time to ensemble!!

[ClimbsRocks]

focus:
within the validations folder:

1. read in each validation file.
   2a. create a list of lists (predictionsAllRows)
2. create a list for each row (predictionsForRow)
3. append each algo's prediction to predictionsForRow
4. once we have read in all the predictions
   read in the validation dataset (with all the features)
5. append predictionsAllRows to our validationData using hstack

post-MVP:
break out the data at this point into validationTrain and validationTest.
test is just going to be our actual test data.

from there, run a RF over the data.

post-post-post MVP:
run a modified version of machineJS over the data. see how we can train the best classifiers possible over the validation data and the predictions from the first round of machineJS.

then ensembler will simply average the results together.

what this means:
leave the current ensembler flow untouched. we will use that again on the second round, once we have run things back through machineJS.
we need to create a new workflow in machineJS to accommodate this (no splitData or dataFormatting. that might be the only difference)

http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.sparse.hstack.html

[ClimbsRocks]

whew, finished!