Remove solo pack benchmark/MLE-bench (kept team contests only)

benchmark/MLE-bench/README.md

@@ -1,304 +0,0 @@
-# MLE-bench
-
-Code for the paper ["MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering"](https://arxiv.org/abs/2410.07095). We have released the code used to construct the dataset, the evaluation logic, as well as the agents we evaluated for this benchmark.
-
-## Leaderboard
-*Update* (04-24-2026): We are currently not taking any new submissions to the leaderboard while we develop an improved process for ensuring submissions are fair and comparable. We will share updates on this process in the future.
-
-| Agent | LLM(s) used | Low == Lite (%) | Medium (%) | High (%) | All (%) | Running Time (hours) | Date | Source Code Available | Grading Reports Available |
-|-------|-------------|-----------------|------------|----------|---------|----------------------|------|----------------------|---------------------------|
-| [Famou-Agent 2.0](https://github.com/baidubce/FM-Agent) | Gemini-3-Pro-Preview | 80.3 ± 1.52 | 64.04 ± 2.32 | 42.22 ± 2.22 | 64.44 ± 1.18 | 24 | 2026-02-23 | X | ✓ |
-| [AIBuildAI](https://github.com/aibuildai/AI-Build-AI) | Claude-Opus-4.6 |  77.27 ± 0.00 |  61.40 ± 0.88 |  46.67 ± 0.00 | 63.11 ± 0.44 | 24 | 2026-03-06 | X | ✓ |
-| [CAIR](https://research.google/teams/cloud-ai-research/) [MARS+](https://arxiv.org/pdf/2602.02660) | Gemini-3-Pro-Preview | 78.79 ± 1.52 | 60.53 ± 1.52 | 44.44 ± 2.22 | 62.67 ± 0.77 | 24 | 2026-02-17 | X | ✓ |
-| [MLEvolve](https://github.com/InternScience/MLEvolve) | Gemini-3-Pro-Preview | 80.30 ± 1.52 | 57.89 ± 1.52 | 42.22 ± 2.22 | 61.33 ± 1.33 | 12 | 2026-02-14 | ✓ | ✓ |
-| [PiEvolve](https://github.com/FractalAIResearchLabs/PiEvolve)<br>(Fractal AI Research) | Gemini-3-Pro-Preview[^4] | 80.30 ± 1.52[^3] | 58.77 ± 0.88[^3] | 40.0 ± 0.00[^3] | 61.33 ± 0.77[^3] | 24 | 2026-01-05 | X | ✓ |
-| [Famou-Agent 2.0](https://github.com/baidubce/FM-Agent) | Gemini-2.5-Pro | 75.76 ± 1.52 | 57.89 ± 1.52 | 40.00 ± 0.00 | 59.56 ± 0.89 | 24 | 2025-12-27 | X | ✓ |
-| [ML-Master 2.0](https://github.com/sjtu-sai-agents/ML-Master) | Deepseek-V3.2-Speciale | 75.76 ± 1.51 | 50.88 ± 3.51 | 42.22 ± 2.22 | 56.44 ± 2.47 | 24 | 2025-12-16 | X | ✓ |
-| [CAIR](https://research.google/teams/cloud-ai-research/) [MARS](https://arxiv.org/pdf/2602.02660) | Gemini-3-Pro-Preview | 74.24 ± 1.52  | 52.63  ±  3.04 | 37.78 ± 2.22  | 56.0 ± 1.54 | 24 | 2026-01-25 | X | ✓ |
-| [PiEvolve](https://github.com/FractalAIResearchLabs/PiEvolve)<br>(Fractal AI Research) | Gemini-3-Pro-Preview[^4] | 74.24 ± 3.03[^3] | 45.61 ±  0.88[^3] | 35.55 ± 2.22[^3] | 52.0 ±  0.77[^3] | 12 | 2026-01-05 | X | ✓ |
-| [Leeroo](https://github.com/Leeroo-AI/kapso) | Gemini-3-Pro-Preview[^4] | 68.18 ± 2.62[^3] | 44.74 ± 1.52[^3] | 40.00 ± 0.00[^3] | 50.67 ± 1.33[^3] | 24 | 2025-12-07 | ✓ | ✓ |
-| [Thesis](https://thesislabs.ai) | gpt-5-codex | 65.15 ± 1.52 | 45.61 ± 7.18 | 31.11 ± 2.22 | 48.44 ± 3.64 | 24 | 2025-11-10 | X | ✓ |
-| [CAIR](https://research.google/teams/cloud-ai-research/) MLE-STAR-Pro-1.5 | Gemini-2.5-Pro | 68.18 ± 2.62 | 34.21 ± 1.52  |  33.33 ±  0.00 |  44.00 ± 1.33 | 24 | 2025-11-25 | X | ✓ |
-| [Famou-Agent](https://github.com/baidubce/FM-Agent) | Gemini-2.5-Pro | 62.12 ± 1.52 | 36.84 ± 1.52 | 33.33 ± 0.00 | 43.56 ± 0.89 | 24 | 2025-10-10 | X | ✓ |
-| [Operand](https://operand.com) ensemble | gpt-5 (low verbosity/effort)[^2] | 63.64 ± 0.00 | 33.33 ± 0.88[^3] | 20.00 ± 0.00[^3] | 39.56 ± 0.44[^3] | 24 | 2025-10-06 | X | ✓ |
-| [CAIR](https://research.google/teams/cloud-ai-research/) MLE-STAR-Pro-1.0 | Gemini-2.5-Pro | 66.67 ± 1.52  | 25.44  ±  0.88 | 31.11 ± 2.22  | 38.67 ± 0.77 | 12 | 2025-11-03 | X | ✓ |
-| [InternAgent](https://github.com/Alpha-Innovator/InternAgent/) | deepseek-r1 | 62.12 ± 3.03 | 26.32 ± 2.63 | 24.44 ± 2.22 | 36.44 ± 1.18 | 12 | 2025-09-12 | X | ✓ |
-| [R&D-Agent](https://github.com/microsoft/RD-Agent) | gpt-5 | 68.18 ± 2.62 | 21.05 ± 1.52 | 22.22 ± 2.22 | 35.11 ± 0.44 | 12 | 2025-09-26 | ✓ | ✓ |
-| [Neo](https://heyneo.so/) multi-agent | undisclosed | 48.48 ± 1.52 | 29.82 ± 2.32 | 24.44 ± 2.22 | 34.22 ± 0.89 | 36 | 2025-07-28 | X | ✓ |
-| [AIRA-dojo](https://github.com/facebookresearch/aira-dojo/) | o3 | 55.00 ± 1.47 | 21.97 ± 1.17 | 21.67 ± 1.07 | 31.60 ± 0.82 | 24 | 2025-05-15 | ✓ | ✓ |
-| [R&D-Agent](https://github.com/microsoft/RD-Agent) | o3 + GPT-4.1 | 51.52 ± 4.01 | 19.30 ± 3.16 | 26.67 ± 0.00 | 30.22 ± 0.89 | 24 | 2025-08-15 | ✓ | ✓ |
-| [ML-Master](https://github.com/zeroxleo/ML-Master) | deepseek-r1 | 48.48 ± 1.52 | 20.18 ± 2.32 | 24.44 ± 2.22 | 29.33 ± 0.77 | 12 | 2025-06-17 | ✓ | ✓ |
-| [R&D-Agent](https://github.com/microsoft/RD-Agent) | o1-preview | 48.18 ± 1.11 | 8.95 ± 1.05 | 18.67 ± 1.33 | 22.40 ± 0.50 | 24 | 2025-05-14 | ✓ | ✓ |
-| [AIDE](https://github.com/wecoai/aideml) | o1-preview | 35.91 ± 1.86 | 8.45 ± 0.43 | 11.67 ± 1.27 | 17.12 ± 0.61 | 24 | 2024-10-08 | ✓ | ✓ |
-| [AIDE](https://github.com/wecoai/aideml) | gpt-4o-2024-08-06 | 18.55 ± 1.26 | 3.06 ± 0.33 | 8.15 ± 0.84 | 8.63 ± 0.54 | 24 | 2024-10-08 | ✓ | ✓ |
-| [AIDE](https://github.com/wecoai/aideml) | claude-3-5-sonnet-20240620 | 19.70 ± 1.52 | 2.63 ± 1.52 | 2.22 ± 2.22 | 7.56 ± 1.60 | 24 | 2024-10-08 | ✓ | ✓ |
-| OpenHands | gpt-4o-2024-08-06 | 12.12 ± 1.52 | 1.75 ± 0.88 | 2.22 ± 2.22 | 4.89 ± 0.44 | 24 | 2024-10-08 | ✓ | ✓ |
-| [AIDE](https://github.com/wecoai/aideml) | llama-3.1-405b-instruct | 10.23 ± 1.14 | 0.66 ± 0.66 | 0.00 ± 0.00 | 3.33 ± 0.38 | 24 | 2024-10-08 | ✓ | ✓ |
-| MLAB | gpt-4o-2024-08-06 | 4.55 ± 0.86 | 0.00 ± 0.00 | 0.00 ± 0.00 | 1.60 ± 0.27 | 24 | 2024-10-08 | ✓ | ✓ |
-
-### Additional Leaderboard Submissions
-
-Additional submissions that are not directly comparable to the main leaderboard (see `Notes` column).
-
-| Agent | LLM(s) used | Low == Lite (%) | Medium (%) | High (%) | All (%) | Running Time (hours) | Date | Notes | Source Code Available | Grading Reports Available |
-|-------|-------------|-----------------|------------|----------|---------|----------------------|------|-------|----------------------|---------------------------|
-| [Disarray](https://disarray.ai) | Ensemble (Claude-Opus-4.5, Claude-Sonnet-4.5, GPT-5.2-Codex, Gemini-3-Pro-Preview) | 90.91 ± 0.00 | 72.81 ± 0.88 | 71.11 ± 2.22 | 77.78 ± 0.44 | 24 | 2026-02-03 | [Test-set feedback](https://github.com/openai/mle-bench/pull/118) | X | ✓ |
-| [LoongFlow](https://github.com/baidu-baige/LoongFlow) | Gemini-3-Flash-Preview | 77.27 ± 0.0[^3] | 63.15 ± 1.51[^3] | 40.0 ± 0.00[^3] | 62.66 ± 0.76[^3] | 24 | 2026-02-09 | [Test-set feedback](https://github.com/openai/mle-bench/pull/119) | ✓ | ✓ |
-
-[^2]: With some light assistance from an ensemble of models including
-    Gemini-2.5-Pro, Grok-4, and Claude 4.1 Opus, distilled by Gemini-2.5-Pro.
-[^3]: Computed by padding incomplete seeds with failing scores.
-[^4]: The architecture is primarily driven by Gemini-3-Pro-Preview, with a subset of modules utilizing GPT-5 and GPT-5-mini.
-
-### Producing Scores for the Leaderboard
-
-To produce the scores for the leaderboard, please organize your grading reports
-in the `runs/` folder organized by run groups, with one grading report per run
-group. Identify the run groups for your submission in
-`runs/run_group_experiments.csv` with an experiment id. Then run
-
-```bash
-uv run python experiments/aggregate_grading_reports.py --experiment-id <exp_id> --split low
-uv run python experiments/aggregate_grading_reports.py --experiment-id <exp_id> --split medium
-uv run python experiments/aggregate_grading_reports.py --experiment-id <exp_id> --split high
-uv run python experiments/aggregate_grading_reports.py --experiment-id <exp_id> --split split75
-```
-
-Report the mean and standard error of the mean (SEM) for each of the splits on
-the reported `any_medal_percentage` metric. The `--split75` flag corresponds to
-the `All (%)` column.
-
-## Benchmarking
-
-This section describes a canonical setup for comparing scores on MLE-bench. We recommend the following:
-- Repeat each evaluation with at least 3 seeds and report the Any Medal (%) score as the mean ± one standard error of the mean. The evaluation (task and grading) itself is deterministic, but agents/LLMs can be quite high-variance!
-- Agent resources - not a strict requirement of the benchmark but please report if you stray from these defaults!
-  - Runtime: 24 hours
-  - Compute: 36 vCPUs with 440GB RAM and one 24GB A10 GPU
-- Include a breakdown of your scores across Low, Medium, High, and All complexity [splits](experiments/splits) (see *Lite evaluation* below for why this is useful).
-
-### Lite Evaluation
-
-Evaluating agents with the above settings on the full 75 competitions of MLE-bench can be expensive. For users preferring a "lite" version of the benchmark, we recommend using the [Low complexity split](https://github.com/openai/mle-bench/blob/main/experiments/splits/low.txt) of our dataset, which consists of only 22 competitions. This reduces the number of runs substantially, while still allowing fair comparison along one column of the table above.
-
-Furthermore, the Low complexity competitions tend to be significantly more lightweight (158GB total dataset size compared to 3.3TB for the full set), so users may additionally consider reducing the runtime or compute resources available to the agents for further cost reduction. However, note that doing so risks degrading the performance of your agent. For example, see [Section 3.3 and 3.4 of our paper](https://arxiv.org/abs/2410.07095) where we have experimented with varying resources on the full competition set.
-
-The Lite dataset contains the following competitions:
-
-| Competition ID                              | Category                   | Dataset Size (GB) |
-|---------------------------------------------|----------------------------|--------------------|
-| aerial-cactus-identification                | Image Classification       | 0.0254            |
-| aptos2019-blindness-detection               | Image Classification       | 10.22             |
-| denoising-dirty-documents                   | Image To Image             | 0.06              |
-| detecting-insults-in-social-commentary      | Text Classification        | 0.002             |
-| dog-breed-identification                    | Image Classification       | 0.75              |
-| dogs-vs-cats-redux-kernels-edition          | Image Classification       | 0.85              |
-| histopathologic-cancer-detection            | Image Regression           | 7.76              |
-| jigsaw-toxic-comment-classification-challenge | Text Classification        | 0.06              |
-| leaf-classification                         | Image Classification       | 0.036             |
-| mlsp-2013-birds                             | Audio Classification       | 0.5851            |
-| new-york-city-taxi-fare-prediction          | Tabular                   | 5.7               |
-| nomad2018-predict-transparent-conductors    | Tabular                   | 0.00624           |
-| plant-pathology-2020-fgvc7                  | Image Classification       | 0.8               |
-| random-acts-of-pizza                        | Text Classification        | 0.003             |
-| ranzcr-clip-catheter-line-classification    | Image Classification       | 13.13             |
-| siim-isic-melanoma-classification           | Image Classification       | 116.16            |
-| spooky-author-identification                | Text Classification        | 0.0019            |
-| tabular-playground-series-dec-2021          | Tabular                   | 0.7               |
-| tabular-playground-series-may-2022          | Tabular                   | 0.57              |
-| text-normalization-challenge-english-language | Seq->Seq                 | 0.01              |
-| text-normalization-challenge-russian-language | Seq->Seq                 | 0.01              |
-| the-icml-2013-whale-challenge-right-whale-redux | Audio Classification     | 0.29314           |
-
-## Setup
-
-Some MLE-bench competition data is stored using [Git-LFS](https://git-lfs.com/).
-Once you have downloaded and installed LFS, run:
-
-```console
-git lfs fetch --all
-git lfs pull
-```
-
-You can install `mlebench` with pip:
-
-```console
-pip install -e .
-```
-
-### Pre-Commit Hooks (Optional)
-
-If you're committing code, you can install the pre-commit hooks by running:
-
-```console
-pre-commit install
-```
-
-## Dataset
-
-The MLE-bench dataset is a collection of 75 Kaggle competitions which we use to evaluate the ML engineering capabilities of AI systems.
-
-Since Kaggle does not provide the held-out test set for each competition, we
-provide preparation scripts that split the publicly available training set into
-a new training and test set.
-
-For each competition, we also provide grading scripts that can be used to
-evaluate the score of a submission.
-
-We use the [Kaggle API](https://github.com/Kaggle/kaggle-api) to download the
-raw datasets. Ensure that you have downloaded your Kaggle credentials
-(`kaggle.json`) and placed it in the `~/.kaggle/` directory (this is the default
-location where the Kaggle API looks for your credentials). To download and prepare the MLE-bench dataset, run the following, which will download and prepare the dataset in your system's default cache directory. Note, we've found this to take two days when running from scratch:
-
-```console
-mlebench prepare --all
-```
-
-To prepare the lite dataset, run:
-
-```console
-mlebench prepare --lite
-```
-
-Alternatively, you can prepare the dataset for a specific competition by
-running:
-
-```console
-mlebench prepare -c <competition-id>
-```
-
-Run `mlebench prepare --help` to see the list of available competitions.
-
-
-
-## Grading Submissions
-
-Answers for competitions must be submitted in CSV format; the required format is described in each competition's description, or shown in a competition's sample submission file. You can grade multiple submissions by using the `mlebench grade` command. Given a JSONL file, where each line corresponds with a submission for one competition, `mlebench grade` will produce a grading report for each competition. The JSONL file must contain the following fields:
-- `competition_id`: the ID of the competition in our dataset.
-- `submission_path`: a `.csv` file with the predictions for the specified
-  competition.
-
-See more information by running `mlebench grade --help`.
-
-You can also grade individual submissions using the `mlebench grade-sample` command. For example, to grade a submission for the Spaceship Titanic competition, you can run:
-
-```console
-mlebench grade-sample <PATH_TO_SUBMISSION> spaceship-titanic
-```
-
-See more information by running `mlebench grade-sample --help`.
-
-## Environment
-
-We provide a base Docker image `mlebench-env` which is the base environment for our agents. This base image contains:
-- Conda environment used to execute our agents. We optionally (default true) install Python packages in this environment which are commonly used across our agents. If you don't want to install these packages, set the `INSTALL_HEAVY_DEPENDENCIES` environment variable to `false` when building the image, by adding `--build-arg INSTALL_HEAVY_DEPENDENCIES=false` to the `docker build` command below
-- Instructions for agents to follow when creating their submission
-- Grading server for agents to use when checking that the structure of their submission is correct
-
-Build this image by running:
-
-```bash
-docker build --platform=linux/amd64 -t mlebench-env -f environment/Dockerfile .
-```
-
-## Agents
-
-We purposefully designed our benchmark to not make any assumptions about the agent that produces submissions, so agents can more easily be evaluated on this benchmark. We evaluated three open-source agents; we discuss this procedure in [agents/README.md](agents/README.md).
-
-## Extras
-
-We include additional features in the MLE-bench repository that may be useful
-for MLE-bench evaluation. These include a rule violation detector and
-a plagiarism detector. We refer readers to
-[extras/README.md](extras/README.md) for more information.
-
-## Examples
-
-We collect example usage of this library in the `examples/` directory, see [examples/README.md](examples/README.md) for more information.
-
-## Experiments
-
-We place the code specific to the experiments from our publication of the
-benchmark in the `experiments/` directory:
-- For instance, our competition splits are available in `experiments/splits/`.
-- For a completed set of runs from a given agent, you can use the provided
-`experiments/make_submission.py` script to compile its submission for grading.
-- We release our methodology for the "familiarity" experiments in `experiments/familiarity/`, see [experiments/familiarity/README.md](experiments/familiarity/README.md) for more information.
-
-## Dev
-
-Note, when running `pytest` locally, be sure to accept the competition rules otherwise the tests will fail.
-
-## Known Issues
-
-There are some known issues with certain MLE-bench competitions. Since we have
-already received leaderboard submissions, we are postponing fixes to avoid
-invalidating the leaderboard. Instead, we plan to release batched fixes in the
-upcoming v2 release of MLE-bench on the
-[openai/frontier-evals](https://github.com/openai/frontier-evals) repo, which will
-include a version column in the leaderboard to distinguish between v1 and v2 results.
-If you wish to make a submission to v1 in the meantime, please still include
-the following competitions in your overall scores. The known issues are
-catalogued below:
-
-- **tensorflow2-question-answering**:
-  - The `validate_submission` function in `grade.py` fails on this competition 
-    because the answer file is `test.jsonl` instead of `test.csv`.
-    [#134](https://github.com/openai/mle-bench/issues/134)
-- **tensorflow-speech-recognition-challenge**:
-  - The prepare.py script incorrectly prepares the test set such that there is a
-    much larger range of test labels than there should be.
-    [#63](https://github.com/openai/mle-bench/issues/63)
-  - The prepare.py script does not properly create a test set where the speaker
-    IDs are disjoint from those in train/val.
-- **icecube-neutrinos-in-deep-ice**: Checksums are mismatch.
-  [#58](https://github.com/openai/mle-bench/issues/58)
-- **ranzcr-clip-catheter-line-classification**: The prepare.py script results in
-  missing columns in the sample submission.
-  [#30](https://github.com/openai/mle-bench/issues/30)
-- **dog-breed-identification**: The MLE-bench test split is created by holding
-  out images from a publicly labeled source corpus derived from the Stanford
-  Dogs Dataset, which agents may discover and leverage.
-  [#128](https://github.com/openai/mle-bench/issues/128)
-- **invasive-species-monitoring**: The prepare.py script archives the prepared
-  `train/` and `test/` directories incorrectly, so `train.7z` and `test.7z`
-  can be missing their image contents in the prepared public dataset.
-  [#122](https://github.com/openai/mle-bench/issues/122)
-- **tabular-playground-series-dec-2021**: The leaderboard is crowded -- very
-  little difference between the top score and the median score.
-- **tabular-playground-series-may-2022**: The leaderboard is crowded -- very
-  little difference between the top score and the median score.
-- **jigsaw-toxic-comment-classification-challenge**: The leaderboard is crowded -- very
-  little difference between the top score and the median score.
-- **champs-scalar-coupling**: test molecules are missing in structures.csv.
-  [#70](https://github.com/openai/mle-bench/pull/70)
-- **multi-modal-gesture-recognition**: public test `.mat` files leak test labels.
-  [#77](https://github.com/openai/mle-bench/issues/77)
-- **smartphone-decimeter-2022**: The public test `span_log.nmea` files leak
-  information that makes achieving a perfect score trivial.
-  [#93](https://github.com/openai/mle-bench/issues/93)
-- **hubmap-kidney-segmentation**: The public test `{image_id}.json` files leak
-  information that makes achieving a close-to-perfect score trivial. They should
-  be removed.
-- **random-acts-of-pizza**: The field `giver_username_if_known` leaks the outcome,
-  enabling trivial perfect prediction. This competition should be dropped.
-  [#108](https://github.com/openai/mle-bench/issues/108)
-
-## Authors
-
-Chan Jun Shern, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Lilian Weng, Aleksander Mądry
-
-## Citation
-
-Please cite using the following BibTeX entry:
-```
-@article{chan2024mle-bench,
-  title={MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering},
-  author={Jun Shern Chan and Neil Chowdhury and Oliver Jaffe and James Aung and Dane Sherburn and Evan Mays and Giulio Starace and Kevin Liu and Leon Maksin and Tejal Patwardhan and Lilian Weng and Aleksander Mądry},
-  year={2024},
-  eprint={2410.07095},
-  archivePrefix={arXiv},
-  primaryClass={cs.CL},
-  url={https://arxiv.org/abs/2410.07095}
-}
-```

benchmark/MLE-bench/mlebench/competitions/h-and-m-personalized-fashion-recommendations/prepare.py

@@ -1,102 +0,0 @@
-import os
-import shutil
-from pathlib import Path
-
-import pandas as pd
-
-from mlebench.utils import read_csv
-
-
-def prepare(raw: Path, public: Path, private: Path):
-    """
-    Splits the data in raw into public and private datasets with appropriate test/train splits.
-    """
-
-    # Create train, test from train split
-    old_train = read_csv(raw / "transactions_train.csv")
-    old_train["purchase_id"] = (
-        old_train["customer_id"].astype(str)
-        + "_"
-        + old_train["article_id"].astype(str)
-        + "_"
-        + old_train["t_dat"].astype(str)
-    )
-
-    # The task is to predict what purchases will be made in the next 7 days.
-    # To create our test set, we will take the purchases made in the last 7 days of the training set.
-    old_train["t_dat_parsed"] = pd.to_datetime(
-        old_train["t_dat"]
-    )  # Parse t_dat to datetime in a new column
-    max_date = old_train["t_dat_parsed"].max()  # Find the maximum date in the t_dat_parsed column
-    old_train["in_last_7_days"] = old_train["t_dat_parsed"] >= (max_date - pd.Timedelta(days=7))
-    new_train = old_train[
-        old_train["in_last_7_days"] == False
-    ].copy()  # Filter rows where t_dat_parsed is more than 7 days from the maximum date
-    new_test = old_train[
-        old_train["in_last_7_days"] == True
-    ].copy()  # Filter rows where t_dat_parsed is within the last 7 days of the time series
-
-    # Train/test checks
-    assert (
-        not new_test["purchase_id"].isin(new_train["purchase_id"]).any()
-    ), "No purchase_ids should be shared between new_test and new_train"
-    new_train = new_train.drop(columns=["purchase_id", "t_dat_parsed", "in_last_7_days"])
-    new_test = new_test.drop(columns=["purchase_id", "t_dat_parsed"])
-
-    # sample submission and answers differ because the task is predicting what articles each
-    # customer will purchase in the 7-day period immediately after the training data ends. Customer
-    # who did not make any purchase during that time are excluded from the scoring.
-
-    # As such we can't put the exact customer ids from test set into the sample submission, as this
-    # would leak which customers made purchases in the test set. Instead, we put _all_ the customer
-    # ids in the sample submission, ask the user to predict for all of them, and then we will filter
-    # out in grade.py the customers who did not make any purchases in the test set.
-
-    # Answers, contains only customers that actually made purchases in the test period.
-    answers = (
-        new_test.groupby("customer_id")["article_id"]
-        .apply(lambda x: " ".join(x.astype(str)))
-        .reset_index()
-    )
-    # rename 'article_id' to 'prediction'
-    answers = answers.rename(columns={"article_id": "prediction"})
-
-    # Sample submission, which contains all customer ids.
-    shutil.copyfile(
-        src=raw / "sample_submission.csv",
-        dst=public / "sample_submission.csv",
-    )
-
-    # Write CSVs
-    # new_test.to_csv(private / "test.csv", index=False)
-    answers.to_csv(private / "answers.csv", index=False)
-    new_train.to_csv(public / "transactions_train.csv", index=False)
-
-    # Copy files and images directory
-    shutil.copyfile(
-        src=raw / "articles.csv",
-        dst=public / "articles.csv",
-    )
-    shutil.copyfile(
-        src=raw / "customers.csv",
-        dst=public / "customers.csv",
-    )
-    shutil.copytree(
-        src=raw / "images",
-        dst=public / "images",
-        dirs_exist_ok=True,
-    )
-
-    # checks
-    expected_train_columns = ["t_dat", "customer_id", "article_id", "price", "sales_channel_id"]
-    assert (
-        new_train.columns.tolist() == expected_train_columns
-    ), f"Unexcpected columns in new_train, expected {expected_train_columns}, got {new_train.columns.tolist()}"
-
-    expected_answer_columns = ["customer_id", "prediction"]
-    assert (
-        answers.columns.tolist() == expected_answer_columns
-    ), f"Unexcpected columns in answers, expected {expected_answer_columns}, got {answers.columns.tolist()}"
-    assert answers["customer_id"].nunique() == len(
-        answers
-    ), "There should be no duplicate customer_ids in answers"

benchmark/MLE-bench/mlebench/competitions/herbarium-2020-fgvc7/checksums.yaml

@@ -1,5 +0,0 @@
-private:
-  answers.csv: 5e7355890f648e4f8be7a68e6c9c71c5
-public:
-  sample_submission.csv: a1bdb830f6582fcb9cffe8cec363a0b1
-zip: 30eafe0eda5d047bce1488c9a3470d57

benchmark/MLE-bench/mlebench/competitions/herbarium-2020-fgvc7/config.yaml

@@ -1,16 +0,0 @@
-id: herbarium-2020-fgvc7
-name: Herbarium 2020 - FGVC7
-competition_type: simple
-awards_medals: false
-prizes: null
-description: mlebench/competitions/herbarium-2020-fgvc7/description.md
-
-dataset:
-  answers: herbarium-2020-fgvc7/prepared/private/answers.csv
-  sample_submission: herbarium-2020-fgvc7/prepared/public/sample_submission.csv
-
-grader:
-  name: macro-f1-score
-  grade_fn: mlebench.competitions.herbarium-2020-fgvc7.grade:grade
-
-preparer: mlebench.competitions.herbarium-2020-fgvc7.prepare:prepare

benchmark/MLE-bench/mlebench/competitions/herbarium-2020-fgvc7/description.md

@@ -1,159 +0,0 @@
-# Overview
-
-## Description
-
-The Herbarium 2020 FGVC7 Challenge is to identify vascular plant species from a large, long-tailed collection herbarium specimens provided by the [New York Botanical Garden](https://www.nybg.org/plant-research-and-conservation/) (NYBG).
-
-The Herbarium 2020 dataset contains over 1M images representing over 32,000 plant species. This is a dataset with a long tail; there are a minimum of 3 specimens per species. However, some species are represented by more than a hundred specimens. This dataset only contains vascular land plants which includes lycophytes, ferns, gymnosperms, and flowering plants. The extinct forms of lycophytes are the major component of coal deposits, ferns are indicators of ecosystem health, gymnosperms provide major habitats for animals, and flowering plants provide all of our crops, vegetables, and fruits.
-
-![specimen1](https://raw.githubusercontent.com/visipedia/herbarium_comp/master/2020/assets/specimen1.jpg)
-![specimen2](!https://raw.githubusercontent.com/visipedia/herbarium_comp/master/2020/assets/specimen2.jpg)
-![specimen3](https://raw.githubusercontent.com/visipedia/herbarium_comp/master/2020/assets/specimen3.jpg)
-![specimen4](https://raw.githubusercontent.com/visipedia/herbarium_comp/master/2020/assets/specimen4.jpg)
-![specimen5](https://raw.githubusercontent.com/visipedia/herbarium_comp/master/2020/assets/specimen5.jpg)
-
-The teams with the most accurate models will be contacted, with the intention of using them on the un-named plant collections in the NYBG herbarium collection, and assessed by the NYBG plant specialists.
-
-### Background
-
-The New York Botanical Garden (NYBG) herbarium contains more than 7.8 million plant and fungal specimens. Herbaria are a massive repository of plant diversity data. These collections not only represent a vast amount of plant diversity, but since herbarium collections include specimens dating back hundreds of years, they provide snapshots of plant diversity through time. The integrity of the plant is maintained in herbaria as a pressed, dried specimen; a specimen collected nearly two hundred years ago by Darwin looks much the same as one collected a month ago by an NYBG botanist. All specimens not only maintain their morphological features but also include collection dates and locations, and the name of the person who collected the specimen. This information, multiplied by millions of plant collections, provides the framework for understanding plant diversity on a massive scale and learning how it has changed over time.
-
-### About
-
-This is an FGVC competition hosted as part of the [FGVC7](https://sites.google.com/corp/view/fgvc7/home) workshop at [CVPR 2020](http://cvpr2020.thecvf.com/) and sponsored by [NYBG](https://www.nybg.org/plant-research-and-conservation/).
-
-Details of this competition are mirrored on the [github page](https://github.com/visipedia/herbarium_comp). Please post in the forum or open an issue if you have any questions or problems with the dataset.
-
-## Evaluation
-
-Submissions are evaluated using the [macro F1 score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html).
-
-F1 is calculated as follows:
-
-$$
-F_1=2 * \frac{\text { precision } * \text { recall }}{\text { precision }+ \text { recall }}
-$$
-
-where:
-
-$$
-\begin{gathered}
-\text { precision }=\frac{T P}{T P+F P} \\
-\text { recall }=\frac{T P}{T P+F N}
-\end{gathered}
-$$
-
-In "macro" F1 a separate F1 score is calculated for each `species` value and then averaged.
-
-### Submission Format
-
-For each image `Id`, you should predict the corresponding image label ("category_id") in the `Predicted` column. The submission file should have the following format:
-
-```
-Id,Predicted
-0,0
-1,27
-2,42
-...
-```
-
-## Timeline
-
-- **March 9, 2020** - Competition start date.
-- **May 19, 2020** - Entry deadline. You must accept the competition rules before this date in order to compete.
-- **May 19, 2020** - Team Merger deadline. This is the last day participants may join or merge teams.
-- **May 26, 2020** - Final submission deadline.
-
-All deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.
-
-## CVPR 2020
-
-This competition is part of the Fine-Grained Visual Categorization [FGVC7](https://sites.google.com/view/fgvc7/home) workshop at the Computer Vision and Pattern Recognition Conference [CVPR 2020](http://cvpr2020.thecvf.com/). A panel will review the top submissions for the competition based on the description of the methods provided. From this, a subset may be invited to present their results at the workshop. Attending the workshop is not required to participate in the competition, however only teams that are attending the workshop will be considered to present their work.
-
-There is no cash prize for this competition. Attendees presenting in person are responsible for all costs associated with travel, expenses, and fees to attend CVPR 2020. PLEASE NOTE: CVPR frequently sells out early, we cannot guarantee CVPR registration after the competition's end. If you are interested in attending, please plan ahead.
-
-You can see a list of all of the FGVC7 competitions [here](https://sites.google.com/corp/view/fgvc7/competitions).
-
-## Citation
-
-Christine Kaeser-Chen, Kiat Chuan Tan, Walter Reade, Maggie Demkin. (2020). Herbarium 2020 - FGVC7. Kaggle. https://kaggle.com/competitions/herbarium-2020-fgvc7
-
-# Data
-
-## Dataset Description
-
-### Data Overview
-
-The training and test set contain images of herbarium specimens, from over 32,000 species of vascular plants. Each image contains exactly one specimen. The text and barcode labels on the specimen images have been blurred to remove category information in the image.
-
-The data has been approximately split 80%/20% for training/test. Each category has at least 1 instance in both the training and test datasets. Note that the test set distribution is slightly different from the training set distribution. The training set contains species with hundreds of examples, but the test set has the number of examples per species capped at a maximum of 10.
-
-### Dataset Details
-
-Each image has different image dimensions, with a maximum of 1000 pixels in the larger dimension. These have been resized from the original image resolution. All images are in JPEG format.
-
-### Dataset Format
-
-This dataset uses the [COCO dataset format](http://cocodataset.org/#format-data) with additional annotation fields. In addition to the species category labels, we also provide region and supercategory information.
-
-The training set metadata (`train/metadata.json`) and test set metadata (`test/metadata.json`) are JSON files in the format below. Naturally, the test set metadata file omits the "annotations", "categories" and "regions" elements.
-
-```
-{
-  "annotations" : [annotation],
-  "categories" : [category],
-  "images" : [image],
-  "info" : info,
-  "licenses": [license],
-  "regions": [region]
-}
-
-info {
-  "year" : int,
-  "version" : str,
-  "url": str,
-  "description" : str,
-  "contributor" : str,
-  "date_created" : datetime
-}
-
-image {
-  "id" : int,
-  "width" : int,
-  "height" : int,
-  "file_name" : str,
-  "license" : int
-}
-
-annotation {
-  "id": int,
-  "image_id": int,
-  "category_id": int,
-  # Region where this specimen was collected.
-  "region_id": int
-}
-
-category {
-  "id" : int,
-  # Species name
-  "name" : str,
-  # We also provide the super-categories for each species.
-  "family": str,
-  "genus": str
-}
-
-region {
-  "id": int
-  "name": str
-}
-
-license {
-  "id": 1,
-  "name": str,
-  "url": str
-}
-```
-
-The training set images are organized in subfolders `train/<subfolder1>/<subfolder2>/<image id>.jpg`.
-
-The test set images are organized in subfolders `test/<subfolder>/<image id>.jpg`.

benchmark/MLE-bench/mlebench/competitions/herbarium-2020-fgvc7/description_obfuscated.md

@@ -1,27 +0,0 @@
-# Task
-
-Build a model to classify the species of plants from images.
-
-# Metric
-
-Macro F1 score. A separate F1 score is calculated for each `species` value and then averaged.
-
-# Submission Format
-
-For each image `Id`, you should predict the corresponding image label ("category_id") in the `Predicted` column. The submission file should have the following format:
-
-```
-Id,Predicted
-0,0
-1,27
-2,42
-...
-```
-
-# Dataset 
-
-This dataset uses the [COCO dataset format](http://cocodataset.org/#format-data) with additional annotation fields. In addition to the species category labels, we also provide region and supercategory information.
-
-The training set images are organized in subfolders `train/<subfolder1>/<subfolder2>/<image id>.jpg`.
-
-The test set images are organized in subfolders `test/<subfolder>/<image id>.jpg`.

benchmark/MLE-bench/mlebench/competitions/herbarium-2020-fgvc7/grade.py

@@ -1,34 +0,0 @@
-import pandas as pd
-from sklearn.metrics import f1_score
-
-from mlebench.grade_helpers import InvalidSubmissionError
-
-
-def prepare_for_metric(submission: pd.DataFrame, answers: pd.DataFrame):
-    """
-    `submission` and `answers` are pd.DataFrame with "Id" and "Predicted" columns
-    """
-    if len(submission) != len(answers):
-        raise InvalidSubmissionError(
-            f"Submission length {len(submission)} != answers length {len(answers)}"
-        )
-    if "Id" not in submission.columns or "Predicted" not in submission.columns:
-        raise InvalidSubmissionError("Submission must have 'Id' and 'Predicted' columns")
-
-    assert "Id" in answers.columns, "Answers must have 'Id' column"
-    assert "Predicted" in answers.columns, "Answers must have 'Predicted' column"
-
-    # Sort submission and answers by "Id"
-    submission = submission.sort_values("Id")
-    answers = answers.sort_values("Id")
-    if (submission["Id"].values != answers["Id"].values).any():
-        raise InvalidSubmissionError("Submission and answers have mismatched 'Id' columns")
-
-    y_true = [int(y) for y in answers["Predicted"]]
-    y_pred = [int(y) for y in submission["Predicted"]]
-    return y_true, y_pred
-
-
-def grade(submission: pd.DataFrame, answers: pd.DataFrame) -> float:
-    y_true, y_pred = prepare_for_metric(submission, answers)
-    return f1_score(y_true=y_true, y_pred=y_pred, average="macro")

benchmark/MLE-bench/mlebench/competitions/herbarium-2020-fgvc7/kernels.txt

@@ -1,35 +0,0 @@
-seraphwedd18/herbarium-consolidating-the-details
-rsingh99/getting-started-with-herbarium-2020
-yasufuminakama/herbarium-2020-pytorch-resnet18-inference
-yasufuminakama/herbarium-2020-pytorch-resnet18-train
-khotijahs1/identify-plant-species-from-herbarium-specimens
-jullang/herbarium-via-resnet50-and-3-step-classification
-jagannathrk/herbarium-2020
-drobchak1988/herbarium-2020-fgvc7-create-tfrecords-tensorflow
-vishnuvardhanvm/herbarium-2020
-tathagatbanerjee/herbarium
-thejravichandran/herbarium-2020-competition
-wojciech1103/herbarium-2020-a-little-bit-fun-with-images
-shaunthesheep/fgvc7-herbarium-2020-data-viz
-sergey55/herbarium-2020-notebook
-thejravichandran/testing-herbarium-2020-v2
-gb00000/herb-nn
-anmol03kumar/few-shot-learning-on-herbarium
-masmask/herbarium-predict-by-efficientnet
-thejravichandran/production-herbarium-2020
-tkm123456/notebook-mykaggle
-rivilcan/herbarium-efficientnetb3
-riabovanderew/h-2fc-ce-d
-michaelschastlivcev/herbarium-2020-pytorch
-blueturtle/plant-detection-resnet50-train
-greendolphin/kernel536e7b756c
-jhayescao/herbarium-2020-digital-garden
-iamprateek/herbarium-2020-classification
-grapestone5321/herbarium-2020-sample-submission
-colorfuldra/herbarium-2020-pytorch
-quratulainarshad/herbarium-via-resnet50-and-3-step-classification
-quratjaffery/herbarium-consolidating-the-details-9d1717
-mariumzia95/herbarium-consolidating-the-details
-quratjaffery/herbarium-consolidating-the-details
-iamricha/herbarium-via-resnet50-and-3-step-classification
-hungwenchen0306/herbarium-classifier-resnet

benchmark/MLE-bench/mlebench/competitions/herbarium-2020-fgvc7/leaderboard.csv

@@ -1,155 +0,0 @@
-scoreNullable,teamId,hasTeamName,submissionDate,score,hasScore
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-0.00002,4525375,True,2020-03-23 02:52:43,0.00002,True
-0.00002,4595957,True,2020-03-23 02:25:33,0.00002,True
-0.00002,4536458,True,2020-03-15 23:17:30,0.00002,True
-0.00002,4525314,True,2020-03-10 13:46:29,0.00002,True
-0.00002,4545294,True,2020-03-14 08:35:28,0.00002,True
-0.00002,4653369,True,2020-03-31 16:00:49,0.00002,True
-0.00002,4528851,True,2020-03-15 03:49:55,0.00002,True
-0.00002,4810745,True,2020-05-03 05:29:25,0.00002,True
-0.00001,4843557,True,2020-05-26 15:17:49,0.00001,True
-0.00001,4529102,True,2020-03-12 06:09:18,0.00001,True
-0.00001,4898764,True,2020-05-26 01:00:47,0.00001,True
-0.00001,4584204,True,2020-04-06 04:13:48,0.00001,True
-0.00001,4580958,True,2020-05-20 21:55:45,0.00001,True
-0.00001,4523118,True,2020-03-17 08:06:31,0.00001,True
-0.00000,4790687,True,2020-05-25 01:13:09,0.00000,True
-0.00000,4791646,True,2020-05-19 04:18:09,0.00000,True
-0.00000,4849422,True,2020-05-19 11:33:57,0.00000,True
-0.00000,4914191,True,2020-05-19 15:38:00,0.00000,True
-0.00000,4811951,True,2020-05-25 16:40:46,0.00000,True
-0.00000,4831491,True,2020-05-26 04:00:44,0.00000,True
-0.00000,4742477,True,2020-04-15 19:55:07,0.00000,True
-0.00000,4788035,True,2020-05-25 17:43:28,0.00000,True
-0.00000,4910605,True,2020-05-24 06:59:01,0.00000,True
-0.00000,4724950,True,2020-04-19 06:46:58,0.00000,True
-0.00000,4533030,True,2020-03-27 06:42:37,0.00000,True
-0.00000,4836621,True,2020-05-26 23:58:22,0.00000,True
-0.00000,4578535,True,2020-03-29 09:19:19,0.00000,True
-0.00000,4572040,True,2020-03-20 13:39:37,0.00000,True
-0.00000,4836652,True,2020-04-30 02:47:40,0.00000,True
-0.00000,4769993,True,2020-05-03 14:09:53,0.00000,True
-0.00000,4811759,True,2020-05-26 09:57:32,0.00000,True
-0.00000,4806193,True,2020-04-29 15:32:47,0.00000,True
-0.00000,4572984,True,2020-03-30 22:10:36,0.00000,True
-0.00000,4788179,True,2020-05-26 20:48:54,0.00000,True
-0.00000,4588810,True,2020-04-02 01:14:05,0.00000,True
-0.00000,4927730,True,2020-05-15 01:55:38,0.00000,True
-0.00000,4860243,True,2020-05-20 13:34:54,0.00000,True
-0.00000,4792497,True,2020-05-26 10:38:09,0.00000,True
-0.00000,4816231,True,2020-05-24 02:05:35,0.00000,True
-0.00000,4937598,True,2020-05-25 15:19:21,0.00000,True
-0.00000,4671758,True,2020-05-25 09:08:37,0.00000,True
-0.00000,4580008,True,2020-03-21 00:40:14,0.00000,True
-0.00000,4723746,True,2020-04-13 05:33:31,0.00000,True
-0.00000,4952718,True,2020-05-25 23:49:16,0.00000,True
-0.00000,4529691,True,2020-05-21 15:56:46,0.00000,True
-0.00000,4829924,True,2020-05-06 11:12:44,0.00000,True
-0.00000,4501447,True,2020-03-05 15:46:01,0.00000,True
-0.00000,4522871,True,2020-03-10 04:00:06,0.00000,True
-0.00000,4635223,True,2020-03-28 19:24:32,0.00000,True
-0.00000,4910781,True,2020-05-11 17:11:23,0.00000,True
-0.00000,4810611,True,2020-05-19 13:30:50,0.00000,True
-0.00000,4861551,True,2020-05-20 08:40:45,0.00000,True
-0.00000,4806526,True,2020-05-24 11:42:52,0.00000,True

benchmark/MLE-bench/mlebench/competitions/herbarium-2020-fgvc7/prepare.py

@@ -1,251 +0,0 @@
-import json
-import random
-import shutil
-from pathlib import Path
-
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from tqdm import tqdm
-
-from mlebench.utils import get_logger
-
-logger = get_logger(__name__)
-
-
-def prepare(raw: Path, public: Path, private: Path):
-    """
-    Splits the raw data into public and private datasets with appropriate test/train splits.
-
-    `train/metadata.json` is the "table of contents" for our data, with the following structure:
-    (More details at https://www.kaggle.com/competitions/herbarium-2020-fgvc7/data)
-    ```
-    {
-        "annotations" : [annotation],
-        "categories" : [category],
-        "images" : [image],
-        "info" : info,
-        "licenses" : [license],
-        "regions" : [region]
-    }
-    ```
-    - `images` and `annotations` are both N-length lists corresponding to the N samples.
-        We'll need to split each of these lists into train and test.
-    - The other fields are dataset-wide metadata that we don't need to touch.
-
-    - test/metadata.json is the same structure as train/metadata.json, but without "annotations", "categories", "regions"
-
-    Other notes:
-    - train/test splits need to occur per category (each category should be in both train and test).
-    - The `test/images` and `train/images` folders have nested subdirs to make it easier to browse
-        - `train/images` is structured as `{category_id[:3]}/{category_id[3:]}/{image_id}.jpg`
-        - `test/images` is structured as `{image_idx[:3]}/{image_idx}.jpg` (to not reveal the category)
-    - When we create the new splits, we re-assign image indices so that we don't give away labels based on the index
-        - train images are indexed within their own category
-        - test images follow a flat index after shuffling the categories
-    """
-
-    dev_mode = False
-    dev_count = 2  # Copy over n images per category when in dev mode
-
-    # Create train, test from train split
-    json_path = raw / "nybg2020/train/metadata.json"
-    with open(json_path, "r", encoding="latin-1") as f:  # utf-8 fails
-        old_train_metadata = json.load(f)
-
-    # Organize data by category so that we can split per-category later
-    annotations_images_by_category = {}  # We'll collect both `annotations` and `images` here
-    for annotation, image in list(
-        zip(old_train_metadata["annotations"], old_train_metadata["images"])
-    ):
-        assert (
-            annotation["image_id"] == image["id"]
-        ), f"Mismatching image_id in annotation and image: {annotation['image_id']} vs {image['id']}"
-        category_id = annotation["category_id"]
-        if category_id not in annotations_images_by_category:
-            annotations_images_by_category[category_id] = []
-        annotations_images_by_category[category_id].append(
-            {
-                "annotation": annotation,
-                "image": image,
-            }
-        )
-
-    # Split train/test
-    train_sample_count = 0  # Useful for tqdm later
-    train_annotations_images_by_category = {}
-    test_annotations_images_by_category = {}
-
-    for category_id, annotations_images in tqdm(
-        annotations_images_by_category.items(), desc="Assigning train/test splits"
-    ):
-        # Create split by "category" (class): Each category needs to be in both train and test (80:20) as per original ratio
-        test_size = 0.2
-        n_samples = len(annotations_images)
-        if n_samples == 1:
-            # If only one sample, put it in train
-            train_annotations_images = annotations_images
-            test_annotations_images = []
-        elif n_samples < 5:  # Minimum 5 samples to ensure at least 1 in test
-            # Ensure at least 1 sample in test
-            test_size = max(1, int(n_samples * test_size))
-            train_annotations_images = annotations_images[:-test_size]
-            test_annotations_images = annotations_images[-test_size:]
-        else:
-            train_annotations_images, test_annotations_images = train_test_split(
-                annotations_images, test_size=test_size, random_state=0
-            )
-
-        train_annotations_images_by_category[category_id] = train_annotations_images
-        test_annotations_images_by_category[category_id] = test_annotations_images
-        train_sample_count += len(train_annotations_images)
-
-    # Add to train set
-    new_train_metadata = (
-        old_train_metadata.copy()
-    )  # Keep 'categories', 'info', 'licenses', 'regions'
-    new_train_metadata.update(
-        {
-            "annotations": [],
-            "images": [],
-        }
-    )
-    with tqdm(
-        desc="Creating new train dataset",
-        total=train_sample_count,
-    ) as pbar:
-        for category_id, annotations_images in train_annotations_images_by_category.items():
-            # Create a nested directory from category_id, e.g. 15504 -> "155/04" or 3 -> "000/03"
-            category_subdir = f"{category_id // 100:03d}/{category_id % 100:02d}"
-            (public / "nybg2020/train/images" / category_subdir).mkdir(exist_ok=True, parents=True)
-            for idx, annotation_image in enumerate(annotations_images):
-                new_annotation = annotation_image["annotation"].copy()
-                new_train_metadata["annotations"].append(new_annotation)
-
-                new_image = annotation_image["image"].copy()
-                new_train_metadata["images"].append(new_image)
-
-                # Copy file from raw to public
-                if (
-                    not dev_mode or idx < dev_count
-                ):  # if dev_mode, only copy the first dev_count images
-                    src_path = raw / "nybg2020/train" / annotation_image["image"]["file_name"]
-                    dst_path = public / "nybg2020/train" / annotation_image["image"]["file_name"]
-                    shutil.copyfile(src=src_path, dst=dst_path)
-
-                pbar.update(1)
-
-    with open(public / "nybg2020/train/metadata.json", "w") as f:
-        json.dump(new_train_metadata, f, indent=4, sort_keys=True)
-
-    if not dev_mode:
-        assert len(list((public / "nybg2020/train/images").glob("**/*.jpg"))) == len(
-            new_train_metadata["images"]
-        ), f"Mismatching number of images in train_images, got {len(list((public / 'nybg2020/train/images').glob('**/*.jpg')))}"
-        assert len(new_train_metadata["annotations"]) == len(
-            new_train_metadata["images"]
-        ), f"Mismatching number of annotations in train_metadata, got {len(new_train_metadata['annotations'])}"
-
-    # Add to test set
-    new_test_metadata = old_train_metadata.copy()
-    del new_test_metadata["categories"]
-    del new_test_metadata["regions"]
-    new_test_metadata.update(
-        {
-            "annotations": [],
-            "images": [],
-        }
-    )
-    # Flatten and shuffle test set so that we don't have all the same categories in a row
-    test_annotations_images = [
-        item for sublist in test_annotations_images_by_category.values() for item in sublist
-    ]
-    random.Random(0).shuffle(test_annotations_images)
-    for idx, annotation_image in tqdm(
-        enumerate(test_annotations_images),
-        desc="Creating new test dataset",
-        total=len(test_annotations_images),
-    ):
-
-        # Make new image id, for test set this is just the index
-        new_image_id = str(idx)
-        # Make new filename from image id e.g. "000/0.jpg"
-        new_file_name = f"images/{idx // 1000:03d}/{idx}.jpg"
-
-        new_annotation = annotation_image["annotation"].copy()
-        new_annotation["image_id"] = new_image_id
-        new_test_metadata["annotations"].append(new_annotation)
-
-        new_image = annotation_image["image"].copy()
-        new_image["id"] = new_image_id
-        new_image["file_name"] = new_file_name
-        new_test_metadata["images"].append(new_image)
-
-        # Copy file from raw to public
-        if not dev_mode or idx < dev_count:  # if dev_mode, only copy the first dev_count images
-            src_path = raw / "nybg2020/train" / annotation_image["image"]["file_name"]
-            dst_path = public / "nybg2020/test" / new_file_name
-            dst_path.parent.mkdir(exist_ok=True, parents=True)
-            shutil.copyfile(src=src_path, dst=dst_path)
-
-    # Save new test metadata
-    with open(public / "nybg2020/test/metadata.json", "w") as f:
-        # The public test data, of course, doesn't have annotations
-        public_new_test = new_test_metadata.copy()
-        del public_new_test["annotations"]
-        assert public_new_test.keys() == {
-            "images",
-            "info",
-            "licenses",
-        }, f"Public test metadata keys should be 'images', 'info', 'licenses', but found {public_new_test.keys()}"
-        json.dump(public_new_test, f, indent=4, sort_keys=True)
-
-    if not dev_mode:
-        assert len(list((public / "nybg2020/test/images").glob("**/*.jpg"))) == len(
-            new_test_metadata["images"]
-        ), f"Mismatching number of images in test_images, got {len(list((public / 'nybg2020/test/images').glob('**/*.jpg')))}"
-        assert len(new_test_metadata["annotations"]) == len(
-            new_test_metadata["images"]
-        ), f"Mismatching number of annotations in test_metadata, got {len(new_test_metadata['annotations'])}"
-        assert len(new_train_metadata["annotations"]) + len(
-            new_test_metadata["annotations"]
-        ) == len(old_train_metadata["annotations"]), (
-            f"Expected {len(old_train_metadata['annotations'])} annotations in total, but found"
-            f"{len(new_train_metadata['annotations'])} in train and {len(new_test_metadata['annotations'])} in test"
-        )
-
-    # Save private test answers
-    answers_rows = []
-    for image, annotation in zip(new_test_metadata["images"], new_test_metadata["annotations"]):
-        assert (
-            image["id"] == annotation["image_id"]
-        ), f"Mismatching image_id in image and annotation: {image['id']} vs {annotation['image_id']}"
-        answers_rows.append(
-            {
-                "Id": image["id"],
-                "Predicted": annotation["category_id"],
-            }
-        )
-    answers_df = pd.DataFrame(answers_rows)
-    answers_df.to_csv(private / "answers.csv", index=False)
-
-    # Create new sample submission that matches raw/sample_submission.csv, but for the new test set
-    sample_rows = []
-    for image in new_test_metadata["images"]:
-        sample_rows.append(
-            {
-                "Id": image["id"],
-                "Predicted": 0,
-            }
-        )
-    sample_df = pd.DataFrame(sample_rows)
-    sample_df.to_csv(public / "sample_submission.csv", index=False)
-
-    assert len(answers_df) == len(
-        new_test_metadata["images"]
-    ), f"Expected {len(new_test_metadata['images'])} rows in answers, but found {len(answers_df)}"
-    assert len(sample_df) == len(
-        answers_df
-    ), f"Expected {len(answers_df)} rows in sample submission, but found {len(sample_df)}"
-    assert answers_df["Id"].equals(
-        sample_df["Id"]
-    ), "Mismatched 'Id' columns between answers and sample submission"

benchmark/MLE-bench/mlebench/competitions/herbarium-2021-fgvc8/checksums.yaml

@@ -1,5 +0,0 @@
-private:
-  answers.csv: ba6fdf67c5f53a1a39bef7a129b38108
-public:
-  sample_submission.csv: e53508ab941ce7f219c6e82fde9a6baa
-zip: c4632c48c0f74ee925ef0e99883ac673

benchmark/MLE-bench/mlebench/competitions/herbarium-2021-fgvc8/config.yaml

@@ -1,16 +0,0 @@
-id: herbarium-2021-fgvc8
-name: Herbarium 2021 - FGVC8
-competition_type: simple
-awards_medals: false
-prizes: null
-description: mlebench/competitions/herbarium-2021-fgvc8/description.md
-
-dataset:
-  answers: herbarium-2021-fgvc8/prepared/private/answers.csv
-  sample_submission: herbarium-2021-fgvc8/prepared/public/sample_submission.csv
-
-grader:
-  name: macro-f1-score
-  grade_fn: mlebench.competitions.herbarium-2021-fgvc8.grade:grade
-
-preparer: mlebench.competitions.herbarium-2021-fgvc8.prepare:prepare

benchmark/MLE-bench/mlebench/competitions/herbarium-2021-fgvc8/description.md

@@ -1,168 +0,0 @@
-# Overview
-
-## Description
-
-*The Herbarium 2021: Half-Earth Challenge* is to identify vascular plant specimens provided by the [New York Botanical Garden](https://www.nybg.org/) (NY), [Bishop Museum](https://www.bishopmuseum.org/) (BPBM), [Naturalis Biodiversity Center](https://www.naturalis.nl/en) (NL), [Queensland Herbarium](https://www.qld.gov.au/environment/plants-animals/plants/herbarium) (BRI), and [Auckland War Memorial Museum](https://www.aucklandmuseum.com/) (AK).
-
-*The Herbarium 2021: Half-Earth Challenge* dataset includes more than **2.5M images** representing nearly **65,000 species** from the Americas and Oceania that have been aligned to a standardized plant list ([LCVP v1.0.2](https://www.nature.com/articles/s41597-020-00702-z)).
-
-This dataset has a long tail; there are a minimum of 3 images per species. However, some species can be represented by more than 100 images. This dataset only includes vascular land plants which include lycophytes, ferns, gymnosperms, and flowering plants. The extinct forms of lycophytes are the major component of coal deposits, ferns are indicators of ecosystem health, gymnosperms provide major habitats for animals, and flowering plants provide almost all of our crops, vegetables, and fruits.
-
-The teams with the most accurate models will be contacted with the intention of using them on the unnamed plant collections in the NYBG herbarium and then be assessed by the NYBG plant specialists for accuracy.
-
-![Herbarium2021](https://i.postimg.cc/htpxH99f/Herbarium2021.png)
-
-### Background
-
-There are approximately 3,000 herbaria world-wide, and they are massive repositories of plant diversity data. These collections not only represent a vast amount of plant diversity, but since herbarium collections include specimens dating back hundreds of years, they provide snapshots of plant diversity through time. The integrity of the plant is maintained in herbaria as a pressed, dried specimen; a specimen collected nearly two hundred years ago by Darwin looks much the same as one collected a month ago by an NYBG botanist. All specimens not only maintain their morphological features but also include collection dates and locations, their reproductive state, and the name of the person who collected the specimen. This information, multiplied by millions of plant collections, provides the framework for understanding plant diversity on a massive scale and learning how it has changed over time. The models developed during this competition are an integral first step to speed the pace of species discovery and save the plants of the world.
-
-There are approximately 400,000 known vascular plant species with an estimated 80,000 still to be discovered. Herbaria contain an overwhelming amount of unnamed and new specimens, and with the threats of climate change, we need new tools to quicken the pace of species discovery. This is more pressing today as a United Nations report indicates that more than one million species are at risk of extinction, and amid this dire prediction is a recent estimate that suggests plants are disappearing more quickly than animals. This year, we have expanded our curated herbarium dataset to vascular plant diversity in the Americas and Oceania.
-
-The most accurate models will be used on the unidentified plant specimens in our herbarium and assessed by our taxonomists thereby producing a tool to quicken the pace of species discovery.
-
-### About
-
-This is an FGVC competition hosted as part of the [FGVC8](https://sites.google.com/view/fgvc8) workshop at [CVPR 2021](http://cvpr2021.thecvf.com/) and sponsored by [NYBG](https://www.nybg.org/).
-
-Details of this competition are mirrored on the [github](https://github.com/visipedia/herbarium_comp) page. Please post in the forum or open an issue if you have any questions or problems with the dataset.
-
-### Acknowledgements
-
-The images are provided by the [New York Botanical Garden](https://www.nybg.org/), [Bishop Museum](https://www.bishopmuseum.org/), [Naturalis Biodiversity Center](https://www.naturalis.nl/en), [Queensland Herbarium](https://www.qld.gov.au/environment/plants-animals/plants/herbarium), and [Auckland War Memorial Museum](https://www.aucklandmuseum.com/).
-
-![New York Botanical Garden, Bishop Museum, Naturalis Biodiversity Center, Queensland Herbarium, and Auckland War Memorial Museum logos](https://i.postimg.cc/fbSLBX54/Logos.png)
-
-## Evaluation
-
-Submissions are evaluated using the [macro F1 score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html).
-
-The F1 score is given by
-
-$$
-F_1=2 * \frac{\text { precision } * \text { recall }}{\text { precision }+ \text { recall }}
-$$
-
-where:
-
-$$
-\begin{gathered}
-\text { precision }=\frac{T P}{T P+F P}, \\
-\text { recall }=\frac{T P}{T P+F N}.
-\end{gathered}
-$$
-
-In "macro" F1 a separate F1 score is calculated for each `species` value and then averaged.
-
-### Submission Format
-
-For each image `Id`, you should predict the corresponding image label (`category_id`) in the `Predicted` column. The submission file should have the following format:
-
-```
-Id,Predicted
-0,1
-1,27
-2,42
-...
-```
-
-## Timeline
-
-- **March 10, 2021** - Competition start date.
-- **May 19, 2021** - Entry deadline. You must accept the competition rules before this date in order to compete.
-- **May 19, 2021** - Team Merger deadline. This is the last day participants may join or merge teams.
-- **May 26, 2021** - Final submission deadline.
-
-All deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.
-
-## CVPR 2021
-
-This competition is part of the Fine-Grained Visual Categorization [FGVC8](https://sites.google.com/view/fgvc8) workshop at the Computer Vision and Pattern Recognition Conference [CVPR 2021](http://cvpr2021.thecvf.com/). A panel will review the top submissions for the competition based on the description of the methods provided. From this, a subset may be invited to present their results at the workshop. Attending the workshop is not required to participate in the competition; however, only teams that are attending the workshop will be considered to present their work.
-
-There is no cash prize for this competition. **CVPR 2021 will take place virtually.** PLEASE NOTE: CVPR frequently sells out early, we cannot guarantee CVPR registration after the competition's end. If you are interested in attending, please plan ahead.
-
-You can see a list of all of the FGVC8 competitions [here](https://sites.google.com/view/fgvc8/competitions?authuser=0).
-
-## Citation
-
-Riccardo de Lutio, Titouan Lorieul, Walter Reade. (2021). Herbarium 2021 - Half-Earth Challenge - FGVC8. Kaggle. https://kaggle.com/competitions/herbarium-2021-fgvc8
-
-# Data
-
-## Dataset Description
-
-### Data Overview
-
-The training and test set contain images of herbarium specimens from nearly 65,000 species of vascular plants. Each image contains exactly one specimen. The text labels on the specimen images have been blurred to remove category information in the image.
-
-The data has been approximately split 80%/20% for training/test. Each category has at least 1 instance in both the training and test datasets. Note that the test set distribution is slightly different from the training set distribution. The training set contains species with hundreds of examples, but the test set has the number of examples per species capped at a maximum of 10.
-
-### Dataset Details
-
-Each image has different image dimensions, with a maximum of 1000 pixels in the larger dimension. These have been resized from the original image resolution. All images are in JPEG format.
-
-### Dataset Format
-
-This dataset uses the [COCO dataset format](https://cocodataset.org/#format-data) with additional annotation fields. In addition to the species category labels, we also provide region and supercategory information.
-
-The training set metadata (`train/metadata.json`) and test set metadata (`test/metadata.json`) are JSON files in the format below. Naturally, the test set metadata file omits the "annotations", "categories," and "regions" elements.
-
-```
-{
-  "annotations" : [annotation],
-  "categories" : [category],
-  "images" : [image],
-  "info" : info,
-  "licenses": [license],
-  "institutions": [region]
-}
-
-info {
-  "year" : int,
-  "version" : str,
-  "url": str,
-  "description" : str,
-  "contributor" : str,
-  "date_created" : datetime
-}
-
-image {
-  "id" : int,
-  "width" : int,
-  "height" : int,
-  "file_name" : str,
-  "license" : int
-}
-
-annotation {
-  "id": int,
-  "image_id": int,
-  "category_id": int,
-  # Region where this specimen was collected.
-  "region_id": int
-}
-
-category {
-  "id" : int,
-  # Species name
-  "name" : str,
-  # We also provide the super-categories for each species.
-  "family": str,
-  "order": str
-}
-
-institution {
-  "id": int
-  "name": str
-}
-
-license {
-  "id": 1,
-  "name": str,
-  "url": str
-}
-
-```
-
-The training set images are organized in subfolders `train/images/<subfolder1>/<subfolder2>/<image id>.jpg`, where `<subfolder1>` combined with `<subfolder2>` corresponds to the `category_id`. For example, a training image with an `image_id` of `1104517` and a `category_id` of `00001`, can be found at `train/images/000/01/1104517.jpg`.
-
-The test set images are organized in subfolders `test/images/<subfolder>/<image id>.jpg`, where `<subfolder>` corresponds to the integer division of the `image_id` by 1000. For example, a test image with and `image_id` of `8005`, can be found at `test/images/008/8005.jpg`.

benchmark/MLE-bench/mlebench/competitions/herbarium-2021-fgvc8/description_obfuscated.md

@@ -1,27 +0,0 @@
-# Task
-
-Build a model to classify the species of plants from images.
-
-# Metric
-
-Macro F1 score. A separate F1 score is calculated for each `species` value and then averaged.
-
-# Submission Format
-
-For each image `Id`, you should predict the corresponding image label (`category_id`) in the `Predicted` column. The submission file should have the following format:
-
-```
-Id,Predicted
-0,1
-1,27
-2,42
-...
-```
-
-# Dataset
-
-This dataset uses the [COCO dataset format](https://cocodataset.org/#format-data) with additional annotation fields. In addition to the species category labels, we also provide region and supercategory information.
-
-The training set images are organized in subfolders `train/images/<subfolder1>/<subfolder2>/<image id>.jpg`, where `<subfolder1>` combined with `<subfolder2>` corresponds to the `category_id`. For example, a training image with an `image_id` of `1104517` and a `category_id` of `00001`, can be found at `train/images/000/01/1104517.jpg`.
-
-The test set images are organized in subfolders `test/images/<subfolder>/<image id>.jpg`, where `<subfolder>` corresponds to the integer division of the `image_id` by 1000. For example, a test image with and `image_id` of `8005`, can be found at `test/images/008/8005.jpg`.

benchmark/MLE-bench/mlebench/competitions/herbarium-2021-fgvc8/grade.py

@@ -1,34 +0,0 @@
-import pandas as pd
-from sklearn.metrics import f1_score
-
-from mlebench.grade_helpers import InvalidSubmissionError
-
-
-def prepare_for_metric(submission: pd.DataFrame, answers: pd.DataFrame):
-    """
-    `submission` and `answers` are pd.DataFrame with "Id" and "Predicted" columns
-    """
-    if len(submission) != len(answers):
-        raise InvalidSubmissionError(
-            f"Submission length {len(submission)} != answers length {len(answers)}"
-        )
-    if "Id" not in submission.columns or "Predicted" not in submission.columns:
-        raise InvalidSubmissionError("Submission must have 'Id' and 'Predicted' columns")
-
-    assert "Id" in answers.columns, "Answers must have 'Id' column"
-    assert "Predicted" in answers.columns, "Answers must have 'Predicted' column"
-
-    # Sort submission and answers by "Id"
-    submission = submission.sort_values("Id")
-    answers = answers.sort_values("Id")
-    if (submission["Id"].values != answers["Id"].values).any():
-        raise InvalidSubmissionError("Submission and answers have mismatched 'Id' columns")
-
-    y_true = [int(y) for y in answers["Predicted"]]
-    y_pred = [int(y) for y in submission["Predicted"]]
-    return y_true, y_pred
-
-
-def grade(submission: pd.DataFrame, answers: pd.DataFrame) -> float:
-    y_true, y_pred = prepare_for_metric(submission, answers)
-    return f1_score(y_true=y_true, y_pred=y_pred, average="macro")

benchmark/MLE-bench/mlebench/competitions/herbarium-2021-fgvc8/kernels.txt

@@ -1,24 +0,0 @@
-khoongweihao/insect-augmentation-et-al
-ihelon/herbarium-2021-exploratory-data-analysis
-ateplyuk/herb2021-pytorch-starter
-debarshichanda/herbarium-2021-pytorch-starter
-yasserhessein/herbarium-2021-uing-vgg16
-sauravmaheshkar/herbarium-2021-pytorch-starter-weights-biases
-tpmeli/herbarium-starter-efficientnet-tf-keras-gpu
-salmanhiro/herbarium-2021-efficientnet-b0-training-starter
-atamazian/herb-2021-tfrecords-effnet-training
-drcapa/herbarium-2021-starter-eda-datagenerator
-twhelan/herbarium2021-creating-smaller-subsets-of-data
-ricardobarbosasousa/herbarium-2021-rbs-resnet
-yeonghyeon/step-by-step-herbarium-2021
-muhammadzubairkhan92/herbarium-2021-exploratory-data-analysis
-sauravmaheshkar/herbarium-2021-resnet18-inference
-atamazian/herb-2021-tfrecords-effnet-inference
-abhaychaudhary01/insect-augmentation-et-al
-sharansmenon/herbarium-pytorch
-ivanfeliperodriguez/tfrecords-creation-with-arbitrary-size
-shayantaherian/herbarium-2021
-anandagdhi/notebook0ac3a4bca3
-anandagdhi/herberium-eda
-siddhartamukherjee/herbarium-2021-resnet34-pytorch-gpu
-oricou/herbarium21-skeleton

benchmark/MLE-bench/mlebench/competitions/herbarium-2021-fgvc8/leaderboard.csv

@@ -1,81 +0,0 @@
-scoreNullable,teamId,hasTeamName,submissionDate,score,hasScore
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benchmark/MLE-bench/mlebench/competitions/herbarium-2021-fgvc8/prepare.py

@@ -1,251 +0,0 @@
-import json
-import random
-import shutil
-from pathlib import Path
-
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from tqdm import tqdm
-
-from mlebench.utils import get_logger
-
-logger = get_logger(__name__)
-
-
-def prepare(raw: Path, public: Path, private: Path):
-    """
-    Splits the raw data into public and private datasets with appropriate test/train splits.
-
-    `train/metadata.json` is the "table of contents" for our data, with the following structure:
-    (More details at https://www.kaggle.com/competitions/herbarium-2021-fgvc8/data)
-    ```
-    {
-        "annotations" : [annotation],
-        "categories" : [category],
-        "images" : [image],
-        "info" : info,
-        "licenses" : [license],
-        "institutions" : [region]
-    }
-    ```
-    - `images` and `annotations` are both N-length lists corresponding to the N samples.
-        We'll need to split each of these lists into train and test.
-    - The other fields are dataset-wide metadata that we don't need to touch.
-
-    - test/metadata.json is the same structure as train/metadata.json, but without "annotations", "categories", "institutions"
-
-    Other notes:
-    - train/test splits need to occur per category (each category should be in both train and test).
-    - The `test/images` and `train/images` folders have nested subdirs to make it easier to browse
-        - `train/images` is structured as `{category_id[:3]}/{category_id[3:]}/{image_id}.jpg`
-        - `test/images` is structured as `{image_idx[:3]}/{image_idx}.jpg` (to not reveal the category)
-    - When we create the new splits, we re-assign image indices so that we don't give away labels based on the index
-        - train images are indexed within their own category
-        - test images follow a flat index after shuffling the categories
-    """
-
-    dev_mode = False
-    dev_count = 2  # Copy over n images per category when in dev mode
-
-    # Create train, test from train split
-    json_path = raw / "train/metadata.json"
-    with open(json_path, "r", encoding="utf-8") as f:
-        old_train_metadata = json.load(f)
-
-    # Organize data by category so that we can split per-category later
-    annotations_images_by_category = {}  # We'll collect both `annotations` and `images` here
-    for annotation, image in list(
-        zip(old_train_metadata["annotations"], old_train_metadata["images"])
-    ):
-        assert (
-            annotation["image_id"] == image["id"]
-        ), f"Mismatching image_id in annotation and image: {annotation['image_id']} vs {image['id']}"
-        category_id = annotation["category_id"]
-        if category_id not in annotations_images_by_category:
-            annotations_images_by_category[category_id] = []
-        annotations_images_by_category[category_id].append(
-            {
-                "annotation": annotation,
-                "image": image,
-            }
-        )
-
-    # Split train/test
-    train_sample_count = 0  # Useful for tqdm later
-    train_annotations_images_by_category = {}
-    test_annotations_images_by_category = {}
-    for category_id, annotations_images in tqdm(
-        annotations_images_by_category.items(), desc="Assigning train/test splits"
-    ):
-        test_size = 0.2
-        # Create split by "category" (class): Each category needs to be in both train and test (80:20) as per original ratio
-        n_samples = len(annotations_images)
-        if n_samples == 1:
-            # If only one sample, put it in train
-            train_annotations_images = annotations_images
-            test_annotations_images = []
-        elif n_samples < 5:  # Minimum 5 samples to ensure at least 1 in test
-            # Ensure at least 1 sample in test
-            test_size = max(1, int(n_samples * test_size))
-            train_annotations_images = annotations_images[:-test_size]
-            test_annotations_images = annotations_images[-test_size:]
-        else:
-            # Original split logic
-            train_annotations_images, test_annotations_images = train_test_split(
-                annotations_images, test_size=test_size, random_state=0
-            )
-
-        train_annotations_images_by_category[category_id] = train_annotations_images
-        test_annotations_images_by_category[category_id] = test_annotations_images
-        train_sample_count += len(train_annotations_images)
-
-    # Add to train set
-    new_train_metadata = (
-        old_train_metadata.copy()
-    )  # Keep 'categories', 'info', 'licenses', 'institutions'
-    new_train_metadata.update(
-        {
-            "annotations": [],
-            "images": [],
-        }
-    )
-    with tqdm(
-        desc="Creating new train dataset",
-        total=train_sample_count,
-    ) as pbar:
-        for category_id, annotations_images in train_annotations_images_by_category.items():
-            # Create a nested directory from category_id, e.g. 15504 -> "155/04" or 3 -> "000/03"
-            category_subdir = f"{category_id // 100:03d}/{category_id % 100:02d}"
-            (public / "train/images" / category_subdir).mkdir(exist_ok=True, parents=True)
-            for idx, annotation_image in enumerate(annotations_images):
-                new_annotation = annotation_image["annotation"].copy()
-                new_train_metadata["annotations"].append(new_annotation)
-
-                new_image = annotation_image["image"].copy()
-                new_train_metadata["images"].append(new_image)
-
-                # Copy file from raw to public
-                if (
-                    not dev_mode or idx < dev_count
-                ):  # if dev_mode, only copy the first dev_count images
-                    src_path = raw / "train" / annotation_image["image"]["file_name"]
-                    dst_path = public / "train" / annotation_image["image"]["file_name"]
-                    shutil.copyfile(src=src_path, dst=dst_path)
-
-                pbar.update(1)
-
-    with open(public / "train/metadata.json", "w") as f:
-        json.dump(new_train_metadata, f, indent=4, sort_keys=True)
-
-    if not dev_mode:
-        assert len(list((public / "train/images").glob("**/*.jpg"))) == len(
-            new_train_metadata["images"]
-        ), f"Mismatching number of images in train_images, got {len(list((public / 'train/images').glob('**/*.jpg')))}"
-        assert len(new_train_metadata["annotations"]) == len(
-            new_train_metadata["images"]
-        ), f"Mismatching number of annotations in train_metadata, got {len(new_train_metadata['annotations'])}"
-
-    # Add to test set
-    new_test_metadata = old_train_metadata.copy()
-    del new_test_metadata["categories"]
-    del new_test_metadata["institutions"]
-    new_test_metadata.update(
-        {
-            "annotations": [],
-            "images": [],
-        }
-    )
-    # Flatten and shuffle test set so that we don't have all the same categories in a row
-    test_annotations_images = [
-        item for sublist in test_annotations_images_by_category.values() for item in sublist
-    ]
-    random.Random(0).shuffle(test_annotations_images)
-    for idx, annotation_image in tqdm(
-        enumerate(test_annotations_images),
-        desc="Creating new test dataset",
-        total=len(test_annotations_images),
-    ):
-
-        # Make new image id, for test set this is just the index
-        new_image_id = str(idx)
-        # Make new filename from image id e.g. "000/0.jpg"
-        new_file_name = f"images/{idx // 1000:03d}/{idx}.jpg"
-
-        new_annotation = annotation_image["annotation"].copy()
-        new_annotation["image_id"] = new_image_id
-        new_test_metadata["annotations"].append(new_annotation)
-
-        new_image = annotation_image["image"].copy()
-        new_image["id"] = new_image_id
-        new_image["file_name"] = new_file_name
-        new_test_metadata["images"].append(new_image)
-
-        # Copy file from raw to public
-        if not dev_mode or idx < dev_count:  # if dev_mode, only copy the first dev_count images
-            src_path = raw / "train" / annotation_image["image"]["file_name"]
-            dst_path = public / "test" / new_file_name
-            dst_path.parent.mkdir(exist_ok=True, parents=True)
-            shutil.copyfile(src=src_path, dst=dst_path)
-
-    # Save new test metadata
-    with open(public / "test/metadata.json", "w") as f:
-        # The public test data, of course, doesn't have annotations
-        public_new_test = new_test_metadata.copy()
-        del public_new_test["annotations"]
-        assert public_new_test.keys() == {
-            "images",
-            "info",
-            "licenses",
-        }, f"Public test metadata keys should be 'images', 'info', 'licenses', but found {public_new_test.keys()}"
-        json.dump(public_new_test, f, indent=4, sort_keys=True)
-
-    if not dev_mode:
-        assert len(list((public / "test/images").glob("**/*.jpg"))) == len(
-            new_test_metadata["images"]
-        ), f"Mismatching number of images in test_images, got {len(list((public / 'test/images').glob('**/*.jpg')))}"
-        assert len(new_test_metadata["annotations"]) == len(
-            new_test_metadata["images"]
-        ), f"Mismatching number of annotations in test_metadata, got {len(new_test_metadata['annotations'])}"
-        assert len(new_train_metadata["annotations"]) + len(
-            new_test_metadata["annotations"]
-        ) == len(old_train_metadata["annotations"]), (
-            f"Expected {len(old_train_metadata['annotations'])} annotations in total, but found"
-            f"{len(new_train_metadata['annotations'])} in train and {len(new_test_metadata['annotations'])} in test"
-        )
-
-    # Save private test answers
-    answers_rows = []
-    for image, annotation in zip(new_test_metadata["images"], new_test_metadata["annotations"]):
-        assert (
-            image["id"] == annotation["image_id"]
-        ), f"Mismatching image_id in image and annotation: {image['id']} vs {annotation['image_id']}"
-        answers_rows.append(
-            {
-                "Id": image["id"],
-                "Predicted": annotation["category_id"],
-            }
-        )
-    answers_df = pd.DataFrame(answers_rows)
-    answers_df.to_csv(private / "answers.csv", index=False)
-
-    # Create new sample submission that matches raw/sample_submission.csv, but for the new test set
-    sample_rows = []
-    for image in new_test_metadata["images"]:
-        sample_rows.append(
-            {
-                "Id": image["id"],
-                "Predicted": 0,
-            }
-        )
-    sample_df = pd.DataFrame(sample_rows)
-    sample_df.to_csv(public / "sample_submission.csv", index=False)
-
-    assert len(answers_df) == len(
-        new_test_metadata["images"]
-    ), f"Expected {len(new_test_metadata['images'])} rows in answers, but found {len(answers_df)}"
-    assert len(sample_df) == len(
-        answers_df
-    ), f"Expected {len(answers_df)} rows in sample submission, but found {len(sample_df)}"
-    assert answers_df["Id"].equals(
-        sample_df["Id"]
-    ), "Mismatched 'Id' columns between answers and sample submission"

benchmark/MLE-bench/mlebench/competitions/herbarium-2022-fgvc9/checksums.yaml

@@ -1,7 +0,0 @@
-private:
-  answers.csv: d7988cc4decb444ed99cbae59381298d
-public:
-  sample_submission.csv: c2b0d6f95036aaa177a4e14a0d627054
-  test_metadata.json: 465f92cf49fb94a793648066989fe817
-  train_metadata.json: 22394926583a35658f6dd26ea0dcc683
-zip: d99e3df24b03dcb2f07fbc4c81b76a5a

benchmark/MLE-bench/mlebench/competitions/herbarium-2022-fgvc9/config.yaml

@@ -1,16 +0,0 @@
-id: herbarium-2022-fgvc9
-name: Herbarium 2022 - FGVC9
-competition_type: simple
-awards_medals: false
-prizes: null
-description: mlebench/competitions/herbarium-2022-fgvc9/description.md
-
-dataset:
-  answers: herbarium-2022-fgvc9/prepared/private/answers.csv
-  sample_submission: herbarium-2022-fgvc9/prepared/public/sample_submission.csv
-
-grader:
-  name: macro-f1-score
-  grade_fn: mlebench.competitions.herbarium-2022-fgvc9.grade:grade
-
-preparer: mlebench.competitions.herbarium-2022-fgvc9.prepare:prepare

benchmark/MLE-bench/mlebench/competitions/herbarium-2022-fgvc9/description.md

@@ -1,154 +0,0 @@
-# Overview
-
-## Description
-
-[![My-Post.jpg](https://i.postimg.cc/15qZZfvt/My-Post.jpg)](https://postimg.cc/Xp4Pf7SS)
-
-*The Herbarium 2022: Flora of North America* is a part of a project of the [New York Botanical Garden](https://www.nybg.org/) funded by the [National Science Foundation](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2054684&HistoricalAwards=false) to build tools to identify novel plant species around the world. The dataset strives to represent all known vascular plant taxa in North America, using images gathered from 60 different botanical institutions around the world.
-
-In botany, a 'flora' is a complete account of the plants found in a geographic region. The dichotomous keys and detailed descriptions of diagnostic morphological features contained within a flora are used by botanists to determine which names to apply to plant specimens. This year's competition dataset aims to encapsulate the flora of North America so that we can test the capability of artificial intelligence to replicate this traditional tool ---a crucial first step to harnessing AI's potential botanical applications.
-
-*The Herbarium 2022: Flora of North America* dataset comprises 1.05 M images of 15,501 vascular plants, which constitute more than 90% of the taxa documented in North America. Our dataset is constrained to include only vascular land plants (lycophytes, ferns, gymnosperms, and flowering plants).
-
-Our dataset has a long-tail distribution. The number of images per taxon is as few as seven and as many as 100 images. Although more images are available, we capped the maximum number in an attempt to ensure sufficient but manageable training data size for competition participants.
-
-## About
-
-This is an FGVC competition hosted as part of the [FGVC9](https://sites.google.com/view/fgvc9) workshop at [CVPR 2022](http://cvpr2022.thecvf.com/) and sponsored by [NYBG](https://www.nybg.org/).
-
-Details of this competition are mirrored on the [github](https://github.com/visipedia/herbarium_comp) page. Please post in the forum or open an issue if you have any questions or problems with the dataset.
-
-## Acknowledgements
-
-The images are provided by the [New York Botanical Garden](https://www.nybg.org/) and 59 other institutions around the world.\
-![herb22banner](https://i.postimg.cc/g0DJMF52/output.png)
-
-## Evaluation
-
-Submissions are evaluated using the [macro F1 score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html). The F1 score is given by
-
-$F_1=2 \frac{\text { precision } \cdot \text { recall }}{\text { precision }+ \text { recall }}$
-
-where:
-
-$\begin{gathered}\text { precision }=\frac{T P}{T P+F P}, \\ \text { recall }=\frac{T P}{T P+F N} .\end{gathered}$
-
-In "macro" F1 a separate F1 score is calculated for each `species` value and then averaged. #Submission Format For each image `Id`, you should predict the corresponding image label (`category_id`) in the `Predicted` column. The submission file should have the following format:
-```
-Id,Predicted
-0,1
-1,27
-2,42
-...
-```
-
-## Timeline
-
--   February 14, 2022 - Competition start date.
--   May 23, 2022 - Entry deadline. You must accept the competition rules before this date in order to compete.
--   May 23, 2022 - Team Merger deadline. This is the last day participants may join or merge teams.
--   May 30, 2022 - Final submission deadline.
-
-All deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.
-
-## CVPR 2022
-
-This competition is part of the Fine-Grained Visual Categorization [FGVC9](https://sites.google.com/view/fgvc9) workshop at the Computer Vision and Pattern Recognition Conference [CVPR 2022](http://cvpr2022.thecvf.com/). A panel will review the top submissions for the competition based on the description of the methods provided. From this, a subset may be invited to present their results at the workshop. Attending the workshop is not required to participate in the competition; however, only teams that are attending the workshop will be considered to present their work.
-
-There is no cash prize for this competition. PLEASE NOTE: CVPR frequently sells out early, we cannot guarantee CVPR registration after the competition's end. If you are interested in attending, please plan ahead.
-
-You can see a list of all of the FGVC9 competitions [here](https://sites.google.com/view/fgvc9/competitions?authuser=0).
-
-## Citation
-
-Brendan Hogan, damon, inversion, John Park, Riccardo de Lutio. (2022). Herbarium 2022 - FGVC9. Kaggle. https://kaggle.com/competitions/herbarium-2022-fgvc9
-
-# Dataset Description
-
-## Data Overview
-
-The training and test sets contain images of herbarium specimens from 15,501 species of vascular plants. Each image contains exactly one specimen. The text labels on the specimen images have been blurred to remove category information in the image.
-
-The data has been approximately split 80%/20% for training/test. Each category has at least 1 instance in both the training and test datasets. Note that the test set distribution is slightly different from the training set distribution. The training set has a number of examples representing species capped at a maximum of 80.
-
-## Dataset Details
-
-### Images
-
-Each image has different image dimensions, with a maximum of 1000 pixels in the larger dimension. These have been resized from the original image resolution. All images are in JPEG format.
-
-### Hierarchical Structure of Classes `category_id`
-
-In addition to the images, we also include a hierarchical taxonomic structure of `category_id`. The `categories` in the `training_metadata.json` contain three levels of hierarchical structure, `family` - `genus` - `species`, from the highest rank to the lowest rank. One can think about this as a directed graph, where families are the root nodes and the species are the leaf nodes. Please note that `species` is only unique under its parent node `genus`, which means that we can find multiple categories with the same `species` name under different `genus` names. This is due to the taxonomic nature that plants are named after, and the `genus`-`species` pair is always unique in our data.
-
-### Phylogenetic Distances Among Genera
-
-What makes this year's data unique is that we include a set of pairwise phylogenetic distances among genera, so that one could test if the difference in morphological features of plant taxa well correspond to their taxonomic distances. You can find the distance data with key name `distances` from `train_metadata.json`.
-
-### Dataset Format
-
-This dataset uses the [COCO dataset format](https://cocodataset.org/#format-data) with additional annotation fields. In addition to the species category labels, we also provide supercategory information.
-
-The training set metadata (`train_metadata.json`) and test set metadata (`test_metadata.json`) are JSON files in the format below. Naturally, the test set metadata file omits the `annotations`, `categories`, and other elements.
-
-```json
-{
-  "annotations" : [annotation],
-  "categories" : [category],
-  "genera" : [genus]
-  "images" : [image],
-  "distances" : [distance],
-  "licenses" : [license],
-  "institutions" : [institution]
-}
-
-annotation {
-  "image_id" : int,
-  "category_id" : int,
-  "genus_id" : int,
-  "institution_id" : int
-}
-
-image {
-  "image_id" : int,
-  "file_name" : str,
-  "license" : int
-}
-
-category {
-  "category_id" : int,
-  "scientificName" : str,
-  # We also provide a super-category for each species.
-  "authors" : str, # correspond to 'authors' field in the wcvp
-  "family" : str, # correspond to 'family' field in the wcvp
-  "genus" : str, # correspond to 'genus' field in the wcvp
-  "species" : str, # correspond to 'species' field in the wcvp
-}
-
-genera {
-  "genus_id" : int,
-  "genus" : str
-}
-
-distance {
-  # We provide the pairwise evolutionary distance between categories (genus_id0 < genus_id1).
-  "genus_id_x" : int,
-  "genus_id_y" : int,
-  "distance" : float
-}
-
-institution {
-  "institution_id" : int
-  "collectionCode" : str
-}
-
-license {
-  "id" : int,
-  "name" : str,
-  "url" : str
-}
-```
-
-The training set images are organized in subfolders `h22-train/images/<subfolder1>/<subfolder2>/<image_id>.jpg`, where `<subfolder1>` and `<subfolder2>` comes from the first three and the last two digits of the image_id. Image_id is a result of combination between `<category_id>` and unique numbers that differentiates images within plant taxa. Please be mindful that `category_id`s are unique, but not complete. Taxa are originally numbered from 1 to 15505, but the competition data has 15501 taxa because we lost four taxa during data cleaning process.
-
-The test set images are organized in subfolders `test/images/<subfolder>/<image id>.jpg`, where `<subfolder>` corresponds to the integer division of the `image_id` by 1000. For example, a test image with and `image_id` of `8005`, can be found at `h22-test/images/008/test-008005.jpg`

benchmark/MLE-bench/mlebench/competitions/ml2021spring-hw2/checksums.yaml

@@ -1,5 +0,0 @@
-private:
-  answers.csv: 9fde0306ed6a02eea1b27686c54ce2bf
-public:
-  sampleSubmission.csv: 4fe69a042cfe9e066c088e603917eec4
-zip: 4bce22f1c15d09d6f224d99853dcb79f

benchmark/MLE-bench/mlebench/competitions/ml2021spring-hw2/config.yaml

@@ -1,16 +0,0 @@
-id: ml2021spring-hw2
-name: ML2021Spring-hw2
-competition_type: simple
-awards_medals: false
-prizes: null
-description: mlebench/competitions/ml2021spring-hw2/description.md
-
-dataset:
-  answers: ml2021spring-hw2/prepared/private/answers.csv
-  sample_submission: ml2021spring-hw2/prepared/public/sampleSubmission.csv
-
-grader:
-  name: accuracy
-  grade_fn: mlebench.competitions.ml2021spring-hw2.grade:grade
-
-preparer: mlebench.competitions.ml2021spring-hw2.prepare:prepare

benchmark/MLE-bench/mlebench/competitions/ml2021spring-hw2/description.md

@@ -1,35 +0,0 @@
-# Overview
-
-## Description
-
-### Task Description
-
-Framewise phoneme classification of TIMIT dataset  
-
-- 1229932 training samples
-- 451552 testing samples
-- Evaluation metric: catagorization accuracy
-
-### Deadlines
-
-- **Kaggle: 2021/04/02 23:59 (UTC+8)**
-- **NTU COOL: 2021/04/04 23:59 (UTC+8)**
-
-### Notes
-
-- Team Name: `<student_ID>_<anything>`
-- If there are any questions, you may discuss with us on NTU COOL or email ( [ntu-ml-2021spring-ta@googlegroups.com](mailto:ntu-ml-2021spring-ta@googlegroups.com) with the title begining with [hw2] )
-
-## Citation
-
-yenmeng. (2021). ML2021Spring-hw2. Kaggle. https://kaggle.com/competitions/ml2021spring-hw2
-
-# Data
-
-## Dataset Description
-
-complete data : `timit_11/`  
-
-- training data: `training_11.npy`  
-- training label: `trainin_label_11.npy`  
-- testing data: `testing_label_11.npy`

benchmark/MLE-bench/mlebench/competitions/ml2021spring-hw2/description_obfuscated.md

@@ -1,7 +0,0 @@
-# Task
-
-Framewise phoneme classification of TIMIT dataset  
-
-# Metric
-
-Catagorization accuracy

benchmark/MLE-bench/mlebench/competitions/ml2021spring-hw2/grade.py

@@ -1,11 +0,0 @@
-import pandas as pd
-from sklearn.metrics import accuracy_score
-
-from mlebench.competitions.utils import prepare_for_accuracy_metric
-
-
-def grade(submission: pd.DataFrame, answers: pd.DataFrame) -> float:
-    accuracy_inputs = prepare_for_accuracy_metric(
-        submission=submission, answers=answers, target_column="ClassId", id_column="Id"
-    )
-    return accuracy_score(y_true=accuracy_inputs["y_true"], y_pred=accuracy_inputs["y_pred"])

benchmark/MLE-bench/mlebench/competitions/ml2021spring-hw2/kernels.txt

@@ -1,3 +0,0 @@
-tamakoyl/2021hw02phoneme
-harrytheorange/2021springhw2
-lizupeng/notebook4bf3cf8e90

benchmark/MLE-bench/mlebench/competitions/ml2021spring-hw2/leaderboard.csv

@@ -1,1525 +0,0 @@
-scoreNullable,teamId,hasTeamName,submissionDate,score,hasScore
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benchmark/MLE-bench/mlebench/competitions/ml2021spring-hw2/prepare.py

@@ -1,58 +0,0 @@
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-from sklearn.model_selection import train_test_split
-
-
-def prepare(raw: Path, public: Path, private: Path):
-    """
-    Create train/test split from old train set, using same train/test proportion
-    """
-    old_train = np.load(raw / "timit_11" / "timit_11" / "train_11.npy")
-    old_train_label = np.load(raw / "timit_11" / "timit_11" / "train_label_11.npy")
-    old_train_idxs = range(len(old_train))
-
-    # Create new splits
-    old_test = np.load(raw / "timit_11" / "timit_11" / "test_11.npy")
-    old_test_prop = len(old_test) / (len(old_train) + len(old_test))  # ~= 0.268
-    new_train_idxs, new_test_idxs = train_test_split(
-        old_train_idxs, test_size=old_test_prop, random_state=0
-    )
-
-    new_train = old_train[new_train_idxs]
-    new_train_label = old_train_label[new_train_idxs]
-    new_test = old_train[new_test_idxs]
-    new_test_label = old_train_label[new_test_idxs]
-
-    answers_df = pd.DataFrame({"Id": range(len(new_test)), "ClassId": new_test_label})
-
-    # Create sample submission
-    sample_submission = answers_df.copy()
-    sample_submission["ClassId"] = 0
-
-    # Save files
-    (public / "timit_11" / "timit_11").mkdir(parents=True, exist_ok=True)
-
-    np.save(public / "timit_11" / "timit_11" / "train_11.npy", new_train)
-    np.save(public / "timit_11" / "timit_11" / "train_label_11.npy", new_train_label)
-    np.save(public / "timit_11" / "timit_11" / "test_11.npy", new_test)
-    sample_submission.to_csv(public / "sampleSubmission.csv", index=False)
-    answers_df.to_csv(private / "answers.csv", index=False)
-
-    # Sanity checks
-    assert (
-        public / "timit_11" / "timit_11" / "train_11.npy"
-    ).exists(), "`train_11.npy` doesn't exist!"
-    assert (
-        public / "timit_11" / "timit_11" / "train_label_11.npy"
-    ).exists(), "`train_label_11.npy` doesn't exist!"
-    assert (
-        public / "timit_11" / "timit_11" / "test_11.npy"
-    ).exists(), "`test_11.npy` doesn't exist!"
-    assert (public / "sampleSubmission.csv").exists(), "`sampleSubmission.csv` doesn't exist!"
-    assert (private / "answers.csv").exists(), "`answers.csv` doesn't exist!"
-
-    assert len(new_train) + len(new_test) == len(
-        old_train
-    ), f"Expected {len(old_train)} samples in combined new train and test splits, got {len(new_train) + len(new_test)}!"

benchmark/MLE-bench/mlebench/competitions/mlsp-2013-birds/checksums.yaml

@@ -1,5 +0,0 @@
-private:
-  answers.csv: f40c0692caff26ca8045b64109371144
-public:
-  sample_submission.csv: 6c0430ab5959d2e718911c3bd7a26a2f
-zip: e3f07a83a78a5d270a40fb1f4e8753f6

benchmark/MLE-bench/mlebench/competitions/mlsp-2013-birds/config.yaml

@@ -1,22 +0,0 @@
-id: mlsp-2013-birds
-name: MLSP 2013 Bird Classification Challenge
-competition_type: simple
-awards_medals: true
-prizes:
-  - position: 1
-    value: 700
-  - position: 2
-    value: 600
-  - position: 3
-    value: 500
-description: mlebench/competitions/mlsp-2013-birds/description.md
-
-dataset:
-  answers: mlsp-2013-birds/prepared/private/answers.csv
-  sample_submission: mlsp-2013-birds/prepared/public/sample_submission.csv
-
-grader:
-  name: auc-roc
-  grade_fn: mlebench.competitions.mlsp-2013-birds.grade:grade
-
-preparer: mlebench.competitions.mlsp-2013-birds.prepare:prepare

benchmark/MLE-bench/mlebench/competitions/mlsp-2013-birds/description.md

@@ -1,269 +0,0 @@
-# Overview
-
-## Description
-
-![overview_img](https://storage.googleapis.com/kaggle-media/competitions/kaggle/3517/media/overview_img.png)
-
-It is important to gain a better understanding of bird behavior and population trends. Birds respond quickly to environmental change, and may also tell us about other organisms (e.g., insects they feed on), while being easier to detect. Traditional methods for collecting data about birds involve costly human effort. A promising alternative is acoustic monitoring. There are many advantages to recording audio of birds compared to human surveys, including increased temporal and spatial resolution and extent, applicability in remote sites, reduced observer bias, and potentially lower costs. However, it is an open problem for signal processing and machine learning to reliably identify bird sounds in real-world audio data collected in an acoustic monitoring scenario. Some of the major challenges include multiple simultaneously vocalizing birds, other sources of non-bird sound (e.g. buzzing insects), and background noise like wind, rain, and motor vehicles.
-
-The goal in this challenge is to predict the set of bird species that are present given a ten-second audio clip. This is a multi-label supervised classification problem. The training data consists of audio recordings paired with the set of species that are present.
-
-### Background
-
-The audio dataset for this challenge was collected in the H. J. Andrews (HJA) Long-Term Experimental Research Forest, in the Cascade mountain range of Oregon. Since 2009, members of the OSU Bioacoustics group have collected over 10TB of audio data in HJA using Songmeter audio recording devices. A Songmeter has two omnidirectional microphones, and records audio in WAV format to flash memory. A Songmeter can be left in the field for several weeks at a time before either its batteries run out, or its memory is full.
-
-HJA has been the site of decades of experiments and data collection in ecology, geology and meteorology. This means, for example, that given an audio recording from a particular day and location in HJA, it is possible to look up the weather, vegetative composition, elevation, and much more. Such data enables unique discoveries through cross-examination, and long-term analysis.
-
-![map](https://storage.googleapis.com/kaggle-media/competitions/kaggle/3517/media/map.png)
-
-Previous experiments on supervised classification using multi-instance and/or multi-label formulations have used audio data collected with song meters in HJA. The dataset for this competition is similar to, but perhaps more difficult than that dataset used in these prior works; in earlier work care was taken to avoid recordings with rain and loud wind, or no birds at all, and all of the recordings came from a single day. In this competition, you will consider a new dataset which includes rain and wind, and represents a sample from two years of audio recording at 13 different locations.
-
-### Conference Attendance
-
-To participate in the conference, participants should email the following information to catherine.huang {at} intel.com no later than August 19, 2013: (1) the names of the team members (each person may belong to at most one team), (2) the name(s) of the host institutions of the researchers, (3) a 1-3 paragraph description of the approach used, (4) their submission score.  Those planning to attend the conference should additionally upload their source code to reproduce results.  Submitted models should follow the [model submission best practices](https://www.kaggle.com/wiki/ModelSubmissionBestPractices) as closely as possible.  You do not need to submit code/models before the deadline to participate in the Kaggle competition.
-
-### Acknowledgements
-
-Collection and preparation of this dataset was partially funded by NSF grant DGE 0333257, NSF-CDI grant 0941748, NSF grant 1055113, NSF grant CCF-1254218, and the College of Engineering, Oregon State University. We would also like to thank Sarah Hadley, Jed Irvine, and others for their contributions in data collection and labeling.
-
-## Evaluation
-
-Submissions are judged on [area under the ROC curve](http://en.wikipedia.org/wiki/Receiver_operating_characteristic).
-
-In Matlab (using the stats toolbox):
-
-```
-[~, ~, ~, auc ] = perfcurve(true_labels, predictions, 1);
-
-```
-
-In R (using the verification package):
-
-```
-auc = roc.area(true_labels, predictions)
-
-```
-
-In python (using the metrics module of scikit-learn):
-
-```
-fpr, tpr, thresholds = metrics.roc_curve(true_labels, predictions, pos_label=1)
-auc = metrics.auc(fpr,tpr)
-```
-
-There are 19 species in the dataset. For each recording in the test set, you will predict the probability that each species is present. The test set labels are hidden from participants in the contest, and have been split into 1/3 "public test" and 2/3 "private test." When you submit your predictions (for the entire test set), Kaggle will immediately calculate your AUC score on the public test set; this is the score you will see for your submission on the Leaderboard. The final winner(s) of the competition will be determined by AUC on the private test set (participants will not be able to see their scores on this set until the competition is over).
-
-### Submission Format
-
-Please note that a new submission parser went live after the launch of this competition, resulting in a minor change to the submission format. See [here](https://www.kaggle.com/c/mlsp-2013-birds/forums/t/4961/new-submission-parser/26853) for details/questions.
-
-Each line of your submission should contain an Id and a prediction.  We combined "rec_id" and "species" into a single "Id" column by multiplying "rec_id" by 100 and then adding in the "species" number. For example a ("rec_id","species") pair of "1,2" was mapped to a single "Id" of "102".  The format looks like this:
-
-```
-Id,Probability
-0,0
-1,0
-2,0
-3,0
-4,0
-5,0
-6,0
-7,0
-8,0
-9,0
-10,0
-11,0
-12,0
-13,0
-14,0
-15,0
-16,0
-17,0
-18,0
-100,0
-101,0
-102,0
-etc...
-```
-
-## Prizes
-
-The MLSP 2013 organizing committee is providing three awards to competition winners:
-
-- 1st place: $700
-- 2nd place: $600
-- 3rd place: $500
-
-All winners will have their registration fee waived to attend MLSP 2013. Selection of winners is based on: (1) the performance of the submitted methods and (2) the requirement that at least one member of each selected team attend the 2013 MLSP Conference. Members of the 2013 MLSP Competition Committee and everyone belonging to any of the labs of the 2013 MLSP Competition Committee) are not eligible for awards.
-
-## Timeline
-
-- **Tuesday, June 18, 2013** - Competition begins
-- **Monday, August 19, 2013** - Final submission deadline and final deadline to send conference-related material.
-
-All deadlines are at 11:59 PM UTC on the corresponding day, unless otherwise noted.
-
-## Winners
-
-**First Place:**
-
-- Gabor F. - with this [model approach](https://www.kaggle.com/c/mlsp-2013-birds/forums/t/5457/congratulations-to-the-winners/29159#post29159) and [code](https://github.com/gaborfodor/MLSP_2013)
-
-**Second Place:**
-
-- Nguyen T. and
-- Ng W. - with this [model approach](https://www.kaggle.com/c/mlsp-2013-birds/forums/t/5457/congratulations-to-the-winners/29017#post29017) and [code](https://www.kaggle.com/c/mlsp-2013-birds/forums/t/5457/congratulations-to-the-winners/29759#post29759)
-
-**Third Place:**
-
-- Anil T. - with this model approach [[1]](https://www.kaggle.com/c/mlsp-2013-birds/forums/t/5457/congratulations-to-the-winners/29101#post29101)[[2]](https://www.kaggle.com/c/mlsp-2013-birds/forums/t/5457/congratulations-to-the-winners/29217#post29217) and [code](https://github.com/anlthms/mlsp-2013)
-
-## Citation
-
-Catherine Huang, fb, Will Cukierski. (2013). MLSP 2013 Bird Classification Challenge. Kaggle. https://kaggle.com/competitions/mlsp-2013-birds
-
-# Data
-
-## Dataset Description
-
-### Data Description
-
-The dataset for this challenge consists of 645 ten-second audio recordings collected in HJA over a two-year period. In addition to the raw WAV audio files, we provide data from several stages of pre-processing, e.g. features that can be used directly for classification. The dataset is described in more detail in the included documentation, mlsp13birdchallenge_documentation.pdf and README.txt.
-
-- mlsp_contest_dataset.zip - Contains all necessary and supplemental files for the competition + additional documentation.
-- mlsp13birdchallenge_documentation.pdf - Main dataset documentation. Has more info than what is on the site.
-
-Please note: rules/changes/modifications on Kaggle.com take precedence over those in the pdf documentation.
-
-### Folder contents
-
-- ** Essential Files ***
-
-(see /essential_data)
-
-These are the most essential files- if you want to do everything from scratch, these are the only files you need.
-
----
-
-/src_wavs
-
-This folder contains the original wav files for the dataset (both training and test sets). These are 10-second mono recordings sampled at 16kHz, 16 bits per sample.
-
----
-
-rec_id2filename.txt
-
-Each audio file has a unique recording identifier ("rec_id"), ranging from 0 to 644. The file rec_id2filename.txt indicates which wav file is associated with each rec_id.
-
----
-
-species_list.txt
-
-There are 19 bird species in the dataset. species_list.txt gives each a number from 0 to 18.
-
----
-
-CVfolds_2.txt
-
-The dataset is split into training and test sets. CVfolds_2.txt gives the fold for each rec_id. 0 is the training set, and 1 is the test set.
-
----
-
-rec_labels_test_hidden.txt
-
-This is your main label training data. For each rec_id, a set of species is listed. The format is:
-
-rec_id,[labels]
-
-for example:
-
-14,0,4
-
-indicates that rec_id=14 has the label set {0,4}
-
-For recordings in the test set, a ? is listed instead of the label set. Your task is to make predictions for these ?s.
-
----
-
-sample_submission.csv
-
-This file is an example of the format you should submit results in. Each line gives 3 numbers:
-
-i,j,p
-
-(i) - the rec_id of a recording *in the test set*. ONLY INCLUDE PREDICTIONS FOR RECORDINGS IN THE TEST SET
-
-(j) - the species/class #. For each rec_id, there should be 19 lines for species 0 through 18.
-
-(p) - your classifier's prediction about the probability that species j is present in rec_id i. THIS MUST BE IN THE RANGE [0,1].
-
-Your submission should have exactly 6138 lines (no blank line at the end), and should include the header as the first line ("rec_id,species,probability").
-
----
-
-- ** Supplementary Files ***
-
-(see /supplemental_data)
-
-There are a lot of steps to go from the raw WAV data to predictions. Some participants may wish to use some supplementary data we provide which gives one implementation of a sequence of processing steps. Participants may use any of this data to improve their classifier.
-
----
-
-/spectrograms
-
-This folder contains BMP image files of spectrograms corresponding to each WAV audio file in the dataset. These spectrograms are computed by dividing the WAV signal into overlapping frames, and applying the FFT with a Hamming window. The FFT returns complex Fourier coefficients. To enhance contrast, we first normalize the spectrogram so that the maximum coefficient magnitude is 1, then take the square root of the normalized magnitude as the pixel value for an image.
-
-The spectrogram has time on the x-axis (from 0 to the duration of the sound), and frequency on the y-axis. The maximum frequency in the spectrogram is half the sampling frequency (16kHz/2 = 8kHz).
-
----
-
-/filtered_spectrograms
-
-This folder contains modified versions of the spectrograms, which have had a stationary noise filter applied. Roughly speaking, it estimates the frequency profile of noise from low-energy frames, then modifies the spectrogram to suppress noise. See "Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach" for more details on the noise reduction.
-
----
-
-/segmentation_examples
-
-For a few recordings in the training set (20 of them), we provide additional annotation of the spectrogram at the pixel level (coarsely drawn). Red pixels (R=255,G=0,B=0) indicate bird sound, and blue pixels (R=0,G=0,B=255) indicate rain or loud wind. These segmentation examples were used to train the baseline method's segmentation system.
-
----
-
-/supervised_segmentation
-
-This folder contains spectrograms with the outlines of segments drawn on top of them. These segments are obtained automatically in the baseline method, using a segmentation algorithm that is trained on the contents of /segmentation_examples. You are not required to use this segmentation, but you can if you want to!!! This segmentation is used in several other data files mentioned below. For example-
-
-segment_mosaic.bmp -- this is a visualization of all of the segments in /supervised_segmentation. Looking at this can give you some idea of the variety of bird sounds present in the dataset.
-
----
-
-segment_features.txt
-
-This text file contains a 38-dimensional feature vector describing each segment in the segmentation shown in /supervised_segmentation. The file is formatted so each line provides the feature vector for one segment. The format is:
-
-rec_id,segment_id,[feature vector]
-
-The first column is the rec_id, the second is an index for the segment within the recording (starting at 0, and going up to whatever number of segments are in that recording). There might be 0 segments in a recording (that doesn't necessarily mean it has nothing in it, just that the baseline segmentation algorithm didn't find anything). So not every rec_id appears in segment_features.txt
-
-Note that segment_features can be thought of as a "multi-instance" representation of the data:
-
-- each "bag" is a recording
-- each "instance" is a segment described by a 38-d feature vector
-
-Combined with bag label sets, this give a multi-instance multi-label (MIML) representation, which has been used in prior work on similar datasets.
-
----
-
-segment_rectangles.txt
-
-You might want to compute your own different features based on rectangles around calls/syllables/segments (rather than irregular blobs), but not worry about doing segmentation from scratch. Good news: we provide some data that can help with this. Bad news: your results might depend on imperfect/bad baseline segmentation.
-
-segment_rectangles.txt contains a bounding box for each segment in the baseline segmentation method. The bounding box is specified by the min/max x/y coordinates for pixels in the spectrogram BMP images.
-
----
-
-histogram_of_segments.txt
-
-Some participants may prefer not to worry about the "multi-instance" structure in the data, and instead focus on a standard multi-label classification scenario, where each recording is described by a fixed-length feature vector. The baseline method uses this approach, and we provide the feature vector that it computes. The feature vector for each recording is obtained based on the 38-d segment features described above. All segments from both training and test datasets are clustered using k-means++ with k=100. This clustering forms a "codebook." For each recording, we find the cluster center that is closest in L2 distance to each segment, and count the number of times each cluster is selected. The vector of counts, normalized to sum to 1, is the "histogram of segments" feature (used in "Multi-Label Classifier Chains for Bird Sound," http://arxiv.org/abs/1304.5862).
-
-A visualization of the clustering is shown in segment_clusters.bmp.

benchmark/MLE-bench/mlebench/competitions/mlsp-2013-birds/description_obfuscated.md

@@ -1,135 +0,0 @@
-# Task
-
-Given a ten-second audio clip, predict the set of bird species that are present.
-
-# Metric
-
-Area under the ROC curve.
-
-There are 19 species in the dataset. For each recording in the test set, you will predict the probability that each species is present.
-
-# Submission Format
-
-Each line of your submission should contain an Id and a prediction (between 0 and 1).  We combined "rec_id" and "species" into a single "Id" column by multiplying "rec_id" by 100 and then adding in the "species" number. For example a ("rec_id","species") pair of "1,2" was mapped to a single "Id" of "102".  The format looks like this:
-```
-Id,Probability
-0,0
-1,0
-2,0
-3,0
-4,0
-5,0
-6,0
-7,0
-8,0
-9,0
-10,0
-11,0
-12,0
-13,0
-14,0
-15,0
-16,0
-17,0
-18,0
-100,0
-101,0
-102,0
-etc...
-```
-
-# Dataset 
-
-The dataset for this challenge consists of 322 ten-second audio recordings collected in HJA over a two-year period. In addition to the raw WAV audio files, we provide data from several stages of pre-processing, e.g. features that can be used directly for classification.
-
-- mlsp_contest_dataset.zip - Contains all necessary and supplemental files for the competition + additional documentation.
-- mlsp13birdchallenge_documentation.pdf - Main dataset documentation. 
-
-sample_submission.csv: This file is an example of the format you should submit results in. 
-
-
-(see /essential_data)
-
-These are the most essential files- if you want to do everything from scratch, these are the only files you need.
-
-/src_wavs
-
-This folder contains the original wav files for the dataset (both training and test sets). These are 10-second mono recordings sampled at 16kHz, 16 bits per sample.
-
-rec_id2filename.txt
-
-Each audio file has a unique recording identifier ("rec_id"), ranging from 0 to 644. The file rec_id2filename.txt indicates which wav file is associated with each rec_id.
-
-species_list.txt
-
-There are 19 bird species in the dataset. species_list.txt gives each a number from 0 to 18.
-
-CVfolds_2.txt
-
-The dataset is split into training and test sets. CVfolds_2.txt gives the fold for each rec_id. 0 is the training set, and 1 is the test set.
-
-rec_labels_test_hidden.txt
-
-This is your main label training data. For each rec_id, a set of species is listed. The format is:
-
-rec_id,[labels]
-
-for example:
-
-14,0,4
-
-indicates that rec_id=14 has the label set {0,4}
-
-For recordings in the test set, a ? is listed instead of the label set. Your task is to make predictions for these ?s.
-
-*** Supplementary Files ***
-
-(see /supplemental_data)
-
-There are a lot of steps to go from the raw WAV data to predictions. Some participants may wish to use some supplementary data we provide which gives one implementation of a sequence of processing steps. Participants may use any of this data to improve their classifier.
-
-/spectrograms
-
-This folder contains BMP image files of spectrograms corresponding to each WAV audio file in the dataset. These spectrograms are computed by dividing the WAV signal into overlapping frames, and applying the FFT with a Hamming window. The FFT returns complex Fourier coefficients. To enhance contrast, we first normalize the spectrogram so that the maximum coefficient magnitude is 1, then take the square root of the normalized magnitude as the pixel value for an image.
-
-The spectrogram has time on the x-axis (from 0 to the duration of the sound), and frequency on the y-axis. The maximum frequency in the spectrogram is half the sampling frequency (16kHz/2 = 8kHz).
-
-/filtered_spectrograms
-
-This folder contains modified versions of the spectrograms, which have had a stationary noise filter applied. Roughly speaking, it estimates the frequency profile of noise from low-energy frames, then modifies the spectrogram to suppress noise. See "Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach" for more details on the noise reduction.
-
-/segmentation_examples
-
-For a few recordings in the training set (20 of them), we provide additional annotation of the spectrogram at the pixel level (coarsely drawn). Red pixels (R=255,G=0,B=0) indicate bird sound, and blue pixels (R=0,G=0,B=255) indicate rain or loud wind. These segmentation examples were used to train the baseline method's segmentation system.
-
-/supervised_segmentation
-
-This folder contains spectrograms with the outlines of segments drawn on top of them. These segments are obtained automatically in the baseline method, using a segmentation algorithm that is trained on the contents of /segmentation_examples. You are not required to use this segmentation, but you can if you want to!!! This segmentation is used in several other data files mentioned below. For example-
-
-segment_mosaic.bmp -- this is a visualization of all of the segments in /supervised_segmentation. Looking at this can give you some idea of the variety of bird sounds present in the dataset.
-
-segment_features.txt
-
-This text file contains a 38-dimensional feature vector describing each segment in the segmentation shown in /supervised_segmentation. The file is formatted so each line provides the feature vector for one segment. The format is:
-
-rec_id,segment_id,[feature vector]
-
-The first column is the rec_id, the second is an index for the segment within the recording (starting at 0, and going up to whatever number of segments are in that recording). There might be 0 segments in a recording (that doesn't necessarily mean it has nothing in it, just that the baseline segmentation algorithm didn't find anything). So not every rec_id appears in segment_features.txt
-
-Note that segment_features can be thought of as a "multi-instance" representation of the data:
-- each "bag" is a recording
-- each "instance" is a segment described by a 38-d feature vector
-
-Combined with bag label sets, this give a multi-instance multi-label (MIML) representation, which has been used in prior work on similar datasets.
-
-segment_rectangles.txt
-
-You might want to compute your own different features based on rectangles around calls/syllables/segments (rather than irregular blobs), but not worry about doing segmentation from scratch. Good news: we provide some data that can help with this. Bad news: your results might depend on imperfect/bad baseline segmentation.
-
-segment_rectangles.txt contains a bounding box for each segment in the baseline segmentation method. The bounding box is specified by the min/max x/y coordinates for pixels in the spectrogram BMP images.
-
-histogram_of_segments.txt
-
-Some participants may prefer not to worry about the "multi-instance" structure in the data, and instead focus on a standard multi-label classification scenario, where each recording is described by a fixed-length feature vector. The baseline method uses this approach, and we provide the feature vector that it computes. The feature vector for each recording is obtained based on the 38-d segment features described above. All segments from both training and test datasets are clustered using k-means++ with k=100. This clustering forms a "codebook." For each recording, we find the cluster center that is closest in L2 distance to each segment, and count the number of times each cluster is selected. The vector of counts, normalized to sum to 1, is the "histogram of segments" feature (used in "Multi-Label Classifier Chains for Bird Sound," http://arxiv.org/abs/1304.5862).
-
-A visualization of the clustering is shown in segment_clusters.bmp.

benchmark/MLE-bench/mlebench/competitions/mlsp-2013-birds/grade.py

@@ -1,11 +0,0 @@
-import pandas as pd
-from sklearn.metrics import roc_auc_score
-
-from mlebench.competitions.utils import prepare_for_auroc_metric
-
-
-def grade(submission: pd.DataFrame, answers: pd.DataFrame) -> float:
-    roc_auc_inputs = prepare_for_auroc_metric(
-        submission=submission, answers=answers, id_col="Id", target_col="Probability"
-    )
-    return roc_auc_score(y_true=roc_auc_inputs["y_true"], y_score=roc_auc_inputs["y_score"])

benchmark/MLE-bench/mlebench/competitions/mlsp-2013-birds/kernels.txt

@@ -1,5 +0,0 @@
-shreyasajal/birdclef-librosa-audio-feature-extraction
-shreyasajal/audio-albumentations-torchaudio-audiomentations
-sagniksanyal/birdclef-2022-torchaudio-audiomentations-skimpy
-titankinansalaatsa/mlsp-2013-bird-classification-challenge-1301180283
-muhammadzubairkhan92/birdclef-2021-librosa-audio-feature-extraction

benchmark/MLE-bench/mlebench/competitions/mlsp-2013-birds/leaderboard.csv

@@ -1,82 +0,0 @@
-scoreNullable,teamId,hasTeamName,submissionDate,score,hasScore
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benchmark/MLE-bench/mlebench/competitions/mlsp-2013-birds/prepare.py

@@ -1,182 +0,0 @@
-import os
-import shutil
-from pathlib import Path
-from typing import Dict
-
-import pandas as pd
-
-from mlebench.utils import extract, read_csv
-
-
-def filter_and_write_file(src: Path, dst: Path, old_id_to_new: Dict[int, int]):
-    """
-    Given txt file that has column 0 as rec_id, filters out rec_ids that are not in old_id_to_new and writes to dst
-    """
-    history_of_segments = open(src).read().splitlines()
-    history_of_segments = history_of_segments[1:]
-    history_of_segments = [
-        (int(i.split(",")[0]), ",".join(i.split(",")[1:])) for i in history_of_segments
-    ]
-    history_of_segments = [
-        (old_id_to_new[i[0]], i[1]) for i in history_of_segments if i[0] in old_id_to_new.keys()
-    ]
-    with open(dst, "w") as f:
-        f.write("rec_id,[histogram of segment features]\n")
-        for rec_id, labels in history_of_segments:
-            f.write(f"{rec_id},{labels}\n")
-
-
-def prepare(raw: Path, public: Path, private: Path):
-    """
-    Splits the data in raw into public and private datasets with appropriate test/train splits.
-    """
-    # extract only what we need
-    extract(raw / "mlsp_contest_dataset.zip", raw)
-
-    (public / "essential_data").mkdir(exist_ok=True)
-    (public / "supplemental_data").mkdir(exist_ok=True)
-
-    # Create train, test from train split
-    cv_folds = read_csv(raw / "mlsp_contest_dataset/essential_data/CVfolds_2.txt")
-    cv_folds = cv_folds[cv_folds["fold"] == 0].reset_index(drop=True)
-    cv_folds.loc[cv_folds.sample(frac=0.2, random_state=0).index, "fold"] = 1
-
-    old_id_to_new = {old_id: new_id for new_id, old_id in enumerate(cv_folds["rec_id"].values)}
-    cv_folds["rec_id"] = cv_folds.index
-    cv_folds.to_csv(public / "essential_data/CVfolds_2.txt", index=False)
-
-    test_rec_ids = cv_folds[cv_folds["fold"] == 1]["rec_id"].values
-    assert len(test_rec_ids) == 64, f"Expected 64 test rec_ids, got {len(test_rec_ids)}"
-
-    # Update id2filename with new split
-    rec_id2filename = read_csv(raw / "mlsp_contest_dataset/essential_data/rec_id2filename.txt")
-    rec_id2filename = rec_id2filename[rec_id2filename["rec_id"].isin(old_id_to_new.keys())]
-    rec_id2filename["rec_id"] = rec_id2filename["rec_id"].map(old_id_to_new)
-    rec_id2filename.to_csv(public / "essential_data/rec_id2filename.txt", index=False)
-    assert len(rec_id2filename) == len(
-        cv_folds
-    ), f"Expected {len(cv_folds)} entires in rec_id2filename, got {len(rec_id2filename)}"
-
-    # Update labels with new split
-    rec_labels = (
-        open(raw / "mlsp_contest_dataset/essential_data/rec_labels_test_hidden.txt")
-        .read()
-        .splitlines()
-    )
-    rec_labels = rec_labels[1:]  # Ignore header line
-    rec_labels_split = []
-    for i in rec_labels:
-        rec_id = i.split(",")[0]
-        labels = ",".join(i.split(",")[1:]) if len(i.split(",")) > 1 else ""
-        rec_labels_split.append((int(rec_id), labels))
-    rec_labels_split = [i for i in rec_labels_split if i[0] in old_id_to_new.keys()]
-    rec_labels_split = [(old_id_to_new[i[0]], i[1]) for i in rec_labels_split]
-
-    # Public labels
-    with open(public / "essential_data/rec_labels_test_hidden.txt", "w") as f:
-        f.write("rec_id,[labels]\n")
-        for rec_id, labels in rec_labels_split:
-            if rec_id in test_rec_ids:
-                labels = "?"
-            if labels == "":  # Write without comma
-                f.write(f"{rec_id}{labels}\n")
-            else:
-                f.write(f"{rec_id},{labels}\n")
-
-    # Private labels. Create csv, with each row containing the label for a (rec_id, species_id) pair
-    data = {"Id": [], "Probability": []}
-    for rec_id, labels in rec_labels_split:
-        if rec_id not in test_rec_ids:
-            continue
-        species_ids = [int(i) for i in labels.split(",") if i != ""]
-        for species_id in range(0, 19):
-            data["Id"].append(rec_id * 100 + species_id)
-            data["Probability"].append(int(species_id in species_ids))
-
-    pd.DataFrame(data).to_csv(private / "answers.csv", index=False)
-    assert (
-        len(pd.DataFrame(data)) == len(test_rec_ids) * 19
-    ), f"Expected {len(test_rec_ids)*19} entires in answers.csv, got {len(pd.DataFrame(data))}"
-
-    # Create new sample submission, following new submission format
-    # http://www.kaggle.com/c/mlsp-2013-birds/forums/t/4961/new-submission-parser
-    data = {
-        "Id": [rec_id * 100 + species_id for rec_id in test_rec_ids for species_id in range(0, 19)],
-        "Probability": 0,
-    }
-    pd.DataFrame(data).to_csv(public / "sample_submission.csv", index=False)
-    assert (
-        len(pd.DataFrame(data)) == len(test_rec_ids) * 19
-    ), f"Expected {len(test_rec_ids)*19} entires in sample_submission.csv, got {len(pd.DataFrame(data))}"
-
-    # Copy over species list
-    shutil.copyfile(
-        src=raw / "mlsp_contest_dataset/essential_data/species_list.txt",
-        dst=public / "essential_data/species_list.txt",
-    )
-
-    # Copy over all src waves from train+test set
-    (public / "essential_data/src_wavs").mkdir(exist_ok=True)
-    for filename in rec_id2filename["filename"]:
-        shutil.copyfile(
-            src=raw / "mlsp_contest_dataset/essential_data/src_wavs" / f"{filename}.wav",
-            dst=public / "essential_data/src_wavs" / f"{filename}.wav",
-        )
-
-    # Copy over train+test filtered spectrograms, segmentation examples, spectrograms, and supervised segmentation
-    (public / "supplemental_data/filtered_spectrograms").mkdir(exist_ok=True)
-    (public / "supplemental_data/segmentation_examples").mkdir(exist_ok=True)
-    (public / "supplemental_data/spectrograms").mkdir(exist_ok=True)
-    (public / "supplemental_data/supervised_segmentation").mkdir(exist_ok=True)
-    for filename in rec_id2filename["filename"]:
-        shutil.copyfile(
-            src=raw
-            / "mlsp_contest_dataset/supplemental_data/filtered_spectrograms"
-            / f"{filename}.bmp",
-            dst=public / "supplemental_data/filtered_spectrograms" / f"{filename}.bmp",
-        )
-        if os.path.exists(
-            raw / "mlsp_contest_dataset/supplemental_data/segmentation_examples" / f"{filename}.bmp"
-        ):
-            shutil.copyfile(
-                src=raw
-                / "mlsp_contest_dataset/supplemental_data/segmentation_examples"
-                / f"{filename}.bmp",
-                dst=public / "supplemental_data/segmentation_examples" / f"{filename}.bmp",
-            )
-        shutil.copyfile(
-            src=raw / "mlsp_contest_dataset/supplemental_data/spectrograms" / f"{filename}.bmp",
-            dst=public / "supplemental_data/spectrograms" / f"{filename}.bmp",
-        )
-        shutil.copyfile(
-            src=raw
-            / "mlsp_contest_dataset/supplemental_data/supervised_segmentation"
-            / f"{filename}.bmp",
-            dst=public / "supplemental_data/supervised_segmentation" / f"{filename}.bmp",
-        )
-
-    # Copy over remaining files
-    shutil.copyfile(
-        src=raw / "mlsp_contest_dataset/supplemental_data/segment_clusters.bmp",
-        dst=public / "supplemental_data/segment_clusters.bmp",
-    )
-    shutil.copyfile(
-        src=raw / "mlsp_contest_dataset/supplemental_data/segment_mosaic.bmp",
-        dst=public / "supplemental_data/segment_mosaic.bmp",
-    )
-
-    filter_and_write_file(
-        src=raw / "mlsp_contest_dataset/supplemental_data/histogram_of_segments.txt",
-        dst=public / "supplemental_data/histogram_of_segments.txt",
-        old_id_to_new=old_id_to_new,
-    )
-    filter_and_write_file(
-        src=raw / "mlsp_contest_dataset/supplemental_data/segment_features.txt",
-        dst=public / "supplemental_data/segment_features.txt",
-        old_id_to_new=old_id_to_new,
-    )
-    filter_and_write_file(
-        src=raw / "mlsp_contest_dataset/supplemental_data/segment_rectangles.txt",
-        dst=public / "supplemental_data/segment_rectangles.txt",
-        old_id_to_new=old_id_to_new,
-    )

benchmark/MLE-bench/mlebench/competitions/movie-review-sentiment-analysis-kernels-only/checksums.yaml

@@ -1,5 +0,0 @@
-private:
-  answers.csv: 82f44bb7f0a1b075afdadfd356c3644b
-public:
-  sampleSubmission.csv: 2da66116279038cc280e8cafcb27a6ea
-zip: 297ae2983c4a07603ed2fd31613c1b5e

benchmark/MLE-bench/mlebench/competitions/movie-review-sentiment-analysis-kernels-only/config.yaml

@@ -1,16 +0,0 @@
-id: movie-review-sentiment-analysis-kernels-only
-name: Movie Review Sentiment Analysis (Kernels Only)
-competition_type: code
-awards_medals: false
-prizes: null
-description: mlebench/competitions/movie-review-sentiment-analysis-kernels-only/description.md
-
-dataset:
-  answers: movie-review-sentiment-analysis-kernels-only/prepared/private/answers.csv
-  sample_submission: movie-review-sentiment-analysis-kernels-only/prepared/public/sampleSubmission.csv
-
-grader:
-  name: accuracy
-  grade_fn: mlebench.competitions.movie-review-sentiment-analysis-kernels-only.grade:grade
-
-preparer: mlebench.competitions.movie-review-sentiment-analysis-kernels-only.prepare:prepare

benchmark/MLE-bench/mlebench/competitions/movie-review-sentiment-analysis-kernels-only/description.md

@@ -1,72 +0,0 @@
-# Overview
-
-## Description
-
-"There's a thin line between likably old-fashioned and fuddy-duddy, and The Count of Monte Cristo ... never quite settles on either side."
-
-The Rotten Tomatoes movie review dataset is a corpus of movie reviews used for sentiment analysis, originally collected by Pang and Lee [1]. In their work on sentiment treebanks, Socher et al. [2] used Amazon's Mechanical Turk to create fine-grained labels for all parsed phrases in the corpus. This competition presents a chance to benchmark your sentiment-analysis ideas on the Rotten Tomatoes dataset. You are asked to label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive. Obstacles like sentence negation, sarcasm, terseness, language ambiguity, and many others make this task very challenging.
-
-![treebank](https://storage.googleapis.com/kaggle-media/competitions/kaggle/3810/media/treebank.png)
-
-Kaggle is hosting this competition for the machine learning community to use for fun and practice. This competition was inspired by the work of [Socher](http://www.socher.org/) et al [2]. We encourage participants to explore the accompanying (and dare we say, fantastic) website that accompanies the paper:
-
-http://nlp.stanford.edu/sentiment/
-
-There you will find have source code, a live demo, and even an online interface to help train the model.
-
-[1] Pang and L. Lee. 2005. *Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales*. In ACL, pages 115–124.
-
-[2] *Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank*, Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Chris Manning, Andrew Ng and Chris Potts. Conference on Empirical Methods in Natural Language Processing (EMNLP 2013).
-
-Image credits: Popcorn - Maura Teague, http://www.flickr.com/photos/93496438@N06/
-
-## Evaluation
-
-Submissions are evaluated on classification accuracy (the percent of labels that are predicted correctly) for every parsed phrase. The sentiment labels are:
-
-0 - negative
-
-1 - somewhat negative
-
-2 - neutral
-
-3 - somewhat positive
-
-4 - positive
-
-### Submission Format
-
-For each phrase in the test set, predict a label for the sentiment. Your submission should have a header and look like the following:
-
-```
-PhraseId,Sentiment
-156061,2
-156062,2
-156063,2
-...
-```
-
-## Citation
-
-Addison Howard, Phil Culliton, Will Cukierski. (2018). Movie Review Sentiment Analysis (Kernels Only). Kaggle. https://kaggle.com/competitions/movie-review-sentiment-analysis-kernels-only
-
-# Data
-
-## Dataset Description
-
-The dataset is comprised of tab-separated files with phrases from the Rotten Tomatoes dataset. The train/test split has been preserved for the purposes of benchmarking, but the sentences have been shuffled from their original order. Each Sentence has been parsed into many phrases by the Stanford parser. Each phrase has a PhraseId. Each sentence has a SentenceId. Phrases that are repeated (such as short/common words) are only included once in the data.
-
-- train.tsv contains the phrases and their associated sentiment labels. We have additionally provided a SentenceId so that you can track which phrases belong to a single sentence.
-- test.tsv contains just phrases. You must assign a sentiment label to each phrase.
-
-The sentiment labels are:
-
-0 - negative
-
-1 - somewhat negative
-
-2 - neutral
-
-3 - somewhat positive
-
-4 - positive

benchmark/MLE-bench/mlebench/competitions/movie-review-sentiment-analysis-kernels-only/description_obfuscated.md

@@ -1,35 +0,0 @@
-# Task
-
-Predict the sentiment of phrases.
-
-# Metric
-
-Classification accuracy.
-
-# Submission Format
-
-For each phrase in the test set, predict a label for the sentiment. Your submission should have a header and look like the following:
-
-```
-PhraseId,Sentiment
-156061,2
-156062,2
-156063,2
-...
-```
-
-# Dataset
-
-The dataset is comprised of tab-separated files with phrases. Each phrase has a PhraseId. Each sentence has a SentenceId.
-
-The sentiment labels are:
-
-0 - negative
-
-1 - somewhat negative
-
-2 - neutral
-
-3 - somewhat positive
-
-4 - positive