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 Trane using pip:
python -m pip install traneHere's a quick demonstration of Trane in action:
import trane
data, metadata = trane.load_airbnb()
problem_generator = trane.ProblemGenerator(
metadata=metadata,
entity_columns=["location"]
)
problems = problem_generator.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 <location> predict if there exists a record
For each <location> predict if there exists a record with <location> equal to <str>
For each <location> predict if there exists a record with <location> not equal to <str>
For each <location> predict if there exists a record with <rating> equal to <str>
For each <location> predict if there exists a record with <rating> not equal to <str>
With Trane's LLM add-on (pip install "trane[llm]"), we can determine the relevant problems with OpenAI:
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."
relevant_problems = analyze(
problems=problems,
instructions=instructions,
context=context,
model="gpt-3.5-turbo-16k"
)
for problem in relevant_problems:
print(problem)
print(f'Reasoning: {problem.get_reasoning()}\n')Output
For each <location> 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 <location> predict the first <location> 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 <location> predict the first <rating> 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 <location> predict the last <location> 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 <location> predict the last <rating> 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.
- Questions or Issues? Create a GitHub issue.
- Want to Chat? Join our Slack community.
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:
@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}
}