Client for www.microprediction.org turnkey community prediction
Client library for www.microprediction.org You publish live data repeatedly, and it gets predicted. Simple, eh? Tap into the collective intelligence of community contributed time series algorithms, or add to the intelligence.
pip install microprediction
This library can also be used to submit predictions.
Participate immediately with a bash script
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/microprediction/microprediction/master/shell_examples/run_default_crawler_from_new_venv.sh)"
Hello world examples
Site documentation at Microprediction.Org
Questions to firstname.lastname@example.org
New skin coming soon...
Provided classes include the following
MicroReader | MicroWriter ---------------------------- | | MicroPoll MicroCrawler (feed creator) (self-navigating algorithm)
A more complete picture would include SimpleCrawler, RegularCrawler, OnlineHorizonCrawler, OnlineStreamCrawler and ReportingCrawler, as well as additional conveniences for creating streams such as ChangePoll, MultiPoll, and MultiChangePoll.
Quickstart: Soliciting predictions
If you have a function that returns a live number, do this:
from microprediction import MicroPoll, create_key feed = MicroPoll(write_key=create_key(), # This takes a while ... see section on mining write_keys below name='my_stream.json', # Name your data stream func=my_feed_func, # Provide a callback function that returns a float interval=20) # Poll every twenty minutes feed.run() # Start the scheduler
Retrieving distributional predictions
Once a stream is created and some crawlers have found it, you can view activity and predictions at www.microprediction.org,
|Stream||Roughly 1 min ahead||Roughly 5 min ahead||Roughly 15 min ahead||Roughly 1 hr ahead|
Here is an actual example: https://www.microprediction.org/stream_dashboard.html?stream=fcx&horizon=70 for a 1 minute ahead CDF. If you wish to use the Python client:
cdf = feed.get_cdf('cop.json',delay=70,values=[0,0.5])
where the delay parameter, in seconds, is the prediction horizon (it is called a delay as the predictions used to compute this CDF have all be quarantine for 70 seconds or more).
The community of algorithms provides predictions roughly 1 min, 5 min, 15 minutes and 1 hr ahead of time. The
get_cdf() above reveals the probability that your future value is less than 0.0, and the probability that it is
less than 0.5. You can view CDFs and activity at MicroPrediction.Org by entering your write key in the dashboard.
A bonus! Based on algorithm predictions, every data point you publish creates another two streams, representing community z-scores for your data point based on predictions made at different times prior (those quarantined the shortest, and longest intervals).
|Z-score relative to 70s ahead predictions||
|Z-score relative to 3555s ahead predictions||
In turn, each of these streams is predicted at four different horizons, as with the base stream. For example:
|Stream||Roughly 1 min ahead||Roughly 5 min ahead||Roughly 15 min ahead||Roughly 1 hr ahead|
Quickstart: Providing predictions
If you have a function that takes a vector of lagged values of a time series and supplies a distributional prediction, a fast way to get going is deriving from MicroCrawler as follows:
from microprediction import MicroCrawler, create_key from microprediction.samplers import differenced_bootstrap class MyCrawler(MicroCrawler): def sample(self, lagged_values, lagged_times=None, name=None, delay=None): my_point_estimate = 0.75*lagged_values+0.25*lagged_values # You can do better scenarios = differenced_bootstrap(lagged=lagged_values, decay=0.01, num=self.num_predictions) # You can do better samples = [ my_point_estimate+s for s in scenarios ] return samples my_write_key = create_key(difficulty=11) # Be patient. Maybe visit www.MUID.org to learn about Memorable Unique Identifiers print(my_write_key) crawler = MyCrawler(write_key=write_key) crawler.run()
Enter your write_key into https://www.microprediction.org/dashboard.html to find out which time series your crawler is good at predicting. Check back in a day, a week or a month.
It is possible to retrieve most quantities at api.microprediction.org with direct web calls such as https://api.microprediction.org/live/cop.json. Use your preferred means such as requests or aiohttp. For example using the former:
import requests lagged_values = requests.get('https://api.microprediction.org/live/lagged_values::cop.json').json() lagged = requests.get('https://api.microprediction.org/lagged/cop.json').json()
However the reader client adds a little convenience.
from microprediction import MicroReader mr = MicroReader() current_value = mr.get('cop.json') lagged_values = mr.get_lagged_values('cop.json') lagged_times = mr.get_lagged_times('cop.json')
Your best reference for the API is the client code https://github.com/microprediction/microprediction/blob/master/microprediction/reader.py
As noted above you may prefer to use MicroPoll or MicroCrawler rather than MicroWriter directly. But here are a few more details on the API wrapper those wanting more control. You can create predictions or feeds using only the writer. Your best reference is the client code https://github.com/microprediction/microprediction/blob/master/microprediction/writer.py
Instantiate a writer
from microprediction import MicroWriter, create_key mw = MicroWriter(write_key=create_key(difficulty=12)) # Sub in your own write_key. MUIDs explained at https://vimeo.com/397352413
In practice you may want to run create_key() separately as it will take many hours, at least for a difficult key. See https://config.microprediction.org/config.json for the current values of min_len, which is the official minimum difficulty to create a stream. If you don't need to create streams but only wish to predict, you can use a lower difficulty like 10 or even 9. But the easier your key, the more likely you are to go bankrupt.
Submitting scenarios (manually)
If MicroCrawler does not suit your needs you can submit predictions:
scenarios = [ i*0.001 for i in range(mw.num_predictions) ] # You can do better ! mw.submit(name='cop.json',values=scenarios, delay=70) # Specify stream name and also prediction horizon
See https://config.microprediction.org/config.json for a list of values that delay can take.
Creating a feed (manually)
If MicroPoll does not serve your needs you can create your stream one data point at a time:
mw = MicroWriter(write_key=write_key) res = mw.set(name='mystream.json',value=3.14157)
However if you don't do this regularly, your stream's history will die and you will lose rights to the name 'mystream.json' established when you made the first call. If you have a long break between data points, such as overnight or over the weekend, consider touching the data stream:
res = mw.touch(name='mystream.json')
to let the system know you still care.
Troubleshooting stream creation
Upgrade the library, which is pretty fluid
pip install --upgrade microprediction
Check https://github.com/microprediction/microconventions/blob/master/microconventions/stream_conventions.py to see if you are violating a stream naming convention
- Must end in
- Must contain only alphanumeric, hyphens, underscores, colons (discouraged) and at most one period.
- Must not contain double colon.
- Must end in
Log into Dashboard with your write_key:
- Check for errors/warnings You can also use
- Was the name already taken?
- Is your
Want more write keys? Cut and paste this bash command into a bash shell:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/microprediction/muid/master/examples/mine_from_venv.sh)"
or use the MUID library (www.muid.org) ...
$pip install muid $python3 >>> import muid >>> muid.mine(skip_intro=True)
See www.muid.org or https://vimeo.com/397352413 for more on MUIDs. Use a URL like http://www.muid.org/validate/fb74baf628d43892020d803614f91f29 to reveal the hidden "spirit animal" in a MUID. The difficulty is the length of the animal, not including the space.
Balances and bankruptcy
write_key has an associated balance. When you create a stream you automatically participate in the prediction of the stream. A benchmark empirical sampling algorithm with some recency adjustment is used for this
purpose. If nobody can do a better job that this, your
write_key balance will neither rise nor fall, on average.
However once smart people and algorithms enter the fray, you can expect this default model to be beaten and the balance on your
write_key to trend downwards.
On an ongoing basis you also need the
write_key balance not to fall below a threshold bankruptcy level. The minimum balance for a key of difficulty 9 is also found at https://api.microprediction.org/config.json and the formula:
supercedes whatever is written here. However at time of writing the bankruptcy levels are:
|write_key difficulty||bankruptcy||write_key difficulty||bankruptcy|
Balance may be transfered from one
write_key to another if the recipient
write_key has a negative balance. You can use the transfer function to keep
write_key alive that you need for sponsoring a stream. You can also ask others to mine (muids)[https://github.com/microprediction/muid] for you and contribute in this fashion, say if you have an important civic nowcast and expect that others
might help maintain it. You cannot use a transfer to
raise the balance associated with a
write_key above zero - that is only possible by means of accurate prediction.
Advanced topic: Higher dimensional prediction with
Multivariate prediction solicitation is available to those with write_keys of difficulty 1 more than the stream minimum (i.e. 12+1). If you want to use this we suggest you start mining now. My making regular calls
mw.cset() you can get all these goodies automatically:
|Functionality||Example dashboard URL|
|Base stream #1||
|Base stream #2||
Copula time series are univariate. An embedding from R^3 or R^2 to R is used (Morton space filling Z-curve). The most up to date reference for these embeddings is at https://github.com/microprediction/microconventions/blob/master/microconventions/zcurve_conventions.py
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