to this rule is if you happen to have a column that is named exactly the same as one of the conversation features that we generate. In that case, your column will be overwritten. Please refer to ``_ for a list of all the features we generate, along with their column names. * **When summarizing features from the utterance level to the conversation and speaker level, we only consider numeric features.** This is perhaps a simplifying assumption more than anything else; although we do extract non-numeric information (for example, a Dale-Chall label of whether an utterance is "Easy" to ready or not; a list of named entities identified), we cannot summarize these efficiently, so they are not considered. + +Inspecting Generated Features +++++++++++++++++++++++++++++++ + +Feature Information +^^^^^^^^^^^^^^^^^^^^^ +Every FeatureBuilder object has an underlying property called the **feature_dict**, which lists information and references about the features included in the toolkit. Assuming that **jury_feature_builder** is the name of your FeatureBuilder, you can access the feature dictionary as follows: + +.. code-block:: python + + jury_feature_builder.feature_dict + +The keys of this dictionary are the formal feature names, and the value is a JSON blob with information about the feature or collection of features. A more nicely-displayed version of this dictionary is also available on our `website `_. + +**New in v.0.1.4**: To access a list of the formal feature names that a FeatureBuilder will generate, you can use the **feature_names** property: + +.. code-block:: python + + jury_feature_builder.feature_names # a list of formal feature names included in featurization (e.g., "Team Burstiness") + +You can also use the **feature_names** property in tandem with the **feature_dict** to learn more about a specific feature; for example, the following code will show the dictionary entry for the first feature in **feature_names**: + +.. code-block:: python + + jury_feature_builder.feature_dict[jury_feature_builder.feature_names[0]] + +Here is some example output (for the RoBERTa sentiment feature): + +.. code-block:: text + + {'columns': ['positive_bert', 'negative_bert', 'neutral_bert'], + 'file': './utils/check_embeddings.py', + 'level': 'Chat', + 'semantic_grouping': 'Emotion', + 'description': 'The extent to which a statement is positive, negative, or neutral, as assigned by Cardiffnlp/twitter-roberta-base-sentiment-latest. The total scores (Positive, Negative, Neutral) sum to 1.', + 'references': '(Hugging Face, 2023)', + 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/positivity_bert.html', + 'function': None>, + 'dependencies': [], + 'preprocess': [], + 'vect_data': False, + 'bert_sentiment_data': True} + +Feature Column Names +^^^^^^^^^^^^^^^^^^^^^ + +Once you call **.featurize()**, you can also obtain a convenient list of the feature columns generated by the toolkit: + +.. code-block:: python + + jury_feature_builder.chat_features # a list of the feature columns generated at the chat (utterance) level + jury_feature_builder.conv_features_base # a list of the base (non-aggregated) feature columns at the conversation level + jury_feature_builder.conv_features_all # a list of all feature columns at the conversation level, including aggregates + +These lists may be useful to you if you'd like to inspect which features in the output dataframe come from the FeatureBuilder; for example: + +.. code-block:: python + + jury_output_chat_level[jury_feature_builder.chat_features] \ No newline at end of file diff --git a/docs/source/features_conceptual/liwc.rst b/docs/source/features_conceptual/liwc.rst index f3af8a97..399cd743 100644 --- a/docs/source/features_conceptual/liwc.rst +++ b/docs/source/features_conceptual/liwc.rst @@ -21,9 +21,17 @@ Implementation **************** For each word in the LIWC lexicon, we use a regular expression to count the number of times the word appears. The regular expression captures word stems where relevant; for example, "certain*" would capture "certainty," "certainly," etc. -All word counts are represented as a scaled rate of **number of words / 100 words**, per `Yeomans et al. (2023) `_, as this makes it easier to compare across conversations and utterances of varying lengths. We compute this as follows: +**Note:** -Rate of word use / 100 words = count / utterance length * (utterance length / 100) +- **In v.1.0.3 and earlier:** + Word counts were presented as a scaled rate of **number of words per 100 words**, computed using the following formula: + + .. code-block:: text + + Rate of word use per 100 words = (count / utterance length) * (utterance length / 100) + +- **In v.1.0.4 and later:** + Lexical values are represented as a **raw count** of the number of times they appear in the utteranc Interpreting the Feature ************************* diff --git a/docs/source/index.rst b/docs/source/index.rst index e3a9e994..9e4be9bf 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -44,6 +44,8 @@ After you import the package and install dependencies, you can then use our tool Using the Package ****************** +Declaring a FeatureBuilder ++++++++++++++++++++++++++++ Once you import the tool, you will be able to declare a FeatureBuilder object, which is the heart of our tool. Here is some sample syntax: .. code-block:: python @@ -60,11 +62,10 @@ Once you import the tool, you will be able to declare a FeatureBuilder object, w timestamp_col= "timestamp", # this is where we'll cache things like sentence vectors; this directory doesn't have to exist; we'll create it for you! vector_directory = "./vector_data/", - # give us names for the utterance (chat), speaker (user), and conversation-level outputs - output_file_path_chat_level = "./my_output_chat_level.csv", - output_file_path_user_level = "./my_output_user_level.csv", - output_file_path_conv_level = "./my_output_conversation_level.csv", - # if true, this will combine successive turns by the same speaker. + # this will be the base file path for which we generate the three outputs; + # you will get your outputs in output/chat/my_output_chat_level.csv; output/conv/my_output_conv_level.csv; and output/user/my_output_user_level. + output_file_base = "my_output" + # it will also store the output into output/turns/my_output_chat_level.csv turns = False, # these features depend on sentence vectors, so they take longer to generate on larger datasets. Add them in manually if you are interested in adding them to your output! custom_features = [ @@ -76,7 +77,70 @@ Once you import the tool, you will be able to declare a FeatureBuilder object, w ) # this line of code runs the FeatureBuilder on your data - my_feature_builder.featurize(col="message") + my_feature_builder.featurize() + +Inspecting Generated Features +++++++++++++++++++++++++++++++ + +Feature Information +^^^^^^^^^^^^^^^^^^^^^ +Every FeatureBuilder object has an underlying property called the **feature_dict**, which lists information and references about the features included in the toolkit. Assuming that **my_feature_builder** is the name of your FeatureBuilder, you can access the feature dictionary as follows: + +.. code-block:: python + + my_feature_builder.feature_dict + +The keys of this dictionary are the formal feature names, and the value is a JSON blob with information about the feature or collection of features. A more nicely-displayed version of this dictionary is also available on our `website `_. + +**New in v.0.1.4**: To access a list of the formal feature names that a FeatureBuilder will generate, you can use the **feature_names** property: + +.. code-block:: python + + my_feature_builder.feature_names # a list of formal feature names included in featurization (e.g., "Team Burstiness") + +You can also use the **feature_names** property in tandem with the **feature_dict** to learn more about a specific feature; for example, the following code will show the dictionary entry for the first feature in **feature_names**: + +.. code-block:: python + + my_feature_builder.feature_dict[my_feature_builder.feature_names[0]] + +Here is some example output (for the RoBERTa sentiment feature): + +.. code-block:: text + + {'columns': ['positive_bert', 'negative_bert', 'neutral_bert'], + 'file': './utils/check_embeddings.py', + 'level': 'Chat', + 'semantic_grouping': 'Emotion', + 'description': 'The extent to which a statement is positive, negative, or neutral, as assigned by Cardiffnlp/twitter-roberta-base-sentiment-latest. The total scores (Positive, Negative, Neutral) sum to 1.', + 'references': '(Hugging Face, 2023)', + 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/positivity_bert.html', + 'function': None>, + 'dependencies': [], + 'preprocess': [], + 'vect_data': False, + 'bert_sentiment_data': True} + +Feature Column Names +^^^^^^^^^^^^^^^^^^^^^ + +Once you call **.featurize()**, you can also obtain a convenient list of the feature columns generated by the toolkit: + +.. code-block:: python + + my_feature_builder.chat_features # a list of the feature columns generated at the chat (utterance) level + my_feature_builder.conv_features_base # a list of the base (non-aggregated) feature columns at the conversation level + my_feature_builder.conv_features_all # a list of all feature columns at the conversation level, including aggregates + +These lists may be useful to you if you'd like to inspect which features in the output dataframe come from the FeatureBuilder; for example: + +.. code-block:: python + + jury_output_chat_level[my_feature_builder.chat_features] + + +Table of Contents +****************** Use the Table of Contents below to learn more about our tool. We recommend that you begin in the "Introduction" section, then explore other sections of the documentation as they become relevant to you. We recommend reading :ref:`basics` for a high-level overview of the requirements and parameters, and then reading through the :ref:`examples` for a detailed walkthrough and discussion of considerations. diff --git a/examples/demo.ipynb b/examples/demo.ipynb index 61070522..07ddd8d3 100644 --- a/examples/demo.ipynb +++ b/examples/demo.ipynb @@ -6,7 +6,7 @@ "source": [ "# Welcome to the Team Communication Toolkit Demo Notebook!\n", "\n", - "Written by [Xinlan Emily Hu](https://xinlanemilyhu.com), and last updated on **September 17, 2024**.\n", + "Written by [Xinlan Emily Hu](https://xinlanemilyhu.com), and last updated on **October 7, 2024**.\n", "\n", "This notebook will walk through how to install the [Team Communication Toolkit](https://pypi.org/project/team-comm-tools/) and use it to analyze conversational data. The goal of the Team Communication Toolkit is to make it easy to bootstrap analyses of multi-party text communication; you can read a little bit more about our tool [here](https://conversational-featurizer.readthedocs.io/en/latest/intro.html)." ] @@ -51,7 +51,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "team_comm_tools 0.1.3\n" + "team_comm_tools 0.1.4\n" ] } ], @@ -452,7 +452,20 @@ "text": [ "Initializing Featurization...\n", "Confirmed that data has conversation_id: conversation_num, speaker_id: speaker_nickname and message: message columns!\n", - "Chat Level Features ...\n", + "Chat Level Features ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:01<00:00, 9.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Generating features for the first 100.0% of messages...\n", "Generating User Level Features ...\n", "Generating Conversation Level Features ...\n", @@ -463,14 +476,12 @@ "source": [ "jury_feature_builder = FeatureBuilder(\n", "\t\tinput_df = juries_df,\n", + " output_file_base = \"jury_tiny_output\", # We use this base string to construct outputs, which will appear at output/chat/, output/conv, and output/user\n", "\t\tspeaker_id_col = \"speaker_nickname\", # This is the column that contains the speaker IDs\n", "\t\tmessage_col = \"message\", # This is the column that contains the messages\n", "\t\ttimestamp_col = \"timestamp\", # This is the column that contains the timestamps\n", "\t\tgrouping_keys = [\"batch_num\", \"round_num\"], # These are the columns that define the conversation identifier\n", "\t\tvector_directory = \"./vector_data/\", # This is the directory where the sentence vectors and cached BERT outputs stored\n", - "\t\toutput_file_path_chat_level = \"./jury_tiny_output_chat_level.csv\", # This is the path to save the utterance (chat)-level features\n", - "\t\toutput_file_path_user_level = \"./jury_tiny_output_user_level.csv\", # This is the path to save the speaker (user)-level features\n", - "\t\toutput_file_path_conv_level = \"./jury_tiny_output_conversation_level.csv\", # This is the path to save the conversation-level features\n", "\t\t\n", "\t\t# Flip this to True if you don't want to automatically combine successive \n", "\t\t# messages from the same speaker as a single \"turn;\"\n", @@ -485,7 +496,603 @@ "\t\t\t\"Forward Flow\",\n", "\t\t\t\"Discursive Diversity\"]\n", ")\n", - "jury_feature_builder.featurize(col=\"message\")" + "jury_feature_builder.featurize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspecting Feature Information\n", + "Every `FeatureBuilder` instance includes a property known as the `feature_dict`, which presents information about the available features within the toolkit. We can retrieve the feature dictionary from the `jury_feature_builder` using `jury_feature_builder.feature_dict`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Named Entity Recognition': {'columns': ['num_named_entity',\n", + " 'named_entities'],\n", + " 'file': './features/named_entity_recognition_features.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Content',\n", + " 'description': 'This feature detects whether a user is talking about (or to) someone else in a conversation.',\n", + " 'references': 'N/A',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/named_entity_recognition.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Sentiment (RoBERTa)': {'columns': ['positive_bert',\n", + " 'negative_bert',\n", + " 'neutral_bert'],\n", + " 'file': './utils/check_embeddings.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Emotion',\n", + " 'description': 'The extent to which a statement is positive, negative, or neutral, as assigned by Cardiffnlp/twitter-roberta-base-sentiment-latest. The total scores (Positive, Negative, Neutral) sum to 1.',\n", + " 'references': '(Hugging Face, 2023)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/positivity_bert.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': True},\n", + " 'Message Length': {'columns': ['num_words', 'num_chars'],\n", + " 'file': './features/basic_features.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Quantity',\n", + " 'description': 'The length of a message in words and characters.',\n", + " 'references': '(Ranganath et al., 2013; Cao et al., 2021)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/message_length.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Message Quantity': {'columns': ['num_messages'],\n", + " 'file': './features/basic_features.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Quantity',\n", + " 'description': 'The total number of messages sent.',\n", + " 'references': '(Cao et al., 2021; Marlow et al., 2018, as objective communication frequency)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/message_quantity.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Information Exchange': {'columns': ['info_exchange_zscore_chats',\n", + " 'info_exchange_zscore_conversation'],\n", + " 'file': './features/info_exchange_zscore.py, ./utils/zscore_chats_and_conversation.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Content',\n", + " 'description': 'A crude measure of task-focused communication: the total number of words spoken, with the number of first-person pronouns (which suggest self-focus) removed. This value is then z-scored to describe the extent to which a message had more/less task-focused communication relative to other messages. We implement two flavors of the z-score: the first scores the messages with respect to other messages in the same conversation; the second scores the messages with respect to all messages in the data.',\n", + " 'references': '(Tausczik & Pennebaker, 2013)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/information_exchange.html#',\n", + " 'function': None>,\n", + " 'dependencies': [ None>],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'LIWC and Other Lexicons': {'columns': ['discrepancies_lexical_wordcount',\n", + " 'hear_lexical_wordcount',\n", + " 'home_lexical_wordcount',\n", + " 'conjunction_lexical_wordcount',\n", + " 'certainty_lexical_wordcount',\n", + " 'inclusive_lexical_wordcount',\n", + " 'bio_lexical_wordcount',\n", + " 'achievement_lexical_wordcount',\n", + " 'adverbs_lexical_wordcount',\n", + " 'anxiety_lexical_wordcount',\n", + " 'third_person_lexical_wordcount',\n", + " 'negation_lexical_wordcount',\n", + " 'swear_lexical_wordcount',\n", + " 'death_lexical_wordcount',\n", + " 'health_lexical_wordcount',\n", + " 'see_lexical_wordcount',\n", + " 'body_lexical_wordcount',\n", + " 'family_lexical_wordcount',\n", + " 'negative_affect_lexical_wordcount',\n", + " 'quantifier_lexical_wordcount',\n", + " 'positive_affect_lexical_wordcount',\n", + " 'insight_lexical_wordcount',\n", + " 'humans_lexical_wordcount',\n", + " 'present_tense_lexical_wordcount',\n", + " 'future_tense_lexical_wordcount',\n", + " 'past_tense_lexical_wordcount',\n", + " 'relative_lexical_wordcount',\n", + " 'sexual_lexical_wordcount',\n", + " 'inhibition_lexical_wordcount',\n", + " 'sadness_lexical_wordcount',\n", + " 'social_lexical_wordcount',\n", + " 'indefinite_pronoun_lexical_wordcount',\n", + " 'religion_lexical_wordcount',\n", + " 'work_lexical_wordcount',\n", + " 'money_lexical_wordcount',\n", + " 'causation_lexical_wordcount',\n", + " 'anger_lexical_wordcount',\n", + " 'first_person_singular_lexical_wordcount',\n", + " 'feel_lexical_wordcount',\n", + " 'tentativeness_lexical_wordcount',\n", + " 'exclusive_lexical_wordcount',\n", + " 'verbs_lexical_wordcount',\n", + " 'friends_lexical_wordcount',\n", + " 'article_lexical_wordcount',\n", + " 'argue_lexical_wordcount',\n", + " 'auxiliary_verbs_lexical_wordcount',\n", + " 'cognitive_mech_lexical_wordcount',\n", + " 'preposition_lexical_wordcount',\n", + " 'first_person_plural_lexical_wordcount',\n", + " 'percept_lexical_wordcount',\n", + " 'second_person_lexical_wordcount',\n", + " 'positive_words_lexical_wordcount',\n", + " 'first_person_lexical_wordcount',\n", + " 'nltk_english_stopwords_lexical_wordcount',\n", + " 'hedge_words_lexical_wordcount'],\n", + " 'file': './features/lexical_features_v2.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': ['Content', 'Emotion', 'Engagement'],\n", + " 'description': 'The extent to which messages reflect words from a variety of lexicons (predominantly LIWC). Each measure is expressed as a rate of word use per 100 words.',\n", + " 'references': '(For LIWC: Niederhoffer & Pennebaker, 2002; Pennebaker et al., 1997; Tausczik & Pennebaker, 2010; for positive words, Hu and Liu (2004); for NLTK English Stopwords: Inspired by Yeomans et al. (2023), which notes the role of stylistic and structural language (e.g., function words), which frequently appear in stopword lists.)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/liwc.html',\n", + " 'function': None>,\n", + " 'dependencies': [ None>],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Questions': {'columns': ['num_question_naive'],\n", + " 'file': './features/question_num.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Engagement',\n", + " 'description': 'Number of questions asked in an utterance. In the naive version, it counts the number of question marks (’?’).',\n", + " 'references': '(Ranganath et al., 2013)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/questions.html',\n", + " 'function': None>,\n", + " 'dependencies': [ None>,\n", + " None>],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Conversational Repair': {'columns': ['NTRI'],\n", + " 'file': './features/other_lexical_features.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Engagement',\n", + " 'description': 'A binary indicator of whether an utterance contains a repair indicator, defined as the following: - “what?” - “sorry” - “excuse me” - “huh?” - “who?” - “pardon?” - “say … again?” - “what’s that?” - “what is that”',\n", + " 'references': '(Ranganath et al., 2013)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/conversational_repair.html',\n", + " 'function': None>,\n", + " 'dependencies': [ None>,\n", + " None>],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Word Type-Token Ratio': {'columns': ['word_TTR'],\n", + " 'file': './features/other_lexical_features.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Content',\n", + " 'description': 'The ratio of word types (the total number of unique words in an utterance) to tokens (the total number of words in an utterance).',\n", + " 'references': '(Reichel et al., 2015)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/word_ttr.html',\n", + " 'function': None>,\n", + " 'dependencies': [ None>,\n", + " None>],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Proportion of First-Person Pronouns': {'columns': ['first_pronouns_proportion'],\n", + " 'file': './features/other_lexical_features.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Content',\n", + " 'description': 'The proportion of words in an utterance that are first-person pronouns (e.g., “I,” “me,” “we,” “us”).',\n", + " 'references': '(Reichel et al., 2015)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/proportion_of_first_person_pronouns.html',\n", + " 'function': None>,\n", + " 'dependencies': [ None>,\n", + " None>],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Function Word Accommodation': {'columns': ['function_word_accommodation'],\n", + " 'file': './features/word_mimicry.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Variance',\n", + " 'description': 'The total number of function words used in a given turn that were also used in the previous turn. Function words are defined as a list of 190 words from the source paper.',\n", + " 'references': '(Ranganath et al., 2013)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/function_word_accommodation.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Content Word Accommodation': {'columns': ['content_word_accommodation'],\n", + " 'file': './features/word_mimicry.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Variance',\n", + " '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.',\n", + " 'references': '(Ranganath et al., 2013)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/content_word_accommodation.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " '(BERT) Mimicry': {'columns': ['mimicry_bert'],\n", + " 'file': './features/word_mimicry.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Variance',\n", + " 'description': 'The cosine similarity of the SBERT vectors between the current utterance and the utterance in the previous turn.',\n", + " 'references': 'Inspired by accommodation (Matarazzo & Wiens, 1977), language style matching (Tausczik & Pennebaker, 2013) and synchrony (Niederhoffer & Pennebaker, 2002), and implemented in a manner similar to forward flow (Gray et al., 2019)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/mimicry_bert.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': True,\n", + " 'bert_sentiment_data': False},\n", + " 'Moving Mimicry': {'columns': ['moving_mimicry'],\n", + " 'file': './features/word_mimicry.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Variance',\n", + " 'description': 'The running average of all BERT Mimicry scores computed so far in a conversation. Captures the extent to which all participants in a conversation mimic each other up until a given point.',\n", + " 'references': 'Inspired by accommodation (Matarazzo & Wiens, 1977), language style matching (Tausczik & Pennebaker, 2013) and synchrony (Niederhoffer & Pennebaker, 2002), and implemented in a manner similar to forward flow (Gray et al., 2019)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/moving_mimicry.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': True,\n", + " 'bert_sentiment_data': False},\n", + " 'Hedge': {'columns': ['hedge_naive'],\n", + " 'file': './features/hedge.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Engagement',\n", + " 'description': 'Captures whether a speaker appears to “hedge” their statement and express lack of certainty; e.g., a score of 1 is assigned if hedge phrases (”I think,” “a little,” “maybe,” “possibly”) are present, and a score of 0 is assigned otherwise.',\n", + " 'references': '(Ranganath et al., 2013; (Danescu-Niculescu-Mizil et al., 2013; Islam et al., 2020)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/hedge.html',\n", + " 'function': None>,\n", + " 'dependencies': [ None>,\n", + " None>],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'TextBlob Subjectivity': {'columns': ['textblob_subjectivity'],\n", + " 'file': './features/textblob_sentiment_analysis.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Content',\n", + " 'description': 'The extent to which a statement is “subjective” (containing personal information) or “objective” (containing factual information), as measured by TextBlob. Ranges from 0 (objective) to 1 (subjective).',\n", + " 'references': '(Cao et al., 2021)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/textblob_subjectivity.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'TextBlob Polarity': {'columns': ['textblob_polarity'],\n", + " 'file': './features/textblob_sentiment_analysis.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Emotion',\n", + " 'description': 'The extent to which a statement is positive or negative; ranges from -1 (negative) to 1 (positive); neutrality is assigned a score of 0.',\n", + " 'references': '(Cao et al., 2021)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/textblob_polarity.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Positivity Z-Score': {'columns': ['positivity_zscore_chats',\n", + " 'positivity_zscore_conversation'],\n", + " 'file': './utils/zscore_chats_and_conversation.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Emotion',\n", + " 'description': 'The relative extent to which an utterance is more (or less) positive, compared to other messages. Here, we use the BERT-assigned positivity score, and calculate two flavors of the z-score: the first scores the messages with respect to other messages in the same conversation; the second scores the messages with respect to all messages in the data.',\n", + " 'references': '(Tausczik & Pennebaker, 2013)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/positivity_z_score.html',\n", + " 'function': None>,\n", + " 'dependencies': [ None>],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': True},\n", + " 'Dale-Chall Score': {'columns': ['dale_chall_score',\n", + " 'dale_chall_classification'],\n", + " 'file': './features/readability.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Content',\n", + " 'description': 'The reading level of the utterance, as calculated by the Dale-Chall Score.',\n", + " 'references': '(Cao et al., 2021)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/dale_chall_score.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Time Difference': {'columns': ['time_diff'],\n", + " 'file': './features/temporal_features.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Pace',\n", + " 'description': 'The response time between successive utterances.',\n", + " 'references': '(Reichel et al., 2015)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/time_difference.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Politeness Strategies': {'columns': ['please_politeness_convokit',\n", + " 'please_start_politeness_convokit',\n", + " 'hashedge_politeness_convokit',\n", + " 'indirect_btw_politeness_convokit',\n", + " 'hedges_politeness_convokit',\n", + " 'factuality_politeness_convokit',\n", + " 'deference_politeness_convokit',\n", + " 'gratitude_politeness_convokit',\n", + " 'apologizing_politeness_convokit',\n", + " '1st_person_pl_politeness_convokit',\n", + " '1st_person_politeness_convokit',\n", + " '1st_person_start_politeness_convokit',\n", + " '2nd_person_politeness_convokit',\n", + " '2nd_person_start_politeness_convokit',\n", + " 'indirect_greeting_politeness_convokit',\n", + " 'direct_question_politeness_convokit',\n", + " 'direct_start_politeness_convokit',\n", + " 'haspositive_politeness_convokit',\n", + " 'hasnegative_politeness_convokit',\n", + " 'subjunctive_politeness_convokit',\n", + " 'indicative_politeness_convokit'],\n", + " 'file': './features/politeness_features.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Engagement',\n", + " 'description': 'A collection of conversational markers that indicates the use of politeness.',\n", + " 'references': '(Danescu-Niculescu-Mizil et al., 2013)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/politeness_strategies.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Politeness / Receptiveness Markers': {'columns': ['Impersonal_Pronoun_receptiveness_yeomans',\n", + " 'First_Person_Single_receptiveness_yeomans',\n", + " 'Hedges_receptiveness_yeomans',\n", + " 'Negation_receptiveness_yeomans',\n", + " 'Subjectivity_receptiveness_yeomans',\n", + " 'Negative_Emotion_receptiveness_yeomans',\n", + " 'Reasoning_receptiveness_yeomans',\n", + " 'Agreement_receptiveness_yeomans',\n", + " 'Second_Person_receptiveness_yeomans',\n", + " 'Adverb_Limiter_receptiveness_yeomans',\n", + " 'Disagreement_receptiveness_yeomans',\n", + " 'Acknowledgement_receptiveness_yeomans',\n", + " 'First_Person_Plural_receptiveness_yeomans',\n", + " 'For_Me_receptiveness_yeomans',\n", + " 'WH_Questions_receptiveness_yeomans',\n", + " 'YesNo_Questions_receptiveness_yeomans',\n", + " 'Bare_Command_receptiveness_yeomans',\n", + " 'Truth_Intensifier_receptiveness_yeomans',\n", + " 'Apology_receptiveness_yeomans',\n", + " 'Ask_Agency_receptiveness_yeomans',\n", + " 'By_The_Way_receptiveness_yeomans',\n", + " 'Can_You_receptiveness_yeomans',\n", + " 'Conjunction_Start_receptiveness_yeomans',\n", + " 'Could_You_receptiveness_yeomans',\n", + " 'Filler_Pause_receptiveness_yeomans',\n", + " 'For_You_receptiveness_yeomans',\n", + " 'Formal_Title_receptiveness_yeomans',\n", + " 'Give_Agency_receptiveness_yeomans',\n", + " 'Affirmation_receptiveness_yeomans',\n", + " 'Gratitude_receptiveness_yeomans',\n", + " 'Hello_receptiveness_yeomans',\n", + " 'Informal_Title_receptiveness_yeomans',\n", + " 'Let_Me_Know_receptiveness_yeomans',\n", + " 'Swearing_receptiveness_yeomans',\n", + " 'Reassurance_receptiveness_yeomans',\n", + " 'Please_receptiveness_yeomans',\n", + " 'Positive_Emotion_receptiveness_yeomans',\n", + " 'Goodbye_receptiveness_yeomans',\n", + " 'Token_count_receptiveness_yeomans'],\n", + " 'file': './features/politeness_v2.py, ./features/politeness_v2_helper.py, ./features/keywords.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Engagement',\n", + " 'description': 'A collection of conversational markers that indicates the use of politeness / receptiveness.',\n", + " 'references': '(Yeomans et al., 2020)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/politeness_receptiveness_markers.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Forward Flow': {'columns': ['forward_flow'],\n", + " 'file': './features/fflow.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Variance',\n", + " 'description': 'The extent to which a conversation “flows forward” — that is, evolves to new topics over time. The forward flow of a given message is the cosine similarity between the SBERT vector of the current message and the average SBERT vector of all previous messages. In other words, it captures how similar a message is to everything that has come before (so far).',\n", + " 'references': '(Gray et al., 2019)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/forward_flow.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': True,\n", + " 'bert_sentiment_data': False},\n", + " 'Certainty': {'columns': ['certainty_rocklage'],\n", + " 'file': './features/certainty.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Content',\n", + " 'description': 'The extent to which a message expresses (un)certainty, as evaluated on a 1-9 scale. Very certain messages (e.g., “I am absolutely sure”) are higher on the scale; very uncertain messages (”I do not know for certain…”) are lower on the scale.',\n", + " 'references': '(Rocklage et al., 2023)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/certainty.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Online Discussion Tags': {'columns': ['num_all_caps',\n", + " 'num_links',\n", + " 'num_reddit_users',\n", + " 'num_emphasis',\n", + " 'num_bullet_points',\n", + " 'num_numbered_points',\n", + " 'num_quotes',\n", + " 'num_block_quote_responses',\n", + " 'num_ellipses',\n", + " 'num_parentheses',\n", + " 'num_emoji'],\n", + " 'file': './features/reddit_tags.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Content',\n", + " 'description': 'Calculates a number of metrics specific to communications in an online setting: 1. Num all caps: Number of words that are in all caps 2. Num links: Number of links to external resources 3. Num Reddit Users: Number of usernames referred to, in u/RedditUser format. 4. Num Emphasis: The number of times someone used **emphasis** in their message 5. Num Bullet Points: The number of bullet points used in a message. 6. Num Line Breaks: The number of line breaks in a message. 7. Num Quotes: The number of “quotes” in a message. 8. Num Block Quotes Responses: The number of times someone uses a block quote (”>”), indicating a longer quotation 9. Num Ellipses: The number of times someone uses ellipses (…) in their message 10. Num Parentheses: The number of sets of fully closed parenthetical statements in a message 11. Num Emoji: The number of emoticons in a message, e.g., “:)”',\n", + " 'references': 'New',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/online_discussions_tags.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Turn-Taking Index': {'columns': ['turn_taking_index'],\n", + " 'file': './features/turn_taking_features.py',\n", + " 'level': 'Conversation',\n", + " 'semantic_grouping': 'Equality',\n", + " 'description': 'Calculates a metric describing the extent to which individuals take turns speaking in a conversation. Adapted from Almaatouq et al. (2023), in which we treat each separate chat as equivalent to an in-game “solution”: ”A group’s turn-taking index for a given round is measured by dividing the number of turns taken … by the total number of [chats] on a particular task instance.”',\n", + " 'references': '(Almaatouq et al., 2023)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/turn_taking_index.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Equal Participation': {'columns': ['gini_coefficient_sum_num_words',\n", + " 'gini_coefficient_sum_num_chars',\n", + " 'gini_coefficient_sum_num_messages'],\n", + " 'file': './utils/gini_coefficient.py',\n", + " 'level': 'Conversation',\n", + " 'semantic_grouping': 'Equality',\n", + " 'description': 'The extent to which each participant in a conversation engages equally, as measured by a Gini coefficient. We calculate three flavors of Gini coefficient, using the number of words, number of characters, and the number of messages, respectively.',\n", + " 'references': '(Tausczik & Pennebaker, 2013)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/gini_coefficient.html',\n", + " 'function': None>,\n", + " 'dependencies': [ None>],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Conversation Level Aggregates': {'columns': [],\n", + " 'file': './utils/summarize_features.py',\n", + " 'level': 'Conversation',\n", + " 'semantic_grouping': 'N/A',\n", + " 'description': 'Aggregation of utterance (chat)-level features at the conversation level',\n", + " 'references': 'N/A',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features/index.html#features-technical',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'User Level Aggregates': {'columns': [],\n", + " 'file': './utils/summarize_features.py, ./features/get_user_network.py, ./features/user_centroids.py',\n", + " 'level': 'Conversation',\n", + " 'semantic_grouping': 'N/A',\n", + " 'description': 'Aggregation of utterance (chat)-level features at the speaker (user) level',\n", + " 'references': 'N/A',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features/index.html#features-technical',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Discursive Diversity': {'columns': ['discursive_diversity',\n", + " 'variance_in_DD',\n", + " 'incongruent_modulation',\n", + " 'within_person_disc_range'],\n", + " 'file': './features/get_all_DD_features.py, ./features/discursive_diversity.py, ./features/variance_in_DD.py, ./features/within_person_discursive_range.py',\n", + " 'level': 'Conversation',\n", + " 'semantic_grouping': 'Variance',\n", + " 'description': 'Calculates metrics related to the extent to which members in a conversation speak similarly. 1. Discursive diversity: 1 - the average pairwise cosine distances between the centroids associated with each speaker in a conversation. 2. Variance in discursive diversity: the extent to which discursive diversity varies across the beginning, middle, and end of a conversation. 3. Incongruent modulation: the total variance, per speaker, between the (beginning, middle) and (middle, end) of a conversation. As described by the pape, this is the “team-level variance in members’ within-person discursive range” from stage 1 to stage 2, and from stage 2 to stage 3. 4. Within-person discursive range: The sum, across all speakers in the conversation, of each speaker’s average distance between their centroids for the (beginning, middle) and (middle, end) of a conversation.',\n", + " 'references': '(Lix et al., 2022)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/discursive_diversity.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': True,\n", + " 'bert_sentiment_data': False},\n", + " 'Team Burstiness': {'columns': ['team_burstiness'],\n", + " 'file': './features/burstiness.py',\n", + " 'level': 'Conversation',\n", + " 'semantic_grouping': 'Pace',\n", + " 'description': 'This conversation-level feature measures the level of burstiness of chats in a conversation. The metric takes a value between -1 and 1, with a higher value indicating higher levels of team burstiness. Teams with higher burstiness would have more spiked patterns in team activity, which tends to indicate a higher sense of responsiveness and connectedness within the team members.',\n", + " 'references': '(Reidl and Woolley, 2017)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/team_burstiness.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False},\n", + " 'Information Diversity': {'columns': ['info_diversity'],\n", + " 'file': './features/information_diversity.py',\n", + " 'level': 'Conversation',\n", + " 'semantic_grouping': 'Variance',\n", + " 'description': \"This conversation-level feature uses topic modeling to measure the level of information diversity across a conversation. We first preprocess the data with lowercasing, lemmatization, removing stop words, and removing short words (less than length 3). We then use the gensim package to create an LDA Model for each conversation, generating a corresponding topic space with its number of dimensions = num_topics. To determine the number of topics used, we use a logarithmic scale relative to the number of chats in the conversation. A team's info diversity is then computed by looking at the average cosine dissimilarity between each chat's topic vector and the mean topic vector across the entire conversation. The value ranges between 0 and 1, with higher values indicating a higher level of information diversity/diversity in topics discussed throughout the conversation. As discussed in the paper above, typical info diversity values are quite small, with the paper having a mean score of 0.04 and standard deviation of 0.05.\",\n", + " 'references': '(Reidl and Wooley, 2017)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/information_diversity.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': False}}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jury_feature_builder.feature_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Starting from v0.1.4, you can use the following properties to access to lists of generated features: \n", + "\n", + "- `feature_names` yields a list of formal feature names; \n", + "- `chat_features` provides feature columns created at the chat (utterance) level;\n", + "- `conv_features_base` for base feature columns at the conversation level; \n", + "- `conv_features_all` encompasses all conversation-level feature columns, including aggregates.\n", + "\n", + "Note that these properties become available only after invoking `.featurize()`. \n", + "\n", + "Using the `feature_names` alongside `feature_dict`, you can learn about a particular feature (here, we show the first element within `feature_names`)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'columns': ['positive_bert', 'negative_bert', 'neutral_bert'],\n", + " 'file': './utils/check_embeddings.py',\n", + " 'level': 'Chat',\n", + " 'semantic_grouping': 'Emotion',\n", + " 'description': 'The extent to which a statement is positive, negative, or neutral, as assigned by Cardiffnlp/twitter-roberta-base-sentiment-latest. The total scores (Positive, Negative, Neutral) sum to 1.',\n", + " 'references': '(Hugging Face, 2023)',\n", + " 'wiki_link': 'https://conversational-featurizer.readthedocs.io/en/latest/features_conceptual/positivity_bert.html',\n", + " 'function': None>,\n", + " 'dependencies': [],\n", + " 'preprocess': [],\n", + " 'vect_data': False,\n", + " 'bert_sentiment_data': True}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jury_feature_builder.feature_dict[jury_feature_builder.feature_names[0]]" ] }, { @@ -520,7 +1127,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -529,13 +1136,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "# Let's get all the features that the toolkit generated;\n", - "# this would by anything in the utterance_features dataframe that isn't in the original dataframe\n", - "utterance_cols = utterance_features.columns.difference(juries_df.columns)" + "# Let's get all the features that the toolkit generated; we can get this easily using the `chat_features` property\n", + "utterance_cols = jury_feature_builder.chat_features" ] }, { @@ -551,7 +1157,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -575,292 +1181,292 @@ " \n", " \n", " \n", - " 1st_person\n", - " 1st_person_pl\n", - " 1st_person_start\n", - " 2nd_person\n", - " 2nd_person_start\n", - " Acknowledgement\n", - " Adverb_Limiter\n", - " Affirmation\n", - " Agreement\n", - " Apology\n", + " positive_bert\n", + " negative_bert\n", + " neutral_bert\n", + " num_words\n", + " num_chars\n", + " num_messages\n", + " info_exchange_zscore_chats\n", + " info_exchange_zscore_conversation\n", + " discrepancies_lexical_wordcount\n", + " hear_lexical_wordcount\n", " ...\n", - " subjunctive\n", - " swear_lexical_per_100\n", - " tentativeness_lexical_per_100\n", - " textblob_polarity\n", - " textblob_subjectivity\n", - " third_person_lexical_per_100\n", - " time_diff\n", - " verbs_lexical_per_100\n", - " word_TTR\n", - " work_lexical_per_100\n", + " num_bullet_points\n", + " num_numbered_points\n", + " num_quotes\n", + " num_block_quote_responses\n", + " num_ellipses\n", + " num_parentheses\n", + " num_emoji\n", + " mimicry_bert\n", + " moving_mimicry\n", + " forward_flow\n", " \n", " \n", " \n", " \n", " count\n", - " 97.00000\n", - " 97.000000\n", " 97.000000\n", " 97.000000\n", " 97.000000\n", " 97.000000\n", " 97.000000\n", + " 97.0\n", + " 9.700000e+01\n", + " 9.700000e+01\n", " 97.000000\n", " 97.000000\n", - " 97.0\n", " ...\n", " 97.0\n", + " 97.0\n", " 97.000000\n", + " 97.0\n", " 97.000000\n", " 97.000000\n", - " 97.000000\n", - " 97.000000\n", - " 97.000000\n", + " 97.0\n", " 97.000000\n", " 97.000000\n", " 97.000000\n", " \n", " \n", " mean\n", - " 0.14433\n", - " 0.051546\n", - " 0.474227\n", - " 0.030928\n", - " 0.020619\n", - " 0.041237\n", + " 0.165439\n", + " 0.397220\n", + " 0.437341\n", + " 15.618557\n", + " 79.927835\n", + " 1.0\n", + " -3.548135e-17\n", + " 1.259016e-17\n", + " 0.463918\n", " 0.092784\n", - 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    8 rows × 153 columns

    \n", + "

    8 rows × 151 columns

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    " ], "text/plain": [ - " 1st_person 1st_person_pl 1st_person_start 2nd_person \\\n", - "count 97.00000 97.000000 97.000000 97.000000 \n", - "mean 0.14433 0.051546 0.474227 0.030928 \n", - "std 0.35325 0.222258 0.501929 0.174022 \n", - "min 0.00000 0.000000 0.000000 0.000000 \n", - "25% 0.00000 0.000000 0.000000 0.000000 \n", - "50% 0.00000 0.000000 0.000000 0.000000 \n", - "75% 0.00000 0.000000 1.000000 0.000000 \n", - "max 1.00000 1.000000 1.000000 1.000000 \n", + " positive_bert negative_bert neutral_bert num_words num_chars \\\n", + "count 97.000000 97.000000 97.000000 97.000000 97.000000 \n", + "mean 0.165439 0.397220 0.437341 15.618557 79.927835 \n", + "std 0.221845 0.322505 0.235022 11.880870 60.299815 \n", + "min 0.005377 0.004957 0.069549 1.000000 2.000000 \n", + "25% 0.015250 0.078356 0.215939 6.000000 31.000000 \n", + "50% 0.047171 0.386957 0.424016 13.000000 71.000000 \n", + "75% 0.234495 0.699443 0.625082 24.000000 123.000000 \n", + "max 0.845765 0.921826 0.906651 47.000000 230.000000 \n", "\n", - " 2nd_person_start Acknowledgement Adverb_Limiter Affirmation \\\n", - "count 97.000000 97.000000 97.000000 97.000000 \n", - "mean 0.020619 0.041237 0.092784 0.072165 \n", - "std 0.142842 0.246540 0.291636 0.260105 \n", - "min 0.000000 0.000000 0.000000 0.000000 \n", - "25% 0.000000 0.000000 0.000000 0.000000 \n", - "50% 0.000000 0.000000 0.000000 0.000000 \n", - "75% 0.000000 0.000000 0.000000 0.000000 \n", - "max 1.000000 2.000000 1.000000 1.000000 \n", + " num_messages info_exchange_zscore_chats \\\n", + "count 97.0 9.700000e+01 \n", + "mean 1.0 -3.548135e-17 \n", + "std 0.0 1.005195e+00 \n", + "min 1.0 -1.213910e+00 \n", + "25% 1.0 -8.665670e-01 \n", + "50% 1.0 -1.718811e-01 \n", + "75% 1.0 6.964764e-01 \n", + "max 1.0 2.606863e+00 \n", "\n", - " Agreement Apology ... subjunctive swear_lexical_per_100 \\\n", - "count 97.000000 97.0 ... 97.0 97.000000 \n", - "mean 0.082474 0.0 ... 0.0 0.003093 \n", - "std 0.276515 0.0 ... 0.0 0.004865 \n", - "min 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... 0.0 0.0 \n", + "std 0.291636 ... 0.0 0.0 \n", + "min 0.000000 ... 0.0 0.0 \n", + "25% 0.000000 ... 0.0 0.0 \n", + "50% 0.000000 ... 0.0 0.0 \n", + "75% 0.000000 ... 0.0 0.0 \n", + "max 1.000000 ... 0.0 0.0 \n", "\n", - " textblob_subjectivity third_person_lexical_per_100 time_diff \\\n", - "count 97.000000 97.000000 97.000000 \n", - "mean 0.286320 0.012577 8.235072 \n", - "std 0.294736 0.014382 7.669224 \n", - "min 0.000000 0.000000 0.000000 \n", - "25% 0.000000 0.000000 2.335000 \n", - "50% 0.300000 0.010000 6.161000 \n", - "75% 0.466667 0.020000 12.744000 \n", - "max 1.000000 0.060000 33.945000 \n", + " num_quotes num_block_quote_responses num_ellipses num_parentheses \\\n", + "count 97.000000 97.0 97.000000 97.000000 \n", + "mean 0.216495 0.0 0.010309 0.020619 \n", + "std 0.461583 0.0 0.101535 0.203069 \n", + "min 0.000000 0.0 0.000000 0.000000 \n", + "25% 0.000000 0.0 0.000000 0.000000 \n", + "50% 0.000000 0.0 0.000000 0.000000 \n", + "75% 0.000000 0.0 0.000000 0.000000 \n", + 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} @@ -879,7 +1485,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -903,22 +1509,21 @@ " \n", " \n", " \n", - " First_Person_Single\n", - " Impersonal_Pronoun\n", - " Negation\n", - " Positive_Emotion\n", - " Token_count\n", - " certainty_rocklage\n", - " dale_chall_score\n", - " function_word_accommodation\n", - " info_exchange_zscore_chats\n", - " info_exchange_zscore_conversation\n", - " num_all_caps\n", - " num_chars\n", " num_words\n", - " positivity_zscore_chats\n", - " positivity_zscore_conversation\n", + " num_chars\n", + " third_person_lexical_wordcount\n", + " present_tense_lexical_wordcount\n", + " relative_lexical_wordcount\n", + " social_lexical_wordcount\n", + " verbs_lexical_wordcount\n", + " auxiliary_verbs_lexical_wordcount\n", + " cognitive_mech_lexical_wordcount\n", + " preposition_lexical_wordcount\n", + " nltk_english_stopwords_lexical_wordcount\n", + " function_word_accommodation\n", + " dale_chall_score\n", " time_diff\n", + " Token_count_receptiveness_yeomans\n", " \n", " \n", " \n", @@ -932,205 +1537,217 @@ " 97.000000\n", " 97.000000\n", " 97.000000\n", - " 9.700000e+01\n", - " 9.700000e+01\n", " 97.000000\n", " 97.000000\n", " 97.000000\n", - " 9.700000e+01\n", - " 9.700000e+01\n", + " 97.000000\n", + " 97.000000\n", + " 97.000000\n", " 97.000000\n", " \n", " \n", " mean\n", - " 0.680412\n", - " 0.597938\n", - " 0.484536\n", - " 0.412371\n", - " 17.958763\n", - " 5.046811\n", - " 1.491515\n", - " 1.494845\n", - " -3.548135e-17\n", - " 1.259016e-17\n", - " 0.432990\n", - " 79.927835\n", " 15.618557\n", - " 1.236125e-16\n", - " -4.234871e-17\n", + " 79.927835\n", + " 1.257732\n", + " 2.030928\n", + " 1.309278\n", + " 2.845361\n", + " 2.804124\n", + " 1.824742\n", + " 3.195876\n", + " 1.711340\n", + " 8.402062\n", + " 1.494845\n", + " 1.491515\n", " 8.235072\n", + " 17.958763\n", " \n", " \n", " std\n", - " 0.771179\n", - " 0.873925\n", - " 0.693923\n", - " 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Negation Positive_Emotion \\\n", - "count 97.000000 97.000000 97.000000 97.000000 \n", - "mean 0.680412 0.597938 0.484536 0.412371 \n", - "std 0.771179 0.873925 0.693923 0.703452 \n", - "min 0.000000 0.000000 0.000000 0.000000 \n", - "25% 0.000000 0.000000 0.000000 0.000000 \n", - "50% 1.000000 0.000000 0.000000 0.000000 \n", - "75% 1.000000 1.000000 1.000000 1.000000 \n", - "max 3.000000 4.000000 2.000000 4.000000 \n", + " num_words num_chars third_person_lexical_wordcount \\\n", + "count 97.000000 97.000000 97.000000 \n", + "mean 15.618557 79.927835 1.257732 \n", + "std 11.880870 60.299815 1.438158 \n", + "min 1.000000 2.000000 0.000000 \n", + "25% 6.000000 31.000000 0.000000 \n", + "50% 13.000000 71.000000 1.000000 \n", + "75% 24.000000 123.000000 2.000000 \n", + "max 47.000000 230.000000 6.000000 \n", + "\n", + " present_tense_lexical_wordcount relative_lexical_wordcount \\\n", + "count 97.000000 97.000000 \n", + "mean 2.030928 1.309278 \n", + "std 1.965609 1.660577 \n", + "min 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1.005195e+00 \n", - "min 0.000000 -1.213910e+00 \n", - "25% 0.000000 -8.665670e-01 \n", - "50% 1.000000 -1.718811e-01 \n", - "75% 2.000000 6.964764e-01 \n", - "max 10.000000 2.606863e+00 \n", + " auxiliary_verbs_lexical_wordcount cognitive_mech_lexical_wordcount \\\n", + "count 97.000000 97.000000 \n", + "mean 1.824742 3.195876 \n", + "std 1.973841 3.154491 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 1.000000 \n", + "50% 1.000000 2.000000 \n", + "75% 3.000000 5.000000 \n", + "max 8.000000 14.000000 \n", "\n", - " info_exchange_zscore_conversation num_all_caps num_chars num_words \\\n", - "count 9.700000e+01 97.000000 97.000000 97.000000 \n", - "mean 1.259016e-17 0.432990 79.927835 15.618557 \n", - "std 1.005195e+00 0.762496 60.299815 11.880870 \n", - "min -1.227733e+00 0.000000 2.000000 1.000000 \n", - "25% -8.512558e-01 0.000000 31.000000 6.000000 \n", - "50% -1.924215e-01 0.000000 71.000000 13.000000 \n", - "75% 6.743418e-01 1.000000 123.000000 24.000000 \n", - "max 2.725274e+00 3.000000 230.000000 47.000000 \n", + " preposition_lexical_wordcount \\\n", + "count 97.000000 \n", + "mean 1.711340 \n", + "std 1.870599 \n", + "min 0.000000 \n", + "25% 0.000000 \n", + "50% 1.000000 \n", + "75% 3.000000 \n", + "max 8.000000 \n", "\n", - " positivity_zscore_chats positivity_zscore_conversation time_diff \n", - "count 9.700000e+01 9.700000e+01 97.000000 \n", - "mean 1.236125e-16 -4.234871e-17 8.235072 \n", - "std 1.005195e+00 1.005195e+00 7.669224 \n", - "min -7.415163e-01 -8.082884e-01 0.000000 \n", - "25% -6.819685e-01 -6.588184e-01 2.335000 \n", - "50% -5.370572e-01 -5.058003e-01 6.161000 \n", - "75% 1.599637e-01 1.701716e-01 12.744000 \n", - "max 3.493734e+00 3.834273e+00 33.945000 " + " nltk_english_stopwords_lexical_wordcount function_word_accommodation \\\n", + "count 97.000000 97.000000 \n", + "mean 8.402062 1.494845 \n", + "std 7.404475 2.062178 \n", + "min 0.000000 0.000000 \n", + "25% 3.000000 0.000000 \n", + "50% 7.000000 1.000000 \n", + "75% 13.000000 2.000000 \n", + "max 30.000000 10.000000 \n", + "\n", + " dale_chall_score time_diff Token_count_receptiveness_yeomans \n", + "count 97.000000 97.000000 97.000000 \n", + "mean 1.491515 8.235072 17.958763 \n", + "std 2.122356 7.669224 13.539231 \n", + "min 0.049600 0.000000 1.000000 \n", + "25% 0.297600 2.335000 7.000000 \n", + "50% 0.744000 6.161000 15.000000 \n", + "75% 1.736000 12.744000 27.000000 \n", + "max 14.311967 33.945000 52.000000 " ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1163,7 +1780,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1198,12 +1815,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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    " ] @@ -1225,7 +1842,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1252,7 +1869,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1281,7 +1898,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1321,12 +1938,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
    " ] @@ -1364,11 +1981,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "conversation_features = pd.read_csv(\"./output/conv/jury_tiny_output_conversation_level.csv\")" + "conversation_features = pd.read_csv(\"./output/conv/jury_tiny_output_conv_level.csv\")" ] }, { @@ -1380,7 +1997,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1404,125 +2021,230 @@ " \n", " \n", " \n", - " average_1st_person\n", - " average_1st_person_pl\n", - " average_1st_person_start\n", - " average_2nd_person\n", - " average_2nd_person_start\n", - " average_Acknowledgement\n", - " average_Adverb_Limiter\n", - " average_Affirmation\n", - " average_Agreement\n", - " average_Apology\n", - " ...\n", - " stdev_verbs_lexical_per_100\n", - " stdev_word_TTR\n", - " stdev_work_lexical_per_100\n", - " sum_num_chars\n", - " sum_num_messages\n", - " sum_num_words\n", - " team_burstiness\n", " turn_taking_index\n", + " gini_coefficient_sum_num_words\n", + " gini_coefficient_sum_num_chars\n", + " gini_coefficient_sum_num_messages\n", + " team_burstiness\n", + " average_positive_bert\n", + " stdev_positive_bert\n", + " min_positive_bert\n", + " max_positive_bert\n", + " average_negative_bert\n", + " ...\n", + " stdev_user_avg_forward_flow\n", + " min_user_avg_forward_flow\n", + " max_user_avg_forward_flow\n", + " info_diversity\n", + " discursive_diversity\n", " variance_in_DD\n", + " incongruent_modulation\n", " within_person_disc_range\n", + " message_original\n", + " message_lower_with_punc\n", " \n", " \n", " \n", " \n", " 0\n", - " 0.134615\n", - " 0.057692\n", - " 0.480769\n", - " 0.057692\n", - " 0.038462\n", - " 0.057692\n", - " 0.057692\n", - " 0.076923\n", - " 0.057692\n", - " 0.0\n", - " ...\n", - " 0.026054\n", - " 0.075993\n", - " 0.007661\n", - " 4293\n", - " 52\n", - " 856\n", - " 0.037380\n", " 0.980392\n", - " 0.005223\n", - " 1.318740\n", + " 0.159463\n", + " 0.166055\n", + " 0.18750\n", + " 0.037380\n", + " 0.192184\n", + " 0.242023\n", + " 0.006599\n", + " 0.837152\n", + " 0.381502\n", + " ...\n", + " 0.052977\n", + " 0.485594\n", + " 0.645007\n", + " 0.282681\n", + " 0.406655\n", + " 0.004126\n", + " 0.060308\n", + " 1.303592\n", + " Hello!\n", + " hello!\n", " \n", " \n", " 1\n", - " 0.155556\n", - " 0.044444\n", - " 0.466667\n", - " 0.000000\n", - " 0.000000\n", - " 0.022222\n", - " 0.133333\n", - " 0.066667\n", - " 0.111111\n", - " 0.0\n", - " ...\n", - " 0.024980\n", - " 0.077148\n", - " 0.004661\n", - " 3460\n", - " 45\n", - " 659\n", - " -0.166857\n", " 1.000000\n", - " 0.002655\n", - " 0.967371\n", + " 0.125695\n", + " 0.124663\n", + " 0.27037\n", + " -0.166857\n", + " 0.134533\n", + " 0.188477\n", + " 0.005377\n", + " 0.845765\n", + " 0.415384\n", + " ...\n", + " 0.070613\n", + " 0.439604\n", + " 0.610393\n", + " 0.267039\n", + " 0.362725\n", + " 0.002987\n", + " 0.024873\n", + " 0.921299\n", + " hi\n", + " hi\n", " \n", " \n", "\n", - "

    2 rows × 1840 columns

    \n", + "

    2 rows × 1839 columns

    \n", "" ], "text/plain": [ - " average_1st_person average_1st_person_pl average_1st_person_start \\\n", - "0 0.134615 0.057692 0.480769 \n", - "1 0.155556 0.044444 0.466667 \n", + " turn_taking_index gini_coefficient_sum_num_words \\\n", + "0 0.980392 0.159463 \n", + "1 1.000000 0.125695 \n", "\n", - " average_2nd_person average_2nd_person_start average_Acknowledgement \\\n", - "0 0.057692 0.038462 0.057692 \n", - "1 0.000000 0.000000 0.022222 \n", + " gini_coefficient_sum_num_chars gini_coefficient_sum_num_messages \\\n", + "0 0.166055 0.18750 \n", + "1 0.124663 0.27037 \n", "\n", - " average_Adverb_Limiter average_Affirmation average_Agreement \\\n", - "0 0.057692 0.076923 0.057692 \n", - "1 0.133333 0.066667 0.111111 \n", + " team_burstiness average_positive_bert stdev_positive_bert \\\n", + "0 0.037380 0.192184 0.242023 \n", + "1 -0.166857 0.134533 0.188477 \n", "\n", - " average_Apology ... stdev_verbs_lexical_per_100 stdev_word_TTR \\\n", - "0 0.0 ... 0.026054 0.075993 \n", - "1 0.0 ... 0.024980 0.077148 \n", + " min_positive_bert max_positive_bert average_negative_bert ... \\\n", + "0 0.006599 0.837152 0.381502 ... \n", + "1 0.005377 0.845765 0.415384 ... \n", "\n", - " stdev_work_lexical_per_100 sum_num_chars sum_num_messages sum_num_words \\\n", - "0 0.007661 4293 52 856 \n", - "1 0.004661 3460 45 659 \n", + " stdev_user_avg_forward_flow min_user_avg_forward_flow \\\n", + "0 0.052977 0.485594 \n", + "1 0.070613 0.439604 \n", "\n", - " team_burstiness turn_taking_index variance_in_DD \\\n", - "0 0.037380 0.980392 0.005223 \n", - "1 -0.166857 1.000000 0.002655 \n", + " max_user_avg_forward_flow info_diversity discursive_diversity \\\n", + "0 0.645007 0.282681 0.406655 \n", + "1 0.610393 0.267039 0.362725 \n", "\n", - " within_person_disc_range \n", - "0 1.318740 \n", - "1 0.967371 \n", + " variance_in_DD incongruent_modulation within_person_disc_range \\\n", + "0 0.004126 0.060308 1.303592 \n", + "1 0.002987 0.024873 0.921299 \n", "\n", - "[2 rows x 1840 columns]" + " message_original message_lower_with_punc \n", + "0 Hello! hello! \n", + "1 hi hi \n", + "\n", + "[2 rows x 1839 columns]" ] }, - "execution_count": 18, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "conversation_cols = conversation_features.columns.difference(juries_df.columns)\n", + "# these are all the conversation features we generate, including aggregations\n", + "conversation_cols = jury_feature_builder.conv_features_all\n", "conversation_features[conversation_cols]" ] }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " turn_taking_index gini_coefficient_sum_num_words \\\n", + "0 0.980392 0.159463 \n", + "1 1.000000 0.125695 \n", + "\n", + " gini_coefficient_sum_num_chars gini_coefficient_sum_num_messages \\\n", + "0 0.166055 0.18750 \n", + "1 0.124663 0.27037 \n", + "\n", + " team_burstiness team_burstiness info_diversity discursive_diversity \\\n", + "0 0.037380 0.037380 0.282681 0.406655 \n", + "1 -0.166857 -0.166857 0.267039 0.362725 \n", + "\n", + " variance_in_DD incongruent_modulation within_person_disc_range \n", + "0 0.004126 0.060308 1.303592 \n", + "1 0.002987 0.024873 0.921299 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# and these are the conversation features that are not aggregations\n", + "conversation_features[jury_feature_builder.conv_features_base]" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1532,7 +2254,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1543,7 +2265,7 @@ "Name: team_burstiness, dtype: float64" ] }, - "execution_count": 19, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1561,7 +2283,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1584,7 +2306,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1621,7 +2343,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1673,9 +2395,9 @@ " 0\n", " 0\n", " niceRhino\n", - " 0.273887\n", - " 0.109928\n", - " 0.616185\n", + " 0.356771\n", + " 0.084720\n", + " 0.558509\n", " 15.875000\n", " 81.625000\n", " 1.0\n", @@ -1688,18 +2410,18 @@ " 0\n", " 2\n", " 0\n", - " 1.639970\n", - " 3.068143\n", - " 5.000301\n", + " 1.731247\n", + " 3.016273\n", + " 4.953423\n", " ['culturedCow' 'spryBison' 'youngLion' 'smallG...\n", " \n", " \n", " 1\n", " 0\n", " culturedCow\n", - " 0.102433\n", - " 0.501847\n", - " 0.395720\n", + " 0.161326\n", + " 0.495867\n", + " 0.342807\n", " 28.000000\n", " 145.000000\n", " 1.0\n", @@ -1712,18 +2434,18 @@ " 0\n", " 0\n", " 0\n", - " 1.819858\n", - " 2.478020\n", - " 2.591912\n", + " 1.823616\n", + " 2.494478\n", + " 2.539859\n", " ['niceRhino' 'spryBison' 'youngLion' 'smallGir...\n", " \n", " \n", " 2\n", " 0\n", " spryBison\n", - " 0.273171\n", - " 0.332005\n", - " 0.394824\n", + " 0.297614\n", + " 0.312863\n", + " 0.389523\n", " 9.777778\n", " 45.222222\n", " 1.0\n", @@ -1736,18 +2458,18 @@ " 0\n", " 0\n", " 0\n", - " 3.074910\n", - " 4.041765\n", - " 5.170760\n", + " 2.924477\n", + " 4.013453\n", + " 5.303996\n", " ['niceRhino' 'culturedCow' 'youngLion' 'smallG...\n", " \n", " \n", " 3\n", " 0\n", " youngLion\n", - " 0.073692\n", - " 0.559233\n", - " 0.367075\n", + " 0.083168\n", + " 0.558676\n", + " 0.358156\n", " 15.875000\n", " 78.375000\n", " 1.0\n", @@ -1760,18 +2482,18 @@ " 0\n", " 0\n", " 0\n", - " 3.693983\n", - " 3.680254\n", - " 4.783784\n", + " 3.689977\n", + " 3.647838\n", + " 4.762858\n", " ['niceRhino' 'culturedCow' 'spryBison' 'smallG...\n", " \n", " \n", " 4\n", " 0\n", " smallGiraffe\n", - " 0.124830\n", - " 0.427087\n", - " 0.448083\n", + " 0.172383\n", + " 0.423620\n", + " 0.403998\n", " 11.375000\n", " 55.375000\n", " 1.0\n", @@ -1784,18 +2506,18 @@ " 0\n", " 0\n", " 0\n", - " 2.361289\n", - " 3.814536\n", - " 4.433901\n", + " 2.305697\n", + " 3.786036\n", + " 4.647392\n", " ['niceRhino' 'culturedCow' 'spryBison' 'youngL...\n", " \n", " \n", " 5\n", " 0\n", " culturedBear\n", - " 0.140187\n", - " 0.162012\n", - " 0.697801\n", + " 0.144971\n", + " 0.174196\n", + " 0.680834\n", " 20.500000\n", " 99.666667\n", " 1.0\n", @@ -1808,18 +2530,18 @@ " 0\n", " 0\n", " 0\n", - " 1.569863\n", - " 2.638946\n", - " 2.922095\n", + " 1.531364\n", + " 2.634271\n", + " 2.913562\n", " ['niceRhino' 'culturedCow' 'spryBison' 'youngL...\n", " \n", " \n", " 6\n", " 0\n", " spryOrangutan\n", - " 0.098869\n", - " 0.611169\n", - " 0.289962\n", + " 0.103209\n", + " 0.648681\n", + " 0.248110\n", " 18.571429\n", " 93.857143\n", " 1.0\n", @@ -1832,18 +2554,18 @@ " 0\n", " 0\n", " 0\n", - " 1.805912\n", - " 2.607455\n", - " 4.516776\n", + " 1.961317\n", + " 2.603867\n", + " 4.515048\n", " ['niceRhino' 'culturedCow' 'spryBison' 'youngL...\n", " \n", " \n", " 7\n", " 0\n", " littleSquirrel\n", - " 0.015630\n", - " 0.459533\n", - " 0.524838\n", + " 0.017574\n", + " 0.420926\n", + " 0.561500\n", " 30.000000\n", " 183.000000\n", " 1.0\n", @@ -1856,18 +2578,18 @@ " 0\n", " 0\n", " 0\n", - " 0.309424\n", - " 0.327158\n", - " 0.521013\n", + " 0.308643\n", + " 0.327093\n", + " 0.521758\n", " ['niceRhino' 'culturedCow' 'spryBison' 'youngL...\n", " \n", " \n", " 8\n", " 1\n", " newLion\n", - " 0.092814\n", - " 0.381585\n", - " 0.525602\n", + " 0.095934\n", + " 0.386197\n", + " 0.517869\n", " 6.333333\n", " 36.083333\n", " 1.0\n", @@ -1880,18 +2602,18 @@ " 0\n", " 0\n", " 0\n", - " 2.840763\n", - " 2.297269\n", - " 7.079996\n", + " 2.755768\n", + " 2.274712\n", + " 7.213783\n", " ['likelyRabbit' 'conventionalMonkey' 'littleCo...\n", " \n", " \n", " 9\n", " 1\n", " likelyRabbit\n", - " 0.237099\n", - " 0.298989\n", - " 0.463913\n", + " 0.280653\n", + " 0.293505\n", + " 0.425842\n", " 11.000000\n", " 55.444444\n", " 1.0\n", @@ -1904,18 +2626,18 @@ " 0\n", " 0\n", " 0\n", - " 2.471947\n", - " 1.657061\n", - " 5.515881\n", + " 2.443192\n", + " 1.628115\n", + " 5.493533\n", " ['newLion' 'conventionalMonkey' 'littleCow' 'n...\n", " \n", " \n", " 10\n", " 1\n", " conventionalMonkey\n", - " 0.073831\n", - " 0.469623\n", - " 0.456546\n", + " 0.079966\n", + " 0.515654\n", + " 0.404380\n", " 14.083333\n", " 75.250000\n", " 1.0\n", @@ -1928,18 +2650,18 @@ " 1\n", " 0\n", " 0\n", - " 2.324324\n", - " 2.544707\n", - " 6.865593\n", + " 2.260409\n", + " 2.522178\n", + " 6.896428\n", " ['newLion' 'likelyRabbit' 'littleCow' 'newPand...\n", " \n", " \n", " 11\n", " 1\n", " littleCow\n", - " 0.113970\n", - " 0.544537\n", - " 0.341493\n", + " 0.144622\n", + " 0.530565\n", + " 0.324812\n", " 25.250000\n", " 128.250000\n", " 1.0\n", @@ -1952,18 +2674,18 @@ " 0\n", " 0\n", " 0\n", - " 0.980274\n", - " 0.611583\n", - " 1.816545\n", + " 0.973790\n", + " 0.595699\n", + " 1.758415\n", " ['newLion' 'likelyRabbit' 'conventionalMonkey'...\n", " \n", " \n", " 12\n", " 1\n", " newPanda\n", - " 0.051978\n", - " 0.429207\n", - " 0.518815\n", + " 0.062017\n", + " 0.439835\n", + " 0.498147\n", " 21.600000\n", " 113.600000\n", " 1.0\n", @@ -1976,18 +2698,18 @@ " 0\n", " 0\n", " 0\n", - " 1.961948\n", - " 1.112893\n", - " 2.242426\n", + " 1.933344\n", + " 1.103848\n", + " 2.211212\n", " ['newLion' 'likelyRabbit' 'conventionalMonkey'...\n", " \n", " \n", " 13\n", " 1\n", " likelyGorilla\n", - " 0.185777\n", - " 0.328654\n", - " 0.485568\n", + " 0.176243\n", + " 0.302361\n", + " 0.521397\n", " 35.333333\n", " 181.333333\n", " 1.0\n", @@ -2000,9 +2722,9 @@ " 0\n", " 0\n", " 0\n", - " 0.936843\n", - " 0.673729\n", - " 1.492236\n", + " 0.949753\n", + " 0.670097\n", + " 1.521269\n", " ['newLion' 'likelyRabbit' 'conventionalMonkey'...\n", " \n", " \n", @@ -2012,36 +2734,36 @@ ], "text/plain": [ " conversation_num speaker_nickname average_positive_bert \\\n", - "0 0 niceRhino 0.273887 \n", - "1 0 culturedCow 0.102433 \n", - "2 0 spryBison 0.273171 \n", - "3 0 youngLion 0.073692 \n", - "4 0 smallGiraffe 0.124830 \n", - "5 0 culturedBear 0.140187 \n", - "6 0 spryOrangutan 0.098869 \n", - "7 0 littleSquirrel 0.015630 \n", - "8 1 newLion 0.092814 \n", - "9 1 likelyRabbit 0.237099 \n", - "10 1 conventionalMonkey 0.073831 \n", - "11 1 littleCow 0.113970 \n", - "12 1 newPanda 0.051978 \n", - "13 1 likelyGorilla 0.185777 \n", + "0 0 niceRhino 0.356771 \n", + "1 0 culturedCow 0.161326 \n", + "2 0 spryBison 0.297614 \n", + "3 0 youngLion 0.083168 \n", + "4 0 smallGiraffe 0.172383 \n", + "5 0 culturedBear 0.144971 \n", + "6 0 spryOrangutan 0.103209 \n", + "7 0 littleSquirrel 0.017574 \n", + "8 1 newLion 0.095934 \n", + "9 1 likelyRabbit 0.280653 \n", + "10 1 conventionalMonkey 0.079966 \n", + "11 1 littleCow 0.144622 \n", + "12 1 newPanda 0.062017 \n", + "13 1 likelyGorilla 0.176243 \n", "\n", " average_negative_bert average_neutral_bert average_num_words \\\n", - "0 0.109928 0.616185 15.875000 \n", - "1 0.501847 0.395720 28.000000 \n", - "2 0.332005 0.394824 9.777778 \n", - "3 0.559233 0.367075 15.875000 \n", - "4 0.427087 0.448083 11.375000 \n", - "5 0.162012 0.697801 20.500000 \n", - "6 0.611169 0.289962 18.571429 \n", - "7 0.459533 0.524838 30.000000 \n", - "8 0.381585 0.525602 6.333333 \n", - "9 0.298989 0.463913 11.000000 \n", - "10 0.469623 0.456546 14.083333 \n", - "11 0.544537 0.341493 25.250000 \n", - "12 0.429207 0.518815 21.600000 \n", - "13 0.328654 0.485568 35.333333 \n", + "0 0.084720 0.558509 15.875000 \n", + "1 0.495867 0.342807 28.000000 \n", + "2 0.312863 0.389523 9.777778 \n", + "3 0.558676 0.358156 15.875000 \n", + "4 0.423620 0.403998 11.375000 \n", + "5 0.174196 0.680834 20.500000 \n", + "6 0.648681 0.248110 18.571429 \n", + "7 0.420926 0.561500 30.000000 \n", + "8 0.386197 0.517869 6.333333 \n", + "9 0.293505 0.425842 11.000000 \n", + "10 0.515654 0.404380 14.083333 \n", + "11 0.530565 0.324812 25.250000 \n", + "12 0.439835 0.498147 21.600000 \n", + "13 0.302361 0.521397 35.333333 \n", "\n", " average_num_chars average_num_messages \\\n", "0 81.625000 1.0 \n", @@ -2108,41 +2830,41 @@ "13 3 0 0 \n", "\n", " sum_num_parentheses sum_num_emoji sum_mimicry_bert sum_moving_mimicry \\\n", - "0 2 0 1.639970 3.068143 \n", - "1 0 0 1.819858 2.478020 \n", - "2 0 0 3.074910 4.041765 \n", - "3 0 0 3.693983 3.680254 \n", - "4 0 0 2.361289 3.814536 \n", - "5 0 0 1.569863 2.638946 \n", - "6 0 0 1.805912 2.607455 \n", - "7 0 0 0.309424 0.327158 \n", - "8 0 0 2.840763 2.297269 \n", - "9 0 0 2.471947 1.657061 \n", - "10 0 0 2.324324 2.544707 \n", - "11 0 0 0.980274 0.611583 \n", - "12 0 0 1.961948 1.112893 \n", - "13 0 0 0.936843 0.673729 \n", + "0 2 0 1.731247 3.016273 \n", + "1 0 0 1.823616 2.494478 \n", + "2 0 0 2.924477 4.013453 \n", + "3 0 0 3.689977 3.647838 \n", + "4 0 0 2.305697 3.786036 \n", + "5 0 0 1.531364 2.634271 \n", + "6 0 0 1.961317 2.603867 \n", + "7 0 0 0.308643 0.327093 \n", + "8 0 0 2.755768 2.274712 \n", + "9 0 0 2.443192 1.628115 \n", + "10 0 0 2.260409 2.522178 \n", + "11 0 0 0.973790 0.595699 \n", + "12 0 0 1.933344 1.103848 \n", + "13 0 0 0.949753 0.670097 \n", "\n", " sum_forward_flow user_list \n", - "0 5.000301 ['culturedCow' 'spryBison' 'youngLion' 'smallG... \n", - "1 2.591912 ['niceRhino' 'spryBison' 'youngLion' 'smallGir... \n", - "2 5.170760 ['niceRhino' 'culturedCow' 'youngLion' 'smallG... \n", - "3 4.783784 ['niceRhino' 'culturedCow' 'spryBison' 'smallG... \n", - "4 4.433901 ['niceRhino' 'culturedCow' 'spryBison' 'youngL... \n", - "5 2.922095 ['niceRhino' 'culturedCow' 'spryBison' 'youngL... \n", - "6 4.516776 ['niceRhino' 'culturedCow' 'spryBison' 'youngL... \n", - "7 0.521013 ['niceRhino' 'culturedCow' 'spryBison' 'youngL... \n", - "8 7.079996 ['likelyRabbit' 'conventionalMonkey' 'littleCo... \n", - "9 5.515881 ['newLion' 'conventionalMonkey' 'littleCow' 'n... \n", - "10 6.865593 ['newLion' 'likelyRabbit' 'littleCow' 'newPand... \n", - "11 1.816545 ['newLion' 'likelyRabbit' 'conventionalMonkey'... \n", - "12 2.242426 ['newLion' 'likelyRabbit' 'conventionalMonkey'... \n", - "13 1.492236 ['newLion' 'likelyRabbit' 'conventionalMonkey'... \n", + "0 4.953423 ['culturedCow' 'spryBison' 'youngLion' 'smallG... \n", + "1 2.539859 ['niceRhino' 'spryBison' 'youngLion' 'smallGir... \n", + "2 5.303996 ['niceRhino' 'culturedCow' 'youngLion' 'smallG... \n", + "3 4.762858 ['niceRhino' 'culturedCow' 'spryBison' 'smallG... \n", + "4 4.647392 ['niceRhino' 'culturedCow' 'spryBison' 'youngL... \n", + "5 2.913562 ['niceRhino' 'culturedCow' 'spryBison' 'youngL... \n", + "6 4.515048 ['niceRhino' 'culturedCow' 'spryBison' 'youngL... \n", + "7 0.521758 ['niceRhino' 'culturedCow' 'spryBison' 'youngL... \n", + "8 7.213783 ['likelyRabbit' 'conventionalMonkey' 'littleCo... \n", + "9 5.493533 ['newLion' 'conventionalMonkey' 'littleCow' 'n... \n", + "10 6.896428 ['newLion' 'likelyRabbit' 'littleCow' 'newPand... \n", + "11 1.758415 ['newLion' 'likelyRabbit' 'conventionalMonkey'... \n", + "12 2.211212 ['newLion' 'likelyRabbit' 'conventionalMonkey'... \n", + "13 1.521269 ['newLion' 'likelyRabbit' 'conventionalMonkey'... \n", "\n", "[14 rows x 307 columns]" ] }, - "execution_count": 22, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2172,7 +2894,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2206,31 +2928,31 @@ " 6\n", " 0\n", " spryOrangutan\n", - " 0.611169\n", + " 0.648681\n", " \n", " \n", " 3\n", " 0\n", " youngLion\n", - " 0.559233\n", + " 0.558676\n", " \n", " \n", " 11\n", " 1\n", " littleCow\n", - " 0.544537\n", - " \n", - " \n", - " 1\n", - " 0\n", - " culturedCow\n", - " 0.501847\n", + " 0.530565\n", " \n", " \n", " 10\n", " 1\n", " conventionalMonkey\n", - " 0.469623\n", + " 0.515654\n", + " \n", + " \n", + " 1\n", + " 0\n", + " culturedCow\n", + " 0.495867\n", " \n", " \n", "\n", @@ -2238,14 +2960,14 @@ ], "text/plain": [ " conversation_num speaker_nickname average_negative_bert\n", - "6 0 spryOrangutan 0.611169\n", - "3 0 youngLion 0.559233\n", - "11 1 littleCow 0.544537\n", - "1 0 culturedCow 0.501847\n", - "10 1 conventionalMonkey 0.469623" + "6 0 spryOrangutan 0.648681\n", + "3 0 youngLion 0.558676\n", + "11 1 littleCow 0.530565\n", + "10 1 conventionalMonkey 0.515654\n", + "1 0 culturedCow 0.495867" ] }, - "execution_count": 23, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -2257,7 +2979,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2321,7 +3043,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -2355,31 +3077,31 @@ " 0\n", " 0\n", " niceRhino\n", - " 0.109928\n", + " 0.084720\n", " \n", " \n", " 5\n", " 0\n", " culturedBear\n", - " 0.162012\n", + " 0.174196\n", " \n", " \n", " 9\n", " 1\n", " likelyRabbit\n", - " 0.298989\n", + " 0.293505\n", " \n", " \n", " 13\n", " 1\n", " likelyGorilla\n", - " 0.328654\n", + " 0.302361\n", " \n", " \n", " 2\n", " 0\n", " spryBison\n", - " 0.332005\n", + " 0.312863\n", " \n", " \n", "\n", @@ -2387,14 +3109,14 @@ ], "text/plain": [ " conversation_num speaker_nickname average_negative_bert\n", - "0 0 niceRhino 0.109928\n", - "5 0 culturedBear 0.162012\n", - "9 1 likelyRabbit 0.298989\n", - "13 1 likelyGorilla 0.328654\n", - "2 0 spryBison 0.332005" + "0 0 niceRhino 0.084720\n", + "5 0 culturedBear 0.174196\n", + "9 1 likelyRabbit 0.293505\n", + "13 1 likelyGorilla 0.302361\n", + "2 0 spryBison 0.312863" ] }, - "execution_count": 25, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -2406,7 +3128,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 29, "metadata": {}, "outputs": [ { diff --git a/examples/featurize.py b/examples/featurize.py index cb751e6f..adfa781a 100644 --- a/examples/featurize.py +++ b/examples/featurize.py @@ -42,38 +42,48 @@ input_df = tiny_juries_df, grouping_keys = ["batch_num", "round_num"], vector_directory = "./vector_data/", - output_file_path_chat_level = "./jury_TINY_output_chat_level.csv", - output_file_path_user_level = "./jury_TINY_output_user_level.csv", - output_file_path_conv_level = "./jury_TINY_output_conversation_level.csv", - turns = False + output_file_base = "jury_TINY_output", # Naming output files using the output_file_base parameter (recommended) + turns = False, + custom_features = [ + "(BERT) Mimicry", + "Moving Mimicry", + "Forward Flow", + "Discursive Diversity"] ) - tiny_juries_feature_builder.featurize(col="message") + tiny_juries_feature_builder.featurize() # Tiny multi-task tiny_multi_task_feature_builder = FeatureBuilder( input_df = tiny_multi_task_df, conversation_id_col = "stageId", vector_directory = "./vector_data/", + # alternatively, you can name each output file separately. NOTE, however, that we don't directly use this path; + # we modify the path to place outputs within the `output/chat`, `output/conv`, and `output/user` folders. output_file_path_chat_level = "./multi_task_TINY_output_chat_level_stageId_cumulative.csv", output_file_path_user_level = "./multi_task_TINY_output_user_level_stageId_cumulative.csv", output_file_path_conv_level = "./multi_task_TINY_output_conversation_level_stageId_cumulative.csv", turns = False ) - tiny_multi_task_feature_builder.featurize(col="message") + tiny_multi_task_feature_builder.featurize() # FULL DATASETS BELOW ------------------------------------- # # Juries # jury_feature_builder = FeatureBuilder( # input_df = juries_df, - # grouping_keys = ["batch_num", "round_num"], + # grouping_keys = ["batch_num", "round_num"], # vector_directory = "./vector_data/", # output_file_path_chat_level = "./jury_output_chat_level.csv", # output_file_path_user_level = "./jury_output_user_level.csv", # output_file_path_conv_level = "./jury_output_conversation_level.csv", - # turns = True + # turns = True, + # custom_features = [ + # "(BERT) Mimicry", + # "Moving Mimicry", + # "Forward Flow", + # "Discursive Diversity"] # ) - # jury_feature_builder.featurize(col="message") + # jury_feature_builder.featurize() # # CSOP (Abdullah) # csop_feature_builder = FeatureBuilder( @@ -84,7 +94,7 @@ # output_file_path_conv_level = "./csop_output_conversation_level.csv", # turns = True # ) - # csop_feature_builder.featurize(col="message") + # csop_feature_builder.featurize() # # CSOP II (Nak Won Rim) @@ -96,4 +106,4 @@ # output_file_path_conv_level = "./csopII_output_conversation_level.csv", # turns = True # ) - # csopII_feature_builder.featurize(col="message") + # csopII_feature_builder.featurize() diff --git a/pyproject.toml b/pyproject.toml index 9122dc06..229cd3c0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta" [project] name = "team_comm_tools" -version = "0.1.3" +version = "0.1.4" requires-python = ">= 3.10" dependencies = [ "chardet>=3.0.4", @@ -14,7 +14,7 @@ dependencies = [ "emoji==1.7.0", "flask==3.0.3", "gensim>=4.3.3", - "nltk==3.8.1", + "nltk==3.9.1", "numpy<2.0.0", "pandas==2.2.2", "pyphen==0.14.0", @@ -36,6 +36,7 @@ dependencies = [ "torchaudio==2.4.1", "torchvision==0.19.1", "transformers==4.44.0", + "tqdm>=4.66.5", "tzdata>=2023.3", "tzlocal==5.2" ] @@ -65,4 +66,4 @@ where = ["src"] 'features/lexicons/function_words.txt', 'features/lexicons/question_words.txt', 'features/assets/*' -] \ No newline at end of file +] diff --git a/requirements.txt b/requirements.txt index 0135f3c1..3c570b21 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,7 +3,7 @@ convokit==3.0.0 emoji==1.7.0 flask==3.0.3 gensim>=4.3.3 -nltk==3.8.1 +nltk==3.9.1 numpy<2.0.0 pandas==2.2.2 pyphen==0.14.0 @@ -25,5 +25,6 @@ torch==2.4.1 torchaudio==2.4.1 torchvision==0.19.1 transformers==4.44.0 +tqdm>=4.66.5 tzdata>=2023.3 -tzlocal==5.2 \ No newline at end of file +tzlocal==5.2 diff --git a/setup.sh b/setup.sh index 3ed17bc7..d414f334 100755 --- a/setup.sh +++ b/setup.sh @@ -21,7 +21,7 @@ echo "Running import_nltk.py..." python -c " import nltk nltk.download('nps_chat') -nltk.download('punkt') +nltk.download('punkt_tab') nltk.download('stopwords') nltk.download('wordnet') " diff --git a/src/team_comm_tools/feature_builder.py b/src/team_comm_tools/feature_builder.py index 54fced50..cb3500d9 100644 --- a/src/team_comm_tools/feature_builder.py +++ b/src/team_comm_tools/feature_builder.py @@ -8,6 +8,7 @@ from pathlib import Path import time import itertools +import warnings # Imports from feature files and classes from team_comm_tools.utils.download_resources import download @@ -28,16 +29,19 @@ class FeatureBuilder: :param input_df: A pandas DataFrame containing the conversation data that you wish to featurize. :type input_df: pd.DataFrame - :param vector_directory: Directory path where the vectors are to be cached. + :param vector_directory: Directory path where the vectors are to be cached. Defaults to "./vector_data/" :type vector_directory: str + + :param output_file_base: Base name for the output files, which will be used to auto-generate filenames for each of the three levels. Defaults to "output." + :type output_file_base: str - :param output_file_path_chat_level: Path where the chat (utterance)-level output csv file is to be generated. + :param output_file_path_chat_level: Path where the chat (utterance)-level output csv file is to be generated. (This parameter will override the base name.) :type output_file_path_chat_level: str - :param output_file_path_user_level: Path where the user (speaker)-level output csv file is to be generated. + :param output_file_path_user_level: Path where the user (speaker)-level output csv file is to be generated. (This parameter will override the base name.) :type output_file_path_user_level: str - :param output_file_path_conv_level: Path where the conversation-level output csv file is to be generated. + :param output_file_path_conv_level: Path where the conversation-level output csv file is to be generated. (This parameter will override the base name.) :type output_file_path_conv_level: str :param custom_features: A list of additional features outside of the default features that should be calculated. @@ -85,6 +89,9 @@ class FeatureBuilder: :param regenerate_vectors: If true, will regenerate vector data even if it already exists. Defaults to False. :type regenerate_vectors: bool, optional + :param compute_vectors_from_preprocessed: If true, computes vectors using preprocessed text (that is, with capitalization and punctuation removed). This was the default behavior for v.0.1.3 and earlier, but we now default to computing metrics on the unpreprocessed text (which INCLUDES capitalization and punctuation). Defaults to False. + :type compute_vectors_from_preprocessed: bool, optional + :return: The FeatureBuilder doesn't return anything; instead, it writes the generated features to files in the specified paths. It will also print out its progress, so you should see "All Done!" in the terminal, which will indicate that the features have been generated. :rtype: None @@ -92,12 +99,13 @@ class FeatureBuilder: def __init__( self, input_df: pd.DataFrame, - vector_directory: str, - output_file_path_chat_level: str, - output_file_path_user_level: str, - output_file_path_conv_level: str, + 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], + analyze_first_pct: list = [1.0], turns: bool=False, conversation_id_col: str = "conversation_num", speaker_id_col: str = "speaker_nickname", @@ -108,17 +116,17 @@ def __init__( within_task = False, ner_training_df: pd.DataFrame = None, ner_cutoff: int = 0.9, - regenerate_vectors: bool = False + regenerate_vectors: bool = False, + compute_vectors_from_preprocessed: bool = False ) -> None: - # Defining input and output paths. + # Defining input and output paths. self.chat_data = input_df.copy() self.orig_data = input_df.copy() self.ner_training = ner_training_df self.vector_directory = vector_directory + print("Initializing Featurization...") - self.output_file_path_conv_level = output_file_path_conv_level - self.output_file_path_user_level = output_file_path_user_level # Set features to generate # TODO --- think through more carefully which ones we want to exclude and why @@ -171,10 +179,16 @@ def __init__( invalid_features_str = ', '.join(invalid_features) print(f"WARNING: Invalid custom features provided. Ignoring `{invalid_features_str}`.") + # keep track of which features we are generating + self.feature_names = self.default_features + self.custom_features + # remove named entities if we didn't pass in the column + if(self.ner_training is None): + self.feature_names.remove("Named Entity Recognition") + # deduplicate functions and append them into a list for calculation self.feature_methods_chat = [] self.feature_methods_conv = [] - for feature in self.default_features + self.custom_features: + for feature in self.feature_names: level, func = self.feature_dict[feature]["level"], self.feature_dict[feature]['function'] if level == 'Chat': if func not in self.feature_methods_chat: @@ -183,13 +197,6 @@ def __init__( if func not in self.feature_methods_conv: self.feature_methods_conv.append(func) - # Basic error detetection - # user didn't specify a file name, or specified one with only nonalphanumeric chars - if not bool(self.output_file_path_conv_level) or not bool(re.sub('[^A-Za-z0-9_]', '', self.output_file_path_conv_level)): - raise ValueError("ERROR: Improper conversation-level output file name detected.") - if not bool(self.output_file_path_user_level) or not bool(re.sub('[^A-Za-z0-9_]', '', self.output_file_path_user_level)): - raise ValueError("ERROR: Improper user (speaker)-level output file name detected.") - # drop all columns that are in our generated feature set --- we don't want to create confusion! chat_features = list(itertools.chain(*[self.feature_dict[feature]["columns"] for feature in self.feature_dict.keys() if self.feature_dict[feature]["level"] == "Chat"])) columns_to_drop = [col for col in chat_features if col in self.chat_data.columns] @@ -218,29 +225,34 @@ def __init__( self.ner_cutoff = ner_cutoff self.regenerate_vectors = regenerate_vectors + if(compute_vectors_from_preprocessed == True): + self.vector_colname = self.message_col # because the message col will eventually get preprocessed + else: + self.vector_colname = self.message_col + "_original" # because this contains the original message + # check grouping rules if self.conversation_id_col not in self.chat_data.columns and len(self.grouping_keys)==0: if(self.conversation_id_col == "conversation_num"): raise ValueError("Conversation identifier not present in data. Did you perhaps forget to pass in a `conversation_id_col`?") raise ValueError("Conversation identifier not present in data.") if self.cumulative_grouping and len(grouping_keys) == 0: - print("WARNING: No grouping keys provided. Ignoring `cumulative_grouping` argument.") + warnings.warn("WARNING: No grouping keys provided. Ignoring `cumulative_grouping` argument.") self.cumulative_grouping = False if self.cumulative_grouping and len(grouping_keys) != 3: - print("WARNING: Can only perform cumulative grouping for three-layer nesting. Ignoring cumulative command and grouping by unique combinations in the grouping_keys.") + warnings.warn("WARNING: Can only perform cumulative grouping for three-layer nesting. Ignoring cumulative command and grouping by unique combinations in the grouping_keys.") self.cumulative_grouping = False self.conversation_id_col = "conversation_num" if self.cumulative_grouping and self.conversation_id_col not in self.grouping_keys: raise ValueError("Conversation identifier for cumulative grouping must be one of the grouping keys.") if self.grouping_keys and not self.cumulative_grouping and self.conversation_id_col != "conversation_num": - print("WARNING: When grouping by the unique combination of a list of keys (`grouping_keys`), the conversation identifier must be auto-generated (`conversation_num`) rather than a user-provided column. Resetting conversation_id.") + warnings.warn("WARNING: When grouping by the unique combination of a list of keys (`grouping_keys`), the conversation identifier must be auto-generated (`conversation_num`) rather than a user-provided column. Resetting conversation_id.") self.conversation_id_col = "conversation_num" self.preprocess_chat_data() # set new identifier column for cumulative grouping. if self.cumulative_grouping and len(grouping_keys) == 3: - print("NOTE: User has requested cumulative grouping. Auto-generating the key `conversation_num` as the conversation identifier for cumulative convrersations.") + warnings.warn("NOTE: User has requested cumulative grouping. Auto-generating the key `conversation_num` as the conversation identifier for cumulative conversations.") self.conversation_id_col = "conversation_num" # Input columns are the columns that come in the raw chat data @@ -268,8 +280,33 @@ def __init__( - The inputted file name must be a valid, non-empty string - The inputted file name must not contain only special characters with no alphanumeric component """ + + # Use the output_file_base parameter to auto-generate paths (since we have a lot of assumptions in how the output path looks) + self.output_file_path_chat_level = output_file_path_chat_level + self.output_file_path_conv_level = output_file_path_conv_level + self.output_file_path_user_level = output_file_path_user_level + + # Ensure output_file_base is alphanumeric + hyphens + if(re.sub('[^A-Za-z0-9_]', '', output_file_base) != output_file_base): + print('here1') + output_file_base = re.sub('[^A-Za-z0-9_]', '', output_file_base) + warnings.warn("WARNING: Special characters detected in output_file_base. These characters have been automatically removed.") + + if self.output_file_path_chat_level is None: + self.output_file_path_chat_level = "./" + output_file_base + "_chat_level.csv" + if self.output_file_path_conv_level is None: + self.output_file_path_conv_level = "./" + output_file_base + "_conv_level.csv" + if self.output_file_path_user_level is None: + self.output_file_path_user_level = "./" + output_file_base + "_user_level.csv" + + # Basic error detetection + if not bool(self.output_file_path_conv_level) or not bool(re.sub('[^A-Za-z0-9_]', '', self.output_file_path_conv_level)): + raise ValueError("ERROR: Improper conversation-level output file name detected.") + if not bool(self.output_file_path_user_level) or not bool(re.sub('[^A-Za-z0-9_]', '', self.output_file_path_user_level)): + raise ValueError("ERROR: Improper user (speaker)-level output file name detected.") + # We assume that the base file name is the last item in the output path; we will use this to name the stored vectors. - if ('/' not in output_file_path_chat_level or + if ('/' not in self.output_file_path_chat_level or '/' not in self.output_file_path_conv_level or '/' not in self.output_file_path_user_level): raise ValueError( @@ -282,7 +319,7 @@ def __init__( ) try: - base_file_name = output_file_path_chat_level.split("/")[-1] + base_file_name = self.output_file_path_chat_level.split("/")[-1] except: raise ValueError("ERROR: Improper chat-level output file name detected.") @@ -290,18 +327,18 @@ def __init__( raise ValueError("ERROR: Improper chat-level output file name detected.") try: - folder_type_name = output_file_path_chat_level.split("/")[-2] + folder_type_name = self.output_file_path_chat_level.split("/")[-2] except IndexError: # user didn't specify a folder, so we will have to append it for them folder_type_name = "turn" if self.turns else "chat" - output_file_path_chat_level = '/'.join(output_file_path_chat_level.split("/")[:-1]) + '/' + folder_type_name + '/' + base_file_name + self.output_file_path_chat_level = '/'.join(self.output_file_path_chat_level.split("/")[:-1]) + '/' + folder_type_name + '/' + base_file_name # We check whether the second to last item is a "folder type": either chat or turn. if folder_type_name not in ["chat", "turn"]: # user didn't specify the folder type, so we will append it for them folder_type_name = "turn" if self.turns else "chat" - output_file_path_chat_level = '/'.join(output_file_path_chat_level.split("/")[:-1]) + '/' + folder_type_name + '/' + base_file_name + self.output_file_path_chat_level = '/'.join(self.output_file_path_chat_level.split("/")[:-1]) + '/' + folder_type_name + '/' + base_file_name # Set file paths, ensuring correct subfolder type is added. - self.output_file_path_chat_level = re.sub(r'chat', r'turn', output_file_path_chat_level) if self.turns else output_file_path_chat_level + self.output_file_path_chat_level = re.sub(r'chat', r'turn', self.output_file_path_chat_level) if self.turns else self.output_file_path_chat_level if self.output_file_path_chat_level.split(".")[-1] != "csv": self.output_file_path_chat_level = self.output_file_path_chat_level + ".csv" if not re.match(r"(.*\/|^)conv\/", self.output_file_path_conv_level): @@ -338,7 +375,7 @@ def __init__( if(not need_sentiment and feature_dict[feature]["bert_sentiment_data"]): need_sentiment = True - check_embeddings(self.chat_data, self.vect_path, self.bert_path, need_sentence, need_sentiment, self.regenerate_vectors, self.message_col) + check_embeddings(self.chat_data, self.vect_path, self.bert_path, need_sentence, need_sentiment, self.regenerate_vectors, message_col = self.vector_colname) if(need_sentence): self.vect_data = pd.read_csv(self.vect_path, encoding='mac_roman') @@ -401,7 +438,7 @@ def merge_conv_data_with_original(self) -> None: if {'index'}.issubset(self.conv_data.columns): self.conv_data = self.conv_data.drop(columns=['index']) - def featurize(self, col: str="message") -> None: + def featurize(self) -> None: """ Main driver function for feature generation. @@ -410,9 +447,6 @@ def featurize(self, col: str="message") -> None: conversation-level features. Finally, the features are saved into the designated output files. - :param col: Column to preprocess, defaults to "message" - :type col: str, optional - :return: None :rtype: None """ @@ -465,6 +499,12 @@ def featurize(self, col: str="message") -> None: # Step 4. Write the feartures into the files defined in the output paths. print("All Done!") + + # Store column names of what we generated, so that the user can easily access them + self.chat_features = list(itertools.chain(*[feature_dict[feature]["columns"] for feature in self.feature_names if feature_dict[feature]["level"] == "Chat"])) + self.conv_features_base = list(itertools.chain(*[feature_dict[feature]["columns"] for feature in self.feature_names if feature_dict[feature]["level"] == "Conversation"])) + self.conv_features_all = [col for col in self.conv_data if col not in self.orig_data and col != 'conversation_num'] + self.save_features() def preprocess_chat_data(self) -> None: @@ -494,7 +534,7 @@ def preprocess_chat_data(self) -> None: # create new column that retains punctuation self.chat_data["message_lower_with_punc"] = self.chat_data[self.message_col].astype(str).apply(preprocess_text_lowercase_but_retain_punctuation) - # Preprocessing the text in `col` and then overwriting the column `col`. + # Preprocessing the text in `message_col` and then overwriting the column `message_col`. # TODO: We should probably use classes to abstract preprocessing module as well? self.chat_data[self.message_col] = self.chat_data[self.message_col].astype(str).apply(preprocess_text) diff --git a/src/team_comm_tools/feature_dict.py b/src/team_comm_tools/feature_dict.py index 90fd4377..5b557091 100644 --- a/src/team_comm_tools/feature_dict.py +++ b/src/team_comm_tools/feature_dict.py @@ -1,9 +1,10 @@ -from .utils.calculate_chat_level_features import ChatLevelFeaturesCalculator -from .utils.calculate_conversation_level_features import ConversationLevelFeaturesCalculator -from .utils.preprocess import * +from team_comm_tools.utils.calculate_chat_level_features import ChatLevelFeaturesCalculator +from team_comm_tools.utils.calculate_conversation_level_features import ConversationLevelFeaturesCalculator +from team_comm_tools.utils.preprocess import * from flask import Flask, jsonify import json +import sys app = Flask(__name__) @@ -89,61 +90,61 @@ }, "LIWC and Other Lexicons": { "columns": [ - "discrepancies_lexical_per_100", - "hear_lexical_per_100", - "home_lexical_per_100", - "conjunction_lexical_per_100", - "certainty_lexical_per_100", - "inclusive_lexical_per_100", - "bio_lexical_per_100", - "achievement_lexical_per_100", - "adverbs_lexical_per_100", - "anxiety_lexical_per_100", - "third_person_lexical_per_100", - "negation_lexical_per_100", - "swear_lexical_per_100", - "death_lexical_per_100", - "health_lexical_per_100", - "see_lexical_per_100", - "body_lexical_per_100", - "family_lexical_per_100", - "negative_affect_lexical_per_100", - "quantifier_lexical_per_100", - "positive_affect_lexical_per_100", - "insight_lexical_per_100", - "humans_lexical_per_100", - "present_tense_lexical_per_100", - "future_tense_lexical_per_100", - "past_tense_lexical_per_100", - "relative_lexical_per_100", - "sexual_lexical_per_100", - "inhibition_lexical_per_100", - "sadness_lexical_per_100", - "social_lexical_per_100", - "indefinite_pronoun_lexical_per_100", - "religion_lexical_per_100", - "work_lexical_per_100", - "money_lexical_per_100", - "causation_lexical_per_100", - "anger_lexical_per_100", - "first_person_singular_lexical_per_100", - "feel_lexical_per_100", - "tentativeness_lexical_per_100", - "exclusive_lexical_per_100", - "verbs_lexical_per_100", - "friends_lexical_per_100", - "article_lexical_per_100", - "argue_lexical_per_100", - "auxiliary_verbs_lexical_per_100", - "cognitive_mech_lexical_per_100", - "preposition_lexical_per_100", - "first_person_plural_lexical_per_100", - "percept_lexical_per_100", - "second_person_lexical_per_100", - "positive_words_lexical_per_100", - "first_person_lexical_per_100", - "nltk_english_stopwords_lexical_per_100", - "hedge_words_lexical_per_100" + "discrepancies_lexical_wordcount", + "hear_lexical_wordcount", + "home_lexical_wordcount", + "conjunction_lexical_wordcount", + "certainty_lexical_wordcount", + "inclusive_lexical_wordcount", + "bio_lexical_wordcount", + "achievement_lexical_wordcount", + "adverbs_lexical_wordcount", + "anxiety_lexical_wordcount", + "third_person_lexical_wordcount", + "negation_lexical_wordcount", + "swear_lexical_wordcount", + "death_lexical_wordcount", + "health_lexical_wordcount", + "see_lexical_wordcount", + "body_lexical_wordcount", + "family_lexical_wordcount", + "negative_affect_lexical_wordcount", + "quantifier_lexical_wordcount", + "positive_affect_lexical_wordcount", + "insight_lexical_wordcount", + "humans_lexical_wordcount", + "present_tense_lexical_wordcount", + "future_tense_lexical_wordcount", + "past_tense_lexical_wordcount", + "relative_lexical_wordcount", + "sexual_lexical_wordcount", + "inhibition_lexical_wordcount", + "sadness_lexical_wordcount", + "social_lexical_wordcount", + "indefinite_pronoun_lexical_wordcount", + "religion_lexical_wordcount", + "work_lexical_wordcount", + "money_lexical_wordcount", + "causation_lexical_wordcount", + "anger_lexical_wordcount", + "first_person_singular_lexical_wordcount", + "feel_lexical_wordcount", + "tentativeness_lexical_wordcount", + "exclusive_lexical_wordcount", + "verbs_lexical_wordcount", + "friends_lexical_wordcount", + "article_lexical_wordcount", + "argue_lexical_wordcount", + "auxiliary_verbs_lexical_wordcount", + "cognitive_mech_lexical_wordcount", + "preposition_lexical_wordcount", + "first_person_plural_lexical_wordcount", + "percept_lexical_wordcount", + "second_person_lexical_wordcount", + "positive_words_lexical_wordcount", + "first_person_lexical_wordcount", + "nltk_english_stopwords_lexical_wordcount", + "hedge_words_lexical_wordcount" ], "file": "./features/lexical_features_v2.py", "level": "Chat", @@ -355,27 +356,27 @@ }, "Politeness Strategies": { "columns": [ - "please", - "please_start", - "hashedge", - "indirect_btw", - "hedges", - "factuality", - "deference", - "gratitude", - "apologizing", - "1st_person_pl", - "1st_person", - "1st_person_start", - "2nd_person", - "2nd_person_start", - "indirect_greeting", - "direct_question", - "direct_start", - "haspositive", - "hasnegative", - "subjunctive", - "indicative" + "please_politeness_convokit", + "please_start_politeness_convokit", + "hashedge_politeness_convokit", + "indirect_btw_politeness_convokit", + "hedges_politeness_convokit", + "factuality_politeness_convokit", + "deference_politeness_convokit", + "gratitude_politeness_convokit", + "apologizing_politeness_convokit", + "1st_person_pl_politeness_convokit", + "1st_person_politeness_convokit", + "1st_person_start_politeness_convokit", + "2nd_person_politeness_convokit", + "2nd_person_start_politeness_convokit", + "indirect_greeting_politeness_convokit", + "direct_question_politeness_convokit", + "direct_start_politeness_convokit", + "haspositive_politeness_convokit", + "hasnegative_politeness_convokit", + "subjunctive_politeness_convokit", + "indicative_politeness_convokit" ], "file": "./features/politeness_features.py", "level": "Chat", @@ -391,45 +392,45 @@ }, "Politeness / Receptiveness Markers": { "columns": [ - "Impersonal_Pronoun", - "First_Person_Single", - "Hedges", - "Negation", - "Subjectivity", - "Negative_Emotion", - "Reasoning", - "Agreement", - "Second_Person", - "Adverb_Limiter", - "Disagreement", - "Acknowledgement", - "First_Person_Plural", - "For_Me", - "WH_Questions", - "YesNo_Questions", - "Bare_Command", - "Truth_Intensifier", - "Apology", - "Ask_Agency", - "By_The_Way", - "Can_You", - "Conjunction_Start", - "Could_You", - "Filler_Pause", - "For_You", - "Formal_Title", - "Give_Agency", - "Affirmation", - "Gratitude", - "Hello", - "Informal_Title", - "Let_Me_Know", - "Swearing", - "Reassurance", - "Please", - "Positive_Emotion", - "Goodbye", - "Token_count" + "Impersonal_Pronoun_receptiveness_yeomans", + "First_Person_Single_receptiveness_yeomans", + "Hedges_receptiveness_yeomans", + "Negation_receptiveness_yeomans", + "Subjectivity_receptiveness_yeomans", + "Negative_Emotion_receptiveness_yeomans", + "Reasoning_receptiveness_yeomans", + "Agreement_receptiveness_yeomans", + "Second_Person_receptiveness_yeomans", + "Adverb_Limiter_receptiveness_yeomans", + "Disagreement_receptiveness_yeomans", + "Acknowledgement_receptiveness_yeomans", + "First_Person_Plural_receptiveness_yeomans", + "For_Me_receptiveness_yeomans", + "WH_Questions_receptiveness_yeomans", + "YesNo_Questions_receptiveness_yeomans", + "Bare_Command_receptiveness_yeomans", + "Truth_Intensifier_receptiveness_yeomans", + "Apology_receptiveness_yeomans", + "Ask_Agency_receptiveness_yeomans", + "By_The_Way_receptiveness_yeomans", + "Can_You_receptiveness_yeomans", + "Conjunction_Start_receptiveness_yeomans", + "Could_You_receptiveness_yeomans", + "Filler_Pause_receptiveness_yeomans", + "For_You_receptiveness_yeomans", + "Formal_Title_receptiveness_yeomans", + "Give_Agency_receptiveness_yeomans", + "Affirmation_receptiveness_yeomans", + "Gratitude_receptiveness_yeomans", + "Hello_receptiveness_yeomans", + "Informal_Title_receptiveness_yeomans", + "Let_Me_Know_receptiveness_yeomans", + "Swearing_receptiveness_yeomans", + "Reassurance_receptiveness_yeomans", + "Please_receptiveness_yeomans", + "Positive_Emotion_receptiveness_yeomans", + "Goodbye_receptiveness_yeomans", + "Token_count_receptiveness_yeomans" ], "file": "./features/politeness_v2.py, ./features/politeness_v2_helper.py, ./features/keywords.py", "level": "Chat", @@ -607,14 +608,15 @@ } } -keys_to_keep = ["columns", "file", "level", "semantic_grouping", "description", "references", "wiki_link"] +def generate_filtered_dict(): -filtered_dict = {feature_name: {key: value for key, value in feature_data.items() if key in keys_to_keep} - for feature_name, feature_data in feature_dict.items()} + keys_to_keep = ["columns", "file", "level", "semantic_grouping", "description", "references", "wiki_link"] -@app.route('/features') -def get_features(): - return jsonify(filtered_dict) + filtered_dict = {feature_name: {key: value for key, value in feature_data.items() if key in keys_to_keep} + for feature_name, feature_data in feature_dict.items()} + with open('./filtered_dict.json', 'w') as json_file: + json.dump(filtered_dict, json_file, indent=4) -if __name__ == '__main__': - app.run(debug=True) +if __name__ == "__main__": + if len(sys.argv) > 1 and sys.argv[1] == 'run': + generate_filtered_dict() \ No newline at end of file diff --git a/src/team_comm_tools/features/lexical_features_v2.py b/src/team_comm_tools/features/lexical_features_v2.py index 196a687a..1cd7ede7 100644 --- a/src/team_comm_tools/features/lexical_features_v2.py +++ b/src/team_comm_tools/features/lexical_features_v2.py @@ -10,29 +10,26 @@ import os from pathlib import Path -def get_liwc_rate(regex, chat): +def get_liwc_count(regex, chat): """" - Computes the LIWC features as a rate per 100 words, per best practice (Yeomans et al. 2023; https://www.mikeyeomans.info/papers/PGCR_yeomans.pdf, p. 42) - - We apply the following formula: - Rate of word use / 100 words = count / chat length * (chat length / 100) + Count the number of LIWC lexicon words Args: regex (str): The regular expression for the lexicon. chat(str): The message (utterance) being analyzed. Returns: - float: The rate at which the message uses words from a given lexicon. + float: The number of lexicon words present in the message """ if(len(chat) > 0): - return (len(re.findall(regex, chat))/(len(chat)))*(len(chat)/100) + return len(re.findall(regex, chat)) else: return 0 def liwc_features(chat_df: pd.DataFrame, message_col) -> pd.DataFrame: """ This function takes in the chat level input dataframe and computes lexical features - (rates at which the message contains contains words from a given lexicon, such as LIWC). + (the number of words from a given lexicon, such as LIWC). Args: chat_df (pd.DataFrame): This is a pandas dataframe of the chat level features. Should contain 'message' column. @@ -52,8 +49,8 @@ def liwc_features(chat_df: pd.DataFrame, message_col) -> pd.DataFrame: # Return the lexical features stacked as columns return pd.concat( # Finding the # of occurrences of lexicons of each type for all the messages. - [pd.DataFrame(chat_df[message_col + "_original"].apply(lambda chat: get_liwc_rate(regex, chat)))\ - .rename({message_col + "_original": lexicon_type + "_lexical_per_100"}, axis=1)\ + [pd.DataFrame(chat_df[message_col + "_original"].apply(lambda chat: get_liwc_count(regex, chat)))\ + .rename({message_col + "_original": lexicon_type + "_lexical_wordcount"}, axis=1)\ for lexicon_type, regex in lexicons_dict.items()], axis=1 ) diff --git a/src/team_comm_tools/lambda_function.py b/src/team_comm_tools/lambda_function.py new file mode 100644 index 00000000..ccd717ab --- /dev/null +++ b/src/team_comm_tools/lambda_function.py @@ -0,0 +1,29 @@ +import json + +def lambda_handler(event, context): + try: + # Open and read the filtered_dict.json file + with open('filtered_dict.json', 'r') as json_file: + filtered_dict = json.load(json_file) + + # Return the filtered_dict in the response body + return { + 'statusCode': 200, + 'body': json.dumps(filtered_dict), + 'headers': { + 'Content-Type': 'application/json' + } + } + + except Exception as e: + # Handle exceptions and return an error message + return { + 'statusCode': 500, + 'body': json.dumps({ + 'message': 'Internal Server Error', + 'error': str(e) + }), + 'headers': { + 'Content-Type': 'application/json' + } + } \ No newline at end of file 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 52c73465..ef55d4be 100644 --- a/src/team_comm_tools/utils/calculate_chat_level_features.py +++ b/src/team_comm_tools/utils/calculate_chat_level_features.py @@ -20,6 +20,9 @@ from .preload_word_lists import * from .zscore_chats_and_conversation import get_zscore_across_all_chats, get_zscore_across_all_conversations +# Loading bar +from tqdm import tqdm + class ChatLevelFeaturesCalculator: """ Initialize variables and objects used by the ChatLevelFeaturesCalculator class. @@ -74,7 +77,7 @@ def calculate_chat_level_features(self, feature_methods: list) -> pd.DataFrame: :rtype: pd.DataFrame """ - for method in feature_methods: + for method in tqdm(feature_methods): method(self) # Return the input dataset with the chat level features appended (as columns) @@ -179,7 +182,7 @@ def calculate_hedge_features(self) -> None: :rtype: None """ # Naive hedge (contains the word or not) - self.chat_data["hedge_naive"] = self.chat_data["hedge_words_lexical_per_100"].apply(is_hedged_sentence_1) + self.chat_data["hedge_naive"] = self.chat_data["hedge_words_lexical_wordcount"].apply(is_hedged_sentence_1) def calculate_textblob_sentiment(self) -> None: """ @@ -319,7 +322,7 @@ def calculate_politeness_sentiment(self) -> None: :rtype: None """ transformed_df = self.chat_data['message_lower_with_punc'].apply(get_politeness_strategies).apply(pd.Series) - transformed_df = transformed_df.rename(columns=lambda x: re.sub('^feature_politeness_==()','',x)[:-2].lower()) + transformed_df = transformed_df.rename(columns=lambda x: re.sub('^feature_politeness_==()','', x)[:-2].lower() + "_politeness_convokit") # Concatenate the transformed dataframe with the original dataframe self.chat_data = pd.concat([self.chat_data, transformed_df], axis=1) @@ -336,7 +339,9 @@ def calculate_politeness_v2(self) -> None: :return: None :rtype: None """ - self.chat_data = pd.concat([self.chat_data, get_politeness_v2(self.chat_data, 'message_lower_with_punc')], axis=1) + receptiveness_df = get_politeness_v2(self.chat_data, 'message_lower_with_punc') + receptiveness_df = receptiveness_df.rename(columns=lambda x: f"{x}_receptiveness_yeomans") + self.chat_data = pd.concat([self.chat_data, receptiveness_df], axis=1) def get_forward_flow(self) -> None: """ diff --git a/src/team_comm_tools/utils/check_embeddings.py b/src/team_comm_tools/utils/check_embeddings.py index 602f42ee..2c56b3b6 100644 --- a/src/team_comm_tools/utils/check_embeddings.py +++ b/src/team_comm_tools/utils/check_embeddings.py @@ -4,6 +4,7 @@ import os import pickle +from tqdm import tqdm from pathlib import Path import torch @@ -179,14 +180,17 @@ def generate_vect(chat_data, output_path, message_col): print(f"Generating SBERT sentence vectors...") - embedding_arr = [row.tolist() for row in model_vect.encode(chat_data[message_col])] + # Ensure empty strings are encoded as NaN + empty_to_nan = [text if text and text.strip() else np.nan for text in chat_data[message_col].tolist()] + embeddings = model_vect.encode(empty_to_nan) + embedding_arr = [row.tolist() for row in tqdm(embeddings, total=len(chat_data[message_col]))] embedding_df = pd.DataFrame({'message': chat_data[message_col], 'message_embedding': embedding_arr}) # Create directories along the path if they don't exist Path(output_path).parent.mkdir(parents=True, exist_ok=True) embedding_df.to_csv(output_path, index=False) -def generate_bert(chat_data, output_path, message_col): +def generate_bert(chat_data, output_path, message_col, batch_size=64): """ Generates RoBERTa sentiment scores for the given chat data and saves them to a CSV file. @@ -196,42 +200,60 @@ def generate_bert(chat_data, output_path, message_col): :type output_path: str :param message_col: A string representing the column name that should be selected as the message. Defaults to "message". :type message_col: str, optional + :param batch_size: The size of each batch for processing sentiment analysis. Defaults to 64. + :type batch_size: int :raises FileNotFoundError: If the output path is invalid. :return: None :rtype: None """ print(f"Generating RoBERTa sentiments...") - messages = chat_data[message_col] - sentiments = messages.apply(get_sentiment) + messages = chat_data[message_col].tolist() + batch_sentiments_df = pd.DataFrame() - sent_arr = [list(dict.values()) for dict in sentiments] + for i in tqdm(range(0, len(messages), batch_size)): + batch = messages[i:i + batch_size] + batch_df = get_sentiment(batch) + batch_sentiments_df = pd.concat([batch_sentiments_df, batch_df], ignore_index=True) - sent_df = pd.DataFrame(sent_arr, columns =['positive_bert', 'negative_bert', 'neutral_bert']) - # Create directories along the path if they don't exist Path(output_path).parent.mkdir(parents=True, exist_ok=True) - sent_df.to_csv(output_path, index=False) + batch_sentiments_df.to_csv(output_path, index=False) -def get_sentiment(text): +def get_sentiment(texts): """ - Analyzes the sentiment of the given text using a BERT model and returns the scores for positive, negative, and neutral sentiments. + Analyzes the sentiment of the given list of texts using a BERT model and returns a DataFrame with scores for positive, negative, and neutral sentiments. - :param text: The input text to analyze. - :type text: str or None - :return: A dictionary with sentiment scores. - :rtype: dict + :param texts: The list of input texts to analyze. + :type texts: list of str + :return: A DataFrame with sentiment scores. + :rtype: pd.DataFrame """ - if (pd.isnull(text)): - return({'positive': np.nan, 'negative': np.nan, 'neutral': np.nan}) - - text = ' '.join(text.split()[:512]) # handle cases when the text is too long: just take the first 512 chars (hacky, but BERT context window cannot be changed) - encoded = tokenizer(text, return_tensors='pt') + # Handle and tokenize non-null and non-empty texts + texts_series = pd.Series(texts) + non_null_non_empty_texts = texts_series[texts_series.apply(lambda x: pd.notnull(x) and x.strip() != '')].tolist() + + if not non_null_non_empty_texts: + # Return a DataFrame with NaN if there are no valid texts to process + return pd.DataFrame(np.nan, index=texts_series.index, columns=['positive_bert', 'negative_bert', 'neutral_bert']) + + encoded = tokenizer(non_null_non_empty_texts, padding=True, truncation=True, max_length=512, return_tensors='pt') output = model_bert(**encoded) - scores = output[0][0].detach().numpy() - scores = softmax(scores) + scores = output[0].detach().numpy() + scores = softmax(scores, axis=1) + + sent_dict = { + 'positive_bert': scores[:, 2], + 'negative_bert': scores[:, 0], + 'neutral_bert': scores[:, 1] + } + + non_null_sent_df = pd.DataFrame(sent_dict) + + # Initialize the DataFrame such that null texts and empty texts get np.nan + sent_df = pd.DataFrame(np.nan, index=texts_series.index, columns=['positive_bert', 'negative_bert', 'neutral_bert']) + sent_df.loc[texts_series.apply(lambda x: pd.notnull(x) and x.strip() != ''), ['positive_bert', 'negative_bert', 'neutral_bert']] = non_null_sent_df.values - # sample output format - return({'positive': scores[2], 'negative': scores[0], 'neutral': scores[1]}) \ No newline at end of file + return sent_df \ No newline at end of file diff --git a/src/team_comm_tools/utils/download_resources.py b/src/team_comm_tools/utils/download_resources.py index c39ac612..efb1b8a0 100644 --- a/src/team_comm_tools/utils/download_resources.py +++ b/src/team_comm_tools/utils/download_resources.py @@ -1,12 +1,22 @@ import nltk import spacy import subprocess +import ssl def download(): + + # Resolves SSL download error to ensure package downloads required NLTK dependencies + try: + _create_unverified_https_context = ssl._create_unverified_context + except AttributeError: + pass + else: + ssl._create_default_https_context = _create_unverified_https_context + # nltk for resource in [ 'corpora/nps_chat', - 'tokenizers/punkt', + 'tokenizers/punkt_tab', 'corpora/stopwords', 'corpora/wordnet']: try: @@ -24,4 +34,4 @@ def download(): raise if __name__ == "__main__": - download() \ No newline at end of file + download() diff --git a/tests/data/cleaned_data/help.ipynb b/tests/data/cleaned_data/help.ipynb deleted file mode 100644 index eb282979..00000000 --- a/tests/data/cleaned_data/help.ipynb +++ /dev/null @@ -1,47 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Timestamp('2024-07-15 23:09:36.779590')" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "pd.to_datetime(\"2024-07-15T23:09:36.779590\")" - ] - } - ], - "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/data/cleaned_data/helper.ipynb b/tests/data/cleaned_data/helper.ipynb deleted file mode 100644 index 5876d361..00000000 --- a/tests/data/cleaned_data/helper.ipynb +++ /dev/null @@ -1,1474 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    conversation_numspeaker_nicknametimestampmessage
    0AJohn2020-04-20T18:27:20.125Z\"I'm curious how do you all feel about using ...
    1AMary2020-04-20T18:27:40.125Z\"I think flashcards are a great way to memori...
    2AJohn2020-04-20T18:28:00.125Z\"I agree I've found them to be really helpful.\"
    3AJessica2020-04-20T18:28:20.125Z\"I'm not a big fan of flashcards I find them ...
    4AMary2020-04-20T18:28:40.125Z\"I think it really depends on the individual ...
    5AJohn2020-04-20T18:29:00.125Z\"That's true I know some people who prefer to...
    6AJessica2020-04-20T18:29:20.125Z\"I've heard of spaced repetition but I've nev...
    7AJohn2020-04-20T18:29:40.125Z\"It's a great way to improve your long-term m...
    8AMary2020-04-20T18:30:00.125Z\"I'll have to give it a try.\"
    9AJessica2020-04-20T18:30:20.125Z\"Do you have any tips for studying effectively?\"
    10AJohn2020-04-20T18:30:40.125Z\"I find that studying in short bursts is more...
    11AMary2020-04-20T18:31:00.125Z\"I agree it's also important to take breaks a...
    12AJessica2020-04-20T18:31:20.125Z\"I'm always struggling to stay motivated any ...
    13AJohn2020-04-20T18:31:40.125Z\"I find it helpful to set realistic goals and...
    14AMary2020-04-20T18:32:00.125Z\"It's also important to find a study buddy or...
    15AJessica2020-04-20T18:32:20.125Z\"Thanks for the advice I'll definitely try it...
    16BA2020-04-20T18:27:20.125Z\"Hi everyone what are your favorite study hab...
    17BB2020-04-20T18:27:40.956Z\"I like to study in a quiet place where I can...
    18BC2020-04-20T18:28:01.789Z\"I prefer to study with a group of friends so...
    19BD2020-04-20T18:28:23.621Z\"I like to study by myself so I can go at my ...
    20BA2020-04-20T18:28:45.452Z\"I think it's important to find a study metho...
    21BB2020-04-20T18:29:07.284Z\"I agree.\"
    22BC2020-04-20T18:29:29.116Z\"I also think it's important to take breaks w...
    23BD2020-04-20T18:29:50.947Z\"Yes it's important to give your brain a chan...
    24BA2020-04-20T18:30:12.778Z\"I find that it's helpful to set a timer and ...
    25BB2020-04-20T18:30:34.610Z\"That's a good idea I'll try that.\"
    26BC2020-04-20T18:30:56.441Z\"I like to listen to music while I study it h...
    27BD2020-04-20T18:31:18.273Z\"I prefer to study in silence so I can focus ...
    28BA2020-04-20T18:31:40.105Z\"I find that it's helpful to create a study s...
    29BB2020-04-20T18:32:01.936Z\"I agree it's important to be consistent with...
    30BC2020-04-20T18:32:23.768Z\"I also think it's important to reward yourse...
    31BD2020-04-20T18:32:45.599Z\"Yes it's important to stay motivated while y...
    32CA2020-04-20T18:27:20.125Z\"I'm really struggling to stay focused when I...
    33CB2020-04-20T18:27:32.456Z\"I know what you mean. I have the same problem.\"
    34CC2020-04-20T18:27:44.567Z\"I've been trying to find a study method that...
    35CB2020-04-20T18:29:22.456Z\"I've found that it helps to break down my st...
    36CC2020-04-20T18:29:44.567Z\"That's a good idea. I'll try that.\"
    37CA2020-04-20T18:29:52.456Z\"I've also found that it helps to study in a ...
    38CD2020-04-20T18:31:24.567Z\"I agree. It's hard to concentrate when there...
    39CA2020-04-20T19:31:32.456Z\"Another thing that I've found helpful is to ...
    40CB2020-04-20T19:31:34.567Z\"That's a good idea. It helps me to stay on t...
    41CC2020-04-20T19:33:22.456Z\"I'm going to try that too.\"
    42CD2020-04-20T19:33:24.567Z\"I've also found that it helps to reward myse...
    43CA2020-04-20T19:33:32.456Z\"That's a good idea. It gives me something to...
    44CB2020-04-20T19:33:44.567Z\"I agree. It helps me to stay motivated.\"
    45CC2020-04-20T20:35:32.456Z\"I've found that it's also important to take ...
    46CD2020-04-20T20:35:44.567Z\"I agree. It helps me to stay focused and avo...
    47CA2020-04-20T20:35:52.456Z\"I've also found that it helps to study with ...
    48CB2020-04-20T20:35:56.567Z\"That's a good idea. It can help you to stay ...
    \n", - "
    " - ], - "text/plain": [ - " conversation_num speaker_nickname timestamp \\\n", - "0 A John 2020-04-20T18:27:20.125Z \n", - "1 A Mary 2020-04-20T18:27:40.125Z \n", - "2 A John 2020-04-20T18:28:00.125Z \n", - "3 A Jessica 2020-04-20T18:28:20.125Z \n", - "4 A Mary 2020-04-20T18:28:40.125Z \n", - "5 A John 2020-04-20T18:29:00.125Z \n", - "6 A Jessica 2020-04-20T18:29:20.125Z \n", - "7 A John 2020-04-20T18:29:40.125Z \n", - "8 A Mary 2020-04-20T18:30:00.125Z \n", - "9 A Jessica 2020-04-20T18:30:20.125Z \n", - "10 A John 2020-04-20T18:30:40.125Z \n", - "11 A Mary 2020-04-20T18:31:00.125Z \n", - "12 A Jessica 2020-04-20T18:31:20.125Z \n", - "13 A John 2020-04-20T18:31:40.125Z \n", - "14 A Mary 2020-04-20T18:32:00.125Z \n", - "15 A Jessica 2020-04-20T18:32:20.125Z \n", - "16 B A 2020-04-20T18:27:20.125Z \n", - "17 B B 2020-04-20T18:27:40.956Z \n", - "18 B C 2020-04-20T18:28:01.789Z \n", - "19 B D 2020-04-20T18:28:23.621Z \n", - "20 B A 2020-04-20T18:28:45.452Z \n", - "21 B B 2020-04-20T18:29:07.284Z \n", - "22 B C 2020-04-20T18:29:29.116Z \n", - "23 B D 2020-04-20T18:29:50.947Z \n", - "24 B A 2020-04-20T18:30:12.778Z \n", - "25 B B 2020-04-20T18:30:34.610Z \n", - "26 B C 2020-04-20T18:30:56.441Z \n", - "27 B D 2020-04-20T18:31:18.273Z \n", - "28 B A 2020-04-20T18:31:40.105Z \n", - "29 B B 2020-04-20T18:32:01.936Z \n", - "30 B C 2020-04-20T18:32:23.768Z \n", - "31 B D 2020-04-20T18:32:45.599Z \n", - "32 C A 2020-04-20T18:27:20.125Z \n", - "33 C B 2020-04-20T18:27:32.456Z \n", - "34 C C 2020-04-20T18:27:44.567Z \n", - "35 C B 2020-04-20T18:29:22.456Z \n", - "36 C C 2020-04-20T18:29:44.567Z \n", - "37 C A 2020-04-20T18:29:52.456Z \n", - "38 C D 2020-04-20T18:31:24.567Z \n", - "39 C A 2020-04-20T19:31:32.456Z \n", - "40 C B 2020-04-20T19:31:34.567Z \n", - "41 C C 2020-04-20T19:33:22.456Z \n", - "42 C D 2020-04-20T19:33:24.567Z \n", - "43 C A 2020-04-20T19:33:32.456Z \n", - "44 C B 2020-04-20T19:33:44.567Z \n", - "45 C C 2020-04-20T20:35:32.456Z \n", - "46 C D 2020-04-20T20:35:44.567Z \n", - "47 C A 2020-04-20T20:35:52.456Z \n", - "48 C B 2020-04-20T20:35:56.567Z \n", - "\n", - " message \n", - "0 \"I'm curious how do you all feel about using ... \n", - "1 \"I think flashcards are a great way to memori... \n", - "2 \"I agree I've found them to be really helpful.\" \n", - "3 \"I'm not a big fan of flashcards I find them ... \n", - "4 \"I think it really depends on the individual ... \n", - "5 \"That's true I know some people who prefer to... \n", - "6 \"I've heard of spaced repetition but I've nev... \n", - "7 \"It's a great way to improve your long-term m... \n", - "8 \"I'll have to give it a try.\" \n", - "9 \"Do you have any tips for studying effectively?\" \n", - "10 \"I find that studying in short bursts is more... \n", - "11 \"I agree it's also important to take breaks a... \n", - "12 \"I'm always struggling to stay motivated any ... \n", - "13 \"I find it helpful to set realistic goals and... \n", - "14 \"It's also important to find a study buddy or... \n", - "15 \"Thanks for the advice I'll definitely try it... \n", - "16 \"Hi everyone what are your favorite study hab... \n", - "17 \"I like to study in a quiet place where I can... \n", - "18 \"I prefer to study with a group of friends so... \n", - "19 \"I like to study by myself so I can go at my ... \n", - "20 \"I think it's important to find a study metho... \n", - "21 \"I agree.\" \n", - "22 \"I also think it's important to take breaks w... \n", - "23 \"Yes it's important to give your brain a chan... \n", - "24 \"I find that it's helpful to set a timer and ... \n", - "25 \"That's a good idea I'll try that.\" \n", - "26 \"I like to listen to music while I study it h... \n", - "27 \"I prefer to study in silence so I can focus ... \n", - "28 \"I find that it's helpful to create a study s... \n", - "29 \"I agree it's important to be consistent with... \n", - "30 \"I also think it's important to reward yourse... \n", - "31 \"Yes it's important to stay motivated while y... \n", - "32 \"I'm really struggling to stay focused when I... \n", - "33 \"I know what you mean. I have the same problem.\" \n", - "34 \"I've been trying to find a study method that... \n", - "35 \"I've found that it helps to break down my st... \n", - "36 \"That's a good idea. I'll try that.\" \n", - "37 \"I've also found that it helps to study in a ... \n", - "38 \"I agree. It's hard to concentrate when there... \n", - "39 \"Another thing that I've found helpful is to ... \n", - "40 \"That's a good idea. It helps me to stay on t... \n", - "41 \"I'm going to try that too.\" \n", - "42 \"I've also found that it helps to reward myse... \n", - "43 \"That's a good idea. It gives me something to... \n", - "44 \"I agree. It helps me to stay motivated.\" \n", - "45 \"I've found that it's also important to take ... \n", - "46 \"I agree. It helps me to stay focused and avo... \n", - "47 \"I've also found that it helps to study with ... \n", - "48 \"That's a good idea. It can help you to stay ... " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv('test_conv_level_complex.csv')\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# add a column to the dataframe called feature and assign value of \"team_burstiness\" to every row\n", - "df['feature'] = 'team_burstiness'" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# write the dataframe to a new csv file\n", - "df.to_csv('test_conv_level_complex.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# parse file as txt and remove all quotes\n", - "with open('test_conv_level_complex.csv', 'r') as file:\n", - " data = file.read()\n", - " data = data.replace('\"', '')\n", - "\n", - "# write the data back to the file\n", - "with open('test_conv_level_complex.csv', 'w') as file:\n", - " file.write(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    " - ], - "text/plain": [ - " conversation_num variance_in_DD\n", - "0 A 0.023184\n", - "1 B 0.003812\n", - "2 C 0.000887\n", - "3 D 0.009075\n", - "4 E 0.022204\n", - "5 F 0.009982\n", - "6 G 0.065572\n", - "7 H 0.046539\n", - "8 I 0.023111" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv(\"../../output/conv/test_conv_level_conv_complex.csv\")\n", - "df[['conversation_num', 'variance_in_DD']]" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('fflow.csv')\n", - "# df['feature'] = 'forward_flow'\n", - "# df['conversation_num'] = 'F'\n", - "# df['message'] = \"Sports are a great way to stay active and healthy.\"\n", - "df['test_type'] = 'unit_eq'\n", - "df[['conversation_num', 'speaker_nickname', 'message', 'feature', 'test_type']].to_csv('fflow.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9004621104890905\n" - ] - } - ], - "source": [ - "df = pd.read_csv('../../output/chat/test_forward_flow_chat.csv')\n", - "\n", - "def get_conversation_batches(dataframe, batch_size=3):\n", - " # group by conversation_num and get the last row from the group\n", - " last_rows = dataframe.groupby('conversation_num').tail(1)\n", - "\n", - " # get 3 row batches of these last rows\n", - " batches = []\n", - " rows = list(last_rows.iterrows())\n", - " for i in range(0, len(rows), batch_size):\n", - " batches.append(rows[i:i + batch_size])\n", - " return batches\n", - "\n", - "batches = get_conversation_batches(df, batch_size=3)\n", - "for batch in batches:\n", - " print(batch[0][1]['forward_flow'])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import datetime as dt\n", - "\n", - "df = pd.read_csv('helper.csv')\n", - "df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n", - "df[\"timestamp\"] = df[\"timestamp\"].apply(lambda x: x.timestamp())\n", - "# # convert time stamp to unix timestamp\n", - "# df[\"timestamp\"] = df[\"timestamp\"].astype(int) // 10**9\n", - "# df['conversation_num'] = 'X'\n", - "df['timestamp']\n", - "\n", - "df.to_csv('helper.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Timestamp('2024-07-15 23:15:36.779590')" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.to_datetime(\"2024-07-15T23:15:36.779590\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1969-12-31 19:00:20\n", - "1 1969-12-31 19:00:40\n", - "2 1969-12-31 19:01:00\n", - "3 1969-12-31 19:01:20\n", - "4 1969-12-31 19:01:40\n", - "Name: timestamp, dtype: datetime64[ns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv('helper.csv')\n", - "df[\"timestamp\"] = df['timestamp'].apply(lambda x: dt.datetime.fromtimestamp(x))\n", - "df['timestamp']\n", - "# pd.to_datetime(df[\"timestamp\"], unit='ms')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "replacement_dict = {\n", - " 'A': 'V',\n", - " 'B': 'W',\n", - " 'C': 'X'\n", - "}\n", - "\n", - "df = pd.read_csv('helper.csv')\n", - "# Add the conversation_num column and apply the replacement\n", - "df['conversation_num'] = df['conversation_num'].replace(replacement_dict)\n", - "df.to_csv('helper.csv', index=False)\n", - "# print(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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This is ***bolded and italicized***",num_emphasis,3 -4,test_B,This is **uneven* in terms of *the emphasis**,num_emphasis,2 -4,test_A,* item 1\n* item 2\n- item 3,num_bullet_points,3 -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 -4,test_A,1. First\n2. Second\n3. Third,num_numbered_points,3 -4,test_B,This is the first line.\nThis is the second line.\nThis is the third line.,num_line_breaks,3 -4,test_A,"I have a line - - - - -here is a new line - - -here is a third line",num_line_breaks,3 -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 -4,test_A,"""This is a quote."" She said, ""Here's another.""",num_quotes,2 -4,test_B,"""You miss 100% of the shots you don't take"" -- Wayne Gretzky",num_quotes,1 -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 -4,test_B,> Quoting someone else\nThis is my reply.,num_block_quote_responses,1 -4,test_A,> Quoting someone else\nThis is my reply.,num_block_quote_responses,1 -4,test_B,>>>> This is a quote but I went overboard with the carat character,num_block_quote_responses,1 -4,test_A,>> This is one where I put too many of the gt's,num_block_quote_responses,1 -4,test_B,"> Hello! -Goodbye!",num_block_quote_responses,1 -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 -4,test_A,Well... I'm not sure... Maybe...,num_ellipses,3 -4,test_B,hm..what if I only use two periods.............or many periods............,num_ellipses,2 -4,test_B,This is a sentence (with some text in parentheses).,num_parentheses,1 -4,test_A,"""Sure,"" I said confidently (thiking to myself: no way!) This was definitely (not) one of my best moments.",num_parentheses,2 -4,test_B,(((((these parentheses are not properly closed.),num_parentheses,1 -4,test_B,((there are multiple parentheses here)),num_parentheses,2 -4,test_A,((1+(1+3+4)^2)+7+(9+8)),num_parentheses,4 -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,1 -1,B,So how should we answer this,Token_count,6 -1,A,We can start here. What is the question?,YesNo_Questions,0 -1,B,I am not sure. Where is the rest of our team?,WH_Questions,1 -1,B,"Please help me figure this out, I really want to do well on this please",Please,2 -2,C,Hey,Hello,1 -2,C,Okay bro lets split it 50/50,Impersonal_Pronoun,1 -2,D,Maybe but how about 60/40? I doubt its fair otherwise,Hedges,2 -2,C,Seems fair,Hedges,1 -1,B,I am not sure. Where is the rest of our team?,First_Person_Single,1 -1,B,"Well please help me figure this out, I really want to do well on this please okay",factuality,1 -2,C,Seems possible,hashedge,1 -2,E,I see what youre thinking but I disagree,Acknowledgement,1 -2,E,We get only one chance so we should understand how to split it,Acknowledgement,2 -2,D,"I just don't agree, I'm making the 60/40 split",Adverb_Limiter,1 -3,G,hey,indirect_greeting,1 -3,G,I think we should try something else,1st_person_start,1 -3,F,Ok whatever. You should leave the team then,2nd_person_start,1 -4,H,Honestly thank you so so much,factuality,1 -4,H,What's the plan?,direct_question,1 -4,I,That is the dumbest idea I've heard; youre actually dumb af,hasnegative,1 -4,H,What's ur problem here?,hasnegative,1 -5,J,Pleasure and an honor to meet you all,haspositive,1 -5,K,We should try that next,haspositive,0 -5,J,Could you please explain why? I don't really understand why you are thinking that,subjunctive,1 -5,K,Sorry sorry I didn't mean to,apologizing,1 -6,L,I don't really want to work with you all but let's get this over with,Impersonal_Pronoun,1 -6,J,Fine by me,Affirmation,1 -6,K,Ok so which part should we do first? the first or second?,YesNo_Questions,1 -7,L,Please don't do that?,please_start,1 -7,L,I don't think that will work,hashedge,1 -7,M,I'm exhuasted rn,hasnegative,0 -7,M,i don't really care please just finish this up,haspositive,0 -7,N,Please don't do that?,Please,1 -7,N,I don't think that will work,Hedges,0 -7,O,I'm exhuasted rn,Negative_Emotion,0 -7,O,i don't really care please just finish this up,Positive_Emotion,0 -8,P,i appreciate all this from you,gratitude,1 -8,P,"well we should start rn, our part is long",1st_person_pl,1 -8,Q,ok forgive me for this error but,apologizing,1 -8,Q,you have to redo the whole thing,2nd_person,0 -8,R,ok so who will work with me? where should we begin?,direct_question,0 -8,S,i appreciate all this from you,Gratitude,1 -8,S,"well we should start rn, our part is long",First_Person_Plural,2 -8,T,ok forgive us for this error but,Apology,0 -8,T,you have to redo the whole thing,Second_Person,1 -8,U,ok so who will work with me? where should we begin?,WH_Questions,2 -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,12 -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,5 -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,3 -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,3 -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,3 -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,3 -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,1 -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,1 -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,1 -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,1 -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,1 -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,1 -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,1 -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,0 -23,A,And I will always love you,Conjunction_Start,1 -23,B,"Can you help me, can you please?",Can_You,2 -23,C,"Can you, will you, could you please be mine?",Could_You,1 -23,D,"This land is your land, this land is my land; this land was made for you and for me",For_You,1 -0,0,unneccessagf shoulds shouldve should'nt,discrepancies_lexical_per_100,0.04 -0,0,wouldnt unneedofek want must've should'nt,discrepancies_lexical_per_100,0.05 -0,0,hopes wish,discrepancies_lexical_per_100,0.02 -0,0,must'nt rather wouldn't ought'nt,discrepancies_lexical_per_100,0.04 -0,0,needn't unwantpotnw hopefulness oughta couldn't,discrepancies_lexical_per_100,0.05 -0,0,musilej,hear_lexical_per_100,0.01 -0,0,listens hearing listenerjp noisy,hear_lexical_per_100,0.04 -0,0,noises noisy harmongoc hearing audiblweds,hear_lexical_per_100,0.05 -0,0,ear yell listenerpvo,hear_lexical_per_100,0.03 -0,0,sang rang ear concertfuw,hear_lexical_per_100,0.04 -0,0,drapeulqv backyard loveseatjproz closet,home_lexical_per_100,0.04 -0,0,curtainqygkr drapekwvh,home_lexical_per_100,0.02 -0,0,bathae,home_lexical_per_100,0.01 -0,0,curtainbu loveseatntr family housing rooms,home_lexical_per_100,0.05 -0,0,bedroomxhpl furniture,home_lexical_per_100,0.02 -0,0,if,conjunction_lexical_per_100,0.01 -0,0,altho while though then how,conjunction_lexical_per_100,0.05 -0,0,when or,conjunction_lexical_per_100,0.02 -0,0,if,conjunction_lexical_per_100,0.01 -0,0,but however,conjunction_lexical_per_100,0.02 -0,0,undoubtni,certainty_lexical_per_100,0.01 -0,0,altogether truthzhf,certainty_lexical_per_100,0.02 -0,0,distinctue definitiveeol commitmentflk forever,certainty_lexical_per_100,0.04 -0,0,total essential,certainty_lexical_per_100,0.02 -0,0,fundamentals completes guarantidjp,certainty_lexical_per_100,0.03 -0,0,both add with,inclusive_lexical_per_100,0.03 -0,0,around we along,inclusive_lexical_per_100,0.03 -0,0,each with,inclusive_lexical_per_100,0.02 -0,0,with inclusg come came around,inclusive_lexical_per_100,0.05 -0,0,each around come,inclusive_lexical_per_100,0.03 -0,0,nausen sensation brunchkjz,bio_lexical_per_100,0.03 -0,0,butt saliverbwp,bio_lexical_per_100,0.02 -0,0,palms,bio_lexical_per_100,0.01 -0,0,liquoriole,bio_lexical_per_100,0.01 -0,0,xanax prescriqd hand sodauce,bio_lexical_per_100,0.04 -0,0,strivl master,achievement_lexical_per_100,0.02 -0,0,originattvf solutionmuyho elitlxoup proficiengiy quittd,achievement_lexical_per_100,0.05 -0,0,finalizuav best quitti capabfs,achievement_lexical_per_100,0.04 -0,0,masters plans,achievement_lexical_per_100,0.02 -0,0,strengthe successqfhs herovqw overconfidence,achievement_lexical_per_100,0.04 -0,0,apparently,adverbs_lexical_per_100,0.01 -0,0,so,adverbs_lexical_per_100,0.01 -0,0,immediately generally very well truly,adverbs_lexical_per_100,0.05 -0,0,rather instead here pushty,adverbs_lexical_per_100,0.04 -0,0,instead,adverbs_lexical_per_100,0.01 -0,0,phobii apprehensnmeyt,anxiety_lexical_per_100,0.02 -0,0,obsesstusbg anguishuiy terrorkm,anxiety_lexical_per_100,0.03 -0,0,restlesst tenseod feared overwhelmfvlxi,anxiety_lexical_per_100,0.04 -0,0,timidfzbh stressetd,anxiety_lexical_per_100,0.02 -0,0,apprehensrm dreadn,anxiety_lexical_per_100,0.02 -0,0,oneself shes he she'll herself,third_person_lexical_per_100,0.05 -0,0,shes he'd himself hes her,third_person_lexical_per_100,0.05 -0,0,she'll,third_person_lexical_per_100,0.01 -0,0,she'll her,third_person_lexical_per_100,0.02 -0,0,she'll him she's hes he,third_person_lexical_per_100,0.05 -0,0,havent haven't ought'nt wont,negation_lexical_per_100,0.04 -0,0,needn't hasn't,negation_lexical_per_100,0.02 -0,0,wouldnt,negation_lexical_per_100,0.01 -0,0,nobodashm hasn't didnt,negation_lexical_per_100,0.03 -0,0,hasn't never,negation_lexical_per_100,0.02 -0,0,titty fuckerusz bitchnsl goddamomde hell,swear_lexical_per_100,0.05 -0,0,sob fuckinoawys titty cuntq,swear_lexical_per_100,0.04 -0,0,cuntn pisstkzme dicks butts,swear_lexical_per_100,0.04 -0,0,fucks jeez sonofako crappy,swear_lexical_per_100,0.04 -0,0,fucks butt heck,swear_lexical_per_100,0.03 -0,0,urnj immortalwai,death_lexical_per_100,0.02 -0,0,od bury demise tombd urnnig,death_lexical_per_100,0.05 -0,0,demise,death_lexical_per_100,0.01 -0,0,immortalyjedn embalmfqydb hearsepdk,death_lexical_per_100,0.03 -0,0,ghostaw alive,death_lexical_per_100,0.02 -0,0,amputu,health_lexical_per_100,0.01 -0,0,nearsighted neurologhl toxv painly throbgvmhn,health_lexical_per_100,0.05 -0,0,wash doselndu mono,health_lexical_per_100,0.03 -0,0,ill,health_lexical_per_100,0.01 -0,0,checkupfli burpzxevw scabo living ICU,health_lexical_per_100,0.05 -0,0,orangemacis squaruei purplabnk,see_lexical_per_100,0.03 -0,0,colourxml roundxgijb sees seen colorht,see_lexical_per_100,0.05 -0,0,staring look yellowkyn triangqmpv,see_lexical_per_100,0.04 -0,0,shiny lookerutn scannv look,see_lexical_per_100,0.04 -0,0,scanni seen,see_lexical_per_100,0.02 -0,0,facialrqth foot lipslt toe titties,body_lexical_per_100,0.05 -0,0,toenailhtqe droolc,body_lexical_per_100,0.02 -0,0,hip dick boobvw,body_lexical_per_100,0.03 -0,0,slenderbmvq ass,body_lexical_per_100,0.02 -0,0,nudeytmcb stomachltbw wake breastqyp eyewut,body_lexical_per_100,0.05 -0,0,moms bro,family_lexical_per_100,0.02 -0,0,nephewz wifevxmlj mom's parentuph,family_lexical_per_100,0.04 -0,0,pa sons fatherhb,family_lexical_per_100,0.03 -0,0,aunte sons grandkidywgxb exes ex,family_lexical_per_100,0.05 -0,0,relatives husbandypoiz mommalqhxo,family_lexical_per_100,0.03 -0,0,sickengwxku,negative_affect_lexical_per_100,0.01 -0,0,hurtefdp lazieqxar impersonal,negative_affect_lexical_per_100,0.03 -0,0,egotisy destroygj,negative_affect_lexical_per_100,0.02 -0,0,unwelcomrfwd,negative_affect_lexical_per_100,0.01 -0,0,jealoustemf unkind,negative_affect_lexical_per_100,0.02 -0,0,significant else every section,quantifier_lexical_per_100,0.04 -0,0,ton lotta,quantifier_lexical_per_100,0.02 -0,0,either best greatest,quantifier_lexical_per_100,0.03 -0,0,best,quantifier_lexical_per_100,0.01 -0,0,singlufb rest fullmzf,quantifier_lexical_per_100,0.03 -0,0,glad great charmbrwsd wins,positive_affect_lexical_per_100,0.04 -0,0,generot helping sincerbucaj,positive_affect_lexical_per_100,0.03 -0,0,freedswu adventursfk gently deliciouseuxl assurm,positive_affect_lexical_per_100,0.05 -0,0,worshipstmjc soulmateeiya treat huggpo,positive_affect_lexical_per_100,0.04 -0,0,grin wealthlg thrillzbrqf casual proudrqu,positive_affect_lexical_per_100,0.05 -0,0,restructurucq,insight_lexical_per_100,0.01 -0,0,know decidux recallwz seems solutionlxh,insight_lexical_per_100,0.05 -0,0,wonder motivsc sensing secret,insight_lexical_per_100,0.04 -0,0,believes,insight_lexical_per_100,0.01 -0,0,suspectc infers,insight_lexical_per_100,0.02 -0,0,man,humans_lexical_per_100,0.01 -0,0,newbornl citizen'phtgi ladies,humans_lexical_per_100,0.03 -0,0,girl's babies persons chicks,humans_lexical_per_100,0.04 -0,0,infant,humans_lexical_per_100,0.01 -0,0,ladies ma'am citizen,humans_lexical_per_100,0.03 -0,0,hasn't makes describe believes I've,present_tense_lexical_per_100,0.05 -0,0,gets how's,present_tense_lexical_per_100,0.02 -0,0,has hopes admits,present_tense_lexical_per_100,0.03 -0,0,let's,present_tense_lexical_per_100,0.01 -0,0,suck wait aren't feels,present_tense_lexical_per_100,0.04 -0,0,wouldnt gonna,future_tense_lexical_per_100,0.02 -0,0,mustnt should shall,future_tense_lexical_per_100,0.03 -0,0,must've,future_tense_lexical_per_100,0.01 -0,0,must should it'll won't shall,future_tense_lexical_per_100,0.05 -0,0,shouldve mustnt,future_tense_lexical_per_100,0.02 -0,0,listened held loved cried changed,past_tense_lexical_per_100,0.05 -0,0,sent sensed tried taken shouldve,past_tense_lexical_per_100,0.05 -0,0,ate sucked tried,past_tense_lexical_per_100,0.03 -0,0,ran described,past_tense_lexical_per_100,0.02 -0,0,didn't,past_tense_lexical_per_100,0.01 -0,0,post distanpiy,relative_lexical_per_100,0.02 -0,0,go marchal updatmwi,relative_lexical_per_100,0.03 -0,0,walking growing gianthy age,relative_lexical_per_100,0.04 -0,0,initiatoxtwa follow,relative_lexical_per_100,0.02 -0,0,hall bending widthxikw,relative_lexical_per_100,0.03 -0,0,loverkfxo pornoiu,sexual_lexical_per_100,0.02 -0,0,virgindtrl humpysbwe prudishybiq,sexual_lexical_per_100,0.03 -0,0,virginc,sexual_lexical_per_100,0.01 -0,0,fucks pubic gay,sexual_lexical_per_100,0.03 -0,0,prostatijvda dick,sexual_lexical_per_100,0.02 -0,0,curbijh boundlby,inhibition_lexical_per_100,0.02 -0,0,deniaxi,inhibition_lexical_per_100,0.01 -0,0,prudishxc deniabxp,inhibition_lexical_per_100,0.02 -0,0,constrictimhr,inhibition_lexical_per_100,0.01 -0,0,withholdyzaqb stops,inhibition_lexical_per_100,0.02 -0,0,remorset ruinc grimwftle,sadness_lexical_per_100,0.03 -0,0,discouragnepqi tragict devastatu,sadness_lexical_per_100,0.03 -0,0,grief unimportant,sadness_lexical_per_100,0.02 -0,0,miss yearnlkw whining tragickh pitiulpnv,sadness_lexical_per_100,0.05 -0,0,miss regretzkbrc missed unhappj,sadness_lexical_per_100,0.04 -0,0,weve son's,social_lexical_per_100,0.02 -0,0,listenerqe girlfriendwsnya,social_lexical_per_100,0.02 -0,0,hers,social_lexical_per_100,0.01 -0,0,he'll sisterqjtfk,social_lexical_per_100,0.02 -0,0,coworkertbpf mates he's mailing interrupm,social_lexical_per_100,0.05 -0,0,somethingjmcpi noboda,indefinite_pronoun_lexical_per_100,0.02 -0,0,somebodp somewhere itll who'll,indefinite_pronoun_lexical_per_100,0.04 -0,0,that'd somethingosdqx,indefinite_pronoun_lexical_per_100,0.02 -0,0,somethingp thatll those which someonecrhbd,indefinite_pronoun_lexical_per_100,0.05 -0,0,it'll it'd,indefinite_pronoun_lexical_per_100,0.02 -0,0,sinnzkc kosher qur'anpy,religion_lexical_per_100,0.03 -0,0,mercy,religion_lexical_per_100,0.01 -0,0,templefydxz hinduejs muhammsla lutheranwv ministerlnfts,religion_lexical_per_100,0.05 -0,0,catholickd,religion_lexical_per_100,0.01 -0,0,sikhc judaprfd muhammehcml sin,religion_lexical_per_100,0.04 -0,0,laidoff tradejnx taxes freshmknd,work_lexical_per_100,0.04 -0,0,incorprhq negotiatjf presentationomwfa,work_lexical_per_100,0.03 -0,0,politics mda,work_lexical_per_100,0.02 -0,0,revieww classes,work_lexical_per_100,0.02 -0,0,masters,work_lexical_per_100,0.01 -0,0,owes cashk bankpsiar checks kronlwik,money_lexical_per_100,0.05 -0,0,dinarcsg businesszosw revenueocp costbjwta owe,money_lexical_per_100,0.05 -0,0,euro auditors store spending,money_lexical_per_100,0.04 -0,0,mortgpzvi overtime,money_lexical_per_100,0.02 -0,0,bucks wagerfv,money_lexical_per_100,0.02 -0,0,effectcaxgn,causation_lexical_per_100,0.01 -0,0,leadmbhfe infer depends creatend,causation_lexical_per_100,0.04 -0,0,depends,causation_lexical_per_100,0.01 -0,0,deducgulp outcomecli affected hows producnbu,causation_lexical_per_100,0.05 -0,0,solutionva launchvoap leadhbi provoku,causation_lexical_per_100,0.04 -0,0,threatxre offencefhcog ludicrouslf,anger_lexical_per_100,0.03 -0,0,enragoqusn,anger_lexical_per_100,0.01 -0,0,sucks brutals sucked threatd destroyj,anger_lexical_per_100,0.05 -0,0,sinister,anger_lexical_per_100,0.01 -0,0,raping battliz warfareid defensjazn molestgv,anger_lexical_per_100,0.05 -0,0,mine I've myself I'd I'll,first_person_singular_lexical_per_100,0.05 -0,0,I'll I've,first_person_singular_lexical_per_100,0.02 -0,0,myself my,first_person_singular_lexical_per_100,0.02 -0,0,ive I'd,first_person_singular_lexical_per_100,0.02 -0,0,myself I'm ive my I've,first_person_singular_lexical_per_100,0.05 -0,0,rub hard warmlvye,feel_lexical_per_100,0.03 -0,0,hardevtqef driegnk feels touchlzf,feel_lexical_per_100,0.04 -0,0,leatherjcf pressed squeezul silkjn,feel_lexical_per_100,0.04 -0,0,skin brushtasj rub colde caressi,feel_lexical_per_100,0.05 -0,0,hottjmuo,feel_lexical_per_100,0.01 -0,0,mysterp ambigun,tentativeness_lexical_per_100,0.02 -0,0,most wondered depend alot vary,tentativeness_lexical_per_100,0.05 -0,0,fairly lucks unresolvn hesitaq option,tentativeness_lexical_per_100,0.05 -0,0,fuzznaxg hypotheticoqwzy barely,tentativeness_lexical_per_100,0.03 -0,0,depend indetermingifjl randomitf hypotheticvzkg supposed,tentativeness_lexical_per_100,0.05 -0,0,exclujvy,exclusive_lexical_per_100,0.01 -0,0,either except if somethingfxdq sometime,exclusive_lexical_per_100,0.05 -0,0,versus,exclusive_lexical_per_100,0.01 -0,0,but exclusxklz except or,exclusive_lexical_per_100,0.04 -0,0,sometime,exclusive_lexical_per_100,0.01 -0,0,youre wished thank went,verbs_lexical_per_100,0.04 -0,0,meant hoped lost,verbs_lexical_per_100,0.03 -0,0,ran carry showed used,verbs_lexical_per_100,0.04 -0,0,theres care cannot made,verbs_lexical_per_100,0.04 -0,0,affected misses theyd kept thatd,verbs_lexical_per_100,0.05 -0,0,gfjgevb,friends_lexical_per_100,0.01 -0,0,exgirll,friends_lexical_per_100,0.01 -0,0,mates,friends_lexical_per_100,0.01 -0,0,mate loveru mates buddyv,friends_lexical_per_100,0.04 -0,0,neighbord partnerzcvdj roomatel girlfriendt gft,friends_lexical_per_100,0.05 -0,0,a an alot,article_lexical_per_100,0.03 -0,0,a an,article_lexical_per_100,0.02 -0,0,a an alot,article_lexical_per_100,0.03 -0,0,the an a alot,article_lexical_per_100,0.04 -0,0,alot,article_lexical_per_100,0.01 -0,0,oh yes I believe,argue_lexical_per_100,0.03 -0,0,u mean and really,argue_lexical_per_100,0.03 -0,0,cause no I know and,argue_lexical_per_100,0.04 -0,0,and I think actually yes well,argue_lexical_per_100,0.05 -0,0,so,argue_lexical_per_100,0.01 -0,0,wouldve whod you've mustn't,auxiliary_verbs_lexical_per_100,0.04 -0,0,let youve itd ought,auxiliary_verbs_lexical_per_100,0.04 -0,0,can don't theyre wasn't,auxiliary_verbs_lexical_per_100,0.04 -0,0,hes must'nt wont did mustn't,auxiliary_verbs_lexical_per_100,0.05 -0,0,shan't theyve oughtve becomes done,auxiliary_verbs_lexical_per_100,0.05 -0,0,meaningi all wanted determining,cognitive_mech_lexical_per_100,0.04 -0,0,appearing suppresshvw proof changes,cognitive_mech_lexical_per_100,0.04 -0,0,rearrangbqlg randomuer containn deducczr practically,cognitive_mech_lexical_per_100,0.05 -0,0,occasionalszxg hazy requirj,cognitive_mech_lexical_per_100,0.03 -0,0,repressydvml,cognitive_mech_lexical_per_100,0.01 -0,0,until,preposition_lexical_per_100,0.01 -0,0,along ahead,preposition_lexical_per_100,0.02 -0,0,below under thru,preposition_lexical_per_100,0.03 -0,0,except insides towardkhyrs between beside,preposition_lexical_per_100,0.05 -0,0,without out about,preposition_lexical_per_100,0.03 -0,0,let's,first_person_plural_lexical_per_100,0.01 -0,0,we'd let's lets,first_person_plural_lexical_per_100,0.03 -0,0,our lets,first_person_plural_lexical_per_100,0.02 -0,0,we've,first_person_plural_lexical_per_100,0.01 -0,0,ours we'd weve ourselves let's,first_person_plural_lexical_per_100,0.05 -0,0,drily,percept_lexical_per_100,0.01 -0,0,grabwktxr,percept_lexical_per_100,0.01 -0,0,savourzw redness,percept_lexical_per_100,0.02 -0,0,speaking,percept_lexical_per_100,0.01 -0,0,souriau tang,percept_lexical_per_100,0.02 -0,0,thine yall,second_person_lexical_per_100,0.02 -0,0,you're youd,second_person_lexical_per_100,0.02 -0,0,y'all you'd thine you're,second_person_lexical_per_100,0.04 -0,0,thee thine youre yours,second_person_lexical_per_100,0.04 -0,0,ye you,second_person_lexical_per_100,0.02 -0,0,mustnt hope mistakwzl hoping,discrepancies_lexical_per_100,0.04 -0,0,would've problemo need lacksuexg expectt,discrepancies_lexical_per_100,0.05 -0,0,couldnt hopeful should,discrepancies_lexical_per_100,0.03 -0,0,desirva,discrepancies_lexical_per_100,0.01 -0,0,wishes wishing must've need,discrepancies_lexical_per_100,0.04 -0,0,noises thundero,hear_lexical_per_100,0.02 -0,0,thunderq inaudibln sang 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-0,0,longingjhbn,negative_affect_lexical_per_100,0.01 -0,0,lamefpbv disadvantagenxtql,negative_affect_lexical_per_100,0.02 -0,0,insincermy remorseoeia,negative_affect_lexical_per_100,0.02 -0,0,difficultna jaded wars vanity,negative_affect_lexical_per_100,0.04 -0,0,difficultnmril warring vulnerabc shitzv,negative_affect_lexical_per_100,0.04 -0,0,tons greater simple,quantifier_lexical_per_100,0.03 -0,0,extent,quantifier_lexical_per_100,0.01 -0,0,mucho much section lotsa extremely,quantifier_lexical_per_100,0.05 -0,0,ton piecg page differenceb whole,quantifier_lexical_per_100,0.05 -0,0,else,quantifier_lexical_per_100,0.01 -0,0,alrightdo,positive_affect_lexical_per_100,0.01 -0,0,helperhnx sunny,positive_affect_lexical_per_100,0.02 -0,0,complimentjlbhe convincfthqs graces,positive_affect_lexical_per_100,0.03 -0,0,lucks romanczk praisyvpch safelmgx hehp,positive_affect_lexical_per_100,0.05 -0,0,talentv interestuthxn,positive_affect_lexical_per_100,0.02 -0,0,reasonpulei 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-0,0,that'll won't I'll,future_tense_lexical_per_100,0.03 -0,0,who'll should'nt,future_tense_lexical_per_100,0.02 -0,0,hated went wondered appeared,past_tense_lexical_per_100,0.04 -0,0,ate,past_tense_lexical_per_100,0.01 -0,0,disliked lost described spent,past_tense_lexical_per_100,0.04 -0,0,ate,past_tense_lexical_per_100,0.01 -0,0,forgote cared believed,past_tense_lexical_per_100,0.03 -0,0,awhile immediately outerki,relative_lexical_per_100,0.03 -0,0,perpetualp internalltsem tiniest,relative_lexical_per_100,0.03 -0,0,right immediateness fademl finishykxwt,relative_lexical_per_100,0.04 -0,0,corners old over,relative_lexical_per_100,0.03 -0,0,fit approachjuzeb,relative_lexical_per_100,0.02 -0,0,erectile condom pornfzqh fucks,sexual_lexical_per_100,0.04 -0,0,fuckinc asses condom queerduvk,sexual_lexical_per_100,0.04 -0,0,pregnange erectionux,sexual_lexical_per_100,0.02 -0,0,orgasmt sexw chlamydia gay,sexual_lexical_per_100,0.04 -0,0,rapistph ovarsg tits,sexual_lexical_per_100,0.03 -0,0,withheld defencjtr,inhibition_lexical_per_100,0.02 -0,0,hesitaz tightq halthvc,inhibition_lexical_per_100,0.03 -0,0,protectab securulxeo interferhvrx deniaanhcy restraindrv,inhibition_lexical_per_100,0.05 -0,0,waits,inhibition_lexical_per_100,0.01 -0,0,controlx waited tidy,inhibition_lexical_per_100,0.03 -0,0,defeats,sadness_lexical_per_100,0.01 -0,0,hopelesspkm heartbrokekible cried loses resigne,sadness_lexical_per_100,0.05 -0,0,whining sadness cry cried disheartengzeaq,sadness_lexical_per_100,0.05 -0,0,sadly damagbntc pessimisrc,sadness_lexical_per_100,0.03 -0,0,hopelessa dissatisfcl fatigucuvjd disillusionqt isolatg,sadness_lexical_per_100,0.05 -0,0,assemblkd meet band guyawg kid,social_lexical_per_100,0.05 -0,0,help excusk peoplelnm organizho yall,social_lexical_per_100,0.05 -0,0,sons,social_lexical_per_100,0.01 -0,0,his participanteir,social_lexical_per_100,0.02 -0,0,grandpap whom mailerut,social_lexical_per_100,0.03 -0,0,this that it'd,indefinite_pronoun_lexical_per_100,0.03 -0,0,these anything thatll everybodhn it'll,indefinite_pronoun_lexical_per_100,0.05 -0,0,noboddup whats,indefinite_pronoun_lexical_per_100,0.02 -0,0,it's who'd thats it whats,indefinite_pronoun_lexical_per_100,0.05 -0,0,anything wholl these,indefinite_pronoun_lexical_per_100,0.03 -0,0,karma salvation orthodoxfdy rosaries sect,religion_lexical_per_100,0.05 -0,0,rosary jesuitgsabe hell,religion_lexical_per_100,0.03 -0,0,hell,religion_lexical_per_100,0.01 -0,0,christianwq muhammyprqh piety sects,religion_lexical_per_100,0.04 -0,0,sunni scripturvko templeme salvation,religion_lexical_per_100,0.04 -0,0,credentialrs com absentp,work_lexical_per_100,0.03 -0,0,mfg reportsuro commercsjezh collabw,work_lexical_per_100,0.04 -0,0,outsourcrfndl,work_lexical_per_100,0.01 -0,0,transfergdnu grad,work_lexical_per_100,0.02 -0,0,econgv goalg,work_lexical_per_100,0.02 -0,0,cashlf,money_lexical_per_100,0.01 -0,0,debti shop consumerzae inheritfkyv,money_lexical_per_100,0.04 -0,0,stocks,money_lexical_per_100,0.01 -0,0,taxaxv cheapi,money_lexical_per_100,0.02 -0,0,incomegfl bet auditing taxansjbf,money_lexical_per_100,0.04 -0,0,thereforrnsq,causation_lexical_per_100,0.01 -0,0,origins,causation_lexical_per_100,0.01 -0,0,motivnt launchvt,causation_lexical_per_100,0.02 -0,0,makes forcev using obedienwayd,causation_lexical_per_100,0.04 -0,0,compliance obedienlpijv pick,causation_lexical_per_100,0.03 -0,0,sucked arrogant ferocwv,anger_lexical_per_100,0.03 -0,0,punishx enragvbj contemptawb,anger_lexical_per_100,0.03 -0,0,maniacxo pissmygt rebelmp,anger_lexical_per_100,0.03 -0,0,stupidncwg hellish paranoilbock warring,anger_lexical_per_100,0.04 -0,0,naga angrwmnl tantrumsv,anger_lexical_per_100,0.03 -0,0,ive I've,first_person_singular_lexical_per_100,0.02 -0,0,Id,first_person_singular_lexical_per_100,0.01 -0,0,Id myself,first_person_singular_lexical_per_100,0.02 -0,0,I'd,first_person_singular_lexical_per_100,0.01 -0,0,i,first_person_singular_lexical_per_100,0.01 -0,0,weightliftd hand driemau drily,feel_lexical_per_100,0.04 -0,0,grippfipun feelingwkaf thinnajy,feel_lexical_per_100,0.03 -0,0,skin'men,feel_lexical_per_100,0.01 -0,0,weight press hot thin silkvr,feel_lexical_per_100,0.05 -0,0,skin'h,feel_lexical_per_100,0.01 -0,0,assumbvd seemed usually,tentativeness_lexical_per_100,0.03 -0,0,option,tentativeness_lexical_per_100,0.01 -0,0,someonesyc wonders theorhe,tentativeness_lexical_per_100,0.03 -0,0,spose hardly seemed alot,tentativeness_lexical_per_100,0.04 -0,0,often appearing,tentativeness_lexical_per_100,0.02 -0,0,either except sometime,exclusive_lexical_per_100,0.03 -0,0,without,exclusive_lexical_per_100,0.01 -0,0,really but whether,exclusive_lexical_per_100,0.03 -0,0,vs,exclusive_lexical_per_100,0.01 -0,0,if,exclusive_lexical_per_100,0.01 -0,0,begin supported,verbs_lexical_per_100,0.02 -0,0,begin held who's describe how's,verbs_lexical_per_100,0.05 -0,0,takes,verbs_lexical_per_100,0.01 -0,0,happened saw mustnt brings,verbs_lexical_per_100,0.04 -0,0,slept took waited,verbs_lexical_per_100,0.03 -0,0,pals bud amigodyzg mate's girlfriendur,friends_lexical_per_100,0.05 -0,0,mates,friends_lexical_per_100,0.01 -0,0,neighborwtey bftxuv comradq,friends_lexical_per_100,0.03 -0,0,colleaguejo buddiesef,friends_lexical_per_100,0.02 -0,0,bfbfi,friends_lexical_per_100,0.01 -0,0,an a alot the,article_lexical_per_100,0.04 -0,0,the,article_lexical_per_100,0.01 -0,0,an,article_lexical_per_100,0.01 -0,0,an the,article_lexical_per_100,0.02 -0,0,an,article_lexical_per_100,0.01 -0,0,u know no,argue_lexical_per_100,0.02 -0,0,really you know I think,argue_lexical_per_100,0.03 -0,0,really I believe,argue_lexical_per_100,0.02 -0,0,cause,argue_lexical_per_100,0.01 -0,0,I know,argue_lexical_per_100,0.01 -0,0,you'd could,auxiliary_verbs_lexical_per_100,0.02 -0,0,couldnt isn't,auxiliary_verbs_lexical_per_100,0.02 -0,0,oughta had be what's,auxiliary_verbs_lexical_per_100,0.04 -0,0,be shant itll,auxiliary_verbs_lexical_per_100,0.03 -0,0,ain't couldnt,auxiliary_verbs_lexical_per_100,0.02 -0,0,avertv,cognitive_mech_lexical_per_100,0.01 -0,0,yearnqgjth,cognitive_mech_lexical_per_100,0.01 -0,0,infer tentativwc wouldn't,cognitive_mech_lexical_per_100,0.03 -0,0,around commit banned fundamental,cognitive_mech_lexical_per_100,0.04 -0,0,obedienoki solutionhk,cognitive_mech_lexical_per_100,0.02 -0,0,around plus,preposition_lexical_per_100,0.02 -0,0,onto,preposition_lexical_per_100,0.01 -0,0,insides in behind out,preposition_lexical_per_100,0.04 -0,0,insides amongzekb unto by than,preposition_lexical_per_100,0.05 -0,0,across above about without,preposition_lexical_per_100,0.04 -0,0,we'd us our weve,first_person_plural_lexical_per_100,0.04 -0,0,we'd we've weve ourselves we,first_person_plural_lexical_per_100,0.05 -0,0,we've we're lets,first_person_plural_lexical_per_100,0.03 -0,0,ourselves,first_person_plural_lexical_per_100,0.01 -0,0,us our we'll let's,first_person_plural_lexical_per_100,0.04 -0,0,greyiyck delectabliktz,percept_lexical_per_100,0.02 -0,0,screen sand,percept_lexical_per_100,0.02 -0,0,sweetness drieqs deoders,percept_lexical_per_100,0.03 -0,0,coldedrm,percept_lexical_per_100,0.01 -0,0,hear caramelzjw wetly tonguel,percept_lexical_per_100,0.04 -0,0,y'all youd you're you'll,second_person_lexical_per_100,0.04 -0,0,u thine,second_person_lexical_per_100,0.02 -0,0,yall yours u your thoust,second_person_lexical_per_100,0.05 -0,0,yall you've youll thee,second_person_lexical_per_100,0.04 -0,0,thine you ye,second_person_lexical_per_100,0.03 -0,0,responsibly evocative faithfully valiantly illustrious,positive_words_lexical_per_100,0.05 -0,0,imaculate vivid likable enjoyable succeed,positive_words_lexical_per_100,0.05 -0,0,smitten sharpest rightfully unreal,positive_words_lexical_per_100,0.04 -0,0,reaffirmation joy goood speedy self-sufficient,positive_words_lexical_per_100,0.05 -0,0,tantalize energize gusto win,positive_words_lexical_per_100,0.04 -0,0,we our i me,first_person_lexical_per_100,0.04 -0,0,ourselves,first_person_lexical_per_100,0.01 -0,0,myself,first_person_lexical_per_100,0.01 -0,0,lets my i,first_person_lexical_per_100,0.03 -0,0,our,first_person_lexical_per_100,0.01 -0,0,but,nltk_english_stopwords_lexical_per_100,0.01 -0,0,were was your,nltk_english_stopwords_lexical_per_100,0.03 -0,0,any shan it is,nltk_english_stopwords_lexical_per_100,0.04 -0,0,our,nltk_english_stopwords_lexical_per_100,0.01 -0,0,because an after other,nltk_english_stopwords_lexical_per_100,0.04 -0,0,probably,hedge_words_lexical_per_100,0.01 -0,0,maybe I guess possibly sort of a little,hedge_words_lexical_per_100,0.05 -0,0,a little possibly I think sort of probably,hedge_words_lexical_per_100,0.05 -0,0,probably sort of,hedge_words_lexical_per_100,0.02 -0,0,I think,hedge_words_lexical_per_100,0.01 \ No newline at end of file +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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Alex loved the feeling of creating something out of nothing, of transforming ideas into functional software that could help people in their daily lives. Byteville was a city that thrived on technology, and programmers like Alex were considered artisans of the modern age. Alex had always been intrigued by the possibilities of coding, but there was one lesson that stood out more than any other during their journey: the paramount importance of testing code. In the early days of Alex's career, they were eager to dive straight into writing code. The thrill of seeing their ideas come to life was intoxicating, and Alex quickly built a reputation for developing features at a swift pace. However, this rapid development came at a cost. Despite the initial excitement, Alex found that their code often contained bugs, leading to frequent crashes and frustrated users. It was a humbling experience, and Alex realized that there was more to being a great programmer than just writing code. One day, Alex was working on a project for a new client. The client had requested a complex application that promised to revolutionize the way people managed their daily tasks. Excitement coursed through Alex's veins, and they threw themselves into the project with gusto. Hours turned into days, and days into weeks, as Alex painstakingly coded every feature that the client had envisioned. Finally, the day came when Alex was ready to present the finished product. The client gathered their team to witness the unveiling, and Alex began the demonstration with confidence. But soon, things started to go awry. Buttons that were supposed to trigger specific actions did nothing. Data that was meant to be saved was lost. The application crashed multiple times, leaving the client and their team frustrated and unimpressed. Alex felt a pang of embarrassment and disappointment. After the failed presentation, Alex decided to seek advice from an experienced programmer named Maya, who was known for her meticulous and bug-free code. Maya had been in the industry for many years and had a wealth of knowledge about best practices in software development. Alex visited Maya's office, and after explaining the situation, Maya nodded knowingly. 'Alex,' she said gently, 'coding is both an art and a science. While your enthusiasm and creativity are wonderful, you must also embrace the discipline of testing. Testing your code is essential to ensure that it functions as intended and to deliver a reliable product to your users.' Maya spent the next several hours teaching Alex about different types of testing. They covered unit testing, which involves testing individual components of the code to ensure they work correctly. They delved into integration testing, where multiple components are tested together to ensure they function seamlessly as a whole. Maya also explained the importance of system testing, where the entire application is tested in an environment that simulates real-world usage. She emphasized the value of automated testing frameworks, which could run tests repeatedly, catch regressions, and provide quick feedback during the development process. Alex began to see testing in a new light. It wasn't just an afterthought or a tedious chore; it was an integral part of the development cycle that could make the difference between a functional, reliable application and a buggy, frustrating one. Inspired by Maya's wisdom, Alex returned to their project with renewed determination. They wrote unit tests for every component, ensuring that each piece of the codebase was robust and free from errors. They created integration tests to check how well different parts of the application worked together. Finally, they conducted system tests to simulate how users would interact with the application in the real world. The process was time-consuming, but Alex quickly discovered that it was worth every minute. The tests caught several issues that would have otherwise slipped through the cracks, and Alex was able to fix these problems before they reached the client. The application became more stable, reliable, and user-friendly. When the time came for the second presentation, Alex stood before the client with newfound confidence. The application ran smoothly, every feature working as intended. The client and their team were impressed, and Alex couldn't help but feel a sense of pride and accomplishment. This was the result of not just hard work, but also a commitment to quality through thorough testing. Word of Alex's successful project spread throughout Byteville, and soon, other programmers and clients began to take notice. Alex became an advocate for the importance of testing code, sharing the lessons they had learned with anyone willing to listen. Testing became a mantra not just for Alex, but for a new generation of programmers who understood that excellence in coding wasn't just about rapid development but also about ensuring reliability and functionality. From that day forward, Alex continued to create innovative applications, each one meticulously tested to ensure it met the highest standards of quality. Byteville thrived on these advancements, and the city's residents knew that whatever challenges lay ahead, they could rely on the software created by dedicated programmers like Alex. And so, the story of Alex and the importance of testing code became a legend in Byteville, a timeless reminder that behind every great line of code is the diligent effort to make sure it works flawlessly." +1,B,This is a much shorter message. +2,B,:-) +2,A, +2,A, \ No newline at end of file diff --git a/tests/run_package_grouping_tests.py b/tests/run_package_grouping_tests.py index 9fefd43a..6df66cc1 100644 --- a/tests/run_package_grouping_tests.py +++ b/tests/run_package_grouping_tests.py @@ -34,7 +34,7 @@ output_file_path_conv_level = "./tiny_multi_task_PT1_level_conv", turns = False, ) - testing_package_task_1.featurize(col="message") + testing_package_task_1.featurize() """ Testing Package Task 1 Advanced Features @@ -64,9 +64,9 @@ output_file_path_chat_level = "./output/chat/tiny_multi_task_case2_level_chat.csv", output_file_path_user_level = "./output/user/tiny_multi_task_case2_level_user.csv", output_file_path_conv_level = "./output/conv/tiny_multi_task_case2_level_conv.csv", - turns = False, + turns = False ) - testing_case_2.featurize(col="message") + testing_case_2.featurize() print("TESTING CASE 3A .....") testing_case_3_a = FeatureBuilder( @@ -82,9 +82,9 @@ output_file_path_chat_level = "./output/chat/tiny_multi_task_case3a_level_chat.csv", output_file_path_user_level = "./output/user/tiny_multi_task_case3a_level_user.csv", output_file_path_conv_level = "./output/conv/tiny_multi_task_case3a_level_conv.csv", - turns = False, + turns = False ) - testing_case_3_a.featurize(col="message") + testing_case_3_a.featurize() print("TESTING CASE 3B .....") testing_case_3_b = FeatureBuilder( @@ -100,9 +100,9 @@ output_file_path_chat_level = "./output/chat/tiny_multi_task_case3b_level_chat.csv", output_file_path_user_level = "./output/user/tiny_multi_task_case3b_level_user.csv", output_file_path_conv_level = "./output/conv/tiny_multi_task_case3b_level_conv.csv", - turns = False, + turns = False ) - testing_case_3_b.featurize(col="message") + testing_case_3_b.featurize() print("TESTING CASE 3C .....") testing_case_3_c = FeatureBuilder( @@ -118,9 +118,9 @@ output_file_path_chat_level = "./output/chat/tiny_multi_task_case3c_level_chat.csv", output_file_path_user_level = "./output/user/tiny_multi_task_case3c_level_user.csv", output_file_path_conv_level = "./output/conv/tiny_multi_task_case3c_level_conv.csv", - turns = False, + turns = False ) - testing_case_3_c.featurize(col="message") + testing_case_3_c.featurize() print("TESTING IMPROPER CASE .....") testing_case_improper = FeatureBuilder( @@ -136,9 +136,9 @@ output_file_path_chat_level = "./output/chat/tiny_multi_task_improper_level_chat.csv", output_file_path_user_level = "./output/user/tiny_multi_task_improper_level_user.csv", output_file_path_conv_level = "./output/conv/tiny_multi_task_improper_level_conv.csv", - turns = False, + turns = False ) - testing_case_improper.featurize(col="message") + testing_case_improper.featurize() """ Test robustness of the FeatureBuilder to taking in an input that contains existing feature names. @@ -150,6 +150,7 @@ testing_chat_existing = FeatureBuilder( input_df = chat_df_existing_output, vector_directory = "./vector_data/", + message_col = "message_original", output_file_path_chat_level = "./output/chat/test_chat_level_existing_chat.csv", output_file_path_user_level = "./output/user/test_chat_level_existing_user.csv", output_file_path_conv_level = "./output/conv/test_chat_level_existing_conv.csv", @@ -158,7 +159,34 @@ "Moving Mimicry", "Forward Flow", "Discursive Diversity" + ], + turns = False + ) + testing_chat_existing.featurize() + + """ + Test robustness of the vector pipeline to weird inputs: + - Super long input + - Input containing only symbols (e.g,. ":-)") + - Empty input + - Input with many spaces + """ + vector_testing_input = pd.read_csv("data/cleaned_data/test_vector_edge_cases.csv", encoding='latin-1') + + test_vectors = FeatureBuilder( + input_df = vector_testing_input, + vector_directory = "./vector_data/", + output_file_path_chat_level = "./output/chat/test_vectors_chat.csv", + output_file_path_user_level = "./output/user/test_vectors_user.csv", + output_file_path_conv_level = "./output/conv/test_vectors_conv.csv", + custom_features = [ + "(BERT) Mimicry", + "Moving Mimicry", + "Forward Flow", + "Discursive Diversity" ], turns = False, + regenerate_vectors = True ) - testing_chat_existing.featurize(col="message") + test_vectors.featurize() + diff --git a/tests/run_tests.py b/tests/run_tests.py index 8fb938a9..aadd767c 100644 --- a/tests/run_tests.py +++ b/tests/run_tests.py @@ -43,9 +43,9 @@ "Forward Flow", "Discursive Diversity" ], - turns = False, + turns = False ) - testing_chat.featurize(col="message") + testing_chat.featurize() testing_conv = FeatureBuilder( input_df = conv_df, @@ -59,9 +59,9 @@ "Forward Flow", "Discursive Diversity" ], - turns = False, + turns = False ) - testing_conv.featurize(col="message") + testing_conv.featurize() test_ner_feature_builder = FeatureBuilder( input_df = test_ner_df, @@ -76,9 +76,9 @@ "Forward Flow", "Discursive Diversity" ], - turns = False, + turns = False ) - test_ner_feature_builder.featurize(col="message") + test_ner_feature_builder.featurize() # testing perturbed chat level features testing_chat_complex = FeatureBuilder( @@ -93,9 +93,9 @@ "Forward Flow", "Discursive Diversity" ], - turns = False, + turns = False ) - testing_chat_complex.featurize(col="message") + testing_chat_complex.featurize() # testing conv features testing_conv_complex = FeatureBuilder( @@ -110,9 +110,9 @@ "Forward Flow", "Discursive Diversity" ], - turns = False, + turns = False ) - testing_conv_complex.featurize(col="message") + testing_conv_complex.featurize() testing_conv_complex_ts = FeatureBuilder( input_df = conv_complex_timestamps_df, @@ -126,9 +126,9 @@ "Forward Flow", "Discursive Diversity" ], - turns = False, + turns = False ) - testing_conv_complex_ts.featurize(col="message") + testing_conv_complex_ts.featurize() # testing forward flow testing_forward_flow = FeatureBuilder( @@ -143,7 +143,7 @@ "Forward Flow", "Discursive Diversity" ], - turns = False, + turns = False ) - testing_forward_flow.featurize(col="message") \ No newline at end of file + 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 42ad513a..4c1844c4 100644 --- a/tests/test_feature_metrics.py +++ b/tests/test_feature_metrics.py @@ -175,8 +175,8 @@ def test_chat_complex(batch): inv_result = batch[1][1][feature] dir_result = batch[2][1][feature] - inv_distance = og_result - inv_result - dir_distance = og_result - dir_result + inv_distance = abs(og_result - inv_result) + dir_distance = abs(og_result - dir_result) # calculate ratio between inv and dir ratio = dir_distance / inv_distance diff --git a/tests/test_package.py b/tests/test_package.py index 0106b230..ac145b66 100644 --- a/tests/test_package.py +++ b/tests/test_package.py @@ -4,6 +4,8 @@ from numpy import nan import logging import itertools +import os +from sklearn.metrics.pairwise import cosine_similarity # Import Test Outputs input_data = pd.read_csv("data/cleaned_data/multi_task_TINY_cols_renamed.csv", encoding='utf-8') @@ -13,6 +15,9 @@ case3b_chatdf = pd.read_csv("./output/chat/tiny_multi_task_case3b_level_chat.csv") case3c_chatdf = pd.read_csv("./output/chat/tiny_multi_task_case3c_level_chat.csv") impropercase_chatdf = pd.read_csv("./output/chat/tiny_multi_task_improper_level_chat.csv") +sentiment_output = pd.read_csv('./vector_data/sentiment/chats/test_vectors_chat.csv') +sbert_output = pd.read_csv('./vector_data/sentence/chats/test_vectors_chat.csv') + # Import the Feature Dictionary from team_comm_tools.feature_dict import feature_dict @@ -167,6 +172,8 @@ def test_improper_case(): file.write(f"Number of unique conversation identifiers in improper case: {improper_ids}\n") file.write(f"Number of unique conversation identifiers in Case 2: {case_2_ids}\n") + raise + def test_robustness_to_existing_column_names(): try: chat_df_orig = pd.read_csv("./output/chat/test_chat_level_chat.csv") # original output @@ -187,4 +194,43 @@ def test_robustness_to_existing_column_names(): file.write("------TEST FAILED------\n") file.write(f"Robustness check for passing in chat dataframe with feature columns failed.\n") - raise \ No newline at end of file + raise + +def get_nan_vector_str(): + current_dir = os.path.dirname(__file__) + nan_vector_file_path = os.path.join(current_dir, '../src/team_comm_tools/features/assets/nan_vector.txt') + nan_vector_file_path = os.path.abspath(nan_vector_file_path) + + f = open(nan_vector_file_path, "r") + return f.read() + +def str_to_vec(str_vec): + vector_list = [float(e) for e in str_vec[1:-1].split(',')] + return np.array(vector_list).reshape(-1, 1) + +def test_empty_vectors_equal(): + try: + # assert that the last two rows are equal; they're both empty + assert(sbert_output.iloc[-1]["message_embedding"]==sbert_output.iloc[-2]["message_embedding"]) + assert(sentiment_output.iloc[-1].equals(sentiment_output.iloc[-2])) + + # assert that the 'positive bert' of the last sentiment is np.nan + assert(np.isnan(float(sentiment_output.iloc[-1]["positive_bert"]))) + + # compare empty vector to nan vector + message_embedding_str = sbert_output.iloc[-1]["message_embedding"] + message_embedding_vec = str_to_vec(message_embedding_str) + nan_vector_str = get_nan_vector_str() + nan_vector = str_to_vec(nan_vector_str) + + # Compute cosine similarity and assert it's essentially 1 + similarity = cosine_similarity([message_embedding_vec.flatten()], [nan_vector.flatten()])[0][0] + assert(round(similarity, 0) == 1.0) + + except AssertionError: + with open('test.log', 'a') as file: + file.write("\n") + file.write("------TEST FAILED------\n") + file.write(f"Empty message vectors / sentence scores are not equal.\n") + + raise diff --git a/website/package-lock.json b/website/package-lock.json index e6ddff2a..73a4125f 100644 --- a/website/package-lock.json +++ b/website/package-lock.json @@ -8,18 +8,18 @@ "name": "website", "version": "0.1.0", "dependencies": { - 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">=8" + }, + "funding": { + "url": "https://github.com/chalk/ansi-styles?sponsor=1" + } + }, + "node_modules/wrap-ansi/node_modules/color-convert": { + "version": "2.0.1", + "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-2.0.1.tgz", + "integrity": "sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==", + "dependencies": { + "color-name": "~1.1.4" + }, + "engines": { + "node": ">=7.0.0" + } + }, + "node_modules/wrap-ansi/node_modules/color-name": { + "version": "1.1.4", + "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.4.tgz", + "integrity": "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==" + }, "node_modules/wrappy": { "version": "1.0.2", "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz", @@ -16565,30 +18238,28 @@ } }, "node_modules/yargs": { - "version": "17.7.2", - "resolved": "https://registry.npmjs.org/yargs/-/yargs-17.7.2.tgz", - "integrity": "sha512-7dSzzRQ++CKnNI/krKnYRV7JKKPUXMEh61soaHKg9mrWEhzFWhFnxPxGl+69cD1Ou63C13NUPCnmIcrvqCuM6w==", - "dev": true, + "version": "16.2.0", + "resolved": "https://registry.npmjs.org/yargs/-/yargs-16.2.0.tgz", + "integrity": "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==", "dependencies": { - "cliui": "^8.0.1", + "cliui": "^7.0.2", "escalade": "^3.1.1", "get-caller-file": "^2.0.5", "require-directory": "^2.1.1", - "string-width": "^4.2.3", + "string-width": "^4.2.0", "y18n": "^5.0.5", - "yargs-parser": "^21.1.1" + "yargs-parser": "^20.2.2" }, "engines": { - "node": ">=12" + "node": ">=10" } }, "node_modules/yargs-parser": { - "version": "21.1.1", - "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz", - "integrity": "sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==", - "dev": true, + "version": "20.2.9", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz", + "integrity": "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==", "engines": { - "node": ">=12" + "node": ">=10" } }, "node_modules/yocto-queue": { diff --git a/website/package.json b/website/package.json index 10cbab84..f379c95c 100644 --- a/website/package.json +++ b/website/package.json @@ -1,5 +1,5 @@ { - "homepage": "https://teamcommtools.seas.upenn.edu/", + "homepage": "https://teamcommtools.seas.upenn.edu/", "name": "website", "version": "0.1.0", "proxy": "https://5f9vk2anlb.execute-api.us-east-2.amazonaws.com/team-comm-tools-features/team-comm-tools", @@ -8,14 +8,15 @@ "react": "^18.3.1", "react-burger-menu": "^3.0.9", "react-dom": "^18.3.1", - "react-icons": "^4.12.0", + "react-icons": "^5.3.0", "react-responsive": "^10.0.0", - "react-router-dom": "^6.23.1", + "react-router-dom": "^6.26.2", "react-scripts": "^5.0.1", - "web-vitals": "^2.1.4" + "web-vitals": "^4.2.3" }, "scripts": { "predeploy": "npm run build", + "add-domain": "echo 'teamcommtools.seas.upenn.edu' > build/CNAME", "deploy": "gh-pages -d build", "build": "react-scripts build", "test": "react-scripts test", @@ -39,5 +40,9 @@ "last 1 firefox version", "last 1 safari version" ] + }, + "devDependencies": { + "@babel/plugin-proposal-private-property-in-object": "^7.21.11", + "gh-pages": "^6.1.1" } } \ No newline at end of file diff --git a/website/public/CNAME b/website/public/CNAME new file mode 100644 index 0000000000000000000000000000000000000000..814eed80d185304510ebc6437f5db729eed53a4e GIT binary patch literal 58 ycmWlOK@I>A5Ci8$`zOH-E3v+hO4Cl8-V;<3os26xAv%qP$^ZLpGu4M=V$puPpA8%U literal 0 HcmV?d00001 diff --git a/website/src/components/pages/Team.js b/website/src/components/pages/Team.js index ff99aab4..91621009 100644 --- a/website/src/components/pages/Team.js +++ b/website/src/components/pages/Team.js @@ -11,8 +11,8 @@ const current = [ image: `${process.env.PUBLIC_URL}/priya.png` }, { - name: 'Evan Rowbotham', - image: `${process.env.PUBLIC_URL}/evan.png` + name: 'Yashveer Singh Sohi', + image: `${process.env.PUBLIC_URL}/yashveer.png` }, { name: 'Yuxuan Zhang', @@ -21,10 +21,6 @@ const current = [ { name: 'Amy Zheng', image: `${process.env.PUBLIC_URL}/amy.png` - }, - { - name: 'Helena Zhou', - image: `${process.env.PUBLIC_URL}/helena.png` } ]; @@ -42,12 +38,16 @@ const alumni = [ image: `${process.env.PUBLIC_URL}/nikhil.png` }, { - name: 'Yashveer Singh Sohi', - image: `${process.env.PUBLIC_URL}/yashveer.png` + name: 'Evan Rowbotham', + image: `${process.env.PUBLIC_URL}/evan.png` }, { name: 'Eric Zhong', image: `${process.env.PUBLIC_URL}/eric.jfif` + }, + { + name: 'Helena Zhou', + image: `${process.env.PUBLIC_URL}/helena.png` } ]; From c2e07915b9ee9286a116def1899bba9b5aacfd5c Mon Sep 17 00:00:00 2001 From: Xinlan Emily Hu Date: Tue, 8 Oct 2024 00:11:05 -0400 Subject: [PATCH 2/3] Update README.md --- README.md | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 3ddc232d..10232b88 100644 --- a/README.md +++ b/README.md @@ -56,11 +56,10 @@ from team_comm_tools import FeatureBuilder Once you import the tool, you will be able to declare a FeatureBuilder object, which is the heart of our tool. Here is some sample syntax: ```python -# this section of code declares a FeatureBuilder object my_feature_builder = FeatureBuilder( input_df = my_pandas_dataframe, # this means there's a column in your data called 'conversation_id' that uniquely identifies a conversation - conversation_id_col = "conversation_id", + conversation_id_col = "conversation_id", # this means there's a column in your data called 'speaker_id' that uniquely identifies a speaker speaker_id_col = "speaker_id", # this means there's a column in your data called 'messagae' that contains the content you want to featurize @@ -69,14 +68,13 @@ my_feature_builder = FeatureBuilder( timestamp_col= "timestamp", # this is where we'll cache things like sentence vectors; this directory doesn't have to exist; we'll create it for you! vector_directory = "./vector_data/", - # give us names for the utterance (chat), speaker (user), and conversation-level outputs - output_file_path_chat_level = "./my_output_chat_level.csv", - output_file_path_user_level = "./my_output_user_level.csv", - output_file_path_conv_level = "./my_output_conversation_level.csv", - # if true, this will combine successive turns by the same speaker. + # this will be the base file path for which we generate the three outputs; + # you will get your outputs in output/chat/my_output_chat_level.csv; output/conv/my_output_conv_level.csv; and output/user/my_output_user_level. + output_file_base = "my_output" + # it will also store the output into output/turns/my_output_chat_level.csv turns = False, # these features depend on sentence vectors, so they take longer to generate on larger datasets. Add them in manually if you are interested in adding them to your output! - custom_features = [ + custom_features = [ "(BERT) Mimicry", "Moving Mimicry", "Forward Flow", @@ -104,7 +102,7 @@ Notably, not all communication features are made equal, as they can be defined a 2. The **speaker**, and 3. The **conversation** -**We generate a separate output file for each level.** When you declare a FeatureBuilder, you will need to specify an output path for each level of analysis. +**We generate a separate output file for each level.** When you declare a FeatureBuilder, you can use the `output_file_base` to define a base path shared among all three levels, and an output path will be automatically generated for each level of analysis. For more information, please refer to the [Introduction on our Read the Docs Page](https://conversational-featurizer.readthedocs.io/en/latest/intro.html#intro). From 627955ab9ffc756a5ba683781d70c96a411b4b01 Mon Sep 17 00:00:00 2001 From: Xinlan Emily Hu Date: Tue, 8 Oct 2024 00:17:01 -0400 Subject: [PATCH 3/3] Update pyproject.toml for post-release 1 (#316) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 229cd3c0..e10f2581 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" +version = "0.1.4.post1" requires-python = ">= 3.10" dependencies = [ "chardet>=3.0.4",