diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000000000000000000000000000000000000..93c39068e7f081aabe0f4b64f2d98847b19d5d27
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,3 @@
+DATA_PATH = ""
+BOT_TOKEN = ""
+CHAT_ID =
diff --git a/.flake8 b/.flake8
new file mode 100644
index 0000000000000000000000000000000000000000..b0302a687f21e9f25c0a73d06ad75d37a925af74
--- /dev/null
+++ b/.flake8
@@ -0,0 +1,33 @@
+[flake8]
+max-complexity = 10
+max_line_length = 138
+exclude =
+ # no need to traverse our git directory
+ .git,
+ # there's no value in checking cache directories
+ __pycache__,
+ # this directory is mostly auto-generated
+ docs/source,
+extend-ignore =
+ # H101: use TODO(NAME)
+ H101,
+ # H202: assertRaises Exception too broad
+ H202,
+ # H233: python 3.x incompatible use of print operator
+ H233,
+ # H301: one import per line
+ H301,
+ # H306: imports not in alphabetical order (time, os)
+ H306,
+ # H401: docstring should not start with a space
+ H401,
+ # H403: multi line docstrings should end on a new line
+ H403,
+ # H404: multi line docstring should start without a leading new line
+ H404,
+ # H405: multi line docstring summary not separated with an empty line
+ H405,
+ # H501: do not use self.__dict__ for string formatting
+ H501,
+ # E203: colons should not have any space before them
+ E203,
diff --git a/.github/workflows/cd.yaml b/.github/workflows/cd.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fc25df8fa4a35d6d241a6675efeb1c3cd5c073fd
--- /dev/null
+++ b/.github/workflows/cd.yaml
@@ -0,0 +1,48 @@
+name: continuous-deployment
+
+on:
+ push:
+ branches:
+ - ci-cd/hugging-face
+ release:
+ types: [published]
+
+jobs:
+ continuous-deployment:
+ runs-on: ubuntu-22.04
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python 3.7.13
+ uses: actions/setup-python@v2
+ with:
+ python-version: 3.7.13
+ - name: Install dependencies
+ run: |
+ curl -sSL https://install.python-poetry.org | python3 -
+ poetry --no-root install
+ - name: Clone Hugging Face repo
+ run: |
+ cd ..
+ git clone https://huggingface.co/spaces/Booguy/linguask
+ - name: Move files to repo
+ run: |
+ cd ..
+ mv ./linguask/.git/ .
+ mv ./linguask/README.md .
+ rm -rf ./linguask/*
+ mv .git ./linguask
+ mv README.md ./linguask
+ cd ./automatic-essay-evaluator
+ rm -rf ./.git
+ rm README.md
+ cd ..
+ cp -a ./automatic-essay-evaluator/. ./linguask
+ - name: Commit all files
+ run: |
+ cd ..
+ cd linguask
+ git add .
+ git config --local user.email "action@github.com"
+ git config --local user.name "GitHub Action"
+ git commit -m ${{ github.ref }}
+ git push https://Booguy:${{ secrets.HF_TOKEN }}@huggingface.co/spaces/Booguy/linguask.git
diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6bc0b857eee9283e35ac4eda98c61829e41150e6
--- /dev/null
+++ b/.github/workflows/ci.yaml
@@ -0,0 +1,41 @@
+name: continuous-integration
+
+on:
+ push:
+ branches:
+ - main
+ pull_request:
+ branches:
+ - main
+
+jobs:
+ continuous-integration:
+ runs-on: ubuntu-22.04
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python 3.7.13
+ uses: actions/setup-python@v2
+ with:
+ python-version: 3.7.13
+ - name: Install dependencies
+ run: |
+ curl -sSL https://install.python-poetry.org | python3 -
+ poetry --no-root install
+ - uses: jamescurtin/isort-action@master
+ - name: Lint with flake8
+ run: |
+ poetry run flake8
+ - name: Lint with static type checker mypy
+ run: |
+ poetry run mypy .
+ - name: Test with pytest
+ run: |
+ poetry run pytest
+ - name: Coverage report
+ run: |
+ poetry run coverage run -m pytest
+ poetry run coverage xml
+ - name: Upload Coverage Report to Codecov
+ uses: codecov/codecov-action@v3
+ with:
+ token: ${{ secrets.CODECOV_TOKEN }}
diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..debf0f39392c67e37c837eda81721e2b5b0437d9
--- /dev/null
+++ b/.github/workflows/docs.yaml
@@ -0,0 +1,46 @@
+name: sphinx-documentation
+
+on:
+ push:
+ branches:
+ - main
+
+jobs:
+ build_job:
+ runs-on: ubuntu-22.04
+ env:
+ GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v2.3.4
+ - name: Set up Python 3.7.13
+ uses: actions/setup-python@v2.2.1
+ with:
+ python-version: 3.7.13
+ - name: Install dependencies
+ run: |
+ curl -sSL https://install.python-poetry.org | python3 -
+ poetry lock
+ poetry --no-root install
+ - name: Make the sphinx docs
+ run: |
+ poetry run make -C docs clean
+ poetry run make -C docs html
+ - name: Commit generated files
+ run: |
+ cd docs/build/html
+ git init
+ touch .nojekyll
+ git add -A
+ git config --local user.email "action@github.com"
+ git config --local user.name "GitHub Action"
+ git config --global --add safe.directory '*'
+ git commit -m ${{ github.ref }}
+ - name: Push to destination branch
+ uses: ad-m/github-push-action@master
+ with:
+ github_token: ${{ secrets.GITHUB_TOKEN }}
+ branch: gh-pages
+ force: true
+ directory: ./docs/build/html
diff --git a/.github/workflows/kaggle.yaml b/.github/workflows/kaggle.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0d81127b1fb31567522df6e35f9a6344dead15ff
--- /dev/null
+++ b/.github/workflows/kaggle.yaml
@@ -0,0 +1,32 @@
+name: kaggle-submission
+
+on:
+ pull_request:
+ branches:
+ - main
+ types: [closed]
+
+
+jobs:
+ kaggle-api:
+ if: ${{ github.event.pull_request.merged }}
+ runs-on: ubuntu-22.04
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python 3.7.13
+ uses: actions/setup-python@v2
+ with:
+ python-version: 3.7.13
+ - name: Install dependencies
+ run: |
+ curl -sSL https://install.python-poetry.org | python3 -
+ poetry lock
+ poetry --no-root install
+ - name: Authenticate to Kaggle
+ run: |
+ mkdir ~/.kaggle
+ echo ${{ secrets.KAGGLE }} >> ~/.kaggle/kaggle.json
+ - name: Push kernel
+ run: poetry run kaggle kernels push -p src/
+ - name: Update weights
+ run: poetry run kaggle datasets version -p data/weights --dir-mode zip -m ${{ github.ref }}
diff --git a/.github/workflows/snyk.yaml b/.github/workflows/snyk.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..12ec9278e2b638c19cf4bd54468df6ff7a09d286
--- /dev/null
+++ b/.github/workflows/snyk.yaml
@@ -0,0 +1,30 @@
+name: snyk-vulnerability-testing
+
+on:
+ push:
+ branches:
+ - main
+ pull_request:
+ branches:
+ - main
+
+jobs:
+ snyk-testing:
+ runs-on: ubuntu-22.04
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python 3.7.13
+ uses: actions/setup-python@v2
+ with:
+ python-version: 3.7.13
+ - name: Install snyk
+ run: |
+ curl https://static.snyk.io/cli/latest/snyk-linux -o snyk
+ chmod +x ./snyk
+ mv ./snyk /usr/local/bin/
+ - name: Authenticate in snyk
+ run: |
+ snyk auth ${{ secrets.SNYK_TOKEN }}
+ - name: Test via snyk
+ run: |
+ snyk test
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..0830c51b7c77d67d62831587622d38dee41c3cc2
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,118 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# MACOS
+.DS_Store
+data/raw/*
+catboost_info/
+.idea/
+requirements-dev.txt
+
+# logging
+checkpoints/
+# hydra-outputs
+outputs/
+# weights
+demo/model.ckpt
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..46a5acd7719cfb3a9d40d136a4307d9811b8d958
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,24 @@
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v4.2.0
+ hooks:
+ - id: trailing-whitespace
+ - id: end-of-file-fixer
+ - id: check-yaml
+ - id: check-added-large-files
+ args: ['--maxkb=5000']
+- repo: https://github.com/pre-commit/mirrors-autopep8
+ rev: v1.6.0
+ hooks:
+ - id: autopep8
+- repo: https://github.com/pycqa/isort
+ rev: 5.10.1
+ hooks:
+ - id: isort
+ name: isort (python)
+- repo: https://github.com/myint/autoflake
+ rev: v1.4
+ hooks:
+ - id: autoflake
+ entry: autoflake
+ args: [--in-place, --remove-all-unused-imports]
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..d99c4601e94f82cff3967682fc585591ac4eff1c
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,7 @@
+FROM python:3.8-slim
+
+RUN curl -sSL https://install.python-poetry.org | python3 -
+
+RUN poetry --no-root install
+
+CMD ["poetry", "run", "make", "build"]
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..694da060c1b1e722e93a8a71aa3c27951913b36b
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Linguask
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..dfe7b5ec12302b448e2bd8b3f5c9b6b198c8c56d
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,23 @@
+build: download_data download_weights run
+
+download_data:
+ mkdir -p data/raw
+ cd data/raw; \
+ rm *; \
+ kaggle competitions download -c feedback-prize-english-language-learning; \
+ unzip feedback-prize-english-language-learning.zip; \
+ rm feedback-prize-english-language-learning.zip
+
+test:
+ flake8
+ isort .
+ pytest -p no:cacheprovider
+
+download_weights:
+ cd ./demo; \
+ kaggle datasets download -d alukaevdanis/feedback-prize-weights; \
+ unzip feedback-prize-weights.zip; \
+ rm feedback-prize-weights.zip
+
+run:
+ PYTHONPATH=. streamlit run demo/app.py
diff --git a/codecov.yml b/codecov.yml
new file mode 100644
index 0000000000000000000000000000000000000000..20939a4b848813f0c667c7cd50a0b4ffd1f1e595
--- /dev/null
+++ b/codecov.yml
@@ -0,0 +1,5 @@
+coverage:
+ status:
+ patch:
+ default:
+ target: 75%
diff --git a/data/.gitkeep b/data/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/weights/.gitignore b/data/weights/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/weights/config.yaml b/data/weights/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c0cce1a7bc197f6495f5449b6189c3e77e1edfde
--- /dev/null
+++ b/data/weights/config.yaml
@@ -0,0 +1,9 @@
+predictor:
+ _target_: src.solutions.constant_predictor.ConstantPredictorSolution
+validator:
+ _target_: src.cross_validate.CrossValidation
+ saving_dir: checkpoints
+ n_splits: 5
+timestamp: ${now:%Y-%m-%d}/${now:%H-%M-%S}
+cwd: ${hydra:runtime.cwd}
+name: sanity checking of ConstantPredictorSolution
diff --git a/data/weights/cv_fold_0/weights.ckpt b/data/weights/cv_fold_0/weights.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..8e217448db2e90c2978cd4461eb147d7843ce7b1
--- /dev/null
+++ b/data/weights/cv_fold_0/weights.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66e4c000f2000eb1cca164f97c441e14d438ca2599c8e79f4de6ecade0de8b32
+size 4
diff --git a/data/weights/cv_fold_1/weights.ckpt b/data/weights/cv_fold_1/weights.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..8e217448db2e90c2978cd4461eb147d7843ce7b1
--- /dev/null
+++ b/data/weights/cv_fold_1/weights.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66e4c000f2000eb1cca164f97c441e14d438ca2599c8e79f4de6ecade0de8b32
+size 4
diff --git a/data/weights/cv_fold_2/weights.ckpt b/data/weights/cv_fold_2/weights.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..8e217448db2e90c2978cd4461eb147d7843ce7b1
--- /dev/null
+++ b/data/weights/cv_fold_2/weights.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66e4c000f2000eb1cca164f97c441e14d438ca2599c8e79f4de6ecade0de8b32
+size 4
diff --git a/data/weights/cv_fold_3/weights.ckpt b/data/weights/cv_fold_3/weights.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..8e217448db2e90c2978cd4461eb147d7843ce7b1
--- /dev/null
+++ b/data/weights/cv_fold_3/weights.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66e4c000f2000eb1cca164f97c441e14d438ca2599c8e79f4de6ecade0de8b32
+size 4
diff --git a/data/weights/cv_fold_4/weights.ckpt b/data/weights/cv_fold_4/weights.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..8e217448db2e90c2978cd4461eb147d7843ce7b1
--- /dev/null
+++ b/data/weights/cv_fold_4/weights.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66e4c000f2000eb1cca164f97c441e14d438ca2599c8e79f4de6ecade0de8b32
+size 4
diff --git a/data/weights/cv_results.csv b/data/weights/cv_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..871928f9bda8fd6bce265f4b536e502d2c9bdb69
--- /dev/null
+++ b/data/weights/cv_results.csv
@@ -0,0 +1,7 @@
+,cohesion,syntax,vocabulary,phraseology,grammar,conventions
+0,0.6608945522512667,0.6302303497468349,0.641527949378647,0.6712010545904032,0.6961855568035836,0.688344859959337
+1,0.6984637874679632,0.6770818988115173,0.6537810547264135,0.6873910050778178,0.7192102801753979,0.6977768863470648
+2,0.667332361584851,0.6364362897377401,0.614977179207726,0.6768457760846118,0.6855281534006166,0.6721059014106314
+3,0.6735313662174706,0.6424358218267724,0.6209266237831667,0.65206733997094,0.6982348955111474,0.6678112510340573
+4,0.6718680299109107,0.6374401330898904,0.6123724356957945,0.6426845869171515,0.7030259769714595,0.654269863059915
+overall,0.6744180194864924,0.644724898642551,0.6287170485583495,0.6660379525281849,0.7004369725724409,0.676061752362201
diff --git a/data/weights/dataset-metadata.json b/data/weights/dataset-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..72c5156b9277ec5bc54bb3975a4e45e8c89222b7
--- /dev/null
+++ b/data/weights/dataset-metadata.json
@@ -0,0 +1,9 @@
+{
+ "title": "Linguask by Vitsyn-Morgunov-Nikulin",
+ "id": "alukaevdanis/weights-linguask",
+ "licenses": [
+ {
+ "name": "CC0-1.0"
+ }
+ ]
+}
diff --git a/data/weights/submission.csv b/data/weights/submission.csv
new file mode 100644
index 0000000000000000000000000000000000000000..152f0a6248b690307867e0b2a1eb906c352d88b0
--- /dev/null
+++ b/data/weights/submission.csv
@@ -0,0 +1,4 @@
+text_id,cohesion,syntax,vocabulary,phraseology,grammar,conventions
+0000C359D63E,3.0,3.0,3.0,3.0,3.0,3.0
+000BAD50D026,3.0,3.0,3.0,3.0,3.0,3.0
+00367BB2546B,3.0,3.0,3.0,3.0,3.0,3.0
diff --git a/data/word_frequencies/unigram_freq.csv b/data/word_frequencies/unigram_freq.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6cec3710dd94f512bae458c388676b1c1378bd93
--- /dev/null
+++ b/data/word_frequencies/unigram_freq.csv
@@ -0,0 +1,333334 @@
+word,count
+the,23135851162
+of,13151942776
+and,12997637966
+to,12136980858
+a,9081174698
+in,8469404971
+for,5933321709
+is,4705743816
+on,3750423199
+that,3400031103
+by,3350048871
+this,3228469771
+with,3183110675
+i,3086225277
+you,2996181025
+it,2813163874
+not,2633487141
+or,2590739907
+be,2398724162
+are,2393614870
+from,2275595356
+at,2272272772
+as,2247431740
+your,2062066547
+all,2022459848
+have,1564202750
+new,1551258643
+more,1544771673
+an,1518266684
+was,1483428678
+we,1390661912
+will,1356293641
+home,1276852170
+can,1242323499
+us,1229112622
+about,1226734006
+if,1134987907
+page,1082121730
+my,1059793441
+has,1046319984
+search,1024093118
+free,1014107316
+but,999899654
+our,998757982
+one,993536631
+other,978481319
+do,950751722
+no,937112320
+information,932594387
+time,908705570
+they,883223816
+site,844310242
+he,842847219
+up,829969374
+may,827822032
+what,812395582
+which,810514085
+their,782849411
+news,755424983
+out,741601852
+use,719980257
+any,710741293
+there,701170205
+see,681410380
+only,661844114
+so,661809559
+his,660177731
+when,650621178
+contact,645824184
+here,639711198
+business,637134177
+who,630927278
+web,619571575
+also,616829742
+now,611387736
+help,611054034
+get,605984508
+pm,604577485
+view,602279334
+online,601317059
+c,596623239
+e,593086170
+first,578161543
+am,576436203
+been,575019382
+would,572644147
+how,571848080
+were,570699558
+me,566617666
+s,565123981
+services,562206804
+some,548829454
+these,541003982
+click,536746424
+its,525627757
+like,520585287
+service,519537222
+x,508609523
+than,502609275
+find,502043038
+price,501651226
+date,488967374
+back,488024109
+top,484213771
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diff --git a/demo/__init__.py b/demo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/demo/app.py b/demo/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..029c2ed25c753309395f37da10d4705492204088
--- /dev/null
+++ b/demo/app.py
@@ -0,0 +1,93 @@
+import re
+
+import seaborn as sns
+import streamlit as st
+
+from demo.utils import load_model, process_text
+
+st.set_page_config(
+ page_title="BERT Keyword Extractor",
+ page_icon="🎈",
+)
+
+
+def _max_width_():
+ max_width_str = "max-width: 1400px;"
+ st.markdown(
+ f"""
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+
+st.header("🔑 Automated Essay Evaluator")
+
+with st.expander("ℹ️ - About this app", expanded=True):
+ st.write(
+ """
+ - This application demonstrates how automated essay evaluation works: given as an input text with max. \
+ length of 512, it scores it (from 1.0 to 4.0) for different criteria: cohesion, syntax, vocabulary, \
+ phraseology, grammar and conventions.
+ - This solution is based on fine-tuned deberta-v3-large model.
+ """
+ )
+
+ st.markdown("")
+
+st.markdown("")
+st.markdown("## 📌 **Paste document**", unsafe_allow_html=True)
+with st.form(key="my_form"):
+ _, c2, _ = st.columns([0.07, 5, 0.07])
+
+ with c2:
+ doc = st.text_area(
+ "Paste your text below (max 500 words)",
+ height=510,
+ )
+
+ MAX_WORDS = 500
+
+ res = len(re.findall(r"\w+", doc))
+ doc = doc[:MAX_WORDS]
+
+ submit_button = st.form_submit_button(label="✨ Assess my text!")
+
+if not submit_button:
+ st.stop()
+
+st.markdown("## 🎈 **Check results**")
+
+st.header("")
+
+cs, c1, c2, c3, cLast = st.columns([2, 1.5, 1.5, 1.5, 2])
+
+st.header("")
+
+model = load_model()
+df = process_text(doc, model)
+
+df.index += 1
+
+# Add styling
+cmGreen = sns.light_palette("green", as_cmap=True)
+cmRed = sns.light_palette("red", as_cmap=True)
+df = df.style.background_gradient(
+ cmap=cmGreen,
+ subset=[
+ "Grade",
+ ],
+)
+
+
+format_dictionary = {
+ "Relevancy": "{:.1%}",
+}
+
+df = df.format(format_dictionary)
+
+st.table(df)
diff --git a/demo/utils.py b/demo/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a7eebbd74652b8e1ff51ea494c7012002cf3ac2
--- /dev/null
+++ b/demo/utils.py
@@ -0,0 +1,29 @@
+import pandas as pd
+import streamlit as st
+import torch
+
+from src.model_finetuning.config import CONFIG
+from src.model_finetuning.model import BertLightningModel
+from src.utils import get_target_columns
+
+
+@st.cache(allow_output_mutation=True)
+def load_model() -> BertLightningModel:
+
+ ckpt_path = "demo/model.ckpt"
+ model = BertLightningModel.load_from_checkpoint(ckpt_path, config=CONFIG, map_location='cpu')
+
+ return model
+
+
+@torch.no_grad()
+def process_text(_text: str, _model: BertLightningModel) -> pd.DataFrame:
+ tokens = _model.tokenizer([_text], return_tensors='pt')
+ outputs = _model(tokens)[0].tolist()
+
+ df = pd.DataFrame({
+ 'Criterion': get_target_columns(),
+ 'Grade': outputs
+ })
+
+ return df
diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..91f74baec3e61c1ec743f25f02d24b732688c123
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,12 @@
+SPHINXOPTS ?=
+SPHINXBUILD ?= sphinx-build
+SOURCEDIR = source
+BUILDDIR = build
+
+help:
+ @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+%: Makefile
+ @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/images/demo.jpeg b/docs/images/demo.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..16ec10cacc3e34fede3d4e2437eaa58f992b135c
Binary files /dev/null and b/docs/images/demo.jpeg differ
diff --git a/docs/images/logo.png b/docs/images/logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..3aa88346b71ab17e50b2dc3f9234d59568deb65b
Binary files /dev/null and b/docs/images/logo.png differ
diff --git a/docs/make.bat b/docs/make.bat
new file mode 100644
index 0000000000000000000000000000000000000000..6247f7e231716482115f34084ac61030743e0715
--- /dev/null
+++ b/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+ set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+ echo.
+ echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+ echo.installed, then set the SPHINXBUILD environment variable to point
+ echo.to the full path of the 'sphinx-build' executable. Alternatively you
+ echo.may add the Sphinx directory to PATH.
+ echo.
+ echo.If you don't have Sphinx installed, grab it from
+ echo.http://sphinx-doc.org/
+ exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/docs/source/_templates/layout.html b/docs/source/_templates/layout.html
new file mode 100644
index 0000000000000000000000000000000000000000..68fb29a6773e46ea748ea1182b02c908a7b865eb
--- /dev/null
+++ b/docs/source/_templates/layout.html
@@ -0,0 +1,31 @@
+{% extends "!layout.html" %}
+
+{% block menu %}
+ {{ super() }}
+
+ Indices
+
+
+
+ {% if menu_links %}
+
+ External links
+
+
+ {% for text, link in menu_links %}
+ - {{ text }}
+ {% endfor %}
+
+ {% endif %}
+{% endblock %}
+
+{% block htmltitle %}
+ {% if title == '' or title == 'Home' %}
+ {{ docstitle|e }}
+ {% else %}
+ {{ title|striptags|e }}{{ titlesuffix }}
+ {% endif %}
+{% endblock %}
diff --git a/docs/source/_templates/notused_packages.rst_t b/docs/source/_templates/notused_packages.rst_t
new file mode 100644
index 0000000000000000000000000000000000000000..bc8412c1b659c1cc46840fc4ed56d79550be1ee2
--- /dev/null
+++ b/docs/source/_templates/notused_packages.rst_t
@@ -0,0 +1,52 @@
+{%- macro automodule(modname, options) -%}
+.. automodule:: {{ modname }}
+{%- for option in options %}
+ :{{ option }}:
+{%- endfor %}
+{%- endmacro %}
+
+{%- macro toctree(docnames) -%}
+.. toctree::
+ :maxdepth: {{ maxdepth }}
+{% for docname in docnames %}
+ {{ docname }}
+{%- endfor %}
+{%- endmacro %}
+
+{%- if is_namespace %}
+{{- [pkgname, "namespace"] | join(" ") | e | heading }}
+{% else %}
+{{- [pkgname, "package"] | join(" ") | e | heading }}
+{% endif %}
+
+{%- if is_namespace %}
+.. py:module:: {{ pkgname }}
+{% endif %}
+
+{%- if modulefirst and not is_namespace %}
+{{ automodule(pkgname, automodule_options) }}
+{% endif %}
+
+{%- if subpackages %}
+Subpackages
+-----------
+
+{{ toctree(subpackages) }}
+{% endif %}
+
+{%- if submodules %}
+Submodules
+----------
+{% if separatemodules %}
+{{ toctree(submodules) }}
+{% else %}
+{%- for submodule in submodules %}
+{% if show_headings %}
+{{- [submodule, "module"] | join(" ") | e | heading(2) }}
+{% endif %}
+{{ automodule(submodule, automodule_options) }}
+{% endfor %}
+{%- endif %}
+{%- endif %}
+
+# https://github.com/sphinx-doc/sphinx/blob/master/sphinx/templates/apidoc
diff --git a/docs/source/conf.py b/docs/source/conf.py
new file mode 100644
index 0000000000000000000000000000000000000000..8484f486c3d67dcaa67829fd81b09606b0ccfdb5
--- /dev/null
+++ b/docs/source/conf.py
@@ -0,0 +1,78 @@
+# Configuration file for the Sphinx documentation builder.
+import os
+import sys
+
+# Need this so sphinx can find lumache.py. Change is .py files are elsewhere than root.
+sys.path.insert(0, os.path.abspath('../..'))
+sys.path.insert(0, os.path.abspath('../../src'))
+
+# -- Project information
+
+project = 'Linguask'
+copyright = '2022'
+author = 'Multiple'
+
+release = '0.1'
+version = '0.1.0'
+
+# -- General configuration
+
+extensions = [
+ 'sphinx.ext.duration',
+ 'sphinx.ext.doctest',
+ 'sphinx.ext.autodoc',
+ 'sphinx.ext.autosummary',
+ 'sphinx.ext.intersphinx',
+ 'autoapi.extension',
+ # 'sphinxcontrib.apidoc',
+]
+
+autoapi_type = 'python'
+autoapi_dirs = ['../../src']
+
+intersphinx_mapping = {
+ 'python': ('https://docs.python.org/3/', None),
+ 'sphinx': ('https://www.sphinx-doc.org/en/master/', None),
+}
+intersphinx_disabled_domains = ['std']
+
+templates_path = ['_templates']
+
+# -- Options for HTML output
+
+html_theme = 'sphinx_rtd_theme'
+
+# -- Options for EPUB output
+epub_show_urls = 'footnote'
+
+html_context = {
+ "display_github": True, # Integrate GitHub
+ "github_repo": "vitsyn-morgunov-and-nikulin/automatic-essay-evaluator", # Repo name
+ "github_version": "main", # Version
+ "conf_py_path": "docs/source/", # Path in the checkout to the docs root
+}
+
+# -- Options for HTML output -------------------------------------------------
+
+html_short_title = "topobathy"
+html_show_sourcelink = False
+html_show_sphinx = True
+html_show_copyright = True
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+repository_url = "https://github.com/Vitsyn-Morgunov-and-Nikulin/automatic-essay-evaluator"
+html_context = {
+ "menu_links": [
+ (
+ ' Source Code',
+ repository_url,
+ ),
+ (
+ ' License',
+ f"{repository_url}/blob/main/LICENSE",
+ ),
+ ],
+}
diff --git a/docs/source/contribute.rst b/docs/source/contribute.rst
new file mode 100644
index 0000000000000000000000000000000000000000..f8540d0d1d90dbbd91e0f130b9cf4eb71e42238c
--- /dev/null
+++ b/docs/source/contribute.rst
@@ -0,0 +1,12 @@
+How to contribute
+=================
+
+In our development process we followed practices described by Uncle Bob in his magnificent "Clean Code". Please, consult this book in case any trouble.
+
+Make a fork of this repository, and develop your own tool. Make sure it is error-free and the test coverage is at least 60 percent. Update :code:`config` files accordingly, and check their operability.
+
+While producing your code, use this famous `git workflow `_. Also note that our branches use prefixes :code:`feature/`, :code:`fix/`, and :code:`ci-cd/`.
+
+Further, send a pull request. In the comment, write the main features of the tool, the technology stack used, and a brief description of the algorithms. This should be enough for us to accept your code.
+
+To check the quality of the code, we use :code:`flake8` and :code:`codacy`.
diff --git a/docs/source/index.rst b/docs/source/index.rst
new file mode 100644
index 0000000000000000000000000000000000000000..cb364d6048d820643effd4fa5236d26af2cde2c5
--- /dev/null
+++ b/docs/source/index.rst
@@ -0,0 +1,14 @@
+Welcome to Linguask!
+===================================
+
+This page documents our solutions to `Feedback Prize Competition `_.
+
+.. note::
+
+ This project is under active development... Meanwhile, check out our relevant version that is `available on this link `_!
+
+.. toctree::
+ :hidden:
+
+ usage
+ contribute
diff --git a/docs/source/usage.rst b/docs/source/usage.rst
new file mode 100644
index 0000000000000000000000000000000000000000..8d04a7ae9ccebbb89f64edb702f6f771fa689d97
--- /dev/null
+++ b/docs/source/usage.rst
@@ -0,0 +1,26 @@
+Getting Started
+=====
+
+
+.. _prerequisites:
+
+Prerequisites
+------------
+1. GNU `make` utility (`link `_)
+2. Python of version 3.7.13 (`link `_)
+3. Packaging manager `poetry` (`link `_)
+4. At least 2Gb on your hard disk
+
+.. code-block:: console
+
+ poetry lock
+ poetry --no-root install
+
+Run application locally
+----------------
+
+To your delight, it's done via a single command:
+
+.. code-block:: console
+
+ poetry run make build
diff --git a/mypy.ini b/mypy.ini
new file mode 100644
index 0000000000000000000000000000000000000000..173d4701a89600301efe5735577b8fe839e1b507
--- /dev/null
+++ b/mypy.ini
@@ -0,0 +1,7 @@
+[mypy]
+disallow_untyped_defs = False
+ignore_missing_imports = True
+ignore_errors = True
+
+[mypy-src.*]
+ignore_errors = False
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000000000000000000000000000000000000..d0dc053630766fa7bf701a7fc7b89f97eef6b2ae
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,3788 @@
+# This file is automatically @generated by Poetry and should not be changed by hand.
+
+[[package]]
+name = "aiohttp"
+version = "3.8.3"
+description = "Async http client/server framework (asyncio)"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "aiohttp-3.8.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:ba71c9b4dcbb16212f334126cc3d8beb6af377f6703d9dc2d9fb3874fd667ee9"},
+ {file = "aiohttp-3.8.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d24b8bb40d5c61ef2d9b6a8f4528c2f17f1c5d2d31fed62ec860f6006142e83e"},
+ {file = "aiohttp-3.8.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f88df3a83cf9df566f171adba39d5bd52814ac0b94778d2448652fc77f9eb491"},
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+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+typing-extensions = {version = ">=3.7.4", markers = "python_version < \"3.8\""}
+
+[[package]]
+name = "zipp"
+version = "3.11.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "zipp-3.11.0-py3-none-any.whl", hash = "sha256:83a28fcb75844b5c0cdaf5aa4003c2d728c77e05f5aeabe8e95e56727005fbaa"},
+ {file = "zipp-3.11.0.tar.gz", hash = "sha256:a7a22e05929290a67401440b39690ae6563279bced5f314609d9d03798f56766"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"]
+testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "3.7.13"
+content-hash = "a4994a8d9f6c072c68ee9ad5588903b3e0cba45b76549ae66116efc7a868a8c6"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..b86b863a2b8c175214c40bc99bfd00ec65237fb5
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,50 @@
+[tool.pytest.ini_options]
+pythonpath = "."
+
+[tool.poetry]
+name = "automatic-essay-evaluator"
+version = "0.1.0"
+description = "Automated tool for evaluation of natural language texts."
+authors = ["Danis Alukaev "]
+license = "MIT"
+readme = "README.md"
+packages = [{include = "automatic_essay_evaluator"}]
+
+[tool.poetry.dependencies]
+python = "3.7.13"
+flake8 = "^5.0.4"
+hydra-core = "^1.3.0"
+python-dotenv = "^0.21.0"
+coverage = "^6.5.0"
+streamlit = "^1.16.0"
+transformers = {extras = ["sentencepiece"], version = "^4.25.1"}
+sphinx = ">=4.0"
+sphinx-rtd-theme = "^1.1.1"
+sphinx-autoapi = "^2.0.0"
+mypy = "^0.991"
+types-requests = "^2.28.11.5"
+pytest-cov = "^4.0.0"
+
+[tool.poetry.group.dev.dependencies]
+flake8 = "^5.0.4"
+hydra-core = "^1.3.0"
+isort = "^5.11.3"
+kaggle = "^1.5.12"
+numpy = "^1.21.6"
+pre-commit = "^2.20.0"
+pytest = "^7.2.0"
+pytorch-lightning = "^1.8.5.post0"
+tqdm = "^4.64.1"
+torch = "^1.13.1"
+transformers = "^4.25.1"
+pyspellchecker = "^0.7.1"
+catboost = "^1.1.1"
+scikit-learn = "^1.0.2"
+wandb = "^0.13.7"
+easydict = "^1.10"
+nltk = "^3.8"
+seaborn = "^0.12.1"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/config/conf/config.yaml b/src/config/conf/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..71e665d2644ee0401a02345e8b420966272f5385
--- /dev/null
+++ b/src/config/conf/config.yaml
@@ -0,0 +1,11 @@
+defaults:
+ - predictor: bert_with_handcrafted_feature_predictor
+ - validator: cross_validator
+ - _self_
+
+timestamp: ${now:%Y-%m-%d}/${now:%H-%M-%S}
+cwd: ${hydra:runtime.cwd}
+
+hydra:
+ job:
+ chdir: True
diff --git a/src/config/conf/experiment/sanity_bert_finetuning_predictor.yaml b/src/config/conf/experiment/sanity_bert_finetuning_predictor.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..428fba8af4bcc337cdb13b507a4a6acf6264c26f
--- /dev/null
+++ b/src/config/conf/experiment/sanity_bert_finetuning_predictor.yaml
@@ -0,0 +1,19 @@
+# @package _global_
+
+name: sanity checking of BertFinetuningPredictor
+
+defaults:
+ - override /predictor: bert_finetuning_predictor
+
+validator:
+ saving_dir: checkpoints
+
+predictor:
+ batch_size: 8
+ num_workers: 8
+ max_length: 64
+ weight_decay: 0.01
+ accelerator: gpu
+ max_epochs: 2
+ train_size: 0.8
+ num_cross_val_splits: 5
diff --git a/src/config/conf/experiment/sanity_bert_with_handcrafted_feature_predictor.yaml b/src/config/conf/experiment/sanity_bert_with_handcrafted_feature_predictor.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2cf9da4c9cc5c9bc4c4743bbcad989ee94ef8e7a
--- /dev/null
+++ b/src/config/conf/experiment/sanity_bert_with_handcrafted_feature_predictor.yaml
@@ -0,0 +1,13 @@
+# @package _global_
+
+name: sanity checking of BertWithHandcraftedFeaturePredictor
+
+defaults:
+ - override /predictor: bert_with_handcrafted_feature_predictor
+
+validator:
+ saving_dir: checkpoints
+ n_splits: 2
+
+predictor:
+ catboost_iter: 5
diff --git a/src/config/conf/experiment/sanity_constant_predictor.yaml b/src/config/conf/experiment/sanity_constant_predictor.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..07392b5b9f0ae0173aa2cc3918ec99a6c371c0dd
--- /dev/null
+++ b/src/config/conf/experiment/sanity_constant_predictor.yaml
@@ -0,0 +1,9 @@
+# @package _global_
+
+name: sanity checking of ConstantPredictorSolution
+
+defaults:
+ - override /predictor: constant_predictor
+
+validator:
+ saving_dir: checkpoints
diff --git a/src/config/conf/experiment/sanity_many_bert_with_handcrafted_feature_predictor.yaml b/src/config/conf/experiment/sanity_many_bert_with_handcrafted_feature_predictor.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..30fd3b4bf3cf074c82baeed474cd26177066115e
--- /dev/null
+++ b/src/config/conf/experiment/sanity_many_bert_with_handcrafted_feature_predictor.yaml
@@ -0,0 +1,15 @@
+# @package _global_
+
+name: sanity checking of ManyBertWithHandcraftedFeaturePredictor
+
+defaults:
+ - override /predictor: many_bert_with_handcrafted_feature_predictor
+
+validator:
+ saving_dir: checkpoints
+
+predictor:
+ model_names:
+ - bert-base-uncased
+ - bert-base-cased
+ catboost_iter: 5
diff --git a/src/config/conf/predictor/bert_finetuning_predictor.yaml b/src/config/conf/predictor/bert_finetuning_predictor.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c67cccdf834c171a7ad5b963379e4d67bce6d0c3
--- /dev/null
+++ b/src/config/conf/predictor/bert_finetuning_predictor.yaml
@@ -0,0 +1,16 @@
+_target_: src.solutions.bert_finetune_solution.BertFinetuningPredictor
+model_name: microsoft/deberta-v3-large
+num_classes: 6
+lr: 2e-5
+batch_size: 8
+num_workers: 8
+max_length: 512
+weight_decay: 0.01
+accelerator: gpu
+max_epochs: 5
+accumulate_grad_batches: 4
+precision: 16
+gradient_clip_val: 1000
+train_size: 0.8
+num_cross_val_splits: 5
+num_frozen_layers: 20
diff --git a/src/config/conf/predictor/bert_with_handcrafted_feature_predictor.yaml b/src/config/conf/predictor/bert_with_handcrafted_feature_predictor.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1219db78716ee9660f9fc78a3dd482d08e88de93
--- /dev/null
+++ b/src/config/conf/predictor/bert_with_handcrafted_feature_predictor.yaml
@@ -0,0 +1,4 @@
+_target_: src.solutions.bert_featurizer_solution.BertWithHandcraftedFeaturePredictor
+model_name: bert-base-uncased
+catboost_iter: 500
+saving_dir: checkpoints
diff --git a/src/config/conf/predictor/constant_predictor.yaml b/src/config/conf/predictor/constant_predictor.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f9b92996ffce0e4f40384d43f07ab6a8598535bf
--- /dev/null
+++ b/src/config/conf/predictor/constant_predictor.yaml
@@ -0,0 +1 @@
+_target_: src.solutions.constant_predictor.ConstantPredictorSolution
diff --git a/src/config/conf/predictor/many_bert_with_handcrafted_feature_predictor.yaml b/src/config/conf/predictor/many_bert_with_handcrafted_feature_predictor.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6926c75c8059148e9ba509ba79729d4fc2701203
--- /dev/null
+++ b/src/config/conf/predictor/many_bert_with_handcrafted_feature_predictor.yaml
@@ -0,0 +1,10 @@
+_target_: src.solutions.many_bert_solution.ManyBertWithHandcraftedFeaturePredictor
+model_names:
+ - bert-base-uncased
+ - bert-base-cased
+ - vblagoje/bert-english-uncased-finetuned-pos
+ - bert-base-multilingual-cased
+ - unitary/toxic-bert
+ - bert-large-uncased
+catboost_iter: 500
+saving_dir: checkpoints
diff --git a/src/config/conf/validator/cross_validator.yaml b/src/config/conf/validator/cross_validator.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bb8e86c2b850c2620d698f79b8a55fb303ebc2ac
--- /dev/null
+++ b/src/config/conf/validator/cross_validator.yaml
@@ -0,0 +1,3 @@
+_target_: src.cross_validate.CrossValidation
+saving_dir: checkpoints/BertWithHandcraftedFeaturePredictor
+n_splits: 5
diff --git a/src/cross_validate.py b/src/cross_validate.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d146155deae028e1e9cc08e3dd77fe0006806f5
--- /dev/null
+++ b/src/cross_validate.py
@@ -0,0 +1,133 @@
+from copy import deepcopy
+from pathlib import Path
+from typing import Optional, Union
+
+import numpy as np
+import pandas as pd
+import torch.cuda
+from sklearn.model_selection import KFold
+
+from src.metrics import MSEMetric
+from src.solutions.base_solution import BaseSolution
+from src.utils import validate_x, validate_y
+
+
+class CrossValidation:
+ device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+ def __init__(self, saving_dir: str, n_splits: int = 5):
+ _saving_dir = Path(saving_dir)
+
+ self.k_fold = KFold(n_splits=n_splits)
+ self.metric = MSEMetric()
+
+ if not _saving_dir.is_dir():
+ _saving_dir.mkdir(exist_ok=True, parents=True)
+ self.saving_dir = _saving_dir
+ self.base_solution: Optional[BaseSolution] = None
+
+ def fit(self, model: BaseSolution, X: pd.DataFrame, y: pd.DataFrame) -> pd.DataFrame:
+ """Makes average fold prediction
+
+ :param model: predictor from BaseSolution class
+ :param X: Dataframe that has text_id and full_text columns
+ :param y: Dataframe that has text_id, cohesion, ... columns
+ :return: Dataframe with class scores for each split and overall CV score
+ """
+
+ validate_x(X)
+ validate_y(y)
+
+ scores = []
+ self.base_solution = model
+ for ii, (train_ind, test_ind) in enumerate(self.k_fold.split(X)):
+ print(f"Training fold={ii}...")
+ X_train, X_test = X.iloc[train_ind], X.iloc[test_ind]
+ y_train, y_test = y.iloc[train_ind], y.iloc[test_ind]
+
+ training_model = deepcopy(model)
+ training_model.fit(X_train, y_train, val_X=X_test, val_y=y_test, fold=ii)
+
+ y_pred = training_model.predict(X_test)
+ class_rmse = self.metric.evaluate_class_rmse(y_pred, y_test)
+ scores.append(class_rmse)
+
+ training_model.save(self.saving_dir / f"cv_fold_{ii}")
+
+ del training_model
+
+ _scores = pd.DataFrame(scores)
+ mean_values = [_scores.mean(axis='rows').values.tolist()]
+ overall = pd.DataFrame(mean_values, columns=_scores.columns, index=['overall'])
+ print("\n")
+
+ _scores = pd.concat([_scores, overall], axis='rows')
+ return _scores
+
+ def predict(self, X: pd.DataFrame) -> pd.DataFrame:
+ """Makes average fold prediction
+
+ :param X: Dataframe that have text_id and full_text columns
+ :return: prediction Dataframe that have text_id, cohesion, ... columns
+ """
+ assert list(self.saving_dir.iterdir()) is not [], "Cross validation is not trained yet"
+
+ validate_x(X)
+
+ predictions = []
+ for ii in range(self.k_fold.n_splits):
+ model_path = self.saving_dir / f"cv_fold_{ii}"
+
+ if not self.base_solution:
+ raise TypeError
+ model = deepcopy(self.base_solution)
+ model.load(model_path)
+ pred = model.predict(X)
+ predictions.append(pred)
+
+ mean_class_predictions = {}
+ for column in ['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']:
+ values = [item[column].values for item in predictions]
+ mean_pred = np.mean(values, axis=0)
+ mean_class_predictions[column] = mean_pred
+
+ mean_class_predictions = pd.DataFrame(mean_class_predictions)
+
+ X = X.copy().drop(columns=['full_text'])
+ X = pd.concat([X, mean_class_predictions], axis='columns')
+
+ return X
+
+ def save(self, path: Union[str, Path]):
+ path = Path(path)
+ if not path.is_dir():
+ path.mkdir(parents=True)
+
+ if not self.base_solution or not self.base_solution.models:
+ raise TypeError
+
+ for ii, model in enumerate(self.base_solution.models):
+ cv_model_path = path / f"cv_fold_{ii}"
+ model.save(cv_model_path)
+
+ print(f"Saved weights successfully to: {path.resolve()}.")
+
+ def load(self, path: Union[str, Path], predictor: BaseSolution):
+ path = Path(path)
+
+ assert path.is_dir(), f"Weights dir. not exists: {path.resolve()}"
+
+ for ii in range(self.k_fold.n_splits):
+ cv_model_path = path / f"cv_fold_{ii}"
+
+ assert cv_model_path.is_dir(), f"Dir. with fold={ii} not exists: {cv_model_path.resolve()}"
+
+ predictor_copy = deepcopy(predictor)
+ predictor_copy.load(cv_model_path)
+
+ if not self.base_solution or not self.base_solution.models:
+ raise TypeError
+
+ self.base_solution.models.append(predictor_copy)
+
+ print(f"Loaded model successfully from: {path.resolve()}.")
diff --git a/src/data_reader.py b/src/data_reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9b22320b31c48c9d90b323ecde83b55da545a46
--- /dev/null
+++ b/src/data_reader.py
@@ -0,0 +1,42 @@
+import os
+from pathlib import Path
+from typing import Tuple
+
+import pandas as pd
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+def set_env_if_kaggle_environ() -> None:
+ if 'KAGGLE_DATA_PROXY_TOKEN' in os.environ:
+ os.environ['DATA_PATH'] = '/kaggle/input/feedback-prize-english-language-learning/'
+
+
+def load_train_test_df(is_testing: bool = False) -> Tuple[pd.DataFrame, pd.DataFrame]:
+ """Loads train/test dataframes
+
+ :param is_testing: If set to true, load subsample of train/test dataframes
+ :return Train and test dataframes
+
+ """
+ set_env_if_kaggle_environ()
+
+ if is_testing:
+ train_df_path = Path("tests/data/train_sample.csv")
+ test_df_path = Path("tests/data/test_sample.csv")
+
+ else:
+ train_df_path = Path(os.environ['DATA_PATH']) / 'train.csv'
+ test_df_path = Path(os.environ['DATA_PATH']) / 'test.csv'
+
+ if not test_df_path.is_file():
+ raise OSError(f"File not found: {test_df_path.absolute()}")
+
+ if not train_df_path.is_file():
+ raise OSError(f"File not found: {train_df_path.absolute()}")
+
+ train_df = pd.read_csv(train_df_path)
+ test_df = pd.read_csv(test_df_path)
+
+ return train_df, test_df
diff --git a/src/feature_extractors/base_extractor.py b/src/feature_extractors/base_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc3b354c144955684dd855b687e819ba4d38df56
--- /dev/null
+++ b/src/feature_extractors/base_extractor.py
@@ -0,0 +1,12 @@
+import pandas as pd
+
+
+class BaseExtractor:
+
+ def generate_features(self, data: pd.Series) -> pd.DataFrame:
+ """Generates features for model
+
+ :param X: Series, that contains texts
+ :return: Dataframe with columns - features, generated from text
+ """
+ raise NotImplementedError(f"Abstract class {type(self).__name__} is used")
diff --git a/src/feature_extractors/bert_pretrain_extractor.py b/src/feature_extractors/bert_pretrain_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..a55678aea4da990755055e186045da2bcb287353
--- /dev/null
+++ b/src/feature_extractors/bert_pretrain_extractor.py
@@ -0,0 +1,82 @@
+from typing import List
+
+import pandas as pd
+import torch
+from tqdm import trange
+from transformers import AutoModel, AutoTokenizer
+
+from src.feature_extractors.base_extractor import BaseExtractor
+
+
+class BertPretrainFeatureExtractor(BaseExtractor):
+ """Extract [CLS] embedding feature from any untrained bert-like models"""
+ device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+ def __init__(self, model_name: str, max_length: int = 512, batch_size=64):
+ self.model_name = model_name
+ self.max_length = max_length
+ self.batch_size = batch_size
+
+ self.model = AutoModel.from_pretrained(self.model_name)
+ self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+
+ @torch.no_grad()
+ def generate_features(self, data: pd.Series) -> pd.DataFrame:
+ """
+ Generates features in batch-mode, obtained from untrained bert model.
+
+ :param data: Series with full_text column
+ :return: Dataframe, that have index - id's from data, and columns - bert features
+ """
+ torch.cuda.empty_cache()
+
+ texts = data.tolist()
+ self.model = self.model.to(self.device)
+
+ classification_outputs = []
+ for ii in trange(
+ 0, len(data), self.batch_size,
+ total=len(data) // self.batch_size + 1,
+ desc="Generating bert features..."
+ ):
+ text_batch = texts[ii: ii + self.batch_size]
+ batch_encoded = self.tokenizer(
+ text_batch,
+ padding=True,
+ truncation=True,
+ max_length=self.max_length,
+ return_tensors='pt'
+ ).to(self.device)
+
+ output = self.model(**batch_encoded)
+ cls_output = output['last_hidden_state'][:, 0].cpu()
+ classification_outputs.append(cls_output)
+
+ self.model = self.model.to("cpu")
+ classification_outputs_tensor = torch.cat(classification_outputs, dim=0)
+ torch.cuda.empty_cache()
+ column_names = [f"{self.model_name}_feat_{ii}" for ii in range(len(classification_outputs_tensor[0]))]
+ return pd.DataFrame(
+ data=classification_outputs_tensor.tolist(),
+ index=data.index,
+ columns=column_names
+ )
+
+
+class ManyBertPretrainFeatureExtractor(BaseExtractor):
+ def __init__(self, model_names: List[str], max_length: int = 512, batch_size=64):
+ super(ManyBertPretrainFeatureExtractor, self).__init__()
+ self.model_names = model_names
+ self.max_length = max_length
+ self.batch_size = batch_size
+
+ def generate_features(self, X: pd.Series) -> pd.DataFrame:
+ extractors = [
+ BertPretrainFeatureExtractor(model_name, self.max_length, self.batch_size)
+ for model_name in self.model_names
+ ]
+ dataframes = [
+ extractor.generate_features(X) for extractor in extractors
+ ]
+
+ return pd.concat(dataframes, axis='columns')
diff --git a/src/feature_extractors/term_frequency_feature_extractor.py b/src/feature_extractors/term_frequency_feature_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..0086991a2241d7304fecb453e6d1559bb37bc472
--- /dev/null
+++ b/src/feature_extractors/term_frequency_feature_extractor.py
@@ -0,0 +1,75 @@
+from collections import defaultdict
+from typing import Dict, List
+
+import nltk
+import numpy as np
+import pandas as pd
+
+from src.feature_extractors.base_extractor import BaseExtractor
+
+
+class TermFrequencyFeatureExtractor(BaseExtractor):
+ """Build a dataframe with a distribution of term frequencies
+
+ Usage:
+ >>> data = pd.read_csv("data/raw/train.csv").set_index("text_id")
+ >>> featurizer = TermFrequencyFeaturizer()
+ >>> X = featurizer.featurize(data.full_text)
+ >>> y = data["vocabulary"]
+ >>> model = catboost.CatBoostRegressor()
+ >>> model.fit(x_train, y_train)
+
+ Possible improvements:
+ - Add word corrections: triying -> trying
+ - Count not only word frequencies, but number of unique words in each hist bin
+ """
+
+ MAX_TERM_FREQUENCY = 23135751162 # it's so in the term frequency dataset
+
+ def __init__(self, n_bins: int = 40):
+ self.term2freq: Dict[str, int] = self._load_term2freq_dict()
+ self.bins = self._make_bins(n_bins)
+ self.feature_names = [
+ f"bin_{round(self.bins[i], 1)}_{round(self.bins[i+1], 1)}"
+ for i in range(len(self.bins) - 1)
+ ]
+ nltk.download("punkt")
+
+ def _make_bins(self, n_bins: int) -> np.ndarray:
+ min_bin = 0
+ max_bin = np.log1p(self.MAX_TERM_FREQUENCY)
+ bins = np.linspace(min_bin, max_bin, n_bins)
+ return bins
+
+ def _load_term2freq_dict(self) -> Dict[str, int]:
+ term_frequencies = pd.read_csv("data/word_frequencies/unigram_freq.csv")
+ term2freq: Dict[str, int] = defaultdict(lambda: 0)
+ term2freq.update(term_frequencies.set_index("word").to_dict()["count"])
+ return term2freq
+
+ def generate_features(self, data: pd.Series) -> pd.DataFrame:
+ """Extracts features from the text in the form of histogram of word frequencies
+
+ Logarithm operation is applied to the frequencies for the sake of distribution
+ normality.
+ """
+ feature_df = data.apply(self._compute_word_frequency_histogram)
+ feature_df.columns = self.feature_names
+ return feature_df
+
+ def _compute_word_frequency_histogram(self, text: str) -> pd.Series:
+ term_frequencies: List[int] = self._compute_term_frequencies_from_text(text)
+ histogram_values: np.ndarray = self._build_histogram(term_frequencies)
+ return pd.Series(histogram_values)
+
+ def _compute_term_frequencies_from_text(self, text: str) -> List[int]:
+ tokens = nltk.tokenize.word_tokenize(text)
+ words = [token.lower() for token in tokens if token.isalpha()]
+ word_frequencies = [self.term2freq[word] for word in words]
+ return word_frequencies
+
+ def _build_histogram(self, values: List[int]) -> np.ndarray:
+ values_log = np.log1p(values)
+ histogram, __ = np.histogram(values_log, bins=self.bins)
+ normalized_histogram = histogram / len(values)
+ return normalized_histogram
diff --git a/src/feature_extractors/text_statistics_extractor.py b/src/feature_extractors/text_statistics_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fa44755ac418308ef1fd070dba9f036b0dec6ac
--- /dev/null
+++ b/src/feature_extractors/text_statistics_extractor.py
@@ -0,0 +1,33 @@
+from functools import reduce
+from typing import Dict, List
+
+import pandas as pd
+from tqdm import tqdm
+
+from src.feature_extractors.base_extractor import BaseExtractor
+from src.feature_extractors.text_statistics_utils import (
+ count_how_many_words_are_repeating, count_misspelled_words,
+ count_punctuation, count_words, preprocess_test)
+from src.spell_checker import SmartSpellChecker
+
+
+class HandcraftedTextFeatureExtractor(BaseExtractor):
+ def __init__(self, spellcheck: SmartSpellChecker):
+ self._spellcheck = spellcheck
+
+ def _generate_features(self, raw_text: str) -> Dict[str, int]:
+ preprocessed_text = preprocess_test(raw_text)
+ cleaned_text = self._spellcheck.correct_text(preprocessed_text)
+
+ features: List[Dict] = [
+ count_punctuation(raw_text),
+ count_misspelled_words(preprocessed_text, self._spellcheck),
+ count_words(cleaned_text),
+ count_how_many_words_are_repeating(cleaned_text)
+ ]
+
+ return reduce(lambda x, y: {**x, **y}, features)
+
+ def generate_features(self, data: pd.Series) -> pd.DataFrame:
+ features = [self._generate_features(text) for text in tqdm(data, desc="Gen. handcraft text features...")]
+ return pd.DataFrame(features, index=data.index)
diff --git a/src/feature_extractors/text_statistics_utils.py b/src/feature_extractors/text_statistics_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a00b7ec4c3275eb867e6361376f6b7095b4b635
--- /dev/null
+++ b/src/feature_extractors/text_statistics_utils.py
@@ -0,0 +1,84 @@
+"""This file contains functions that can generates hand-crafted features from the text"""
+
+__all__ = [
+ 'count_words',
+ 'count_punctuation',
+ 'count_how_many_words_are_repeating',
+ 'count_misspelled_words',
+ 'preprocess_test'
+]
+
+import re
+from collections import Counter
+from string import punctuation
+from typing import Dict
+
+from src.spell_checker import SmartSpellChecker
+
+underscores_to_replace = {
+ 'Generic_Name': 'name',
+ 'OTHER_NAME': 'name',
+ 'STUDENT_NAME': 'name',
+ 'Generic_Namea': 'name',
+ 'PROPER_NAME': 'proper name',
+ 'PROEPR_NAME': 'proper name ',
+ 'Generic_School': 'school',
+ 'SCHOOL_NAME': 'school',
+ 'Generic_school': 'school',
+ 'TEACHER_NAME': 'teacher',
+ 'Generic_City': 'city',
+ 'LOCATION_NAME': 'location',
+ 'STORE_NAME': 'store',
+ 'RESTAURANT_NAME': 'restaurant',
+ 'LANGUAGE_NAME': 'language',
+}
+
+
+def preprocess_test(text: str) -> str:
+ # Removes digits, special signs, double spaces and tabulation, underscores
+ for key, value in underscores_to_replace.items():
+ text = text.replace(key, value)
+
+ text = re.sub(r"[\d%@\\#$&^\"_()*+\-/]", " ", text)
+ text = re.sub(r"\n|\t", " ", text)
+ text = re.sub(r'(?<=[.,:;!?])(?=\S)', " ", text) # Add space after punctuation
+ text = re.sub(r"\s+", " ", text)
+
+ return text
+
+
+def get_word_counter(text: str) -> Dict[str, int]:
+ # removes punctuation and count words in sentence
+ text = re.sub(r"[.,!?;:]", " ", text)
+
+ return Counter(text.split())
+
+
+def count_punctuation(text: str) -> Dict[str, int]:
+ features = {}
+
+ for symbol in (punctuation + " "):
+ features[f'count_{symbol}'] = text.count(symbol)
+
+ return features
+
+
+def count_how_many_words_are_repeating(text: str) -> Dict[str, int]:
+ word_count = get_word_counter(text)
+ features = {}
+
+ # For each text count how many unique words repeated >= times
+ for ii in range(3, 10):
+ n_words_repeated = len([word for word in word_count if word_count[word] >= ii])
+ features[f'{ii}_word_repeated'] = n_words_repeated
+
+ return features
+
+
+def count_misspelled_words(text: str, spellcheck: SmartSpellChecker) -> Dict[str, int]:
+ unknown_words = spellcheck.unknown(get_word_counter(text))
+ return {'n_misspelled_words': len(unknown_words)}
+
+
+def count_words(text: str) -> Dict[str, int]:
+ return {'length': len(get_word_counter(text))}
diff --git a/src/kaggle.ipynb b/src/kaggle.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..53944b002ecb38569a8fd75d9ed253dcd3af50ef
--- /dev/null
+++ b/src/kaggle.ipynb
@@ -0,0 +1,99 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from pathlib import Path\n",
+ "from typing import Optional, Union\n",
+ "\n",
+ "import pandas as pd\n",
+ "from dotenv import load_dotenv\n",
+ "\n",
+ "\n",
+ "load_dotenv()\n",
+ "\n",
+ "from pathlib import Path\n",
+ "from typing import Optional\n",
+ "\n",
+ "import pandas as pd\n",
+ "\n",
+ "\n",
+ "def set_env_if_kaggle_environ():\n",
+ " if 'KAGGLE_DATA_PROXY_TOKEN' in os.environ:\n",
+ " os.environ['DATA_PATH'] = '/kaggle/input/feedback-prize-english-language-learning/'\n",
+ "\n",
+ "\n",
+ "class ConstantPredictorSolution():\n",
+ " def __init__(self, config: Optional[dict] = None):\n",
+ " super().__init__()\n",
+ "\n",
+ " def fit(self, X: pd.DataFrame, y: pd.DataFrame):\n",
+ " pass\n",
+ "\n",
+ " def predict(self, X: pd.DataFrame) -> pd.DataFrame:\n",
+ " submission_df = []\n",
+ "\n",
+ " for _, row in X.iterrows():\n",
+ " submission_df.append({\n",
+ " 'text_id': row.text_id,\n",
+ " 'cohesion': 3.0,\n",
+ " 'syntax': 3.0,\n",
+ " 'vocabulary': 3.0,\n",
+ " 'phraseology': 3.0,\n",
+ " 'grammar': 3.0,\n",
+ " 'conventions': 3.0\n",
+ " })\n",
+ "\n",
+ " return pd.DataFrame(submission_df)\n",
+ "\n",
+ " def save(self, directory: Union[str,Path]):\n",
+ " pass\n",
+ "\n",
+ " def load(self, directory: Union[str, Path]):\n",
+ " pass\n",
+ "\n",
+ "\n",
+ "def main():\n",
+ " set_env_if_kaggle_environ()\n",
+ "\n",
+ " test_df_path = Path(os.environ['DATA_PATH']) / 'test.csv'\n",
+ " if not test_df_path.is_file():\n",
+ " raise OSError(f\"File not found: {test_df_path.absolute()}\")\n",
+ "\n",
+ " predictor = ConstantPredictorSolution()\n",
+ "\n",
+ " test_df = pd.read_csv(test_df_path)\n",
+ " submission_df = predictor.predict(test_df)\n",
+ "\n",
+ " submission_df.to_csv(\"submission.csv\", index=False)\n",
+ "\n",
+ "\n",
+ "if __name__ == '__main__':\n",
+ " main()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.6 ('essay')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python",
+ "version": "3.10.6"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "d78f2c2de95f7b748174daf85b42b570c91869441d974f177c5a7efccbbdf121"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/kernel-metadata.json b/src/kernel-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..570fdf07d4c2b89e26e14bcc77836585828fc7a5
--- /dev/null
+++ b/src/kernel-metadata.json
@@ -0,0 +1,13 @@
+{
+ "id": "alukaevdanis/feedback-prize-english-language-learning",
+ "title": "feedback-prize-english-language-learning",
+ "code_file": "kaggle.ipynb",
+ "language": "python",
+ "kernel_type": "notebook",
+ "is_private": "true",
+ "enable_gpu": "true",
+ "enable_internet": "false",
+ "dataset_sources": [],
+ "competition_sources": ["feedback-prize-english-language-learning"],
+ "kernel_sources": []
+}
diff --git a/src/main.py b/src/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..598a4f7a3cd3bfcc1eeb3f3df80e64e4ef8d3c5b
--- /dev/null
+++ b/src/main.py
@@ -0,0 +1,52 @@
+import traceback
+
+import hydra
+from hydra.utils import instantiate
+from omegaconf import DictConfig
+from transformers import logging as transformer_log
+
+from src.data_reader import load_train_test_df
+from src.utils import (get_x_columns, pretty_cfg, report_to_telegram,
+ save_experiment, seed_everything)
+
+seed_everything()
+transformer_log.set_verbosity_error()
+
+
+def run(cfg):
+ predictor = instantiate(cfg.predictor)
+ validator = instantiate(cfg.validator)
+
+ train_df, test_df = load_train_test_df()
+ x_columns = get_x_columns()
+ train_x, train_y = train_df[x_columns], train_df.drop(columns=['full_text'])
+
+ results = validator.fit(predictor, train_x, train_y)
+
+ cv_mean = results.iloc[len(results) - 1].mean()
+ print("CV results")
+ print(results)
+ print(f"CV mean: {cv_mean}")
+
+ submission_df = validator.predict(test_df)
+
+ save_experiment(cfg, submission_df, results, validator.saving_dir)
+
+ return cv_mean
+
+
+@hydra.main(version_base=None, config_path="config/conf", config_name="config")
+def main(cfg: DictConfig):
+ try:
+ metric = run(cfg)
+ message = f"✅ Successful run from {cfg.timestamp}!\n\n"
+ message += f"Metric: {metric:.5f} MCRMSE\n\n"
+ message += f"Configuration:\n{pretty_cfg(cfg)}"
+ except Exception:
+ message = f"🚫 Run from {cfg.timestamp} failed!\n\n"
+ message += traceback.format_exc()
+ report_to_telegram(message)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/metrics.py b/src/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..3af34c46fb35dab0cd21d421103535da8458cc71
--- /dev/null
+++ b/src/metrics.py
@@ -0,0 +1,28 @@
+from typing import Dict
+
+import pandas as pd
+from sklearn.metrics import mean_squared_error
+
+from src.utils import validate_y
+
+
+class MSEMetric:
+ def __init__(self):
+ super().__init__()
+
+ @staticmethod
+ def evaluate_class_rmse(y_pred: pd.DataFrame, y_true: pd.DataFrame) -> Dict[str, float]:
+ validate_y(y_pred)
+ validate_y(y_true)
+
+ result = {}
+
+ for column in y_pred.drop(columns=['text_id']).columns:
+ result[column] = mean_squared_error(y_pred[column], y_true[column], squared=False)
+
+ return result
+
+ def evaluate(self, y_pred: pd.DataFrame, y_true: pd.DataFrame) -> float:
+ result = self.evaluate_class_rmse(y_pred, y_true)
+
+ return sum(result.values()) / len(result)
diff --git a/src/model_finetuning/config.py b/src/model_finetuning/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe8444990a42071129ce5f00b48fc29efe5aca44
--- /dev/null
+++ b/src/model_finetuning/config.py
@@ -0,0 +1,21 @@
+CONFIG = dict(
+ model_name="microsoft/deberta-v3-large",
+ num_classes=6,
+ lr=2e-5,
+
+ batch_size=8,
+ num_workers=8,
+ max_length=512,
+ weight_decay=0.01,
+
+ accelerator='gpu',
+ max_epochs=5,
+ accumulate_grad_batches=4,
+ precision=16,
+ gradient_clip_val=1000,
+ train_size=0.8,
+ num_cross_val_splits=5,
+ num_frozen_layers=20, # out of 24 in deberta
+)
+
+# we can also try nn.SmoothL1Loss
diff --git a/src/model_finetuning/dataloader.py b/src/model_finetuning/dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..c93b59ebc284b4cd97f17766b611d9689b36faec
--- /dev/null
+++ b/src/model_finetuning/dataloader.py
@@ -0,0 +1,96 @@
+import pandas as pd
+import pytorch_lightning as pl
+import torch
+from torch.utils.data import DataLoader, Dataset
+from transformers import BertTokenizer
+
+from src.utils import get_target_columns
+
+
+def collate_fn(data):
+ input_ids = []
+ token_type_ids = []
+ attention_mask = []
+ labels = []
+
+ for item in data:
+ input_ids.append(item['input_ids'].squeeze())
+ token_type_ids.append(item['token_type_ids'].squeeze())
+ attention_mask.append(item['attention_mask'].squeeze())
+ labels.append(item['labels'].squeeze())
+
+ return {
+ "input_ids": torch.stack(input_ids),
+ 'token_type_ids': torch.stack(token_type_ids),
+ 'attention_mask': torch.stack(attention_mask),
+ 'labels': torch.stack(labels)
+ }
+
+
+class ClassificationDataset(Dataset):
+ def __init__(self, tokenizer: BertTokenizer, df: pd.DataFrame, config: dict):
+ self.config = config
+ self.tokenizer = tokenizer
+
+ self.df = df
+
+ self.features = self.tokenizer(
+ text=df.full_text.tolist(),
+ max_length=self.config['max_length'],
+ padding=True,
+ truncation=True,
+ return_attention_mask=True,
+ add_special_tokens=True,
+ return_tensors='pt',
+ )
+ if 'cohesion' in self.df.columns:
+ self.features['labels'] = torch.as_tensor(df[get_target_columns()].values, dtype=torch.float32)
+ else:
+ data = torch.ones(size=(len(df), 6), dtype=torch.float32) * -1.
+ self.features['labels'] = data
+
+ def __getitem__(self, item):
+ """Returns dict with input_ids, token_type_ids, attention_mask, labels
+ """
+ return {
+ 'input_ids': self.features['input_ids'][item],
+ 'token_type_ids': self.features['token_type_ids'][item],
+ 'attention_mask': self.features['attention_mask'][item],
+ 'labels': self.features['labels'][item]
+ }
+
+ def __len__(self):
+ return len(self.df)
+
+
+class ClassificationDataloader(pl.LightningDataModule):
+ def __init__(
+ self,
+ tokenizer: BertTokenizer,
+ train_df: pd.DataFrame,
+ val_df: pd.DataFrame,
+ config: dict
+ ):
+ super().__init__()
+ self.config = config
+
+ self.train_data = ClassificationDataset(tokenizer, train_df, config)
+ self.val_data = ClassificationDataset(tokenizer, val_df, config)
+
+ def train_dataloader(self):
+ return DataLoader(
+ dataset=self.train_data,
+ shuffle=True,
+ batch_size=self.config['batch_size'],
+ num_workers=self.config['num_workers'],
+ collate_fn=collate_fn
+ )
+
+ def val_dataloader(self):
+ return DataLoader(
+ dataset=self.val_data,
+ shuffle=False,
+ batch_size=self.config['batch_size'],
+ num_workers=self.config['num_workers'],
+ collate_fn=collate_fn
+ )
diff --git a/src/model_finetuning/losses.py b/src/model_finetuning/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..aeb49456cead84cd4ed8667c1c29e673f27675af
--- /dev/null
+++ b/src/model_finetuning/losses.py
@@ -0,0 +1,29 @@
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+
+class MCRMSELoss(nn.Module):
+
+ def __init__(self):
+ super(MCRMSELoss, self).__init__()
+ self.mse = nn.MSELoss(reduction='none')
+
+ def forward(self, y_pred: Tensor, y_true: Tensor):
+ """Calculate mean column-wise rmse on columns
+
+ :param y_pred: tensor of shape (bs, 6)
+ :param y_true: tensor of shape (bs, 6)
+ :return: tensor of shape 0 (scalar with grad)
+ """
+
+ mse = self.mse(y_pred, y_true).mean(0) # column-wise mean
+ rmse = torch.sqrt(mse + 1e-7)
+
+ return rmse.mean()
+
+ def class_mcrmse(self, y_pred: Tensor, y_true: Tensor):
+ mse = self.mse(y_pred, y_true).mean(0) # column-wise mean
+ rmse = torch.sqrt(mse + 1e-7)
+
+ return rmse.squeeze()
diff --git a/src/model_finetuning/model.py b/src/model_finetuning/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1c0b7f680e72d5869e5b25b4146676b9988ccff
--- /dev/null
+++ b/src/model_finetuning/model.py
@@ -0,0 +1,134 @@
+"""
+ Copy pasted model from https://www.kaggle.com/code/yasufuminakama/fb3-deberta-v3-base-baseline-train/notebook
+"""
+import pytorch_lightning as pl
+import torch
+import torch.nn as nn
+from torch.optim import AdamW
+from torch.optim.lr_scheduler import CosineAnnealingLR
+from transformers import AutoConfig, AutoModel, AutoTokenizer
+
+from src.data_reader import load_train_test_df
+from src.model_finetuning.losses import MCRMSELoss
+
+
+def num_train_samples():
+ train_df, _ = load_train_test_df()
+ return len(train_df)
+
+
+class MeanPooling(nn.Module):
+ # taking mean of last hidden state with mask
+
+ def forward(self, last_hidden_state, attention_mask):
+ input_mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()
+ sum_embeddings = torch.sum(last_hidden_state * input_mask_expanded, 1)
+ sum_mask = input_mask_expanded.sum(1)
+ sum_mask = torch.clamp(sum_mask, min=1e-9)
+ mean_embeddings = sum_embeddings / sum_mask
+ return mean_embeddings
+
+
+class BertLightningModel(pl.LightningModule):
+
+ def __init__(self, config: dict):
+ super(BertLightningModel, self).__init__()
+
+ self.config = config
+
+ huggingface_config = AutoConfig.from_pretrained(self.config['model_name'], output_hidden_states=True)
+ huggingface_config.hidden_dropout = 0.
+ huggingface_config.hidden_dropout_prob = 0.
+ huggingface_config.attention_dropout = 0.
+ huggingface_config.attention_probs_dropout_prob = 0.
+
+ self.tokenizer = AutoTokenizer.from_pretrained(self.config['model_name'])
+ self.model = AutoModel.from_pretrained(self.config['model_name'], config=huggingface_config)
+
+ self.pool = MeanPooling()
+
+ self.fc = nn.Linear(in_features=1024, out_features=6)
+
+ self.loss = MCRMSELoss()
+
+ # freezing first 20 layers of DeBERTa from 24
+ modules = [self.model.embeddings, self.model.encoder.layer[:self.config['num_frozen_layers']]]
+ for module in modules:
+ for param in module.parameters():
+ param.requires_grad = False
+
+ self.class_metric = None
+ self.best_metric = None
+
+ def forward(self, inputs):
+ outputs = self.model(**inputs)
+ last_hidden_state = outputs.last_hidden_state
+
+ bert_features = self.pool(last_hidden_state, inputs['attention_mask'])
+
+ logits = self.fc(bert_features)
+
+ return logits
+
+ def training_step(self, batch, batch_idx):
+ inputs = batch
+ labels = inputs.pop("labels", None)
+ logits = self(inputs)
+ loss = self.loss(logits, labels)
+
+ self.log('train/loss', loss)
+
+ return {
+ 'loss': loss,
+ 'mc_rmse': loss
+ }
+
+ def training_epoch_end(self, outputs):
+ mean_mc_rmse = sum(output['mc_rmse'].item() for output in outputs) / len(outputs)
+ self.log("train/epoch_loss", mean_mc_rmse)
+
+ def validation_step(self, batch, batch_idx):
+ inputs = batch
+ labels = inputs.pop("labels", None)
+ logits = self(inputs)
+ loss = self.loss(logits, labels)
+ class_rmse = self.loss.class_mcrmse(logits, labels)
+
+ self.log('val/loss', loss)
+
+ return {
+ 'loss': loss,
+ 'mc_rmse': loss,
+ 'class_mc_rmse': class_rmse
+ }
+
+ def validation_epoch_end(self, outputs):
+ mean_mc_rmse = sum(output['mc_rmse'].item() for output in outputs) / len(outputs)
+ class_metrics = torch.stack([output['class_mc_rmse'] for output in outputs]).mean(0).tolist()
+ class_metrics = [round(item, 4) for item in class_metrics]
+ self.log('val/epoch_loss', mean_mc_rmse)
+
+ if self.best_metric is None or mean_mc_rmse < self.best_metric:
+ self.best_metric = mean_mc_rmse
+ self.class_metric = class_metrics
+
+ def configure_optimizers(self):
+ # weight_decay = self.config['weight_decay']
+ lr = self.config['lr']
+
+ # In original solution authors add weight decaying to some parameters
+
+ optimizer = AdamW(self.parameters(), lr=lr, weight_decay=0.0, eps=1e-6, betas=(0.9, 0.999))
+
+ scheduler = CosineAnnealingLR(
+ optimizer,
+ T_max=self.config['max_epochs'],
+ )
+ return [optimizer], [scheduler]
+
+ def predict_step(self, batch, batch_idx: int, dataloader_idx: int = 0):
+ inputs = batch
+ inputs.pop("labels", None)
+ logits = self(inputs)
+
+ return logits
diff --git a/src/model_finetuning/train.py b/src/model_finetuning/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bbe7247e55a3ed88b839e905124aa134d785633
--- /dev/null
+++ b/src/model_finetuning/train.py
@@ -0,0 +1,127 @@
+import os
+from pathlib import Path
+
+import pandas as pd
+import pytorch_lightning as pl
+import torch
+import wandb
+from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
+from pytorch_lightning.loggers import WandbLogger # noqa
+from sklearn.model_selection import train_test_split
+from torch import Tensor
+
+from src.data_reader import load_train_test_df
+from src.model_finetuning.config import CONFIG
+from src.model_finetuning.dataloader import ClassificationDataloader
+from src.model_finetuning.losses import MCRMSELoss
+from src.model_finetuning.model import BertLightningModel
+from src.utils import get_target_columns, seed_everything
+
+seed_everything()
+
+
+def train(
+ config: dict,
+ train_df: pd.DataFrame,
+ val_df: pd.DataFrame,
+ verbose: bool = False
+) -> BertLightningModel:
+ log_dir = Path("logs/")
+ log_dir.mkdir(exist_ok=True)
+
+ model = BertLightningModel(config)
+
+ dataloader = ClassificationDataloader(
+ tokenizer=model.tokenizer,
+ train_df=train_df,
+ val_df=val_df,
+ config=config
+ )
+
+ logger = WandbLogger(
+ project="automated_essay_evaluator",
+ entity="parmezano",
+ config=config,
+ log_model='all',
+ group=os.environ['GROUP_NAME'],
+ )
+
+ if not wandb.run:
+ raise TypeError
+
+ wandb.run.log_code(".")
+ wandb.watch(model, criterion=MCRMSELoss())
+
+ lr_monitor = LearningRateMonitor(logging_interval='step')
+
+ model_checkpoint = ModelCheckpoint(
+ dirpath=str(log_dir.resolve()),
+ monitor='val/epoch_loss',
+ verbose=True,
+ mode='min',
+ auto_insert_metric_name=True,
+ save_weights_only=True,
+ )
+
+ trainer = pl.Trainer(
+ logger=logger,
+ callbacks=[lr_monitor, model_checkpoint],
+ accelerator=config['accelerator'],
+ max_epochs=config['max_epochs'],
+ accumulate_grad_batches=config['accumulate_grad_batches'],
+ gradient_clip_val=config['gradient_clip_val'],
+ precision=config['precision'],
+
+ # this is for debug
+ # max_epochs=1,
+ # limit_train_batches=1,
+ # limit_val_batches=1,
+ # limit_predict_batches=1,
+ )
+ train_dataloader = dataloader.train_dataloader()
+ val_dataloader = dataloader.val_dataloader()
+ trainer.fit(model, train_dataloader, val_dataloader)
+ wandb.finish()
+
+ if verbose:
+ print(f"Best class metric: {model.best_metric}, class scores: {model.class_metric}")
+
+ model = BertLightningModel.load_from_checkpoint(model_checkpoint.best_model_path, config=config)
+ return model
+
+
+def predict(config: dict, model: BertLightningModel, df: pd.DataFrame) -> pd.DataFrame:
+ trainer = pl.Trainer(
+ accelerator=config['accelerator'],
+ gradient_clip_val=config['gradient_clip_val'],
+ precision=config['precision'],
+ )
+
+ predict_dataloader = ClassificationDataloader(
+ tokenizer=model.tokenizer,
+ train_df=df,
+ val_df=df,
+ config=config
+ ).val_dataloader()
+
+ validation_predictions = trainer.predict(model, predict_dataloader, return_predictions=True)
+ if not validation_predictions:
+ raise TypeError
+ validation_predictions_conv = [Tensor(_) for _ in validation_predictions]
+ validation_predictions_tensor = torch.vstack(validation_predictions_conv)
+ validation_predictions_df = pd.DataFrame({
+ column: validation_predictions_tensor[:, ii] for ii, column in enumerate(get_target_columns())
+ }, index=df.index)
+ validation_predictions_df['text_id'] = df['text_id']
+
+ return validation_predictions
+
+
+def main():
+ train_data, _ = load_train_test_df()
+ train_df, val_df = train_test_split(train_data, train_size=CONFIG['train_size'])
+ train(config=CONFIG, train_df=train_df, val_df=val_df, verbose=True)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/src/solutions/__init__.py b/src/solutions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/solutions/base_solution.py b/src/solutions/base_solution.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dad55d44d7edb8abdbbb2cdd671fd6d61c120e5
--- /dev/null
+++ b/src/solutions/base_solution.py
@@ -0,0 +1,35 @@
+from pathlib import Path
+from typing import Any, List, Optional, Union
+
+import pandas as pd
+
+
+class BaseSolution:
+ """Base class for any competition solution."""
+
+ def __init__(self, config: Optional[dict] = None):
+ self.models: List[Any] = []
+
+ def fit(self, X: pd.DataFrame, y: pd.DataFrame, **kwargs):
+ raise NotImplementedError(f"Abstract class {type(self).__name__} is used")
+
+ def predict(self, X: pd.DataFrame) -> pd.DataFrame:
+ raise NotImplementedError(f"Abstract class {type(self).__name__} is used")
+
+ def save(self, directory: Union[str, Path]) -> None:
+ """Stores model to the directory.
+
+ The directory must be empty.
+ """
+ raise NotImplementedError(f"Abstract class {type(self).__name__} is used")
+
+ def load(self, directory: Union[str, Path]) -> None:
+ """Loads model from the directory.
+
+ Initializes the solution correctly even if the config dict
+ wasn't specified in the constructor.
+
+ The directory should not contain files other than those produced by
+ the `save` method of the same class.
+ """
+ raise NotImplementedError(f"Abstract class {type(self).__name__} is used")
diff --git a/src/solutions/bert_featurizer_solution.py b/src/solutions/bert_featurizer_solution.py
new file mode 100644
index 0000000000000000000000000000000000000000..089710140465d7384e09bc6c6184d5410764c43a
--- /dev/null
+++ b/src/solutions/bert_featurizer_solution.py
@@ -0,0 +1,153 @@
+from pathlib import Path
+from typing import Union
+
+import pandas as pd
+import torch.cuda
+from catboost import CatBoostRegressor
+from transformers import logging as transformers_logging
+
+from src.cross_validate import CrossValidation
+from src.feature_extractors.bert_pretrain_extractor import \
+ BertPretrainFeatureExtractor
+from src.feature_extractors.text_statistics_extractor import \
+ HandcraftedTextFeatureExtractor
+from src.solutions.base_solution import BaseSolution
+from src.solutions.constant_predictor import load_train_test_df
+from src.spell_checker import SmartSpellChecker
+from src.text_preprocessings.spellcheck_preprocessing import \
+ SpellcheckTextPreprocessor
+from src.utils import get_x_columns, seed_everything, validate_x, validate_y
+
+transformers_logging.set_verbosity_error()
+
+seed_everything()
+
+spellcheck = SmartSpellChecker()
+
+
+class BertWithHandcraftedFeaturePredictor(BaseSolution):
+ device = 'GPU' if torch.cuda.is_available() else None
+
+ def __init__(
+ self,
+ model_name: str,
+ catboost_iter: int,
+ saving_dir: str
+ ):
+ super(BertWithHandcraftedFeaturePredictor, self).__init__()
+
+ self.feature_extractor = HandcraftedTextFeatureExtractor(spellcheck)
+ self.text_preprocessing = SpellcheckTextPreprocessor(spellcheck)
+ self.bert = BertPretrainFeatureExtractor(model_name=model_name)
+
+ # classification model for each column
+ self.columns = ['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']
+ self.models = [
+ CatBoostRegressor(
+ iterations=catboost_iter,
+ task_type="CPU", # self.device,
+ verbose=True,
+ ) for _ in range(len(self.columns))
+ ]
+
+ def transform_data(self, X: pd.Series) -> pd.DataFrame:
+ cleaned_text = self.text_preprocessing.preprocess_data(X)
+ bert_features = self.bert.generate_features(cleaned_text)
+ bert_features = self.bert.generate_features(X)
+ handcrafted_features = self.feature_extractor.generate_features(X)
+ features_df = pd.concat([bert_features, handcrafted_features], axis='columns')
+
+ features_df = bert_features
+
+ return features_df
+
+ def fit(self, X: pd.DataFrame, y: pd.DataFrame, **kwargs) -> None:
+ validate_x(X)
+ validate_y(y)
+
+ features_df = self.transform_data(X.full_text)
+
+ for ii, column in enumerate(self.columns):
+ print(f"-> Training model on: {column}...")
+ model = self.models[ii]
+ target = y[column]
+
+ model.fit(X=features_df, y=target)
+
+ def predict(self, X: pd.DataFrame) -> pd.DataFrame:
+ validate_x(X)
+
+ features_df = self.transform_data(X.full_text)
+
+ prediction = {}
+ for ii, column in enumerate(self.columns):
+ print(f"-> Predicting model on: {column}")
+ model = self.models[ii]
+ prediction[column] = model.predict(features_df)
+
+ y_pred = pd.DataFrame(prediction, index=X.index)
+ y_pred['text_id'] = X.text_id
+
+ return y_pred
+
+ def save(self, directory: Union[str, Path]) -> None:
+ directory = Path(directory)
+ if not directory.exists():
+ directory.mkdir(parents=True)
+
+ for ii, model in enumerate(self.models):
+ column = self.columns[ii]
+ path = directory / f'catboost_{column}.cbm'
+ model.save_model(str(path))
+
+ def load(self, directory: Union[str, Path]) -> None:
+ directory = Path(directory)
+ if not directory.is_dir():
+ raise OSError(f"Dir. {directory.absolute()} does not exist")
+
+ for ii, model in enumerate(self.models):
+ column = self.columns[ii]
+ path = directory / f'catboost_{column}.cbm'
+ model.load_model(str(path))
+
+
+def main():
+ config = {
+ 'model_name': 'bert-base-uncased',
+ 'catboost_iter': 5000,
+ 'n_splits': 5,
+ 'saving_dir': 'checkpoints/BertWithHandcraftedFeaturePredictor',
+ }
+ saving_dir = Path("checkpoints/bert_featurizer_solution")
+ saving_dir.mkdir(exist_ok=True, parents=True)
+
+ train_df, test_df = load_train_test_df()
+ # train_df = train_df.iloc[:10]
+
+ x_columns = get_x_columns()
+ train_x, train_y = train_df[x_columns], train_df.drop(columns=['full_text'])
+
+ predictor = BertWithHandcraftedFeaturePredictor(
+ model_name='bert-base-uncased',
+ catboost_iter=5000,
+ saving_dir='checkpoints/BertWithHandcraftedFeaturePredictor'
+ )
+ cv = CrossValidation(saving_dir=str(saving_dir), n_splits=config['n_splits'])
+
+ results = cv.fit(predictor, train_x, train_y)
+ print("CV results")
+ print(results)
+ results.to_csv("cv_results.csv")
+
+ print(f"CV mean: {results.iloc[len(results) - 1].mean()}")
+
+ submission_df = cv.predict(test_df)
+ submission_df.to_csv("submission.csv", index=False)
+
+ cv.save(config['saving_dir'])
+
+ print("Finished training!")
+
+
+if __name__ == '__main__':
+ main()
diff --git a/src/solutions/bert_finetune_solution.py b/src/solutions/bert_finetune_solution.py
new file mode 100644
index 0000000000000000000000000000000000000000..b201ac5888460c6864ea2634e5027b7ac09b0b86
--- /dev/null
+++ b/src/solutions/bert_finetune_solution.py
@@ -0,0 +1,129 @@
+import os
+from pathlib import Path
+from typing import Union
+
+import pandas as pd
+import pytorch_lightning as pl
+import wandb
+from transformers import logging
+
+from src.cross_validate import CrossValidation
+from src.data_reader import load_train_test_df
+from src.model_finetuning.config import CONFIG
+from src.model_finetuning.model import BertLightningModel
+from src.model_finetuning.train import predict, train
+from src.solutions.base_solution import BaseSolution
+from src.utils import get_random_string, get_x_columns
+
+logging.set_verbosity_error()
+
+
+os.environ['GROUP_NAME'] = 'train_deberta_model-' + get_random_string(6)
+
+
+class BertFinetuningPredictor(BaseSolution):
+
+ def __init__(
+ self,
+ model_name="microsoft/deberta-v3-large",
+ num_classes=6,
+ lr=2e-5,
+ batch_size=8,
+ num_workers=8,
+ max_length=512,
+ weight_decay=0.01,
+ accelerator='gpu',
+ max_epochs=5,
+ accumulate_grad_batches=4,
+ precision=16,
+ gradient_clip_val=1000,
+ train_size=0.8,
+ num_cross_val_splits=5,
+ num_frozen_layers=20,
+ ):
+ super(BertFinetuningPredictor, self).__init__()
+ self.config = dict(
+ model_name=model_name,
+ num_classes=num_classes,
+ lr=lr,
+ batch_size=batch_size,
+ num_workers=num_workers,
+ max_length=max_length,
+ weight_decay=weight_decay,
+ accelerator=accelerator,
+ max_epochs=max_epochs,
+ accumulate_grad_batches=accumulate_grad_batches,
+ precision=precision,
+ gradient_clip_val=gradient_clip_val,
+ train_size=train_size,
+ num_cross_val_splits=num_cross_val_splits,
+ num_frozen_layers=num_frozen_layers,
+ )
+
+ def fit(self, X: pd.DataFrame, y: pd.DataFrame, **kwargs):
+ train_df = pd.concat([X, y], axis='columns')
+
+ val_X, val_y, = kwargs['val_X'], kwargs['val_y']
+ val_df = pd.concat([val_X, val_y], axis='columns')
+
+ self.model: BertLightningModel = train(self.config, train_df, val_df, verbose=False)
+
+ def predict(self, X: pd.DataFrame) -> pd.DataFrame:
+ assert self.model is not None, "Model is not trained yet"
+
+ predictions = predict(self.config, self.model, X)
+ return predictions
+
+ def save(self, directory: Union[str, Path]) -> None:
+ directory = Path(directory)
+ if not directory.is_dir():
+ directory.mkdir(parents=True)
+
+ trainer = pl.Trainer(accelerator=self.config['accelerator'])
+ trainer.model = self.model
+ # trainer.save_checkpoint(directory / "lightning_model.ckpt", weights_only=True)
+
+ def load(self, directory: Union[str, Path]) -> None:
+ filepath = Path(directory) / "lightning_model.ckpt"
+
+ if not filepath.is_file():
+ raise OSError(f"File not found: {filepath.resolve()}")
+
+ self.model = BertLightningModel.load_from_checkpoint(str(filepath), config=self.config)
+
+
+def main():
+ config = CONFIG
+ saving_dir = Path("checkpoints/finetune_bert")
+ train_df, test_df = load_train_test_df()
+
+ x_columns = get_x_columns()
+ train_x, train_y = train_df[x_columns], train_df.drop(columns=['full_text'])
+
+ predictor = BertFinetuningPredictor(config)
+ cv = CrossValidation(saving_dir=str(saving_dir), n_splits=config['num_cross_val_splits'])
+
+ results = cv.fit(predictor, train_x, train_y)
+ print(f"CV metric: {results.iloc[len(results) - 1].mean()}")
+ print("CV results")
+ print(results)
+ results.to_csv(saving_dir / "cv_results.csv")
+
+ submission_df = cv.predict(test_df)
+ submission_df.to_csv(saving_dir / "submission.csv", index=False)
+
+ wandb.init(
+ project="automated_essay_evaluator",
+ entity="parmezano",
+ group=os.environ['GROUP_NAME'],
+ name='weights_cv'
+ )
+ art = wandb.Artifact("bert-finetune-solution", type="model")
+ art.add_dir(str(saving_dir.absolute()), name='data/')
+ wandb.log_artifact(art)
+
+ print("Finished training!")
+
+
+if __name__ == '__main__':
+ main()
diff --git a/src/solutions/constant_predictor.py b/src/solutions/constant_predictor.py
new file mode 100644
index 0000000000000000000000000000000000000000..a871fb00205c8c6809be79693750138b09bcea3a
--- /dev/null
+++ b/src/solutions/constant_predictor.py
@@ -0,0 +1,78 @@
+from pathlib import Path
+from typing import Union
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+from src.data_reader import load_train_test_df
+from src.metrics import MSEMetric
+from src.solutions.base_solution import BaseSolution
+
+
+class ConstantPredictorSolution(BaseSolution):
+ def __init__(self, const=3.0):
+ super().__init__()
+ self.const = const
+
+ def fit(self, X: pd.DataFrame, y: pd.DataFrame, **kwargs) -> None:
+ pass
+
+ def predict(self, X: pd.DataFrame) -> pd.DataFrame:
+ submission_df = []
+
+ for _, row in X.iterrows():
+ submission_df.append({
+ 'text_id': row.text_id,
+ 'cohesion': self.const,
+ 'syntax': self.const,
+ 'vocabulary': self.const,
+ 'phraseology': self.const,
+ 'grammar': self.const,
+ 'conventions': self.const
+ })
+
+ return pd.DataFrame(submission_df)
+
+ def save(self, directory: Union[str, Path]) -> None:
+ directory = Path(directory)
+ if not directory.exists():
+ directory.mkdir(parents=True)
+
+ path = directory / "weights.ckpt"
+ with open(path, 'w') as file:
+ file.write(str(self.const))
+
+ def load(self, directory: Union[str, Path]) -> None:
+ directory = Path(directory)
+ if not directory.exists():
+ directory.mkdir(parents=True)
+
+ path = directory / "weights.ckpt"
+ with open(path, 'r') as file:
+ self.const = float(file.read())
+
+ def to(self, device: str) -> 'BaseSolution':
+ return self
+
+
+def main():
+ train_df, test_df = load_train_test_df()
+
+ predictor = ConstantPredictorSolution()
+
+ _, test_data = train_test_split(train_df, test_size=0.2)
+ y_pred = predictor.predict(test_data)
+
+ y_true = test_data[['text_id', 'cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']]
+ metric = MSEMetric()
+
+ print(f"Calculation class metric: {metric.evaluate_class_rmse(y_pred, y_true)}")
+ print(f"Calculation class metric: {metric.evaluate_class_rmse(y_pred, y_true)}")
+
+ submission_df = predictor.predict(test_df)
+
+ submission_df.to_csv("submission.csv", index=False)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/src/solutions/many_bert_solution.py b/src/solutions/many_bert_solution.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f9b1e23f197e44df2732685cd203314d7477dd0
--- /dev/null
+++ b/src/solutions/many_bert_solution.py
@@ -0,0 +1,161 @@
+import os
+from pathlib import Path
+from typing import Union
+
+import pandas as pd
+import torch.cuda
+from catboost import CatBoostRegressor
+from catboost.utils import get_gpu_device_count
+from easydict import EasyDict as edict
+
+from src.cross_validate import CrossValidation
+from src.feature_extractors.bert_pretrain_extractor import \
+ ManyBertPretrainFeatureExtractor
+from src.feature_extractors.text_statistics_extractor import \
+ HandcraftedTextFeatureExtractor
+from src.solutions.base_solution import BaseSolution
+from src.solutions.constant_predictor import load_train_test_df
+from src.spell_checker import SmartSpellChecker
+from src.text_preprocessings.spellcheck_preprocessing import \
+ SpellcheckTextPreprocessor
+from src.utils import get_x_columns, seed_everything, validate_x, validate_y
+
+seed_everything()
+
+spellcheck = SmartSpellChecker()
+
+
+class ManyBertWithHandcraftedFeaturePredictor(BaseSolution):
+
+ def __init__(
+ self,
+ model_names: list,
+ catboost_iter: int,
+ saving_dir: str,
+ ):
+ super(ManyBertWithHandcraftedFeaturePredictor, self).__init__()
+
+ self.feature_extractor = HandcraftedTextFeatureExtractor(spellcheck)
+ self.text_preprocessing = SpellcheckTextPreprocessor(spellcheck)
+ self.berts = ManyBertPretrainFeatureExtractor(model_names=model_names)
+
+ self.device = 'GPU' if torch.cuda.is_available() else None
+ self.task_type = 'GPU' if get_gpu_device_count() > 0 else 'CPU'
+
+ # classification model for each column
+ self.columns = ['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']
+ self.models = [
+ CatBoostRegressor(
+ iterations=catboost_iter,
+ task_type=self.task_type,
+ verbose=True,
+ ) for _ in range(len(self.columns))
+ ]
+
+ def transform_data(self, X: pd.Series) -> pd.DataFrame:
+ cleaned_text = self.text_preprocessing.preprocess_data(X)
+ bert_features = self.berts.generate_features(cleaned_text)
+ handcrafted_features = self.feature_extractor.generate_features(X)
+ features_df = pd.concat([bert_features, handcrafted_features], axis='columns')
+ return features_df
+
+ def fit(self, X: pd.DataFrame, y: pd.DataFrame, **kwargs) -> None:
+ validate_x(X)
+ validate_y(y)
+
+ features_df = self.transform_data(X.full_text)
+
+ for ii, column in enumerate(self.columns):
+ print(f"-> Training model on: {column}...")
+ model = self.models[ii]
+ target = y[column]
+ torch.cuda.empty_cache()
+ model.fit(X=features_df, y=target)
+ torch.cuda.empty_cache()
+
+ def predict(self, X: pd.DataFrame) -> pd.DataFrame:
+ validate_x(X)
+
+ features_df = self.transform_data(X.full_text)
+
+ prediction = {}
+ for ii, column in enumerate(self.columns):
+ print(f"-> Predicting model on: {column}")
+ model = self.models[ii]
+ prediction[column] = model.predict(features_df)
+
+ y_pred = pd.DataFrame(prediction, index=X.index)
+ y_pred['text_id'] = X.text_id
+
+ return y_pred
+
+ def save(self, directory: Union[str, Path]) -> None:
+ directory = Path(directory)
+ if not directory.exists():
+ directory.mkdir(parents=True)
+
+ for ii, model in enumerate(self.models):
+ column = self.columns[ii]
+ path = directory / f'catboost_{column}.cbm'
+ model.save_model(str(path))
+
+ def load(self, directory: Union[str, Path]) -> None:
+ directory = Path(directory)
+ if not directory.is_dir():
+ raise OSError(f"Dir. {directory.absolute()} does not exist")
+
+ for ii, model in enumerate(self.models):
+ column = self.columns[ii]
+ path = directory / f'catboost_{column}.cbm'
+ model.load_model(str(path))
+
+
+def main():
+ config = edict(
+ dict(
+ model_names=[
+ 'bert-base-uncased',
+ 'bert-base-cased',
+ 'vblagoje/bert-english-uncased-finetuned-pos',
+ 'bert-base-multilingual-cased',
+ 'unitary/toxic-bert',
+ 'bert-large-uncased'
+ ],
+ catboost_iter=5000,
+ n_splits=5,
+ saving_dir='checkpoints/ManyBertWithHandcraftedFeaturePredictor',
+ )
+ )
+
+ train_df, test_df = load_train_test_df()
+
+ x_columns = get_x_columns()
+ train_x, train_y = train_df[x_columns], train_df.drop(columns=['full_text'])
+
+ predictor = ManyBertWithHandcraftedFeaturePredictor(
+ model_names=config.model_names,
+ catboost_iter=config.catboost_iter,
+ saving_dir=config.saving_dir,
+ )
+ cv = CrossValidation(saving_dir=config.saving_dir, n_splits=config.n_splits)
+
+ results = cv.fit(predictor, train_x, train_y)
+ print("CV results")
+ print(results)
+
+ print(f"CV mean: {results.iloc[len(results) - 1].mean()}")
+
+ cv.save(config.saving_dir)
+
+ submission_df = cv.predict(test_df)
+ submission_path = os.path.join(config.saving_dir, "submission.csv")
+ submission_df.to_csv(submission_path, index=False)
+
+ cv_results_path = os.path.join(config.saving_dir, "cv_results.csv")
+ results.to_csv(cv_results_path)
+
+ print("Finished training!")
+
+
+if __name__ == '__main__':
+ main()
diff --git a/src/spell_checker.py b/src/spell_checker.py
new file mode 100644
index 0000000000000000000000000000000000000000..804b686aff79ef34f4ec49bac17bfca9292de711
--- /dev/null
+++ b/src/spell_checker.py
@@ -0,0 +1,87 @@
+__all__ = ['SmartSpellChecker']
+
+import re
+from collections import Counter
+from functools import lru_cache
+from typing import Iterable
+
+from spellchecker import SpellChecker
+
+
+def get_word_counter(text: str):
+ # removes punctuation and count words in sentence
+ text = re.sub(r"[.,!?;:]", " ", text)
+
+ return Counter(text.split())
+
+
+custom_mappings = {
+ 'alot': 'a lot',
+ 'classwork': 'class work',
+ 'everytime': 'every time',
+ 'loosing': 'losing',
+ 'clases': 'classes',
+ 'payed': 'paid',
+ 'learnd': 'learned',
+ 'ect': 'etc',
+ 'wasnt': "wasn't",
+ 'wich': 'which',
+ "sol's": 'souls',
+ 'thigs': 'things',
+ 'activies': 'activities',
+ 'oline': 'online',
+ 'thru': 'through',
+ 'inconclusion': 'in conclusion',
+}
+
+skipped_mappings = {
+ ' u ': ' you ',
+ 'youll': "you will",
+ 'wont': "won't"}
+
+exclude_words_from_check = {
+ "you're", 'covid'
+}
+
+black_list = {'ther', "waldo's", "f's", ""}
+
+
+class SmartSpellChecker:
+ def __init__(self):
+ self.spellcheck = SpellChecker()
+
+ @lru_cache(maxsize=None)
+ def correct_word(self, mismatch: str):
+ if mismatch in custom_mappings:
+ return custom_mappings[mismatch]
+
+ if mismatch in black_list:
+ return ""
+
+ if mismatch in exclude_words_from_check:
+ return None
+
+ # sometimes spellcheck thinks 'b' or 'c' if misspelled words
+ # this condition > 2 is needed
+ if len(mismatch) <= 2:
+ return None
+
+ return self.spellcheck.correction(mismatch)
+
+ def correct_text(self, text: str):
+ for key, value in skipped_mappings.items():
+ if key in text:
+ text = text.replace(key, value)
+
+ word_count = get_word_counter(text)
+
+ unknown_words = self.unknown(word_count)
+ for misspell in unknown_words:
+ correct = self.correct_word(misspell)
+ if correct is not None:
+ text = text.replace(misspell, correct)
+
+ return text
+
+ def unknown(self, words: Iterable):
+ return self.spellcheck.unknown(words)
diff --git a/src/tests/test_term_frequency_feature_extractor.py b/src/tests/test_term_frequency_feature_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..f24669874084f09bfda02a65decd82d1c7b65d89
--- /dev/null
+++ b/src/tests/test_term_frequency_feature_extractor.py
@@ -0,0 +1,17 @@
+import pandas as pd
+
+from src.data_reader import load_train_test_df
+from src.feature_extractors.term_frequency_feature_extractor import \
+ TermFrequencyFeatureExtractor
+
+
+def test_text_feature_generation():
+ N_BINS = 20
+ train_df, __ = load_train_test_df(is_testing=True)
+
+ feature_extractor = TermFrequencyFeatureExtractor(n_bins=N_BINS)
+ feature_df: pd.DataFrame = feature_extractor.generate_features(train_df.full_text)
+
+ assert len(feature_df.columns) == N_BINS - 1
+ assert len(feature_df) == 5
+ assert not feature_df.isna().values.any()
diff --git a/src/text_preprocessings/base_preprocessing.py b/src/text_preprocessings/base_preprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..685d9f251795c28094006d0b945963bebbb0383d
--- /dev/null
+++ b/src/text_preprocessings/base_preprocessing.py
@@ -0,0 +1,13 @@
+import pandas as pd
+
+
+class BasePreprocessor:
+
+ def preprocess_data(self, data: pd.Series) -> pd.Series:
+ """Performs preprocessing of raw texts, returns cleaned texts
+
+ :param X: raw texts
+ :return: cleaned texts
+ """
+
+ raise NotImplementedError(f"Abstract class {type(self).__name__} is used")
diff --git a/src/text_preprocessings/spellcheck_preprocessing.py b/src/text_preprocessings/spellcheck_preprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9120615067a9e2140d73c49381f483ead8fca5b
--- /dev/null
+++ b/src/text_preprocessings/spellcheck_preprocessing.py
@@ -0,0 +1,29 @@
+from functools import lru_cache
+
+import pandas as pd
+from tqdm import tqdm
+
+from src.feature_extractors.text_statistics_utils import preprocess_test
+from src.spell_checker import SmartSpellChecker
+from src.text_preprocessings.base_preprocessing import BasePreprocessor
+
+
+class SpellcheckTextPreprocessor(BasePreprocessor):
+ def __init__(self, spellcheck: SmartSpellChecker):
+ super(SpellcheckTextPreprocessor, self).__init__()
+ self._spellcheck = spellcheck
+
+ def preprocess_data(self, data: pd.Series) -> pd.Series:
+ out_texts = []
+
+ for text in tqdm(data, desc="Preprocessing texts (correcting mistakes, removing tokens, etc.)..."):
+ text = self._preprocess_text(text)
+ out_texts.append(text)
+
+ return pd.Series(out_texts, index=data.index)
+
+ @lru_cache()
+ def _preprocess_text(self, text: str) -> str:
+ text = preprocess_test(text)
+ text = self._spellcheck.correct_text(text)
+ return text
diff --git a/src/utils.py b/src/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..918e931b1899513c51f723d55b667188ec5d688e
--- /dev/null
+++ b/src/utils.py
@@ -0,0 +1,96 @@
+import json
+import os
+import random
+import shutil
+import string
+from distutils.dir_util import copy_tree
+from typing import List
+
+import numpy as np
+import pandas as pd
+import requests
+import torch
+from dotenv import load_dotenv
+from omegaconf import OmegaConf
+
+load_dotenv()
+
+
+def seed_everything(seed: int = 42) -> None:
+ random.seed(seed)
+ os.environ['PYTHONHASHSEED'] = str(seed)
+ np.random.seed(seed)
+
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed(seed)
+ torch.backends.cudnn.deterministic = True
+ torch.backends.cudnn.benchmark = False
+
+
+def pandas_set_print_options():
+ pd.set_option('display.max_rows', 500)
+ pd.set_option('display.max_columns', 500)
+ pd.set_option('display.width', 1000)
+
+
+def get_target_columns() -> List[str]:
+ return ['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']
+
+
+def get_x_columns() -> List[str]:
+ return ['text_id', 'full_text']
+
+
+def validate_x(X: pd.DataFrame) -> None:
+ columns = set(X.columns)
+
+ if len(columns) != 2 or any(col not in get_x_columns() for col in columns):
+ print(X)
+ raise RuntimeError(f"X has incorrect columns: it should contain only {get_x_columns()}")
+
+
+def validate_y(y: pd.DataFrame) -> None:
+ columns = set(y.columns)
+
+ y_needed_columns = get_target_columns() + ['text_id']
+
+ if len(columns) != 7 or any(col not in y_needed_columns for col in columns):
+ print(y)
+ raise RuntimeError(f"y has incorrect columns: it should contain only {y_needed_columns}")
+
+
+def get_random_string(length) -> str:
+ # choose from all lowercase letter
+ letters = string.ascii_lowercase
+ result_str = ''.join(random.choice(letters) for _ in range(length))
+ return result_str
+
+
+def report_to_telegram(message):
+ requests.get(
+ 'https://api.telegram.org/bot{bot_token}/sendMessage?chat_id={chat_id}&text={text}'.format(
+ bot_token=os.environ['BOT_TOKEN'],
+ chat_id=os.environ['CHAT_ID'],
+ text=message)
+ )
+
+
+def pretty_cfg(cfg):
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+ cfg_json = json.dumps(cfg_dict, indent=2)
+ return cfg_json
+
+
+def save_experiment(cfg, submission_df, results, saving_dir):
+ submission_path = os.path.join(saving_dir, "submission.csv")
+ submission_df.to_csv(submission_path, index=False)
+
+ cv_results_path = os.path.join(saving_dir, "cv_results.csv")
+ results.to_csv(cv_results_path)
+
+ weight_path = os.path.join(cfg.cwd, "data/weights")
+ copy_tree(saving_dir, weight_path)
+
+ src_config = os.path.join(".hydra", "config.yaml")
+ dst_config = os.path.join(weight_path, "config.yaml")
+ shutil.copy(src_config, dst_config)
diff --git a/tests/data/test_sample.csv b/tests/data/test_sample.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f60331d0a0cfb4b9e98657b0e7a521b78ecf5d79
--- /dev/null
+++ b/tests/data/test_sample.csv
@@ -0,0 +1,26 @@
+text_id,full_text
+0000C359D63E,"when a person has no experience on a job their is always going to be good people to help you and try to explane the job you need to get done in life you were not born with knowing everything. Life is bassicly about learing new things every single day even though without experience because life is simple and we must live happy and around with the people we love. When a person thinks they know everything in life they dont do good because they trying to make the other person less then others you must be kind to those the dont have experience because you may not know some day you will go to a different country. When you dont know anyting because you not from their so you going to need help from others to explain you about the culture or how to eat a food because you have to no experience on the new country. You must help a person the has no experience because maybe you may need help from the person the you didnt want to help.
+
+yes, even thought you may not have experience in the type of job you seek,you can learn and teach others.
+
+If you dont have experence in a restaurant for the job you seek for you will learn. For example a person the has no experence working in a restaurant,the only place they will offer you would be to be a diswasher. But you want to dream big because everytime a person has big dreams they can learn the job whereever, you want to be like a cooking person or a kitchen manager. In a job there is always going to be people the they dont want to see you in a better place because they may think you dont deserve to be there but you the only one the knows how hard you being working to achieve your dream .In life you always going to have proof without experience to see how good you are to learn a new job in the kitchen so if you can learn quick. They can give you a good place for you teach others the has no experience.
+
+My dad has always talk with my cousins that when their is no experience you can always learn and fight for what you want. When my cosuin came to America he wanted to play travel soccer, he went to tryout but the coach told him the has any experience of talking english so he wouldn't make it. But later on he learn how to speak then, he went to tryout for fc virginia he made it also everyone was talking about him because hes a great soccer player also, hes a great person. Then he came to the house and thanks my dad because of the edvice he gave to him the even though you dont have experience,you can always learn and fight for you goals. My dad is man the wants the best for hes family because when he was a kid he wanted to be a loyer but it was hard for hes parents because they were really poor and the corruption was really bad in the country.
+
+In every job their is always going to be a person with no experience. For example people with no experience those are the ones the learn the job and when they learn the job very well they always try not to make a mistake because thay want to get the job done with quality. Because everytime you do a job for someone else they want to see good quality on you before they give the kind of job they want you to get done for them. The people the has no experience in a job that doesnt make them a less person because we all are humans and we must have the same equal rights. we all know the everytime you aplied for a job the first thing they asked you is about if you have experience but its okay to say no because they can teach you and you can get it fast.
+
+I think yes, you can be a good candidate to be hire without no experience because every person in the world needs to have a opportunity to try something new. People today in life they dont need to have experience to go find a job why because today in every work you get one week of train which people can learn the job just in one week. Because they will practice the job and every time they get practice they will going to get better and better. Practices makes everything better so dont be scared to applied for job only because it says you must have experience no just go believe in youself. If you believe in you everything will be good in like ad you will be going great just be you even though you have experience or not we all deserve a chance."
+000BAD50D026,"Do you think students would benefit from being able attend classes from home?
+
+Yes! its benefit for student who attend classes from home. Because some student want to attend classes from home because they thinks it's very important for them . And they think they can learned fast, and understand than they student who attend classes from school. For example my friend told me that she's attand classes from home it's good for her, because they is some subject she didn't understand when she attend classes from school but when she attend the home classes she good for that subject. she like science she think science its important for her but she didn't understand anything any science but she understand science because she attend classes from home. And some parent doesn't want they are children to attend classes from school, because many christian doesn't want they are children to attend to classes from school because they doesn't believe science and they want they are children to be disrespectful for teacher or for them. Maybe student can deciding to attend classes from home because it's benefit for them and it's good for the people who have lot of money. It's not any people who can attend classes from home.
+
+some student attend classes from home because they hate bull or trouble. because they is student who come to school to bull other student or to fight with other. I know they is some people who scared about fight or bull by someone. For example the was a girl who attend very day to school and they was three girl they bull and beat her everyday and try to kill her self because she think she doesn't have anyone who can fight for her and when her parent sigh her want to kill herself, immediately they stop her, and they say they can not let they children to go back for school and they think every school it's like that school. so that why attand class from home it's good sometime. They are student who attend classes from home for the reason because to provide the experience and to show that they can try to learned themselves. In order to achieve anything at all in life a attend classes from home would must being take a deciding whether. "
+00367BB2546B,"Thomas Jefferson once states that ""it is wonderful how much we can get done when we are always doing something,I agree to this statement. Some people say that it is always better to always be doing something than to be inactive and so nothing at all to chase after on your dreams. always doing something lets you feel a sense of accomplishment,it makes you want to strive to do even more than what you did,you can also learn something along the way.
+
+The first reason that always doing is better than doing nothing is that it gives you a sense of you accomplishing something. when you finish a task you feel successful with yourself in saying that you pursued something and kept going. for example,when you graduate from school and move on to collage,you feel successful you also know that you worked as hard as you can to complete your schooling and onto collage or even to the real world to work.
+
+Secondly,accomplishing your goals makes you want to strive to do more. when you complete something you may think to yourself,""if I did this than what else can i did with my life?"". Accomplishing something could make you want to do even more. For example, when you finish such as school then later move out of the house and live on you own,it gives you a sense of you being an independent person and you having the capability to live on you own and also to manage you own life how you want it to be. lastly it gives you the chance to go higher in your career.
+
+Finally it give you the opportunity to learn new and useful thing along the way that you may be able to use in your own life. by accomplishing something you can learn how to help you self to accomplish new and better things in you life. For example ,by finishing school you will take all that you learned and you can apply it it some aspect of your life to help you succeed in life. You can also apply it to the job you pursue or even to get a higher education.
+
+Although some may disagree with me and say that inactivity is helpful when trying to accomplish something because it will not cause stress on you ,what this fails to show is that it lets you become a better person in your day to day life. in conclusion always doing is better than doing nothing because it helps you achieve more in you life,makes you feel accomplished in life, and shows that you can always strive to do more in you life "
diff --git a/tests/data/train_sample.csv b/tests/data/train_sample.csv
new file mode 100644
index 0000000000000000000000000000000000000000..dd855c39f40eb510f475a58274a75056dd0e9a8f
--- /dev/null
+++ b/tests/data/train_sample.csv
@@ -0,0 +1,42 @@
+text_id,full_text,cohesion,syntax,vocabulary,phraseology,grammar,conventions
+0016926B079C,"I think that students would benefit from learning at home,because they wont have to change and get up early in the morning to shower and do there hair. taking only classes helps them because at there house they'll be pay more attention. they will be comfortable at home.
+
+The hardest part of school is getting ready. you wake up go brush your teeth and go to your closet and look at your cloths. after you think you picked a outfit u go look in the mirror and youll either not like it or you look and see a stain. Then you'll have to change. with the online classes you can wear anything and stay home and you wont need to stress about what to wear.
+
+most students usually take showers before school. they either take it before they sleep or when they wake up. some students do both to smell good. that causes them do miss the bus and effects on there lesson time cause they come late to school. when u have online classes u wont need to miss lessons cause you can get everything set up and go take a shower and when u get out your ready to go.
+
+when your home your comfortable and you pay attention. it gives then an advantage to be smarter and even pass there classmates on class work. public schools are difficult even if you try. some teacher dont know how to teach it in then way that students understand it. that causes students to fail and they may repeat the class. ",3.5,3.5,3.0,3.0,4.0,3.0
+0022683E9EA5,"When a problem is a change you have to let it do the best on you no matter what is happening it can change your mind. sometimes you need to wake up and look what is around you because problems are the best way to change what you want to change along time ago. A
+
+problem is a change for you because it can make you see different and help you to understand how tings wok.
+
+First of all it can make you see different then the others. For example i remember that when i came to the United States i think that nothing was going to change me because i think that nothing was going to change me because everything was different that my country and then i realist that wrong because a problem may change you but sometimes can not change the way it is, but i remember that i was really shy but i think that change a lot because sometimes my problems make me think that there is more thing that i never see in my life but i just need to see it from a different way and dont let nothing happened and ruing the change that i want to make because of just a problem. For example i think that nothing was going to change me and that i dont need to be shy anymore became i need to start seeing everything in a different ways because you can get mad at every one but you need to know what is going to happened after,
+
+people may see you different but the only way that you know how to change is to do the best and don't let nothing or not body to change nothing about you. The way you want to change not one have that and can't do nothing about it because is your choice and your problems and you can decide what to do with it.
+
+second of all can help you to understand how things work. For instance my mom have a lot of problems but she have faith when she is around people, my mom is scare of high and i'm not scare of high i did not understand why my mos is scare of high and in not scare of high and every time i see my mom in a airplane it make me laugh because she is scare and is funny, but i see it from a different way and i like the high but also she have to understand that hoe things work in other people because it can no be the same as you. For example i think that my mom and me are different because we are and i have to understand that she does not like high and i need to understand that. to help someone to understand how things work you need to start to see how things work in that persons life.
+
+A problem is a change for you and can make you a different and help you to understand. Everyone has a different opinion and a different was to understand then others. everyone can see the different opinion and what other people think.",2.5,2.5,3.0,2.0,2.0,2.5
+00299B378633,"Dear, Principal
+
+If u change the school policy of having a grade b average that unfair. Because many students have a C average. So that means that they cant go out for sports or other activities they want to do bad. That's like taking everything they have. What if kids want to become good at something, but now they cant because of that school policy. If they have a C average they should still be able to go out for sports or activities. A C average isn't that bad, its higher then a D average. If the school police was if you have a D average of lower they shouldn't do sports or activities. If they have a D average in school for not working hard, that's means that they in ain't going to try hard. If they have a C average and there trying hard they should be able to out for sports or activities. What if all the good people in sports have a C average in school, that means that they cant play and were going to lose every game we have. That's a good policy to get grade's up but don't take away something they care about. Everyone should be able to go out for sports if they want to. If the school policy happens, schools going to be boarding now, because now students cant go out for sports or other activities. The students that are doing good in school should feel good about themselves but we shouldn't take the other students away from the others ones. If we do this policy student will try to raised their grade but if they cant what happens they them. Should they just be out of it and think that schools boarding. If they do this its like taking away their video games. All I'm saying is that they have the right to go out for sports or activities.",3.0,3.5,3.0,3.0,3.0,2.5
+003885A45F42,"The best time in life is when you become yourself. I agree that the greatest accomplishment, is when you be yourself in a world that constantly trying to make you something else. Because you make your own choices, you become more happy, and you respect others.
+
+First, you make your own choices by being yourself. Becoming yourself means that you should be able to make your own choices and not be shy or afraid of what you're doing. Because you're defining yourself by doing those things that you want. Some people follow others, therefore, they don't make their own choices. People are afraid to make their own choice because they don't want to get rejected or be wrong. In this world, most of the teenagers act and follow the people who are strong and bullies. The reason they follow them and make the same choices they make, is because they're afraid that they will get beaten up and be left out. Personally when I came to the United States I used to follow other, because I thought they were amazing. But one day, I knew I was walking in the wrong path and I left them. Making your own choices makes you happy because you get to do what you like and what you want to do. It's hard to make your own choices, but one day you're going to have to make your own choices. The more you mature the more it will become easier for you to not be afraid of what you're doing and how other people are looking at it, because at the end of the day It's your life and you do what you think is right.
+
+Second, You become more happy by being yourself because you make your own choices, You take responsibilities and you do whatever makes you happy. Also, some people act like they're happy while not being themselves, but matter of fact, they're depressed from the inside. Being yourself makes you more happy because you feel like you achieved something big. Also, Some religious kids who become themselves love everyone. In the bible it says ""Those who know God should love others, because God is love.""
+
+Not being yourself will get you depressed because you don't do everything you want to do, but you do what others wanna do because you want to fit in. Some kids who come from a different country will do stuff they don't want to do just to fit in, because they don't want to be lonely. Some people are afraid they won't be happy because they would lose a lot of friends for being themselves, but they have to know that those people should be there for them and support them for what he's doing since it's his friends.
+
+Lastly, Respecting others will gain them to respect you. Some teenagers like to make fun of others, not listen to what they have to say, and they like to beat kids up. Also some kids don't like to not listen to their parents, and that is also disrespectful. The bible says "" Honor your father and mother.""
+
+If you respect your parents and listen to them, then they would trust you and not ask you lots of questions. Respecting other should be really important, because you don't want others to not respect you. Also, Some people are disrespectful because they were never respected. Important things to do to be respectful is, Listen to what the other person has to say, Stand up for them, Being friends with them Doing all of that will gain you respect and you would have good friends. Also, if you see someone bullying a kid don't watch, and stand up for them, don't be afraid to stand up for someone because you're doing the right thing.
+
+Some may disagree with me and say that it's not that big of a deal, and it doesn't feel like a big accomplishment. But with all do respect, I agree that it's a big accomplishment because nowadays it's really hard to become yourself.
+
+In conclusion, It's hard to become yourself in this world, because others are holding that opportunity back from you and you're afraid to fight it back. That's why I agree that to be yourself in a world that is constantly trying to make you something else is the greatest accomplishment. ",4.5,4.5,4.5,4.5,4.0,5.0
+0049B1DF5CCC,"Small act of kindness can impact in other people can change people to become better persons you can have an impact of kindess with a homeles that can change his life or with some who needed they are going to know you are a nice person if you are a nice person everywhere you go people is going to like your personality so you have to be a nice person with others like a old women triying to cross the road thats a impact of kindness when you do that you feel a greate person you can change people in the way they think by helping others treating nice other people give them some advice when you see someone need it help someone older then you give food and new clothes to a homeless person is a big act of kindess maybe you wount change his life but you would change one day of his life when you do thoose stuffs you feel emotional.
+
+Another example of an act of kindess is when you help your friends to study for a test or explain them or when they need money you give some money is an act of kindess because you are helping your friend.
+
+Another example is not trowing trash on the floor if you put the trash where belong that is an act of kindess because benefits the earth and the people. ",2.5,3.0,3.0,3.0,2.5,2.5
diff --git a/tests/test_constant_predictor.py b/tests/test_constant_predictor.py
new file mode 100644
index 0000000000000000000000000000000000000000..c07d256a2a64012da8e08fce14c7b9dd6e5a1e5a
--- /dev/null
+++ b/tests/test_constant_predictor.py
@@ -0,0 +1,11 @@
+from src.data_reader import load_train_test_df
+from src.solutions.constant_predictor import ConstantPredictorSolution
+
+
+def test_constant_predictor():
+ _, test_df = load_train_test_df(is_testing=True)
+
+ predictor = ConstantPredictorSolution()
+ submission_df = predictor.predict(test_df)
+
+ assert submission_df.shape == (len(test_df), 7)
diff --git a/tests/test_cross_validation.py b/tests/test_cross_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..c61a3a1be2af161b177016c1186a567988655818
--- /dev/null
+++ b/tests/test_cross_validation.py
@@ -0,0 +1,23 @@
+from src.cross_validate import CrossValidation
+from src.data_reader import load_train_test_df
+from src.solutions.constant_predictor import ConstantPredictorSolution
+from src.utils import pandas_set_print_options
+
+pandas_set_print_options()
+
+
+def test_cross_validation():
+ train_df, _ = load_train_test_df(is_testing=True)
+
+ x_columns = ['text_id', 'full_text']
+ X, y = train_df[x_columns], train_df.drop(columns=['full_text'])
+
+ n_splits = 3
+ cv = CrossValidation(saving_dir='/tmp/sdfjsld', n_splits=n_splits)
+ predictor = ConstantPredictorSolution()
+ cv_scores = cv.fit(predictor, X, y)
+ assert cv_scores.shape == (n_splits + 1, 6)
+
+ prediction_df = cv.predict(train_df[x_columns])
+
+ assert prediction_df.shape == (len(train_df), 7)
diff --git a/tests/test_feature_extractor.py b/tests/test_feature_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3768e9a34e6b06360c4e30e2c3823dedcea7d6d
--- /dev/null
+++ b/tests/test_feature_extractor.py
@@ -0,0 +1,15 @@
+from src.data_reader import load_train_test_df
+from src.feature_extractors.bert_pretrain_extractor import \
+ BertPretrainFeatureExtractor
+
+
+def test_pretrain_feature_extractor():
+ models = ['distilbert-base-uncased-finetuned-sst-2-english', 'bert-base-uncased']
+ train_df, _ = load_train_test_df(is_testing=True)
+
+ for model_name in models:
+ feature_extractor = BertPretrainFeatureExtractor(model_name=model_name)
+
+ output_features = feature_extractor.generate_features(train_df.full_text)
+
+ assert len(output_features) == 5 and len(output_features.columns) == 768
diff --git a/tests/test_handcrafted_text_feature_extractor.py b/tests/test_handcrafted_text_feature_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d69dfe1d6078a81a3751f67aedf86905a3bf73d
--- /dev/null
+++ b/tests/test_handcrafted_text_feature_extractor.py
@@ -0,0 +1,16 @@
+from src.data_reader import load_train_test_df
+from src.feature_extractors.text_statistics_extractor import \
+ HandcraftedTextFeatureExtractor
+from src.spell_checker import SmartSpellChecker
+
+
+def test_text_feature_generation():
+ train_df, _ = load_train_test_df(is_testing=True)
+
+ spellcheck = SmartSpellChecker()
+
+ feature_extractor = HandcraftedTextFeatureExtractor(spellcheck)
+ feature_df = feature_extractor.generate_features(train_df.full_text)
+
+ assert len(feature_df.columns) == 42
+ assert len(feature_df) == 5
diff --git a/tests/test_metric.py b/tests/test_metric.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffe27cf508b9bbad193c45021ded132890f83164
--- /dev/null
+++ b/tests/test_metric.py
@@ -0,0 +1,17 @@
+import numpy as np
+import torch
+from sklearn.metrics import mean_squared_error
+
+from src.model_finetuning.losses import MCRMSELoss
+
+
+def test_sklearn_metric_matches_torch():
+ a = torch.randn(10, 6)
+ b = torch.randn(10, 6)
+
+ sklearn_loss = []
+ for ii in range(6):
+ loss = mean_squared_error(a[:, ii], b[:, ii], squared=False)
+ sklearn_loss.append(loss)
+
+ assert np.isclose(np.mean(sklearn_loss), MCRMSELoss().forward(a, b))
diff --git a/tests/test_reproducibility.py b/tests/test_reproducibility.py
new file mode 100644
index 0000000000000000000000000000000000000000..be27dc2040837305ee407b9bdf2b2d55d4b2314a
--- /dev/null
+++ b/tests/test_reproducibility.py
@@ -0,0 +1,22 @@
+from random import randrange
+
+import numpy as np
+import torch
+
+from src.utils import seed_everything
+
+
+def test_reproducibility():
+ seed_everything()
+ python_random_a = randrange(100)
+ np_random_a = np.random.randint(100, size=1)[0]
+ torch_random_a = torch.randint(100, size=(1, ))[0]
+
+ seed_everything()
+ python_random_b = randrange(100)
+ np_random_b = np.random.randint(100, size=1)[0]
+ torch_random_b = torch.randint(100, size=(1, ))[0]
+
+ assert python_random_a == python_random_b
+ assert np_random_a == np_random_b
+ assert torch_random_a == torch_random_b
diff --git a/tests/test_text_cleaning.py b/tests/test_text_cleaning.py
new file mode 100644
index 0000000000000000000000000000000000000000..45070a25e2e4f24192102e75f1d5ebfe8bec593c
--- /dev/null
+++ b/tests/test_text_cleaning.py
@@ -0,0 +1,15 @@
+from src.data_reader import load_train_test_df
+from src.spell_checker import SmartSpellChecker
+from src.text_preprocessings.spellcheck_preprocessing import \
+ SpellcheckTextPreprocessor
+
+
+def test_text_cleaning():
+ train_df, _ = load_train_test_df(is_testing=True)
+
+ spellcheck = SmartSpellChecker()
+ text_preprocessor = SpellcheckTextPreprocessor(spellcheck)
+
+ cleaned_texts = text_preprocessor.preprocess_data(train_df.full_text)
+
+ assert len(cleaned_texts) == 5
diff --git a/tests/void_test.py b/tests/void_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..1dd90445bc79c774df79888b538f772269a14055
--- /dev/null
+++ b/tests/void_test.py
@@ -0,0 +1,2 @@
+def test_true():
+ assert True is True