> 1. How do I interpret the "Field Contributions"? How are those number calculated?
Those numbers are how much the error decreases (as a percent) if you include that field. Let's say you are using the MAPE error, which is the default. A field contribution of 30.16 means that if you include only that extra field (and no others), the error will go down to original_error * (1-30.16%). Without knowing the specifics, I'm not sure why wind speed didn't help. With streaming data often the field combination results are counterintuitive but true. I'll try to go over this point in my chalk talk next week. Also, did you plot the data to see if there is a large day of week contribution? Maybe that is indeed the biggest factor? BTW, did you use a large swarm? A medium swarm doesn't go beyond two-field combinations, I believe. --Subutai On Thu, Oct 8, 2015 at 5:49 PM, Matthew Taylor <[email protected]> wrote: > Hello NuPIC, > > I've got weather data that looks like this [1] for every day for the > past several years. I'm trying to correlate this weather data with the > number of 311 calls made in the same area over time. I'm swarming over > a selection of weather input fields and the debris call count [2]. > Weather certainly should contribute somehow to people calling for tree > debris pickup. > > So far, I have swarmed twice with the following results. > > #1 included "rain", "snow", "precip", and "max wind speed" and the > field contributions looked like this: > > Field Contributions: > { u'debris': 30.163726239876382, > u'maxwspd': -1.373108683713905, > u'precip': 2.1176366006787224, > u'rain': 0.0, > u'snow': -3.0830847929189784, > u'timestamp_dayOfWeek': 32.13034654690986, > u'timestamp_timeOfDay': 3.9764609868384224, > u'timestamp_weekend': 15.442651796208624} > > The best model params returned only encoded "debris" and day of week / > weekend. I expected "max wind speed" to contribute much more to debris > calls. > > #2 included "hail", "mean wind speed", "temperature variation", and > "precip". The field contributions after swarming looked like this: > > Field Contributions: > { u'debris': 28.19563250430966, > u'hail': 1.7711291936725424, > u'meanwindspdm': -6.274956215526072, > u'precip': 0.0, > u'tempvariation': -6.395026451990224, > u'timestamp_dayOfWeek': 30.21767519999757, > u'timestamp_timeOfDay': 1.2703697906231544, > u'timestamp_weekend': 13.05969551380973} > > Still, it seems that wind and temperature variation do not contribute > to better predictions of debris calls. You can see all my code and CSV > data I am swarming over here: > https://github.com/rhyolight/multivariate-example > > So, a couple of questions I have now are: > > 1. How do I interpret the "Field Contributions"? How are those number > calculated? > 2. What am I doing wrong? Weather certainly does contribute to 311 > Tree Debris calls in the real world. Is my data not good enough? > > [1] https://gist.github.com/rhyolight/5631429c950529a7c947 > [2] > https://github.com/rhyolight/multivariate-example/blob/master/weather_debris_data.csv > > Thanks in advance, > --------- > Matt Taylor > OS Community Flag-Bearer > Numenta > >
