Welcome to collate’s documentation!¶
Contents:
collate¶
Aggregated feature generation made easy.
- Free software for noncommercial use: UChicago open source license.
- Documentation: https://collate.readthedocs.io.
Overview¶
Collate allows you to easily specify and execute statements like “find the number of restaurants in a given zip code that have had food safety violations within the past year.” The real power is that it allows you to vary both the spatial and temporal windows, choosing not just zip code and one year, but a range over multiple partitions and times. Specifying features is also easier and more efficient than writing raw sql. Collate will automatically generate and execute all the required SQL scripts to aggregate the data across many groups in an efficient manner. We mainly use the results as features in machine learning models.
Inputs¶
Take for example food inspections data from the City of Chicago. The table looks like this:
| inspection_id | license_no | zip | inspection_date | results | violations | ... |
|---|---|---|---|---|---|---|
| 1966765 | 80273 | 60636 | 2016-10-18 | No Entry | ... | |
| 1966314 | 2092894 | 60640 | 2016-10-11 | Pass | …CORRECTED… | ... |
| 1966286 | 2215628 | 60661 | 2016-10-11 | Pass w/ C… | …HAZARDOUS… | ... |
| 1966220 | 2424039 | 60620 | 2016-10-07 | Pass | ... |
There are two spatial levels in the data: the specific restaurant (by its license number) and the zip code. And there is a date.
An example of an aggregate feature is the number of failed inspections. In raw SQL this could be calculated, for each restaurant, as so:
SELECT license_no, sum((results = 'Fail')::int) as failed_sum
FROM food_inspections GROUP BY license_no;
In collate, this aggregated column would be defined as:
Aggregate({"failed": "(results = 'Fail')::int"}, "sum")
Note that the SQL query is split into two parts: the first argument to Aggregate
is the computation to be performed and gives it a name (as a dictionary key), and
the second argument is the reduction function to perform. Splitting the SQL like
this makes it easy to generate lots of composable features as the outer product
of these two lists. For example, you may also be interested in the proportion
of inspections that resulted in a failure in addition to the total number. This is
easy to specify with the average value of the failed computation:
Aggregate({"failed": "(results = 'Fail')::int"}, ["sum","avg"])
Aggregations in collate easily aggregate this single feature across different spatiotemporal groups, e.g.:
Aggregate({"failed": "(results = 'Fail')::int"}, ["sum","avg"])
st = SpacetimeAggregation([fail],
from_obj='food_inspections',
groups=['license_no','zip'],
intervals={"license_no":["2 year", "3 year"], "zip": ["1 year"]},
dates=["2016-01-01", "2015-01-01"],
date_column="inspection_date",
schema='test_collate')
The SpacetimeAggregation object encapsulates the FROM section of the query
(in this case it’s simply the inspections table), as well as the GROUP BY
columns. Not only will this create information about the individual restaurants
(grouping by license_no), it also creates “neighborhood” columns that add
information about the region in which the restaurant is operating (by grouping by
zip).
Even more powerful is the sophisticated date range partitioning that the
SpacetimeAggregation object provides. It will create multiple queries in
order to create the summary statistics over the past 1, 2, or 3 years, looking
back from either Jan 1, 2015 or Jan 1 2016. Executing this set of queries with:
st.execute(engine.connect()) # with a SQLAlchemy engine object
will create three new tables in the test_collate schema. The table
food_inspections_license_no will contain four feature columns for each
license that describe the total number and proportion of failures over the past
two or three years, with a date column that states whether it was looking
before 2016 or 2015. Similarly, a food_inspections_zip table will have two
feature columns for every zip code in the database, looking at the total and
average number of failures in that neighborhood over the year prior to the date
in the date column. Finally, the food_inspections_aggregate table joins
these results together to make it easier to look at both neighborhood and
restaurant-level effects for any given restaurant.
Outputs¶
The main output of a collate aggregation is a database table with all of the aggregated features joined to a list of entities.
TODO: sample rows from the above aggregation.
Usage Examples¶
Multiple quantities¶
TODO
Multiple functions¶
TODO
Tuple quantity¶
TODO
Date substitution¶
TODO
Categorical counts¶
TODO
Naming of features¶
TODO
More complicated from_obj¶
TODO
Technical details¶
Installation¶
Stable release¶
To install collate, run this command in your terminal:
$ pip install collate
This is the preferred method to install collate, as it will always install the most recent stable release.
If you don’t have pip installed, this Python installation guide can guide you through the process.
From sources¶
The sources for collate can be downloaded from the Github repo.
You can either clone the public repository:
$ git clone git://github.com/dssg/collate
Or download the tarball:
$ curl -OL https://github.com/dssg/collate/tarball/master
Once you have a copy of the source, you can install it with:
$ python setup.py install
Contributing¶
Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.
You can contribute in many ways:
Types of Contributions¶
Report Bugs¶
Report bugs at https://github.com/dssg/collate/issues.
If you are reporting a bug, please include:
- Your operating system name and version.
- Any details about your local setup that might be helpful in troubleshooting.
- Detailed steps to reproduce the bug.
Fix Bugs¶
Look through the GitHub issues for bugs. Anything tagged with “bug” and “help wanted” is open to whoever wants to implement it.
Implement Features¶
Look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whoever wants to implement it.
Write Documentation¶
collate could always use more documentation, whether as part of the official collate docs, in docstrings, or even on the web in blog posts, articles, and such.
Submit Feedback¶
The best way to send feedback is to file an issue at https://github.com/dssg/collate/issues.
If you are proposing a feature:
- Explain in detail how it would work.
- Keep the scope as narrow as possible, to make it easier to implement.
- Remember that this is a volunteer-driven project, and that contributions are welcome :)
Get Started!¶
Ready to contribute? Here’s how to set up collate for local development.
Fork the collate repo on GitHub.
Clone your fork locally:
$ git clone git@github.com:your_name_here/collate.git
Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:
$ mkvirtualenv collate $ cd collate/ $ python setup.py developCreate a branch for local development:
$ git checkout -b name-of-your-bugfix-or-feature
Now you can make your changes locally.
When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:
$ flake8 collate tests $ python setup.py test or py.test $ toxTo get flake8 and tox, just pip install them into your virtualenv.
Commit your changes and push your branch to GitHub:
$ git add . $ git commit -m "Your detailed description of your changes." $ git push origin name-of-your-bugfix-or-featureSubmit a pull request through the GitHub website.
Pull Request Guidelines¶
Before you submit a pull request, check that it meets these guidelines:
- The pull request should include tests.
- If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
- The pull request should work for Python 2.6, 2.7, 3.3, 3.4 and 3.5, and for PyPy. Check https://travis-ci.org/dssg/collate/pull_requests and make sure that the tests pass for all supported Python versions.