Metadata-Version: 2.1
Name: microprediction
Version: 0.9.9
Summary: Client for www.microprediction.org
Home-page: https://github.com/microprediction/microprediction
Author: microprediction
Author-email: info@microprediction.org
License: MIT
Description: # microprediction
        
        Client library for www.microprediction.org
        
        Tap into the collective intelligence of community contributed time series algorithms:
        
            pip install microprediction 
            
        This library can also be used to both solicit and contribute distributional predictions. 
        
        ## Microprediction.Org
        
        The simplest way to solicit good predictions of live public data. 
            
        - Creation of feeds (to solicit predictions) is introduced below but also at https://www.microprediction.org/publishing.html 
        
        - Crawling (letting loose an algorithm to explore all the time series) is also explained at https://www.microprediction.org/crawling.html
        
        Questions to  info@microprediction.org 
        
        
        ![](https://i.imgur.com/6FpjUaR.png)
        
        # Class Hierarchy 
        
        The following classes are provided:
        
            MicroReader
               |
            MicroWriter ----------------------------
               |                                   |
            MicroPoll                         MicroCrawler
            (feed creator)               (self-navigating algorithm)
                
        # 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               |
            |-------------|---------------------------------|---------------------------------|--------------------------------------|-----------------------------------|
            | my_stream   | `stream=my_stream&horizon=70`   |  `stream=my_stream&horizon=310` | `stream=my_stream&horizon=910`       | `stream=my_stream&horizon=3555`   | 
        
        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. 
        
        
        ## Z-Scores
        
        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). 
        
        |  Stream                                      |                                                                                   |
        |----------------------------------------------|-----------------------------------------------------------------------------------|
        |  Base stream                                 |  `https://www.microprediction.org/stream_dashboard.html?stream=cop`               |
        |  Z-score relative to 70s ahead predictions   |  `https://www.microprediction.org/stream_dashboard.html?stream=z1~cop~70`         |
        |  Z-score relative to 3555s ahead predictions  |  `https://www.microprediction.org/stream_dashboard.html?stream=z1~cop~3555`        |
        
        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 |
        |--------------|---------------------------------|-------------------------------------|-------------------------------------|---------------------
        | cop          | `stream=cop&horizon=70`         |  `stream=cop&horizon=310`           | `stream=cop&horizon=910`            | `stream=cop&horizon=3555` 
        | `z1~cop~3555`| `stream=z1~cop~3555&horizon=70` |  `stream=z1~cop~3555&horizon=310`   | `stream=z1~cop~3555&horizon=910`    | `stream=z1~cop~3555&horizon=3555'
          
             
        # 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]+0.25*lagged_values[1]                                     # 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. 
         
        
        
            
        # Read client
        
        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 
            
        # Write client
        
        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 
        
        In principle:
        
            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.  
        
        # More on mining write_keys 
        
        If you want to collect some write_keys off to the side, you can 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.     
        
        
                 
        # Maintaining your balance 
        
        Every participating 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
         -1.0*( abs(self.min_balance)*(16**(write_key_len-9)) ) supercedes whatever is written here. However at time of writing the bankruptcy levels are:
        
        |  write_key difficulty   |  bankruptcy         |  write_key difficulty   |  bankruptcy         |
        |-------------------------|---------------------|-------------------------|---------------------|
        |  8                      |  -0.01              |     11                  |   -256              |
        |  9                      |  -1.0               |     12                  |   -4,096            |
        | 10                      |  -16.0              |     13                  |   -65,536           |
               
        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
        a write_key alive that you need for sponsoring a stream. You can also ask others to mine muids 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 cset() 
        
        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
         to mw.cset( ) you can get all these goodies automatically:
                 
        |  Functionality          |  Example dashboard URL                                                            |
        |-------------------------|-----------------------------------------------------------------------------------|
        |  Base stream #1         |  `https://www.microprediction.org/stream_dashboard.html?stream=cop`               |
        |  Base stream #2         |  `https://www.microprediction.org/stream_dashboard.html?stream=fcx`               |
        |  Z-scores               |  `https://www.microprediction.org/stream_dashboard.html?stream=z1~cop~310`        |
        |  Bivariate copula       |  `https://www.microprediction.org/stream_dashboard.html?stream=z2~cop~pe~910`     |
        |  Trivariate copula      |  `https://www.microprediction.org/stream_dashboard.html?stream=z3~cop~fcx~pe~910` |         
                 
        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
                 
                         
                 
        # Troubleshooting stream creation
                
        0. Upgrade the library, which is pretty fluid
           1. pip install --upgrade microprediction 
                
        1. Check https://github.com/microprediction/microconventions/blob/master/microconventions/stream_conventions.py to see if you are violating a stream naming convention
           1. Must end in .json  
           2. Must contain only alphanumeric, hyphens, underscores, colons (discouraged) and at most one period.
           3. Must not contain double colon. 
           
        2. Log into Dashboard with your write_key:
           1. https://www.microprediction.org/dashboard.html
           2. Check for errors/warnings You can also use  mw.get_errors(), mw.get_warnings(), mw.get_confirmations()
           3. Was the name already taken? 
           4. Is you write_key bankrupt? 
        
        3. Get in touch: 
           1. File an issue at https://github.com/microprediction/microprediction/issues if you believe there is a problem
           2. Post a question on Quora and request answer from user 'Peter Cotton' if you need advice. 
        
        
                 
        
            
         
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
