Add files using upload-large-folder tool

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

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

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

@@ -0,0 +1,16 @@
+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

@@ -0,0 +1,35 @@
+# 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

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

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

@@ -0,0 +1,11 @@
+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

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

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

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

@@ -0,0 +1,58 @@
+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

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

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

@@ -0,0 +1,22 @@
+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

@@ -0,0 +1,269 @@
+# 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

@@ -0,0 +1,135 @@
+# 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

@@ -0,0 +1,11 @@
+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

@@ -0,0 +1,5 @@
+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

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

@@ -0,0 +1,182 @@
+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

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

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

@@ -0,0 +1,16 @@
+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

@@ -0,0 +1,72 @@
+# 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

@@ -0,0 +1,35 @@
+# 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