https://github.com/trane-dev/trane

An open source python library for automated prediction engineering

Science Score: 26.0%

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Low similarity (14.9%) to scientific vocabulary

Keywords

auto-labeling autolabeling automated-machine-learning automated-prediction-engineering automl data-science machine-learning prediction-engineering python

Last synced: 6 months ago · JSON representation

Repository

An open source python library for automated prediction engineering

Basic Info

Host: GitHub
Owner: trane-dev
License: mit
Language: Python
Default Branch: main
Homepage: https://www.trane.dev
Size: 29.4 MB

Statistics

Stars: 45
Watchers: 11
Forks: 16
Open Issues: 26
Releases: 8

Topics

auto-labeling autolabeling automated-machine-learning automated-prediction-engineering automl data-science machine-learning prediction-engineering python

Created over 8 years ago · Last pushed 8 months ago

Metadata Files

Readme Contributing License

Trane is a software package that automatically generates problems for temporal datasets and produces labels for supervised learning. Its goal is to streamline the machine learning problem-solving process.

Install

Install Trane using pip:

shell python -m pip install trane

Usage

Here's a quick demonstration of Trane in action:

```python import trane

data, metadata = trane.loadairbnb() problemgenerator = trane.ProblemGenerator( metadata=metadata, entitycolumns=["location"] ) problems = problemgenerator.generate()

for problem in problems[:5]: print(problem) ```

A few of the generated problems:

```

Generated 40 total problems

Classification problems: 5

Regression problems: 35

For each predict if there exists a record For each predict if there exists a record with equal to For each predict if there exists a record with not equal to For each predict if there exists a record with equal to For each predict if there exists a record with not equal to ```

With Trane's LLM add-on (pip install "trane[llm]"), we can determine the relevant problems with OpenAI: ```python from trane.llm import analyze

instructions = "determine 5 most relevant problems about user's booking preferences. Do not include 'predict the first/last X' problems" context = "Airbnb data listings in major cities, including information about hosts, pricing, location, and room type, along with over 5 million historical reviews." relevantproblems = analyze( problems=problems, instructions=instructions, context=context, model="gpt-3.5-turbo-16k" ) for problem in relevantproblems: print(problem) print(f'Reasoning: {problem.get_reasoning()}\n') Outputtext For each predict if there exists a record Reasoning: This problem can help identify locations with missing data or locations that have not been booked at all.

For each predict the first in all related records Reasoning: Predicting the first location in all related records can provide insights into the most frequently booked locations for each city.

For each predict the first in all related records Reasoning: Predicting the first rating in all related records can provide insights into the average satisfaction level of guests for each location.

For each predict the last in all related records Reasoning: Predicting the last location in all related records can provide insights into the most recent bookings for each city.

For each predict the last in all related records Reasoning: Predicting the last rating in all related records can provide insights into the recent satisfaction level of guests for each location. ```

Community

Questions or Issues? Create a GitHub issue.
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Cite Trane

If you find Trane beneficial, consider citing our paper:

Ben Schreck, Kalyan Veeramachaneni. What Would a Data Scientist Ask? Automatically Formulating and Solving Predictive Problems. IEEE DSAA 2016, 440-451.

BibTeX entry:

bibtex @inproceedings{schreck2016would, title={What Would a Data Scientist Ask? Automatically Formulating and Solving Predictive Problems}, author={Schreck, Benjamin and Veeramachaneni, Kalyan}, booktitle={Data Science and Advanced Analytics (DSAA), 2016 IEEE International Conference on}, pages={440--451}, year={2016}, organization={IEEE} }

Owner

Name: Trane
Login: trane-dev
Kind: organization

Repositories: 1
Profile: https://github.com/trane-dev

GitHub Events

Total

Watch event: 1
Delete event: 16
Push event: 30
Pull request event: 31
Create event: 17

Last Year

Watch event: 1
Delete event: 16
Push event: 30
Pull request event: 31
Create event: 17

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 0
Total pull requests: 6
Average time to close issues: N/A
Average time to close pull requests: less than a minute
Total issue authors: 0
Total pull request authors: 1
Average comments per issue: 0
Average comments per pull request: 0.0
Merged pull requests: 5
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 6
Average time to close issues: N/A
Average time to close pull requests: less than a minute
Issue authors: 0
Pull request authors: 1
Average comments per issue: 0
Average comments per pull request: 0.0
Merged pull requests: 5
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

gsheni (1)

Pull Request Authors

Trane-bot (47)
gsheni (5)
dependabot[bot] (1)

Top Labels

Issue Labels

Pull Request Labels

dependencies (1)

Packages

Total packages: 1
Total downloads:
- pypi 71 last-month

Total dependent packages: 0
Total dependent repositories: 1
Total versions: 9
Total maintainers: 2

pypi.org: trane

automatically generate prediction problems and labels for supervised learning.

Documentation: https://trane.readthedocs.io/
License: MIT License
Latest release: 0.8.0
published about 2 years ago

Versions: 9
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 71 Last month

Rankings

Dependent packages count: 7.4%

Forks count: 9.4%

Stargazers count: 10.5%

Average: 13.9%

Downloads: 19.8%

Dependent repos count: 22.2%

Maintainers (2)

mit_dai_lab gsheni