e.g.

{
  "user":{"user_id":"xxxx"},
 "like_num":"xx",
 "set_id":"xxx",
 "items":[
    {"price":"xxxx","item_id":"xxxxxx","category_id1":"xx","category_id2":"xxxxx"},
    ...
  ],
  "publish_date":"yyyy-mm-dd"
}

Is your feature request related to a problem? Please describe.

Prepare the Benchmark Proposal Guide document to clarify how to contribute Shift28M by proposing benchmarks.

Describe the solution you'd like

create ./BENCHMARK.md

Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.

Additional context
Add any other context or screenshots about the feature request here.

The following question should be answered:

Did the individuals in question consent to the collection and use of their data? If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented.

Is your feature request related to a problem? Please describe.

Prepare the Task Proposal Guide document to clarify how to contribute Shift28M by proposing tasks.

Describe the solution you'd like

create ./TASK_PROPOSAL.md

Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.

Additional context
Add any other context or screenshots about the feature request here.

The following question should be answered:

Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide a description.

The following question should be answered:

Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a future user? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.

The following question should be answered:

Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.

Is your feature request related to a problem? Please describe.

It is useful to provide the download option when we construct the dataset.

Describe the solution you'd like

dataset = Dataset(download=True)

Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.

Additional context
Add any other context or screenshots about the feature request here.

The following question should be answered:

Is there a label or target associated with each instance? If so, please provide a description.

The following question should be answered:

For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.

The following question should be answered:

If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate).

The following question should be answered:

Does the dataset relate to people? If not, you may skip the remaining questions in this section.

The following question should be answered:

Does the dataset relate to people? If not, you may skip the remaining questions in this section.

The following question should be answered:

Were the individuals in question notified about the data collection? If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself.

The following question should be answered:

How many instances are there in total (of each type, if appropriate)?

The following question should be answered:

Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).

The following question should be answered:

Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.

Create Datasheet for the SHIFT28M.

The template is here.

Is your feature request related to a problem? Please describe.

Based on numlikes_tabular.py, we should create the benchmark template.

Describe the solution you'd like

• Clarify experimental settings which should be reported.

Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.

Additional context
Add any other context or screenshots about the feature request here.

Brief description

The goal of this task is to estimate the number of likes from the tabular information (without images).

Type of Task

regression

How interesting is this task?

It is reasonable as the first task.

Note

References

The following question should be answered:

What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? How were these mechanisms or procedures validated?

The following question should be answered:

Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctorpatient confidentiality, data that includes the content of individuals non-public communications)? If so, please provide a description.

The following question should be answered:

Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.

The following question should be answered:

Were any ethical review processes conducted (e.g., by an institutional review board)? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.

Parent Task

#2

Model List

• IWERM
• AIWERM
• RIWERM

Note

p_tr = norm.pdf(mu_tr, sigma_tr)
p_te = norm.pdf(mu_te, sigma_te)

model = LinearRegression(sample_weight = p_tr / p_te)

References

The following question should be answered:

If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)?

The following question should be answered:

Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit.

The following question should be answered:

Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)?

Brief description

The regression problem for the item price from the item image.

Type of Task

regression

How interesting is this task?

This task includes the target shift assumption.

Note

References

To show the properties of the original dataset, let make basic statistical analysis.

The following question should be answered:

Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.

The following question should be answered:

Composition

Any other comments?

The following question should be answered:

Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why.

The following question should be answered:

Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created.

Is your feature request related to a problem? Please describe.

The current benchmark code is WIP.
We should clean it and fix it as the template of benchmarks.

Describe the solution you'd like

Refactoring current code.

Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.

Additional context
Add any other context or screenshots about the feature request here.

Brief description

The regression problem for the sum of item prices from the number of likes.

Type of Task

• regression

How interesting is this task?

This task includes both covariate shift and target shift assumptions.

Note

References

Parent Task

#2

Model List

• Linear Regression
• Random Forest
• MLP

The following question should be answered:

How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how.

The following question should be answered:
What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.

Is your feature request related to a problem? Please describe.
Implementation of the visualizer for dataset shifts

Describe the solution you'd like
A clear and concise description of what you want to happen.

Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.

Additional context
Add any other context or screenshots about the feature request here.

Is your feature request related to a problem? Please describe.

#2

Implementation of the Scikit-learn like API:

from shift28m.datasets import load_numlikes_tabular

X, y = load_numlikes_tabular()

Describe the solution you'd like

• implement datasets.load_numlikes_tabular
• unit tests for datasets.load_numlikes_tabular
• docstring for datasets.load_numlikes_tabular

Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.

Additional context
Add any other context or screenshots about the feature request here.

Is your feature request related to a problem? Please describe.

For the purpose of simplicity, we want to make a class for each task.

Describe the solution you'd like

class NumLikesRegression():
  def __init__(self):
    self.df = ...

  def load_dataset(self):
    return (x_train, y_train), (x_test, y_test)

Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.

Additional context
This is the sub-issue of #2

The following question should be answered:

Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.

The following question should be answered:

What data does each instance consist of? “Raw” data (e.g., unprocessed text or images)or features? In either case, please provide a description.

The following question should be answered:

Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)?

Before publishing, we need to determine the license of the repository, e.g.,

• MIT
• Apache
• BSD
• GPL

After researching which license is appropriate, please add the LICENSE to the repository.

The following question should be answered:

Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.

We need to prepare some sample images that can be published in the repository and paper.

The following question should be answered:

Who created this dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?