Can you describe the weather model? In general the idea is to rerank the top N docs, because it will be too slow to classify the whole result set.
In this scenario the search engine ranking will already be returning relevant candidate documents and the model is only used to get a better ordering of the top docs. Joel Bernstein http://joelsolr.blogspot.com/ On Tue, Aug 22, 2017 at 12:32 PM, Joe Obernberger < joseph.obernber...@gmail.com> wrote: > Hi All - One of the really neat features of solr 6 is the ability to > create machine learning models (information gain) and then use those models > as a query. If I want a user to be able to execute a query for the text > Hawaii and use a machine learning model related to weather data, how can I > correctly rank the results? It looks like I would need to classify all the > documents in some date range (assuming the query is date restricted), look > at the probability_d and pick the top n documents. Is there a better way > to do this? > > I'm using a stream like this: > classify(model(models,id="WeatherModel",cacheMillis=5000), > search(COL1,df="FULL_DOCUMENT",q="Hawaii AND > DocTimestamp:[2017-07-23T04:00:00Z TO > 2017-08-23T03:59:00Z]",fl="ClusterText,id",sort="id > asc",rows="10000"),field="ClusterText") > > This sends this to all the shards who can return at most 10,000 docs each. > > Thanks! > > -Joe > >
